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 collections import OrderedDict
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from models.archs.arch_util import initialize_weights, ConvBnRelu, ConvBnLelu, ConvBnSilu
from models.archs.RRDBNet_arch import ResidualDenseBlock_5C
from models.archs.spinenet_arch import SpineNet
from switched_conv_util import save_attention_to_image
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):
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, bn=bn, bias=False)] +
[ConvBnLelu(filters_mid, filters_mid, kernel_size, bn=bn, bias=False) for i in range(depth-2)] +
[ConvBnLelu(filters_mid, filters_out, kernel_size, lelu=False, bn=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 = ConvBnLelu(filters, filters * 2, stride=2, bn=False, bias=False)
self.bnconv2 = ConvBnLelu(filters * 2, filters * 2, bn=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):
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convs.append(ConvBnSilu(current_filters, current_filters + filter_growth, bn=True, bias=False))
current_filters += filter_growth
return nn.Sequential(*convs), current_filters
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class ConfigurableSwitchComputer(nn.Module):
def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, init_temp=20,
enable_negative_transforms=False, add_scalable_noise_to_transforms=False, init_scalar=1):
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super(ConfigurableSwitchComputer, self).__init__()
self.enable_negative_transforms = enable_negative_transforms
tc = transform_count
if self.enable_negative_transforms:
tc = transform_count * 2
self.multiplexer = multiplexer_net(tc)
self.pre_transform = pre_transform_block()
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self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
self.add_noise = add_scalable_noise_to_transforms
self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3)))
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# And the switch itself, including learned scalars
self.switch = BareConvSwitch(initial_temperature=init_temp)
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self.switch_scale = nn.Parameter(torch.full((1,), float(init_scalar)))
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.
self.psc_scale = nn.Parameter(torch.full((1,), float(1e-3)))
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self.bias = nn.Parameter(torch.zeros(1))
def forward(self, x, output_attention_weights=False):
identity = x
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if self.add_noise:
rand_feature = torch.randn_like(x) * self.noise_scale
x = x + rand_feature
x = self.pre_transform(x)
xformed = [t.forward(x) for t in self.transforms]
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if self.enable_negative_transforms:
xformed.extend([-t for t in xformed])
m = self.multiplexer(identity)
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# Interpolate the multiplexer across the entire shape of the image.
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
outputs = identity + self.post_switch_conv(outputs) * self.psc_scale
outputs = outputs + self.bias
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if output_attention_weights:
return outputs, attention
else:
return outputs
def set_temperature(self, temp):
self.switch.set_attention_temperature(temp)
class ConvBasisMultiplexer(nn.Module):
def __init__(self, input_channels, base_filters, growth, reductions, processing_depth, multiplexer_channels, use_bn=True):
super(ConvBasisMultiplexer, self).__init__()
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self.filter_conv = ConvBnSilu(input_channels, base_filters, bias=True)
self.reduction_blocks = nn.Sequential(OrderedDict([('block%i:' % (i,), HalvingProcessingBlock(base_filters * 2 ** i)) for i in range(reductions)]))
reduction_filters = base_filters * 2 ** reductions
self.processing_blocks, self.output_filter_count = create_sequential_growing_processing_block(reduction_filters, growth, processing_depth)
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)
self.cbl2 = ConvBnSilu(self.output_filter_count - (gap // 2), self.output_filter_count - (3 * gap // 4), bn=use_bn, bias=False)
self.cbl3 = ConvBnSilu(self.output_filter_count - (3 * gap // 4), multiplexer_channels, bias=True)
def forward(self, x):
x = self.filter_conv(x)
x = self.reduction_blocks(x)
x = self.processing_blocks(x)
x = self.cbl1(x)
x = self.cbl2(x)
x = self.cbl3(x)
return x
class SpineNetMultiplexer(nn.Module):
def __init__(self, input_channels, transform_count):
super(SpineNetMultiplexer, self).__init__()
self.backbone = SpineNet('49', in_channels=input_channels)
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self.rdc1 = ConvBnSilu(256, 128, kernel_size=3, bias=False)
self.rdc2 = ConvBnSilu(128, 64, kernel_size=3, bias=False)
self.rdc3 = ConvBnSilu(64, transform_count, bias=False, bn=False, relu=False)
def forward(self, x):
spine = self.backbone(x)
feat = self.rdc1(spine[0])
feat = self.rdc2(feat)
feat = self.rdc3(feat)
return feat
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class ConfigurableSwitchedResidualGenerator2(nn.Module):
def __init__(self, switch_filters, switch_growths, 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,
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heightened_final_step=50000, upsample_factor=1, enable_negative_transforms=False,
add_scalable_noise_to_transforms=False):
super(ConfigurableSwitchedResidualGenerator2, self).__init__()
switches = []
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self.initial_conv = ConvBnLelu(3, transformation_filters, bn=False, lelu=False, bias=True)
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, bias=True)
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self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, bn=False, bias=True)
self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, bn=False, bias=True)
self.final_conv = ConvBnLelu(transformation_filters, 3, bn=False, lelu=False, bias=True)
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):
multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, filters, growth, sw_reduce, sw_proc, trans_count)
switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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),
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)
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
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assert self.upsample_factor == 2 or self.upsample_factor == 4
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def forward(self, x):
x = self.initial_conv(x)
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self.attentions = []
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swx = x
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)
x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest"))
if self.upsample_factor > 2:
x = F.interpolate(x, scale_factor=2, mode="nearest")
x = self.upconv2(x)
return self.final_conv(self.hr_conv(x)),
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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
class Interpolate(nn.Module):
def __init__(self, factor):
super(Interpolate, self).__init__()
self.factor = factor
def forward(self, x):
return F.interpolate(x, scale_factor=self.factor)
class ConfigurableSwitchedResidualGenerator3(nn.Module):
def __init__(self, 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, enable_negative_transforms=False,
add_scalable_noise_to_transforms=False):
super(ConfigurableSwitchedResidualGenerator3, self).__init__()
switches = []
for trans_count, kernel, layers in zip(trans_counts, trans_kernel_sizes, trans_layers):
multiplx_fn = functools.partial(SpineNetMultiplexer, 3)
switches.append(ConfigurableSwitchComputer(base_filters=3, multiplexer_net=multiplx_fn,
pre_transform_block=functools.partial(nn.Sequential,
ConvBnLelu(3, transformation_filters, kernel_size=1, stride=4, bn=False, lelu=False, bias=False),
ResidualDenseBlock_5C(
transformation_filters),
ResidualDenseBlock_5C(
transformation_filters)),
transform_block=functools.partial(nn.Sequential,
ResidualDenseBlock_5C(transformation_filters),
Interpolate(4),
ConvBnLelu(transformation_filters, transformation_filters // 2, kernel_size=3, bias=False, bn=False),
ConvBnLelu(transformation_filters // 2, 3, kernel_size=1, bias=False, bn=False, lelu=False)),
transform_count=trans_count, init_temp=initial_temp,
enable_negative_transforms=enable_negative_transforms,
add_scalable_noise_to_transforms=add_scalable_noise_to_transforms,
init_scalar=.01))
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):
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):
x, att = sw.forward(x, True)
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))]
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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