407 lines
16 KiB
Python
407 lines
16 KiB
Python
import functools
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import os
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import random
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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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import torchvision
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from models.arch_util import make_layer, default_init_weights, ConvGnSilu, ConvGnLelu
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from trainer.networks import register_model
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from utils.util import checkpoint, sequential_checkpoint, opt_get
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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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mid_channels (int): Channel number of intermediate features.
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growth_channels (int): Channels for each growth.
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"""
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def __init__(self, mid_channels=64, growth_channels=32, init_weight=.1):
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super(ResidualDenseBlock, self).__init__()
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for i in range(5):
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out_channels = mid_channels if i == 4 else growth_channels
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self.add_module(
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f'conv{i+1}',
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nn.Conv2d(mid_channels + i * growth_channels, out_channels, 3,
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1, 1))
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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for i in range(5):
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default_init_weights(getattr(self, f'conv{i+1}'), init_weight)
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def forward(self, x):
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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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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# Emperically, 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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mid_channels (int): Channel number of intermediate features.
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growth_channels (int): Channels for each growth.
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"""
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def __init__(self, mid_channels, growth_channels=32, reduce_to=None):
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super(RRDB, self).__init__()
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self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels)
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self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
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self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)
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if reduce_to is not None:
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self.reducer = ConvGnLelu(mid_channels, reduce_to, kernel_size=3, activation=False, norm=False, bias=True)
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self.recover_ch = mid_channels - reduce_to
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else:
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self.reducer = None
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def forward(self, x, return_residual=False):
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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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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if self.reducer is not None:
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out = self.reducer(out)
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b, f, h, w = out.shape
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out = torch.cat([out, torch.zeros((b, self.recover_ch, h, w), device=out.device)], dim=1)
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if return_residual:
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return 0.2 * out
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else:
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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 RRDBWithBypass(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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mid_channels (int): Channel number of intermediate features.
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growth_channels (int): Channels for each growth.
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"""
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def __init__(self, mid_channels, growth_channels=32, reduce_to=None, randomly_add_noise_to_bypass=False):
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super(RRDBWithBypass, self).__init__()
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self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels)
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self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
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self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)
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if reduce_to is not None:
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self.reducer = ConvGnLelu(mid_channels, reduce_to, kernel_size=3, activation=False, norm=False, bias=True)
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self.recover_ch = mid_channels - reduce_to
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bypass_channels = mid_channels + reduce_to
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else:
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self.reducer = None
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bypass_channels = mid_channels * 2
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self.bypass = nn.Sequential(ConvGnSilu(bypass_channels, mid_channels, kernel_size=3, bias=True, activation=True, norm=True),
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ConvGnSilu(mid_channels, mid_channels//2, kernel_size=3, bias=False, activation=True, norm=False),
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ConvGnSilu(mid_channels//2, 1, kernel_size=3, bias=False, activation=False, norm=False),
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nn.Sigmoid())
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self.randomly_add_bypass_noise = randomly_add_noise_to_bypass
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def forward(self, x):
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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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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if self.reducer is not None:
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out = self.reducer(out)
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b, f, h, w = out.shape
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out = torch.cat([out, torch.zeros((b, self.recover_ch, h, w), device=out.device)], dim=1)
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bypass = self.bypass(torch.cat([x, out], dim=1))
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# The purpose of random noise is to induce usage of bypass maps that would otherwise be "dead". Theoretically
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# if these maps provide value, the noise should trigger gradients to flow into the bypass conv network again.
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if self.randomly_add_bypass_noise and random.random() < .2:
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rnoise = torch.rand_like(bypass) * .02
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bypass = (bypass + rnoise).clamp(0, 1)
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self.bypass_map = bypass.detach().clone()
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# Empirically, we use 0.2 to scale the residual for better performance
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return out * 0.2 * bypass + 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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Currently, it supports x4 upsampling scale factor.
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Args:
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in_channels (int): Channel number of inputs.
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out_channels (int): Channel number of outputs.
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mid_channels (int): Channel number of intermediate features.
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Default: 64
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num_blocks (int): Block number in the trunk network. Defaults: 23
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growth_channels (int): Channels for each growth. Default: 32.
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"""
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def __init__(self,
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in_channels,
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out_channels,
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mid_channels=64,
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num_blocks=23,
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growth_channels=32,
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body_block=RRDB,
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blocks_per_checkpoint=1,
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scale=4,
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additive_mode="not", # Options: "not", "additive", "additive_enforced"
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headless=False,
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feature_channels=64, # Only applicable when headless=True. How many channels are used at the trunk level.
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output_mode="hq_only", # Options: "hq_only", "hq+features", "features_only"
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initial_stride=1,
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use_ref=False, # When set, a reference image is expected as input and synthesized if not found. Useful for video SR.
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):
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super(RRDBNet, self).__init__()
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assert output_mode in ['hq_only', 'hq+features', 'features_only']
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assert additive_mode in ['not', 'additive', 'additive_enforced']
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self.num_blocks = num_blocks
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self.blocks_per_checkpoint = blocks_per_checkpoint
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self.scale = scale
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self.in_channels = in_channels
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self.output_mode = output_mode
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self.use_ref = use_ref
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first_conv_stride = initial_stride if not self.use_ref else scale
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first_conv_ksize = 3 if first_conv_stride == 1 else 7
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first_conv_padding = 1 if first_conv_stride == 1 else 3
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if headless:
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self.conv_first = None
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self.reduce_ch = feature_channels
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reduce_to = feature_channels
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self.conv_ref_first = ConvGnLelu(3, feature_channels, 7, stride=2, norm=False, activation=False, bias=True)
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else:
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self.conv_first = nn.Conv2d(in_channels, mid_channels, first_conv_ksize, first_conv_stride, first_conv_padding)
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self.reduce_ch = mid_channels
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reduce_to = None
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self.body = make_layer(
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body_block,
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num_blocks,
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mid_channels=mid_channels,
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growth_channels=growth_channels,
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reduce_to=reduce_to)
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self.conv_body = nn.Conv2d(self.reduce_ch, self.reduce_ch, 3, 1, 1)
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# upsample
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self.conv_up1 = nn.Conv2d(self.reduce_ch, self.reduce_ch, 3, 1, 1)
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self.conv_up2 = nn.Conv2d(self.reduce_ch, self.reduce_ch, 3, 1, 1)
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if scale >= 8:
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self.conv_up3 = nn.Conv2d(self.reduce_ch, self.reduce_ch, 3, 1, 1)
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else:
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self.conv_up3 = None
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self.conv_hr = nn.Conv2d(self.reduce_ch, self.reduce_ch, 3, 1, 1)
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self.conv_last = nn.Conv2d(self.reduce_ch, out_channels, 3, 1, 1)
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self.additive_mode = additive_mode
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if additive_mode == "additive_enforced":
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self.add_enforced_pool = nn.AvgPool2d(kernel_size=scale, stride=scale)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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for m in [
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self.conv_first, self.conv_body, self.conv_up1,
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self.conv_up2, self.conv_up3, self.conv_hr, self.conv_last
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]:
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if m is not None:
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default_init_weights(m, 0.1)
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def forward(self, x, ref=None):
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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if self.conv_first is None:
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# Headless mode -> embedding inputs.
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if ref is not None:
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ref = self.conv_ref_first(ref)
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feat = torch.cat([x, ref], dim=1)
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else:
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feat = x
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else:
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# "Normal" mode -> image input.
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if self.use_ref:
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x_lg = F.interpolate(x, scale_factor=self.scale, mode="bicubic")
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if ref is None:
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ref = torch.zeros_like(x_lg)
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x_lg = torch.cat([x_lg, ref], dim=1)
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else:
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x_lg = x
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feat = self.conv_first(x_lg)
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feat = sequential_checkpoint(self.body, self.num_blocks // self.blocks_per_checkpoint, feat)
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feat = feat[:, :self.reduce_ch]
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body_feat = self.conv_body(feat)
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feat = feat + body_feat
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if self.output_mode == "features_only":
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return feat
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# upsample
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out = self.lrelu(
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self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
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if self.scale >= 4:
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out = self.lrelu(
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self.conv_up2(F.interpolate(out, scale_factor=2, mode='nearest')))
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if self.scale >= 8:
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out = self.lrelu(
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self.conv_up3(F.interpolate(out, scale_factor=2, mode='nearest')))
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else:
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out = self.lrelu(self.conv_up2(out))
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out = self.conv_last(self.lrelu(self.conv_hr(out)))
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if "additive" in self.additive_mode:
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x_interp = F.interpolate(x, scale_factor=self.scale, mode='bilinear')
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if self.additive_mode == 'additive':
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out = out + x_interp
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elif self.additive_mode == 'additive_enforced':
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out_pooled = self.add_enforced_pool(out)
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out = out - F.interpolate(out_pooled, scale_factor=self.scale, mode='nearest')
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out = out + x_interp
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if self.output_mode == "hq+features":
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return out, feat
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return out
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def visual_dbg(self, step, path):
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for i, bm in enumerate(self.body):
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if hasattr(bm, 'bypass_map'):
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torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1)))
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class DiscRDB(nn.Module):
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def __init__(self, mid_channels=64, growth_channels=32):
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super(DiscRDB, self).__init__()
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for i in range(5):
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out_channels = mid_channels if i == 4 else growth_channels
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actnorm = i != 5
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self.add_module(
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f'conv{i+1}',
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ConvGnLelu(mid_channels + i * growth_channels, out_channels, kernel_size=3, norm=actnorm, activation=actnorm, bias=True)
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)
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self.lrelu = nn.LeakyReLU(negative_slope=.2)
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for i in range(5):
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default_init_weights(getattr(self, f'conv{i+1}'), 1)
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def forward(self, x):
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x1 = self.conv1(x)
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x2 = self.conv2(torch.cat((x, x1), 1))
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x3 = self.conv3(torch.cat((x, x1, x2), 1))
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x4 = 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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return self.lrelu(x5 + x)
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class DiscRRDB(nn.Module):
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def __init__(self, mid_channels, growth_channels=32):
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super(DiscRRDB, self).__init__()
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self.rdb1 = DiscRDB(mid_channels, growth_channels)
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self.rdb2 = DiscRDB(mid_channels, growth_channels)
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self.rdb3 = DiscRDB(mid_channels, growth_channels)
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self.gn = nn.GroupNorm(num_groups=8, num_channels=mid_channels)
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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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return self.gn(out + x)
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class RRDBDiscriminator(nn.Module):
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def __init__(self,
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in_channels,
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mid_channels=64,
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num_blocks=23,
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growth_channels=32,
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blocks_per_checkpoint=1
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):
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super(RRDBDiscriminator, self).__init__()
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self.num_blocks = num_blocks
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self.blocks_per_checkpoint = blocks_per_checkpoint
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self.in_channels = in_channels
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self.conv_first = ConvGnLelu(in_channels, mid_channels, 3, stride=4, activation=False, norm=False, bias=True)
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self.body = make_layer(
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DiscRRDB,
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num_blocks,
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mid_channels=mid_channels,
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growth_channels=growth_channels)
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self.tail = nn.Sequential(
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ConvGnLelu(mid_channels, mid_channels // 2, kernel_size=1, activation=True, norm=False, bias=True),
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ConvGnLelu(mid_channels // 2, mid_channels // 4, kernel_size=1, activation=True, norm=False, bias=True),
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ConvGnLelu(mid_channels // 4, 1, kernel_size=1, activation=False, norm=False, bias=True)
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)
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self.pred_ = None
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def forward(self, x):
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feat = self.conv_first(x)
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feat = sequential_checkpoint(self.body, self.num_blocks // self.blocks_per_checkpoint, feat)
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pred = checkpoint(self.tail, feat)
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self.pred_ = pred.detach().clone()
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return pred
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def visual_dbg(self, step, path):
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if self.pred_ is not None:
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self.pred_ = F.sigmoid(self.pred_)
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torchvision.utils.save_image(self.pred_.cpu().float(), os.path.join(path, "%i_predictions.png" % (step,)))
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@register_model
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def register_RRDBNetBypass(opt_net, opt):
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additive_mode = opt_net['additive_mode'] if 'additive_mode' in opt_net.keys() else 'not'
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output_mode = opt_net['output_mode'] if 'output_mode' in opt_net.keys() else 'hq_only'
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gc = opt_net['gc'] if 'gc' in opt_net.keys() else 32
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initial_stride = opt_net['initial_stride'] if 'initial_stride' in opt_net.keys() else 1
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bypass_noise = opt_get(opt_net, ['bypass_noise'], False)
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block = functools.partial(RRDBWithBypass, randomly_add_noise_to_bypass=bypass_noise)
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return RRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
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mid_channels=opt_net['nf'], num_blocks=opt_net['nb'], additive_mode=additive_mode,
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output_mode=output_mode, body_block=block, scale=opt_net['scale'], growth_channels=gc,
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initial_stride=initial_stride)
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@register_model
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def register_RRDBNet(opt_net, opt):
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additive_mode = opt_net['additive_mode'] if 'additive_mode' in opt_net.keys() else 'not'
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output_mode = opt_net['output_mode'] if 'output_mode' in opt_net.keys() else 'hq_only'
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gc = opt_net['gc'] if 'gc' in opt_net.keys() else 32
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initial_stride = opt_net['initial_stride'] if 'initial_stride' in opt_net.keys() else 1
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return RRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
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mid_channels=opt_net['nf'], num_blocks=opt_net['nb'], additive_mode=additive_mode,
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output_mode=output_mode, body_block=RRDB, scale=opt_net['scale'], growth_channels=gc,
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initial_stride=initial_stride) |