forked from mrq/DL-Art-School
4x spsr ref (not workin)
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James Betker
parent
623f3b99b2
commit
0e859a8082
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@ -470,3 +470,127 @@ class SwitchedSpsrWithRef(nn.Module):
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val["switch_%i_specificity" % (i,)] = means[i]
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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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val["switch_%i_histogram" % (i,)] = hists[i]
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return val
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return val
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class SwitchedSpsrWithRef4x(nn.Module):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
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super(SwitchedSpsrWithRef4x, 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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switch_filters = nf
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self.transformation_counts = xforms
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self.reference_processor = ReferenceImageBranch(transformation_filters)
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multiplx_fn = functools.partial(ReferencingConvMultiplexer, transformation_filters, switch_filters, self.transformation_counts)
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pretransform_fn = functools.partial(AdaInConvBlock, 512, transformation_filters, transformation_filters)
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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.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=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=True)
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self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
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self.stage1_up_fea = UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, 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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mplex_grad = functools.partial(ReferencingConvMultiplexer, nf * 2, nf * 2, self.transformation_counts // 2)
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self.sw_grad = ConfigurableSwitchComputer(transformation_filters, mplex_grad,
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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=True)
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self.stage1_up_grad = UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False)
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# Upsampling
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self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=True, bias=False)
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self.grad_hr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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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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transform_fn_cat = functools.partial(MultiConvBlock, transformation_filters * 2, int(transformation_filters * 1.5),
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transformation_filters, kernel_size=3, depth=4,
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weight_init_factor=.1)
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pretransform_fn_cat = functools.partial(AdaInConvBlock, 512, transformation_filters * 2, transformation_filters * 2)
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self._branch_pretrain_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn_cat, transform_block=transform_fn_cat,
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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=True)
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self.stage2_up_fea = UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False)
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self.stage2_up_grad = UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False)
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self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
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self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=True, bias=False)
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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._branch_pretrain_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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def forward(self, x, ref, center_coord):
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x_grad = self.get_g_nopadding(x)
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ref = self.reference_processor(ref, center_coord)
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x = self.model_fea_conv(x)
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x1, a1 = self.sw1((x, ref), True)
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x2, a2 = self.sw2((x1, ref), True)
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x_fea = self.feature_lr_conv(x2)
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x_fea = self.stage1_up_fea(x_fea)
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x_fea = self.feature_hr_conv2(x_fea)
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x_b_fea = self.b_fea_conv(x_grad)
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x_grad, a3 = self.sw_grad((x_b_fea, ref), att_in=(torch.cat([x1, x_b_fea], dim=1), ref), output_attention_weights=True)
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x_grad = self.grad_lr_conv(x_grad)
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x_grad = self.stage1_up_grad(x_grad)
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x_grad = self.grad_hr_conv(x_grad)
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x_out_branch = self.stage2_up_grad(x_grad)
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x_out_branch = self.grad_branch_output_conv(x_out_branch)
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x__branch_pretrain_cat = torch.cat([x_grad, x_fea], dim=1)
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x__branch_pretrain_cat, a4 = self._branch_pretrain_sw((x__branch_pretrain_cat, ref), att_in=(x_fea, ref), identity=x_fea, output_attention_weights=True)
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x_out = self.final_lr_conv(x__branch_pretrain_cat)
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x_out = self.stage2_up_fea(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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self.attentions = [a1, a2, a3, a4]
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return x_out_branch, 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 % 200 == 0:
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output_path = os.path.join(experiments_path, "attention_maps", "a%i")
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prefix = "attention_map_%i_%%i.png" % (step,)
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[save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
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def get_debug_values(self, step):
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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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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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@ -126,6 +126,10 @@ def define_G(opt, net_key='network_G', scale=None):
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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netG = spsr.SwitchedSpsrWithRef(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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netG = spsr.SwitchedSpsrWithRef(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
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elif which_model == "spsr_switched_with_ref4x":
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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netG = spsr.SwitchedSpsrWithRef4x(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
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# image corruption
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# image corruption
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elif which_model == 'HighToLowResNet':
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elif which_model == 'HighToLowResNet':
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