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trainer.py
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trainer.py
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import logging
import os
import random
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn.modules.loss import CrossEntropyLoss
from torch.utils.data import DataLoader
from tqdm import tqdm
from utils import DiceLoss, test_single_volume
from torchvision import transforms
import matplotlib.pyplot as plt
import pandas as pd
import datetime
from datasets.dataset_synapse import Synapse_dataset, RandomGenerator
def inference(model, testloader, args, test_save_path=None):
model.eval()
metric_list = 0.0
for i_batch, sampled_batch in tqdm(enumerate(testloader)):
h, w = sampled_batch["image"].size()[2:]
image, label, case_name = sampled_batch["image"], sampled_batch["label"], sampled_batch['case_name'][0]
metric_i = test_single_volume(image, label, model, classes=args.num_classes, patch_size=[args.img_size, args.img_size],
test_save_path=test_save_path, case=case_name, z_spacing=args.z_spacing)
metric_list += np.array(metric_i)
logging.info(' idx %d case %s mean_dice %f mean_hd95 %f' % (i_batch, case_name, np.mean(metric_i, axis=0)[0], np.mean(metric_i, axis=0)[1]))
metric_list = metric_list / len(testloader.dataset)
for i in range(1, args.num_classes):
logging.info('Mean class %d mean_dice %f mean_hd95 %f' % (i, metric_list[i-1][0], metric_list[i-1][1]))
performance = np.mean(metric_list, axis=0)[0]
mean_hd95 = np.mean(metric_list, axis=0)[1]
logging.info('Testing performance in best val model: mean_dice : %f mean_hd95 : %f' % (performance, mean_hd95))
return performance, mean_hd95
def plot_result(dice, h, snapshot_path,args):
dict = {'mean_dice': dice, 'mean_hd95': h}
df = pd.DataFrame(dict)
plt.figure(0)
df['mean_dice'].plot()
resolution_value = 1200
plt.title('Mean Dice')
date_and_time = datetime.datetime.now()
filename = f'{args.model_name}_' + str(date_and_time)+'dice'+'.png'
save_mode_path = os.path.join(snapshot_path, filename)
plt.savefig(save_mode_path, format="png", dpi=resolution_value)
plt.figure(1)
df['mean_hd95'].plot()
plt.title('Mean hd95')
filename = f'{args.model_name}_' + str(date_and_time)+'hd95'+'.png'
save_mode_path = os.path.join(snapshot_path, filename)
#save csv
filename = f'{args.model_name}_' + str(date_and_time)+'results'+'.csv'
save_mode_path = os.path.join(snapshot_path, filename)
df.to_csv(save_mode_path, sep='\t')
def trainer(args, model, snapshot_path):
date_and_time = datetime.datetime.now()
os.makedirs(os.path.join(snapshot_path, 'test'), exist_ok=True)
test_save_path = os.path.join(snapshot_path, 'test')
# Save logs
logging.basicConfig(filename=snapshot_path + f"/{args.model_name}" + str(date_and_time) + "_log.txt", level=logging.INFO,
format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S')
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
logging.info(str(args))
base_lr = args.base_lr
num_classes = args.num_classes
batch_size = args.batch_size * args.n_gpu
db_train = Synapse_dataset(base_dir=args.root_path, list_dir=args.list_dir, split="train",
transform=transforms.Compose(
[RandomGenerator(output_size=[args.img_size, args.img_size])]))
db_test = Synapse_dataset(base_dir=args.test_path, split="test_vol", list_dir=args.list_dir)
testloader = DataLoader(db_test, batch_size=1, shuffle=False, num_workers=1)
print("The length of train set is: {}".format(len(db_train)))
def worker_init_fn(worker_id):
random.seed(args.seed + worker_id)
trainloader = DataLoader(db_train, batch_size=batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True,
worker_init_fn=worker_init_fn)
if args.n_gpu > 1:
model = nn.DataParallel(model)
model.train()
ce_loss = CrossEntropyLoss()
dice_loss = DiceLoss(num_classes)
optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001)
writer = SummaryWriter(snapshot_path + '/log')
iter_num = 0
max_epoch = args.max_epochs
max_iterations = args.max_epochs * len(trainloader)
logging.info("{} iterations per epoch. {} max iterations ".format(len(trainloader), max_iterations))
best_performance = 0.0
iterator = tqdm(range(max_epoch), ncols=70)
dice_=[]
hd95_= []
for epoch_num in iterator:
for i_batch, sampled_batch in enumerate(trainloader):
image_batch, label_batch = sampled_batch['image'], sampled_batch['label']
image_batch, label_batch = image_batch.cuda(), label_batch.cuda()
B, C, H, W = image_batch.shape
image_batch = image_batch.expand(B, 3, H, W)
outputs = model(image_batch)
loss_ce = ce_loss(outputs, label_batch[:].long())
loss_dice = dice_loss(outputs, label_batch, softmax=True)
loss = 0.4 * loss_ce + 0.6 * loss_dice
optimizer.zero_grad()
loss.backward()
optimizer.step()
lr_ = base_lr * (1.0 - iter_num / max_iterations) ** 0.9
for param_group in optimizer.param_groups:
param_group['lr'] = lr_
iter_num = iter_num + 1
writer.add_scalar('info/lr', lr_, iter_num)
writer.add_scalar('info/total_loss', loss, iter_num)
writer.add_scalar('info/loss_ce', loss_ce, iter_num)
writer.add_scalar('info/loss_dice', loss_dice, iter_num)
logging.info('iteration %d : loss : %f, loss_ce: %f loss_dice: %f' % (iter_num, loss.item(), loss_ce.item(), loss_dice.item()))
try:
if iter_num % 10 == 0:
image = image_batch[1, 0:1, :, :]
image = (image - image.min()) / (image.max() - image.min())
writer.add_image('train/Image', image, iter_num)
outputs = torch.argmax(torch.softmax(outputs, dim=1), dim=1, keepdim=True)
writer.add_image('train/Prediction', outputs[1, ...] * 50, iter_num)
labs = label_batch[1, ...].unsqueeze(0) * 50
writer.add_image('train/GroundTruth', labs, iter_num)
except: pass
# Test
if (epoch_num + 1) % args.eval_interval == 0:
filename = f'{args.model_name}_epoch_{epoch_num}.pth'
save_mode_path = os.path.join(snapshot_path, filename)
torch.save(model.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
logging.info("*" * 20)
logging.info(f"Running Inference after epoch {epoch_num}")
print(f"Epoch {epoch_num}")
mean_dice, mean_hd95 = inference(model, testloader, args, test_save_path=test_save_path)
dice_.append(mean_dice)
hd95_.append(mean_hd95)
model.train()
if epoch_num >= max_epoch - 1:
filename = f'{args.model_name}_epoch_{epoch_num}.pth'
save_mode_path = os.path.join(snapshot_path, filename)
torch.save(model.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
if not (epoch_num + 1) % args.eval_interval == 0:
logging.info("*" * 20)
logging.info(f"Running Inference after epoch {epoch_num} (Last Epoch)")
print(f"Epoch {epoch_num}, Last Epcoh")
mean_dice, mean_hd95 = inference(model, testloader, args, test_save_path=test_save_path)
dice_.append(mean_dice)
hd95_.append(mean_hd95)
model.train()
iterator.close()
break
plot_result(dice_,hd95_,snapshot_path,args)
writer.close()
return "Training Finished!"