Upload model
Browse files- config.json +18 -0
- config.py +23 -0
- model.py +22 -0
- model.safetensors +3 -0
- rrdbnet.py +194 -0
config.json
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{
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"architectures": [
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"RealESRGANModel"
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],
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"auto_map": {
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"AutoConfig": "config.RealESRGANConfig",
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"AutoModel": "model.RealESRGANModel"
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},
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"model_type": "realesrgan",
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"num_block": 23,
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"num_feat": 64,
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"num_grow_ch": 32,
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"num_in_ch": 3,
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"num_out_ch": 3,
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"scale": 4,
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"torch_dtype": "float32",
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"transformers_version": "4.38.1"
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}
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config.py
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from transformers import PretrainedConfig
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class RealESRGANConfig(PretrainedConfig):
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model_type = "realesrgan"
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def __init__(
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self,
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num_in_ch: int = 3,
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num_out_ch: int = 3,
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num_feat: int = 64,
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num_block: int = 23,
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num_grow_ch: int = 32,
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scale: int = 4,
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**kwargs,
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):
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self.num_in_ch = num_in_ch
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self.num_out_ch = num_out_ch
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self.num_feat = num_feat
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self.num_block = num_block
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self.num_grow_ch = num_grow_ch
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self.scale = scale
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super().__init__(**kwargs)
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model.py
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from transformers import PreTrainedModel
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from .config import RealESRGANConfig
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from .rrdbnet import RRDBNet
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class RealESRGANModel(PreTrainedModel):
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config_class = RealESRGANConfig
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def __init__(self, config):
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super().__init__(config)
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self.model = RRDBNet(
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num_in_ch=config.num_in_ch,
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num_out_ch=config.num_out_ch,
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num_feat=config.num_feat,
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num_block=config.num_block,
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num_grow_ch=config.num_grow_ch,
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scale=config.scale,
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)
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def forward(self, tensor):
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return self.model.forward(tensor)
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:94ffebe0816db7d0f0837f5b5d49ab75144af01797ff5c010a92f314217c32d9
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size 66862076
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rrdbnet.py
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import torch
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from torch import nn as nn
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from torch.nn import functional as F
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from torch.nn import init as init
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from torch.nn.modules.batchnorm import _BatchNorm
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def pixel_unshuffle(x, scale):
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"""Pixel unshuffle.
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Args:
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x (Tensor): Input feature with shape (b, c, hh, hw).
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scale (int): Downsample ratio.
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Returns:
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Tensor: the pixel unshuffled feature.
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"""
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b, c, hh, hw = x.size()
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out_channel = c * (scale**2)
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assert hh % scale == 0 and hw % scale == 0
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h = hh // scale
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w = hw // scale
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x_view = x.view(b, c, h, scale, w, scale)
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return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
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@torch.no_grad()
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def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
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"""Initialize network weights.
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Args:
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module_list (list[nn.Module] | nn.Module): Modules to be initialized.
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scale (float): Scale initialized weights, especially for residual
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blocks. Default: 1.
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bias_fill (float): The value to fill bias. Default: 0
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kwargs (dict): Other arguments for initialization function.
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"""
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if not isinstance(module_list, list):
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module_list = [module_list]
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for module in module_list:
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for m in module.modules():
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if isinstance(m, nn.Conv2d):
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init.kaiming_normal_(m.weight, **kwargs)
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m.weight.data *= scale
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if m.bias is not None:
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m.bias.data.fill_(bias_fill)
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elif isinstance(m, nn.Linear):
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init.kaiming_normal_(m.weight, **kwargs)
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m.weight.data *= scale
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if m.bias is not None:
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m.bias.data.fill_(bias_fill)
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elif isinstance(m, _BatchNorm):
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init.constant_(m.weight, 1)
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if m.bias is not None:
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m.bias.data.fill_(bias_fill)
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def make_layer(basic_block, num_basic_block, **kwarg):
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"""Make layers by stacking the same blocks.
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Args:
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basic_block (nn.module): nn.module class for basic block.
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num_basic_block (int): number of blocks.
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Returns:
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nn.Sequential: Stacked blocks in nn.Sequential.
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"""
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layers = []
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for _ in range(num_basic_block):
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layers.append(basic_block(**kwarg))
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return nn.Sequential(*layers)
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class ResidualDenseBlock(nn.Module):
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"""Residual Dense Block.
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Used in RRDB block in ESRGAN.
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Args:
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num_feat (int): Channel number of intermediate features.
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num_grow_ch (int): Channels for each growth.
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"""
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def __init__(self, num_feat=64, num_grow_ch=32):
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super(ResidualDenseBlock, self).__init__()
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self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
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self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
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self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
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self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
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self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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# initialization
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default_init_weights(
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[self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1
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)
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def forward(self, x):
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x1 = self.lrelu(self.conv1(x))
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x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
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x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
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x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
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x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
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# Empirically, we use 0.2 to scale the residual for better performance
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return x5 * 0.2 + x
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class RRDB(nn.Module):
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"""Residual in Residual Dense Block.
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Used in RRDB-Net in ESRGAN.
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Args:
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num_feat (int): Channel number of intermediate features.
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num_grow_ch (int): Channels for each growth.
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"""
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def __init__(self, num_feat, num_grow_ch=32):
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super(RRDB, self).__init__()
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self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
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self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
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self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
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def forward(self, x):
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out = self.rdb1(x)
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out = self.rdb2(out)
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out = self.rdb3(out)
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# Empirically, we use 0.2 to scale the residual for better performance
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return out * 0.2 + x
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class RRDBNet(nn.Module):
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"""Networks consisting of Residual in Residual Dense Block, which is used
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in ESRGAN.
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ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
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We extend ESRGAN for scale x2 and scale x1.
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Note: This is one option for scale 1, scale 2 in RRDBNet.
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We first employ the pixel-unshuffle an inverse operation of pixelshuffle to reduce
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the spatial size and enlarge the channel size before feeding inputs
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into the main ESRGAN architecture.
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Args:
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num_in_ch (int): Channel number of inputs.
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num_out_ch (int): Channel number of outputs.
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num_feat (int): Channel number of intermediate features.
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Default: 64
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num_block (int): Block number in the trunk network. Defaults: 23
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num_grow_ch (int): Channels for each growth. Default: 32.
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"""
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def __init__(
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self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32
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):
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super(RRDBNet, self).__init__()
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self.scale = scale
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if scale == 2:
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num_in_ch = num_in_ch * 4
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elif scale == 1:
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num_in_ch = num_in_ch * 16
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self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
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self.body = make_layer(
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RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch
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)
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self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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# upsample
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self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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def forward(self, x):
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if self.scale == 2:
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feat = pixel_unshuffle(x, scale=2)
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elif self.scale == 1:
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feat = pixel_unshuffle(x, scale=4)
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else:
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feat = x
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feat = self.conv_first(feat)
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body_feat = self.conv_body(self.body(feat))
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feat = feat + body_feat
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# upsample
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feat = self.lrelu(
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self.conv_up1(F.interpolate(feat, scale_factor=2, mode="nearest"))
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)
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feat = self.lrelu(
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self.conv_up2(F.interpolate(feat, scale_factor=2, mode="nearest"))
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)
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out = self.conv_last(self.lrelu(self.conv_hr(feat)))
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return out
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