From 6a43b0ab81f937d68bb9c4147c388309e692f29d Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 13:18:32 -0700 Subject: [PATCH 01/32] make the tool a package --- README.md | 19 +- {highdim => inputs/highdim}/input11D | 0 input2D => inputs/input2D | 0 input2D_bins => inputs/input2D_bins | 0 input3D => inputs/input3D | 0 main_iterative.py | 18 +- phaseSpaceSampling/TrainingLog/log_iter0.csv | 148 +++++++++++ phaseSpaceSampling/TrainingLog/log_iter1.csv | 148 +++++++++++ .../TrainingLog/modelWeights_iter0.npz | Bin 0 -> 82126 bytes .../TrainingLog/modelWeights_iter0.pt | Bin 0 -> 46978 bytes .../TrainingLog/modelWeights_iter1.npz | Bin 0 -> 82126 bytes .../TrainingLog/modelWeights_iter1.pt | Bin 0 -> 48258 bytes phaseSpaceSampling/__init__.py | 9 + .../cutils}/__init__.py | 0 {cutils => phaseSpaceSampling/cutils}/io.py | 0 {cutils => phaseSpaceSampling/cutils}/misc.py | 6 +- .../downSampledData_100000_it0.npz | Bin 0 -> 1200508 bytes .../downSampledData_100000_it1.npz | Bin 0 -> 1200508 bytes .../downSampledData_10000_it0.npz | Bin 0 -> 120508 bytes .../downSampledData_10000_it1.npz | Bin 0 -> 120508 bytes .../downSampledData_1000_it0.npz | Bin 0 -> 12508 bytes .../downSampledData_1000_it1.npz | Bin 0 -> 12508 bytes phaseSpaceSampling/main_iterative.py | 251 ++++++++++++++++++ {nde => phaseSpaceSampling/nde}/__init__.py | 0 .../nde}/distributions/__init__.py | 0 .../nde}/distributions/base.py | 15 +- .../nde}/distributions/discrete.py | 11 +- .../nde}/distributions/discrete_test.py | 2 +- .../nde}/distributions/mixture.py | 0 .../nde}/distributions/normal.py | 18 +- .../nde}/distributions/normal_test.py | 2 +- .../nde}/distributions/uniform.py | 4 +- .../nde}/flows/__init__.py | 0 .../nde}/flows/autoregressive.py | 6 +- .../nde}/flows/autoregressive_test.py | 2 +- {nde => phaseSpaceSampling/nde}/flows/base.py | 19 +- .../nde}/flows/base_test.py | 6 +- .../nde}/flows/realnvp.py | 8 +- .../nde}/flows/realnvp_test.py | 2 +- .../nde}/transforms/__init__.py | 0 .../nde}/transforms/autoregressive.py | 21 +- .../nde}/transforms/autoregressive_test.py | 4 +- .../nde}/transforms/base.py | 4 +- .../nde}/transforms/base_test.py | 6 +- .../nde}/transforms/conv.py | 7 +- .../nde}/transforms/conv_test.py | 4 +- .../nde}/transforms/coupling.py | 13 +- .../nde}/transforms/coupling_test.py | 11 +- .../nde}/transforms/linear.py | 16 +- .../nde}/transforms/linear_test.py | 11 +- .../nde}/transforms/lu.py | 4 +- .../nde}/transforms/lu_test.py | 10 +- .../nde}/transforms/made.py | 5 +- .../nde}/transforms/made_test.py | 2 +- .../nde}/transforms/nonlinearities.py | 35 +-- .../nde}/transforms/nonlinearities_test.py | 6 +- .../nde}/transforms/normalization.py | 11 +- .../nde}/transforms/normalization_test.py | 6 +- .../nde}/transforms/orthogonal.py | 10 +- .../nde}/transforms/orthogonal_test.py | 12 +- .../nde}/transforms/permutations.py | 11 +- .../nde}/transforms/permutations_test.py | 4 +- .../nde}/transforms/qr.py | 4 +- .../nde}/transforms/qr_test.py | 10 +- .../nde}/transforms/reshape.py | 7 +- .../nde}/transforms/reshape_test.py | 4 +- .../nde}/transforms/splines/__init__.py | 0 .../nde}/transforms/splines/cubic.py | 13 +- .../nde}/transforms/splines/cubic_test.py | 2 +- .../nde}/transforms/splines/linear.py | 7 +- .../nde}/transforms/splines/linear_test.py | 2 +- .../nde}/transforms/splines/quadratic.py | 9 +- .../nde}/transforms/splines/quadratic_test.py | 2 +- .../transforms/splines/rational_quadratic.py | 9 +- .../splines/rational_quadratic_test.py | 2 +- .../nde}/transforms/standard.py | 2 +- .../nde}/transforms/standard_test.py | 4 +- .../nde}/transforms/svd.py | 4 +- .../nde}/transforms/svd_test.py | 8 +- .../nde}/transforms/transform_test.py | 2 +- {nn => phaseSpaceSampling/nn}/__init__.py | 0 {nn => phaseSpaceSampling/nn}/attention.py | 6 +- {nn => phaseSpaceSampling/nn}/conv.py | 0 {nn => phaseSpaceSampling/nn}/mlp.py | 0 {nn => phaseSpaceSampling/nn}/mlp_test.py | 2 +- {nn => phaseSpaceSampling/nn}/resnet.py | 4 +- {nn => phaseSpaceSampling/nn}/unet.py | 4 +- .../postProcess/plotLoss.py | 0 .../visualizeDownSampled_subplots.py | 0 sampler.py => phaseSpaceSampling/sampler.py | 19 +- phaseSpaceSampling/scaler.npz | Bin 0 -> 542 bytes phaseSpaceSampling/utils/.torchutils.py.swp | Bin 0 -> 16384 bytes .../utils}/dataUtils.py | 14 +- .../utils}/flowUtils.py | 15 +- {utils => phaseSpaceSampling/utils}/io.py | 0 .../utils/oldinit.py | 0 .../utils}/parallel.py | 0 .../utils}/plotFun.py | 6 +- .../utils}/torchutils.py | 10 +- .../utils}/torchutils_test.py | 0 .../utils}/typechecks.py | 0 pyproject.toml | 59 ---- readmeImages/103_phaseSpaceSampling.png | Bin 36998 -> 0 bytes readmeImages/103_randomSampling.png | Bin 32136 -> 0 bytes readmeImages/104_phaseSpaceSampling.png | Bin 130119 -> 0 bytes readmeImages/104_randomSampling.png | Bin 57042 -> 0 bytes readmeImages/fulldataset.png | Bin 143932 -> 0 bytes readmeImages/probabilityMap.png | Bin 42575 -> 0 bytes setup.py | 30 +++ run2D.sh => tests/run2D.sh | 0 run2D_bins.sh => tests/run2D_bins.sh | 0 run3D.sh => tests/run3D.sh | 0 112 files changed, 846 insertions(+), 321 deletions(-) rename {highdim => inputs/highdim}/input11D (100%) rename input2D => inputs/input2D (100%) rename input2D_bins => inputs/input2D_bins (100%) rename input3D => inputs/input3D (100%) create mode 100644 phaseSpaceSampling/TrainingLog/log_iter0.csv create mode 100644 phaseSpaceSampling/TrainingLog/log_iter1.csv create mode 100644 phaseSpaceSampling/TrainingLog/modelWeights_iter0.npz create mode 100644 phaseSpaceSampling/TrainingLog/modelWeights_iter0.pt create mode 100644 phaseSpaceSampling/TrainingLog/modelWeights_iter1.npz create mode 100644 phaseSpaceSampling/TrainingLog/modelWeights_iter1.pt create mode 100644 phaseSpaceSampling/__init__.py rename {cutils => phaseSpaceSampling/cutils}/__init__.py (100%) rename {cutils => phaseSpaceSampling/cutils}/io.py (100%) rename {cutils => phaseSpaceSampling/cutils}/misc.py (71%) create mode 100644 phaseSpaceSampling/downSampledData_100000_it0.npz create mode 100644 phaseSpaceSampling/downSampledData_100000_it1.npz create mode 100644 phaseSpaceSampling/downSampledData_10000_it0.npz create mode 100644 phaseSpaceSampling/downSampledData_10000_it1.npz create mode 100644 phaseSpaceSampling/downSampledData_1000_it0.npz create mode 100644 phaseSpaceSampling/downSampledData_1000_it1.npz create mode 100644 phaseSpaceSampling/main_iterative.py rename {nde => phaseSpaceSampling/nde}/__init__.py (100%) rename {nde => phaseSpaceSampling/nde}/distributions/__init__.py (100%) rename {nde => phaseSpaceSampling/nde}/distributions/base.py (88%) rename {nde => phaseSpaceSampling/nde}/distributions/discrete.py (86%) rename {nde => phaseSpaceSampling/nde}/distributions/discrete_test.py (98%) rename {nde => phaseSpaceSampling/nde}/distributions/mixture.py (100%) rename {nde => phaseSpaceSampling/nde}/distributions/normal.py (86%) rename {nde => phaseSpaceSampling/nde}/distributions/normal_test.py (99%) rename {nde => phaseSpaceSampling/nde}/distributions/uniform.py (76%) rename {nde => phaseSpaceSampling/nde}/flows/__init__.py (100%) rename {nde => phaseSpaceSampling/nde}/flows/autoregressive.py (93%) rename {nde => phaseSpaceSampling/nde}/flows/autoregressive_test.py (94%) rename {nde => phaseSpaceSampling/nde}/flows/base.py (79%) rename {nde => phaseSpaceSampling/nde}/flows/base_test.py (96%) rename {nde => phaseSpaceSampling/nde}/flows/realnvp.py (91%) rename {nde => phaseSpaceSampling/nde}/flows/realnvp_test.py (95%) rename {nde => phaseSpaceSampling/nde}/transforms/__init__.py (100%) rename {nde => phaseSpaceSampling/nde}/transforms/autoregressive.py (96%) rename {nde => phaseSpaceSampling/nde}/transforms/autoregressive_test.py (97%) rename {nde => phaseSpaceSampling/nde}/transforms/base.py (98%) rename {nde => phaseSpaceSampling/nde}/transforms/base_test.py (96%) rename {nde => phaseSpaceSampling/nde}/transforms/conv.py (90%) rename {nde => phaseSpaceSampling/nde}/transforms/conv_test.py (78%) rename {nde => phaseSpaceSampling/nde}/transforms/coupling.py (98%) rename {nde => phaseSpaceSampling/nde}/transforms/coupling_test.py (96%) rename {nde => phaseSpaceSampling/nde}/transforms/linear.py (93%) rename {nde => phaseSpaceSampling/nde}/transforms/linear_test.py (97%) rename {nde => phaseSpaceSampling/nde}/transforms/lu.py (97%) rename {nde => phaseSpaceSampling/nde}/transforms/lu_test.py (91%) rename {nde => phaseSpaceSampling/nde}/transforms/made.py (99%) rename {nde => phaseSpaceSampling/nde}/transforms/made_test.py (99%) rename {nde => phaseSpaceSampling/nde}/transforms/nonlinearities.py (94%) rename {nde => phaseSpaceSampling/nde}/transforms/nonlinearities_test.py (96%) rename {nde => phaseSpaceSampling/nde}/transforms/normalization.py (96%) rename {nde => phaseSpaceSampling/nde}/transforms/normalization_test.py (96%) rename {nde => phaseSpaceSampling/nde}/transforms/orthogonal.py (93%) rename {nde => phaseSpaceSampling/nde}/transforms/orthogonal_test.py (88%) rename {nde => phaseSpaceSampling/nde}/transforms/permutations.py (89%) rename {nde => phaseSpaceSampling/nde}/transforms/permutations_test.py (92%) rename {nde => phaseSpaceSampling/nde}/transforms/qr.py (97%) rename {nde => phaseSpaceSampling/nde}/transforms/qr_test.py (91%) rename {nde => phaseSpaceSampling/nde}/transforms/reshape.py (95%) rename {nde => phaseSpaceSampling/nde}/transforms/reshape_test.py (95%) rename {nde => phaseSpaceSampling/nde}/transforms/splines/__init__.py (100%) rename {nde => phaseSpaceSampling/nde}/transforms/splines/cubic.py (95%) rename {nde => phaseSpaceSampling/nde}/transforms/splines/cubic_test.py (97%) rename {nde => phaseSpaceSampling/nde}/transforms/splines/linear.py (95%) rename {nde => phaseSpaceSampling/nde}/transforms/splines/linear_test.py (96%) rename {nde => phaseSpaceSampling/nde}/transforms/splines/quadratic.py (95%) rename {nde => phaseSpaceSampling/nde}/transforms/splines/quadratic_test.py (97%) rename {nde => phaseSpaceSampling/nde}/transforms/splines/rational_quadratic.py (96%) rename {nde => phaseSpaceSampling/nde}/transforms/splines/rational_quadratic_test.py (97%) rename {nde => phaseSpaceSampling/nde}/transforms/standard.py (97%) rename {nde => phaseSpaceSampling/nde}/transforms/standard_test.py (96%) rename {nde => phaseSpaceSampling/nde}/transforms/svd.py (96%) rename {nde => phaseSpaceSampling/nde}/transforms/svd_test.py (91%) rename {nde => phaseSpaceSampling/nde}/transforms/transform_test.py (95%) rename {nn => phaseSpaceSampling/nn}/__init__.py (100%) rename {nn => phaseSpaceSampling/nn}/attention.py (98%) rename {nn => phaseSpaceSampling/nn}/conv.py (100%) rename {nn => phaseSpaceSampling/nn}/mlp.py (100%) rename {nn => phaseSpaceSampling/nn}/mlp_test.py (96%) rename {nn => phaseSpaceSampling/nn}/resnet.py (98%) rename {nn => phaseSpaceSampling/nn}/unet.py (94%) rename plotLoss.py => phaseSpaceSampling/postProcess/plotLoss.py (100%) rename visualizeDownSampled_subplots.py => phaseSpaceSampling/postProcess/visualizeDownSampled_subplots.py (100%) rename sampler.py => phaseSpaceSampling/sampler.py (97%) create mode 100644 phaseSpaceSampling/scaler.npz create mode 100644 phaseSpaceSampling/utils/.torchutils.py.swp rename {utils => phaseSpaceSampling/utils}/dataUtils.py (89%) rename {utils => phaseSpaceSampling/utils}/flowUtils.py (84%) rename {utils => phaseSpaceSampling/utils}/io.py (100%) rename utils/__init__.py => phaseSpaceSampling/utils/oldinit.py (100%) rename {utils => phaseSpaceSampling/utils}/parallel.py (100%) rename {utils => phaseSpaceSampling/utils}/plotFun.py (94%) rename {utils => phaseSpaceSampling/utils}/torchutils.py (95%) rename {utils => phaseSpaceSampling/utils}/torchutils_test.py (100%) rename {utils => phaseSpaceSampling/utils}/typechecks.py (100%) delete mode 100644 pyproject.toml delete mode 100644 readmeImages/103_phaseSpaceSampling.png delete mode 100644 readmeImages/103_randomSampling.png delete mode 100644 readmeImages/104_phaseSpaceSampling.png delete mode 100644 readmeImages/104_randomSampling.png delete mode 100644 readmeImages/fulldataset.png delete mode 100644 readmeImages/probabilityMap.png create mode 100644 setup.py rename run2D.sh => tests/run2D.sh (100%) rename run2D_bins.sh => tests/run2D_bins.sh (100%) rename run3D.sh => tests/run3D.sh (100%) diff --git a/README.md b/README.md index 29d7c25..e5e7738 100644 --- a/README.md +++ b/README.md @@ -6,17 +6,12 @@ ## Installation for other users -### Option 1: From `conda` (recommended) +### From `conda` 1. `conda create --name uips python=3.10` 2. `conda activate uips` 3. `pip install -r requirements.txt` -### Option 2: From `poetry` - -This requires [poetry](https://python-poetry.org/docs/#installation) -1. `poetry update` - ## Purpose The purpose of the tool is to perform a smart downselection of a large number of datapoints. Typically, large numerical simulations generate billions, or even trillions of datapoints. However, there may be redundancy in the dataset which unnecessarily constrains memory and computing requirements. Here, redundancy is defined as closeness in feature space. The method is called phase-space sampling. @@ -79,22 +74,22 @@ A script is provided to visualize the losses. Execute `python plotLoss.py input` Suppose one wants to downsample an dataset where $N=10^7$ and $d=2$. First, the code estimates the probability map of the data in order to identify where are located redundant data points. An example dataset (left) and associated probability map (right) are shown below

- - + +

Next, the code uses the probability map to define a sampling probability which downselect samples that uniformly span the feature space. The probability map is obtained by training a Neural Spline Flow which implementation was obtained from [Neural Spline Flow repository](https://github.com/bayesiains/nsf). The number of samples in the final dataset can be controlled via the input file.

- - + +

For comparison, a random sampling gives the following result

- - + +

## Example 11D diff --git a/highdim/input11D b/inputs/highdim/input11D similarity index 100% rename from highdim/input11D rename to inputs/highdim/input11D diff --git a/input2D b/inputs/input2D similarity index 100% rename from input2D rename to inputs/input2D diff --git a/input2D_bins b/inputs/input2D_bins similarity index 100% rename from input2D_bins rename to inputs/input2D_bins diff --git a/input3D b/inputs/input3D similarity index 100% rename from input3D rename to inputs/input3D diff --git a/main_iterative.py b/main_iterative.py index 87ee471..1ff9954 100644 --- a/main_iterative.py +++ b/main_iterative.py @@ -6,17 +6,17 @@ # import matplotlib.pyplot as plt -sys.path.append("utils") import argparse import time -import parallel as par -from dataUtils import prepareData -from plotFun import * +import phaseSpaceSampling.utils.parallel as par +from phaseSpaceSampling.utils.dataUtils import prepareData +from phaseSpaceSampling.utils.torchutils import get_num_parameters +from phaseSpaceSampling.utils.plotFun import * +from phaseSpaceSampling import DATA_DIR from prettyPlot.parser import parse_input_file -import sampler -import utils +import phaseSpaceSampling.sampler as sampler parser = argparse.ArgumentParser(description="Downsampler") parser.add_argument( @@ -78,11 +78,11 @@ if use_gpu: # GPU SETTING device = torch.device("cuda") - torch.set_default_tensor_type("torch.cuda.FloatTensor") + torch.set_default_dtype(torch.cuda.float32) else: # CPU SETTING device = torch.device("cpu") - torch.set_default_tensor_type("torch.FloatTensor") + torch.set_default_dtype(torch.float32) # REPRODUCIBILITY torch.manual_seed(int(inpt["seed"]) + par.irank) np.random.seed(int(inpt["seed"]) + par.irank) @@ -127,7 +127,7 @@ # Create the normalizing flow flow = sampler.createFlow(dim, pdf_iter, inpt) # flow = flow.to(device) - n_params = utils.get_num_parameters(flow) + n_params = get_num_parameters(flow) par.printRoot( "There are {} trainable parameters in this model.".format(n_params) ) diff --git a/phaseSpaceSampling/TrainingLog/log_iter0.csv b/phaseSpaceSampling/TrainingLog/log_iter0.csv new file mode 100644 index 0000000..91048d0 --- /dev/null +++ b/phaseSpaceSampling/TrainingLog/log_iter0.csv @@ -0,0 +1,148 @@ +step;nll_loss +10;14.064168930053711 +20;13.886690139770508 +30;13.762961387634277 +40;13.783608436584473 +50;13.679816246032715 +60;13.17573070526123 +70;12.778334617614746 +80;12.39841365814209 +90;12.478351593017578 +100;12.381379127502441 +110;12.107513427734375 +120;11.604694366455078 +130;11.120548248291016 +140;10.883197784423828 +150;10.723276138305664 +160;10.64442253112793 +170;10.42137336730957 +180;9.866634368896484 +190;9.385425567626953 +200;8.945733070373535 +210;8.902589797973633 +220;8.80291748046875 +230;8.60580062866211 +240;8.033447265625 +250;7.385888576507568 +260;7.058380126953125 +270;6.975914001464844 +280;6.874386310577393 +290;6.605739116668701 +300;6.016022682189941 +310;5.397113800048828 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a/phaseSpaceSampling/__init__.py b/phaseSpaceSampling/__init__.py new file mode 100644 index 0000000..6b1dacd --- /dev/null +++ b/phaseSpaceSampling/__init__.py @@ -0,0 +1,9 @@ +"""Phase space sampling""" +import os + +PSS_DIR = os.path.dirname(os.path.realpath(__file__)) +INPUT_DIR = os.path.join(PSS_DIR, "inputs") +DATA_DIR = os.path.join(PSS_DIR, "../data") +DOC_DIR = os.path.join(PSS_DIR, "../documentation") +ML_DIR = os.path.join(PSS_DIR, "../data-efficientML") +UTIL_DIR = os.path.join(PSS_DIR, "../utils") diff --git a/cutils/__init__.py b/phaseSpaceSampling/cutils/__init__.py similarity index 100% rename from cutils/__init__.py rename to phaseSpaceSampling/cutils/__init__.py diff --git a/cutils/io.py b/phaseSpaceSampling/cutils/io.py similarity index 100% rename from cutils/io.py rename to phaseSpaceSampling/cutils/io.py diff --git a/cutils/misc.py b/phaseSpaceSampling/cutils/misc.py similarity index 71% rename from cutils/misc.py rename to phaseSpaceSampling/cutils/misc.py index 1ee8427..2d28d83 100644 --- a/cutils/misc.py +++ b/phaseSpaceSampling/cutils/misc.py @@ -1,12 +1,12 @@ -import utils +from phaseSpaceSampling.utils.torchutils import tensor2numpy def gridimshow(image, ax): if image.shape[0] == 1: - image = utils.tensor2numpy(image[0, ...]) + image = tensor2numpy(image[0, ...]) ax.imshow(1 - image, cmap='Greys') else: - image = utils.tensor2numpy(image.permute(1, 2, 0)) + image = tensor2numpy(image.permute(1, 2, 0)) ax.imshow(image) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) diff --git a/phaseSpaceSampling/downSampledData_100000_it0.npz b/phaseSpaceSampling/downSampledData_100000_it0.npz new file mode 100644 index 0000000000000000000000000000000000000000..e9be2a47ae512d947bf822d6177236a0502667be GIT binary patch literal 1200508 zcmbSy^;cBi8!ie+D4>)mNUA7EDm}9|2uKUk-QC>|Vq;>rVxXcZb}`RBb|><&6-*En z6)Z&bp6|W?!98p717~KUkr-DP zXPTW`Hb`Rl|GoFW_xJlC!jUYoa283bL@5ii2BNLMo{Aof)7(4|lnMa#9PL6h;*`6XC0-I@`S}8CSLam`$n^{@Y?J^w^pN%@Y~)t6MUf 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/dev/null +++ b/phaseSpaceSampling/main_iterative.py @@ -0,0 +1,251 @@ +import os +import sys + +import numpy as np +import torch + +# import matplotlib.pyplot as plt + +import argparse +import time + +from phaseSpaceSampling.utils import parallel as par +from phaseSpaceSampling.utils.dataUtils import prepareData +from phaseSpaceSampling.utils.torchutils import get_num_parameters +from phaseSpaceSampling.utils.plotFun import * +from phaseSpaceSampling import DATA_DIR +from prettyPlot.parser import parse_input_file + +import phaseSpaceSampling.sampler as sampler + +parser = argparse.ArgumentParser(description="Downsampler") +parser.add_argument( + "-i", + "--input", + type=str, + metavar="", + required=False, + help="Input file", + default="input", +) +args, unknown = parser.parse_known_args() + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ~~~~ Parse input +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +inpt = parse_input_file(args.input) +use_normalizing_flow = inpt["pdf_method"].lower() == "normalizingflow" +use_bins = inpt["pdf_method"].lower() == "bins" + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ~~~~ Parameters to save +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +# List of sample size +nSamples = [int(float(n)) for n in inpt["nSamples"].split()] +# Data size used to adjust the sampling probability +nWorkingDataAdjustment = int(float(inpt["nWorkingDataAdjustment"])) +if nWorkingDataAdjustment < 0: + use_serial_adjustment = False +else: + use_serial_adjustment = True +# Data size used to learn the data probability +nWorkingDatas = [int(float(n)) for n in inpt["nWorkingData"].split()] +if len(nWorkingDatas) == 1: + nWorkingDatas = nWorkingDatas * int(inpt["num_pdf_iter"]) +for nWorkingData in nWorkingDatas: + if not nWorkingData in nSamples: + nSamples += [nWorkingData] +# Do we compute the neighbor distance criterion +computeCriterion = inpt["computeDistanceCriterion"] == "True" +try: + nSampleCriterionLimit = int(inpt["nSampleCriterionLimit"]) +except: + nSampleCriterionLimit = int(1e5) + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ~~~~ Environment +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +os.environ["KMP_DUPLICATE_LIB_OK"] = "True" +use_gpu = ( + (inpt["use_gpu"] == "True") + and (torch.cuda.is_available()) + and (par.irank == par.iroot) +) +if use_gpu: + # GPU SETTING + device = torch.device("cuda") + torch.set_default_dtype(torch.cuda.float32) +else: + # CPU SETTING + device = torch.device("cpu") + torch.set_default_dtype(torch.float32) +# REPRODUCIBILITY +torch.manual_seed(int(inpt["seed"]) + par.irank) +np.random.seed(int(inpt["seed"]) + par.irank) + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ~~~~ Prepare Data and scatter across processors +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +data_to_downsample_, dataInd_, working_data, nFullData = prepareData(inpt) + +dim = data_to_downsample_.shape[1] + +# Compute uniform sampling criterion of random data +randomCriterion = np.zeros(len(nSamples)) +if par.irank == par.iroot and computeCriterion: + par.printRoot("RANDOM: ") + for inSample, nSample in enumerate(nSamples): + if nSample <= nSampleCriterionLimit: + random_sampled_data = working_data[:nSample, :] + mean, std = sampler.computeDistanceToClosestNeighbor( + sampler.rescaleData(random_sampled_data, inpt) + ) + randomCriterion[inSample] = mean + par.printRoot( + f"\t nSample {nSample} mean dist = {mean:.4f}, std dist = {std:.4f}" + ) + +# Prepare arrays used for sanity checks +meanCriterion = np.zeros((int(inpt["num_pdf_iter"]), len(nSamples))) +stdCriterion = np.zeros((int(inpt["num_pdf_iter"]), len(nSamples))) +flow_nll_loss = np.zeros(int(inpt["num_pdf_iter"])) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ~~~~ Downsample +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +data_for_pdf_est = working_data + +for pdf_iter in range(int(inpt["num_pdf_iter"])): + if use_normalizing_flow: + # Create the normalizing flow + flow = sampler.createFlow(dim, pdf_iter, inpt) + # flow = flow.to(device) + n_params = get_num_parameters(flow) + par.printRoot( + "There are {} trainable parameters in this model.".format(n_params) + ) + + # Train (happens on 1 proc) + flow_nll_loss[pdf_iter] = sampler.trainFlow( + data_for_pdf_est, flow, pdf_iter, inpt + ) + sampler.checkLoss(pdf_iter, flow_nll_loss) + + # Evaluate probability: This is the expensive step (happens on multi processors) + log_density_np_ = sampler.evalLogProbNF( + flow, data_to_downsample_, pdf_iter, inpt + ) + + if use_bins: + bin_pdfH, bin_pdfEdges = sampler.trainBinPDF( + data_for_pdf_est, pdf_iter, inpt + ) + # Evaluate probability: This is the expensive step (happens on multi processors) + log_density_np_ = sampler.evalLogProbBIN( + data_to_downsample_, pdf_iter, inpt + ) + + if use_serial_adjustment: + log_density_np_for_adjust = par.gatherNelementsInArray( + log_density_np_, nWorkingDataAdjustment + ) + else: + log_density_np_for_adjust = None + + # Correct probability estimate + if pdf_iter > 0: + log_density_np_ = log_density_np_ - log_samplingProb_ + if use_serial_adjustment: + log_density_np_for_adjust = par.gatherNelementsInArray( + log_density_np_, nWorkingDataAdjustment + ) + else: + log_density_np_for_adjust = None + + par.printRoot(f"TRAIN ITER {pdf_iter}") + + for inSample, nSample in enumerate(nSamples): + # Downsample + ( + downSampledData, + downSampledIndices, + samplingProb_, + log_samplingProb_, + ) = sampler.downSample( + data_to_downsample_, + dataInd_, + log_density_np_, + log_density_np_for_adjust, + nSample, + nFullData, + inpt, + ) + + # Plot + # cornerPlotScatter(downSampledData,title='downSampled npts='+str(nSample)+', iter='+str(pdf_iter)) + # Get criterion + if computeCriterion and par.irank == par.iroot: + if nSample <= nSampleCriterionLimit: + mean, std = sampler.computeDistanceToClosestNeighbor( + sampler.rescaleData(downSampledData, inpt) + ) + meanCriterion[pdf_iter, inSample] = mean + stdCriterion[pdf_iter, inSample] = std + par.printRoot( + f"\t nSample {nSample} mean dist = {mean:.4f}, std dist = {std:.4f}" + ) + + if pdf_iter == int(inpt["num_pdf_iter"]) - 1: + # Last pdf iter : Root proc saves downsampled data, and checks the outcome + sampler.checkProcedure( + meanCriterion[:, inSample], + nSample, + randomCriterion[inSample], + ) + if par.irank == par.iroot: + np.savez( + f"{inpt['prefixDownsampledData']}_{nSample}_it{pdf_iter}.npz", + data=downSampledData, + indices=downSampledIndices, + ) + + if not (pdf_iter == int(inpt["num_pdf_iter"]) - 1): + # Prepare data for the next training iteration + ( + downSampledData, + _, + samplingProb_, + log_samplingProb_, + ) = sampler.downSample( + data_to_downsample_, + dataInd_, + log_density_np_, + log_density_np_for_adjust, + nWorkingDatas[pdf_iter + 1], + nFullData, + inpt, + ) + data_for_pdf_est = downSampledData + +# Advise for best sampling +if par.irank == par.iroot: + if np.amax(meanCriterion) > 0: + print("\n") + maxCrit = np.argmax(meanCriterion, axis=0) + nSamples_asked = [int(float(n)) for n in inpt["nSamples"].split()] + for iSample, nSample in enumerate(nSamples_asked): + print( + f"For sample {nSample} use {inpt['prefixDownsampledData']}_{nSample}_it{maxCrit[iSample]}.npz" + ) + print("\n") + + +# if par.irank==par.iroot: +# plt.show() diff --git a/nde/__init__.py b/phaseSpaceSampling/nde/__init__.py similarity index 100% rename from nde/__init__.py rename to phaseSpaceSampling/nde/__init__.py diff --git a/nde/distributions/__init__.py b/phaseSpaceSampling/nde/distributions/__init__.py similarity index 100% rename from nde/distributions/__init__.py rename to phaseSpaceSampling/nde/distributions/__init__.py diff --git a/nde/distributions/base.py b/phaseSpaceSampling/nde/distributions/base.py similarity index 88% rename from nde/distributions/base.py rename to phaseSpaceSampling/nde/distributions/base.py index 7b60686..555ee51 100644 --- a/nde/distributions/base.py +++ b/phaseSpaceSampling/nde/distributions/base.py @@ -4,7 +4,8 @@ from torch import nn -import utils +from phaseSpaceSampling.utils.typechecks import is_positive_int +from phaseSpaceSampling.utils.torchutils import merge_leading_dims, repeat_rows, split_leading_dim class NoMeanException(Exception): @@ -52,7 +53,7 @@ def sample(self, num_samples, context=None, batch_size=None): A Tensor containing the samples, with shape [num_samples, ...] if context is None, or [context_size, num_samples, ...] if context is given. """ - if not utils.is_positive_int(num_samples): + if not is_positive_int(num_samples): raise TypeError('Number of samples must be a positive integer.') if context is not None: @@ -62,7 +63,7 @@ def sample(self, num_samples, context=None, batch_size=None): return self._sample(num_samples, context) else: - if not utils.is_positive_int(batch_size): + if not is_positive_int(batch_size): raise TypeError('Batch size must be a positive integer.') num_batches = num_samples // batch_size @@ -94,16 +95,16 @@ def sample_and_log_prob(self, num_samples, context=None): if context is not None: # Merge the context dimension with sample dimension in order to call log_prob. - samples = utils.merge_leading_dims(samples, num_dims=2) - context = utils.repeat_rows(context, num_reps=num_samples) + samples = merge_leading_dims(samples, num_dims=2) + context = repeat_rows(context, num_reps=num_samples) assert samples.shape[0] == context.shape[0] log_prob = self.log_prob(samples, context=context) if context is not None: # Split the context dimension from sample dimension. - samples = utils.split_leading_dim(samples, shape=[-1, num_samples]) - log_prob = utils.split_leading_dim(log_prob, shape=[-1, num_samples]) + samples = split_leading_dim(samples, shape=[-1, num_samples]) + log_prob = split_leading_dim(log_prob, shape=[-1, num_samples]) return samples, log_prob diff --git a/nde/distributions/discrete.py b/phaseSpaceSampling/nde/distributions/discrete.py similarity index 86% rename from nde/distributions/discrete.py rename to phaseSpaceSampling/nde/distributions/discrete.py index ea853db..86af941 100644 --- a/nde/distributions/discrete.py +++ b/phaseSpaceSampling/nde/distributions/discrete.py @@ -4,9 +4,10 @@ from torch.nn import functional as F -import utils -from nde import distributions +from phaseSpaceSampling.nde import distributions +from phaseSpaceSampling.utils.torchutils import sum_except_batch, repeat_rows, split_leading_dim + class ConditionalIndependentBernoulli(distributions.Distribution): @@ -50,20 +51,20 @@ def _log_prob(self, inputs, context): # Compute log prob. log_prob = -inputs * F.softplus(-logits) - (1.0 - inputs) * F.softplus(logits) - log_prob = utils.sum_except_batch(log_prob, num_batch_dims=1) + log_prob = sum_except_batch(log_prob, num_batch_dims=1) return log_prob def _sample(self, num_samples, context): # Compute parameters. logits = self._compute_params(context) probs = torch.sigmoid(logits) - probs = utils.repeat_rows(probs, num_samples) + probs = repeat_rows(probs, num_samples) # Generate samples. context_size = context.shape[0] noise = torch.rand(context_size * num_samples, *self._shape) samples = (noise < probs).float() - return utils.split_leading_dim(samples, [context_size, num_samples]) + return split_leading_dim(samples, [context_size, num_samples]) def _mean(self, context): logits = self._compute_params(context) diff --git a/nde/distributions/discrete_test.py b/phaseSpaceSampling/nde/distributions/discrete_test.py similarity index 98% rename from nde/distributions/discrete_test.py rename to phaseSpaceSampling/nde/distributions/discrete_test.py index 6e46bcb..5cb1e3b 100644 --- a/nde/distributions/discrete_test.py +++ b/phaseSpaceSampling/nde/distributions/discrete_test.py @@ -3,7 +3,7 @@ import torch import torchtestcase import unittest -from nde.distributions import discrete +from phaseSpaceSampling.nde.distributions import discrete class ConditionalIndependentBernoulliTest(torchtestcase.TorchTestCase): diff --git a/nde/distributions/mixture.py b/phaseSpaceSampling/nde/distributions/mixture.py similarity index 100% rename from nde/distributions/mixture.py rename to phaseSpaceSampling/nde/distributions/mixture.py diff --git a/nde/distributions/normal.py b/phaseSpaceSampling/nde/distributions/normal.py similarity index 86% rename from nde/distributions/normal.py rename to phaseSpaceSampling/nde/distributions/normal.py index 04069fb..09cfc1d 100644 --- a/nde/distributions/normal.py +++ b/phaseSpaceSampling/nde/distributions/normal.py @@ -3,9 +3,9 @@ import numpy as np import torch -import utils -from nde import distributions +from phaseSpaceSampling.nde import distributions +from phaseSpaceSampling.utils.torchutils import sum_except_batch, split_leading_dim, repeat_rows class StandardNormal(distributions.Distribution): @@ -21,7 +21,7 @@ def _log_prob(self, inputs, context): if inputs.shape[1:] != self._shape: raise ValueError('Expected input of shape {}, got {}'.format( self._shape, inputs.shape[1:])) - neg_energy = -0.5 * utils.sum_except_batch(inputs ** 2, num_batch_dims=1) + neg_energy = -0.5 * sum_except_batch(inputs ** 2, num_batch_dims=1) return neg_energy - self._log_z def _sample(self, num_samples, context): @@ -31,7 +31,7 @@ def _sample(self, num_samples, context): # The value of the context is ignored, only its size is taken into account. context_size = context.shape[0] samples = torch.randn(context_size * num_samples, *self._shape) - return utils.split_leading_dim(samples, [context_size, num_samples]) + return split_leading_dim(samples, [context_size, num_samples]) def _mean(self, context): if context is None: @@ -89,8 +89,8 @@ def _log_prob(self, inputs, context): # Compute log prob. norm_inputs = (inputs - means) * torch.exp(-log_stds) - log_prob = -0.5 * utils.sum_except_batch(norm_inputs ** 2, num_batch_dims=1) - log_prob -= utils.sum_except_batch(log_stds, num_batch_dims=1) + log_prob = -0.5 * sum_except_batch(norm_inputs ** 2, num_batch_dims=1) + log_prob -= sum_except_batch(log_stds, num_batch_dims=1) log_prob -= self._log_z return log_prob @@ -98,14 +98,14 @@ def _sample(self, num_samples, context): # Compute parameters. means, log_stds = self._compute_params(context) stds = torch.exp(log_stds) - means = utils.repeat_rows(means, num_samples) - stds = utils.repeat_rows(stds, num_samples) + means = repeat_rows(means, num_samples) + stds = repeat_rows(stds, num_samples) # Generate samples. context_size = context.shape[0] noise = torch.randn(context_size * num_samples, *self._shape) samples = means + stds * noise - return utils.split_leading_dim(samples, [context_size, num_samples]) + return split_leading_dim(samples, [context_size, num_samples]) def _mean(self, context): means, _ = self._compute_params(context) diff --git a/nde/distributions/normal_test.py b/phaseSpaceSampling/nde/distributions/normal_test.py similarity index 99% rename from nde/distributions/normal_test.py rename to phaseSpaceSampling/nde/distributions/normal_test.py index 48d8243..9db29d0 100644 --- a/nde/distributions/normal_test.py +++ b/phaseSpaceSampling/nde/distributions/normal_test.py @@ -3,7 +3,7 @@ import torch import torchtestcase import unittest -from nde.distributions import normal +from phaseSpaceSampling.nde.distributions import normal class StandardNormalTest(torchtestcase.TorchTestCase): diff --git a/nde/distributions/uniform.py b/phaseSpaceSampling/nde/distributions/uniform.py similarity index 76% rename from nde/distributions/uniform.py rename to phaseSpaceSampling/nde/distributions/uniform.py index d2342db..52fd575 100644 --- a/nde/distributions/uniform.py +++ b/phaseSpaceSampling/nde/distributions/uniform.py @@ -1,11 +1,11 @@ from torch import distributions -import utils +import phaseSpaceSampling.utils.torchutils import sum_except_batch class TweakedUniform(distributions.Uniform): def log_prob(self, value, context): - return utils.sum_except_batch(super().log_prob(value)) + return sum_except_batch(super().log_prob(value)) # result = super().log_prob(value) # if len(result.shape) == 2 and result.shape[1] == 1: # return result.reshape(-1) diff --git a/nde/flows/__init__.py b/phaseSpaceSampling/nde/flows/__init__.py similarity index 100% rename from nde/flows/__init__.py rename to phaseSpaceSampling/nde/flows/__init__.py diff --git a/nde/flows/autoregressive.py b/phaseSpaceSampling/nde/flows/autoregressive.py similarity index 93% rename from nde/flows/autoregressive.py rename to phaseSpaceSampling/nde/flows/autoregressive.py index 69e4092..68bab77 100644 --- a/nde/flows/autoregressive.py +++ b/phaseSpaceSampling/nde/flows/autoregressive.py @@ -2,9 +2,9 @@ from torch.nn import functional as F -from nde import distributions -from nde import flows -from nde import transforms +from phaseSpaceSampling.nde import distributions +from phaseSpaceSampling.nde import flows +from phaseSpaceSampling.nde import transforms class MaskedAutoregressiveFlow(flows.Flow): diff --git a/nde/flows/autoregressive_test.py b/phaseSpaceSampling/nde/flows/autoregressive_test.py similarity index 94% rename from nde/flows/autoregressive_test.py rename to phaseSpaceSampling/nde/flows/autoregressive_test.py index f5f0baa..9548582 100644 --- a/nde/flows/autoregressive_test.py +++ b/phaseSpaceSampling/nde/flows/autoregressive_test.py @@ -4,7 +4,7 @@ import torchtestcase import unittest -from nde.flows import autoregressive as ar +from phaseSpaceSampling.nde.flows import autoregressive as ar class MaskedAutoregressiveFlowTest(torchtestcase.TorchTestCase): diff --git a/nde/flows/base.py b/phaseSpaceSampling/nde/flows/base.py similarity index 79% rename from nde/flows/base.py rename to phaseSpaceSampling/nde/flows/base.py index 1fbcc54..56ac4fe 100644 --- a/nde/flows/base.py +++ b/phaseSpaceSampling/nde/flows/base.py @@ -1,8 +1,7 @@ """Basic definitions for the flows module.""" -import utils - -from nde import distributions +from phaseSpaceSampling.nde import distributions +from phaseSpaceSampling.utils.torchutils import merge_leading_dims, repeat_rows, split_leading_dim class Flow(distributions.Distribution): @@ -30,14 +29,14 @@ def _sample(self, num_samples, context): if context is not None: # Merge the context dimension with sample dimension in order to apply the transform. - noise = utils.merge_leading_dims(noise, num_dims=2) - context = utils.repeat_rows(context, num_reps=num_samples) + noise = merge_leading_dims(noise, num_dims=2) + context = repeat_rows(context, num_reps=num_samples) samples, _ = self._transform.inverse(noise, context=context) if context is not None: # Split the context dimension from sample dimension. - samples = utils.split_leading_dim(samples, shape=[-1, num_samples]) + samples = split_leading_dim(samples, shape=[-1, num_samples]) return samples @@ -50,15 +49,15 @@ def sample_and_log_prob(self, num_samples, context=None): if context is not None: # Merge the context dimension with sample dimension in order to apply the transform. - noise = utils.merge_leading_dims(noise, num_dims=2) - context = utils.repeat_rows(context, num_reps=num_samples) + noise = merge_leading_dims(noise, num_dims=2) + context = repeat_rows(context, num_reps=num_samples) samples, logabsdet = self._transform.inverse(noise, context=context) if context is not None: # Split the context dimension from sample dimension. - samples = utils.split_leading_dim(samples, shape=[-1, num_samples]) - logabsdet = utils.split_leading_dim(logabsdet, shape=[-1, num_samples]) + samples = split_leading_dim(samples, shape=[-1, num_samples]) + logabsdet = split_leading_dim(logabsdet, shape=[-1, num_samples]) return samples, log_prob - logabsdet diff --git a/nde/flows/base_test.py b/phaseSpaceSampling/nde/flows/base_test.py similarity index 96% rename from nde/flows/base_test.py rename to phaseSpaceSampling/nde/flows/base_test.py index 4834c4c..9d62926 100644 --- a/nde/flows/base_test.py +++ b/phaseSpaceSampling/nde/flows/base_test.py @@ -3,9 +3,9 @@ import torch import torchtestcase import unittest -from nde import transforms -from nde import distributions -from nde.flows import base +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde import distributions +from phaseSpaceSampling.nde.flows import base class FlowTest(torchtestcase.TorchTestCase): diff --git a/nde/flows/realnvp.py b/phaseSpaceSampling/nde/flows/realnvp.py similarity index 91% rename from nde/flows/realnvp.py rename to phaseSpaceSampling/nde/flows/realnvp.py index 85920f4..f106f9f 100644 --- a/nde/flows/realnvp.py +++ b/phaseSpaceSampling/nde/flows/realnvp.py @@ -3,10 +3,10 @@ import torch from torch.nn import functional as F -from nde import distributions -from nde import flows -from nde import transforms -import nn as nn_ +from phaseSpaceSampling.nde import distributions +from phaseSpaceSampling.nde import flows +from phaseSpaceSampling.nde import transforms +import phaseSpaceSampling.nn as nn_ class SimpleRealNVP(flows.Flow): diff --git a/nde/flows/realnvp_test.py b/phaseSpaceSampling/nde/flows/realnvp_test.py similarity index 95% rename from nde/flows/realnvp_test.py rename to phaseSpaceSampling/nde/flows/realnvp_test.py index e5f6e16..a2652b2 100644 --- a/nde/flows/realnvp_test.py +++ b/phaseSpaceSampling/nde/flows/realnvp_test.py @@ -4,7 +4,7 @@ import torchtestcase import unittest -from nde.flows import realnvp +from phaseSpaceSampling.nde.flows import realnvp class SimpleRealNVPTest(torchtestcase.TorchTestCase): diff --git a/nde/transforms/__init__.py b/phaseSpaceSampling/nde/transforms/__init__.py similarity index 100% rename from nde/transforms/__init__.py rename to phaseSpaceSampling/nde/transforms/__init__.py diff --git a/nde/transforms/autoregressive.py b/phaseSpaceSampling/nde/transforms/autoregressive.py similarity index 96% rename from nde/transforms/autoregressive.py rename to phaseSpaceSampling/nde/transforms/autoregressive.py index 275e4c8..fc150a1 100644 --- a/nde/transforms/autoregressive.py +++ b/phaseSpaceSampling/nde/transforms/autoregressive.py @@ -4,10 +4,11 @@ import torch from torch.nn import functional as F -import utils -from nde import transforms -from nde.transforms import made as made_module -from nde.transforms import splines +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms import made as made_module +from phaseSpaceSampling.nde.transforms import splines + +from phaseSpaceSampling.utils.torchutils import sum_except_batch class AutoregressiveTransform(transforms.Transform): """Transforms each input variable with an invertible elementwise transformation. @@ -80,7 +81,7 @@ def _elementwise_forward(self, inputs, autoregressive_params): scale = torch.sigmoid(unconstrained_scale + 2.) + 1e-3 log_scale = torch.log(scale) outputs = scale * inputs + shift - logabsdet = utils.sum_except_batch(log_scale, num_batch_dims=1) + logabsdet = sum_except_batch(log_scale, num_batch_dims=1) return outputs, logabsdet def _elementwise_inverse(self, inputs, autoregressive_params): @@ -88,7 +89,7 @@ def _elementwise_inverse(self, inputs, autoregressive_params): scale = torch.sigmoid(unconstrained_scale + 2.) + 1e-3 log_scale = torch.log(scale) outputs = (inputs - shift) / scale - logabsdet = -utils.sum_except_batch(log_scale, num_batch_dims=1) + logabsdet = -sum_except_batch(log_scale, num_batch_dims=1) return outputs, logabsdet def _unconstrained_scale_and_shift(self, autoregressive_params): @@ -146,7 +147,7 @@ def _elementwise(self, inputs, autoregressive_params, inverse=False): unnormalized_pdf=unnormalized_pdf, inverse=inverse) - return outputs, utils.sum_except_batch(logabsdet) + return outputs, sum_except_batch(logabsdet) def _elementwise_forward(self, inputs, autoregressive_params): return self._elementwise(inputs, autoregressive_params) @@ -236,7 +237,7 @@ def _elementwise(self, inputs, autoregressive_params, inverse=False): **spline_kwargs ) - return outputs, utils.sum_except_batch(logabsdet) + return outputs, sum_except_batch(logabsdet) def _elementwise_forward(self, inputs, autoregressive_params): return self._elementwise(inputs, autoregressive_params) @@ -301,7 +302,7 @@ def _elementwise(self, inputs, autoregressive_params, inverse=False): unnorm_derivatives_right=unnorm_derivatives_right, inverse=inverse ) - return outputs, utils.sum_except_batch(logabsdet) + return outputs, sum_except_batch(logabsdet) def _elementwise_forward(self, inputs, autoregressive_params): return self._elementwise(inputs, autoregressive_params) @@ -399,7 +400,7 @@ def _elementwise(self, inputs, autoregressive_params, inverse=False): **spline_kwargs ) - return outputs, utils.sum_except_batch(logabsdet) + return outputs, sum_except_batch(logabsdet) def _elementwise_forward(self, inputs, autoregressive_params): return self._elementwise(inputs, autoregressive_params) diff --git a/nde/transforms/autoregressive_test.py b/phaseSpaceSampling/nde/transforms/autoregressive_test.py similarity index 97% rename from nde/transforms/autoregressive_test.py rename to phaseSpaceSampling/nde/transforms/autoregressive_test.py index f171b37..98c1f28 100644 --- a/nde/transforms/autoregressive_test.py +++ b/phaseSpaceSampling/nde/transforms/autoregressive_test.py @@ -3,8 +3,8 @@ import torch import unittest -from nde.transforms import autoregressive -from nde.transforms.transform_test import TransformTest +from phaseSpaceSampling.nde.transforms import autoregressive +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class MaskedAffineAutoregressiveTransformTest(TransformTest): diff --git a/nde/transforms/base.py b/phaseSpaceSampling/nde/transforms/base.py similarity index 98% rename from nde/transforms/base.py rename to phaseSpaceSampling/nde/transforms/base.py index 457c66d..cea35f8 100644 --- a/nde/transforms/base.py +++ b/phaseSpaceSampling/nde/transforms/base.py @@ -5,7 +5,7 @@ from torch import nn -import utils +from phaseSpaceSampling.utils.typechecks import is_positive_int class InverseNotAvailable(Exception): @@ -78,7 +78,7 @@ def __init__(self, num_transforms, split_dim=1): num_transforms: int, total number of transforms to be added. split_dim: dimension along which to split. """ - if not utils.is_positive_int(split_dim): + if not is_positive_int(split_dim): raise TypeError('Split dimension must be a positive integer.') super().__init__() diff --git a/nde/transforms/base_test.py b/phaseSpaceSampling/nde/transforms/base_test.py similarity index 96% rename from nde/transforms/base_test.py rename to phaseSpaceSampling/nde/transforms/base_test.py index 4fdccb4..2ffe322 100644 --- a/nde/transforms/base_test.py +++ b/phaseSpaceSampling/nde/transforms/base_test.py @@ -4,9 +4,9 @@ import numpy as np import torch -from nde.transforms import base -from nde.transforms import standard -from nde.transforms.transform_test import TransformTest +from phaseSpaceSampling.nde.transforms import base +from phaseSpaceSampling.nde.transforms import standard +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class CompositeTransformTest(TransformTest): diff --git a/nde/transforms/conv.py b/phaseSpaceSampling/nde/transforms/conv.py similarity index 90% rename from nde/transforms/conv.py rename to phaseSpaceSampling/nde/transforms/conv.py index 7f6a18a..4edf581 100644 --- a/nde/transforms/conv.py +++ b/phaseSpaceSampling/nde/transforms/conv.py @@ -1,6 +1,5 @@ -import utils -from nde import transforms - +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.utils.torchutils import sum_except_batch class OneByOneConvolution(transforms.LULinear): """An invertible 1x1 convolution with a fixed permutation, as introduced in the Glow paper. @@ -27,7 +26,7 @@ def _lu_forward_inverse(self, inputs, inverse=False): outputs = outputs.reshape(b, h, w, c).permute(0, 3, 1, 2) logabsdet = logabsdet.reshape(b, h, w) - return outputs, utils.sum_except_batch(logabsdet) + return outputs, sum_except_batch(logabsdet) def forward(self, inputs, context=None): if inputs.dim() != 4: diff --git a/nde/transforms/conv_test.py b/phaseSpaceSampling/nde/transforms/conv_test.py similarity index 78% rename from nde/transforms/conv_test.py rename to phaseSpaceSampling/nde/transforms/conv_test.py index 54b2843..56db6b7 100644 --- a/nde/transforms/conv_test.py +++ b/phaseSpaceSampling/nde/transforms/conv_test.py @@ -1,8 +1,8 @@ import torch import unittest -from nde import transforms -from nde.transforms.transform_test import TransformTest +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class OneByOneConvolutionTest(TransformTest): diff --git a/nde/transforms/coupling.py b/phaseSpaceSampling/nde/transforms/coupling.py similarity index 98% rename from nde/transforms/coupling.py rename to phaseSpaceSampling/nde/transforms/coupling.py index adae5ad..02f2e4d 100644 --- a/nde/transforms/coupling.py +++ b/phaseSpaceSampling/nde/transforms/coupling.py @@ -5,11 +5,10 @@ import torch from torch.nn import functional as F -import utils - -from nde import transforms -from nde.transforms import splines +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms import splines +from phaseSpaceSampling.utils.torchutils import sum_except_batch class CouplingTransform(transforms.Transform): """A base class for coupling layers. Supports 2D inputs (NxD), as well as 4D inputs for @@ -153,14 +152,14 @@ def _coupling_transform_forward(self, inputs, transform_params): scale, shift = self._scale_and_shift(transform_params) log_scale = torch.log(scale) outputs = inputs * scale + shift - logabsdet = utils.sum_except_batch(log_scale, num_batch_dims=1) + logabsdet = sum_except_batch(log_scale, num_batch_dims=1) return outputs, logabsdet def _coupling_transform_inverse(self, inputs, transform_params): scale, shift = self._scale_and_shift(transform_params) log_scale = torch.log(scale) outputs = (inputs - shift) / scale - logabsdet = -utils.sum_except_batch(log_scale, num_batch_dims=1) + logabsdet = -sum_except_batch(log_scale, num_batch_dims=1) return outputs, logabsdet @@ -199,7 +198,7 @@ def _coupling_transform(self, inputs, transform_params, inverse=False): outputs, logabsdet = self._piecewise_cdf(inputs, transform_params, inverse) - return outputs, utils.sum_except_batch(logabsdet) + return outputs, sum_except_batch(logabsdet) def _piecewise_cdf(self, inputs, transform_params, inverse=False): raise NotImplementedError() diff --git a/nde/transforms/coupling_test.py b/phaseSpaceSampling/nde/transforms/coupling_test.py similarity index 96% rename from nde/transforms/coupling_test.py rename to phaseSpaceSampling/nde/transforms/coupling_test.py index 4f9c2cb..a1f796e 100644 --- a/nde/transforms/coupling_test.py +++ b/phaseSpaceSampling/nde/transforms/coupling_test.py @@ -6,11 +6,12 @@ from torch import nn -import nn as nn_ -import utils +import phaseSpaceSampling.nn as nn_ -from nde.transforms import coupling -from nde.transforms.transform_test import TransformTest +from phaseSpaceSampling.nde.transforms import coupling +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest + +from phaseSpaceSampling.utils.torchutils import create_mid_split_binary_mask def create_coupling_transform(cls, shape, **kwargs): if len(shape) == 1: @@ -27,7 +28,7 @@ def create_net(in_channels, out_channels): hidden_channels=16 ) - mask = utils.create_mid_split_binary_mask(shape[0]) + mask = create_mid_split_binary_mask(shape[0]) return cls( mask=mask, diff --git a/nde/transforms/linear.py b/phaseSpaceSampling/nde/transforms/linear.py similarity index 93% rename from nde/transforms/linear.py rename to phaseSpaceSampling/nde/transforms/linear.py index 61e3fb5..83921df 100644 --- a/nde/transforms/linear.py +++ b/phaseSpaceSampling/nde/transforms/linear.py @@ -6,10 +6,12 @@ from torch import nn from torch.nn import functional as F, init -import utils -from nde import transforms +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.utils.typechecks import is_positive_int, is_bool +from phaseSpaceSampling.utils.torchutils import random_orthogonal +import phaseSpaceSampling as pss class LinearCache(object): """Helper class to store the cache of a linear transform. @@ -32,7 +34,7 @@ class Linear(transforms.Transform): """Abstract base class for linear transforms that parameterize a weight matrix.""" def __init__(self, features, using_cache=False): - if not utils.is_positive_int(features): + if not is_positive_int(features): raise TypeError('Number of features must be a positive integer.') super().__init__() @@ -88,7 +90,7 @@ def train(self, mode=True): return super().train(mode) def use_cache(self, mode=True): - if not utils.is_bool(mode): + if not is_bool(mode): raise TypeError('Mode must be boolean.') self.using_cache = mode @@ -147,7 +149,7 @@ def __init__(self, features, orthogonal_initialization=True, using_cache=False): super().__init__(features, using_cache) if orthogonal_initialization: - self._weight = nn.Parameter(utils.random_orthogonal(features)) + self._weight = nn.Parameter(random_orthogonal(features)) else: self._weight = nn.Parameter(torch.empty(features, features)) stdv = 1.0 / np.sqrt(features) @@ -163,7 +165,7 @@ def forward_no_cache(self, inputs): """ batch_size = inputs.shape[0] outputs = F.linear(inputs, self._weight, self.bias) - logabsdet = utils.logabsdet(self._weight) + logabsdet = logabsdet(self._weight) logabsdet = logabsdet * torch.ones(batch_size) return outputs, logabsdet @@ -220,4 +222,4 @@ def logabsdet(self): where: D = num of features """ - return utils.logabsdet(self._weight) + return pss.utils.torchutils.logabsdet(self._weight) diff --git a/nde/transforms/linear_test.py b/phaseSpaceSampling/nde/transforms/linear_test.py similarity index 97% rename from nde/transforms/linear_test.py rename to phaseSpaceSampling/nde/transforms/linear_test.py index f69bd90..85e9116 100644 --- a/nde/transforms/linear_test.py +++ b/phaseSpaceSampling/nde/transforms/linear_test.py @@ -5,12 +5,11 @@ from unittest.mock import MagicMock -import utils - -from nde.transforms import linear -from nde.transforms.transform_test import TransformTest -from nde.transforms.linear import Linear +from phaseSpaceSampling.nde.transforms import linear +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +from phaseSpaceSampling.nde.transforms.linear import Linear +import phaseSpaceSampling as pss class LinearTest(TransformTest): def setUp(self): @@ -191,7 +190,7 @@ def setUp(self): self.weight = self.transform._weight self.weight_inverse = torch.inverse(self.weight) - self.logabsdet = utils.logabsdet(self.weight) + self.logabsdet = pss.utils.torchutils.logabsdet(self.weight) self.eps = 1e-5 diff --git a/nde/transforms/lu.py b/phaseSpaceSampling/nde/transforms/lu.py similarity index 97% rename from nde/transforms/lu.py rename to phaseSpaceSampling/nde/transforms/lu.py index 5537203..d1250bc 100644 --- a/nde/transforms/lu.py +++ b/phaseSpaceSampling/nde/transforms/lu.py @@ -4,7 +4,7 @@ from torch import nn from torch.nn import functional as F, init -from nde.transforms.linear import Linear +from phaseSpaceSampling.nde.transforms.linear import Linear class LULinear(Linear): @@ -117,4 +117,4 @@ def logabsdet(self): where: D = num of features """ - return torch.sum(torch.log(self.upper_diag)) \ No newline at end of file + return torch.sum(torch.log(self.upper_diag)) diff --git a/nde/transforms/lu_test.py b/phaseSpaceSampling/nde/transforms/lu_test.py similarity index 91% rename from nde/transforms/lu_test.py rename to phaseSpaceSampling/nde/transforms/lu_test.py index a79ed53..0173a07 100644 --- a/nde/transforms/lu_test.py +++ b/phaseSpaceSampling/nde/transforms/lu_test.py @@ -3,11 +3,9 @@ import torch import unittest -import utils - -from nde.transforms import lu -from nde.transforms.transform_test import TransformTest - +from phaseSpaceSampling.nde.transforms import lu +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +import phaseSpaceSampling as pss class LULinearTest(TransformTest): @@ -18,7 +16,7 @@ def setUp(self): lower, upper = self.transform._create_lower_upper() self.weight = lower @ upper self.weight_inverse = torch.inverse(self.weight) - self.logabsdet = utils.logabsdet(self.weight) + self.logabsdet = pss.utils.torchutils.logabsdet(self.weight) self.eps = 1e-5 diff --git a/nde/transforms/made.py b/phaseSpaceSampling/nde/transforms/made.py similarity index 99% rename from nde/transforms/made.py rename to phaseSpaceSampling/nde/transforms/made.py index 7026fc1..894c959 100644 --- a/nde/transforms/made.py +++ b/phaseSpaceSampling/nde/transforms/made.py @@ -3,11 +3,10 @@ import torch -import utils - from torch import nn from torch.nn import functional as F, init +from phaseSpaceSampling.utils.torchutils import tile def _get_input_degrees(in_features): """Returns the degrees an input to MADE should have.""" @@ -45,7 +44,7 @@ def _get_mask_and_degrees(cls, random_mask, is_output): if is_output: - out_degrees = utils.tile( + out_degrees = tile( _get_input_degrees(autoregressive_features), out_features // autoregressive_features ) diff --git a/nde/transforms/made_test.py b/phaseSpaceSampling/nde/transforms/made_test.py similarity index 99% rename from nde/transforms/made_test.py rename to phaseSpaceSampling/nde/transforms/made_test.py index 119181e..1d82b17 100644 --- a/nde/transforms/made_test.py +++ b/phaseSpaceSampling/nde/transforms/made_test.py @@ -4,7 +4,7 @@ import torchtestcase import unittest -from nde.transforms import made +from phaseSpaceSampling.nde.transforms import made class ShapeTest(torchtestcase.TorchTestCase): diff --git a/nde/transforms/nonlinearities.py b/phaseSpaceSampling/nde/transforms/nonlinearities.py similarity index 94% rename from nde/transforms/nonlinearities.py rename to phaseSpaceSampling/nde/transforms/nonlinearities.py index 8e61ad5..5a4d6c3 100644 --- a/nde/transforms/nonlinearities.py +++ b/phaseSpaceSampling/nde/transforms/nonlinearities.py @@ -6,16 +6,17 @@ from torch import nn from torch.nn import functional as F -import utils -from nde import transforms -from nde.transforms import splines +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms import splines + +from phaseSpaceSampling.utils.torchutils import sum_except_batch class Tanh(transforms.Transform): def forward(self, inputs, context=None): outputs = torch.tanh(inputs) logabsdet = torch.log(1 - outputs ** 2) - logabsdet = utils.sum_except_batch(logabsdet, num_batch_dims=1) + logabsdet = sum_except_batch(logabsdet, num_batch_dims=1) return outputs, logabsdet def inverse(self, inputs, context=None): @@ -23,7 +24,7 @@ def inverse(self, inputs, context=None): raise transforms.InputOutsideDomain() outputs = 0.5 * torch.log((1 + inputs) / (1 - inputs)) logabsdet = - torch.log(1 - inputs ** 2) - logabsdet = utils.sum_except_batch(logabsdet, num_batch_dims=1) + logabsdet = sum_except_batch(logabsdet, num_batch_dims=1) return outputs, logabsdet @@ -59,7 +60,7 @@ def forward(self, inputs, context=None): logabsdet[mask_middle] = torch.log(1 - outputs[mask_middle] ** 2) logabsdet[mask_right] = torch.log(self.alpha / inputs[mask_right]) logabsdet[mask_left] = torch.log(-self.alpha / inputs[mask_left]) - logabsdet = utils.sum_except_batch(logabsdet, num_batch_dims=1) + logabsdet = sum_except_batch(logabsdet, num_batch_dims=1) return outputs, logabsdet @@ -79,7 +80,7 @@ def inverse(self, inputs, context=None): logabsdet[mask_middle] = -torch.log(1 - inputs[mask_middle] ** 2) logabsdet[mask_right] = -np.log(self.alpha * self.beta) + inputs[mask_right] / self.alpha logabsdet[mask_left] = -np.log(self.alpha * self.beta) - inputs[mask_left] / self.alpha - logabsdet = utils.sum_except_batch(logabsdet, num_batch_dims=1) + logabsdet = sum_except_batch(logabsdet, num_batch_dims=1) return outputs, logabsdet @@ -96,14 +97,14 @@ def forward(self, inputs, context=None): outputs = F.leaky_relu(inputs, negative_slope=self.negative_slope) mask = (inputs < 0).type(torch.Tensor) logabsdet = self.log_negative_slope * mask - logabsdet = utils.sum_except_batch(logabsdet, num_batch_dims=1) + logabsdet = sum_except_batch(logabsdet, num_batch_dims=1) return outputs, logabsdet def inverse(self, inputs, context=None): outputs = F.leaky_relu(inputs, negative_slope=(1 / self.negative_slope)) mask = (inputs < 0).type(torch.Tensor) logabsdet = -self.log_negative_slope * mask - logabsdet = utils.sum_except_batch(logabsdet, num_batch_dims=1) + logabsdet = sum_except_batch(logabsdet, num_batch_dims=1) return outputs, logabsdet @@ -116,7 +117,7 @@ def __init__(self, temperature=1, eps=1e-6): def forward(self, inputs, context=None): inputs = self.temperature * inputs outputs = torch.sigmoid(inputs) - logabsdet = utils.sum_except_batch( + logabsdet = sum_except_batch( torch.log(self.temperature) - F.softplus(-inputs) - F.softplus(inputs) ) return outputs, logabsdet @@ -128,7 +129,7 @@ def inverse(self, inputs, context=None): inputs = torch.clamp(inputs, self.eps, 1 - self.eps) outputs = (1 / self.temperature) * (torch.log(inputs) - torch.log1p(-inputs)) - logabsdet = - utils.sum_except_batch( + logabsdet = - sum_except_batch( torch.log(self.temperature) - F.softplus( -self.temperature * outputs) - F.softplus(self.temperature * outputs) ) @@ -146,7 +147,7 @@ def __init__(self, location=None, scale=None, features=None): def forward(self, inputs, context=None): outputs = (1 / np.pi) * torch.atan(inputs) + 0.5 - logabsdet = utils.sum_except_batch( + logabsdet = sum_except_batch( - np.log(np.pi) - torch.log(1 + inputs ** 2) ) return outputs, logabsdet @@ -156,7 +157,7 @@ def inverse(self, inputs, context=None): raise transforms.InputOutsideDomain() outputs = torch.tan(np.pi * (inputs - 0.5)) - logabsdet = - utils.sum_except_batch( + logabsdet = - sum_except_batch( - np.log(np.pi) - torch.log(1 + outputs ** 2) ) return outputs, logabsdet @@ -215,7 +216,7 @@ def _spline(self, inputs, inverse=False): tail_bound=self.tail_bound ) - return outputs, utils.sum_except_batch(logabsdet) + return outputs, sum_except_batch(logabsdet) def forward(self, inputs, context=None): return self._spline(inputs, inverse=False) @@ -268,7 +269,7 @@ def _spline(self, inputs, inverse=False): **spline_kwargs ) - return outputs, utils.sum_except_batch(logabsdet) + return outputs, sum_except_batch(logabsdet) def forward(self, inputs, context=None): return self._spline(inputs, inverse=False) @@ -326,7 +327,7 @@ def _spline(self, inputs, inverse=False): **spline_kwargs ) - return outputs, utils.sum_except_batch(logabsdet) + return outputs, sum_except_batch(logabsdet) def forward(self, inputs, context=None): return self._spline(inputs, inverse=False) @@ -397,7 +398,7 @@ def _spline(self, inputs, inverse=False): **spline_kwargs ) - return outputs, utils.sum_except_batch(logabsdet) + return outputs, sum_except_batch(logabsdet) def forward(self, inputs, context=None): return self._spline(inputs, inverse=False) diff --git a/nde/transforms/nonlinearities_test.py b/phaseSpaceSampling/nde/transforms/nonlinearities_test.py similarity index 96% rename from nde/transforms/nonlinearities_test.py rename to phaseSpaceSampling/nde/transforms/nonlinearities_test.py index 3a5c1cd..01b9d53 100644 --- a/nde/transforms/nonlinearities_test.py +++ b/phaseSpaceSampling/nde/transforms/nonlinearities_test.py @@ -3,9 +3,9 @@ import torch import unittest -import nde.transforms.splines.quadratic -from nde.transforms import base, nonlinearities as nl, standard -from nde.transforms.transform_test import TransformTest +import phaseSpaceSampling.nde.transforms.splines.quadratic +from phaseSpaceSampling.nde.transforms import base, nonlinearities as nl, standard +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class TanhTest(TransformTest): diff --git a/nde/transforms/normalization.py b/phaseSpaceSampling/nde/transforms/normalization.py similarity index 96% rename from nde/transforms/normalization.py rename to phaseSpaceSampling/nde/transforms/normalization.py index efd0ec8..3119ce4 100644 --- a/nde/transforms/normalization.py +++ b/phaseSpaceSampling/nde/transforms/normalization.py @@ -5,10 +5,9 @@ from torch import nn from torch.nn import functional as F -import utils - -from nde import transforms +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.utils.typechecks import is_positive_int # class BatchNorm(transforms.Transform): # """Transform that performs batch normalization. @@ -19,7 +18,7 @@ # """ # # def __init__(self, features, eps=1e-5, momentum=0.1, affine=True): -# if not utils.is_positive_int(features): +# if not is_positive_int(features): # raise TypeError('Number of features must be a positive integer.') # super().__init__() # @@ -81,7 +80,7 @@ class BatchNorm(transforms.Transform): """ def __init__(self, features, eps=1e-5, momentum=0.1, affine=True): - if not utils.is_positive_int(features): + if not is_positive_int(features): raise TypeError('Number of features must be a positive integer.') super().__init__() @@ -140,7 +139,7 @@ def __init__(self, features): Reference: > D. Kingma et. al., Glow: Generative flow with invertible 1x1 convolutions, NeurIPS 2018. """ - if not utils.is_positive_int(features): + if not is_positive_int(features): raise TypeError('Number of features must be a positive integer.') super().__init__() diff --git a/nde/transforms/normalization_test.py b/phaseSpaceSampling/nde/transforms/normalization_test.py similarity index 96% rename from nde/transforms/normalization_test.py rename to phaseSpaceSampling/nde/transforms/normalization_test.py index 6e7217f..0b33d0b 100644 --- a/nde/transforms/normalization_test.py +++ b/phaseSpaceSampling/nde/transforms/normalization_test.py @@ -3,9 +3,9 @@ import torch import unittest -from nde.transforms import base -from nde.transforms import normalization as norm -from nde.transforms.transform_test import TransformTest +from phaseSpaceSampling.nde.transforms import base +from phaseSpaceSampling.nde.transforms import normalization as norm +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class BatchNormTest(TransformTest): diff --git a/nde/transforms/orthogonal.py b/phaseSpaceSampling/nde/transforms/orthogonal.py similarity index 93% rename from nde/transforms/orthogonal.py rename to phaseSpaceSampling/nde/transforms/orthogonal.py index 922394d..c994b7f 100644 --- a/nde/transforms/orthogonal.py +++ b/phaseSpaceSampling/nde/transforms/orthogonal.py @@ -4,10 +4,8 @@ from torch import nn -import utils - -from nde import transforms - +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.utils.typechecks import is_positive_int class HouseholderSequence(transforms.Transform): """A sequence of Householder transforms. @@ -25,9 +23,9 @@ def __init__(self, features, num_transforms): Raises: TypeError: if arguments are not the right type. """ - if not utils.is_positive_int(features): + if not is_positive_int(features): raise TypeError('Number of features must be a positive integer.') - if not utils.is_positive_int(num_transforms): + if not is_positive_int(num_transforms): raise TypeError('Number of transforms must be a positive integer.') super().__init__() diff --git a/nde/transforms/orthogonal_test.py b/phaseSpaceSampling/nde/transforms/orthogonal_test.py similarity index 88% rename from nde/transforms/orthogonal_test.py rename to phaseSpaceSampling/nde/transforms/orthogonal_test.py index 7633a12..71256ca 100644 --- a/nde/transforms/orthogonal_test.py +++ b/phaseSpaceSampling/nde/transforms/orthogonal_test.py @@ -3,11 +3,9 @@ import torch import unittest -import utils - -from nde.transforms import orthogonal -from nde.transforms.transform_test import TransformTest - +from phaseSpaceSampling.nde.transforms import orthogonal +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +import phaseSpaceSampling as pss class HouseholderSequenceTest(TransformTest): @@ -26,7 +24,7 @@ def test_forward(self): self.assert_tensor_is_good(logabsdet, [batch_size]) self.eps = 1e-5 self.assertEqual(outputs, inputs @ matrix.t()) - self.assertEqual(logabsdet, utils.logabsdet(matrix) * torch.ones(batch_size)) + self.assertEqual(logabsdet, pss.utils.torchutils.logabsdet(matrix) * torch.ones(batch_size)) def test_inverse(self): features = 100 @@ -43,7 +41,7 @@ def test_inverse(self): self.assert_tensor_is_good(logabsdet, [batch_size]) self.eps = 1e-5 self.assertEqual(outputs, inputs @ matrix) - self.assertEqual(logabsdet, utils.logabsdet(matrix) * torch.ones(batch_size)) + self.assertEqual(logabsdet, pss.utils.torchutils.logabsdet(matrix) * torch.ones(batch_size)) def test_matrix(self): features = 100 diff --git a/nde/transforms/permutations.py b/phaseSpaceSampling/nde/transforms/permutations.py similarity index 89% rename from nde/transforms/permutations.py rename to phaseSpaceSampling/nde/transforms/permutations.py index 8f04289..cfd85ac 100644 --- a/nde/transforms/permutations.py +++ b/phaseSpaceSampling/nde/transforms/permutations.py @@ -1,10 +1,9 @@ """Implementations of permutation-like transforms.""" import torch -import utils - -from nde import transforms +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.utils.typechecks import is_positive_int class Permutation(transforms.Transform): """Permutes inputs on a given dimension using a given permutation.""" @@ -12,7 +11,7 @@ class Permutation(transforms.Transform): def __init__(self, permutation, dim=1): if permutation.ndimension() != 1: raise ValueError('Permutation must be a 1D tensor.') - if not utils.is_positive_int(dim): + if not is_positive_int(dim): raise ValueError('dim must be a positive integer.') super().__init__() @@ -46,7 +45,7 @@ class RandomPermutation(Permutation): """Permutes using a random, but fixed, permutation. Only works with 1D inputs.""" def __init__(self, features, dim=1): - if not utils.is_positive_int(features): + if not is_positive_int(features): raise ValueError('Number of features must be a positive integer.') super().__init__(torch.randperm(features), dim) @@ -55,6 +54,6 @@ class ReversePermutation(Permutation): """Reverses the elements of the input. Only works with 1D inputs.""" def __init__(self, features, dim=1): - if not utils.is_positive_int(features): + if not is_positive_int(features): raise ValueError('Number of features must be a positive integer.') super().__init__(torch.arange(features - 1, -1, -1), dim) diff --git a/nde/transforms/permutations_test.py b/phaseSpaceSampling/nde/transforms/permutations_test.py similarity index 92% rename from nde/transforms/permutations_test.py rename to phaseSpaceSampling/nde/transforms/permutations_test.py index cb964f0..f669ba3 100644 --- a/nde/transforms/permutations_test.py +++ b/phaseSpaceSampling/nde/transforms/permutations_test.py @@ -3,8 +3,8 @@ import torch import unittest -from nde.transforms import permutations -from nde.transforms.transform_test import TransformTest +from phaseSpaceSampling.nde.transforms import permutations +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class PermutationTest(TransformTest): diff --git a/nde/transforms/qr.py b/phaseSpaceSampling/nde/transforms/qr.py similarity index 97% rename from nde/transforms/qr.py rename to phaseSpaceSampling/nde/transforms/qr.py index 935e1af..3883c2e 100644 --- a/nde/transforms/qr.py +++ b/phaseSpaceSampling/nde/transforms/qr.py @@ -4,8 +4,8 @@ from torch import nn from torch.nn import functional as F, init -from nde import transforms -from nde.transforms.linear import Linear +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.linear import Linear class QRLinear(Linear): diff --git a/nde/transforms/qr_test.py b/phaseSpaceSampling/nde/transforms/qr_test.py similarity index 91% rename from nde/transforms/qr_test.py rename to phaseSpaceSampling/nde/transforms/qr_test.py index 70b66c0..48eb6ba 100644 --- a/nde/transforms/qr_test.py +++ b/phaseSpaceSampling/nde/transforms/qr_test.py @@ -1,11 +1,9 @@ import torch import unittest -import utils - -from nde.transforms import qr -from nde.transforms.transform_test import TransformTest - +from phaseSpaceSampling.nde.transforms import qr +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +import phaseSpaceSampling as pss class QRLinearTest(TransformTest): @@ -17,7 +15,7 @@ def setUp(self): orthogonal = self.transform.orthogonal.matrix() self.weight = orthogonal @ upper self.weight_inverse = torch.inverse(self.weight) - self.logabsdet = utils.logabsdet(self.weight) + self.logabsdet = pss.utils.torchutils.logabsdet(self.weight) self.eps = 1e-5 diff --git a/nde/transforms/reshape.py b/phaseSpaceSampling/nde/transforms/reshape.py similarity index 95% rename from nde/transforms/reshape.py rename to phaseSpaceSampling/nde/transforms/reshape.py index 0b42289..1089b08 100644 --- a/nde/transforms/reshape.py +++ b/phaseSpaceSampling/nde/transforms/reshape.py @@ -1,8 +1,7 @@ import torch -import utils -from nde import transforms - +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.utils.typechecks import is_int class SqueezeTransform(transforms.Transform): """A transformation defined for image data that trades spatial dimensions for channel @@ -17,7 +16,7 @@ class SqueezeTransform(transforms.Transform): def __init__(self, factor=2): super(SqueezeTransform, self).__init__() - if not utils.is_int(factor) or factor <= 1: + if not is_int(factor) or factor <= 1: raise ValueError('Factor must be an integer > 1.') self.factor = factor diff --git a/nde/transforms/reshape_test.py b/phaseSpaceSampling/nde/transforms/reshape_test.py similarity index 95% rename from nde/transforms/reshape_test.py rename to phaseSpaceSampling/nde/transforms/reshape_test.py index 74b262e..57b6a2d 100644 --- a/nde/transforms/reshape_test.py +++ b/phaseSpaceSampling/nde/transforms/reshape_test.py @@ -1,8 +1,8 @@ import torch import unittest -from nde.transforms.transform_test import TransformTest -from nde import transforms +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +from phaseSpaceSampling.nde import transforms class SqueezeTransformTest(TransformTest): diff --git a/nde/transforms/splines/__init__.py b/phaseSpaceSampling/nde/transforms/splines/__init__.py similarity index 100% rename from nde/transforms/splines/__init__.py rename to phaseSpaceSampling/nde/transforms/splines/__init__.py diff --git a/nde/transforms/splines/cubic.py b/phaseSpaceSampling/nde/transforms/splines/cubic.py similarity index 95% rename from nde/transforms/splines/cubic.py rename to phaseSpaceSampling/nde/transforms/splines/cubic.py index 3afb077..bc98b24 100644 --- a/nde/transforms/splines/cubic.py +++ b/phaseSpaceSampling/nde/transforms/splines/cubic.py @@ -3,8 +3,9 @@ import torch from torch.nn import functional as F -import utils -from nde import transforms +from phaseSpaceSampling.nde import transforms + +from phaseSpaceSampling.utils.torchutils import searchsorted, cbrt DEFAULT_MIN_BIN_WIDTH = 1e-3 DEFAULT_MIN_BIN_HEIGHT = 1e-3 @@ -122,9 +123,9 @@ def cubic_spline(inputs, d = cumheights[..., :-1] if inverse: - bin_idx = utils.searchsorted(cumheights, inputs)[..., None] + bin_idx = searchsorted(cumheights, inputs)[..., None] else: - bin_idx = utils.searchsorted(cumwidths, inputs)[..., None] + bin_idx = searchsorted(cumwidths, inputs)[..., None] inputs_a = a.gather(-1, bin_idx)[..., 0] inputs_b = b.gather(-1, bin_idx)[..., 0] @@ -156,8 +157,8 @@ def cubic_spline(inputs, # Deal with one root cases. - p = utils.cbrt((-depressed_1[one_root_mask] + torch.sqrt(-discriminant[one_root_mask])) / 2.) - q = utils.cbrt((-depressed_1[one_root_mask] - torch.sqrt(-discriminant[one_root_mask])) / 2.) + p = cbrt((-depressed_1[one_root_mask] + torch.sqrt(-discriminant[one_root_mask])) / 2.) + q = cbrt((-depressed_1[one_root_mask] - torch.sqrt(-discriminant[one_root_mask])) / 2.) outputs[one_root_mask] = ((p + q) - inputs_b_[one_root_mask] diff --git a/nde/transforms/splines/cubic_test.py b/phaseSpaceSampling/nde/transforms/splines/cubic_test.py similarity index 97% rename from nde/transforms/splines/cubic_test.py rename to phaseSpaceSampling/nde/transforms/splines/cubic_test.py index 3fc071b..c9e2cfa 100644 --- a/nde/transforms/splines/cubic_test.py +++ b/phaseSpaceSampling/nde/transforms/splines/cubic_test.py @@ -1,7 +1,7 @@ import torch import torchtestcase -from nde.transforms import splines +from phaseSpaceSampling.nde.transforms import splines class CubicSplineTest(torchtestcase.TorchTestCase): diff --git a/nde/transforms/splines/linear.py b/phaseSpaceSampling/nde/transforms/splines/linear.py similarity index 95% rename from nde/transforms/splines/linear.py rename to phaseSpaceSampling/nde/transforms/splines/linear.py index 3b5fee0..201ed0a 100644 --- a/nde/transforms/splines/linear.py +++ b/phaseSpaceSampling/nde/transforms/splines/linear.py @@ -4,8 +4,9 @@ from torch.nn import functional as F import numpy as np -import utils -from nde import transforms +from phaseSpaceSampling.nde import transforms + +from phaseSpaceSampling.utils.torchutils import searchsorted def unconstrained_linear_spline(inputs, unnormalized_pdf, inverse=False, @@ -58,7 +59,7 @@ def linear_spline(inputs, unnormalized_pdf, cdf = F.pad(cdf, pad=(1, 0), mode='constant', value=0.0) if inverse: - inv_bin_idx = utils.searchsorted(cdf, inputs) + inv_bin_idx = searchsorted(cdf, inputs) bin_boundaries = (torch.linspace(0, 1, num_bins+1) .view([1] * inputs.dim() + [-1]) diff --git a/nde/transforms/splines/linear_test.py b/phaseSpaceSampling/nde/transforms/splines/linear_test.py similarity index 96% rename from nde/transforms/splines/linear_test.py rename to phaseSpaceSampling/nde/transforms/splines/linear_test.py index ac8dbd9..57162a6 100644 --- a/nde/transforms/splines/linear_test.py +++ b/phaseSpaceSampling/nde/transforms/splines/linear_test.py @@ -1,7 +1,7 @@ import torch import torchtestcase -from nde.transforms import splines +from phaseSpaceSampling.nde.transforms import splines class LinearSplineTest(torchtestcase.TorchTestCase): def test_forward_inverse_are_consistent(self): diff --git a/nde/transforms/splines/quadratic.py b/phaseSpaceSampling/nde/transforms/splines/quadratic.py similarity index 95% rename from nde/transforms/splines/quadratic.py rename to phaseSpaceSampling/nde/transforms/splines/quadratic.py index 2bee354..36dd431 100644 --- a/nde/transforms/splines/quadratic.py +++ b/phaseSpaceSampling/nde/transforms/splines/quadratic.py @@ -3,8 +3,9 @@ import torch from torch.nn import functional as F -import utils -from nde import transforms +from phaseSpaceSampling.nde import transforms + +from phaseSpaceSampling.utils.torchutils import searchsorted DEFAULT_MIN_BIN_WIDTH = 1e-3 DEFAULT_MIN_BIN_HEIGHT = 1e-3 @@ -100,9 +101,9 @@ def quadratic_spline(inputs, bin_locations = F.pad(bin_locations, pad=(1, 0), mode='constant', value=0.0) if inverse: - bin_idx = utils.searchsorted(bin_left_cdf, inputs)[..., None] + bin_idx = searchsorted(bin_left_cdf, inputs)[..., None] else: - bin_idx = utils.searchsorted(bin_locations, inputs)[..., None] + bin_idx = searchsorted(bin_locations, inputs)[..., None] input_bin_locations = bin_locations.gather(-1, bin_idx)[..., 0] input_bin_widths = widths.gather(-1, bin_idx)[..., 0] diff --git a/nde/transforms/splines/quadratic_test.py b/phaseSpaceSampling/nde/transforms/splines/quadratic_test.py similarity index 97% rename from nde/transforms/splines/quadratic_test.py rename to phaseSpaceSampling/nde/transforms/splines/quadratic_test.py index cb52f1e..286f634 100644 --- a/nde/transforms/splines/quadratic_test.py +++ b/phaseSpaceSampling/nde/transforms/splines/quadratic_test.py @@ -1,7 +1,7 @@ import torch import torchtestcase -from nde.transforms import splines +from phaseSpaceSampling.nde.transforms import splines class QuadraticSplineTest(torchtestcase.TorchTestCase): def test_forward_inverse_are_consistent(self): diff --git a/nde/transforms/splines/rational_quadratic.py b/phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py similarity index 96% rename from nde/transforms/splines/rational_quadratic.py rename to phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py index e98d0fd..8ff369a 100644 --- a/nde/transforms/splines/rational_quadratic.py +++ b/phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py @@ -1,10 +1,11 @@ import torch from torch.nn import functional as F -import utils import numpy as np -from nde import transforms +from phaseSpaceSampling.nde import transforms + +from phaseSpaceSampling.utils.torchutils import searchsorted DEFAULT_MIN_BIN_WIDTH = 1e-3 DEFAULT_MIN_BIN_HEIGHT = 1e-3 @@ -91,9 +92,9 @@ def rational_quadratic_spline(inputs, heights = cumheights[..., 1:] - cumheights[..., :-1] if inverse: - bin_idx = utils.searchsorted(cumheights, inputs)[..., None] + bin_idx = searchsorted(cumheights, inputs)[..., None] else: - bin_idx = utils.searchsorted(cumwidths, inputs)[..., None] + bin_idx = searchsorted(cumwidths, inputs)[..., None] input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] input_bin_widths = widths.gather(-1, bin_idx)[..., 0] diff --git a/nde/transforms/splines/rational_quadratic_test.py b/phaseSpaceSampling/nde/transforms/splines/rational_quadratic_test.py similarity index 97% rename from nde/transforms/splines/rational_quadratic_test.py rename to phaseSpaceSampling/nde/transforms/splines/rational_quadratic_test.py index 77fef5b..0856f21 100644 --- a/nde/transforms/splines/rational_quadratic_test.py +++ b/phaseSpaceSampling/nde/transforms/splines/rational_quadratic_test.py @@ -1,7 +1,7 @@ import torch import torchtestcase -from nde.transforms import splines +from phaseSpaceSampling.nde.transforms import splines class RationalQuadraticSplineTest(torchtestcase.TorchTestCase): def test_forward_inverse_are_consistent(self): diff --git a/nde/transforms/standard.py b/phaseSpaceSampling/nde/transforms/standard.py similarity index 97% rename from nde/transforms/standard.py rename to phaseSpaceSampling/nde/transforms/standard.py index 121233a..fa4d1fd 100644 --- a/nde/transforms/standard.py +++ b/phaseSpaceSampling/nde/transforms/standard.py @@ -1,7 +1,7 @@ """Implementations of some standard transforms.""" import torch -from nde import transforms +from phaseSpaceSampling.nde import transforms class IdentityTransform(transforms.Transform): diff --git a/nde/transforms/standard_test.py b/phaseSpaceSampling/nde/transforms/standard_test.py similarity index 96% rename from nde/transforms/standard_test.py rename to phaseSpaceSampling/nde/transforms/standard_test.py index 8a41749..bc19a45 100644 --- a/nde/transforms/standard_test.py +++ b/phaseSpaceSampling/nde/transforms/standard_test.py @@ -4,8 +4,8 @@ import torch import unittest -from nde.transforms import standard -from nde.transforms.transform_test import TransformTest +from phaseSpaceSampling.nde.transforms import standard +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class IdentityTransformTest(TransformTest): diff --git a/nde/transforms/svd.py b/phaseSpaceSampling/nde/transforms/svd.py similarity index 96% rename from nde/transforms/svd.py rename to phaseSpaceSampling/nde/transforms/svd.py index cd07fa0..31cf0c8 100644 --- a/nde/transforms/svd.py +++ b/phaseSpaceSampling/nde/transforms/svd.py @@ -4,8 +4,8 @@ from torch import nn from torch.nn import init -from nde import transforms -from nde.transforms.linear import Linear +from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.linear import Linear class SVDLinear(Linear): diff --git a/nde/transforms/svd_test.py b/phaseSpaceSampling/nde/transforms/svd_test.py similarity index 91% rename from nde/transforms/svd_test.py rename to phaseSpaceSampling/nde/transforms/svd_test.py index 08dd516..c829da4 100644 --- a/nde/transforms/svd_test.py +++ b/phaseSpaceSampling/nde/transforms/svd_test.py @@ -1,11 +1,11 @@ import torch import unittest -import utils -from nde.transforms import svd -from nde.transforms.transform_test import TransformTest +from phaseSpaceSampling.nde.transforms import svd +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +from phaseSpaceSampling.utils.torchutils import logabsdet class SVDLinearTest(TransformTest): @@ -19,7 +19,7 @@ def setUp(self): orthogonal_2 = self.transform.orthogonal_2.matrix() self.weight = orthogonal_1 @ diagonal @ orthogonal_2 self.weight_inverse = torch.inverse(self.weight) - self.logabsdet = utils.logabsdet(self.weight) + self.logabsdet = logabsdet(self.weight) self.eps = 1e-5 diff --git a/nde/transforms/transform_test.py b/phaseSpaceSampling/nde/transforms/transform_test.py similarity index 95% rename from nde/transforms/transform_test.py rename to phaseSpaceSampling/nde/transforms/transform_test.py index 2b90c1d..f6fdea6 100644 --- a/nde/transforms/transform_test.py +++ b/phaseSpaceSampling/nde/transforms/transform_test.py @@ -1,7 +1,7 @@ import torch import torchtestcase -from nde.transforms import base +from phaseSpaceSampling.nde.transforms import base class TransformTest(torchtestcase.TorchTestCase): diff --git a/nn/__init__.py b/phaseSpaceSampling/nn/__init__.py similarity index 100% rename from nn/__init__.py rename to phaseSpaceSampling/nn/__init__.py diff --git a/nn/attention.py b/phaseSpaceSampling/nn/attention.py similarity index 98% rename from nn/attention.py rename to phaseSpaceSampling/nn/attention.py index 65eeb64..db264a9 100644 --- a/nn/attention.py +++ b/phaseSpaceSampling/nn/attention.py @@ -3,7 +3,7 @@ from torch import nn from torch.nn import functional as F -import utils +from phaseSpaceSampling.utils.torchutils import get_num_parameters # # Projection of x onto y @@ -211,10 +211,10 @@ def main(): hidden_channels=32, num_blocks=4 ) - print(utils.get_num_parameters(net)) + print(get_num_parameters(net)) outputs = net(inputs) print(outputs.shape) if __name__ == '__main__': - main() \ No newline at end of file + main() diff --git a/nn/conv.py b/phaseSpaceSampling/nn/conv.py similarity index 100% rename from nn/conv.py rename to phaseSpaceSampling/nn/conv.py diff --git a/nn/mlp.py b/phaseSpaceSampling/nn/mlp.py similarity index 100% rename from nn/mlp.py rename to phaseSpaceSampling/nn/mlp.py diff --git a/nn/mlp_test.py b/phaseSpaceSampling/nn/mlp_test.py similarity index 96% rename from nn/mlp_test.py rename to phaseSpaceSampling/nn/mlp_test.py index adea93f..eb7de23 100644 --- a/nn/mlp_test.py +++ b/phaseSpaceSampling/nn/mlp_test.py @@ -4,7 +4,7 @@ import torchtestcase import unittest -from nn import mlp +from phaseSpaceSampling.nn import mlp class MLPTest(torchtestcase.TorchTestCase): diff --git a/nn/resnet.py b/phaseSpaceSampling/nn/resnet.py similarity index 98% rename from nn/resnet.py rename to phaseSpaceSampling/nn/resnet.py index 6c04899..011f9d4 100644 --- a/nn/resnet.py +++ b/phaseSpaceSampling/nn/resnet.py @@ -3,7 +3,7 @@ from torch import nn from torch.nn import functional as F, init -import utils +from phaseSpaceSampling.utils.torchutils import get_num_parameters class ResidualBlock(nn.Module): @@ -221,7 +221,7 @@ def main(): dropout_probability=0.1, use_batch_norm=True ) - print(utils.get_num_parameters(net)) + print(get_num_parameters(net)) outputs = net(inputs, context) print(outputs.shape) diff --git a/nn/unet.py b/phaseSpaceSampling/nn/unet.py similarity index 94% rename from nn/unet.py rename to phaseSpaceSampling/nn/unet.py index 4bfabf8..875e9cb 100644 --- a/nn/unet.py +++ b/phaseSpaceSampling/nn/unet.py @@ -3,7 +3,7 @@ from torch import nn from torch.nn import functional as F -import utils +from phaseSpaceSampling.utils.typechecks import is_power_of_two class UNet(nn.Module): @@ -15,7 +15,7 @@ def __init__(self, nonlinearity=F.relu): super().__init__() - assert utils.is_power_of_two(max_hidden_features), \ + assert is_power_of_two(max_hidden_features), \ '\'max_hidden_features\' must be a power of two.' assert max_hidden_features // 2 ** num_layers > 1, \ '\'num_layers\' must be {} or fewer'.format(int(np.log2(max_hidden_features) - 1)) diff --git a/plotLoss.py b/phaseSpaceSampling/postProcess/plotLoss.py similarity index 100% rename from plotLoss.py rename to phaseSpaceSampling/postProcess/plotLoss.py diff --git a/visualizeDownSampled_subplots.py b/phaseSpaceSampling/postProcess/visualizeDownSampled_subplots.py similarity index 100% rename from visualizeDownSampled_subplots.py rename to phaseSpaceSampling/postProcess/visualizeDownSampled_subplots.py diff --git a/sampler.py b/phaseSpaceSampling/sampler.py similarity index 97% rename from sampler.py rename to phaseSpaceSampling/sampler.py index 2cdff5c..7109df0 100644 --- a/sampler.py +++ b/phaseSpaceSampling/sampler.py @@ -7,18 +7,17 @@ from torch.utils import data from torch.utils.data import DataLoader -sys.path.append("utils") import os import time -import parallel as par -from flowUtils import * +import phaseSpaceSampling.utils.parallel as par +from phaseSpaceSampling.utils.flowUtils import create_base_transform +from phaseSpaceSampling.utils.torchutils import tensor2numpy from prettyPlot.progressBar import print_progress_bar from sklearn.neighbors import NearestNeighbors -import nn as nn_ -import utils -from nde import distributions, flows, transforms +import phaseSpaceSampling.nn as nn_ +from phaseSpaceSampling.nde import distributions, flows, transforms def str2float(str): @@ -176,10 +175,10 @@ def trainFlow(np_data, flow, pdf_iter, inpt): # Perform training on GPU if possible if use_gpu: device = torch.device("cuda") - torch.set_default_tensor_type("torch.cuda.FloatTensor") + torch.set_default_dtype(torch.cuda.float32) else: device = torch.device("cpu") - torch.set_default_tensor_type("torch.FloatTensor") + torch.set_default_dtype(torch.float32) flow = flow.to(device) # Train @@ -225,7 +224,7 @@ def trainFlow(np_data, flow, pdf_iter, inpt): if use_gpu: # Send model back to cpu so that all cpu can read it device = torch.device("cpu") - torch.set_default_tensor_type("torch.FloatTensor") + torch.set_default_dtype(torch.float32) flow = flow.to(device) torch.save( @@ -379,7 +378,7 @@ def evalLogProbNF(flow, np_data_to_downsample, pdf_iter, inpt): batch = batch.to(device) log_density = flow.log_prob(batch) log_density_np = np.concatenate( - (log_density_np, utils.tensor2numpy(log_density)) + (log_density_np, tensor2numpy(log_density)) ) print_progress_bar( step + 1, diff --git a/phaseSpaceSampling/scaler.npz b/phaseSpaceSampling/scaler.npz new file mode 100644 index 0000000000000000000000000000000000000000..6e4e3f78349a0af890aaaa531b2e186d06d044cd GIT binary patch literal 542 zcmWIWW@Zs#fB;2?A6K=tCjdDh%*7zWkeitomYAcLS5V2wAOIExsRYS_z+}Hr-+)L) zhBAg~^_0}&iK>k-N5}~{r&(Hzcf#tT8-kD#EQY~7l&(FpX@>5D&e`sKERui xNrV|!%z#uuKm#L)M#K}kKGaZvY64OXjAual;9(Nr&B_LnWdgziAUy+YE&#w-X3PKp literal 0 HcmV?d00001 diff --git a/phaseSpaceSampling/utils/.torchutils.py.swp b/phaseSpaceSampling/utils/.torchutils.py.swp new file 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-1,7 +1,9 @@ import sys - +import os import numpy as np -import parallel as par + +import phaseSpaceSampling.utils.parallel as par +from phaseSpaceSampling import DATA_DIR # from memory_profiler import profile @@ -33,6 +35,14 @@ def checkData(shape, N, d, nWorkingData, nWorkingDataAdjustment, useNF): def prepareData(inpt): # Set parameters from input dataFile = inpt["dataFile"] + if not os.path.isfile(dataFile): + new_dataFile = os.path.join(DATA_DIR, os.path.split(dataFile)[-1]) + par.printRoot(f"WARNING: {dataFile} not found trying {new_dataFile} ...") + if not os.path.isfile(new_dataFile): + par.printRoot(f"ERROR: could not open data {dataFile} or {new_dataFile}") + sys.exit() + else: + dataFile = new_dataFile preShuffled = inpt["preShuffled"] == "True" scalerFile = inpt["scalerFile"] nWorkingDatas = [int(float(n)) for n in inpt["nWorkingData"].split()] diff --git a/utils/flowUtils.py b/phaseSpaceSampling/utils/flowUtils.py similarity index 84% rename from utils/flowUtils.py rename to phaseSpaceSampling/utils/flowUtils.py index 5f1a067..65361b4 100644 --- a/utils/flowUtils.py +++ b/phaseSpaceSampling/utils/flowUtils.py @@ -3,10 +3,9 @@ import torch from torch.utils import data -sys.path.append("../utils") -import nn as nn_ -import utils -from nde import distributions, flows, transforms +import phaseSpaceSampling.nn as nn_ +from phaseSpaceSampling.nde import distributions, flows, transforms +from phaseSpaceSampling.utils.torchutils import create_alternating_binary_mask, create_random_binary_mask def create_base_transform( @@ -20,7 +19,7 @@ def create_base_transform( ): if base_transform_type == "affine": return transforms.AffineCouplingTransform( - mask=utils.create_alternating_binary_mask( + mask=create_alternating_binary_mask( features=dim, even=(i % 2 == 0) ), transform_net_create_fn=lambda in_features, out_features: nn_.ResidualNet( @@ -33,7 +32,7 @@ def create_base_transform( ) else: return transforms.PiecewiseRationalQuadraticCouplingTransform( - mask=utils.create_alternating_binary_mask( + mask=create_alternating_binary_mask( features=dim, even=(i % 2 == 0) ), transform_net_create_fn=lambda in_features, out_features: nn_.ResidualNet( @@ -61,7 +60,7 @@ def create_base_transform_random( ): if base_transform_type == "affine": return transforms.AffineCouplingTransform( - mask=utils.create_random_binary_mask(features=dim), + mask=create_random_binary_mask(features=dim), transform_net_create_fn=lambda in_features, out_features: nn_.ResidualNet( in_features=in_features, out_features=out_features, @@ -72,7 +71,7 @@ def create_base_transform_random( ) else: return transforms.PiecewiseRationalQuadraticCouplingTransform( - mask=utils.create_random_binary_mask(features=dim), + mask=create_random_binary_mask(features=dim), transform_net_create_fn=lambda in_features, out_features: nn_.ResidualNet( in_features=in_features, out_features=out_features, diff --git a/utils/io.py b/phaseSpaceSampling/utils/io.py similarity index 100% rename from utils/io.py rename to phaseSpaceSampling/utils/io.py diff --git a/utils/__init__.py b/phaseSpaceSampling/utils/oldinit.py similarity index 100% rename from utils/__init__.py rename to phaseSpaceSampling/utils/oldinit.py diff --git a/utils/parallel.py b/phaseSpaceSampling/utils/parallel.py similarity index 100% rename from utils/parallel.py rename to phaseSpaceSampling/utils/parallel.py diff --git a/utils/plotFun.py b/phaseSpaceSampling/utils/plotFun.py similarity index 94% rename from utils/plotFun.py rename to phaseSpaceSampling/utils/plotFun.py index 53c58b8..27691e1 100644 --- a/utils/plotFun.py +++ b/phaseSpaceSampling/utils/plotFun.py @@ -1,11 +1,11 @@ import numpy as np -import parallel as par +from phaseSpaceSampling.utils.parallel import irank, iroot from matplotlib import cm from prettyPlot.plotting import plt, pretty_labels def cornerPlotScatter(data, title=None): - if not par.irank == par.iroot: + if not irank == iroot: return nDim = data.shape[1] @@ -36,7 +36,7 @@ def cornerPlotScatter(data, title=None): def cornerPlotScatterColor(data, colorData): - if not par.irank == par.iroot: + if not irank == iroot: return nDim = data.shape[1] diff --git a/utils/torchutils.py b/phaseSpaceSampling/utils/torchutils.py similarity index 95% rename from utils/torchutils.py rename to phaseSpaceSampling/utils/torchutils.py index f8c8771..c6bfb63 100644 --- a/utils/torchutils.py +++ b/phaseSpaceSampling/utils/torchutils.py @@ -2,11 +2,11 @@ import torch -import utils +from phaseSpaceSampling.utils.typechecks import is_positive_int, is_nonnegative_int def tile(x, n): - if not utils.is_positive_int(n): + if not is_positive_int(n): raise TypeError("Argument 'n' must be a positive integer.") x_ = x.reshape(-1) x_ = x_.repeat(n) @@ -18,7 +18,7 @@ def tile(x, n): def sum_except_batch(x, num_batch_dims=1): """Sums all elements of `x` except for the first `num_batch_dims` dimensions.""" - if not utils.is_nonnegative_int(num_batch_dims): + if not is_nonnegative_int(num_batch_dims): raise TypeError( "Number of batch dimensions must be a non-negative integer." ) @@ -34,7 +34,7 @@ def split_leading_dim(x, shape): def merge_leading_dims(x, num_dims): """Reshapes the tensor `x` such that the first `num_dims` dimensions are merged to one.""" - if not utils.is_positive_int(num_dims): + if not is_positive_int(num_dims): raise TypeError("Number of leading dims must be a positive integer.") if num_dims > x.dim(): raise ValueError( @@ -46,7 +46,7 @@ def merge_leading_dims(x, num_dims): def repeat_rows(x, num_reps): """Each row of tensor `x` is repeated `num_reps` times along leading dimension.""" - if not utils.is_positive_int(num_reps): + if not is_positive_int(num_reps): raise TypeError("Number of repetitions must be a positive integer.") shape = x.shape x = x.unsqueeze(1) diff --git a/utils/torchutils_test.py b/phaseSpaceSampling/utils/torchutils_test.py similarity index 100% rename from utils/torchutils_test.py rename to phaseSpaceSampling/utils/torchutils_test.py diff --git a/utils/typechecks.py b/phaseSpaceSampling/utils/typechecks.py similarity index 100% rename from utils/typechecks.py rename to phaseSpaceSampling/utils/typechecks.py diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index f0aaf1e..0000000 --- a/pyproject.toml +++ /dev/null @@ -1,59 +0,0 @@ -[tool.poetry] -name = "uips" -version = "0.1.0" -description = "" -authors = ["Malik Hassanaly " ] - -[tool.poetry.dependencies] -python = ">=3.8,<3.11" -torch = {url = "https://download.pytorch.org/whl/cpu/torch-1.7.1-cp39-none-macosx_10_9_x86_64.whl"} -scikit-learn = "^1.2.0" - -[tool.poetry.dev-dependencies] -pytest = "^5.2" -black = {version = "^22.3"} -isort = "^5.9.3" -ipython = "^8.2.0" -flake8 = "^4.0.1" -flake8-bugbear = "^22.4.25" -pep8-naming = "^0.12.1" -flake8-docstrings = "^1.6.0" -numpy = "^1.19.1" -matplotlib = "^3.2.2" -mpi4py = "^3.0.3" -imageio = "2.9.0" - -[build-system] -requires = ["poetry-core>=1.0.0"] 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zBzZvp3TKe%QUjTzt{}UB4+?x~Q2eb3%G>u}s0>3s} z;I}CPQMQJmzX1ZNK7<%4L5Pz%jDDvKqmSxC%ya{Yr^b*l-T@L<@?lb!Jxo2S1L@-> zAU)C&X1AF`?n)iVYts{xHxx~Gg~j5!%NMA^%JJ4v@lFyd#s|QfNKe?ncZ7YBzHn%| z795SVg%jg_pnicE2ez3(XQUV-C>y~8WgB?BKm}g3iTV63#z+ec;KMet4sA1nzUhk4 z_uB)$8d<{k-~KS@1n^f`9VFZJLB3rb6a-Qr|Hl-xCP;y{iYw^MkOrgonqa(L8jOw2 zz)0W;cJJlD{+KT~6p7bC#Q4+;RG3YBA&;KKXUMDf)%_L7{R-dn(z^g;B&hk{5<9WvJ0I+E}IXE zt35zD+Yxl4EJ1fr9SoQX81GO5hYw;b?4l1YgBIX6=nFm*)xl?n6Och&2)ZWbe^3z; zt|`O#M*%P~I{=bR3?S`;7Gx3`nBgJ=1yL4IFz5qCOdtMPmRA#|E3 zz`cp8aL>d89;=G=+G=;`U8n@TkM!WnpdJiPGy*A8Es&aI4pJ3jK1bPt;zvbL`YQ*D zqtrmt)f0xeI)KGTKG>)!gX<(CaGT@`UdP?RXQvAIRcOFaQ%wk(WC_6)E)aa(3x;!& z5GF2<7^MKCj!Qwbt0Rm#t_m@e3?X)=1dJUe1>@E9U;-xxlRlb4%1lMbsIZ32zy2_d zlYyC!O<>kv9hhA)1ad?^koQpzicQU+WRfH-o@4{1*Ts7%LPAno>i_g(!GGQ*{QF)c v@$dV;elz%ATm8?M693lx&#*{Btiu2M6~*WX8QFh}B*o86aUAdV@7I3-*^Wij From 3f61d6dac9def46e3ddb2f361e1ac6215def0162 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 17:12:25 -0700 Subject: [PATCH 09/32] remove training logs by default --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 29c3a43..e2baef5 100644 --- a/.gitignore +++ b/.gitignore @@ -146,4 +146,4 @@ cython_debug/ # output Figures -TrainingLog +TrainingLog* From 1577b54483f7f5be71b00310f722a493ae483f2d Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 17:15:54 -0700 Subject: [PATCH 10/32] rename dangerous global vars --- phaseSpaceSampling/__init__.py | 9 ++++----- phaseSpaceSampling/utils/fileFinder.py | 6 +++--- postProcess/plotLoss.py | 4 ++-- postProcess/visualizeDownSampled_subplots.py | 4 ++-- 4 files changed, 11 insertions(+), 12 deletions(-) diff --git a/phaseSpaceSampling/__init__.py b/phaseSpaceSampling/__init__.py index 98966e8..9080071 100644 --- a/phaseSpaceSampling/__init__.py +++ b/phaseSpaceSampling/__init__.py @@ -2,8 +2,7 @@ import os PSS_DIR = os.path.dirname(os.path.realpath(__file__)) -INPUT_DIR = os.path.join(PSS_DIR, "../inputs") -DATA_DIR = os.path.join(PSS_DIR, "../data") -DOC_DIR = os.path.join(PSS_DIR, "../documentation") -ML_DIR = os.path.join(PSS_DIR, "../data-efficientML") -UTIL_DIR = os.path.join(PSS_DIR, "../utils") +PSS_INPUT_DIR = os.path.join(PSS_DIR, "../inputs") +PSS_DATA_DIR = os.path.join(PSS_DIR, "../data") +PSS_DOC_DIR = os.path.join(PSS_DIR, "../documentation") +PSS_ML_DIR = os.path.join(PSS_DIR, "../data-efficientML") diff --git a/phaseSpaceSampling/utils/fileFinder.py b/phaseSpaceSampling/utils/fileFinder.py index 26b1bb1..b4f4357 100644 --- a/phaseSpaceSampling/utils/fileFinder.py +++ b/phaseSpaceSampling/utils/fileFinder.py @@ -2,12 +2,12 @@ import sys import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling import DATA_DIR, INPUT_DIR +from phaseSpaceSampling import PSS_DATA_DIR, PSS_INPUT_DIR def find_input(inpt_file): if not os.path.isfile(inpt_file): - new_inpt_file = os.path.join(INPUT_DIR, os.path.split(inpt_file)[-1]) + new_inpt_file = os.path.join(PSS_INPUT_DIR, os.path.split(inpt_file)[-1]) par.printRoot( f"WARNING: {inpt_file} not found trying {new_inpt_file} ..." ) @@ -25,7 +25,7 @@ def find_input(inpt_file): def find_data(data_file): if not os.path.isfile(data_file): - new_data_file = os.path.join(DATA_DIR, os.path.split(data_file)[-1]) + new_data_file = os.path.join(PSS_DATA_DIR, os.path.split(data_file)[-1]) par.printRoot( f"WARNING: {data_file} not found trying {new_data_file} ..." ) diff --git a/postProcess/plotLoss.py b/postProcess/plotLoss.py index d95a444..cf9250c 100644 --- a/postProcess/plotLoss.py +++ b/postProcess/plotLoss.py @@ -8,7 +8,7 @@ from prettyPlot.plotting import pretty_labels import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling import INPUT_DIR +from phaseSpaceSampling import PSS_INPUT_DIR parser = argparse.ArgumentParser(description="Loss plotting") parser.add_argument( @@ -27,7 +27,7 @@ # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ inpt_file = args.input if not os.path.isfile(inpt_file): - new_inpt_file = os.path.join(INPUT_DIR, os.path.split(inpt_file)[-1]) + new_inpt_file = os.path.join(PSS_INPUT_DIR, os.path.split(inpt_file)[-1]) par.printRoot(f"WARNING: {inpt_file} not found trying {new_inpt_file} ...") if not os.path.isfile(new_inpt_file): par.printRoot( diff --git a/postProcess/visualizeDownSampled_subplots.py b/postProcess/visualizeDownSampled_subplots.py index 4546d6d..e631899 100644 --- a/postProcess/visualizeDownSampled_subplots.py +++ b/postProcess/visualizeDownSampled_subplots.py @@ -8,7 +8,7 @@ from prettyPlot.plotting import pretty_labels import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling import INPUT_DIR +from phaseSpaceSampling import PSS_INPUT_DIR def plotScatterProjection(data, fullData, fieldNames, lims): @@ -71,7 +71,7 @@ def plotScatterProjection(data, fullData, fieldNames, lims): # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ inpt_file = args.input if not os.path.isfile(inpt_file): - new_inpt_file = os.path.join(INPUT_DIR, os.path.split(inpt_file)[-1]) + new_inpt_file = os.path.join(PSS_INPUT_DIR, os.path.split(inpt_file)[-1]) par.printRoot(f"WARNING: {inpt_file} not found trying {new_inpt_file} ...") if not os.path.isfile(new_inpt_file): par.printRoot( From 837f9b034513cb865b230b25e022dd5c6d12b535 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 17:16:24 -0700 Subject: [PATCH 11/32] format --- main.py | 1 + phaseSpaceSampling/utils/fileFinder.py | 8 +++-- phaseSpaceSampling/wrapper.py | 49 +++++++++++++------------- 3 files changed, 31 insertions(+), 27 deletions(-) diff --git a/main.py b/main.py index 30de786..738666a 100644 --- a/main.py +++ b/main.py @@ -1,4 +1,5 @@ import argparse + from phaseSpaceSampling.wrapper import downsample_dataset_from_input parser = argparse.ArgumentParser(description="Downsampler") diff --git a/phaseSpaceSampling/utils/fileFinder.py b/phaseSpaceSampling/utils/fileFinder.py index b4f4357..43fe8b8 100644 --- a/phaseSpaceSampling/utils/fileFinder.py +++ b/phaseSpaceSampling/utils/fileFinder.py @@ -7,7 +7,9 @@ def find_input(inpt_file): if not os.path.isfile(inpt_file): - new_inpt_file = os.path.join(PSS_INPUT_DIR, os.path.split(inpt_file)[-1]) + new_inpt_file = os.path.join( + PSS_INPUT_DIR, os.path.split(inpt_file)[-1] + ) par.printRoot( f"WARNING: {inpt_file} not found trying {new_inpt_file} ..." ) @@ -25,7 +27,9 @@ def find_input(inpt_file): def find_data(data_file): if not os.path.isfile(data_file): - new_data_file = os.path.join(PSS_DATA_DIR, os.path.split(data_file)[-1]) + new_data_file = os.path.join( + PSS_DATA_DIR, os.path.split(data_file)[-1] + ) par.printRoot( f"WARNING: {data_file} not found trying {new_data_file} ..." ) diff --git a/phaseSpaceSampling/wrapper.py b/phaseSpaceSampling/wrapper.py index 6bc8496..e6be4d0 100644 --- a/phaseSpaceSampling/wrapper.py +++ b/phaseSpaceSampling/wrapper.py @@ -15,7 +15,6 @@ def downsample_dataset_from_input(inpt_file): - inpt_file = find_input(inpt_file) inpt = parse_input_file(inpt_file) use_normalizing_flow = inpt["pdf_method"].lower() == "normalizingflow" @@ -24,7 +23,7 @@ def downsample_dataset_from_input(inpt_file): # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ~~~~ Parameters to save # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - + # List of sample size nSamples = [int(float(n)) for n in inpt["nSamples"].split()] # Data size used to adjust the sampling probability @@ -46,12 +45,11 @@ def downsample_dataset_from_input(inpt_file): nSampleCriterionLimit = int(inpt["nSampleCriterionLimit"]) except: nSampleCriterionLimit = int(1e5) - - + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ~~~~ Environment # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - + os.environ["KMP_DUPLICATE_LIB_OK"] = "True" use_gpu = ( (inpt["use_gpu"] == "True") @@ -69,16 +67,15 @@ def downsample_dataset_from_input(inpt_file): # REPRODUCIBILITY torch.manual_seed(int(inpt["seed"]) + par.irank) np.random.seed(int(inpt["seed"]) + par.irank) - - + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ~~~~ Prepare Data and scatter across processors # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - + data_to_downsample_, dataInd_, working_data, nFullData = prepareData(inpt) - + dim = data_to_downsample_.shape[1] - + # Compute uniform sampling criterion of random data randomCriterion = np.zeros(len(nSamples)) if par.irank == par.iroot and computeCriterion: @@ -93,18 +90,18 @@ def downsample_dataset_from_input(inpt_file): par.printRoot( f"\t nSample {nSample} mean dist = {mean:.4f}, std dist = {std:.4f}" ) - + # Prepare arrays used for sanity checks meanCriterion = np.zeros((int(inpt["num_pdf_iter"]), len(nSamples))) stdCriterion = np.zeros((int(inpt["num_pdf_iter"]), len(nSamples))) flow_nll_loss = np.zeros(int(inpt["num_pdf_iter"])) - + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ~~~~ Downsample # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - + data_for_pdf_est = working_data - + for pdf_iter in range(int(inpt["num_pdf_iter"])): if use_normalizing_flow: # Create the normalizing flow @@ -112,20 +109,22 @@ def downsample_dataset_from_input(inpt_file): # flow = flow.to(device) n_params = get_num_parameters(flow) par.printRoot( - "There are {} trainable parameters in this model.".format(n_params) + "There are {} trainable parameters in this model.".format( + n_params + ) ) - + # Train (happens on 1 proc) flow_nll_loss[pdf_iter] = sampler.trainFlow( data_for_pdf_est, flow, pdf_iter, inpt ) sampler.checkLoss(pdf_iter, flow_nll_loss) - + # Evaluate probability: This is the expensive step (happens on multi processors) log_density_np_ = sampler.evalLogProbNF( flow, data_to_downsample_, pdf_iter, inpt ) - + if use_bins: bin_pdfH, bin_pdfEdges = sampler.trainBinPDF( data_for_pdf_est, pdf_iter, inpt @@ -134,14 +133,14 @@ def downsample_dataset_from_input(inpt_file): log_density_np_ = sampler.evalLogProbBIN( data_to_downsample_, pdf_iter, inpt ) - + if use_serial_adjustment: log_density_np_for_adjust = par.gatherNelementsInArray( log_density_np_, nWorkingDataAdjustment ) else: log_density_np_for_adjust = None - + # Correct probability estimate if pdf_iter > 0: log_density_np_ = log_density_np_ - log_samplingProb_ @@ -151,9 +150,9 @@ def downsample_dataset_from_input(inpt_file): ) else: log_density_np_for_adjust = None - + par.printRoot(f"TRAIN ITER {pdf_iter}") - + for inSample, nSample in enumerate(nSamples): # Downsample ( @@ -170,7 +169,7 @@ def downsample_dataset_from_input(inpt_file): nFullData, inpt, ) - + # Plot # cornerPlotScatter(downSampledData,title='downSampled npts='+str(nSample)+', iter='+str(pdf_iter)) # Get criterion @@ -184,7 +183,7 @@ def downsample_dataset_from_input(inpt_file): par.printRoot( f"\t nSample {nSample} mean dist = {mean:.4f}, std dist = {std:.4f}" ) - + if pdf_iter == int(inpt["num_pdf_iter"]) - 1: # Last pdf iter : Root proc saves downsampled data, and checks the outcome sampler.checkProcedure( @@ -198,7 +197,7 @@ def downsample_dataset_from_input(inpt_file): data=downSampledData, indices=downSampledIndices, ) - + if not (pdf_iter == int(inpt["num_pdf_iter"]) - 1): # Prepare data for the next training iteration ( From 20adb0085f299628b2f96cd94e36e7449f12af19 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 17:40:28 -0700 Subject: [PATCH 12/32] reorganize tests --- .github/workflows/ci.yml | 13 +- README.md | 6 +- {tests => inputs}/input_test | 0 {tests => inputs}/input_test_bins | 0 main_iterative.py | 251 ----------------------------- main.py => tests/main.py | 0 tests/run3D.sh | 9 -- tests/test_downsampling.py | 11 ++ {tests => tutorials}/run2D.sh | 0 {tests => tutorials}/run2D_bins.sh | 0 10 files changed, 20 insertions(+), 270 deletions(-) rename {tests => inputs}/input_test (100%) rename {tests => inputs}/input_test_bins (100%) delete mode 100644 main_iterative.py rename main.py => tests/main.py (100%) delete mode 100644 tests/run3D.sh create mode 100644 tests/test_downsampling.py rename {tests => tutorials}/run2D.sh (100%) rename {tests => tutorials}/run2D_bins.sh (100%) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 0524181..00c5984 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -32,17 +32,16 @@ jobs: run: | python -m pip install --upgrade pip python -m pip install . + pip install pytest - name: Formatting run: | source .github/linters/formatting.sh format . true codespell - - name: Normalizing flow test + - name: Pytests run: | - python main_iterative.py -i tests/input_test - - name: Bins test + python -m pytest tests -v --disable-warnings + - name: Parallel test run: | - python main_iterative.py -i tests/input_test_bins - - name: Parallel normalizing flow test - run: | - mpiexec -np 2 python main_iterative.py -i tests/input_test + cd tests + mpiexec -np 2 python main.py -i ../inputs/input_test diff --git a/README.md b/README.md index effafe1..634a5ff 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ The purpose of the tool is to perform a smart downselection of a large number of ## Running the example -`bash tests/run2D.sh`: Example of downsampling a 2D combustion dataset. First the downsampling is performed (`mpiexec -np 4 python main.py -i inputs/input2D`). Then the loss function for each flow iteration is plotted (`python postProcess/plotLoss.py -i inputs/input2D`). Finally, the samples are visualized (`python postProcess/visualizeDownSampled_subplots.py -i inputs/input2D`). All figures are saved under the folder `Figures`. +`bash tutorials/run2D.sh`: Example of downsampling a 2D combustion dataset. First the downsampling is performed (`mpiexec -np 4 python main.py -i inputs/input2D`). Then the loss function for each flow iteration is plotted (`python postProcess/plotLoss.py -i inputs/input2D`). Finally, the samples are visualized (`python postProcess/visualizeDownSampled_subplots.py -i inputs/input2D`). All figures are saved under the folder `Figures`. ## Parallelization @@ -35,7 +35,7 @@ Running on GPU only accelerate execution by ~30% for the examples provided here. Parallelization tested with up to 36 cores on Eagle. -Parallelization tested with up to 4 cores on MacOS Catalina v10.15.7. +Parallelization tested with up to 4 cores on MacOS Monterey v12.7.1. ## Data @@ -47,7 +47,7 @@ The dataset to downsample has size $N \times d$ where $N \gg d$. The first dimen ## Hyperparameters -All hyperparameters can be controlled via an input file (see `tests/run2D.sh`). +All hyperparameters can be controlled via an input file (see `tutorials/run2D.sh`). We recommend fixing the number of flow calculation iteration to 2. When increasing the number of dimensions, we recommend adjusting the hyperparameters. A 2-dimensional example (`inputs/input2D`) and an 11-dimensional (`inputs/highdim/input11D`) example are provided to guide the user. diff --git a/tests/input_test b/inputs/input_test similarity index 100% rename from tests/input_test rename to inputs/input_test diff --git a/tests/input_test_bins b/inputs/input_test_bins similarity index 100% rename from tests/input_test_bins rename to inputs/input_test_bins diff --git a/main_iterative.py b/main_iterative.py deleted file mode 100644 index 8700b8b..0000000 --- a/main_iterative.py +++ /dev/null @@ -1,251 +0,0 @@ -import argparse -import os -import sys -import time - -import numpy as np -import torch -from prettyPlot.parser import parse_input_file - -import phaseSpaceSampling.sampler as sampler -import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling.utils.dataUtils import prepareData -from phaseSpaceSampling.utils.fileFinder import find_input -from phaseSpaceSampling.utils.plotFun import * -from phaseSpaceSampling.utils.torchutils import get_num_parameters - -# import matplotlib.pyplot as plt - - -parser = argparse.ArgumentParser(description="Downsampler") -parser.add_argument( - "-i", - "--input", - type=str, - metavar="", - required=False, - help="Input file", - default="input", -) -args, unknown = parser.parse_known_args() - -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# ~~~~ Parse input -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -inpt_file = find_input(args.input) -inpt = parse_input_file(inpt_file) -use_normalizing_flow = inpt["pdf_method"].lower() == "normalizingflow" -use_bins = inpt["pdf_method"].lower() == "bins" - -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# ~~~~ Parameters to save -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -# List of sample size -nSamples = [int(float(n)) for n in inpt["nSamples"].split()] -# Data size used to adjust the sampling probability -nWorkingDataAdjustment = int(float(inpt["nWorkingDataAdjustment"])) -if nWorkingDataAdjustment < 0: - use_serial_adjustment = False -else: - use_serial_adjustment = True -# Data size used to learn the data probability -nWorkingDatas = [int(float(n)) for n in inpt["nWorkingData"].split()] -if len(nWorkingDatas) == 1: - nWorkingDatas = nWorkingDatas * int(inpt["num_pdf_iter"]) -for nWorkingData in nWorkingDatas: - if not nWorkingData in nSamples: - nSamples += [nWorkingData] -# Do we compute the neighbor distance criterion -computeCriterion = inpt["computeDistanceCriterion"] == "True" -try: - nSampleCriterionLimit = int(inpt["nSampleCriterionLimit"]) -except: - nSampleCriterionLimit = int(1e5) - - -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# ~~~~ Environment -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -os.environ["KMP_DUPLICATE_LIB_OK"] = "True" -use_gpu = ( - (inpt["use_gpu"] == "True") - and (torch.cuda.is_available()) - and (par.irank == par.iroot) -) -if use_gpu: - # GPU SETTING - device = torch.device("cuda") - torch.set_default_dtype(torch.cuda.float32) -else: - # CPU SETTING - device = torch.device("cpu") - torch.set_default_dtype(torch.float32) -# REPRODUCIBILITY -torch.manual_seed(int(inpt["seed"]) + par.irank) -np.random.seed(int(inpt["seed"]) + par.irank) - - -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# ~~~~ Prepare Data and scatter across processors -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -data_to_downsample_, dataInd_, working_data, nFullData = prepareData(inpt) - -dim = data_to_downsample_.shape[1] - -# Compute uniform sampling criterion of random data -randomCriterion = np.zeros(len(nSamples)) -if par.irank == par.iroot and computeCriterion: - par.printRoot("RANDOM: ") - for inSample, nSample in enumerate(nSamples): - if nSample <= nSampleCriterionLimit: - random_sampled_data = working_data[:nSample, :] - mean, std = sampler.computeDistanceToClosestNeighbor( - sampler.rescaleData(random_sampled_data, inpt) - ) - randomCriterion[inSample] = mean - par.printRoot( - f"\t nSample {nSample} mean dist = {mean:.4f}, std dist = {std:.4f}" - ) - -# Prepare arrays used for sanity checks -meanCriterion = np.zeros((int(inpt["num_pdf_iter"]), len(nSamples))) -stdCriterion = np.zeros((int(inpt["num_pdf_iter"]), len(nSamples))) -flow_nll_loss = np.zeros(int(inpt["num_pdf_iter"])) - -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# ~~~~ Downsample -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -data_for_pdf_est = working_data - -for pdf_iter in range(int(inpt["num_pdf_iter"])): - if use_normalizing_flow: - # Create the normalizing flow - flow = sampler.createFlow(dim, pdf_iter, inpt) - # flow = flow.to(device) - n_params = get_num_parameters(flow) - par.printRoot( - "There are {} trainable parameters in this model.".format(n_params) - ) - - # Train (happens on 1 proc) - flow_nll_loss[pdf_iter] = sampler.trainFlow( - data_for_pdf_est, flow, pdf_iter, inpt - ) - sampler.checkLoss(pdf_iter, flow_nll_loss) - - # Evaluate probability: This is the expensive step (happens on multi processors) - log_density_np_ = sampler.evalLogProbNF( - flow, data_to_downsample_, pdf_iter, inpt - ) - - if use_bins: - bin_pdfH, bin_pdfEdges = sampler.trainBinPDF( - data_for_pdf_est, pdf_iter, inpt - ) - # Evaluate probability: This is the expensive step (happens on multi processors) - log_density_np_ = sampler.evalLogProbBIN( - data_to_downsample_, pdf_iter, inpt - ) - - if use_serial_adjustment: - log_density_np_for_adjust = par.gatherNelementsInArray( - log_density_np_, nWorkingDataAdjustment - ) - else: - log_density_np_for_adjust = None - - # Correct probability estimate - if pdf_iter > 0: - log_density_np_ = log_density_np_ - log_samplingProb_ - if use_serial_adjustment: - log_density_np_for_adjust = par.gatherNelementsInArray( - log_density_np_, nWorkingDataAdjustment - ) - else: - log_density_np_for_adjust = None - - par.printRoot(f"TRAIN ITER {pdf_iter}") - - for inSample, nSample in enumerate(nSamples): - # Downsample - ( - downSampledData, - downSampledIndices, - samplingProb_, - log_samplingProb_, - ) = sampler.downSample( - data_to_downsample_, - dataInd_, - log_density_np_, - log_density_np_for_adjust, - nSample, - nFullData, - inpt, - ) - - # Plot - # cornerPlotScatter(downSampledData,title='downSampled npts='+str(nSample)+', iter='+str(pdf_iter)) - # Get criterion - if computeCriterion and par.irank == par.iroot: - if nSample <= nSampleCriterionLimit: - mean, std = sampler.computeDistanceToClosestNeighbor( - sampler.rescaleData(downSampledData, inpt) - ) - meanCriterion[pdf_iter, inSample] = mean - stdCriterion[pdf_iter, inSample] = std - par.printRoot( - f"\t nSample {nSample} mean dist = {mean:.4f}, std dist = {std:.4f}" - ) - - if pdf_iter == int(inpt["num_pdf_iter"]) - 1: - # Last pdf iter : Root proc saves downsampled data, and checks the outcome - sampler.checkProcedure( - meanCriterion[:, inSample], - nSample, - randomCriterion[inSample], - ) - if par.irank == par.iroot: - np.savez( - f"{inpt['prefixDownsampledData']}_{nSample}_it{pdf_iter}.npz", - data=downSampledData, - indices=downSampledIndices, - ) - - if not (pdf_iter == int(inpt["num_pdf_iter"]) - 1): - # Prepare data for the next training iteration - ( - downSampledData, - _, - samplingProb_, - log_samplingProb_, - ) = sampler.downSample( - data_to_downsample_, - dataInd_, - log_density_np_, - log_density_np_for_adjust, - nWorkingDatas[pdf_iter + 1], - nFullData, - inpt, - ) - data_for_pdf_est = downSampledData - -# Advise for best sampling -if par.irank == par.iroot: - if np.amax(meanCriterion) > 0: - print("\n") - maxCrit = np.argmax(meanCriterion, axis=0) - nSamples_asked = [int(float(n)) for n in inpt["nSamples"].split()] - for iSample, nSample in enumerate(nSamples_asked): - print( - f"For sample {nSample} use {inpt['prefixDownsampledData']}_{nSample}_it{maxCrit[iSample]}.npz" - ) - print("\n") - - -# if par.irank==par.iroot: -# plt.show() diff --git a/main.py b/tests/main.py similarity index 100% rename from main.py rename to tests/main.py diff --git a/tests/run3D.sh b/tests/run3D.sh deleted file mode 100644 index f1a8c48..0000000 --- a/tests/run3D.sh +++ /dev/null @@ -1,9 +0,0 @@ -# Run the downsampling script -mpiexec -np 4 python main.py -i input3D - -# Plot the loss -python postProcess/plotLoss.py -i input3D - -# Plot the sampling results -python postProcess/visualizeDownSampled_subplots.py -i input3D - diff --git a/tests/test_downsampling.py b/tests/test_downsampling.py new file mode 100644 index 0000000..34f7595 --- /dev/null +++ b/tests/test_downsampling.py @@ -0,0 +1,11 @@ +from phaseSpaceSampling.wrapper import downsample_dataset_from_input +from phaseSpaceSampling import PSS_INPUT_DIR +import os + +def test_nf_input(): + input_file = os.path.join(PSS_INPUT_DIR, "input_test") + downsample_dataset_from_input(input_file) + +def test_bins_input(): + input_file = os.path.join(PSS_INPUT_DIR, "input_test_bins") + downsample_dataset_from_input(input_file) diff --git a/tests/run2D.sh b/tutorials/run2D.sh similarity index 100% rename from tests/run2D.sh rename to tutorials/run2D.sh diff --git a/tests/run2D_bins.sh b/tutorials/run2D_bins.sh similarity index 100% rename from tests/run2D_bins.sh rename to tutorials/run2D_bins.sh From 5a29b22ef42af64b27a195483c566ef05a8be656 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 17:41:16 -0700 Subject: [PATCH 13/32] rename parallel test --- .github/workflows/ci.yml | 2 +- tests/test_parallel.py | 17 +++++++++++++++++ 2 files changed, 18 insertions(+), 1 deletion(-) create mode 100644 tests/test_parallel.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 00c5984..213c7d3 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -44,4 +44,4 @@ jobs: - name: Parallel test run: | cd tests - mpiexec -np 2 python main.py -i ../inputs/input_test + mpiexec -np 2 python test_parallel.py -i ../inputs/input_test diff --git a/tests/test_parallel.py b/tests/test_parallel.py new file mode 100644 index 0000000..738666a --- /dev/null +++ b/tests/test_parallel.py @@ -0,0 +1,17 @@ +import argparse + +from phaseSpaceSampling.wrapper import downsample_dataset_from_input + +parser = argparse.ArgumentParser(description="Downsampler") +parser.add_argument( + "-i", + "--input", + type=str, + metavar="", + required=False, + help="Input file", + default="input", +) +args, unknown = parser.parse_known_args() + +downsample_dataset_from_input(args.input) From d0005bca699f9b7fe7f6045a1cca2d6cd42b20b6 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 17:43:58 -0700 Subject: [PATCH 14/32] update tutorials --- tutorials/run2D.sh | 2 +- tutorials/run2D_bins.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/run2D.sh b/tutorials/run2D.sh index c20be52..0090bfc 100644 --- a/tutorials/run2D.sh +++ b/tutorials/run2D.sh @@ -1,5 +1,5 @@ # Run the downsampling script -mpiexec -np 4 python main.py -i input2D +mpiexec -np 4 python tests/test_parallel.py -i input2D # Plot the loss python postProcess/plotLoss.py -i input2D diff --git a/tutorials/run2D_bins.sh b/tutorials/run2D_bins.sh index 1ccb459..935c2bf 100644 --- a/tutorials/run2D_bins.sh +++ b/tutorials/run2D_bins.sh @@ -1,5 +1,5 @@ # Run the downsampling script -mpiexec -np 4 python main.py -i input2D_bins +mpiexec -np 4 python tests/test_parallel.py -i input2D_bins # Plot the sampling results python postProcess/visualizeDownSampled_subplots.py -i input2D_bins From ce813420bb8d3b52ebd61f4ef943d3a25c66152a Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 17:46:48 -0700 Subject: [PATCH 15/32] reorganize test and update doc --- .github/workflows/ci.yml | 2 +- README.md | 8 ++++---- tests/{test_parallel.py => main_from_input.py} | 0 tutorials/run2D.sh | 2 +- tutorials/run2D_bins.sh | 2 +- 5 files changed, 7 insertions(+), 7 deletions(-) rename tests/{test_parallel.py => main_from_input.py} (100%) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 213c7d3..3aec303 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -44,4 +44,4 @@ jobs: - name: Parallel test run: | cd tests - mpiexec -np 2 python test_parallel.py -i ../inputs/input_test + mpiexec -np 2 python main_from_input.py -i ../inputs/input_test diff --git a/README.md b/README.md index 634a5ff..8c13f50 100644 --- a/README.md +++ b/README.md @@ -18,18 +18,18 @@ The purpose of the tool is to perform a smart downselection of a large number of ## Running the example -`bash tutorials/run2D.sh`: Example of downsampling a 2D combustion dataset. First the downsampling is performed (`mpiexec -np 4 python main.py -i inputs/input2D`). Then the loss function for each flow iteration is plotted (`python postProcess/plotLoss.py -i inputs/input2D`). Finally, the samples are visualized (`python postProcess/visualizeDownSampled_subplots.py -i inputs/input2D`). All figures are saved under the folder `Figures`. +`bash tutorials/run2D.sh`: Example of downsampling a 2D combustion dataset. First the downsampling is performed (`mpiexec -np 4 python tests/main_from_input.py -i inputs/input2D`). Then the loss function for each flow iteration is plotted (`python postProcess/plotLoss.py -i inputs/input2D`). Finally, the samples are visualized (`python postProcess/visualizeDownSampled_subplots.py -i inputs/input2D`). All figures are saved under the folder `Figures`. ## Parallelization -The code is GPU+MPI-parallelized: a) the dataset is loaded and shuffled in parallel b) the probability evaluation (the most expensive step) is done in parallel c) downsampling is done in parallel d) only the training is offloaded to a GPU if available. Memory usage of root processor is higher than other since it is the only one in charge of the normalizing flow training and sampling probability adjustment. To run the code in parallel, `mpiexec -np num_procs python main.py -i inputs/input2D`. +The code is GPU+MPI-parallelized: a) the dataset is loaded and shuffled in parallel b) the probability evaluation (the most expensive step) is done in parallel c) downsampling is done in parallel d) only the training is offloaded to a GPU if available. Memory usage of root processor is higher than other since it is the only one in charge of the normalizing flow training and sampling probability adjustment. To run the code in parallel, `mpiexec -np num_procs python tests/main_from_input.py -i inputs/input2D`. In the code, arrays with suffix `_` denote data distributed over the processors. The computation of nearest neighbor distance is parallelized using the sklearn implementation. It will be accelerated on systems where hyperthreading is enabled (your laptop, but NOT the Eagle HPC) -When using GPU+MPI-parallelism on Eagle, you need to specify the number of MPI tasks (`srun -n 36 python main.py`) -When using MPI-parallelism alone on Eagle, you do not need to specify the number of MPI tasks (`srun python main.py`) +When using GPU+MPI-parallelism on Eagle, you need to specify the number of MPI tasks (`srun -n 36 python tests/main_from_input.py`) +When using MPI-parallelism alone on Eagle, you do not need to specify the number of MPI tasks (`srun python tests/main_from_input.py`) Running on GPU only accelerate execution by ~30% for the examples provided here. Running with many MPI-tasks linearly decreases the execution time for probability evaluation, as well as memory per core requirements. diff --git a/tests/test_parallel.py b/tests/main_from_input.py similarity index 100% rename from tests/test_parallel.py rename to tests/main_from_input.py diff --git a/tutorials/run2D.sh b/tutorials/run2D.sh index 0090bfc..19bfe0a 100644 --- a/tutorials/run2D.sh +++ b/tutorials/run2D.sh @@ -1,5 +1,5 @@ # Run the downsampling script -mpiexec -np 4 python tests/test_parallel.py -i input2D +mpiexec -np 4 python tests/main_from_input.py -i input2D # Plot the loss python postProcess/plotLoss.py -i input2D diff --git a/tutorials/run2D_bins.sh b/tutorials/run2D_bins.sh index 935c2bf..3d09033 100644 --- a/tutorials/run2D_bins.sh +++ b/tutorials/run2D_bins.sh @@ -1,5 +1,5 @@ # Run the downsampling script -mpiexec -np 4 python tests/test_parallel.py -i input2D_bins +mpiexec -np 4 python tests/main_from_input.py -i input2D_bins # Plot the sampling results python postProcess/visualizeDownSampled_subplots.py -i input2D_bins From 4f247040a53b4941c2e6b4404b87d14ca6daa4e2 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 18:22:19 -0700 Subject: [PATCH 16/32] format --- tests/test_downsampling.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/tests/test_downsampling.py b/tests/test_downsampling.py index 34f7595..467c9b7 100644 --- a/tests/test_downsampling.py +++ b/tests/test_downsampling.py @@ -1,11 +1,14 @@ -from phaseSpaceSampling.wrapper import downsample_dataset_from_input +import os + from phaseSpaceSampling import PSS_INPUT_DIR -import os +from phaseSpaceSampling.wrapper import downsample_dataset_from_input + def test_nf_input(): input_file = os.path.join(PSS_INPUT_DIR, "input_test") downsample_dataset_from_input(input_file) + def test_bins_input(): input_file = os.path.join(PSS_INPUT_DIR, "input_test_bins") downsample_dataset_from_input(input_file) From b3ec394f7e4be422701f0937c34d5d68b4f4b1fd Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 18:32:48 -0700 Subject: [PATCH 17/32] rename package --- README.md | 4 ++-- phaseSpaceSampling/scaler.npz | Bin 542 -> 0 bytes postProcess/plotLoss.py | 4 ++-- postProcess/visualizeDownSampled_subplots.py | 4 ++-- setup.py | 4 ++-- tests/main.py | 2 +- tests/main_from_input.py | 2 +- tests/test_downsampling.py | 4 ++-- {phaseSpaceSampling => uips}/__init__.py | 0 {phaseSpaceSampling => uips}/cutils/__init__.py | 0 {phaseSpaceSampling => uips}/cutils/io.py | 0 {phaseSpaceSampling => uips}/cutils/misc.py | 2 +- {phaseSpaceSampling => uips}/nde/__init__.py | 0 .../nde/distributions/__init__.py | 0 .../nde/distributions/base.py | 4 ++-- .../nde/distributions/discrete.py | 4 ++-- .../nde/distributions/discrete_test.py | 2 +- .../nde/distributions/mixture.py | 0 .../nde/distributions/normal.py | 4 ++-- .../nde/distributions/normal_test.py | 2 +- .../nde/distributions/uniform.py | 2 +- .../nde/flows/__init__.py | 0 .../nde/flows/autoregressive.py | 4 ++-- .../nde/flows/autoregressive_test.py | 2 +- {phaseSpaceSampling => uips}/nde/flows/base.py | 4 ++-- .../nde/flows/base_test.py | 4 ++-- {phaseSpaceSampling => uips}/nde/flows/realnvp.py | 4 ++-- .../nde/flows/realnvp_test.py | 2 +- .../nde/transforms/__init__.py | 0 .../nde/transforms/autoregressive.py | 8 ++++---- .../nde/transforms/autoregressive_test.py | 4 ++-- .../nde/transforms/base.py | 2 +- .../nde/transforms/base_test.py | 4 ++-- .../nde/transforms/conv.py | 6 +++--- .../nde/transforms/conv_test.py | 4 ++-- .../nde/transforms/coupling.py | 8 ++++---- .../nde/transforms/coupling_test.py | 8 ++++---- .../nde/transforms/linear.py | 8 ++++---- .../nde/transforms/linear_test.py | 8 ++++---- {phaseSpaceSampling => uips}/nde/transforms/lu.py | 2 +- .../nde/transforms/lu_test.py | 6 +++--- .../nde/transforms/made.py | 2 +- .../nde/transforms/made_test.py | 2 +- .../nde/transforms/nonlinearities.py | 6 +++--- .../nde/transforms/nonlinearities_test.py | 10 +++++----- .../nde/transforms/normalization.py | 4 ++-- .../nde/transforms/normalization_test.py | 6 +++--- .../nde/transforms/orthogonal.py | 4 ++-- .../nde/transforms/orthogonal_test.py | 6 +++--- .../nde/transforms/permutations.py | 4 ++-- .../nde/transforms/permutations_test.py | 4 ++-- {phaseSpaceSampling => uips}/nde/transforms/qr.py | 4 ++-- .../nde/transforms/qr_test.py | 6 +++--- .../nde/transforms/reshape.py | 4 ++-- .../nde/transforms/reshape_test.py | 4 ++-- .../nde/transforms/splines/__init__.py | 0 .../nde/transforms/splines/cubic.py | 4 ++-- .../nde/transforms/splines/cubic_test.py | 2 +- .../nde/transforms/splines/linear.py | 4 ++-- .../nde/transforms/splines/linear_test.py | 2 +- .../nde/transforms/splines/quadratic.py | 4 ++-- .../nde/transforms/splines/quadratic_test.py | 2 +- .../nde/transforms/splines/rational_quadratic.py | 4 ++-- .../transforms/splines/rational_quadratic_test.py | 2 +- .../nde/transforms/standard.py | 2 +- .../nde/transforms/standard_test.py | 4 ++-- .../nde/transforms/svd.py | 4 ++-- .../nde/transforms/svd_test.py | 6 +++--- .../nde/transforms/transform_test.py | 2 +- {phaseSpaceSampling => uips}/nn/__init__.py | 0 {phaseSpaceSampling => uips}/nn/attention.py | 2 +- {phaseSpaceSampling => uips}/nn/conv.py | 0 {phaseSpaceSampling => uips}/nn/mlp.py | 0 {phaseSpaceSampling => uips}/nn/mlp_test.py | 2 +- {phaseSpaceSampling => uips}/nn/resnet.py | 2 +- {phaseSpaceSampling => uips}/nn/unet.py | 2 +- {phaseSpaceSampling => uips}/sampler.py | 10 +++++----- {phaseSpaceSampling => uips}/utils/dataUtils.py | 4 ++-- {phaseSpaceSampling => uips}/utils/fileFinder.py | 4 ++-- {phaseSpaceSampling => uips}/utils/flowUtils.py | 6 +++--- {phaseSpaceSampling => uips}/utils/io.py | 0 {phaseSpaceSampling => uips}/utils/oldinit.py | 0 {phaseSpaceSampling => uips}/utils/parallel.py | 0 {phaseSpaceSampling => uips}/utils/plotFun.py | 2 +- {phaseSpaceSampling => uips}/utils/torchutils.py | 2 +- .../utils/torchutils_test.py | 0 {phaseSpaceSampling => uips}/utils/typechecks.py | 0 {phaseSpaceSampling => uips}/wrapper.py | 12 ++++++------ 88 files changed, 144 insertions(+), 144 deletions(-) delete mode 100644 phaseSpaceSampling/scaler.npz rename {phaseSpaceSampling => uips}/__init__.py (100%) rename {phaseSpaceSampling => uips}/cutils/__init__.py (100%) rename {phaseSpaceSampling => uips}/cutils/io.py (100%) rename {phaseSpaceSampling => uips}/cutils/misc.py (89%) rename {phaseSpaceSampling => uips}/nde/__init__.py (100%) rename {phaseSpaceSampling => uips}/nde/distributions/__init__.py (100%) rename {phaseSpaceSampling => uips}/nde/distributions/base.py (97%) rename {phaseSpaceSampling => uips}/nde/distributions/discrete.py (96%) rename {phaseSpaceSampling => uips}/nde/distributions/discrete_test.py (98%) rename {phaseSpaceSampling => uips}/nde/distributions/mixture.py (100%) rename {phaseSpaceSampling => uips}/nde/distributions/normal.py (97%) rename {phaseSpaceSampling => uips}/nde/distributions/normal_test.py (99%) rename {phaseSpaceSampling => uips}/nde/distributions/uniform.py (87%) rename {phaseSpaceSampling => uips}/nde/flows/__init__.py (100%) rename {phaseSpaceSampling => uips}/nde/flows/autoregressive.py (94%) rename {phaseSpaceSampling => uips}/nde/flows/autoregressive_test.py (94%) rename {phaseSpaceSampling => uips}/nde/flows/base.py (96%) rename {phaseSpaceSampling => uips}/nde/flows/base_test.py (97%) rename {phaseSpaceSampling => uips}/nde/flows/realnvp.py (94%) rename {phaseSpaceSampling => uips}/nde/flows/realnvp_test.py (95%) rename {phaseSpaceSampling => uips}/nde/transforms/__init__.py (100%) rename {phaseSpaceSampling => uips}/nde/transforms/autoregressive.py (98%) rename {phaseSpaceSampling => uips}/nde/transforms/autoregressive_test.py (97%) rename {phaseSpaceSampling => uips}/nde/transforms/base.py (99%) rename {phaseSpaceSampling => uips}/nde/transforms/base_test.py (97%) rename {phaseSpaceSampling => uips}/nde/transforms/conv.py (88%) rename {phaseSpaceSampling => uips}/nde/transforms/conv_test.py (75%) rename {phaseSpaceSampling => uips}/nde/transforms/coupling.py (98%) rename {phaseSpaceSampling => uips}/nde/transforms/coupling_test.py (97%) rename {phaseSpaceSampling => uips}/nde/transforms/linear.py (96%) rename {phaseSpaceSampling => uips}/nde/transforms/linear_test.py (97%) rename {phaseSpaceSampling => uips}/nde/transforms/lu.py (98%) rename {phaseSpaceSampling => uips}/nde/transforms/lu_test.py (94%) rename {phaseSpaceSampling => uips}/nde/transforms/made.py (99%) rename {phaseSpaceSampling => uips}/nde/transforms/made_test.py (99%) rename {phaseSpaceSampling => uips}/nde/transforms/nonlinearities.py (98%) rename {phaseSpaceSampling => uips}/nde/transforms/nonlinearities_test.py (94%) rename {phaseSpaceSampling => uips}/nde/transforms/normalization.py (98%) rename {phaseSpaceSampling => uips}/nde/transforms/normalization_test.py (96%) rename {phaseSpaceSampling => uips}/nde/transforms/orthogonal.py (95%) rename {phaseSpaceSampling => uips}/nde/transforms/orthogonal_test.py (95%) rename {phaseSpaceSampling => uips}/nde/transforms/permutations.py (94%) rename {phaseSpaceSampling => uips}/nde/transforms/permutations_test.py (92%) rename {phaseSpaceSampling => uips}/nde/transforms/qr.py (96%) rename {phaseSpaceSampling => uips}/nde/transforms/qr_test.py (94%) rename {phaseSpaceSampling => uips}/nde/transforms/reshape.py (96%) rename {phaseSpaceSampling => uips}/nde/transforms/reshape_test.py (94%) rename {phaseSpaceSampling => uips}/nde/transforms/splines/__init__.py (100%) rename {phaseSpaceSampling => uips}/nde/transforms/splines/cubic.py (98%) rename {phaseSpaceSampling => uips}/nde/transforms/splines/cubic_test.py (97%) rename {phaseSpaceSampling => uips}/nde/transforms/splines/linear.py (96%) rename {phaseSpaceSampling => uips}/nde/transforms/splines/linear_test.py (96%) rename {phaseSpaceSampling => uips}/nde/transforms/splines/quadratic.py (97%) rename {phaseSpaceSampling => uips}/nde/transforms/splines/quadratic_test.py (97%) rename {phaseSpaceSampling => uips}/nde/transforms/splines/rational_quadratic.py (97%) rename {phaseSpaceSampling => uips}/nde/transforms/splines/rational_quadratic_test.py (97%) rename {phaseSpaceSampling => uips}/nde/transforms/standard.py (96%) rename {phaseSpaceSampling => uips}/nde/transforms/standard_test.py (96%) rename {phaseSpaceSampling => uips}/nde/transforms/svd.py (95%) rename {phaseSpaceSampling => uips}/nde/transforms/svd_test.py (93%) rename {phaseSpaceSampling => uips}/nde/transforms/transform_test.py (95%) rename {phaseSpaceSampling => uips}/nn/__init__.py (100%) rename {phaseSpaceSampling => uips}/nn/attention.py (99%) rename {phaseSpaceSampling => uips}/nn/conv.py (100%) rename {phaseSpaceSampling => uips}/nn/mlp.py (100%) rename {phaseSpaceSampling => uips}/nn/mlp_test.py (96%) rename {phaseSpaceSampling => uips}/nn/resnet.py (99%) rename {phaseSpaceSampling => uips}/nn/unet.py (97%) rename {phaseSpaceSampling => uips}/sampler.py (98%) rename {phaseSpaceSampling => uips}/utils/dataUtils.py (97%) rename {phaseSpaceSampling => uips}/utils/fileFinder.py (91%) rename {phaseSpaceSampling => uips}/utils/flowUtils.py (94%) rename {phaseSpaceSampling => uips}/utils/io.py (100%) rename {phaseSpaceSampling => uips}/utils/oldinit.py (100%) rename {phaseSpaceSampling => uips}/utils/parallel.py (100%) rename {phaseSpaceSampling => uips}/utils/plotFun.py (97%) rename {phaseSpaceSampling => uips}/utils/torchutils.py (98%) rename {phaseSpaceSampling => uips}/utils/torchutils_test.py (100%) rename {phaseSpaceSampling => uips}/utils/typechecks.py (100%) rename {phaseSpaceSampling => uips}/wrapper.py (96%) diff --git a/README.md b/README.md index 8c13f50..f9f7993 100644 --- a/README.md +++ b/README.md @@ -77,8 +77,8 @@ Suppose one wants to downsample an dataset where $N=10^7$ and $d=2$. First, the Next, the code uses the probability map to define a sampling probability which downselect samples that uniformly span the feature space. The probability map is obtained by training a Neural Spline Flow which implementation was obtained from [Neural Spline Flow repository](https://github.com/bayesiains/nsf). The number of samples in the final dataset can be controlled via the input file.

      - - + +

      For comparison, a random sampling gives the following result diff --git a/phaseSpaceSampling/scaler.npz b/phaseSpaceSampling/scaler.npz deleted file mode 100644 index 6e4e3f78349a0af890aaaa531b2e186d06d044cd..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 542 zcmWIWW@Zs#fB;2?A6K=tCjdDh%*7zWkeitomYAcLS5V2wAOIExsRYS_z+}Hr-+)L) zhBAg~^_0}&iK>k-N5}~{r&(Hzcf#tT8-kD#EQY~7l&(FpX@>5D&e`sKERui xNrV|!%z#uuKm#L)M#K}kKGaZvY64OXjAual;9(Nr&B_LnWdgziAUy+YE&#w-X3PKp diff --git a/postProcess/plotLoss.py b/postProcess/plotLoss.py index cf9250c..fbcd45c 100644 --- a/postProcess/plotLoss.py +++ b/postProcess/plotLoss.py @@ -7,8 +7,8 @@ from prettyPlot.parser import parse_input_file from prettyPlot.plotting import pretty_labels -import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling import PSS_INPUT_DIR +import uips.utils.parallel as par +from uips import PSS_INPUT_DIR parser = argparse.ArgumentParser(description="Loss plotting") parser.add_argument( diff --git a/postProcess/visualizeDownSampled_subplots.py b/postProcess/visualizeDownSampled_subplots.py index e631899..7d01093 100644 --- a/postProcess/visualizeDownSampled_subplots.py +++ b/postProcess/visualizeDownSampled_subplots.py @@ -7,8 +7,8 @@ from prettyPlot.parser import parse_input_file from prettyPlot.plotting import pretty_labels -import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling import PSS_INPUT_DIR +import uips.utils.parallel as par +from uips import PSS_INPUT_DIR def plotScatterProjection(data, fullData, fieldNames, lims): diff --git a/setup.py b/setup.py index 6df53cc..6ec4074 100644 --- a/setup.py +++ b/setup.py @@ -3,13 +3,13 @@ from setuptools import setup setup( - name="phaseSpaceSampling", + name="uips", version="0.0.1", description="Reduce a large and high-dimensional dataset by downselecting data uniformly in phase space", url="https://github.com/NREL/Phase-space-sampling", author="Malik Hassanaly", license="BSD 3-Clause", - package_dir={"phaseSpaceSampling": "phaseSpaceSampling"}, + package_dir={"uips": "uips"}, classifiers=[ "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", diff --git a/tests/main.py b/tests/main.py index 738666a..5b328cc 100644 --- a/tests/main.py +++ b/tests/main.py @@ -1,6 +1,6 @@ import argparse -from phaseSpaceSampling.wrapper import downsample_dataset_from_input +from uips.wrapper import downsample_dataset_from_input parser = argparse.ArgumentParser(description="Downsampler") parser.add_argument( diff --git a/tests/main_from_input.py b/tests/main_from_input.py index 738666a..5b328cc 100644 --- a/tests/main_from_input.py +++ b/tests/main_from_input.py @@ -1,6 +1,6 @@ import argparse -from phaseSpaceSampling.wrapper import downsample_dataset_from_input +from uips.wrapper import downsample_dataset_from_input parser = argparse.ArgumentParser(description="Downsampler") parser.add_argument( diff --git a/tests/test_downsampling.py b/tests/test_downsampling.py index 467c9b7..0fc8b41 100644 --- a/tests/test_downsampling.py +++ b/tests/test_downsampling.py @@ -1,7 +1,7 @@ import os -from phaseSpaceSampling import PSS_INPUT_DIR -from phaseSpaceSampling.wrapper import downsample_dataset_from_input +from uips import PSS_INPUT_DIR +from uips.wrapper import downsample_dataset_from_input def test_nf_input(): diff --git a/phaseSpaceSampling/__init__.py b/uips/__init__.py similarity index 100% rename from phaseSpaceSampling/__init__.py rename to uips/__init__.py diff --git a/phaseSpaceSampling/cutils/__init__.py b/uips/cutils/__init__.py similarity index 100% rename from phaseSpaceSampling/cutils/__init__.py rename to uips/cutils/__init__.py diff --git a/phaseSpaceSampling/cutils/io.py b/uips/cutils/io.py similarity index 100% rename from phaseSpaceSampling/cutils/io.py rename to uips/cutils/io.py diff --git a/phaseSpaceSampling/cutils/misc.py b/uips/cutils/misc.py similarity index 89% rename from phaseSpaceSampling/cutils/misc.py rename to uips/cutils/misc.py index 8bcf0e6..cd1846f 100644 --- a/phaseSpaceSampling/cutils/misc.py +++ b/uips/cutils/misc.py @@ -1,4 +1,4 @@ -from phaseSpaceSampling.utils.torchutils import tensor2numpy +from uips.utils.torchutils import tensor2numpy def gridimshow(image, ax): diff --git a/phaseSpaceSampling/nde/__init__.py b/uips/nde/__init__.py similarity index 100% rename from phaseSpaceSampling/nde/__init__.py rename to uips/nde/__init__.py diff --git a/phaseSpaceSampling/nde/distributions/__init__.py b/uips/nde/distributions/__init__.py similarity index 100% rename from phaseSpaceSampling/nde/distributions/__init__.py rename to uips/nde/distributions/__init__.py diff --git a/phaseSpaceSampling/nde/distributions/base.py b/uips/nde/distributions/base.py similarity index 97% rename from phaseSpaceSampling/nde/distributions/base.py rename to uips/nde/distributions/base.py index 24e667a..ff35524 100644 --- a/phaseSpaceSampling/nde/distributions/base.py +++ b/uips/nde/distributions/base.py @@ -3,12 +3,12 @@ import torch from torch import nn -from phaseSpaceSampling.utils.torchutils import ( +from uips.utils.torchutils import ( merge_leading_dims, repeat_rows, split_leading_dim, ) -from phaseSpaceSampling.utils.typechecks import is_positive_int +from uips.utils.typechecks import is_positive_int class NoMeanException(Exception): diff --git a/phaseSpaceSampling/nde/distributions/discrete.py b/uips/nde/distributions/discrete.py similarity index 96% rename from phaseSpaceSampling/nde/distributions/discrete.py rename to uips/nde/distributions/discrete.py index 2631c3b..33b6efe 100644 --- a/phaseSpaceSampling/nde/distributions/discrete.py +++ b/uips/nde/distributions/discrete.py @@ -3,8 +3,8 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde import distributions -from phaseSpaceSampling.utils.torchutils import ( +from uips.nde import distributions +from uips.utils.torchutils import ( repeat_rows, split_leading_dim, sum_except_batch, diff --git a/phaseSpaceSampling/nde/distributions/discrete_test.py b/uips/nde/distributions/discrete_test.py similarity index 98% rename from phaseSpaceSampling/nde/distributions/discrete_test.py rename to uips/nde/distributions/discrete_test.py index fdd81f2..9b44d2f 100644 --- a/phaseSpaceSampling/nde/distributions/discrete_test.py +++ b/uips/nde/distributions/discrete_test.py @@ -5,7 +5,7 @@ import torch import torchtestcase -from phaseSpaceSampling.nde.distributions import discrete +from uips.nde.distributions import discrete class ConditionalIndependentBernoulliTest(torchtestcase.TorchTestCase): diff --git a/phaseSpaceSampling/nde/distributions/mixture.py b/uips/nde/distributions/mixture.py similarity index 100% rename from phaseSpaceSampling/nde/distributions/mixture.py rename to uips/nde/distributions/mixture.py diff --git a/phaseSpaceSampling/nde/distributions/normal.py b/uips/nde/distributions/normal.py similarity index 97% rename from phaseSpaceSampling/nde/distributions/normal.py rename to uips/nde/distributions/normal.py index 65934f0..b2c3aee 100644 --- a/phaseSpaceSampling/nde/distributions/normal.py +++ b/uips/nde/distributions/normal.py @@ -3,8 +3,8 @@ import numpy as np import torch -from phaseSpaceSampling.nde import distributions -from phaseSpaceSampling.utils.torchutils import ( +from uips.nde import distributions +from uips.utils.torchutils import ( repeat_rows, split_leading_dim, sum_except_batch, diff --git a/phaseSpaceSampling/nde/distributions/normal_test.py b/uips/nde/distributions/normal_test.py similarity index 99% rename from phaseSpaceSampling/nde/distributions/normal_test.py rename to uips/nde/distributions/normal_test.py index b896ae6..1adabb8 100644 --- a/phaseSpaceSampling/nde/distributions/normal_test.py +++ b/uips/nde/distributions/normal_test.py @@ -5,7 +5,7 @@ import torch import torchtestcase -from phaseSpaceSampling.nde.distributions import normal +from uips.nde.distributions import normal class StandardNormalTest(torchtestcase.TorchTestCase): diff --git a/phaseSpaceSampling/nde/distributions/uniform.py b/uips/nde/distributions/uniform.py similarity index 87% rename from phaseSpaceSampling/nde/distributions/uniform.py rename to uips/nde/distributions/uniform.py index 958d982..99fc1ff 100644 --- a/phaseSpaceSampling/nde/distributions/uniform.py +++ b/uips/nde/distributions/uniform.py @@ -1,6 +1,6 @@ from torch import distributions -from phaseSpaceSampling.utils.torchutils import sum_except_batch +from uips.utils.torchutils import sum_except_batch class TweakedUniform(distributions.Uniform): diff --git a/phaseSpaceSampling/nde/flows/__init__.py b/uips/nde/flows/__init__.py similarity index 100% rename from phaseSpaceSampling/nde/flows/__init__.py rename to uips/nde/flows/__init__.py diff --git a/phaseSpaceSampling/nde/flows/autoregressive.py b/uips/nde/flows/autoregressive.py similarity index 94% rename from phaseSpaceSampling/nde/flows/autoregressive.py rename to uips/nde/flows/autoregressive.py index 94a9e4f..a8d5651 100644 --- a/phaseSpaceSampling/nde/flows/autoregressive.py +++ b/uips/nde/flows/autoregressive.py @@ -2,8 +2,8 @@ from torch.nn import functional as F -from phaseSpaceSampling.nde import distributions, transforms -from phaseSpaceSampling.nde.flows.base import Flow +from uips.nde import distributions, transforms +from uips.nde.flows.base import Flow class MaskedAutoregressiveFlow(Flow): diff --git a/phaseSpaceSampling/nde/flows/autoregressive_test.py b/uips/nde/flows/autoregressive_test.py similarity index 94% rename from phaseSpaceSampling/nde/flows/autoregressive_test.py rename to uips/nde/flows/autoregressive_test.py index 7784a61..80b1655 100644 --- a/phaseSpaceSampling/nde/flows/autoregressive_test.py +++ b/uips/nde/flows/autoregressive_test.py @@ -5,7 +5,7 @@ import torch import torchtestcase -from phaseSpaceSampling.nde.flows import autoregressive as ar +from uips.nde.flows import autoregressive as ar class MaskedAutoregressiveFlowTest(torchtestcase.TorchTestCase): diff --git a/phaseSpaceSampling/nde/flows/base.py b/uips/nde/flows/base.py similarity index 96% rename from phaseSpaceSampling/nde/flows/base.py rename to uips/nde/flows/base.py index 6516f84..3dee518 100644 --- a/phaseSpaceSampling/nde/flows/base.py +++ b/uips/nde/flows/base.py @@ -1,7 +1,7 @@ """Basic definitions for the flows module.""" -from phaseSpaceSampling.nde import distributions -from phaseSpaceSampling.utils.torchutils import ( +from uips.nde import distributions +from uips.utils.torchutils import ( merge_leading_dims, repeat_rows, split_leading_dim, diff --git a/phaseSpaceSampling/nde/flows/base_test.py b/uips/nde/flows/base_test.py similarity index 97% rename from phaseSpaceSampling/nde/flows/base_test.py rename to uips/nde/flows/base_test.py index 0162170..7b3a0a5 100644 --- a/phaseSpaceSampling/nde/flows/base_test.py +++ b/uips/nde/flows/base_test.py @@ -5,8 +5,8 @@ import torch import torchtestcase -from phaseSpaceSampling.nde import distributions, transforms -from phaseSpaceSampling.nde.flows import base +from uips.nde import distributions, transforms +from uips.nde.flows import base class FlowTest(torchtestcase.TorchTestCase): diff --git a/phaseSpaceSampling/nde/flows/realnvp.py b/uips/nde/flows/realnvp.py similarity index 94% rename from phaseSpaceSampling/nde/flows/realnvp.py rename to uips/nde/flows/realnvp.py index 0dec777..0aa019e 100644 --- a/phaseSpaceSampling/nde/flows/realnvp.py +++ b/uips/nde/flows/realnvp.py @@ -3,8 +3,8 @@ import torch from torch.nn import functional as F -import phaseSpaceSampling.nn as nn_ -from phaseSpaceSampling.nde import distributions, flows, transforms +import uips.nn as nn_ +from uips.nde import distributions, flows, transforms class SimpleRealNVP(flows.Flow): diff --git a/phaseSpaceSampling/nde/flows/realnvp_test.py b/uips/nde/flows/realnvp_test.py similarity index 95% rename from phaseSpaceSampling/nde/flows/realnvp_test.py rename to uips/nde/flows/realnvp_test.py index c05e9f8..200b438 100644 --- a/phaseSpaceSampling/nde/flows/realnvp_test.py +++ b/uips/nde/flows/realnvp_test.py @@ -5,7 +5,7 @@ import torch import torchtestcase -from phaseSpaceSampling.nde.flows import realnvp +from uips.nde.flows import realnvp class SimpleRealNVPTest(torchtestcase.TorchTestCase): diff --git a/phaseSpaceSampling/nde/transforms/__init__.py b/uips/nde/transforms/__init__.py similarity index 100% rename from phaseSpaceSampling/nde/transforms/__init__.py rename to uips/nde/transforms/__init__.py diff --git a/phaseSpaceSampling/nde/transforms/autoregressive.py b/uips/nde/transforms/autoregressive.py similarity index 98% rename from phaseSpaceSampling/nde/transforms/autoregressive.py rename to uips/nde/transforms/autoregressive.py index b92e916..d75635e 100644 --- a/phaseSpaceSampling/nde/transforms/autoregressive.py +++ b/uips/nde/transforms/autoregressive.py @@ -4,10 +4,10 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde.transforms import made as made_module -from phaseSpaceSampling.nde.transforms import splines -from phaseSpaceSampling.nde.transforms.base import Transform -from phaseSpaceSampling.utils.torchutils import sum_except_batch +from uips.nde.transforms import made as made_module +from uips.nde.transforms import splines +from uips.nde.transforms.base import Transform +from uips.utils.torchutils import sum_except_batch class AutoregressiveTransform(Transform): diff --git a/phaseSpaceSampling/nde/transforms/autoregressive_test.py b/uips/nde/transforms/autoregressive_test.py similarity index 97% rename from phaseSpaceSampling/nde/transforms/autoregressive_test.py rename to uips/nde/transforms/autoregressive_test.py index c7bc4a1..60f51e8 100644 --- a/phaseSpaceSampling/nde/transforms/autoregressive_test.py +++ b/uips/nde/transforms/autoregressive_test.py @@ -4,8 +4,8 @@ import torch -from phaseSpaceSampling.nde.transforms import autoregressive -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +from uips.nde.transforms import autoregressive +from uips.nde.transforms.transform_test import TransformTest class MaskedAffineAutoregressiveTransformTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/base.py b/uips/nde/transforms/base.py similarity index 99% rename from phaseSpaceSampling/nde/transforms/base.py rename to uips/nde/transforms/base.py index 1de47ff..3628547 100644 --- a/phaseSpaceSampling/nde/transforms/base.py +++ b/uips/nde/transforms/base.py @@ -4,7 +4,7 @@ import torch from torch import nn -from phaseSpaceSampling.utils.typechecks import is_positive_int +from uips.utils.typechecks import is_positive_int class InverseNotAvailable(Exception): diff --git a/phaseSpaceSampling/nde/transforms/base_test.py b/uips/nde/transforms/base_test.py similarity index 97% rename from phaseSpaceSampling/nde/transforms/base_test.py rename to uips/nde/transforms/base_test.py index 1df4c12..e3f1af2 100644 --- a/phaseSpaceSampling/nde/transforms/base_test.py +++ b/uips/nde/transforms/base_test.py @@ -4,8 +4,8 @@ import numpy as np import torch -from phaseSpaceSampling.nde.transforms import base, standard -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +from uips.nde.transforms import base, standard +from uips.nde.transforms.transform_test import TransformTest class CompositeTransformTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/conv.py b/uips/nde/transforms/conv.py similarity index 88% rename from phaseSpaceSampling/nde/transforms/conv.py rename to uips/nde/transforms/conv.py index 8cbee50..79c3410 100644 --- a/phaseSpaceSampling/nde/transforms/conv.py +++ b/uips/nde/transforms/conv.py @@ -1,6 +1,6 @@ -from phaseSpaceSampling.nde.transforms.lu import LULinear -from phaseSpaceSampling.nde.transforms.permutations import RandomPermutation -from phaseSpaceSampling.utils.torchutils import sum_except_batch +from uips.nde.transforms.lu import LULinear +from uips.nde.transforms.permutations import RandomPermutation +from uips.utils.torchutils import sum_except_batch class OneByOneConvolution(LULinear): diff --git a/phaseSpaceSampling/nde/transforms/conv_test.py b/uips/nde/transforms/conv_test.py similarity index 75% rename from phaseSpaceSampling/nde/transforms/conv_test.py rename to uips/nde/transforms/conv_test.py index a79bf15..db40047 100644 --- a/phaseSpaceSampling/nde/transforms/conv_test.py +++ b/uips/nde/transforms/conv_test.py @@ -2,8 +2,8 @@ import torch -from phaseSpaceSampling.nde.conv import OneByOneConvolution -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +from uips.nde.conv import OneByOneConvolution +from uips.nde.transforms.transform_test import TransformTest class OneByOneConvolutionTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/coupling.py b/uips/nde/transforms/coupling.py similarity index 98% rename from phaseSpaceSampling/nde/transforms/coupling.py rename to uips/nde/transforms/coupling.py index 287dbb0..b5cb315 100644 --- a/phaseSpaceSampling/nde/transforms/coupling.py +++ b/uips/nde/transforms/coupling.py @@ -5,15 +5,15 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde.transforms import splines -from phaseSpaceSampling.nde.transforms.base import Transform -from phaseSpaceSampling.nde.transforms.nonlinearities import ( +from uips.nde.transforms import splines +from uips.nde.transforms.base import Transform +from uips.nde.transforms.nonlinearities import ( PiecewiseCubicCDF, PiecewiseLinearCDF, PiecewiseQuadraticCDF, PiecewiseRationalQuadraticCDF, ) -from phaseSpaceSampling.utils.torchutils import sum_except_batch +from uips.utils.torchutils import sum_except_batch class CouplingTransform(Transform): diff --git a/phaseSpaceSampling/nde/transforms/coupling_test.py b/uips/nde/transforms/coupling_test.py similarity index 97% rename from phaseSpaceSampling/nde/transforms/coupling_test.py rename to uips/nde/transforms/coupling_test.py index a7c6b3a..9d72a3b 100644 --- a/phaseSpaceSampling/nde/transforms/coupling_test.py +++ b/uips/nde/transforms/coupling_test.py @@ -6,10 +6,10 @@ import torch from torch import nn -import phaseSpaceSampling.nn as nn_ -from phaseSpaceSampling.nde.transforms import coupling -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest -from phaseSpaceSampling.utils.torchutils import create_mid_split_binary_mask +import uips.nn as nn_ +from uips.nde.transforms import coupling +from uips.nde.transforms.transform_test import TransformTest +from uips.utils.torchutils import create_mid_split_binary_mask def create_coupling_transform(cls, shape, **kwargs): diff --git a/phaseSpaceSampling/nde/transforms/linear.py b/uips/nde/transforms/linear.py similarity index 96% rename from phaseSpaceSampling/nde/transforms/linear.py rename to uips/nde/transforms/linear.py index 67c9544..71a41af 100644 --- a/phaseSpaceSampling/nde/transforms/linear.py +++ b/uips/nde/transforms/linear.py @@ -6,10 +6,10 @@ from torch.nn import functional as F from torch.nn import init -import phaseSpaceSampling as pss -from phaseSpaceSampling.nde.transforms.base import Transform -from phaseSpaceSampling.utils.torchutils import random_orthogonal -from phaseSpaceSampling.utils.typechecks import is_bool, is_positive_int +import uips as pss +from uips.nde.transforms.base import Transform +from uips.utils.torchutils import random_orthogonal +from uips.utils.typechecks import is_bool, is_positive_int class LinearCache(object): diff --git a/phaseSpaceSampling/nde/transforms/linear_test.py b/uips/nde/transforms/linear_test.py similarity index 97% rename from phaseSpaceSampling/nde/transforms/linear_test.py rename to uips/nde/transforms/linear_test.py index 96b6725..c6d143e 100644 --- a/phaseSpaceSampling/nde/transforms/linear_test.py +++ b/uips/nde/transforms/linear_test.py @@ -5,10 +5,10 @@ import torch -import phaseSpaceSampling as pss -from phaseSpaceSampling.nde.transforms import linear -from phaseSpaceSampling.nde.transforms.linear import Linear -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +import uips as pss +from uips.nde.transforms import linear +from uips.nde.transforms.linear import Linear +from uips.nde.transforms.transform_test import TransformTest class LinearTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/lu.py b/uips/nde/transforms/lu.py similarity index 98% rename from phaseSpaceSampling/nde/transforms/lu.py rename to uips/nde/transforms/lu.py index e9d56c6..09cabb1 100644 --- a/phaseSpaceSampling/nde/transforms/lu.py +++ b/uips/nde/transforms/lu.py @@ -4,7 +4,7 @@ from torch.nn import functional as F from torch.nn import init -from phaseSpaceSampling.nde.transforms.linear import Linear +from uips.nde.transforms.linear import Linear class LULinear(Linear): diff --git a/phaseSpaceSampling/nde/transforms/lu_test.py b/uips/nde/transforms/lu_test.py similarity index 94% rename from phaseSpaceSampling/nde/transforms/lu_test.py rename to uips/nde/transforms/lu_test.py index 00041ca..91bd56b 100644 --- a/phaseSpaceSampling/nde/transforms/lu_test.py +++ b/uips/nde/transforms/lu_test.py @@ -4,9 +4,9 @@ import torch -import phaseSpaceSampling as pss -from phaseSpaceSampling.nde.transforms import lu -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +import uips as pss +from uips.nde.transforms import lu +from uips.nde.transforms.transform_test import TransformTest class LULinearTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/made.py b/uips/nde/transforms/made.py similarity index 99% rename from phaseSpaceSampling/nde/transforms/made.py rename to uips/nde/transforms/made.py index 2eecc59..867e7c5 100644 --- a/phaseSpaceSampling/nde/transforms/made.py +++ b/uips/nde/transforms/made.py @@ -6,7 +6,7 @@ from torch.nn import functional as F from torch.nn import init -from phaseSpaceSampling.utils.torchutils import tile +from uips.utils.torchutils import tile def _get_input_degrees(in_features): diff --git a/phaseSpaceSampling/nde/transforms/made_test.py b/uips/nde/transforms/made_test.py similarity index 99% rename from phaseSpaceSampling/nde/transforms/made_test.py rename to uips/nde/transforms/made_test.py index c02ab7c..a370c5b 100644 --- a/phaseSpaceSampling/nde/transforms/made_test.py +++ b/uips/nde/transforms/made_test.py @@ -5,7 +5,7 @@ import torch import torchtestcase -from phaseSpaceSampling.nde.transforms import made +from uips.nde.transforms import made class ShapeTest(torchtestcase.TorchTestCase): diff --git a/phaseSpaceSampling/nde/transforms/nonlinearities.py b/uips/nde/transforms/nonlinearities.py similarity index 98% rename from phaseSpaceSampling/nde/transforms/nonlinearities.py rename to uips/nde/transforms/nonlinearities.py index 6f5a8ff..6c4fcdf 100644 --- a/phaseSpaceSampling/nde/transforms/nonlinearities.py +++ b/uips/nde/transforms/nonlinearities.py @@ -5,14 +5,14 @@ from torch import nn from torch.nn import functional as F -from phaseSpaceSampling.nde.transforms import splines -from phaseSpaceSampling.nde.transforms.base import ( +from uips.nde.transforms import splines +from uips.nde.transforms.base import ( CompositeTransform, InputOutsideDomain, InverseTransform, Transform, ) -from phaseSpaceSampling.utils.torchutils import sum_except_batch +from uips.utils.torchutils import sum_except_batch class Tanh(Transform): diff --git a/phaseSpaceSampling/nde/transforms/nonlinearities_test.py b/uips/nde/transforms/nonlinearities_test.py similarity index 94% rename from phaseSpaceSampling/nde/transforms/nonlinearities_test.py rename to uips/nde/transforms/nonlinearities_test.py index 08591fc..c6a71ec 100644 --- a/phaseSpaceSampling/nde/transforms/nonlinearities_test.py +++ b/uips/nde/transforms/nonlinearities_test.py @@ -4,11 +4,11 @@ import torch -import phaseSpaceSampling.nde.transforms.splines.quadratic -from phaseSpaceSampling.nde.transforms import base -from phaseSpaceSampling.nde.transforms import nonlinearities as nl -from phaseSpaceSampling.nde.transforms import standard -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +import uips.nde.transforms.splines.quadratic +from uips.nde.transforms import base +from uips.nde.transforms import nonlinearities as nl +from uips.nde.transforms import standard +from uips.nde.transforms.transform_test import TransformTest class TanhTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/normalization.py b/uips/nde/transforms/normalization.py similarity index 98% rename from phaseSpaceSampling/nde/transforms/normalization.py rename to uips/nde/transforms/normalization.py index 32c0597..4c3b5bd 100644 --- a/phaseSpaceSampling/nde/transforms/normalization.py +++ b/uips/nde/transforms/normalization.py @@ -4,11 +4,11 @@ from torch import nn from torch.nn import functional as F -from phaseSpaceSampling.nde.transforms.base import ( +from uips.nde.transforms.base import ( InverseNotAvailable, Transform, ) -from phaseSpaceSampling.utils.typechecks import is_positive_int +from uips.utils.typechecks import is_positive_int # class BatchNorm(Transform): # """Transform that performs batch normalization. diff --git a/phaseSpaceSampling/nde/transforms/normalization_test.py b/uips/nde/transforms/normalization_test.py similarity index 96% rename from phaseSpaceSampling/nde/transforms/normalization_test.py rename to uips/nde/transforms/normalization_test.py index 0b4a763..2eb77c5 100644 --- a/phaseSpaceSampling/nde/transforms/normalization_test.py +++ b/uips/nde/transforms/normalization_test.py @@ -4,9 +4,9 @@ import torch -from phaseSpaceSampling.nde.transforms import base -from phaseSpaceSampling.nde.transforms import normalization as norm -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +from uips.nde.transforms import base +from uips.nde.transforms import normalization as norm +from uips.nde.transforms.transform_test import TransformTest class BatchNormTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/orthogonal.py b/uips/nde/transforms/orthogonal.py similarity index 95% rename from phaseSpaceSampling/nde/transforms/orthogonal.py rename to uips/nde/transforms/orthogonal.py index 7be102e..b9a8648 100644 --- a/phaseSpaceSampling/nde/transforms/orthogonal.py +++ b/uips/nde/transforms/orthogonal.py @@ -3,8 +3,8 @@ import torch from torch import nn -from phaseSpaceSampling.nde.transforms.base import Transform -from phaseSpaceSampling.utils.typechecks import is_positive_int +from uips.nde.transforms.base import Transform +from uips.utils.typechecks import is_positive_int class HouseholderSequence(Transform): diff --git a/phaseSpaceSampling/nde/transforms/orthogonal_test.py b/uips/nde/transforms/orthogonal_test.py similarity index 95% rename from phaseSpaceSampling/nde/transforms/orthogonal_test.py rename to uips/nde/transforms/orthogonal_test.py index 3f2f828..ae4328a 100644 --- a/phaseSpaceSampling/nde/transforms/orthogonal_test.py +++ b/uips/nde/transforms/orthogonal_test.py @@ -4,9 +4,9 @@ import torch -import phaseSpaceSampling as pss -from phaseSpaceSampling.nde.transforms import orthogonal -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +import uips as pss +from uips.nde.transforms import orthogonal +from uips.nde.transforms.transform_test import TransformTest class HouseholderSequenceTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/permutations.py b/uips/nde/transforms/permutations.py similarity index 94% rename from phaseSpaceSampling/nde/transforms/permutations.py rename to uips/nde/transforms/permutations.py index fb7002b..3d24f01 100644 --- a/phaseSpaceSampling/nde/transforms/permutations.py +++ b/uips/nde/transforms/permutations.py @@ -2,8 +2,8 @@ import torch -from phaseSpaceSampling.nde.transforms.base import Transform -from phaseSpaceSampling.utils.typechecks import is_positive_int +from uips.nde.transforms.base import Transform +from uips.utils.typechecks import is_positive_int class Permutation(Transform): diff --git a/phaseSpaceSampling/nde/transforms/permutations_test.py b/uips/nde/transforms/permutations_test.py similarity index 92% rename from phaseSpaceSampling/nde/transforms/permutations_test.py rename to uips/nde/transforms/permutations_test.py index 7e0f946..51d4370 100644 --- a/phaseSpaceSampling/nde/transforms/permutations_test.py +++ b/uips/nde/transforms/permutations_test.py @@ -4,8 +4,8 @@ import torch -from phaseSpaceSampling.nde.transforms import permutations -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +from uips.nde.transforms import permutations +from uips.nde.transforms.transform_test import TransformTest class PermutationTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/qr.py b/uips/nde/transforms/qr.py similarity index 96% rename from phaseSpaceSampling/nde/transforms/qr.py rename to uips/nde/transforms/qr.py index 5202d9a..64605e4 100644 --- a/phaseSpaceSampling/nde/transforms/qr.py +++ b/uips/nde/transforms/qr.py @@ -4,8 +4,8 @@ from torch.nn import functional as F from torch.nn import init -from phaseSpaceSampling.nde.transforms.linear import Linear -from phaseSpaceSampling.nde.transforms.orthogonal import HouseholderSequence +from uips.nde.transforms.linear import Linear +from uips.nde.transforms.orthogonal import HouseholderSequence class QRLinear(Linear): diff --git a/phaseSpaceSampling/nde/transforms/qr_test.py b/uips/nde/transforms/qr_test.py similarity index 94% rename from phaseSpaceSampling/nde/transforms/qr_test.py rename to uips/nde/transforms/qr_test.py index dbdc9eb..fccffb7 100644 --- a/phaseSpaceSampling/nde/transforms/qr_test.py +++ b/uips/nde/transforms/qr_test.py @@ -2,9 +2,9 @@ import torch -import phaseSpaceSampling as pss -from phaseSpaceSampling.nde.transforms import qr -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +import uips as pss +from uips.nde.transforms import qr +from uips.nde.transforms.transform_test import TransformTest class QRLinearTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/reshape.py b/uips/nde/transforms/reshape.py similarity index 96% rename from phaseSpaceSampling/nde/transforms/reshape.py rename to uips/nde/transforms/reshape.py index 557dfc1..06283de 100644 --- a/phaseSpaceSampling/nde/transforms/reshape.py +++ b/uips/nde/transforms/reshape.py @@ -1,7 +1,7 @@ import torch -from phaseSpaceSampling.nde.transforms.base import Transform -from phaseSpaceSampling.utils.typechecks import is_int +from uips.nde.transforms.base import Transform +from uips.utils.typechecks import is_int class SqueezeTransform(Transform): diff --git a/phaseSpaceSampling/nde/transforms/reshape_test.py b/uips/nde/transforms/reshape_test.py similarity index 94% rename from phaseSpaceSampling/nde/transforms/reshape_test.py rename to uips/nde/transforms/reshape_test.py index 87fdb27..8c0ba5d 100644 --- a/phaseSpaceSampling/nde/transforms/reshape_test.py +++ b/uips/nde/transforms/reshape_test.py @@ -2,8 +2,8 @@ import torch -from phaseSpaceSampling.nde.reshape import SqueezeTransform -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +from uips.nde.reshape import SqueezeTransform +from uips.nde.transforms.transform_test import TransformTest class SqueezeTransformTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/splines/__init__.py b/uips/nde/transforms/splines/__init__.py similarity index 100% rename from phaseSpaceSampling/nde/transforms/splines/__init__.py rename to uips/nde/transforms/splines/__init__.py diff --git a/phaseSpaceSampling/nde/transforms/splines/cubic.py b/uips/nde/transforms/splines/cubic.py similarity index 98% rename from phaseSpaceSampling/nde/transforms/splines/cubic.py rename to uips/nde/transforms/splines/cubic.py index ebf85d5..330c6f1 100644 --- a/phaseSpaceSampling/nde/transforms/splines/cubic.py +++ b/uips/nde/transforms/splines/cubic.py @@ -3,8 +3,8 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde.transforms.base import InputOutsideDomain -from phaseSpaceSampling.utils.torchutils import cbrt, searchsorted +from uips.nde.transforms.base import InputOutsideDomain +from uips.utils.torchutils import cbrt, searchsorted DEFAULT_MIN_BIN_WIDTH = 1e-3 DEFAULT_MIN_BIN_HEIGHT = 1e-3 diff --git a/phaseSpaceSampling/nde/transforms/splines/cubic_test.py b/uips/nde/transforms/splines/cubic_test.py similarity index 97% rename from phaseSpaceSampling/nde/transforms/splines/cubic_test.py rename to uips/nde/transforms/splines/cubic_test.py index a3d70cd..a0d7401 100644 --- a/phaseSpaceSampling/nde/transforms/splines/cubic_test.py +++ b/uips/nde/transforms/splines/cubic_test.py @@ -1,7 +1,7 @@ import torch import torchtestcase -from phaseSpaceSampling.nde.transforms import splines +from uips.nde.transforms import splines class CubicSplineTest(torchtestcase.TorchTestCase): diff --git a/phaseSpaceSampling/nde/transforms/splines/linear.py b/uips/nde/transforms/splines/linear.py similarity index 96% rename from phaseSpaceSampling/nde/transforms/splines/linear.py rename to uips/nde/transforms/splines/linear.py index 0b9cb85..dac85c7 100644 --- a/phaseSpaceSampling/nde/transforms/splines/linear.py +++ b/uips/nde/transforms/splines/linear.py @@ -4,8 +4,8 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde.transforms.base import InputOutsideDomain -from phaseSpaceSampling.utils.torchutils import searchsorted +from uips.nde.transforms.base import InputOutsideDomain +from uips.utils.torchutils import searchsorted def unconstrained_linear_spline( diff --git a/phaseSpaceSampling/nde/transforms/splines/linear_test.py b/uips/nde/transforms/splines/linear_test.py similarity index 96% rename from phaseSpaceSampling/nde/transforms/splines/linear_test.py rename to uips/nde/transforms/splines/linear_test.py index 3ae61e7..0087d09 100644 --- a/phaseSpaceSampling/nde/transforms/splines/linear_test.py +++ b/uips/nde/transforms/splines/linear_test.py @@ -1,7 +1,7 @@ import torch import torchtestcase -from phaseSpaceSampling.nde.transforms import splines +from uips.nde.transforms import splines class LinearSplineTest(torchtestcase.TorchTestCase): diff --git a/phaseSpaceSampling/nde/transforms/splines/quadratic.py b/uips/nde/transforms/splines/quadratic.py similarity index 97% rename from phaseSpaceSampling/nde/transforms/splines/quadratic.py rename to uips/nde/transforms/splines/quadratic.py index a634846..06ae2e3 100644 --- a/phaseSpaceSampling/nde/transforms/splines/quadratic.py +++ b/uips/nde/transforms/splines/quadratic.py @@ -3,8 +3,8 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde.transforms.base import InputOutsideDomain -from phaseSpaceSampling.utils.torchutils import searchsorted +from uips.nde.transforms.base import InputOutsideDomain +from uips.utils.torchutils import searchsorted DEFAULT_MIN_BIN_WIDTH = 1e-3 DEFAULT_MIN_BIN_HEIGHT = 1e-3 diff --git a/phaseSpaceSampling/nde/transforms/splines/quadratic_test.py b/uips/nde/transforms/splines/quadratic_test.py similarity index 97% rename from phaseSpaceSampling/nde/transforms/splines/quadratic_test.py rename to uips/nde/transforms/splines/quadratic_test.py index 6abc27b..93f6579 100644 --- a/phaseSpaceSampling/nde/transforms/splines/quadratic_test.py +++ b/uips/nde/transforms/splines/quadratic_test.py @@ -1,7 +1,7 @@ import torch import torchtestcase -from phaseSpaceSampling.nde.transforms import splines +from uips.nde.transforms import splines class QuadraticSplineTest(torchtestcase.TorchTestCase): diff --git a/phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py b/uips/nde/transforms/splines/rational_quadratic.py similarity index 97% rename from phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py rename to uips/nde/transforms/splines/rational_quadratic.py index 593ba86..1eddb29 100644 --- a/phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py +++ b/uips/nde/transforms/splines/rational_quadratic.py @@ -2,8 +2,8 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde.transforms.base import InputOutsideDomain -from phaseSpaceSampling.utils.torchutils import searchsorted +from uips.nde.transforms.base import InputOutsideDomain +from uips.utils.torchutils import searchsorted DEFAULT_MIN_BIN_WIDTH = 1e-3 DEFAULT_MIN_BIN_HEIGHT = 1e-3 diff --git a/phaseSpaceSampling/nde/transforms/splines/rational_quadratic_test.py b/uips/nde/transforms/splines/rational_quadratic_test.py similarity index 97% rename from phaseSpaceSampling/nde/transforms/splines/rational_quadratic_test.py rename to uips/nde/transforms/splines/rational_quadratic_test.py index c79f4c2..2eb32ac 100644 --- a/phaseSpaceSampling/nde/transforms/splines/rational_quadratic_test.py +++ b/uips/nde/transforms/splines/rational_quadratic_test.py @@ -1,7 +1,7 @@ import torch import torchtestcase -from phaseSpaceSampling.nde.transforms import splines +from uips.nde.transforms import splines class RationalQuadraticSplineTest(torchtestcase.TorchTestCase): diff --git a/phaseSpaceSampling/nde/transforms/standard.py b/uips/nde/transforms/standard.py similarity index 96% rename from phaseSpaceSampling/nde/transforms/standard.py rename to uips/nde/transforms/standard.py index 8cc119c..bdbd38e 100644 --- a/phaseSpaceSampling/nde/transforms/standard.py +++ b/uips/nde/transforms/standard.py @@ -2,7 +2,7 @@ import torch -from phaseSpaceSampling.nde.transforms.base import Transform +from uips.nde.transforms.base import Transform class IdentityTransform(Transform): diff --git a/phaseSpaceSampling/nde/transforms/standard_test.py b/uips/nde/transforms/standard_test.py similarity index 96% rename from phaseSpaceSampling/nde/transforms/standard_test.py rename to uips/nde/transforms/standard_test.py index 5966bb3..a46f598 100644 --- a/phaseSpaceSampling/nde/transforms/standard_test.py +++ b/uips/nde/transforms/standard_test.py @@ -5,8 +5,8 @@ import numpy as np import torch -from phaseSpaceSampling.nde.transforms import standard -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +from uips.nde.transforms import standard +from uips.nde.transforms.transform_test import TransformTest class IdentityTransformTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/svd.py b/uips/nde/transforms/svd.py similarity index 95% rename from phaseSpaceSampling/nde/transforms/svd.py rename to uips/nde/transforms/svd.py index fb165b6..8f23b64 100644 --- a/phaseSpaceSampling/nde/transforms/svd.py +++ b/uips/nde/transforms/svd.py @@ -3,8 +3,8 @@ from torch import nn from torch.nn import init -from phaseSpaceSampling.nde.transforms.linear import Linear -from phaseSpaceSampling.nde.transforms.orthogonal import HouseholderSequence +from uips.nde.transforms.linear import Linear +from uips.nde.transforms.orthogonal import HouseholderSequence class SVDLinear(Linear): diff --git a/phaseSpaceSampling/nde/transforms/svd_test.py b/uips/nde/transforms/svd_test.py similarity index 93% rename from phaseSpaceSampling/nde/transforms/svd_test.py rename to uips/nde/transforms/svd_test.py index f07a3bf..c668a43 100644 --- a/phaseSpaceSampling/nde/transforms/svd_test.py +++ b/uips/nde/transforms/svd_test.py @@ -2,9 +2,9 @@ import torch -from phaseSpaceSampling.nde.transforms import svd -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest -from phaseSpaceSampling.utils.torchutils import logabsdet +from uips.nde.transforms import svd +from uips.nde.transforms.transform_test import TransformTest +from uips.utils.torchutils import logabsdet class SVDLinearTest(TransformTest): diff --git a/phaseSpaceSampling/nde/transforms/transform_test.py b/uips/nde/transforms/transform_test.py similarity index 95% rename from phaseSpaceSampling/nde/transforms/transform_test.py rename to uips/nde/transforms/transform_test.py index c27452a..939cd15 100644 --- a/phaseSpaceSampling/nde/transforms/transform_test.py +++ b/uips/nde/transforms/transform_test.py @@ -1,7 +1,7 @@ import torch import torchtestcase -from phaseSpaceSampling.nde.transforms import base +from uips.nde.transforms import base class TransformTest(torchtestcase.TorchTestCase): diff --git a/phaseSpaceSampling/nn/__init__.py b/uips/nn/__init__.py similarity index 100% rename from phaseSpaceSampling/nn/__init__.py rename to uips/nn/__init__.py diff --git a/phaseSpaceSampling/nn/attention.py b/uips/nn/attention.py similarity index 99% rename from phaseSpaceSampling/nn/attention.py rename to uips/nn/attention.py index 0867dd5..25fa939 100644 --- a/phaseSpaceSampling/nn/attention.py +++ b/uips/nn/attention.py @@ -2,7 +2,7 @@ from torch import nn from torch.nn import functional as F -from phaseSpaceSampling.utils.torchutils import get_num_parameters +from uips.utils.torchutils import get_num_parameters # # Projection of x onto y # def proj(x, y): diff --git a/phaseSpaceSampling/nn/conv.py b/uips/nn/conv.py similarity index 100% rename from phaseSpaceSampling/nn/conv.py rename to uips/nn/conv.py diff --git a/phaseSpaceSampling/nn/mlp.py b/uips/nn/mlp.py similarity index 100% rename from phaseSpaceSampling/nn/mlp.py rename to uips/nn/mlp.py diff --git a/phaseSpaceSampling/nn/mlp_test.py b/uips/nn/mlp_test.py similarity index 96% rename from phaseSpaceSampling/nn/mlp_test.py rename to uips/nn/mlp_test.py index da6c062..6ce6b67 100644 --- a/phaseSpaceSampling/nn/mlp_test.py +++ b/uips/nn/mlp_test.py @@ -5,7 +5,7 @@ import torch import torchtestcase -from phaseSpaceSampling.nn import mlp +from uips.nn import mlp class MLPTest(torchtestcase.TorchTestCase): diff --git a/phaseSpaceSampling/nn/resnet.py b/uips/nn/resnet.py similarity index 99% rename from phaseSpaceSampling/nn/resnet.py rename to uips/nn/resnet.py index a6ac232..e4588e6 100644 --- a/phaseSpaceSampling/nn/resnet.py +++ b/uips/nn/resnet.py @@ -3,7 +3,7 @@ from torch.nn import functional as F from torch.nn import init -from phaseSpaceSampling.utils.torchutils import get_num_parameters +from uips.utils.torchutils import get_num_parameters class ResidualBlock(nn.Module): diff --git a/phaseSpaceSampling/nn/unet.py b/uips/nn/unet.py similarity index 97% rename from phaseSpaceSampling/nn/unet.py rename to uips/nn/unet.py index 0939c02..80c74e2 100644 --- a/phaseSpaceSampling/nn/unet.py +++ b/uips/nn/unet.py @@ -2,7 +2,7 @@ from torch import nn from torch.nn import functional as F -from phaseSpaceSampling.utils.typechecks import is_power_of_two +from uips.utils.typechecks import is_power_of_two class UNet(nn.Module): diff --git a/phaseSpaceSampling/sampler.py b/uips/sampler.py similarity index 98% rename from phaseSpaceSampling/sampler.py rename to uips/sampler.py index 4f6ccac..9705af6 100644 --- a/phaseSpaceSampling/sampler.py +++ b/uips/sampler.py @@ -11,11 +11,11 @@ from torch.utils import data from torch.utils.data import DataLoader -import phaseSpaceSampling.nn as nn_ -import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling.nde import distributions, flows, transforms -from phaseSpaceSampling.utils.flowUtils import create_base_transform -from phaseSpaceSampling.utils.torchutils import tensor2numpy +import uips.nn as nn_ +import uips.utils.parallel as par +from uips.nde import distributions, flows, transforms +from uips.utils.flowUtils import create_base_transform +from uips.utils.torchutils import tensor2numpy def str2float(str): diff --git a/phaseSpaceSampling/utils/dataUtils.py b/uips/utils/dataUtils.py similarity index 97% rename from phaseSpaceSampling/utils/dataUtils.py rename to uips/utils/dataUtils.py index 7b78f00..dae5362 100644 --- a/phaseSpaceSampling/utils/dataUtils.py +++ b/uips/utils/dataUtils.py @@ -3,8 +3,8 @@ import numpy as np -import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling.utils.fileFinder import find_data +import uips.utils.parallel as par +from uips.utils.fileFinder import find_data # from memory_profiler import profile diff --git a/phaseSpaceSampling/utils/fileFinder.py b/uips/utils/fileFinder.py similarity index 91% rename from phaseSpaceSampling/utils/fileFinder.py rename to uips/utils/fileFinder.py index 43fe8b8..31a340f 100644 --- a/phaseSpaceSampling/utils/fileFinder.py +++ b/uips/utils/fileFinder.py @@ -1,8 +1,8 @@ import os import sys -import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling import PSS_DATA_DIR, PSS_INPUT_DIR +import uips.utils.parallel as par +from uips import PSS_DATA_DIR, PSS_INPUT_DIR def find_input(inpt_file): diff --git a/phaseSpaceSampling/utils/flowUtils.py b/uips/utils/flowUtils.py similarity index 94% rename from phaseSpaceSampling/utils/flowUtils.py rename to uips/utils/flowUtils.py index a1ab5a9..c0bd294 100644 --- a/phaseSpaceSampling/utils/flowUtils.py +++ b/uips/utils/flowUtils.py @@ -3,9 +3,9 @@ import torch from torch.utils import data -import phaseSpaceSampling.nn as nn_ -from phaseSpaceSampling.nde import distributions, flows, transforms -from phaseSpaceSampling.utils.torchutils import ( +import uips.nn as nn_ +from uips.nde import distributions, flows, transforms +from uips.utils.torchutils import ( create_alternating_binary_mask, create_random_binary_mask, ) diff --git a/phaseSpaceSampling/utils/io.py b/uips/utils/io.py similarity index 100% rename from phaseSpaceSampling/utils/io.py rename to uips/utils/io.py diff --git a/phaseSpaceSampling/utils/oldinit.py b/uips/utils/oldinit.py similarity index 100% rename from phaseSpaceSampling/utils/oldinit.py rename to uips/utils/oldinit.py diff --git a/phaseSpaceSampling/utils/parallel.py b/uips/utils/parallel.py similarity index 100% rename from phaseSpaceSampling/utils/parallel.py rename to uips/utils/parallel.py diff --git a/phaseSpaceSampling/utils/plotFun.py b/uips/utils/plotFun.py similarity index 97% rename from phaseSpaceSampling/utils/plotFun.py rename to uips/utils/plotFun.py index 64d0e92..6ad7555 100644 --- a/phaseSpaceSampling/utils/plotFun.py +++ b/uips/utils/plotFun.py @@ -2,7 +2,7 @@ from matplotlib import cm from prettyPlot.plotting import plt, pretty_labels -from phaseSpaceSampling.utils.parallel import irank, iroot +from uips.utils.parallel import irank, iroot def cornerPlotScatter(data, title=None): diff --git a/phaseSpaceSampling/utils/torchutils.py b/uips/utils/torchutils.py similarity index 98% rename from phaseSpaceSampling/utils/torchutils.py rename to uips/utils/torchutils.py index fdbeae3..0ccb1ce 100644 --- a/phaseSpaceSampling/utils/torchutils.py +++ b/uips/utils/torchutils.py @@ -2,7 +2,7 @@ import torch -from phaseSpaceSampling.utils.typechecks import ( +from uips.utils.typechecks import ( is_nonnegative_int, is_positive_int, ) diff --git a/phaseSpaceSampling/utils/torchutils_test.py b/uips/utils/torchutils_test.py similarity index 100% rename from phaseSpaceSampling/utils/torchutils_test.py rename to uips/utils/torchutils_test.py diff --git a/phaseSpaceSampling/utils/typechecks.py b/uips/utils/typechecks.py similarity index 100% rename from phaseSpaceSampling/utils/typechecks.py rename to uips/utils/typechecks.py diff --git a/phaseSpaceSampling/wrapper.py b/uips/wrapper.py similarity index 96% rename from phaseSpaceSampling/wrapper.py rename to uips/wrapper.py index e6be4d0..af53c5e 100644 --- a/phaseSpaceSampling/wrapper.py +++ b/uips/wrapper.py @@ -6,12 +6,12 @@ import torch from prettyPlot.parser import parse_input_file -import phaseSpaceSampling.sampler as sampler -import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling.utils.dataUtils import prepareData -from phaseSpaceSampling.utils.fileFinder import find_input -from phaseSpaceSampling.utils.plotFun import * -from phaseSpaceSampling.utils.torchutils import get_num_parameters +import uips.sampler as sampler +import uips.utils.parallel as par +from uips.utils.dataUtils import prepareData +from uips.utils.fileFinder import find_input +from uips.utils.plotFun import * +from uips.utils.torchutils import get_num_parameters def downsample_dataset_from_input(inpt_file): From 9de847f489eea3ec70a791dff9203c978228967d Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 18:38:18 -0700 Subject: [PATCH 18/32] dist script --- dist.sh | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 dist.sh diff --git a/dist.sh b/dist.sh new file mode 100644 index 0000000..e9df190 --- /dev/null +++ b/dist.sh @@ -0,0 +1,2 @@ +python -m build +python -m twine upload --verbose --repository pypi dist/* From 670083074940803189b8b5402efc1bae4b32abcb Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 18:39:18 -0700 Subject: [PATCH 19/32] format --- uips/nde/transforms/normalization.py | 5 +---- uips/utils/torchutils.py | 5 +---- 2 files changed, 2 insertions(+), 8 deletions(-) diff --git a/uips/nde/transforms/normalization.py b/uips/nde/transforms/normalization.py index 4c3b5bd..cf68c75 100644 --- a/uips/nde/transforms/normalization.py +++ b/uips/nde/transforms/normalization.py @@ -4,10 +4,7 @@ from torch import nn from torch.nn import functional as F -from uips.nde.transforms.base import ( - InverseNotAvailable, - Transform, -) +from uips.nde.transforms.base import InverseNotAvailable, Transform from uips.utils.typechecks import is_positive_int # class BatchNorm(Transform): diff --git a/uips/utils/torchutils.py b/uips/utils/torchutils.py index 0ccb1ce..85ac0e4 100644 --- a/uips/utils/torchutils.py +++ b/uips/utils/torchutils.py @@ -2,10 +2,7 @@ import torch -from uips.utils.typechecks import ( - is_nonnegative_int, - is_positive_int, -) +from uips.utils.typechecks import is_nonnegative_int, is_positive_int def tile(x, n): From abefaea006aed14d9e6f29610dbdac41be788715 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 18:44:52 -0700 Subject: [PATCH 20/32] update version --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 6ec4074..aa5e83d 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ setup( name="uips", - version="0.0.1", + version="0.0.2", description="Reduce a large and high-dimensional dataset by downselecting data uniformly in phase space", url="https://github.com/NREL/Phase-space-sampling", author="Malik Hassanaly", From e6bde163b458e29a645bc6891b8f1bbf803a2155 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 19:10:34 -0700 Subject: [PATCH 21/32] make sure mluq works --- setup.py | 2 +- uips/utils/parallel.py | 5 +++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index aa5e83d..2e3da18 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ setup( name="uips", - version="0.0.2", + version="0.0.3", description="Reduce a large and high-dimensional dataset by downselecting data uniformly in phase space", url="https://github.com/NREL/Phase-space-sampling", author="Malik Hassanaly", diff --git a/uips/utils/parallel.py b/uips/utils/parallel.py index 5ec3173..0a83cce 100644 --- a/uips/utils/parallel.py +++ b/uips/utils/parallel.py @@ -159,6 +159,11 @@ def allmaxScalar(A): return result +def allminScalar(A): + result = comm.allreduce(A, op=MPI.MIN) + return result + + def bcast(A): A = comm.bcast(A, root=0) return A From 5912a903e6ce0b518ace79bee4485c69101e80a0 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 19:26:49 -0700 Subject: [PATCH 22/32] fix readme images link --- .../{103_phaseSpaceSampling.png => 103_uips.png} | Bin .../{104_phaseSpaceSampling.png => 104_uips.png} | Bin 2 files changed, 0 insertions(+), 0 deletions(-) rename documentation/readmeImages/{103_phaseSpaceSampling.png => 103_uips.png} (100%) rename documentation/readmeImages/{104_phaseSpaceSampling.png => 104_uips.png} (100%) diff --git a/documentation/readmeImages/103_phaseSpaceSampling.png b/documentation/readmeImages/103_uips.png similarity index 100% rename from documentation/readmeImages/103_phaseSpaceSampling.png rename to documentation/readmeImages/103_uips.png diff --git a/documentation/readmeImages/104_phaseSpaceSampling.png b/documentation/readmeImages/104_uips.png similarity index 100% rename from documentation/readmeImages/104_phaseSpaceSampling.png rename to documentation/readmeImages/104_uips.png From 2f49ce645bd82d8586d079de3299421bc41cfd4f Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 19:30:03 -0700 Subject: [PATCH 23/32] add badges --- README.md | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f9f7993..fd32d12 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,4 @@ -# Phase-space sampling of large datasets [![UIPS-CI](https://github.com/NREL/Phase-space-sampling/actions/workflows/ci.yml/badge.svg)](https://github.com/NREL/Phase-space-sampling/actions/workflows/ci.yml) - +# Phase-space sampling of large datasets [![UIPS-CI](https://github.com/NREL/Phase-space-sampling/actions/workflows/ci.yml/badge.svg)](https://github.com/NREL/Phase-space-sampling/actions/workflows/ci.yml) [![UIPS-pypi](https://badge.fury.io/py/uips.svg)](https://badge.fury.io/py/uips) ## Installation for NREL HPC users 1. `module load openmpi/4.1.0/gcc-8.4.0` 2. `conda activate /projects/mluq/condaEnvs/uips` @@ -96,6 +95,14 @@ Input file is provided in `inputs/highdim/input11D` The folder `data-efficientML` is NOT necessary for using the phase-space sampling package. It only contains the code necessary to reproduce the results shown in the paper: +## Formatting [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) + +Code formatting and import sorting are done automatically with `black` and `isort`. + +Fix imports and format : `pip install black isort; bash fixFormat.sh` + +Spelling is checked but not automatically fixed using `codespell` + ## Reference [Published version (open access)](https://www.cambridge.org/core/journals/data-centric-engineering/article/uniforminphasespace-data-selection-with-iterative-normalizing-flows/E6212E3FCB5A7EE7B1399BA49667B84C) From 9b36d047555ea4100201c29eee36152bd4002321 Mon Sep 17 00:00:00 2001 From: Malik Date: Thu, 30 Nov 2023 08:47:33 -0700 Subject: [PATCH 24/32] add standalone tutorials and update global var names --- README.md | 51 ++++++++++++++++++-- postProcess/plotLoss.py | 4 +- postProcess/visualizeDownSampled_subplots.py | 4 +- setup.py | 4 +- tests/main_from_input.py | 2 +- tests/test_downsampling.py | 6 +-- tutorials/uips_normal.py | 38 +++++++++++++++ uips/__init__.py | 10 ++-- uips/utils/fileFinder.py | 6 +-- uips/wrapper.py | 12 ++++- 10 files changed, 116 insertions(+), 21 deletions(-) create mode 100644 tutorials/uips_normal.py diff --git a/README.md b/README.md index fd32d12..7fc88ea 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,61 @@ # Phase-space sampling of large datasets [![UIPS-CI](https://github.com/NREL/Phase-space-sampling/actions/workflows/ci.yml/badge.svg)](https://github.com/NREL/Phase-space-sampling/actions/workflows/ci.yml) [![UIPS-pypi](https://badge.fury.io/py/uips.svg)](https://badge.fury.io/py/uips) + ## Installation for NREL HPC users 1. `module load openmpi/4.1.0/gcc-8.4.0` 2. `conda activate /projects/mluq/condaEnvs/uips` -## Installation for other users - -### From `conda` +## Installation developers 1. `conda create --name uips python=3.10` 2. `conda activate uips` 3. `pip install .` +## Installation for users + +`pip install uips` + +``` +import os + +import numpy as np +from prettyPlot.parser import parse_input_file +from prettyPlot.plotting import plt, pretty_labels, pretty_legend + +from uips import UIPS_INPUT_DIR +from uips.wrapper import downsample_dataset_from_input + +ndat = int(1e5) +nSampl = 100 +ds = np.random.multivariate_normal([0, 0], np.eye(2), size=ndat) +np.save("norm.npy", ds) + +inpt = parse_input_file(os.path.join(UIPS_INPUT_DIR, "input2D")) +inpt["dataFile"] = "norm.npy" +inpt["nWorkingData"] = f"{ndat} {ndat}" +inpt["nEpochs"] = f"5 20" +inpt["nSamples"] = f"{nSampl}" +best_files = downsample_dataset_from_input(inpt) + + +downsampled_ds = {} +for nsamp in best_files: + downsampled_ds[nsamp] = np.load(best_files[nsamp])["data"] + +fig = plt.figure() +plt.plot(ds[:, 0], ds[:, 1], "o", color="k", label="full DS") +plt.plot( + downsampled_ds[nSampl][:, 0], + downsampled_ds[nSampl][:, 1], + "o", + color="r", + label="downsampled", +) +pretty_labels("", "", 14) +pretty_legend() +plt.savefig("normal_downsample.png") + +``` + ## Purpose The purpose of the tool is to perform a smart downselection of a large number of datapoints. Typically, large numerical simulations generate billions, or even trillions of datapoints. However, there may be redundancy in the dataset which unnecessarily constrains memory and computing requirements. Here, redundancy is defined as closeness in feature space. The method is called phase-space sampling. diff --git a/postProcess/plotLoss.py b/postProcess/plotLoss.py index fbcd45c..c598c9f 100644 --- a/postProcess/plotLoss.py +++ b/postProcess/plotLoss.py @@ -8,7 +8,7 @@ from prettyPlot.plotting import pretty_labels import uips.utils.parallel as par -from uips import PSS_INPUT_DIR +from uips import UIPS_INPUT_DIR parser = argparse.ArgumentParser(description="Loss plotting") parser.add_argument( @@ -27,7 +27,7 @@ # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ inpt_file = args.input if not os.path.isfile(inpt_file): - new_inpt_file = os.path.join(PSS_INPUT_DIR, os.path.split(inpt_file)[-1]) + new_inpt_file = os.path.join(UIPS_INPUT_DIR, os.path.split(inpt_file)[-1]) par.printRoot(f"WARNING: {inpt_file} not found trying {new_inpt_file} ...") if not os.path.isfile(new_inpt_file): par.printRoot( diff --git a/postProcess/visualizeDownSampled_subplots.py b/postProcess/visualizeDownSampled_subplots.py index 7d01093..029e13f 100644 --- a/postProcess/visualizeDownSampled_subplots.py +++ b/postProcess/visualizeDownSampled_subplots.py @@ -8,7 +8,7 @@ from prettyPlot.plotting import pretty_labels import uips.utils.parallel as par -from uips import PSS_INPUT_DIR +from uips import UIPS_INPUT_DIR def plotScatterProjection(data, fullData, fieldNames, lims): @@ -71,7 +71,7 @@ def plotScatterProjection(data, fullData, fieldNames, lims): # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ inpt_file = args.input if not os.path.isfile(inpt_file): - new_inpt_file = os.path.join(PSS_INPUT_DIR, os.path.split(inpt_file)[-1]) + new_inpt_file = os.path.join(UIPS_INPUT_DIR, os.path.split(inpt_file)[-1]) par.printRoot(f"WARNING: {inpt_file} not found trying {new_inpt_file} ...") if not os.path.isfile(new_inpt_file): par.printRoot( diff --git a/setup.py b/setup.py index 2e3da18..deeb51b 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ setup( name="uips", - version="0.0.3", + version="0.0.4", description="Reduce a large and high-dimensional dataset by downselecting data uniformly in phase space", url="https://github.com/NREL/Phase-space-sampling", author="Malik Hassanaly", @@ -16,6 +16,8 @@ "Natural Language :: English", "Programming Language :: Python :: 3.10", ], + package_data={"": ["input2D", "input2D_bins"]}, + include_package_data=True, python_requires=">=3.10", install_requires=[ "openmpi", diff --git a/tests/main_from_input.py b/tests/main_from_input.py index 5b328cc..4b3036f 100644 --- a/tests/main_from_input.py +++ b/tests/main_from_input.py @@ -14,4 +14,4 @@ ) args, unknown = parser.parse_known_args() -downsample_dataset_from_input(args.input) +downsample_dataset_from_input_file(args.input) diff --git a/tests/test_downsampling.py b/tests/test_downsampling.py index 0fc8b41..69c5c84 100644 --- a/tests/test_downsampling.py +++ b/tests/test_downsampling.py @@ -1,14 +1,14 @@ import os -from uips import PSS_INPUT_DIR +from uips import UIPS_INPUT_DIR from uips.wrapper import downsample_dataset_from_input def test_nf_input(): - input_file = os.path.join(PSS_INPUT_DIR, "input_test") + input_file = os.path.join(UIPS_INPUT_DIR, "input_test") downsample_dataset_from_input(input_file) def test_bins_input(): - input_file = os.path.join(PSS_INPUT_DIR, "input_test_bins") + input_file = os.path.join(UIPS_INPUT_DIR, "input_test_bins") downsample_dataset_from_input(input_file) diff --git a/tutorials/uips_normal.py b/tutorials/uips_normal.py new file mode 100644 index 0000000..b368252 --- /dev/null +++ b/tutorials/uips_normal.py @@ -0,0 +1,38 @@ +import os + +import numpy as np +from prettyPlot.parser import parse_input_file +from prettyPlot.plotting import plt, pretty_labels, pretty_legend + +from uips import UIPS_INPUT_DIR +from uips.wrapper import downsample_dataset_from_input + +ndat = int(1e5) +nSampl = 100 +ds = np.random.multivariate_normal([0, 0], np.eye(2), size=ndat) +np.save("norm.npy", ds) + +inpt = parse_input_file(os.path.join(UIPS_INPUT_DIR, "input2D")) +inpt["dataFile"] = "norm.npy" +inpt["nWorkingData"] = f"{ndat} {ndat}" +inpt["nEpochs"] = f"5 20" +inpt["nSamples"] = f"{nSampl}" +best_files = downsample_dataset_from_input(inpt) + + +downsampled_ds = {} +for nsamp in best_files: + downsampled_ds[nsamp] = np.load(best_files[nsamp])["data"] + +fig = plt.figure() +plt.plot(ds[:, 0], ds[:, 1], "o", color="k", label="full DS") +plt.plot( + downsampled_ds[nSampl][:, 0], + downsampled_ds[nSampl][:, 1], + "o", + color="r", + label="downsampled", +) +pretty_labels("", "", 14) +pretty_legend() +plt.savefig("normal_downsample.png") diff --git a/uips/__init__.py b/uips/__init__.py index 9080071..1159149 100644 --- a/uips/__init__.py +++ b/uips/__init__.py @@ -1,8 +1,8 @@ """Phase space sampling""" import os -PSS_DIR = os.path.dirname(os.path.realpath(__file__)) -PSS_INPUT_DIR = os.path.join(PSS_DIR, "../inputs") -PSS_DATA_DIR = os.path.join(PSS_DIR, "../data") -PSS_DOC_DIR = os.path.join(PSS_DIR, "../documentation") -PSS_ML_DIR = os.path.join(PSS_DIR, "../data-efficientML") +UIPS_DIR = os.path.dirname(os.path.realpath(__file__)) +UIPS_INPUT_DIR = os.path.join(UIPS_DIR, "../inputs") +UIPS_DATA_DIR = os.path.join(UIPS_DIR, "../data") +UIPS_DOC_DIR = os.path.join(UIPS_DIR, "../documentation") +UIPS_ML_DIR = os.path.join(UIPS_DIR, "../data-efficientML") diff --git a/uips/utils/fileFinder.py b/uips/utils/fileFinder.py index 31a340f..e888cd2 100644 --- a/uips/utils/fileFinder.py +++ b/uips/utils/fileFinder.py @@ -2,13 +2,13 @@ import sys import uips.utils.parallel as par -from uips import PSS_DATA_DIR, PSS_INPUT_DIR +from uips import UIPS_DATA_DIR, UIPS_INPUT_DIR def find_input(inpt_file): if not os.path.isfile(inpt_file): new_inpt_file = os.path.join( - PSS_INPUT_DIR, os.path.split(inpt_file)[-1] + UIPS_INPUT_DIR, os.path.split(inpt_file)[-1] ) par.printRoot( f"WARNING: {inpt_file} not found trying {new_inpt_file} ..." @@ -28,7 +28,7 @@ def find_input(inpt_file): def find_data(data_file): if not os.path.isfile(data_file): new_data_file = os.path.join( - PSS_DATA_DIR, os.path.split(data_file)[-1] + UIPS_DATA_DIR, os.path.split(data_file)[-1] ) par.printRoot( f"WARNING: {data_file} not found trying {new_data_file} ..." diff --git a/uips/wrapper.py b/uips/wrapper.py index af53c5e..b2835bf 100644 --- a/uips/wrapper.py +++ b/uips/wrapper.py @@ -14,9 +14,13 @@ from uips.utils.torchutils import get_num_parameters -def downsample_dataset_from_input(inpt_file): +def downsample_dataset_from_input_file(inpt_file): inpt_file = find_input(inpt_file) inpt = parse_input_file(inpt_file) + return downsample_dataset_from_input(inpt) + + +def downsample_dataset_from_input(inpt): use_normalizing_flow = inpt["pdf_method"].lower() == "normalizingflow" use_bins = inpt["pdf_method"].lower() == "bins" @@ -218,6 +222,7 @@ def downsample_dataset_from_input(inpt_file): # Advise for best sampling if par.irank == par.iroot: + best_files = {} if np.amax(meanCriterion) > 0: print("\n") maxCrit = np.argmax(meanCriterion, axis=0) @@ -226,8 +231,13 @@ def downsample_dataset_from_input(inpt_file): print( f"For sample {nSample} use {inpt['prefixDownsampledData']}_{nSample}_it{maxCrit[iSample]}.npz" ) + best_files[ + nSample + ] = f"{inpt['prefixDownsampledData']}_{nSample}_it{maxCrit[iSample]}.npz" print("\n") + return best_files + # if par.irank==par.iroot: # plt.show() From 6f78b9c227d416321d5405521481c569e86d591b Mon Sep 17 00:00:00 2001 From: Malik Date: Thu, 30 Nov 2023 08:54:25 -0700 Subject: [PATCH 25/32] update docs and make sure test runs --- README.md | 8 ++++++++ tests/main_from_input.py | 2 +- uips/wrapper.py | 2 +- 3 files changed, 10 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 7fc88ea..d4e31a0 100644 --- a/README.md +++ b/README.md @@ -10,10 +10,18 @@ 2. `conda activate uips` 3. `pip install .` +Test + +``` +bash tutorials/run2D.sh +``` + ## Installation for users `pip install uips` +Test + ``` import os diff --git a/tests/main_from_input.py b/tests/main_from_input.py index 4b3036f..7e2a1bc 100644 --- a/tests/main_from_input.py +++ b/tests/main_from_input.py @@ -1,6 +1,6 @@ import argparse -from uips.wrapper import downsample_dataset_from_input +from uips.wrapper import downsample_dataset_from_input_file parser = argparse.ArgumentParser(description="Downsampler") parser.add_argument( diff --git a/uips/wrapper.py b/uips/wrapper.py index b2835bf..9f33df9 100644 --- a/uips/wrapper.py +++ b/uips/wrapper.py @@ -220,9 +220,9 @@ def downsample_dataset_from_input(inpt): ) data_for_pdf_est = downSampledData + best_files = {} # Advise for best sampling if par.irank == par.iroot: - best_files = {} if np.amax(meanCriterion) > 0: print("\n") maxCrit = np.argmax(meanCriterion, axis=0) From 26ab4557be365a5dd06e6b9c35091fa7d055142c Mon Sep 17 00:00:00 2001 From: Malik Date: Thu, 30 Nov 2023 08:57:08 -0700 Subject: [PATCH 26/32] fix doc --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d4e31a0..e3f94cd 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ 1. `module load openmpi/4.1.0/gcc-8.4.0` 2. `conda activate /projects/mluq/condaEnvs/uips` -## Installation developers +## Installation for developers 1. `conda create --name uips python=3.10` 2. `conda activate uips` From 217fc3f081225cac0eb3e31d9e9dd54baccee6be Mon Sep 17 00:00:00 2001 From: Malik Date: Thu, 30 Nov 2023 08:59:55 -0700 Subject: [PATCH 27/32] fix tests --- tests/test_downsampling.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_downsampling.py b/tests/test_downsampling.py index 69c5c84..0db7728 100644 --- a/tests/test_downsampling.py +++ b/tests/test_downsampling.py @@ -1,14 +1,14 @@ import os from uips import UIPS_INPUT_DIR -from uips.wrapper import downsample_dataset_from_input +from uips.wrapper import downsample_dataset_from_input_file def test_nf_input(): input_file = os.path.join(UIPS_INPUT_DIR, "input_test") - downsample_dataset_from_input(input_file) + downsample_dataset_from_input_file(input_file) def test_bins_input(): input_file = os.path.join(UIPS_INPUT_DIR, "input_test_bins") - downsample_dataset_from_input(input_file) + downsample_dataset_from_input_file(input_file) From 3671b92ed7938def361179bfcb07e72f6220b872 Mon Sep 17 00:00:00 2001 From: Malik Date: Thu, 30 Nov 2023 09:13:03 -0700 Subject: [PATCH 28/32] make sure test can run --- inputs/input_test | 2 +- inputs/input_test_bins | 2 +- tests/test_downsampling.py | 10 +++++++++- 3 files changed, 11 insertions(+), 3 deletions(-) diff --git a/inputs/input_test b/inputs/input_test index 0038e75..1046e7e 100644 --- a/inputs/input_test +++ b/inputs/input_test @@ -57,7 +57,7 @@ printTiming : True ! ~~~~ Data ! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ! -dataFile : data/combustion2DToDownsampleSmall.npy +dataFile : ../data/combustion2DToDownsampleSmall.npy nDatReduced : 1e5 preShuffled : True scalerFile : scaler.npz diff --git a/inputs/input_test_bins b/inputs/input_test_bins index 9464250..c7492b0 100644 --- a/inputs/input_test_bins +++ b/inputs/input_test_bins @@ -54,7 +54,7 @@ printTiming : True ! ~~~~ Data ! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ! -dataFile : data/combustion2DToDownsampleSmall.npy +dataFile : ../data/combustion2DToDownsampleSmall.npy nDatReduced : 1e5 preShuffled : True scalerFile : scaler.npz diff --git a/tests/test_downsampling.py b/tests/test_downsampling.py index 0db7728..f807554 100644 --- a/tests/test_downsampling.py +++ b/tests/test_downsampling.py @@ -1,7 +1,10 @@ import os from uips import UIPS_INPUT_DIR -from uips.wrapper import downsample_dataset_from_input_file +from uips.wrapper import ( + downsample_dataset_from_input, + downsample_dataset_from_input_file, +) def test_nf_input(): @@ -12,3 +15,8 @@ def test_nf_input(): def test_bins_input(): input_file = os.path.join(UIPS_INPUT_DIR, "input_test_bins") downsample_dataset_from_input_file(input_file) + + +if __name__ == "__main__": + test_nf_input() + test_bins_input() From b0c26c82b6dd71af23c512eccea9ae780c85d6d8 Mon Sep 17 00:00:00 2001 From: Malik Date: Thu, 30 Nov 2023 09:23:07 -0700 Subject: [PATCH 29/32] move inputs to package --- uips/__init__.py | 2 +- {inputs => uips/inputs}/highdim/input11D | 0 {inputs => uips/inputs}/input2D | 0 {inputs => uips/inputs}/input2D_bins | 0 {inputs => uips/inputs}/input3D | 0 uips/inputs/input_moons.py | 0 {inputs => uips/inputs}/input_test | 0 {inputs => uips/inputs}/input_test_bins | 0 8 files changed, 1 insertion(+), 1 deletion(-) rename {inputs => uips/inputs}/highdim/input11D (100%) rename {inputs => uips/inputs}/input2D (100%) rename {inputs => uips/inputs}/input2D_bins (100%) rename {inputs => uips/inputs}/input3D (100%) create mode 100644 uips/inputs/input_moons.py rename {inputs => uips/inputs}/input_test (100%) rename {inputs => uips/inputs}/input_test_bins (100%) diff --git a/uips/__init__.py b/uips/__init__.py index 1159149..053aa43 100644 --- a/uips/__init__.py +++ b/uips/__init__.py @@ -2,7 +2,7 @@ import os UIPS_DIR = os.path.dirname(os.path.realpath(__file__)) -UIPS_INPUT_DIR = os.path.join(UIPS_DIR, "../inputs") +UIPS_INPUT_DIR = os.path.join(UIPS_DIR, "inputs") UIPS_DATA_DIR = os.path.join(UIPS_DIR, "../data") UIPS_DOC_DIR = os.path.join(UIPS_DIR, "../documentation") UIPS_ML_DIR = os.path.join(UIPS_DIR, "../data-efficientML") diff --git a/inputs/highdim/input11D b/uips/inputs/highdim/input11D similarity index 100% rename from inputs/highdim/input11D rename to uips/inputs/highdim/input11D diff --git a/inputs/input2D b/uips/inputs/input2D similarity index 100% rename from inputs/input2D rename to uips/inputs/input2D diff --git a/inputs/input2D_bins b/uips/inputs/input2D_bins similarity index 100% rename from inputs/input2D_bins rename to uips/inputs/input2D_bins diff --git a/inputs/input3D b/uips/inputs/input3D similarity index 100% rename from inputs/input3D rename to uips/inputs/input3D diff --git a/uips/inputs/input_moons.py b/uips/inputs/input_moons.py new file mode 100644 index 0000000..e69de29 diff --git a/inputs/input_test b/uips/inputs/input_test similarity index 100% rename from inputs/input_test rename to uips/inputs/input_test diff --git a/inputs/input_test_bins b/uips/inputs/input_test_bins similarity index 100% rename from inputs/input_test_bins rename to uips/inputs/input_test_bins From 94cb11e5c3f93f4f0f94be415ed3d227b05c92d9 Mon Sep 17 00:00:00 2001 From: Malik Date: Thu, 30 Nov 2023 09:23:35 -0700 Subject: [PATCH 30/32] update version --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index deeb51b..48dfb9f 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ setup( name="uips", - version="0.0.4", + version="0.0.5", description="Reduce a large and high-dimensional dataset by downselecting data uniformly in phase space", url="https://github.com/NREL/Phase-space-sampling", author="Malik Hassanaly", From fcf4b8d9d6d32c2c3d48093264257e823b1eaf5e Mon Sep 17 00:00:00 2001 From: Malik Date: Thu, 30 Nov 2023 09:31:49 -0700 Subject: [PATCH 31/32] make sure parallel test runs --- .github/workflows/ci.yml | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 3aec303..fba47c4 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -44,4 +44,4 @@ jobs: - name: Parallel test run: | cd tests - mpiexec -np 2 python main_from_input.py -i ../inputs/input_test + mpiexec -np 2 python main_from_input.py -i ../uips/inputs/input_test diff --git a/setup.py b/setup.py index 48dfb9f..a39b5bb 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ setup( name="uips", - version="0.0.5", + version="0.0.6", description="Reduce a large and high-dimensional dataset by downselecting data uniformly in phase space", url="https://github.com/NREL/Phase-space-sampling", author="Malik Hassanaly", From aa63fef5602e5a62ff313a41e04efdf92d3cd38b Mon Sep 17 00:00:00 2001 From: Malik Date: Thu, 30 Nov 2023 09:42:16 -0700 Subject: [PATCH 32/32] update tutorials --- README.md | 35 ++++++++++++++++++----------------- setup.py | 2 +- tutorials/uips_normal.py | 34 ++++++++++++++++++---------------- 3 files changed, 37 insertions(+), 34 deletions(-) diff --git a/README.md b/README.md index e3f94cd..1685734 100644 --- a/README.md +++ b/README.md @@ -31,6 +31,7 @@ from prettyPlot.plotting import plt, pretty_labels, pretty_legend from uips import UIPS_INPUT_DIR from uips.wrapper import downsample_dataset_from_input +import uips.utils.parallel as par ndat = int(1e5) nSampl = 100 @@ -42,26 +43,26 @@ inpt["dataFile"] = "norm.npy" inpt["nWorkingData"] = f"{ndat} {ndat}" inpt["nEpochs"] = f"5 20" inpt["nSamples"] = f"{nSampl}" -best_files = downsample_dataset_from_input(inpt) - -downsampled_ds = {} -for nsamp in best_files: - downsampled_ds[nsamp] = np.load(best_files[nsamp])["data"] +best_files = downsample_dataset_from_input(inpt) -fig = plt.figure() -plt.plot(ds[:, 0], ds[:, 1], "o", color="k", label="full DS") -plt.plot( - downsampled_ds[nSampl][:, 0], - downsampled_ds[nSampl][:, 1], - "o", - color="r", - label="downsampled", -) -pretty_labels("", "", 14) -pretty_legend() -plt.savefig("normal_downsample.png") +if par.irank == par.iroot: + downsampled_ds = {} + for nsamp in best_files: + downsampled_ds[nsamp] = np.load(best_files[nsamp])["data"] + fig = plt.figure() + plt.plot(ds[:, 0], ds[:, 1], "o", color="k", label="full DS") + plt.plot( + downsampled_ds[nSampl][:, 0], + downsampled_ds[nSampl][:, 1], + "o", + color="r", + label="downsampled", + ) + pretty_labels("", "", 14) + pretty_legend() + plt.savefig("normal_downsample.png") ``` ## Purpose diff --git a/setup.py b/setup.py index a39b5bb..11ed706 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ setup( name="uips", - version="0.0.6", + version="0.0.7", description="Reduce a large and high-dimensional dataset by downselecting data uniformly in phase space", url="https://github.com/NREL/Phase-space-sampling", author="Malik Hassanaly", diff --git a/tutorials/uips_normal.py b/tutorials/uips_normal.py index b368252..3fc7e22 100644 --- a/tutorials/uips_normal.py +++ b/tutorials/uips_normal.py @@ -4,6 +4,7 @@ from prettyPlot.parser import parse_input_file from prettyPlot.plotting import plt, pretty_labels, pretty_legend +import uips.utils.parallel as par from uips import UIPS_INPUT_DIR from uips.wrapper import downsample_dataset_from_input @@ -17,22 +18,23 @@ inpt["nWorkingData"] = f"{ndat} {ndat}" inpt["nEpochs"] = f"5 20" inpt["nSamples"] = f"{nSampl}" -best_files = downsample_dataset_from_input(inpt) +best_files = downsample_dataset_from_input(inpt) -downsampled_ds = {} -for nsamp in best_files: - downsampled_ds[nsamp] = np.load(best_files[nsamp])["data"] -fig = plt.figure() -plt.plot(ds[:, 0], ds[:, 1], "o", color="k", label="full DS") -plt.plot( - downsampled_ds[nSampl][:, 0], - downsampled_ds[nSampl][:, 1], - "o", - color="r", - label="downsampled", -) -pretty_labels("", "", 14) -pretty_legend() -plt.savefig("normal_downsample.png") +if par.irank == par.iroot: + downsampled_ds = {} + for nsamp in best_files: + downsampled_ds[nsamp] = np.load(best_files[nsamp])["data"] + fig = plt.figure() + plt.plot(ds[:, 0], ds[:, 1], "o", color="k", label="full DS") + plt.plot( + downsampled_ds[nSampl][:, 0], + downsampled_ds[nSampl][:, 1], + "o", + color="r", + label="downsampled", + ) + pretty_labels("", "", 14) + pretty_legend() + plt.savefig("normal_downsample.png")

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log_density_np_, nWorkingDataAdjustment - ) - else: - log_density_np_for_adjust = None - - par.printRoot(f"TRAIN ITER {pdf_iter}") - - for inSample, nSample in enumerate(nSamples): - # Downsample - ( - downSampledData, - downSampledIndices, - samplingProb_, - log_samplingProb_, - ) = sampler.downSample( - data_to_downsample_, - dataInd_, - log_density_np_, - log_density_np_for_adjust, - nSample, - nFullData, - inpt, - ) - - # Plot - # cornerPlotScatter(downSampledData,title='downSampled npts='+str(nSample)+', iter='+str(pdf_iter)) - # Get criterion - if computeCriterion and par.irank == par.iroot: - if nSample <= nSampleCriterionLimit: - mean, std = sampler.computeDistanceToClosestNeighbor( - sampler.rescaleData(downSampledData, inpt) - ) - meanCriterion[pdf_iter, inSample] = mean - stdCriterion[pdf_iter, inSample] = std - par.printRoot( - f"\t nSample {nSample} mean dist = {mean:.4f}, std dist = {std:.4f}" - ) - - if pdf_iter == int(inpt["num_pdf_iter"]) - 1: - # Last pdf iter : Root proc saves downsampled data, and checks the outcome - sampler.checkProcedure( - meanCriterion[:, inSample], - nSample, - randomCriterion[inSample], - ) - if par.irank == par.iroot: - np.savez( - f"{inpt['prefixDownsampledData']}_{nSample}_it{pdf_iter}.npz", - data=downSampledData, - indices=downSampledIndices, - ) - - if not (pdf_iter == int(inpt["num_pdf_iter"]) - 1): - # Prepare data for the next training iteration - ( - downSampledData, - _, - samplingProb_, - log_samplingProb_, - ) = sampler.downSample( - data_to_downsample_, - dataInd_, - log_density_np_, - log_density_np_for_adjust, - nWorkingDatas[pdf_iter + 1], - nFullData, - inpt, - ) - data_for_pdf_est = downSampledData - -# Advise for best sampling -if par.irank == par.iroot: - if np.amax(meanCriterion) > 0: - print("\n") - maxCrit = np.argmax(meanCriterion, axis=0) - nSamples_asked = [int(float(n)) for n in inpt["nSamples"].split()] - for iSample, nSample in enumerate(nSamples_asked): - print( - f"For sample {nSample} use {inpt['prefixDownsampledData']}_{nSample}_it{maxCrit[iSample]}.npz" - ) - print("\n") - - -# if par.irank==par.iroot: -# plt.show() diff --git a/phaseSpaceSampling/nde/distributions/__init__.py b/phaseSpaceSampling/nde/distributions/__init__.py index a8d4cbd..d2b908d 100644 --- a/phaseSpaceSampling/nde/distributions/__init__.py +++ b/phaseSpaceSampling/nde/distributions/__init__.py @@ -1,7 +1,3 @@ -from .base import Distribution -from .base import NoMeanException - -from .normal import StandardNormal -from .normal import ConditionalDiagonalNormal - +from .base import Distribution, NoMeanException from .discrete import ConditionalIndependentBernoulli +from .normal import ConditionalDiagonalNormal, StandardNormal diff --git a/phaseSpaceSampling/nde/distributions/base.py b/phaseSpaceSampling/nde/distributions/base.py index 555ee51..24e667a 100644 --- a/phaseSpaceSampling/nde/distributions/base.py +++ b/phaseSpaceSampling/nde/distributions/base.py @@ -1,15 +1,19 @@ """Basic definitions for the distributions module.""" import torch - from torch import nn -from phaseSpaceSampling.utils.typechecks import is_positive_int -from phaseSpaceSampling.utils.torchutils import merge_leading_dims, repeat_rows, split_leading_dim +from phaseSpaceSampling.utils.torchutils import ( + merge_leading_dims, + repeat_rows, + split_leading_dim, +) +from phaseSpaceSampling.utils.typechecks import is_positive_int class NoMeanException(Exception): """Exception to be thrown when a mean function doesn't exist.""" + pass @@ -17,7 +21,9 @@ class Distribution(nn.Module): """Base class for all distribution objects.""" def forward(self, *args): - raise RuntimeError('Forward method cannot be called for a Distribution object.') + raise RuntimeError( + "Forward method cannot be called for a Distribution object." + ) def log_prob(self, inputs, context=None): """Calculate log probability under the distribution. @@ -34,7 +40,9 @@ def log_prob(self, inputs, context=None): if context is not None: context = torch.as_tensor(context) if inputs.shape[0] != context.shape[0]: - raise ValueError('Number of input items must be equal to number of context items.') + raise ValueError( + "Number of input items must be equal to number of context items." + ) return self._log_prob(inputs, context) def _log_prob(self, inputs, context): @@ -54,7 +62,7 @@ def sample(self, num_samples, context=None, batch_size=None): [context_size, num_samples, ...] if context is given. """ if not is_positive_int(num_samples): - raise TypeError('Number of samples must be a positive integer.') + raise TypeError("Number of samples must be a positive integer.") if context is not None: context = torch.as_tensor(context) @@ -64,11 +72,13 @@ def sample(self, num_samples, context=None, batch_size=None): else: if not is_positive_int(batch_size): - raise TypeError('Batch size must be a positive integer.') + raise TypeError("Batch size must be a positive integer.") num_batches = num_samples // batch_size num_leftover = num_samples % batch_size - samples = [self._sample(batch_size, context) for _ in range(num_batches)] + samples = [ + self._sample(batch_size, context) for _ in range(num_batches) + ] if num_leftover > 0: samples.append(self._sample(num_leftover, context)) return torch.cat(samples, dim=0) diff --git a/phaseSpaceSampling/nde/distributions/discrete.py b/phaseSpaceSampling/nde/distributions/discrete.py index 86af941..2631c3b 100644 --- a/phaseSpaceSampling/nde/distributions/discrete.py +++ b/phaseSpaceSampling/nde/distributions/discrete.py @@ -1,13 +1,14 @@ """Implementations of discrete distributions.""" import torch - from torch.nn import functional as F - from phaseSpaceSampling.nde import distributions -from phaseSpaceSampling.utils.torchutils import sum_except_batch, repeat_rows, split_leading_dim - +from phaseSpaceSampling.utils.torchutils import ( + repeat_rows, + split_leading_dim, + sum_except_batch, +) class ConditionalIndependentBernoulli(distributions.Distribution): @@ -31,26 +32,32 @@ def __init__(self, shape, context_encoder=None): def _compute_params(self, context): """Compute the logits from context.""" if context is None: - raise ValueError('Context can\'t be None.') + raise ValueError("Context can't be None.") logits = self._context_encoder(context) if logits.shape[0] != context.shape[0]: raise RuntimeError( - 'The batch dimension of the parameters is inconsistent with the input.') + "The batch dimension of the parameters is inconsistent with the input." + ) return logits.reshape(logits.shape[0], *self._shape) def _log_prob(self, inputs, context): if inputs.shape[1:] != self._shape: - raise ValueError('Expected input of shape {}, got {}'.format( - self._shape, inputs.shape[1:])) + raise ValueError( + "Expected input of shape {}, got {}".format( + self._shape, inputs.shape[1:] + ) + ) # Compute parameters. logits = self._compute_params(context) assert logits.shape == inputs.shape # Compute log prob. - log_prob = -inputs * F.softplus(-logits) - (1.0 - inputs) * F.softplus(logits) + log_prob = -inputs * F.softplus(-logits) - (1.0 - inputs) * F.softplus( + logits + ) log_prob = sum_except_batch(log_prob, num_batch_dims=1) return log_prob diff --git a/phaseSpaceSampling/nde/distributions/discrete_test.py b/phaseSpaceSampling/nde/distributions/discrete_test.py index 5cb1e3b..fdd81f2 100644 --- a/phaseSpaceSampling/nde/distributions/discrete_test.py +++ b/phaseSpaceSampling/nde/distributions/discrete_test.py @@ -1,13 +1,14 @@ """Tests for discrete distributions.""" +import unittest + import torch import torchtestcase -import unittest + from phaseSpaceSampling.nde.distributions import discrete class ConditionalIndependentBernoulliTest(torchtestcase.TorchTestCase): - def test_log_prob(self): batch_size = 10 input_shape = [2, 3, 4] @@ -31,7 +32,10 @@ def test_sample(self): context = torch.randn(context_size, *context_shape) samples = dist.sample(num_samples, context=context) self.assertIsInstance(samples, torch.Tensor) - self.assertEqual(samples.shape, torch.Size([context_size, num_samples] + input_shape)) + self.assertEqual( + samples.shape, + torch.Size([context_size, num_samples] + input_shape), + ) self.assertFalse(torch.isnan(samples).any()) self.assertFalse(torch.isinf(samples).any()) binary = (samples == 1.0) | (samples == 0.0) @@ -45,13 +49,20 @@ def test_sample_and_log_prob_with_context(self): dist = discrete.ConditionalIndependentBernoulli(input_shape) context = torch.randn(context_size, *context_shape) - samples, log_prob = dist.sample_and_log_prob(num_samples, context=context) + samples, log_prob = dist.sample_and_log_prob( + num_samples, context=context + ) self.assertIsInstance(samples, torch.Tensor) self.assertIsInstance(log_prob, torch.Tensor) - self.assertEqual(samples.shape, torch.Size([context_size, num_samples] + input_shape)) - self.assertEqual(log_prob.shape, torch.Size([context_size, num_samples])) + self.assertEqual( + samples.shape, + torch.Size([context_size, num_samples] + input_shape), + ) + self.assertEqual( + log_prob.shape, torch.Size([context_size, num_samples]) + ) self.assertFalse(torch.isnan(log_prob).any()) self.assertFalse(torch.isinf(log_prob).any()) @@ -77,5 +88,5 @@ def test_mean(self): self.assert_tensor_less_equal(means, 1.0) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/distributions/mixture.py b/phaseSpaceSampling/nde/distributions/mixture.py index cf02d68..e2e9b5f 100644 --- a/phaseSpaceSampling/nde/distributions/mixture.py +++ b/phaseSpaceSampling/nde/distributions/mixture.py @@ -1,5 +1,4 @@ import torch - from torch import distributions @@ -8,11 +7,11 @@ def __init__(self, mixture_distribution, components_distribution): self.mixture_distribution = mixture_distribution self.components_distribution = components_distribution - has_rsample=False + has_rsample = False super().__init__( batch_shape=self.components_distribution.batch_shape, - event_shape=self.components_distribution.event_shape + event_shape=self.components_distribution.event_shape, ) def expand(self, batch_shape, _instance=None): @@ -22,7 +21,7 @@ def expand(self, batch_shape, _instance=None): def arg_constraints(self): return dict( self.mixture_distribution.arg_constraints, - **self.components_distribution.arg_constraints + **self.components_distribution.arg_constraints, ) @property @@ -38,11 +37,17 @@ def variance(self): raise NotImplementedError def sample(self, sample_shape=torch.Size()): - mixture_mask = self.mixture_distribution.sample(sample_shape) # [S, B, D, M] + mixture_mask = self.mixture_distribution.sample( + sample_shape + ) # [S, B, D, M] if len(mixture_mask.shape) == 3: mixture_mask = mixture_mask[:, None, ...] - components_samples = self.components_distribution.rsample(sample_shape) # [S, B, D, M] - samples = torch.sum(mixture_mask * components_samples, dim=-1) # [S, B, D] + components_samples = self.components_distribution.rsample( + sample_shape + ) # [S, B, D, M] + samples = torch.sum( + mixture_mask * components_samples, dim=-1 + ) # [S, B, D] return samples def rsample(self, sample_shape=torch.Size()): @@ -50,9 +55,13 @@ def rsample(self, sample_shape=torch.Size()): def log_prob(self, value): # pad value for evaluation under component density - value = value.permute(2, 0, 1) # [S, B, D] - value = value[..., None].repeat(1, 1, 1, self.batch_shape[-1]) # [S, B, D, M] - log_prob_components = self.components_distribution.log_prob(value).permute(1, 2, 3, 0) + value = value.permute(2, 0, 1) # [S, B, D] + value = value[..., None].repeat( + 1, 1, 1, self.batch_shape[-1] + ) # [S, B, D, M] + log_prob_components = self.components_distribution.log_prob( + value + ).permute(1, 2, 3, 0) # calculate numerically stable log coefficients, and pad log_prob_mixture = self.mixture_distribution.logits @@ -76,5 +85,5 @@ def main(): pass -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/phaseSpaceSampling/nde/distributions/normal.py b/phaseSpaceSampling/nde/distributions/normal.py index 09cfc1d..65934f0 100644 --- a/phaseSpaceSampling/nde/distributions/normal.py +++ b/phaseSpaceSampling/nde/distributions/normal.py @@ -3,9 +3,12 @@ import numpy as np import torch - from phaseSpaceSampling.nde import distributions -from phaseSpaceSampling.utils.torchutils import sum_except_batch, split_leading_dim, repeat_rows +from phaseSpaceSampling.utils.torchutils import ( + repeat_rows, + split_leading_dim, + sum_except_batch, +) class StandardNormal(distributions.Distribution): @@ -19,9 +22,12 @@ def __init__(self, shape): def _log_prob(self, inputs, context): # Note: the context is ignored. if inputs.shape[1:] != self._shape: - raise ValueError('Expected input of shape {}, got {}'.format( - self._shape, inputs.shape[1:])) - neg_energy = -0.5 * sum_except_batch(inputs ** 2, num_batch_dims=1) + raise ValueError( + "Expected input of shape {}, got {}".format( + self._shape, inputs.shape[1:] + ) + ) + neg_energy = -0.5 * sum_except_batch(inputs**2, num_batch_dims=1) return neg_energy - self._log_z def _sample(self, num_samples, context): @@ -63,15 +69,17 @@ def __init__(self, shape, context_encoder=None): def _compute_params(self, context): """Compute the means and log stds form the context.""" if context is None: - raise ValueError('Context can\'t be None.') + raise ValueError("Context can't be None.") params = self._context_encoder(context) if params.shape[-1] % 2 != 0: raise RuntimeError( - 'The context encoder must return a tensor whose last dimension is even.') + "The context encoder must return a tensor whose last dimension is even." + ) if params.shape[0] != context.shape[0]: raise RuntimeError( - 'The batch dimension of the parameters is inconsistent with the input.') + "The batch dimension of the parameters is inconsistent with the input." + ) split = params.shape[-1] // 2 means = params[..., :split].reshape(params.shape[0], *self._shape) @@ -80,8 +88,11 @@ def _compute_params(self, context): def _log_prob(self, inputs, context): if inputs.shape[1:] != self._shape: - raise ValueError('Expected input of shape {}, got {}'.format( - self._shape, inputs.shape[1:])) + raise ValueError( + "Expected input of shape {}, got {}".format( + self._shape, inputs.shape[1:] + ) + ) # Compute parameters. means, log_stds = self._compute_params(context) @@ -89,7 +100,7 @@ def _log_prob(self, inputs, context): # Compute log prob. norm_inputs = (inputs - means) * torch.exp(-log_stds) - log_prob = -0.5 * sum_except_batch(norm_inputs ** 2, num_batch_dims=1) + log_prob = -0.5 * sum_except_batch(norm_inputs**2, num_batch_dims=1) log_prob -= sum_except_batch(log_stds, num_batch_dims=1) log_prob -= self._log_z return log_prob diff --git a/phaseSpaceSampling/nde/distributions/normal_test.py b/phaseSpaceSampling/nde/distributions/normal_test.py index 9db29d0..b896ae6 100644 --- a/phaseSpaceSampling/nde/distributions/normal_test.py +++ b/phaseSpaceSampling/nde/distributions/normal_test.py @@ -1,13 +1,14 @@ """Tests for Normal distributions.""" +import unittest + import torch import torchtestcase -import unittest + from phaseSpaceSampling.nde.distributions import normal class StandardNormalTest(torchtestcase.TorchTestCase): - def test_log_prob(self): batch_size = 10 input_shape = [2, 3, 4] @@ -37,10 +38,14 @@ def test_sample(self): self.assertFalse(torch.isnan(samples).any()) self.assertFalse(torch.isinf(samples).any()) if context is None: - self.assertEqual(samples.shape, torch.Size([num_samples] + input_shape)) + self.assertEqual( + samples.shape, torch.Size([num_samples] + input_shape) + ) else: self.assertEqual( - samples.shape, torch.Size([context_size, num_samples] + input_shape)) + samples.shape, + torch.Size([context_size, num_samples] + input_shape), + ) def test_sample_and_log_prob(self): num_samples = 10 @@ -51,7 +56,9 @@ def test_sample_and_log_prob(self): self.assertIsInstance(samples, torch.Tensor) self.assertIsInstance(log_prob_1, torch.Tensor) self.assertIsInstance(log_prob_2, torch.Tensor) - self.assertEqual(samples.shape, torch.Size([num_samples] + input_shape)) + self.assertEqual( + samples.shape, torch.Size([num_samples] + input_shape) + ) self.assertEqual(log_prob_1.shape, torch.Size([num_samples])) self.assertEqual(log_prob_2.shape, torch.Size([num_samples])) self.assertEqual(log_prob_1, log_prob_2) @@ -63,11 +70,18 @@ def test_sample_and_log_prob_with_context(self): context_shape = [5, 6] dist = normal.StandardNormal(input_shape) context = torch.randn(context_size, *context_shape) - samples, log_prob = dist.sample_and_log_prob(num_samples, context=context) + samples, log_prob = dist.sample_and_log_prob( + num_samples, context=context + ) self.assertIsInstance(samples, torch.Tensor) self.assertIsInstance(log_prob, torch.Tensor) - self.assertEqual(samples.shape, torch.Size([context_size, num_samples] + input_shape)) - self.assertEqual(log_prob.shape, torch.Size([context_size, num_samples])) + self.assertEqual( + samples.shape, + torch.Size([context_size, num_samples] + input_shape), + ) + self.assertEqual( + log_prob.shape, torch.Size([context_size, num_samples]) + ) def test_mean(self): context_size = 20 @@ -85,11 +99,12 @@ def test_mean(self): if context is None: self.assertEqual(means.shape, torch.Size(input_shape)) else: - self.assertEqual(means.shape, torch.Size([context_size] + input_shape)) + self.assertEqual( + means.shape, torch.Size([context_size] + input_shape) + ) class ConditionalDiagonalNormalTest(torchtestcase.TorchTestCase): - def test_log_prob(self): batch_size = 10 input_shape = [2, 3, 4] @@ -112,7 +127,10 @@ def test_sample(self): context = torch.randn(context_size, *context_shape) samples = dist.sample(num_samples, context=context) self.assertIsInstance(samples, torch.Tensor) - self.assertEqual(samples.shape, torch.Size([context_size, num_samples] + input_shape)) + self.assertEqual( + samples.shape, + torch.Size([context_size, num_samples] + input_shape), + ) self.assertFalse(torch.isnan(samples).any()) self.assertFalse(torch.isinf(samples).any()) @@ -123,11 +141,18 @@ def test_sample_and_log_prob_with_context(self): context_shape = [2, 3, 8] dist = normal.ConditionalDiagonalNormal(input_shape) context = torch.randn(context_size, *context_shape) - samples, log_prob = dist.sample_and_log_prob(num_samples, context=context) + samples, log_prob = dist.sample_and_log_prob( + num_samples, context=context + ) self.assertIsInstance(samples, torch.Tensor) self.assertIsInstance(log_prob, torch.Tensor) - self.assertEqual(samples.shape, torch.Size([context_size, num_samples] + input_shape)) - self.assertEqual(log_prob.shape, torch.Size([context_size, num_samples])) + self.assertEqual( + samples.shape, + torch.Size([context_size, num_samples] + input_shape), + ) + self.assertEqual( + log_prob.shape, torch.Size([context_size, num_samples]) + ) def test_mean(self): context_size = 20 @@ -142,5 +167,5 @@ def test_mean(self): self.assertEqual(means.shape, torch.Size([context_size] + input_shape)) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/distributions/uniform.py b/phaseSpaceSampling/nde/distributions/uniform.py index 52fd575..545b02d 100644 --- a/phaseSpaceSampling/nde/distributions/uniform.py +++ b/phaseSpaceSampling/nde/distributions/uniform.py @@ -1,6 +1,8 @@ +import import +import sum_except_batch from torch import distributions -import phaseSpaceSampling.utils.torchutils import sum_except_batch +import phaseSpaceSampling.utils.torchutils class TweakedUniform(distributions.Uniform): diff --git a/phaseSpaceSampling/nde/flows/__init__.py b/phaseSpaceSampling/nde/flows/__init__.py index 3641f36..5790bc2 100644 --- a/phaseSpaceSampling/nde/flows/__init__.py +++ b/phaseSpaceSampling/nde/flows/__init__.py @@ -1,5 +1,3 @@ -from .base import Flow - from .autoregressive import MaskedAutoregressiveFlow - +from .base import Flow from .realnvp import SimpleRealNVP diff --git a/phaseSpaceSampling/nde/flows/autoregressive.py b/phaseSpaceSampling/nde/flows/autoregressive.py index 68bab77..b48e4cc 100644 --- a/phaseSpaceSampling/nde/flows/autoregressive.py +++ b/phaseSpaceSampling/nde/flows/autoregressive.py @@ -2,9 +2,7 @@ from torch.nn import functional as F -from phaseSpaceSampling.nde import distributions -from phaseSpaceSampling.nde import flows -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde import distributions, flows, transforms class MaskedAutoregressiveFlow(flows.Flow): @@ -15,19 +13,20 @@ class MaskedAutoregressiveFlow(flows.Flow): > Advances in Neural Information Processing Systems, 2017. """ - def __init__(self, - features, - hidden_features, - num_layers, - num_blocks_per_layer, - use_residual_blocks=True, - use_random_masks=False, - use_random_permutations=False, - activation=F.relu, - dropout_probability=0., - batch_norm_within_layers=False, - batch_norm_between_layers=False): - + def __init__( + self, + features, + hidden_features, + num_layers, + num_blocks_per_layer, + use_residual_blocks=True, + use_random_masks=False, + use_random_permutations=False, + activation=F.relu, + dropout_probability=0.0, + batch_norm_within_layers=False, + batch_norm_between_layers=False, + ): if use_random_permutations: permutation_constructor = transforms.RandomPermutation else: diff --git a/phaseSpaceSampling/nde/flows/autoregressive_test.py b/phaseSpaceSampling/nde/flows/autoregressive_test.py index 9548582..7784a61 100644 --- a/phaseSpaceSampling/nde/flows/autoregressive_test.py +++ b/phaseSpaceSampling/nde/flows/autoregressive_test.py @@ -1,14 +1,14 @@ """Tests for autoregressive flows.""" +import unittest + import torch import torchtestcase -import unittest from phaseSpaceSampling.nde.flows import autoregressive as ar class MaskedAutoregressiveFlowTest(torchtestcase.TorchTestCase): - def test_log_prob(self): batch_size = 10 features = 20 @@ -37,5 +37,5 @@ def test_sample(self): self.assertEqual(samples.shape, torch.Size([num_samples, features])) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/flows/base.py b/phaseSpaceSampling/nde/flows/base.py index 56ac4fe..6516f84 100644 --- a/phaseSpaceSampling/nde/flows/base.py +++ b/phaseSpaceSampling/nde/flows/base.py @@ -1,7 +1,11 @@ """Basic definitions for the flows module.""" from phaseSpaceSampling.nde import distributions -from phaseSpaceSampling.utils.torchutils import merge_leading_dims, repeat_rows, split_leading_dim +from phaseSpaceSampling.utils.torchutils import ( + merge_leading_dims, + repeat_rows, + split_leading_dim, +) class Flow(distributions.Distribution): @@ -45,7 +49,9 @@ def sample_and_log_prob(self, num_samples, context=None): For flows, this is more efficient that calling `sample` and `log_prob` separately. """ - noise, log_prob = self._distribution.sample_and_log_prob(num_samples, context=context) + noise, log_prob = self._distribution.sample_and_log_prob( + num_samples, context=context + ) if context is not None: # Merge the context dimension with sample dimension in order to apply the transform. diff --git a/phaseSpaceSampling/nde/flows/base_test.py b/phaseSpaceSampling/nde/flows/base_test.py index 9d62926..0162170 100644 --- a/phaseSpaceSampling/nde/flows/base_test.py +++ b/phaseSpaceSampling/nde/flows/base_test.py @@ -1,15 +1,15 @@ """Tests for the basic flow definitions.""" +import unittest + import torch import torchtestcase -import unittest -from phaseSpaceSampling.nde import transforms -from phaseSpaceSampling.nde import distributions + +from phaseSpaceSampling.nde import distributions, transforms from phaseSpaceSampling.nde.flows import base class FlowTest(torchtestcase.TorchTestCase): - def test_log_prob(self): batch_size = 10 input_shape = [2, 3, 4] @@ -41,10 +41,14 @@ def test_sample(self): samples = flow.sample(num_samples, context=context) self.assertIsInstance(samples, torch.Tensor) if context is None: - self.assertEqual(samples.shape, torch.Size([num_samples] + input_shape)) + self.assertEqual( + samples.shape, torch.Size([num_samples] + input_shape) + ) else: self.assertEqual( - samples.shape, torch.Size([context_size, num_samples] + input_shape)) + samples.shape, + torch.Size([context_size, num_samples] + input_shape), + ) def test_sample_and_log_prob(self): num_samples = 10 @@ -58,7 +62,9 @@ def test_sample_and_log_prob(self): self.assertIsInstance(samples, torch.Tensor) self.assertIsInstance(log_prob_1, torch.Tensor) self.assertIsInstance(log_prob_2, torch.Tensor) - self.assertEqual(samples.shape, torch.Size([num_samples] + input_shape)) + self.assertEqual( + samples.shape, torch.Size([num_samples] + input_shape) + ) self.assertEqual(log_prob_1.shape, torch.Size([num_samples])) self.assertEqual(log_prob_2.shape, torch.Size([num_samples])) self.assertEqual(log_prob_1, log_prob_2) @@ -73,11 +79,18 @@ def test_sample_and_log_prob_with_context(self): distribution=distributions.StandardNormal(input_shape), ) context = torch.randn(context_size, *context_shape) - samples, log_prob = flow.sample_and_log_prob(num_samples, context=context) + samples, log_prob = flow.sample_and_log_prob( + num_samples, context=context + ) self.assertIsInstance(samples, torch.Tensor) self.assertIsInstance(log_prob, torch.Tensor) - self.assertEqual(samples.shape, torch.Size([context_size, num_samples] + input_shape)) - self.assertEqual(log_prob.shape, torch.Size([context_size, num_samples])) + self.assertEqual( + samples.shape, + torch.Size([context_size, num_samples] + input_shape), + ) + self.assertEqual( + log_prob.shape, torch.Size([context_size, num_samples]) + ) def test_transform_to_noise(self): batch_size = 10 @@ -97,5 +110,5 @@ def test_transform_to_noise(self): self.assertEqual(noise.shape, torch.Size([batch_size] + shape)) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/flows/realnvp.py b/phaseSpaceSampling/nde/flows/realnvp.py index f106f9f..0dec777 100644 --- a/phaseSpaceSampling/nde/flows/realnvp.py +++ b/phaseSpaceSampling/nde/flows/realnvp.py @@ -3,10 +3,8 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde import distributions -from phaseSpaceSampling.nde import flows -from phaseSpaceSampling.nde import transforms import phaseSpaceSampling.nn as nn_ +from phaseSpaceSampling.nde import distributions, flows, transforms class SimpleRealNVP(flows.Flow): @@ -18,17 +16,18 @@ class SimpleRealNVP(flows.Flow): > L. Dinh et al., Density estimation using Real NVP, ICLR 2017. """ - def __init__(self, - features, - hidden_features, - num_layers, - num_blocks_per_layer, - use_volume_preserving=False, - activation=F.relu, - dropout_probability=0., - batch_norm_within_layers=False, - batch_norm_between_layers=False): - + def __init__( + self, + features, + hidden_features, + num_layers, + num_blocks_per_layer, + use_volume_preserving=False, + activation=F.relu, + dropout_probability=0.0, + batch_norm_within_layers=False, + batch_norm_between_layers=False, + ): if use_volume_preserving: coupling_constructor = transforms.AdditiveCouplingTransform else: @@ -45,14 +44,13 @@ def create_resnet(in_features, out_features): num_blocks=num_blocks_per_layer, activation=activation, dropout_probability=dropout_probability, - use_batch_norm=batch_norm_within_layers + use_batch_norm=batch_norm_within_layers, ) layers = [] for _ in range(num_layers): transform = coupling_constructor( - mask=mask, - transform_net_create_fn=create_resnet + mask=mask, transform_net_create_fn=create_resnet ) layers.append(transform) mask *= -1 diff --git a/phaseSpaceSampling/nde/flows/realnvp_test.py b/phaseSpaceSampling/nde/flows/realnvp_test.py index a2652b2..c05e9f8 100644 --- a/phaseSpaceSampling/nde/flows/realnvp_test.py +++ b/phaseSpaceSampling/nde/flows/realnvp_test.py @@ -1,14 +1,14 @@ """Tests for Real NVP.""" +import unittest + import torch import torchtestcase -import unittest from phaseSpaceSampling.nde.flows import realnvp class SimpleRealNVPTest(torchtestcase.TorchTestCase): - def test_log_prob(self): batch_size = 10 features = 20 @@ -37,5 +37,5 @@ def test_sample(self): self.assertEqual(samples.shape, torch.Size([num_samples, features])) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/__init__.py b/phaseSpaceSampling/nde/transforms/__init__.py index 9344213..6de3386 100644 --- a/phaseSpaceSampling/nde/transforms/__init__.py +++ b/phaseSpaceSampling/nde/transforms/__init__.py @@ -1,62 +1,45 @@ -from .base import ( - InverseNotAvailable, - InputOutsideDomain, - Transform, - CompositeTransform, - MultiscaleCompositeTransform, - InverseTransform -) - from .autoregressive import ( MaskedAffineAutoregressiveTransform, + MaskedPiecewiseCubicAutoregressiveTransform, MaskedPiecewiseLinearAutoregressiveTransform, MaskedPiecewiseQuadraticAutoregressiveTransform, - MaskedPiecewiseCubicAutoregressiveTransform, - MaskedPiecewiseRationalQuadraticAutoregressiveTransform + MaskedPiecewiseRationalQuadraticAutoregressiveTransform, +) +from .base import ( + CompositeTransform, + InputOutsideDomain, + InverseNotAvailable, + InverseTransform, + MultiscaleCompositeTransform, + Transform, +) +from .conv import OneByOneConvolution +from .coupling import ( + AdditiveCouplingTransform, + AffineCouplingTransform, + PiecewiseCubicCouplingTransform, + PiecewiseLinearCouplingTransform, + PiecewiseQuadraticCouplingTransform, + PiecewiseRationalQuadraticCouplingTransform, ) - from .linear import NaiveLinear from .lu import LULinear -from .qr import QRLinear -from .svd import SVDLinear - from .nonlinearities import ( CompositeCDFTransform, LeakyReLU, Logit, LogTanh, + PiecewiseCubicCDF, PiecewiseLinearCDF, PiecewiseQuadraticCDF, - PiecewiseCubicCDF, PiecewiseRationalQuadraticCDF, Sigmoid, - Tanh + Tanh, ) - -from .normalization import ( - BatchNorm, - ActNorm -) - +from .normalization import ActNorm, BatchNorm from .orthogonal import HouseholderSequence - -from .permutations import Permutation -from .permutations import RandomPermutation -from .permutations import ReversePermutation - -from .coupling import ( - AffineCouplingTransform, - AdditiveCouplingTransform, - PiecewiseLinearCouplingTransform, - PiecewiseQuadraticCouplingTransform, - PiecewiseCubicCouplingTransform, - PiecewiseRationalQuadraticCouplingTransform -) - -from .standard import ( - IdentityTransform, - AffineScalarTransform, -) - -from .reshape import SqueezeTransform, ReshapeTransform -from .conv import OneByOneConvolution \ No newline at end of file +from .permutations import Permutation, RandomPermutation, ReversePermutation +from .qr import QRLinear +from .reshape import ReshapeTransform, SqueezeTransform +from .standard import AffineScalarTransform, IdentityTransform +from .svd import SVDLinear diff --git a/phaseSpaceSampling/nde/transforms/autoregressive.py b/phaseSpaceSampling/nde/transforms/autoregressive.py index fc150a1..117c558 100644 --- a/phaseSpaceSampling/nde/transforms/autoregressive.py +++ b/phaseSpaceSampling/nde/transforms/autoregressive.py @@ -7,9 +7,9 @@ from phaseSpaceSampling.nde import transforms from phaseSpaceSampling.nde.transforms import made as made_module from phaseSpaceSampling.nde.transforms import splines - from phaseSpaceSampling.utils.torchutils import sum_except_batch + class AutoregressiveTransform(transforms.Transform): """Transforms each input variable with an invertible elementwise transformation. @@ -19,13 +19,16 @@ class AutoregressiveTransform(transforms.Transform): NOTE: Calculating the inverse transform is D times slower than calculating the forward transform, where D is the dimensionality of the input to the transform. """ + def __init__(self, autoregressive_net): super(AutoregressiveTransform, self).__init__() self.autoregressive_net = autoregressive_net def forward(self, inputs, context=None): autoregressive_params = self.autoregressive_net(inputs, context) - outputs, logabsdet = self._elementwise_forward(inputs, autoregressive_params) + outputs, logabsdet = self._elementwise_forward( + inputs, autoregressive_params + ) return outputs, logabsdet def inverse(self, inputs, context=None): @@ -34,7 +37,9 @@ def inverse(self, inputs, context=None): logabsdet = None for _ in range(num_inputs): autoregressive_params = self.autoregressive_net(outputs, context) - outputs, logabsdet = self._elementwise_inverse(inputs, autoregressive_params) + outputs, logabsdet = self._elementwise_inverse( + inputs, autoregressive_params + ) return outputs, logabsdet def _output_dim_multiplier(self): @@ -48,16 +53,18 @@ def _elementwise_inverse(self, inputs, autoregressive_params): class MaskedAffineAutoregressiveTransform(AutoregressiveTransform): - def __init__(self, - features, - hidden_features, - context_features=None, - num_blocks=2, - use_residual_blocks=True, - random_mask=False, - activation=F.relu, - dropout_probability=0., - use_batch_norm=False): + def __init__( + self, + features, + hidden_features, + context_features=None, + num_blocks=2, + use_residual_blocks=True, + random_mask=False, + activation=F.relu, + dropout_probability=0.0, + use_batch_norm=False, + ): self.features = features made = made_module.MADE( features=features, @@ -77,16 +84,20 @@ def _output_dim_multiplier(self): return 2 def _elementwise_forward(self, inputs, autoregressive_params): - unconstrained_scale, shift = self._unconstrained_scale_and_shift(autoregressive_params) - scale = torch.sigmoid(unconstrained_scale + 2.) + 1e-3 + unconstrained_scale, shift = self._unconstrained_scale_and_shift( + autoregressive_params + ) + scale = torch.sigmoid(unconstrained_scale + 2.0) + 1e-3 log_scale = torch.log(scale) outputs = scale * inputs + shift logabsdet = sum_except_batch(log_scale, num_batch_dims=1) return outputs, logabsdet def _elementwise_inverse(self, inputs, autoregressive_params): - unconstrained_scale, shift = self._unconstrained_scale_and_shift(autoregressive_params) - scale = torch.sigmoid(unconstrained_scale + 2.) + 1e-3 + unconstrained_scale, shift = self._unconstrained_scale_and_shift( + autoregressive_params + ) + scale = torch.sigmoid(unconstrained_scale + 2.0) + 1e-3 log_scale = torch.log(scale) outputs = (inputs - shift) / scale logabsdet = -sum_except_batch(log_scale, num_batch_dims=1) @@ -106,17 +117,19 @@ def _unconstrained_scale_and_shift(self, autoregressive_params): class MaskedPiecewiseLinearAutoregressiveTransform(AutoregressiveTransform): - def __init__(self, - num_bins, - features, - hidden_features, - context_features=None, - num_blocks=2, - use_residual_blocks=True, - random_mask=False, - activation=F.relu, - dropout_probability=0., - use_batch_norm=False): + def __init__( + self, + num_bins, + features, + hidden_features, + context_features=None, + num_blocks=2, + use_residual_blocks=True, + random_mask=False, + activation=F.relu, + dropout_probability=0.0, + use_batch_norm=False, + ): self.num_bins = num_bins self.features = features made = made_module.MADE( @@ -139,13 +152,13 @@ def _output_dim_multiplier(self): def _elementwise(self, inputs, autoregressive_params, inverse=False): batch_size = inputs.shape[0] - unnormalized_pdf = autoregressive_params.view(batch_size, - self.features, - self._output_dim_multiplier()) + unnormalized_pdf = autoregressive_params.view( + batch_size, self.features, self._output_dim_multiplier() + ) - outputs, logabsdet = splines.linear_spline(inputs=inputs, - unnormalized_pdf=unnormalized_pdf, - inverse=inverse) + outputs, logabsdet = splines.linear_spline( + inputs=inputs, unnormalized_pdf=unnormalized_pdf, inverse=inverse + ) return outputs, sum_except_batch(logabsdet) @@ -157,23 +170,24 @@ def _elementwise_inverse(self, inputs, autoregressive_params): class MaskedPiecewiseQuadraticAutoregressiveTransform(AutoregressiveTransform): - def __init__(self, - features, - hidden_features, - context_features=None, - num_bins=10, - num_blocks=2, - tails=None, - tail_bound=1., - use_residual_blocks=True, - random_mask=False, - activation=F.relu, - dropout_probability=0., - use_batch_norm=False, - min_bin_width=splines.rational_quadratic.DEFAULT_MIN_BIN_WIDTH, - min_bin_height=splines.rational_quadratic.DEFAULT_MIN_BIN_HEIGHT, - min_derivative=splines.rational_quadratic.DEFAULT_MIN_DERIVATIVE - ): + def __init__( + self, + features, + hidden_features, + context_features=None, + num_bins=10, + num_blocks=2, + tails=None, + tail_bound=1.0, + use_residual_blocks=True, + random_mask=False, + activation=F.relu, + dropout_probability=0.0, + use_batch_norm=False, + min_bin_width=splines.rational_quadratic.DEFAULT_MIN_BIN_WIDTH, + min_bin_height=splines.rational_quadratic.DEFAULT_MIN_BIN_HEIGHT, + min_derivative=splines.rational_quadratic.DEFAULT_MIN_DERIVATIVE, + ): self.num_bins = num_bins self.min_bin_width = min_bin_width self.min_bin_height = min_bin_height @@ -196,7 +210,7 @@ def __init__(self, super().__init__(made) def _output_dim_multiplier(self): - if self.tails == 'linear': + if self.tails == "linear": return self.num_bins * 2 - 1 else: return self.num_bins * 2 + 1 @@ -204,25 +218,29 @@ def _output_dim_multiplier(self): def _elementwise(self, inputs, autoregressive_params, inverse=False): batch_size = inputs.shape[0] - transform_params = autoregressive_params.view(batch_size, - self.features, - self._output_dim_multiplier()) + transform_params = autoregressive_params.view( + batch_size, self.features, self._output_dim_multiplier() + ) - unnormalized_widths = transform_params[..., :self.num_bins] - unnormalized_heights = transform_params[..., self.num_bins:] + unnormalized_widths = transform_params[..., : self.num_bins] + unnormalized_heights = transform_params[..., self.num_bins :] - if hasattr(self.autoregressive_net, 'hidden_features'): - unnormalized_widths /= np.sqrt(self.autoregressive_net.hidden_features) - unnormalized_heights /= np.sqrt(self.autoregressive_net.hidden_features) + if hasattr(self.autoregressive_net, "hidden_features"): + unnormalized_widths /= np.sqrt( + self.autoregressive_net.hidden_features + ) + unnormalized_heights /= np.sqrt( + self.autoregressive_net.hidden_features + ) if self.tails is None: spline_fn = splines.quadratic_spline spline_kwargs = {} - elif self.tails == 'linear': + elif self.tails == "linear": spline_fn = splines.unconstrained_quadratic_spline spline_kwargs = { - 'tails': self.tails, - 'tail_bound': self.tail_bound + "tails": self.tails, + "tail_bound": self.tail_bound, } else: raise ValueError @@ -234,7 +252,7 @@ def _elementwise(self, inputs, autoregressive_params, inverse=False): inverse=inverse, min_bin_width=self.min_bin_width, min_bin_height=self.min_bin_height, - **spline_kwargs + **spline_kwargs, ) return outputs, sum_except_batch(logabsdet) @@ -247,17 +265,19 @@ def _elementwise_inverse(self, inputs, autoregressive_params): class MaskedPiecewiseCubicAutoregressiveTransform(AutoregressiveTransform): - def __init__(self, - num_bins, - features, - hidden_features, - context_features=None, - num_blocks=2, - use_residual_blocks=True, - random_mask=False, - activation=F.relu, - dropout_probability=0., - use_batch_norm=False): + def __init__( + self, + num_bins, + features, + hidden_features, + context_features=None, + num_blocks=2, + use_residual_blocks=True, + random_mask=False, + activation=F.relu, + dropout_probability=0.0, + use_batch_norm=False, + ): self.num_bins = num_bins self.features = features made = made_module.MADE( @@ -280,19 +300,25 @@ def _output_dim_multiplier(self): def _elementwise(self, inputs, autoregressive_params, inverse=False): batch_size = inputs.shape[0] - transform_params = autoregressive_params.view(batch_size, - self.features, - self.num_bins * 2 + 2) + transform_params = autoregressive_params.view( + batch_size, self.features, self.num_bins * 2 + 2 + ) - unnormalized_widths = transform_params[...,:self.num_bins] - unnormalized_heights = transform_params[...,self.num_bins:2*self.num_bins] - derivatives = transform_params[...,2*self.num_bins:] + unnormalized_widths = transform_params[..., : self.num_bins] + unnormalized_heights = transform_params[ + ..., self.num_bins : 2 * self.num_bins + ] + derivatives = transform_params[..., 2 * self.num_bins :] unnorm_derivatives_left = derivatives[..., 0][..., None] unnorm_derivatives_right = derivatives[..., 1][..., None] - if hasattr(self.autoregressive_net, 'hidden_features'): - unnormalized_widths /= np.sqrt(self.autoregressive_net.hidden_features) - unnormalized_heights /= np.sqrt(self.autoregressive_net.hidden_features) + if hasattr(self.autoregressive_net, "hidden_features"): + unnormalized_widths /= np.sqrt( + self.autoregressive_net.hidden_features + ) + unnormalized_heights /= np.sqrt( + self.autoregressive_net.hidden_features + ) outputs, logabsdet = splines.cubic_spline( inputs=inputs, @@ -300,7 +326,7 @@ def _elementwise(self, inputs, autoregressive_params, inverse=False): unnormalized_heights=unnormalized_heights, unnorm_derivatives_left=unnorm_derivatives_left, unnorm_derivatives_right=unnorm_derivatives_right, - inverse=inverse + inverse=inverse, ) return outputs, sum_except_batch(logabsdet) @@ -311,24 +337,27 @@ def _elementwise_inverse(self, inputs, autoregressive_params): return self._elementwise(inputs, autoregressive_params, inverse=True) -class MaskedPiecewiseRationalQuadraticAutoregressiveTransform(AutoregressiveTransform): - def __init__(self, - features, - hidden_features, - context_features=None, - num_bins=10, - tails=None, - tail_bound=1., - num_blocks=2, - use_residual_blocks=True, - random_mask=False, - activation=F.relu, - dropout_probability=0., - use_batch_norm=False, - min_bin_width=splines.rational_quadratic.DEFAULT_MIN_BIN_WIDTH, - min_bin_height=splines.rational_quadratic.DEFAULT_MIN_BIN_HEIGHT, - min_derivative=splines.rational_quadratic.DEFAULT_MIN_DERIVATIVE - ): +class MaskedPiecewiseRationalQuadraticAutoregressiveTransform( + AutoregressiveTransform +): + def __init__( + self, + features, + hidden_features, + context_features=None, + num_bins=10, + tails=None, + tail_bound=1.0, + num_blocks=2, + use_residual_blocks=True, + random_mask=False, + activation=F.relu, + dropout_probability=0.0, + use_batch_norm=False, + min_bin_width=splines.rational_quadratic.DEFAULT_MIN_BIN_WIDTH, + min_bin_height=splines.rational_quadratic.DEFAULT_MIN_BIN_HEIGHT, + min_derivative=splines.rational_quadratic.DEFAULT_MIN_DERIVATIVE, + ): self.num_bins = num_bins self.min_bin_width = min_bin_width self.min_bin_height = min_bin_height @@ -352,7 +381,7 @@ def __init__(self, super().__init__(autoregressive_net) def _output_dim_multiplier(self): - if self.tails == 'linear': + if self.tails == "linear": return self.num_bins * 3 - 1 elif self.tails is None: return self.num_bins * 3 + 1 @@ -363,27 +392,31 @@ def _elementwise(self, inputs, autoregressive_params, inverse=False): batch_size, features = inputs.shape[0], inputs.shape[1] transform_params = autoregressive_params.view( - batch_size, - features, - self._output_dim_multiplier() + batch_size, features, self._output_dim_multiplier() ) - unnormalized_widths = transform_params[...,:self.num_bins] - unnormalized_heights = transform_params[...,self.num_bins:2*self.num_bins] - unnormalized_derivatives = transform_params[...,2*self.num_bins:] + unnormalized_widths = transform_params[..., : self.num_bins] + unnormalized_heights = transform_params[ + ..., self.num_bins : 2 * self.num_bins + ] + unnormalized_derivatives = transform_params[..., 2 * self.num_bins :] - if hasattr(self.autoregressive_net, 'hidden_features'): - unnormalized_widths /= np.sqrt(self.autoregressive_net.hidden_features) - unnormalized_heights /= np.sqrt(self.autoregressive_net.hidden_features) + if hasattr(self.autoregressive_net, "hidden_features"): + unnormalized_widths /= np.sqrt( + self.autoregressive_net.hidden_features + ) + unnormalized_heights /= np.sqrt( + self.autoregressive_net.hidden_features + ) if self.tails is None: spline_fn = splines.rational_quadratic_spline spline_kwargs = {} - elif self.tails == 'linear': + elif self.tails == "linear": spline_fn = splines.unconstrained_rational_quadratic_spline spline_kwargs = { - 'tails': self.tails, - 'tail_bound': self.tail_bound + "tails": self.tails, + "tail_bound": self.tail_bound, } else: raise ValueError @@ -397,7 +430,7 @@ def _elementwise(self, inputs, autoregressive_params, inverse=False): min_bin_width=self.min_bin_width, min_bin_height=self.min_bin_height, min_derivative=self.min_derivative, - **spline_kwargs + **spline_kwargs, ) return outputs, sum_except_batch(logabsdet) @@ -417,12 +450,12 @@ def main(): hidden_features=32, context_features=24, num_bins=10, - tails='linear', - num_blocks=2 + tails="linear", + num_blocks=2, ) outputs, logabsdet = transform(inputs, context) print(outputs.shape) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/phaseSpaceSampling/nde/transforms/autoregressive_test.py b/phaseSpaceSampling/nde/transforms/autoregressive_test.py index 98c1f28..c7bc4a1 100644 --- a/phaseSpaceSampling/nde/transforms/autoregressive_test.py +++ b/phaseSpaceSampling/nde/transforms/autoregressive_test.py @@ -1,23 +1,27 @@ """Tests for the autoregressive transforms.""" -import torch import unittest +import torch + from phaseSpaceSampling.nde.transforms import autoregressive from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class MaskedAffineAutoregressiveTransformTest(TransformTest): - def test_forward(self): batch_size = 10 features = 20 inputs = torch.randn(batch_size, features) - for use_residual_blocks, random_mask in [(False, False), - (False, True), - (True, False)]: - with self.subTest(use_residual_blocks=use_residual_blocks, - random_mask=random_mask): + for use_residual_blocks, random_mask in [ + (False, False), + (False, True), + (True, False), + ]: + with self.subTest( + use_residual_blocks=use_residual_blocks, + random_mask=random_mask, + ): transform = autoregressive.MaskedAffineAutoregressiveTransform( features=features, hidden_features=30, @@ -33,11 +37,15 @@ def test_inverse(self): batch_size = 10 features = 20 inputs = torch.randn(batch_size, features) - for use_residual_blocks, random_mask in [(False, False), - (False, True), - (True, False)]: - with self.subTest(use_residual_blocks=use_residual_blocks, - random_mask=random_mask): + for use_residual_blocks, random_mask in [ + (False, False), + (False, True), + (True, False), + ]: + with self.subTest( + use_residual_blocks=use_residual_blocks, + random_mask=random_mask, + ): transform = autoregressive.MaskedAffineAutoregressiveTransform( features=features, hidden_features=30, @@ -54,11 +62,15 @@ def test_forward_inverse_are_consistent(self): features = 20 inputs = torch.randn(batch_size, features) self.eps = 1e-6 - for use_residual_blocks, random_mask in [(False, False), - (False, True), - (True, False)]: - with self.subTest(use_residual_blocks=use_residual_blocks, - random_mask=random_mask): + for use_residual_blocks, random_mask in [ + (False, False), + (False, True), + (True, False), + ]: + with self.subTest( + use_residual_blocks=use_residual_blocks, + random_mask=random_mask, + ): transform = autoregressive.MaskedAffineAutoregressiveTransform( features=features, hidden_features=30, @@ -76,12 +88,14 @@ def test_forward_inverse_are_consistent(self): inputs = torch.rand(batch_size, features) self.eps = 1e-3 - transform = autoregressive.MaskedPiecewiseLinearAutoregressiveTransform( - num_bins=10, - features=features, - hidden_features=30, - num_blocks=5, - use_residual_blocks=True + transform = ( + autoregressive.MaskedPiecewiseLinearAutoregressiveTransform( + num_bins=10, + features=features, + hidden_features=30, + num_blocks=5, + use_residual_blocks=True, + ) ) self.assert_forward_inverse_are_consistent(transform, inputs) @@ -94,12 +108,14 @@ def test_forward_inverse_are_consistent(self): inputs = torch.rand(batch_size, features) self.eps = 1e-4 - transform = autoregressive.MaskedPiecewiseQuadraticAutoregressiveTransform( - num_bins=10, - features=features, - hidden_features=30, - num_blocks=5, - use_residual_blocks=True + transform = ( + autoregressive.MaskedPiecewiseQuadraticAutoregressiveTransform( + num_bins=10, + features=features, + hidden_features=30, + num_blocks=5, + use_residual_blocks=True, + ) ) self.assert_forward_inverse_are_consistent(transform, inputs) @@ -117,11 +133,11 @@ def test_forward_inverse_are_consistent(self): features=features, hidden_features=30, num_blocks=5, - use_residual_blocks=True + use_residual_blocks=True, ) self.assert_forward_inverse_are_consistent(transform, inputs) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/base.py b/phaseSpaceSampling/nde/transforms/base.py index cea35f8..1de47ff 100644 --- a/phaseSpaceSampling/nde/transforms/base.py +++ b/phaseSpaceSampling/nde/transforms/base.py @@ -2,19 +2,20 @@ import numpy as np import torch - from torch import nn -from phaseSpaceSampling.utils.typechecks import is_positive_int +from phaseSpaceSampling.utils.typechecks import is_positive_int class InverseNotAvailable(Exception): """Exception to be thrown when a transform does not have an inverse.""" + pass class InputOutsideDomain(Exception): """Exception to be thrown when the input to a transform is not within its domain.""" + pass @@ -79,7 +80,7 @@ def __init__(self, num_transforms, split_dim=1): split_dim: dimension along which to split. """ if not is_positive_int(split_dim): - raise TypeError('Split dimension must be a positive integer.') + raise TypeError("Split dimension must be a positive integer.") super().__init__() self._transforms = nn.ModuleList() @@ -102,23 +103,36 @@ def add_transform(self, transform, transform_output_shape): if len(self._transforms) == self._num_transforms: raise RuntimeError( - 'Adding more than {} transforms is not allowed.'.format(self._num_transforms)) + "Adding more than {} transforms is not allowed.".format( + self._num_transforms + ) + ) if (self._split_dim - 1) >= len(transform_output_shape): - raise ValueError('No split_dim in output shape') + raise ValueError("No split_dim in output shape") if transform_output_shape[self._split_dim - 1] < 2: - raise ValueError('Size of dimension {} must be at least 2.'.format(self._split_dim)) + raise ValueError( + "Size of dimension {} must be at least 2.".format( + self._split_dim + ) + ) self._transforms.append(transform) - if len(self._transforms) != self._num_transforms: # Unless last transform. + if ( + len(self._transforms) != self._num_transforms + ): # Unless last transform. output_shape = list(transform_output_shape) - output_shape[self._split_dim - 1] = (output_shape[self._split_dim - 1] + 1) // 2 + output_shape[self._split_dim - 1] = ( + output_shape[self._split_dim - 1] + 1 + ) // 2 output_shape = tuple(output_shape) hidden_shape = list(transform_output_shape) - hidden_shape[self._split_dim - 1] = hidden_shape[self._split_dim - 1] // 2 + hidden_shape[self._split_dim - 1] = ( + hidden_shape[self._split_dim - 1] // 2 + ) hidden_shape = tuple(hidden_shape) else: # No splitting for last transform. @@ -130,10 +144,12 @@ def add_transform(self, transform, transform_output_shape): def forward(self, inputs, context=None): if self._split_dim >= inputs.dim(): - raise ValueError('No split_dim in inputs.') + raise ValueError("No split_dim in inputs.") if self._num_transforms != len(self._transforms): - raise RuntimeError('Expecting exactly {} transform(s) ' - 'to be added.'.format(self._num_transforms)) + raise RuntimeError( + "Expecting exactly {} transform(s) " + "to be added.".format(self._num_transforms) + ) batch_size = inputs.shape[0] @@ -142,9 +158,9 @@ def cascade(): for i, transform in enumerate(self._transforms[:-1]): transform_outputs, logabsdet = transform(hiddens, context) - outputs, hiddens = torch.chunk(transform_outputs, - chunks=2, - dim=self._split_dim) + outputs, hiddens = torch.chunk( + transform_outputs, chunks=2, dim=self._split_dim + ) assert outputs.shape[1:] == self._output_shapes[i] yield outputs, logabsdet @@ -164,32 +180,44 @@ def cascade(): def inverse(self, inputs, context=None): if inputs.dim() != 2: - raise ValueError('Expecting NxD inputs') + raise ValueError("Expecting NxD inputs") if self._num_transforms != len(self._transforms): - raise RuntimeError('Expecting exactly {} transform(s) ' - 'to be added.'.format(self._num_transforms)) + raise RuntimeError( + "Expecting exactly {} transform(s) " + "to be added.".format(self._num_transforms) + ) batch_size = inputs.shape[0] - rev_inv_transforms = [transform.inverse for transform in self._transforms[::-1]] + rev_inv_transforms = [ + transform.inverse for transform in self._transforms[::-1] + ] - split_indices = np.cumsum([np.prod(shape) for shape in self._output_shapes]) + split_indices = np.cumsum( + [np.prod(shape) for shape in self._output_shapes] + ) split_indices = np.insert(split_indices, 0, 0) split_inputs = [] for i in range(len(self._output_shapes)): - flat_input = inputs[:, split_indices[i]:split_indices[i+1]] + flat_input = inputs[:, split_indices[i] : split_indices[i + 1]] split_inputs.append(flat_input.view(-1, *self._output_shapes[i])) rev_split_inputs = split_inputs[::-1] total_logabsdet = torch.zeros(batch_size) # We don't do the splitting for the last (here first) transform. - hiddens, logabsdet = rev_inv_transforms[0](rev_split_inputs[0], context) + hiddens, logabsdet = rev_inv_transforms[0]( + rev_split_inputs[0], context + ) total_logabsdet += logabsdet - for inv_transform, input_chunk in zip(rev_inv_transforms[1:], rev_split_inputs[1:]): - tmp_concat_inputs = torch.cat([input_chunk, hiddens], dim=self._split_dim) + for inv_transform, input_chunk in zip( + rev_inv_transforms[1:], rev_split_inputs[1:] + ): + tmp_concat_inputs = torch.cat( + [input_chunk, hiddens], dim=self._split_dim + ) hiddens, logabsdet = inv_transform(tmp_concat_inputs, context) total_logabsdet += logabsdet diff --git a/phaseSpaceSampling/nde/transforms/base_test.py b/phaseSpaceSampling/nde/transforms/base_test.py index 2ffe322..1df4c12 100644 --- a/phaseSpaceSampling/nde/transforms/base_test.py +++ b/phaseSpaceSampling/nde/transforms/base_test.py @@ -4,12 +4,11 @@ import numpy as np import torch -from phaseSpaceSampling.nde.transforms import base -from phaseSpaceSampling.nde.transforms import standard +from phaseSpaceSampling.nde.transforms import base, standard from phaseSpaceSampling.nde.transforms.transform_test import TransformTest -class CompositeTransformTest(TransformTest): +class CompositeTransformTest(TransformTest): def test_forward(self): batch_size = 10 shape = [2, 3, 4] @@ -46,29 +45,32 @@ def test_inverse(self): self.assertEqual(outputs, outputs_ref) self.assertEqual(logabsdet, logabsdet_ref) + class MultiscaleCompositeTransformTest(TransformTest): def create_transform(self, shape, split_dim=1): - mct = base.MultiscaleCompositeTransform(num_transforms=4, - split_dim=split_dim) - for transform in [ standard.AffineScalarTransform(scale=2.), - standard.AffineScalarTransform(scale=4.), - standard.AffineScalarTransform(scale=0.5), - standard.AffineScalarTransform(scale=0.25)]: + mct = base.MultiscaleCompositeTransform( + num_transforms=4, split_dim=split_dim + ) + for transform in [ + standard.AffineScalarTransform(scale=2.0), + standard.AffineScalarTransform(scale=4.0), + standard.AffineScalarTransform(scale=0.5), + standard.AffineScalarTransform(scale=0.25), + ]: shape = mct.add_transform(transform, shape) return mct - def test_forward(self): batch_size = 5 - for shape in [(32,4,4), - (64,), - (65,)]: + for shape in [(32, 4, 4), (64,), (65,)]: with self.subTest(shape=shape): inputs = torch.ones(batch_size, *shape) transform = self.create_transform(shape) outputs, logabsdet = transform(inputs) - self.assert_tensor_is_good(outputs, [batch_size] + [np.prod(shape)]) + self.assert_tensor_is_good( + outputs, [batch_size] + [np.prod(shape)] + ) self.assert_tensor_is_good(logabsdet, [batch_size]) def test_forward_bad_shape(self): @@ -93,22 +95,21 @@ def test_inverse_not_flat(self): def test_forward_inverse_are_consistent(self): batch_size = 5 - for shape in [(32,4,4), - (64,), - (65,), - (21,)]: + for shape in [(32, 4, 4), (64,), (65,), (21,)]: with self.subTest(shape=shape): transform = self.create_transform(shape) inputs = torch.randn(batch_size, *shape).view(batch_size, -1) self.assert_forward_inverse_are_consistent(transform, inputs) -class InverseTransformTest(TransformTest): +class InverseTransformTest(TransformTest): def test_forward(self): batch_size = 10 shape = [2, 3, 4] inputs = torch.randn(batch_size, *shape) - transform = base.InverseTransform(standard.AffineScalarTransform(scale=-2.0)) + transform = base.InverseTransform( + standard.AffineScalarTransform(scale=-2.0) + ) reference = standard.AffineScalarTransform(scale=-0.5) outputs, logabsdet = transform(inputs) outputs_ref, logabsdet_ref = reference(inputs) @@ -121,7 +122,9 @@ def test_inverse(self): batch_size = 10 shape = [2, 3, 4] inputs = torch.randn(batch_size, *shape) - transform = base.InverseTransform(standard.AffineScalarTransform(scale=-2.0)) + transform = base.InverseTransform( + standard.AffineScalarTransform(scale=-2.0) + ) reference = standard.AffineScalarTransform(scale=-0.5) outputs, logabsdet = transform.inverse(inputs) outputs_ref, logabsdet_ref = reference.inverse(inputs) @@ -131,5 +134,5 @@ def test_inverse(self): self.assertEqual(logabsdet, logabsdet_ref) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/conv.py b/phaseSpaceSampling/nde/transforms/conv.py index 4edf581..42a2594 100644 --- a/phaseSpaceSampling/nde/transforms/conv.py +++ b/phaseSpaceSampling/nde/transforms/conv.py @@ -1,22 +1,21 @@ from phaseSpaceSampling.nde import transforms from phaseSpaceSampling.utils.torchutils import sum_except_batch + class OneByOneConvolution(transforms.LULinear): """An invertible 1x1 convolution with a fixed permutation, as introduced in the Glow paper. Reference: > D. Kingma et. al., Glow: Generative flow with invertible 1x1 convolutions, NeurIPS 2018. """ - def __init__(self, - num_channels, - using_cache=False, - identity_init=True): + + def __init__(self, num_channels, using_cache=False, identity_init=True): super().__init__(num_channels, using_cache, identity_init) self.permutation = transforms.RandomPermutation(num_channels, dim=1) def _lu_forward_inverse(self, inputs, inverse=False): b, c, h, w = inputs.shape - inputs = inputs.permute(0, 2, 3, 1).reshape(b*h*w, c) + inputs = inputs.permute(0, 2, 3, 1).reshape(b * h * w, c) if inverse: outputs, logabsdet = super().inverse(inputs) @@ -30,7 +29,7 @@ def _lu_forward_inverse(self, inputs, inverse=False): def forward(self, inputs, context=None): if inputs.dim() != 4: - raise ValueError('Inputs must be a 4D tensor.') + raise ValueError("Inputs must be a 4D tensor.") inputs, _ = self.permutation(inputs) @@ -38,7 +37,7 @@ def forward(self, inputs, context=None): def inverse(self, inputs, context=None): if inputs.dim() != 4: - raise ValueError('Inputs must be a 4D tensor.') + raise ValueError("Inputs must be a 4D tensor.") outputs, logabsdet = self._lu_forward_inverse(inputs, inverse=True) diff --git a/phaseSpaceSampling/nde/transforms/conv_test.py b/phaseSpaceSampling/nde/transforms/conv_test.py index 56db6b7..63bf41b 100644 --- a/phaseSpaceSampling/nde/transforms/conv_test.py +++ b/phaseSpaceSampling/nde/transforms/conv_test.py @@ -1,6 +1,7 @@ -import torch import unittest +import torch + from phaseSpaceSampling.nde import transforms from phaseSpaceSampling.nde.transforms.transform_test import TransformTest @@ -15,5 +16,5 @@ def test_forward_and_inverse_are_consistent(self): self.assert_forward_inverse_are_consistent(transform, inputs) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/coupling.py b/phaseSpaceSampling/nde/transforms/coupling.py index 02f2e4d..d898cbf 100644 --- a/phaseSpaceSampling/nde/transforms/coupling.py +++ b/phaseSpaceSampling/nde/transforms/coupling.py @@ -7,18 +7,17 @@ from phaseSpaceSampling.nde import transforms from phaseSpaceSampling.nde.transforms import splines - from phaseSpaceSampling.utils.torchutils import sum_except_batch + class CouplingTransform(transforms.Transform): """A base class for coupling layers. Supports 2D inputs (NxD), as well as 4D inputs for images (NxCxHxW). For images the splitting is done on the channel dimension, using the provided 1D mask.""" - def __init__(self, - mask, - transform_net_create_fn, - unconditional_transform=None): + def __init__( + self, mask, transform_net_create_fn, unconditional_transform=None + ): """ Constructor. @@ -29,22 +28,29 @@ def __init__(self, """ mask = torch.as_tensor(mask) if mask.dim() != 1: - raise ValueError('Mask must be a 1-dim tensor.') + raise ValueError("Mask must be a 1-dim tensor.") if mask.numel() <= 0: - raise ValueError('Mask can\'t be empty.') + raise ValueError("Mask can't be empty.") super().__init__() self.features = len(mask) features_vector = torch.arange(self.features) - self.register_buffer('identity_features', features_vector.masked_select(mask <= 0)) - self.register_buffer('transform_features', features_vector.masked_select(mask > 0)) + self.register_buffer( + "identity_features", features_vector.masked_select(mask <= 0) + ) + self.register_buffer( + "transform_features", features_vector.masked_select(mask > 0) + ) - assert self.num_identity_features + self.num_transform_features == self.features + assert ( + self.num_identity_features + self.num_transform_features + == self.features + ) self.transform_net = transform_net_create_fn( self.num_identity_features, - self.num_transform_features * self._transform_dim_multiplier() + self.num_transform_features * self._transform_dim_multiplier(), ) if unconditional_transform is None: @@ -64,24 +70,27 @@ def num_transform_features(self): def forward(self, inputs, context=None): if inputs.dim() not in [2, 4]: - raise ValueError('Inputs must be a 2D or a 4D tensor.') + raise ValueError("Inputs must be a 2D or a 4D tensor.") if inputs.shape[1] != self.features: - raise ValueError('Expected features = {}, got {}.'.format( - self.features, inputs.shape[1])) + raise ValueError( + "Expected features = {}, got {}.".format( + self.features, inputs.shape[1] + ) + ) identity_split = inputs[:, self.identity_features, ...] transform_split = inputs[:, self.transform_features, ...] transform_params = self.transform_net(identity_split, context) transform_split, logabsdet = self._coupling_transform_forward( - inputs=transform_split, - transform_params=transform_params + inputs=transform_split, transform_params=transform_params ) if self.unconditional_transform is not None: - identity_split, logabsdet_identity =\ - self.unconditional_transform(identity_split, context) + identity_split, logabsdet_identity = self.unconditional_transform( + identity_split, context + ) logabsdet += logabsdet_identity outputs = torch.empty_like(inputs) @@ -92,24 +101,27 @@ def forward(self, inputs, context=None): def inverse(self, inputs, context=None): if inputs.dim() not in [2, 4]: - raise ValueError('Inputs must be a 2D or a 4D tensor.') + raise ValueError("Inputs must be a 2D or a 4D tensor.") if inputs.shape[1] != self.features: - raise ValueError('Expected features = {}, got {}.'.format( - self.features, inputs.shape[1])) + raise ValueError( + "Expected features = {}, got {}.".format( + self.features, inputs.shape[1] + ) + ) identity_split = inputs[:, self.identity_features, ...] transform_split = inputs[:, self.transform_features, ...] logabsdet = 0.0 if self.unconditional_transform is not None: - identity_split, logabsdet = self.unconditional_transform.inverse(identity_split, - context) + identity_split, logabsdet = self.unconditional_transform.inverse( + identity_split, context + ) transform_params = self.transform_net(identity_split, context) transform_split, logabsdet_split = self._coupling_transform_inverse( - inputs=transform_split, - transform_params=transform_params + inputs=transform_split, transform_params=transform_params ) logabsdet += logabsdet_split @@ -138,12 +150,15 @@ class AffineCouplingTransform(CouplingTransform): Reference: > L. Dinh et al., Density estimation using Real NVP, ICLR 2017. """ + def _transform_dim_multiplier(self): return 2 def _scale_and_shift(self, transform_params): - unconstrained_scale = transform_params[:, self.num_transform_features:, ...] - shift = transform_params[:, :self.num_transform_features, ...] + unconstrained_scale = transform_params[ + :, self.num_transform_features :, ... + ] + shift = transform_params[:, : self.num_transform_features, ...] # scale = (F.softplus(unconstrained_scale) + 1e-3).clamp(0, 3) scale = torch.sigmoid(unconstrained_scale + 2) + 1e-3 return scale, shift @@ -170,6 +185,7 @@ class AdditiveCouplingTransform(AffineCouplingTransform): > L. Dinh et al., NICE: Non-linear Independent Components Estimation, > arXiv:1410.8516, 2014. """ + def _transform_dim_multiplier(self): return 1 @@ -181,7 +197,9 @@ def _scale_and_shift(self, transform_params): class PiecewiseCouplingTransform(CouplingTransform): def _coupling_transform_forward(self, inputs, transform_params): - return self._coupling_transform(inputs, transform_params, inverse=False) + return self._coupling_transform( + inputs, transform_params, inverse=False + ) def _coupling_transform_inverse(self, inputs, transform_params): return self._coupling_transform(inputs, transform_params, inverse=True) @@ -190,13 +208,17 @@ def _coupling_transform(self, inputs, transform_params, inverse=False): if inputs.dim() == 4: b, c, h, w = inputs.shape # For images, reshape transform_params from Bx(C*?)xHxW to BxCxHxWx? - transform_params = transform_params.reshape(b, c, -1, h, w).permute(0, 1, 3, 4, 2) + transform_params = transform_params.reshape( + b, c, -1, h, w + ).permute(0, 1, 3, 4, 2) elif inputs.dim() == 2: b, d = inputs.shape # For 2D data, reshape transform_params from Bx(D*?) to BxDx? transform_params = transform_params.reshape(b, d, -1) - outputs, logabsdet = self._piecewise_cdf(inputs, transform_params, inverse) + outputs, logabsdet = self._piecewise_cdf( + inputs, transform_params, inverse + ) return outputs, sum_except_batch(logabsdet) @@ -209,30 +231,38 @@ class PiecewiseLinearCouplingTransform(PiecewiseCouplingTransform): Reference: > Müller et al., Neural Importance Sampling, arXiv:1808.03856, 2018. """ - def __init__(self, - mask, - transform_net_create_fn, - num_bins=10, - tails=None, - tail_bound=1., - apply_unconditional_transform=False, - img_shape=None): + + def __init__( + self, + mask, + transform_net_create_fn, + num_bins=10, + tails=None, + tail_bound=1.0, + apply_unconditional_transform=False, + img_shape=None, + ): self.num_bins = num_bins self.tails = tails self.tail_bound = tail_bound if apply_unconditional_transform: - unconditional_transform = lambda features: transforms.PiecewiseLinearCDF( - shape=[features] + (img_shape if img_shape else []), - num_bins=num_bins, - tails=tails, - tail_bound=tail_bound + unconditional_transform = ( + lambda features: transforms.PiecewiseLinearCDF( + shape=[features] + (img_shape if img_shape else []), + num_bins=num_bins, + tails=tails, + tail_bound=tail_bound, + ) ) else: unconditional_transform = None - super().__init__(mask, transform_net_create_fn, - unconditional_transform=unconditional_transform) + super().__init__( + mask, + transform_net_create_fn, + unconditional_transform=unconditional_transform, + ) def _transform_dim_multiplier(self): return self.num_bins @@ -244,7 +274,7 @@ def _piecewise_cdf(self, inputs, transform_params, inverse=False): return splines.linear_spline( inputs=inputs, unnormalized_pdf=unnormalized_pdf, - inverse=inverse + inverse=inverse, ) else: return splines.unconstrained_linear_spline( @@ -252,24 +282,28 @@ def _piecewise_cdf(self, inputs, transform_params, inverse=False): unnormalized_pdf=unnormalized_pdf, inverse=inverse, tails=self.tails, - tail_bound=self.tail_bound + tail_bound=self.tail_bound, ) + class PiecewiseQuadraticCouplingTransform(PiecewiseCouplingTransform): """ Reference: > Müller et al., Neural Importance Sampling, arXiv:1808.03856, 2018. """ - def __init__(self, - mask, - transform_net_create_fn, - num_bins=10, - tails=None, - tail_bound=1., - apply_unconditional_transform=False, - img_shape=None, - min_bin_width=splines.quadratic.DEFAULT_MIN_BIN_WIDTH, - min_bin_height=splines.quadratic.DEFAULT_MIN_BIN_HEIGHT): + + def __init__( + self, + mask, + transform_net_create_fn, + num_bins=10, + tails=None, + tail_bound=1.0, + apply_unconditional_transform=False, + img_shape=None, + min_bin_width=splines.quadratic.DEFAULT_MIN_BIN_WIDTH, + min_bin_height=splines.quadratic.DEFAULT_MIN_BIN_HEIGHT, + ): self.num_bins = num_bins self.tails = tails self.tail_bound = tail_bound @@ -277,31 +311,36 @@ def __init__(self, self.min_bin_height = min_bin_height if apply_unconditional_transform: - unconditional_transform = lambda features: transforms.PiecewiseQuadraticCDF( - shape=[features] + (img_shape if img_shape else []), - num_bins=num_bins, - tails=tails, - tail_bound=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height + unconditional_transform = ( + lambda features: transforms.PiecewiseQuadraticCDF( + shape=[features] + (img_shape if img_shape else []), + num_bins=num_bins, + tails=tails, + tail_bound=tail_bound, + min_bin_width=min_bin_width, + min_bin_height=min_bin_height, + ) ) else: unconditional_transform = None - super().__init__(mask, transform_net_create_fn, - unconditional_transform=unconditional_transform) + super().__init__( + mask, + transform_net_create_fn, + unconditional_transform=unconditional_transform, + ) def _transform_dim_multiplier(self): - if self.tails == 'linear': + if self.tails == "linear": return self.num_bins * 2 - 1 else: return self.num_bins * 2 + 1 def _piecewise_cdf(self, inputs, transform_params, inverse=False): - unnormalized_widths = transform_params[..., :self.num_bins] - unnormalized_heights = transform_params[..., self.num_bins:] + unnormalized_widths = transform_params[..., : self.num_bins] + unnormalized_heights = transform_params[..., self.num_bins :] - if hasattr(self.transform_net, 'hidden_features'): + if hasattr(self.transform_net, "hidden_features"): unnormalized_widths /= np.sqrt(self.transform_net.hidden_features) unnormalized_heights /= np.sqrt(self.transform_net.hidden_features) @@ -311,8 +350,8 @@ def _piecewise_cdf(self, inputs, transform_params, inverse=False): else: spline_fn = splines.unconstrained_quadratic_spline spline_kwargs = { - 'tails': self.tails, - 'tail_bound': self.tail_bound + "tails": self.tails, + "tail_bound": self.tail_bound, } return spline_fn( @@ -322,22 +361,23 @@ def _piecewise_cdf(self, inputs, transform_params, inverse=False): inverse=inverse, min_bin_width=self.min_bin_width, min_bin_height=self.min_bin_height, - **spline_kwargs + **spline_kwargs, ) class PiecewiseCubicCouplingTransform(PiecewiseCouplingTransform): - def __init__(self, - mask, - transform_net_create_fn, - num_bins=10, - tails=None, - tail_bound=1., - apply_unconditional_transform=False, - img_shape=None, - min_bin_width=splines.cubic.DEFAULT_MIN_BIN_WIDTH, - min_bin_height=splines.cubic.DEFAULT_MIN_BIN_HEIGHT): - + def __init__( + self, + mask, + transform_net_create_fn, + num_bins=10, + tails=None, + tail_bound=1.0, + apply_unconditional_transform=False, + img_shape=None, + min_bin_width=splines.cubic.DEFAULT_MIN_BIN_WIDTH, + min_bin_height=splines.cubic.DEFAULT_MIN_BIN_HEIGHT, + ): self.num_bins = num_bins self.min_bin_width = min_bin_width self.min_bin_height = min_bin_height @@ -345,30 +385,41 @@ def __init__(self, self.tail_bound = tail_bound if apply_unconditional_transform: - unconditional_transform = lambda features: transforms.PiecewiseCubicCDF( - shape=[features] + (img_shape if img_shape else []), - num_bins=num_bins, - tails=tails, - tail_bound=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height + unconditional_transform = ( + lambda features: transforms.PiecewiseCubicCDF( + shape=[features] + (img_shape if img_shape else []), + num_bins=num_bins, + tails=tails, + tail_bound=tail_bound, + min_bin_width=min_bin_width, + min_bin_height=min_bin_height, + ) ) else: unconditional_transform = None - super().__init__(mask, transform_net_create_fn, - unconditional_transform=unconditional_transform) + super().__init__( + mask, + transform_net_create_fn, + unconditional_transform=unconditional_transform, + ) def _transform_dim_multiplier(self): return self.num_bins * 2 + 2 def _piecewise_cdf(self, inputs, transform_params, inverse=False): - unnormalized_widths = transform_params[..., :self.num_bins] - unnormalized_heights = transform_params[..., self.num_bins:2*self.num_bins] - unnorm_derivatives_left = transform_params[..., 2*self.num_bins][..., None] - unnorm_derivatives_right = transform_params[..., 2*self.num_bins + 1][..., None] - - if hasattr(self.transform_net, 'hidden_features'): + unnormalized_widths = transform_params[..., : self.num_bins] + unnormalized_heights = transform_params[ + ..., self.num_bins : 2 * self.num_bins + ] + unnorm_derivatives_left = transform_params[..., 2 * self.num_bins][ + ..., None + ] + unnorm_derivatives_right = transform_params[ + ..., 2 * self.num_bins + 1 + ][..., None] + + if hasattr(self.transform_net, "hidden_features"): unnormalized_widths /= np.sqrt(self.transform_net.hidden_features) unnormalized_heights /= np.sqrt(self.transform_net.hidden_features) @@ -378,8 +429,8 @@ def _piecewise_cdf(self, inputs, transform_params, inverse=False): else: spline_fn = splines.unconstrained_cubic_spline spline_kwargs = { - 'tails': self.tails, - 'tail_bound': self.tail_bound + "tails": self.tails, + "tail_bound": self.tail_bound, } return spline_fn( @@ -391,21 +442,24 @@ def _piecewise_cdf(self, inputs, transform_params, inverse=False): inverse=inverse, min_bin_width=self.min_bin_width, min_bin_height=self.min_bin_height, - **spline_kwargs + **spline_kwargs, ) class PiecewiseRationalQuadraticCouplingTransform(PiecewiseCouplingTransform): - def __init__(self, mask, transform_net_create_fn, - num_bins=10, - tails=None, - tail_bound=1., - apply_unconditional_transform=False, - img_shape=None, - min_bin_width=splines.rational_quadratic.DEFAULT_MIN_BIN_WIDTH, - min_bin_height=splines.rational_quadratic.DEFAULT_MIN_BIN_HEIGHT, - min_derivative=splines.rational_quadratic.DEFAULT_MIN_DERIVATIVE): - + def __init__( + self, + mask, + transform_net_create_fn, + num_bins=10, + tails=None, + tail_bound=1.0, + apply_unconditional_transform=False, + img_shape=None, + min_bin_width=splines.rational_quadratic.DEFAULT_MIN_BIN_WIDTH, + min_bin_height=splines.rational_quadratic.DEFAULT_MIN_BIN_HEIGHT, + min_derivative=splines.rational_quadratic.DEFAULT_MIN_DERIVATIVE, + ): self.num_bins = num_bins self.min_bin_width = min_bin_width self.min_bin_height = min_bin_height @@ -414,40 +468,49 @@ def __init__(self, mask, transform_net_create_fn, self.tail_bound = tail_bound if apply_unconditional_transform: - unconditional_transform = lambda features: transforms.PiecewiseRationalQuadraticCDF( - shape=[features] + (img_shape if img_shape else []), - num_bins=num_bins, - tails=tails, - tail_bound=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative + unconditional_transform = ( + lambda features: transforms.PiecewiseRationalQuadraticCDF( + shape=[features] + (img_shape if img_shape else []), + num_bins=num_bins, + tails=tails, + tail_bound=tail_bound, + min_bin_width=min_bin_width, + min_bin_height=min_bin_height, + min_derivative=min_derivative, + ) ) else: unconditional_transform = None - super().__init__(mask, transform_net_create_fn, - unconditional_transform=unconditional_transform) + super().__init__( + mask, + transform_net_create_fn, + unconditional_transform=unconditional_transform, + ) def _transform_dim_multiplier(self): - if self.tails == 'linear': + if self.tails == "linear": return self.num_bins * 3 - 1 else: return self.num_bins * 3 + 1 def _piecewise_cdf(self, inputs, transform_params, inverse=False): - unnormalized_widths = transform_params[..., :self.num_bins] - unnormalized_heights = transform_params[..., self.num_bins:2*self.num_bins] - unnormalized_derivatives = transform_params[..., 2 * self.num_bins:] + unnormalized_widths = transform_params[..., : self.num_bins] + unnormalized_heights = transform_params[ + ..., self.num_bins : 2 * self.num_bins + ] + unnormalized_derivatives = transform_params[..., 2 * self.num_bins :] - if hasattr(self.transform_net, 'hidden_features'): + if hasattr(self.transform_net, "hidden_features"): unnormalized_widths /= np.sqrt(self.transform_net.hidden_features) unnormalized_heights /= np.sqrt(self.transform_net.hidden_features) - elif hasattr(self.transform_net, 'hidden_channels'): + elif hasattr(self.transform_net, "hidden_channels"): unnormalized_widths /= np.sqrt(self.transform_net.hidden_channels) unnormalized_heights /= np.sqrt(self.transform_net.hidden_channels) else: - warnings.warn('Inputs to the softmax are not scaled down: initialization might be bad.') + warnings.warn( + "Inputs to the softmax are not scaled down: initialization might be bad." + ) if self.tails is None: spline_fn = splines.rational_quadratic_spline @@ -455,8 +518,8 @@ def _piecewise_cdf(self, inputs, transform_params, inverse=False): else: spline_fn = splines.unconstrained_rational_quadratic_spline spline_kwargs = { - 'tails': self.tails, - 'tail_bound': self.tail_bound + "tails": self.tails, + "tail_bound": self.tail_bound, } return spline_fn( @@ -468,6 +531,5 @@ def _piecewise_cdf(self, inputs, transform_params, inverse=False): min_bin_width=self.min_bin_width, min_bin_height=self.min_bin_height, min_derivative=self.min_derivative, - **spline_kwargs + **spline_kwargs, ) - diff --git a/phaseSpaceSampling/nde/transforms/coupling_test.py b/phaseSpaceSampling/nde/transforms/coupling_test.py index a1f796e..a7c6b3a 100644 --- a/phaseSpaceSampling/nde/transforms/coupling_test.py +++ b/phaseSpaceSampling/nde/transforms/coupling_test.py @@ -1,117 +1,138 @@ """Tests for the coupling Transforms.""" import itertools -import torch import unittest +import torch from torch import nn import phaseSpaceSampling.nn as nn_ - from phaseSpaceSampling.nde.transforms import coupling from phaseSpaceSampling.nde.transforms.transform_test import TransformTest - from phaseSpaceSampling.utils.torchutils import create_mid_split_binary_mask + def create_coupling_transform(cls, shape, **kwargs): if len(shape) == 1: + def create_net(in_features, out_features): - return nn_.ResidualNet(in_features, out_features, - hidden_features=30, - num_blocks=5) + return nn_.ResidualNet( + in_features, out_features, hidden_features=30, num_blocks=5 + ) + else: + def create_net(in_channels, out_channels): # return nn.Conv2d(in_channels, out_channels, kernel_size=1) return nn_.ConvResidualNet( in_channels=in_channels, out_channels=out_channels, - hidden_channels=16 + hidden_channels=16, ) mask = create_mid_split_binary_mask(shape[0]) - return cls( - mask=mask, - transform_net_create_fn=create_net, - **kwargs - ), mask + return cls(mask=mask, transform_net_create_fn=create_net, **kwargs), mask + batch_size = 10 + class AffineTransformTest(TransformTest): - shapes = [[20], - [2, 4, 4]] + shapes = [[20], [2, 4, 4]] def test_forward(self): for shape in self.shapes: inputs = torch.randn(batch_size, *shape) - transform, mask = create_coupling_transform(coupling.AffineCouplingTransform, shape) + transform, mask = create_coupling_transform( + coupling.AffineCouplingTransform, shape + ) outputs, logabsdet = transform(inputs) with self.subTest(shape=shape): self.assert_tensor_is_good(outputs, [batch_size] + shape) self.assert_tensor_is_good(logabsdet, [batch_size]) - self.assertEqual(outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...]) + self.assertEqual( + outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...] + ) def test_inverse(self): for shape in self.shapes: inputs = torch.randn(batch_size, *shape) - transform, mask = create_coupling_transform(coupling.AffineCouplingTransform, shape) + transform, mask = create_coupling_transform( + coupling.AffineCouplingTransform, shape + ) outputs, logabsdet = transform(inputs) with self.subTest(shape=shape): self.assert_tensor_is_good(outputs, [batch_size] + shape) self.assert_tensor_is_good(logabsdet, [batch_size]) - self.assertEqual(outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...]) + self.assertEqual( + outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...] + ) def test_forward_inverse_are_consistent(self): self.eps = 1e-6 for shape in self.shapes: inputs = torch.randn(batch_size, *shape) - transform, mask = create_coupling_transform(coupling.AffineCouplingTransform, shape) + transform, mask = create_coupling_transform( + coupling.AffineCouplingTransform, shape + ) with self.subTest(shape=shape): self.assert_forward_inverse_are_consistent(transform, inputs) + class AdditiveTransformTest(TransformTest): - shapes = [[20], - [2, 4, 4]] + shapes = [[20], [2, 4, 4]] def test_forward(self): for shape in self.shapes: inputs = torch.randn(batch_size, *shape) - transform, mask = create_coupling_transform(coupling.AdditiveCouplingTransform, shape) + transform, mask = create_coupling_transform( + coupling.AdditiveCouplingTransform, shape + ) outputs, logabsdet = transform(inputs) with self.subTest(shape=shape): self.assert_tensor_is_good(outputs, [batch_size] + shape) self.assert_tensor_is_good(logabsdet, [batch_size]) - self.assertEqual(outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...]) + self.assertEqual( + outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...] + ) self.assertEqual(logabsdet, torch.zeros(batch_size)) def test_inverse(self): for shape in self.shapes: inputs = torch.randn(batch_size, *shape) - transform, mask = create_coupling_transform(coupling.AdditiveCouplingTransform, shape) + transform, mask = create_coupling_transform( + coupling.AdditiveCouplingTransform, shape + ) outputs, logabsdet = transform(inputs) with self.subTest(shape=shape): self.assert_tensor_is_good(outputs, [batch_size] + shape) self.assert_tensor_is_good(logabsdet, [batch_size]) - self.assertEqual(outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...]) + self.assertEqual( + outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...] + ) self.assertEqual(logabsdet, torch.zeros(batch_size)) def test_forward_inverse_are_consistent(self): self.eps = 1e-6 for shape in self.shapes: inputs = torch.randn(batch_size, *shape) - transform, mask = create_coupling_transform(coupling.AdditiveCouplingTransform, shape) + transform, mask = create_coupling_transform( + coupling.AdditiveCouplingTransform, shape + ) with self.subTest(shape=shape): self.assert_forward_inverse_are_consistent(transform, inputs) + class PiecewiseCouplingTransformTest(TransformTest): - classes = [coupling.PiecewiseLinearCouplingTransform, - coupling.PiecewiseQuadraticCouplingTransform, - coupling.PiecewiseCubicCouplingTransform, - coupling.PiecewiseRationalQuadraticCouplingTransform] + classes = [ + coupling.PiecewiseLinearCouplingTransform, + coupling.PiecewiseQuadraticCouplingTransform, + coupling.PiecewiseCubicCouplingTransform, + coupling.PiecewiseRationalQuadraticCouplingTransform, + ] - shapes = [[20], - [2, 4, 4]] + shapes = [[20], [2, 4, 4]] def test_forward(self): for shape in self.shapes: @@ -122,19 +143,25 @@ def test_forward(self): with self.subTest(cls=cls, shape=shape): self.assert_tensor_is_good(outputs, [batch_size] + shape) self.assert_tensor_is_good(logabsdet, [batch_size]) - self.assertEqual(outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...]) + self.assertEqual( + outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...] + ) def test_forward_unconstrained(self): batch_size = 10 for shape in self.shapes: for cls in self.classes: - inputs = 3. * torch.randn(batch_size, *shape) - transform, mask = create_coupling_transform(cls, shape, tails='linear') + inputs = 3.0 * torch.randn(batch_size, *shape) + transform, mask = create_coupling_transform( + cls, shape, tails="linear" + ) outputs, logabsdet = transform(inputs) with self.subTest(cls=cls, shape=shape): self.assert_tensor_is_good(outputs, [batch_size] + shape) self.assert_tensor_is_good(logabsdet, [batch_size]) - self.assertEqual(outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...]) + self.assertEqual( + outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...] + ) def test_inverse(self): for shape in self.shapes: @@ -145,18 +172,24 @@ def test_inverse(self): with self.subTest(cls=cls, shape=shape): self.assert_tensor_is_good(outputs, [batch_size] + shape) self.assert_tensor_is_good(logabsdet, [batch_size]) - self.assertEqual(outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...]) + self.assertEqual( + outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...] + ) def test_inverse_unconstrained(self): for shape in self.shapes: for cls in self.classes: - inputs = 3. * torch.randn(batch_size, *shape) - transform, mask = create_coupling_transform(cls, shape, tails='linear') + inputs = 3.0 * torch.randn(batch_size, *shape) + transform, mask = create_coupling_transform( + cls, shape, tails="linear" + ) outputs, logabsdet = transform(inputs) with self.subTest(cls=cls, shape=shape): self.assert_tensor_is_good(outputs, [batch_size] + shape) self.assert_tensor_is_good(logabsdet, [batch_size]) - self.assertEqual(outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...]) + self.assertEqual( + outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...] + ) def test_forward_inverse_are_consistent(self): for shape in self.shapes: @@ -164,33 +197,44 @@ def test_forward_inverse_are_consistent(self): inputs = torch.rand(batch_size, *shape) transform, mask = create_coupling_transform(cls, shape) with self.subTest(cls=cls, shape=shape): - self.eps = 1e-4 # TODO: can do better? - self.assert_forward_inverse_are_consistent(transform, inputs) + self.eps = 1e-4 # TODO: can do better? + self.assert_forward_inverse_are_consistent( + transform, inputs + ) def test_forward_inverse_are_consistent_unconstrained(self): self.eps = 1e-5 for shape in self.shapes: for cls in self.classes: - inputs = 3. * torch.randn(batch_size, *shape) - transform, mask = create_coupling_transform(cls, shape, tails='linear') + inputs = 3.0 * torch.randn(batch_size, *shape) + transform, mask = create_coupling_transform( + cls, shape, tails="linear" + ) with self.subTest(cls=cls, shape=shape): - self.eps = 1e-4 # TODO: can do better? - self.assert_forward_inverse_are_consistent(transform, inputs) + self.eps = 1e-4 # TODO: can do better? + self.assert_forward_inverse_are_consistent( + transform, inputs + ) def test_forward_unconditional(self): for shape in self.shapes: for cls in self.classes: inputs = torch.rand(batch_size, *shape) img_shape = shape[1:] if len(shape) > 1 else None - transform, mask = create_coupling_transform(cls, shape, - apply_unconditional_transform=True, - img_shape=img_shape) + transform, mask = create_coupling_transform( + cls, + shape, + apply_unconditional_transform=True, + img_shape=img_shape, + ) outputs, logabsdet = transform(inputs) with self.subTest(cls=cls, shape=shape): self.assert_tensor_is_good(outputs, [batch_size] + shape) self.assert_tensor_is_good(logabsdet, [batch_size]) - self.assertNotEqual(outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...]) + self.assertNotEqual( + outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...] + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/linear.py b/phaseSpaceSampling/nde/transforms/linear.py index 83921df..0ada04f 100644 --- a/phaseSpaceSampling/nde/transforms/linear.py +++ b/phaseSpaceSampling/nde/transforms/linear.py @@ -2,16 +2,15 @@ import numpy as np import torch - from torch import nn -from torch.nn import functional as F, init - +from torch.nn import functional as F +from torch.nn import init +import phaseSpaceSampling as pss from phaseSpaceSampling.nde import transforms - -from phaseSpaceSampling.utils.typechecks import is_positive_int, is_bool from phaseSpaceSampling.utils.torchutils import random_orthogonal -import phaseSpaceSampling as pss +from phaseSpaceSampling.utils.typechecks import is_bool, is_positive_int + class LinearCache(object): """Helper class to store the cache of a linear transform. @@ -35,7 +34,7 @@ class Linear(transforms.Transform): def __init__(self, features, using_cache=False): if not is_positive_int(features): - raise TypeError('Number of features must be a positive integer.') + raise TypeError("Number of features must be a positive integer.") super().__init__() self.features = features @@ -56,7 +55,10 @@ def forward(self, inputs, context=None): def _check_forward_cache(self): if self.cache.weight is None and self.cache.logabsdet is None: - self.cache.weight, self.cache.logabsdet = self.weight_and_logabsdet() + ( + self.cache.weight, + self.cache.logabsdet, + ) = self.weight_and_logabsdet() elif self.cache.weight is None: self.cache.weight = self.weight() @@ -75,7 +77,10 @@ def inverse(self, inputs, context=None): def _check_inverse_cache(self): if self.cache.inverse is None and self.cache.logabsdet is None: - self.cache.inverse, self.cache.logabsdet = self.weight_inverse_and_logabsdet() + ( + self.cache.inverse, + self.cache.logabsdet, + ) = self.weight_inverse_and_logabsdet() elif self.cache.inverse is None: self.cache.inverse = self.weight_inverse() @@ -91,7 +96,7 @@ def train(self, mode=True): def use_cache(self, mode=True): if not is_bool(mode): - raise TypeError('Mode must be boolean.') + raise TypeError("Mode must be boolean.") self.using_cache = mode def weight_and_logabsdet(self): @@ -135,7 +140,9 @@ class NaiveLinear(Linear): of the input. """ - def __init__(self, features, orthogonal_initialization=True, using_cache=False): + def __init__( + self, features, orthogonal_initialization=True, using_cache=False + ): """Constructor. Args: @@ -179,7 +186,9 @@ def inverse_no_cache(self, inputs): """ batch_size = inputs.shape[0] outputs = inputs - self.bias - outputs, lu = torch.gesv(outputs.t(), self._weight) # Linear-system solver. + outputs, lu = torch.gesv( + outputs.t(), self._weight + ) # Linear-system solver. outputs = outputs.t() # The linear-system solver returns the LU decomposition of the weights, which we # can use to obtain the log absolute determinant directly. @@ -189,7 +198,7 @@ def inverse_no_cache(self, inputs): def weight(self): """Cost: - weight = O(1) + weight = O(1) """ return self._weight @@ -212,7 +221,9 @@ def weight_inverse_and_logabsdet(self): """ # If both weight inverse and logabsdet are needed, it's cheaper to compute both together. identity = torch.eye(self.features, self.features) - weight_inv, lu = torch.gesv(identity, self._weight) # Linear-system solver. + weight_inv, lu = torch.gesv( + identity, self._weight + ) # Linear-system solver. logabsdet = torch.sum(torch.log(torch.abs(torch.diag(lu)))) return weight_inv, logabsdet diff --git a/phaseSpaceSampling/nde/transforms/linear_test.py b/phaseSpaceSampling/nde/transforms/linear_test.py index 85e9116..96b6725 100644 --- a/phaseSpaceSampling/nde/transforms/linear_test.py +++ b/phaseSpaceSampling/nde/transforms/linear_test.py @@ -1,15 +1,15 @@ """Tests for linear transforms.""" -import torch import unittest - from unittest.mock import MagicMock +import torch + +import phaseSpaceSampling as pss from phaseSpaceSampling.nde.transforms import linear -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest from phaseSpaceSampling.nde.transforms.linear import Linear +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest -import phaseSpaceSampling as pss class LinearTest(TransformTest): def setUp(self): @@ -20,7 +20,9 @@ def setUp(self): inverse = torch.randn(features, features) logabsdet = torch.randn(1) self.transform = Linear(features) - self.transform.bias.data = torch.randn(features) # Just so bias isn't zero. + self.transform.bias.data = torch.randn( + features + ) # Just so bias isn't zero. self.inputs = torch.randn(batch_size, features) self.outputs_fwd = self.inputs @ weight.t() + self.transform.bias @@ -30,9 +32,11 @@ def setUp(self): # Mocks for abstract methods. self.transform.forward_no_cache = MagicMock( - return_value=(self.outputs_fwd, self.logabsdet_fwd)) + return_value=(self.outputs_fwd, self.logabsdet_fwd) + ) self.transform.inverse_no_cache = MagicMock( - return_value=(self.outputs_inv, self.logabsdet_inv)) + return_value=(self.outputs_inv, self.logabsdet_inv) + ) self.transform.weight = MagicMock(return_value=weight) self.transform.weight_inverse = MagicMock(return_value=inverse) self.transform.logabsdet = MagicMock(return_value=logabsdet) @@ -150,7 +154,9 @@ def test_forward_train_invalidates_cache(self): self.transform.logabsdet.reset_mock() self.transform.train() # Cache should be invalidated here. - self.assertTrue(self.transform.using_cache) # Using cache should still be enabled. + self.assertTrue( + self.transform.using_cache + ) # Using cache should still be enabled. self.transform.eval() outputs, logabsdet = self.transform(self.inputs) @@ -171,7 +177,9 @@ def test_inverse_train_invalidates_cache(self): self.transform.logabsdet.reset_mock() self.transform.train() # Cache should be disabled and invalidated here. - self.assertTrue(self.transform.using_cache) # Using cache should still be enabled. + self.assertTrue( + self.transform.using_cache + ) # Using cache should still be enabled. self.transform.eval() outputs, logabsdet = self.transform.inverse(self.inputs) @@ -183,7 +191,6 @@ def test_inverse_train_invalidates_cache(self): class NaiveLinearTest(TransformTest): - def setUp(self): self.features = 3 self.transform = linear.NaiveLinear(features=self.features) @@ -229,7 +236,9 @@ def test_weight(self): def test_weight_inverse(self): weight_inverse = self.transform.weight_inverse() - self.assert_tensor_is_good(weight_inverse, [self.features, self.features]) + self.assert_tensor_is_good( + weight_inverse, [self.features, self.features] + ) self.assertEqual(weight_inverse, self.weight_inverse) def test_logabsdet(self): @@ -243,5 +252,5 @@ def test_forward_inverse_are_consistent(self): self.assert_forward_inverse_are_consistent(self.transform, inputs) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/lu.py b/phaseSpaceSampling/nde/transforms/lu.py index d1250bc..e9d56c6 100644 --- a/phaseSpaceSampling/nde/transforms/lu.py +++ b/phaseSpaceSampling/nde/transforms/lu.py @@ -1,8 +1,8 @@ import numpy as np import torch - from torch import nn -from torch.nn import functional as F, init +from torch.nn import functional as F +from torch.nn import init from phaseSpaceSampling.nde.transforms.linear import Linear @@ -10,7 +10,9 @@ class LULinear(Linear): """A linear transform where we parameterize the LU decomposition of the weights.""" - def __init__(self, features, using_cache=False, identity_init=True, eps=1e-3): + def __init__( + self, features, using_cache=False, identity_init=True, eps=1e-3 + ): super().__init__(features, using_cache) self.eps = eps @@ -43,12 +45,16 @@ def _initialize(self, identity_init): def _create_lower_upper(self): lower = self.lower_entries.new_zeros(self.features, self.features) - lower[self.lower_indices[0], self.lower_indices[1]] = self.lower_entries + lower[ + self.lower_indices[0], self.lower_indices[1] + ] = self.lower_entries # The diagonal of L is taken to be all-ones without loss of generality. - lower[self.diag_indices[0], self.diag_indices[1]] = 1. + lower[self.diag_indices[0], self.diag_indices[1]] = 1.0 upper = self.upper_entries.new_zeros(self.features, self.features) - upper[self.upper_indices[0], self.upper_indices[1]] = self.upper_entries + upper[ + self.upper_indices[0], self.upper_indices[1] + ] = self.upper_entries upper[self.diag_indices[0], self.diag_indices[1]] = self.upper_diag return lower, upper @@ -77,8 +83,12 @@ def inverse_no_cache(self, inputs): """ lower, upper = self._create_lower_upper() outputs = inputs - self.bias - outputs, _ = torch.triangular_solve(outputs.t(), lower, upper=False, unitriangular=True) - outputs, _ = torch.triangular_solve(outputs, upper, upper=True, unitriangular=False) + outputs, _ = torch.triangular_solve( + outputs.t(), lower, upper=False, unitriangular=True + ) + outputs, _ = torch.triangular_solve( + outputs, upper, upper=True, unitriangular=False + ) outputs = outputs.t() logabsdet = -self.logabsdet() @@ -103,8 +113,12 @@ def weight_inverse(self): """ lower, upper = self._create_lower_upper() identity = torch.eye(self.features, self.features) - lower_inverse, _ = torch.trtrs(identity, lower, upper=False, unitriangular=True) - weight_inverse, _ = torch.trtrs(lower_inverse, upper, upper=True, unitriangular=False) + lower_inverse, _ = torch.trtrs( + identity, lower, upper=False, unitriangular=True + ) + weight_inverse, _ = torch.trtrs( + lower_inverse, upper, upper=True, unitriangular=False + ) return weight_inverse @property diff --git a/phaseSpaceSampling/nde/transforms/lu_test.py b/phaseSpaceSampling/nde/transforms/lu_test.py index 0173a07..00041ca 100644 --- a/phaseSpaceSampling/nde/transforms/lu_test.py +++ b/phaseSpaceSampling/nde/transforms/lu_test.py @@ -1,14 +1,15 @@ """Tests for the LU linear transforms.""" -import torch import unittest +import torch + +import phaseSpaceSampling as pss from phaseSpaceSampling.nde.transforms import lu from phaseSpaceSampling.nde.transforms.transform_test import TransformTest -import phaseSpaceSampling as pss -class LULinearTest(TransformTest): +class LULinearTest(TransformTest): def setUp(self): self.features = 3 self.transform = lu.LULinear(features=self.features) @@ -55,7 +56,9 @@ def test_weight(self): def test_weight_inverse(self): weight_inverse = self.transform.weight_inverse() - self.assert_tensor_is_good(weight_inverse, [self.features, self.features]) + self.assert_tensor_is_good( + weight_inverse, [self.features, self.features] + ) self.assertEqual(weight_inverse, self.weight_inverse) def test_logabsdet(self): @@ -69,5 +72,5 @@ def test_forward_inverse_are_consistent(self): self.assert_forward_inverse_are_consistent(self.transform, inputs) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/made.py b/phaseSpaceSampling/nde/transforms/made.py index 894c959..2eecc59 100644 --- a/phaseSpaceSampling/nde/transforms/made.py +++ b/phaseSpaceSampling/nde/transforms/made.py @@ -2,11 +2,12 @@ # TODO: should be moved to module nn. import torch - from torch import nn -from torch.nn import functional as F, init +from torch.nn import functional as F +from torch.nn import init + +from phaseSpaceSampling.utils.torchutils import tile -from phaseSpaceSampling.utils.torchutils import tile def _get_input_degrees(in_features): """Returns the degrees an input to MADE should have.""" @@ -16,37 +17,41 @@ def _get_input_degrees(in_features): class MaskedLinear(nn.Linear): """A linear module with a masked weight matrix.""" - def __init__(self, - in_degrees, - out_features, - autoregressive_features, - random_mask, - is_output, - bias=True): + def __init__( + self, + in_degrees, + out_features, + autoregressive_features, + random_mask, + is_output, + bias=True, + ): super().__init__( - in_features=len(in_degrees), - out_features=out_features, - bias=bias) + in_features=len(in_degrees), out_features=out_features, bias=bias + ) mask, degrees = self._get_mask_and_degrees( in_degrees=in_degrees, out_features=out_features, autoregressive_features=autoregressive_features, random_mask=random_mask, - is_output=is_output) - self.register_buffer('mask', mask) - self.register_buffer('degrees', degrees) + is_output=is_output, + ) + self.register_buffer("mask", mask) + self.register_buffer("degrees", degrees) @classmethod - def _get_mask_and_degrees(cls, - in_degrees, - out_features, - autoregressive_features, - random_mask, - is_output): + def _get_mask_and_degrees( + cls, + in_degrees, + out_features, + autoregressive_features, + random_mask, + is_output, + ): if is_output: out_degrees = tile( _get_input_degrees(autoregressive_features), - out_features // autoregressive_features + out_features // autoregressive_features, ) mask = (out_degrees[..., None] > in_degrees).float() @@ -58,7 +63,8 @@ def _get_mask_and_degrees(cls, low=min_in_degree, high=autoregressive_features, size=[out_features], - dtype=torch.long) + dtype=torch.long, + ) else: max_ = max(1, autoregressive_features - 1) min_ = min(1, autoregressive_features - 1) @@ -78,14 +84,16 @@ class MaskedFeedforwardBlock(nn.Module): the number of input features. """ - def __init__(self, - in_degrees, - autoregressive_features, - context_features=None, - random_mask=False, - activation=F.relu, - dropout_probability=0., - use_batch_norm=False): + def __init__( + self, + in_degrees, + autoregressive_features, + context_features=None, + random_mask=False, + activation=F.relu, + dropout_probability=0.0, + use_batch_norm=False, + ): super().__init__() features = len(in_degrees) @@ -129,17 +137,21 @@ def forward(self, inputs, context=None): class MaskedResidualBlock(nn.Module): """A residual block containing masked linear modules.""" - def __init__(self, - in_degrees, - autoregressive_features, - context_features=None, - random_mask=False, - activation=F.relu, - dropout_probability=0., - use_batch_norm=False, - zero_initialization=True): + def __init__( + self, + in_degrees, + autoregressive_features, + context_features=None, + random_mask=False, + activation=F.relu, + dropout_probability=0.0, + use_batch_norm=False, + zero_initialization=True, + ): if random_mask: - raise ValueError('Masked residual block can\'t be used with random masks.') + raise ValueError( + "Masked residual block can't be used with random masks." + ) super().__init__() features = len(in_degrees) @@ -149,10 +161,9 @@ def __init__(self, # Batch norm. self.use_batch_norm = use_batch_norm if use_batch_norm: - self.batch_norm_layers = nn.ModuleList([ - nn.BatchNorm1d(features, eps=1e-3) - for _ in range(2) - ]) + self.batch_norm_layers = nn.ModuleList( + [nn.BatchNorm1d(features, eps=1e-3) for _ in range(2)] + ) # Masked linear. linear_0 = MaskedLinear( @@ -172,8 +183,10 @@ def __init__(self, self.linear_layers = nn.ModuleList([linear_0, linear_1]) self.degrees = linear_1.degrees if torch.all(self.degrees >= in_degrees).item() != 1: - raise RuntimeError('In a masked residual block, the output degrees can\'t be' - ' less than the corresponding input degrees.') + raise RuntimeError( + "In a masked residual block, the output degrees can't be" + " less than the corresponding input degrees." + ) # Activation and dropout self.activation = activation @@ -197,8 +210,7 @@ def forward(self, inputs, context=None): temps = self.linear_layers[1](temps) if context is not None: temps = F.glu( - torch.cat((temps, self.context_layer(context)), dim=1), - dim=1 + torch.cat((temps, self.context_layer(context)), dim=1), dim=1 ) return inputs + temps @@ -210,19 +222,23 @@ class MADE(nn.Module): Optionally, it can use batch norm or dropout within blocks (default is no). """ - def __init__(self, - features, - hidden_features, - context_features=None, - num_blocks=2, - output_multiplier=1, - use_residual_blocks=True, - random_mask=False, - activation=F.relu, - dropout_probability=0., - use_batch_norm=False): + def __init__( + self, + features, + hidden_features, + context_features=None, + num_blocks=2, + output_multiplier=1, + use_residual_blocks=True, + random_mask=False, + activation=F.relu, + dropout_probability=0.0, + use_batch_norm=False, + ): if use_residual_blocks and random_mask: - raise ValueError('Residual blocks can\'t be used with random masks.') + raise ValueError( + "Residual blocks can't be used with random masks." + ) super().__init__() # Initial layer. @@ -231,7 +247,7 @@ def __init__(self, out_features=hidden_features, autoregressive_features=features, random_mask=random_mask, - is_output=False + is_output=False, ) if context_features is not None: @@ -265,7 +281,7 @@ def __init__(self, out_features=features * output_multiplier, autoregressive_features=features, random_mask=random_mask, - is_output=True + is_output=True, ) def forward(self, inputs, context=None): diff --git a/phaseSpaceSampling/nde/transforms/made_test.py b/phaseSpaceSampling/nde/transforms/made_test.py index 1d82b17..c02ab7c 100644 --- a/phaseSpaceSampling/nde/transforms/made_test.py +++ b/phaseSpaceSampling/nde/transforms/made_test.py @@ -1,14 +1,14 @@ """Tests for MADE.""" +import unittest + import torch import torchtestcase -import unittest from phaseSpaceSampling.nde.transforms import made class ShapeTest(torchtestcase.TorchTestCase): - def test_conditional(self): features = 100 hidden_features = 200 @@ -20,11 +20,15 @@ def test_conditional(self): inputs = torch.randn(batch_size, features) conditional_inputs = torch.randn(batch_size, conditional_features) - for use_residual_blocks, random_mask in [(False, False), - (False, True), - (True, False)]: - with self.subTest(use_residual_blocks=use_residual_blocks, - random_mask=random_mask): + for use_residual_blocks, random_mask in [ + (False, False), + (False, True), + (True, False), + ]: + with self.subTest( + use_residual_blocks=use_residual_blocks, + random_mask=random_mask, + ): model = made.MADE( features=features, hidden_features=hidden_features, @@ -37,7 +41,9 @@ def test_conditional(self): outputs = model(inputs, conditional_inputs) self.assertEqual(outputs.dim(), 2) self.assertEqual(outputs.shape[0], batch_size) - self.assertEqual(outputs.shape[1], output_multiplier * features) + self.assertEqual( + outputs.shape[1], output_multiplier * features + ) def test_unconditional(self): features = 100 @@ -48,11 +54,15 @@ def test_unconditional(self): inputs = torch.randn(batch_size, features) - for use_residual_blocks, random_mask in [(False, False), - (False, True), - (True, False)]: - with self.subTest(use_residual_blocks=use_residual_blocks, - random_mask=random_mask): + for use_residual_blocks, random_mask in [ + (False, False), + (False, True), + (True, False), + ]: + with self.subTest( + use_residual_blocks=use_residual_blocks, + random_mask=random_mask, + ): model = made.MADE( features=features, hidden_features=hidden_features, @@ -64,22 +74,27 @@ def test_unconditional(self): outputs = model(inputs) self.assertEqual(outputs.dim(), 2) self.assertEqual(outputs.shape[0], batch_size) - self.assertEqual(outputs.shape[1], output_multiplier * features) + self.assertEqual( + outputs.shape[1], output_multiplier * features + ) class ConnectivityTest(torchtestcase.TorchTestCase): - def test_gradients(self): features = 10 hidden_features = 256 num_blocks = 20 output_multiplier = 3 - for use_residual_blocks, random_mask in [(False, False), - (False, True), - (True, False)]: - with self.subTest(use_residual_blocks=use_residual_blocks, - random_mask=random_mask): + for use_residual_blocks, random_mask in [ + (False, False), + (False, True), + (True, False), + ]: + with self.subTest( + use_residual_blocks=use_residual_blocks, + random_mask=random_mask, + ): model = made.MADE( features=features, hidden_features=hidden_features, @@ -120,7 +135,9 @@ def test_total_mask_sequential(self): total_mask = block.linear_layers[0].mask @ total_mask total_mask = block.linear_layers[1].mask @ total_mask else: - self.assertIsInstance(block, made.MaskedFeedforwardBlock) + self.assertIsInstance( + block, made.MaskedFeedforwardBlock + ) total_mask = block.linear.mask @ total_mask total_mask = model.final_layer.mask @ total_mask total_mask = (total_mask > 0).float() @@ -147,8 +164,10 @@ def test_total_mask_random(self): total_mask = block.linear.mask @ total_mask total_mask = model.final_layer.mask @ total_mask total_mask = (total_mask > 0).float() - self.assertEqual(torch.triu(total_mask), torch.zeros([features, features])) + self.assertEqual( + torch.triu(total_mask), torch.zeros([features, features]) + ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/nonlinearities.py b/phaseSpaceSampling/nde/transforms/nonlinearities.py index 5a4d6c3..a28c46d 100644 --- a/phaseSpaceSampling/nde/transforms/nonlinearities.py +++ b/phaseSpaceSampling/nde/transforms/nonlinearities.py @@ -2,20 +2,18 @@ import numpy as np import torch - from torch import nn from torch.nn import functional as F - from phaseSpaceSampling.nde import transforms from phaseSpaceSampling.nde.transforms import splines +from phaseSpaceSampling.utils.torchutils import sum_except_batch -from phaseSpaceSampling.utils.torchutils import sum_except_batch class Tanh(transforms.Transform): def forward(self, inputs, context=None): outputs = torch.tanh(inputs) - logabsdet = torch.log(1 - outputs ** 2) + logabsdet = torch.log(1 - outputs**2) logabsdet = sum_except_batch(logabsdet, num_batch_dims=1) return outputs, logabsdet @@ -23,7 +21,7 @@ def inverse(self, inputs, context=None): if torch.min(inputs) <= -1 or torch.max(inputs) >= 1: raise transforms.InputOutsideDomain() outputs = 0.5 * torch.log((1 + inputs) / (1 - inputs)) - logabsdet = - torch.log(1 - inputs ** 2) + logabsdet = -torch.log(1 - inputs**2) logabsdet = sum_except_batch(logabsdet, num_batch_dims=1) return outputs, logabsdet @@ -36,25 +34,30 @@ class LogTanh(transforms.Transform): def __init__(self, cut_point=1): if cut_point <= 0: - raise ValueError('Cut point must be positive.') + raise ValueError("Cut point must be positive.") super().__init__() self.cut_point = cut_point self.inv_cut_point = np.tanh(cut_point) self.alpha = (1 - np.tanh(np.tanh(cut_point))) / cut_point - self.beta = np.exp((np.tanh(cut_point) - self.alpha * np.log(cut_point)) - / self.alpha) + self.beta = np.exp( + (np.tanh(cut_point) - self.alpha * np.log(cut_point)) / self.alpha + ) def forward(self, inputs, context=None): - mask_right = (inputs > self.cut_point) - mask_left = (inputs < -self.cut_point) + mask_right = inputs > self.cut_point + mask_left = inputs < -self.cut_point mask_middle = ~(mask_right | mask_left) outputs = torch.zeros_like(inputs) outputs[mask_middle] = torch.tanh(inputs[mask_middle]) - outputs[mask_right] = self.alpha * torch.log(self.beta * inputs[mask_right]) - outputs[mask_left] = self.alpha * -torch.log(-self.beta * inputs[mask_left]) + outputs[mask_right] = self.alpha * torch.log( + self.beta * inputs[mask_right] + ) + outputs[mask_left] = self.alpha * -torch.log( + -self.beta * inputs[mask_left] + ) logabsdet = torch.zeros_like(inputs) logabsdet[mask_middle] = torch.log(1 - outputs[mask_middle] ** 2) @@ -65,21 +68,29 @@ def forward(self, inputs, context=None): return outputs, logabsdet def inverse(self, inputs, context=None): - - mask_right = (inputs > self.inv_cut_point) - mask_left = (inputs < -self.inv_cut_point) + mask_right = inputs > self.inv_cut_point + mask_left = inputs < -self.inv_cut_point mask_middle = ~(mask_right | mask_left) outputs = torch.zeros_like(inputs) - outputs[mask_middle] = 0.5 * torch.log((1 + inputs[mask_middle]) - / (1 - inputs[mask_middle])) - outputs[mask_right] = torch.exp(inputs[mask_right] / self.alpha) / self.beta - outputs[mask_left] = -torch.exp(-inputs[mask_left] / self.alpha) / self.beta + outputs[mask_middle] = 0.5 * torch.log( + (1 + inputs[mask_middle]) / (1 - inputs[mask_middle]) + ) + outputs[mask_right] = ( + torch.exp(inputs[mask_right] / self.alpha) / self.beta + ) + outputs[mask_left] = ( + -torch.exp(-inputs[mask_left] / self.alpha) / self.beta + ) logabsdet = torch.zeros_like(inputs) logabsdet[mask_middle] = -torch.log(1 - inputs[mask_middle] ** 2) - logabsdet[mask_right] = -np.log(self.alpha * self.beta) + inputs[mask_right] / self.alpha - logabsdet[mask_left] = -np.log(self.alpha * self.beta) - inputs[mask_left] / self.alpha + logabsdet[mask_right] = ( + -np.log(self.alpha * self.beta) + inputs[mask_right] / self.alpha + ) + logabsdet[mask_left] = ( + -np.log(self.alpha * self.beta) - inputs[mask_left] / self.alpha + ) logabsdet = sum_except_batch(logabsdet, num_batch_dims=1) return outputs, logabsdet @@ -88,10 +99,12 @@ def inverse(self, inputs, context=None): class LeakyReLU(transforms.Transform): def __init__(self, negative_slope=1e-2): if negative_slope <= 0: - raise ValueError('Slope must be positive.') + raise ValueError("Slope must be positive.") super().__init__() self.negative_slope = negative_slope - self.log_negative_slope = torch.log(torch.as_tensor(self.negative_slope)) + self.log_negative_slope = torch.log( + torch.as_tensor(self.negative_slope) + ) def forward(self, inputs, context=None): outputs = F.leaky_relu(inputs, negative_slope=self.negative_slope) @@ -101,7 +114,9 @@ def forward(self, inputs, context=None): return outputs, logabsdet def inverse(self, inputs, context=None): - outputs = F.leaky_relu(inputs, negative_slope=(1 / self.negative_slope)) + outputs = F.leaky_relu( + inputs, negative_slope=(1 / self.negative_slope) + ) mask = (inputs < 0).type(torch.Tensor) logabsdet = -self.log_negative_slope * mask logabsdet = sum_except_batch(logabsdet, num_batch_dims=1) @@ -118,7 +133,9 @@ def forward(self, inputs, context=None): inputs = self.temperature * inputs outputs = torch.sigmoid(inputs) logabsdet = sum_except_batch( - torch.log(self.temperature) - F.softplus(-inputs) - F.softplus(inputs) + torch.log(self.temperature) + - F.softplus(-inputs) + - F.softplus(inputs) ) return outputs, logabsdet @@ -128,10 +145,13 @@ def inverse(self, inputs, context=None): inputs = torch.clamp(inputs, self.eps, 1 - self.eps) - outputs = (1 / self.temperature) * (torch.log(inputs) - torch.log1p(-inputs)) - logabsdet = - sum_except_batch( - torch.log(self.temperature) - F.softplus( - -self.temperature * outputs) - F.softplus(self.temperature * outputs) + outputs = (1 / self.temperature) * ( + torch.log(inputs) - torch.log1p(-inputs) + ) + logabsdet = -sum_except_batch( + torch.log(self.temperature) + - F.softplus(-self.temperature * outputs) + - F.softplus(self.temperature * outputs) ) return outputs, logabsdet @@ -148,7 +168,7 @@ def __init__(self, location=None, scale=None, features=None): def forward(self, inputs, context=None): outputs = (1 / np.pi) * torch.atan(inputs) + 0.5 logabsdet = sum_except_batch( - - np.log(np.pi) - torch.log(1 + inputs ** 2) + -np.log(np.pi) - torch.log(1 + inputs**2) ) return outputs, logabsdet @@ -157,38 +177,36 @@ def inverse(self, inputs, context=None): raise transforms.InputOutsideDomain() outputs = torch.tan(np.pi * (inputs - 0.5)) - logabsdet = - sum_except_batch( - - np.log(np.pi) - torch.log(1 + outputs ** 2) + logabsdet = -sum_except_batch( + -np.log(np.pi) - torch.log(1 + outputs**2) ) return outputs, logabsdet class CauchyCDFInverse(transforms.InverseTransform): def __init__(self, location=None, scale=None, features=None): - super().__init__(CauchyCDF( - location=location, - scale=scale, - features=features - )) + super().__init__( + CauchyCDF(location=location, scale=scale, features=features) + ) class CompositeCDFTransform(transforms.CompositeTransform): def __init__(self, squashing_transform, cdf_transform): - super().__init__([ - squashing_transform, - cdf_transform, - transforms.InverseTransform(squashing_transform) - ]) + super().__init__( + [ + squashing_transform, + cdf_transform, + transforms.InverseTransform(squashing_transform), + ] + ) + def _share_across_batch(params, batch_size): - return params[None,...].expand(batch_size, *params.shape) + return params[None, ...].expand(batch_size, *params.shape) + class PiecewiseLinearCDF(transforms.Transform): - def __init__(self, - shape, - num_bins=10, - tails=None, - tail_bound=1.): + def __init__(self, shape, num_bins=10, tails=None, tail_bound=1.0): super().__init__() self.tail_bound = tail_bound @@ -199,13 +217,15 @@ def __init__(self, def _spline(self, inputs, inverse=False): batch_size = inputs.shape[0] - unnormalized_pdf = _share_across_batch(self.unnormalized_pdf, batch_size) + unnormalized_pdf = _share_across_batch( + self.unnormalized_pdf, batch_size + ) if self.tails is None: outputs, logabsdet = splines.linear_spline( inputs=inputs, unnormalized_pdf=unnormalized_pdf, - inverse=inverse + inverse=inverse, ) else: outputs, logabsdet = splines.unconstrained_linear_spline( @@ -213,7 +233,7 @@ def _spline(self, inputs, inverse=False): unnormalized_pdf=unnormalized_pdf, inverse=inverse, tails=self.tails, - tail_bound=self.tail_bound + tail_bound=self.tail_bound, ) return outputs, sum_except_batch(logabsdet) @@ -224,14 +244,17 @@ def forward(self, inputs, context=None): def inverse(self, inputs, context=None): return self._spline(inputs, inverse=True) + class PiecewiseQuadraticCDF(transforms.Transform): - def __init__(self, - shape, - num_bins=10, - tails=None, - tail_bound=1., - min_bin_width=splines.quadratic.DEFAULT_MIN_BIN_WIDTH, - min_bin_height=splines.quadratic.DEFAULT_MIN_BIN_HEIGHT): + def __init__( + self, + shape, + num_bins=10, + tails=None, + tail_bound=1.0, + min_bin_width=splines.quadratic.DEFAULT_MIN_BIN_WIDTH, + min_bin_height=splines.quadratic.DEFAULT_MIN_BIN_HEIGHT, + ): super().__init__() self.min_bin_width = min_bin_width self.min_bin_height = min_bin_height @@ -240,14 +263,22 @@ def __init__(self, self.unnormalized_widths = nn.Parameter(torch.randn(*shape, num_bins)) - num_heights = (num_bins - 1) if self.tails == 'linear' else (num_bins + 1) - self.unnormalized_heights = nn.Parameter(torch.randn(*shape, num_heights)) + num_heights = ( + (num_bins - 1) if self.tails == "linear" else (num_bins + 1) + ) + self.unnormalized_heights = nn.Parameter( + torch.randn(*shape, num_heights) + ) def _spline(self, inputs, inverse=False): batch_size = inputs.shape[0] - unnormalized_widths = _share_across_batch(self.unnormalized_widths, batch_size) - unnormalized_heights = _share_across_batch(self.unnormalized_heights, batch_size) + unnormalized_widths = _share_across_batch( + self.unnormalized_widths, batch_size + ) + unnormalized_heights = _share_across_batch( + self.unnormalized_heights, batch_size + ) if self.tails is None: spline_fn = splines.quadratic_spline @@ -255,8 +286,8 @@ def _spline(self, inputs, inverse=False): else: spline_fn = splines.unconstrained_quadratic_spline spline_kwargs = { - 'tails': self.tails, - 'tail_bound': self.tail_bound + "tails": self.tails, + "tail_bound": self.tail_bound, } outputs, logabsdet = spline_fn( @@ -266,7 +297,7 @@ def _spline(self, inputs, inverse=False): inverse=inverse, min_bin_width=self.min_bin_width, min_bin_height=self.min_bin_height, - **spline_kwargs + **spline_kwargs, ) return outputs, sum_except_batch(logabsdet) @@ -277,14 +308,17 @@ def forward(self, inputs, context=None): def inverse(self, inputs, context=None): return self._spline(inputs, inverse=True) + class PiecewiseCubicCDF(transforms.Transform): - def __init__(self, - shape, - num_bins=10, - tails=None, - tail_bound=1., - min_bin_width=splines.cubic.DEFAULT_MIN_BIN_WIDTH, - min_bin_height=splines.cubic.DEFAULT_MIN_BIN_HEIGHT): + def __init__( + self, + shape, + num_bins=10, + tails=None, + tail_bound=1.0, + min_bin_width=splines.cubic.DEFAULT_MIN_BIN_WIDTH, + min_bin_height=splines.cubic.DEFAULT_MIN_BIN_HEIGHT, + ): super().__init__() self.min_bin_width = min_bin_width @@ -300,10 +334,18 @@ def __init__(self, def _spline(self, inputs, inverse=False): batch_size = inputs.shape[0] - unnormalized_widths = _share_across_batch(self.unnormalized_widths, batch_size) - unnormalized_heights = _share_across_batch(self.unnormalized_heights, batch_size) - unnorm_derivatives_left = _share_across_batch(self.unnorm_derivatives_left, batch_size) - unnorm_derivatives_right = _share_across_batch(self.unnorm_derivatives_right, batch_size) + unnormalized_widths = _share_across_batch( + self.unnormalized_widths, batch_size + ) + unnormalized_heights = _share_across_batch( + self.unnormalized_heights, batch_size + ) + unnorm_derivatives_left = _share_across_batch( + self.unnorm_derivatives_left, batch_size + ) + unnorm_derivatives_right = _share_across_batch( + self.unnorm_derivatives_right, batch_size + ) if self.tails is None: spline_fn = splines.cubic_spline @@ -311,8 +353,8 @@ def _spline(self, inputs, inverse=False): else: spline_fn = splines.unconstrained_cubic_spline spline_kwargs = { - 'tails': self.tails, - 'tail_bound': self.tail_bound + "tails": self.tails, + "tail_bound": self.tail_bound, } outputs, logabsdet = spline_fn( @@ -324,7 +366,7 @@ def _spline(self, inputs, inverse=False): inverse=inverse, min_bin_width=self.min_bin_width, min_bin_height=self.min_bin_height, - **spline_kwargs + **spline_kwargs, ) return outputs, sum_except_batch(logabsdet) @@ -335,16 +377,19 @@ def forward(self, inputs, context=None): def inverse(self, inputs, context=None): return self._spline(inputs, inverse=True) + class PiecewiseRationalQuadraticCDF(transforms.Transform): - def __init__(self, - shape, - num_bins=10, - tails=None, - tail_bound=1., - identity_init=False, - min_bin_width=splines.rational_quadratic.DEFAULT_MIN_BIN_WIDTH, - min_bin_height=splines.rational_quadratic.DEFAULT_MIN_BIN_HEIGHT, - min_derivative=splines.rational_quadratic.DEFAULT_MIN_DERIVATIVE): + def __init__( + self, + shape, + num_bins=10, + tails=None, + tail_bound=1.0, + identity_init=False, + min_bin_width=splines.rational_quadratic.DEFAULT_MIN_BIN_WIDTH, + min_bin_height=splines.rational_quadratic.DEFAULT_MIN_BIN_HEIGHT, + min_derivative=splines.rational_quadratic.DEFAULT_MIN_DERIVATIVE, + ): super().__init__() self.min_bin_width = min_bin_width @@ -355,26 +400,47 @@ def __init__(self, self.tails = tails if identity_init: - self.unnormalized_widths = nn.Parameter(torch.zeros(*shape, num_bins)) - self.unnormalized_heights = nn.Parameter(torch.zeros(*shape, num_bins)) + self.unnormalized_widths = nn.Parameter( + torch.zeros(*shape, num_bins) + ) + self.unnormalized_heights = nn.Parameter( + torch.zeros(*shape, num_bins) + ) constant = np.log(np.exp(1 - min_derivative) - 1) - num_derivatives = (num_bins - 1) if self.tails == 'linear' else (num_bins + 1) - self.unnormalized_derivatives = nn.Parameter(constant * torch.ones(*shape, - num_derivatives)) + num_derivatives = ( + (num_bins - 1) if self.tails == "linear" else (num_bins + 1) + ) + self.unnormalized_derivatives = nn.Parameter( + constant * torch.ones(*shape, num_derivatives) + ) else: - self.unnormalized_widths = nn.Parameter(torch.rand(*shape, num_bins)) - self.unnormalized_heights = nn.Parameter(torch.rand(*shape, num_bins)) + self.unnormalized_widths = nn.Parameter( + torch.rand(*shape, num_bins) + ) + self.unnormalized_heights = nn.Parameter( + torch.rand(*shape, num_bins) + ) - num_derivatives = (num_bins - 1) if self.tails == 'linear' else (num_bins + 1) - self.unnormalized_derivatives = nn.Parameter(torch.rand(*shape, num_derivatives)) + num_derivatives = ( + (num_bins - 1) if self.tails == "linear" else (num_bins + 1) + ) + self.unnormalized_derivatives = nn.Parameter( + torch.rand(*shape, num_derivatives) + ) def _spline(self, inputs, inverse=False): batch_size = inputs.shape[0] - unnormalized_widths=_share_across_batch(self.unnormalized_widths, batch_size) - unnormalized_heights=_share_across_batch(self.unnormalized_heights, batch_size) - unnormalized_derivatives=_share_across_batch(self.unnormalized_derivatives, batch_size) + unnormalized_widths = _share_across_batch( + self.unnormalized_widths, batch_size + ) + unnormalized_heights = _share_across_batch( + self.unnormalized_heights, batch_size + ) + unnormalized_derivatives = _share_across_batch( + self.unnormalized_derivatives, batch_size + ) if self.tails is None: spline_fn = splines.rational_quadratic_spline @@ -382,8 +448,8 @@ def _spline(self, inputs, inverse=False): else: spline_fn = splines.unconstrained_rational_quadratic_spline spline_kwargs = { - 'tails': self.tails, - 'tail_bound': self.tail_bound + "tails": self.tails, + "tail_bound": self.tail_bound, } outputs, logabsdet = spline_fn( @@ -395,7 +461,7 @@ def _spline(self, inputs, inverse=False): min_bin_width=self.min_bin_width, min_bin_height=self.min_bin_height, min_derivative=self.min_derivative, - **spline_kwargs + **spline_kwargs, ) return outputs, sum_except_batch(logabsdet) @@ -405,5 +471,3 @@ def forward(self, inputs, context=None): def inverse(self, inputs, context=None): return self._spline(inputs, inverse=True) - - diff --git a/phaseSpaceSampling/nde/transforms/nonlinearities_test.py b/phaseSpaceSampling/nde/transforms/nonlinearities_test.py index 01b9d53..08591fc 100644 --- a/phaseSpaceSampling/nde/transforms/nonlinearities_test.py +++ b/phaseSpaceSampling/nde/transforms/nonlinearities_test.py @@ -1,10 +1,13 @@ """Tests for the invertible non-linearities.""" -import torch import unittest +import torch + import phaseSpaceSampling.nde.transforms.splines.quadratic -from phaseSpaceSampling.nde.transforms import base, nonlinearities as nl, standard +from phaseSpaceSampling.nde.transforms import base +from phaseSpaceSampling.nde.transforms import nonlinearities as nl +from phaseSpaceSampling.nde.transforms import standard from phaseSpaceSampling.nde.transforms.transform_test import TransformTest @@ -22,17 +25,21 @@ class TestPiecewiseCDF(TransformTest): def setUp(self): self.shape = [2, 3, 4] self.batch_size = 10 - self.transforms = [nl.PiecewiseLinearCDF(self.shape), - nl.PiecewiseQuadraticCDF(self.shape), - nl.PiecewiseCubicCDF(self.shape), - nl.PiecewiseRationalQuadraticCDF(self.shape)] + self.transforms = [ + nl.PiecewiseLinearCDF(self.shape), + nl.PiecewiseQuadraticCDF(self.shape), + nl.PiecewiseCubicCDF(self.shape), + nl.PiecewiseRationalQuadraticCDF(self.shape), + ] def test_raises_domain_exception(self): for transform in self.transforms: with self.subTest(transform=transform): for value in [-1.0, -0.1, 1.1, 2.0]: with self.assertRaises(base.InputOutsideDomain): - inputs = torch.full([self.batch_size] + self.shape, value) + inputs = torch.full( + [self.batch_size] + self.shape, value + ) transform.forward(inputs) def test_zeros_to_zeros(self): @@ -58,14 +65,17 @@ def test_forward_inverse_are_consistent(self): self.eps = 1e-4 self.assert_forward_inverse_are_consistent(transform, inputs) + class TestUnconstrainedPiecewiseCDF(TransformTest): def test_forward_inverse_are_consistent(self): - shape = [2,3,4] + shape = [2, 3, 4] batch_size = 10 - transforms = [nl.PiecewiseLinearCDF(shape, tails='linear'), - nl.PiecewiseQuadraticCDF(shape, tails='linear'), - nl.PiecewiseCubicCDF(shape, tails='linear'), - nl.PiecewiseRationalQuadraticCDF(shape, tails='linear')] + transforms = [ + nl.PiecewiseLinearCDF(shape, tails="linear"), + nl.PiecewiseQuadraticCDF(shape, tails="linear"), + nl.PiecewiseCubicCDF(shape, tails="linear"), + nl.PiecewiseRationalQuadraticCDF(shape, tails="linear"), + ] for transform in transforms: with self.subTest(transform=transform): @@ -78,8 +88,12 @@ class LogitTest(TransformTest): def test_forward_zero_and_one(self): batch_size = 10 shape = [5, 10, 15] - inputs = torch.cat([torch.zeros(batch_size // 2, *shape), - torch.ones(batch_size // 2, *shape)]) + inputs = torch.cat( + [ + torch.zeros(batch_size // 2, *shape), + torch.ones(batch_size // 2, *shape), + ] + ) transform = nl.Logit() outputs, logabsdet = transform(inputs) @@ -89,7 +103,6 @@ def test_forward_zero_and_one(self): class NonlinearitiesTest(TransformTest): - def test_forward(self): batch_size = 10 shape = [5, 10, 15] @@ -100,7 +113,9 @@ def test_forward(self): nl.LeakyReLU(), nl.Sigmoid(), nl.Logit(), - nl.CompositeCDFTransform(nl.Sigmoid(), standard.IdentityTransform()) + nl.CompositeCDFTransform( + nl.Sigmoid(), standard.IdentityTransform() + ), ] for transform in transforms: with self.subTest(transform=transform): @@ -118,7 +133,9 @@ def test_inverse(self): nl.LeakyReLU(), nl.Sigmoid(), nl.Logit(), - nl.CompositeCDFTransform(nl.Sigmoid(), standard.IdentityTransform()) + nl.CompositeCDFTransform( + nl.Sigmoid(), standard.IdentityTransform() + ), ] for transform in transforms: with self.subTest(transform=transform): @@ -136,7 +153,9 @@ def test_forward_inverse_are_consistent(self): nl.LeakyReLU(), nl.Sigmoid(), nl.Logit(), - nl.CompositeCDFTransform(nl.Sigmoid(), standard.IdentityTransform()) + nl.CompositeCDFTransform( + nl.Sigmoid(), standard.IdentityTransform() + ), ] self.eps = 1e-3 for transform in transforms: @@ -144,5 +163,5 @@ def test_forward_inverse_are_consistent(self): self.assert_forward_inverse_are_consistent(transform, inputs) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/normalization.py b/phaseSpaceSampling/nde/transforms/normalization.py index 3119ce4..579bf49 100644 --- a/phaseSpaceSampling/nde/transforms/normalization.py +++ b/phaseSpaceSampling/nde/transforms/normalization.py @@ -1,12 +1,10 @@ """Implementation of normalization-based transforms.""" import numpy as np import torch - from torch import nn from torch.nn import functional as F from phaseSpaceSampling.nde import transforms - from phaseSpaceSampling.utils.typechecks import is_positive_int # class BatchNorm(transforms.Transform): @@ -81,17 +79,19 @@ class BatchNorm(transforms.Transform): def __init__(self, features, eps=1e-5, momentum=0.1, affine=True): if not is_positive_int(features): - raise TypeError('Number of features must be a positive integer.') + raise TypeError("Number of features must be a positive integer.") super().__init__() self.momentum = momentum self.eps = eps constant = np.log(np.exp(1 - eps) - 1) - self.unconstrained_weight = nn.Parameter(constant * torch.ones(features)) + self.unconstrained_weight = nn.Parameter( + constant * torch.ones(features) + ) self.bias = nn.Parameter(torch.zeros(features)) - self.register_buffer('running_mean', torch.zeros(features)) - self.register_buffer('running_var', torch.zeros(features)) + self.register_buffer("running_mean", torch.zeros(features)) + self.register_buffer("running_var", torch.zeros(features)) @property def weight(self): @@ -99,16 +99,25 @@ def weight(self): def forward(self, inputs, context=None): if inputs.dim() != 2: - raise ValueError('Expected 2-dim inputs, got inputs of shape: {}'.format(inputs.shape)) + raise ValueError( + "Expected 2-dim inputs, got inputs of shape: {}".format( + inputs.shape + ) + ) if self.training: mean, var = inputs.mean(0), inputs.var(0) - self.running_mean.mul_(1 - self.momentum).add_(mean * self.momentum) + self.running_mean.mul_(1 - self.momentum).add_( + mean * self.momentum + ) self.running_var.mul_(1 - self.momentum).add_(var * self.momentum) else: mean, var = self.running_mean, self.running_var - outputs = self.weight * ((inputs - mean) / torch.sqrt((var + self.eps))) + self.bias + outputs = ( + self.weight * ((inputs - mean) / torch.sqrt((var + self.eps))) + + self.bias + ) logabsdet_ = torch.log(self.weight) - 0.5 * torch.log(var + self.eps) logabsdet = torch.sum(logabsdet_) * torch.ones(inputs.shape[0]) @@ -118,13 +127,24 @@ def forward(self, inputs, context=None): def inverse(self, inputs, context=None): if self.training: raise transforms.InverseNotAvailable( - 'Batch norm inverse is only available in eval mode, not in training mode.') + "Batch norm inverse is only available in eval mode, not in training mode." + ) if inputs.dim() != 2: - raise ValueError('Expected 2-dim inputs, got inputs of shape: {}'.format(inputs.shape)) - - outputs = torch.sqrt(self.running_var + self.eps) * ((inputs - self.bias) / self.weight) + self.running_mean - - logabsdet_ = - torch.log(self.weight) + 0.5 * torch.log(self.running_var + self.eps) + raise ValueError( + "Expected 2-dim inputs, got inputs of shape: {}".format( + inputs.shape + ) + ) + + outputs = ( + torch.sqrt(self.running_var + self.eps) + * ((inputs - self.bias) / self.weight) + + self.running_mean + ) + + logabsdet_ = -torch.log(self.weight) + 0.5 * torch.log( + self.running_var + self.eps + ) logabsdet = torch.sum(logabsdet_) * torch.ones(inputs.shape[0]) return outputs, logabsdet @@ -140,7 +160,7 @@ def __init__(self, features): > D. Kingma et. al., Glow: Generative flow with invertible 1x1 convolutions, NeurIPS 2018. """ if not is_positive_int(features): - raise TypeError('Number of features must be a positive integer.') + raise TypeError("Number of features must be a positive integer.") super().__init__() self.initialized = False @@ -159,7 +179,7 @@ def _broadcastable_scale_shift(self, inputs): def forward(self, inputs, context=None): if inputs.dim() not in [2, 4]: - raise ValueError('Expecting inputs to be a 2D or a 4D tensor.') + raise ValueError("Expecting inputs to be a 2D or a 4D tensor.") if self.training and not self.initialized: self._initialize(inputs) @@ -169,32 +189,36 @@ def forward(self, inputs, context=None): if inputs.dim() == 4: batch_size, _, h, w = inputs.shape - logabsdet = h * w * torch.sum(self.log_scale) * torch.ones(batch_size) + logabsdet = ( + h * w * torch.sum(self.log_scale) * torch.ones(batch_size) + ) else: - batch_size,_ = inputs.shape + batch_size, _ = inputs.shape logabsdet = torch.sum(self.log_scale) * torch.ones(batch_size) return outputs, logabsdet def inverse(self, inputs, context=None): if inputs.dim() not in [2, 4]: - raise ValueError('Expecting inputs to be a 2D or a 4D tensor.') + raise ValueError("Expecting inputs to be a 2D or a 4D tensor.") scale, shift = self._broadcastable_scale_shift(inputs) outputs = (inputs - shift) / scale if inputs.dim() == 4: batch_size, _, h, w = inputs.shape - logabsdet = - h * w * torch.sum(self.log_scale) * torch.ones(batch_size) + logabsdet = ( + -h * w * torch.sum(self.log_scale) * torch.ones(batch_size) + ) else: batch_size, _ = inputs.shape - logabsdet = - torch.sum(self.log_scale) * torch.ones(batch_size) + logabsdet = -torch.sum(self.log_scale) * torch.ones(batch_size) return outputs, logabsdet def _initialize(self, inputs): """Data-dependent initialization, s.t. post-actnorm activations have zero mean and unit - variance. """ + variance.""" if inputs.dim() == 4: num_channels = inputs.shape[1] inputs = inputs.permute(0, 2, 3, 1).reshape(-1, num_channels) @@ -206,6 +230,3 @@ def _initialize(self, inputs): self.shift.data = -mu self.initialized = True - - - diff --git a/phaseSpaceSampling/nde/transforms/normalization_test.py b/phaseSpaceSampling/nde/transforms/normalization_test.py index 0b33d0b..0b4a763 100644 --- a/phaseSpaceSampling/nde/transforms/normalization_test.py +++ b/phaseSpaceSampling/nde/transforms/normalization_test.py @@ -1,15 +1,15 @@ """Tests for the normalization-based transforms.""" -import torch import unittest +import torch + from phaseSpaceSampling.nde.transforms import base from phaseSpaceSampling.nde.transforms import normalization as norm from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class BatchNormTest(TransformTest): - def test_forward(self): features = 100 batch_size = 50 @@ -19,7 +19,9 @@ def test_forward(self): for affine in [True, True]: with self.subTest(affine=affine): inputs = torch.randn(batch_size, features) - transform = norm.BatchNorm(features=features, affine=affine, eps=bn_eps) + transform = norm.BatchNorm( + features=features, affine=affine, eps=bn_eps + ) outputs, logabsdet = transform(inputs) self.assert_tensor_is_good(outputs, [batch_size, features]) @@ -27,7 +29,9 @@ def test_forward(self): mean, var = inputs.mean(0), inputs.var(0) outputs_ref = (inputs - mean) / torch.sqrt(var + bn_eps) - logabsdet_ref = torch.sum(torch.log(1.0 / torch.sqrt(var + bn_eps))) + logabsdet_ref = torch.sum( + torch.log(1.0 / torch.sqrt(var + bn_eps)) + ) logabsdet_ref = torch.full([batch_size], logabsdet_ref.item()) if affine: outputs_ref *= transform.weight @@ -47,7 +51,9 @@ def test_forward(self): mean = transform.running_mean var = transform.running_var outputs_ref = (inputs - mean) / torch.sqrt(var + bn_eps) - logabsdet_ref = torch.sum(torch.log(1.0 / torch.sqrt(var + bn_eps))) + logabsdet_ref = torch.sum( + torch.log(1.0 / torch.sqrt(var + bn_eps)) + ) logabsdet_ref = torch.full([batch_size], logabsdet_ref.item()) if affine: outputs_ref *= transform.weight @@ -89,11 +95,9 @@ def test_forward_inverse_are_consistent(self): class ActNormTest(TransformTest): - def test_forward(self): batch_size = 50 - for shape in [(100,), - (32,8,8)]: + for shape in [(100,), (32, 8, 8)]: with self.subTest(shape=shape): inputs = torch.randn(batch_size, *shape) transform = norm.ActNorm(shape[0]) @@ -104,8 +108,7 @@ def test_forward(self): def test_inverse(self): batch_size = 50 - for shape in [(100,), - (32,8,8)]: + for shape in [(100,), (32, 8, 8)]: with self.subTest(shape=shape): inputs = torch.randn(batch_size, *shape) transform = norm.ActNorm(shape[0]) @@ -116,15 +119,14 @@ def test_inverse(self): def test_forward_inverse_are_consistent(self): batch_size = 50 - for shape in [(100,), - (32,8,8)]: + for shape in [(100,), (32, 8, 8)]: with self.subTest(shape=shape): inputs = torch.randn(batch_size, *shape) transform = norm.ActNorm(shape[0]) - transform.forward(inputs) # One forward pass to initialize + transform.forward(inputs) # One forward pass to initialize self.eps = 1e-6 self.assert_forward_inverse_are_consistent(transform, inputs) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/orthogonal.py b/phaseSpaceSampling/nde/transforms/orthogonal.py index c994b7f..9d8838e 100644 --- a/phaseSpaceSampling/nde/transforms/orthogonal.py +++ b/phaseSpaceSampling/nde/transforms/orthogonal.py @@ -1,12 +1,12 @@ """Implementations of orthogonal transforms.""" import torch - from torch import nn from phaseSpaceSampling.nde import transforms from phaseSpaceSampling.utils.typechecks import is_positive_int + class HouseholderSequence(transforms.Transform): """A sequence of Householder transforms. @@ -24,9 +24,9 @@ def __init__(self, features, num_transforms): TypeError: if arguments are not the right type. """ if not is_positive_int(features): - raise TypeError('Number of features must be a positive integer.') + raise TypeError("Number of features must be a positive integer.") if not is_positive_int(num_transforms): - raise TypeError('Number of transforms must be a positive integer.') + raise TypeError("Number of transforms must be a positive integer.") super().__init__() self.features = features @@ -52,11 +52,13 @@ def _apply_transforms(inputs, q_vectors): - A Tensor of shape [N, D], the outputs. - A Tensor of shape [N], the log absolute determinants of the total transform. """ - squared_norms = torch.sum(q_vectors ** 2, dim=-1) + squared_norms = torch.sum(q_vectors**2, dim=-1) outputs = inputs for q_vector, squared_norm in zip(q_vectors, squared_norms): temp = outputs @ q_vector # Inner product. - temp = torch.ger(temp, (2.0 / squared_norm) * q_vector) # Outer product. + temp = torch.ger( + temp, (2.0 / squared_norm) * q_vector + ) # Outer product. outputs = outputs - temp batch_size = inputs.shape[0] logabsdet = torch.zeros(batch_size) diff --git a/phaseSpaceSampling/nde/transforms/orthogonal_test.py b/phaseSpaceSampling/nde/transforms/orthogonal_test.py index 71256ca..3f2f828 100644 --- a/phaseSpaceSampling/nde/transforms/orthogonal_test.py +++ b/phaseSpaceSampling/nde/transforms/orthogonal_test.py @@ -1,14 +1,15 @@ """Tests for the orthogonal transforms.""" -import torch import unittest +import torch + +import phaseSpaceSampling as pss from phaseSpaceSampling.nde.transforms import orthogonal from phaseSpaceSampling.nde.transforms.transform_test import TransformTest -import phaseSpaceSampling as pss -class HouseholderSequenceTest(TransformTest): +class HouseholderSequenceTest(TransformTest): def test_forward(self): features = 100 batch_size = 50 @@ -16,7 +17,8 @@ def test_forward(self): for num_transforms in [1, 2, 11, 12]: with self.subTest(num_transforms=num_transforms): transform = orthogonal.HouseholderSequence( - features=features, num_transforms=num_transforms) + features=features, num_transforms=num_transforms + ) matrix = transform.matrix() inputs = torch.randn(batch_size, features) outputs, logabsdet = transform(inputs) @@ -24,7 +26,11 @@ def test_forward(self): self.assert_tensor_is_good(logabsdet, [batch_size]) self.eps = 1e-5 self.assertEqual(outputs, inputs @ matrix.t()) - self.assertEqual(logabsdet, pss.utils.torchutils.logabsdet(matrix) * torch.ones(batch_size)) + self.assertEqual( + logabsdet, + pss.utils.torchutils.logabsdet(matrix) + * torch.ones(batch_size), + ) def test_inverse(self): features = 100 @@ -33,7 +39,8 @@ def test_inverse(self): for num_transforms in [1, 2, 11, 12]: with self.subTest(num_transforms=num_transforms): transform = orthogonal.HouseholderSequence( - features=features, num_transforms=num_transforms) + features=features, num_transforms=num_transforms + ) matrix = transform.matrix() inputs = torch.randn(batch_size, features) outputs, logabsdet = transform.inverse(inputs) @@ -41,7 +48,11 @@ def test_inverse(self): self.assert_tensor_is_good(logabsdet, [batch_size]) self.eps = 1e-5 self.assertEqual(outputs, inputs @ matrix) - self.assertEqual(logabsdet, pss.utils.torchutils.logabsdet(matrix) * torch.ones(batch_size)) + self.assertEqual( + logabsdet, + pss.utils.torchutils.logabsdet(matrix) + * torch.ones(batch_size), + ) def test_matrix(self): features = 100 @@ -49,14 +60,21 @@ def test_matrix(self): for num_transforms in [1, 2, 11, 12]: with self.subTest(num_transforms=num_transforms): transform = orthogonal.HouseholderSequence( - features=features, num_transforms=num_transforms) + features=features, num_transforms=num_transforms + ) matrix = transform.matrix() self.assert_tensor_is_good(matrix, [features, features]) self.eps = 1e-5 - self.assertEqual(matrix @ matrix.t(), torch.eye(features, features)) - self.assertEqual(matrix.t() @ matrix, torch.eye(features, features)) + self.assertEqual( + matrix @ matrix.t(), torch.eye(features, features) + ) + self.assertEqual( + matrix.t() @ matrix, torch.eye(features, features) + ) self.assertEqual(matrix.t(), torch.inverse(matrix)) - det_ref = torch.tensor(1.0 if num_transforms % 2 == 0 else -1.0) + det_ref = torch.tensor( + 1.0 if num_transforms % 2 == 0 else -1.0 + ) self.assertEqual(matrix.det(), det_ref) def test_forward_inverse_are_consistent(self): @@ -65,7 +83,8 @@ def test_forward_inverse_are_consistent(self): inputs = torch.randn(batch_size, features) transforms = [ orthogonal.HouseholderSequence( - features=features, num_transforms=num_transforms) + features=features, num_transforms=num_transforms + ) for num_transforms in [1, 2, 11, 12] ] self.eps = 1e-5 @@ -74,5 +93,5 @@ def test_forward_inverse_are_consistent(self): self.assert_forward_inverse_are_consistent(transform, inputs) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/permutations.py b/phaseSpaceSampling/nde/transforms/permutations.py index cfd85ac..c321550 100644 --- a/phaseSpaceSampling/nde/transforms/permutations.py +++ b/phaseSpaceSampling/nde/transforms/permutations.py @@ -5,18 +5,19 @@ from phaseSpaceSampling.nde import transforms from phaseSpaceSampling.utils.typechecks import is_positive_int + class Permutation(transforms.Transform): """Permutes inputs on a given dimension using a given permutation.""" def __init__(self, permutation, dim=1): if permutation.ndimension() != 1: - raise ValueError('Permutation must be a 1D tensor.') + raise ValueError("Permutation must be a 1D tensor.") if not is_positive_int(dim): - raise ValueError('dim must be a positive integer.') + raise ValueError("dim must be a positive integer.") super().__init__() self._dim = dim - self.register_buffer('_permutation', permutation) + self.register_buffer("_permutation", permutation) @property def _inverse_permutation(self): @@ -27,8 +28,11 @@ def _permute(inputs, permutation, dim): if dim >= inputs.ndimension(): raise ValueError("No dimension {} in inputs.".format(dim)) if inputs.shape[dim] != len(permutation): - raise ValueError("Dimension {} in inputs must be of size {}." - .format(dim, len(permutation))) + raise ValueError( + "Dimension {} in inputs must be of size {}.".format( + dim, len(permutation) + ) + ) batch_size = inputs.shape[0] outputs = torch.index_select(inputs, dim, permutation) logabsdet = torch.zeros(batch_size) @@ -46,7 +50,7 @@ class RandomPermutation(Permutation): def __init__(self, features, dim=1): if not is_positive_int(features): - raise ValueError('Number of features must be a positive integer.') + raise ValueError("Number of features must be a positive integer.") super().__init__(torch.randperm(features), dim) @@ -55,5 +59,5 @@ class ReversePermutation(Permutation): def __init__(self, features, dim=1): if not is_positive_int(features): - raise ValueError('Number of features must be a positive integer.') + raise ValueError("Number of features must be a positive integer.") super().__init__(torch.arange(features - 1, -1, -1), dim) diff --git a/phaseSpaceSampling/nde/transforms/permutations_test.py b/phaseSpaceSampling/nde/transforms/permutations_test.py index f669ba3..7e0f946 100644 --- a/phaseSpaceSampling/nde/transforms/permutations_test.py +++ b/phaseSpaceSampling/nde/transforms/permutations_test.py @@ -1,14 +1,14 @@ """Tests for permutations.""" -import torch import unittest +import torch + from phaseSpaceSampling.nde.transforms import permutations from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class PermutationTest(TransformTest): - def test_forward(self): batch_size = 10 features = 100 @@ -48,5 +48,5 @@ def test_forward_inverse_are_consistent(self): self.assert_forward_inverse_are_consistent(transform, inputs) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/qr.py b/phaseSpaceSampling/nde/transforms/qr.py index 3883c2e..2713e9a 100644 --- a/phaseSpaceSampling/nde/transforms/qr.py +++ b/phaseSpaceSampling/nde/transforms/qr.py @@ -1,8 +1,8 @@ import numpy as np import torch - from torch import nn -from torch.nn import functional as F, init +from torch.nn import functional as F +from torch.nn import init from phaseSpaceSampling.nde import transforms from phaseSpaceSampling.nde.transforms.linear import Linear @@ -23,7 +23,8 @@ def __init__(self, features, num_householder, using_cache=False): # Parameterization for Q self.orthogonal = transforms.HouseholderSequence( - features=features, num_transforms=num_householder) + features=features, num_transforms=num_householder + ) self._initialize() @@ -35,8 +36,12 @@ def _initialize(self): def _create_upper(self): upper = torch.zeros(self.features, self.features) - upper[self.upper_indices[0], self.upper_indices[1]] = self.upper_entries - upper[self.diag_indices[0], self.diag_indices[1]] = torch.exp(self.log_upper_diag) + upper[ + self.upper_indices[0], self.upper_indices[1] + ] = self.upper_entries + upper[self.diag_indices[0], self.diag_indices[1]] = torch.exp( + self.log_upper_diag + ) return upper def forward_no_cache(self, inputs): @@ -51,7 +56,9 @@ def forward_no_cache(self, inputs): upper = self._create_upper() outputs = F.linear(inputs, upper) - outputs, _ = self.orthogonal(outputs) # Ignore logabsdet as we know it's zero. + outputs, _ = self.orthogonal( + outputs + ) # Ignore logabsdet as we know it's zero. outputs += self.bias logabsdet = self.logabsdet() * torch.ones(outputs.shape[0]) @@ -69,7 +76,9 @@ def inverse_no_cache(self, inputs): """ upper = self._create_upper() outputs = inputs - self.bias - outputs, _ = self.orthogonal.inverse(outputs) # Ignore logabsdet since we know it's zero. + outputs, _ = self.orthogonal.inverse( + outputs + ) # Ignore logabsdet since we know it's zero. outputs, _ = torch.trtrs(outputs.t(), upper, upper=True) outputs = outputs.t() logabsdet = -self.logabsdet() diff --git a/phaseSpaceSampling/nde/transforms/qr_test.py b/phaseSpaceSampling/nde/transforms/qr_test.py index 48eb6ba..dbdc9eb 100644 --- a/phaseSpaceSampling/nde/transforms/qr_test.py +++ b/phaseSpaceSampling/nde/transforms/qr_test.py @@ -1,12 +1,13 @@ -import torch import unittest +import torch + +import phaseSpaceSampling as pss from phaseSpaceSampling.nde.transforms import qr from phaseSpaceSampling.nde.transforms.transform_test import TransformTest -import phaseSpaceSampling as pss -class QRLinearTest(TransformTest): +class QRLinearTest(TransformTest): def setUp(self): self.features = 3 self.transform = qr.QRLinear(features=self.features, num_householder=4) @@ -24,7 +25,9 @@ def test_forward_no_cache(self): inputs = torch.randn(batch_size, self.features) outputs, logabsdet = self.transform.forward_no_cache(inputs) - outputs_ref = torch.matmul(inputs, self.weight.t()) + self.transform.bias + outputs_ref = ( + torch.matmul(inputs, self.weight.t()) + self.transform.bias + ) logabsdet_ref = torch.full([batch_size], self.logabsdet.item()) self.assert_tensor_is_good(outputs, [batch_size, self.features]) @@ -54,7 +57,9 @@ def test_weight(self): def test_weight_inverse(self): weight_inverse = self.transform.weight_inverse() - self.assert_tensor_is_good(weight_inverse, [self.features, self.features]) + self.assert_tensor_is_good( + weight_inverse, [self.features, self.features] + ) self.assertEqual(weight_inverse, self.weight_inverse) def test_logabsdet(self): @@ -68,5 +73,5 @@ def test_forward_inverse_are_consistent(self): self.assert_forward_inverse_are_consistent(self.transform, inputs) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/reshape.py b/phaseSpaceSampling/nde/transforms/reshape.py index 1089b08..62ac806 100644 --- a/phaseSpaceSampling/nde/transforms/reshape.py +++ b/phaseSpaceSampling/nde/transforms/reshape.py @@ -3,6 +3,7 @@ from phaseSpaceSampling.nde import transforms from phaseSpaceSampling.utils.typechecks import is_int + class SqueezeTransform(transforms.Transform): """A transformation defined for image data that trades spatial dimensions for channel dimensions, i.e. "squeezes" the inputs along the channel dimensions. @@ -13,51 +14,71 @@ class SqueezeTransform(transforms.Transform): Reference: > L. Dinh et al., Density estimation using Real NVP, ICLR 2017. """ + def __init__(self, factor=2): super(SqueezeTransform, self).__init__() if not is_int(factor) or factor <= 1: - raise ValueError('Factor must be an integer > 1.') + raise ValueError("Factor must be an integer > 1.") self.factor = factor def get_output_shape(self, c, h, w): - return (c * self.factor * self.factor, - h // self.factor, - w // self.factor) + return ( + c * self.factor * self.factor, + h // self.factor, + w // self.factor, + ) def forward(self, inputs, context=None): if inputs.dim() != 4: - raise ValueError('Expecting inputs with 4 dimensions') + raise ValueError("Expecting inputs with 4 dimensions") batch_size, c, h, w = inputs.size() if h % self.factor != 0 or w % self.factor != 0: - raise ValueError('Input image size not compatible with the factor.') - - inputs = inputs.view(batch_size, c, h // self.factor, self.factor, w // self.factor, - self.factor) + raise ValueError( + "Input image size not compatible with the factor." + ) + + inputs = inputs.view( + batch_size, + c, + h // self.factor, + self.factor, + w // self.factor, + self.factor, + ) inputs = inputs.permute(0, 1, 3, 5, 2, 4).contiguous() - inputs = inputs.view(batch_size, c * self.factor * self.factor, h // self.factor, - w // self.factor) + inputs = inputs.view( + batch_size, + c * self.factor * self.factor, + h // self.factor, + w // self.factor, + ) return inputs, torch.zeros(batch_size) def inverse(self, inputs, context=None): if inputs.dim() != 4: - raise ValueError('Expecting inputs with 4 dimensions') + raise ValueError("Expecting inputs with 4 dimensions") batch_size, c, h, w = inputs.size() if c < 4 or c % 4 != 0: - raise ValueError('Invalid number of channel dimensions.') + raise ValueError("Invalid number of channel dimensions.") - inputs = inputs.view(batch_size, c // self.factor ** 2, self.factor, self.factor, h, w) + inputs = inputs.view( + batch_size, c // self.factor**2, self.factor, self.factor, h, w + ) inputs = inputs.permute(0, 1, 4, 2, 5, 3).contiguous() - inputs = inputs.view(batch_size, c // self.factor ** 2, h * self.factor, w * self.factor) + inputs = inputs.view( + batch_size, c // self.factor**2, h * self.factor, w * self.factor + ) return inputs, torch.zeros(batch_size) + class ReshapeTransform(transforms.Transform): def __init__(self, input_shape, output_shape): super().__init__() @@ -66,12 +87,22 @@ def __init__(self, input_shape, output_shape): def forward(self, inputs, context=None): if tuple(inputs.shape[1:]) != self.input_shape: - raise RuntimeError('Unexpected inputs shape ({}, but expecting {})' - .format(tuple(inputs.shape[1:]), self.input_shape)) - return inputs.reshape(-1, *self.output_shape), torch.zeros(inputs.shape[0]) + raise RuntimeError( + "Unexpected inputs shape ({}, but expecting {})".format( + tuple(inputs.shape[1:]), self.input_shape + ) + ) + return inputs.reshape(-1, *self.output_shape), torch.zeros( + inputs.shape[0] + ) def inverse(self, inputs, context=None): if tuple(inputs.shape[1:]) != self.output_shape: - raise RuntimeError('Unexpected inputs shape ({}, but expecting {})' - .format(tuple(inputs.shape[1:]), self.output_shape)) - return inputs.reshape(-1, *self.input_shape), torch.zeros(inputs.shape[0]) + raise RuntimeError( + "Unexpected inputs shape ({}, but expecting {})".format( + tuple(inputs.shape[1:]), self.output_shape + ) + ) + return inputs.reshape(-1, *self.input_shape), torch.zeros( + inputs.shape[0] + ) diff --git a/phaseSpaceSampling/nde/transforms/reshape_test.py b/phaseSpaceSampling/nde/transforms/reshape_test.py index 57b6a2d..543c4c6 100644 --- a/phaseSpaceSampling/nde/transforms/reshape_test.py +++ b/phaseSpaceSampling/nde/transforms/reshape_test.py @@ -1,8 +1,9 @@ -import torch import unittest -from phaseSpaceSampling.nde.transforms.transform_test import TransformTest +import torch + from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class SqueezeTransformTest(TransformTest): @@ -11,13 +12,14 @@ def setUp(self): def test_forward(self): batch_size = 10 - for shape in [[32, 4, 4], - [16, 8, 8]]: + for shape in [[32, 4, 4], [16, 8, 8]]: with self.subTest(shape=shape): c, h, w = shape inputs = torch.randn(batch_size, c, h, w) outputs, logabsdet = self.transform(inputs) - self.assert_tensor_is_good(outputs, [batch_size, c * 4, h // 2, w // 2]) + self.assert_tensor_is_good( + outputs, [batch_size, c * 4, h // 2, w // 2] + ) self.assert_tensor_is_good(logabsdet, [batch_size]) self.assertEqual(logabsdet, torch.zeros(batch_size)) @@ -26,22 +28,18 @@ def test_forward_values(self): outputs, _ = self.transform(inputs) def assert_channel_equal(channel, values): - self.assertEqual(outputs[0, channel, ...], torch.LongTensor(values)) + self.assertEqual( + outputs[0, channel, ...], torch.LongTensor(values) + ) - assert_channel_equal(0, [[1,3], - [9,11]]) - assert_channel_equal(1, [[2,4], - [10,12]]) - assert_channel_equal(2, [[5,7], - [13,15]]) - assert_channel_equal(3, [[6,8], - [14,16]]) + assert_channel_equal(0, [[1, 3], [9, 11]]) + assert_channel_equal(1, [[2, 4], [10, 12]]) + assert_channel_equal(2, [[5, 7], [13, 15]]) + assert_channel_equal(3, [[6, 8], [14, 16]]) def test_forward_wrong_shape(self): batch_size = 10 - for shape in [[32, 3, 3], - [32, 5, 5], - [32, 4]]: + for shape in [[32, 3, 3], [32, 5, 5], [32, 4]]: with self.subTest(shape=shape): inputs = torch.randn(batch_size, *shape) with self.assertRaises(ValueError): @@ -49,23 +47,22 @@ def test_forward_wrong_shape(self): def test_forward_inverse_are_consistent(self): batch_size = 10 - for shape in [[32, 4, 4], - [16, 8, 8]]: + for shape in [[32, 4, 4], [16, 8, 8]]: with self.subTest(shape=shape): c, h, w = shape inputs = torch.randn(batch_size, c, h, w) - self.assert_forward_inverse_are_consistent(self.transform, inputs) + self.assert_forward_inverse_are_consistent( + self.transform, inputs + ) def test_inverse_wrong_shape(self): batch_size = 10 - for shape in [[3, 4, 4], - [33, 4, 4], - [32, 4]]: + for shape in [[3, 4, 4], [33, 4, 4], [32, 4]]: with self.subTest(shape=shape): inputs = torch.randn(batch_size, *shape) with self.assertRaises(ValueError): self.transform.inverse(inputs) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/splines/__init__.py b/phaseSpaceSampling/nde/transforms/splines/__init__.py index 91605d8..a1092dd 100644 --- a/phaseSpaceSampling/nde/transforms/splines/__init__.py +++ b/phaseSpaceSampling/nde/transforms/splines/__init__.py @@ -1,20 +1,7 @@ -from .linear import ( - linear_spline, - unconstrained_linear_spline -) - -from .quadratic import ( - quadratic_spline, - unconstrained_quadratic_spline -) - -from .cubic import ( - cubic_spline, - unconstrained_cubic_spline -) - +from .cubic import cubic_spline, unconstrained_cubic_spline +from .linear import linear_spline, unconstrained_linear_spline +from .quadratic import quadratic_spline, unconstrained_quadratic_spline from .rational_quadratic import ( rational_quadratic_spline, - unconstrained_rational_quadratic_spline + unconstrained_rational_quadratic_spline, ) - diff --git a/phaseSpaceSampling/nde/transforms/splines/cubic.py b/phaseSpaceSampling/nde/transforms/splines/cubic.py index bc98b24..ba61bf5 100644 --- a/phaseSpaceSampling/nde/transforms/splines/cubic.py +++ b/phaseSpaceSampling/nde/transforms/splines/cubic.py @@ -4,66 +4,83 @@ from torch.nn import functional as F from phaseSpaceSampling.nde import transforms - -from phaseSpaceSampling.utils.torchutils import searchsorted, cbrt +from phaseSpaceSampling.utils.torchutils import cbrt, searchsorted DEFAULT_MIN_BIN_WIDTH = 1e-3 DEFAULT_MIN_BIN_HEIGHT = 1e-3 DEFAULT_EPS = 1e-5 DEFAULT_QUADRATIC_THRESHOLD = 1e-3 -def unconstrained_cubic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnorm_derivatives_left, - unnorm_derivatives_right, - inverse=False, - tail_bound=1., - tails='linear', - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - eps = DEFAULT_EPS, - quadratic_threshold = DEFAULT_QUADRATIC_THRESHOLD): +def unconstrained_cubic_spline( + inputs, + unnormalized_widths, + unnormalized_heights, + unnorm_derivatives_left, + unnorm_derivatives_right, + inverse=False, + tail_bound=1.0, + tails="linear", + min_bin_width=DEFAULT_MIN_BIN_WIDTH, + min_bin_height=DEFAULT_MIN_BIN_HEIGHT, + eps=DEFAULT_EPS, + quadratic_threshold=DEFAULT_QUADRATIC_THRESHOLD, +): inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) outside_interval_mask = ~inside_interval_mask outputs = torch.zeros_like(inputs) logabsdet = torch.zeros_like(inputs) - if tails == 'linear': + if tails == "linear": outputs[outside_interval_mask] = inputs[outside_interval_mask] logabsdet[outside_interval_mask] = 0 else: - raise RuntimeError('{} tails are not implemented.'.format(tails)) + raise RuntimeError("{} tails are not implemented.".format(tails)) - outputs[inside_interval_mask], logabsdet[inside_interval_mask] = cubic_spline( + ( + outputs[inside_interval_mask], + logabsdet[inside_interval_mask], + ) = cubic_spline( inputs=inputs[inside_interval_mask], unnormalized_widths=unnormalized_widths[inside_interval_mask, :], unnormalized_heights=unnormalized_heights[inside_interval_mask, :], - unnorm_derivatives_left=unnorm_derivatives_left[inside_interval_mask, :], - unnorm_derivatives_right=unnorm_derivatives_right[inside_interval_mask, :], + unnorm_derivatives_left=unnorm_derivatives_left[ + inside_interval_mask, : + ], + unnorm_derivatives_right=unnorm_derivatives_right[ + inside_interval_mask, : + ], inverse=inverse, - left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound, + left=-tail_bound, + right=tail_bound, + bottom=-tail_bound, + top=tail_bound, min_bin_width=min_bin_width, min_bin_height=min_bin_height, eps=eps, - quadratic_threshold=quadratic_threshold + quadratic_threshold=quadratic_threshold, ) return outputs, logabsdet -def cubic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnorm_derivatives_left, - unnorm_derivatives_right, - inverse=False, - left=0., right=1., bottom=0., top=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - eps=DEFAULT_EPS, - quadratic_threshold=DEFAULT_QUADRATIC_THRESHOLD): + +def cubic_spline( + inputs, + unnormalized_widths, + unnormalized_heights, + unnorm_derivatives_left, + unnorm_derivatives_right, + inverse=False, + left=0.0, + right=1.0, + bottom=0.0, + top=1.0, + min_bin_width=DEFAULT_MIN_BIN_WIDTH, + min_bin_height=DEFAULT_MIN_BIN_HEIGHT, + eps=DEFAULT_EPS, + quadratic_threshold=DEFAULT_QUADRATIC_THRESHOLD, +): """ References: > Blinn, J. F. (2007). How to solve a cubic equation, part 5: Back to numerics. IEEE Computer @@ -77,9 +94,9 @@ def cubic_spline(inputs, num_bins = unnormalized_widths.shape[-1] if min_bin_width * num_bins > 1.0: - raise ValueError('Minimal bin width too large for the number of bins') + raise ValueError("Minimal bin width too large for the number of bins") if min_bin_height * num_bins > 1.0: - raise ValueError('Minimal bin height too large for the number of bins') + raise ValueError("Minimal bin height too large for the number of bins") if inverse: inputs = (inputs - bottom) / (top - bottom) @@ -91,34 +108,51 @@ def cubic_spline(inputs, cumwidths = torch.cumsum(widths, dim=-1) cumwidths[..., -1] = 1 - cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0) + cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0) heights = F.softmax(unnormalized_heights, dim=-1) heights = min_bin_height + (1 - min_bin_height * num_bins) * heights cumheights = torch.cumsum(heights, dim=-1) cumheights[..., -1] = 1 - cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0) + cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0) slopes = heights / widths - min_something_1 = torch.min(torch.abs(slopes[..., :-1]), - torch.abs(slopes[..., 1:])) + min_something_1 = torch.min( + torch.abs(slopes[..., :-1]), torch.abs(slopes[..., 1:]) + ) min_something_2 = ( - 0.5 * (widths[..., 1:] * slopes[..., :-1] + widths[..., :-1] * slopes[..., 1:]) - / (widths[..., :-1] + widths[..., 1:]) + 0.5 + * ( + widths[..., 1:] * slopes[..., :-1] + + widths[..., :-1] * slopes[..., 1:] + ) + / (widths[..., :-1] + widths[..., 1:]) ) min_something = torch.min(min_something_1, min_something_2) - derivatives_left = torch.sigmoid(unnorm_derivatives_left) * 3 * slopes[..., 0][..., None] - derivatives_right = torch.sigmoid(unnorm_derivatives_right) * 3 * slopes[..., -1][..., None] + derivatives_left = ( + torch.sigmoid(unnorm_derivatives_left) * 3 * slopes[..., 0][..., None] + ) + derivatives_right = ( + torch.sigmoid(unnorm_derivatives_right) + * 3 + * slopes[..., -1][..., None] + ) - derivatives = min_something * (torch.sign(slopes[..., :-1]) + torch.sign(slopes[..., 1:])) - derivatives = torch.cat([derivatives_left, - derivatives, - derivatives_right], dim=-1) + derivatives = min_something * ( + torch.sign(slopes[..., :-1]) + torch.sign(slopes[..., 1:]) + ) + derivatives = torch.cat( + [derivatives_left, derivatives, derivatives_right], dim=-1 + ) - a = (derivatives[..., :-1] + derivatives[..., 1:] - 2 * slopes) / widths.pow(2) - b = (3 * slopes - 2 * derivatives[..., :-1] - derivatives[..., 1:]) / widths + a = ( + derivatives[..., :-1] + derivatives[..., 1:] - 2 * slopes + ) / widths.pow(2) + b = ( + 3 * slopes - 2 * derivatives[..., :-1] - derivatives[..., 1:] + ) / widths c = derivatives[..., :-1] d = cumheights[..., :-1] @@ -137,37 +171,56 @@ def cubic_spline(inputs, if inverse: # Modified coefficients for solving the cubic. - inputs_b_ = (inputs_b / inputs_a) / 3. - inputs_c_ = (inputs_c / inputs_a) / 3. + inputs_b_ = (inputs_b / inputs_a) / 3.0 + inputs_c_ = (inputs_c / inputs_a) / 3.0 inputs_d_ = (inputs_d - inputs) / inputs_a delta_1 = -inputs_b_.pow(2) + inputs_c_ delta_2 = -inputs_c_ * inputs_b_ + inputs_d_ delta_3 = inputs_b_ * inputs_d_ - inputs_c_.pow(2) - discriminant = 4. * delta_1 * delta_3 - delta_2.pow(2) + discriminant = 4.0 * delta_1 * delta_3 - delta_2.pow(2) - depressed_1 = -2. * inputs_b_ * delta_1 + delta_2 + depressed_1 = -2.0 * inputs_b_ * delta_1 + delta_2 depressed_2 = delta_1 - three_roots_mask = discriminant >= 0 # Discriminant == 0 might be a problem in practice. + three_roots_mask = ( + discriminant >= 0 + ) # Discriminant == 0 might be a problem in practice. one_root_mask = discriminant < 0 outputs = torch.zeros_like(inputs) # Deal with one root cases. - p = cbrt((-depressed_1[one_root_mask] + torch.sqrt(-discriminant[one_root_mask])) / 2.) - q = cbrt((-depressed_1[one_root_mask] - torch.sqrt(-discriminant[one_root_mask])) / 2.) - - outputs[one_root_mask] = ((p + q) - - inputs_b_[one_root_mask] - + input_left_cumwidths[one_root_mask]) + p = cbrt( + ( + -depressed_1[one_root_mask] + + torch.sqrt(-discriminant[one_root_mask]) + ) + / 2.0 + ) + q = cbrt( + ( + -depressed_1[one_root_mask] + - torch.sqrt(-discriminant[one_root_mask]) + ) + / 2.0 + ) + + outputs[one_root_mask] = ( + (p + q) + - inputs_b_[one_root_mask] + + input_left_cumwidths[one_root_mask] + ) # Deal with three root cases. - theta = torch.atan2(torch.sqrt(discriminant[three_roots_mask]), -depressed_1[three_roots_mask]) - theta /= 3. + theta = torch.atan2( + torch.sqrt(discriminant[three_roots_mask]), + -depressed_1[three_roots_mask], + ) + theta /= 3.0 cubic_root_1 = torch.cos(theta) cubic_root_2 = torch.sin(theta) @@ -177,49 +230,78 @@ def cubic_spline(inputs, root_3 = -0.5 * cubic_root_1 + 0.5 * math.sqrt(3) * cubic_root_2 root_scale = 2 * torch.sqrt(-depressed_2[three_roots_mask]) - root_shift = (-inputs_b_[three_roots_mask] + input_left_cumwidths[three_roots_mask]) + root_shift = ( + -inputs_b_[three_roots_mask] + + input_left_cumwidths[three_roots_mask] + ) root_1 = root_1 * root_scale + root_shift root_2 = root_2 * root_scale + root_shift root_3 = root_3 * root_scale + root_shift - root1_mask = ((input_left_cumwidths[three_roots_mask] - eps) < root_1).float() - root1_mask *= (root_1 < (input_right_cumwidths[three_roots_mask] + eps)).float() - - root2_mask = ((input_left_cumwidths[three_roots_mask] - eps) < root_2).float() - root2_mask *= (root_2 < (input_right_cumwidths[three_roots_mask] + eps)).float() - - root3_mask = ((input_left_cumwidths[three_roots_mask] - eps) < root_3).float() - root3_mask *= (root_3 < (input_right_cumwidths[three_roots_mask] + eps)).float() + root1_mask = ( + (input_left_cumwidths[three_roots_mask] - eps) < root_1 + ).float() + root1_mask *= ( + root_1 < (input_right_cumwidths[three_roots_mask] + eps) + ).float() + + root2_mask = ( + (input_left_cumwidths[three_roots_mask] - eps) < root_2 + ).float() + root2_mask *= ( + root_2 < (input_right_cumwidths[three_roots_mask] + eps) + ).float() + + root3_mask = ( + (input_left_cumwidths[three_roots_mask] - eps) < root_3 + ).float() + root3_mask *= ( + root_3 < (input_right_cumwidths[three_roots_mask] + eps) + ).float() roots = torch.stack([root_1, root_2, root_3], dim=-1) masks = torch.stack([root1_mask, root2_mask, root3_mask], dim=-1) - mask_index = torch.argsort(masks, dim=-1, descending=True)[..., 0][..., None] - outputs[three_roots_mask] = torch.gather(roots, dim=-1, index=mask_index).view(-1) + mask_index = torch.argsort(masks, dim=-1, descending=True)[..., 0][ + ..., None + ] + outputs[three_roots_mask] = torch.gather( + roots, dim=-1, index=mask_index + ).view(-1) # Deal with a -> 0 (almost quadratic) cases. quadratic_mask = inputs_a.abs() < quadratic_threshold a = inputs_b[quadratic_mask] b = inputs_c[quadratic_mask] - c = (inputs_d[quadratic_mask] - inputs[quadratic_mask]) - alpha = (-b + torch.sqrt(b.pow(2) - 4*a*c)) / (2*a) + c = inputs_d[quadratic_mask] - inputs[quadratic_mask] + alpha = (-b + torch.sqrt(b.pow(2) - 4 * a * c)) / (2 * a) outputs[quadratic_mask] = alpha + input_left_cumwidths[quadratic_mask] - shifted_outputs = (outputs - input_left_cumwidths) - logabsdet = -torch.log((3 * inputs_a * shifted_outputs.pow(2) + - 2 * inputs_b * shifted_outputs + - inputs_c)) + shifted_outputs = outputs - input_left_cumwidths + logabsdet = -torch.log( + ( + 3 * inputs_a * shifted_outputs.pow(2) + + 2 * inputs_b * shifted_outputs + + inputs_c + ) + ) else: - shifted_inputs = (inputs - input_left_cumwidths) - outputs = (inputs_a * shifted_inputs.pow(3) + - inputs_b * shifted_inputs.pow(2) + - inputs_c * shifted_inputs + - inputs_d) - - logabsdet = torch.log((3 * inputs_a * shifted_inputs.pow(2) + - 2 * inputs_b * shifted_inputs + - inputs_c)) + shifted_inputs = inputs - input_left_cumwidths + outputs = ( + inputs_a * shifted_inputs.pow(3) + + inputs_b * shifted_inputs.pow(2) + + inputs_c * shifted_inputs + + inputs_d + ) + + logabsdet = torch.log( + ( + 3 * inputs_a * shifted_inputs.pow(2) + + 2 * inputs_b * shifted_inputs + + inputs_c + ) + ) if inverse: outputs = outputs * (right - left) + left diff --git a/phaseSpaceSampling/nde/transforms/splines/cubic_test.py b/phaseSpaceSampling/nde/transforms/splines/cubic_test.py index c9e2cfa..a3d70cd 100644 --- a/phaseSpaceSampling/nde/transforms/splines/cubic_test.py +++ b/phaseSpaceSampling/nde/transforms/splines/cubic_test.py @@ -7,7 +7,7 @@ class CubicSplineTest(torchtestcase.TorchTestCase): def test_forward_inverse_are_consistent(self): num_bins = 10 - shape = [2,3,4] + shape = [2, 3, 4] unnormalized_widths = torch.randn(*shape, num_bins) unnormalized_heights = torch.randn(*shape, num_bins) @@ -21,7 +21,7 @@ def call_spline_fn(inputs, inverse=False): unnormalized_heights=unnormalized_heights, unnorm_derivatives_left=unnorm_derivatives_left, unnorm_derivatives_right=unnorm_derivatives_right, - inverse=inverse + inverse=inverse, ) inputs = torch.rand(*shape) @@ -30,12 +30,15 @@ def call_spline_fn(inputs, inverse=False): self.eps = 1e-4 self.assertEqual(inputs, inputs_inv) - self.assertEqual(logabsdet + logabsdet_inv, torch.zeros_like(logabsdet)) + self.assertEqual( + logabsdet + logabsdet_inv, torch.zeros_like(logabsdet) + ) + class UnconstrainedCubicSplineTest(torchtestcase.TorchTestCase): def test_forward_inverse_are_consistent(self): num_bins = 10 - shape = [2,3,4] + shape = [2, 3, 4] unnormalized_widths = torch.randn(*shape, num_bins) unnormalized_heights = torch.randn(*shape, num_bins) @@ -49,13 +52,15 @@ def call_spline_fn(inputs, inverse=False): unnormalized_heights=unnormalized_heights, unnorm_derivatives_left=unnorm_derivatives_left, unnorm_derivatives_right=unnorm_derivatives_right, - inverse=inverse + inverse=inverse, ) - inputs = 3 * torch.randn(*shape) # Note inputs are outside [0,1]. + inputs = 3 * torch.randn(*shape) # Note inputs are outside [0,1]. outputs, logabsdet = call_spline_fn(inputs, inverse=False) inputs_inv, logabsdet_inv = call_spline_fn(outputs, inverse=True) self.eps = 1e-4 self.assertEqual(inputs, inputs_inv) - self.assertEqual(logabsdet + logabsdet_inv, torch.zeros_like(logabsdet)) + self.assertEqual( + logabsdet + logabsdet_inv, torch.zeros_like(logabsdet) + ) diff --git a/phaseSpaceSampling/nde/transforms/splines/linear.py b/phaseSpaceSampling/nde/transforms/splines/linear.py index 201ed0a..687af66 100644 --- a/phaseSpaceSampling/nde/transforms/splines/linear.py +++ b/phaseSpaceSampling/nde/transforms/splines/linear.py @@ -1,41 +1,53 @@ import math +import numpy as np import torch from torch.nn import functional as F -import numpy as np from phaseSpaceSampling.nde import transforms - from phaseSpaceSampling.utils.torchutils import searchsorted -def unconstrained_linear_spline(inputs, unnormalized_pdf, - inverse=False, - tail_bound=1., - tails='linear'): + +def unconstrained_linear_spline( + inputs, unnormalized_pdf, inverse=False, tail_bound=1.0, tails="linear" +): inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) outside_interval_mask = ~inside_interval_mask outputs = torch.zeros_like(inputs) logabsdet = torch.zeros_like(inputs) - if tails == 'linear': + if tails == "linear": outputs[outside_interval_mask] = inputs[outside_interval_mask] logabsdet[outside_interval_mask] = 0 else: - raise RuntimeError('{} tails are not implemented.'.format(tails)) + raise RuntimeError("{} tails are not implemented.".format(tails)) - outputs[inside_interval_mask], logabsdet[inside_interval_mask] = linear_spline( + ( + outputs[inside_interval_mask], + logabsdet[inside_interval_mask], + ) = linear_spline( inputs=inputs[inside_interval_mask], unnormalized_pdf=unnormalized_pdf[inside_interval_mask, :], inverse=inverse, - left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound + left=-tail_bound, + right=tail_bound, + bottom=-tail_bound, + top=tail_bound, ) return outputs, logabsdet -def linear_spline(inputs, unnormalized_pdf, - inverse=False, - left=0., right=1., bottom=0., top=1.): + +def linear_spline( + inputs, + unnormalized_pdf, + inverse=False, + left=0.0, + right=1.0, + bottom=0.0, + top=1.0, +): """ Reference: > Müller et al., Neural Importance Sampling, arXiv:1808.03856, 2018. @@ -55,18 +67,21 @@ def linear_spline(inputs, unnormalized_pdf, pdf = F.softmax(unnormalized_pdf, dim=-1) cdf = torch.cumsum(pdf, dim=-1) - cdf[..., -1] = 1. - cdf = F.pad(cdf, pad=(1, 0), mode='constant', value=0.0) + cdf[..., -1] = 1.0 + cdf = F.pad(cdf, pad=(1, 0), mode="constant", value=0.0) if inverse: inv_bin_idx = searchsorted(cdf, inputs) - bin_boundaries = (torch.linspace(0, 1, num_bins+1) - .view([1] * inputs.dim() + [-1]) - .expand(*inputs.shape, -1)) + bin_boundaries = ( + torch.linspace(0, 1, num_bins + 1) + .view([1] * inputs.dim() + [-1]) + .expand(*inputs.shape, -1) + ) - slopes = ((cdf[..., 1:] - cdf[..., :-1]) - / (bin_boundaries[..., 1:] - bin_boundaries[..., :-1])) + slopes = (cdf[..., 1:] - cdf[..., :-1]) / ( + bin_boundaries[..., 1:] - bin_boundaries[..., :-1] + ) offsets = cdf[..., 1:] - slopes * bin_boundaries[..., 1:] inv_bin_idx = inv_bin_idx.unsqueeze(-1) diff --git a/phaseSpaceSampling/nde/transforms/splines/linear_test.py b/phaseSpaceSampling/nde/transforms/splines/linear_test.py index 57162a6..3ae61e7 100644 --- a/phaseSpaceSampling/nde/transforms/splines/linear_test.py +++ b/phaseSpaceSampling/nde/transforms/splines/linear_test.py @@ -3,10 +3,11 @@ from phaseSpaceSampling.nde.transforms import splines + class LinearSplineTest(torchtestcase.TorchTestCase): def test_forward_inverse_are_consistent(self): num_bins = 10 - shape = [2,3,4] + shape = [2, 3, 4] unnormalized_pdf = torch.randn(*shape, num_bins) @@ -14,7 +15,7 @@ def call_spline_fn(inputs, inverse=False): return splines.linear_spline( inputs=inputs, unnormalized_pdf=unnormalized_pdf, - inverse=inverse + inverse=inverse, ) inputs = torch.rand(*shape) @@ -23,12 +24,15 @@ def call_spline_fn(inputs, inverse=False): self.eps = 1e-4 self.assertEqual(inputs, inputs_inv) - self.assertEqual(logabsdet + logabsdet_inv, torch.zeros_like(logabsdet)) + self.assertEqual( + logabsdet + logabsdet_inv, torch.zeros_like(logabsdet) + ) + class UnconstrainedLinearSplineTest(torchtestcase.TorchTestCase): def test_forward_inverse_are_consistent(self): num_bins = 10 - shape = [2,3,4] + shape = [2, 3, 4] unnormalized_pdf = torch.randn(*shape, num_bins) @@ -36,13 +40,15 @@ def call_spline_fn(inputs, inverse=False): return splines.unconstrained_linear_spline( inputs=inputs, unnormalized_pdf=unnormalized_pdf, - inverse=inverse + inverse=inverse, ) - inputs = 3 * torch.randn(*shape) # Note inputs are outside [0,1]. + inputs = 3 * torch.randn(*shape) # Note inputs are outside [0,1]. outputs, logabsdet = call_spline_fn(inputs, inverse=False) inputs_inv, logabsdet_inv = call_spline_fn(outputs, inverse=True) self.eps = 1e-4 self.assertEqual(inputs, inputs_inv) - self.assertEqual(logabsdet + logabsdet_inv, torch.zeros_like(logabsdet)) + self.assertEqual( + logabsdet + logabsdet_inv, torch.zeros_like(logabsdet) + ) diff --git a/phaseSpaceSampling/nde/transforms/splines/quadratic.py b/phaseSpaceSampling/nde/transforms/splines/quadratic.py index 36dd431..969895b 100644 --- a/phaseSpaceSampling/nde/transforms/splines/quadratic.py +++ b/phaseSpaceSampling/nde/transforms/splines/quadratic.py @@ -4,21 +4,22 @@ from torch.nn import functional as F from phaseSpaceSampling.nde import transforms - from phaseSpaceSampling.utils.torchutils import searchsorted DEFAULT_MIN_BIN_WIDTH = 1e-3 DEFAULT_MIN_BIN_HEIGHT = 1e-3 -def unconstrained_quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - inverse=False, - tail_bound=1., - tails='linear', - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT): +def unconstrained_quadratic_spline( + inputs, + unnormalized_widths, + unnormalized_heights, + inverse=False, + tail_bound=1.0, + tails="linear", + min_bin_width=DEFAULT_MIN_BIN_WIDTH, + min_bin_height=DEFAULT_MIN_BIN_HEIGHT, +): inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) outside_interval_mask = ~inside_interval_mask @@ -27,32 +28,44 @@ def unconstrained_quadratic_spline(inputs, num_bins = unnormalized_widths.shape[-1] - if tails == 'linear': + if tails == "linear": outputs[outside_interval_mask] = inputs[outside_interval_mask] logabsdet[outside_interval_mask] = 0 assert unnormalized_heights.shape[-1] == num_bins - 1 else: - raise RuntimeError('{} tails are not implemented.'.format(tails)) + raise RuntimeError("{} tails are not implemented.".format(tails)) - outputs[inside_interval_mask], logabsdet[inside_interval_mask] = quadratic_spline( + ( + outputs[inside_interval_mask], + logabsdet[inside_interval_mask], + ) = quadratic_spline( inputs=inputs[inside_interval_mask], unnormalized_widths=unnormalized_widths[inside_interval_mask, :], unnormalized_heights=unnormalized_heights[inside_interval_mask, :], inverse=inverse, - left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound, + left=-tail_bound, + right=tail_bound, + bottom=-tail_bound, + top=tail_bound, min_bin_width=min_bin_width, - min_bin_height=min_bin_height + min_bin_height=min_bin_height, ) return outputs, logabsdet -def quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - inverse=False, - left=0., right=1., bottom=0., top=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT): + +def quadratic_spline( + inputs, + unnormalized_widths, + unnormalized_heights, + inverse=False, + left=0.0, + right=1.0, + bottom=0.0, + top=1.0, + min_bin_width=DEFAULT_MIN_BIN_WIDTH, + min_bin_height=DEFAULT_MIN_BIN_HEIGHT, +): if not inverse and (torch.min(inputs) < left or torch.max(inputs) > right): raise transforms.InputOutsideDomain() elif inverse and (torch.min(inputs) < bottom or torch.max(inputs) > top): @@ -66,9 +79,9 @@ def quadratic_spline(inputs, num_bins = unnormalized_widths.shape[-1] if min_bin_width * num_bins > 1.0: - raise ValueError('Minimal bin width too large for the number of bins') + raise ValueError("Minimal bin width too large for the number of bins") if min_bin_height * num_bins > 1.0: - raise ValueError('Minimal bin height too large for the number of bins') + raise ValueError("Minimal bin height too large for the number of bins") widths = F.softmax(unnormalized_widths, dim=-1) widths = min_bin_width + (1 - min_bin_width * num_bins) * widths @@ -79,26 +92,46 @@ def quadratic_spline(inputs, # Set boundary heights s.t. after normalization they are exactly 1. first_widths = 0.5 * widths[..., 0] last_widths = 0.5 * widths[..., -1] - numerator = (0.5 * first_widths * unnorm_heights_exp[..., 0] - + 0.5 * last_widths * unnorm_heights_exp[..., -1] - + torch.sum(((unnorm_heights_exp[..., :-1] + unnorm_heights_exp[..., 1:]) / 2) - * widths[..., 1:-1], dim=-1)) + numerator = ( + 0.5 * first_widths * unnorm_heights_exp[..., 0] + + 0.5 * last_widths * unnorm_heights_exp[..., -1] + + torch.sum( + ( + ( + unnorm_heights_exp[..., :-1] + + unnorm_heights_exp[..., 1:] + ) + / 2 + ) + * widths[..., 1:-1], + dim=-1, + ) + ) constant = numerator / (1 - 0.5 * first_widths - 0.5 * last_widths) constant = constant[..., None] - unnorm_heights_exp = torch.cat([constant, unnorm_heights_exp, constant], dim=-1) - - unnormalized_area = torch.sum(((unnorm_heights_exp[..., :-1] + unnorm_heights_exp[..., 1:]) / 2) - * widths, dim=-1)[..., None] + unnorm_heights_exp = torch.cat( + [constant, unnorm_heights_exp, constant], dim=-1 + ) + + unnormalized_area = torch.sum( + ((unnorm_heights_exp[..., :-1] + unnorm_heights_exp[..., 1:]) / 2) + * widths, + dim=-1, + )[..., None] heights = unnorm_heights_exp / unnormalized_area heights = min_bin_height + (1 - min_bin_height) * heights - bin_left_cdf = torch.cumsum(((heights[..., :-1] + heights[..., 1:]) / 2) * widths, dim=-1) - bin_left_cdf[..., -1] = 1. - bin_left_cdf = F.pad(bin_left_cdf, pad=(1, 0), mode='constant', value=0.0) + bin_left_cdf = torch.cumsum( + ((heights[..., :-1] + heights[..., 1:]) / 2) * widths, dim=-1 + ) + bin_left_cdf[..., -1] = 1.0 + bin_left_cdf = F.pad(bin_left_cdf, pad=(1, 0), mode="constant", value=0.0) bin_locations = torch.cumsum(widths, dim=-1) - bin_locations[..., -1] = 1. - bin_locations = F.pad(bin_locations, pad=(1, 0), mode='constant', value=0.0) + bin_locations[..., -1] = 1.0 + bin_locations = F.pad( + bin_locations, pad=(1, 0), mode="constant", value=0.0 + ) if inverse: bin_idx = searchsorted(bin_left_cdf, inputs)[..., None] @@ -111,7 +144,7 @@ def quadratic_spline(inputs, input_left_cdf = bin_left_cdf.gather(-1, bin_idx)[..., 0] input_left_heights = heights.gather(-1, bin_idx)[..., 0] - input_right_heights = heights.gather(-1, bin_idx+1)[..., 0] + input_right_heights = heights.gather(-1, bin_idx + 1)[..., 0] a = 0.5 * (input_right_heights - input_left_heights) * input_bin_widths b = input_left_heights * input_bin_widths @@ -119,17 +152,25 @@ def quadratic_spline(inputs, if inverse: c_ = c - inputs - alpha = (-b + torch.sqrt(b.pow(2) - 4*a*c_)) / (2*a) + alpha = (-b + torch.sqrt(b.pow(2) - 4 * a * c_)) / (2 * a) outputs = alpha * input_bin_widths + input_bin_locations outputs = torch.clamp(outputs, 0, 1) - logabsdet = -torch.log((alpha * (input_right_heights - input_left_heights) - + input_left_heights)) + logabsdet = -torch.log( + ( + alpha * (input_right_heights - input_left_heights) + + input_left_heights + ) + ) else: alpha = (inputs - input_bin_locations) / input_bin_widths outputs = a * alpha.pow(2) + b * alpha + c outputs = torch.clamp(outputs, 0, 1) - logabsdet = torch.log((alpha * (input_right_heights - input_left_heights) - + input_left_heights)) + logabsdet = torch.log( + ( + alpha * (input_right_heights - input_left_heights) + + input_left_heights + ) + ) if inverse: outputs = outputs * (right - left) + left diff --git a/phaseSpaceSampling/nde/transforms/splines/quadratic_test.py b/phaseSpaceSampling/nde/transforms/splines/quadratic_test.py index 286f634..6abc27b 100644 --- a/phaseSpaceSampling/nde/transforms/splines/quadratic_test.py +++ b/phaseSpaceSampling/nde/transforms/splines/quadratic_test.py @@ -3,10 +3,11 @@ from phaseSpaceSampling.nde.transforms import splines + class QuadraticSplineTest(torchtestcase.TorchTestCase): def test_forward_inverse_are_consistent(self): num_bins = 10 - shape = [2,3,4] + shape = [2, 3, 4] unnormalized_widths = torch.randn(*shape, num_bins) unnormalized_heights = torch.randn(*shape, num_bins + 1) @@ -16,7 +17,7 @@ def call_spline_fn(inputs, inverse=False): inputs=inputs, unnormalized_widths=unnormalized_widths, unnormalized_heights=unnormalized_heights, - inverse=inverse + inverse=inverse, ) inputs = torch.rand(*shape) @@ -25,12 +26,15 @@ def call_spline_fn(inputs, inverse=False): self.eps = 1e-4 self.assertEqual(inputs, inputs_inv) - self.assertEqual(logabsdet + logabsdet_inv, torch.zeros_like(logabsdet)) + self.assertEqual( + logabsdet + logabsdet_inv, torch.zeros_like(logabsdet) + ) + class UnconstrainedQuadraticSplineTest(torchtestcase.TorchTestCase): def test_forward_inverse_are_consistent(self): num_bins = 10 - shape = [2,3,4] + shape = [2, 3, 4] unnormalized_widths = torch.randn(*shape, num_bins) unnormalized_heights = torch.randn(*shape, num_bins - 1) @@ -40,13 +44,15 @@ def call_spline_fn(inputs, inverse=False): inputs=inputs, unnormalized_widths=unnormalized_widths, unnormalized_heights=unnormalized_heights, - inverse=inverse + inverse=inverse, ) - inputs = 3 * torch.randn(*shape) # Note inputs are outside [0,1]. + inputs = 3 * torch.randn(*shape) # Note inputs are outside [0,1]. outputs, logabsdet = call_spline_fn(inputs, inverse=False) inputs_inv, logabsdet_inv = call_spline_fn(outputs, inverse=True) self.eps = 1e-4 self.assertEqual(inputs, inputs_inv) - self.assertEqual(logabsdet + logabsdet_inv, torch.zeros_like(logabsdet)) + self.assertEqual( + logabsdet + logabsdet_inv, torch.zeros_like(logabsdet) + ) diff --git a/phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py b/phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py index 8ff369a..2da0dd7 100644 --- a/phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py +++ b/phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py @@ -1,33 +1,34 @@ +import numpy as np import torch from torch.nn import functional as F -import numpy as np - from phaseSpaceSampling.nde import transforms - from phaseSpaceSampling.utils.torchutils import searchsorted DEFAULT_MIN_BIN_WIDTH = 1e-3 DEFAULT_MIN_BIN_HEIGHT = 1e-3 DEFAULT_MIN_DERIVATIVE = 1e-3 -def unconstrained_rational_quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails='linear', - tail_bound=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): + +def unconstrained_rational_quadratic_spline( + inputs, + unnormalized_widths, + unnormalized_heights, + unnormalized_derivatives, + inverse=False, + tails="linear", + tail_bound=1.0, + min_bin_width=DEFAULT_MIN_BIN_WIDTH, + min_bin_height=DEFAULT_MIN_BIN_HEIGHT, + min_derivative=DEFAULT_MIN_DERIVATIVE, +): inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) outside_interval_mask = ~inside_interval_mask outputs = torch.zeros_like(inputs) logabsdet = torch.zeros_like(inputs) - if tails == 'linear': + if tails == "linear": unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) constant = np.log(np.exp(1 - min_derivative) - 1) unnormalized_derivatives[..., 0] = constant @@ -36,45 +37,59 @@ def unconstrained_rational_quadratic_spline(inputs, outputs[outside_interval_mask] = inputs[outside_interval_mask] logabsdet[outside_interval_mask] = 0 else: - raise RuntimeError('{} tails are not implemented.'.format(tails)) + raise RuntimeError("{} tails are not implemented.".format(tails)) - outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline( + ( + outputs[inside_interval_mask], + logabsdet[inside_interval_mask], + ) = rational_quadratic_spline( inputs=inputs[inside_interval_mask], unnormalized_widths=unnormalized_widths[inside_interval_mask, :], unnormalized_heights=unnormalized_heights[inside_interval_mask, :], - unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], + unnormalized_derivatives=unnormalized_derivatives[ + inside_interval_mask, : + ], inverse=inverse, - left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound, + left=-tail_bound, + right=tail_bound, + bottom=-tail_bound, + top=tail_bound, min_bin_width=min_bin_width, min_bin_height=min_bin_height, - min_derivative=min_derivative + min_derivative=min_derivative, ) return outputs, logabsdet -def rational_quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - left=0., right=1., bottom=0., top=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): + +def rational_quadratic_spline( + inputs, + unnormalized_widths, + unnormalized_heights, + unnormalized_derivatives, + inverse=False, + left=0.0, + right=1.0, + bottom=0.0, + top=1.0, + min_bin_width=DEFAULT_MIN_BIN_WIDTH, + min_bin_height=DEFAULT_MIN_BIN_HEIGHT, + min_derivative=DEFAULT_MIN_DERIVATIVE, +): if torch.min(inputs) < left or torch.max(inputs) > right: raise transforms.InputOutsideDomain() num_bins = unnormalized_widths.shape[-1] if min_bin_width * num_bins > 1.0: - raise ValueError('Minimal bin width too large for the number of bins') + raise ValueError("Minimal bin width too large for the number of bins") if min_bin_height * num_bins > 1.0: - raise ValueError('Minimal bin height too large for the number of bins') + raise ValueError("Minimal bin height too large for the number of bins") widths = F.softmax(unnormalized_widths, dim=-1) widths = min_bin_width + (1 - min_bin_width * num_bins) * widths cumwidths = torch.cumsum(widths, dim=-1) - cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0) + cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0) cumwidths = (right - left) * cumwidths + left cumwidths[..., 0] = left cumwidths[..., -1] = right @@ -85,7 +100,7 @@ def rational_quadratic_spline(inputs, heights = F.softmax(unnormalized_heights, dim=-1) heights = min_bin_height + (1 - min_bin_height * num_bins) * heights cumheights = torch.cumsum(heights, dim=-1) - cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0) + cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0) cumheights = (top - bottom) * cumheights + bottom cumheights[..., 0] = bottom cumheights[..., -1] = top @@ -104,20 +119,20 @@ def rational_quadratic_spline(inputs, input_delta = delta.gather(-1, bin_idx)[..., 0] input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] - input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] + input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[ + ..., 0 + ] input_heights = heights.gather(-1, bin_idx)[..., 0] if inverse: - a = (((inputs - input_cumheights) * (input_derivatives - + input_derivatives_plus_one - - 2 * input_delta) - + input_heights * (input_delta - input_derivatives))) - b = (input_heights * input_derivatives - - (inputs - input_cumheights) * (input_derivatives - + input_derivatives_plus_one - - 2 * input_delta)) - c = - input_delta * (inputs - input_cumheights) + a = (inputs - input_cumheights) * ( + input_derivatives + input_derivatives_plus_one - 2 * input_delta + ) + input_heights * (input_delta - input_derivatives) + b = input_heights * input_derivatives - (inputs - input_cumheights) * ( + input_derivatives + input_derivatives_plus_one - 2 * input_delta + ) + c = -input_delta * (inputs - input_cumheights) discriminant = b.pow(2) - 4 * a * c assert (discriminant >= 0).all() @@ -126,27 +141,41 @@ def rational_quadratic_spline(inputs, outputs = root * input_bin_widths + input_cumwidths theta_one_minus_theta = root * (1 - root) - denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta) - derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - root).pow(2)) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) + denominator = input_delta + ( + (input_derivatives + input_derivatives_plus_one - 2 * input_delta) + * theta_one_minus_theta + ) + derivative_numerator = input_delta.pow(2) * ( + input_derivatives_plus_one * root.pow(2) + + 2 * input_delta * theta_one_minus_theta + + input_derivatives * (1 - root).pow(2) + ) + logabsdet = torch.log(derivative_numerator) - 2 * torch.log( + denominator + ) return outputs, -logabsdet else: theta = (inputs - input_cumwidths) / input_bin_widths theta_one_minus_theta = theta * (1 - theta) - numerator = input_heights * (input_delta * theta.pow(2) - + input_derivatives * theta_one_minus_theta) - denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta) + numerator = input_heights * ( + input_delta * theta.pow(2) + + input_derivatives * theta_one_minus_theta + ) + denominator = input_delta + ( + (input_derivatives + input_derivatives_plus_one - 2 * input_delta) + * theta_one_minus_theta + ) outputs = input_cumheights + numerator / denominator - derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - theta).pow(2)) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) + derivative_numerator = input_delta.pow(2) * ( + input_derivatives_plus_one * theta.pow(2) + + 2 * input_delta * theta_one_minus_theta + + input_derivatives * (1 - theta).pow(2) + ) + logabsdet = torch.log(derivative_numerator) - 2 * torch.log( + denominator + ) return outputs, logabsdet diff --git a/phaseSpaceSampling/nde/transforms/splines/rational_quadratic_test.py b/phaseSpaceSampling/nde/transforms/splines/rational_quadratic_test.py index 0856f21..c79f4c2 100644 --- a/phaseSpaceSampling/nde/transforms/splines/rational_quadratic_test.py +++ b/phaseSpaceSampling/nde/transforms/splines/rational_quadratic_test.py @@ -3,36 +3,40 @@ from phaseSpaceSampling.nde.transforms import splines + class RationalQuadraticSplineTest(torchtestcase.TorchTestCase): def test_forward_inverse_are_consistent(self): - num_bins = 10 - shape = [2,3,4] - - unnormalized_widths = torch.randn(*shape, num_bins) - unnormalized_heights = torch.randn(*shape, num_bins) - unnormalized_derivatives = torch.randn(*shape, num_bins + 1) - - def call_spline_fn(inputs, inverse=False): - return splines.rational_quadratic_spline( - inputs=inputs, - unnormalized_widths=unnormalized_widths, - unnormalized_heights=unnormalized_heights, - unnormalized_derivatives=unnormalized_derivatives, - inverse=inverse - ) - - inputs = torch.rand(*shape) - outputs, logabsdet = call_spline_fn(inputs, inverse=False) - inputs_inv, logabsdet_inv = call_spline_fn(outputs, inverse=True) - - self.eps = 1e-4 - self.assertEqual(inputs, inputs_inv) - self.assertEqual(logabsdet + logabsdet_inv, torch.zeros_like(logabsdet)) + num_bins = 10 + shape = [2, 3, 4] + + unnormalized_widths = torch.randn(*shape, num_bins) + unnormalized_heights = torch.randn(*shape, num_bins) + unnormalized_derivatives = torch.randn(*shape, num_bins + 1) + + def call_spline_fn(inputs, inverse=False): + return splines.rational_quadratic_spline( + inputs=inputs, + unnormalized_widths=unnormalized_widths, + unnormalized_heights=unnormalized_heights, + unnormalized_derivatives=unnormalized_derivatives, + inverse=inverse, + ) + + inputs = torch.rand(*shape) + outputs, logabsdet = call_spline_fn(inputs, inverse=False) + inputs_inv, logabsdet_inv = call_spline_fn(outputs, inverse=True) + + self.eps = 1e-4 + self.assertEqual(inputs, inputs_inv) + self.assertEqual( + logabsdet + logabsdet_inv, torch.zeros_like(logabsdet) + ) + class UnconstrainedRationalQuadraticSplineTest(torchtestcase.TorchTestCase): def test_forward_inverse_are_consistent(self): num_bins = 10 - shape = [2,3,4] + shape = [2, 3, 4] unnormalized_widths = torch.randn(*shape, num_bins) unnormalized_heights = torch.randn(*shape, num_bins) @@ -44,13 +48,15 @@ def call_spline_fn(inputs, inverse=False): unnormalized_widths=unnormalized_widths, unnormalized_heights=unnormalized_heights, unnormalized_derivatives=unnormalized_derivatives, - inverse=inverse + inverse=inverse, ) - inputs = 3 * torch.randn(*shape) # Note inputs are outside [0,1]. + inputs = 3 * torch.randn(*shape) # Note inputs are outside [0,1]. outputs, logabsdet = call_spline_fn(inputs, inverse=False) inputs_inv, logabsdet_inv = call_spline_fn(outputs, inverse=True) self.eps = 1e-4 self.assertEqual(inputs, inputs_inv) - self.assertEqual(logabsdet + logabsdet_inv, torch.zeros_like(logabsdet)) + self.assertEqual( + logabsdet + logabsdet_inv, torch.zeros_like(logabsdet) + ) diff --git a/phaseSpaceSampling/nde/transforms/standard.py b/phaseSpaceSampling/nde/transforms/standard.py index fa4d1fd..7b79226 100644 --- a/phaseSpaceSampling/nde/transforms/standard.py +++ b/phaseSpaceSampling/nde/transforms/standard.py @@ -1,6 +1,7 @@ """Implementations of some standard transforms.""" import torch + from phaseSpaceSampling.nde import transforms @@ -23,12 +24,18 @@ def __init__(self, shift=None, scale=None): super().__init__() if shift is None and scale is None: - raise ValueError('At least one of scale and shift must be provided.') - if scale == 0.: - raise ValueError('Scale cannot be zero.') - - self.register_buffer('_shift', torch.tensor(shift if (shift is not None) else 0.)) - self.register_buffer('_scale', torch.tensor(scale if (scale is not None) else 1.)) + raise ValueError( + "At least one of scale and shift must be provided." + ) + if scale == 0.0: + raise ValueError("Scale cannot be zero.") + + self.register_buffer( + "_shift", torch.tensor(shift if (shift is not None) else 0.0) + ) + self.register_buffer( + "_scale", torch.tensor(scale if (scale is not None) else 1.0) + ) @property def _log_scale(self): @@ -36,14 +43,18 @@ def _log_scale(self): def forward(self, inputs, context=None): batch_size = inputs.shape[0] - num_dims = torch.prod(torch.tensor(inputs.shape[1:]), dtype=torch.float) + num_dims = torch.prod( + torch.tensor(inputs.shape[1:]), dtype=torch.float + ) outputs = inputs * self._scale + self._shift logabsdet = torch.full([batch_size], self._log_scale * num_dims) return outputs, logabsdet def inverse(self, inputs, context=None): batch_size = inputs.shape[0] - num_dims = torch.prod(torch.tensor(inputs.shape[1:]), dtype=torch.float) + num_dims = torch.prod( + torch.tensor(inputs.shape[1:]), dtype=torch.float + ) outputs = (inputs - self._shift) / self._scale logabsdet = torch.full([batch_size], -self._log_scale * num_dims) return outputs, logabsdet diff --git a/phaseSpaceSampling/nde/transforms/standard_test.py b/phaseSpaceSampling/nde/transforms/standard_test.py index bc19a45..5966bb3 100644 --- a/phaseSpaceSampling/nde/transforms/standard_test.py +++ b/phaseSpaceSampling/nde/transforms/standard_test.py @@ -1,15 +1,15 @@ """Tests for the standard transforms.""" +import unittest + import numpy as np import torch -import unittest from phaseSpaceSampling.nde.transforms import standard from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class IdentityTransformTest(TransformTest): - def test_forward(self): batch_size = 10 shape = [2, 3, 4] @@ -41,7 +41,6 @@ def test_forward_inverse_are_consistent(self): class AffineScalarTransformTest(TransformTest): - def test_forward(self): batch_size = 10 shape = [2, 3, 4] @@ -49,18 +48,22 @@ def test_forward(self): def test_case(scale, shift, true_outputs, true_logabsdet): with self.subTest(scale=scale, shift=shift): - transform = standard.AffineScalarTransform(scale=scale, shift=shift) + transform = standard.AffineScalarTransform( + scale=scale, shift=shift + ) outputs, logabsdet = transform(inputs) self.assert_tensor_is_good(outputs, [batch_size] + shape) self.assert_tensor_is_good(logabsdet, [batch_size]) self.assertEqual(outputs, true_outputs) - self.assertEqual(logabsdet, - torch.full([batch_size], true_logabsdet * np.prod(shape))) + self.assertEqual( + logabsdet, + torch.full([batch_size], true_logabsdet * np.prod(shape)), + ) self.eps = 1e-6 - test_case(None, 2., inputs + 2., 0) - test_case(2., None, inputs * 2., np.log(2.)) - test_case(2., 2., inputs * 2. + 2., np.log(2.)) + test_case(None, 2.0, inputs + 2.0, 0) + test_case(2.0, None, inputs * 2.0, np.log(2.0)) + test_case(2.0, 2.0, inputs * 2.0 + 2.0, np.log(2.0)) def test_inverse(self): batch_size = 10 @@ -69,18 +72,22 @@ def test_inverse(self): def test_case(scale, shift, true_outputs, true_logabsdet): with self.subTest(scale=scale, shift=shift): - transform = standard.AffineScalarTransform(scale=scale, shift=shift) + transform = standard.AffineScalarTransform( + scale=scale, shift=shift + ) outputs, logabsdet = transform.inverse(inputs) self.assert_tensor_is_good(outputs, [batch_size] + shape) self.assert_tensor_is_good(logabsdet, [batch_size]) self.assertEqual(outputs, true_outputs) - self.assertEqual(logabsdet, - torch.full([batch_size], true_logabsdet * np.prod(shape))) + self.assertEqual( + logabsdet, + torch.full([batch_size], true_logabsdet * np.prod(shape)), + ) self.eps = 1e-6 - test_case(None, 2., inputs - 2., 0) - test_case(2., None, inputs / 2., -np.log(2.)) - test_case(2., 2., (inputs - 2.) / 2., -np.log(2.)) + test_case(None, 2.0, inputs - 2.0, 0) + test_case(2.0, None, inputs / 2.0, -np.log(2.0)) + test_case(2.0, 2.0, (inputs - 2.0) / 2.0, -np.log(2.0)) def test_forward_inverse_are_consistent(self): batch_size = 10 @@ -88,14 +95,16 @@ def test_forward_inverse_are_consistent(self): inputs = torch.randn(batch_size, *shape) def test_case(scale, shift): - transform = standard.AffineScalarTransform(scale=scale, shift=shift) + transform = standard.AffineScalarTransform( + scale=scale, shift=shift + ) self.assert_forward_inverse_are_consistent(transform, inputs) self.eps = 1e-6 - test_case(None, 2.) - test_case(2., None) - test_case(2., 2.) + test_case(None, 2.0) + test_case(2.0, None) + test_case(2.0, 2.0) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/svd.py b/phaseSpaceSampling/nde/transforms/svd.py index 31cf0c8..1a2acea 100644 --- a/phaseSpaceSampling/nde/transforms/svd.py +++ b/phaseSpaceSampling/nde/transforms/svd.py @@ -1,6 +1,5 @@ import numpy as np import torch - from torch import nn from torch.nn import init @@ -16,14 +15,16 @@ def __init__(self, features, num_householder, using_cache=False): # First orthogonal matrix (U). self.orthogonal_1 = transforms.HouseholderSequence( - features=features, num_transforms=num_householder) + features=features, num_transforms=num_householder + ) # Logs of diagonal entries of the diagonal matrix (S). self.log_diagonal = nn.Parameter(torch.zeros(features)) # Second orthogonal matrix (V^T). self.orthogonal_2 = transforms.HouseholderSequence( - features=features, num_transforms=num_householder) + features=features, num_transforms=num_householder + ) self._initialize() @@ -32,7 +33,6 @@ def _initialize(self): init.uniform_(self.log_diagonal, -stdv, stdv) init.constant_(self.bias, 0.0) - def forward_no_cache(self, inputs): """Cost: output = O(KDN) @@ -42,9 +42,13 @@ def forward_no_cache(self, inputs): D = num of features N = num of inputs """ - outputs, _ = self.orthogonal_2(inputs) # Ignore logabsdet as we know it's zero. + outputs, _ = self.orthogonal_2( + inputs + ) # Ignore logabsdet as we know it's zero. outputs *= torch.exp(self.log_diagonal) - outputs, _ = self.orthogonal_1(outputs) # Ignore logabsdet as we know it's zero. + outputs, _ = self.orthogonal_1( + outputs + ) # Ignore logabsdet as we know it's zero. outputs += self.bias logabsdet = self.logabsdet() * torch.ones(outputs.shape[0]) @@ -61,9 +65,13 @@ def inverse_no_cache(self, inputs): N = num of inputs """ outputs = inputs - self.bias - outputs, _ = self.orthogonal_1.inverse(outputs) # Ignore logabsdet since we know it's zero. + outputs, _ = self.orthogonal_1.inverse( + outputs + ) # Ignore logabsdet since we know it's zero. outputs *= torch.exp(-self.log_diagonal) - outputs, _ = self.orthogonal_2.inverse(outputs) # Ignore logabsdet since we know it's zero. + outputs, _ = self.orthogonal_2.inverse( + outputs + ) # Ignore logabsdet since we know it's zero. logabsdet = -self.logabsdet() logabsdet = logabsdet * torch.ones(outputs.shape[0]) return outputs, logabsdet diff --git a/phaseSpaceSampling/nde/transforms/svd_test.py b/phaseSpaceSampling/nde/transforms/svd_test.py index c829da4..f07a3bf 100644 --- a/phaseSpaceSampling/nde/transforms/svd_test.py +++ b/phaseSpaceSampling/nde/transforms/svd_test.py @@ -1,18 +1,21 @@ -import torch import unittest +import torch from phaseSpaceSampling.nde.transforms import svd from phaseSpaceSampling.nde.transforms.transform_test import TransformTest - from phaseSpaceSampling.utils.torchutils import logabsdet -class SVDLinearTest(TransformTest): +class SVDLinearTest(TransformTest): def setUp(self): self.features = 3 - self.transform = svd.SVDLinear(features=self.features, num_householder=4) - self.transform.bias.data = torch.randn(self.features) # Just so bias isn't zero. + self.transform = svd.SVDLinear( + features=self.features, num_householder=4 + ) + self.transform.bias.data = torch.randn( + self.features + ) # Just so bias isn't zero. diagonal = torch.diag(torch.exp(self.transform.log_diagonal)) orthogonal_1 = self.transform.orthogonal_1.matrix() @@ -58,7 +61,9 @@ def test_weight(self): def test_weight_inverse(self): weight_inverse = self.transform.weight_inverse() - self.assert_tensor_is_good(weight_inverse, [self.features, self.features]) + self.assert_tensor_is_good( + weight_inverse, [self.features, self.features] + ) self.assertEqual(weight_inverse, self.weight_inverse) def test_logabsdet(self): @@ -72,5 +77,5 @@ def test_forward_inverse_are_consistent(self): self.assert_forward_inverse_are_consistent(self.transform, inputs) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nde/transforms/transform_test.py b/phaseSpaceSampling/nde/transforms/transform_test.py index f6fdea6..c27452a 100644 --- a/phaseSpaceSampling/nde/transforms/transform_test.py +++ b/phaseSpaceSampling/nde/transforms/transform_test.py @@ -25,8 +25,7 @@ def assert_forward_inverse_are_consistent(self, transform, inputs): self.assertEqual(logabsdet, torch.zeros(inputs.shape[:1])) def assertNotEqual(self, first, second, msg=None): - if ((self._eps and (first - second).abs().max().item() < self._eps) or - (not self._eps and torch.equal(first, second))): + if (self._eps and (first - second).abs().max().item() < self._eps) or ( + not self._eps and torch.equal(first, second) + ): self._fail_with_message(msg, "The tensors are _not_ different!") - - diff --git a/phaseSpaceSampling/nn/__init__.py b/phaseSpaceSampling/nn/__init__.py index 57933b9..40b230b 100644 --- a/phaseSpaceSampling/nn/__init__.py +++ b/phaseSpaceSampling/nn/__init__.py @@ -1,17 +1,10 @@ -from .resnet import ( - ResidualNet, - ConvResidualNet, -) - -from .unet import UNet - from .attention import ConvAttentionNet - -from .mlp import MLP - from .conv import ( - SylvesterFlowConvEncoderNet, - SylvesterFlowConvDecoderNet, + ConvDecoder, ConvEncoder, - ConvDecoder -) \ No newline at end of file + SylvesterFlowConvDecoderNet, + SylvesterFlowConvEncoderNet, +) +from .mlp import MLP +from .resnet import ConvResidualNet, ResidualNet +from .unet import UNet diff --git a/phaseSpaceSampling/nn/attention.py b/phaseSpaceSampling/nn/attention.py index db264a9..0867dd5 100644 --- a/phaseSpaceSampling/nn/attention.py +++ b/phaseSpaceSampling/nn/attention.py @@ -1,11 +1,9 @@ import torch - from torch import nn from torch.nn import functional as F from phaseSpaceSampling.utils.torchutils import get_num_parameters - # # Projection of x onto y # def proj(x, y): # return torch.mm(y, x.t()) * y / torch.mm(y, y.t()) @@ -140,16 +138,36 @@ def __init__(self, channels, which_conv=nn.Conv2d, heads=8): self.channels = channels self.which_conv = which_conv self.heads = heads - self.theta = self.which_conv(self.channels, self.channels // heads, kernel_size=1, padding=0, - bias=False) - self.phi = self.which_conv(self.channels, self.channels // heads, kernel_size=1, padding=0, - bias=False) - self.g = self.which_conv(self.channels, self.channels // 2, kernel_size=1, padding=0, - bias=False) - self.o = self.which_conv(self.channels // 2, self.channels, kernel_size=1, padding=0, - bias=False) + self.theta = self.which_conv( + self.channels, + self.channels // heads, + kernel_size=1, + padding=0, + bias=False, + ) + self.phi = self.which_conv( + self.channels, + self.channels // heads, + kernel_size=1, + padding=0, + bias=False, + ) + self.g = self.which_conv( + self.channels, + self.channels // 2, + kernel_size=1, + padding=0, + bias=False, + ) + self.o = self.which_conv( + self.channels // 2, + self.channels, + kernel_size=1, + padding=0, + bias=False, + ) # Learnable gain parameter - self.gamma = nn.Parameter(torch.tensor(0.), requires_grad=True) + self.gamma = nn.Parameter(torch.tensor(0.0), requires_grad=True) def forward(self, inputs, y=None): # Apply convs @@ -157,45 +175,56 @@ def forward(self, inputs, y=None): phi = F.max_pool2d(self.phi(inputs), [2, 2]) g = F.max_pool2d(self.g(inputs), [2, 2]) # Perform reshapes - theta = theta.view(-1, self.channels // self.heads, inputs.shape[2] * inputs.shape[3]) - phi = phi.view(-1, self.channels // self.heads, inputs.shape[2] * inputs.shape[3] // 4) - g = g.view(-1, self.channels // 2, inputs.shape[2] * inputs.shape[3] // 4) + theta = theta.view( + -1, self.channels // self.heads, inputs.shape[2] * inputs.shape[3] + ) + phi = phi.view( + -1, + self.channels // self.heads, + inputs.shape[2] * inputs.shape[3] // 4, + ) + g = g.view( + -1, self.channels // 2, inputs.shape[2] * inputs.shape[3] // 4 + ) # Matmul and softmax to get attention maps beta = F.softmax(torch.bmm(theta.transpose(1, 2), phi), -1) # Attention map times g path - o = self.o(torch.bmm(g, beta.transpose(1, 2)).view(-1, self.channels // 2, inputs.shape[2], - inputs.shape[3])) + o = self.o( + torch.bmm(g, beta.transpose(1, 2)).view( + -1, self.channels // 2, inputs.shape[2], inputs.shape[3] + ) + ) outputs = self.gamma * o + inputs return outputs class ConvAttentionNet(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - num_blocks - ): + def __init__(self, in_channels, out_channels, hidden_channels, num_blocks): super().__init__() - self.initial_layer = nn.Conv2d(in_channels, hidden_channels, kernel_size=1, padding=0) - self.attention_blocks = nn.ModuleList([ - AttentionBlock( - channels=hidden_channels, - which_conv=nn.Conv2d, - heads=8 - ) for _ in range(num_blocks) - ]) + self.initial_layer = nn.Conv2d( + in_channels, hidden_channels, kernel_size=1, padding=0 + ) + self.attention_blocks = nn.ModuleList( + [ + AttentionBlock( + channels=hidden_channels, which_conv=nn.Conv2d, heads=8 + ) + for _ in range(num_blocks) + ] + ) # if use_batch_norm: - self.batch_norm_layers = nn.ModuleList([ - nn.BatchNorm2d( - num_features=hidden_channels - ) - ]) - self.final_layer = nn.Conv2d(hidden_channels, out_channels, kernel_size=1, padding=0) + self.batch_norm_layers = nn.ModuleList( + [nn.BatchNorm2d(num_features=hidden_channels)] + ) + self.final_layer = nn.Conv2d( + hidden_channels, out_channels, kernel_size=1, padding=0 + ) def forward(self, inputs): temps = self.initial_layer(inputs) - for attention, batch_norm in zip(self.attention_blocks, self.batch_norm_layers): + for attention, batch_norm in zip( + self.attention_blocks, self.batch_norm_layers + ): temps = attention(temps) temps = batch_norm(temps) outputs = self.final_layer(temps) @@ -209,12 +238,12 @@ def main(): in_channels=channels, out_channels=2 * channels, hidden_channels=32, - num_blocks=4 + num_blocks=4, ) print(get_num_parameters(net)) outputs = net(inputs) print(outputs.shape) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/phaseSpaceSampling/nn/conv.py b/phaseSpaceSampling/nn/conv.py index c9c8813..ec0cdfb 100644 --- a/phaseSpaceSampling/nn/conv.py +++ b/phaseSpaceSampling/nn/conv.py @@ -1,16 +1,28 @@ import torch - from torch import nn from torch.nn import functional as F class GatedConv2d(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, stride, - padding, dilation=1): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation=1, + ): super(GatedConv2d, self).__init__() - self.conv = nn.Conv2d(in_channels, 2 * out_channels, kernel_size, - stride, padding, dilation) + self.conv = nn.Conv2d( + in_channels, + 2 * out_channels, + kernel_size, + stride, + padding, + dilation, + ) def forward(self, inputs): temps = self.conv(inputs) @@ -19,13 +31,27 @@ def forward(self, inputs): class GatedConvTranspose2d(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, - stride, padding, output_padding=0, dilation=1): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + output_padding=0, + dilation=1, + ): super(GatedConvTranspose2d, self).__init__() - self.conv_transpose = nn.ConvTranspose2d(in_channels, 2 * out_channels, - kernel_size, stride, padding, - output_padding, dilation=dilation) + self.conv_transpose = nn.ConvTranspose2d( + in_channels, + 2 * out_channels, + kernel_size, + stride, + padding, + output_padding, + dilation=dilation, + ) def forward(self, inputs): temps = self.conv_transpose(inputs) @@ -39,61 +65,62 @@ def __init__(self, context_features, last_kernel_shape=(7, 7)): self.context_features = context_features self.last_kernel_shape = last_kernel_shape - self.gated_conv_layers = nn.ModuleList([ - GatedConv2d( - in_channels=1, - out_channels=32, - kernel_size=5, - padding=2, - stride=1 - ), - GatedConv2d( # 2 - in_channels=32, - out_channels=32, - kernel_size=5, - padding=2, - stride=2 - ), - GatedConv2d( # 3 - in_channels=32, - out_channels=64, - kernel_size=5, - padding=2, - stride=1 - ), - GatedConv2d( # 4 - in_channels=64, - out_channels=64, - kernel_size=5, - padding=2, - stride=2 - ), - GatedConv2d( # 5 - in_channels=64, - out_channels=64, - kernel_size=5, - padding=2, - stride=1 - ), - GatedConv2d( # 6 - in_channels=64, - out_channels=64, - kernel_size=5, - padding=2, - stride=1 - ), - GatedConv2d( # 7 - in_channels=64, - out_channels=256, - kernel_size=self.last_kernel_shape, - padding=0, - stride=1 - ) - ]) + self.gated_conv_layers = nn.ModuleList( + [ + GatedConv2d( + in_channels=1, + out_channels=32, + kernel_size=5, + padding=2, + stride=1, + ), + GatedConv2d( # 2 + in_channels=32, + out_channels=32, + kernel_size=5, + padding=2, + stride=2, + ), + GatedConv2d( # 3 + in_channels=32, + out_channels=64, + kernel_size=5, + padding=2, + stride=1, + ), + GatedConv2d( # 4 + in_channels=64, + out_channels=64, + kernel_size=5, + padding=2, + stride=2, + ), + GatedConv2d( # 5 + in_channels=64, + out_channels=64, + kernel_size=5, + padding=2, + stride=1, + ), + GatedConv2d( # 6 + in_channels=64, + out_channels=64, + kernel_size=5, + padding=2, + stride=1, + ), + GatedConv2d( # 7 + in_channels=64, + out_channels=256, + kernel_size=self.last_kernel_shape, + padding=0, + stride=1, + ), + ] + ) self.context_layer = nn.Linear( - in_features=256, - out_features=self.context_features + in_features=256, out_features=self.context_features ) def forward(self, inputs): @@ -113,59 +140,61 @@ def __init__(self, latent_features, last_kernel_shape=(7, 7)): self.latent_features = latent_features self.last_kernel_shape = last_kernel_shape - self.gated_conv_transpose_layers = nn.ModuleList([ - GatedConvTranspose2d( - in_channels=self.latent_features, - out_channels=64, - kernel_size=self.last_kernel_shape, - padding=0, - stride=1 - ), - GatedConvTranspose2d( # 2 - in_channels=64, - out_channels=64, - kernel_size=5, - padding=2, - stride=1 - ), - GatedConvTranspose2d( # 3 - in_channels=64, - out_channels=32, - kernel_size=5, - padding=2, - stride=2, - output_padding=1 - ), - GatedConvTranspose2d( # 4 - in_channels=32, - out_channels=32, - kernel_size=5, - padding=2, - stride=1 - ), - GatedConvTranspose2d( # 5 - in_channels=32, - out_channels=32, - kernel_size=5, - padding=2, - stride=2, - output_padding=1 - ), - GatedConv2d( # 6 - in_channels=32, - out_channels=32, - kernel_size=5, - padding=2, - stride=1 - ), - GatedConv2d( # 7 - in_channels=32, - out_channels=1, - kernel_size=1, - padding=0, - stride=1 - ) - ]) + self.gated_conv_transpose_layers = nn.ModuleList( + [ + GatedConvTranspose2d( + in_channels=self.latent_features, + out_channels=64, + kernel_size=self.last_kernel_shape, + padding=0, + stride=1, + ), + GatedConvTranspose2d( # 2 + in_channels=64, + out_channels=64, + kernel_size=5, + padding=2, + stride=1, + ), + GatedConvTranspose2d( # 3 + in_channels=64, + out_channels=32, + kernel_size=5, + padding=2, + stride=2, + output_padding=1, + ), + GatedConvTranspose2d( # 4 + in_channels=32, + out_channels=32, + kernel_size=5, + padding=2, + stride=1, + ), + GatedConvTranspose2d( # 5 + in_channels=32, + out_channels=32, + kernel_size=5, + padding=2, + stride=2, + output_padding=1, + ), + GatedConv2d( # 6 + in_channels=32, + out_channels=32, + kernel_size=5, + padding=2, + stride=1, + ), + GatedConv2d( # 7 + in_channels=32, + out_channels=1, + kernel_size=1, + padding=0, + stride=1, + ), + ] + ) def forward(self, inputs): temps = inputs[..., None, None] @@ -178,8 +207,14 @@ def forward(self, inputs): class ResidualBlock(nn.Module): - def __init__(self, in_channels, resample=None, activation=F.relu, - dropout_probability=0., first=False): + def __init__( + self, + in_channels, + resample=None, + activation=F.relu, + dropout_probability=0.0, + first=False, + ): super().__init__() self.in_channels = in_channels self.resample = resample @@ -189,7 +224,7 @@ def __init__(self, in_channels, resample=None, activation=F.relu, in_channels=in_channels, out_channels=in_channels, kernel_size=3, - padding=1 + padding=1, ) if resample is None: @@ -198,31 +233,31 @@ def __init__(self, in_channels, resample=None, activation=F.relu, in_channels=in_channels, out_channels=in_channels, kernel_size=3, - padding=1 + padding=1, ) - elif resample == 'down': + elif resample == "down": self.shortcut_layer = nn.Conv2d( in_channels=in_channels, out_channels=2 * in_channels, kernel_size=3, stride=2, - padding=1 + padding=1, ) self.residual_2_layer = nn.Conv2d( in_channels=in_channels, out_channels=2 * in_channels, kernel_size=3, stride=2, - padding=1 + padding=1, ) - elif resample == 'up': + elif resample == "up": self.shortcut_layer = nn.ConvTranspose2d( in_channels=in_channels, out_channels=in_channels // 2, kernel_size=3, stride=2, padding=1, - output_padding=0 if first else 1 + output_padding=0 if first else 1, ) self.residual_2_layer = nn.ConvTranspose2d( in_channels=in_channels, @@ -230,7 +265,7 @@ def __init__(self, in_channels, resample=None, activation=F.relu, kernel_size=3, stride=2, padding=1, - output_padding=0 if first else 1 + output_padding=0 if first else 1, ) if dropout_probability > 0: @@ -239,7 +274,6 @@ def __init__(self, in_channels, resample=None, activation=F.relu, self.dropout = None def forward(self, inputs): - shortcut = self.shortcut_layer(inputs) residual_1 = self.activation(inputs) residual_1 = self.residual_layer_1(residual_1) @@ -252,31 +286,53 @@ def forward(self, inputs): class ConvEncoder(nn.Module): - def __init__(self, context_features, channels_multiplier, - activation=F.relu, dropout_probability=0.): + def __init__( + self, + context_features, + channels_multiplier, + activation=F.relu, + dropout_probability=0.0, + ): super().__init__() self.context_features = context_features self.channels_multiplier = channels_multiplier self.activation = activation self.initial_layer = nn.Conv2d(1, channels_multiplier, kernel_size=1) - self.residual_blocks = nn.ModuleList([ - ResidualBlock(in_channels=channels_multiplier, - dropout_probability=dropout_probability), - ResidualBlock(in_channels=channels_multiplier, resample='down', - dropout_probability=dropout_probability), - ResidualBlock(in_channels=channels_multiplier * 2, - dropout_probability=dropout_probability), - ResidualBlock(in_channels=channels_multiplier * 2, resample='down', - dropout_probability=dropout_probability), - ResidualBlock(in_channels=channels_multiplier * 4, - dropout_probability=dropout_probability), - ResidualBlock(in_channels=channels_multiplier * 4, resample='down', - dropout_probability=dropout_probability) - ]) + self.residual_blocks = nn.ModuleList( + [ + ResidualBlock( + in_channels=channels_multiplier, + dropout_probability=dropout_probability, + ), + ResidualBlock( + in_channels=channels_multiplier, + resample="down", + dropout_probability=dropout_probability, + ), + ResidualBlock( + in_channels=channels_multiplier * 2, + dropout_probability=dropout_probability, + ), + ResidualBlock( + in_channels=channels_multiplier * 2, + resample="down", + dropout_probability=dropout_probability, + ), + ResidualBlock( + in_channels=channels_multiplier * 4, + dropout_probability=dropout_probability, + ), + ResidualBlock( + in_channels=channels_multiplier * 4, + resample="down", + dropout_probability=dropout_probability, + ), + ] + ) self.final_layer = nn.Linear( in_features=(4 * 4 * channels_multiplier * 8), - out_features=context_features + out_features=context_features, ) def forward(self, inputs): @@ -284,13 +340,20 @@ def forward(self, inputs): for residual_block in self.residual_blocks: temps = residual_block(temps) temps = self.activation(temps) - outputs = self.final_layer(temps.reshape(-1, 4 * 4 * self.channels_multiplier * 8)) + outputs = self.final_layer( + temps.reshape(-1, 4 * 4 * self.channels_multiplier * 8) + ) return outputs class ConvDecoder(nn.Module): - def __init__(self, latent_features, channels_multiplier, - activation=F.relu, dropout_probability=0.): + def __init__( + self, + latent_features, + channels_multiplier, + activation=F.relu, + dropout_probability=0.0, + ): super().__init__() self.latent_features = latent_features self.channels_multiplier = channels_multiplier @@ -298,26 +361,42 @@ def __init__(self, latent_features, channels_multiplier, self.initial_layer = nn.Linear( in_features=latent_features, - out_features=(4 * 4 * channels_multiplier * 8) + out_features=(4 * 4 * channels_multiplier * 8), + ) + self.residual_blocks = nn.ModuleList( + [ + ResidualBlock( + in_channels=channels_multiplier * 8, + dropout_probability=dropout_probability, + ), + ResidualBlock( + in_channels=channels_multiplier * 8, + resample="up", + first=True, + dropout_probability=dropout_probability, + ), + ResidualBlock( + in_channels=channels_multiplier * 4, + dropout_probability=dropout_probability, + ), + ResidualBlock( + in_channels=channels_multiplier * 4, + resample="up", + dropout_probability=dropout_probability, + ), + ResidualBlock( + in_channels=channels_multiplier * 2, + dropout_probability=dropout_probability, + ), + ResidualBlock( + in_channels=channels_multiplier * 2, + resample="up", + dropout_probability=dropout_probability, + ), + ] ) - self.residual_blocks = nn.ModuleList([ - ResidualBlock(in_channels=channels_multiplier * 8, - dropout_probability=dropout_probability), - ResidualBlock(in_channels=channels_multiplier * 8, resample='up', first=True, - dropout_probability=dropout_probability), - ResidualBlock(in_channels=channels_multiplier * 4, - dropout_probability=dropout_probability), - ResidualBlock(in_channels=channels_multiplier * 4, resample='up', - dropout_probability=dropout_probability), - ResidualBlock(in_channels=channels_multiplier * 2, - dropout_probability=dropout_probability), - ResidualBlock(in_channels=channels_multiplier * 2, resample='up', - dropout_probability=dropout_probability) - ]) self.final_layer = nn.Conv2d( - in_channels=channels_multiplier, - out_channels=1, - kernel_size=1 + in_channels=channels_multiplier, out_channels=1, kernel_size=1 ) def forward(self, inputs): @@ -342,5 +421,5 @@ def main(): outputs = net(outputs) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/phaseSpaceSampling/nn/mlp.py b/phaseSpaceSampling/nn/mlp.py index ea7efa6..604adf6 100644 --- a/phaseSpaceSampling/nn/mlp.py +++ b/phaseSpaceSampling/nn/mlp.py @@ -2,20 +2,21 @@ import numpy as np import torch - -from torch.nn import functional as F from torch import nn +from torch.nn import functional as F class MLP(nn.Module): """A standard multi-layer perceptron.""" - def __init__(self, - in_shape, - out_shape, - hidden_sizes, - activation=F.relu, - activate_output=False): + def __init__( + self, + in_shape, + out_shape, + hidden_sizes, + activation=F.relu, + activate_output=False, + ): """ Args: in_shape: tuple, list or torch.Size, the shape of the input. @@ -32,19 +33,26 @@ def __init__(self, self._activate_output = activate_output if len(hidden_sizes) == 0: - raise ValueError('List of hidden sizes can\'t be empty.') + raise ValueError("List of hidden sizes can't be empty.") self._input_layer = nn.Linear(np.prod(in_shape), hidden_sizes[0]) - self._hidden_layers = nn.ModuleList([ - nn.Linear(in_size, out_size) - for in_size, out_size in zip(hidden_sizes[:-1], hidden_sizes[1:]) - ]) + self._hidden_layers = nn.ModuleList( + [ + nn.Linear(in_size, out_size) + for in_size, out_size in zip( + hidden_sizes[:-1], hidden_sizes[1:] + ) + ] + ) self._output_layer = nn.Linear(hidden_sizes[-1], np.prod(out_shape)) def forward(self, inputs): if inputs.shape[1:] != self._in_shape: - raise ValueError('Expected inputs of shape {}, got {}.'.format( - self._in_shape, inputs.shape[1:])) + raise ValueError( + "Expected inputs of shape {}, got {}.".format( + self._in_shape, inputs.shape[1:] + ) + ) inputs = inputs.reshape(-1, np.prod(self._in_shape)) outputs = self._input_layer(inputs) diff --git a/phaseSpaceSampling/nn/mlp_test.py b/phaseSpaceSampling/nn/mlp_test.py index eb7de23..da6c062 100644 --- a/phaseSpaceSampling/nn/mlp_test.py +++ b/phaseSpaceSampling/nn/mlp_test.py @@ -1,14 +1,14 @@ """Tests for multi-layer perceptrons.""" +import unittest + import torch import torchtestcase -import unittest from phaseSpaceSampling.nn import mlp class MLPTest(torchtestcase.TorchTestCase): - def test_forward(self): batch_size = 10 in_shape = [2, 3, 4] @@ -24,7 +24,9 @@ def test_forward(self): ) outputs = model(inputs) self.assertIsInstance(outputs, torch.Tensor) - self.assertEqual(outputs.shape, torch.Size([batch_size] + out_shape)) + self.assertEqual( + outputs.shape, torch.Size([batch_size] + out_shape) + ) self.assertFalse(torch.isnan(outputs).any()) self.assertFalse(torch.isinf(outputs).any()) @@ -36,5 +38,5 @@ def test_forward(self): ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/phaseSpaceSampling/nn/resnet.py b/phaseSpaceSampling/nn/resnet.py index 011f9d4..a6ac232 100644 --- a/phaseSpaceSampling/nn/resnet.py +++ b/phaseSpaceSampling/nn/resnet.py @@ -1,36 +1,36 @@ import torch - from torch import nn -from torch.nn import functional as F, init +from torch.nn import functional as F +from torch.nn import init -from phaseSpaceSampling.utils.torchutils import get_num_parameters +from phaseSpaceSampling.utils.torchutils import get_num_parameters class ResidualBlock(nn.Module): """A general-purpose residual block. Works only with 1-dim inputs.""" - def __init__(self, - features, - context_features, - activation=F.relu, - dropout_probability=0., - use_batch_norm=False, - zero_initialization=True): + def __init__( + self, + features, + context_features, + activation=F.relu, + dropout_probability=0.0, + use_batch_norm=False, + zero_initialization=True, + ): super().__init__() self.activation = activation self.use_batch_norm = use_batch_norm if use_batch_norm: - self.batch_norm_layers = nn.ModuleList([ - nn.BatchNorm1d(features, eps=1e-3) - for _ in range(2) - ]) + self.batch_norm_layers = nn.ModuleList( + [nn.BatchNorm1d(features, eps=1e-3) for _ in range(2)] + ) if context_features is not None: self.context_layer = nn.Linear(context_features, features) - self.linear_layers = nn.ModuleList([ - nn.Linear(features, features) - for _ in range(2) - ]) + self.linear_layers = nn.ModuleList( + [nn.Linear(features, features) for _ in range(2)] + ) self.dropout = nn.Dropout(p=dropout_probability) if zero_initialization: init.uniform_(self.linear_layers[-1].weight, -1e-3, 1e-3) @@ -49,11 +49,7 @@ def forward(self, inputs, context=None): temps = self.linear_layers[1](temps) if context is not None: temps = F.glu( - torch.cat( - (temps, self.context_layer(context)), - dim=1 - ), - dim=1 + torch.cat((temps, self.context_layer(context)), dim=1), dim=1 ) return inputs + temps @@ -61,40 +57,45 @@ def forward(self, inputs, context=None): class ResidualNet(nn.Module): """A general-purpose residual network. Works only with 1-dim inputs.""" - def __init__(self, - in_features, - out_features, - hidden_features, - context_features=None, - num_blocks=2, - activation=F.relu, - dropout_probability=0., - use_batch_norm=False): + def __init__( + self, + in_features, + out_features, + hidden_features, + context_features=None, + num_blocks=2, + activation=F.relu, + dropout_probability=0.0, + use_batch_norm=False, + ): super().__init__() self.hidden_features = hidden_features self.context_features = context_features if context_features is not None: - self.initial_layer = nn.Linear(in_features + context_features, hidden_features) + self.initial_layer = nn.Linear( + in_features + context_features, hidden_features + ) else: self.initial_layer = nn.Linear(in_features, hidden_features) - self.blocks = nn.ModuleList([ - ResidualBlock( - features=hidden_features, - context_features=context_features, - activation=activation, - dropout_probability=dropout_probability, - use_batch_norm=use_batch_norm, - ) for _ in range(num_blocks) - ]) + self.blocks = nn.ModuleList( + [ + ResidualBlock( + features=hidden_features, + context_features=context_features, + activation=activation, + dropout_probability=dropout_probability, + use_batch_norm=use_batch_norm, + ) + for _ in range(num_blocks) + ] + ) self.final_layer = nn.Linear(hidden_features, out_features) def forward(self, inputs, context=None): if context is None: temps = self.initial_layer(inputs) else: - temps = self.initial_layer( - torch.cat((inputs, context), dim=1) - ) + temps = self.initial_layer(torch.cat((inputs, context), dim=1)) for block in self.blocks: temps = block(temps, context=context) outputs = self.final_layer(temps) @@ -102,13 +103,15 @@ def forward(self, inputs, context=None): class ConvResidualBlock(nn.Module): - def __init__(self, - channels, - context_channels=None, - activation=F.relu, - dropout_probability=0., - use_batch_norm=False, - zero_initialization=True): + def __init__( + self, + channels, + context_channels=None, + activation=F.relu, + dropout_probability=0.0, + use_batch_norm=False, + zero_initialization=True, + ): super().__init__() self.activation = activation @@ -117,18 +120,19 @@ def __init__(self, in_channels=context_channels, out_channels=channels, kernel_size=1, - padding=0 + padding=0, ) self.use_batch_norm = use_batch_norm if use_batch_norm: - self.batch_norm_layers = nn.ModuleList([ - nn.BatchNorm2d(channels, eps=1e-3) + self.batch_norm_layers = nn.ModuleList( + [nn.BatchNorm2d(channels, eps=1e-3) for _ in range(2)] + ) + self.conv_layers = nn.ModuleList( + [ + nn.Conv2d(channels, channels, kernel_size=3, padding=1) for _ in range(2) - ]) - self.conv_layers = nn.ModuleList([ - nn.Conv2d(channels, channels, kernel_size=3, padding=1) - for _ in range(2) - ]) + ] + ) self.dropout = nn.Dropout(p=dropout_probability) if zero_initialization: init.uniform_(self.conv_layers[-1].weight, -1e-3, 1e-3) @@ -147,26 +151,23 @@ def forward(self, inputs, context=None): temps = self.conv_layers[1](temps) if context is not None: temps = F.glu( - torch.cat( - (temps, self.context_layer(context)), - dim=1 - ), - dim=1 + torch.cat((temps, self.context_layer(context)), dim=1), dim=1 ) return inputs + temps class ConvResidualNet(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - context_channels=None, - num_blocks=2, - activation=F.relu, - dropout_probability=0., - use_batch_norm=False - ): + def __init__( + self, + in_channels, + out_channels, + hidden_channels, + context_channels=None, + num_blocks=2, + activation=F.relu, + dropout_probability=0.0, + use_batch_norm=False, + ): super().__init__() self.context_channels = context_channels self.hidden_channels = hidden_channels @@ -175,33 +176,36 @@ def __init__(self, in_channels=in_channels + context_channels, out_channels=hidden_channels, kernel_size=1, - padding=0 + padding=0, ) else: self.initial_layer = nn.Conv2d( in_channels=in_channels, out_channels=hidden_channels, kernel_size=1, - padding=0 + padding=0, ) - self.blocks = nn.ModuleList([ - ConvResidualBlock( - channels=hidden_channels, - context_channels=context_channels, - activation=activation, - dropout_probability=dropout_probability, - use_batch_norm=use_batch_norm, - ) for _ in range(num_blocks) - ]) - self.final_layer = nn.Conv2d(hidden_channels, out_channels, kernel_size=1, padding=0) + self.blocks = nn.ModuleList( + [ + ConvResidualBlock( + channels=hidden_channels, + context_channels=context_channels, + activation=activation, + dropout_probability=dropout_probability, + use_batch_norm=use_batch_norm, + ) + for _ in range(num_blocks) + ] + ) + self.final_layer = nn.Conv2d( + hidden_channels, out_channels, kernel_size=1, padding=0 + ) def forward(self, inputs, context=None): if context is None: temps = self.initial_layer(inputs) else: - temps = self.initial_layer( - torch.cat((inputs, context), dim=1) - ) + temps = self.initial_layer(torch.cat((inputs, context), dim=1)) for block in self.blocks: temps = block(temps, context) outputs = self.final_layer(temps) @@ -219,12 +223,12 @@ def main(): context_channels=channels // 2, num_blocks=2, dropout_probability=0.1, - use_batch_norm=True + use_batch_norm=True, ) print(get_num_parameters(net)) outputs = net(inputs, context) print(outputs.shape) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/phaseSpaceSampling/nn/unet.py b/phaseSpaceSampling/nn/unet.py index 875e9cb..0939c02 100644 --- a/phaseSpaceSampling/nn/unet.py +++ b/phaseSpaceSampling/nn/unet.py @@ -1,5 +1,4 @@ import numpy as np - from torch import nn from torch.nn import functional as F @@ -7,43 +6,54 @@ class UNet(nn.Module): - def __init__(self, - in_features, - max_hidden_features, - num_layers, - out_features, - nonlinearity=F.relu): + def __init__( + self, + in_features, + max_hidden_features, + num_layers, + out_features, + nonlinearity=F.relu, + ): super().__init__() - assert is_power_of_two(max_hidden_features), \ - '\'max_hidden_features\' must be a power of two.' - assert max_hidden_features // 2 ** num_layers > 1, \ - '\'num_layers\' must be {} or fewer'.format(int(np.log2(max_hidden_features) - 1)) + assert is_power_of_two( + max_hidden_features + ), "'max_hidden_features' must be a power of two." + assert ( + max_hidden_features // 2**num_layers > 1 + ), "'num_layers' must be {} or fewer".format( + int(np.log2(max_hidden_features) - 1) + ) self.nonlinearity = nonlinearity self.num_layers = num_layers self.initial_layer = nn.Linear(in_features, max_hidden_features) - self.down_layers = nn.ModuleList([ - nn.Linear( - in_features=max_hidden_features // 2 ** i, - out_features=max_hidden_features // 2 ** (i + 1) - ) - for i in range(num_layers) - ]) + self.down_layers = nn.ModuleList( + [ + nn.Linear( + in_features=max_hidden_features // 2**i, + out_features=max_hidden_features // 2 ** (i + 1), + ) + for i in range(num_layers) + ] + ) self.middle_layer = nn.Linear( - in_features=max_hidden_features // 2 ** num_layers, - out_features=max_hidden_features // 2 ** num_layers) - - self.up_layers = nn.ModuleList([ - nn.Linear( - in_features=max_hidden_features // 2 ** (i + 1), - out_features=max_hidden_features // 2 ** i - ) - for i in range(num_layers - 1, -1, -1) - ]) + in_features=max_hidden_features // 2**num_layers, + out_features=max_hidden_features // 2**num_layers, + ) + + self.up_layers = nn.ModuleList( + [ + nn.Linear( + in_features=max_hidden_features // 2 ** (i + 1), + out_features=max_hidden_features // 2**i, + ) + for i in range(num_layers - 1, -1, -1) + ] + ) self.final_layer = nn.Linear(max_hidden_features, out_features) diff --git a/phaseSpaceSampling/postProcess/plotLoss.py b/phaseSpaceSampling/postProcess/plotLoss.py index cd36087..d95a444 100644 --- a/phaseSpaceSampling/postProcess/plotLoss.py +++ b/phaseSpaceSampling/postProcess/plotLoss.py @@ -1,13 +1,15 @@ +import argparse import os import sys import matplotlib.pyplot as plt import numpy as np - from prettyPlot.parser import parse_input_file from prettyPlot.plotting import pretty_labels -import argparse +import phaseSpaceSampling.utils.parallel as par +from phaseSpaceSampling import INPUT_DIR + parser = argparse.ArgumentParser(description="Loss plotting") parser.add_argument( "-i", @@ -23,13 +25,24 @@ # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ~~~~ Parse input # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +inpt_file = args.input +if not os.path.isfile(inpt_file): + new_inpt_file = os.path.join(INPUT_DIR, os.path.split(inpt_file)[-1]) + par.printRoot(f"WARNING: {inpt_file} not found trying {new_inpt_file} ...") + if not os.path.isfile(new_inpt_file): + par.printRoot( + f"ERROR: could not open data {inpt_file} or {new_inpt_file}" + ) + sys.exit() + else: + inpt_file = new_inpt_file -inpt = parse_input_file(args.input) +inpt = parse_input_file(inpt_file) -if inpt["pdf_method"].lower() == "normalizingflow": +if inpt["pdf_method"].lower() == "normalizingflow": nIter = int(inpt["num_pdf_iter"]) - + # Folder where figures are saved figureFolder = "Figures" os.makedirs(figureFolder, exist_ok=True) @@ -40,7 +53,9 @@ f"TrainingLog/log_iter{i}.csv", delimiter=";", skip_header=1 ) axs[i].plot(Loss[:, 0], Loss[:, 1], color="k", linewidth=3) - pretty_labels("Step", "Loss", 14, title=f"iteration {i}", ax=axs[i]) + pretty_labels( + "Step", "Loss", 14, title=f"iteration {i}", ax=axs[i] + ) else: fig = plt.figure() Loss = np.genfromtxt( @@ -48,6 +63,6 @@ ) plt.plot(Loss[:, 0], Loss[:, 1], color="k", linewidth=3) pretty_labels("Step", "Loss", 14, title=f"iteration 0") - + plt.savefig(figureFolder + "/loss.png") plt.close() diff --git a/phaseSpaceSampling/postProcess/visualizeDownSampled_subplots.py b/phaseSpaceSampling/postProcess/visualizeDownSampled_subplots.py index 600f524..4546d6d 100644 --- a/phaseSpaceSampling/postProcess/visualizeDownSampled_subplots.py +++ b/phaseSpaceSampling/postProcess/visualizeDownSampled_subplots.py @@ -1,13 +1,14 @@ -import sys - -import numpy as np - import os +import sys import matplotlib.pyplot as plt +import numpy as np from mpl_toolkits.axes_grid1 import make_axes_locatable -from prettyPlot.plotting import pretty_labels from prettyPlot.parser import parse_input_file +from prettyPlot.plotting import pretty_labels + +import phaseSpaceSampling.utils.parallel as par +from phaseSpaceSampling import INPUT_DIR def plotScatterProjection(data, fullData, fieldNames, lims): @@ -48,9 +49,11 @@ def plotScatterProjection(data, fullData, fieldNames, lims): tick.set_rotation(33) ax.set_ylim(lims[1]) - plt.tight_layout() + plt.tight_layout() + import argparse + parser = argparse.ArgumentParser(description="Visualize downsampled data") parser.add_argument( "-i", @@ -66,8 +69,19 @@ def plotScatterProjection(data, fullData, fieldNames, lims): # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ~~~~ Parse input # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +inpt_file = args.input +if not os.path.isfile(inpt_file): + new_inpt_file = os.path.join(INPUT_DIR, os.path.split(inpt_file)[-1]) + par.printRoot(f"WARNING: {inpt_file} not found trying {new_inpt_file} ...") + if not os.path.isfile(new_inpt_file): + par.printRoot( + f"ERROR: could not open data {inpt_file} or {new_inpt_file}" + ) + sys.exit() + else: + inpt_file = new_inpt_file -inpt = parse_input_file(args.input) +inpt = parse_input_file(inpt_file) # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ~~~~ Parameters to save @@ -120,7 +134,10 @@ def plotScatterProjection(data, fullData, fieldNames, lims): downSampledData = fullData[np.load(dataFile)["indices"], :] plotScatterProjection(downSampledData, fullData, fieldNames, lims) plt.savefig( - os.path.join(figureFolder, f"{inpt['prefixDownsampledData']}_{nSample}_it{pdf_iter}.png") + os.path.join( + figureFolder, + f"{inpt['prefixDownsampledData']}_{nSample}_it{pdf_iter}.png", + ) ) plt.close() print("DONE!") diff --git a/phaseSpaceSampling/sampler.py b/phaseSpaceSampling/sampler.py index 7109df0..4f6ccac 100644 --- a/phaseSpaceSampling/sampler.py +++ b/phaseSpaceSampling/sampler.py @@ -1,23 +1,21 @@ +import os import sys +import time import numpy as np import torch +from prettyPlot.progressBar import print_progress_bar +from sklearn.neighbors import NearestNeighbors from torch import optim from torch.nn.utils import clip_grad_norm_ from torch.utils import data from torch.utils.data import DataLoader -import os -import time - -import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling.utils.flowUtils import create_base_transform -from phaseSpaceSampling.utils.torchutils import tensor2numpy -from prettyPlot.progressBar import print_progress_bar -from sklearn.neighbors import NearestNeighbors - import phaseSpaceSampling.nn as nn_ +import phaseSpaceSampling.utils.parallel as par from phaseSpaceSampling.nde import distributions, flows, transforms +from phaseSpaceSampling.utils.flowUtils import create_base_transform +from phaseSpaceSampling.utils.torchutils import tensor2numpy def str2float(str): @@ -195,7 +193,7 @@ def trainFlow(np_data, flow, pdf_iter, inpt): prefix="Loss = ? Step %d / %d " % (0, totalSteps), suffix="Complete", length=50, - extraCond=(par.irank==par.iroot), + extraCond=(par.irank == par.iroot), ) for epoch in range(EPOCHS): for step, batch in enumerate(data_loader): @@ -216,7 +214,7 @@ def trainFlow(np_data, flow, pdf_iter, inpt): % (loss.item(), epoch * nBatch + step + 1, totalSteps), suffix="Complete", length=50, - extraCond=(par.irank==par.iroot), + extraCond=(par.irank == par.iroot), ) if ((epoch * nBatch + step + 1) % 10) == 0: logTraining(epoch * nBatch + step + 1, loss, pdf_iter) @@ -372,7 +370,7 @@ def evalLogProbNF(flow, np_data_to_downsample, pdf_iter, inpt): prefix="Eval Step %d / %d " % (0, nBatch), suffix="Complete", length=50, - extraCond=(par.irank==par.iroot), + extraCond=(par.irank == par.iroot), ) for step, batch in enumerate(to_downsample_loader): batch = batch.to(device) @@ -386,7 +384,7 @@ def evalLogProbNF(flow, np_data_to_downsample, pdf_iter, inpt): prefix="Eval Step %d / %d " % (step + 1, nBatch), suffix="Complete", length=50, - extraCond=(par.irank==par.iroot), + extraCond=(par.irank == par.iroot), ) # Timer diff --git a/phaseSpaceSampling/utils/dataUtils.py b/phaseSpaceSampling/utils/dataUtils.py index a93da22..be1596c 100644 --- a/phaseSpaceSampling/utils/dataUtils.py +++ b/phaseSpaceSampling/utils/dataUtils.py @@ -1,5 +1,6 @@ -import sys import os +import sys + import numpy as np import phaseSpaceSampling.utils.parallel as par @@ -37,12 +38,16 @@ def prepareData(inpt): dataFile = inpt["dataFile"] if not os.path.isfile(dataFile): new_dataFile = os.path.join(DATA_DIR, os.path.split(dataFile)[-1]) - par.printRoot(f"WARNING: {dataFile} not found trying {new_dataFile} ...") + par.printRoot( + f"WARNING: {dataFile} not found trying {new_dataFile} ..." + ) if not os.path.isfile(new_dataFile): - par.printRoot(f"ERROR: could not open data {dataFile} or {new_dataFile}") - sys.exit() + par.printRoot( + f"ERROR: could not open data {dataFile} or {new_dataFile}" + ) + sys.exit() else: - dataFile = new_dataFile + dataFile = new_dataFile preShuffled = inpt["preShuffled"] == "True" scalerFile = inpt["scalerFile"] nWorkingDatas = [int(float(n)) for n in inpt["nWorkingData"].split()] diff --git a/phaseSpaceSampling/utils/flowUtils.py b/phaseSpaceSampling/utils/flowUtils.py index 65361b4..a1ab5a9 100644 --- a/phaseSpaceSampling/utils/flowUtils.py +++ b/phaseSpaceSampling/utils/flowUtils.py @@ -5,7 +5,10 @@ import phaseSpaceSampling.nn as nn_ from phaseSpaceSampling.nde import distributions, flows, transforms -from phaseSpaceSampling.utils.torchutils import create_alternating_binary_mask, create_random_binary_mask +from phaseSpaceSampling.utils.torchutils import ( + create_alternating_binary_mask, + create_random_binary_mask, +) def create_base_transform( diff --git a/phaseSpaceSampling/utils/plotFun.py b/phaseSpaceSampling/utils/plotFun.py index 27691e1..64d0e92 100644 --- a/phaseSpaceSampling/utils/plotFun.py +++ b/phaseSpaceSampling/utils/plotFun.py @@ -1,8 +1,9 @@ import numpy as np -from phaseSpaceSampling.utils.parallel import irank, iroot from matplotlib import cm from prettyPlot.plotting import plt, pretty_labels +from phaseSpaceSampling.utils.parallel import irank, iroot + def cornerPlotScatter(data, title=None): if not irank == iroot: diff --git a/phaseSpaceSampling/utils/torchutils.py b/phaseSpaceSampling/utils/torchutils.py index c6bfb63..fdbeae3 100644 --- a/phaseSpaceSampling/utils/torchutils.py +++ b/phaseSpaceSampling/utils/torchutils.py @@ -2,7 +2,10 @@ import torch -from phaseSpaceSampling.utils.typechecks import is_positive_int, is_nonnegative_int +from phaseSpaceSampling.utils.typechecks import ( + is_nonnegative_int, + is_positive_int, +) def tile(x, n): diff --git a/phaseSpaceSampling/utils/torchutils_test.py b/phaseSpaceSampling/utils/torchutils_test.py index 5702a2f..06d7180 100644 --- a/phaseSpaceSampling/utils/torchutils_test.py +++ b/phaseSpaceSampling/utils/torchutils_test.py @@ -4,7 +4,6 @@ import torch import torchtestcase - from utils import torchutils diff --git a/setup.py b/setup.py index 6bd8c26..6df53cc 100644 --- a/setup.py +++ b/setup.py @@ -1,9 +1,10 @@ import os + from setuptools import setup setup( - name='phaseSpaceSampling', - version='0.0.1', + name="phaseSpaceSampling", + version="0.0.1", description="Reduce a large and high-dimensional dataset by downselecting data uniformly in phase space", url="https://github.com/NREL/Phase-space-sampling", author="Malik Hassanaly", @@ -15,16 +16,16 @@ "Natural Language :: English", "Programming Language :: Python :: 3.10", ], - python_requires='>=3.10', + python_requires=">=3.10", install_requires=[ - "openmpi", - "torch", - "numpy", - "mpi4py", - "scikit-learn", - "prettyPlot>=0.0.10", - "black", - "isort", - "codespell", - ], + "openmpi", + "torch", + "numpy", + "mpi4py", + "scikit-learn", + "prettyPlot>=0.0.10", + "black", + "isort", + "codespell", + ], ) diff --git a/tests/run2D.sh b/tests/run2D.sh index 54ce161..35cf4b4 100644 --- a/tests/run2D.sh +++ b/tests/run2D.sh @@ -2,8 +2,8 @@ mpiexec -np 4 python main_iterative.py -i input2D # Plot the loss -python plotLoss.py -i input2D +python phaseSpaceSampling/postProcess/plotLoss.py -i input2D # Plot the sampling results -python visualizeDownSampled_subplots.py -i input2D +python phaseSpaceSampling/postProcess/visualizeDownSampled_subplots.py -i input2D From 10fc5bfaf5d70a8193e32b38e1da3cd794859d10 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 13:50:02 -0700 Subject: [PATCH 03/32] format --- phaseSpaceSampling/nde/distributions/uniform.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/phaseSpaceSampling/nde/distributions/uniform.py b/phaseSpaceSampling/nde/distributions/uniform.py index 545b02d..958d982 100644 --- a/phaseSpaceSampling/nde/distributions/uniform.py +++ b/phaseSpaceSampling/nde/distributions/uniform.py @@ -1,8 +1,6 @@ -import import -import sum_except_batch from torch import distributions -import phaseSpaceSampling.utils.torchutils +from phaseSpaceSampling.utils.torchutils import sum_except_batch class TweakedUniform(distributions.Uniform): @@ -15,4 +13,4 @@ def log_prob(self, value, context): # return result def sample(self, num_samples, context): - return super().sample((num_samples, )) + return super().sample((num_samples,)) From 35319a2f1d15ae88931da80e6ace5bebdf4b6eb4 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 14:25:25 -0700 Subject: [PATCH 04/32] fix circular imports --- .gitignore | 3 ++ .../nde/flows/autoregressive.py | 5 +-- .../nde/transforms/autoregressive.py | 4 +-- phaseSpaceSampling/nde/transforms/conv.py | 7 ++-- .../nde/transforms/conv_test.py | 4 +-- phaseSpaceSampling/nde/transforms/coupling.py | 13 +++---- phaseSpaceSampling/nde/transforms/linear.py | 4 +-- .../nde/transforms/nonlinearities.py | 34 +++++++++--------- .../nde/transforms/normalization.py | 12 +++---- .../nde/transforms/orthogonal.py | 4 +-- .../nde/transforms/permutations.py | 4 +-- phaseSpaceSampling/nde/transforms/qr.py | 4 +-- phaseSpaceSampling/nde/transforms/reshape.py | 6 ++-- .../nde/transforms/reshape_test.py | 4 +-- .../nde/transforms/splines/cubic.py | 6 ++-- .../nde/transforms/splines/linear.py | 6 ++-- .../nde/transforms/splines/quadratic.py | 6 ++-- .../transforms/splines/rational_quadratic.py | 4 +-- phaseSpaceSampling/nde/transforms/standard.py | 6 ++-- phaseSpaceSampling/nde/transforms/svd.py | 6 ++-- phaseSpaceSampling/utils/.torchutils.py.swp | Bin 16384 -> 0 bytes 21 files changed, 74 insertions(+), 68 deletions(-) delete mode 100644 phaseSpaceSampling/utils/.torchutils.py.swp diff --git a/.gitignore b/.gitignore index 9a88f91..29c3a43 100644 --- a/.gitignore +++ b/.gitignore @@ -137,6 +137,9 @@ dmypy.json # Cython debug symbols cython_debug/ +# tmp +*.swp + # data *.npz .DS_Store diff --git a/phaseSpaceSampling/nde/flows/autoregressive.py b/phaseSpaceSampling/nde/flows/autoregressive.py index b48e4cc..d21d5ba 100644 --- a/phaseSpaceSampling/nde/flows/autoregressive.py +++ b/phaseSpaceSampling/nde/flows/autoregressive.py @@ -2,10 +2,11 @@ from torch.nn import functional as F -from phaseSpaceSampling.nde import distributions, flows, transforms +from phaseSpaceSampling.nde.flows.base import Flow +from phaseSpaceSampling.nde import distributions, transforms -class MaskedAutoregressiveFlow(flows.Flow): +class MaskedAutoregressiveFlow(Flow): """An autoregressive flow that uses affine transforms with masking. Reference: diff --git a/phaseSpaceSampling/nde/transforms/autoregressive.py b/phaseSpaceSampling/nde/transforms/autoregressive.py index 117c558..a9905c1 100644 --- a/phaseSpaceSampling/nde/transforms/autoregressive.py +++ b/phaseSpaceSampling/nde/transforms/autoregressive.py @@ -4,13 +4,13 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import Transform from phaseSpaceSampling.nde.transforms import made as made_module from phaseSpaceSampling.nde.transforms import splines from phaseSpaceSampling.utils.torchutils import sum_except_batch -class AutoregressiveTransform(transforms.Transform): +class AutoregressiveTransform(Transform): """Transforms each input variable with an invertible elementwise transformation. The parameters of each invertible elementwise transformation can be functions of previous input diff --git a/phaseSpaceSampling/nde/transforms/conv.py b/phaseSpaceSampling/nde/transforms/conv.py index 42a2594..8cbee50 100644 --- a/phaseSpaceSampling/nde/transforms/conv.py +++ b/phaseSpaceSampling/nde/transforms/conv.py @@ -1,8 +1,9 @@ -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.lu import LULinear +from phaseSpaceSampling.nde.transforms.permutations import RandomPermutation from phaseSpaceSampling.utils.torchutils import sum_except_batch -class OneByOneConvolution(transforms.LULinear): +class OneByOneConvolution(LULinear): """An invertible 1x1 convolution with a fixed permutation, as introduced in the Glow paper. Reference: @@ -11,7 +12,7 @@ class OneByOneConvolution(transforms.LULinear): def __init__(self, num_channels, using_cache=False, identity_init=True): super().__init__(num_channels, using_cache, identity_init) - self.permutation = transforms.RandomPermutation(num_channels, dim=1) + self.permutation = RandomPermutation(num_channels, dim=1) def _lu_forward_inverse(self, inputs, inverse=False): b, c, h, w = inputs.shape diff --git a/phaseSpaceSampling/nde/transforms/conv_test.py b/phaseSpaceSampling/nde/transforms/conv_test.py index 63bf41b..a79bf15 100644 --- a/phaseSpaceSampling/nde/transforms/conv_test.py +++ b/phaseSpaceSampling/nde/transforms/conv_test.py @@ -2,7 +2,7 @@ import torch -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.conv import OneByOneConvolution from phaseSpaceSampling.nde.transforms.transform_test import TransformTest @@ -11,7 +11,7 @@ def test_forward_and_inverse_are_consistent(self): batch_size = 10 c, h, w = 3, 28, 28 inputs = torch.randn(batch_size, c, h, w) - transform = transforms.OneByOneConvolution(c) + transform = OneByOneConvolution(c) self.eps = 1e-6 self.assert_forward_inverse_are_consistent(transform, inputs) diff --git a/phaseSpaceSampling/nde/transforms/coupling.py b/phaseSpaceSampling/nde/transforms/coupling.py index d898cbf..ca28fa7 100644 --- a/phaseSpaceSampling/nde/transforms/coupling.py +++ b/phaseSpaceSampling/nde/transforms/coupling.py @@ -5,12 +5,13 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import Transform +from phaseSpaceSampling.nde.transforms.nonlinearities import PiecewiseRationalQuadraticCDF, PiecewiseCubicCDF, PiecewiseLinearCDF, PiecewiseQuadraticCDF from phaseSpaceSampling.nde.transforms import splines from phaseSpaceSampling.utils.torchutils import sum_except_batch -class CouplingTransform(transforms.Transform): +class CouplingTransform(Transform): """A base class for coupling layers. Supports 2D inputs (NxD), as well as 4D inputs for images (NxCxHxW). For images the splitting is done on the channel dimension, using the provided 1D mask.""" @@ -248,7 +249,7 @@ def __init__( if apply_unconditional_transform: unconditional_transform = ( - lambda features: transforms.PiecewiseLinearCDF( + lambda features: PiecewiseLinearCDF( shape=[features] + (img_shape if img_shape else []), num_bins=num_bins, tails=tails, @@ -312,7 +313,7 @@ def __init__( if apply_unconditional_transform: unconditional_transform = ( - lambda features: transforms.PiecewiseQuadraticCDF( + lambda features: PiecewiseQuadraticCDF( shape=[features] + (img_shape if img_shape else []), num_bins=num_bins, tails=tails, @@ -386,7 +387,7 @@ def __init__( if apply_unconditional_transform: unconditional_transform = ( - lambda features: transforms.PiecewiseCubicCDF( + lambda features: PiecewiseCubicCDF( shape=[features] + (img_shape if img_shape else []), num_bins=num_bins, tails=tails, @@ -469,7 +470,7 @@ def __init__( if apply_unconditional_transform: unconditional_transform = ( - lambda features: transforms.PiecewiseRationalQuadraticCDF( + lambda features: PiecewiseRationalQuadraticCDF( shape=[features] + (img_shape if img_shape else []), num_bins=num_bins, tails=tails, diff --git a/phaseSpaceSampling/nde/transforms/linear.py b/phaseSpaceSampling/nde/transforms/linear.py index 0ada04f..67c9544 100644 --- a/phaseSpaceSampling/nde/transforms/linear.py +++ b/phaseSpaceSampling/nde/transforms/linear.py @@ -7,7 +7,7 @@ from torch.nn import init import phaseSpaceSampling as pss -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import Transform from phaseSpaceSampling.utils.torchutils import random_orthogonal from phaseSpaceSampling.utils.typechecks import is_bool, is_positive_int @@ -29,7 +29,7 @@ def invalidate(self): self.logabsdet = None -class Linear(transforms.Transform): +class Linear(Transform): """Abstract base class for linear transforms that parameterize a weight matrix.""" def __init__(self, features, using_cache=False): diff --git a/phaseSpaceSampling/nde/transforms/nonlinearities.py b/phaseSpaceSampling/nde/transforms/nonlinearities.py index a28c46d..2ea6ab8 100644 --- a/phaseSpaceSampling/nde/transforms/nonlinearities.py +++ b/phaseSpaceSampling/nde/transforms/nonlinearities.py @@ -5,12 +5,12 @@ from torch import nn from torch.nn import functional as F -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import Transform, InputOutsideDomain, InverseTransform, CompositeTransform from phaseSpaceSampling.nde.transforms import splines from phaseSpaceSampling.utils.torchutils import sum_except_batch -class Tanh(transforms.Transform): +class Tanh(Transform): def forward(self, inputs, context=None): outputs = torch.tanh(inputs) logabsdet = torch.log(1 - outputs**2) @@ -19,14 +19,14 @@ def forward(self, inputs, context=None): def inverse(self, inputs, context=None): if torch.min(inputs) <= -1 or torch.max(inputs) >= 1: - raise transforms.InputOutsideDomain() + raise InputOutsideDomain() outputs = 0.5 * torch.log((1 + inputs) / (1 - inputs)) logabsdet = -torch.log(1 - inputs**2) logabsdet = sum_except_batch(logabsdet, num_batch_dims=1) return outputs, logabsdet -class LogTanh(transforms.Transform): +class LogTanh(Transform): """Tanh with unbounded output. Constructed by selecting a cut_point, and replacing values to the right of cut_point with alpha * log(beta * x), and to the left of -cut_point with -alpha * log(-beta * x). alpha and beta are set to match the value and the first derivative of @@ -96,7 +96,7 @@ def inverse(self, inputs, context=None): return outputs, logabsdet -class LeakyReLU(transforms.Transform): +class LeakyReLU(Transform): def __init__(self, negative_slope=1e-2): if negative_slope <= 0: raise ValueError("Slope must be positive.") @@ -123,7 +123,7 @@ def inverse(self, inputs, context=None): return outputs, logabsdet -class Sigmoid(transforms.Transform): +class Sigmoid(Transform): def __init__(self, temperature=1, eps=1e-6): super().__init__() self.eps = eps @@ -141,7 +141,7 @@ def forward(self, inputs, context=None): def inverse(self, inputs, context=None): if torch.min(inputs) < 0 or torch.max(inputs) > 1: - raise transforms.InputOutsideDomain() + raise InputOutsideDomain() inputs = torch.clamp(inputs, self.eps, 1 - self.eps) @@ -156,12 +156,12 @@ def inverse(self, inputs, context=None): return outputs, logabsdet -class Logit(transforms.InverseTransform): +class Logit(InverseTransform): def __init__(self, temperature=1, eps=1e-6): super().__init__(Sigmoid(temperature=temperature, eps=eps)) -class CauchyCDF(transforms.Transform): +class CauchyCDF(Transform): def __init__(self, location=None, scale=None, features=None): super().__init__() @@ -174,7 +174,7 @@ def forward(self, inputs, context=None): def inverse(self, inputs, context=None): if torch.min(inputs) < 0 or torch.max(inputs) > 1: - raise transforms.InputOutsideDomain() + raise InputOutsideDomain() outputs = torch.tan(np.pi * (inputs - 0.5)) logabsdet = -sum_except_batch( @@ -183,20 +183,20 @@ def inverse(self, inputs, context=None): return outputs, logabsdet -class CauchyCDFInverse(transforms.InverseTransform): +class CauchyCDFInverse(InverseTransform): def __init__(self, location=None, scale=None, features=None): super().__init__( CauchyCDF(location=location, scale=scale, features=features) ) -class CompositeCDFTransform(transforms.CompositeTransform): +class CompositeCDFTransform(CompositeTransform): def __init__(self, squashing_transform, cdf_transform): super().__init__( [ squashing_transform, cdf_transform, - transforms.InverseTransform(squashing_transform), + InverseTransform(squashing_transform), ] ) @@ -205,7 +205,7 @@ def _share_across_batch(params, batch_size): return params[None, ...].expand(batch_size, *params.shape) -class PiecewiseLinearCDF(transforms.Transform): +class PiecewiseLinearCDF(Transform): def __init__(self, shape, num_bins=10, tails=None, tail_bound=1.0): super().__init__() @@ -245,7 +245,7 @@ def inverse(self, inputs, context=None): return self._spline(inputs, inverse=True) -class PiecewiseQuadraticCDF(transforms.Transform): +class PiecewiseQuadraticCDF(Transform): def __init__( self, shape, @@ -309,7 +309,7 @@ def inverse(self, inputs, context=None): return self._spline(inputs, inverse=True) -class PiecewiseCubicCDF(transforms.Transform): +class PiecewiseCubicCDF(Transform): def __init__( self, shape, @@ -378,7 +378,7 @@ def inverse(self, inputs, context=None): return self._spline(inputs, inverse=True) -class PiecewiseRationalQuadraticCDF(transforms.Transform): +class PiecewiseRationalQuadraticCDF(Transform): def __init__( self, shape, diff --git a/phaseSpaceSampling/nde/transforms/normalization.py b/phaseSpaceSampling/nde/transforms/normalization.py index 579bf49..da80b2c 100644 --- a/phaseSpaceSampling/nde/transforms/normalization.py +++ b/phaseSpaceSampling/nde/transforms/normalization.py @@ -4,10 +4,10 @@ from torch import nn from torch.nn import functional as F -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import Transform, InverseNotAvailable from phaseSpaceSampling.utils.typechecks import is_positive_int -# class BatchNorm(transforms.Transform): +# class BatchNorm(Transform): # """Transform that performs batch normalization. # # Limitations: @@ -48,7 +48,7 @@ # # def inverse(self, inputs): # if self.training: -# raise transforms.InverseNotAvailable( +# raise InverseNotAvailable( # 'Batch norm inverse is only available in eval mode, not in training mode.') # if inputs.dim() != 2: # raise ValueError('Expected 2-dim inputs, got inputs of shape: {}'.format(inputs.shape)) @@ -69,7 +69,7 @@ # return outputs, logabsdet -class BatchNorm(transforms.Transform): +class BatchNorm(Transform): """Transform that performs batch normalization. Limitations: @@ -126,7 +126,7 @@ def forward(self, inputs, context=None): def inverse(self, inputs, context=None): if self.training: - raise transforms.InverseNotAvailable( + raise InverseNotAvailable( "Batch norm inverse is only available in eval mode, not in training mode." ) if inputs.dim() != 2: @@ -150,7 +150,7 @@ def inverse(self, inputs, context=None): return outputs, logabsdet -class ActNorm(transforms.Transform): +class ActNorm(Transform): def __init__(self, features): """ Transform that performs activation normalization. Works for 2D and 4D inputs. For 4D diff --git a/phaseSpaceSampling/nde/transforms/orthogonal.py b/phaseSpaceSampling/nde/transforms/orthogonal.py index 9d8838e..7be102e 100644 --- a/phaseSpaceSampling/nde/transforms/orthogonal.py +++ b/phaseSpaceSampling/nde/transforms/orthogonal.py @@ -3,11 +3,11 @@ import torch from torch import nn -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import Transform from phaseSpaceSampling.utils.typechecks import is_positive_int -class HouseholderSequence(transforms.Transform): +class HouseholderSequence(Transform): """A sequence of Householder transforms. This class can be used as a way of parameterizing an orthogonal matrix. diff --git a/phaseSpaceSampling/nde/transforms/permutations.py b/phaseSpaceSampling/nde/transforms/permutations.py index c321550..fb7002b 100644 --- a/phaseSpaceSampling/nde/transforms/permutations.py +++ b/phaseSpaceSampling/nde/transforms/permutations.py @@ -2,11 +2,11 @@ import torch -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import Transform from phaseSpaceSampling.utils.typechecks import is_positive_int -class Permutation(transforms.Transform): +class Permutation(Transform): """Permutes inputs on a given dimension using a given permutation.""" def __init__(self, permutation, dim=1): diff --git a/phaseSpaceSampling/nde/transforms/qr.py b/phaseSpaceSampling/nde/transforms/qr.py index 2713e9a..b978f9b 100644 --- a/phaseSpaceSampling/nde/transforms/qr.py +++ b/phaseSpaceSampling/nde/transforms/qr.py @@ -4,7 +4,7 @@ from torch.nn import functional as F from torch.nn import init -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.orthogonal import HouseholderSequence from phaseSpaceSampling.nde.transforms.linear import Linear @@ -22,7 +22,7 @@ def __init__(self, features, num_householder, using_cache=False): self.log_upper_diag = nn.Parameter(torch.zeros(features)) # Parameterization for Q - self.orthogonal = transforms.HouseholderSequence( + self.orthogonal = HouseholderSequence( features=features, num_transforms=num_householder ) diff --git a/phaseSpaceSampling/nde/transforms/reshape.py b/phaseSpaceSampling/nde/transforms/reshape.py index 62ac806..557dfc1 100644 --- a/phaseSpaceSampling/nde/transforms/reshape.py +++ b/phaseSpaceSampling/nde/transforms/reshape.py @@ -1,10 +1,10 @@ import torch -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import Transform from phaseSpaceSampling.utils.typechecks import is_int -class SqueezeTransform(transforms.Transform): +class SqueezeTransform(Transform): """A transformation defined for image data that trades spatial dimensions for channel dimensions, i.e. "squeezes" the inputs along the channel dimensions. @@ -79,7 +79,7 @@ def inverse(self, inputs, context=None): return inputs, torch.zeros(batch_size) -class ReshapeTransform(transforms.Transform): +class ReshapeTransform(Transform): def __init__(self, input_shape, output_shape): super().__init__() self.input_shape = input_shape diff --git a/phaseSpaceSampling/nde/transforms/reshape_test.py b/phaseSpaceSampling/nde/transforms/reshape_test.py index 543c4c6..a4306ee 100644 --- a/phaseSpaceSampling/nde/transforms/reshape_test.py +++ b/phaseSpaceSampling/nde/transforms/reshape_test.py @@ -2,13 +2,13 @@ import torch -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.reshape SqueezeTransform from phaseSpaceSampling.nde.transforms.transform_test import TransformTest class SqueezeTransformTest(TransformTest): def setUp(self): - self.transform = transforms.SqueezeTransform() + self.transform = SqueezeTransform() def test_forward(self): batch_size = 10 diff --git a/phaseSpaceSampling/nde/transforms/splines/cubic.py b/phaseSpaceSampling/nde/transforms/splines/cubic.py index ba61bf5..ebf85d5 100644 --- a/phaseSpaceSampling/nde/transforms/splines/cubic.py +++ b/phaseSpaceSampling/nde/transforms/splines/cubic.py @@ -3,7 +3,7 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import InputOutsideDomain from phaseSpaceSampling.utils.torchutils import cbrt, searchsorted DEFAULT_MIN_BIN_WIDTH = 1e-3 @@ -87,9 +87,9 @@ def cubic_spline( Graphics and Applications, 27(3):78–89. """ if not inverse and (torch.min(inputs) < left or torch.max(inputs) > right): - raise transforms.InputOutsideDomain() + raise InputOutsideDomain() elif inverse and (torch.min(inputs) < bottom or torch.max(inputs) > top): - raise transforms.InputOutsideDomain() + raise InputOutsideDomain() num_bins = unnormalized_widths.shape[-1] diff --git a/phaseSpaceSampling/nde/transforms/splines/linear.py b/phaseSpaceSampling/nde/transforms/splines/linear.py index 687af66..0b9cb85 100644 --- a/phaseSpaceSampling/nde/transforms/splines/linear.py +++ b/phaseSpaceSampling/nde/transforms/splines/linear.py @@ -4,7 +4,7 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import InputOutsideDomain from phaseSpaceSampling.utils.torchutils import searchsorted @@ -53,9 +53,9 @@ def linear_spline( > Müller et al., Neural Importance Sampling, arXiv:1808.03856, 2018. """ if not inverse and (torch.min(inputs) < left or torch.max(inputs) > right): - raise transforms.InputOutsideDomain() + raise InputOutsideDomain() elif inverse and (torch.min(inputs) < bottom or torch.max(inputs) > top): - raise transforms.InputOutsideDomain() + raise InputOutsideDomain() if inverse: inputs = (inputs - bottom) / (top - bottom) diff --git a/phaseSpaceSampling/nde/transforms/splines/quadratic.py b/phaseSpaceSampling/nde/transforms/splines/quadratic.py index 969895b..a634846 100644 --- a/phaseSpaceSampling/nde/transforms/splines/quadratic.py +++ b/phaseSpaceSampling/nde/transforms/splines/quadratic.py @@ -3,7 +3,7 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import InputOutsideDomain from phaseSpaceSampling.utils.torchutils import searchsorted DEFAULT_MIN_BIN_WIDTH = 1e-3 @@ -67,9 +67,9 @@ def quadratic_spline( min_bin_height=DEFAULT_MIN_BIN_HEIGHT, ): if not inverse and (torch.min(inputs) < left or torch.max(inputs) > right): - raise transforms.InputOutsideDomain() + raise InputOutsideDomain() elif inverse and (torch.min(inputs) < bottom or torch.max(inputs) > top): - raise transforms.InputOutsideDomain() + raise InputOutsideDomain() if inverse: inputs = (inputs - bottom) / (top - bottom) diff --git a/phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py b/phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py index 2da0dd7..593ba86 100644 --- a/phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py +++ b/phaseSpaceSampling/nde/transforms/splines/rational_quadratic.py @@ -2,7 +2,7 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import InputOutsideDomain from phaseSpaceSampling.utils.torchutils import searchsorted DEFAULT_MIN_BIN_WIDTH = 1e-3 @@ -77,7 +77,7 @@ def rational_quadratic_spline( min_derivative=DEFAULT_MIN_DERIVATIVE, ): if torch.min(inputs) < left or torch.max(inputs) > right: - raise transforms.InputOutsideDomain() + raise InputOutsideDomain() num_bins = unnormalized_widths.shape[-1] diff --git a/phaseSpaceSampling/nde/transforms/standard.py b/phaseSpaceSampling/nde/transforms/standard.py index 7b79226..8cc119c 100644 --- a/phaseSpaceSampling/nde/transforms/standard.py +++ b/phaseSpaceSampling/nde/transforms/standard.py @@ -2,10 +2,10 @@ import torch -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.base import Transform -class IdentityTransform(transforms.Transform): +class IdentityTransform(Transform): """Transform that leaves input unchanged.""" def forward(self, inputs, context=None): @@ -17,7 +17,7 @@ def inverse(self, inputs, context=None): return self(inputs, context) -class AffineScalarTransform(transforms.Transform): +class AffineScalarTransform(Transform): """Computes X = X * scale + shift, where scale and shift are scalars, and scale is non-zero.""" def __init__(self, shift=None, scale=None): diff --git a/phaseSpaceSampling/nde/transforms/svd.py b/phaseSpaceSampling/nde/transforms/svd.py index 1a2acea..d8a6c8a 100644 --- a/phaseSpaceSampling/nde/transforms/svd.py +++ b/phaseSpaceSampling/nde/transforms/svd.py @@ -3,7 +3,7 @@ from torch import nn from torch.nn import init -from phaseSpaceSampling.nde import transforms +from phaseSpaceSampling.nde.transforms.orthogonal import HouseholderSequence from phaseSpaceSampling.nde.transforms.linear import Linear @@ -14,7 +14,7 @@ def __init__(self, features, num_householder, using_cache=False): super().__init__(features, using_cache) # First orthogonal matrix (U). - self.orthogonal_1 = transforms.HouseholderSequence( + self.orthogonal_1 = HouseholderSequence( features=features, num_transforms=num_householder ) @@ -22,7 +22,7 @@ def __init__(self, features, num_householder, using_cache=False): self.log_diagonal = nn.Parameter(torch.zeros(features)) # Second orthogonal matrix (V^T). - 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c4303e9a5b27ffcb1649c969edff4c0eb66632ab Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 14:26:40 -0700 Subject: [PATCH 05/32] format --- .../nde/flows/autoregressive.py | 2 +- .../nde/transforms/autoregressive.py | 2 +- phaseSpaceSampling/nde/transforms/coupling.py | 53 +++++++++---------- .../nde/transforms/nonlinearities.py | 7 ++- .../nde/transforms/normalization.py | 5 +- phaseSpaceSampling/nde/transforms/qr.py | 2 +- .../nde/transforms/reshape_test.py | 2 +- phaseSpaceSampling/nde/transforms/svd.py | 2 +- 8 files changed, 41 insertions(+), 34 deletions(-) diff --git a/phaseSpaceSampling/nde/flows/autoregressive.py b/phaseSpaceSampling/nde/flows/autoregressive.py index d21d5ba..94a9e4f 100644 --- a/phaseSpaceSampling/nde/flows/autoregressive.py +++ b/phaseSpaceSampling/nde/flows/autoregressive.py @@ -2,8 +2,8 @@ from torch.nn import functional as F -from phaseSpaceSampling.nde.flows.base import Flow from phaseSpaceSampling.nde import distributions, transforms +from phaseSpaceSampling.nde.flows.base import Flow class MaskedAutoregressiveFlow(Flow): diff --git a/phaseSpaceSampling/nde/transforms/autoregressive.py b/phaseSpaceSampling/nde/transforms/autoregressive.py index a9905c1..b92e916 100644 --- a/phaseSpaceSampling/nde/transforms/autoregressive.py +++ b/phaseSpaceSampling/nde/transforms/autoregressive.py @@ -4,9 +4,9 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde.transforms.base import Transform from phaseSpaceSampling.nde.transforms import made as made_module from phaseSpaceSampling.nde.transforms import splines +from phaseSpaceSampling.nde.transforms.base import Transform from phaseSpaceSampling.utils.torchutils import sum_except_batch diff --git a/phaseSpaceSampling/nde/transforms/coupling.py b/phaseSpaceSampling/nde/transforms/coupling.py index ca28fa7..287dbb0 100644 --- a/phaseSpaceSampling/nde/transforms/coupling.py +++ b/phaseSpaceSampling/nde/transforms/coupling.py @@ -5,9 +5,14 @@ import torch from torch.nn import functional as F -from phaseSpaceSampling.nde.transforms.base import Transform -from phaseSpaceSampling.nde.transforms.nonlinearities import PiecewiseRationalQuadraticCDF, PiecewiseCubicCDF, PiecewiseLinearCDF, PiecewiseQuadraticCDF from phaseSpaceSampling.nde.transforms import splines +from phaseSpaceSampling.nde.transforms.base import Transform +from phaseSpaceSampling.nde.transforms.nonlinearities import ( + PiecewiseCubicCDF, + PiecewiseLinearCDF, + PiecewiseQuadraticCDF, + PiecewiseRationalQuadraticCDF, +) from phaseSpaceSampling.utils.torchutils import sum_except_batch @@ -248,13 +253,11 @@ def __init__( self.tail_bound = tail_bound if apply_unconditional_transform: - unconditional_transform = ( - lambda features: PiecewiseLinearCDF( - shape=[features] + (img_shape if img_shape else []), - num_bins=num_bins, - tails=tails, - tail_bound=tail_bound, - ) + unconditional_transform = lambda features: PiecewiseLinearCDF( + shape=[features] + (img_shape if img_shape else []), + num_bins=num_bins, + tails=tails, + tail_bound=tail_bound, ) else: unconditional_transform = None @@ -312,15 +315,13 @@ def __init__( self.min_bin_height = min_bin_height if apply_unconditional_transform: - unconditional_transform = ( - lambda features: PiecewiseQuadraticCDF( - shape=[features] + (img_shape if img_shape else []), - num_bins=num_bins, - tails=tails, - tail_bound=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - ) + unconditional_transform = lambda features: PiecewiseQuadraticCDF( + shape=[features] + (img_shape if img_shape else []), + num_bins=num_bins, + tails=tails, + tail_bound=tail_bound, + min_bin_width=min_bin_width, + min_bin_height=min_bin_height, ) else: unconditional_transform = None @@ -386,15 +387,13 @@ def __init__( self.tail_bound = tail_bound if apply_unconditional_transform: - unconditional_transform = ( - lambda features: PiecewiseCubicCDF( - shape=[features] + (img_shape if img_shape else []), - num_bins=num_bins, - tails=tails, - tail_bound=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - ) + unconditional_transform = lambda features: PiecewiseCubicCDF( + shape=[features] + (img_shape if img_shape else []), + num_bins=num_bins, + tails=tails, + tail_bound=tail_bound, + min_bin_width=min_bin_width, + min_bin_height=min_bin_height, ) else: unconditional_transform = None diff --git a/phaseSpaceSampling/nde/transforms/nonlinearities.py b/phaseSpaceSampling/nde/transforms/nonlinearities.py index 2ea6ab8..6f5a8ff 100644 --- a/phaseSpaceSampling/nde/transforms/nonlinearities.py +++ b/phaseSpaceSampling/nde/transforms/nonlinearities.py @@ -5,8 +5,13 @@ from torch import nn from torch.nn import functional as F -from phaseSpaceSampling.nde.transforms.base import Transform, InputOutsideDomain, InverseTransform, CompositeTransform from phaseSpaceSampling.nde.transforms import splines +from phaseSpaceSampling.nde.transforms.base import ( + CompositeTransform, + InputOutsideDomain, + InverseTransform, + Transform, +) from phaseSpaceSampling.utils.torchutils import sum_except_batch diff --git a/phaseSpaceSampling/nde/transforms/normalization.py b/phaseSpaceSampling/nde/transforms/normalization.py index da80b2c..32c0597 100644 --- a/phaseSpaceSampling/nde/transforms/normalization.py +++ b/phaseSpaceSampling/nde/transforms/normalization.py @@ -4,7 +4,10 @@ from torch import nn from torch.nn import functional as F -from phaseSpaceSampling.nde.transforms.base import Transform, InverseNotAvailable +from phaseSpaceSampling.nde.transforms.base import ( + InverseNotAvailable, + Transform, +) from phaseSpaceSampling.utils.typechecks import is_positive_int # class BatchNorm(Transform): diff --git a/phaseSpaceSampling/nde/transforms/qr.py b/phaseSpaceSampling/nde/transforms/qr.py index b978f9b..5202d9a 100644 --- a/phaseSpaceSampling/nde/transforms/qr.py +++ b/phaseSpaceSampling/nde/transforms/qr.py @@ -4,8 +4,8 @@ from torch.nn import functional as F from torch.nn import init -from phaseSpaceSampling.nde.transforms.orthogonal import HouseholderSequence from phaseSpaceSampling.nde.transforms.linear import Linear +from phaseSpaceSampling.nde.transforms.orthogonal import HouseholderSequence class QRLinear(Linear): diff --git a/phaseSpaceSampling/nde/transforms/reshape_test.py b/phaseSpaceSampling/nde/transforms/reshape_test.py index a4306ee..87fdb27 100644 --- a/phaseSpaceSampling/nde/transforms/reshape_test.py +++ b/phaseSpaceSampling/nde/transforms/reshape_test.py @@ -2,7 +2,7 @@ import torch -from phaseSpaceSampling.nde.reshape SqueezeTransform +from phaseSpaceSampling.nde.reshape import SqueezeTransform from phaseSpaceSampling.nde.transforms.transform_test import TransformTest diff --git a/phaseSpaceSampling/nde/transforms/svd.py b/phaseSpaceSampling/nde/transforms/svd.py index d8a6c8a..fb165b6 100644 --- a/phaseSpaceSampling/nde/transforms/svd.py +++ b/phaseSpaceSampling/nde/transforms/svd.py @@ -3,8 +3,8 @@ from torch import nn from torch.nn import init -from phaseSpaceSampling.nde.transforms.orthogonal import HouseholderSequence from phaseSpaceSampling.nde.transforms.linear import Linear +from phaseSpaceSampling.nde.transforms.orthogonal import HouseholderSequence class SVDLinear(Linear): From 4839bdbc7e59f846000e3e2ddfd5771255203353 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 14:45:09 -0700 Subject: [PATCH 06/32] add file finder functions --- main_iterative.py | 15 ++-------- phaseSpaceSampling/utils/dataUtils.py | 16 ++-------- phaseSpaceSampling/utils/fileFinder.py | 41 ++++++++++++++++++++++++++ 3 files changed, 45 insertions(+), 27 deletions(-) create mode 100644 phaseSpaceSampling/utils/fileFinder.py diff --git a/main_iterative.py b/main_iterative.py index d5aa59b..8700b8b 100644 --- a/main_iterative.py +++ b/main_iterative.py @@ -9,8 +9,8 @@ import phaseSpaceSampling.sampler as sampler import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling import DATA_DIR, INPUT_DIR from phaseSpaceSampling.utils.dataUtils import prepareData +from phaseSpaceSampling.utils.fileFinder import find_input from phaseSpaceSampling.utils.plotFun import * from phaseSpaceSampling.utils.torchutils import get_num_parameters @@ -33,18 +33,7 @@ # ~~~~ Parse input # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -inpt_file = args.input -if not os.path.isfile(inpt_file): - new_inpt_file = os.path.join(INPUT_DIR, os.path.split(inpt_file)[-1]) - par.printRoot(f"WARNING: {inpt_file} not found trying {new_inpt_file} ...") - if not os.path.isfile(new_inpt_file): - par.printRoot( - f"ERROR: could not open data {inpt_file} or {new_inpt_file}" - ) - sys.exit() - else: - inpt_file = new_inpt_file - +inpt_file = find_input(args.input) inpt = parse_input_file(inpt_file) use_normalizing_flow = inpt["pdf_method"].lower() == "normalizingflow" use_bins = inpt["pdf_method"].lower() == "bins" diff --git a/phaseSpaceSampling/utils/dataUtils.py b/phaseSpaceSampling/utils/dataUtils.py index be1596c..7b78f00 100644 --- a/phaseSpaceSampling/utils/dataUtils.py +++ b/phaseSpaceSampling/utils/dataUtils.py @@ -4,7 +4,7 @@ import numpy as np import phaseSpaceSampling.utils.parallel as par -from phaseSpaceSampling import DATA_DIR +from phaseSpaceSampling.utils.fileFinder import find_data # from memory_profiler import profile @@ -35,19 +35,7 @@ def checkData(shape, N, d, nWorkingData, nWorkingDataAdjustment, useNF): # @profile def prepareData(inpt): # Set parameters from input - dataFile = inpt["dataFile"] - if not os.path.isfile(dataFile): - new_dataFile = os.path.join(DATA_DIR, os.path.split(dataFile)[-1]) - par.printRoot( - f"WARNING: {dataFile} not found trying {new_dataFile} ..." - ) - if not os.path.isfile(new_dataFile): - par.printRoot( - f"ERROR: could not open data {dataFile} or {new_dataFile}" - ) - sys.exit() - else: - dataFile = new_dataFile + dataFile = find_data(inpt["dataFile"]) preShuffled = inpt["preShuffled"] == "True" scalerFile = inpt["scalerFile"] nWorkingDatas = [int(float(n)) for n in inpt["nWorkingData"].split()] diff --git a/phaseSpaceSampling/utils/fileFinder.py b/phaseSpaceSampling/utils/fileFinder.py new file mode 100644 index 0000000..26b1bb1 --- /dev/null +++ b/phaseSpaceSampling/utils/fileFinder.py @@ -0,0 +1,41 @@ +import os +import sys + +import phaseSpaceSampling.utils.parallel as par +from phaseSpaceSampling import DATA_DIR, INPUT_DIR + + +def find_input(inpt_file): + if not os.path.isfile(inpt_file): + new_inpt_file = os.path.join(INPUT_DIR, os.path.split(inpt_file)[-1]) + par.printRoot( + f"WARNING: {inpt_file} not found trying {new_inpt_file} ..." + ) + if not os.path.isfile(new_inpt_file): + par.printRoot( + f"ERROR: could not open data {inpt_file} or {new_inpt_file}" + ) + sys.exit() + return None + else: + return new_inpt_file + else: + return inpt_file + + +def find_data(data_file): + if not os.path.isfile(data_file): + new_data_file = os.path.join(DATA_DIR, os.path.split(data_file)[-1]) + par.printRoot( + f"WARNING: {data_file} not found trying {new_data_file} ..." + ) + if not os.path.isfile(new_data_file): + par.printRoot( + f"ERROR: could not open data {data_file} or {new_data_file}" + ) + sys.exit() + return None + else: + return new_data_file + else: + return data_file From a41260b6fd59986ea2f6b8627159ad83b4633a5b Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 15:00:56 -0700 Subject: [PATCH 07/32] make a function to downsample from input --- README.md | 24 +- main.py | 16 ++ phaseSpaceSampling/wrapper.py | 234 ++++++++++++++++++ .../postProcess => postProcess}/plotLoss.py | 0 .../visualizeDownSampled_subplots.py | 0 tests/run2D.sh | 6 +- tests/run2D_bins.sh | 4 +- tests/run3D.sh | 6 +- 8 files changed, 268 insertions(+), 22 deletions(-) create mode 100644 main.py create mode 100644 phaseSpaceSampling/wrapper.py rename {phaseSpaceSampling/postProcess => postProcess}/plotLoss.py (100%) rename {phaseSpaceSampling/postProcess => postProcess}/visualizeDownSampled_subplots.py (100%) diff --git a/README.md b/README.md index e5e7738..effafe1 100644 --- a/README.md +++ b/README.md @@ -10,30 +10,26 @@ 1. `conda create --name uips python=3.10` 2. `conda activate uips` -3. `pip install -r requirements.txt` +3. `pip install .` ## Purpose The purpose of the tool is to perform a smart downselection of a large number of datapoints. Typically, large numerical simulations generate billions, or even trillions of datapoints. However, there may be redundancy in the dataset which unnecessarily constrains memory and computing requirements. Here, redundancy is defined as closeness in feature space. The method is called phase-space sampling. -## Running the example without poetry +## Running the example -`bash run2D.sh`: Example of downsampling a 2D combustion dataset. First the downsampling is performed (`mpiexec -np 4 python main_iterative.py input`). Then the loss function for each flow iteration is plotted (`python plotLoss.py input`). Finally, the samples are visualized (`python visualizeDownSampled_subplots.py input`). All figures are saved under the folder `Figures`. - -## Running the example with poetry - -Add `poetry run` before `python ...` +`bash tests/run2D.sh`: Example of downsampling a 2D combustion dataset. First the downsampling is performed (`mpiexec -np 4 python main.py -i inputs/input2D`). Then the loss function for each flow iteration is plotted (`python postProcess/plotLoss.py -i inputs/input2D`). Finally, the samples are visualized (`python postProcess/visualizeDownSampled_subplots.py -i inputs/input2D`). All figures are saved under the folder `Figures`. ## Parallelization -The code is GPU+MPI-parallelized: a) the dataset is loaded and shuffled in parallel b) the probability evaluation (the most expensive step) is done in parallel c) downsampling is done in parallel d) only the training is offloaded to a GPU if available. Memory usage of root processor is higher than other since it is the only one in charge of the normalizing flow training and sampling probability adjustment. To run the code in parallel, `mpiexec -np num_procs python main_iterative.py input`. +The code is GPU+MPI-parallelized: a) the dataset is loaded and shuffled in parallel b) the probability evaluation (the most expensive step) is done in parallel c) downsampling is done in parallel d) only the training is offloaded to a GPU if available. Memory usage of root processor is higher than other since it is the only one in charge of the normalizing flow training and sampling probability adjustment. To run the code in parallel, `mpiexec -np num_procs python main.py -i inputs/input2D`. In the code, arrays with suffix `_` denote data distributed over the processors. The computation of nearest neighbor distance is parallelized using the sklearn implementation. It will be accelerated on systems where hyperthreading is enabled (your laptop, but NOT the Eagle HPC) -When using GPU+MPI-parallelism on Eagle, you need to specify the number of MPI tasks (`srun -n 36 python main_iterative.py`) -When using MPI-parallelism alone on Eagle, you do not need to specify the number of MPI tasks (`srun python main_iterative.py`) +When using GPU+MPI-parallelism on Eagle, you need to specify the number of MPI tasks (`srun -n 36 python main.py`) +When using MPI-parallelism alone on Eagle, you do not need to specify the number of MPI tasks (`srun python main.py`) Running on GPU only accelerate execution by ~30% for the examples provided here. Running with many MPI-tasks linearly decreases the execution time for probability evaluation, as well as memory per core requirements. @@ -51,9 +47,9 @@ The dataset to downsample has size $N \times d$ where $N \gg d$. The first dimen ## Hyperparameters -All hyperparameters can be controlled via an input file (see `run2D.sh`). +All hyperparameters can be controlled via an input file (see `tests/run2D.sh`). We recommend fixing the number of flow calculation iteration to 2. -When increasing the number of dimensions, we recommend adjusting the hyperparameters. A 2-dimensional example (`input`) and an 11-dimensional (`highdim/input11D`) example are provided to guide the user. +When increasing the number of dimensions, we recommend adjusting the hyperparameters. A 2-dimensional example (`inputs/input2D`) and an 11-dimensional (`inputs/highdim/input11D`) example are provided to guide the user. ## Sanity checks @@ -67,7 +63,7 @@ The computational cost associated with the nearest neighbor computations scales During training of the normalizing flow, the negative log likelihood is displayed. The user should ensure that the normalizing flow has learned something useful about the distribution by ensuring that the loss is close to being converged. The log of the loss is displayed as a csv file in the folder `TrainingLog`. The loss of the second training iteration should be higher than the first iteration. If this is not the case or if more iterations are needed, the normalizing flow trained may need to be better converged. A warning message will be issued in that case. -A script is provided to visualize the losses. Execute `python plotLoss.py input` where `input` is the name of the input file used to perform the downsampling. +A script is provided to visualize the losses. Execute `python plotLoss.py -i inputs/input2D` where `input2D` is the name of the input file used to perform the downsampling. ## Example 2D @@ -94,7 +90,7 @@ For comparison, a random sampling gives the following result ## Example 11D -Input file is provided in `highdim/input11D` +Input file is provided in `inputs/highdim/input11D` ## Data efficient ML diff --git a/main.py b/main.py new file mode 100644 index 0000000..30de786 --- /dev/null +++ b/main.py @@ -0,0 +1,16 @@ +import argparse +from phaseSpaceSampling.wrapper import downsample_dataset_from_input + +parser = argparse.ArgumentParser(description="Downsampler") +parser.add_argument( + "-i", + "--input", + type=str, + metavar="", + required=False, + help="Input file", + default="input", +) +args, unknown = parser.parse_known_args() + +downsample_dataset_from_input(args.input) diff --git a/phaseSpaceSampling/wrapper.py b/phaseSpaceSampling/wrapper.py new file mode 100644 index 0000000..6bc8496 --- /dev/null +++ b/phaseSpaceSampling/wrapper.py @@ -0,0 +1,234 @@ +import os +import sys +import time + +import numpy as np +import torch +from prettyPlot.parser import parse_input_file + +import phaseSpaceSampling.sampler as sampler +import phaseSpaceSampling.utils.parallel as par +from phaseSpaceSampling.utils.dataUtils import prepareData +from phaseSpaceSampling.utils.fileFinder import find_input +from phaseSpaceSampling.utils.plotFun import * +from phaseSpaceSampling.utils.torchutils import get_num_parameters + + +def downsample_dataset_from_input(inpt_file): + + inpt_file = find_input(inpt_file) + inpt = parse_input_file(inpt_file) + use_normalizing_flow = inpt["pdf_method"].lower() == "normalizingflow" + use_bins = inpt["pdf_method"].lower() == "bins" + + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # ~~~~ Parameters to save + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + # List of sample size + nSamples = [int(float(n)) for n in inpt["nSamples"].split()] + # Data size used to adjust the sampling probability + nWorkingDataAdjustment = int(float(inpt["nWorkingDataAdjustment"])) + if nWorkingDataAdjustment < 0: + use_serial_adjustment = False + else: + use_serial_adjustment = True + # Data size used to learn the data probability + nWorkingDatas = [int(float(n)) for n in inpt["nWorkingData"].split()] + if len(nWorkingDatas) == 1: + nWorkingDatas = nWorkingDatas * int(inpt["num_pdf_iter"]) + for nWorkingData in nWorkingDatas: + if not nWorkingData in nSamples: + nSamples += [nWorkingData] + # Do we compute the neighbor distance criterion + computeCriterion = inpt["computeDistanceCriterion"] == "True" + try: + nSampleCriterionLimit = int(inpt["nSampleCriterionLimit"]) + except: + nSampleCriterionLimit = int(1e5) + + + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # ~~~~ Environment + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + os.environ["KMP_DUPLICATE_LIB_OK"] = "True" + use_gpu = ( + (inpt["use_gpu"] == "True") + and (torch.cuda.is_available()) + and (par.irank == par.iroot) + ) + if use_gpu: + # GPU SETTING + device = torch.device("cuda") + torch.set_default_dtype(torch.cuda.float32) + else: + # CPU SETTING + device = torch.device("cpu") + torch.set_default_dtype(torch.float32) + # REPRODUCIBILITY + torch.manual_seed(int(inpt["seed"]) + par.irank) + np.random.seed(int(inpt["seed"]) + par.irank) + + + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # ~~~~ Prepare Data and scatter across processors + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + data_to_downsample_, dataInd_, working_data, nFullData = prepareData(inpt) + + dim = data_to_downsample_.shape[1] + + # Compute uniform sampling criterion of random data + randomCriterion = np.zeros(len(nSamples)) + if par.irank == par.iroot and computeCriterion: + par.printRoot("RANDOM: ") + for inSample, nSample in enumerate(nSamples): + if nSample <= nSampleCriterionLimit: + random_sampled_data = working_data[:nSample, :] + mean, std = sampler.computeDistanceToClosestNeighbor( + sampler.rescaleData(random_sampled_data, inpt) + ) + randomCriterion[inSample] = mean + par.printRoot( + f"\t nSample {nSample} mean dist = {mean:.4f}, std dist = {std:.4f}" + ) + + # Prepare arrays used for sanity checks + meanCriterion = np.zeros((int(inpt["num_pdf_iter"]), len(nSamples))) + stdCriterion = np.zeros((int(inpt["num_pdf_iter"]), len(nSamples))) + flow_nll_loss = np.zeros(int(inpt["num_pdf_iter"])) + + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # ~~~~ Downsample + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + data_for_pdf_est = working_data + + for pdf_iter in range(int(inpt["num_pdf_iter"])): + if use_normalizing_flow: + # Create the normalizing flow + flow = sampler.createFlow(dim, pdf_iter, inpt) + # flow = flow.to(device) + n_params = get_num_parameters(flow) + par.printRoot( + "There are {} trainable parameters in this model.".format(n_params) + ) + + # Train (happens on 1 proc) + flow_nll_loss[pdf_iter] = sampler.trainFlow( + data_for_pdf_est, flow, pdf_iter, inpt + ) + sampler.checkLoss(pdf_iter, flow_nll_loss) + + # Evaluate probability: This is the expensive step (happens on multi processors) + log_density_np_ = sampler.evalLogProbNF( + flow, data_to_downsample_, pdf_iter, inpt + ) + + if use_bins: + bin_pdfH, bin_pdfEdges = sampler.trainBinPDF( + data_for_pdf_est, pdf_iter, inpt + ) + # Evaluate probability: This is the expensive step (happens on multi processors) + log_density_np_ = sampler.evalLogProbBIN( + data_to_downsample_, pdf_iter, inpt + ) + + if use_serial_adjustment: + log_density_np_for_adjust = par.gatherNelementsInArray( + log_density_np_, nWorkingDataAdjustment + ) + else: + log_density_np_for_adjust = None + + # Correct probability estimate + if pdf_iter > 0: + log_density_np_ = log_density_np_ - log_samplingProb_ + if use_serial_adjustment: + log_density_np_for_adjust = par.gatherNelementsInArray( + log_density_np_, nWorkingDataAdjustment + ) + else: + log_density_np_for_adjust = None + + par.printRoot(f"TRAIN ITER {pdf_iter}") + + for inSample, nSample in enumerate(nSamples): + # Downsample + ( + downSampledData, + downSampledIndices, + samplingProb_, + log_samplingProb_, + ) = sampler.downSample( + data_to_downsample_, + dataInd_, + log_density_np_, + log_density_np_for_adjust, + nSample, + nFullData, + inpt, + ) + + # Plot + # cornerPlotScatter(downSampledData,title='downSampled npts='+str(nSample)+', iter='+str(pdf_iter)) + # Get criterion + if computeCriterion and par.irank == par.iroot: + if nSample <= nSampleCriterionLimit: + mean, std = sampler.computeDistanceToClosestNeighbor( + sampler.rescaleData(downSampledData, inpt) + ) + meanCriterion[pdf_iter, inSample] = mean + stdCriterion[pdf_iter, inSample] = std + par.printRoot( + f"\t nSample {nSample} mean dist = {mean:.4f}, std dist = {std:.4f}" + ) + + if pdf_iter == int(inpt["num_pdf_iter"]) - 1: + # Last pdf iter : Root proc saves downsampled data, and checks the outcome + sampler.checkProcedure( + meanCriterion[:, inSample], + nSample, + randomCriterion[inSample], + ) + if par.irank == par.iroot: + np.savez( + f"{inpt['prefixDownsampledData']}_{nSample}_it{pdf_iter}.npz", + data=downSampledData, + indices=downSampledIndices, + ) + + if not (pdf_iter == int(inpt["num_pdf_iter"]) - 1): + # Prepare data for the next training iteration + ( + downSampledData, + _, + samplingProb_, + log_samplingProb_, + ) = sampler.downSample( + data_to_downsample_, + dataInd_, + log_density_np_, + log_density_np_for_adjust, + nWorkingDatas[pdf_iter + 1], + nFullData, + inpt, + ) + data_for_pdf_est = downSampledData + + # Advise for best sampling + if par.irank == par.iroot: + if np.amax(meanCriterion) > 0: + print("\n") + maxCrit = np.argmax(meanCriterion, axis=0) + nSamples_asked = [int(float(n)) for n in inpt["nSamples"].split()] + for iSample, nSample in enumerate(nSamples_asked): + print( + f"For sample {nSample} use {inpt['prefixDownsampledData']}_{nSample}_it{maxCrit[iSample]}.npz" + ) + print("\n") + + +# if par.irank==par.iroot: +# plt.show() diff --git a/phaseSpaceSampling/postProcess/plotLoss.py b/postProcess/plotLoss.py similarity index 100% rename from phaseSpaceSampling/postProcess/plotLoss.py rename to postProcess/plotLoss.py diff --git a/phaseSpaceSampling/postProcess/visualizeDownSampled_subplots.py b/postProcess/visualizeDownSampled_subplots.py similarity index 100% rename from phaseSpaceSampling/postProcess/visualizeDownSampled_subplots.py rename to postProcess/visualizeDownSampled_subplots.py diff --git a/tests/run2D.sh b/tests/run2D.sh index 35cf4b4..c20be52 100644 --- a/tests/run2D.sh +++ b/tests/run2D.sh @@ -1,9 +1,9 @@ # Run the downsampling script -mpiexec -np 4 python main_iterative.py -i input2D +mpiexec -np 4 python main.py -i input2D # Plot the loss -python phaseSpaceSampling/postProcess/plotLoss.py -i input2D +python postProcess/plotLoss.py -i input2D # Plot the sampling results -python phaseSpaceSampling/postProcess/visualizeDownSampled_subplots.py -i input2D +python postProcess/visualizeDownSampled_subplots.py -i input2D diff --git a/tests/run2D_bins.sh b/tests/run2D_bins.sh index e10bb71..1ccb459 100644 --- a/tests/run2D_bins.sh +++ b/tests/run2D_bins.sh @@ -1,6 +1,6 @@ # Run the downsampling script -mpiexec -np 4 python main_iterative.py -i input2D_bins +mpiexec -np 4 python main.py -i input2D_bins # Plot the sampling results -python visualizeDownSampled_subplots.py -i input2D_bins +python postProcess/visualizeDownSampled_subplots.py -i input2D_bins diff --git a/tests/run3D.sh b/tests/run3D.sh index 6a12a12..f1a8c48 100644 --- a/tests/run3D.sh +++ b/tests/run3D.sh @@ -1,9 +1,9 @@ # Run the downsampling script -mpiexec -np 4 python main_iterative.py -i input3D +mpiexec -np 4 python main.py -i input3D # Plot the loss -python plotLoss.py -i input3D +python postProcess/plotLoss.py -i input3D # Plot the sampling results -python visualizeDownSampled_subplots.py -i input3D +python postProcess/visualizeDownSampled_subplots.py -i input3D From cd8f955422fc67c24040202a35ea86bd027786d4 Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 17:11:34 -0700 Subject: [PATCH 08/32] remove logs --- phaseSpaceSampling/TrainingLog/log_iter0.csv | 148 ------------------ phaseSpaceSampling/TrainingLog/log_iter1.csv | 148 ------------------ .../TrainingLog/modelWeights_iter0.npz | Bin 82126 -> 0 bytes .../TrainingLog/modelWeights_iter0.pt | Bin 46978 -> 0 bytes .../TrainingLog/modelWeights_iter1.npz | Bin 82126 -> 0 bytes .../TrainingLog/modelWeights_iter1.pt | Bin 48258 -> 0 bytes .../downSampledData_100000_it0.npz | Bin 1200508 -> 0 bytes .../downSampledData_100000_it1.npz | Bin 1200508 -> 0 bytes .../downSampledData_10000_it0.npz | Bin 120508 -> 0 bytes .../downSampledData_10000_it1.npz | Bin 120508 -> 0 bytes .../downSampledData_1000_it0.npz | Bin 12508 -> 0 bytes .../downSampledData_1000_it1.npz | Bin 12508 -> 0 bytes 12 files changed, 296 deletions(-) delete mode 100644 phaseSpaceSampling/TrainingLog/log_iter0.csv delete mode 100644 phaseSpaceSampling/TrainingLog/log_iter1.csv delete mode 100644 phaseSpaceSampling/TrainingLog/modelWeights_iter0.npz delete mode 100644 phaseSpaceSampling/TrainingLog/modelWeights_iter0.pt delete mode 100644 phaseSpaceSampling/TrainingLog/modelWeights_iter1.npz delete mode 100644 phaseSpaceSampling/TrainingLog/modelWeights_iter1.pt delete mode 100644 phaseSpaceSampling/downSampledData_100000_it0.npz delete mode 100644 phaseSpaceSampling/downSampledData_100000_it1.npz delete mode 100644 phaseSpaceSampling/downSampledData_10000_it0.npz delete mode 100644 phaseSpaceSampling/downSampledData_10000_it1.npz delete mode 100644 phaseSpaceSampling/downSampledData_1000_it0.npz delete mode 100644 phaseSpaceSampling/downSampledData_1000_it1.npz diff --git a/phaseSpaceSampling/TrainingLog/log_iter0.csv b/phaseSpaceSampling/TrainingLog/log_iter0.csv deleted file mode 100644 index 91048d0..0000000 --- a/phaseSpaceSampling/TrainingLog/log_iter0.csv +++ /dev/null @@ -1,148 +0,0 @@ -step;nll_loss 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tests/run2D.sh diff --git a/run2D_bins.sh b/tests/run2D_bins.sh similarity index 100% rename from run2D_bins.sh rename to tests/run2D_bins.sh diff --git a/run3D.sh b/tests/run3D.sh similarity index 100% rename from run3D.sh rename to tests/run3D.sh From cc73612c6b253fa96ab0c8348224764a1c5b2cca Mon Sep 17 00:00:00 2001 From: Malik Date: Wed, 29 Nov 2023 13:39:07 -0700 Subject: [PATCH 02/32] format and ensure that tests pass --- .github/workflows/ci.yml | 5 +- .gitignore | 8 + data-efficientML/artificialCase/trainGP.py | 2 +- data-efficientML/artificialCase/trainNN.py | 9 +- .../artificialCase/util/myNN_better.py | 5 +- data-efficientML/combustionCase/trainGP.py | 9 +- data-efficientML/combustionCase/trainNN.py | 8 +- .../combustionCase/util/myNN_better.py | 5 +- .../readmeImages/103_phaseSpaceSampling.png | Bin 0 -> 36998 bytes .../readmeImages/103_randomSampling.png | Bin 0 -> 32136 bytes .../readmeImages/104_phaseSpaceSampling.png | Bin 0 -> 130119 bytes .../readmeImages/104_randomSampling.png | Bin 0 -> 57042 bytes documentation/readmeImages/fulldataset.png | Bin 0 -> 143932 bytes documentation/readmeImages/probabilityMap.png | Bin 0 -> 42575 bytes fixFormat.sh | 3 +- main_iterative.py | 31 +- phaseSpaceSampling/__init__.py | 2 +- phaseSpaceSampling/cutils/__init__.py | 3 +- phaseSpaceSampling/cutils/io.py | 32 +- phaseSpaceSampling/cutils/misc.py | 16 +- phaseSpaceSampling/main_iterative.py | 251 ----------- .../nde/distributions/__init__.py | 8 +- phaseSpaceSampling/nde/distributions/base.py | 26 +- .../nde/distributions/discrete.py | 25 +- .../nde/distributions/discrete_test.py | 25 +- .../nde/distributions/mixture.py | 31 +- .../nde/distributions/normal.py | 33 +- .../nde/distributions/normal_test.py | 55 ++- .../nde/distributions/uniform.py | 4 +- phaseSpaceSampling/nde/flows/__init__.py | 4 +- .../nde/flows/autoregressive.py | 31 +- .../nde/flows/autoregressive_test.py | 6 +- phaseSpaceSampling/nde/flows/base.py | 10 +- phaseSpaceSampling/nde/flows/base_test.py | 35 +- phaseSpaceSampling/nde/flows/realnvp.py | 32 +- phaseSpaceSampling/nde/flows/realnvp_test.py | 6 +- phaseSpaceSampling/nde/transforms/__init__.py | 71 ++- .../nde/transforms/autoregressive.py | 273 +++++++----- .../nde/transforms/autoregressive_test.py | 78 ++-- phaseSpaceSampling/nde/transforms/base.py | 76 +++- .../nde/transforms/base_test.py | 47 +- phaseSpaceSampling/nde/transforms/conv.py | 13 +- .../nde/transforms/conv_test.py | 5 +- phaseSpaceSampling/nde/transforms/coupling.py | 328 ++++++++------ .../nde/transforms/coupling_test.py | 146 ++++--- phaseSpaceSampling/nde/transforms/linear.py | 39 +- .../nde/transforms/linear_test.py | 33 +- phaseSpaceSampling/nde/transforms/lu.py | 34 +- phaseSpaceSampling/nde/transforms/lu_test.py | 13 +- phaseSpaceSampling/nde/transforms/made.py | 144 +++--- .../nde/transforms/made_test.py | 65 ++- .../nde/transforms/nonlinearities.py | 274 +++++++----- .../nde/transforms/nonlinearities_test.py | 57 ++- .../nde/transforms/normalization.py | 73 ++-- .../nde/transforms/normalization_test.py | 30 +- .../nde/transforms/orthogonal.py | 12 +- .../nde/transforms/orthogonal_test.py | 45 +- .../nde/transforms/permutations.py | 18 +- .../nde/transforms/permutations_test.py | 6 +- phaseSpaceSampling/nde/transforms/qr.py | 23 +- phaseSpaceSampling/nde/transforms/qr_test.py | 17 +- phaseSpaceSampling/nde/transforms/reshape.py | 73 +++- .../nde/transforms/reshape_test.py | 45 +- .../nde/transforms/splines/__init__.py | 21 +- .../nde/transforms/splines/cubic.py | 256 +++++++---- .../nde/transforms/splines/cubic_test.py | 19 +- .../nde/transforms/splines/linear.py | 55 ++- .../nde/transforms/splines/linear_test.py | 20 +- .../nde/transforms/splines/quadratic.py | 125 ++++-- .../nde/transforms/splines/quadratic_test.py | 20 +- .../transforms/splines/rational_quadratic.py | 141 +++--- .../splines/rational_quadratic_test.py | 60 +-- phaseSpaceSampling/nde/transforms/standard.py | 27 +- .../nde/transforms/standard_test.py | 49 ++- phaseSpaceSampling/nde/transforms/svd.py | 24 +- phaseSpaceSampling/nde/transforms/svd_test.py | 19 +- .../nde/transforms/transform_test.py | 7 +- phaseSpaceSampling/nn/__init__.py | 21 +- phaseSpaceSampling/nn/attention.py | 107 +++-- phaseSpaceSampling/nn/conv.py | 411 +++++++++++------- phaseSpaceSampling/nn/mlp.py | 38 +- phaseSpaceSampling/nn/mlp_test.py | 10 +- phaseSpaceSampling/nn/resnet.py | 188 ++++---- phaseSpaceSampling/nn/unet.py | 66 +-- phaseSpaceSampling/postProcess/plotLoss.py | 29 +- .../visualizeDownSampled_subplots.py | 33 +- phaseSpaceSampling/sampler.py | 24 +- phaseSpaceSampling/utils/dataUtils.py | 15 +- phaseSpaceSampling/utils/flowUtils.py | 5 +- phaseSpaceSampling/utils/plotFun.py | 3 +- phaseSpaceSampling/utils/torchutils.py | 5 +- phaseSpaceSampling/utils/torchutils_test.py | 1 - setup.py | 27 +- tests/run2D.sh | 4 +- 94 files changed, 2612 insertions(+), 1976 deletions(-) create mode 100644 documentation/readmeImages/103_phaseSpaceSampling.png create mode 100644 documentation/readmeImages/103_randomSampling.png create mode 100644 documentation/readmeImages/104_phaseSpaceSampling.png create mode 100644 documentation/readmeImages/104_randomSampling.png create mode 100644 documentation/readmeImages/fulldataset.png create mode 100644 documentation/readmeImages/probabilityMap.png delete mode 100644 phaseSpaceSampling/main_iterative.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 62ac022..0524181 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -31,12 +31,11 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - python -m pip install -r requirements.txt + python -m pip install . - name: Formatting run: | source .github/linters/formatting.sh - format *.py true - format utils true + format . true codespell - name: Normalizing flow test run: | diff --git a/.gitignore b/.gitignore index a81c8ee..9a88f91 100644 --- a/.gitignore +++ b/.gitignore @@ -136,3 +136,11 @@ dmypy.json # Cython debug symbols cython_debug/ + +# data +*.npz +.DS_Store + +# output +Figures +TrainingLog diff --git a/data-efficientML/artificialCase/trainGP.py b/data-efficientML/artificialCase/trainGP.py index c395ecc..6f02735 100644 --- a/data-efficientML/artificialCase/trainGP.py +++ b/data-efficientML/artificialCase/trainGP.py @@ -7,10 +7,10 @@ import os import warnings +from prettyPlot.progressBar import print_progress_bar from sklearn.gaussian_process.kernels import RBF from sklearn.gaussian_process.kernels import ConstantKernel as C from sklearn.gaussian_process.kernels import WhiteKernel -from prettyPlot.progressBar import print_progress_bar def partitionData(nData, nBatch): diff --git a/data-efficientML/artificialCase/trainNN.py b/data-efficientML/artificialCase/trainNN.py index 235f8ec..a4ee88e 100644 --- a/data-efficientML/artificialCase/trainNN.py +++ b/data-efficientML/artificialCase/trainNN.py @@ -7,11 +7,12 @@ # NN Stuff import tensorflow as tf from myNN_better import * +from parallel import irank, iroot +from prettyPlot.progressBar import print_progress_bar from sklearn.model_selection import train_test_split from tensorflow import keras from tensorflow.keras import layers, regularizers -from prettyPlot.progressBar import print_progress_bar -from parallel import irank, iroot + def partitionData(nData, nBatch): # ~~~~ Partition the data across batches @@ -43,7 +44,7 @@ def getPrediction(model, data): prefix="Eval " + str(0) + " / " + str(nBatch), suffix="Complete", length=50, - extraCond=(irank==iroot), + extraCond=(irank == iroot), ) for ibatch in range(nBatch): start_ = startData_b[ibatch] @@ -55,7 +56,7 @@ def getPrediction(model, data): prefix="Eval " + str(ibatch + 1) + " / " + str(nBatch), suffix="Complete", length=50, - extraCond=(irank==iroot), + extraCond=(irank == iroot), ) return result diff --git a/data-efficientML/artificialCase/util/myNN_better.py b/data-efficientML/artificialCase/util/myNN_better.py index d2bde36..7d233c5 100644 --- a/data-efficientML/artificialCase/util/myNN_better.py +++ b/data-efficientML/artificialCase/util/myNN_better.py @@ -2,13 +2,13 @@ import numpy as np import tensorflow as tf +from prettyPlot.progressBar import print_progress_bar from tensorflow.keras import backend as K from tensorflow.keras import layers, losses, optimizers, regularizers from tensorflow.keras.callbacks import CSVLogger from tensorflow.keras.constraints import max_norm, unit_norm from tensorflow.keras.layers import * from tensorflow.keras.models import Model -from prettyPlot.progressBar import print_progress_bar @tf.function @@ -57,7 +57,6 @@ def call(self, xTrain): def train( self, batch_size, epochs, xTrain, yTrain, learningRate, lrScheduler ): - self.checkDataShape(xTrain, yTrain) self.prepareLog() @@ -88,7 +87,6 @@ def train( step, (x_batch_train, y_batch_train), ) in enumerate(train_dataset): - mse = self.train_step( x_batch_train, y_batch_train, dsOptimizer ) @@ -125,7 +123,6 @@ def prepareLog(self): f.close() def logTraining(self, epoch, mse, bestLoss): - f = open(self.logLossFolder + "/log.csv", "a+") f.write(str(int(epoch)) + ";" + str(mse.numpy()) + "\n") f.close() diff --git a/data-efficientML/combustionCase/trainGP.py b/data-efficientML/combustionCase/trainGP.py index dfc3f8a..7bb3e56 100644 --- a/data-efficientML/combustionCase/trainGP.py +++ b/data-efficientML/combustionCase/trainGP.py @@ -7,11 +7,12 @@ import os import warnings +from parallel import irank, iroot +from prettyPlot.progressBar import print_progress_bar from sklearn.gaussian_process.kernels import RBF from sklearn.gaussian_process.kernels import ConstantKernel as C from sklearn.gaussian_process.kernels import WhiteKernel -from prettyPlot.progressBar import print_progress_bar -from parallel import irank, iroot + def partitionData(nData, nBatch): # ~~~~ Partition the data across batches @@ -43,7 +44,7 @@ def getPrediction(gpr, data): prefix="Get prob " + str(0) + " / " + str(nBatch), suffix="Complete", length=50, - extraCond=(irank==iroot), + extraCond=(irank == iroot), ) for ibatch in range(nBatch): start_ = startData_b[ibatch] @@ -55,7 +56,7 @@ def getPrediction(gpr, data): prefix="Get prob " + str(ibatch + 1) + " / " + str(nBatch), suffix="Complete", length=50, - extraCond=(irank==iroot), + extraCond=(irank == iroot), ) return result diff --git a/data-efficientML/combustionCase/trainNN.py b/data-efficientML/combustionCase/trainNN.py index 730909c..f2426c1 100644 --- a/data-efficientML/combustionCase/trainNN.py +++ b/data-efficientML/combustionCase/trainNN.py @@ -7,12 +7,12 @@ # NN Stuff import tensorflow as tf from myNN_better import * +from parallel import irank, iroot +from prettyPlot.progressBar import print_progress_bar from sklearn.model_selection import train_test_split from tensorflow import keras from tensorflow.keras import layers, regularizers -from prettyPlot.progressBar import print_progress_bar -from parallel import irank, iroot def partitionData(nData, nBatch): # ~~~~ Partition the data across batches @@ -44,7 +44,7 @@ def getPrediction(model, data): prefix="Eval " + str(0) + " / " + str(nBatch), suffix="Complete", length=50, - extraCond=(irank==iroot), + extraCond=(irank == iroot), ) for ibatch in range(nBatch): start_ = startData_b[ibatch] @@ -56,7 +56,7 @@ def getPrediction(model, data): prefix="Eval " + str(ibatch + 1) + " / " + str(nBatch), suffix="Complete", length=50, - extraCond=(irank==iroot), + extraCond=(irank == iroot), ) return result diff --git a/data-efficientML/combustionCase/util/myNN_better.py b/data-efficientML/combustionCase/util/myNN_better.py index d2bde36..7d233c5 100644 --- a/data-efficientML/combustionCase/util/myNN_better.py +++ b/data-efficientML/combustionCase/util/myNN_better.py @@ -2,13 +2,13 @@ import numpy as np import tensorflow as tf +from prettyPlot.progressBar import print_progress_bar from tensorflow.keras import backend as K from tensorflow.keras import layers, losses, optimizers, regularizers from tensorflow.keras.callbacks import CSVLogger from tensorflow.keras.constraints import max_norm, unit_norm from tensorflow.keras.layers import * from tensorflow.keras.models import Model -from prettyPlot.progressBar import print_progress_bar @tf.function @@ -57,7 +57,6 @@ def call(self, xTrain): def train( self, batch_size, epochs, xTrain, yTrain, learningRate, lrScheduler ): - self.checkDataShape(xTrain, yTrain) self.prepareLog() @@ -88,7 +87,6 @@ def train( step, (x_batch_train, y_batch_train), ) in enumerate(train_dataset): - mse = self.train_step( x_batch_train, y_batch_train, dsOptimizer ) @@ -125,7 +123,6 @@ def prepareLog(self): f.close() def logTraining(self, epoch, mse, bestLoss): - f = open(self.logLossFolder + "/log.csv", "a+") f.write(str(int(epoch)) + ";" + str(mse.numpy()) + "\n") f.close() diff --git a/documentation/readmeImages/103_phaseSpaceSampling.png 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