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trainer.py
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trainer.py
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import torch
import time, random, cv2, sys
from math import ceil
import numpy as np
from itertools import cycle
import torch.nn.functional as F
from torchvision.utils import make_grid
from torchvision import transforms
from base import BaseTrainer
from utils.helpers import colorize_mask
from utils.metrics import eval_metrics, AverageMeter
from tqdm import tqdm
from PIL import Image
from utils.helpers import DeNormalize
class Trainer(BaseTrainer):
def __init__(self, model, resume, config, supervised_loader, unsupervised_loader, iter_per_epoch,
val_loader=None, train_logger=None):
super(Trainer, self).__init__(model, resume, config, iter_per_epoch, train_logger)
self.supervised_loader = supervised_loader
self.unsupervised_loader = unsupervised_loader
self.val_loader = val_loader
self.ignore_index = self.val_loader.dataset.ignore_index
self.wrt_mode, self.wrt_step = 'train_', 0
self.log_step = config['trainer'].get('log_per_iter', int(np.sqrt(self.val_loader.batch_size)))
if config['trainer']['log_per_iter']:
self.log_step = int(self.log_step / self.val_loader.batch_size) + 1
self.num_classes = self.val_loader.dataset.num_classes
self.mode = self.model.module.mode
# TRANSORMS FOR VISUALIZATION
self.restore_transform = transforms.Compose([
DeNormalize(self.val_loader.MEAN, self.val_loader.STD),
transforms.ToPILImage()])
self.viz_transform = transforms.Compose([
transforms.Resize((400, 400)),
transforms.ToTensor()])
self.start_time = time.time()
def _train_epoch(self, epoch):
self.html_results.save()
self.logger.info('\n')
self.model.train()
if self.mode == 'supervised':
dataloader = iter(self.supervised_loader)
tbar = tqdm(range(len(self.supervised_loader)), ncols=135)
else:
dataloader = iter(zip(cycle(self.supervised_loader), self.unsupervised_loader))
tbar = tqdm(range(len(self.unsupervised_loader)), ncols=135)
self._reset_metrics()
for batch_idx in tbar:
if self.mode == 'supervised':
(input_l, target_l), (input_ul, target_ul) = next(dataloader), (None, None)
else:
(input_l, target_l), (input_ul, target_ul) = next(dataloader)
input_ul, target_ul = input_ul.cuda(non_blocking=True), target_ul.cuda(non_blocking=True)
input_l, target_l = input_l.cuda(non_blocking=True), target_l.cuda(non_blocking=True)
self.optimizer.zero_grad()
total_loss, cur_losses, outputs = self.model(x_l=input_l, target_l=target_l, x_ul=input_ul,
curr_iter=batch_idx, target_ul=target_ul, epoch=epoch-1)
total_loss = total_loss.mean()
total_loss.backward()
self.optimizer.step()
self._update_losses(cur_losses)
self._compute_metrics(outputs, target_l, target_ul, epoch-1)
logs = self._log_values(cur_losses)
if batch_idx % self.log_step == 0:
self.wrt_step = (epoch - 1) * len(self.unsupervised_loader) + batch_idx
self._write_scalars_tb(logs)
if batch_idx % int(len(self.unsupervised_loader)*0.9) == 0:
self._write_img_tb(input_l, target_l, input_ul, target_ul, outputs, epoch)
del input_l, target_l, input_ul, target_ul
del total_loss, cur_losses, outputs
tbar.set_description('T ({}) | Ls {:.2f} Lu {:.2f} Lw {:.2f} PW {:.2f} m1 {:.2f} m2 {:.2f}|'.format(
epoch, self.loss_sup.average, self.loss_unsup.average, self.loss_weakly.average,
self.pair_wise.average, self.mIoU_l, self.mIoU_ul))
self.lr_scheduler.step(epoch=epoch-1)
return logs
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'
total_loss_val = AverageMeter()
total_inter, total_union = 0, 0
total_correct, total_label = 0, 0
tbar = tqdm(self.val_loader, ncols=130)
with torch.no_grad():
val_visual = []
for batch_idx, (data, target) in enumerate(tbar):
target, data = target.cuda(non_blocking=True), data.cuda(non_blocking=True)
H, W = target.size(1), target.size(2)
up_sizes = (ceil(H / 8) * 8, ceil(W / 8) * 8)
pad_h, pad_w = up_sizes[0] - data.size(2), up_sizes[1] - data.size(3)
data = F.pad(data, pad=(0, pad_w, 0, pad_h), mode='reflect')
output = self.model(data)
output = output[:, :, :H, :W]
# LOSS
loss = F.cross_entropy(output, target, ignore_index=self.ignore_index)
total_loss_val.update(loss.item())
correct, labeled, inter, union = eval_metrics(output, target, self.num_classes, self.ignore_index)
total_inter, total_union = total_inter+inter, total_union+union
total_correct, total_label = total_correct+correct, total_label+labeled
# LIST OF IMAGE TO VIZ (15 images)
if len(val_visual) < 15:
if isinstance(data, list): data = data[0]
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 = 1.0 * total_correct / (np.spacing(1) + total_label)
IoU = 1.0 * total_inter / (np.spacing(1) + total_union)
mIoU = IoU.mean()
seg_metrics = {"Pixel_Accuracy": np.round(pixAcc, 3), "Mean_IoU": np.round(mIoU, 3),
"Class_IoU": dict(zip(range(self.num_classes), np.round(IoU, 3)))}
tbar.set_description('EVAL ({}) | Loss: {:.3f}, PixelAcc: {:.2f}, Mean IoU: {:.2f} |'.format( epoch,
total_loss_val.average, pixAcc, mIoU))
self._add_img_tb(val_visual, 'val')
# METRICS TO TENSORBOARD
self.wrt_step = (epoch) * len(self.val_loader)
self.writer.add_scalar(f'{self.wrt_mode}/loss', total_loss_val.average, self.wrt_step)
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': total_loss_val.average,
**seg_metrics
}
self.html_results.add_results(epoch=epoch, seg_resuts=log)
self.html_results.save()
if (time.time() - self.start_time) / 3600 > 22:
self._save_checkpoint(epoch, save_best=self.improved)
return log
def _reset_metrics(self):
self.loss_sup = AverageMeter()
self.loss_unsup = AverageMeter()
self.loss_weakly = AverageMeter()
self.pair_wise = AverageMeter()
self.total_inter_l, self.total_union_l = 0, 0
self.total_correct_l, self.total_label_l = 0, 0
self.total_inter_ul, self.total_union_ul = 0, 0
self.total_correct_ul, self.total_label_ul = 0, 0
self.mIoU_l, self.mIoU_ul = 0, 0
self.pixel_acc_l, self.pixel_acc_ul = 0, 0
self.class_iou_l, self.class_iou_ul = {}, {}
def _update_losses(self, cur_losses):
if "loss_sup" in cur_losses.keys():
self.loss_sup.update(cur_losses['loss_sup'].mean().item())
if "loss_unsup" in cur_losses.keys():
self.loss_unsup.update(cur_losses['loss_unsup'].mean().item())
if "loss_weakly" in cur_losses.keys():
self.loss_weakly.update(cur_losses['loss_weakly'].mean().item())
if "pair_wise" in cur_losses.keys():
self.pair_wise.update(cur_losses['pair_wise'].mean().item())
def _compute_metrics(self, outputs, target_l, target_ul, epoch):
seg_metrics_l = eval_metrics(outputs['sup_pred'], target_l, self.num_classes, self.ignore_index)
self._update_seg_metrics(*seg_metrics_l, True)
seg_metrics_l = self._get_seg_metrics(True)
self.pixel_acc_l, self.mIoU_l, self.class_iou_l = seg_metrics_l.values()
if self.mode == 'semi':
seg_metrics_ul = eval_metrics(outputs['unsup_pred'], target_ul, self.num_classes, self.ignore_index)
self._update_seg_metrics(*seg_metrics_ul, False)
seg_metrics_ul = self._get_seg_metrics(False)
self.pixel_acc_ul, self.mIoU_ul, self.class_iou_ul = seg_metrics_ul.values()
def _update_seg_metrics(self, correct, labeled, inter, union, supervised=True):
if supervised:
self.total_correct_l += correct
self.total_label_l += labeled
self.total_inter_l += inter
self.total_union_l += union
else:
self.total_correct_ul += correct
self.total_label_ul += labeled
self.total_inter_ul += inter
self.total_union_ul += union
def _get_seg_metrics(self, supervised=True):
if supervised:
pixAcc = 1.0 * self.total_correct_l / (np.spacing(1) + self.total_label_l)
IoU = 1.0 * self.total_inter_l / (np.spacing(1) + self.total_union_l)
else:
pixAcc = 1.0 * self.total_correct_ul / (np.spacing(1) + self.total_label_ul)
IoU = 1.0 * self.total_inter_ul / (np.spacing(1) + self.total_union_ul)
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)))
}
def _log_values(self, cur_losses):
logs = {}
if "loss_sup" in cur_losses.keys():
logs['loss_sup'] = self.loss_sup.average
if "loss_unsup" in cur_losses.keys():
logs['loss_unsup'] = self.loss_unsup.average
if "loss_weakly" in cur_losses.keys():
logs['loss_weakly'] = self.loss_weakly.average
if "pair_wise" in cur_losses.keys():
logs['pair_wise'] = self.pair_wise.average
logs['mIoU_labeled'] = self.mIoU_l
logs['pixel_acc_labeled'] = self.pixel_acc_l
if self.mode == 'semi':
logs['mIoU_unlabeled'] = self.mIoU_ul
logs['pixel_acc_unlabeled'] = self.pixel_acc_ul
return logs
def _write_scalars_tb(self, logs):
for k, v in logs.items():
if 'class_iou' not in k: self.writer.add_scalar(f'train/{k}', v, self.wrt_step)
for i, opt_group in enumerate(self.optimizer.param_groups):
self.writer.add_scalar(f'train/Learning_rate_{i}', opt_group['lr'], self.wrt_step)
current_rampup = self.model.module.unsup_loss_w.current_rampup
self.writer.add_scalar('train/Unsupervised_rampup', current_rampup, self.wrt_step)
def _add_img_tb(self, val_visual, wrt_mode):
val_img = []
palette = self.val_loader.dataset.palette
for imgs in val_visual:
imgs = [self.restore_transform(i) if (isinstance(i, torch.Tensor) and len(i.shape) == 3)
else colorize_mask(i, palette) for i in imgs]
imgs = [i.convert('RGB') for i in imgs]
imgs = [self.viz_transform(i) for i in imgs]
val_img.extend(imgs)
val_img = torch.stack(val_img, 0)
val_img = make_grid(val_img.cpu(), nrow=val_img.size(0)//len(val_visual), padding=5)
self.writer.add_image(f'{wrt_mode}/inputs_targets_predictions', val_img, self.wrt_step)
def _write_img_tb(self, input_l, target_l, input_ul, target_ul, outputs, epoch):
outputs_l_np = outputs['sup_pred'].data.max(1)[1].cpu().numpy()
targets_l_np = target_l.data.cpu().numpy()
imgs = [[i.data.cpu(), j, k] for i, j, k in zip(input_l, outputs_l_np, targets_l_np)]
self._add_img_tb(imgs, 'supervised')
if self.mode == 'semi':
outputs_ul_np = outputs['unsup_pred'].data.max(1)[1].cpu().numpy()
targets_ul_np = target_ul.data.cpu().numpy()
imgs = [[i.data.cpu(), j, k] for i, j, k in zip(input_ul, outputs_ul_np, targets_ul_np)]
self._add_img_tb(imgs, 'unsupervised')