C++：双向列表list

import cv2
import math
import numpy as np
import os
import queue
import threading
import torch
from basicsr.utils.download_util import load_file_from_url
from torch.nn import functional as F

ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))


class RealESRGANer():
    """A helper class for upsampling images with RealESRGAN.

    Args:
        scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
        model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
        model (nn.Module): The defined network. Default: None.
        tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
            input images into tiles, and then process each of them. Finally, they will be merged into one image.
            0 denotes for do not use tile. Default: 0.
        tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
        pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
        half (float): Whether to use half precision during inference. Default: False.
    """

    def __init__(self,
                 scale,
                 model_path,
                 dni_weight=None,
                 model=None,
                 tile=0,
                 tile_pad=10,
                 pre_pad=10,
                 half=False,
                 device=None,
                 gpu_id=None):
        self.scale = scale
        self.tile_size = tile
        self.tile_pad = tile_pad
        self.pre_pad = pre_pad
        self.mod_scale = None
        self.half = half

        # initialize model
        if gpu_id:
            self.device = torch.device(
                f'cuda:{gpu_id}' if torch.cuda.is_available() else 'cpu') if device is None else device
        else:
            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device

        if isinstance(model_path, list):
            # dni
            assert len(model_path) == len(dni_weight), 'model_path and dni_weight should have the save length.'
            loadnet = self.dni(model_path[0], model_path[1], dni_weight)
        else:
            # if the model_path starts with https, it will first download models to the folder: weights
            if model_path.startswith('https://'):
                model_path = load_file_from_url(
                    url=model_path, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
            loadnet = torch.load(model_path, map_location=torch.device('cpu'))

        # prefer to use params_ema
        if 'params_ema' in loadnet:
            keyname = 'params_ema'
        else:
            keyname = 'params'
        model.load_state_dict(loadnet[keyname], strict=True)

        model.eval()
        self.model = model.to(self.device)
        if self.half:
            self.model = self.model.half()

    def dni(self, net_a, net_b, dni_weight, key='params', loc='cpu'):
        """Deep network interpolation.

        ``Paper: Deep Network Interpolation for Continuous Imagery Effect Transition``
        """
        net_a = torch.load(net_a, map_location=torch.device(loc))
        net_b = torch.load(net_b, map_location=torch.device(loc))
        for k, v_a in net_a[key].items():
            net_a[key][k] = dni_weight[0] * v_a + dni_weight[1] * net_b[key][k]
        return net_a

    def pre_process(self, img):
        """Pre-process, such as pre-pad and mod pad, so that the images can be divisible
        """
        img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
        self.img = img.unsqueeze(0).to(self.device)
        if self.half:
            self.img = self.img.half()

        # pre_pad
        if self.pre_pad != 0:
            self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
        # mod pad for divisible borders
        if self.scale == 2:
            self.mod_scale = 2
        elif self.scale == 1:
            self.mod_scale = 4
        if self.mod_scale is not None:
            self.mod_pad_h, self.mod_pad_w = 0, 0
            _, _, h, w = self.img.size()
            if (h % self.mod_scale != 0):
                self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
            if (w % self.mod_scale != 0):
                self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
            self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')

    def process(self):
        # model inference
        # self.output = self.model(self.img)

        # cv2.imwrite("/home/sunyingli/y_channel.jpg", ((self.img[:, 2, :, :].cpu().numpy()*255).astype(np.uint8)[0]))
        self.output = self.model(self.img[:, 2, :, :].unsqueeze(1))

        print("Input_size: " + str(self.img[:, 2, :, :].unsqueeze(1).shape))

        from thop import profile
        # 2 相当于只取Y通道
        f = open(os.devnull, "w")
        import sys
        sys.stdout = f
        flops, params = profile(self.model, inputs=(self.img[:, 2, :, :].unsqueeze(1),))
        sys.stdout = sys.__stdout__
        f.close()
        # 将FLOPs转换为TOPs,计算精度为32位
        tops = flops*24 / 10**12
        print("24 frames per second computing power: ")
        # 打印结果
        print("FLOPs:", flops)
        print("TOPs:", tops)
        print("Params:", params)
        print("-"*60)


    def tile_process(self):
        """It will first crop input images to tiles, and then process each tile.
        Finally, all the processed tiles are merged into one images.

        Modified from: https://github.com/ata4/esrgan-launcher
        """
        batch, channel, height, width = self.img.shape
        output_height = height * self.scale
        output_width = width * self.scale
        output_shape = (batch, channel, output_height, output_width)

        # start with black image
        self.output = self.img.new_zeros(output_shape)
        tiles_x = math.ceil(width / self.tile_size)
        tiles_y = math.ceil(height / self.tile_size)

        # loop over all tiles
        for y in range(tiles_y):
            for x in range(tiles_x):
                # extract tile from input image
                ofs_x = x * self.tile_size
                ofs_y = y * self.tile_size
                # input tile area on total image
                input_start_x = ofs_x
                input_end_x = min(ofs_x + self.tile_size, width)
                input_start_y = ofs_y
                input_end_y = min(ofs_y + self.tile_size, height)

                # input tile area on total image with padding
                input_start_x_pad = max(input_start_x - self.tile_pad, 0)
                input_end_x_pad = min(input_end_x + self.tile_pad, width)
                input_start_y_pad = max(input_start_y - self.tile_pad, 0)
                input_end_y_pad = min(input_end_y + self.tile_pad, height)

                # input tile dimensions
                input_tile_width = input_end_x - input_start_x
                input_tile_height = input_end_y - input_start_y
                tile_idx = y * tiles_x + x + 1
                input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]

                # upscale tile
                try:
                    with torch.no_grad():
                        output_tile = self.model(input_tile)
                except RuntimeError as error:
                    print('Error', error)
                print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')

                # output tile area on total image
                output_start_x = input_start_x * self.scale
                output_end_x = input_end_x * self.scale
                output_start_y = input_start_y * self.scale
                output_end_y = input_end_y * self.scale

                # output tile area without padding
                output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
                output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
                output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
                output_end_y_tile = output_start_y_tile + input_tile_height * self.scale

                # put tile into output image
                self.output[:, :, output_start_y:output_end_y,
                            output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
                                                                       output_start_x_tile:output_end_x_tile]

    def post_process(self):
        # remove extra pad
        if self.mod_scale is not None:
            _, _, h, w = self.output.size()
            self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
        # remove prepad
        if self.pre_pad != 0:
            _, _, h, w = self.output.size()
            self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
        return self.output

    @torch.no_grad()
    def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'):
        h_input, w_input = img.shape[0:2]
        # img: numpy
        img = img.astype(np.float32)
        if np.max(img) > 256:  # 16-bit image
            max_range = 65535
            print('\tInput is a 16-bit image')
        else:
            max_range = 255
        img = img / max_range
        if len(img.shape) == 2:  # gray image
            img_mode = 'L'
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
        elif img.shape[2] == 4:  # RGBA image with alpha channel
            img_mode = 'RGBA'
            alpha = img[:, :, 3]
            img = img[:, :, 0:3]
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            if alpha_upsampler == 'realesrgan':
                alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
        else:
            img_mode = 'RGB'
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

            # # 将图像转换为YUV通道
            # img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
            # # 分离YUV通道
            # y, u, v = cv2.split(img_yuv)

        # ------------------- process image (without the alpha channel) ------------------- #
        self.pre_process(img)
        if self.tile_size > 0:
            self.tile_process()
        else:
            self.process()
        output_img = self.post_process()
        output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy()



        # output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
        
        
        
        if img_mode == 'L':
            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)

        # ------------------- process the alpha channel if necessary ------------------- #
        if img_mode == 'RGBA':
            if alpha_upsampler == 'realesrgan':
                self.pre_process(alpha)
                if self.tile_size > 0:
                    self.tile_process()
                else:
                    self.process()
                output_alpha = self.post_process()
                output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
                output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
                output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
            else:  # use the cv2 resize for alpha channel
                h, w = alpha.shape[0:2]
                output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR)

            # merge the alpha channel
            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
            output_img[:, :, 3] = output_alpha

        # ------------------------------ return ------------------------------ #
        if max_range == 65535:  # 16-bit image
            output = (output_img * 65535.0).round().astype(np.uint16)
        else:
            output = (output_img * 255.0).round().astype(np.uint8)

        if outscale is not None and outscale != float(self.scale):
            output = cv2.resize(
                output, (
                    int(w_input * outscale),
                    int(h_input * outscale),
                ), interpolation=cv2.INTER_LANCZOS4)

        return output, img_mode


class PrefetchReader(threading.Thread):
    """Prefetch images.

    Args:
        img_list (list[str]): A image list of image paths to be read.
        num_prefetch_queue (int): Number of prefetch queue.
    """

    def __init__(self, img_list, num_prefetch_queue):
        super().__init__()
        self.que = queue.Queue(num_prefetch_queue)
        self.img_list = img_list

    def run(self):
        for img_path in self.img_list:
            img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
            self.que.put(img)

        self.que.put(None)

    def __next__(self):
        next_item = self.que.get()
        if next_item is None:
            raise StopIteration
        return next_item

    def __iter__(self):
        return self


class IOConsumer(threading.Thread):

    def __init__(self, opt, que, qid):
        super().__init__()
        self._queue = que
        self.qid = qid
        self.opt = opt

    def run(self):
        while True:
            msg = self._queue.get()
            if isinstance(msg, str) and msg == 'quit':
                break

            output = msg['output']
            save_path = msg['save_path']
            cv2.imwrite(save_path, output)
        print(f'IO worker {self.qid} is done.')

import argparse
import cv2
import glob
import os
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.download_util import load_file_from_url

from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
import numpy as np


def main():
    """Inference demo for Real-ESRGAN.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument('-i', '--input', type=str, default='/home/sunyingli/inputs/test_004_lq', help='Input image or folder')
    parser.add_argument(
        '-n',
        '--model_name',
        type=str,
        default='RealESRGAN_x2plus',
        help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus | '
              'realesr-animevideov3 | realesr-general-x4v3'))
    parser.add_argument('-o', '--output', type=str, default='/home/sunyingli/inputs/test_004_lq_2X', help='Output folder')
    parser.add_argument(
        '-dn',
        '--denoise_strength',
        type=float,
        default=0.5,
        help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. '
              'Only used for the realesr-general-x4v3 model'))
    parser.add_argument('-s', '--outscale', type=float, default=2, help='The final upsampling scale of the image')
    parser.add_argument(
        # '--model_path', type=str, default="/home/sunyingli/Real-ESRGAN/experiments/train_realesrnet_x2plus_32_1_16_4channel__123conv_1rdb1_net_oneresize_no_conv_hr_pairdata/models/net_g_520000.pth", help='[Option] Model path. Usually, you do not need to specify it')
        '--model_path', type=str, default="/home/sunyingli/Real-ESRGAN/experiments/train_realesrnet_x2plus_32_1_16_4channel__123conv_1rdb1_net_oneresize_no_conv_hr_pairdata_0806/models/net_g_410000.pth", help='[Option] Model path. Usually, you do not need to specify it')
    parser.add_argument('--suffix', type=str, default='', help='Suffix of the restored image')
    parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
    parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
    parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
    parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
    parser.add_argument(
        '--fp32', action='store_true', help='Use fp32 precision during inference. Default: fp16 (half precision).')
    parser.add_argument(
        '--alpha_upsampler',
        type=str,
        default='realesrgan',
        help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
    parser.add_argument(
        '--ext',
        type=str,
        default='auto',
        help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
    parser.add_argument(
        '-g', '--gpu-id', type=int, default=3, help='gpu device to use (default=None) can be 0,1,2 for multi-gpu')

    args = parser.parse_args()

    # determine models according to model names
    args.model_name = args.model_name.split('.')[0]
    if args.model_name == 'RealESRGAN_x4plus':  # x4 RRDBNet model
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
        netscale = 4
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
    elif args.model_name == 'RealESRNet_x4plus':  # x4 RRDBNet model
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
        netscale = 4
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
    elif args.model_name == 'RealESRGAN_x4plus_anime_6B':  # x4 RRDBNet model with 6 blocks
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
        netscale = 4
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
    elif args.model_name == 'RealESRGAN_x2plus':  # x2 RRDBNet model
        model = RRDBNet(num_in_ch=1, num_out_ch=1, num_feat=32, num_block=1, num_grow_ch=16, scale=2)
        netscale = 2
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
    elif args.model_name == 'realesr-animevideov3':  # x4 VGG-style model (XS size)
        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
        netscale = 4
        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
    elif args.model_name == 'realesr-general-x4v3':  # x4 VGG-style model (S size)
        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
        netscale = 4
        file_url = [
            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
        ]

    # determine model paths
    if args.model_path is not None:
        model_path = args.model_path
    else:
        model_path = os.path.join('weights', args.model_name + '.pth')
        if not os.path.isfile(model_path):
            ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
            for url in file_url:
                # model_path will be updated
                model_path = load_file_from_url(
                    url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)

    # use dni to control the denoise strength
    dni_weight = None
    if args.model_name == 'realesr-general-x4v3' and args.denoise_strength != 1:
        wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
        model_path = [model_path, wdn_model_path]
        dni_weight = [args.denoise_strength, 1 - args.denoise_strength]

    # restorer
    upsampler = RealESRGANer(
        scale=netscale,
        model_path=model_path,
        dni_weight=dni_weight,
        model=model,
        tile=args.tile,
        tile_pad=args.tile_pad,
        pre_pad=args.pre_pad,
        half=not args.fp32,
        gpu_id=args.gpu_id)

    if args.face_enhance:  # Use GFPGAN for face enhancement
        from gfpgan import GFPGANer
        face_enhancer = GFPGANer(
            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
            upscale=args.outscale,
            arch='clean',
            channel_multiplier=2,
            bg_upsampler=upsampler)
    os.makedirs(args.output, exist_ok=True)

    if os.path.isfile(args.input):
        paths = [args.input]
    else:
        paths = sorted(glob.glob(os.path.join(args.input, '*')))

    for idx, path in enumerate(paths):
        imgname, extension = os.path.splitext(os.path.basename(path))
        print('Testing', idx, imgname)

        img_BGR = cv2.imread(path)
        img_yuv = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2YUV)
        img = img_yuv

        img_resized = cv2.resize(img_BGR, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
        img_YUV = cv2.cvtColor(img_resized, cv2.COLOR_BGR2YUV)
        # 分离YUV通道
        y, u, v = cv2.split(img_YUV)

        if len(img.shape) == 3 and img.shape[2] == 4:
            img_mode = 'RGBA'
        else:
            img_mode = None

        try:
        # if True:
            if args.face_enhance:
                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
            else:
                output, _ = upsampler.enhance(img, outscale=args.outscale)
        except RuntimeError as error:
            print('Error', error)
            print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
        else:
            if args.ext == 'auto':
                extension = extension[1:]
            else:
                extension = args.ext
            if img_mode == 'RGBA':  # RGBA images should be saved in png format
                extension = 'png'
            if args.suffix == '':
                save_path = os.path.join(args.output, f'{imgname}.{extension}')
            else:
                save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')

            # cv2.imwrite(save_path, output)



            # 保存为彩色图像
            # 创建一个空白的彩色图像
            h, w = output.shape
            img_YUV_OUT = np.zeros((h, w, 3), dtype=np.uint8)

            # 为图像的Y、U、V通道赋值
            img_YUV_OUT[:,:,0] = output  # Y通道
            img_YUV_OUT[:,:,1] = u  # U通道
            img_YUV_OUT[:,:,2] = v  # V通道

            # 将图像从YUV颜色空间转换回BGR颜色空间
            img_BGR_OUT = cv2.cvtColor(img_YUV_OUT, cv2.COLOR_YUV2BGR)

            # 保存合成后的图像
            cv2.imwrite(save_path, img_BGR_OUT)


if __name__ == '__main__':
    main()

import torch
from torch import nn as nn
from torch.nn import functional as F

from basicsr.utils.registry import ARCH_REGISTRY
from .arch_util import default_init_weights, make_layer, pixel_unshuffle
import time

class ResidualDenseBlock(nn.Module):
    """Residual Dense Block.

    Used in RRDB block in ESRGAN.

    Args:
        num_feat (int): Channel number of intermediate features.
        num_grow_ch (int): Channels for each growth.
    """

    def __init__(self, num_feat=64, num_grow_ch=32):
        super(ResidualDenseBlock, self).__init__()
        # 5 
        # self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
        # self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
        # self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
        # self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
        # self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
        # self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
        # default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)


        # 3
        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
        self.conv5 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_feat, 3, 1, 1)
        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
        default_init_weights([self.conv1, self.conv2, self.conv5], 0.1)

    def forward(self, x):
        x1 = self.lrelu(self.conv1(x))
        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
        # x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
        # x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
        # x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
        # # Empirically, we use 0.2 to scale the residual for better performance
        # return x5 * 0.2 + x
        ##############################################################################################
        x5 = self.conv5(torch.cat((x, x1, x2), 1))
        # Empirically, we use 0.2 to scale the residual for better performance
        return x5 * 0.2 + x
        ##############################################################################################


class RRDB(nn.Module):
    """Residual in Residual Dense Block.

    Used in RRDB-Net in ESRGAN.

    Args:
        num_feat (int): Channel number of intermediate features.
        num_grow_ch (int): Channels for each growth.
    """

    def __init__(self, num_feat, num_grow_ch=32):
        super(RRDB, self).__init__()
        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
        # self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
        # self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)

    def forward(self, x):
        out = self.rdb1(x)
        # out = self.rdb2(out)
        # out = self.rdb3(out)
        # Empirically, we use 0.2 to scale the residual for better performance
        return out * 0.2 + x


# @ARCH_REGISTRY.register()
# class RRDBNet(nn.Module):
#     """Networks consisting of Residual in Residual Dense Block, which is used
#     in ESRGAN.

#     ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.

#     We extend ESRGAN for scale x2 and scale x1.
#     Note: This is one option for scale 1, scale 2 in RRDBNet.
#     We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
#     and enlarge the channel size before feeding inputs into the main ESRGAN architecture.

#     Args:
#         num_in_ch (int): Channel number of inputs.
#         num_out_ch (int): Channel number of outputs.
#         num_feat (int): Channel number of intermediate features.
#             Default: 64
#         num_block (int): Block number in the trunk network. Defaults: 23
#         num_grow_ch (int): Channels for each growth. Default: 32.
#     """

#     def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
#         super(RRDBNet, self).__init__()
#         self.scale = scale
#         if scale == 2:
#             num_in_ch = num_in_ch * 4
#         elif scale == 1:
#             num_in_ch = num_in_ch * 16
#         self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
#         self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
#         self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
#         # upsample
#         self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
#         self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
#         self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
#         self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

#         self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

#     def forward(self, x):
#         if self.scale == 2:
#             feat = pixel_unshuffle(x, scale=2)
#         elif self.scale == 1:
#             feat = pixel_unshuffle(x, scale=4)
#         else:
#             feat = x
#         feat = self.conv_first(feat)
#         body_feat = self.conv_body(self.body(feat))
#         feat = feat + body_feat
#         # upsample
#         feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
#         feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
#         out = self.conv_last(self.lrelu(self.conv_hr(feat)))
#         return out



def pixel_shuffle_resume(x, scale):
    """ Pixel shuffle.

    Args:
        x (Tensor): Input feature with shape (b, c, h, w).
        scale (int): Upsample ratio.

    Returns:
        Tensor: the pixel shuffled feature.
    """
    # scale = scale * 2
    
    b, c, h, w = x.size()
    out_channel = 1
    assert h % 2 == 0 and w % 2 == 0
    hh = h * scale
    ww = w  * scale
    # x_view = x.view(b, out_channel, 2, 4, h, w)
    # return x_view.permute(0, 1, 4, 2, 5, 3).reshape(b, out_channel, hh, ww)
    x_view = x.view(b, out_channel, scale, scale, h, w)
    return x_view.permute(0, 1, 4, 2, 5, 3).reshape(b, out_channel, hh, ww)

@ARCH_REGISTRY.register()
class RRDBNet(nn.Module):
    """Networks consisting of Residual in Residual Dense Block, which is used
    in ESRGAN.

    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.

    We extend ESRGAN for scale x2 and scale x1.
    Note: This is one option for scale 1, scale 2 in RRDBNet.
    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.

    Args:
        num_in_ch (int): Channel number of inputs.
        num_out_ch (int): Channel number of outputs.
        num_feat (int): Channel number of intermediate features.
            Default: 64
        num_block (int): Block number in the trunk network. Defaults: 23
        num_grow_ch (int): Channels for each growth. Default: 32.
    """

    def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
        super(RRDBNet, self).__init__()
        self.scale = scale
        if scale == 2:
            num_in_ch = num_in_ch * 4
        elif scale == 1:
            num_in_ch = num_in_ch * 16
        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        # upsample
        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        # self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        # self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        self.conv_last = nn.Conv2d(num_feat, num_out_ch * 4, 3, 1, 1)

        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, x):
        # start_time = time.time()
        if self.scale == 2:
            feat = pixel_unshuffle(x, scale=2)
        elif self.scale == 1:
            feat = pixel_unshuffle(x, scale=4)
        else:
            feat = x
        # end_time = time.time()
        # print(f"pixel_unshuffle: {end_time - start_time} 秒")


        # start_time = time.time()
        feat = self.conv_first(feat)
        body_feat = self.conv_body(self.body(feat))
        feat = feat + body_feat
        # upsample
        # end_time = time.time()
        # print(f"body_feat: {end_time - start_time} 秒")


        # start_time = time.time()
        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='bilinear')))
        # end_time = time.time()
        # print(f"interpolate: {end_time - start_time} 秒")

        # feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
        # out = self.conv_last(self.lrelu(self.conv_hr(feat)))

        # start_time = time.time()
        out = self.conv_last(feat)
        # end_time = time.time()
        # print(f"self.conv_last(feat): {end_time - start_time} 秒")

        # start_time = time.time()
        # result = pixel_shuffle_resume(out, self.scale)
        # end_time = time.time()
        # print(f"pixel_shuffle_resume(out, self.scale): {end_time - start_time} 秒")
        # return result

        return pixel_shuffle_resume(out, self.scale)

import collections.abc
import math
import torch
import torchvision
import warnings
from distutils.version import LooseVersion
from itertools import repeat
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm

from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv
from basicsr.utils import get_root_logger


@torch.no_grad()
def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
    """Initialize network weights.

    Args:
        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
        scale (float): Scale initialized weights, especially for residual
            blocks. Default: 1.
        bias_fill (float): The value to fill bias. Default: 0
        kwargs (dict): Other arguments for initialization function.
    """
    if not isinstance(module_list, list):
        module_list = [module_list]
    for module in module_list:
        for m in module.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, **kwargs)
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.fill_(bias_fill)
            elif isinstance(m, nn.Linear):
                init.kaiming_normal_(m.weight, **kwargs)
                m.weight.data *= scale
                if m.bias is not None:
                    m.bias.data.fill_(bias_fill)
            elif isinstance(m, _BatchNorm):
                init.constant_(m.weight, 1)
                if m.bias is not None:
                    m.bias.data.fill_(bias_fill)


def make_layer(basic_block, num_basic_block, **kwarg):
    """Make layers by stacking the same blocks.

    Args:
        basic_block (nn.module): nn.module class for basic block.
        num_basic_block (int): number of blocks.

    Returns:
        nn.Sequential: Stacked blocks in nn.Sequential.
    """
    layers = []
    for _ in range(num_basic_block):
        layers.append(basic_block(**kwarg))
    return nn.Sequential(*layers)


class ResidualBlockNoBN(nn.Module):
    """Residual block without BN.

    Args:
        num_feat (int): Channel number of intermediate features.
            Default: 64.
        res_scale (float): Residual scale. Default: 1.
        pytorch_init (bool): If set to True, use pytorch default init,
            otherwise, use default_init_weights. Default: False.
    """

    def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
        super(ResidualBlockNoBN, self).__init__()
        self.res_scale = res_scale
        self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
        self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
        self.relu = nn.ReLU(inplace=True)

        if not pytorch_init:
            default_init_weights([self.conv1, self.conv2], 0.1)

    def forward(self, x):
        identity = x
        out = self.conv2(self.relu(self.conv1(x)))
        return identity + out * self.res_scale


class Upsample(nn.Sequential):
    """Upsample module.

    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.
    """

    def __init__(self, scale, num_feat):
        m = []
        if (scale & (scale - 1)) == 0:  # scale = 2^n
            for _ in range(int(math.log(scale, 2))):
                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
                m.append(nn.PixelShuffle(2))
        elif scale == 3:
            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
            m.append(nn.PixelShuffle(3))
        else:
            raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
        super(Upsample, self).__init__(*m)


def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True):
    """Warp an image or feature map with optical flow.

    Args:
        x (Tensor): Tensor with size (n, c, h, w).
        flow (Tensor): Tensor with size (n, h, w, 2), normal value.
        interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
        padding_mode (str): 'zeros' or 'border' or 'reflection'.
            Default: 'zeros'.
        align_corners (bool): Before pytorch 1.3, the default value is
            align_corners=True. After pytorch 1.3, the default value is
            align_corners=False. Here, we use the True as default.

    Returns:
        Tensor: Warped image or feature map.
    """
    assert x.size()[-2:] == flow.size()[1:3]
    _, _, h, w = x.size()
    # create mesh grid
    grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x))
    grid = torch.stack((grid_x, grid_y), 2).float()  # W(x), H(y), 2
    grid.requires_grad = False

    vgrid = grid + flow
    # scale grid to [-1,1]
    vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
    vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
    vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
    output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners)

    # TODO, what if align_corners=False
    return output


def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False):
    """Resize a flow according to ratio or shape.

    Args:
        flow (Tensor): Precomputed flow. shape [N, 2, H, W].
        size_type (str): 'ratio' or 'shape'.
        sizes (list[int | float]): the ratio for resizing or the final output
            shape.
            1) The order of ratio should be [ratio_h, ratio_w]. For
            downsampling, the ratio should be smaller than 1.0 (i.e., ratio
            < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
            ratio > 1.0).
            2) The order of output_size should be [out_h, out_w].
        interp_mode (str): The mode of interpolation for resizing.
            Default: 'bilinear'.
        align_corners (bool): Whether align corners. Default: False.

    Returns:
        Tensor: Resized flow.
    """
    _, _, flow_h, flow_w = flow.size()
    if size_type == 'ratio':
        output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
    elif size_type == 'shape':
        output_h, output_w = sizes[0], sizes[1]
    else:
        raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.')

    input_flow = flow.clone()
    ratio_h = output_h / flow_h
    ratio_w = output_w / flow_w
    input_flow[:, 0, :, :] *= ratio_w
    input_flow[:, 1, :, :] *= ratio_h
    resized_flow = F.interpolate(
        input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners)
    return resized_flow


# # TODO: may write a cpp file
# def pixel_unshuffle(x, scale):
#     """ Pixel unshuffle.

#     Args:
#         x (Tensor): Input feature with shape (b, c, hh, hw).
#         scale (int): Downsample ratio.

#     Returns:
#         Tensor: the pixel unshuffled feature.
#     """
#     b, c, hh, hw = x.size()
#     out_channel = c * (scale**2)
#     assert hh % scale == 0 and hw % scale == 0
#     h = hh // scale
#     w = hw // scale
#     x_view = x.view(b, c, h, scale, w, scale)
#     return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)


# TODO: may write a cpp file
def pixel_unshuffle(x, scale):
    """ Pixel unshuffle.

    Args:
        x (Tensor): Input feature with shape (b, c, hh, hw).
        scale (int): Downsample ratio.

    Returns:
        Tensor: the pixel unshuffled feature.
    """
    # scale = scale * 2
    b, c, hh, hw = x.size()
    out_channel = c * (scale**2)
    assert hh % scale == 0 and hw % scale == 0
    h = hh // scale
    w = hw // scale
    x_view = x.view(b, c, h, scale, w, scale)
    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)


class DCNv2Pack(ModulatedDeformConvPack):
    """Modulated deformable conv for deformable alignment.

    Different from the official DCNv2Pack, which generates offsets and masks
    from the preceding features, this DCNv2Pack takes another different
    features to generate offsets and masks.

    ``Paper: Delving Deep into Deformable Alignment in Video Super-Resolution``
    """

    def forward(self, x, feat):
        out = self.conv_offset(feat)
        o1, o2, mask = torch.chunk(out, 3, dim=1)
        offset = torch.cat((o1, o2), dim=1)
        mask = torch.sigmoid(mask)

        offset_absmean = torch.mean(torch.abs(offset))
        if offset_absmean > 50:
            logger = get_root_logger()
            logger.warning(f'Offset abs mean is {offset_absmean}, larger than 50.')

        if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'):
            return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding,
                                                 self.dilation, mask)
        else:
            return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding,
                                         self.dilation, self.groups, self.deformable_groups)


def _no_grad_trunc_normal_(tensor, mean, std, a, b):
    # From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
    # Cut & paste from PyTorch official master until it's in a few official releases - RW
    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    def norm_cdf(x):
        # Computes standard normal cumulative distribution function
        return (1. + math.erf(x / math.sqrt(2.))) / 2.

    if (mean < a - 2 * std) or (mean > b + 2 * std):
        warnings.warn(
            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
            'The distribution of values may be incorrect.',
            stacklevel=2)

    with torch.no_grad():
        # Values are generated by using a truncated uniform distribution and
        # then using the inverse CDF for the normal distribution.
        # Get upper and lower cdf values
        low = norm_cdf((a - mean) / std)
        up = norm_cdf((b - mean) / std)

        # Uniformly fill tensor with values from [low, up], then translate to
        # [2l-1, 2u-1].
        tensor.uniform_(2 * low - 1, 2 * up - 1)

        # Use inverse cdf transform for normal distribution to get truncated
        # standard normal
        tensor.erfinv_()

        # Transform to proper mean, std
        tensor.mul_(std * math.sqrt(2.))
        tensor.add_(mean)

        # Clamp to ensure it's in the proper range
        tensor.clamp_(min=a, max=b)
        return tensor


def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
    r"""Fills the input Tensor with values drawn from a truncated
    normal distribution.

    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py

    The values are effectively drawn from the
    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
    with values outside :math:`[a, b]` redrawn until they are within
    the bounds. The method used for generating the random values works
    best when :math:`a \leq \text{mean} \leq b`.

    Args:
        tensor: an n-dimensional `torch.Tensor`
        mean: the mean of the normal distribution
        std: the standard deviation of the normal distribution
        a: the minimum cutoff value
        b: the maximum cutoff value

    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.trunc_normal_(w)
    """
    return _no_grad_trunc_normal_(tensor, mean, std, a, b)


# From PyTorch
def _ntuple(n):

    def parse(x):
        if isinstance(x, collections.abc.Iterable):
            return x
        return tuple(repeat(x, n))

    return parse


to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple

```python
在这里插入代码片

import numpy as np
import random
import torch
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.srgan_model import SRGANModel
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.utils.registry import MODEL_REGISTRY
from collections import OrderedDict
from torch.nn import functional as F
import time

@MODEL_REGISTRY.register()
class RealESRGANModel(SRGANModel):
    """RealESRGAN Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.

    It mainly performs:
    1. randomly synthesize LQ images in GPU tensors
    2. optimize the networks with GAN training.
    """

    def __init__(self, opt):
        super(RealESRGANModel, 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)
            self.gt_usm = 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_usm, 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.gt_usm), self.lq = paired_random_crop([self.gt, self.gt_usm], self.lq, gt_size,
                                                                 self.opt['scale'])

            # training pair pool
            #################################################################
            self.lq = self.lq[:, 0, :, :].unsqueeze(1)
            self.gt = self.gt[:, 0, :, :].unsqueeze(1)
            self.gt_usm = self.gt_usm[:, 0, :, :].unsqueeze(1)
            ###################################################################
            self._dequeue_and_enqueue()
            # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue
            self.gt_usm = self.usm_sharpener(self.gt)
            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)
            ##########################################
            self.lq = self.lq[:, 0, :, :].unsqueeze(1)
            ##########################################
            if 'gt' in data:

                self.gt = data['gt'].to(self.device)
                self.gt_usm = self.usm_sharpener(self.gt)

                ############################################
                self.gt = self.gt[:, 0, :, :].unsqueeze(1)
                self.gt_usm = self.gt_usm[:, 0, :, :].unsqueeze(1)
                ############################################

    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
        # do not use the synthetic process during validation
        self.is_train = False
        super(RealESRGANModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
        self.is_train = True

    def optimize_parameters(self, current_iter):
        # usm sharpening
        l1_gt = self.gt_usm
        percep_gt = self.gt_usm
        gan_gt = self.gt_usm
        if self.opt['l1_gt_usm'] is False:
            l1_gt = self.gt
        if self.opt['percep_gt_usm'] is False:
            percep_gt = self.gt
        if self.opt['gan_gt_usm'] is False:
            gan_gt = self.gt

        # optimize net_g
        for p in self.net_d.parameters():
            p.requires_grad = False

        self.optimizer_g.zero_grad()
        self.output = self.net_g(self.lq)

        l_g_total = 0
        loss_dict = OrderedDict()
        if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
            # pixel loss
            if self.cri_pix:
                l_g_pix = self.cri_pix(self.output, l1_gt)
                l_g_total += l_g_pix
                loss_dict['l_g_pix'] = l_g_pix
            # perceptual loss
            if self.cri_perceptual:
                l_g_percep, l_g_style = self.cri_perceptual(self.output, percep_gt)
                if l_g_percep is not None:
                    l_g_total += l_g_percep
                    loss_dict['l_g_percep'] = l_g_percep
                if l_g_style is not None:
                    l_g_total += l_g_style
                    loss_dict['l_g_style'] = l_g_style
            # gan loss
            fake_g_pred = self.net_d(self.output)
            l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
            l_g_total += l_g_gan
            loss_dict['l_g_gan'] = l_g_gan

            l_g_total.backward()
            self.optimizer_g.step()

        # optimize net_d
        for p in self.net_d.parameters():
            p.requires_grad = True

        self.optimizer_d.zero_grad()
        # real
        real_d_pred = self.net_d(gan_gt)
        l_d_real = self.cri_gan(real_d_pred, True, is_disc=True)
        loss_dict['l_d_real'] = l_d_real
        loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
        l_d_real.backward()
        # fake
        fake_d_pred = self.net_d(self.output.detach().clone())  # clone for pt1.9
        l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True)
        loss_dict['l_d_fake'] = l_d_fake
        loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
        l_d_fake.backward()
        self.optimizer_d.step()

        if self.ema_decay > 0:
            self.model_ema(decay=self.ema_decay)

        self.log_dict = self.reduce_loss_dict(loss_dict)

import numpy as np
import random
import torch
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
from torch.nn import functional as F


@MODEL_REGISTRY.register()
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'])

            ####################################################################
            self.lq = self.lq[:, 0, :, :].unsqueeze(1)
            self.gt = self.gt[:, 0, :, :].unsqueeze(1)
            ####################################################################          
            
            # 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)
            ##########################################
            self.lq = self.lq[:, 0, :, :].unsqueeze(1)
            ##########################################
            if 'gt' in data:
                self.gt = data['gt'].to(self.device)
                self.gt_usm = self.usm_sharpener(self.gt)
                
                ############################################
                self.gt = self.gt[:, 0, :, :].unsqueeze(1)
                self.gt_usm = self.gt_usm[:, 0, :, :].unsqueeze(1)
                ############################################

    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