trainer.py

import argparse
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 utils import DiceLoss
from torchvision import transforms
from utils import test_single_volume
from torch.nn import functional as F
from datasets.dataset_synapse import Synapse_dataset, RandomGenerator

import matplotlib.pyplot as plt
import pandas as pd
import datetime

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_synapse(args, model, snapshot_path):
    grad_clipping = args.grad_clipping
    use_scheduler = args.use_scheduler
    # accumulation_steps = 2
    os.makedirs(os.path.join(snapshot_path, 'test'), exist_ok=True)
    test_save_path = os.path.join(snapshot_path, 'test')

    logging.basicConfig(filename=snapshot_path + "/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
    # max_iterations = args.max_iterations

    x_transforms = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5])
    ])
    y_transforms = transforms.ToTensor()

    db_train = Synapse_dataset(base_dir=args.root_path, list_dir=args.list_dir, split="train",img_size=args.img_size,
                               norm_x_transform = x_transforms, norm_y_transform = y_transforms)

    print("The length of train set is: {}".format(len(db_train)))
    print(f"The model: {args.model_name}")
    logging.info(f"The model: {args.model_name}")
    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)

    db_test = Synapse_dataset(base_dir=args.test_path, split="test_vol", list_dir=args.list_dir, img_size=args.img_size)
    testloader = DataLoader(db_test, batch_size=1, shuffle=False, num_workers=1)

    if args.n_gpu > 1:
        model = nn.DataParallel(model)

    model.train()

    

    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))
    
    ce_loss = CrossEntropyLoss()
    dice_loss = DiceLoss(num_classes)
    optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001)
    if use_scheduler: 
        scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=max_iterations,verbose=True)
   
    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']
            # print("data shape---------", image_batch.shape, label_batch.shape)
            image_batch, label_batch = image_batch.cuda(), label_batch.squeeze(1).cuda()
            outputs = model(image_batch)
            # outputs = F.interpolate(outputs, size=label_batch.shape[1:], mode='bilinear', align_corners=False)
            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
            # print("loss-----------", loss)
            optimizer.zero_grad()
            loss.backward()
            if grad_clipping:
                nn.utils.clip_grad_norm_(model.parameters(), max_norm=5, norm_type=2)
            optimizer.step()

            if use_scheduler:
                scheduler.step()
                lr_ = scheduler.get_last_lr()[-1]
            else:
                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 : lr: %f, loss : %f, loss_ce: %f, loss_dice: %f' % (iter_num, lr_, loss.item(), loss_ce.item(), loss_dice.item()))

            if iter_num % 20 == 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)

            
        
        # Test
        eval_interval_initial = 20
        eval_interval = args.eval_interval 
        if epoch_num >= int(max_epoch / 2) and epoch_num < int(max_epoch-100) and (epoch_num + 1) % eval_interval_initial == 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 >= int(max_epoch-100) and (epoch_num + 1) % 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 Epoch")
                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!"