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, ConvBnRelu, ConvBnLelu, ConvBnSilu
from switched_conv_util import save_attention_to_image
class MultiConvBlock(nn.Module):
def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth, scale_init=1, bn=False):
assert depth >= 2
super(MultiConvBlock, self).__init__()
self.noise_scale = nn.Parameter(torch.full((1,), fill_value=.01))
self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, norm=bn, bias=False)] +
[ConvBnLelu(filters_mid, filters_mid, kernel_size, norm=bn, bias=False) for i in range(depth - 2)] +
[ConvBnLelu(filters_mid, filters_out, kernel_size, activation=False, norm=False, bias=False)])
self.scale = nn.Parameter(torch.full((1,), fill_value=scale_init))
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, x, noise=None):
if noise is not None:
noise = noise * self.noise_scale
x = x + noise
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 = ConvBnSilu(filters, filters * 2, stride=2, norm=False, bias=False)
self.bnconv2 = ConvBnSilu(filters * 2, filters * 2, norm=True, bias=False)
def forward(self, x):
x = self.bnconv1(x)
return self.bnconv2(x)
# Creates a nested series of convolutional blocks. Each block processes the input data in-place and adds
# filter_growth filters. Return is (nn.Sequential, ending_filters)
def create_sequential_growing_processing_block(filters_init, filter_growth, num_convs):
convs = []
current_filters = filters_init
for i in range(num_convs):
convs.append(ConvBnSilu(current_filters, current_filters + filter_growth, norm=True, bias=False))
current_filters += filter_growth
return nn.Sequential(*convs), current_filters
class SwitchComputer(nn.Module):
def __init__(self, channels_in, filters, growth, transform_block, transform_count, reduction_blocks, processing_blocks=0,
init_temp=20, enable_negative_transforms=False, add_scalable_noise_to_transforms=False):
super(SwitchComputer, self).__init__()
self.enable_negative_transforms = enable_negative_transforms
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, final_filters = create_sequential_growing_processing_block(final_filters, growth, processing_blocks)
self.post_interpolate_decimate = ConvBnSilu(final_filters, filters, kernel_size=1, activation=False, norm=False)
self.interpolate_process = ConvBnSilu(filters, filters)
self.interpolate_process2 = ConvBnSilu(filters, filters)
tc = transform_count
if self.enable_negative_transforms:
tc = transform_count * 2
assert filters > tc
self.final_switch_conv = nn.Conv2d(filters, tc, 1, 1, 0)
self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
self.add_noise = add_scalable_noise_to_transforms
# 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):
if self.add_noise:
rand_feature = torch.randn_like(x)
xformed = [t.forward(x, rand_feature) for t in self.transforms]
else:
xformed = [t.forward(x) for t in self.transforms]
if self.enable_negative_transforms:
xformed.extend([-t for t in xformed])
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)
# Interpolate the multiplexer across the entire shape of the image perform some post-processing before feeding into switch.
multiplexer = F.interpolate(multiplexer, size=x.shape[2:], mode='nearest')
multiplexer = self.post_interpolate_decimate(multiplexer)
multiplexer = self.interpolate_process(multiplexer)
multiplexer = self.interpolate_process2(multiplexer)
multiplexer = self.final_switch_conv.forward(multiplexer)
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_growths, 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, enable_negative_transforms=False,
add_scalable_noise_to_transforms=False):
super(ConfigurableSwitchedResidualGenerator, self).__init__()
switches = []
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):
switches.append(SwitchComputer(3, filters, growth, functools.partial(MultiConvBlock, 3, mid_filters, 3, kernel_size=kernel, depth=layers), trans_count, sw_reduce, sw_proc, initial_temp, enable_negative_transforms=enable_negative_transforms, add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
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