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import os
import time
import numpy as np
from PIL import Image
import torch
from torch.autograd import Variable
import torchtask
from torchtask.utils import logger, cmd, tool
from torchtask.nn import func
def add_parser_arguments(parser):
torchtask.trainer_template.add_parser_arguments(parser)
def harmonizer_trainer(args, model_dict, optimizer_dict, lrer_dict, criterion_dict, task_func):
model_funcs = [model_dict['model']]
optimizer_funcs = [optimizer_dict['model']]
lrer_funcs = [lrer_dict['model']]
criterion_funcs = [criterion_dict['model']]
algorithm = HarmonizerTrainer(args)
algorithm.build(model_funcs, optimizer_funcs, lrer_funcs, criterion_funcs, task_func)
return algorithm
class HarmonizerTrainer(torchtask.trainer_template.TaskTrainer):
def __init__(self, args):
super(HarmonizerTrainer, self).__init__(args)
self.model = None
self.optimizer = None
self.lrer = None
self.criterion = None
def _build(self, model_funcs, optimizer_funcs, lrer_funcs, criterion_funcs, task_func):
self.task_func = task_func
self.model = func.create_model(model_funcs[0], 'model', args=self.args)
self.models = {'model': self.model}
self.optimizer = optimizer_funcs[0](self.model.module.param_groups)
self.optimizers = {'optimizer': self.optimizer}
self.lrer = lrer_funcs[0](self.optimizer)
self.lrers = {'lrer': self.lrer}
self.criterion = criterion_funcs[0](self.args)
self.criterions = {'criterion': self.criterion}
def _train(self, data_loader, epoch):
self.meters.reset()
lbs = self.args.labeled_batch_size
self.model.train()
timer = time.time()
for idx, (inp, gt) in enumerate(data_loader):
# pre-process input tensor and ground truth tensor
inp, gt = self._batch_prehandle(inp, gt, True)
x, mask = inp
# forword the model
self.optimizer.zero_grad()
resulter, debugger = self.model(inp)
pred_outputs = tool.dict_value(resulter, 'outputs')
# calculate loss for the fine labeled data
l_pred_outputs = func.split_tensor_tuple(pred_outputs, 0, lbs)
l_pred = (l_pred_outputs, )
l_gt = func.split_tensor_tuple(gt, 0, lbs)
l_inp = func.split_tensor_tuple(inp, 0, lbs)
l_image_losses = self.criterion(l_pred, l_gt, l_inp)
# if self.args.dynamic_loss:
sum_losses = l_image_losses[0].detach()
for i in range(1, len(l_image_losses)):
sum_losses = sum_losses + \
(l_image_losses[i].detach() - l_image_losses[i-1].detach()) * ((l_image_losses[i].detach() - l_image_losses[i-1].detach()) > 0).float()
sum_losses = sum_losses + 1e-9
sum_losses = sum_losses.detach()
scaled_l_image_losses = [torch.mean(l_image_losses[0] / sum_losses)]
self.meters.update('fine_filter_0_loss', torch.mean(l_image_losses[0] / sum_losses).item())
for i in range(1, len(l_image_losses)):
loss = (l_image_losses[i] - l_image_losses[i-1].detach()) / sum_losses
loss = loss * (loss > 0).float()
loss = torch.mean(loss)
scaled_l_image_losses.append(loss)
self.meters.update('fine_filter_{0}_loss'.format(i), loss.item())
# calculate loss for the coarse labeled data
if not self.args.ignore_additional:
u_pred_outputs = func.split_tensor_tuple(pred_outputs, lbs, self.args.batch_size)
u_pred_outputs = (u_pred_outputs[-1], )
u_pred = (u_pred_outputs, )
u_gt = func.split_tensor_tuple(gt, lbs, self.args.batch_size)
u_gt = (u_gt[-1], )
u_inp = func.split_tensor_tuple(inp, lbs, self.args.batch_size)
u_image_losses = self.criterion(u_pred, u_gt, u_inp)
u_image_loss = torch.mean(u_image_losses[0]) * 10
self.meters.update('coarse_filter_loss', u_image_loss.item())
else:
self.meters.update('coarse_filter_loss', torch.mean(torch.zeros(1)).item())
# calculate the sum of all losses
loss = 0
for l_image_loss in scaled_l_image_losses:
loss = loss + l_image_loss
loss = loss + u_image_loss
# backward and update
loss.backward()
self.optimizer.step()
# logging
self.meters.update('batch_time', time.time() - timer)
if idx % self.args.log_freq == 0:
logger.log_info('step: [{0}][{1}/{2}]\tbatch-time: {meters[batch_time]:.3f}'.format(epoch+1, idx, len(data_loader), meters=self.meters))
logger.log_info('\tfine-filter-0-loss: {meters[fine_filter_0_loss]:.6f}'.format(meters=self.meters))
logger.log_info('\tfine-filter-1-loss: {meters[fine_filter_1_loss]:.6f}'.format(meters=self.meters))
logger.log_info('\tfine-filter-2-loss: {meters[fine_filter_2_loss]:.6f}'.format(meters=self.meters))
logger.log_info('\tfine-filter-3-loss: {meters[fine_filter_3_loss]:.6f}'.format(meters=self.meters))
logger.log_info('\tfine-filter-4-loss: {meters[fine_filter_4_loss]:.6f}'.format(meters=self.meters))
logger.log_info('\tfine-filter-5-loss: {meters[fine_filter_5_loss]:.6f}'.format(meters=self.meters))
logger.log_info('\tcoarse-filter-loss: {meters[coarse_filter_loss]:.6f}'.format(meters=self.meters))
# visualization
if self.args.visualize and idx % self.args.visual_freq == 0:
self._visualization(
epoch, idx, True,
func.split_tensor_tuple(inp, 0, 1, reduce_dim=True),
func.split_tensor_tuple(pred_outputs, 0, 1, reduce_dim=True),
func.split_tensor_tuple(gt, 0, 1, reduce_dim=True))
# update iteration-based lrers
if not self.args.is_epoch_lrer:
self.lrer.step()
timer = time.time()
# update epoch-based lrers
if self.args.is_epoch_lrer:
self.lrer.step()
def _validate(self, data_loader, epoch):
self.meters.reset()
self.model.eval()
timer = time.time()
for idx, (inp, gt) in enumerate(data_loader):
inp, gt = self._batch_prehandle(inp, gt, False)
x, mask = inp
resulter, debugger = self.model(inp)
pred_outputs = tool.dict_value(resulter, 'outputs')
pred = (pred_outputs[-1], )
gt = (gt[-1], )
# calculate loss for the fine labeled data
losses = self.criterion.forward(pred, gt, inp)
loss = 0
for _loss in losses:
loss = loss + _loss
loss = loss / len(losses)
self.meters.update('loss', loss.item())
self.task_func.metrics(pred_outputs[-1].detach(), gt[-1], mask, self.meters, id_str='IH')
self.meters.update('batch_time', time.time() - timer)
if idx % self.args.log_freq == 0:
logger.log_info('step: [{0}][{1}/{2}]\tbatch-time: {meters[batch_time]:.3f}\n'
'loss: {meters[loss]:.6f}\n'
.format(epoch+1, idx, len(data_loader), meters=self.meters))
if self.args.visualize:
self._visualization(
epoch, idx, False,
func.split_tensor_tuple(inp, 0, 1, reduce_dim=True),
func.split_tensor_tuple((pred_outputs[-1], ), 0, 1, reduce_dim=True),
func.split_tensor_tuple(gt, 0, 1, reduce_dim=True))
timer = time.time()
metrics_info = {'IH': ''}
for key in sorted(list(self.meters.keys())):
if self.task_func.METRIC_STR in key:
for id_str in metrics_info.keys():
if key.startswith(id_str):
metrics_info[id_str] += '{0}: {1:.6}\t'.format(key, self.meters[key])
logger.log_info('Validation metrics:\n task-metrics\t=>\t{0}\n'.format(metrics_info['IH'].replace('_', '-')))
def _visualization(self, epoch, idx, is_train, inp, pred, gt):
visualize_path = self.args.visual_train_path if is_train else self.args.visual_val_path
out_path = os.path.join(visualize_path, '{0}_{1}'.format(epoch, idx))
x, mask = inp
x = (np.transpose(x.cpu().numpy(), (1, 2, 0)))
Image.fromarray((x * 255).astype('uint8')).save(out_path + '_1_0_x.jpg')
mask = mask[0].data.cpu().numpy()
Image.fromarray((mask * 255).astype('uint8'), mode='L').save(out_path + '_2_0_mask.jpg')
for idx, (pred_, gt_) in enumerate(zip(pred, gt)):
pred_ = (np.transpose(pred_.detach().cpu().numpy(), (1, 2, 0)))
Image.fromarray((pred_ * 255).astype('uint8')).save(out_path + '_1_{0}_pred_filter.jpg'.format(idx+1))
if torch.mean(gt_) != -999:
gt_ = (np.transpose(gt_.cpu().numpy(), (1, 2, 0)))
Image.fromarray((gt_ * 255).astype('uint8')).save(out_path + '_2_{0}_gt_filter.jpg'.format(idx+1))
def _save_checkpoint(self, epoch):
state = {
'epoch': epoch,
'model': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'lrer': self.lrer.state_dict(),
}
checkpoint = os.path.join(self.args.checkpoint_path, 'checkpoint_{0}.ckpt'.format(epoch))
torch.save(state, checkpoint)
def _load_checkpoint(self):
checkpoint = torch.load(self.args.resume)
self.model.load_state_dict(checkpoint['model'])
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.lrer.load_state_dict(checkpoint['lrer'])
return checkpoint['epoch']
def _batch_prehandle(self, inp, gt, is_train):
lbs = self.args.labeled_batch_size
ubs = self.args.additional_batch_size
# convert all input and ground truth to Variables
inp_var = []
for i in inp:
inp_var.append(Variable(i).cuda())
inp = tuple(inp_var)
gt_var = []
for g in gt:
gt_var.append(Variable(g).cuda())
gt = tuple(gt_var)
filter_num = len(self.model.module.model.filter_types)
if is_train:
# ----------------------------------------------------------------
# for fine labeled data, we generate the adjusted input
# ----------------------------------------------------------------
l_inp = func.split_tensor_tuple(inp, 0, lbs)
l_gt = func.split_tensor_tuple(gt, 0, lbs)
_, l_mask = l_inp
l_gt_image, = l_gt
n = l_gt_image.shape[0]
l_rand_arguments = [self._rand_adjustment_values(n) for _ in range(0, filter_num)]
l_x = self.model.module.adjust(l_gt_image, l_mask, l_rand_arguments)
l_inp = (l_x[-1], l_mask)
l_gt = []
for _ in reversed(l_x[:-1]):
l_gt.append(_)
l_gt.append(l_gt_image)
if not self.args.ignore_additional:
# ----------------------------------------------------------------
# for coarse labeled data, we use the existising adjusted input
# ----------------------------------------------------------------
u_inp = func.split_tensor_tuple(inp, lbs, self.args.batch_size)
u_gt = func.split_tensor_tuple(gt, lbs, self.args.batch_size)
u_gt_image, = u_gt
none_value = torch.ones(ubs).view(ubs, 1).cuda() * -999
none_im = u_gt_image.cuda() * 0 - 999
u_gt = [none_im for _ in range(0, filter_num)]
u_gt[-1] = u_gt_image
inp = func.combine_tensor_tuple(l_inp, u_inp, 0)
gt = func.combine_tensor_tuple(l_gt, u_gt, 0)
else:
inp = l_inp
gt = l_gt
else:
gt_image, = gt
none_value = torch.ones(1).view(1, 1).cuda() * -999
none_im = gt_image.cuda() * 0 - 999
gt = [none_im for _ in range(0, filter_num)]
gt[-1] = gt_image
return inp, gt
def _rand_adjustment_values(self, n):
x = torch.FloatTensor(np.random.uniform(-1, 1, n))
x = x.view(n, 1).cuda()
return x
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