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NEU_train.py
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NEU_train.py
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#import libraries
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0" # specify which GPU(s) to be used
import time
import warnings
import random
import numpy as np
from torch.optim.lr_scheduler import ReduceLROnPlateau
import torch
import torch.optim as optim
from tensorboardX import SummaryWriter
from losses import*
from metrics import*
from NEU_dataloaders import*
from model import*
from NEU_utilities import get_logger, create_dir
import torch.backends.cudnn as cudnn
warnings.filterwarnings("ignore")
seed = 1234
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
# save_path = '/media/disk2t_/Dejene/DD/NEU-DualSeg/logs/'
class Trainer(object):
'''This class takes care of training and validation of our model'''
def __init__(self, model):
self._init_logger()
self.num_workers = 6
self.patience = 0
# self.best_dice = 0
# self.best_loss_score = False
self.batch_size = {"train": 16, "val": 16}
self.accumulation_steps = 128 // self.batch_size['train']
self.lr = 5e-4
self.num_epochs = 300
self.best_loss = float("inf")
self.phases = ["train", "val"]
self.device = torch.device("cuda")
# torch.set_default_tensor_type("torch.cuda.FloatTensor")
self.net = model
# self.criterion = torch.nn.BCEWithLogitsLoss()
# self.criterion = BCESoftDiceLoss(w1=0.4, w2=0.8)
self.criterion = BCEDiceLoss()
# self.criterion = FocalLoss()
# self.criterion = DiceLoss()
# self.optimizer = RAdam(self.net.parameters(), lr=self.lr)
self.optimizer = optim.Adam(self.net.parameters(), lr=self.lr)
self.scheduler = ReduceLROnPlateau(self.optimizer, mode="min", patience=10, verbose=True)
self.net = self.net.to(self.device)
# self.save_path = '/media/disk2t_/Dejene/DD/supervised_neu/ResT-small-FPNhead-notebook/Checkpoints/'
cudnn.benchmark = True
self.dataloaders = {
phase: dataloaders(
data_folder=data_folder,
df_path=train_df_path,
phase=phase,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
batch_size=self.batch_size[phase],
num_workers=self.num_workers,
)
for phase in self.phases
}
self.losses = {phase: [] for phase in self.phases}
self.iou_scores = {phase: [] for phase in self.phases}
self.dice_scores = {phase: [] for phase in self.phases}
# self.save_tbx_log = self.save_path + '/tbx_logs'
# self.writer = SummaryWriter(self.save_tbx_log)
def forward(self, images, targets):
images = images.to(self.device)
masks = targets.to(self.device)
outputs = self.net(images)
loss = self.criterion(outputs, masks)
return loss, outputs
def _init_logger(self):
log_dir = '.../model_weights/'
self.logger = get_logger(log_dir)
print('RUNDIR: {}'.format(log_dir))
self.save_path = log_dir
self.save_tbx_log = self.save_path + '/tbx_log'
self.writer = SummaryWriter(self.save_tbx_log)
def iterate(self, epoch, phase):
meter = Meter(phase, epoch)
start = time.strftime("%H:%M:%S")
print(f"Starting epoch: {epoch} | phase: {phase} | ⏰: {start}")
batch_size = self.batch_size[phase]
self.net.train(phase == "train")
dataloader = self.dataloaders[phase]
running_loss = 0.0
total_batches = len(dataloader)
# tk0 = tqdm(dataloader, total=total_batches)
self.optimizer.zero_grad()
for itr, batch in enumerate(dataloader): # replace `dataloader` with `tk0` for tqdm
images, targets = batch
loss, outputs = self.forward(images, targets)
loss = loss / self.accumulation_steps
if phase == "train":
loss.backward()
if (itr + 1 ) % self.accumulation_steps == 0:
self.optimizer.step()
self.optimizer.zero_grad()
running_loss += loss.item()
outputs = outputs.detach().cpu()
meter.update(targets, outputs)
# tk0.set_postfix(loss=(running_loss / ((itr + 1))))
epoch_loss = (running_loss * self.accumulation_steps) / total_batches
dice, iou = epoch_log(phase, epoch, epoch_loss, meter, start)
if phase == "train":
self.writer.add_scalar('Train/Loss', epoch_loss, epoch)
self.writer.add_scalar('Train/DSC', dice, epoch)
self.writer.add_scalar('Train/IoU', iou, epoch)
else:
self.writer.add_scalar('Val/Loss', epoch_loss, epoch)
self.writer.add_scalar('Val/DSC', dice, epoch)
self.writer.add_scalar('Val/IoU', iou, epoch)
# self.writer.add_scalar('Info/lr', lr_, epoch)
self.losses[phase].append(epoch_loss)
self.dice_scores[phase].append(dice)
self.iou_scores[phase].append(iou)
# self.writer.add_scalar('Val_Dices', dice['val'], epoch)
torch.cuda.empty_cache()
return epoch_loss, dice
def start(self):
for epoch in range(0, self.num_epochs):
self.iterate(epoch, "train")
state = {
"epoch": epoch,
"best_loss": self.best_loss,
"state_dict": self.net.state_dict(),
"optimizer": self.optimizer.state_dict(),
}
with torch.no_grad():
val_loss, dice = self.iterate(epoch, "val")
# self.writer.add_scalar('Val/val_loss', val_loss, epoch)
# self.scheduler.step(val_loss)
self.scheduler.step(val_loss)
if val_loss < self.best_loss:
# if self.best_dice < dice:
print("******** New optimal found, saving state ********")
state["best_loss"] = self.best_loss = val_loss
Checkpoints_Path = self.save_path + '/Checkpoints'
if not os.path.exists(Checkpoints_Path):
os.makedirs(Checkpoints_Path)
torch.save(state, Checkpoints_Path + '/NEU_ResT_S_UperHead.pth')
self.patience = 0
# self.logger.info('current patience :{}'.format(self.patience))
else:
# self.save_best_model_1 = False
self.patience += 1
# self.logger.info('current patience :{}'.format(self.patience))
for param_group in self.optimizer.param_groups:
lr_ = param_group['lr'] #For plotting the learning rate change during the training process
self.writer.add_scalar('Info/lr', lr_, epoch)
self.logger.info('current patience :{}'.format(self.patience))
print('==================================================================================')
print()
if __name__ == '__main__':
# Training data path
train_df_path = ".../data/DD/NEU_data/NEU_train_files.csv"
data_folder = ".../data/NEU_data/"
# test_data_folder = ".../data/NEU_data/test_set"
model_trainer = Trainer(model)
model_trainer.start()