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