414 lines
15 KiB
Python
414 lines
15 KiB
Python
# ------------------------------------------------------------------------
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# Copyright (c) 2022 megvii-model. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from BasicSR (https://github.com/xinntao/BasicSR)
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# Copyright 2018-2020 BasicSR Authors
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# ------------------------------------------------------------------------
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import importlib
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import torch
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import torch.nn.functional as F
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from collections import OrderedDict
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from copy import deepcopy
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from os import path as osp
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from tqdm import tqdm
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from basicsr.models.archs import define_network
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from basicsr.models.base_model import BaseModel
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from basicsr.utils import get_root_logger, imwrite, tensor2img
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from basicsr.utils.dist_util import get_dist_info
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loss_module = importlib.import_module('basicsr.models.losses')
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metric_module = importlib.import_module('basicsr.metrics')
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class ImageRestorationModel(BaseModel):
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"""Base Deblur model for single image deblur."""
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def __init__(self, opt):
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super(ImageRestorationModel, self).__init__(opt)
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# define network
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self.net_g = define_network(deepcopy(opt['network_g']))
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self.net_g = self.model_to_device(self.net_g)
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# load pretrained models
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load_path = self.opt['path'].get('pretrain_network_g', None)
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if load_path is not None:
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self.load_network(self.net_g, load_path,
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self.opt['path'].get('strict_load_g', True), param_key=self.opt['path'].get('param_key', 'params'))
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if self.is_train:
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self.init_training_settings()
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self.scale = int(opt['scale'])
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def init_training_settings(self):
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self.net_g.train()
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train_opt = self.opt['train']
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# define losses
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if train_opt.get('pixel_opt'):
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pixel_type = train_opt['pixel_opt'].pop('type')
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cri_pix_cls = getattr(loss_module, pixel_type)
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self.cri_pix = cri_pix_cls(**train_opt['pixel_opt']).to(
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self.device)
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else:
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self.cri_pix = None
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if train_opt.get('perceptual_opt'):
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percep_type = train_opt['perceptual_opt'].pop('type')
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cri_perceptual_cls = getattr(loss_module, percep_type)
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self.cri_perceptual = cri_perceptual_cls(
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**train_opt['perceptual_opt']).to(self.device)
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else:
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self.cri_perceptual = None
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if self.cri_pix is None and self.cri_perceptual is None:
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raise ValueError('Both pixel and perceptual losses are None.')
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# set up optimizers and schedulers
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self.setup_optimizers()
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self.setup_schedulers()
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def setup_optimizers(self):
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train_opt = self.opt['train']
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optim_params = []
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for k, v in self.net_g.named_parameters():
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if v.requires_grad:
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# if k.startswith('module.offsets') or k.startswith('module.dcns'):
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# optim_params_lowlr.append(v)
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# else:
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optim_params.append(v)
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# else:
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# logger = get_root_logger()
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# logger.warning(f'Params {k} will not be optimized.')
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# print(optim_params)
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# ratio = 0.1
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optim_type = train_opt['optim_g'].pop('type')
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if optim_type == 'Adam':
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self.optimizer_g = torch.optim.Adam([{'params': optim_params}],
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**train_opt['optim_g'])
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elif optim_type == 'SGD':
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self.optimizer_g = torch.optim.SGD(optim_params,
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**train_opt['optim_g'])
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elif optim_type == 'AdamW':
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self.optimizer_g = torch.optim.AdamW([{'params': optim_params}],
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**train_opt['optim_g'])
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pass
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else:
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raise NotImplementedError(
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f'optimizer {optim_type} is not supperted yet.')
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self.optimizers.append(self.optimizer_g)
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def feed_data(self, data, is_val=False):
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self.lq = data['lq'].to(self.device)
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if 'gt' in data:
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self.gt = data['gt'].to(self.device)
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def grids(self):
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b, c, h, w = self.gt.size()
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self.original_size = (b, c, h, w)
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assert b == 1
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if 'crop_size_h' in self.opt['val']:
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crop_size_h = self.opt['val']['crop_size_h']
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else:
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crop_size_h = int(self.opt['val'].get('crop_size_h_ratio') * h)
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if 'crop_size_w' in self.opt['val']:
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crop_size_w = self.opt['val'].get('crop_size_w')
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else:
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crop_size_w = int(self.opt['val'].get('crop_size_w_ratio') * w)
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crop_size_h, crop_size_w = crop_size_h // self.scale * self.scale, crop_size_w // self.scale * self.scale
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#adaptive step_i, step_j
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num_row = (h - 1) // crop_size_h + 1
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num_col = (w - 1) // crop_size_w + 1
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import math
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step_j = crop_size_w if num_col == 1 else math.ceil((w - crop_size_w) / (num_col - 1) - 1e-8)
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step_i = crop_size_h if num_row == 1 else math.ceil((h - crop_size_h) / (num_row - 1) - 1e-8)
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scale = self.scale
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step_i = step_i//scale*scale
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step_j = step_j//scale*scale
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parts = []
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idxes = []
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i = 0 # 0~h-1
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last_i = False
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while i < h and not last_i:
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j = 0
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if i + crop_size_h >= h:
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i = h - crop_size_h
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last_i = True
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last_j = False
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while j < w and not last_j:
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if j + crop_size_w >= w:
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j = w - crop_size_w
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last_j = True
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parts.append(self.lq[:, :, i // scale :(i + crop_size_h) // scale, j // scale:(j + crop_size_w) // scale])
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idxes.append({'i': i, 'j': j})
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j = j + step_j
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i = i + step_i
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self.origin_lq = self.lq
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self.lq = torch.cat(parts, dim=0)
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self.idxes = idxes
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def grids_inverse(self):
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preds = torch.zeros(self.original_size)
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b, c, h, w = self.original_size
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count_mt = torch.zeros((b, 1, h, w))
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if 'crop_size_h' in self.opt['val']:
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crop_size_h = self.opt['val']['crop_size_h']
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else:
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crop_size_h = int(self.opt['val'].get('crop_size_h_ratio') * h)
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if 'crop_size_w' in self.opt['val']:
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crop_size_w = self.opt['val'].get('crop_size_w')
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else:
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crop_size_w = int(self.opt['val'].get('crop_size_w_ratio') * w)
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crop_size_h, crop_size_w = crop_size_h // self.scale * self.scale, crop_size_w // self.scale * self.scale
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for cnt, each_idx in enumerate(self.idxes):
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i = each_idx['i']
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j = each_idx['j']
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preds[0, :, i: i + crop_size_h, j: j + crop_size_w] += self.outs[cnt]
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count_mt[0, 0, i: i + crop_size_h, j: j + crop_size_w] += 1.
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self.output = (preds / count_mt).to(self.device)
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self.lq = self.origin_lq
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def optimize_parameters(self, current_iter, tb_logger):
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self.optimizer_g.zero_grad()
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if self.opt['train'].get('mixup', False):
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self.mixup_aug()
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preds = self.net_g(self.lq)
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if not isinstance(preds, list):
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preds = [preds]
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self.output = preds[-1]
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l_total = 0
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loss_dict = OrderedDict()
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# pixel loss
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if self.cri_pix:
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l_pix = 0.
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for pred in preds:
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l_pix += self.cri_pix(pred, self.gt)
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# print('l pix ... ', l_pix)
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l_total += l_pix
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loss_dict['l_pix'] = l_pix
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# perceptual loss
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if self.cri_perceptual:
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l_percep, l_style = self.cri_perceptual(self.output, self.gt)
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#
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if l_percep is not None:
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l_total += l_percep
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loss_dict['l_percep'] = l_percep
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if l_style is not None:
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l_total += l_style
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loss_dict['l_style'] = l_style
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l_total = l_total + 0. * sum(p.sum() for p in self.net_g.parameters())
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l_total.backward()
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use_grad_clip = self.opt['train'].get('use_grad_clip', True)
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if use_grad_clip:
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torch.nn.utils.clip_grad_norm_(self.net_g.parameters(), 0.01)
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self.optimizer_g.step()
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self.log_dict = self.reduce_loss_dict(loss_dict)
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def test(self):
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self.net_g.eval()
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with torch.no_grad():
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n = len(self.lq)
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outs = []
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m = self.opt['val'].get('max_minibatch', n)
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i = 0
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while i < n:
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j = i + m
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if j >= n:
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j = n
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pred = self.net_g(self.lq[i:j])
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if isinstance(pred, list):
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pred = pred[-1]
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outs.append(pred.detach().cpu())
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i = j
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self.output = torch.cat(outs, dim=0)
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self.net_g.train()
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def dist_validation(self, dataloader, current_iter, tb_logger, save_img, rgb2bgr, use_image):
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dataset_name = dataloader.dataset.opt['name']
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with_metrics = self.opt['val'].get('metrics') is not None
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if with_metrics:
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self.metric_results = {
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metric: 0
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for metric in self.opt['val']['metrics'].keys()
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}
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rank, world_size = get_dist_info()
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if rank == 0:
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pbar = tqdm(total=len(dataloader), unit='image')
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cnt = 0
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for idx, val_data in enumerate(dataloader):
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if idx % world_size != rank:
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continue
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img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0]
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self.feed_data(val_data, is_val=True)
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if self.opt['val'].get('grids', False):
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self.grids()
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self.test()
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if self.opt['val'].get('grids', False):
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self.grids_inverse()
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visuals = self.get_current_visuals()
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sr_img = tensor2img([visuals['result']], rgb2bgr=rgb2bgr)
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if 'gt' in visuals:
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gt_img = tensor2img([visuals['gt']], rgb2bgr=rgb2bgr)
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del self.gt
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# tentative for out of GPU memory
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del self.lq
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del self.output
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torch.cuda.empty_cache()
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if save_img:
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if sr_img.shape[2] == 6:
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L_img = sr_img[:, :, :3]
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R_img = sr_img[:, :, 3:]
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# visual_dir = osp.join('visual_results', dataset_name, self.opt['name'])
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visual_dir = osp.join('visual_results', self.opt['name'], dataset_name)
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imwrite(L_img, osp.join(visual_dir, f'{img_name}_L.png'))
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imwrite(R_img, osp.join(visual_dir, f'{img_name}_R.png'))
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else:
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if self.opt['is_train']:
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save_img_path = osp.join(self.opt['path']['visualization'],
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img_name,
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f'{img_name}_{current_iter}.png')
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save_gt_img_path = osp.join(self.opt['path']['visualization'],
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img_name,
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f'{img_name}_{current_iter}_gt.png')
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else:
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save_img_path = osp.join(
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self.opt['path']['visualization'], dataset_name,
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f'{img_name}.png')
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save_gt_img_path = osp.join(
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self.opt['path']['visualization'], dataset_name,
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f'{img_name}_gt.png')
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imwrite(sr_img, save_img_path)
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imwrite(gt_img, save_gt_img_path)
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if with_metrics:
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# calculate metrics
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opt_metric = deepcopy(self.opt['val']['metrics'])
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if use_image:
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for name, opt_ in opt_metric.items():
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metric_type = opt_.pop('type')
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self.metric_results[name] += getattr(
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metric_module, metric_type)(sr_img, gt_img, **opt_)
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else:
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for name, opt_ in opt_metric.items():
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metric_type = opt_.pop('type')
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self.metric_results[name] += getattr(
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metric_module, metric_type)(visuals['result'], visuals['gt'], **opt_)
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cnt += 1
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if rank == 0:
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for _ in range(world_size):
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pbar.update(1)
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pbar.set_description(f'Test {img_name}')
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if rank == 0:
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pbar.close()
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# current_metric = 0.
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collected_metrics = OrderedDict()
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if with_metrics:
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for metric in self.metric_results.keys():
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collected_metrics[metric] = torch.tensor(self.metric_results[metric]).float().to(self.device)
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collected_metrics['cnt'] = torch.tensor(cnt).float().to(self.device)
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self.collected_metrics = collected_metrics
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keys = []
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metrics = []
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for name, value in self.collected_metrics.items():
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keys.append(name)
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metrics.append(value)
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metrics = torch.stack(metrics, 0)
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torch.distributed.reduce(metrics, dst=0)
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if self.opt['rank'] == 0:
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metrics_dict = {}
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cnt = 0
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for key, metric in zip(keys, metrics):
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if key == 'cnt':
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cnt = float(metric)
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continue
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metrics_dict[key] = float(metric)
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for key in metrics_dict:
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metrics_dict[key] /= cnt
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self._log_validation_metric_values(current_iter, dataloader.dataset.opt['name'],
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tb_logger, metrics_dict)
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return 0.
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def nondist_validation(self, *args, **kwargs):
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logger = get_root_logger()
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logger.warning('nondist_validation is not implemented. Run dist_validation.')
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self.dist_validation(*args, **kwargs)
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def _log_validation_metric_values(self, current_iter, dataset_name,
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tb_logger, metric_dict):
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log_str = f'Validation {dataset_name}, \t'
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for metric, value in metric_dict.items():
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log_str += f'\t # {metric}: {value:.4f}'
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logger = get_root_logger()
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logger.info(log_str)
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log_dict = OrderedDict()
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# for name, value in loss_dict.items():
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for metric, value in metric_dict.items():
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log_dict[f'm_{metric}'] = value
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self.log_dict = log_dict
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def get_current_visuals(self):
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out_dict = OrderedDict()
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out_dict['lq'] = self.lq.detach().cpu()
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out_dict['result'] = self.output.detach().cpu()
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if hasattr(self, 'gt'):
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out_dict['gt'] = self.gt.detach().cpu()
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return out_dict
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def save(self, epoch, current_iter):
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self.save_network(self.net_g, 'net_g', current_iter)
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self.save_training_state(epoch, current_iter)
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