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| import numpy as np | |
| import random | |
| import torch | |
| from torch.nn import functional as F | |
| from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt | |
| from basicsr.data.transforms import paired_random_crop | |
| from basicsr.models.sr_model import SRModel | |
| from basicsr.utils import DiffJPEG, USMSharp | |
| from basicsr.utils.img_process_util import filter2D | |
| from basicsr.utils.registry import MODEL_REGISTRY | |
| class RealESRNetModel(SRModel): | |
| """RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. | |
| It is trained without GAN losses. | |
| It mainly performs: | |
| 1. randomly synthesize LQ images in GPU tensors | |
| 2. optimize the networks with GAN training. | |
| """ | |
| def __init__(self, opt): | |
| super(RealESRNetModel, self).__init__(opt) | |
| self.jpeger = DiffJPEG(differentiable=False).cuda() # simulate JPEG compression artifacts | |
| self.usm_sharpener = USMSharp().cuda() # do usm sharpening | |
| self.queue_size = opt.get('queue_size', 180) | |
| def _dequeue_and_enqueue(self): | |
| """It is the training pair pool for increasing the diversity in a batch. | |
| Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a | |
| batch could not have different resize scaling factors. Therefore, we employ this training pair pool | |
| to increase the degradation diversity in a batch. | |
| """ | |
| # initialize | |
| b, c, h, w = self.lq.size() | |
| if not hasattr(self, 'queue_lr'): | |
| assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}' | |
| self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda() | |
| _, c, h, w = self.gt.size() | |
| self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda() | |
| self.queue_ptr = 0 | |
| if self.queue_ptr == self.queue_size: # the pool is full | |
| # do dequeue and enqueue | |
| # shuffle | |
| idx = torch.randperm(self.queue_size) | |
| self.queue_lr = self.queue_lr[idx] | |
| self.queue_gt = self.queue_gt[idx] | |
| # get first b samples | |
| lq_dequeue = self.queue_lr[0:b, :, :, :].clone() | |
| gt_dequeue = self.queue_gt[0:b, :, :, :].clone() | |
| # update the queue | |
| self.queue_lr[0:b, :, :, :] = self.lq.clone() | |
| self.queue_gt[0:b, :, :, :] = self.gt.clone() | |
| self.lq = lq_dequeue | |
| self.gt = gt_dequeue | |
| else: | |
| # only do enqueue | |
| self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone() | |
| self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone() | |
| self.queue_ptr = self.queue_ptr + b | |
| def feed_data(self, data): | |
| """Accept data from dataloader, and then add two-order degradations to obtain LQ images. | |
| """ | |
| if self.is_train and self.opt.get('high_order_degradation', True): | |
| # training data synthesis | |
| self.gt = data['gt'].to(self.device) | |
| # USM sharpen the GT images | |
| if self.opt['gt_usm'] is True: | |
| self.gt = self.usm_sharpener(self.gt) | |
| self.kernel1 = data['kernel1'].to(self.device) | |
| self.kernel2 = data['kernel2'].to(self.device) | |
| self.sinc_kernel = data['sinc_kernel'].to(self.device) | |
| ori_h, ori_w = self.gt.size()[2:4] | |
| # ----------------------- The first degradation process ----------------------- # | |
| # blur | |
| out = filter2D(self.gt, self.kernel1) | |
| # random resize | |
| updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0] | |
| if updown_type == 'up': | |
| scale = np.random.uniform(1, self.opt['resize_range'][1]) | |
| elif updown_type == 'down': | |
| scale = np.random.uniform(self.opt['resize_range'][0], 1) | |
| else: | |
| scale = 1 | |
| mode = random.choice(['area', 'bilinear', 'bicubic']) | |
| out = F.interpolate(out, scale_factor=scale, mode=mode) | |
| # add noise | |
| gray_noise_prob = self.opt['gray_noise_prob'] | |
| if np.random.uniform() < self.opt['gaussian_noise_prob']: | |
| out = random_add_gaussian_noise_pt( | |
| out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob) | |
| else: | |
| out = random_add_poisson_noise_pt( | |
| out, | |
| scale_range=self.opt['poisson_scale_range'], | |
| gray_prob=gray_noise_prob, | |
| clip=True, | |
| rounds=False) | |
| # JPEG compression | |
| jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range']) | |
| out = torch.clamp(out, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts | |
| out = self.jpeger(out, quality=jpeg_p) | |
| # ----------------------- The second degradation process ----------------------- # | |
| # blur | |
| if np.random.uniform() < self.opt['second_blur_prob']: | |
| out = filter2D(out, self.kernel2) | |
| # random resize | |
| updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0] | |
| if updown_type == 'up': | |
| scale = np.random.uniform(1, self.opt['resize_range2'][1]) | |
| elif updown_type == 'down': | |
| scale = np.random.uniform(self.opt['resize_range2'][0], 1) | |
| else: | |
| scale = 1 | |
| mode = random.choice(['area', 'bilinear', 'bicubic']) | |
| out = F.interpolate( | |
| out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode) | |
| # add noise | |
| gray_noise_prob = self.opt['gray_noise_prob2'] | |
| if np.random.uniform() < self.opt['gaussian_noise_prob2']: | |
| out = random_add_gaussian_noise_pt( | |
| out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob) | |
| else: | |
| out = random_add_poisson_noise_pt( | |
| out, | |
| scale_range=self.opt['poisson_scale_range2'], | |
| gray_prob=gray_noise_prob, | |
| clip=True, | |
| rounds=False) | |
| # JPEG compression + the final sinc filter | |
| # We also need to resize images to desired sizes. We group [resize back + sinc filter] together | |
| # as one operation. | |
| # We consider two orders: | |
| # 1. [resize back + sinc filter] + JPEG compression | |
| # 2. JPEG compression + [resize back + sinc filter] | |
| # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines. | |
| if np.random.uniform() < 0.5: | |
| # resize back + the final sinc filter | |
| mode = random.choice(['area', 'bilinear', 'bicubic']) | |
| out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) | |
| out = filter2D(out, self.sinc_kernel) | |
| # JPEG compression | |
| jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) | |
| out = torch.clamp(out, 0, 1) | |
| out = self.jpeger(out, quality=jpeg_p) | |
| else: | |
| # JPEG compression | |
| jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) | |
| out = torch.clamp(out, 0, 1) | |
| out = self.jpeger(out, quality=jpeg_p) | |
| # resize back + the final sinc filter | |
| mode = random.choice(['area', 'bilinear', 'bicubic']) | |
| out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) | |
| out = filter2D(out, self.sinc_kernel) | |
| # clamp and round | |
| self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255. | |
| # random crop | |
| gt_size = self.opt['gt_size'] | |
| self.gt, self.lq = paired_random_crop(self.gt, self.lq, gt_size, self.opt['scale']) | |
| # training pair pool | |
| self._dequeue_and_enqueue() | |
| self.lq = self.lq.contiguous() # for the warning: grad and param do not obey the gradient layout contract | |
| else: | |
| # for paired training or validation | |
| self.lq = data['lq'].to(self.device) | |
| if 'gt' in data: | |
| self.gt = data['gt'].to(self.device) | |
| self.gt_usm = self.usm_sharpener(self.gt) | |
| def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): | |
| # do not use the synthetic process during validation | |
| self.is_train = False | |
| super(RealESRNetModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img) | |
| self.is_train = True | |