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trainer3D.py
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trainer3D.py
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#Partial implementation taken from https://github.com/Beckschen/TransUNet
#Partial implementation taken from https://github.com/raoyongming/GFNet
import argparse
import datetime
import logging
import os
import random
import sys
import time
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 torchvision import transforms
from dataset_train2D import dataset_train, RandomGenerator
from dataset_test3D import dataset3D
def trainer_dataset(args, model, snapshot_path):
base_lr = args.base_lr
num_classes = args.num_classes
batch_size = args.batch_size * args.n_gpu
# max_iterations = args.max_iterations
db_train = dataset_train(base_dir=args.root_path, list_dir=args.list_dir, split="train_ADNI_2D",
transform=transforms.Compose(
[RandomGenerator(output_size=[args.img_size, args.img_size])]))
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=8, pin_memory=True,
worker_init_fn=worker_init_fn)
db_valid = dataset3D(base_dir=args.volume_path, list_dir=args.list_dir, split="valid_ADNI_3D")
data_loader_val = DataLoader(db_valid, batch_size=1, shuffle=False, num_workers=1)
print("The length of validation set is: {}".format(len(db_valid)))
if args.n_gpu > 1:
model = nn.DataParallel(model)
model.train()
ce_loss = CrossEntropyLoss()
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) # max_epoch = max_iterations // len(trainloader) + 1
logging.info("{} iterations per epoch. {} max iterations ".format(len(trainloader), max_iterations))
best_performance = -1.0
max_accuracy = 0.0
max_accuracy_all3D = 0.0
iterator = tqdm(range(max_epoch), ncols=70)
for epoch_num in iterator:
start_time = time.time()
model.train()
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()
# print(image_batch.shape, label_batch.shape)
loss = model(image_batch, label_batch)
# loss = ce_loss(outputs, label_batch[:])
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)
logging.info('iteration %d : loss : %f' % (iter_num, loss.item()))
##Evaluation:
model.eval()
tot_correct = tot_incorrect = total = 0
for data in data_loader_val:
x, y = data
x = x.to(args.device)
y = y.to(args.device)
correct = 0
incorrect = 0
for slice_number in range(args.range_start, args.range_end):
#temp_x = x[:, :, :, slice_number, :] #For MNI
temp_x = x[:, :, :, :, slice_number] #For FreeSurfer
with torch.no_grad():
logits = model(temp_x)[0]
preds = torch.argmax(logits, dim=-1)
if preds == y:
correct = correct + 1
temp_correct_preds = preds
else:
incorrect = incorrect + 1
temp_incorrect_preds = preds
if correct >= 7:
tot_correct = tot_correct + 1
else:
tot_incorrect = tot_incorrect + 1
total = total + 1
accuracy = tot_correct / total
print(total, tot_correct, tot_incorrect)
print("test/accuracy = {}".format(accuracy))
if accuracy > best_performance:
best_performance = accuracy
best_epoch = epoch_num
save_mode_path = os.path.join(snapshot_path, 'best_model.pth')
torch.save(model.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
print("Best accuracy = {}".format(best_performance))
print("Best epoch = {}".format(best_epoch))
if epoch_num >= max_epoch - 1:
save_mode_path = os.path.join(snapshot_path, 'epoch_' + str(epoch_num) + '.pth')
torch.save(model.state_dict(), save_mode_path)
logging.info("save model to {}".format(save_mode_path))
iterator.close()
break
writer.close()
return "Training Finished!"