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train_model.py
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train_model.py
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# -*- coding: utf-8 -*-
import torch.optim
import torch.nn as nn
import time
from tensorboardX import SummaryWriter
import os
import numpy as np
import random
from torch.backends import cudnn
import Config
from Load_Dataset import RandomGenerator, ValGenerator, ImageToImage2D, LV2D
from nets.LViT import LViT
from torch.utils.data import DataLoader
import logging
from Train_one_epoch import train_one_epoch, print_summary
import Config as config
from torchvision import transforms
from utils import CosineAnnealingWarmRestarts, WeightedDiceBCE, WeightedDiceCE, read_text, read_text_LV, save_on_batch
from thop import profile
def logger_config(log_path):
loggerr = logging.getLogger()
loggerr.setLevel(level=logging.INFO)
handler = logging.FileHandler(log_path, encoding='UTF-8')
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(message)s')
handler.setFormatter(formatter)
console = logging.StreamHandler()
console.setLevel(logging.INFO)
loggerr.addHandler(handler)
loggerr.addHandler(console)
return loggerr
def save_checkpoint(state, save_path):
'''
Save the current model.
If the model is the best model since beginning of the training
it will be copy
'''
logger.info('\t Saving to {}'.format(save_path))
if not os.path.isdir(save_path):
os.makedirs(save_path)
epoch = state['epoch'] # epoch no
best_model = state['best_model'] # bool
model = state['model'] # model type
if best_model:
filename = save_path + '/' + \
'best_model-{}.pth.tar'.format(model)
else:
filename = save_path + '/' + \
'model-{}-{:02d}.pth.tar'.format(model, epoch)
torch.save(state, filename)
def worker_init_fn(worker_id):
random.seed(config.seed + worker_id)
##################################################################################
# =================================================================================
# Main Loop: load model,
# =================================================================================
##################################################################################
def main_loop(batch_size=config.batch_size, model_type='', tensorboard=True):
# Load train and val data
train_tf = transforms.Compose([RandomGenerator(output_size=[config.img_size, config.img_size])])
val_tf = ValGenerator(output_size=[config.img_size, config.img_size])
if config.task_name == 'MoNuSeg':
train_text = read_text(config.train_dataset + 'Train_text.xlsx')
val_text = read_text(config.val_dataset + 'Val_text.xlsx')
train_dataset = ImageToImage2D(config.train_dataset, config.task_name, train_text, train_tf,
image_size=config.img_size)
val_dataset = ImageToImage2D(config.val_dataset, config.task_name, val_text, val_tf, image_size=config.img_size)
elif config.task_name == 'Covid19':
text = read_text(config.task_dataset + 'Train_Val_text.xlsx')
train_dataset = ImageToImage2D(config.train_dataset, config.task_name, text, train_tf,
image_size=config.img_size)
val_dataset = ImageToImage2D(config.val_dataset, config.task_name, text, val_tf, image_size=config.img_size)
train_loader = DataLoader(train_dataset,
batch_size=config.batch_size,
shuffle=True,
worker_init_fn=worker_init_fn,
num_workers=8,
pin_memory=True)
val_loader = DataLoader(val_dataset,
batch_size=config.batch_size,
shuffle=True,
worker_init_fn=worker_init_fn,
num_workers=8,
pin_memory=True)
lr = config.learning_rate
logger.info(model_type)
if model_type == 'LViT':
config_vit = config.get_CTranS_config()
logger.info('transformer head num: {}'.format(config_vit.transformer.num_heads))
logger.info('transformer layers num: {}'.format(config_vit.transformer.num_layers))
logger.info('transformer expand ratio: {}'.format(config_vit.expand_ratio))
model = LViT(config_vit, n_channels=config.n_channels, n_classes=config.n_labels)
elif model_type == 'LViT_pretrain':
config_vit = config.get_CTranS_config()
logger.info('transformer head num: {}'.format(config_vit.transformer.num_heads))
logger.info('transformer layers num: {}'.format(config_vit.transformer.num_layers))
logger.info('transformer expand ratio: {}'.format(config_vit.expand_ratio))
model = LViT(config_vit, n_channels=config.n_channels, n_classes=config.n_labels)
pretrained_UNet_model_path = "MoNuSeg/LViT/Test_session_05.23_10h55/models/best_model-LViT.pth.tar"
pretrained_UNet = torch.load(pretrained_UNet_model_path, map_location='cuda')
pretrained_UNet = pretrained_UNet['state_dict']
model2_dict = model.state_dict()
state_dict = {k: v for k, v in pretrained_UNet.items() if k in model2_dict.keys()}
print(state_dict.keys())
model2_dict.update(state_dict)
model.load_state_dict(model2_dict)
logger.info('Load successful!')
else:
raise TypeError('Please enter a valid name for the model type')
input = torch.randn(2, 3, 224, 224)
text = torch.randn(2, 10, 768)
flops, params = profile(model, inputs=(input, text, ))
print('flops:{}'.format(flops))
print('params:{}'.format(params))
model = model.cuda()
if torch.cuda.device_count() > 1:
print("Let's use {0} GPUs!".format(torch.cuda.device_count()))
model = nn.DataParallel(model)
criterion = WeightedDiceBCE(dice_weight=0.5, BCE_weight=0.5)
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=lr) # Choose optimize
if config.cosineLR is True:
lr_scheduler = CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=1, eta_min=1e-4)
else:
lr_scheduler = None
if tensorboard:
log_dir = config.tensorboard_folder
logger.info('log dir: '.format(log_dir))
if not os.path.isdir(log_dir):
os.makedirs(log_dir)
writer = SummaryWriter(log_dir)
else:
writer = None
max_dice = 0.0
best_epoch = 1
for epoch in range(config.epochs): # loop over the dataset multiple times
logger.info('\n========= Epoch [{}/{}] ========='.format(epoch + 1, config.epochs + 1))
logger.info(config.session_name)
# train for one epoch
model.train(True)
logger.info('Training with batch size : {}'.format(batch_size))
train_one_epoch(train_loader, model, criterion, optimizer, writer, epoch, None, model_type, logger) # sup
# evaluate on validation set
logger.info('Validation')
with torch.no_grad():
model.eval()
val_loss, val_dice = train_one_epoch(val_loader, model, criterion,
optimizer, writer, epoch, lr_scheduler, model_type, logger)
# =============================================================
# Save best model
# =============================================================
if val_dice > max_dice:
if epoch + 1 > 5:
logger.info(
'\t Saving best model, mean dice increased from: {:.4f} to {:.4f}'.format(max_dice, val_dice))
max_dice = val_dice
best_epoch = epoch + 1
save_checkpoint({'epoch': epoch,
'best_model': True,
'model': model_type,
'state_dict': model.state_dict(),
'val_loss': val_loss,
'optimizer': optimizer.state_dict()}, config.model_path)
else:
logger.info('\t Mean dice:{:.4f} does not increase, '
'the best is still: {:.4f} in epoch {}'.format(val_dice, max_dice, best_epoch))
early_stopping_count = epoch - best_epoch + 1
logger.info('\t early_stopping_count: {}/{}'.format(early_stopping_count, config.early_stopping_patience))
if early_stopping_count > config.early_stopping_patience:
logger.info('\t early_stopping!')
break
return model
if __name__ == '__main__':
deterministic = True
if not deterministic:
cudnn.benchmark = True
cudnn.deterministic = False
else:
cudnn.benchmark = False
cudnn.deterministic = True
random.seed(config.seed)
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
if not os.path.isdir(config.save_path):
os.makedirs(config.save_path)
logger = logger_config(log_path=config.logger_path)
model = main_loop(model_type=config.model_name, tensorboard=True)