384 lines
10 KiB
Python
384 lines
10 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from . import block as B
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class Get_gradient_nopadding(nn.Module):
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def __init__(self):
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super(Get_gradient_nopadding, self).__init__()
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kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
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kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]
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kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
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kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
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self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False) # type: ignore
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self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False) # type: ignore
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def forward(self, x):
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x_list = []
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for i in range(x.shape[1]):
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x_i = x[:, i]
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x_i_v = F.conv2d(x_i.unsqueeze(1), self.weight_v, padding=1)
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x_i_h = F.conv2d(x_i.unsqueeze(1), self.weight_h, padding=1)
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x_i = torch.sqrt(torch.pow(x_i_v, 2) + torch.pow(x_i_h, 2) + 1e-6)
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x_list.append(x_i)
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x = torch.cat(x_list, dim=1)
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return x
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class SPSRNet(nn.Module):
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def __init__(
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self,
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state_dict,
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norm=None,
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act: str = "leakyrelu",
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upsampler: str = "upconv",
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mode: B.ConvMode = "CNA",
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):
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super(SPSRNet, self).__init__()
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self.model_arch = "SPSR"
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self.sub_type = "SR"
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self.state = state_dict
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self.norm = norm
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self.act = act
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self.upsampler = upsampler
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self.mode = mode
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self.num_blocks = self.get_num_blocks()
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self.in_nc: int = self.state["model.0.weight"].shape[1]
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self.out_nc: int = self.state["f_HR_conv1.0.bias"].shape[0]
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self.scale = self.get_scale(4)
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self.num_filters: int = self.state["model.0.weight"].shape[0]
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self.supports_fp16 = True
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self.supports_bfp16 = True
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self.min_size_restriction = None
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n_upscale = int(math.log(self.scale, 2))
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if self.scale == 3:
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n_upscale = 1
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fea_conv = B.conv_block(
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self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None
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)
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rb_blocks = [
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B.RRDB(
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self.num_filters,
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kernel_size=3,
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gc=32,
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stride=1,
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bias=True,
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pad_type="zero",
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norm_type=norm,
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act_type=act,
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mode="CNA",
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)
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for _ in range(self.num_blocks)
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]
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LR_conv = B.conv_block(
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self.num_filters,
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self.num_filters,
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kernel_size=3,
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norm_type=norm,
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act_type=None,
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mode=mode,
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)
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if upsampler == "upconv":
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upsample_block = B.upconv_block
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elif upsampler == "pixelshuffle":
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upsample_block = B.pixelshuffle_block
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else:
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raise NotImplementedError(f"upsample mode [{upsampler}] is not found")
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if self.scale == 3:
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a_upsampler = upsample_block(
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self.num_filters, self.num_filters, 3, act_type=act
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)
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else:
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a_upsampler = [
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upsample_block(self.num_filters, self.num_filters, act_type=act)
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for _ in range(n_upscale)
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]
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self.HR_conv0_new = B.conv_block(
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self.num_filters,
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self.num_filters,
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kernel_size=3,
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norm_type=None,
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act_type=act,
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)
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self.HR_conv1_new = B.conv_block(
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self.num_filters,
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self.num_filters,
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kernel_size=3,
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norm_type=None,
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act_type=None,
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)
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self.model = B.sequential(
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fea_conv,
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B.ShortcutBlockSPSR(B.sequential(*rb_blocks, LR_conv)),
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*a_upsampler,
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self.HR_conv0_new,
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)
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self.get_g_nopadding = Get_gradient_nopadding()
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self.b_fea_conv = B.conv_block(
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self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None
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)
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self.b_concat_1 = B.conv_block(
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2 * self.num_filters,
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self.num_filters,
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kernel_size=3,
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norm_type=None,
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act_type=None,
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)
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self.b_block_1 = B.RRDB(
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self.num_filters * 2,
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kernel_size=3,
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gc=32,
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stride=1,
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bias=True,
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pad_type="zero",
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norm_type=norm,
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act_type=act,
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mode="CNA",
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)
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self.b_concat_2 = B.conv_block(
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2 * self.num_filters,
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self.num_filters,
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kernel_size=3,
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norm_type=None,
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act_type=None,
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)
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self.b_block_2 = B.RRDB(
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self.num_filters * 2,
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kernel_size=3,
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gc=32,
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stride=1,
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bias=True,
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pad_type="zero",
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norm_type=norm,
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act_type=act,
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mode="CNA",
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)
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self.b_concat_3 = B.conv_block(
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2 * self.num_filters,
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self.num_filters,
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kernel_size=3,
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norm_type=None,
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act_type=None,
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)
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self.b_block_3 = B.RRDB(
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self.num_filters * 2,
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kernel_size=3,
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gc=32,
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stride=1,
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bias=True,
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pad_type="zero",
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norm_type=norm,
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act_type=act,
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mode="CNA",
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)
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self.b_concat_4 = B.conv_block(
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2 * self.num_filters,
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self.num_filters,
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kernel_size=3,
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norm_type=None,
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act_type=None,
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)
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self.b_block_4 = B.RRDB(
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self.num_filters * 2,
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kernel_size=3,
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gc=32,
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stride=1,
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bias=True,
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pad_type="zero",
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norm_type=norm,
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act_type=act,
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mode="CNA",
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)
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self.b_LR_conv = B.conv_block(
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self.num_filters,
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self.num_filters,
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kernel_size=3,
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norm_type=norm,
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act_type=None,
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mode=mode,
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)
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if upsampler == "upconv":
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upsample_block = B.upconv_block
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elif upsampler == "pixelshuffle":
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upsample_block = B.pixelshuffle_block
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else:
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raise NotImplementedError(f"upsample mode [{upsampler}] is not found")
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if self.scale == 3:
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b_upsampler = upsample_block(
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self.num_filters, self.num_filters, 3, act_type=act
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)
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else:
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b_upsampler = [
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upsample_block(self.num_filters, self.num_filters, act_type=act)
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for _ in range(n_upscale)
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]
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b_HR_conv0 = B.conv_block(
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self.num_filters,
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self.num_filters,
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kernel_size=3,
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norm_type=None,
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act_type=act,
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)
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b_HR_conv1 = B.conv_block(
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self.num_filters,
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self.num_filters,
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kernel_size=3,
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norm_type=None,
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act_type=None,
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)
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self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1)
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self.conv_w = B.conv_block(
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self.num_filters, self.out_nc, kernel_size=1, norm_type=None, act_type=None
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)
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self.f_concat = B.conv_block(
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self.num_filters * 2,
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self.num_filters,
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kernel_size=3,
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norm_type=None,
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act_type=None,
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)
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self.f_block = B.RRDB(
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self.num_filters * 2,
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kernel_size=3,
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gc=32,
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stride=1,
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bias=True,
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pad_type="zero",
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norm_type=norm,
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act_type=act,
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mode="CNA",
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)
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self.f_HR_conv0 = B.conv_block(
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self.num_filters,
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self.num_filters,
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kernel_size=3,
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norm_type=None,
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act_type=act,
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)
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self.f_HR_conv1 = B.conv_block(
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self.num_filters, self.out_nc, kernel_size=3, norm_type=None, act_type=None
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)
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self.load_state_dict(self.state, strict=False)
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def get_scale(self, min_part: int = 4) -> int:
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n = 0
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for part in list(self.state):
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parts = part.split(".")
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if len(parts) == 3:
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part_num = int(parts[1])
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if part_num > min_part and parts[0] == "model" and parts[2] == "weight":
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n += 1
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return 2**n
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def get_num_blocks(self) -> int:
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nb = 0
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for part in list(self.state):
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parts = part.split(".")
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n_parts = len(parts)
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if n_parts == 5 and parts[2] == "sub":
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nb = int(parts[3])
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return nb
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def forward(self, x):
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x_grad = self.get_g_nopadding(x)
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x = self.model[0](x)
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x, block_list = self.model[1](x)
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x_ori = x
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for i in range(5):
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x = block_list[i](x)
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x_fea1 = x
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for i in range(5):
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x = block_list[i + 5](x)
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x_fea2 = x
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for i in range(5):
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x = block_list[i + 10](x)
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x_fea3 = x
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for i in range(5):
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x = block_list[i + 15](x)
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x_fea4 = x
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x = block_list[20:](x)
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# short cut
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x = x_ori + x
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x = self.model[2:](x)
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x = self.HR_conv1_new(x)
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x_b_fea = self.b_fea_conv(x_grad)
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x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1)
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x_cat_1 = self.b_block_1(x_cat_1)
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x_cat_1 = self.b_concat_1(x_cat_1)
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x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1)
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x_cat_2 = self.b_block_2(x_cat_2)
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x_cat_2 = self.b_concat_2(x_cat_2)
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x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1)
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x_cat_3 = self.b_block_3(x_cat_3)
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x_cat_3 = self.b_concat_3(x_cat_3)
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x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1)
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x_cat_4 = self.b_block_4(x_cat_4)
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x_cat_4 = self.b_concat_4(x_cat_4)
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x_cat_4 = self.b_LR_conv(x_cat_4)
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# short cut
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x_cat_4 = x_cat_4 + x_b_fea
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x_branch = self.b_module(x_cat_4)
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# x_out_branch = self.conv_w(x_branch)
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########
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x_branch_d = x_branch
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x_f_cat = torch.cat([x_branch_d, x], dim=1)
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x_f_cat = self.f_block(x_f_cat)
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x_out = self.f_concat(x_f_cat)
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x_out = self.f_HR_conv0(x_out)
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x_out = self.f_HR_conv1(x_out)
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#########
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# return x_out_branch, x_out, x_grad
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return x_out
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