PyTorch_Unet_FCN_example.py

import matplotlib.pyplot as plt
import numpy as np
import helper
import simulation

# Generate some random images
input_images, target_masks = simulation.generate_random_data(192, 192, count=3)

# for x in [input_images, target_masks]:
#     print(x.shape)
#     print(x.min(), x.max())

# Change channel-order and make 3 channels for matplot
input_images_rgb = [x.astype(np.uint8) for x in input_images]


# Map each channel (i.e. class) to each color
target_masks_rgb = [helper.masks_to_colorimg(x) for x in target_masks]


# Left: Input image (black and white), Right: Target mask (6ch)
# helper.plot_side_by_side([input_images_rgb, target_masks_rgb])

#                                                                                                                                Prepare Dataset and DataLoader

from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, datasets, models

class SimDataset(Dataset):
    def __init__(self, count, transform=None):
        self.input_images, self.target_masks = simulation.generate_random_data(192, 192, count=count)
        self.transform = transform

    def __len__(self):
        return len(self.input_images)

    def __getitem__(self, idx):
        image = self.input_images[idx]
        mask = self.target_masks[idx]
        if self.transform:
            image = self.transform(image)

        return [image, mask]

# use the same transformations for train/val in this example
trans = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # imagenet
])

train_set = SimDataset(2000, transform = trans)
val_set = SimDataset(200, transform = trans)

image_datasets = {
    'train': train_set, 'val': val_set
}

batch_size = 25

dataloaders = {
    'train': DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0),
    'val': DataLoader(val_set, batch_size=batch_size, shuffle=True, num_workers=0)
}

#                                                                                                                           Check the outputs from DataLoader

import torchvision.utils

def reverse_transform(inp):
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    inp = (inp * 255).astype(np.uint8)

    return inp

# Get a batch of training data
inputs, masks = next(iter(dataloaders['train']))

print(inputs.shape, masks.shape)

plt.imshow(reverse_transform(inputs[3]))
plt.show()
plt.imshow(reverse_transform(masks[3]))
plt.show()
#                                                                                                                                   Create the UNet module
"""
import torch
import torch.nn as nn
from torchvision import models

def convrelu(in_channels, out_channels, kernel, padding):
    return nn.Sequential(
        nn.Conv2d(in_channels, out_channels, kernel, padding=padding),
        nn.ReLU(inplace=True),
    )

class ResNetUNet(nn.Module):
    def __init__(self, n_class):
        super().__init__()

        self.base_model = models.resnet18(pretrained=True)
        self.base_layers = list(self.base_model.children())

        self.layer0 = nn.Sequential(*self.base_layers[:3]) # size=(N, 64, x.H/2, x.W/2)
        self.layer0_1x1 = convrelu(64, 64, 1, 0)
        self.layer1 = nn.Sequential(*self.base_layers[3:5]) # size=(N, 64, x.H/4, x.W/4)
        self.layer1_1x1 = convrelu(64, 64, 1, 0)
        self.layer2 = self.base_layers[5]  # size=(N, 128, x.H/8, x.W/8)
        self.layer2_1x1 = convrelu(128, 128, 1, 0)
        self.layer3 = self.base_layers[6]  # size=(N, 256, x.H/16, x.W/16)
        self.layer3_1x1 = convrelu(256, 256, 1, 0)
        self.layer4 = self.base_layers[7]  # size=(N, 512, x.H/32, x.W/32)
        self.layer4_1x1 = convrelu(512, 512, 1, 0)

        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)

        self.conv_up3 = convrelu(256 + 512, 512, 3, 1)
        self.conv_up2 = convrelu(128 + 512, 256, 3, 1)
        self.conv_up1 = convrelu(64 + 256, 256, 3, 1)
        self.conv_up0 = convrelu(64 + 256, 128, 3, 1)

        self.conv_original_size0 = convrelu(3, 64, 3, 1)
        self.conv_original_size1 = convrelu(64, 64, 3, 1)
        self.conv_original_size2 = convrelu(64 + 128, 64, 3, 1)

        self.conv_last = nn.Conv2d(64, n_class, 1)

    def forward(self, input):
        x_original = self.conv_original_size0(input)
        x_original = self.conv_original_size1(x_original)

        layer0 = self.layer0(input)
        layer1 = self.layer1(layer0)
        layer2 = self.layer2(layer1)
        layer3 = self.layer3(layer2)
        layer4 = self.layer4(layer3)

        layer4 = self.layer4_1x1(layer4)
        x = self.upsample(layer4)
        layer3 = self.layer3_1x1(layer3)
        x = torch.cat([x, layer3], dim=1)
        x = self.conv_up3(x)

        x = self.upsample(x)
        layer2 = self.layer2_1x1(layer2)
        x = torch.cat([x, layer2], dim=1)
        x = self.conv_up2(x)

        x = self.upsample(x)
        layer1 = self.layer1_1x1(layer1)
        x = torch.cat([x, layer1], dim=1)
        x = self.conv_up1(x)

        x = self.upsample(x)
        layer0 = self.layer0_1x1(layer0)
        x = torch.cat([x, layer0], dim=1)
        x = self.conv_up0(x)

        x = self.upsample(x)
        x = torch.cat([x, x_original], dim=1)
        x = self.conv_original_size2(x)

        out = self.conv_last(x)

        return out

#                                                                                                                                        Model summary

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ResNetUNet(n_class=6)
model = model.to(device)

# check keras-like model summary using torchsummary
from torchsummary import summary
summary(model, input_size=(3, 224, 224))

#                                                                                                                           Define the main training loop

from collections import defaultdict
import torch.nn.functional as F
from loss import dice_loss

def calc_loss(pred, target, metrics, bce_weight=0.5):
    bce = F.binary_cross_entropy_with_logits(pred, target)

    pred = torch.sigmoid(pred)
    dice = dice_loss(pred, target)

    loss = bce * bce_weight + dice * (1 - bce_weight)

    metrics['bce'] += bce.data.cpu().numpy() * target.size(0)
    metrics['dice'] += dice.data.cpu().numpy() * target.size(0)
    metrics['loss'] += loss.data.cpu().numpy() * target.size(0)

    return loss

def print_metrics(metrics, epoch_samples, phase):
    outputs = []
    for k in metrics.keys():
        outputs.append("{}: {:4f}".format(k, metrics[k] / epoch_samples))

    print("{}: {}".format(phase, ", ".join(outputs)))

def train_model(model, optimizer, scheduler, num_epochs=25):
    best_model_wts = copy.deepcopy(model.state_dict())
    best_loss = 1e10

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        since = time.time()

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                scheduler.step()
                for param_group in optimizer.param_groups:
                    print("LR", param_group['lr'])

                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            metrics = defaultdict(float)
            epoch_samples = 0

            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    loss = calc_loss(outputs, labels, metrics)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                epoch_samples += inputs.size(0)

            print_metrics(metrics, epoch_samples, phase)
            epoch_loss = metrics['loss'] / epoch_samples

            # deep copy the model
            if phase == 'val' and epoch_loss < best_loss:
                print("saving best model")
                best_loss = epoch_loss
                best_model_wts = copy.deepcopy(model.state_dict())

        time_elapsed = time.time() - since
        print('{:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))

    print('Best val loss: {:4f}'.format(best_loss))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model

#                                                                                                                                  Training

import torch
import torch.optim as optim
from torch.optim import lr_scheduler
import time
import copy

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)

num_class = 6
model = ResNetUNet(num_class).to(device)

# freeze backbone layers
#for l in model.base_layers:
#    for param in l.parameters():
#        param.requires_grad = False

optimizer_ft = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-4)

exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=30, gamma=0.1)

model = train_model(model, optimizer_ft, exp_lr_scheduler, num_epochs=2)

#                                                                                                                           Use the trained model

import math

model.eval()   # Set model to the evaluation mode

# Create another simulation dataset for test
test_dataset = SimDataset(3, transform = trans)
test_loader = DataLoader(test_dataset, batch_size=3, shuffle=False, num_workers=0)

# Get the first batch
inputs, labels = next(iter(test_loader))
inputs = inputs.to(device)
labels = labels.to(device)

# Predict
pred = model(inputs)
# The loss functions include the sigmoid function.
pred = torch.sigmoid(pred)
pred = pred.data.cpu().numpy()
print(pred.shape)

# Change channel-order and make 3 channels for matplot
input_images_rgb = [reverse_transform(x) for x in inputs.cpu()]

# Map each channel (i.e. class) to each color
target_masks_rgb = [helper.masks_to_colorimg(x) for x in labels.cpu().numpy()]
pred_rgb = [helper.masks_to_colorimg(x) for x in pred]

helper.plot_side_by_side([input_images_rgb, target_masks_rgb, pred_rgb])

"""