# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import os.path as osp from collections import OrderedDict import numpy as np import cv2 import paddle import paddle.nn.functional as F from paddle.static import InputSpec import paddlers import paddlers.models.ppgan as ppgan import paddlers.rs_models.res as cmres import paddlers.models.ppgan.metrics as metrics import paddlers.utils.logging as logging from paddlers.models import res_losses from paddlers.transforms import Resize, decode_image from paddlers.transforms.functions import calc_hr_shape from paddlers.utils import get_single_card_bs from paddlers.utils.checkpoint import res_pretrain_weights_dict from .base import BaseModel from .utils.res_adapters import GANAdapter, OptimizerAdapter from .utils.infer_nets import InferResNet __all__ = ["DRN", "LESRCNN", "ESRGAN"] class BaseRestorer(BaseModel): MIN_MAX = (0., 1.) TEST_OUT_KEY = None def __init__(self, model_name, losses=None, sr_factor=None, **params): self.init_params = locals() if 'with_net' in self.init_params: del self.init_params['with_net'] super(BaseRestorer, self).__init__('restorer') self.model_name = model_name self.losses = losses self.sr_factor = sr_factor if params.get('with_net', True): params.pop('with_net', None) self.net = self.build_net(**params) self.find_unused_parameters = True def build_net(self, **params): # Currently, only use models from cmres. if not hasattr(cmres, self.model_name): raise ValueError("ERROR: There is no model named {}.".format( model_name)) net = dict(**cmres.__dict__)[self.model_name](**params) return net def _build_inference_net(self): # For GAN models, only the generator will be used for inference. if isinstance(self.net, GANAdapter): infer_net = InferResNet( self.net.generator, out_key=self.TEST_OUT_KEY) else: infer_net = InferResNet(self.net, out_key=self.TEST_OUT_KEY) infer_net.eval() return infer_net def _fix_transforms_shape(self, image_shape): if hasattr(self, 'test_transforms'): if self.test_transforms is not None: has_resize_op = False resize_op_idx = -1 normalize_op_idx = len(self.test_transforms.transforms) for idx, op in enumerate(self.test_transforms.transforms): name = op.__class__.__name__ if name == 'Normalize': normalize_op_idx = idx if 'Resize' in name: has_resize_op = True resize_op_idx = idx if not has_resize_op: self.test_transforms.transforms.insert( normalize_op_idx, Resize(target_size=image_shape)) else: self.test_transforms.transforms[resize_op_idx] = Resize( target_size=image_shape) def _get_test_inputs(self, image_shape): if image_shape is not None: if len(image_shape) == 2: image_shape = [1, 3] + image_shape self._fix_transforms_shape(image_shape[-2:]) else: image_shape = [None, 3, -1, -1] self.fixed_input_shape = image_shape input_spec = [ InputSpec( shape=image_shape, name='image', dtype='float32') ] return input_spec def run(self, net, inputs, mode): outputs = OrderedDict() if mode == 'test': tar_shape = inputs[1] if self.status == 'Infer': net_out = net(inputs[0]) res_map_list = self.postprocess( net_out, tar_shape, transforms=inputs[2]) else: if isinstance(net, GANAdapter): net_out = net.generator(inputs[0]) else: net_out = net(inputs[0]) if self.TEST_OUT_KEY is not None: net_out = net_out[self.TEST_OUT_KEY] pred = self.postprocess( net_out, tar_shape, transforms=inputs[2]) res_map_list = [] for res_map in pred: res_map = self._tensor_to_images(res_map) res_map_list.append(res_map) outputs['res_map'] = res_map_list if mode == 'eval': if isinstance(net, GANAdapter): net_out = net.generator(inputs[0]) else: net_out = net(inputs[0]) if self.TEST_OUT_KEY is not None: net_out = net_out[self.TEST_OUT_KEY] tar = inputs[1] tar_shape = [tar.shape[-2:]] pred = self.postprocess( net_out, tar_shape, transforms=inputs[2])[0] # NCHW pred = self._tensor_to_images(pred) outputs['pred'] = pred tar = self._tensor_to_images(tar) outputs['tar'] = tar if mode == 'train': # This is used by non-GAN models. # For GAN models, self.run_gan() should be used. net_out = net(inputs[0]) loss = self.losses(net_out, inputs[1]) outputs['loss'] = loss return outputs def run_gan(self, net, inputs, mode, gan_mode): raise NotImplementedError def default_loss(self): return res_losses.L1Loss() def default_optimizer(self, parameters, learning_rate, num_epochs, num_steps_each_epoch, lr_decay_power=0.9): decay_step = num_epochs * num_steps_each_epoch lr_scheduler = paddle.optimizer.lr.PolynomialDecay( learning_rate, decay_step, end_lr=0, power=lr_decay_power) optimizer = paddle.optimizer.Momentum( learning_rate=lr_scheduler, parameters=parameters, momentum=0.9, weight_decay=4e-5) return optimizer def train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, optimizer=None, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrain_weights=None, learning_rate=0.01, lr_decay_power=0.9, early_stop=False, early_stop_patience=5, use_vdl=True, resume_checkpoint=None): """ Train the model. Args: num_epochs (int): Number of epochs. train_dataset (paddlers.datasets.ResDataset): Training dataset. train_batch_size (int, optional): Total batch size among all cards used in training. Defaults to 2. eval_dataset (paddlers.datasets.ResDataset|None, optional): Evaluation dataset. If None, the model will not be evaluated during training process. Defaults to None. optimizer (paddle.optimizer.Optimizer|None, optional): Optimizer used in training. If None, a default optimizer will be used. Defaults to None. save_interval_epochs (int, optional): Epoch interval for saving the model. Defaults to 1. log_interval_steps (int, optional): Step interval for printing training information. Defaults to 2. save_dir (str, optional): Directory to save the model. Defaults to 'output'. pretrain_weights (str|None, optional): None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to None. learning_rate (float, optional): Learning rate for training. Defaults to .01. lr_decay_power (float, optional): Learning decay power. Defaults to .9. early_stop (bool, optional): Whether to adopt early stop strategy. Defaults to False. early_stop_patience (int, optional): Early stop patience. Defaults to 5. use_vdl (bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True. resume_checkpoint (str|None, optional): Path of the checkpoint to resume training from. If None, no training checkpoint will be resumed. At most Aone of `resume_checkpoint` and `pretrain_weights` can be set simultaneously. Defaults to None. """ if self.status == 'Infer': logging.error( "Exported inference model does not support training.", exit=True) if pretrain_weights is not None and resume_checkpoint is not None: logging.error( "`pretrain_weights` and `resume_checkpoint` cannot be set simultaneously.", exit=True) if self.losses is None: self.losses = self.default_loss() if optimizer is None: num_steps_each_epoch = train_dataset.num_samples // train_batch_size if isinstance(self.net, GANAdapter): parameters = {'params_g': [], 'params_d': []} for net_g in self.net.generators: parameters['params_g'].append(net_g.parameters()) for net_d in self.net.discriminators: parameters['params_d'].append(net_d.parameters()) else: parameters = self.net.parameters() self.optimizer = self.default_optimizer( parameters, learning_rate, num_epochs, num_steps_each_epoch, lr_decay_power) else: self.optimizer = optimizer if pretrain_weights is not None: if not osp.exists(pretrain_weights): if self.model_name not in res_pretrain_weights_dict: logging.warning( "Path of pretrained weights ('{}') does not exist!". format(pretrain_weights)) pretrain_weights = None elif pretrain_weights not in res_pretrain_weights_dict[ self.model_name]: logging.warning( "Path of pretrained weights ('{}') does not exist!". format(pretrain_weights)) pretrain_weights = res_pretrain_weights_dict[ self.model_name][0] logging.warning( "`pretrain_weights` is forcibly set to '{}'. " "If you don't want to use pretrained weights, " "please set `pretrain_weights` to None.".format( pretrain_weights)) else: if osp.splitext(pretrain_weights)[-1] != '.pdparams': logging.error( "Invalid pretrained weights. Please specify a .pdparams file.", exit=True) pretrained_dir = osp.join(save_dir, 'pretrain') is_backbone_weights = pretrain_weights == 'IMAGENET' self.initialize_net( pretrain_weights=pretrain_weights, save_dir=pretrained_dir, resume_checkpoint=resume_checkpoint, is_backbone_weights=is_backbone_weights) self.train_loop( num_epochs=num_epochs, train_dataset=train_dataset, train_batch_size=train_batch_size, eval_dataset=eval_dataset, save_interval_epochs=save_interval_epochs, log_interval_steps=log_interval_steps, save_dir=save_dir, early_stop=early_stop, early_stop_patience=early_stop_patience, use_vdl=use_vdl) def quant_aware_train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, optimizer=None, save_interval_epochs=1, log_interval_steps=2, save_dir='output', learning_rate=0.0001, lr_decay_power=0.9, early_stop=False, early_stop_patience=5, use_vdl=True, resume_checkpoint=None, quant_config=None): """ Quantization-aware training. Args: num_epochs (int): Number of epochs. train_dataset (paddlers.datasets.ResDataset): Training dataset. train_batch_size (int, optional): Total batch size among all cards used in training. Defaults to 2. eval_dataset (paddlers.datasets.ResDataset|None, optional): Evaluation dataset. If None, the model will not be evaluated during training process. Defaults to None. optimizer (paddle.optimizer.Optimizer|None, optional): Optimizer used in training. If None, a default optimizer will be used. Defaults to None. save_interval_epochs (int, optional): Epoch interval for saving the model. Defaults to 1. log_interval_steps (int, optional): Step interval for printing training information. Defaults to 2. save_dir (str, optional): Directory to save the model. Defaults to 'output'. learning_rate (float, optional): Learning rate for training. Defaults to .0001. lr_decay_power (float, optional): Learning decay power. Defaults to .9. early_stop (bool, optional): Whether to adopt early stop strategy. Defaults to False. early_stop_patience (int, optional): Early stop patience. Defaults to 5. use_vdl (bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True. quant_config (dict|None, optional): Quantization configuration. If None, a default rule of thumb configuration will be used. Defaults to None. resume_checkpoint (str|None, optional): Path of the checkpoint to resume quantization-aware training from. If None, no training checkpoint will be resumed. Defaults to None. """ self._prepare_qat(quant_config) self.train( num_epochs=num_epochs, train_dataset=train_dataset, train_batch_size=train_batch_size, eval_dataset=eval_dataset, optimizer=optimizer, save_interval_epochs=save_interval_epochs, log_interval_steps=log_interval_steps, save_dir=save_dir, pretrain_weights=None, learning_rate=learning_rate, lr_decay_power=lr_decay_power, early_stop=early_stop, early_stop_patience=early_stop_patience, use_vdl=use_vdl, resume_checkpoint=resume_checkpoint) def evaluate(self, eval_dataset, batch_size=1, return_details=False): """ Evaluate the model. Args: eval_dataset (paddlers.datasets.ResDataset): Evaluation dataset. batch_size (int, optional): Total batch size among all cards used for evaluation. Defaults to 1. return_details (bool, optional): Whether to return evaluation details. Defaults to False. Returns: If `return_details` is False, return collections.OrderedDict with key-value pairs: {"psnr": peak signal-to-noise ratio, "ssim": structural similarity}. """ self._check_transforms(eval_dataset.transforms, 'eval') self.net.eval() nranks = paddle.distributed.get_world_size() local_rank = paddle.distributed.get_rank() if nranks > 1: # Initialize parallel environment if not done. if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized( ): paddle.distributed.init_parallel_env() # TODO: Distributed evaluation if batch_size > 1: logging.warning( "Restorer only supports single card evaluation with batch_size=1 " "during evaluation, so batch_size is forcibly set to 1.") batch_size = 1 if nranks < 2 or local_rank == 0: self.eval_data_loader = self.build_data_loader( eval_dataset, batch_size=batch_size, mode='eval') # XXX: Hard-code crop_border and test_y_channel psnr = metrics.PSNR(crop_border=4, test_y_channel=True) ssim = metrics.SSIM(crop_border=4, test_y_channel=True) logging.info( "Start to evaluate(total_samples={}, total_steps={})...".format( eval_dataset.num_samples, eval_dataset.num_samples)) with paddle.no_grad(): for step, data in enumerate(self.eval_data_loader): data.append(eval_dataset.transforms.transforms) outputs = self.run(self.net, data, 'eval') psnr.update(outputs['pred'], outputs['tar']) ssim.update(outputs['pred'], outputs['tar']) # DO NOT use psnr.accumulate() here, otherwise the program hangs in multi-card training. assert len(psnr.results) > 0 assert len(ssim.results) > 0 eval_metrics = OrderedDict( zip(['psnr', 'ssim'], [np.mean(psnr.results), np.mean(ssim.results)])) if return_details: # TODO: Add details return eval_metrics, None return eval_metrics def predict(self, img_file, transforms=None): """ Do inference. Args: img_file (list[np.ndarray|str] | str | np.ndarray): Image path or decoded image data, which also could constitute a list, meaning all images to be predicted as a mini-batch. transforms (paddlers.transforms.Compose|None, optional): Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None. Returns: If `img_file` is a tuple of string or np.array, the result is a dict with the following key-value pairs: res_map (np.ndarray): Restored image (HWC). If `img_file` is a list, the result is a list composed of dicts with the above keys. """ if transforms is None and not hasattr(self, 'test_transforms'): raise ValueError("transforms need to be defined, now is None.") if transforms is None: transforms = self.test_transforms if isinstance(img_file, (str, np.ndarray)): images = [img_file] else: images = img_file batch_im, batch_tar_shape = self.preprocess(images, transforms, self.model_type) self.net.eval() data = (batch_im, batch_tar_shape, transforms.transforms) outputs = self.run(self.net, data, 'test') res_map_list = outputs['res_map'] if isinstance(img_file, list): prediction = [{'res_map': m} for m in res_map_list] else: prediction = {'res_map': res_map_list[0]} return prediction def preprocess(self, images, transforms, to_tensor=True): self._check_transforms(transforms, 'test') batch_im = list() batch_tar_shape = list() for im in images: if isinstance(im, str): im = decode_image(im, read_raw=True) ori_shape = im.shape[:2] sample = {'image': im} im = transforms(sample)[0] batch_im.append(im) batch_tar_shape.append(self._get_target_shape(ori_shape)) if to_tensor: batch_im = paddle.to_tensor(batch_im) else: batch_im = np.asarray(batch_im) return batch_im, batch_tar_shape def _get_target_shape(self, ori_shape): if self.sr_factor is None: return ori_shape else: return calc_hr_shape(ori_shape, self.sr_factor) @staticmethod def get_transforms_shape_info(batch_tar_shape, transforms): batch_restore_list = list() for tar_shape in batch_tar_shape: restore_list = list() h, w = tar_shape[0], tar_shape[1] for op in transforms: if op.__class__.__name__ == 'Resize': restore_list.append(('resize', (h, w))) h, w = op.target_size elif op.__class__.__name__ == 'ResizeByShort': restore_list.append(('resize', (h, w))) im_short_size = min(h, w) im_long_size = max(h, w) scale = float(op.short_size) / float(im_short_size) if 0 < op.max_size < np.round(scale * im_long_size): scale = float(op.max_size) / float(im_long_size) h = int(round(h * scale)) w = int(round(w * scale)) elif op.__class__.__name__ == 'ResizeByLong': restore_list.append(('resize', (h, w))) im_long_size = max(h, w) scale = float(op.long_size) / float(im_long_size) h = int(round(h * scale)) w = int(round(w * scale)) elif op.__class__.__name__ == 'Pad': if op.target_size: target_h, target_w = op.target_size else: target_h = int( (np.ceil(h / op.size_divisor) * op.size_divisor)) target_w = int( (np.ceil(w / op.size_divisor) * op.size_divisor)) if op.pad_mode == -1: offsets = op.offsets elif op.pad_mode == 0: offsets = [0, 0] elif op.pad_mode == 1: offsets = [(target_h - h) // 2, (target_w - w) // 2] else: offsets = [target_h - h, target_w - w] restore_list.append(('padding', (h, w), offsets)) h, w = target_h, target_w batch_restore_list.append(restore_list) return batch_restore_list def postprocess(self, batch_pred, batch_tar_shape, transforms): batch_restore_list = BaseRestorer.get_transforms_shape_info( batch_tar_shape, transforms) if self.status == 'Infer': return self._infer_postprocess( batch_res_map=batch_pred, batch_restore_list=batch_restore_list) results = [] if batch_pred.dtype == paddle.float32: mode = 'bilinear' else: mode = 'nearest' for pred, restore_list in zip(batch_pred, batch_restore_list): pred = paddle.unsqueeze(pred, axis=0) for item in restore_list[::-1]: h, w = item[1][0], item[1][1] if item[0] == 'resize': pred = F.interpolate( pred, (h, w), mode=mode, data_format='NCHW') elif item[0] == 'padding': x, y = item[2] pred = pred[:, :, y:y + h, x:x + w] else: pass results.append(pred) return results def _infer_postprocess(self, batch_res_map, batch_restore_list): res_maps = [] for res_map, restore_list in zip(batch_res_map, batch_restore_list): if not isinstance(res_map, np.ndarray): res_map = paddle.unsqueeze(res_map, axis=0) for item in restore_list[::-1]: h, w = item[1][0], item[1][1] if item[0] == 'resize': if isinstance(res_map, np.ndarray): res_map = cv2.resize( res_map, (w, h), interpolation=cv2.INTER_LINEAR) else: res_map = F.interpolate( res_map, (h, w), mode='bilinear', data_format='NHWC') elif item[0] == 'padding': x, y = item[2] if isinstance(res_map, np.ndarray): res_map = res_map[y:y + h, x:x + w] else: res_map = res_map[:, y:y + h, x:x + w, :] else: pass res_map = res_map.squeeze() if not isinstance(res_map, np.ndarray): res_map = res_map.numpy() res_map = self._normalize(res_map) res_maps.append(res_map.squeeze()) return res_maps def _check_transforms(self, transforms, mode): super()._check_transforms(transforms, mode) if not isinstance(transforms.arrange, paddlers.transforms.ArrangeRestorer): raise TypeError( "`transforms.arrange` must be an ArrangeRestorer object.") def build_data_loader(self, dataset, batch_size, mode='train'): if dataset.num_samples < batch_size: raise ValueError( 'The volume of dataset({}) must be larger than batch size({}).' .format(dataset.num_samples, batch_size)) if mode != 'train': return paddle.io.DataLoader( dataset, batch_size=batch_size, shuffle=dataset.shuffle, drop_last=False, collate_fn=dataset.batch_transforms, num_workers=dataset.num_workers, return_list=True, use_shared_memory=False) else: return super(BaseRestorer, self).build_data_loader(dataset, batch_size, mode) def set_losses(self, losses): self.losses = losses def _tensor_to_images(self, tensor, transpose=True, squeeze=True, quantize=True): if transpose: tensor = paddle.transpose(tensor, perm=[0, 2, 3, 1]) # NHWC if squeeze: tensor = tensor.squeeze() images = tensor.numpy().astype('float32') images = self._normalize( images, copy=True, clip=True, quantize=quantize) return images def _normalize(self, im, copy=False, clip=True, quantize=True): if copy: im = im.copy() if clip: im = np.clip(im, self.MIN_MAX[0], self.MIN_MAX[1]) im -= im.min() im /= im.max() + 1e-32 if quantize: im *= 255 im = im.astype('uint8') return im class DRN(BaseRestorer): TEST_OUT_KEY = -1 def __init__(self, losses=None, sr_factor=4, scales=(2, 4), n_blocks=30, n_feats=16, n_colors=3, rgb_range=1.0, negval=0.2, lq_loss_weight=0.1, dual_loss_weight=0.1, **params): if sr_factor != max(scales): raise ValueError(f"`sr_factor` must be equal to `max(scales)`.") params.update({ 'scale': scales, 'n_blocks': n_blocks, 'n_feats': n_feats, 'n_colors': n_colors, 'rgb_range': rgb_range, 'negval': negval }) self.lq_loss_weight = lq_loss_weight self.dual_loss_weight = dual_loss_weight self.scales = scales super(DRN, self).__init__( model_name='DRN', losses=losses, sr_factor=sr_factor, **params) def build_net(self, **params): from ppgan.modules.init import init_weights generators = [ppgan.models.generators.DRNGenerator(**params)] init_weights(generators[-1]) for scale in params['scale']: dual_model = ppgan.models.generators.drn.DownBlock( params['negval'], params['n_feats'], params['n_colors'], 2) generators.append(dual_model) init_weights(generators[-1]) return GANAdapter(generators, []) def default_optimizer(self, parameters, *args, **kwargs): optims_g = [ super(DRN, self).default_optimizer(params_g, *args, **kwargs) for params_g in parameters['params_g'] ] return OptimizerAdapter(*optims_g) def run_gan(self, net, inputs, mode, gan_mode='forward_primary'): if mode != 'train': raise ValueError("`mode` is not 'train'.") outputs = OrderedDict() if gan_mode == 'forward_primary': sr = net.generator(inputs[0]) lr = [inputs[0]] lr.extend([ F.interpolate( inputs[0], scale_factor=s, mode='bicubic') for s in self.scales[:-1] ]) loss = self.losses(sr[-1], inputs[1]) for i in range(1, len(sr)): if self.lq_loss_weight > 0: loss += self.losses(sr[i - 1 - len(sr)], lr[i - len(sr)]) * self.lq_loss_weight outputs['loss_prim'] = loss outputs['sr'] = sr outputs['lr'] = lr elif gan_mode == 'forward_dual': sr, lr = inputs[0], inputs[1] sr2lr = [] n_scales = len(self.scales) for i in range(n_scales): sr2lr_i = net.generators[1 + i](sr[i - n_scales]) sr2lr.append(sr2lr_i) loss = self.losses(sr2lr[0], lr[0]) for i in range(1, n_scales): if self.dual_loss_weight > 0.0: loss += self.losses(sr2lr[i], lr[i]) * self.dual_loss_weight outputs['loss_dual'] = loss else: raise ValueError("Invalid `gan_mode`!") return outputs def train_step(self, step, data, net): outputs = self.run_gan( net, data, mode='train', gan_mode='forward_primary') outputs.update( self.run_gan( net, (outputs['sr'], outputs['lr']), mode='train', gan_mode='forward_dual')) self.optimizer.clear_grad() (outputs['loss_prim'] + outputs['loss_dual']).backward() self.optimizer.step() return { 'loss': outputs['loss_prim'] + outputs['loss_dual'], 'loss_prim': outputs['loss_prim'], 'loss_dual': outputs['loss_dual'] } class LESRCNN(BaseRestorer): def __init__(self, losses=None, sr_factor=4, multi_scale=False, group=1, **params): params.update({ 'scale': sr_factor, 'multi_scale': multi_scale, 'group': group }) super(LESRCNN, self).__init__( model_name='LESRCNN', losses=losses, sr_factor=sr_factor, **params) def build_net(self, **params): net = ppgan.models.generators.LESRCNNGenerator(**params) return net class ESRGAN(BaseRestorer): def __init__(self, losses=None, sr_factor=4, use_gan=True, in_channels=3, out_channels=3, nf=64, nb=23, **params): if sr_factor != 4: raise ValueError("`sr_factor` must be 4.") params.update({ 'in_nc': in_channels, 'out_nc': out_channels, 'nf': nf, 'nb': nb }) self.use_gan = use_gan super(ESRGAN, self).__init__( model_name='ESRGAN', losses=losses, sr_factor=sr_factor, **params) def build_net(self, **params): from ppgan.modules.init import init_weights generator = ppgan.models.generators.RRDBNet(**params) init_weights(generator) if self.use_gan: discriminator = ppgan.models.discriminators.VGGDiscriminator128( in_channels=params['out_nc'], num_feat=64) net = GANAdapter( generators=[generator], discriminators=[discriminator]) else: net = generator return net def default_loss(self): if self.use_gan: return { 'pixel': res_losses.L1Loss(loss_weight=0.01), 'perceptual': res_losses.PerceptualLoss( layer_weights={'34': 1.0}, perceptual_weight=1.0, style_weight=0.0, norm_img=False), 'gan': res_losses.GANLoss( gan_mode='vanilla', loss_weight=0.005) } else: return res_losses.L1Loss() def default_optimizer(self, parameters, *args, **kwargs): if self.use_gan: optim_g = super(ESRGAN, self).default_optimizer( parameters['params_g'][0], *args, **kwargs) optim_d = super(ESRGAN, self).default_optimizer( parameters['params_d'][0], *args, **kwargs) return OptimizerAdapter(optim_g, optim_d) else: return super(ESRGAN, self).default_optimizer(parameters, *args, **kwargs) def run_gan(self, net, inputs, mode, gan_mode='forward_g'): if mode != 'train': raise ValueError("`mode` is not 'train'.") outputs = OrderedDict() if gan_mode == 'forward_g': loss_g = 0 g_pred = net.generator(inputs[0]) loss_pix = self.losses['pixel'](g_pred, inputs[1]) loss_perc, loss_sty = self.losses['perceptual'](g_pred, inputs[1]) loss_g += loss_pix if loss_perc is not None: loss_g += loss_perc if loss_sty is not None: loss_g += loss_sty self._set_requires_grad(net.discriminator, False) real_d_pred = net.discriminator(inputs[1]).detach() fake_g_pred = net.discriminator(g_pred) loss_g_real = self.losses['gan']( real_d_pred - paddle.mean(fake_g_pred), False, is_disc=False) * 0.5 loss_g_fake = self.losses['gan']( fake_g_pred - paddle.mean(real_d_pred), True, is_disc=False) * 0.5 loss_g_gan = loss_g_real + loss_g_fake outputs['g_pred'] = g_pred.detach() outputs['loss_g_pps'] = loss_g outputs['loss_g_gan'] = loss_g_gan elif gan_mode == 'forward_d': self._set_requires_grad(net.discriminator, True) # Real fake_d_pred = net.discriminator(inputs[0]).detach() real_d_pred = net.discriminator(inputs[1]) loss_d_real = self.losses['gan']( real_d_pred - paddle.mean(fake_d_pred), True, is_disc=True) * 0.5 # Fake fake_d_pred = net.discriminator(inputs[0].detach()) loss_d_fake = self.losses['gan']( fake_d_pred - paddle.mean(real_d_pred.detach()), False, is_disc=True) * 0.5 outputs['loss_d'] = loss_d_real + loss_d_fake else: raise ValueError("Invalid `gan_mode`!") return outputs def train_step(self, step, data, net): if self.use_gan: optim_g, optim_d = self.optimizer outputs = self.run_gan( net, data, mode='train', gan_mode='forward_g') optim_g.clear_grad() (outputs['loss_g_pps'] + outputs['loss_g_gan']).backward() optim_g.step() outputs.update( self.run_gan( net, (outputs['g_pred'], data[1]), mode='train', gan_mode='forward_d')) optim_d.clear_grad() outputs['loss_d'].backward() optim_d.step() outputs['loss'] = outputs['loss_g_pps'] + outputs[ 'loss_g_gan'] + outputs['loss_d'] return { 'loss': outputs['loss'], 'loss_g_pps': outputs['loss_g_pps'], 'loss_g_gan': outputs['loss_g_gan'], 'loss_d': outputs['loss_d'] } else: return super(ESRGAN, self).train_step(step, data, net) def _set_requires_grad(self, net, requires_grad): for p in net.parameters(): p.trainable = requires_grad class RCAN(BaseRestorer): def __init__(self, losses=None, sr_factor=4, n_resgroups=10, n_resblocks=20, n_feats=64, n_colors=3, rgb_range=1.0, kernel_size=3, reduction=16, **params): params.update({ 'n_resgroups': n_resgroups, 'n_resblocks': n_resblocks, 'n_feats': n_feats, 'n_colors': n_colors, 'rgb_range': rgb_range, 'kernel_size': kernel_size, 'reduction': reduction }) super(RCAN, self).__init__( model_name='RCAN', losses=losses, sr_factor=sr_factor, **params)