import math import torch import torch.nn as nn import torch.nn.functional as F from models.archs import SPSR_util as B from .RRDBNet_arch import RRDB from models.archs.arch_util import ConvGnLelu, UpconvBlock, ConjoinBlock2 from models.archs.SwitchedResidualGenerator_arch import MultiConvBlock, ConvBasisMultiplexer, ConfigurableSwitchComputer from switched_conv_util import save_attention_to_image_rgb from switched_conv import compute_attention_specificity import functools import os class ImageGradient(nn.Module): def __init__(self): super(ImageGradient, 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).cuda() self.weight_v = nn.Parameter(data = kernel_v, requires_grad = False).cuda() def forward(self, x): x0 = x[:, 0] x1 = x[:, 1] x2 = x[:, 2] x0_v = F.conv2d(x0.unsqueeze(1), self.weight_v, padding=2) x0_h = F.conv2d(x0.unsqueeze(1), self.weight_h, padding=2) x1_v = F.conv2d(x1.unsqueeze(1), self.weight_v, padding=2) x1_h = F.conv2d(x1.unsqueeze(1), self.weight_h, padding=2) x2_v = F.conv2d(x2.unsqueeze(1), self.weight_v, padding=2) x2_h = F.conv2d(x2.unsqueeze(1), self.weight_h, padding=2) x0 = torch.sqrt(torch.pow(x0_v, 2) + torch.pow(x0_h, 2) + 1e-6) x1 = torch.sqrt(torch.pow(x1_v, 2) + torch.pow(x1_h, 2) + 1e-6) x2 = torch.sqrt(torch.pow(x2_v, 2) + torch.pow(x2_h, 2) + 1e-6) x = torch.cat([x0, x1, x2], dim=1) return x class ImageGradientNoPadding(nn.Module): def __init__(self): super(ImageGradientNoPadding, 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) self.weight_v = nn.Parameter(data = kernel_v, requires_grad = False) 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 #################### # Generator #################### class SPSRNet(nn.Module): def __init__(self, in_nc, out_nc, nf, nb, gc=32, upscale=4, norm_type=None, \ act_type='leakyrelu', mode='CNA', upsample_mode='upconv'): super(SPSRNet, self).__init__() n_upscale = int(math.log(upscale, 2)) if upscale == 3: n_upscale = 1 fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None) rb_blocks = [RRDB(nf, gc=32) for _ in range(nb)] LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode) if upsample_mode == 'upconv': upsample_block = B.upconv_block elif upsample_mode == 'pixelshuffle': upsample_block = B.pixelshuffle_block else: raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode)) if upscale == 3: upsampler = upsample_block(nf, nf, 3, act_type=act_type) else: upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)] self.HR_conv0_new = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type) self.HR_conv1_new = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=None) self.model = B.sequential(fea_conv, B.ShortcutBlock(B.sequential(*rb_blocks, LR_conv)),\ *upsampler, self.HR_conv0_new) self.get_g_nopadding = ImageGradientNoPadding() self.b_fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None) self.b_concat_1 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None) self.b_block_1 = RRDB(nf*2, gc=32) self.b_concat_2 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None) self.b_block_2 = RRDB(nf*2, gc=32) self.b_concat_3 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None) self.b_block_3 = RRDB(nf*2, gc=32) self.b_concat_4 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None) self.b_block_4 = RRDB(nf*2, gc=32) self.b_LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode) if upsample_mode == 'upconv': upsample_block = B.upconv_block elif upsample_mode == 'pixelshuffle': upsample_block = B.pixelshuffle_block else: raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode)) if upscale == 3: b_upsampler = upsample_block(nf, nf, 3, act_type=act_type) else: b_upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)] b_HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type) b_HR_conv1 = B.conv_block(nf, nf, 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(nf, out_nc, kernel_size=1, norm_type=None, act_type=None) # Note: "_branch_pretrain" is a special tag used to denote parameters that get pretrained before the rest. self._branch_pretrain_concat = B.conv_block(nf*2, nf, kernel_size=3, norm_type=None, act_type=None) self._branch_pretrain_block = RRDB(nf*2, gc=32) self._branch_pretrain_HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type) self._branch_pretrain_HR_conv1 = B.conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None) 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__branch_pretrain_cat = torch.cat([x_branch_d, x], dim=1) x__branch_pretrain_cat = self._branch_pretrain_block(x__branch_pretrain_cat) x_out = self._branch_pretrain_concat(x__branch_pretrain_cat) x_out = self._branch_pretrain_HR_conv0(x_out) x_out = self._branch_pretrain_HR_conv1(x_out) ######### return x_out_branch, x_out, x_gradn class SwitchedSpsr(nn.Module): def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4): super(SwitchedSpsr, self).__init__() n_upscale = int(math.log(upscale, 2)) # switch options transformation_filters = nf switch_filters = nf switch_reductions = 3 switch_processing_layers = 2 self.transformation_counts = xforms multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions, switch_processing_layers, self.transformation_counts, use_exp2=True) pretransform_fn = functools.partial(ConvGnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1) transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5), transformation_filters, kernel_size=3, depth=3, weight_init_factor=.1) # Feature branch self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False) self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, pre_transform_block=pretransform_fn, transform_block=transform_fn, attention_norm=True, transform_count=self.transformation_counts, init_temp=10, add_scalable_noise_to_transforms=True) self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, pre_transform_block=pretransform_fn, transform_block=transform_fn, attention_norm=True, transform_count=self.transformation_counts, init_temp=10, add_scalable_noise_to_transforms=True) self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False) self.feature_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False) # Grad branch self.get_g_nopadding = ImageGradientNoPadding() self.b_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False) mplex_grad = functools.partial(ConvBasisMultiplexer, nf * 2, nf * 2, switch_reductions, switch_processing_layers, self.transformation_counts // 2, use_exp2=True) self.sw_grad = ConfigurableSwitchComputer(transformation_filters, mplex_grad, pre_transform_block=pretransform_fn, transform_block=transform_fn, attention_norm=True, transform_count=self.transformation_counts // 2, init_temp=10, add_scalable_noise_to_transforms=True) # Upsampling self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=True, bias=False) self.grad_hr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False) # Conv used to output grad branch shortcut. self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False) # Conjoin branch. # Note: "_branch_pretrain" is a special tag used to denote parameters that get pretrained before the rest. transform_fn_cat = functools.partial(MultiConvBlock, transformation_filters * 2, int(transformation_filters * 1.5), transformation_filters, kernel_size=3, depth=4, weight_init_factor=.1) pretransform_fn_cat = functools.partial(ConvGnLelu, transformation_filters * 2, transformation_filters * 2, norm=False, bias=False, weight_init_factor=.1) self._branch_pretrain_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, pre_transform_block=pretransform_fn_cat, transform_block=transform_fn_cat, attention_norm=True, transform_count=self.transformation_counts, init_temp=10, add_scalable_noise_to_transforms=True) self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)]) self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)]) self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False) self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=True, bias=False) self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=3, norm=False, activation=False, bias=False) self.switches = [self.sw1, self.sw2, self.sw_grad, self._branch_pretrain_sw] self.attentions = None self.init_temperature = 10 self.final_temperature_step = 10000 def forward(self, x): x_grad = self.get_g_nopadding(x) x = self.model_fea_conv(x) x1, a1 = self.sw1(x, True) x2, a2 = self.sw2(x1, True) x_fea = self.feature_lr_conv(x2) x_fea = self.feature_hr_conv2(x_fea) x_b_fea = self.b_fea_conv(x_grad) x_grad, a3 = self.sw_grad(x_b_fea, att_in=torch.cat([x1, x_b_fea], dim=1), output_attention_weights=True) x_grad = self.grad_lr_conv(x_grad) x_grad = self.grad_hr_conv(x_grad) x_out_branch = self.upsample_grad(x_grad) x_out_branch = self.grad_branch_output_conv(x_out_branch) x__branch_pretrain_cat = torch.cat([x_grad, x_fea], dim=1) x__branch_pretrain_cat, a4 = self._branch_pretrain_sw(x__branch_pretrain_cat, att_in=x_fea, identity=x_fea, output_attention_weights=True) x_out = self.final_lr_conv(x__branch_pretrain_cat) x_out = self.upsample(x_out) x_out = self.final_hr_conv1(x_out) x_out = self.final_hr_conv2(x_out) self.attentions = [a1, a2, a3, a4] return x_out_branch, x_out, x_grad def set_temperature(self, temp): [sw.set_temperature(temp) for sw in self.switches] def update_for_step(self, step, experiments_path='.'): if self.attentions: temp = max(1, 1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step) self.set_temperature(temp) if step % 200 == 0: output_path = os.path.join(experiments_path, "attention_maps", "a%i") prefix = "attention_map_%i_%%i.png" % (step,) [save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))] def get_debug_values(self, step): temp = self.switches[0].switch.temperature mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions] means = [i[0] for i in mean_hists] hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists] val = {"switch_temperature": temp} for i in range(len(means)): val["switch_%i_specificity" % (i,)] = means[i] val["switch_%i_histogram" % (i,)] = hists[i] return val