282 lines
9.9 KiB
Python
282 lines
9.9 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import functools
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import math
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import re
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from collections import OrderedDict
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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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# Borrowed from https://github.com/rlaphoenix/VSGAN/blob/master/vsgan/archs/ESRGAN.py
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# Which enhanced stuff that was already here
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class RRDBNet(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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) -> None:
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"""
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ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks.
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By Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao,
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and Chen Change Loy.
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This is old-arch Residual in Residual Dense Block Network and is not
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the newest revision that's available at github.com/xinntao/ESRGAN.
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This is on purpose, the newest Network has severely limited the
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potential use of the Network with no benefits.
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This network supports model files from both new and old-arch.
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Args:
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norm: Normalization layer
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act: Activation layer
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upsampler: Upsample layer. upconv, pixel_shuffle
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mode: Convolution mode
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"""
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super(RRDBNet, self).__init__()
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self.model_arch = "ESRGAN"
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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.state_map = {
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# currently supports old, new, and newer RRDBNet arch models
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# ESRGAN, BSRGAN/RealSR, Real-ESRGAN
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"model.0.weight": ("conv_first.weight",),
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"model.0.bias": ("conv_first.bias",),
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"model.1.sub./NB/.weight": ("trunk_conv.weight", "conv_body.weight"),
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"model.1.sub./NB/.bias": ("trunk_conv.bias", "conv_body.bias"),
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r"model.1.sub.\1.RDB\2.conv\3.0.\4": (
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r"RRDB_trunk\.(\d+)\.RDB(\d)\.conv(\d+)\.(weight|bias)",
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r"body\.(\d+)\.rdb(\d)\.conv(\d+)\.(weight|bias)",
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),
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}
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if "params_ema" in self.state:
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self.state = self.state["params_ema"]
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# self.model_arch = "RealESRGAN"
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self.num_blocks = self.get_num_blocks()
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self.plus = any("conv1x1" in k for k in self.state.keys())
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if self.plus:
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self.model_arch = "ESRGAN+"
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self.state = self.new_to_old_arch(self.state)
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self.key_arr = list(self.state.keys())
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self.in_nc: int = self.state[self.key_arr[0]].shape[1]
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self.out_nc: int = self.state[self.key_arr[-1]].shape[0]
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self.scale: int = self.get_scale()
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self.num_filters: int = self.state[self.key_arr[0]].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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# Detect if pixelunshuffle was used (Real-ESRGAN)
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if self.in_nc in (self.out_nc * 4, self.out_nc * 16) and self.out_nc in (
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self.in_nc / 4,
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self.in_nc / 16,
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):
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self.shuffle_factor = int(math.sqrt(self.in_nc / self.out_nc))
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else:
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self.shuffle_factor = None
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upsample_block = {
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"upconv": B.upconv_block,
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"pixel_shuffle": B.pixelshuffle_block,
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}.get(self.upsampler)
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if upsample_block is None:
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raise NotImplementedError(f"Upsample mode [{self.upsampler}] is not found")
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if self.scale == 3:
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upsample_blocks = upsample_block(
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in_nc=self.num_filters,
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out_nc=self.num_filters,
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upscale_factor=3,
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act_type=self.act,
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)
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else:
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upsample_blocks = [
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upsample_block(
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in_nc=self.num_filters, out_nc=self.num_filters, act_type=self.act
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)
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for _ in range(int(math.log(self.scale, 2)))
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]
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self.model = B.sequential(
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# fea conv
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B.conv_block(
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in_nc=self.in_nc,
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out_nc=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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B.ShortcutBlock(
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B.sequential(
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# rrdb blocks
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*[
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B.RRDB(
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nf=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=self.norm,
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act_type=self.act,
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mode="CNA",
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plus=self.plus,
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)
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for _ in range(self.num_blocks)
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],
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# lr conv
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B.conv_block(
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in_nc=self.num_filters,
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out_nc=self.num_filters,
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kernel_size=3,
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norm_type=self.norm,
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act_type=None,
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mode=self.mode,
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),
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)
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),
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*upsample_blocks,
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# hr_conv0
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B.conv_block(
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in_nc=self.num_filters,
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out_nc=self.num_filters,
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kernel_size=3,
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norm_type=None,
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act_type=self.act,
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),
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# hr_conv1
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B.conv_block(
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in_nc=self.num_filters,
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out_nc=self.out_nc,
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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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)
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# Adjust these properties for calculations outside of the model
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if self.shuffle_factor:
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self.in_nc //= self.shuffle_factor**2
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self.scale //= self.shuffle_factor
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self.load_state_dict(self.state, strict=False)
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def new_to_old_arch(self, state):
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"""Convert a new-arch model state dictionary to an old-arch dictionary."""
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if "params_ema" in state:
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state = state["params_ema"]
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if "conv_first.weight" not in state:
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# model is already old arch, this is a loose check, but should be sufficient
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return state
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# add nb to state keys
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for kind in ("weight", "bias"):
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self.state_map[f"model.1.sub.{self.num_blocks}.{kind}"] = self.state_map[
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f"model.1.sub./NB/.{kind}"
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]
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del self.state_map[f"model.1.sub./NB/.{kind}"]
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old_state = OrderedDict()
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for old_key, new_keys in self.state_map.items():
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for new_key in new_keys:
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if r"\1" in old_key:
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for k, v in state.items():
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sub = re.sub(new_key, old_key, k)
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if sub != k:
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old_state[sub] = v
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else:
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if new_key in state:
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old_state[old_key] = state[new_key]
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# upconv layers
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max_upconv = 0
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for key in state.keys():
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match = re.match(r"(upconv|conv_up)(\d)\.(weight|bias)", key)
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if match is not None:
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_, key_num, key_type = match.groups()
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old_state[f"model.{int(key_num) * 3}.{key_type}"] = state[key]
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max_upconv = max(max_upconv, int(key_num) * 3)
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# final layers
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for key in state.keys():
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if key in ("HRconv.weight", "conv_hr.weight"):
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old_state[f"model.{max_upconv + 2}.weight"] = state[key]
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elif key in ("HRconv.bias", "conv_hr.bias"):
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old_state[f"model.{max_upconv + 2}.bias"] = state[key]
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elif key in ("conv_last.weight",):
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old_state[f"model.{max_upconv + 4}.weight"] = state[key]
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elif key in ("conv_last.bias",):
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old_state[f"model.{max_upconv + 4}.bias"] = state[key]
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# Sort by first numeric value of each layer
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def compare(item1, item2):
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parts1 = item1.split(".")
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parts2 = item2.split(".")
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int1 = int(parts1[1])
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int2 = int(parts2[1])
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return int1 - int2
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sorted_keys = sorted(old_state.keys(), key=functools.cmp_to_key(compare))
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# Rebuild the output dict in the right order
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out_dict = OrderedDict((k, old_state[k]) for k in sorted_keys)
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return out_dict
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def get_scale(self, min_part: int = 6) -> int:
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n = 0
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for part in list(self.state):
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parts = part.split(".")[1:]
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if len(parts) == 2:
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part_num = int(parts[0])
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if part_num > min_part and parts[1] == "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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nbs = []
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state_keys = self.state_map[r"model.1.sub.\1.RDB\2.conv\3.0.\4"] + (
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r"model\.\d+\.sub\.(\d+)\.RDB(\d+)\.conv(\d+)\.0\.(weight|bias)",
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)
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for state_key in state_keys:
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for k in self.state:
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m = re.search(state_key, k)
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if m:
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nbs.append(int(m.group(1)))
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if nbs:
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break
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return max(*nbs) + 1
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def forward(self, x):
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if self.shuffle_factor:
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_, _, h, w = x.size()
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mod_pad_h = (
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self.shuffle_factor - h % self.shuffle_factor
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) % self.shuffle_factor
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mod_pad_w = (
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self.shuffle_factor - w % self.shuffle_factor
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) % self.shuffle_factor
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x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect")
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x = torch.pixel_unshuffle(x, downscale_factor=self.shuffle_factor)
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x = self.model(x)
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return x[:, :, : h * self.scale, : w * self.scale]
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return self.model(x)
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