forked from mrq/DL-Art-School
Add updated spsr net for test
This commit is contained in:
James Betker
parent
55475d2ac1
commit
a18ece62ee
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@ -4,8 +4,8 @@ import torch.nn as nn
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import torch.nn.functional as F
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from models.archs import SPSR_util as B
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from .RRDBNet_arch import RRDB
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from models.archs.arch_util import ConvGnLelu, UpconvBlock, ConjoinBlock
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from models.archs.SwitchedResidualGenerator_arch import MultiConvBlock, ConvBasisMultiplexer, ConfigurableSwitchComputer, ReferencingConvMultiplexer, ReferenceImageBranch, AdaInConvBlock
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from models.archs.arch_util import ConvGnLelu, UpconvBlock, ConjoinBlock, ConvGnSilu
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from models.archs.SwitchedResidualGenerator_arch import MultiConvBlock, ConvBasisMultiplexer, ConfigurableSwitchComputer, ReferencingConvMultiplexer, ReferenceImageBranch, AdaInConvBlock, ProcessingBranchWithStochasticity
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from switched_conv_util import save_attention_to_image_rgb
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from switched_conv import compute_attention_specificity
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import functools
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@ -473,3 +473,133 @@ class SwitchedSpsrWithRef(nn.Module):
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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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class MultiplexerWithReducer(nn.Module):
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def __init__(self, base_filters, multiplx_create_fn, transform_count):
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super(MultiplexerWithReducer, self).__init__()
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self.proc1 = ConvGnSilu(base_filters*2, base_filters*2, bias=False)
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self.proc2 = ConvGnSilu(base_filters*2, base_filters*2, bias=False)
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self.reduce = ConvGnSilu(base_filters*2, base_filters, activation=False, norm=False, bias=True)
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self.conjoin = ConjoinBlock(base_filters)
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self.mplex = multiplx_create_fn(transform_count)
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def forward(self, x, ref):
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x = self.proc1(x)
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x = self.proc2(x)
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x = self.reduce(x)
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return self.mplex(x, ref)
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class SwitchedSpsrWithRef2(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(SwitchedSpsrWithRef2, 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)
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pretransform_fn = functools.partial(AdaInConvBlock, 512, transformation_filters, transformation_filters)
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transform_fn = functools.partial(ProcessingBranchWithStochasticity, transformation_filters, transformation_filters, transformation_filters // 8, 3)
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# For conjoining two input streams.
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conjoin_multiplex_fn = functools.partial(MultiplexerWithReducer, nf, multiplx_fn)
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conjoin_pretransform_fn = functools.partial(AdaInConvBlock, 512, transformation_filters * 2, transformation_filters * 2)
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conjoin_transform_fn = functools.partial(ProcessingBranchWithStochasticity, transformation_filters * 2, transformation_filters, transformation_filters // 8, 4)
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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=False)
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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)
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self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
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self.feature_lr_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.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
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self.sw_grad = ConfigurableSwitchComputer(transformation_filters, conjoin_multiplex_fn,
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pre_transform_block=conjoin_pretransform_fn, transform_block=conjoin_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)
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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_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=True, bias=False)
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self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=True, 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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self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, conjoin_multiplex_fn,
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pre_transform_block=conjoin_pretransform_fn, transform_block=conjoin_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)
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self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
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self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=True, activation=True, bias=False) for _ in range(n_upscale)])
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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=True)
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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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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.feature_lr_conv2(x_fea)
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x_grad = self.grad_conv(x_grad)
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x_grad, a3 = self.sw_grad((torch.cat([x_grad, x1], dim=1), ref),
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identity=x_grad, output_attention_weights=True)
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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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x_out, a4 = self.conjoin_sw((torch.cat([x_fea, x_grad], dim=1), ref),
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identity=x_fea, output_attention_weights=True)
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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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self.attentions = [a1, a2, a3, a4]
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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 % 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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@ -138,6 +138,19 @@ class AdaInConvBlock(nn.Module):
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return self.post_fuse_conv(x)
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class ProcessingBranchWithStochasticity(nn.Module):
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def __init__(self, nf_in, nf_out, noise_filters, depth):
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super(ProcessingBranchWithStochasticity, self).__init__()
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nf_gap = nf_out - nf_in
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self.noise_filters = noise_filters
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self.processor = MultiConvBlock(nf_in + noise_filters, nf_in + nf_gap // 2, nf_out, kernel_size=3, depth=depth, weight_init_factor = .1)
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def forward(self, x):
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b, c, h, w = x.shape
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noise = torch.randn((b, self.noise_filters, h, w), device=x.device)
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return self.processor(torch.cat([x, noise], dim=1))
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# This is similar to ConvBasisMultiplexer, except that it takes a linear reference tensor as a second input to
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# provide better results. It also has fixed parameterization in several places
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class ReferencingConvMultiplexer(nn.Module):
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@ -440,4 +440,4 @@ class UpconvBlock(nn.Module):
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def forward(self, x):
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x = F.interpolate(x, scale_factor=2, mode="nearest")
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return self.process(x)
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return self.process(x)
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@ -1,20 +1,14 @@
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import torch
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import logging
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from munch import munchify
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import models.archs.SRResNet_arch as SRResNet_arch
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import models.archs.discriminator_vgg_arch as SRGAN_arch
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import models.archs.DiscriminatorResnet_arch as DiscriminatorResnet_arch
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import models.archs.DiscriminatorResnet_arch_passthrough as DiscriminatorResnet_arch_passthrough
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import models.archs.FlatProcessorNetNew_arch as FlatProcessorNetNew_arch
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import models.archs.RRDBNet_arch as RRDBNet_arch
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import models.archs.HighToLowResNet as HighToLowResNet
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import models.archs.NestedSwitchGenerator as ng
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import models.archs.feature_arch as feature_arch
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import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
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import models.archs.SRG1_arch as srg1
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import models.archs.ProgressiveSrg_arch as psrg
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import models.archs.SPSR_arch as spsr
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import models.archs.arch_util as arch_util
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import functools
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from collections import OrderedDict
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logger = logging.getLogger('base')
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@ -61,10 +55,13 @@ 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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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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elif which_model == "spsr_switched_with_ref4x":
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elif which_model == "spsr_switched_with_ref2":
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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,
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netG = spsr.SwitchedSpsrWithRef2(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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elif which_model == "csnln":
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import model.csnln as csnln
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netG = csnln.CSNLN(munchify(opt_net))
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else:
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raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))
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@ -32,7 +32,7 @@ def init_dist(backend='nccl', **kwargs):
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def main():
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#### options
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_spsr_switched2_fullimgref_gan_no_branch.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_csnln.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
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help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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