DL-Art-School/codes/models/archs/SwitchedResidualGenerator_arch.py

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import torch
from torch import nn
from switched_conv import BareConvSwitch, compute_attention_specificity
import torch.nn.functional as F
import functools
from models.archs.arch_util import initialize_weights
from switched_conv_util import save_attention_to_image
class ConvBnLelu(nn.Module):
def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, lelu=True):
super(ConvBnLelu, self).__init__()
padding_map = {1: 0, 3: 1, 5: 2, 7: 3}
assert kernel_size in padding_map.keys()
self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size])
self.bn = nn.BatchNorm2d(filters_out)
if lelu:
self.lelu = nn.LeakyReLU(negative_slope=.1)
else:
self.lelu = None
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.lelu:
return self.lelu(x)
else:
return x
class ResidualBranch(nn.Module):
def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth):
assert depth >= 2
super(ResidualBranch, self).__init__()
self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size)] +
[ConvBnLelu(filters_mid, filters_mid, kernel_size) for i in range(depth-2)] +
[ConvBnLelu(filters_mid, filters_out, kernel_size, lelu=False)])
self.scale = nn.Parameter(torch.ones(1))
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, x):
for m in self.bnconvs:
x = m.forward(x)
return x * self.scale + self.bias
# VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation
# Doubles the input filter count.
class HalvingProcessingBlock(nn.Module):
def __init__(self, filters):
super(HalvingProcessingBlock, self).__init__()
self.bnconv1 = ConvBnLelu(filters, filters * 2, stride=2)
self.bnconv2 = ConvBnLelu(filters * 2, filters * 2)
def forward(self, x):
x = self.bnconv1(x)
return self.bnconv2(x)
class SwitchComputer(nn.Module):
def __init__(self, channels_in, filters, transform_block, transform_count, reduction_blocks, processing_blocks=0, init_temp=20):
super(SwitchComputer, self).__init__()
self.filter_conv = ConvBnLelu(channels_in, filters)
self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(filters * 2 ** i) for i in range(reduction_blocks)])
final_filters = filters * 2 ** reduction_blocks
self.processing_blocks = nn.ModuleList([ConvBnLelu(final_filters, final_filters) for i in range(processing_blocks)])
proc_block_filters = max(final_filters // 2, transform_count)
self.proc_switch_conv = ConvBnLelu(final_filters, proc_block_filters)
self.final_switch_conv = nn.Conv2d(proc_block_filters, transform_count, 1, 1, 0)
self.transforms = nn.ModuleList([transform_block() for i in range(transform_count)])
# And the switch itself, including learned scalars
self.switch = BareConvSwitch(initial_temperature=init_temp)
self.scale = nn.Parameter(torch.ones(1))
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, x, output_attention_weights=False):
xformed = [t.forward(x) for t in self.transforms]
multiplexer = self.filter_conv(x)
for block in self.reduction_blocks:
multiplexer = block.forward(multiplexer)
for block in self.processing_blocks:
multiplexer = block.forward(multiplexer)
multiplexer = self.proc_switch_conv(multiplexer)
multiplexer = self.final_switch_conv.forward(multiplexer)
# Interpolate the multiplexer across the entire shape of the image.
multiplexer = F.interpolate(multiplexer, size=x.shape[2:], mode='nearest')
outputs, attention = self.switch(xformed, multiplexer, True)
outputs = outputs * self.scale + self.bias
if output_attention_weights:
return outputs, attention
else:
return outputs
def set_temperature(self, temp):
self.switch.set_attention_temperature(temp)
class ConfigurableSwitchedResidualGenerator(nn.Module):
def __init__(self, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
trans_layers, trans_filters_mid, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
heightened_final_step=50000, upsample_factor=1):
super(ConfigurableSwitchedResidualGenerator, self).__init__()
switches = []
for filters, sw_reduce, sw_proc, trans_count, kernel, layers, mid_filters in zip(switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers, trans_filters_mid):
switches.append(SwitchComputer(3, filters, functools.partial(ResidualBranch, 3, mid_filters, 3, kernel_size=kernel, depth=layers), trans_count, sw_reduce, sw_proc, initial_temp))
initialize_weights(switches, 1)
# Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image.
initialize_weights([s.transforms for s in switches], .2 / len(switches))
self.switches = nn.ModuleList(switches)
self.transformation_counts = trans_counts
self.init_temperature = initial_temp
self.final_temperature_step = final_temperature_step
self.heightened_temp_min = heightened_temp_min
self.heightened_final_step = heightened_final_step
self.attentions = None
self.upsample_factor = upsample_factor
def forward(self, x):
# This network is entirely a "repair" network and operates on full-resolution images. Upsample first if that
# is called for, then repair.
if self.upsample_factor > 1:
x = F.interpolate(x, scale_factor=self.upsample_factor, mode="nearest")
self.attentions = []
for i, sw in enumerate(self.switches):
sw_out, att = sw.forward(x, True)
x = x + sw_out
self.attentions.append(att)
return x,
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, int(self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step))
if temp == 1 and self.heightened_final_step and self.heightened_final_step != 1:
# Once the temperature passes (1) it enters an inverted curve to match the linear curve from above.
# without this, the attention specificity "spikes" incredibly fast in the last few iterations.
h_steps_total = self.heightened_final_step - self.final_temperature_step
h_steps_current = min(step - self.final_temperature_step, h_steps_total)
# The "gap" will represent the steps that need to be traveled as a linear function.
h_gap = 1 / self.heightened_temp_min
temp = h_gap * h_steps_current / h_steps_total
# Invert temperature to represent reality on this side of the curve
temp = 1 / temp
self.set_temperature(temp)
if step % 50 == 0:
[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))]
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