From 4d29b7729e4d2e9f57f91df09f991e6d93ff0b31 Mon Sep 17 00:00:00 2001
From: James Betker <jbetker@gmail.com>
Date: Sun, 27 Sep 2020 11:18:45 -0600
Subject: [PATCH] Model arch cleanup

---
 codes/models/archs/AttentionResnet.py         |  80 -------
 .../models/archs/FlatProcessorNetNew_arch.py  | 134 -----------
 codes/models/archs/FlatProcessorNet_arch.py   | 122 ----------
 codes/models/archs/HighToLowResNet.py         |  86 -------
 codes/models/archs/NestedSwitchGenerator.py   | 226 ------------------
 5 files changed, 648 deletions(-)
 delete mode 100644 codes/models/archs/AttentionResnet.py
 delete mode 100644 codes/models/archs/FlatProcessorNetNew_arch.py
 delete mode 100644 codes/models/archs/FlatProcessorNet_arch.py
 delete mode 100644 codes/models/archs/HighToLowResNet.py
 delete mode 100644 codes/models/archs/NestedSwitchGenerator.py

diff --git a/codes/models/archs/AttentionResnet.py b/codes/models/archs/AttentionResnet.py
deleted file mode 100644
index ea57889a..00000000
--- a/codes/models/archs/AttentionResnet.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import torch
-import torch.nn as nn
-import numpy as np
-import torch.nn.functional as F
-
-def conv3x3(in_planes, out_planes, stride=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
-
-def conv5x5(in_planes, out_planes, stride=1):
-    """5x5 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=5, stride=stride,
-                     padding=2, bias=False)
-
-def conv7x7(in_planes, out_planes, stride=1):
-    """7x7 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=7, stride=stride,
-                     padding=3, bias=False)
-
-def conv1x1(in_planes, out_planes, stride=1):
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-class SequenceDistributed(nn.Module):
-    def __init__(self, module, batch_first=False):
-        super(SequenceDistributed, self).__init__()
-        self.module = module
-        self.batch_first = batch_first
-
-    def forward(self, x):
-        if len(x.size()) <= 2:
-            return self.module(x)
-
-        # Squash samples and timesteps into a single axis
-        x_reshape = x.contiguous().view(-1, x.size(-1))  # (samples * timesteps, input_size)
-
-        y = self.module(x_reshape)
-
-        # We have to reshape Y
-        if self.batch_first:
-            y = y.contiguous().view(x.size(0), -1, y.size(-1))  # (samples, timesteps, output_size)
-        else:
-            y = y.view(-1, x.size(1), y.size(-1))  # (timesteps, samples, output_size)
-
-        return y
-
-# Input into this block is of shape (sequence, filters, width, height)
-# Output is (attention_hidden_size, width, height)
-class ConvAttentionBlock(nn.Module):
-
-    def __init__(self, planes, attention_hidden_size=8, query_conv=conv1x1, key_conv=conv1x1, value_conv=conv1x1):
-        super(ConvAttentionBlock, self).__init__()
-        self.query_conv_dist = SequenceDistributed(query_conv(planes, attention_hidden_size))
-        self.key_conv_dist = SequenceDistributed(key_conv(planes, attention_hidden_size))
-        self.value_conv_dist = value_conv(planes, attention_hidden_size)
-        self.hidden_size = attention_hidden_size
-
-    def forward(self, x):
-        # All values come out of this with the shape (batch, sequence, hidden, width, height)
-        query = self.query_conv_dist(x)
-        key = self.key_conv_dist(x)
-        value = self.value_conv_dist(x)
-
-        # Permute to (batch, width, height, sequence, hidden)
-        query = query.permute(0, 3, 4, 1, 2)
-        key = key.permute(0, 3, 4, 1, 2)
-        value = value.permute(0, 3, 4, 1, 2)
-
-        # Perform attention operation.
-        scores = torch.matmul(query, key.transpose(-2, -1)) / torch.sqrt(self.hidden_size)
-        scores = torch.softmax(scores, dim=-1)
-        result = torch.matmul(scores, value)
-
-        # Collapse out the sequence dim.
-        result = torch.sum(result, dim=-2)
-
-        # Permute back to (batch, hidden, width, height)
-        result = result.permute(0, 3, 1, 2)
-        return result
diff --git a/codes/models/archs/FlatProcessorNetNew_arch.py b/codes/models/archs/FlatProcessorNetNew_arch.py
deleted file mode 100644
index bc164fcf..00000000
--- a/codes/models/archs/FlatProcessorNetNew_arch.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import torch
-import torch.nn as nn
-import numpy as np
-import torch.nn.functional as F
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-class FixupBasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(FixupBasicBlock, self).__init__()
-        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
-        self.bias1a = nn.Parameter(torch.zeros(1))
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = nn.BatchNorm2d(planes, affine=True)
-        self.bias1b = nn.Parameter(torch.zeros(1))
-        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-        self.bias2a = nn.Parameter(torch.zeros(1))
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = nn.BatchNorm2d(planes, affine=True)
-        self.scale = nn.Parameter(torch.ones(1))
-        self.bias2b = nn.Parameter(torch.zeros(1))
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-
-        out = self.conv1(x + self.bias1a)
-        out = self.lrelu(out + self.bias1b)
-
-        out = self.conv2(out + self.bias2a)
-        out = out * self.scale + self.bias2b
-
-        if self.downsample is not None:
-            identity = self.downsample(x + self.bias1a)
-
-        out += identity
-        out = self.lrelu(out)
-
-        return out
-
-
-class FixupResNet(nn.Module):
-
-    def __init__(self, block, num_filters, layers, num_classes=1000):
-        super(FixupResNet, self).__init__()
-        self.num_layers = sum(layers)
-        self.bias1 = nn.Parameter(torch.zeros(1))
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        self.pixel_shuffle = nn.PixelShuffle(2)
-
-        # 4 input channels, including the noise.
-        self.conv1 = nn.Conv2d(4, num_filters, kernel_size=7, stride=2, padding=3,
-                               bias=False)
-
-        self.inplanes = num_filters
-        self.down_layer1 = self._make_layer(block, num_filters, layers[0])
-        self.down_layer2 = self._make_layer(block, num_filters, layers[1], stride=2)
-        self.down_layer3 = self._make_layer(block, num_filters * 4, layers[2], stride=2)
-        self.down_layer4 = self._make_layer(block, num_filters * 16, layers[3], stride=2)
-
-        self.inplanes = num_filters * 4
-        self.up_layer1 = self._make_layer(block, num_filters * 4, layers[4], stride=1)
-        self.inplanes = num_filters
-        self.up_layer2 = self._make_layer(block, num_filters, layers[5], stride=1)
-
-        self.defilter = nn.Conv2d(num_filters, 3, kernel_size=5, stride=1, padding=2, bias=False)
-
-        for m in self.modules():
-            if isinstance(m, FixupBasicBlock):
-                nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.5))
-                nn.init.constant_(m.conv2.weight, 0)
-                if m.downsample is not None:
-                    nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
-            elif isinstance(m, nn.Linear):
-                nn.init.constant_(m.weight, 0)
-                nn.init.constant_(m.bias, 0)
-
-    def _make_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = conv1x1(self.inplanes, planes * block.expansion, stride)
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        skip = x
-
-        # Noise has the same shape as the input with only one channel.
-        rand_feature = torch.randn((x.shape[0], 1) + x.shape[2:], device=x.device, dtype=x.dtype)
-        x = torch.cat([x, rand_feature], dim=1)
-
-        x = self.conv1(x)
-        x = self.lrelu(x + self.bias1)
-
-        x = self.down_layer1(x)
-        x = self.down_layer2(x)
-        x = self.down_layer3(x)
-        x = self.down_layer4(x)
-
-        x = self.pixel_shuffle(x)
-        x = self.up_layer1(x)
-        x = self.pixel_shuffle(x)
-        x = self.up_layer2(x)
-
-        x = self.defilter(x)
-
-        base = F.interpolate(skip, scale_factor=.25, mode='bilinear', align_corners=False)
-        return x + base
-
-
-def fixup_resnet34(num_filters, **kwargs):
-    """Constructs a Fixup-ResNet-34 model.
-    """
-    model = FixupResNet(FixupBasicBlock, num_filters, [3, 4, 6, 3, 2, 2], **kwargs)
-    return model
\ No newline at end of file
diff --git a/codes/models/archs/FlatProcessorNet_arch.py b/codes/models/archs/FlatProcessorNet_arch.py
deleted file mode 100644
index 504487b8..00000000
--- a/codes/models/archs/FlatProcessorNet_arch.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import functools
-import torch.nn as nn
-import torch.nn.functional as F
-import models.archs.arch_util as arch_util
-import torch
-
-class ReduceAnnealer(nn.Module):
-    '''
-    Reduces an image dimensionality by half and performs a specified number of residual blocks on it before
-    `annealing` the filter count to the same as the input filter count.
-
-    To reduce depth, accepts an interpolated "trunk" input which is summed with the output of the RA block before
-    returning.
-
-    Returns a tuple in the forward pass. The first return is the annealed output. The second is the output before
-    annealing (e.g. number_filters=input*4) which can be be used for upsampling.
-    '''
-
-    def __init__(self, number_filters, residual_blocks):
-        super(ReduceAnnealer, self).__init__()
-        self.reducer = nn.Conv2d(number_filters, number_filters*4, 3, stride=2, padding=1, bias=True)
-        self.res_trunk = arch_util.make_layer(functools.partial(arch_util.ResidualBlock, nf=number_filters*4), residual_blocks)
-        self.annealer = nn.Conv2d(number_filters*4, number_filters, 3, stride=1, padding=1, bias=True)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-        arch_util.initialize_weights([self.reducer, self.annealer], .1)
-        self.bn_reduce = nn.BatchNorm2d(number_filters*4, affine=True)
-        self.bn_anneal = nn.BatchNorm2d(number_filters*4, affine=True)
-
-    def forward(self, x, interpolated_trunk):
-        out = self.lrelu(self.bn_reduce(self.reducer(x)))
-        out = self.lrelu(self.bn_anneal(self.res_trunk(out)))
-        annealed = self.lrelu(self.annealer(out)) + interpolated_trunk
-        return annealed, out
-
-class Assembler(nn.Module):
-    '''
-    Upsamples a given input using PixelShuffle. Then upsamples this input further and adds in a residual raw input from
-    a corresponding upstream ReduceAnnealer. Finally performs processing using ResNet blocks.
-    '''
-    def __init__(self, number_filters, residual_blocks):
-        super(Assembler, self).__init__()
-        self.pixel_shuffle = nn.PixelShuffle(2)
-        self.upsampler = nn.Conv2d(number_filters, number_filters*4, 3, stride=1, padding=1, bias=True)
-        self.res_trunk = arch_util.make_layer(functools.partial(arch_util.ResidualBlock, nf=number_filters*4), residual_blocks)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-        self.bn = nn.BatchNorm2d(number_filters*4, affine=True)
-        self.bn_up = nn.BatchNorm2d(number_filters*4, affine=True)
-
-    def forward(self, input, skip_raw):
-        out = self.pixel_shuffle(input)
-        out = self.bn_up(self.upsampler(out)) + skip_raw
-        out = self.lrelu(self.bn(self.res_trunk(out)))
-        return out
-
-class FlatProcessorNet(nn.Module):
-    '''
-    Specialized network that tries to perform a near-equal amount of processing on each of 5 downsampling steps. Image
-    is then upsampled to a specified size with a similarly flat amount of processing.
-
-    This network automatically applies a noise vector on the inputs to provide entropy for processing.
-     '''
-    def __init__(self, in_nc=3, out_nc=3, nf=64, reduce_anneal_blocks=4, assembler_blocks=2, downscale=4):
-        super(FlatProcessorNet, self).__init__()
-
-        assert downscale in [1, 2, 4], "Requested downscale not supported; %i" % (downscale, )
-        self.downscale = downscale
-
-        # We will always apply a noise channel to the inputs, account for that here.
-        in_nc += 1
-
-        # We need two layers to move the image into the filter space in which we will perform most of the work.
-        self.conv_first = nn.Conv2d(in_nc, nf, 3, stride=1, padding=1, bias=True)
-        self.conv_last = nn.Conv2d(nf*4, out_nc, 3, stride=1, padding=1, bias=True)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-
-        # Torch modules need to have all submodules as explicit class members. So make those, then add them into an
-        # array for easier logic in forward().
-        self.ra1 = ReduceAnnealer(nf, reduce_anneal_blocks)
-        self.ra2 = ReduceAnnealer(nf, reduce_anneal_blocks)
-        self.ra3 = ReduceAnnealer(nf, reduce_anneal_blocks)
-        self.ra4 = ReduceAnnealer(nf, reduce_anneal_blocks)
-        self.ra5 = ReduceAnnealer(nf, reduce_anneal_blocks)
-        self.reducers = [self.ra1, self.ra2, self.ra3, self.ra4, self.ra5]
-
-        # Produce assemblers for all possible downscale variants. Some may not be used.
-        self.assembler1 = Assembler(nf, assembler_blocks)
-        self.assembler2 = Assembler(nf, assembler_blocks)
-        self.assembler3 = Assembler(nf, assembler_blocks)
-        self.assembler4 = Assembler(nf, assembler_blocks)
-        self.assemblers = [self.assembler1, self.assembler2, self.assembler3, self.assembler4]
-
-        # Initialization
-        arch_util.initialize_weights([self.conv_first, self.conv_last], .1)
-
-    def forward(self, x):
-        # Noise has the same shape as the input with only one channel.
-        rand_feature = torch.randn((x.shape[0], 1) + x.shape[2:], device=x.device, dtype=x.dtype)
-        out = torch.cat([x, rand_feature], dim=1)
-
-        out = self.lrelu(self.conv_first(out))
-        features_trunk = out
-        raw_values = []
-        downsamples = 1
-        for ra in self.reducers:
-            downsamples *= 2
-            interpolated = F.interpolate(features_trunk, scale_factor=1/downsamples, mode='bilinear', align_corners=False)
-            out, raw = ra(out, interpolated)
-            raw_values.append(raw)
-
-        i = -1
-        out = raw_values[-1]
-        while downsamples != self.downscale:
-            out = self.assemblers[i](out, raw_values[i-1])
-            i -= 1
-            downsamples = int(downsamples / 2)
-
-        out = self.conv_last(out)
-
-        basis = x
-        if downsamples != 1:
-            basis = F.interpolate(x, scale_factor=1/downsamples, mode='bilinear', align_corners=False)
-        return basis + out
diff --git a/codes/models/archs/HighToLowResNet.py b/codes/models/archs/HighToLowResNet.py
deleted file mode 100644
index 623978c9..00000000
--- a/codes/models/archs/HighToLowResNet.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import functools
-import torch.nn as nn
-import torch.nn.functional as F
-import models.archs.arch_util as arch_util
-import torch
-
-
-class HighToLowResNet(nn.Module):
-    ''' ResNet that applies a noise channel to the input, then downsamples it four times using strides. Finally, the
-     input is upsampled to the desired downscale. Currently downscale=1,2,4 is supported.
-     '''
-    def __init__(self, in_nc=3, out_nc=3, nf=64, nb=16, downscale=4):
-        super(HighToLowResNet, self).__init__()
-
-        assert downscale in [1, 2, 4], "Requested downscale not supported; %i" % (downscale, )
-        self.downscale = downscale
-
-        # We will always apply a noise channel to the inputs, account for that here.
-        in_nc += 1
-
-        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
-
-        # All sub-modules must be explicit members. Make it so. Then add them to a list.
-        self.trunk1 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf), 4)
-        self.trunk2 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*2), 6)
-        self.trunk3 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*4), 12)
-        self.trunk4 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*8), 12)
-        self.trunks = [self.trunk1, self.trunk2, self.trunk3, self.trunk4]
-        self.trunkshapes = [4, 6, 12, 12]
-
-        self.r1 = nn.Conv2d(nf, nf*2, 3, stride=2, padding=1, bias=True)
-        self.r2 = nn.Conv2d(nf*2, nf*4, 3, stride=2, padding=1, bias=True)
-        self.r3 = nn.Conv2d(nf*4, nf*8, 3, stride=2, padding=1, bias=True)
-        self.reducers = [self.r1, self.r2, self.r3]
-
-        self.pixel_shuffle = nn.PixelShuffle(2)
-
-        self.a1 = nn.Conv2d(nf*2, nf*4, 3, stride=1, padding=1, bias=True)
-        self.a2 = nn.Conv2d(nf, nf*4, 3, stride=1, padding=1, bias=True)
-        self.a3 = nn.Conv2d(nf, nf, 3, stride=1, padding=1, bias=True)
-        self.assemblers = [self.a1, self.a2, self.a3]
-
-        if self.downscale == 1:
-            nf_last = nf
-        elif self.downscale == 2:
-            nf_last = nf * 4
-        elif self.downscale == 4:
-            nf_last = nf * 4
-
-        self.conv_last = nn.Conv2d(nf_last, out_nc, 3, stride=1, padding=1, bias=True)
-
-        # activation function
-        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-
-        # initialization
-        arch_util.initialize_weights([self.conv_first, self.conv_last] + self.reducers + self.assemblers,
-                                     .1)
-
-    def forward(self, x):
-        # Noise has the same shape as the input with only one channel.
-        rand_feature = torch.randn((x.shape[0], 1) + x.shape[2:], device=x.device, dtype=x.dtype)
-        out = torch.cat([x, rand_feature], dim=1)
-
-        out = self.lrelu(self.conv_first(out))
-        skips = []
-        for i in range(4):
-            skips.append(out)
-            out = self.trunks[i](out)
-            if i < 3:
-                out = self.lrelu(self.reducers[i](out))
-
-        target_width = x.shape[-1] / self.downscale
-        i = 0
-        while out.shape[-1] != target_width:
-            out = self.pixel_shuffle(out)
-            out = self.lrelu(self.assemblers[i](out))
-            out = out + skips[-i-2]
-            i += 1
-
-        # TODO: Figure out where this magic number '12' comes from and fix it.
-        out = 12 * self.conv_last(out)
-        if self.downscale == 1:
-            base = x
-        else:
-            base = F.interpolate(x, scale_factor=1/self.downscale, mode='bilinear', align_corners=False)
-        return out + base
diff --git a/codes/models/archs/NestedSwitchGenerator.py b/codes/models/archs/NestedSwitchGenerator.py
deleted file mode 100644
index eafbb84b..00000000
--- a/codes/models/archs/NestedSwitchGenerator.py
+++ /dev/null
@@ -1,226 +0,0 @@
-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
\ No newline at end of file