train3D_monai_version.py

import os

from torch.nn.functional import threshold
fold_num = 0
gpu_bias = 2
os.environ['CUDA_VISIBLE_DEVICES'] = f'0, 1, 3'
# os.environ['CUDA_VISIBLE_DEVICES'] = f'{fold_num*2+gpu_bias}, {fold_num*2+1+gpu_bias}'

import time
import json
import argparse
from numpy import Inf

import torch
import torch.nn as nn

from dataset.CT_pancreas_monai import CachePanDataset, EvaPanDataset
from monai.data import DataLoader, list_data_collate
from model.trans_3DUnet import get_model_dict
from loss.multi_criterions import get_criterions
# from utils.utils import train_on_epoch, eval_on_epoch, save_model
# from utils.utils_3D_2 import train_on_epoch, eval_on_epoch, save_model
from utils.utils_3D_monai import train_on_epoch, eval_on_epoch, save_model, get_weight
from torch.utils.tensorboard import SummaryWriter

from tqdm import tqdm

def get_parse():
    parser = argparse.ArgumentParser()

    '''
    parser.add_argument('--dir_data', type=str,
                        default='../../data/CT_Pancreas/Sloan_data',
                        help='direction for the dataset')
    '''
    parser.add_argument('--dir_data', type=str,
                        default='/data/datasets/zheyuan/Raw_Pancreas',
                        help='direction for the dataset')
    parser.add_argument('--is_transform', type=bool,
                        default=True, help='apply transform or not')
    parser.add_argument('--split_ratio', type=float,
                        default=0.9, help='split ratio for training')
    parser.add_argument('--is_pretrained', type=bool,
                        default=False, help='pretained or not')
    parser.add_argument('--pretrained_dir', type=str,
                        default='./out/log/20220204-19_1', help='pretrained dir')
    parser.add_argument('--model_name', type=str,
                        default='MaskTransUnet', help='model name for training')
    parser.add_argument('--batch_size', type=int,
                        default=4, help='patient batch size')
    parser.add_argument('--depth_size', type=int,
                        default=32, help='patient depth size')
    parser.add_argument('--num_samples', type=int,
                        default=6, help='num samples')

    # num layers [32, 32, 64, 64, 128]
    # previous [16, 32, 64, 128, 256]
    # [32, 64, 64, 128, 256]
    parser.add_argument('--num_layers', type=list, 
                        default=[16, 32, 64, 128, 256], help='number of layer for each layer')
    # 320-160-80-40-20: 160-80-40-20-10
    # 256-128-64-32-16:  80-40-20-10-5
    parser.add_argument('--roi_size_list', type=list, 
                        default=[100, 65, 40, 25, 10], help='size of roi for each layer')
    parser.add_argument('--is_roi_list', type=list, 
                        default=[False, True, True, True, True], help='using roi for each layer')

    '''
    parser.add_argument('--num_layers', type=list, 
                        default=[16, 32, 32, 64], help='number of layer for each layer')
    '''
    parser.add_argument('--dim_input', type=int,
                        default=1, help='input dimension or modality')
    parser.add_argument('--dim_output', type=int,
                        default=3, help='output dimension or classes')
    parser.add_argument('--kernel_size', type=int,
                        default=3, help='kernel_size for convolution')

    parser.add_argument('--device', type=str,
                        default='cuda', help='device for training')
    parser.add_argument('--epochs', type=int,
                        default=800, help='epochs for training')

    parser.add_argument('--eval_epoch', type=int,
                        default=5, help='the interval epoch for eval')
    parser.add_argument('--log_dir', type=str,
                        default='./runs/log', help='device for training')
    parser.add_argument('--model_dir', type=str,
                        default='./out/log', help='device for training')
    parser.add_argument('--criterion_list', type=list,
                        default=['CrossEntroLoss', 'DiceClassLoss', 'DiceClassLoss2'],
                        help='device for training')
    parser.add_argument('--criterion_weight', type=list,
                        default=[10, 1, 2],
                        help='device for training')
    parser.add_argument('--weight_list', type=list,
                        default=[0.05, 0.05, 0.1, 0.1, 1.0],
                        help='weight list for training')
    parser.add_argument('--final_weight', type=list,
                        default=[2., 1.5, 0.5, 0.5, 0.4],
                        help='weight list for training')
    parser.add_argument('--initial_weight', type=list,
                        default=[0.2, 0.2, 0.3, 0.3, 0.4],
                        help='device for training')

    args = parser.parse_args()
    return args

def get_model(args, fold_num, device):
    model_fn = get_model_dict(args.model_name)
    model = model_fn(num_layers=args.num_layers,
                     roi_size_list=args.roi_size_list,
                     is_roi_list=args.is_roi_list,
                     dim_input=args.dim_input,
                     dim_output=args.dim_output,
                     kernel_size=args.kernel_size)

    if args.is_pretrained:
        pretrained_dir = os.path.join(args.pretrained_dir, f'fold_{fold_num}', 'temp_model.pt')
        # state_dict = torch.load(pretrained_dir).state_dict()
        # model.load_state_dict(state_dict)
        model.load_state_dict(torch.load(pretrained_dir))

    model = nn.DataParallel(model.to(device))
    return model

def get_dynamic_weight(args, T, warmup_step):
    weight_list = args.weight_list
    initial_weight = args.initial_weight
    final_weight = args.final_weight
    out_list = []

    for i in range(len(weight_list)):
        y = [get_weight(j-warmup_step, T=T, 
                        default_weight=weight_list[i], 
                        initial_weight=initial_weight[i],
                        final_weight=final_weight[i])
                        for j in range(args.epochs)]
        out_list.append(y)

    out_list = list(zip(*out_list))
    return out_list

def get_criterion_list(args):
    criterions = []
    criterion_name = args.criterion_list
    temp_list = ['CrossEntroLoss', 'DiceClassLoss', 'DiceClassLoss2']
    temp_list2 = ['CrossEntroLoss', 'DiceClassLoss', 'DiceClassLoss2']
    eval_list = ['DiceClassLoss0', 'DiceClassLoss', 'DiceClassLoss2', 'RecallLoss', 'PrecisionLoss','LocalizationLoss']

    for i in range(len(args.num_layers)):
        if i < (len(args.num_layers)-2):
            criterions.append(get_criterions(temp_list))
        elif i == (len(args.num_layers)-2):
            criterions.append(get_criterions(temp_list2))
        else:
            criterions.append(get_criterions(criterion_name))
    
    eval_criterions = get_criterions(eval_list)
    return criterions, eval_criterions

def main(args):
    # torch.autograd.set_detect_anomaly(True)
    num_device = torch.cuda.device_count()

    root = args.dir_data
    is_transform = args.is_transform
    depth_size = args.depth_size
    num_samples = args.num_samples
    batch_size = args.batch_size * num_device
    step_times = num_samples // 2

    with open('split_dataset_8.json', 'r') as f:
        dataset_ids = json.load(f)

    train_ids = dataset_ids[f'train_id fold_{fold_num}']
    test_ids = dataset_ids[f'test_id fold_{fold_num}']

    train_pandataset = CachePanDataset(root=root,
                                       depth_size=depth_size,
                                       num_samples=num_samples,
                                       ids=train_ids)
    
    test_pandataset = EvaPanDataset(root=root,
                                    depth_size=depth_size,
                                    ids=test_ids)

    train_panDl = DataLoader(dataset=train_pandataset, batch_size=batch_size,
                             num_workers=12, shuffle=True, pin_memory=True, collate_fn=list_data_collate)
    test_panDl = DataLoader(dataset=test_pandataset, batch_size=1,
                            shuffle=False, pin_memory=True, collate_fn=list_data_collate)

    device = torch.device(args.device if torch.cuda.is_available() else 'cpu')
    model = get_model(args, fold_num, device=device)

    warmup_step = 10
    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
    # optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9)
    sheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer, 
                                                          mode='min',
                                                          factor=0.6,
                                                          patience=4,
                                                          threshold=1e-2,
                                                          cooldown=1,
                                                          min_lr=1e-7)
    '''
    sheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=250, 
                                               gamma=0.5)

    sheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
                                                          T_max=100,
                                                          eta_min=1e-6)
    '''
    epochs = args.epochs
    patient_batchsize = batch_size
    patient_epochs = num_samples

    criterions, eval_criterions = get_criterion_list(args)
    # print(criterions)
    criterion_weight = args.criterion_weight
    # writer = SummaryWriter(os.path.join(args.log_dir, time.strftime("%Y%m%d-%H%M")))
    writer = SummaryWriter(os.path.join(args.log_dir, time.strftime("%Y%m%d-%H_1"), f'fold_{fold_num}'))
    # writer = SummaryWriter(os.path.join(args.log_dir, '20211109-1112'))
    # model_dir = os.path.join(args.model_dir, time.strftime("%Y%m%d-%H%M"))
    model_dir = os.path.join(args.model_dir, time.strftime("%Y%m%d-%H_1"), f'fold_{fold_num}')

    if not os.path.exists(model_dir):
        os.makedirs(model_dir)

    global_step = 0
    smooth_ratio = 0
    best_eval_loss = Inf
    eval_loss = 50
    best_train_loss = Inf
    train_loss = Inf
    smooth_eval_loss = Inf
    smooth_train_loss = Inf
    T = 12

    dynamic_weight_list = get_dynamic_weight(args, T, warmup_step)

    for i in tqdm(range(epochs)):
        dynamic_weight =  dynamic_weight_list[i]
        # dynamic_weight =  args.weight_list
        if i % args.eval_epoch == 0:
            eval_loss, global_step = eval_on_epoch(model=model,
                                                   dataloader=test_panDl, 
                                                   criterions=eval_criterions,
                                                   device=device, 
                                                   writer=writer,
                                                   patient_epochs=patient_epochs,
                                                   patient_batchsize=patient_batchsize,
                                                   global_step=global_step)
            
            sheduler.step(eval_loss)

            if i != 0:
                smooth_eval_loss = eval_loss
                smooth_train_loss = train_loss
            else:
                smooth_eval_loss = (1-smooth_ratio)*eval_loss + \
                                        smooth_ratio*smooth_eval_loss
                smooth_train_loss = (1-smooth_ratio)*train_loss + \
                                        smooth_ratio*smooth_train_loss

            if smooth_eval_loss <= best_eval_loss:
                best_eval_loss = smooth_eval_loss
                best_train_loss = smooth_train_loss
                print('Best train_loss:', best_train_loss)
                print('Best eval loss', eval_loss)

                save_model(model.module.state_dict(),
                        os.path.join(model_dir, f'temp_model.pt'))

        if i < warmup_step:
            dynamic_weight =  dynamic_weight_list[0]

        train_loss, global_step = train_on_epoch(model=model,
                                                 dataloader=train_panDl,
                                                 optimizer=optimizer,
                                                 criterions=criterions,
                                                 criterion_weight=criterion_weight,
                                                 step_times=step_times,
                                                 device=device, 
                                                 writer=writer,
                                                 patient_epochs=patient_epochs,
                                                 patient_batchsize=patient_batchsize,
                                                 global_step=global_step,
                                                 dynamic_weight=dynamic_weight)

        # sheduler.step()

    print('Best train_loss:', best_train_loss)
    print('Best eval loss', eval_loss)
    writer.close()
    save_model(model.module, os.path.join(model_dir, 'model.pt'))



if __name__ == '__main__':
    args = get_parse()
    main(args)