Merge branch 'dev' into main

disent/dataset/_base.py

@@ -354,6 +354,9 @@ def dataset_sample_elems(self, num_samples: int, mode: str, return_indices: bool
     # Batches -- Ground Truth Only                                          #
     # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
 
+    # TODO: batches should be obtained from indices
+    #       - the wrapped gt datasets should handle generating these indices, eg. factor traversals etc.
+
     @groundtruth_only
     def dataset_batch_from_factors(self, factors: np.ndarray, mode: str, collate: bool = True):
         """Get a batch of observations X from a batch of factors Y."""

disent/frameworks/ae/_unsupervised__ae.py

@@ -22,7 +22,6 @@
 #  SOFTWARE.
 #  ~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~
 
-import logging
 from dataclasses import dataclass
 from numbers import Number
 from typing import Any
@@ -34,7 +33,7 @@
 
 import torch
 
-from disent.frameworks.ae._ae_mixin import _AeAndVaeMixin
+from disent.frameworks._ae_mixin import _AeAndVaeMixin
 from disent.frameworks.helper.util import detach_all
 from disent.model import AutoEncoder
 from disent.util.iters import map_all

disent/frameworks/vae/_unsupervised__dfcvae.py

@@ -40,6 +40,7 @@
 
 from disent.frameworks.helper.util import compute_ave_loss
 from disent.frameworks.vae._unsupervised__betavae import BetaVae
+from disent.nn.loss.reduction import batch_loss_reduction
 from disent.nn.loss.reduction import get_mean_loss_scale
 from disent.dataset.transform.functional import check_tensor
 
@@ -132,6 +133,24 @@ def __init__(self, feature_layers: Optional[List[Union[str, int]]] = None, input
         assert input_mode in {'none', 'clamp', 'assert'}
         self.input_mode = input_mode
 
+    def compute_pairwise_loss(self, x_recon, x_targ, reduction='mean'):
+        """
+        THIS DOES NOT HAVE LOSS SCALING, LIKE `compute_loss`
+
+        x_recon and x_targ data should be an unnormalized RGB batch of
+        data [B x C x H x W] in the range [0, 1].
+        """
+        features_recon = self._extract_features(x_recon)
+        features_targ = self._extract_features(x_targ)
+        # compute losses
+        feature_loss = 0.0
+        for (f_recon, f_targ) in zip(features_recon, features_targ):
+            loss = F.mse_loss(f_recon, f_targ, reduction='none')
+            feature_loss += batch_loss_reduction(loss, reduction=reduction)
+        # checks
+        assert (feature_loss.ndim == 1) and (len(feature_loss) == len(x_recon))
+        return feature_loss
+
     def compute_loss(self, x_recon, x_targ, reduction='mean'):
         """
         x_recon and x_targ data should be an unnormalized RGB batch of

disent/frameworks/vae/_unsupervised__dotvae.py

@@ -33,7 +33,7 @@
 
 from disent.frameworks.helper.reconstructions import make_reconstruction_loss
 from disent.frameworks.helper.reconstructions import ReconLossHandler
-from disent.frameworks.vae import AdaNegTripletVae
+from disent.frameworks.vae._supervised__adaneg_tvae import AdaNegTripletVae
 from disent.nn.loss.triplet_mining import configured_idx_mine
 
 

disent/frameworks/vae/_unsupervised__vae.py

@@ -34,7 +34,7 @@
 import torch
 from torch.distributions import Distribution
 
-from disent.frameworks.ae._ae_mixin import _AeAndVaeMixin
+from disent.frameworks._ae_mixin import _AeAndVaeMixin
 from disent.frameworks.helper.latent_distributions import LatentDistsHandler
 from disent.frameworks.helper.latent_distributions import make_latent_distribution
 from disent.frameworks.helper.util import detach_all

requirements.txt

@@ -5,7 +5,7 @@ pip>=21.0
 numpy>=1.19.0
 torch>=1.9.0
 torchvision>=0.10.0
-pytorch-lightning>=1.4.0
+pytorch-lightning>=1.4.0,<1.7
 torch_optimizer>=0.1.0
 scipy>=1.7.0
 scikit-learn>=0.24.2

setup.py

@@ -48,7 +48,7 @@
     author="Nathan Juraj Michlo",
     author_email="[email protected]",
 
-    version="0.6.0",
+    version="0.6.1",
     python_requires=">=3.8",  # we make use of standard library features only in 3.8
     packages=setuptools.find_packages(),
 

tests/test_000_import.py

@@ -0,0 +1,31 @@
+#  ~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~
+#  MIT License
+#
+#  Copyright (c) 2022 Nathan Juraj Michlo
+#
+#  Permission is hereby granted, free of charge, to any person obtaining a copy
+#  of this software and associated documentation files (the "Software"), to deal
+#  in the Software without restriction, including without limitation the rights
+#  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+#  copies of the Software, and to permit persons to whom the Software is
+#  furnished to do so, subject to the following conditions:
+#
+#  The above copyright notice and this permission notice shall be included in
+#  all copies or substantial portions of the Software.
+#
+#  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+#  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+#  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+#  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+#  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+#  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+#  SOFTWARE.
+#  ~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~
+
+
+# THIS TEST FILE SHOULD ALWAYS BE LOADED AND RUN FIRST
+from disent.frameworks.vae import BetaVae
+
+
+def test_000_import():
+    assert BetaVae