From 0e859a8082e6f2f7f7e4fada6f7db0e24d3138cd Mon Sep 17 00:00:00 2001
From: James Betker <jbetker@gmail.com>
Date: Sat, 29 Aug 2020 09:27:18 -0600
Subject: [PATCH] 4x spsr ref (not workin)
---
codes/models/archs/SPSR_arch.py | 124 ++++++++++++++++++++++++++++++++
codes/models/networks.py | 4 ++
2 files changed, 128 insertions(+)
diff --git a/codes/models/archs/SPSR_arch.py b/codes/models/archs/SPSR_arch.py
index 29996bd9..7a4acbc1 100644
--- a/codes/models/archs/SPSR_arch.py
+++ b/codes/models/archs/SPSR_arch.py
@@ -470,3 +470,127 @@ class SwitchedSpsrWithRef(nn.Module):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val
+
+
+class SwitchedSpsrWithRef4x(nn.Module):
+ def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
+ super(SwitchedSpsrWithRef4x, self).__init__()
+ n_upscale = int(math.log(upscale, 2))
+
+ # switch options
+ transformation_filters = nf
+ switch_filters = nf
+ self.transformation_counts = xforms
+ self.reference_processor = ReferenceImageBranch(transformation_filters)
+ multiplx_fn = functools.partial(ReferencingConvMultiplexer, transformation_filters, switch_filters, self.transformation_counts)
+ pretransform_fn = functools.partial(AdaInConvBlock, 512, transformation_filters, transformation_filters)
+ 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.stage1_up_fea = UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=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(ReferencingConvMultiplexer, nf * 2, nf * 2, self.transformation_counts // 2)
+ 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)
+ self.stage1_up_grad = UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False)
+
+ # 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.
+ 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(AdaInConvBlock, 512, transformation_filters * 2, transformation_filters * 2)
+ 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.stage2_up_fea = UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False)
+ self.stage2_up_grad = UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False)
+ 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 = init_temperature
+ self.final_temperature_step = 10000
+
+ def forward(self, x, ref, center_coord):
+ x_grad = self.get_g_nopadding(x)
+ ref = self.reference_processor(ref, center_coord)
+ x = self.model_fea_conv(x)
+
+ x1, a1 = self.sw1((x, ref), True)
+ x2, a2 = self.sw2((x1, ref), True)
+ x_fea = self.feature_lr_conv(x2)
+ x_fea = self.stage1_up_fea(x_fea)
+ 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, ref), att_in=(torch.cat([x1, x_b_fea], dim=1), ref), output_attention_weights=True)
+ x_grad = self.grad_lr_conv(x_grad)
+ x_grad = self.stage1_up_grad(x_grad)
+ x_grad = self.grad_hr_conv(x_grad)
+ x_out_branch = self.stage2_up_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, ref), att_in=(x_fea, ref), identity=x_fea, output_attention_weights=True)
+ x_out = self.final_lr_conv(x__branch_pretrain_cat)
+ x_out = self.stage2_up_fea(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
diff --git a/codes/models/networks.py b/codes/models/networks.py
index 78196ef1..6ebb7935 100644
--- a/codes/models/networks.py
+++ b/codes/models/networks.py
@@ -126,6 +126,10 @@ def define_G(opt, net_key='network_G', scale=None):
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = spsr.SwitchedSpsrWithRef(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
+ elif which_model == "spsr_switched_with_ref4x":
+ xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
+ netG = spsr.SwitchedSpsrWithRef4x(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
+ init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
# image corruption
elif which_model == 'HighToLowResNet':