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from abc import abstractmethod
import torch
import numpy as np
from torch.optim import AdamW, lr_scheduler
from src.config.config_setup import build_model, get_dataloader
from monai.losses import DiceCELoss, DiceLoss
import torch.nn as nn
from src.utils.util import save_checkpoint
import time
import os
import torch.distributed as dist
from torch.cuda import amp
import torchio as tio
class Trainer_basic(object):
def __init__(self, args, logger):
self.args = args
self.logger = logger
a = time.time()
use_small = True if self.args.use_small_dataset else False
self.train_data, self.val_data = get_dataloader(args, split='train', use_small=use_small), get_dataloader(args, split='val', use_small=use_small)
if self.args.use_sam3d_turbo:
self.sam = build_model(args, checkpoint='../src/ckpt/sam_med3d_turbo.pth')
else:
self.sam = build_model(args)
if self.args.ddp:
self.sam = self.sam.module
self.best_dice, self.best_epoch, self.start_epoch = 0, 0, 0
self.pooling_layer = nn.AvgPool3d((self.args.boundary_kernel_size, self.args.boundary_kernel_size, 1), stride=1,
padding=(int((self.args.boundary_kernel_size - 1) / 2),
int((self.args.boundary_kernel_size - 1) / 2),
0)).cuda()
self.setup()
print('dataloaders are created, models are loaded, and others are set, spent {} for rank {}'
.format(round(time.time() - a, 2), self.args.rank))
def run(self):
self.scaler = amp.GradScaler()
for epoch_num in range(self.start_epoch, self.args.max_epoch):
self.sam.train()
if self.args.ddp:
# dist.barrier() # set a barrier until all processes are at same point
self.train_data.sampler.set_epoch(epoch_num)
self.train(epoch_num)
if self.args.ddp and self.args.rank == 0:
print('doing validation on rank=0')
current_mean_dice = self.validate(epoch_num) if self.args.data != 'lits' else self.validate_sliding_window(epoch_num)
else:
current_mean_dice = self.validate(epoch_num) if self.args.data != 'lits' else self.validate_sliding_window(epoch_num)
# https://medium.com/codex/a-comprehensive-tutorial-to-pytorch-distributeddataparallel-1f4b42bb1b51
# if self.args.ddp:
# dist.barrier()
self.save_model(current_mean_dice, epoch_num)
@abstractmethod
def forward(self, model, image, label, iter_nums, train, return_each_iter):
pass
def train(self, epoch_num):
loss_summary = []
for idx, (image, label, image_path) in enumerate(self.train_data):
self.optimizer.zero_grad()
# increase speed based on gradient accumulation
# my_context = self.sam.no_sync if self.args.rank != -1 and idx % self.args.accumulation_steps != 0 else nullcontext
# with my_context():
image, label = image.to(self.args.device), label.to(self.args.device)
with amp.autocast():
loss, _ = self.forward(self.sam, image, label, iter_nums=self.args.iter_nums, train=True)
loss_summary.append(loss.detach().cpu().numpy())
self.scaler.scale(loss).backward()
self.scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(self.sam.parameters(), 1.0)
self.scaler.step(self.optimizer)
self.scaler.update()
print('epoch: {}/{}, iter: {}/{}'.format(epoch_num, self.args.max_epoch, idx, len(self.train_data))
+ ": loss:" + str(round(loss_summary[-1].flatten()[0], 4))
+ ": rank:" + str(self.args.rank))
self.logger.info(
'epoch: {}/{}, iter: {}/{}'.format(epoch_num, self.args.max_epoch, idx, len(self.train_data))
+ ": loss:" + str(round(loss_summary[-1].flatten()[0], 4))
+ ": rank:" + str(self.args.rank))
print('current lr: {}'.format(self.optimizer.param_groups[0]["lr"]))
# If the first iteration creates NaN gradients (e.g. due to a high scaling factor and thus gradient overflow),
# the optimizer.step() will be skipped and you might get this warning.
self.update_lr(epoch_num, warm_up=self.args.warm_up)
self.logger.info("- Train metrics: " + str(np.mean(loss_summary)))
def validate_sliding_window(self, epoch_num):
self.sam.eval()
with torch.no_grad():
dice_list = []
for idx, batch in enumerate(self.val_data):
if isinstance(batch, (list, tuple)) and len(batch) >= 2:
subject_dict, image_path = batch[0], batch[1]
else:
raise ValueError("Unexpected validation batch format for lits dataset")
if subject_dict['label']['data'][0].sum() <= 0:
self.logger.info(image_path, 'label volume too small, and it has been skipped for validation')
continue
mean_dice = 0
subject = tio.Subject(image=tio.ScalarImage(tensor=subject_dict['image']['data'][0].float(), affine=subject_dict['image']['affine'][0]),
label=tio.LabelMap(tensor=subject_dict['label']['data'][0].float(), affine=subject_dict['label']['affine'][0]))
grid_sampler = tio.inference.GridSampler(subject, 128, 16)
patch_loader = torch.utils.data.DataLoader(grid_sampler, batch_size=1)
aggregator = tio.inference.GridAggregator(grid_sampler)
masks_final = torch.zeros([self.args.iter_nums, len(patch_loader), 128, 128, 128])
location_list = []
for idx_patch, patches_batch in enumerate(patch_loader):
image, label = patches_batch['image'][tio.DATA].to(self.args.device), patches_batch['label'][tio.DATA].to(self.args.device)
locations = patches_batch[tio.LOCATION]
if torch.count_nonzero(label) == 0:
print('found empty patch')
masks = torch.zeros([self.args.iter_nums, 1, 128, 128, 128])
else:
_, masks = self.forward(self.sam, image, label, iter_nums=self.args.iter_nums, train=False, return_each_iter=True)
print(masks.shape)
masks_final[:, idx_patch, :] = masks.squeeze(1)
location_list.append(locations)
mean_dice_sub_list = []
for iter_num in range(self.args.iter_nums):
for l_i in range(0, len(location_list)):
location = location_list[l_i]
a = masks_final[iter_num, l_i, :].unsqueeze(0).unsqueeze(0)
mask = a
aggregator.add_batch(mask, location)
masks_iter_final = aggregator.get_output_tensor()
mean_dice_sub_list.append(self.get_dice_score(torch.sigmoid(masks_iter_final), subject.label.data))
mean_dice_sub = np.mean(mean_dice_sub_list)
mean_dice += mean_dice_sub
dice_list.append(mean_dice)
print(mean_dice_sub)
self.logger.info(
'epoch: {}/{}, iter: {}/{}'.format(epoch_num, self.args.max_epoch, idx, len(self.val_data)) +
' subject: ' + str(image_path) + ' mean dice over clicks:' + str(mean_dice) +
' stich left and right side (total size): ' + str(label.size(1)))
self.logger.info("- Val metrics mean dice: " + str(np.mean(dice_list)))
return dice_list
def validate(self, epoch_num):
self.sam.eval()
device = self.args.device
with torch.no_grad():
dice_list = []
for idx, (image, label, image_path, _) in enumerate(self.val_data):
mean_dice = 0
image, label = image.to(device), label.to(device)
#with amp.autocast():
if self.args.data == 'kits' and image.size(1) > 1:
label_final, masks_final = torch.zeros([1, 1, int(image.size(2) * 2), image.size(3), image.size(4)]), torch.zeros([self.args.iter_nums, 1, int(image.size(2) * 2), image.size(3), image.size(4)])
for channel_num in range(image.size(1)):
_, masks = self.forward(self.sam, image[:, channel_num, :].unsqueeze(1), label[:, channel_num, :].unsqueeze(1), iter_nums=self.args.iter_nums, train=False, return_each_iter=True)
start_point, end_pont = 0 + channel_num * image.size(2), image.size(2) + channel_num * image.size(2)
masks_final[:, 0, start_point: end_pont, :] = masks[:, 0, :]
label_final[0, 0, start_point: end_pont, :] = label[0, channel_num, :]
mean_dice_sub_list = []
for iter_num in range(self.args.iter_nums):
mean_dice_sub_list.append(self.get_dice_score(torch.sigmoid(masks_final[iter_num]), label_final[0]))
mean_dice_sub = np.mean(mean_dice_sub_list)
else:
mean_dice_sub, masks = self.forward(self.sam, image, label, iter_nums=self.args.iter_nums, train=False)
mean_dice += mean_dice_sub
dice_list.append(mean_dice)
print(mean_dice_sub)
self.logger.info(
'epoch: {}/{}, iter: {}/{}'.format(epoch_num, self.args.max_epoch, idx, len(self.val_data)) +
' subject: ' + str(image_path) + ' mean dice over clicks:' + str(mean_dice) +
' stich left and right side (total size): ' + str(label.size(1)))
self.logger.info("- Val metrics mean dice: " + str(np.mean(dice_list)))
return dice_list
def calculate_loss(self, mask, prev_masks, pred_dice, label, labels_input, iter_num, inter=False):
mask_probs, prev_masks_prob = torch.sigmoid(mask), torch.sigmoid(prev_masks)
seg_edge = abs(label - self.pooling_layer(label))
mask_edge = abs(mask_probs - self.pooling_layer(mask_probs))
edge_number = torch.count_nonzero(mask_edge) + 1
pred_dice_score_loss = 0
for batch_index in range(mask.size(0)):
target_dice = 1 - self.loss_validation(mask[batch_index].unsqueeze(0), label[batch_index].unsqueeze(0))[0,0,0,0,0]
target_dice = torch.tensor([target_dice])[0].to(self.args.device)
pred_dice_score_loss += self.loss_boundary(pred_dice[batch_index], target_dice) * 1
loss = self.loss_segmentation(mask, label) + self.loss_boundary(mask_edge, seg_edge) * 10
loss = loss + pred_dice_score_loss
return loss
def get_dice_score(self, prev_masks, label, batch=False):
def compute_dice(mask_pred, mask_gt):
mask_threshold = 0.5
mask_pred = (mask_pred > mask_threshold)
mask_gt = (mask_gt > 0)
volume_sum = mask_gt.sum() + mask_pred.sum()
if volume_sum == 0:
return np.NaN
volume_intersect = (mask_gt & mask_pred).sum()
return 2 * volume_intersect / volume_sum
pred_masks = (prev_masks > 0.5)
true_masks = (label > 0)
dice_list = []
for i in range(true_masks.shape[0]):
dice_list.append(compute_dice(pred_masks[i], true_masks[i]))
if batch:
return dice_list
else:
return (sum(dice_list) / len(dice_list)).item()
def save_model(self, current_dice, epoch_num):
is_best = False
if np.mean(current_dice) > self.best_dice:
self.best_dice = np.mean(current_dice)
self.best_epoch = epoch_num
is_best = True
if not self.args.ddp or (self.args.ddp and self.args.rank == 0):
save_checkpoint({"epoch": epoch_num + 1,
"best_val_loss": self.best_dice,
"model_state_dict": self.sam.state_dict(),
"optimizer": self.optimizer.state_dict(),
"lr_scheduler": self.lr_scheduler.state_dict(),
},
is_best=is_best,
checkpoint=self.args.save_dir)
self.logger.info("- Val metrics best mean dice: {} at epoch {} " .format(self.best_dice, self.best_epoch))
def setup(self):
self.setup_loss()
self.setup_optimizier()
self.setup_scheduler()
if self.args.resume:
if self.args.ddp:
dist.barrier()
checkpoint = 'best.pth.tar' if self.args.resume_best else 'last.pth.tar'
ckpt = torch.load(os.path.join(self.args.save_dir, checkpoint))
self.start_epoch = ckpt["epoch"]
self.best_epoch = self.start_epoch
self.best_dice = ckpt["best_val_loss"]
self.sam.load_state_dict(ckpt["model_state_dict"], strict=True)
self.optimizer.load_state_dict(ckpt["optimizer"])
#self.lr_scheduler.load_state_dict(ckpt['lr_scheduler'])lr_scheduler_regular
self.lr_scheduler_regular.load_state_dict(ckpt['lr_scheduler'])
self.logger.info(f"Resume training from epoch {self.start_epoch}!")
del ckpt
torch.cuda.empty_cache()
def setup_loss(self):
self.loss_boundary = nn.MSELoss()
self.mse_none = nn.MSELoss(reduction='none')
self.loss_segmentation = DiceCELoss(sigmoid=True, squared_pred=True, reduction='mean')
self.loss_Dice = DiceLoss(sigmoid=True)
self.loss_validation = DiceLoss(sigmoid=True, reduction='none')
self.l1 = nn.L1Loss()
self.inter_loss = DiceCELoss(sigmoid=True, squared_pred=True, reduction='mean')
def setup_optimizier(self):
self.optimizer = AdamW([
{'params': self.sam.image_encoder.parameters()},
{'params': self.sam.prompt_encoder.parameters()},
{'params': self.sam.mask_decoder.parameters()},
], lr=self.args.lr)
def setup_scheduler(self):
if self.args.lr_scheduler == 'linear':
self.lr_scheduler_regular = lr_scheduler.LinearLR(self.optimizer, start_factor=1.0, end_factor=0.01, total_iters=500)
else:
self.lr_scheduler_regular = lr_scheduler.ExponentialLR(self.optimizer, gamma=0.98)
if self.args.warm_up:
self.linear_warmup_scheduler = lr_scheduler.LinearLR(self.optimizer, start_factor=0.01, end_factor=1.0, total_iters=10)
def update_lr(self, epoch, warmup_epoch=10, warm_up=False):
if warm_up:
if epoch < warmup_epoch:
self.lr_scheduler = self.linear_warmup_scheduler
else:
self.lr_scheduler = self.lr_scheduler_regular
else:
self.lr_scheduler = self.lr_scheduler_regular
self.lr_scheduler.step()
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