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train.py
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train.py
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import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
import numpy as np
import os
import sys
sys.path.insert(0, 'model/')
sys.path.insert(0, 'utils/')
from model.unet import *
from model.unet_plus_plus import *
from model.backboned_unet import *
from utils.dataloader import *
from utils.image_utils import *
SAVE_PATH = {'unet': 'checkpoints\\unet',
'unet_plus_plus': 'checkpoints\\unet_plus_plus',
'backboned_unet': 'checkpoints\\backboned_unet'}
DATA_PATH = 'data\\kaggle_3m'
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
class DiceLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceLoss, self).__init__()
def forward(self, inputs, targets, smooth=1):
#comment out if your model contains a sigmoid or equivalent activation layer
inputs = torch.sigmoid(inputs)
#flatten label and prediction tensors
inputs = inputs.view(-1)
targets = targets.view(-1)
intersection = (inputs * targets).sum()
dice = (2.*intersection + smooth)/(inputs.sum() + targets.sum() + smooth)
return 1 - dice
class DiceBCELoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceBCELoss, self).__init__()
def forward(self, inputs, targets, smooth=1):
#comment out if your model contains a sigmoid or equivalent activation layer
inputs = torch.sigmoid(inputs)
bce_weight = 0.5
#flatten label and prediction tensors
inputs = inputs.view(-1)
targets = targets.view(-1)
intersection = (inputs * targets).sum()
dice_loss = 1 - (2.*intersection + smooth)/(inputs.sum() + targets.sum() + smooth)
BCE = F.binary_cross_entropy(inputs, targets, reduction='mean')
loss_final = BCE * bce_weight + dice_loss * (1 - bce_weight)
return loss_final
def train(model_name = 'unet', epochs = 20, backbone_name = 'resnet50'):
assert model_name in ['unet', 'backboned_unet', 'unet_plus_plus']
assert backbone_name in ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'vgg16', 'vgg19', 'densenet121', 'densenet161', 'densenet169', 'densenet201']
dataset = MRIDataset(DATA_PATH)
train, not_train = random_split(dataset, [3143, 786], generator=torch.Generator().manual_seed(0))
val, test = random_split(not_train, [393, 393], generator=torch.Generator().manual_seed(0))
train_loader = DataLoader(dataset=train, batch_size=10, shuffle=True)
val_loader = DataLoader(dataset=val, batch_size=10)
if model_name == 'unet':
model = Unet()
elif model_name == 'unet_plus_plus':
model = NestedUNet(num_classes=1)
elif model_name == 'backboned_unet':
model = BackbonedUnet(backbone_name=backbone_name)
model.to(device)
if model_name != 'backboned_unet':
if len(os.listdir(SAVE_PATH[model_name])) > 0:
model.load_state_dict(torch.load(os.path.join(SAVE_PATH[model_name], 'best.pth'), map_location=torch.device('cpu')))
else:
if len(os.listdir(SAVE_PATH[model_name])) > 0:
model.load_state_dict(torch.load(os.path.join(SAVE_PATH[model_name], '{}_best.pth'.format(backbone_name)), map_location=torch.device('cpu')))
criterion = DiceBCELoss()
learning_rate = 1e-3
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
train_loss = []
val_loss = []
best_val_loss = float('inf')
for epoch in range(epochs):
print('Epoch {}/{}'.format(epoch+1, epochs))
running_train_loss = []
for (image, mask) in tqdm(train_loader):
image = image.to(device, dtype = torch.float32)
mask = mask.to(device, dtype = torch.float32)
pred = model.forward(image)
loss = criterion(pred, mask)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_train_loss.append(loss.item())
running_val_loss = []
with torch.no_grad():
for image,mask in val_loader:
image = image.to(device,dtype=torch.float)
mask = mask.to(device,dtype=torch.float)
pred_mask = model.forward(image)
loss = criterion(pred_mask,mask)
running_val_loss.append(loss.item())
epoch_train_loss = np.mean(running_train_loss)
print('Train loss: {}'.format(epoch_train_loss))
train_loss.append(epoch_train_loss)
epoch_val_loss = np.mean(running_val_loss)
print('Validation loss: {}'.format(epoch_val_loss))
val_loss.append(epoch_val_loss)
#torch.save(model.state_dict(), os.path.join(SAVE_PATH[model_name], 'epoch_{}.pth'.format(epoch+1)))
if epoch_val_loss < best_val_loss:
best_val_loss = epoch_val_loss
if model_name != 'backboned_unet':
torch.save(model.state_dict(), os.path.join(SAVE_PATH[model_name], 'best.pth'))
else:
torch.save(model.state_dict(), os.path.join(SAVE_PATH[model_name], '{}_best.pth').format(backbone_name))
return train_loss, val_loss
if __name__ == '__main__':
train_loss, val_loss = train(model_name='backboned_unet', epochs=20, backbone_name='vgg16')
plot_hist(train_loss, val_loss)