initial commit

.gitignore

@@ -0,0 +1,8 @@
+notebooks/.ipynb_checkpoints
+.idea/
+modules/__pycache__/
+*/*/*.pyc
+*/*/.ipynb_checkpoints
+*/*/__pycache__/
+notebooks/
+misc_stuff/

environment.yml

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+name: pytorch-CycleGAN-and-pix2pix
+channels:
+  - anaconda
+  - conda-forge
+  - defaults
+dependencies:
+  - h5py=2.8.0=py35h989c5e5_3
+  - six=1.11.0=py35_1
+  - ca-certificates=2018.11.29=ha4d7672_0
+  - certifi=2018.8.24=py35_1001
+  - cloudpickle=0.6.1=py_0
+  - cycler=0.10.0=py_1
+  - dask-core=1.0.0=py_0
+  - decorator=4.3.0=py_0
+  - matplotlib=2.2.3=py35h8e2386c_0
+  - networkx=2.2=py_1
+  - openssl=1.0.2p=h470a237_1
+  - pyqt=5.6.0=py35h8210e8a_7
+  - python-dateutil=2.7.5=py_0
+  - pywavelets=1.0.1=py35h7eb728f_0
+  - scikit-image=0.14.0=py35hfc679d8_1
+  - sip=4.18.1=py35hfc679d8_0
+  - toolz=0.9.0=py_1
+  - tornado=5.1.1=py35h470a237_0
+  - blas=1.0=mkl
+  - cffi=1.11.5=py35he75722e_1
+  - cudatoolkit=9.0=h13b8566_0
+  - cudnn=7.1.2=cuda9.0_0
+  - dbus=1.13.2=h714fa37_1
+  - expat=2.2.6=he6710b0_0
+  - fontconfig=2.13.0=h9420a91_0
+  - freetype=2.9.1=h8a8886c_1
+  - glib=2.56.2=hd408876_0
+  - gst-plugins-base=1.14.0=hbbd80ab_1
+  - gstreamer=1.14.0=hb453b48_1
+  - hdf5=1.10.2=hba1933b_1
+  - icu=58.2=h9c2bf20_1
+  - imageio=2.4.1=py35_0
+  - intel-openmp=2019.1=144
+  - jpeg=9b=h024ee3a_2
+  - kiwisolver=1.0.1=py35hf484d3e_0
+  - libedit=3.1.20170329=h6b74fdf_2
+  - libffi=3.2.1=hd88cf55_4
+  - libgcc-ng=8.2.0=hdf63c60_1
+  - libgfortran-ng=7.3.0=hdf63c60_0
+  - libpng=1.6.35=hbc83047_0
+  - libstdcxx-ng=8.2.0=hdf63c60_1
+  - libtiff=4.0.9=he85c1e1_2
+  - libuuid=1.0.3=h1bed415_2
+  - libxcb=1.13=h1bed415_1
+  - libxml2=2.9.8=h26e45fe_1
+  - mkl=2018.0.3=1
+  - mkl_fft=1.0.6=py35h7dd41cf_0
+  - mkl_random=1.0.1=py35h4414c95_1
+  - nccl=1.3.5=cuda9.0_0
+  - ncurses=6.1=hf484d3e_0
+  - ninja=1.8.2=py35h6bb024c_1
+  - numpy=1.15.2=py35h1d66e8a_0
+  - numpy-base=1.15.2=py35h81de0dd_0
+  - olefile=0.46=py35_0
+  - pcre=8.42=h439df22_0
+  - pillow=5.2.0=py35heded4f4_0
+  - pip=10.0.1=py35_0
+  - pycparser=2.19=py35_0
+  - pyparsing=2.2.1=py35_0
+  - python=3.5.5=hc3d631a_4
+  - pytorch=0.4.1=py35ha74772b_0
+  - pytz=2018.5=py35_0
+  - qt=5.6.3=h39df351_1
+  - readline=7.0=h7b6447c_5
+  - scipy=1.1.0=py35hfa4b5c9_1
+  - setuptools=40.2.0=py35_0
+  - sqlite=3.25.3=h7b6447c_0
+  - tk=8.6.8=hbc83047_0
+  - wheel=0.31.1=py35_0
+  - xz=5.2.4=h14c3975_4
+  - zlib=1.2.11=ha838bed_2
+  - pip:
+    - backcall==0.1.0
+    - bleach==3.0.2
+    - chardet==3.0.4
+    - dask==1.0.0
+    - defusedxml==0.5.0
+    - dominate==2.3.1
+    - entrypoints==0.2.3
+    - idna==2.7
+    - ipykernel==5.1.0
+    - ipython==7.2.0
+    - ipython-genutils==0.2.0
+    - ipywidgets==7.4.2
+    - jedi==0.13.1
+    - jinja2==2.10
+    - jsonschema==2.6.0
+    - jupyter==1.0.0
+    - jupyter-client==5.2.3
+    - jupyter-console==6.0.0
+    - jupyter-core==4.4.0
+    - markupsafe==1.1.0
+    - mistune==0.8.4
+    - nbconvert==5.4.0
+    - nbformat==4.4.0
+    - notebook==5.7.2
+    - pandocfilters==1.4.2
+    - parso==0.3.1
+    - pexpect==4.6.0
+    - pickleshare==0.7.5
+    - prometheus-client==0.4.2
+    - prompt-toolkit==2.0.7
+    - ptyprocess==0.6.0
+    - pygments==2.3.0
+    - pyzmq==17.1.2
+    - qtconsole==4.4.3
+    - requests==2.20.1
+    - send2trash==1.5.0
+    - terminado==0.8.1
+    - testpath==0.4.2
+    - torch==0.4.1
+    - torchfile==0.1.0
+    - torchvision==0.2.1
+    - tqdm==4.28.1
+    - traitlets==4.3.2
+    - urllib3==1.24.1
+    - visdom==0.1.7
+    - wcwidth==0.1.7
+    - webencodings==0.5.1
+    - widgetsnbextension==3.4.2
+prefix: /home/asa224/anaconda3/envs/pytorch-CycleGAN-and-pix2pix
+

mmgan_hgg.sh

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+#!/usr/bin/env bash
+python train_mmgan_brats2018.py --grade=HGG --train_patient_idx=200 --test_pats=10 --batch_size=4 --dataset=BRATS2018 --n_epochs=60 --model_name=mmgan_hgg_zeros_cl --log_level=info --n_cpu=4 --c_learning=1 --z_type=zeros

mmgan_lgg.sh

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+#!/usr/bin/env bash
+python train_mmgan_brats2018.py --grade=LGG --train_patient_idx=70 --test_pats=5 --batch_size=4 --dataset=BRATS2018 --n_epochs=60 --model_name=mmgan_lgg_zeros_cl --log_level=info --n_cpu=8 --c_learning=1 --z_type=zeros

modules/advanced_gans/datasets.py

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+import glob
+import random
+import os
+import numpy as np
+
+from torch.utils.data import Dataset
+from PIL import Image
+import torchvision.transforms as transforms
+
+class ImageDataset(Dataset):
+    def __init__(self, root, transforms_=None, mode='train'):
+        self.transform = transforms.Compose(transforms_)
+
+        self.files = sorted(glob.glob(os.path.join(root, mode) + '/*.*'))
+        if mode == 'train':
+            self.files.extend(sorted(glob.glob(os.path.join(root, 'test') + '/*.*')))
+
+    def __getitem__(self, index):
+
+        img = Image.open(self.files[index % len(self.files)])
+        w, h = img.size
+        img_A = img.crop((0, 0, w/2, h))
+        img_B = img.crop((w/2, 0, w, h))
+
+        if np.random.random() < 0.5:
+            img_A = Image.fromarray(np.array(img_A)[:, ::-1, :], 'RGB')
+            img_B = Image.fromarray(np.array(img_B)[:, ::-1, :], 'RGB')
+
+        img_A = self.transform(img_A)
+        img_B = self.transform(img_B)
+
+        return {'A': img_A, 'B': img_B}
+
+    def __len__(self):
+        return len(self.files)

modules/advanced_gans/models.py

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+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+import numpy as np
+import random
+np.random.seed(1337)
+torch.manual_seed(1337)
+random.seed(1337)
+torch.backends.cudnn.deterministic = True
+def weights_init_normal(m):
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1:
+        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
+    elif classname.find('BatchNorm2d') != -1:
+        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
+        torch.nn.init.constant_(m.bias.data, 0.0)
+
+##############################
+#           U-NET
+##############################
+
+class UNetDown(nn.Module):
+    def __init__(self, in_size, out_size, normalize=True, dropout=0.0):
+        super(UNetDown, self).__init__()
+        layers = [nn.Conv2d(in_size, out_size, 4, 2, 1, bias=False)]
+        if normalize:
+            layers.append(nn.InstanceNorm2d(out_size))
+        layers.append(nn.LeakyReLU(0.2))
+        if dropout:
+            layers.append(nn.Dropout(dropout))
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.model(x)
+
+class UNetUp(nn.Module):
+    def __init__(self, in_size, out_size, dropout=0.0):
+        super(UNetUp, self).__init__()
+        layers = [  nn.ConvTranspose2d(in_size, out_size, 4, 2, 1, bias=False),
+                    nn.InstanceNorm2d(out_size),
+                    nn.ReLU(inplace=True)]
+        if dropout:
+            layers.append(nn.Dropout(dropout))
+
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x, skip_input):
+        x = self.model(x)
+        x = torch.cat((x, skip_input), 1)
+
+        return x
+
+class GeneratorUNet(nn.Module):
+    def __init__(self, in_channels=3, out_channels=3, with_tanh=False, with_relu=False):
+        super(GeneratorUNet, self).__init__()
+
+        # original dropout was 0.5
+        self.down1 = UNetDown(in_channels, 64, normalize=False)
+        self.down2 = UNetDown(64, 128)
+        self.down3 = UNetDown(128, 256)
+        self.down4 = UNetDown(256, 512, dropout=0.2)
+        self.down5 = UNetDown(512, 512, dropout=0.2)
+        self.down6 = UNetDown(512, 512, dropout=0.2)
+        self.down7 = UNetDown(512, 512, dropout=0.2)
+        self.down8 = UNetDown(512, 512, normalize=False, dropout=0.2)
+
+        self.up1 = UNetUp(512, 512, dropout=0.2)
+        self.up2 = UNetUp(1024, 512, dropout=0.2)
+        self.up3 = UNetUp(1024, 512, dropout=0.2)
+        self.up4 = UNetUp(1024, 512, dropout=0.2)
+        self.up5 = UNetUp(1024, 256)
+        self.up6 = UNetUp(512, 128)
+        self.up7 = UNetUp(256, 64)
+
+        if with_tanh:
+
+            self.final = nn.Sequential(
+                nn.Upsample(scale_factor=2),
+                nn.ZeroPad2d((1, 0, 1, 0)),
+                nn.Conv2d(128, out_channels, 4, padding=1),
+                nn.Tanh()
+            )
+        elif with_relu:
+            # this is for ISLES2015
+            self.final = nn.Sequential(
+                nn.Upsample(scale_factor=2),
+                nn.ZeroPad2d((1, 0, 1, 0)),
+                nn.Conv2d(128, out_channels, 4, padding=1),
+                nn.ReLU()
+            )
+
+        else:
+            self.final = nn.Sequential(
+                nn.Upsample(scale_factor=2),
+                nn.ZeroPad2d((1, 0, 1, 0)),
+                nn.Conv2d(128, out_channels, 4, padding=1)
+            )
+
+
+    def forward(self, x):
+        # U-Net generator with skip connections from encoder to decoder
+        d1 = self.down1(x)
+        d2 = self.down2(d1)
+        d3 = self.down3(d2)
+        d4 = self.down4(d3)
+        d5 = self.down5(d4)
+        d6 = self.down6(d5)
+        d7 = self.down7(d6)
+        d8 = self.down8(d7)
+        u1 = self.up1(d8, d7)
+        u2 = self.up2(u1, d6)
+        u3 = self.up3(u2, d5)
+        u4 = self.up4(u3, d4)
+        u5 = self.up5(u4, d3)
+        u6 = self.up6(u5, d2)
+        u7 = self.up7(u6, d1)
+
+        return self.final(u7)
+
+
+##############################
+#        Discriminator
+##############################
+
+class Discriminator(nn.Module):
+    def __init__(self, in_channels=3, out_channels=4, dataset='BRATS2018'):
+        super(Discriminator, self).__init__()
+
+        # inp, stride, pad, dil, kernel = (256, 2, 1, 1, 8)
+        # np.floor(((inp + 2*pad - dil*(kernel - 1) - 1)/stride) + 1)
+
+        if 'BRATS' in dataset:
+            def discriminator_block(in_filters, out_filters, normalization=True):
+                """Returns downsampling layers of each discriminator block"""
+                layers = [nn.Conv2d(in_filters, out_filters, 4, stride=2, padding=1)]
+                if normalization:
+                    layers.append(nn.InstanceNorm2d(out_filters))
+                layers.append(nn.LeakyReLU(0.2, inplace=True))
+                return layers
+
+            self.model = nn.Sequential(
+                *discriminator_block(in_channels*2, 64, normalization=False),
+                *discriminator_block(64, 128),
+                *discriminator_block(128, 256),
+                *discriminator_block(256, 512),
+                nn.ZeroPad2d((1, 0, 1, 0)),
+                nn.Conv2d(512, out_channels, 4, padding=1, bias=False)
+            )
+        else:
+            # FOR ISLES2015
+            def discriminator_block(in_filters, out_filters, normalization=True):
+                """Returns downsampling layers of each discriminator block"""
+                layers = [nn.Conv2d(in_filters, out_filters, 4, stride=4, padding=1)]
+                if normalization:
+                    layers.append(nn.InstanceNorm2d(out_filters))
+                layers.append(nn.LeakyReLU(0.2, inplace=True))
+                return layers
+
+            self.model = nn.Sequential(
+                *discriminator_block(in_channels * 2, 8, normalization=False),
+                *discriminator_block(8, 8),
+                nn.ZeroPad2d((1, 0, 1, 0)),
+                nn.Conv2d(8, out_channels, 4, padding=1, bias=False)
+            )
+
+    def forward(self, img_A, img_B):
+        # Concatenate image and condition image by channels to produce input
+        img_input = torch.cat((img_A, img_B), 1)
+        return self.model(img_input)