316 lines
17 KiB
Python
316 lines
17 KiB
Python
import torch
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from torch import nn
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from switched_conv import BareConvSwitch, compute_attention_specificity
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import torch.nn.functional as F
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import functools
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from collections import OrderedDict
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from models.archs.arch_util import initialize_weights, ConvBnRelu, ConvBnLelu, ConvBnSilu
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from models.archs.RRDBNet_arch import ResidualDenseBlock_5C
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from models.archs.spinenet_arch import SpineNet
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from switched_conv_util import save_attention_to_image
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class MultiConvBlock(nn.Module):
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def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth, scale_init=1, bn=False):
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assert depth >= 2
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super(MultiConvBlock, self).__init__()
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self.noise_scale = nn.Parameter(torch.full((1,), fill_value=.01))
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self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, bn=bn, bias=False)] +
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[ConvBnLelu(filters_mid, filters_mid, kernel_size, bn=bn, bias=False) for i in range(depth-2)] +
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[ConvBnLelu(filters_mid, filters_out, kernel_size, lelu=False, bn=False, bias=False)])
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self.scale = nn.Parameter(torch.full((1,), fill_value=scale_init))
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self.bias = nn.Parameter(torch.zeros(1))
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def forward(self, x, noise=None):
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if noise is not None:
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noise = noise * self.noise_scale
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x = x + noise
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for m in self.bnconvs:
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x = m.forward(x)
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return x * self.scale + self.bias
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# VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation
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# Doubles the input filter count.
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class HalvingProcessingBlock(nn.Module):
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def __init__(self, filters):
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super(HalvingProcessingBlock, self).__init__()
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self.bnconv1 = ConvBnLelu(filters, filters * 2, stride=2, bn=False, bias=False)
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self.bnconv2 = ConvBnLelu(filters * 2, filters * 2, bn=True, bias=False)
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def forward(self, x):
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x = self.bnconv1(x)
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return self.bnconv2(x)
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# Creates a nested series of convolutional blocks. Each block processes the input data in-place and adds
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# filter_growth filters. Return is (nn.Sequential, ending_filters)
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def create_sequential_growing_processing_block(filters_init, filter_growth, num_convs):
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convs = []
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current_filters = filters_init
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for i in range(num_convs):
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convs.append(ConvBnSilu(current_filters, current_filters + filter_growth, bn=True, bias=False))
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current_filters += filter_growth
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return nn.Sequential(*convs), current_filters
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class ConfigurableSwitchComputer(nn.Module):
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def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, init_temp=20,
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enable_negative_transforms=False, add_scalable_noise_to_transforms=False, init_scalar=1):
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super(ConfigurableSwitchComputer, self).__init__()
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self.enable_negative_transforms = enable_negative_transforms
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tc = transform_count
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if self.enable_negative_transforms:
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tc = transform_count * 2
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self.multiplexer = multiplexer_net(tc)
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self.pre_transform = pre_transform_block()
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self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
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self.add_noise = add_scalable_noise_to_transforms
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self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3)))
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# And the switch itself, including learned scalars
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self.switch = BareConvSwitch(initial_temperature=init_temp)
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self.switch_scale = nn.Parameter(torch.full((1,), float(init_scalar)))
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self.post_switch_conv = ConvBnLelu(base_filters, base_filters, bn=False, bias=False)
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# The post_switch_conv gets a near-zero scale. The network can decide to magnify it (or not) depending on its needs.
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self.psc_scale = nn.Parameter(torch.full((1,), float(1e-3)))
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self.bias = nn.Parameter(torch.zeros(1))
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def forward(self, x, output_attention_weights=False):
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identity = x
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if self.add_noise:
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rand_feature = torch.randn_like(x) * self.noise_scale
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x = x + rand_feature
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x = self.pre_transform(x)
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xformed = [t.forward(x) for t in self.transforms]
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if self.enable_negative_transforms:
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xformed.extend([-t for t in xformed])
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m = self.multiplexer(identity)
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# Interpolate the multiplexer across the entire shape of the image.
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m = F.interpolate(m, size=xformed[0].shape[2:], mode='nearest')
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outputs, attention = self.switch(xformed, m, True)
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outputs = identity + outputs * self.switch_scale
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outputs = identity + self.post_switch_conv(outputs) * self.psc_scale
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outputs = outputs + self.bias
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if output_attention_weights:
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return outputs, attention
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else:
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return outputs
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def set_temperature(self, temp):
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self.switch.set_attention_temperature(temp)
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class ConvBasisMultiplexer(nn.Module):
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def __init__(self, input_channels, base_filters, growth, reductions, processing_depth, multiplexer_channels, use_bn=True):
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super(ConvBasisMultiplexer, self).__init__()
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self.filter_conv = ConvBnSilu(input_channels, base_filters, bias=True)
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self.reduction_blocks = nn.Sequential(OrderedDict([('block%i:' % (i,), HalvingProcessingBlock(base_filters * 2 ** i)) for i in range(reductions)]))
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reduction_filters = base_filters * 2 ** reductions
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self.processing_blocks, self.output_filter_count = create_sequential_growing_processing_block(reduction_filters, growth, processing_depth)
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gap = self.output_filter_count - multiplexer_channels
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# Hey silly - if you're going to interpolate later, do it here instead. Then add some processing layers to let the model adjust it properly.
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self.cbl1 = ConvBnSilu(self.output_filter_count, self.output_filter_count - (gap // 2), bn=use_bn, bias=False)
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self.cbl2 = ConvBnSilu(self.output_filter_count - (gap // 2), self.output_filter_count - (3 * gap // 4), bn=use_bn, bias=False)
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self.cbl3 = ConvBnSilu(self.output_filter_count - (3 * gap // 4), multiplexer_channels, bias=True)
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def forward(self, x):
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x = self.filter_conv(x)
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x = self.reduction_blocks(x)
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x = self.processing_blocks(x)
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x = self.cbl1(x)
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x = self.cbl2(x)
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x = self.cbl3(x)
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return x
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class SpineNetMultiplexer(nn.Module):
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def __init__(self, input_channels, transform_count):
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super(SpineNetMultiplexer, self).__init__()
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self.backbone = SpineNet('49', in_channels=input_channels)
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self.rdc1 = ConvBnSilu(256, 128, kernel_size=3, bias=False)
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self.rdc2 = ConvBnSilu(128, 64, kernel_size=3, bias=False)
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self.rdc3 = ConvBnSilu(64, transform_count, bias=False, bn=False, relu=False)
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def forward(self, x):
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spine = self.backbone(x)
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feat = self.rdc1(spine[0])
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feat = self.rdc2(feat)
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feat = self.rdc3(feat)
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return feat
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class ConfigurableSwitchedResidualGenerator2(nn.Module):
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def __init__(self, switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
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trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
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heightened_final_step=50000, upsample_factor=1, enable_negative_transforms=False,
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add_scalable_noise_to_transforms=False):
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super(ConfigurableSwitchedResidualGenerator2, self).__init__()
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switches = []
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self.initial_conv = ConvBnLelu(3, transformation_filters, bn=False, lelu=False, bias=True)
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self.sw_conv = ConvBnLelu(transformation_filters, transformation_filters, lelu=False, bias=True)
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self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, bn=False, biasd=True)
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self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, bn=False, bias=True)
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self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, bn=False, bias=True)
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self.final_conv = ConvBnLelu(transformation_filters, 3, bn=False, lelu=False, bias=True)
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for filters, growth, sw_reduce, sw_proc, trans_count, kernel, layers in zip(switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers):
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multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, filters, growth, sw_reduce, sw_proc, trans_count)
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switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=functools.partial(ConvBnLelu, transformation_filters, transformation_filters, bn=False, bias=False),
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transform_block=functools.partial(MultiConvBlock, transformation_filters, transformation_filters + growth, transformation_filters, kernel_size=kernel, depth=layers),
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transform_count=trans_count, init_temp=initial_temp, enable_negative_transforms=enable_negative_transforms,
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add_scalable_noise_to_transforms=add_scalable_noise_to_transforms, init_scalar=.2))
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self.switches = nn.ModuleList(switches)
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self.transformation_counts = trans_counts
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self.init_temperature = initial_temp
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self.final_temperature_step = final_temperature_step
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self.heightened_temp_min = heightened_temp_min
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self.heightened_final_step = heightened_final_step
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self.attentions = None
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self.upsample_factor = upsample_factor
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def forward(self, x):
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x = self.initial_conv(x)
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self.attentions = []
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swx = x
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for i, sw in enumerate(self.switches):
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swx, att = sw.forward(swx, True)
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self.attentions.append(att)
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x = swx + self.sw_conv(x)
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assert x == 2 or x == 4
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x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest"))
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if self.upsample_factor > 2:
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x = F.interpolate(x, scale_factor=2, mode="nearest")
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x = self.upconv2(x)
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return self.final_conv(self.hr_conv(x)),
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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, int(self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step))
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if temp == 1 and self.heightened_final_step and self.heightened_final_step != 1:
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# Once the temperature passes (1) it enters an inverted curve to match the linear curve from above.
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# without this, the attention specificity "spikes" incredibly fast in the last few iterations.
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h_steps_total = self.heightened_final_step - self.final_temperature_step
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h_steps_current = min(step - self.final_temperature_step, h_steps_total)
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# The "gap" will represent the steps that need to be traveled as a linear function.
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h_gap = 1 / self.heightened_temp_min
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temp = h_gap * h_steps_current / h_steps_total
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# Invert temperature to represent reality on this side of the curve
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temp = 1 / temp
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self.set_temperature(temp)
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if step % 50 == 0:
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[save_attention_to_image(experiments_path, self.attentions[i], self.transformation_counts[i], step, "a%i" % (i+1,)) for i in range(len(self.switches))]
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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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class Interpolate(nn.Module):
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def __init__(self, factor):
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super(Interpolate, self).__init__()
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self.factor = factor
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def forward(self, x):
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return F.interpolate(x, scale_factor=self.factor)
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class ConfigurableSwitchedResidualGenerator3(nn.Module):
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def __init__(self, trans_counts,
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trans_kernel_sizes,
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trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000,
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heightened_temp_min=1,
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heightened_final_step=50000, upsample_factor=1, enable_negative_transforms=False,
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add_scalable_noise_to_transforms=False):
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super(ConfigurableSwitchedResidualGenerator3, self).__init__()
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switches = []
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for trans_count, kernel, layers in zip(trans_counts, trans_kernel_sizes, trans_layers):
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multiplx_fn = functools.partial(SpineNetMultiplexer, 3)
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switches.append(ConfigurableSwitchComputer(base_filters=3, multiplexer_net=multiplx_fn,
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pre_transform_block=functools.partial(nn.Sequential,
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ConvBnLelu(3, transformation_filters, kernel_size=1, stride=4, bn=False, lelu=False, bias=False),
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ResidualDenseBlock_5C(
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transformation_filters),
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ResidualDenseBlock_5C(
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transformation_filters)),
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transform_block=functools.partial(nn.Sequential,
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ResidualDenseBlock_5C(transformation_filters),
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Interpolate(4),
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ConvBnLelu(transformation_filters, transformation_filters // 2, kernel_size=3, bias=False, bn=False),
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ConvBnLelu(transformation_filters // 2, 3, kernel_size=1, bias=False, bn=False, lelu=False)),
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transform_count=trans_count, init_temp=initial_temp,
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enable_negative_transforms=enable_negative_transforms,
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add_scalable_noise_to_transforms=add_scalable_noise_to_transforms,
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init_scalar=.01))
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self.switches = nn.ModuleList(switches)
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self.transformation_counts = trans_counts
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self.init_temperature = initial_temp
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self.final_temperature_step = final_temperature_step
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self.heightened_temp_min = heightened_temp_min
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self.heightened_final_step = heightened_final_step
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self.attentions = None
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self.upsample_factor = upsample_factor
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def forward(self, x):
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if self.upsample_factor > 1:
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x = F.interpolate(x, scale_factor=self.upsample_factor, mode="nearest")
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self.attentions = []
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for i, sw in enumerate(self.switches):
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x, att = sw.forward(x, True)
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self.attentions.append(att)
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return x,
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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, int(
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self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step))
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if temp == 1 and self.heightened_final_step and self.heightened_final_step != 1:
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# Once the temperature passes (1) it enters an inverted curve to match the linear curve from above.
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# without this, the attention specificity "spikes" incredibly fast in the last few iterations.
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h_steps_total = self.heightened_final_step - self.final_temperature_step
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h_steps_current = min(step - self.final_temperature_step, h_steps_total)
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# The "gap" will represent the steps that need to be traveled as a linear function.
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h_gap = 1 / self.heightened_temp_min
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temp = h_gap * h_steps_current / h_steps_total
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# Invert temperature to represent reality on this side of the curve
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temp = 1 / temp
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self.set_temperature(temp)
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if step % 50 == 0:
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[save_attention_to_image(experiments_path, self.attentions[i], self.transformation_counts[i], step,
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"a%i" % (i + 1,)) for i in range(len(self.switches))]
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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 |