181 lines
8.7 KiB
Python
181 lines
8.7 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
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from switched_conv_util import save_attention_to_image
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class ConvBnLelu(nn.Module):
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def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, lelu=True, bn=True):
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super(ConvBnLelu, self).__init__()
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padding_map = {1: 0, 3: 1, 5: 2, 7: 3}
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assert kernel_size in padding_map.keys()
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self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size])
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if bn:
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self.bn = nn.BatchNorm2d(filters_out)
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else:
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self.bn = None
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if lelu:
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self.lelu = nn.LeakyReLU(negative_slope=.1)
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else:
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self.lelu = None
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def forward(self, x):
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x = self.conv(x)
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if self.bn:
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x = self.bn(x)
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if self.lelu:
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return self.lelu(x)
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else:
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return x
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class ResidualBranch(nn.Module):
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def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth):
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assert depth >= 2
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super(ResidualBranch, self).__init__()
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self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, bn=False)] +
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[ConvBnLelu(filters_mid, filters_mid, kernel_size, bn=False) for i in range(depth-2)] +
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[ConvBnLelu(filters_mid, filters_out, kernel_size, lelu=False, bn=False)])
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self.scale = nn.Parameter(torch.ones(1))
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self.bias = nn.Parameter(torch.zeros(1))
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def forward(self, x):
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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)
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self.bnconv2 = ConvBnLelu(filters * 2, filters * 2)
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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(ConvBnLelu(current_filters, current_filters + filter_growth))
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current_filters += filter_growth
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return nn.Sequential(*convs), current_filters
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class SwitchComputer(nn.Module):
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def __init__(self, channels_in, filters, growth, transform_block, transform_count, reduction_blocks, processing_blocks=0, init_temp=20):
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super(SwitchComputer, self).__init__()
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self.filter_conv = ConvBnLelu(channels_in, filters)
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self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(filters * 2 ** i) for i in range(reduction_blocks)])
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final_filters = filters * 2 ** reduction_blocks
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self.processing_blocks, final_filters = create_sequential_growing_processing_block(final_filters, growth, processing_blocks)
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proc_block_filters = max(final_filters // 2, transform_count)
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self.proc_switch_conv = ConvBnLelu(final_filters, proc_block_filters)
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self.final_switch_conv = nn.Conv2d(proc_block_filters, transform_count, 1, 1, 0)
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self.transforms = nn.ModuleList([transform_block() for i in range(transform_count)])
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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.scale = nn.Parameter(torch.ones(1))
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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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xformed = [t.forward(x) for t in self.transforms]
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multiplexer = self.filter_conv(x)
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for block in self.reduction_blocks:
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multiplexer = block.forward(multiplexer)
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for block in self.processing_blocks:
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multiplexer = block.forward(multiplexer)
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multiplexer = self.proc_switch_conv(multiplexer)
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multiplexer = self.final_switch_conv.forward(multiplexer)
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# Interpolate the multiplexer across the entire shape of the image.
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multiplexer = F.interpolate(multiplexer, size=x.shape[2:], mode='nearest')
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outputs, attention = self.switch(xformed, multiplexer, True)
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outputs = outputs * self.scale + 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 ConfigurableSwitchedResidualGenerator(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, trans_filters_mid, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
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heightened_final_step=50000, upsample_factor=1):
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super(ConfigurableSwitchedResidualGenerator, self).__init__()
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switches = []
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for filters, growth, sw_reduce, sw_proc, trans_count, kernel, layers, mid_filters in zip(switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers, trans_filters_mid):
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switches.append(SwitchComputer(3, filters, growth, functools.partial(ResidualBranch, 3, mid_filters, 3, kernel_size=kernel, depth=layers), trans_count, sw_reduce, sw_proc, initial_temp))
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initialize_weights(switches, 1)
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# Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image.
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initialize_weights([s.transforms for s in switches], .2 / len(switches))
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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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# This network is entirely a "repair" network and operates on full-resolution images. Upsample first if that
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# is called for, then repair.
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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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sw_out, att = sw.forward(x, True)
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x = x + sw_out
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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(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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