import torch
from torch import nn
from models.archs.arch_util import ConvBnLelu, ConvBnRelu, MultiConvBlock
from switched_conv import BareConvSwitch, compute_attention_specificity
from switched_conv_util import save_attention_to_image
from functools import partial
import torch.nn.functional as F
from collections import OrderedDict
class Switch(nn.Module):
def __init__(self, transform_block, transform_count, init_temp=20, pass_chain_forward=False, add_scalable_noise_to_transforms=False):
super(Switch, self).__init__()
self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
self.add_noise = add_scalable_noise_to_transforms
self.pass_chain_forward = pass_chain_forward
# 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))
# x is the input fed to the transform blocks.
# m is the output of the multiplexer which will be used to select from those transform blocks.
# chain is a chain of shared processing outputs used by the individual transforms.
def forward(self, x, m, chain):
if self.pass_chain_forward:
pcf = [t(x, chain) for t in self.transforms]
xformed = [o[0] for o in pcf]
atts = [o[1] for o in pcf]
else:
if self.add_noise:
rand_feature = torch.randn_like(x)
xformed = [t(x, rand_feature) for t in self.transforms]
else:
xformed = [t(x) for t in self.transforms]
# Interpolate the multiplexer across the entire shape of the image.
m = F.interpolate(m, size=x.shape[2:], mode='nearest')
outputs, attention = self.switch(xformed, m, True)
outputs = outputs * self.scale + self.bias
if self.pass_chain_forward:
# Apply attention weights to collected [atts] and return the aggregate.
atts = torch.stack(atts, dim=3)
attention = atts * attention.unsqueeze(dim=-1)
attention = torch.flatten(attention, 3)
return outputs, attention
def set_temperature(self, temp):
self.switch.set_attention_temperature(temp)
if self.pass_chain_forward:
[t.set_temperature(temp) for t in self.transforms]
# Convolutional image processing block that optionally reduces image size by a factor of 2 using stride and performs a
# series of conv blocks on it
class Processor(nn.Module):
def __init__(self, base_filters, processing_depth, reduce=False):
super(Processor, self).__init__()
self.output_filter_count = base_filters * (2 if reduce else 1)
self.pre = ConvBnRelu(base_filters, base_filters, kernel_size=3, bias=True)
self.initial = ConvBnRelu(base_filters, self.output_filter_count, kernel_size=1, stride=2 if reduce else 1, bias=False)
self.blocks = nn.Sequential(OrderedDict(
[(str(i), ConvBnRelu(self.output_filter_count, self.output_filter_count, kernel_size=3, bias=False)) for i in range(processing_depth)]))
def forward(self, x):
x = self.pre(x)
x = self.initial(x)
x = self.blocks(x)
return (x - .39) / .58
# Convolutional image processing block that constricts an input image with a large number of filters to a small number
# of filters over a fixed number of layers.
class Constrictor(nn.Module):
def __init__(self, filters, output_filters):
super(Constrictor, self).__init__()
assert(filters > output_filters)
gap = filters - output_filters
gap_div_4 = int(gap / 4)
self.cbl1 = ConvBnRelu(filters, filters - (gap_div_4 * 2), kernel_size=1, norm=True, bias=True)
self.cbl2 = ConvBnRelu(filters - (gap_div_4 * 2), filters - (gap_div_4 * 3), kernel_size=1, norm=True, bias=False)
self.cbl3 = ConvBnRelu(filters - (gap_div_4 * 3), output_filters, kernel_size=1, activation=False, norm=False, bias=False)
def forward(self, x):
x = self.cbl1(x)
x = self.cbl2(x)
x = self.cbl3(x)
return x / 2.67
class RecursiveSwitchedTransform(nn.Module):
def __init__(self, transform_filters, filters_count_list, nesting_depth, transforms_at_leaf,
trans_kernel_size, trans_num_layers, trans_scale_init=1, initial_temp=20, add_scalable_noise_to_transforms=False):
super(RecursiveSwitchedTransform, self).__init__()
self.depth = nesting_depth
at_leaf = (self.depth == 0)
if at_leaf:
transform = partial(MultiConvBlock, transform_filters, transform_filters, transform_filters, kernel_size=trans_kernel_size, depth=trans_num_layers, scale_init=trans_scale_init)
else:
transform = partial(RecursiveSwitchedTransform, transform_filters, filters_count_list,
nesting_depth - 1, transforms_at_leaf, trans_kernel_size, trans_num_layers, trans_scale_init, initial_temp, add_scalable_noise_to_transforms)
selection_breadth = transforms_at_leaf if at_leaf else 2
self.switch = Switch(transform, selection_breadth, initial_temp, pass_chain_forward=not at_leaf, add_scalable_noise_to_transforms=add_scalable_noise_to_transforms)
self.multiplexer = Constrictor(filters_count_list[self.depth], selection_breadth)
def forward(self, x, processing_trunk_chain):
proc_out = processing_trunk_chain[self.depth]
m = self.multiplexer(proc_out)
return self.switch(x, m, processing_trunk_chain)
def set_temperature(self, temp):
self.switch.set_temperature(temp)
class NestedSwitchComputer(nn.Module):
def __init__(self, transform_filters, switch_base_filters, num_switch_processing_layers, nesting_depth, transforms_at_leaf,
trans_kernel_size, trans_num_layers, trans_scale_init, initial_temp=20, add_scalable_noise_to_transforms=False):
super(NestedSwitchComputer, self).__init__()
processing_trunk = []
filters = []
current_filters = switch_base_filters
reduce = False # Don't reduce the first layer, but reduce after that.
for _ in range(nesting_depth):
processing_trunk.append(Processor(current_filters, num_switch_processing_layers, reduce=reduce))
current_filters = processing_trunk[-1].output_filter_count
filters.append(current_filters)
reduce = True
self.multiplexer_init_conv = ConvBnLelu(transform_filters, switch_base_filters, kernel_size=7, activation=False, norm=False)
self.processing_trunk = nn.ModuleList(processing_trunk)
self.switch = RecursiveSwitchedTransform(transform_filters, filters, nesting_depth-1, transforms_at_leaf, trans_kernel_size, trans_num_layers-1, trans_scale_init, initial_temp=initial_temp, add_scalable_noise_to_transforms=add_scalable_noise_to_transforms)
self.anneal = ConvBnLelu(transform_filters, transform_filters, kernel_size=1, norm=False)
def forward(self, x):
feed_forward = x
trunk = []
trunk_input = self.multiplexer_init_conv(x)
for m in self.processing_trunk:
trunk_input = (m(trunk_input) - 3.3) / 12.5
trunk.append(trunk_input)
self.trunk = trunk[-1]
x, att = self.switch(x, trunk)
x = x + feed_forward
return feed_forward + self.anneal(x) / .86, att
def set_temperature(self, temp):
self.switch.set_temperature(temp)
class NestedSwitchedGenerator(nn.Module):
def __init__(self, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
heightened_final_step=50000, upsample_factor=1, add_scalable_noise_to_transforms=False):
super(NestedSwitchedGenerator, self).__init__()
self.initial_conv = ConvBnLelu(3, transformation_filters, kernel_size=7, activation=False, norm=False)
self.proc_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False)
self.final_conv = ConvBnLelu(transformation_filters, 3, kernel_size=1, activation=False, norm=False)
switches = []
for sw_reduce, sw_proc, trans_count, kernel, layers in zip(switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers):
switches.append(NestedSwitchComputer(transform_filters=transformation_filters, switch_base_filters=switch_filters, num_switch_processing_layers=sw_proc,
nesting_depth=sw_reduce, transforms_at_leaf=trans_count, trans_kernel_size=kernel, trans_num_layers=layers,
trans_scale_init=.2, initial_temp=initial_temp, add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
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):
x = self.initial_conv(x) / .2
self.attentions = []
for i, sw in enumerate(self.switches):
x, att = sw(x)
self.attentions.append(att)
if self.upsample_factor > 1:
x = F.interpolate(x, scale_factor=self.upsample_factor, mode="nearest")
x = self.proc_conv(x) / .85
x = self.final_conv(x) / 4.6
return x / 16,
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.switch.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