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 @MODEL_REGISTRY.register(suffix='basicsr') 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) @torch.no_grad() 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 @torch.no_grad() 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