DL-Art-School/codes/models/archs/SwitchedResidualGenerator_arch.py

import torch
from torch import nn
from switched_conv import BareConvSwitch, compute_attention_specificity
import torch.nn.functional as F
import functools
from models.archs.arch_util import initialize_weights
from switched_conv_util import save_attention_to_image


class ConvBnLelu(nn.Module):
    def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, lelu=True):
        super(ConvBnLelu, self).__init__()
        padding_map = {1: 0, 3: 1, 5: 2, 7: 3}
        assert kernel_size in padding_map.keys()
        self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size])
        self.bn = nn.BatchNorm2d(filters_out)
        if lelu:
            self.lelu = nn.LeakyReLU(negative_slope=.1)
        else:
            self.lelu = None

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        if self.lelu:
            return self.lelu(x)
        else:
            return x


class ResidualBranch(nn.Module):
    def __init__(self, filters_in, filters_out, kernel_size, depth):
        super(ResidualBranch, self).__init__()
        self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_out, kernel_size)] +
                                     [ConvBnLelu(filters_out, filters_out, kernel_size) for i in range(depth-2)] +
                                     [ConvBnLelu(filters_out, filters_out, kernel_size, lelu=False)])
        self.scale = nn.Parameter(torch.ones(1))
        self.bias = nn.Parameter(torch.zeros(1))

    def forward(self, x):
        for m in self.bnconvs:
            x = m.forward(x)
        return x * self.scale + self.bias


# VGG-style layer with Conv->BN->Activation->Conv(stride2)->BN->Activation
class HalvingProcessingBlock(nn.Module):
    def __init__(self, filters):
        super(HalvingProcessingBlock, self).__init__()
        self.bnconv1 = ConvBnLelu(filters, filters)
        self.bnconv2 = ConvBnLelu(filters, filters * 2, stride=2)

    def forward(self, x):
        x = self.bnconv1(x)
        return self.bnconv2(x)


class SwitchComputer(nn.Module):
    def __init__(self, channels_in, filters, transform_block, transform_count, reductions, init_temp=20):
        super(SwitchComputer, self).__init__()
        self.filter_conv = ConvBnLelu(channels_in, filters)
        self.blocks = nn.ModuleList([HalvingProcessingBlock(filters * 2 ** i) for i in range(reductions)])
        final_filters = filters * 2 ** reductions
        proc_block_filters = max(final_filters // 2, transform_count)
        self.proc_switch_conv = ConvBnLelu(final_filters, proc_block_filters)
        self.final_switch_conv = nn.Conv2d(proc_block_filters, transform_count, 1, 1, 0)

        # Always include the identity transform (all zeros), hence transform_count-10
        self.transforms = nn.ModuleList([transform_block() for i in range(transform_count-1)])

        # And the switch itself
        self.switch = BareConvSwitch(initial_temperature=init_temp)

    def forward(self, x, output_attention_weights=False):
        xformed = [t.forward(x) for t in self.transforms]
        # Append the identity transform.
        xformed.append(torch.zeros_like(xformed[0]))

        multiplexer = self.filter_conv(x)
        for block in self.blocks:
            multiplexer = block.forward(multiplexer)
        multiplexer = self.proc_switch_conv(multiplexer)
        multiplexer = self.final_switch_conv.forward(multiplexer)
        # Interpolate the multiplexer across the entire shape of the image.
        multiplexer = F.interpolate(multiplexer, size=x.shape[2:], mode='nearest')

        return self.switch(xformed, multiplexer, output_attention_weights)

    def set_temperature(self, temp):
        self.switch.set_attention_temperature(temp)

class SwitchedResidualGenerator(nn.Module):
    def __init__(self, switch_filters, initial_temp=20, final_temperature_step=50000):
        super(SwitchedResidualGenerator, self).__init__()
        self.switch1 = SwitchComputer(3, switch_filters, functools.partial(ResidualBranch, 3, 3, kernel_size=7, depth=3), 4, 4, initial_temp)
        self.switch2 = SwitchComputer(3, switch_filters, functools.partial(ResidualBranch, 3, 3, kernel_size=5, depth=3), 8, 3, initial_temp)
        self.switch3 = SwitchComputer(3, switch_filters, functools.partial(ResidualBranch, 3, 3, kernel_size=3, depth=3), 16, 2, initial_temp)
        self.switch4 = SwitchComputer(3, switch_filters, functools.partial(ResidualBranch, 3, 3, kernel_size=3, depth=2), 32, 1, initial_temp)
        initialize_weights([self.switch1, self.switch2, self.switch3, self.switch4], 1)
        # Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image.
        initialize_weights([self.switch1.transforms, self.switch2.transforms, self.switch3.transforms, self.switch4.transforms], .05)

        self.init_temperature = initial_temp
        self.final_temperature_step = final_temperature_step
        self.running_sum = [0, 0, 0, 0]
        self.running_count = 0

    def forward(self, x):
        sw1, self.a1 = self.switch1.forward(x, True)
        x = x + sw1
        sw2, self.a2 = self.switch2.forward(x, True)
        x = x + sw2
        sw3, self.a3 = self.switch3.forward(x, True)
        x = x + sw3
        sw4, self.a4 = self.switch4.forward(x, True)
        x = x + sw4

        a1mean, _ = compute_attention_specificity(self.a1, 2)
        a2mean, _ = compute_attention_specificity(self.a2, 2)
        a3mean, _ = compute_attention_specificity(self.a3, 2)
        a4mean, _ = compute_attention_specificity(self.a4, 2)
        running_sum = [
            self.running_sum[0] + a1mean,
            self.running_sum[1] + a2mean,
            self.running_sum[2] + a3mean,
            self.running_sum[3] + a4mean,
        ]
        self.running_count += 1

        return (x,)

    def set_temperature(self, temp):
        self.switch1.set_temperature(temp)
        self.switch2.set_temperature(temp)
        self.switch3.set_temperature(temp)
        self.switch4.set_temperature(temp)

    def get_debug_values(self, step):
        # Take the chance to update the temperature here.
        temp = max(1, int(self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step))
        self.set_temperature(temp)

        if step % 250 == 0:
            save_attention_to_image(self.a1, 4, step, "a1")
            save_attention_to_image(self.a2, 8, step, "a2")
            save_attention_to_image(self.a3, 16, step, "a3", 2)
            save_attention_to_image(self.a4, 32, step, "a4", 4)

        val = {"switch_temperature": temp}
        for i in range(len(self.running_sum)):
            val["switch_%i_specificity" % (i,)] = self.running_sum[i] / self.running_count
            self.running_sum[i] = 0
        self.running_count = 0
        return val


class ConfigurableSwitchedResidualGenerator(nn.Module):
    def __init__(self, switch_filters, switch_depths, trans_counts, trans_kernel_sizes, trans_layers, initial_temp=20, final_temperature_step=50000):
        super(ConfigurableSwitchedResidualGenerator, self).__init__()
        switches = []
        for filters, depth, trans_count, kernel, layers in zip(switch_filters, switch_depths, trans_counts, trans_kernel_sizes, trans_layers):
            switches.append(SwitchComputer(3, filters, functools.partial(ResidualBranch, 3, 3, kernel_size=kernel, depth=layers), trans_count, depth, initial_temp))
        initialize_weights(switches, 1)
        # Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image.
        initialize_weights([s.transforms for s in switches], .05)
        self.switches = nn.ModuleList(switches)
        self.transformation_counts = trans_counts
        self.init_temperature = initial_temp
        self.final_temperature_step = final_temperature_step
        self.running_sum = [0 for i in range(len(switches))]
        self.running_count = 0

    def forward(self, x):
        self.attentions = []
        for i, sw in enumerate(self.switches):
            x, att = sw.forward(x, True)
            self.attentions.append(att)
            spec, _ = compute_attention_specificity(att, 2)
            self.running_sum[i] += spec

        self.running_count += 1

        return (x,)

    def set_temperature(self, temp):
        [sw.set_temperature(temp) for sw in self.switches]

    def get_debug_values(self, step):
        # Take the chance to update the temperature here.
        temp = max(1, int(self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step))
        self.set_temperature(temp)

        if step % 250 == 0:
            [save_attention_to_image(self.attentions[i], self.transformation_counts[i], step, "a%i" % (i+1,), l_mult=float(self.transformation_counts[i]/4)) for i in range(len(self.switches))]

        val = {"switch_temperature": temp}
        for i in range(len(self.running_sum)):
            val["switch_%i_specificity" % (i,)] = self.running_sum[i] / self.running_count
            self.running_sum[i] = 0
        self.running_count = 0
        return val