848 lines
47 KiB
Python
848 lines
47 KiB
Python
import functools
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import os
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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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import torchvision
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from utils.util import checkpoint
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from models.archs import SPSR_util as B
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from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer, ReferenceImageBranch, \
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QueryKeyMultiplexer, QueryKeyPyramidMultiplexer, ConvBasisMultiplexer
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from models.archs.arch_util import ConvGnLelu, UpconvBlock, MultiConvBlock, ReferenceJoinBlock
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from switched_conv.switched_conv import compute_attention_specificity
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from switched_conv.switched_conv_util import save_attention_to_image_rgb
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from .RRDBNet_arch import RRDB
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class ImageGradient(nn.Module):
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def __init__(self):
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super(ImageGradient, self).__init__()
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kernel_v = [[0, -1, 0],
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[0, 0, 0],
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[0, 1, 0]]
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kernel_h = [[0, 0, 0],
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[-1, 0, 1],
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[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).cuda()
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self.weight_v = nn.Parameter(data = kernel_v, requires_grad = False).cuda()
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def forward(self, x):
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x0 = x[:, 0]
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x1 = x[:, 1]
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x2 = x[:, 2]
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x0_v = F.conv2d(x0.unsqueeze(1), self.weight_v, padding=2)
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x0_h = F.conv2d(x0.unsqueeze(1), self.weight_h, padding=2)
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x1_v = F.conv2d(x1.unsqueeze(1), self.weight_v, padding=2)
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x1_h = F.conv2d(x1.unsqueeze(1), self.weight_h, padding=2)
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x2_v = F.conv2d(x2.unsqueeze(1), self.weight_v, padding=2)
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x2_h = F.conv2d(x2.unsqueeze(1), self.weight_h, padding=2)
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x0 = torch.sqrt(torch.pow(x0_v, 2) + torch.pow(x0_h, 2) + 1e-6)
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x1 = torch.sqrt(torch.pow(x1_v, 2) + torch.pow(x1_h, 2) + 1e-6)
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x2 = torch.sqrt(torch.pow(x2_v, 2) + torch.pow(x2_h, 2) + 1e-6)
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x = torch.cat([x0, x1, x2], dim=1)
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return x
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class ImageGradientNoPadding(nn.Module):
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def __init__(self):
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super(ImageGradientNoPadding, self).__init__()
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kernel_v = [[0, -1, 0],
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[0, 0, 0],
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[0, 1, 0]]
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kernel_h = [[0, 0, 0],
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[-1, 0, 1],
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[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)
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self.weight_v = nn.Parameter(data = kernel_v, requires_grad = False)
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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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####################
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# Generator
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####################
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class SPSRNetSimplified(nn.Module):
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def __init__(self, in_nc, out_nc, nf, nb, upscale=4):
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super(SPSRNetSimplified, self).__init__()
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n_upscale = int(math.log(upscale, 2))
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
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self.model_shortcut_blk = nn.Sequential(*[RRDB(nf, gc=32) for _ in range(nb)])
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self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False)
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self.model_upsampler = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
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self.feature_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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self.feature_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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# Grad branch
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self.get_g_nopadding = ImageGradientNoPadding()
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self.b_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
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self.b_concat_decimate_1 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self.b_proc_block_1 = RRDB(nf, gc=32)
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self.b_concat_decimate_2 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self.b_proc_block_2 = RRDB(nf, gc=32)
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self.b_concat_decimate_3 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self.b_proc_block_3 = RRDB(nf, gc=32)
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self.b_concat_decimate_4 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self.b_proc_block_4 = RRDB(nf, gc=32)
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# Upsampling
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self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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b_upsampler = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
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grad_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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grad_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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self.branch_upsample = B.sequential(*b_upsampler, grad_hr_conv1, grad_hr_conv2)
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# Conv used to output grad branch shortcut.
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self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
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# Conjoin branch.
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# Note: "_branch_pretrain" is a special tag used to denote parameters that get pretrained before the rest.
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self._branch_pretrain_concat = ConvGnLelu(nf * 2, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self._branch_pretrain_block = RRDB(nf * 2, gc=32)
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self._branch_pretrain_HR_conv0 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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self._branch_pretrain_HR_conv1 = ConvGnLelu(nf, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
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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_fea_conv(x)
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x_ori = x
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for i in range(5):
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x = self.model_shortcut_blk[i](x)
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x_fea1 = x
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for i in range(5):
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x = self.model_shortcut_blk[i + 5](x)
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x_fea2 = x
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for i in range(5):
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x = self.model_shortcut_blk[i + 10](x)
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x_fea3 = x
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for i in range(5):
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x = self.model_shortcut_blk[i + 15](x)
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x_fea4 = x
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x = self.model_shortcut_blk[20:](x)
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x = self.feature_lr_conv(x)
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# short cut
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x = x_ori + x
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x = self.model_upsampler(x)
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x = self.feature_hr_conv1(x)
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x = self.feature_hr_conv2(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_concat_decimate_1(x_cat_1)
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x_cat_1 = self.b_proc_block_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_concat_decimate_2(x_cat_2)
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x_cat_2 = self.b_proc_block_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_concat_decimate_3(x_cat_3)
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x_cat_3 = self.b_proc_block_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_concat_decimate_4(x_cat_4)
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x_cat_4 = self.b_proc_block_4(x_cat_4)
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x_cat_4 = self.grad_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.branch_upsample(x_cat_4)
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x_out_branch = self.grad_branch_output_conv(x_branch)
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########
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x_branch_d = x_branch
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x__branch_pretrain_cat = torch.cat([x_branch_d, x], dim=1)
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x__branch_pretrain_cat = self._branch_pretrain_block(x__branch_pretrain_cat)
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x_out = self._branch_pretrain_concat(x__branch_pretrain_cat)
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x_out = self._branch_pretrain_HR_conv0(x_out)
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x_out = self._branch_pretrain_HR_conv1(x_out)
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#########
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return x_out_branch, x_out, x_grad
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class Spsr5(nn.Module):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=2, init_temperature=10):
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super(Spsr5, self).__init__()
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n_upscale = int(math.log(upscale, 2))
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# switch options
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transformation_filters = nf
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self.transformation_counts = xforms
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multiplx_fn = functools.partial(QueryKeyMultiplexer, transformation_filters, reductions=multiplexer_reductions)
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pretransform_fn = functools.partial(ConvGnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
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transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5),
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transformation_filters, kernel_size=3, depth=3,
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weight_init_factor=.1)
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
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self.noise_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
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self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
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self.get_g_nopadding = ImageGradientNoPadding()
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self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
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self.noise_ref_join_grad = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
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self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False)
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self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts // 2, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.grad_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
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self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
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# Join branch (grad+fea)
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self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
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self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3)
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self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
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self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True)
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self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
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self.switches = [self.sw1, self.sw2, self.sw_grad, self.conjoin_sw]
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self.attentions = None
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self.init_temperature = init_temperature
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self.final_temperature_step = 10000
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self.lr = None
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def forward(self, x, embedding):
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# The attention_maps debugger outputs <x>. Save that here.
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self.lr = x.detach().cpu()
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noise_stds = []
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x_grad = self.get_g_nopadding(x)
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x = self.model_fea_conv(x)
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x1 = x
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x1, a1 = self.sw1(x1, identity=x, att_in=(x1, embedding))
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x2 = x1
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x2, nstd = self.noise_ref_join(x2, torch.randn_like(x2))
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x2, a2 = self.sw2(x2, identity=x1, att_in=(x2, embedding))
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noise_stds.append(nstd)
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x_grad = self.grad_conv(x_grad)
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x_grad_identity = x_grad
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x_grad, nstd = self.noise_ref_join_grad(x_grad, torch.randn_like(x_grad))
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x_grad, grad_fea_std = self.grad_ref_join(x_grad, x1)
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x_grad, a3 = self.sw_grad(x_grad, identity=x_grad_identity, att_in=(x_grad, embedding))
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x_grad = self.grad_lr_conv(x_grad)
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x_grad = self.grad_lr_conv2(x_grad)
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x_grad_out = self.upsample_grad(x_grad)
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x_grad_out = self.grad_branch_output_conv(x_grad_out)
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noise_stds.append(nstd)
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x_out = x2
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x_out, nstd = self.noise_ref_join_conjoin(x_out, torch.randn_like(x_out))
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x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad)
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x_out, a4 = self.conjoin_sw(x_out, identity=x2, att_in=(x_out, embedding))
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x_out = self.final_lr_conv(x_out)
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x_out = self.upsample(x_out)
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x_out = self.final_hr_conv1(x_out)
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x_out = self.final_hr_conv2(x_out)
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noise_stds.append(nstd)
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self.attentions = [a1, a2, a3, a4]
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self.noise_stds = torch.stack(noise_stds).mean().detach().cpu()
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self.grad_fea_std = grad_fea_std.detach().cpu()
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self.fea_grad_std = fea_grad_std.detach().cpu()
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return x_grad_out, x_out, x_grad
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def set_temperature(self, temp):
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[sw.set_temperature(temp) for sw in self.switches]
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def update_for_step(self, step, experiments_path='.'):
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if self.attentions:
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temp = max(1, 1 + self.init_temperature *
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(self.final_temperature_step - step) / self.final_temperature_step)
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self.set_temperature(temp)
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if step % 500 == 0:
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output_path = os.path.join(experiments_path, "attention_maps")
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prefix = "amap_%i_a%i_%%i.png"
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[save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
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torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
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def get_debug_values(self, step, net_name):
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temp = self.switches[0].switch.temperature
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mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
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means = [i[0] for i in mean_hists]
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hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
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val = {"switch_temperature": temp,
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"noise_branch_std_dev": self.noise_stds,
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"grad_branch_feat_intg_std_dev": self.grad_fea_std,
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"conjoin_branch_grad_intg_std_dev": self.fea_grad_std}
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for i in range(len(means)):
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val["switch_%i_specificity" % (i,)] = means[i]
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val["switch_%i_histogram" % (i,)] = hists[i]
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return val
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# Variant of Spsr5 which uses multiplexer blocks that are not derived from an embedding. Also makes a few "best practices"
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# adjustments learned over the past few weeks (no noise, kernel_size=7
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class Spsr6(nn.Module):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
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super(Spsr6, self).__init__()
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n_upscale = int(math.log(upscale, 2))
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# switch options
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transformation_filters = nf
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self.transformation_counts = xforms
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multiplx_fn = functools.partial(QueryKeyPyramidMultiplexer, transformation_filters, reductions=multiplexer_reductions)
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transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5),
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transformation_filters, kernel_size=3, depth=3,
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weight_init_factor=.1)
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
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self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
|
pre_transform_block=None, transform_block=transform_fn,
|
|
attention_norm=True,
|
|
transform_count=self.transformation_counts, init_temp=init_temperature,
|
|
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
|
self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
|
pre_transform_block=None, transform_block=transform_fn,
|
|
attention_norm=True,
|
|
transform_count=self.transformation_counts, init_temp=init_temperature,
|
|
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
|
self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
|
|
self.feature_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
|
|
|
|
# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
|
|
self.get_g_nopadding = ImageGradientNoPadding()
|
|
self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
|
|
self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False)
|
|
self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
|
pre_transform_block=None, transform_block=transform_fn,
|
|
attention_norm=True,
|
|
transform_count=self.transformation_counts // 2, init_temp=init_temperature,
|
|
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
|
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
self.grad_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=1, norm=False, activation=True, bias=True)
|
|
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
|
|
self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
|
|
|
|
# Join branch (grad+fea)
|
|
self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
|
|
self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3)
|
|
self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
|
pre_transform_block=None, transform_block=transform_fn,
|
|
attention_norm=True,
|
|
transform_count=self.transformation_counts, init_temp=init_temperature,
|
|
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
|
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
|
|
self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True)
|
|
self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
|
|
self.switches = [self.sw1, self.sw2, self.sw_grad, self.conjoin_sw]
|
|
self.attentions = None
|
|
self.init_temperature = init_temperature
|
|
self.final_temperature_step = 10000
|
|
self.lr = None
|
|
|
|
def forward(self, x):
|
|
# The attention_maps debugger outputs <x>. Save that here.
|
|
self.lr = x.detach().cpu()
|
|
|
|
x_grad = self.get_g_nopadding(x)
|
|
|
|
x = self.model_fea_conv(x)
|
|
x1 = x
|
|
x1, a1 = self.sw1(x1, identity=x)
|
|
|
|
x2 = x1
|
|
x2, a2 = self.sw2(x2, identity=x1)
|
|
|
|
x_grad = self.grad_conv(x_grad)
|
|
x_grad_identity = x_grad
|
|
x_grad, grad_fea_std = self.grad_ref_join(x_grad, x1)
|
|
x_grad, a3 = self.sw_grad(x_grad, identity=x_grad_identity)
|
|
x_grad = self.grad_lr_conv(x_grad)
|
|
x_grad = self.grad_lr_conv2(x_grad)
|
|
x_grad_out = self.upsample_grad(x_grad)
|
|
x_grad_out = self.grad_branch_output_conv(x_grad_out)
|
|
|
|
x_out = x2
|
|
x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad)
|
|
x_out, a4 = self.conjoin_sw(x_out, identity=x2)
|
|
x_out = self.final_lr_conv(x_out)
|
|
x_out = checkpoint(self.upsample, x_out)
|
|
x_out = checkpoint(self.final_hr_conv1, x_out)
|
|
x_out = self.final_hr_conv2(x_out)
|
|
|
|
self.attentions = [a1, a2, a3, a4]
|
|
self.grad_fea_std = grad_fea_std.detach().cpu()
|
|
self.fea_grad_std = fea_grad_std.detach().cpu()
|
|
return x_grad_out, 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 % 500 == 0:
|
|
output_path = os.path.join(experiments_path, "attention_maps")
|
|
prefix = "amap_%i_a%i_%%i.png"
|
|
[save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
|
|
torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
|
|
|
|
def get_debug_values(self, step, net_name):
|
|
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,
|
|
"grad_branch_feat_intg_std_dev": self.grad_fea_std,
|
|
"conjoin_branch_grad_intg_std_dev": self.fea_grad_std}
|
|
for i in range(len(means)):
|
|
val["switch_%i_specificity" % (i,)] = means[i]
|
|
val["switch_%i_histogram" % (i,)] = hists[i]
|
|
return val
|
|
|
|
# Variant of Spsr6 which uses multiplexer blocks that feed off of a reference embedding. Also computes that embedding.
|
|
class Spsr7(nn.Module):
|
|
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, recurrent=False, init_temperature=10):
|
|
super(Spsr7, self).__init__()
|
|
n_upscale = int(math.log(upscale, 2))
|
|
|
|
# processing the input embedding
|
|
self.reference_embedding = ReferenceImageBranch(nf)
|
|
|
|
self.recurrent = recurrent
|
|
if recurrent:
|
|
self.model_recurrent_conv = ConvGnLelu(3, nf, kernel_size=3, stride=2, norm=False, activation=False,
|
|
bias=True)
|
|
self.model_fea_recurrent_combine = ConvGnLelu(nf * 2, nf, 1, activation=False, norm=False, bias=False, weight_init_factor=.01)
|
|
|
|
# switch options
|
|
self.nf = nf
|
|
transformation_filters = nf
|
|
self.transformation_counts = xforms
|
|
multiplx_fn = functools.partial(QueryKeyMultiplexer, transformation_filters, embedding_channels=512, reductions=multiplexer_reductions)
|
|
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=7, norm=False, activation=False)
|
|
self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
|
pre_transform_block=None, transform_block=transform_fn,
|
|
attention_norm=True,
|
|
transform_count=self.transformation_counts, init_temp=init_temperature,
|
|
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
|
self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
|
pre_transform_block=None, transform_block=transform_fn,
|
|
attention_norm=True,
|
|
transform_count=self.transformation_counts, init_temp=init_temperature,
|
|
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
|
|
|
# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
|
|
self.get_g_nopadding = ImageGradientNoPadding()
|
|
self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False, bias=False)
|
|
self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False)
|
|
|
|
self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
|
pre_transform_block=None, transform_block=transform_fn,
|
|
attention_norm=True,
|
|
transform_count=self.transformation_counts // 2, init_temp=init_temperature,
|
|
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
|
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
self.grad_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=1, norm=False, activation=True, bias=True)
|
|
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
|
|
self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
|
|
|
|
# Join branch (grad+fea)
|
|
self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
|
|
self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3)
|
|
self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
|
pre_transform_block=None, transform_block=transform_fn,
|
|
attention_norm=True,
|
|
transform_count=self.transformation_counts, init_temp=init_temperature,
|
|
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
|
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
|
|
self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True)
|
|
self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
|
|
self.switches = [self.sw1, self.sw2, self.sw_grad, self.conjoin_sw]
|
|
self.attentions = None
|
|
self.init_temperature = init_temperature
|
|
self.final_temperature_step = 10000
|
|
self.lr = None
|
|
|
|
def forward(self, x, ref, ref_center, update_attention_norm=True, recurrent=None):
|
|
# The attention_maps debugger outputs <x>. Save that here.
|
|
self.lr = x.detach().cpu()
|
|
|
|
x_grad = self.get_g_nopadding(x)
|
|
ref_code = self.reference_embedding(ref, ref_center)
|
|
ref_embedding = ref_code.view(-1, self.nf * 8, 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
|
|
|
|
x = self.model_fea_conv(x)
|
|
if self.recurrent:
|
|
rec = self.model_recurrent_conv(recurrent)
|
|
br = self.model_fea_recurrent_combine(torch.cat([x, rec], dim=1))
|
|
x = x + br
|
|
|
|
x1 = x
|
|
x1, a1 = self.sw1(x1, identity=x, att_in=(x1, ref_embedding), do_checkpointing=True)
|
|
|
|
x2 = x1
|
|
x2, a2 = self.sw2(x2, identity=x1, att_in=(x2, ref_embedding), do_checkpointing=True)
|
|
|
|
x_grad = self.grad_conv(x_grad)
|
|
x_grad_identity = x_grad
|
|
x_grad, grad_fea_std = checkpoint(self.grad_ref_join, x_grad, x1)
|
|
x_grad, a3 = self.sw_grad(x_grad, identity=x_grad_identity, att_in=(x_grad, ref_embedding), do_checkpointing=True)
|
|
x_grad = checkpoint(self.grad_lr_conv, x_grad)
|
|
x_grad = checkpoint(self.grad_lr_conv2, x_grad)
|
|
x_grad_out = checkpoint(self.upsample_grad, x_grad)
|
|
x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
|
|
|
|
x_out = x2
|
|
x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad)
|
|
x_out, a4 = self.conjoin_sw(x_out, identity=x2, att_in=(x_out, ref_embedding), do_checkpointing=True)
|
|
x_out = checkpoint(self.final_lr_conv, x_out)
|
|
x_out = checkpoint(self.upsample, x_out)
|
|
x_out = checkpoint(self.final_hr_conv1, x_out)
|
|
x_out = checkpoint(self.final_hr_conv2, x_out)
|
|
|
|
self.attentions = [a1, a2, a3, a4]
|
|
self.grad_fea_std = grad_fea_std.detach().cpu()
|
|
self.fea_grad_std = fea_grad_std.detach().cpu()
|
|
return x_grad_out, x_out
|
|
|
|
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 % 500 == 0:
|
|
output_path = os.path.join(experiments_path, "attention_maps")
|
|
prefix = "amap_%i_a%i_%%i.png"
|
|
[save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
|
|
torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
|
|
|
|
def get_debug_values(self, step, net_name):
|
|
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,
|
|
"grad_branch_feat_intg_std_dev": self.grad_fea_std,
|
|
"conjoin_branch_grad_intg_std_dev": self.fea_grad_std}
|
|
for i in range(len(means)):
|
|
val["switch_%i_specificity" % (i,)] = means[i]
|
|
val["switch_%i_histogram" % (i,)] = hists[i]
|
|
return val
|
|
|
|
|
|
class AttentionBlock(nn.Module):
|
|
def __init__(self, nf, num_transforms, multiplexer_reductions, init_temperature=10, has_ref=True):
|
|
super(AttentionBlock, self).__init__()
|
|
self.nf = nf
|
|
self.transformation_counts = num_transforms
|
|
multiplx_fn = functools.partial(QueryKeyMultiplexer, nf, embedding_channels=512, reductions=multiplexer_reductions)
|
|
transform_fn = functools.partial(MultiConvBlock, nf, int(nf * 1.5),
|
|
nf, kernel_size=3, depth=4,
|
|
weight_init_factor=.1)
|
|
if has_ref:
|
|
self.ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False)
|
|
else:
|
|
self.ref_join = None
|
|
self.switch = ConfigurableSwitchComputer(nf, multiplx_fn,
|
|
pre_transform_block=None, transform_block=transform_fn,
|
|
attention_norm=True,
|
|
transform_count=self.transformation_counts, init_temp=init_temperature,
|
|
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
|
|
|
def forward(self, x, mplex_ref=None, ref=None):
|
|
if self.ref_join is not None:
|
|
branch, ref_std = self.ref_join(x, ref)
|
|
return self.switch(branch, identity=x, att_in=(branch, mplex_ref)) + (ref_std,)
|
|
else:
|
|
return self.switch(x, identity=x, att_in=(x, mplex_ref))
|
|
|
|
|
|
# SPSR7 with incremental improvements and also using the new AttentionBlock to save gpu memory.
|
|
class Spsr9(nn.Module):
|
|
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
|
|
super(Spsr9, self).__init__()
|
|
n_upscale = int(math.log(upscale, 2))
|
|
self.nf = nf
|
|
self.transformation_counts = xforms
|
|
|
|
# processing the input embedding
|
|
self.reference_embedding = ReferenceImageBranch(nf)
|
|
|
|
|
|
# Feature branch
|
|
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
|
|
self.sw1 = AttentionBlock(nf, self.transformation_counts, multiplexer_reductions, init_temperature, False)
|
|
self.sw2 = AttentionBlock(nf, self.transformation_counts, multiplexer_reductions, init_temperature, False)
|
|
|
|
# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
|
|
self.get_g_nopadding = ImageGradientNoPadding()
|
|
self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False, bias=False)
|
|
self.sw_grad = AttentionBlock(nf, self.transformation_counts // 2, multiplexer_reductions, init_temperature, True)
|
|
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
self.grad_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=1, norm=False, activation=True, bias=True)
|
|
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
|
|
self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
|
|
|
|
# Join branch (grad+fea)
|
|
self.conjoin_sw = AttentionBlock(nf, self.transformation_counts, multiplexer_reductions, init_temperature, True)
|
|
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
|
|
self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True)
|
|
self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
|
|
self.switches = [self.sw1.switch, self.sw2.switch, self.sw_grad.switch, self.conjoin_sw.switch]
|
|
self.attentions = None
|
|
self.init_temperature = init_temperature
|
|
self.final_temperature_step = 10000
|
|
self.lr = None
|
|
|
|
def forward(self, x, ref, ref_center, update_attention_norm=True):
|
|
# The attention_maps debugger outputs <x>. Save that here.
|
|
self.lr = x.detach().cpu()
|
|
|
|
for sw in self.switches:
|
|
sw.set_update_attention_norm(update_attention_norm)
|
|
|
|
x_grad = self.get_g_nopadding(x)
|
|
ref_code = checkpoint(self.reference_embedding, ref, ref_center)
|
|
ref_embedding = ref_code.view(-1, self.nf * 8, 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
|
|
|
|
x = self.model_fea_conv(x)
|
|
x1 = x
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x1, a1 = checkpoint(self.sw1, x1, ref_embedding)
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x2 = x1
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x2, a2 = checkpoint(self.sw2, x2, ref_embedding)
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|
|
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x_grad = self.grad_conv(x_grad)
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x_grad_identity = x_grad
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|
x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, ref_embedding, x1)
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|
x_grad = self.grad_lr_conv(x_grad)
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|
x_grad = self.grad_lr_conv2(x_grad)
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|
x_grad_out = checkpoint(self.upsample_grad, x_grad)
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|
x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
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|
|
|
x_out = x2
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|
x_out, a4, fea_grad_std = checkpoint(self.conjoin_sw, x_out, ref_embedding, x_grad)
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|
x_out = self.final_lr_conv(x_out)
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|
x_out = checkpoint(self.upsample, x_out)
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|
x_out = checkpoint(self.final_hr_conv1, x_out)
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|
x_out = checkpoint(self.final_hr_conv2, x_out)
|
|
|
|
self.attentions = [a1, a2, a3, a4]
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|
self.grad_fea_std = grad_fea_std.detach().cpu()
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|
self.fea_grad_std = fea_grad_std.detach().cpu()
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|
return x_grad_out, x_out
|
|
|
|
def set_temperature(self, temp):
|
|
[sw.set_temperature(temp) for sw in self.switches]
|
|
|
|
def update_for_step(self, step, experiments_path='.'):
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|
if self.attentions:
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|
temp = max(1, 1 + self.init_temperature *
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|
(self.final_temperature_step - step) / self.final_temperature_step)
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|
self.set_temperature(temp)
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|
if step % 500 == 0:
|
|
output_path = os.path.join(experiments_path, "attention_maps")
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|
prefix = "amap_%i_a%i_%%i.png"
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|
[save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
|
|
torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
|
|
|
|
def get_debug_values(self, step, net_name):
|
|
temp = self.switches[0].switch.temperature
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|
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]
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|
val = {"switch_temperature": temp,
|
|
"grad_branch_feat_intg_std_dev": self.grad_fea_std,
|
|
"conjoin_branch_grad_intg_std_dev": self.fea_grad_std}
|
|
for i in range(len(means)):
|
|
val["switch_%i_specificity" % (i,)] = means[i]
|
|
val["switch_%i_histogram" % (i,)] = hists[i]
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|
return val
|
|
|
|
|
|
class SwitchedSpsr(nn.Module):
|
|
def __init__(self, in_nc, nf, xforms=8, upscale=4, init_temperature=10):
|
|
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=init_temperature,
|
|
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=init_temperature,
|
|
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=init_temperature,
|
|
add_scalable_noise_to_transforms=True)
|
|
# Upsampling
|
|
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, 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, 3, 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=init_temperature,
|
|
add_scalable_noise_to_transforms=True)
|
|
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=True) for _ in range(n_upscale)])
|
|
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=True) 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=True)
|
|
self.final_hr_conv2 = ConvGnLelu(nf, 3, 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 = init_temperature
|
|
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, do_checkpointing=True)
|
|
x2, a2 = self.sw2(x1, do_checkpointing=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, do_checkpointing=True)
|
|
x_grad = checkpoint(self.grad_lr_conv, x_grad)
|
|
x_grad = checkpoint(self.grad_hr_conv, x_grad)
|
|
x_out_branch = checkpoint(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 = checkpoint(self.final_lr_conv, x__branch_pretrain_cat)
|
|
x_out = checkpoint(self.upsample, x_out)
|
|
x_out = checkpoint(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, net):
|
|
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 |