Enable vqvae to use a switched_conv variant

codes/models/SwitchedResidualGenerator_arch.py

@@ -1,594 +0,0 @@
-import functools
-import os
-from collections import OrderedDict
-
-import torch
-import torch.nn.functional as F
-import torchvision
-from torch import nn
-
-from models.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock, ExpansionBlock2, MultiConvBlock
-from models.switched_conv.switched_conv import BareConvSwitch, compute_attention_specificity, AttentionNorm
-from models.switched_conv.switched_conv_util import save_attention_to_image_rgb
-from trainer.networks import register_model
-from utils.util import checkpoint
-
-
-# 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 = ConvGnSilu(filters, filters * 2, stride=2, norm=False, bias=False)
-        self.bnconv2 = ConvGnSilu(filters * 2, filters * 2, norm=True, bias=False)
-
-    def forward(self, x):
-        x = self.bnconv1(x)
-        return self.bnconv2(x)
-
-
-# This is a classic u-net architecture with the goal of assigning each individual pixel an individual transform
-# switching set.
-class ConvBasisMultiplexer(nn.Module):
-    def __init__(self, input_channels, base_filters, reductions, processing_depth, multiplexer_channels, use_gn=True, use_exp2=False):
-        super(ConvBasisMultiplexer, self).__init__()
-        self.filter_conv = ConvGnSilu(input_channels, base_filters, bias=True)
-        self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(base_filters * 2 ** i) for i in range(reductions)])
-        reduction_filters = base_filters * 2 ** reductions
-        self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, bias=False)) for i in range(processing_depth)]))
-        if use_exp2:
-            self.expansion_blocks = nn.ModuleList([ExpansionBlock2(reduction_filters // (2 ** i)) for i in range(reductions)])
-        else:
-            self.expansion_blocks = nn.ModuleList([ExpansionBlock(reduction_filters // (2 ** i)) for i in range(reductions)])
-
-        gap = base_filters - multiplexer_channels
-        cbl1_out = ((base_filters - (gap // 2)) // 4) * 4   # Must be multiples of 4 to use with group norm.
-        self.cbl1 = ConvGnSilu(base_filters, cbl1_out, norm=use_gn, bias=False, num_groups=4)
-        cbl2_out = ((base_filters - (3 * gap // 4)) // 4) * 4
-        self.cbl2 = ConvGnSilu(cbl1_out, cbl2_out, norm=use_gn, bias=False, num_groups=4)
-        self.cbl3 = ConvGnSilu(cbl2_out, multiplexer_channels, bias=True, norm=False)
-
-    def forward(self, x):
-        x = self.filter_conv(x)
-        reduction_identities = []
-        for b in self.reduction_blocks:
-            reduction_identities.append(x)
-            x = b(x)
-        x = self.processing_blocks(x)
-        for i, b in enumerate(self.expansion_blocks):
-            x = b(x, reduction_identities[-i - 1])
-
-        x = self.cbl1(x)
-        x = self.cbl2(x)
-        x = self.cbl3(x)
-        return x
-
-
-# torch.gather() which operates across 2d images.
-def gather_2d(input, index):
-    b, c, h, w = input.shape
-    nodim = input.view(b, c, h * w)
-    ind_nd = index[:, 0]*w + index[:, 1]
-    ind_nd = ind_nd.unsqueeze(1)
-    ind_nd = ind_nd.repeat((1, c))
-    ind_nd = ind_nd.unsqueeze(2)
-    result = torch.gather(nodim, dim=2, index=ind_nd)
-    result = result.squeeze()
-    if b == 1:
-        result = result.unsqueeze(0)
-    return result
-
-
-class ConfigurableSwitchComputer(nn.Module):
-    def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, attention_norm,
-                 post_transform_block=None,
-                 init_temp=20, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=False, post_switch_conv=True,
-                 anorm_multiplier=16):
-        super(ConfigurableSwitchComputer, self).__init__()
-
-        tc = transform_count
-        self.multiplexer = multiplexer_net(tc)
-
-        if pre_transform_block:
-            self.pre_transform = pre_transform_block()
-        else:
-            self.pre_transform = None
-        self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
-        self.add_noise = add_scalable_noise_to_transforms
-        self.feed_transforms_into_multiplexer = feed_transforms_into_multiplexer
-        self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3)))
-
-        # And the switch itself, including learned scalars
-        self.switch = BareConvSwitch(initial_temperature=init_temp, attention_norm=AttentionNorm(transform_count, accumulator_size=anorm_multiplier * transform_count) if attention_norm else None)
-        self.switch_scale = nn.Parameter(torch.full((1,), float(1)))
-        self.post_transform_block = post_transform_block
-        if post_switch_conv:
-            self.post_switch_conv = ConvBnLelu(base_filters, base_filters, norm=False, bias=True)
-            # The post_switch_conv gets a low scale initially. The network can decide to magnify it (or not)
-            # depending on its needs.
-            self.psc_scale = nn.Parameter(torch.full((1,), float(.1)))
-        else:
-            self.post_switch_conv = None
-        self.update_norm = True
-
-    def set_update_attention_norm(self, set_val):
-        self.update_norm = set_val
-
-    # Regarding inputs: it is acceptable to pass in a tuple/list as an input for (x), but the first element
-    # *must* be the actual parameter that gets fed through the network - it is assumed to be the identity.
-    def forward(self, x, att_in=None, identity=None, output_attention_weights=True, fixed_scale=1, do_checkpointing=False,
-                output_att_logits=False):
-        if isinstance(x, tuple):
-            x1 = x[0]
-        else:
-            x1 = x
-
-        if att_in is None:
-            att_in = x
-
-        if identity is None:
-            identity = x1
-
-        if self.add_noise:
-            rand_feature = torch.randn_like(x1) * self.noise_scale
-            if isinstance(x, tuple):
-                x = (x1 + rand_feature,) + x[1:]
-            else:
-                x = x1 + rand_feature
-
-        if not isinstance(x, tuple):
-            x = (x,)
-        if self.pre_transform:
-            x = self.pre_transform(*x)
-        if not isinstance(x, tuple):
-            x = (x,)
-        if do_checkpointing:
-            xformed = [checkpoint(t, *x) for t in self.transforms]
-        else:
-            xformed = [t(*x) for t in self.transforms]
-
-        if not isinstance(att_in, tuple):
-            att_in = (att_in,)
-        if self.feed_transforms_into_multiplexer:
-            att_in = att_in + (torch.stack(xformed, dim=1),)
-        if do_checkpointing:
-            m = checkpoint(self.multiplexer, *att_in)
-        else:
-            m = self.multiplexer(*att_in)
-
-        # It is assumed that [xformed] and [m] are collapsed into tensors at this point.
-        outputs, attention, att_logits = self.switch(xformed, m, True, self.update_norm, output_attention_logits=True)
-        if self.post_transform_block is not None:
-            outputs = self.post_transform_block(outputs)
-
-        outputs = identity + outputs * self.switch_scale * fixed_scale
-        if self.post_switch_conv is not None:
-            outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale * fixed_scale
-        if output_attention_weights:
-            if output_att_logits:
-                return outputs, attention, att_logits
-            else:
-                return outputs, attention
-        else:
-            return outputs
-
-    def set_temperature(self, temp):
-        self.switch.set_attention_temperature(temp)
-
-
-class ConfigurableSwitchedResidualGenerator2(nn.Module):
-    def __init__(self, switch_depth, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
-                 trans_layers, transformation_filters, attention_norm, 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(ConfigurableSwitchedResidualGenerator2, self).__init__()
-        switches = []
-        self.initial_conv = ConvBnLelu(3, transformation_filters, norm=False, activation=False, bias=True)
-        self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
-        self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
-        self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
-        self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
-        for _ in range(switch_depth):
-            multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions, switch_processing_layers, trans_counts)
-            pretransform_fn = functools.partial(ConvBnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
-            transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5), transformation_filters, kernel_size=trans_kernel_sizes, depth=trans_layers, weight_init_factor=.1)
-            switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
-                                                       pre_transform_block=pretransform_fn, transform_block=transform_fn,
-                                                       attention_norm=attention_norm,
-                                                       transform_count=trans_counts, init_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
-        assert self.upsample_factor == 2 or self.upsample_factor == 4
-
-    def forward(self, x):
-        # This is a common bug when evaluating SRG2 generators. It needs to be configured properly in eval mode. Just fail.
-        if not self.train:
-            assert self.switches[0].switch.temperature == 1
-
-        x = self.initial_conv(x)
-
-        self.attentions = []
-        for i, sw in enumerate(self.switches):
-            x, att = sw.forward(x, True)
-            self.attentions.append(att)
-
-        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)
-        x = self.final_conv(self.hr_conv(x))
-        return x, 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,
-                1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)
-            if temp == 1 and self.heightened_final_step and step > self.final_temperature_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:
-                output_path = os.path.join(experiments_path, "attention_maps", "a%i")
-                prefix = "attention_map_%i_%%i.png" % (step,)
-                [save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
-
-    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
-
-
-# Computes a linear latent by performing processing on the reference image and returning the filters of a single point,
-# which should be centered on the image patch being processed.
-#
-# Output is base_filters * 8.
-class ReferenceImageBranch(nn.Module):
-    def __init__(self, base_filters=64):
-        super(ReferenceImageBranch, self).__init__()
-        self.features = nn.Sequential(ConvGnSilu(4, base_filters, kernel_size=7, bias=True),
-                                      HalvingProcessingBlock(base_filters),
-                                      ConvGnSilu(base_filters*2, base_filters*2, activation=True, norm=True, bias=False),
-                                      HalvingProcessingBlock(base_filters*2),
-                                      ConvGnSilu(base_filters*4, base_filters*4, activation=True, norm=True, bias=False),
-                                      HalvingProcessingBlock(base_filters*4),
-                                      ConvGnSilu(base_filters*8, base_filters*8, activation=True, norm=True, bias=False),
-                                      ConvGnSilu(base_filters*8, base_filters*8, activation=True, norm=True, bias=False))
-
-    # center_point is a [b,2] long tensor describing the center point of where the patch was taken from the reference
-    # image.
-    def forward(self, x, center_point):
-        x = self.features(x)
-        return gather_2d(x, center_point // 8)  # Divide by 8 to scale the center_point down.
-
-
-# Mutiplexer that combines a structured embedding with a contextual switch input to guide alterations to that input.
-#
-# Implemented as basically a u-net which reduces the input into the same structural space as the embedding, combines the
-# two, then expands back into the original feature space.
-class EmbeddingMultiplexer(nn.Module):
-    # Note: reductions=2 if the encoder is using interpolated input, otherwise reductions=3.
-    def __init__(self, nf, multiplexer_channels, reductions=2):
-        super(EmbeddingMultiplexer, self).__init__()
-        self.embedding_process = MultiConvBlock(256, 256, 256, kernel_size=3, depth=3, norm=True)
-
-        self.filter_conv = ConvGnSilu(nf, nf, activation=True, norm=False, bias=True)
-        self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(nf * 2 ** i) for i in range(reductions)])
-        reduction_filters = nf * 2 ** reductions
-        self.processing_blocks = nn.Sequential(
-            ConvGnSilu(reduction_filters + 256, reduction_filters + 256, kernel_size=1, activation=True, norm=False, bias=True),
-            ConvGnSilu(reduction_filters + 256, reduction_filters + 128, kernel_size=1, activation=True, norm=True, bias=False),
-            ConvGnSilu(reduction_filters + 128, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False),
-            ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False))
-        self.expansion_blocks = nn.ModuleList([ExpansionBlock2(reduction_filters // (2 ** i)) for i in range(reductions)])
-
-        gap = nf - multiplexer_channels
-        cbl1_out = ((nf - (gap // 2)) // 4) * 4   # Must be multiples of 4 to use with group norm.
-        self.cbl1 = ConvGnSilu(nf, cbl1_out, norm=True, bias=False, num_groups=4)
-        cbl2_out = ((nf - (3 * gap // 4)) // 4) * 4
-        self.cbl2 = ConvGnSilu(cbl1_out, cbl2_out, norm=True, bias=False, num_groups=4)
-        self.cbl3 = ConvGnSilu(cbl2_out, multiplexer_channels, bias=True, norm=False)
-
-    def forward(self, x, embedding):
-        x = self.filter_conv(x)
-        embedding = self.embedding_process(embedding)
-
-        reduction_identities = []
-        for b in self.reduction_blocks:
-            reduction_identities.append(x)
-            x = b(x)
-        x = self.processing_blocks(torch.cat([x, embedding], dim=1))
-        for i, b in enumerate(self.expansion_blocks):
-            x = b(x, reduction_identities[-i - 1])
-
-        x = self.cbl1(x)
-        x = self.cbl2(x)
-        x = self.cbl3(x)
-        return x
-
-
-class QueryKeyMultiplexer(nn.Module):
-    def __init__(self, nf, multiplexer_channels, embedding_channels=256, reductions=2):
-        super(QueryKeyMultiplexer, self).__init__()
-
-        # Blocks used to create the query
-        self.input_process = ConvGnSilu(nf, nf, activation=True, norm=False, bias=True)
-        self.embedding_process = ConvGnSilu(embedding_channels, 256, activation=True, norm=False, bias=True)
-        self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(nf * 2 ** i) for i in range(reductions)])
-        reduction_filters = nf * 2 ** reductions
-        self.processing_blocks = nn.Sequential(
-            ConvGnSilu(reduction_filters + 256, reduction_filters + 256, kernel_size=1, activation=True, norm=False, bias=True),
-            ConvGnSilu(reduction_filters + 256, reduction_filters + 128, kernel_size=1, activation=True, norm=True, bias=False),
-            ConvGnSilu(reduction_filters + 128, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False),
-            ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False))
-        self.expansion_blocks = nn.ModuleList([ExpansionBlock2(reduction_filters // (2 ** i)) for i in range(reductions)])
-
-        # Blocks used to create the key
-        self.key_process = ConvGnSilu(nf, nf, kernel_size=1, activation=True, norm=False, bias=True)
-
-        # Postprocessing blocks.
-        self.query_key_combine = ConvGnSilu(nf*2, nf, kernel_size=1, activation=True, norm=False, bias=False)
-        self.cbl1 = ConvGnSilu(nf, nf // 2, kernel_size=1, norm=True, bias=False, num_groups=4)
-        self.cbl2 = ConvGnSilu(nf // 2, 1, kernel_size=1, norm=False, bias=False)
-
-    def forward(self, x, embedding, transformations):
-        q = self.input_process(x)
-        embedding = self.embedding_process(embedding)
-        reduction_identities = []
-        for b in self.reduction_blocks:
-            reduction_identities.append(q)
-            q = b(q)
-        q = self.processing_blocks(torch.cat([q, embedding], dim=1))
-        for i, b in enumerate(self.expansion_blocks):
-            q = b(q, reduction_identities[-i - 1])
-
-        b, t, f, h, w = transformations.shape
-        k = transformations.view(b * t, f, h, w)
-        k = self.key_process(k)
-
-        q = q.view(b, 1, f, h, w).repeat(1, t, 1, 1, 1).view(b * t, f, h, w)
-        v = self.query_key_combine(torch.cat([q, k], dim=1))
-
-        v = self.cbl1(v)
-        v = self.cbl2(v)
-
-        return v.view(b, t, h, w)
-
-
-class QueryKeyPyramidMultiplexer(nn.Module):
-    def __init__(self, nf, multiplexer_channels, reductions=3):
-        super(QueryKeyPyramidMultiplexer, self).__init__()
-
-        # Blocks used to create the query
-        self.input_process = ConvGnSilu(nf, nf, activation=True, norm=False, bias=True)
-        self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(nf * 2 ** i) for i in range(reductions)])
-        reduction_filters = nf * 2 ** reductions
-        self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, kernel_size=1, norm=True, bias=False)) for i in range(3)]))
-        self.expansion_blocks = nn.ModuleList([ExpansionBlock2(reduction_filters // (2 ** i)) for i in range(reductions)])
-
-        # Blocks used to create the key
-        self.key_process = ConvGnSilu(nf, nf, kernel_size=1, activation=True, norm=False, bias=True)
-
-        # Postprocessing blocks.
-        self.query_key_combine = ConvGnSilu(nf*2, nf, kernel_size=3, activation=True, norm=False, bias=False)
-        self.cbl0 = ConvGnSilu(nf, nf, kernel_size=3, activation=True, norm=True, bias=False)
-        self.cbl1 = ConvGnSilu(nf, nf // 2, kernel_size=1, norm=True, bias=False, num_groups=4)
-        self.cbl2 = ConvGnSilu(nf // 2, 1, kernel_size=1, norm=False, bias=False)
-
-    def forward(self, x, transformations):
-        q = self.input_process(x)
-        reduction_identities = []
-        for b in self.reduction_blocks:
-            reduction_identities.append(q)
-            q = b(q)
-        q = self.processing_blocks(q)
-        for i, b in enumerate(self.expansion_blocks):
-            q = b(q, reduction_identities[-i - 1])
-
-        b, t, f, h, w = transformations.shape
-        k = transformations.view(b * t, f, h, w)
-        k = self.key_process(k)
-
-        q = q.view(b, 1, f, h, w).repeat(1, t, 1, 1, 1).view(b * t, f, h, w)
-        v = self.query_key_combine(torch.cat([q, k], dim=1))
-        v = self.cbl0(v)
-        v = self.cbl1(v)
-        v = self.cbl2(v)
-
-        return v.view(b, t, h, w)
-
-
-# Base class for models that utilize ConfigurableSwitchComputer. Provides basis functionality like logging
-# switch temperature, distribution and images, as well as managing attention norms.
-class SwitchModelBase(nn.Module):
-    def __init__(self, init_temperature=10, final_temperature_step=10000):
-        super(SwitchModelBase, self).__init__()
-        self.switches = []  # The implementing class is expected to set this to a list of all ConfigurableSwitchComputers.
-        self.attentions = []  # The implementing class is expected to set this in forward() to the output of the attention blocks.
-        self.lr = None  # The implementing class is expected to set this to the input image fed into the generator. If not
-                        # set, the attention logger will not output an image reference.
-        self.init_temperature = init_temperature
-        self.final_temperature_step = final_temperature_step
-
-    def set_temperature(self, temp):
-        [sw.set_temperature(temp) for sw in self.switches]
-
-    def update_for_step(self, step, experiments_path='.'):
-        # All-reduce the attention norm.
-        for sw in self.switches:
-            sw.switch.reduce_norm_params()
-
-        temp = max(1, 1 + self.init_temperature *
-                   (self.final_temperature_step - step) / self.final_temperature_step)
-        self.set_temperature(temp)
-        if step % 100 == 0:
-            output_path = os.path.join(experiments_path, "attention_maps")
-            prefix = "amap_%i_a%i_%%i.png"
-            [save_attention_to_image_rgb(output_path, self.attentions[i], self.attentions[i].shape[3], prefix % (step, i), step,
-                                         output_mag=False) for i in range(len(self.attentions))]
-            if self.lr is not None:
-                torchvision.utils.save_image(self.lr[:, :3], os.path.join(experiments_path, "attention_maps",
-                                                                          "amap_%i_base_image.png" % (step,)))
-
-    # This is a bit awkward. We want this plot to show up in TB as a histogram, but we are getting an intensity
-    # plot out of the attention norm tensor. So we need to convert it back into a list of indexes, then feed into TB.
-    def compute_anorm_histogram(self):
-        intensities = [sw.switch.attention_norm.compute_buffer_norm().clone().detach().cpu() for sw in self.switches]
-        result = []
-        for intensity in intensities:
-            intensity = intensity * 10
-            bins = torch.tensor(list(range(len(intensity))))
-            intensity = intensity.long()
-            result.append(bins.repeat_interleave(intensity, 0))
-        return result
-
-    def get_debug_values(self, step, net_name):
-        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]
-        anorms = self.compute_anorm_histogram()
-        val = {"switch_temperature": temp}
-        for i in range(len(means)):
-            val["switch_%i_specificity" % (i,)] = means[i]
-            val["switch_%i_histogram" % (i,)] = hists[i]
-            val["switch_%i_attention_norm_histogram" % (i,)] = anorms[i]
-        return val
-
-
-class ConfigurableSwitchedResidualGenerator2(nn.Module):
-    def __init__(self, switch_depth, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
-                 trans_layers, transformation_filters, attention_norm, 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(ConfigurableSwitchedResidualGenerator2, self).__init__()
-        switches = []
-        self.initial_conv = ConvBnLelu(3, transformation_filters, kernel_size=7, norm=False, activation=False, bias=True)
-        self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
-        self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
-        self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
-        self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
-        for _ in range(switch_depth):
-            multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions, switch_processing_layers, trans_counts)
-            pretransform_fn = functools.partial(ConvBnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
-            transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5), transformation_filters, kernel_size=trans_kernel_sizes, depth=trans_layers, weight_init_factor=.1)
-            switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
-                                                       pre_transform_block=pretransform_fn, transform_block=transform_fn,
-                                                       attention_norm=attention_norm,
-                                                       transform_count=trans_counts, init_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
-        self.lr = None
-        assert self.upsample_factor == 2 or self.upsample_factor == 4
-
-    def forward(self, x):
-        self.lr = x.detach().cpu()
-
-        # This is a common bug when evaluating SRG2 generators. It needs to be configured properly in eval mode. Just fail.
-        if not self.train:
-            assert self.switches[0].switch.temperature == 1
-
-        x = self.initial_conv(x)
-
-        self.attentions = []
-        for i, sw in enumerate(self.switches):
-            x, att = checkpoint(sw, x)
-            self.attentions.append(att)
-
-        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)
-        x = self.final_conv(self.hr_conv(x))
-        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,
-                1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)
-            if temp == 1 and self.heightened_final_step and step > self.final_temperature_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 % 100 == 0:
-                output_path = os.path.join(experiments_path, "attention_maps")
-                prefix = "amap_%i_a%i_%%i.png"
-                [save_attention_to_image_rgb(output_path, self.attentions[i], self.attentions[i].shape[3], prefix % (step, i), step,
-                                             output_mag=False) for i in range(len(self.attentions))]
-                if self.lr is not None:
-                    torchvision.utils.save_image(self.lr[:, :3], os.path.join(experiments_path, "attention_maps",
-                                                                              "amap_%i_base_image.png" % (step,)))
-
-    def get_debug_values(self, step, net_name):
-        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
-
-@register_model
-def register_ConfigurableSwitchedResidualGenerator2(opt_net, opt):
-     return ConfigurableSwitchedResidualGenerator2(switch_depth=opt_net['switch_depth'],
-                                                            switch_filters=opt_net['switch_filters'],
-                                                            switch_reductions=opt_net['switch_reductions'],
-                                                            switch_processing_layers=opt_net[
-                                                                'switch_processing_layers'],
-                                                            trans_counts=opt_net['trans_counts'],
-                                                            trans_kernel_sizes=opt_net['trans_kernel_sizes'],
-                                                            trans_layers=opt_net['trans_layers'],
-                                                            transformation_filters=opt_net['transformation_filters'],
-                                                            attention_norm=opt_net['attention_norm'],
-                                                            initial_temp=opt_net['temperature'],
-                                                            final_temperature_step=opt_net['temperature_final_step'],
-                                                            heightened_temp_min=opt_net['heightened_temp_min'],
-                                                            heightened_final_step=opt_net['heightened_final_step'],
-                                                            upsample_factor=scale,
-                                                            add_scalable_noise_to_transforms=opt_net['add_noise'],
-                                                            for_video=opt_net['for_video'])

codes/models/srg2_classic.py

@@ -1,223 +0,0 @@
-import os
-
-import torch
-import torchvision
-from matplotlib import cm
-from torch import nn
-import torch.nn.functional as F
-import functools
-from collections import OrderedDict
-
-from models.SwitchedResidualGenerator_arch import HalvingProcessingBlock, ConfigurableSwitchComputer
-from models.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock, MultiConvBlock
-from models.switched_conv.switched_conv import BareConvSwitch, AttentionNorm
-from trainer.networks import register_model
-from utils.util import checkpoint
-
-
-# This is a classic u-net architecture with the goal of assigning each individual pixel an individual transform
-# switching set.
-class ConvBasisMultiplexer(nn.Module):
-    def __init__(self, input_channels, base_filters, reductions, processing_depth, multiplexer_channels, use_gn=True):
-        super(ConvBasisMultiplexer, self).__init__()
-        self.filter_conv = ConvGnSilu(input_channels, base_filters, bias=True)
-        self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(base_filters * 2 ** i) for i in range(reductions)])
-        reduction_filters = base_filters * 2 ** reductions
-        self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, bias=False)) for i in range(processing_depth)]))
-        self.expansion_blocks = nn.ModuleList([ExpansionBlock(reduction_filters // (2 ** i)) for i in range(reductions)])
-
-        gap = base_filters - multiplexer_channels
-        cbl1_out = ((base_filters - (gap // 2)) // 4) * 4   # Must be multiples of 4 to use with group norm.
-        self.cbl1 = ConvGnSilu(base_filters, cbl1_out, norm=use_gn, bias=False, num_groups=4)
-        cbl2_out = ((base_filters - (3 * gap // 4)) // 4) * 4
-        self.cbl2 = ConvGnSilu(cbl1_out, cbl2_out, norm=use_gn, bias=False, num_groups=4)
-        self.cbl3 = ConvGnSilu(cbl2_out, multiplexer_channels, bias=True, norm=False)
-
-    def forward(self, x):
-        x = self.filter_conv(x)
-        reduction_identities = []
-        for b in self.reduction_blocks:
-            reduction_identities.append(x)
-            x = b(x)
-        x = self.processing_blocks(x)
-        for i, b in enumerate(self.expansion_blocks):
-            x = b(x, reduction_identities[-i - 1])
-
-        x = self.cbl1(x)
-        x = self.cbl2(x)
-        x = self.cbl3(x)
-        return x
-
-
-def compute_attention_specificity(att_weights, topk=3):
-    att = att_weights.detach()
-    vals, indices = torch.topk(att, topk, dim=-1)
-    avg = torch.sum(vals, dim=-1)
-    avg = avg.flatten().mean()
-    return avg.item(), indices.flatten().detach()
-
-
-# Copied from torchvision.utils.save_image. Allows specifying pixel format.
-def save_image(tensor, fp, nrow=8, padding=2,
-               normalize=False, range=None, scale_each=False, pad_value=0, format=None, pix_format=None):
-    from PIL import Image
-    grid = torchvision.utils.make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value,
-                                       normalize=normalize, range=range, scale_each=scale_each)
-    # Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
-    ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
-    im = Image.fromarray(ndarr, mode=pix_format).convert('RGB')
-    im.save(fp, format=format)
-
-
-def save_attention_to_image(folder, attention_out, attention_size, step, fname_part="map", l_mult=1.0):
-    magnitude, indices = torch.topk(attention_out, 1, dim=-1)
-    magnitude = magnitude.squeeze(3)
-    indices = indices.squeeze(3)
-    # indices is an integer tensor (b,w,h) where values are on the range [0,attention_size]
-    # magnitude is a float tensor (b,w,h) [0,1] representing the magnitude of that attention.
-    # Use HSV colorspace to show this. Hue is mapped to the indices, Lightness is mapped to intensity,
-    # Saturation is left fixed.
-    hue = indices.float() / attention_size
-    saturation = torch.full_like(hue, .8)
-    value = magnitude * l_mult
-    hsv_img = torch.stack([hue, saturation, value], dim=1)
-
-    output_path=os.path.join(folder, "attention_maps", fname_part)
-    os.makedirs(output_path, exist_ok=True)
-    save_image(hsv_img, os.path.join(output_path, "attention_map_%i.png" % (step,)), pix_format="HSV")
-
-
-def save_attention_to_image_rgb(output_folder, attention_out, attention_size, file_prefix, step, cmap_discrete_name='viridis'):
-    magnitude, indices = torch.topk(attention_out, 3, dim=-1)
-    magnitude = magnitude.cpu()
-    indices = indices.cpu()
-    magnitude /= torch.max(torch.abs(torch.min(magnitude)), torch.abs(torch.max(magnitude)))
-    colormap = cm.get_cmap(cmap_discrete_name, attention_size)
-    colormap_mag = cm.get_cmap(cmap_discrete_name)
-    os.makedirs(os.path.join(output_folder), exist_ok=True)
-    for i in range(3):
-        img = torch.tensor(colormap(indices[:,:,:,i].detach().numpy()))
-        img = img.permute((0, 3, 1, 2))
-        save_image(img, os.path.join(output_folder, file_prefix + "_%i_%s.png" % (step, "rgb_%i" % (i,))), pix_format="RGBA")
-
-        mag_image = torch.tensor(colormap_mag(magnitude[:,:,:,i].detach().numpy()))
-        mag_image = mag_image.permute((0, 3, 1, 2))
-        save_image(mag_image, os.path.join(output_folder, file_prefix + "_%i_%s.png" % (step, "mag_%i" % (i,))), pix_format="RGBA")
-
-
-class ConfigurableSwitchedResidualGenerator2(nn.Module):
-    def __init__(self, switch_depth, 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, for_video=False):
-        super(ConfigurableSwitchedResidualGenerator2, self).__init__()
-        switches = []
-        self.for_video = for_video
-        if for_video:
-            self.initial_conv = ConvBnLelu(6, transformation_filters, stride=upsample_factor, norm=False, activation=False, bias=True)
-        else:
-            self.initial_conv = ConvBnLelu(3, transformation_filters, norm=False, activation=False, bias=True)
-        self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
-        self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
-        self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
-        self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
-        for _ in range(switch_depth):
-            multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions, switch_processing_layers, trans_counts)
-            pretransform_fn = functools.partial(ConvBnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
-            transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5), transformation_filters, kernel_size=trans_kernel_sizes, depth=trans_layers, weight_init_factor=.1)
-            switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn, attention_norm=True,
-                                                       pre_transform_block=pretransform_fn, transform_block=transform_fn,
-                                                       transform_count=trans_counts, init_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
-        assert self.upsample_factor == 2 or self.upsample_factor == 4
-
-    def forward(self, x, ref=None):
-        if self.for_video:
-            x_lg = F.interpolate(x, scale_factor=self.upsample_factor, mode="bicubic")
-            if ref is None:
-                ref = torch.zeros_like(x_lg)
-            x_lg = torch.cat([x_lg, ref], dim=1)
-        else:
-            x_lg = x
-        x = self.initial_conv(x_lg)
-
-        self.attentions = []
-        for i, sw in enumerate(self.switches):
-            x, att = checkpoint(sw, x)
-            self.attentions.append(att)
-
-        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)
-        x = self.final_conv(self.hr_conv(x))
-        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,
-                1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)
-            if temp == 1 and self.heightened_final_step and step > self.final_temperature_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, step, "a%i" % (i+1,), l_mult=10) for i in  range(len(self.attentions))]
-
-    def get_debug_values(self, step, net_name):
-        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, mode="nearest"):
-        super(Interpolate, self).__init__()
-        self.factor = factor
-        self.mode = mode
-
-    def forward(self, x):
-        return F.interpolate(x, scale_factor=self.factor, mode=self.mode)
-
-@register_model
-def register_srg2classic(opt_net, opt):
-    return ConfigurableSwitchedResidualGenerator2(switch_depth=opt_net['switch_depth'],
-                                                        switch_filters=opt_net['switch_filters'],
-                                                        switch_reductions=opt_net['switch_reductions'],
-                                                        switch_processing_layers=opt_net['switch_processing_layers'],
-                                                        trans_counts=opt_net['trans_counts'],
-                                                        trans_kernel_sizes=opt_net['trans_kernel_sizes'],
-                                                        trans_layers=opt_net['trans_layers'],
-                                                        transformation_filters=opt_net['transformation_filters'],
-                                                        initial_temp=opt_net['temperature'],
-                                                        final_temperature_step=opt_net['temperature_final_step'],
-                                                        heightened_temp_min=opt_net['heightened_temp_min'],
-                                                        heightened_final_step=opt_net['heightened_final_step'],
-                                                        upsample_factor=scale,
-                                                        add_scalable_noise_to_transforms=opt_net['add_noise'])

codes/models/switched_conv

@@ -1 +0,0 @@
-Subproject commit cb520afd4da97796bfca398feeef18a7bd18475c

codes/models/vqvae/kmeans_mask_producer.py

@@ -0,0 +1,48 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torchvision.models.resnet import Bottleneck
+
+from models.pixel_level_contrastive_learning.resnet_unet import UResNet50
+from trainer.networks import register_model
+from utils.kmeans import kmeans_predict
+from utils.util import opt_get
+
+
+class UResnetMaskProducer(nn.Module):
+    def __init__(self, pretrained_uresnet_path, kmeans_centroid_path, mask_scales=[.125,.25,.5,1]):
+        super().__init__()
+        _, centroids = torch.load(kmeans_centroid_path)
+        self.centroids = nn.Parameter(centroids)
+        self.ures = UResNet50(Bottleneck, [3,4,6,3], out_dim=512).to('cuda')
+        self.mask_scales = mask_scales
+
+        sd = torch.load(pretrained_uresnet_path)
+        # An assumption is made that the state_dict came from a byol model. Strip out unnecessary weights..
+        resnet_sd = {}
+        for k, v in sd.items():
+            if 'target_encoder.net.' in k:
+                resnet_sd[k.replace('target_encoder.net.', '')] = v
+
+        self.ures.load_state_dict(resnet_sd, strict=True)
+        self.ures.eval()
+
+    def forward(self, x):
+        with torch.no_grad():
+            latents = self.ures(x)
+            b,c,h,w = latents.shape
+            latents = latents.permute(0,2,3,1).reshape(b*h*w,c)
+            masks = kmeans_predict(latents, self.centroids).float()
+            masks = masks.reshape(b,1,h,w)
+            interpolated_masks = {}
+            for sf in self.mask_scales:
+                dim_h, dim_w = int(sf*x.shape[-2]), int(sf*x.shape[-1])
+                imask = F.interpolate(masks, size=(dim_h,dim_w), mode="nearest")
+                interpolated_masks[dim_w] = imask.long()
+            return interpolated_masks
+
+
+@register_model
+def register_uresnet_mask_producer(opt_net, opt):
+    kw = opt_get(opt_net, ['kwargs'], {})
+    return UResnetMaskProducer(**kw)

codes/models/vqvae/scaled_weight_conv.py

@@ -0,0 +1,130 @@
+from typing import Optional, List
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.nn.modules.conv import _ConvNd, _ConvTransposeNd
+from torch.nn.modules.utils import _ntuple
+import torch.nn.functional as F
+
+
+_pair = _ntuple(2)
+
+
+# Indexes the <p> index of input=b,c,h,w,p by the long tensor index=b,1,h,w. Result is b,c,h,w.
+# Frankly - IMO - this is what torch.gather should do.
+def index_2d(input, index):
+    index = index.repeat(1,input.shape[1],1,1)
+    e = torch.eye(input.shape[-1], device=input.device)
+    result = e[index] * input
+    return result.sum(-1)
+
+
+# Drop-in implementation of Conv2d that can apply masked scales&shifts to the convolution weights.
+class ScaledWeightConv(_ConvNd):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size,
+        stride = 1,
+        padding = 0,
+        dilation = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = 'zeros',
+        breadth: int = 8,
+    ):
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        super().__init__(
+            in_channels, out_channels, _pair(kernel_size), stride, padding, dilation,
+            False, _pair(0), groups, bias, padding_mode)
+
+        self.weight_scales = nn.ParameterList([nn.Parameter(torch.ones(out_channels, in_channels, kernel_size, kernel_size)) for _ in range(breadth)])
+        self.shifts = nn.ParameterList([nn.Parameter(torch.zeros(out_channels, in_channels, kernel_size, kernel_size)) for _ in range(breadth)])
+        for w, s in zip(self.weight_scales, self.shifts):
+            w.FOR_SCALE_SHIFT = True
+            s.FOR_SCALE_SHIFT = True
+        # This should probably be configurable at some point.
+        for p in self.parameters():
+            if not hasattr(p, "FOR_SCALE_SHIFT"):
+                p.DO_NOT_TRAIN = True
+
+    def _weighted_conv_forward(self, input, weight):
+        if self.padding_mode != 'zeros':
+            return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
+                            weight, self.bias, self.stride,
+                            _pair(0), self.dilation, self.groups)
+        return F.conv2d(input, weight, self.bias, self.stride,
+                        self.padding, self.dilation, self.groups)
+
+    def forward(self, input: Tensor, masks: dict) -> Tensor:
+        # This is an exceptionally inefficient way of achieving this functionality. The hope is that if this is any
+        # good at all, this can be made more efficient by performing a single conv pass with multiple masks.
+        weighted_convs = [self._weighted_conv_forward(input, self.weight * scale + shift) for scale, shift in zip(self.weight_scales, self.shifts)]
+        weighted_convs = torch.stack(weighted_convs, dim=-1)
+
+        needed_mask = weighted_convs.shape[-2]
+        assert needed_mask in masks.keys()
+
+        return index_2d(weighted_convs, masks[needed_mask])
+
+
+# Drop-in implementation of ConvTranspose2d that can apply masked scales&shifts to the convolution weights.
+class ScaledWeightConvTranspose(_ConvTransposeNd):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size,
+        stride = 1,
+        padding = 0,
+        output_padding = 0,
+        groups: int = 1,
+        bias: bool = True,
+        dilation: int = 1,
+        padding_mode: str = 'zeros',
+        breadth: int = 8,
+    ):
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        output_padding = _pair(output_padding)
+        super().__init__(
+            in_channels, out_channels, _pair(kernel_size), stride, padding, dilation,
+            True, output_padding, groups, bias, padding_mode)
+
+        self.weight_scales = nn.ParameterList([nn.Parameter(torch.ones(in_channels, out_channels, kernel_size, kernel_size)) for _ in range(breadth)])
+        self.shifts = nn.ParameterList([nn.Parameter(torch.zeros(in_channels, out_channels, kernel_size, kernel_size)) for _ in range(breadth)])
+        for w, s in zip(self.weight_scales, self.shifts):
+            w.FOR_SCALE_SHIFT = True
+            s.FOR_SCALE_SHIFT = True
+        # This should probably be configurable at some point.
+        for nm, p in self.named_parameters():
+            if nm == 'weight':
+                p.DO_NOT_TRAIN = True
+
+    def _conv_transpose_forward(self, input, weight, output_size) -> Tensor:
+        if self.padding_mode != 'zeros':
+            raise ValueError('Only `zeros` padding mode is supported for ConvTranspose2d')
+
+        output_padding = self._output_padding(
+            input, output_size, self.stride, self.padding, self.kernel_size, self.dilation)
+
+        return F.conv_transpose2d(
+            input, weight, self.bias, self.stride, self.padding,
+            output_padding, self.groups, self.dilation)
+
+    def forward(self, input: Tensor, masks: dict, output_size: Optional[List[int]] = None) -> Tensor:
+        # This is an exceptionally inefficient way of achieving this functionality. The hope is that if this is any
+        # good at all, this can be made more efficient by performing a single conv pass with multiple masks.
+        weighted_convs = [self._conv_transpose_forward(input, self.weight * scale + shift, output_size)
+                          for scale, shift in zip(self.weight_scales, self.shifts)]
+        weighted_convs = torch.stack(weighted_convs, dim=-1)
+
+        needed_mask = weighted_convs.shape[-2]
+        assert needed_mask in masks.keys()
+
+        return index_2d(weighted_convs, masks[needed_mask])

codes/models/vqvae/weighted_conv_vqvae.py

@@ -0,0 +1,267 @@
+# Copyright 2018 The Sonnet Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import torch.distributed as distributed
+
+from models.vqvae.scaled_weight_conv import ScaledWeightConv, ScaledWeightConvTranspose
+from trainer.networks import register_model
+from utils.util import checkpoint, opt_get
+
+
+class Quantize(nn.Module):
+    def __init__(self, dim, n_embed, decay=0.99, eps=1e-5):
+        super().__init__()
+
+        self.dim = dim
+        self.n_embed = n_embed
+        self.decay = decay
+        self.eps = eps
+
+        embed = torch.randn(dim, n_embed)
+        self.register_buffer("embed", embed)
+        self.register_buffer("cluster_size", torch.zeros(n_embed))
+        self.register_buffer("embed_avg", embed.clone())
+
+    def forward(self, input):
+        flatten = input.reshape(-1, self.dim)
+        dist = (
+            flatten.pow(2).sum(1, keepdim=True)
+            - 2 * flatten @ self.embed
+            + self.embed.pow(2).sum(0, keepdim=True)
+        )
+        _, embed_ind = (-dist).max(1)
+        embed_onehot = F.one_hot(embed_ind, self.n_embed).type(flatten.dtype)
+        embed_ind = embed_ind.view(*input.shape[:-1])
+        quantize = self.embed_code(embed_ind)
+
+        if self.training:
+            embed_onehot_sum = embed_onehot.sum(0)
+            embed_sum = flatten.transpose(0, 1) @ embed_onehot
+
+            if distributed.is_initialized() and distributed.get_world_size() > 1:
+                distributed.all_reduce(embed_onehot_sum)
+                distributed.all_reduce(embed_sum)
+
+            self.cluster_size.data.mul_(self.decay).add_(
+                embed_onehot_sum, alpha=1 - self.decay
+            )
+            self.embed_avg.data.mul_(self.decay).add_(embed_sum, alpha=1 - self.decay)
+            n = self.cluster_size.sum()
+            cluster_size = (
+                (self.cluster_size + self.eps) / (n + self.n_embed * self.eps) * n
+            )
+            embed_normalized = self.embed_avg / cluster_size.unsqueeze(0)
+            self.embed.data.copy_(embed_normalized)
+
+        diff = (quantize.detach() - input).pow(2).mean()
+        quantize = input + (quantize - input).detach()
+
+        return quantize, diff, embed_ind
+
+    def embed_code(self, embed_id):
+        return F.embedding(embed_id, self.embed.transpose(0, 1))
+
+
+class ResBlock(nn.Module):
+    def __init__(self, in_channel, channel, breadth):
+        super().__init__()
+
+        self.conv = nn.ModuleList([
+            nn.ReLU(inplace=True),
+            ScaledWeightConv(in_channel, channel, 3, padding=1, breadth=breadth),
+            nn.ReLU(inplace=True),
+            ScaledWeightConv(channel, in_channel, 1, breadth=breadth),
+        ])
+
+    def forward(self, input, masks):
+        out = input
+        for m in self.conv:
+            if isinstance(m, ScaledWeightConv):
+                out = m(out, masks)
+            else:
+                out = m(out)
+        out += input
+
+        return out
+
+
+class Encoder(nn.Module):
+    def __init__(self, in_channel, channel, n_res_block, n_res_channel, stride, breadth):
+        super().__init__()
+
+        if stride == 4:
+            blocks = [
+                ScaledWeightConv(in_channel, channel // 2, 4, stride=2, padding=1, breadth=breadth),
+                nn.ReLU(inplace=True),
+                ScaledWeightConv(channel // 2, channel, 4, stride=2, padding=1, breadth=breadth),
+                nn.ReLU(inplace=True),
+                ScaledWeightConv(channel, channel, 3, padding=1, breadth=breadth),
+            ]
+
+        elif stride == 2:
+            blocks = [
+                ScaledWeightConv(in_channel, channel // 2, 4, stride=2, padding=1, breadth=breadth),
+                nn.ReLU(inplace=True),
+                ScaledWeightConv(channel // 2, channel, 3, padding=1, breadth=breadth),
+            ]
+
+        for i in range(n_res_block):
+            blocks.append(ResBlock(channel, n_res_channel, breadth=breadth))
+
+        blocks.append(nn.ReLU(inplace=True))
+
+        self.blocks = nn.ModuleList(blocks)
+
+    def forward(self, input):
+        for block in self.blocks:
+            if isinstance(block, ScaledWeightConv) or isinstance(block, ResBlock):
+                input = block(input, self.masks)
+            else:
+                input = block(input)
+        return input
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self, in_channel, out_channel, channel, n_res_block, n_res_channel, stride, breadth
+    ):
+        super().__init__()
+
+        blocks = [ScaledWeightConv(in_channel, channel, 3, padding=1, breadth=breadth)]
+
+        for i in range(n_res_block):
+            blocks.append(ResBlock(channel, n_res_channel, breadth=breadth))
+
+        blocks.append(nn.ReLU(inplace=True))
+
+        if stride == 4:
+            blocks.extend(
+                [
+                    ScaledWeightConvTranspose(channel, channel // 2, 4, stride=2, padding=1, breadth=breadth),
+                    nn.ReLU(inplace=True),
+                    ScaledWeightConvTranspose(
+                        channel // 2, out_channel, 4, stride=2, padding=1, breadth=breadth
+                    ),
+                ]
+            )
+
+        elif stride == 2:
+            blocks.append(
+                ScaledWeightConvTranspose(channel, out_channel, 4, stride=2, padding=1, breadth=breadth)
+            )
+
+        self.blocks = nn.ModuleList(blocks)
+
+    def forward(self, input):
+        for block in self.blocks:
+            if isinstance(block, ScaledWeightConvTranspose) or isinstance(block, ResBlock) \
+                    or isinstance(block, ScaledWeightConv):
+                input = block(input, self.masks)
+            else:
+                input = block(input)
+        return input
+
+
+class VQVAE(nn.Module):
+    def __init__(
+        self,
+        in_channel=3,
+        channel=128,
+        n_res_block=2,
+        n_res_channel=32,
+        codebook_dim=64,
+        codebook_size=512,
+        breadth=8,
+        decay=0.99,
+    ):
+        super().__init__()
+
+        self.enc_b = Encoder(in_channel, channel, n_res_block, n_res_channel, stride=4, breadth=breadth)
+        self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2, breadth=breadth)
+        self.quantize_conv_t = ScaledWeightConv(channel, codebook_dim, 1, breadth=breadth)
+        self.quantize_t = Quantize(codebook_dim, codebook_size)
+        self.dec_t = Decoder(
+            codebook_dim, codebook_dim, channel, n_res_block, n_res_channel, stride=2, breadth=breadth
+        )
+        self.quantize_conv_b = ScaledWeightConv(codebook_dim + channel, codebook_dim, 1, breadth=breadth)
+        self.quantize_b = Quantize(codebook_dim, codebook_size)
+        self.upsample_t = ScaledWeightConvTranspose(
+            codebook_dim, codebook_dim, 4, stride=2, padding=1, breadth=breadth
+        )
+        self.dec = Decoder(
+            codebook_dim + codebook_dim,
+            in_channel,
+            channel,
+            n_res_block,
+            n_res_channel,
+            stride=4,
+            breadth=breadth
+        )
+
+    def forward(self, input, masks):
+        # This awkward injection point is necessary to enable checkpointing to work.
+        for m in [self.enc_b, self.enc_t, self.dec_t, self.dec]:
+            m.masks = masks
+
+        quant_t, quant_b, diff, _, _ = self.encode(input, masks)
+        dec = self.decode(quant_t, quant_b, masks)
+
+        return dec, diff
+
+    def encode(self, input, masks):
+        enc_b = checkpoint(self.enc_b, input)
+        enc_t = checkpoint(self.enc_t, enc_b)
+
+        quant_t = self.quantize_conv_t(enc_t, masks).permute(0, 2, 3, 1)
+        quant_t, diff_t, id_t = self.quantize_t(quant_t)
+        quant_t = quant_t.permute(0, 3, 1, 2)
+        diff_t = diff_t.unsqueeze(0)
+
+        dec_t = checkpoint(self.dec_t, quant_t)
+        enc_b = torch.cat([dec_t, enc_b], 1)
+
+        quant_b = self.quantize_conv_b(enc_b, masks).permute(0, 2, 3, 1)
+        quant_b, diff_b, id_b = self.quantize_b(quant_b)
+        quant_b = quant_b.permute(0, 3, 1, 2)
+        diff_b = diff_b.unsqueeze(0)
+
+        return quant_t, quant_b, diff_t + diff_b, id_t, id_b
+
+    def decode(self, quant_t, quant_b, masks):
+        upsample_t = self.upsample_t(quant_t, masks)
+        quant = torch.cat([upsample_t, quant_b], 1)
+        dec = checkpoint(self.dec, quant)
+
+        return dec
+
+    def decode_code(self, code_t, code_b):
+        quant_t = self.quantize_t.embed_code(code_t)
+        quant_t = quant_t.permute(0, 3, 1, 2)
+        quant_b = self.quantize_b.embed_code(code_b)
+        quant_b = quant_b.permute(0, 3, 1, 2)
+
+        dec = self.decode(quant_t, quant_b, masks)
+
+        return dec
+
+
+@register_model
+def register_weighted_vqvae(opt_net, opt):
+    kw = opt_get(opt_net, ['kwargs'], {})
+    return VQVAE(**kw)

codes/train.py

@@ -295,7 +295,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_stylesr.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/train_imgset_vqvae_stage1/train_imgset_vqvae_stage1_5.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()

codes/utils/kmeans.py

@@ -115,8 +115,7 @@ def kmeans(
 def kmeans_predict(
         X,
         cluster_centers,
-        distance='euclidean',
+        distance='euclidean'
-        device=torch.device('cpu')
 ):
     """
     predict using cluster centers
@@ -126,8 +125,6 @@ def kmeans_predict(
     :param device: (torch.device) device [default: 'cpu']
     :return: (torch.tensor) cluster ids
     """
-    print(f'predicting on {device}..')
-
     if distance == 'euclidean':
         pairwise_distance_function = pairwise_distance
     elif distance == 'cosine':
@@ -135,22 +132,13 @@ def kmeans_predict(
     else:
         raise NotImplementedError
 
-    # convert to float
-    X = X.float()
-
-    # transfer to device
-    X = X.to(device)
-
     dis = pairwise_distance_function(X, cluster_centers)
     choice_cluster = torch.argmin(dis, dim=1)
 
-    return choice_cluster.cpu()
+    return choice_cluster
 
 
-def pairwise_distance(data1, data2, device=torch.device('cpu')):
+def pairwise_distance(data1, data2):
-    # transfer to device
-    data1, data2 = data1.to(device), data2.to(device)
-
     # N*1*M
     A = data1.unsqueeze(dim=1)
 
@@ -163,10 +151,7 @@ def pairwise_distance(data1, data2, device=torch.device('cpu')):
     return dis
 
 
-def pairwise_cosine(data1, data2, device=torch.device('cpu')):
+def pairwise_cosine(data1, data2):
-    # transfer to device
-    data1, data2 = data1.to(device), data2.to(device)
-
     # N*1*M
     A = data1.unsqueeze(dim=1)