├── .gitignore
├── .idea
    ├── misc.xml
    ├── modules.xml
    ├── unet_pytorch.iml
    ├── vcs.xml
    └── workspace.xml
├── README.md
├── assets
    └── unet.png
├── crf
    └── crf.py
├── data
    └── membrane
    │   ├── test
    │       ├── 0.png
    │       ├── 1.png
    │       ├── 10.png
    │       ├── 11.png
    │       ├── 12.png
    │       ├── 13.png
    │       ├── 14.png
    │       ├── 15.png
    │       ├── 16.png
    │       ├── 17.png
    │       ├── 18.png
    │       ├── 19.png
    │       ├── 2.png
    │       ├── 20.png
    │       ├── 21.png
    │       ├── 22.png
    │       ├── 23.png
    │       ├── 24.png
    │       ├── 25.png
    │       ├── 26.png
    │       ├── 27.png
    │       ├── 28.png
    │       ├── 29.png
    │       ├── 3.png
    │       ├── 4.png
    │       ├── 5.png
    │       ├── 6.png
    │       ├── 7.png
    │       ├── 8.png
    │       └── 9.png
    │   └── train
    │       ├── image
    │           ├── 0.png
    │           ├── 1.png
    │           ├── 10.png
    │           ├── 11.png
    │           ├── 12.png
    │           ├── 13.png
    │           ├── 14.png
    │           ├── 15.png
    │           ├── 16.png
    │           ├── 17.png
    │           ├── 18.png
    │           ├── 19.png
    │           ├── 2.png
    │           ├── 20.png
    │           ├── 21.png
    │           ├── 22.png
    │           ├── 23.png
    │           ├── 24.png
    │           ├── 25.png
    │           ├── 26.png
    │           ├── 27.png
    │           ├── 28.png
    │           ├── 29.png
    │           ├── 3.png
    │           ├── 4.png
    │           ├── 5.png
    │           ├── 6.png
    │           ├── 7.png
    │           ├── 8.png
    │           └── 9.png
    │       └── label
    │           ├── 0.png
    │           ├── 1.png
    │           ├── 10.png
    │           ├── 11.png
    │           ├── 12.png
    │           ├── 13.png
    │           ├── 14.png
    │           ├── 15.png
    │           ├── 16.png
    │           ├── 17.png
    │           ├── 18.png
    │           ├── 19.png
    │           ├── 2.png
    │           ├── 20.png
    │           ├── 21.png
    │           ├── 22.png
    │           ├── 23.png
    │           ├── 24.png
    │           ├── 25.png
    │           ├── 26.png
    │           ├── 27.png
    │           ├── 28.png
    │           ├── 29.png
    │           ├── 3.png
    │           ├── 4.png
    │           ├── 5.png
    │           ├── 6.png
    │           ├── 7.png
    │           ├── 8.png
    │           └── 9.png
├── data_aug.py
├── dice_loss.py
├── load.py
├── main.py
├── model
    ├── __pycache__
    │   └── unet.cpython-36.pyc
    └── unet.py
└── utils.py


/.gitignore:
--------------------------------------------------------------------------------
1 | data/
2 | 


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/README.md:
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 1 | # Unet-Pytorch
 2 | 
 3 | > Unet for skin lesion segmentation in ISIC2018 implemented with pytorch
 4 | 
 5 | ### Network Architecture
 6 | 
 7 | ![image-20190221234154564](./assets/unet.png)
 8 | 
 9 | ### Environment
10 | 
11 | * Pytorch 1.0
12 | * CUDA 10.0
13 | 
14 | ### Results
15 | 
16 | 


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/assets/unet.png:
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https://raw.githubusercontent.com/JoshuaSXA/Unet-Pytorch/7e5452dfbc1276bbf3be1530831e1b13bad02229/assets/unet.png


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/crf/crf.py:
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 1 | import numpy as np
 2 | import pydensecrf.densecrf as dcrf
 3 | 
 4 | def dense_crf(img, output_probs):
 5 |     h = output_probs.shape[0]
 6 |     w = output_probs.shape[1]
 7 | 
 8 |     output_probs = np.expand_dims(output_probs, 0)
 9 |     output_probs = np.append(1 - output_probs, output_probs, axis=0)
10 | 
11 |     d = dcrf.DenseCRF2D(w, h, 2)
12 |     U = -np.log(output_probs)
13 |     U = U.reshape((2, -1))
14 |     U = np.ascontiguousarray(U)
15 |     img = np.ascontiguousarray(img)
16 | 
17 |     d.setUnaryEnergy(U)
18 | 
19 |     d.addPairwiseGaussian(sxy=20, compat=3)
20 |     d.addPairwiseBilateral(sxy=30, srgb=20, rgbim=img, compat=10)
21 | 
22 |     Q = d.inference(5)
23 |     Q = np.argmax(np.array(Q), axis=0).reshape((h, w))
24 | 
25 |     return Q


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/data_aug.py:
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 1 | from PIL import Image
 2 | import random
 3 | import math
 4 | import torch
 5 | import types
 6 | 
 7 | # Random rotation.
 8 | def random_rotate(img, mask, degrees):
 9 |     if isinstance(degrees, int):
10 |         if degrees < 0:
11 |             raise ValueError("If degrees is a single number, it must be positive.")
12 |         degrees = (-degrees, degrees)
13 |     else:
14 |         if len(degrees) != 2:
15 |             raise ValueError("If degrees is a sequence, it must be of len 2.")
16 |         degrees = degrees
17 |     random_degree = random.randint(degrees[0], degrees[1])
18 |     img_rotate = img.rotate(random_degree)
19 |     mask_rotate = mask.rotate(random_degree)
20 |     return img_rotate, mask_rotate
21 | 
22 | # Apply random cropping to original images, and resize the cropped result to a fixed size.
23 | def random_resized_crop(img, mask, size=(512, 512), scale=(0.6, 1.0), ratio=(4. / 5., 5. / 4.)):
24 |     area = img.size[0] * img.size[1]
25 |     target_area = random.uniform(*scale) * area
26 |     aspect_ratio = random.uniform(*ratio)
27 |     w = int(round(math.sqrt(target_area * aspect_ratio)))
28 |     h = int(round(math.sqrt(target_area / aspect_ratio)))
29 |     w = min(w, img.size[0])
30 |     h = min(h, img.size[1])
31 |     axis_x = round(random.uniform(0, img.size[0] - w))
32 |     axis_y = round(random.uniform(0, img.size[1] - h))
33 |     box = (axis_x, axis_y, axis_x + w, axis_y + h)
34 |     print(box)
35 |     img_roi = img.crop(box)
36 |     img_roi = img_roi.resize(size)
37 |     mask_roi = mask.crop(box)
38 |     mask_roi = mask_roi.resize(size)
39 |     return img_roi, mask_roi
40 | 
41 | # Apply random horizontal flip to original images with 0.5 as probability.
42 | def random_horizontal_flip(img, mask):
43 |     if_trans = True if random.random() >= 0.5 else False
44 |     if if_trans:
45 |         img = img.transpose(Image.FLIP_LEFT_RIGHT)
46 |         mask = mask.transpose(Image.FLIP_LEFT_RIGHT)
47 |     return img, mask
48 | 
49 | # Apply random vertical flip to original images with 0.5 as probability.
50 | def random_vertical_flip(img, mask):
51 |     if_trans = True if random.random() >= 0.5 else False
52 |     if if_trans:
53 |         img = img.transpose(Image.FLIP_TOP_BOTTOM)
54 |         mask = mask.transpose(Image.FLIP_TOP_BOTTOM)
55 |     return img, mask
56 | 
57 | def image_resize(img, mask, size=(512, 512)):
58 |     return img.resize(size), mask.resize(size)
59 | 
60 | def image_to_tensor(img, mask):
61 |     img = img / 255.0
62 |     mask = mask / 255.0
63 |     img = torch.from_numpy(img).permute(2, 0, 1)
64 |     mask = torch.from_numpy(mask).unsqueeze(0)
65 |     return img, mask
66 | 
67 | 
68 | 
69 | #
70 | # def compose(image, mask, trans_opt=[]):
71 | #     for i in range(len(trans_opt)):
72 | #         trans_func = trans_opt[i]
73 | #         if not isinstance(trans_func, types.FunctionType):
74 | #             assert "Invalid function type."
75 | #             return image, mask
76 | #         image, mask = trans_func(image, mask)


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/dice_loss.py:
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 1 | import torch
 2 | from torch.autograd import Function, Variable
 3 | 
 4 | class DiceCoeff(Function):
 5 |     """Dice coeff for individual examples"""
 6 | 
 7 |     def forward(self, input, target):
 8 |         self.save_for_backward(input, target)
 9 |         eps = 0.0001
10 |         self.inter = torch.dot(input.view(-1), target.view(-1))
11 |         self.union = torch.sum(input) + torch.sum(target) + eps
12 | 
13 |         t = (2 * self.inter.float() + eps) / self.union.float()
14 |         return t
15 | 
16 |     # This function has only a single output, so it gets only one gradient
17 |     def backward(self, grad_output):
18 | 
19 |         input, target = self.saved_variables
20 |         grad_input = grad_target = None
21 | 
22 |         if self.needs_input_grad[0]:
23 |             grad_input = grad_output * 2 * (target * self.union - self.inter) \
24 |                          / (self.union * self.union)
25 |         if self.needs_input_grad[1]:
26 |             grad_target = None
27 | 
28 |         return grad_input, grad_target
29 | 
30 | 
31 | def dice_coeff(input, target):
32 |     """Dice coeff for batches"""
33 |     if input.is_cuda:
34 |         s = torch.FloatTensor(1).cuda().zero_()
35 |     else:
36 |         s = torch.FloatTensor(1).zero_()
37 | 
38 |     for i, c in enumerate(zip(input, target)):
39 |         s = s + 1 - DiceCoeff().forward(c[0], c[1])
40 | 
41 |     return s / (i + 1)


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/load.py:
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 1 | import torch
 2 | from torchvision import transforms
 3 | import torch.utils.data as Data
 4 | import os
 5 | import numpy as np
 6 | from glob import glob
 7 | from PIL import Image
 8 | from data_aug import *
 9 | 
10 | 
11 | class CustomDataset(Data.Dataset):
12 |     def __len__(self):
13 |         return len(self._img_frames)
14 | 
15 |     def __init__(self, image_frames, mask_frames, transform=None):
16 |         super(CustomDataset, self).__init__()
17 |         self._img_frames = image_frames
18 |         self._mask_frames = mask_frames
19 |         self._transform = transform
20 | 
21 |     def __getitem__(self, index):
22 |         img_path = self._img_frames[index]
23 |         mask_path = self._mask_frames[index]
24 |         img = Image.open(img_path)
25 |         mask = Image.open(mask_path)
26 | 
27 |         if self._transform is not None:
28 |             img, mask = self._transform(img, mask)
29 |         img, mask = image_resize(img, mask, (512, 512))
30 | 
31 |         img = np.array(img).astype("float32")
32 |         mask = np.array(mask).astype("float32")
33 |         img, mask = image_to_tensor(img, mask)
34 |         return (img, mask)
35 | 
36 | 
37 | class MyDataLoader(object):
38 |     def __init__(self, image_path, mask_path, split_ratio=0.05,transforms=None):
39 |         img_frames = sorted(glob(os.path.join(image_path, "*")))
40 |         mask_frames = sorted(glob(os.path.join(mask_path, "*")))
41 |         self._split_ratio = split_ratio
42 |         train_frames, val_frames = self.split_dataset(img_frames, mask_frames)
43 |         self._train_dataset = CustomDataset(train_frames['image'], train_frames['mask'], transforms)
44 |         self._val_dataset = CustomDataset(val_frames['image'], val_frames['mask'], transforms)
45 | 
46 |     def split_dataset(self, img_frames, mask_frames):
47 |         total_len = len(img_frames)
48 |         val_len = round(total_len * self._split_ratio)
49 |         train_img_frames = img_frames[:-val_len]
50 |         train_mask_frames = mask_frames[:-val_len]
51 |         val_img_frames = img_frames[-val_len:]
52 |         val_mask_frames = mask_frames[-val_len:]
53 |         return {"image":train_img_frames, "mask":train_mask_frames}, {"image":val_img_frames, "mask":val_mask_frames}
54 | 
55 |     def get_train_dataloader(self, batch_size=4, shuffle=True, num_works=4):
56 |         return Data.DataLoader(self._train_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_works)
57 | 
58 |     def get_val_dataloader(self, batch_size=4, num_works=4):
59 |         return Data.DataLoader(self._val_dataset, batch_size=batch_size, num_workers=num_works)
60 | 
61 | 


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/main.py:
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 1 | import torch.optim as optim
 2 | import torch.nn as nn
 3 | from model.unet import UNet
 4 | from load import *
 5 | from dice_loss import *
 6 | import utils
 7 | 
 8 | 
 9 | def eval_net(net, dataloader):
10 |     """Evaluation without the densecrf with the dice coefficient"""
11 | 
12 |     # choose the device gpu / cpu
13 |     device = 'cuda' if torch.cuda.is_available() else 'cpu'
14 |     # net
15 |     net = net.to(device)
16 |     net.eval()
17 |     tot = 0
18 |     for (img, mask) in dataloader:
19 |         inputs, targets = img.to(device), mask.to(device)
20 |         outputs = net(inputs)
21 |         len = list(outputs.size())[0]
22 | 
23 |         for i in range(len):
24 |             mask_pred = outputs[i]
25 |             true_mask = targets[i]
26 |             mask_pred = (mask_pred > 0.5).float()
27 |             tot += dice_coeff(mask_pred, true_mask).item()
28 |     return tot / (i + 1)
29 | 
30 | 
31 | 
32 | def train_net(net, epochs=10, batch_size=4,lr=0.1, val_percent=0.05, save_cp=True):
33 |     # choose the device gpu / cpu
34 |     device = 'cuda' if torch.cuda.is_available() else 'cpu'
35 | 
36 |     # net
37 |     net = net.to(device)
38 |     net.apply(utils.weights_initializer)
39 | 
40 |     # dataloader
41 |     dataloader = MyDataLoader(image_path="./data/ISIC2018/image/", mask_path="./data/ISIC2018/mask/")
42 |     train_loader = dataloader.get_train_dataloader(batch_size=4, shuffle=True, num_works=0)
43 |     val_loader = dataloader.get_val_dataloader(batch_size=4, num_works=0)
44 | 
45 |     # optimizer and criterion
46 |     optimizer = optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)
47 |     criterion = nn.BCELoss()
48 | 
49 |     dir_checkpoint = './checkpoints/'
50 | 
51 |     # train
52 |     for epoch in range(epochs):
53 |         epoch_train_loss = 0.0
54 |         net.train()
55 |         for (img, mask) in train_loader:
56 |             inputs, targets = img.to(device), mask.to(device)
57 |             optimizer.zero_grad()
58 |             outputs = net(inputs)
59 |             loss = criterion(outputs, targets)
60 |             loss.backward()
61 |             optimizer.step()
62 |             epoch_train_loss += loss.item()
63 |         val_dice_loss = eval_net(net, val_loader)
64 |         print('Epoch %d, total train loss is %.5f, test dice loss is %.5f' % ((epoch + 1),epoch_train_loss, val_dice_loss) )
65 |         if save_cp:
66 |             torch.save(net.state_dict(),
67 |                        dir_checkpoint + 'CP{}.pth'.format(epoch + 1))
68 |             print('Checkpoint {} saved !'.format(epoch + 1))
69 | 
70 | 
71 | if __name__ == '__main__':
72 |     net = UNet(n_channels=3, n_classes=1)
73 |     train_net(net)
74 | 


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/model/__pycache__/unet.cpython-36.pyc:
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https://raw.githubusercontent.com/JoshuaSXA/Unet-Pytorch/7e5452dfbc1276bbf3be1530831e1b13bad02229/model/__pycache__/unet.cpython-36.pyc


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/model/unet.py:
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  1 | import torch
  2 | import torch.nn as nn
  3 | import torch.nn.functional as F
  4 | 
  5 | 
  6 | class double_conv(nn.Module):
  7 | 
  8 |     def __init__(self, in_dim, out_dim):
  9 |         super(double_conv, self).__init__()
 10 |         self.conv = nn.Sequential(
 11 |             nn.Conv2d(in_dim, out_dim, 3, padding=1),
 12 |             nn.BatchNorm2d(out_dim),
 13 |             nn.ReLU(),
 14 |             nn.Conv2d(out_dim, out_dim, 3, padding=1),
 15 |             nn.BatchNorm2d(out_dim),
 16 |             nn.ReLU()
 17 |         )
 18 | 
 19 |     def forward(self, x):
 20 |         x = self.conv(x)
 21 |         return x
 22 | 
 23 | 
 24 | class inconv(nn.Module):
 25 |     def __init__(self, in_dim, out_dim):
 26 |         super(inconv, self).__init__()
 27 |         self.conv = double_conv(in_dim, out_dim)
 28 | 
 29 |     def forward(self, x):
 30 |         x = self.conv(x)
 31 |         return x
 32 | 
 33 | 
 34 | class down(nn.Module):
 35 |     def __init__(self, in_dim, out_dim):
 36 |         super(down, self).__init__()
 37 |         self.mpconv = nn.Sequential(
 38 |             nn.MaxPool2d(2),
 39 |             double_conv(in_dim, out_dim)
 40 |         )
 41 | 
 42 |     def forward(self, x):
 43 |         x = self.mpconv(x)
 44 |         return x
 45 | 
 46 | 
 47 | class up(nn.Module):
 48 |     def __init__(self, in_dim, out_dim):
 49 |         super(up, self).__init__()
 50 |         self.up = nn.ConvTranspose2d(in_dim // 2, in_dim // 2, 2, stride=2)
 51 |         self.conv = double_conv(in_dim, out_dim)
 52 | 
 53 |     def forward(self, x1, x2):
 54 |         x1 = self.up(x1)
 55 | 
 56 |         # input is CHW
 57 |         diffY = x2.size()[2] - x1.size()[2]
 58 |         diffX = x2.size()[3] - x1.size()[3]
 59 | 
 60 |         x1 = F.pad(x1, (diffX // 2, diffX - diffX // 2,
 61 |                         diffY // 2, diffY - diffY // 2))
 62 | 
 63 |         x = torch.cat([x2, x1], dim=1)
 64 |         x = self.conv(x)
 65 |         return x
 66 | 
 67 | 
 68 | class outconv(nn.Module):
 69 |     def __init__(self, in_dim, out_dim):
 70 |         super(outconv, self).__init__()
 71 |         self.conv = nn.Conv2d(in_dim, out_dim, 1)
 72 | 
 73 |     def forward(self, x):
 74 |         x = self.conv(x)
 75 |         return x
 76 | 
 77 | 
 78 | 
 79 | class UNet(nn.Module):
 80 |     def __init__(self, n_channels, n_classes):
 81 |         super(UNet, self).__init__()
 82 |         self.inc = inconv(n_channels, 64)
 83 |         # down stream
 84 |         self.down1 = down(64, 128)
 85 |         self.down2 = down(128, 256)
 86 |         self.down3 = down(256, 512)
 87 |         self.down4 = down(512, 512)
 88 |         # up stream
 89 |         self.up1 = up(1024, 256)
 90 |         self.up2 = up(512, 128)
 91 |         self.up3 = up(256, 64)
 92 |         self.up4 = up(128, 64)
 93 |         self.outc = outconv(64, n_classes)
 94 | 
 95 |     def forward(self, x):
 96 |         x1 = self.inc(x)
 97 |         x2 = self.down1(x1)
 98 |         x3 = self.down2(x2)
 99 |         x4 = self.down3(x3)
100 |         x5 = self.down4(x4)
101 |         x = self.up1(x5, x4)
102 |         x = self.up2(x, x3)
103 |         x = self.up3(x, x2)
104 |         x = self.up4(x, x1)
105 |         x = self.outc(x)
106 |         return F.sigmoid(x)
107 | 


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/utils.py:
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 1 | import torch.nn as nn
 2 | import matplotlib.pyplot as plt
 3 | import numpy as np
 4 | 
 5 | def weights_initializer(model):
 6 |     classname = model.__class__.__name__
 7 |     if classname.find("Conv2d") != -1:
 8 |         nn.init.normal_(model.weight.data, mean=0, std=0.01)
 9 |         nn.init.constant_(model.bias.data, 0.0)
10 |     elif classname.find('Linear') != -1:
11 |         nn.init.normal_(model.weight.data, mean=0, std=0.01)
12 |         nn.init.constant_(model.bias.data, 0.0)
13 | 
14 | def show_image(img, img_size=(512, 512)):
15 |     img = img.view(*img_size)
16 |     img = img.numpy()
17 |     plt.axis("off")
18 |     plt.imshow(img)
19 |     plt.show()
20 | 
21 | 
22 | 


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