Some small details of the repair

Lornatang · Jun 1, 2021 · b6f8b41 · b6f8b41
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diff --git a/.gitignore b/.gitignore
@@ -58,6 +58,7 @@ coverage.xml
 *.log
 local_settings.py
 db.sqlite3
+db.sqlite3-journal
 
 # Flask stuff:
 instance/
@@ -113,7 +114,6 @@ venv.bak/
 
 # mkdocs documentation
 /site
-
 # mypy
 .mypy_cache/
 .dmypy.json
@@ -122,24 +122,34 @@ dmypy.json
 # Pyre type checker
 .pyre/
 
-# IDE file
+# custom
 .idea
 .vscode
 
-# Mac file
+# Mac configure file.
 .DS_Store
 
-# custom
+# Dataset
 data/MNIST
-data/TFD
-data/cifar-10-python
-data/cifar-10-python.tar.gz
-data/cifar-10-batches-py
-
-output
-test
-*.jpg
-*.jpeg
-*.png
+data/Camelyon16
+data/DF2K
+data/DIV2K
+data/DF2K+OST
+data/Digital-pathology
+data/ImageNet2012
+data/Paper
+
+# Program run create directory.
+benchmarks
+tests
+videos
+runs
+weights
+
+# Program run create file.
 *.bmp
-*.pth
+*.png
+*.mp4
+*.zip
+*.csv
+*.json
diff --git a/README.md b/README.md
@@ -2,8 +2,7 @@
 
 ### Overview
 
-This repository contains an op-for-op PyTorch reimplementation
-of [Conditional Generative Adversarial Networks](http://xxx.itp.ac.cn/pdf/1411.1784).
+This repository contains an op-for-op PyTorch reimplementation of [Conditional Generative Adversarial Networks](http://arxiv.org/pdf/1411.1784).
 
 ### Table of contents
 
@@ -23,19 +22,17 @@ of [Conditional Generative Adversarial Networks](http://xxx.itp.ac.cn/pdf/1411.1
 
 If you're new to CGANs, here's an abstract straight from the paper:
 
-Generative Adversarial Nets were recently introduced as a novel way to train generative models. In this work we
-introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data,
-y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits
-conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide
-preliminary examples of an application to image tagging in which we demonstrate how this approach can generate
-descriptive tags which are not part of training labels.
+Generative Adversarial Nets were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of
+generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator.
+We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal
+model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags
+which are not part of training labels.
 
 ### Model Description
 
-We have two networks, G (Generator) and D (Discriminator).The Generator is a network for generating images. It receives
-a random noise z and generates images from this noise, which is called G(z).Discriminator is a discriminant network that
-discriminates whether an image is real. The input is x, x is a picture, and the output is D of x is the probability that
-x is a real picture, and if it's 1, it's 100% real, and if it's 0, it's not real.
+We have two networks, G (Generator) and D (Discriminator).The Generator is a network for generating images. It receives a random noise z and generates
+images from this noise, which is called G(z).Discriminator is a discriminant network that discriminates whether an image is real. The input is x, x is
+a picture, and the output is D of x is the probability that x is a real picture, and if it's 1, it's 100% real, and if it's 0, it's not real.
 
 ### Installation
 
@@ -67,15 +64,14 @@ import torchvision.utils as vutils
 device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
 
 # Load the model into the specified device.
-model = torch.hub.load("Lornatang/CGAN-PyTorch", "mnist", pretrained=True, progress=True, verbose=False)
+model = torch.hub.load("Lornatang/CGAN-PyTorch", "cgan", pretrained=True, progress=True, verbose=False)
 model.eval()
 model = model.to(device)
 
 # Create random noise image.
 num_images = 64
-number = 1
-conditional = torch.randint(number, number + 1, (num_images,), device=device)
-noise = torch.randn(num_images, 100, device=device)
+noise = torch.randn([num_images, 100], device=device)
+conditional = torch.randint(1, 2, (num_images,), device=device)
 
 # The noise is input into the generator model to generate the image.
 with torch.no_grad():
@@ -90,30 +86,22 @@ vutils.save_image(generated_images, "mnist.png", normalize=True)
 Using pre training model to generate pictures.
 
 ```text
-usage: test.py [-h] [-a ARCH] [--num-classes NUM_CLASSES]
-               [--number {0,1,2,3,4,5,6,7,8,9}] [-n NUM_IMAGES] [--outf PATH]
-               [--device DEVICE]
-
-An implementation of CGAN algorithm using PyTorch framework.
+usage: test.py [-h] [-a ARCH] [--number {0,1,2,3,4,5,6,7,8,9}] [--num-images NUM_IMAGES] [--model-path PATH] [--pretrained] [--seed SEED] [--gpu GPU]
 
 optional arguments:
   -h, --help            show this help message and exit
-  -a ARCH, --arch ARCH  model architecture: _gan | discriminator |
-                        load_state_dict_from_url | mnist (default: mnist)
-  --num-classes NUM_CLASSES
-                        Number of classes for dataset. (default: 10).
+  -a ARCH, --arch ARCH  model architecture: cgan. (Default: `cgan`)
   --number {0,1,2,3,4,5,6,7,8,9}
-                        Specifies the generated number. (default: 1).
-  -n NUM_IMAGES, --num-images NUM_IMAGES
-                        How many samples are generated at one time. (default:
-                        64).
-  --outf PATH           The location of the image in the evaluation process.
-                        (default: ``test``).
-  --device DEVICE       device id i.e. `0` or `0,1` or `cpu`. (default:
-                        ``cpu``).
+                        Specifies the generated number. (Default: 1)
+  --num-images NUM_IMAGES
+                        How many samples are generated at one time. (Default: 64)
+  --model-path PATH     Path to latest checkpoint for model.
+  --pretrained          Use pre-trained model.
+  --seed SEED           Seed for initializing training. (Default: 666)
+  --gpu GPU             GPU id to use.
 
 # Example (e.g. MNIST)
-$ python3 test.py -a mnist --number 1
+$ python3 test.py -a cgan --number 1 --gpu 0
 ```
 
 <span align="center"><img src="assets/mnist.gif" alt="">
@@ -122,67 +110,53 @@ $ python3 test.py -a mnist --number 1
 ### Train (e.g. MNIST)
 
 ```text
-usage: train.py [-h] [-a ARCH] [-j N] [--start-iter N] [--iters N] [-b N]
-                [--lr LR] [--image-size IMAGE_SIZE] [--channels CHANNELS]
-                [--num-classes NUM_CLASSES] [--pretrained] [--netD PATH]
-                [--netG PATH] [--manualSeed MANUALSEED] [--device DEVICE]
+usage: train.py [-h] [-a ARCH] [-j N] [--epochs N] [--start-epoch N] [-b N] [--lr LR] [--image-size IMAGE_SIZE] [--channels CHANNELS] [--num-classes NUM_CLASSES] [--netD PATH] [--netG PATH] [--pretrained] [--world-size WORLD_SIZE] [--rank RANK]
+                [--dist-url DIST_URL] [--dist-backend DIST_BACKEND] [--seed SEED] [--gpu GPU] [--multiprocessing-distributed]
                 DIR
 
-An implementation of GAN algorithm using PyTorch framework.
-
 positional arguments:
-  DIR                   path to dataset
+  DIR                   Path to dataset.
 
 optional arguments:
   -h, --help            show this help message and exit
-  -a ARCH, --arch ARCH  model architecture: _gan | discriminator |
-                        load_state_dict_from_url | mnist (default: mnist)
-  -j N, --workers N     Number of data loading workers. (default:8)
-  --start-iter N        manual iter number (useful on restarts)
-  --iters N             The number of iterations is needed in the training of
-                        model. (default: 50000)
-  -b N, --batch-size N  mini-batch size (default: 64), this is the total batch
-                        size of all GPUs on the current node when using Data
-                        Parallel or Distributed Data Parallel.
-  --lr LR               Learning rate. (default:0.0002)
+  -a ARCH, --arch ARCH  Model architecture: cgan. (Default: cgan)
+  -j N, --workers N     Number of data loading workers. (Default: 4)
+  --epochs N            Number of total epochs to run. (Default: 128)
+  --start-epoch N       Manual epoch number (useful on restarts). (Default: 0)
+  -b N, --batch-size N  Mini-batch size (default: 64), this is the total batch size of all GPUs on the current node when using Data Parallel or Distributed Data Parallel.
+  --lr LR               Learning rate. (Default: 0.0002)
   --image-size IMAGE_SIZE
-                        The height / width of the input image to network.
-                        (default: 28).
-  --channels CHANNELS   The number of channels of the image. (default: 1).
+                        Image size of high resolution image. (Default: 28)
+  --channels CHANNELS   The number of channels of the image. (Default: 1)
   --num-classes NUM_CLASSES
-                        Number of classes for dataset. (default: 10).
+                        Number of classes for dataset. (Default: 10)
+  --netD PATH           Path to Discriminator checkpoint.
+  --netG PATH           Path to Generator checkpoint.
   --pretrained          Use pre-trained model.
-  --netD PATH           Path to latest discriminator checkpoint. (default:
-                        ````).
-  --netG PATH           Path to latest generator checkpoint. (default: ````).
-  --manualSeed MANUALSEED
-                        Seed for initializing training. (default:1111)
-  --device DEVICE       device id i.e. `0` or `0,1` or `cpu`. (default:
-                        ``0``).
+  --world-size WORLD_SIZE
+                        Number of nodes for distributed training.
+  --rank RANK           Node rank for distributed training. (Default: -1)
+  --dist-url DIST_URL   url used to set up distributed training. (Default: `tcp://59.110.31.55:12345`)
+  --dist-backend DIST_BACKEND
+                        Distributed backend. (Default: `nccl`)
+  --seed SEED           Seed for initializing training.
+  --gpu GPU             GPU id to use.
+  --multiprocessing-distributed
+                        Use multi-processing distributed training to launch N processes per node, which has N GPUs. This is the fastest way to use PyTorch for either single node or multi node data parallel training.
 
 # Example (e.g. MNIST)
-$ python3 train.py data -a mnist --image-size 28 --channels 1 --num-classes 10 --pretrained --device 0
+$ python3 train.py -a cgan --gpu 0 data
 ```
 
 If you want to load weights that you've trained before, run the following command.
 
 ```bash
-$ python3 train.py data \
-                   --dataset mnist \
-                   -a mnist \
-                   --image-size 28 \
-                   --channels 1 \
-                   --num-classes 10 \
-                   --start-iter 10000 \
-                   --netG weights/mnist_G_iter_10000.pth \
-                   --netD weights/mnist_D_iter_10000.pth \
-                   --device 0
+$ python3 train.py -a cgan data --netD weights/Discriminator_epoch8.pth --netG weights/Generator_epoch8.pth --start-epoch 8 --gpu 0 data
 ```
 
 ### Contributing
 
-If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions,
-simply post them as GitHub issues.
+If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.
 
 I look forward to seeing what the community does with these models!
 
@@ -194,12 +168,11 @@ I look forward to seeing what the community does with these models!
 
 **Abstract**
 
-Generative Adversarial Nets were recently introduced as a novel way to train generative models. In this work we
-introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data,
-y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits
-conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide
-preliminary examples of an application to image tagging in which we demonstrate how this approach can generate
-descriptive tags which are not part of training labels.
+Generative Adversarial Nets were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of
+generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator.
+We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal
+model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags
+which are not part of training labels.
 
 [[Paper]](http://xxx.itp.ac.cn/pdf/1411.1784)
 

diff --git a/cgan_pytorch/__init__.py b/cgan_pytorch/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Dakewe Biotech Corporation. All Rights Reserved.
+# Copyright 2021 Dakewe Biotech Corporation. All Rights Reserved.
 # Licensed under the Apache License, Version 2.0 (the "License");
 #   you may not use this file except in compliance with the License.
 #   You may obtain a copy of the License at
@@ -11,7 +11,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+from .activation import *
 from .models import *
 from .utils import *
 
-__version__ = "0.1.0"
+__version__ = "0.2.0"