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‎.gitignore

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outputs/
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src/models/
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data/
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logs/
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.idea/
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dgx/scripts/
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.ipynb_checkpoints/
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src/*jpg
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notebooks/.ipynb_checkpoints/*
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exps/unit_local/configs/
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exps/unit_local/backup/
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src/yaml_generator.py
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*.tar.gz
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*ipynb
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*.zip
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*.pkl
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*.pyc

‎Dockerfile

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FROM nvidia/cuda:8.0-cudnn7-runtime-ubuntu16.04
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# Set anaconda path
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ENV ANACONDA /opt/anaconda
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ENV PATH $ANACONDA/bin:$PATH
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# Download anaconda and install it
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RUN apt-get update && apt-get install -y wget build-essential
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RUN apt-get update && apt-get install -y libopencv-dev python-opencv
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RUN apt-get update && apt-get install -y --no-install-recommends \
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build-essential \
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cmake \
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git \
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curl \
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ca-certificates \
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libjpeg-dev \
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libpng-dev
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RUN wget https://repo.continuum.io/archive/Anaconda2-5.0.1-Linux-x86_64.sh -P /tmp
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RUN bash /tmp/Anaconda2-5.0.1-Linux-x86_64.sh -b -p $ANACONDA
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RUN rm /tmp/Anaconda2-5.0.1-Linux-x86_64.sh -rf
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RUN conda install -y pytorch torchvision cuda80 -c pytorch
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RUN conda install -y -c anaconda pip
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RUN conda install -y -c menpo opencv
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RUN conda install -y -c anaconda yaml
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RUN pip install tensorboard
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‎LICENSE.md

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‎__init__.py

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"""
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Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
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Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
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"""

‎configs/edges2shoes.yaml

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# Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
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# Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
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train:
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# logger options
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image_save_iter: 10 # How often do you want to save output images during training
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image_display_iter: 10 # How often do you want to display output images during training
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display_size: 8 # How many images do you want to display each time
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snapshot_save_iter: 10 # How often do you want to save trained models
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log_iter: 1 # How often do you want to log the training stats
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# optimization options
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max_iter: 1000000 # maximum number of training iterations
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batch_size: 1 # batch size
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weight_decay: 0.0001 # weight decay
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beta1: 0.5 # Adam parameter
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beta2: 0.999 # Adam parameter
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init: kaiming # initialization [gaussian/kaiming/xavier]
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lr: 0.0001 # initial learning rate
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lr_policy: step # learning rate scheduler
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step_size: 100000 # how often to decay learning rate
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gamma: 0.5 # how much to decay learning rate
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gan_w: 1 # weight of adversarial loss
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recon_x_a_w: 10 # weight of image reconstruction loss
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recon_x_b_w: 10 # weight of image reconstruction loss
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recon_s_a_w: 1 # weight of style reconstruction loss
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recon_s_b_w: 1 # weight of style reconstruction loss
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recon_c_w: 1 # weight of content reconstruction loss
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recon_x_a_cyc_w: 0 # weight of explicit style augmented cycle consistency loss
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recon_x_b_cyc_w: 0 # weight of explicit style augmented cycle consistency loss
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vgg_w: 0
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# model options
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gen:
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dim: 64 # number of filters in the bottommost layer
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mlp_dim: 512 # number of filters in MLP
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style_dim: 8 # length of style code
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activ: relu # activation function [relu/lrelu/prelu/selu/tanh]
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style_norm: none
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upsample_norm: ln
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n_downsample: 2 # number of downsampling layers in content encoder
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n_res: 4 # number of residual blocks in content encoder/decoder
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pad_type: zero
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downsample_style: 0
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dis:
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dim: 64 # number of filters in the bottommost layer
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norm: none # normalization layer [none/bn/in/ln]
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activ: lrelu # activation function [relu/lrelu/prelu/selu/tanh]
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n_layer: 4 # number of layers in D
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gan_type: lsgan # GAN loss [lsgan/nsgan]
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num_scales: 3 # number of scales
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pad_type: zero
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# data options
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input_dim_a: 1 # number of image channels [1/3]
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input_dim_b: 3 # number of image channels [1/3]
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num_workers: 8 # number of data loading threads
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new_size: 256 # first resize the shortest image side to this size
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crop_image_height: 256 # random crop image of this height
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crop_image_width: 256 # random crop image of this width
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root_a: /cosmo/datasets/edges2shoes/ # dataset folder location
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train_list_a: trainA.txt # image list
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test_list_a: testA.txt # image list
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root_b: /cosmo/datasets/edges2shoes/ # dataset folder location
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train_list_b: trainB.txt # image list
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test_list_b: testB.txt # image listprojects/MUNIT/outputs/inception_models/all_4_dogs/all_3_cat_best.pt

‎data.py

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import torch.utils.data as data
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import os.path
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def default_loader(path):
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return Image.open(path).convert('RGB')
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def default_flist_reader(flist):
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"""
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flist format: impath label\nimpath label\n ...(same to caffe's filelist)
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"""
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imlist = []
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with open(flist, 'r') as rf:
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for line in rf.readlines():
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impath = line.strip()
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imlist.append(impath)
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return imlist
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class ImageFilelist(data.Dataset):
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def __init__(self, root, flist, transform=None,
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flist_reader=default_flist_reader, loader=default_loader):
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self.root = root
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self.imlist = flist_reader(os.path.join(self.root, flist))
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self.transform = transform
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self.loader = loader
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def __getitem__(self, index):
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impath = self.imlist[index]
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img = self.loader(os.path.join(self.root, impath))
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if self.transform is not None:
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img = self.transform(img)
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return img
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def __len__(self):
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return len(self.imlist)
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class ImageLabelFilelist(data.Dataset):
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def __init__(self, root, flist, transform=None,
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flist_reader=default_flist_reader, loader=default_loader):
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self.root = root
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self.imlist = flist_reader(os.path.join(self.root, flist))
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self.transform = transform
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self.loader = loader
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self.classes = sorted(list(set([path.split('/')[0] for path in self.imlist])))
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self.class_to_idx = {self.classes[i]: i for i in range(len(self.classes))}
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self.imgs = [(impath, self.class_to_idx[impath.split('/')[0]]) for impath in self.imlist]
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def __getitem__(self, index):
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impath, label = self.imgs[index]
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img = self.loader(os.path.join(self.root, impath))
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if self.transform is not None:
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img = self.transform(img)
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return img, label
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def __len__(self):
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return len(self.imgs)
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###############################################################################
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# Code from
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# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
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# Modified the original code so that it also loads images from the current
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# directory as well as the subdirectories
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###############################################################################
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import torch.utils.data as data
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from PIL import Image
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import os
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import os.path
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IMG_EXTENSIONS = [
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'.jpg', '.JPG', '.jpeg', '.JPEG',
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'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
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]
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def is_image_file(filename):
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return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
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def make_dataset(dir):
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images = []
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assert os.path.isdir(dir), '%s is not a valid directory' % dir
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for root, _, fnames in sorted(os.walk(dir)):
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for fname in fnames:
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if is_image_file(fname):
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path = os.path.join(root, fname)
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images.append(path)
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return images
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class ImageFolder(data.Dataset):
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def __init__(self, root, transform=None, return_paths=False,
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loader=default_loader):
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imgs = sorted(make_dataset(root))
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if len(imgs) == 0:
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raise(RuntimeError("Found 0 images in: " + root + "\n"
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"Supported image extensions are: " +
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",".join(IMG_EXTENSIONS)))
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self.root = root
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self.imgs = imgs
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self.transform = transform
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self.return_paths = return_paths
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self.loader = loader
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def __getitem__(self, index):
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path = self.imgs[index]
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img = self.loader(path)
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if self.transform is not None:
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img = self.transform(img)
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if self.return_paths:
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return img, path
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else:
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return img
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def __len__(self):
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return len(self.imgs)

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