setuptools packaging

.pylintrc

@@ -0,0 +1,49 @@
+[MASTER]
+extension-pkg-whitelist = numpy
+
+[TYPECHECK]
+generated-members = np.*, torch.*
+ignored-classes = np.*
+
+[MESSAGES CONTROL]
+# as we have legacy code in ocrolib which
+# does not adhere to most if this, and
+# pylint does not offer per-subdirectory
+# configuration yet (#618), we must be
+# overly tolerant here:
+disable =
+    ungrouped-imports,
+    multiple-imports,
+    bad-continuation,
+    missing-docstring,
+    no-self-use,
+    superfluous-parens,
+    invalid-name,
+    line-too-long,
+    too-many-arguments,
+    too-many-branches,
+    too-many-statements,
+    too-many-locals,
+    too-few-public-methods,
+    too-many-instance-attributes,
+    wrong-import-order,
+    duplicate-code,
+    #bad-whitespace,
+    consider-using-f-string,
+    unused-variable,
+    import-outside-toplevel,
+    wildcard-import,
+    unused-wildcard-import,
+    too-many-return-statements,
+    unidiomatic-typecheck,
+    multiple-statements,
+    no-else-return,
+    unnecessary-semicolon,
+    consider-using-dict-comprehension,
+    redefined-builtin
+
+# allow indented whitespace (as required by interpreter):
+no-space-check=empty-line
+
+# allow non-snake-case identifiers:
+good-names=n,i

README.md

@@ -1,144 +1,148 @@
-<img src='imgs/teaser_720.gif' align="right" width=360>
-
-<br><br><br><br>
-
-# pix2pixHD
-### [Project](https://tcwang0509.github.io/pix2pixHD/) | [Youtube](https://youtu.be/3AIpPlzM_qs) | [Paper](https://arxiv.org/pdf/1711.11585.pdf) <br>
-Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translation. It can be used for turning semantic label maps into photo-realistic images or synthesizing portraits from face label maps. <br><br>
-[High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://tcwang0509.github.io/pix2pixHD/)  
- [Ting-Chun Wang](https://tcwang0509.github.io/)<sup>1</sup>, [Ming-Yu Liu](http://mingyuliu.net/)<sup>1</sup>, [Jun-Yan Zhu](http://people.eecs.berkeley.edu/~junyanz/)<sup>2</sup>, Andrew Tao<sup>1</sup>, [Jan Kautz](http://jankautz.com/)<sup>1</sup>, [Bryan Catanzaro](http://catanzaro.name/)<sup>1</sup>  
- <sup>1</sup>NVIDIA Corporation, <sup>2</sup>UC Berkeley  
- In CVPR 2018.  
-
-## Image-to-image translation at 2k/1k resolution
-- Our label-to-streetview results
-<p align='center'>  
-  <img src='imgs/teaser_label.png' width='400'/>
-  <img src='imgs/teaser_ours.jpg' width='400'/>
-</p>
-- Interactive editing results
-<p align='center'>  
-  <img src='imgs/teaser_style.gif' width='400'/>
-  <img src='imgs/teaser_label.gif' width='400'/>
-</p>
-- Additional streetview results
-<p align='center'>
-  <img src='imgs/cityscapes_1.jpg' width='400'/>
-  <img src='imgs/cityscapes_2.jpg' width='400'/>
-</p>
-<p align='center'>
-  <img src='imgs/cityscapes_3.jpg' width='400'/>
-  <img src='imgs/cityscapes_4.jpg' width='400'/>
-</p>
-
-- Label-to-face and interactive editing results
-<p align='center'>
-  <img src='imgs/face1_1.jpg' width='250'/>
-  <img src='imgs/face1_2.jpg' width='250'/>
-  <img src='imgs/face1_3.jpg' width='250'/>
-</p>
-<p align='center'>
-  <img src='imgs/face2_1.jpg' width='250'/>
-  <img src='imgs/face2_2.jpg' width='250'/>
-  <img src='imgs/face2_3.jpg' width='250'/>
-</p>
-
-- Our editing interface
-<p align='center'>
-  <img src='imgs/city_short.gif' width='330'/>
-  <img src='imgs/face_short.gif' width='450'/>
-</p>
-
-## Prerequisites
-- Linux or macOS
-- Python 2 or 3
-- NVIDIA GPU (11G memory or larger) + CUDA cuDNN
-
-## Getting Started
-### Installation
-- Install PyTorch and dependencies from http://pytorch.org
-- Install python libraries [dominate](https://github.com/Knio/dominate).
-```bash
-pip install dominate
-```
-- Clone this repo:
-```bash
-git clone https://github.com/NVIDIA/pix2pixHD
-cd pix2pixHD
-```
-
-
-### Testing
-- A few example Cityscapes test images are included in the `datasets` folder.
-- Please download the pre-trained Cityscapes model from [here](https://drive.google.com/file/d/1h9SykUnuZul7J3Nbms2QGH1wa85nbN2-/view?usp=sharing) (google drive link), and put it under `./checkpoints/label2city_1024p/`
-- Test the model (`bash ./scripts/test_1024p.sh`):
-```bash
-#!./scripts/test_1024p.sh
-python test.py --name label2city_1024p --netG local --ngf 32 --resize_or_crop none
-```
-The test results will be saved to a html file here: `./results/label2city_1024p/test_latest/index.html`.
-
-More example scripts can be found in the `scripts` directory.
-
-
-### Dataset
-- We use the Cityscapes dataset. To train a model on the full dataset, please download it from the [official website](https://www.cityscapes-dataset.com/) (registration required).
-After downloading, please put it under the `datasets` folder in the same way the example images are provided.
-
-
-### Training
-- Train a model at 1024 x 512 resolution (`bash ./scripts/train_512p.sh`):
-```bash
-#!./scripts/train_512p.sh
-python train.py --name label2city_512p
-```
-- To view training results, please checkout intermediate results in `./checkpoints/label2city_512p/web/index.html`.
-If you have tensorflow installed, you can see tensorboard logs in `./checkpoints/label2city_512p/logs` by adding `--tf_log` to the training scripts.
-
-### Multi-GPU training
-- Train a model using multiple GPUs (`bash ./scripts/train_512p_multigpu.sh`):
-```bash
-#!./scripts/train_512p_multigpu.sh
-python train.py --name label2city_512p --batchSize 8 --gpu_ids 0,1,2,3,4,5,6,7
-```
-Note: this is not tested and we trained our model using single GPU only. Please use at your own discretion.
-
-### Training with Automatic Mixed Precision (AMP) for faster speed
-- To train with mixed precision support, please first install apex from: https://github.com/NVIDIA/apex
-- You can then train the model by adding `--fp16`. For example,
-```bash
-#!./scripts/train_512p_fp16.sh
-python -m torch.distributed.launch train.py --name label2city_512p --fp16
-```
-In our test case, it trains about 80% faster with AMP on a Volta machine.
-
-### Training at full resolution
-- To train the images at full resolution (2048 x 1024) requires a GPU with 24G memory (`bash ./scripts/train_1024p_24G.sh`), or 16G memory if using mixed precision (AMP).
-- If only GPUs with 12G memory are available, please use the 12G script (`bash ./scripts/train_1024p_12G.sh`), which will crop the images during training. Performance is not guaranteed using this script.
-
-### Training with your own dataset
-- If you want to train with your own dataset, please generate label maps which are one-channel whose pixel values correspond to the object labels (i.e. 0,1,...,N-1, where N is the number of labels). This is because we need to generate one-hot vectors from the label maps. Please also specity `--label_nc N` during both training and testing.
-- If your input is not a label map, please just specify `--label_nc 0` which will directly use the RGB colors as input. The folders should then be named `train_A`, `train_B` instead of `train_label`, `train_img`, where the goal is to translate images from A to B.
-- If you don't have instance maps or don't want to use them, please specify `--no_instance`.
-- The default setting for preprocessing is `scale_width`, which will scale the width of all training images to `opt.loadSize` (1024) while keeping the aspect ratio. If you want a different setting, please change it by using the `--resize_or_crop` option. For example, `scale_width_and_crop` first resizes the image to have width `opt.loadSize` and then does random cropping of size `(opt.fineSize, opt.fineSize)`. `crop` skips the resizing step and only performs random cropping. If you don't want any preprocessing, please specify `none`, which will do nothing other than making sure the image is divisible by 32.
-
-## More Training/Test Details
-- Flags: see `options/train_options.py` and `options/base_options.py` for all the training flags; see `options/test_options.py` and `options/base_options.py` for all the test flags.
-- Instance map: we take in both label maps and instance maps as input. If you don't want to use instance maps, please specify the flag `--no_instance`.
-
-
-## Citation
-
-If you find this useful for your research, please use the following.
-
-```
-@inproceedings{wang2018pix2pixHD,
-  title={High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs},
-  author={Ting-Chun Wang and Ming-Yu Liu and Jun-Yan Zhu and Andrew Tao and Jan Kautz and Bryan Catanzaro},  
-  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
-  year={2018}
-}
-```
-
-## Acknowledgments
-This code borrows heavily from [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).
+<img src='imgs/teaser_720.gif' align="right" width=360>
+
+<br><br><br><br>
+
+# pix2pixHD
+### [Project](https://tcwang0509.github.io/pix2pixHD/) | [Youtube](https://youtu.be/3AIpPlzM_qs) | [Paper](https://arxiv.org/pdf/1711.11585.pdf) <br>
+Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translation. It can be used for turning semantic label maps into photo-realistic images or synthesizing portraits from face label maps. <br><br>
+[High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://tcwang0509.github.io/pix2pixHD/)  
+ [Ting-Chun Wang](https://tcwang0509.github.io/)<sup>1</sup>, [Ming-Yu Liu](http://mingyuliu.net/)<sup>1</sup>, [Jun-Yan Zhu](http://people.eecs.berkeley.edu/~junyanz/)<sup>2</sup>, Andrew Tao<sup>1</sup>, [Jan Kautz](http://jankautz.com/)<sup>1</sup>, [Bryan Catanzaro](http://catanzaro.name/)<sup>1</sup>  
+ <sup>1</sup>NVIDIA Corporation, <sup>2</sup>UC Berkeley  
+ In CVPR 2018.  
+
+## Image-to-image translation at 2k/1k resolution
+- Our label-to-streetview results
+<p align='center'>  
+  <img src='imgs/teaser_label.png' width='400'/>
+  <img src='imgs/teaser_ours.jpg' width='400'/>
+</p>
+- Interactive editing results
+<p align='center'>  
+  <img src='imgs/teaser_style.gif' width='400'/>
+  <img src='imgs/teaser_label.gif' width='400'/>
+</p>
+- Additional streetview results
+<p align='center'>
+  <img src='imgs/cityscapes_1.jpg' width='400'/>
+  <img src='imgs/cityscapes_2.jpg' width='400'/>
+</p>
+<p align='center'>
+  <img src='imgs/cityscapes_3.jpg' width='400'/>
+  <img src='imgs/cityscapes_4.jpg' width='400'/>
+</p>
+
+- Label-to-face and interactive editing results
+<p align='center'>
+  <img src='imgs/face1_1.jpg' width='250'/>
+  <img src='imgs/face1_2.jpg' width='250'/>
+  <img src='imgs/face1_3.jpg' width='250'/>
+</p>
+<p align='center'>
+  <img src='imgs/face2_1.jpg' width='250'/>
+  <img src='imgs/face2_2.jpg' width='250'/>
+  <img src='imgs/face2_3.jpg' width='250'/>
+</p>
+
+- Our editing interface
+<p align='center'>
+  <img src='imgs/city_short.gif' width='330'/>
+  <img src='imgs/face_short.gif' width='450'/>
+</p>
+
+## Prerequisites
+- Linux or macOS
+- Python 2 or 3
+- [optionally:] NVIDIA GPU (11G memory or larger) + CUDA cuDNN
+
+## Getting Started
+### Installation
+- Create a [virtual environment](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/), and activate it
+- Clone this repo:
+
+        git clone https://github.com/NVIDIA/pix2pixHD
+        pip install pix2pixHD
+
+
+
+### Testing
+- A few example Cityscapes test images are included in the `datasets` folder.
+- Please download the pre-trained Cityscapes model from [here](https://drive.google.com/file/d/1h9SykUnuZul7J3Nbms2QGH1wa85nbN2-/view?usp=sharing) (google drive link), and put it under `./checkpoints/label2city_1024p/`
+- Test the model (`bash ./scripts/test_1024p.sh`):
+```bash
+#!./scripts/test_1024p.sh
+pix2pixhd-test --name label2city_1024p --netG local --ngf 32 --resize_or_crop none
+```
+The test results will be saved to a html file here: `./results/label2city_1024p/test_latest/index.html`.
+
+More example scripts can be found in the `scripts` directory.
+
+For other options, cf.
+
+    pix2pixhd-test --help
+
+
+### Dataset
+- We use the Cityscapes dataset. To train a model on the full dataset, please download it from the [official website](https://www.cityscapes-dataset.com/) (registration required).
+After downloading, please put it under the `datasets` folder in the same way the example images are provided.
+
+
+### Training
+- Train a model at 1024 x 512 resolution (`bash ./scripts/train_512p.sh`):
+```bash
+#!./scripts/train_512p.sh
+pix2pixhd-train --name label2city_512p
+```
+- To view training results, please checkout intermediate results in `./checkpoints/label2city_512p/web/index.html`.
+If you have tensorflow installed, you can see tensorboard logs in `./checkpoints/label2city_512p/logs` by adding `--tf_log` to the training scripts.
+
+For other options, cf.
+
+    pix2pixhd-train --help
+
+### Multi-GPU training
+- Train a model using multiple GPUs (`bash ./scripts/train_512p_multigpu.sh`):
+```bash
+#!./scripts/train_512p_multigpu.sh
+pix2pixhd-train --name label2city_512p --batchSize 8 --gpu_ids 0,1,2,3,4,5,6,7
+```
+Note: this is not tested and we trained our model using single GPU only. Please use at your own discretion.
+
+### Training with Automatic Mixed Precision (AMP) for faster speed
+- To train with mixed precision support, please first install apex from: https://github.com/NVIDIA/apex
+- You can then train the model by adding `--fp16`. For example,
+```bash
+#!./scripts/train_512p_fp16.sh
+python -m torch.distributed.launch pix2pixhd/train.py --name label2city_512p --fp16
+```
+In our test case, it trains about 80% faster with AMP on a Volta machine.
+
+### Training at full resolution
+- To train the images at full resolution (2048 x 1024) requires a GPU with 24G memory (`bash ./scripts/train_1024p_24G.sh`), or 16G memory if using mixed precision (AMP).
+- If only GPUs with 12G memory are available, please use the 12G script (`bash ./scripts/train_1024p_12G.sh`), which will crop the images during training. Performance is not guaranteed using this script.
+
+### Training with your own dataset
+- If you want to train with your own dataset, please generate label maps which are one-channel whose pixel values correspond to the object labels (i.e. 0,1,...,N-1, where N is the number of labels). This is because we need to generate one-hot vectors from the label maps. Please also specity `--label_nc N` during both training and testing.
+- If your input is not a label map, please just specify `--label_nc 0` which will directly use the RGB colors as input. The folders should then be named `train_A`, `train_B` instead of `train_label`, `train_img`, where the goal is to translate images from A to B.
+- If you don't have instance maps or don't want to use them, please specify `--no_instance`.
+- The default setting for preprocessing is `scale_width`, which will scale the width of all training images to `opt.loadSize` (1024) while keeping the aspect ratio. If you want a different setting, please change it by using the `--resize_or_crop` option. For example, `scale_width_and_crop` first resizes the image to have width `opt.loadSize` and then does random cropping of size `(opt.fineSize, opt.fineSize)`. `crop` skips the resizing step and only performs random cropping. If you don't want any preprocessing, please specify `none`, which will do nothing other than making sure the image is divisible by 32.
+
+## More Training/Test Details
+- Flags: see `options/train_options.py` and `options/base_options.py` for all the training flags; see `options/test_options.py` and `options/base_options.py` for all the test flags.
+- Instance map: we take in both label maps and instance maps as input. If you don't want to use instance maps, please specify the flag `--no_instance`.
+
+
+## Citation
+
+If you find this useful for your research, please use the following.
+
+```bibtex
+@inproceedings{wang2018pix2pixHD,
+  title={High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs},
+  author={Ting-Chun Wang and Ming-Yu Liu and Jun-Yan Zhu and Andrew Tao and Jan Kautz and Bryan Catanzaro},  
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  year={2018}
+}
+```
+
+## Acknowledgments
+This code borrows heavily from [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).