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deep_segmentation.py
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import os
import time
import random
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as nd
import math
import torch
import torch.utils
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import dataloaders as dl
import augmentation as aug
import cost_functions as cf
import utils
import paths
from networks import segmentation_network as sn
training_path = None
validation_path = None
models_path = paths.models_path
figures_path = paths.figures_path
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def training(training_params):
model_name = training_params['model_name']
num_epochs = training_params['num_epochs']
batch_size = training_params['batch_size']
learning_rate = training_params['learning_rate']
initial_path = training_params['initial_path']
decay_rate = training_params['decay_rate']
model_save_path = os.path.join(models_path, model_name)
model = sn.load_network(device, path=initial_path)
model = model.to(device)
parameters = model.parameters()
optimizer = optim.Adam(parameters, learning_rate)
scheduler = optim.lr_scheduler.LambdaLR(optimizer, lambda epoch: decay_rate**epoch)
training_loader = dl.SegmentationLoader(training_path)
validation_loader = dl.SegmentationLoader(validation_path)
training_dataloader = torch.utils.data.DataLoader(training_loader, batch_size = batch_size, shuffle = True, num_workers = 4, collate_fn = dl.collate_to_list_segmentation)
validation_dataloader = torch.utils.data.DataLoader(validation_loader, batch_size = batch_size, shuffle = True, num_workers = 4, collate_fn = dl.collate_to_list_segmentation)
cost_function = cf.dice_loss
cost_function_params = dict()
# Training starts here
train_history = []
val_history = []
training_size = len(training_dataloader.dataset)
validation_size = len(validation_dataloader.dataset)
print("Training size: ", training_size)
print("Validation size: ", validation_size)
for epoch in range(num_epochs):
bet = time.time()
print("Current epoch: ", str(epoch + 1) + "/" + str(num_epochs))
# Training
train_running_loss = 0.0
model.train()
for sources, targets, source_masks, target_masks in training_dataloader:
optimizer.zero_grad()
with torch.set_grad_enabled(True):
for i in range(len(sources)):
source = sources[i].to(device)
target = targets[i].to(device)
source = source + 0.00001*torch.randn((source.size(0), source.size(1))).to(device)
target = target + 0.00001*torch.randn((source.size(0), source.size(1))).to(device)
source_mask = source_masks[i].to(device).view(1, 1, source.size(0), source.size(1))
target_mask = target_masks[i].to(device).view(1, 1, target.size(0), target.size(1))
source_mask_pred = model(source.view(1, 1, source.size(0), source.size(1)))
target_mask_pred = model(target.view(1, 1, target.size(0), target.size(1)))
loss_src = cost_function(source_mask_pred, source_mask, device=device, **cost_function_params)
loss_tgt = cost_function(target_mask_pred, target_mask, device=device, **cost_function_params)
loss = (loss_src + loss_tgt) / 2
train_running_loss += loss.item()
loss.backward()
optimizer.step()
print("Train Loss: ", train_running_loss / training_size)
train_history.append(train_running_loss / training_size)
# Validation
val_running_loss = 0.0
model.eval()
for sources, targets, source_masks, target_masks in validation_dataloader:
with torch.set_grad_enabled(False):
for i in range(len(sources)):
source = sources[i].to(device)
target = targets[i].to(device)
source = source + 0.00001*torch.randn((source.size(0), source.size(1))).to(device)
target = target + 0.00001*torch.randn((source.size(0), source.size(1))).to(device)
source_mask = source_masks[i].to(device).view(1, 1, source.size(0), source.size(1))
target_mask = target_masks[i].to(device).view(1, 1, target.size(0), target.size(1))
source_mask_pred = model(source.view(1, 1, source.size(0), source.size(1)))
target_mask_pred = model(target.view(1, 1, target.size(0), target.size(1)))
loss_src = cost_function(source_mask_pred, source_mask, device=device, **cost_function_params)
loss_tgt = cost_function(target_mask_pred, target_mask, device=device, **cost_function_params)
loss = (loss_src + loss_tgt) / 2
val_running_loss += loss.item()
print("Val Loss: ", val_running_loss / validation_size)
val_history.append(val_running_loss / validation_size)
scheduler.step()
eet = time.time()
print("Epoch time: ", eet - bet, "seconds.")
print("Estimated time to end: ", (eet - bet)*(num_epochs-epoch), "seconds.")
if model_save_path is not None:
torch.save(model.state_dict(), model_save_path)
plt.figure()
plt.plot(train_history, "r-")
plt.plot(val_history, "b-")
plt.grid(True)
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend(["Train", "Validation"])
plt.savefig(os.path.join(figures_path, model_name + ".png"), bbox_inches = 'tight', pad_inches = 0)
plt.show()
def visualization(model_name):
model_path = os.path.join(models_path, model_name)
model = sn.load_network(device, path=model_path)
model = model.to(device)
batch_size = 4
validation_loader = dl.SegmentationLoader(training_path)
validation_dataloader = torch.utils.data.DataLoader(validation_loader, batch_size = batch_size, shuffle = True, num_workers = 4, collate_fn = dl.collate_to_list_segmentation)
cost_function = cf.dice_loss
cost_function_params = dict()
model.eval()
for sources, targets, source_masks, target_masks in validation_dataloader:
with torch.set_grad_enabled(False):
for i in range(len(sources)):
source = sources[i].to(device)
target = targets[i].to(device)
source = source + 0.00001*torch.randn((source.size(0), source.size(1))).to(device)
target = target + 0.00001*torch.randn((source.size(0), source.size(1))).to(device)
source_mask = source_masks[i].to(device).view(1, 1, source.size(0), source.size(1))
target_mask = target_masks[i].to(device).view(1, 1, target.size(0), target.size(1))
source_mask_pred = model(source.view(1, 1, source.size(0), source.size(1)))
target_mask_pred = model(target.view(1, 1, target.size(0), target.size(1)))
loss_src = cost_function(source_mask_pred, source_mask, device=device, **cost_function_params)
loss_tgt = cost_function(target_mask_pred, target_mask, device=device, **cost_function_params)
print("Loss src: ", loss_src.item())
print("Loss tgt: ", loss_tgt.item())
plt.figure()
plt.subplot(2, 2, 1)
plt.imshow(source_mask[0, 0, :, :].detach().cpu().numpy(), cmap='gray')
plt.axis('off')
plt.title("S")
plt.subplot(2, 2, 2)
plt.imshow(source_mask_pred[0, 0, :, :].detach().cpu().numpy(), cmap='gray')
plt.axis('off')
plt.title("SPred")
plt.subplot(2, 2, 3)
plt.imshow(target_mask[0, 0, :, :].detach().cpu().numpy(), cmap='gray')
plt.axis('off')
plt.title("R")
plt.subplot(2, 2, 4)
plt.imshow(target_mask_pred[0, 0, :, :].detach().cpu().numpy(), cmap='gray')
plt.axis('off')
plt.title("TPred")
plt.show()
def segmentation(source, target, model, device="cpu"):
with torch.set_grad_enabled(False):
output_min_size = 512
new_shape = utils.calculate_new_shape_min((source.size(0), source.size(1)), output_min_size)
resampled_source = utils.resample_tensor(source, new_shape, device=device)
resampled_target = utils.resample_tensor(target, new_shape, device=device)
source_mask = model(resampled_source.view(1, 1, resampled_source.size(0), resampled_source.size(1)))[0, 0, :, :]
target_mask = model(resampled_target.view(1, 1, resampled_target.size(0), resampled_target.size(1)))[0, 0, :, :]
source_mask = utils.resample_tensor(source_mask, (source.size(0), source.size(1)), device=device) > 0.5
target_mask = utils.resample_tensor(target_mask, (target.size(0), target.size(1)), device=device) > 0.5
return source_mask, target_mask
if __name__ == "__main__":
training_params = dict()
training_params['model_name'] = None # TO DEFINE
training_params['num_epochs'] = 100
training_params['batch_size'] = 4
training_params['learning_rate'] = 0.001
training_params['initial_path'] = None
training_params['decay_rate'] = 0.98
training(training_params)
model_name = None # TO DEFINE
visualization(model_name)