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trainer.py
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trainer.py
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import torch
import time
import numpy as np
from torchvision.utils import make_grid
from torchvision import transforms
from utils import transforms as local_transforms
from base import BaseTrainer, DataPrefetcher
from utils.helpers import colorize_mask
from utils.metrics import eval_metrics, AverageMeter
from tqdm import tqdm
class Trainer(BaseTrainer):
def __init__(self, model, loss, resume, config, train_loader, val_loader=None, train_logger=None, prefetch=True):
super(Trainer, self).__init__(model, loss, resume, config, train_loader, val_loader, train_logger)
self.wrt_mode, self.wrt_step = 'train_', 0
self.log_step = config['trainer'].get('log_per_iter', int(np.sqrt(self.train_loader.batch_size)))
if config['trainer']['log_per_iter']: self.log_step = int(self.log_step / self.train_loader.batch_size) + 1
self.num_classes = self.train_loader.dataset.num_classes
# TRANSORMS FOR VISUALIZATION
self.restore_transform = transforms.Compose([
local_transforms.DeNormalize(self.train_loader.MEAN, self.train_loader.STD),
transforms.ToPILImage()])
self.viz_transform = transforms.Compose([
transforms.Resize((400, 400)),
transforms.ToTensor()])
if self.device == torch.device('cpu'): prefetch = False
if prefetch:
self.train_loader = DataPrefetcher(train_loader, device=self.device)
self.val_loader = DataPrefetcher(val_loader, device=self.device)
torch.backends.cudnn.benchmark = True
def _train_epoch(self, epoch):
self.logger.info('\n')
self.model.train()
if self.config['arch']['args']['freeze_bn']:
if isinstance(self.model, torch.nn.DataParallel): self.model.module.freeze_bn()
else: self.model.freeze_bn()
self.wrt_mode = 'train'
tic = time.time()
self._reset_metrics()
tbar = tqdm(self.train_loader, ncols=130)
for batch_idx, (data, target) in enumerate(tbar):
self.data_time.update(time.time() - tic)
#data, target = data.to(self.device), target.to(self.device)
self.lr_scheduler.step(epoch=epoch-1)
# LOSS & OPTIMIZE
self.optimizer.zero_grad()
output = self.model(data)
if self.config['arch']['type'][:3] == 'PSP':
assert output[0].size()[2:] == target.size()[1:]
assert output[0].size()[1] == self.num_classes
loss = self.loss(output[0], target)
loss += self.loss(output[1], target) * 0.4
output = output[0]
else:
assert output.size()[2:] == target.size()[1:]
assert output.size()[1] == self.num_classes
loss = self.loss(output, target)
if isinstance(self.loss, torch.nn.DataParallel):
loss = loss.mean()
loss.backward()
self.optimizer.step()
self.total_loss.update(loss.item())
# measure elapsed time
self.batch_time.update(time.time() - tic)
tic = time.time()
# LOGGING & TENSORBOARD
if batch_idx % self.log_step == 0:
self.wrt_step = (epoch - 1) * len(self.train_loader) + batch_idx
self.writer.add_scalar(f'{self.wrt_mode}/loss', loss.item(), self.wrt_step)
# FOR EVAL
seg_metrics = eval_metrics(output, target, self.num_classes)
self._update_seg_metrics(*seg_metrics)
pixAcc, mIoU, _ = self._get_seg_metrics().values()
# PRINT INFO
tbar.set_description('TRAIN ({}) | Loss: {:.3f} | Acc {:.2f} mIoU {:.2f} | B {:.2f} D {:.2f} |'.format(
epoch, self.total_loss.average,
pixAcc, mIoU,
self.batch_time.average, self.data_time.average))
# METRICS TO TENSORBOARD
seg_metrics = self._get_seg_metrics()
for k, v in list(seg_metrics.items())[:-1]:
self.writer.add_scalar(f'{self.wrt_mode}/{k}', v, self.wrt_step)
for i, opt_group in enumerate(self.optimizer.param_groups):
self.writer.add_scalar(f'{self.wrt_mode}/Learning_rate_{i}', opt_group['lr'], self.wrt_step)
#self.writer.add_scalar(f'{self.wrt_mode}/Momentum_{k}', opt_group['momentum'], self.wrt_step)
# RETURN LOSS & METRICS
log = {'loss': self.total_loss.average,
**seg_metrics}
#if self.lr_scheduler is not None: self.lr_scheduler.step()
return log
def _valid_epoch(self, epoch):
if self.val_loader is None:
self.logger.warning('Not data loader was passed for the validation step, No validation is performed !')
return {}
self.logger.info('\n###### EVALUATION ######')
self.model.eval()
self.wrt_mode = 'val'
self._reset_metrics()
tbar = tqdm(self.val_loader, ncols=130)
with torch.no_grad():
val_visual = []
for batch_idx, (data, target) in enumerate(tbar):
#data, target = data.to(self.device), target.to(self.device)
# LOSS
output = self.model(data)
loss = self.loss(output, target)
if isinstance(self.loss, torch.nn.DataParallel):
loss = loss.mean()
self.total_loss.update(loss.item())
seg_metrics = eval_metrics(output, target, self.num_classes)
self._update_seg_metrics(*seg_metrics)
# LIST OF IMAGE TO VIZ (15 images)
if len(val_visual) < 15:
target_np = target.data.cpu().numpy()
output_np = output.data.max(1)[1].cpu().numpy()
val_visual.append([data[0].data.cpu(), target_np[0], output_np[0]])
# PRINT INFO
pixAcc, mIoU, _ = self._get_seg_metrics().values()
tbar.set_description('EVAL ({}) | Loss: {:.3f}, PixelAcc: {:.2f}, Mean IoU: {:.2f} |'.format( epoch,
self.total_loss.average,
pixAcc, mIoU))
# WRTING & VISUALIZING THE MASKS
val_img = []
palette = self.train_loader.dataset.palette
for d, t, o in val_visual:
d = self.restore_transform(d)
t, o = colorize_mask(t, palette), colorize_mask(o, palette)
d, t, o = d.convert('RGB'), t.convert('RGB'), o.convert('RGB')
[d, t, o] = [self.viz_transform(x) for x in [d, t, o]]
val_img.extend([d, t, o])
val_img = torch.stack(val_img, 0)
val_img = make_grid(val_img.cpu(), nrow=3, padding=5)
self.writer.add_image(f'{self.wrt_mode}/inputs_targets_predictions', val_img, self.wrt_step)
# METRICS TO TENSORBOARD
self.wrt_step = (epoch) * len(self.val_loader)
self.writer.add_scalar(f'{self.wrt_mode}/loss', self.total_loss.average, self.wrt_step)
seg_metrics = self._get_seg_metrics()
for k, v in list(seg_metrics.items())[:-1]:
self.writer.add_scalar(f'{self.wrt_mode}/{k}', v, self.wrt_step)
log = {
'val_loss': self.total_loss.average,
**seg_metrics
}
return log
def _reset_metrics(self):
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.total_loss = AverageMeter()
self.total_inter, self.total_union = 0, 0
self.total_correct, self.total_label = 0, 0
def _update_seg_metrics(self, correct, labeled, inter, union):
self.total_correct += correct
self.total_label += labeled
self.total_inter += inter
self.total_union += union
def _get_seg_metrics(self):
pixAcc = 1.0 * self.total_correct / (np.spacing(1) + self.total_label)
IoU = 1.0 * self.total_inter / (np.spacing(1) + self.total_union)
mIoU = IoU.mean()
return {
"Pixel_Accuracy": np.round(pixAcc, 3),
"Mean_IoU": np.round(mIoU, 3),
"Class_IoU": dict(zip(range(self.num_classes), np.round(IoU, 3)))
}