models/BigGAN_networks.py

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT

import numpy as np
import math
import functools

import torch
import torch.nn as nn
from torch.nn import init
import torch.optim as optim
import torch.nn.functional as F
from torch.nn import Parameter as P

from . import BigGAN_layers as layers
from .sync_batchnorm import SynchronizedBatchNorm2d as SyncBatchNorm2d
from util.util import to_device, load_network
from .networks import init_weights
from params import *

# Attention is passed in in the format '32_64' to mean applying an attention
# block at both resolution 32x32 and 64x64. Just '64' will apply at 64x64.

from models.blocks import LinearBlock, Conv2dBlock, ResBlocks, ActFirstResBlock


class Decoder(nn.Module):
    def __init__(
        self,
        ups=3,
        n_res=2,
        dim=512,
        out_dim=1,
        res_norm="adain",
        activ="relu",
        pad_type="reflect",
    ):
        super(Decoder, self).__init__()

        self.model = []
        self.model += [ResBlocks(n_res, dim, res_norm, activ, pad_type=pad_type)]
        for i in range(ups):
            self.model += [
                nn.Upsample(scale_factor=2),
                Conv2dBlock(
                    dim,
                    dim // 2,
                    5,
                    1,
                    2,
                    norm="in",
                    activation=activ,
                    pad_type=pad_type,
                ),
            ]
            dim //= 2
        self.model += [
            Conv2dBlock(
                dim, out_dim, 7, 1, 3, norm="none", activation="tanh", pad_type=pad_type
            )
        ]
        self.model = nn.Sequential(*self.model)

    def forward(self, x):
        y = self.model(x)

        return y


def G_arch(ch=64, attention="64", ksize="333333", dilation="111111"):
    arch = {}
    arch[512] = {
        "in_channels": [ch * item for item in [16, 16, 8, 8, 4, 2, 1]],
        "out_channels": [ch * item for item in [16, 8, 8, 4, 2, 1, 1]],
        "upsample": [(2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2)],
        "resolution": [8, 16, 32, 64, 128, 256, 512],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 10)
        },
    }
    arch[256] = {
        "in_channels": [ch * item for item in [16, 16, 8, 8, 4, 2]],
        "out_channels": [ch * item for item in [16, 8, 8, 4, 2, 1]],
        "upsample": [(2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2)],
        "resolution": [8, 16, 32, 64, 128, 256],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 9)
        },
    }
    arch[128] = {
        "in_channels": [ch * item for item in [16, 16, 8, 4, 2]],
        "out_channels": [ch * item for item in [16, 8, 4, 2, 1]],
        "upsample": [(2, 2), (2, 2), (2, 2), (2, 2), (2, 2)],
        "resolution": [8, 16, 32, 64, 128],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 8)
        },
    }
    arch[64] = {
        "in_channels": [ch * item for item in [16, 16, 8, 4]],
        "out_channels": [ch * item for item in [16, 8, 4, 2]],
        "upsample": [(2, 2), (2, 2), (2, 2), (2, 2)],
        "resolution": [8, 16, 32, 64],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 7)
        },
    }

    arch[63] = {
        "in_channels": [ch * item for item in [16, 16, 8, 4]],
        "out_channels": [ch * item for item in [16, 8, 4, 2]],
        "upsample": [(2, 2), (2, 2), (2, 2), (2, 1)],
        "resolution": [8, 16, 32, 64],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 7)
        },
        "kernel1": [3, 3, 3, 3],
        "kernel2": [3, 3, 1, 1],
    }

    arch[32] = {
        "in_channels": [ch * item for item in [4, 4, 4]],
        "out_channels": [ch * item for item in [4, 4, 4]],
        "upsample": [(2, 2), (2, 2), (2, 2)],
        "resolution": [8, 16, 32],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 6)
        },
    }

    arch[32] = {
        "in_channels": [ch * item for item in [4, 4, 4]],
        "out_channels": [ch * item for item in [4, 4, 4]],
        "upsample": [(2, 2), (2, 2), (2, 2)],
        "resolution": [8, 16, 32],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 6)
        },
        "kernel1": [3, 3, 3],
        "kernel2": [3, 3, 1],
    }

    arch[129] = {
        "in_channels": [ch * item for item in [16, 16, 8, 8, 4, 2, 1]],
        "out_channels": [ch * item for item in [16, 8, 8, 4, 2, 1, 1]],
        "upsample": [(2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (1, 2), (1, 2)],
        "resolution": [8, 16, 32, 64, 128, 256, 512],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 10)
        },
    }

    arch[33] = {
        "in_channels": [ch * item for item in [16, 16, 8, 4, 2]],
        "out_channels": [ch * item for item in [16, 8, 4, 2, 1]],
        "upsample": [(2, 2), (2, 2), (2, 2), (1, 2), (1, 2)],
        "resolution": [8, 16, 32, 64, 128],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 8)
        },
    }

    arch[31] = {
        "in_channels": [ch * item for item in [16, 16, 4, 2]],
        "out_channels": [ch * item for item in [16, 4, 2, 1]],
        "upsample": [(2, 2), (2, 2), (2, 2), (1, 2)],
        "resolution": [8, 16, 32, 64],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 7)
        },
        "kernel1": [3, 3, 3, 3],
        "kernel2": [3, 1, 1, 1],
    }

    arch[16] = {
        "in_channels": [ch * item for item in [8, 4, 2]],
        "out_channels": [ch * item for item in [4, 2, 1]],
        "upsample": [(2, 2), (2, 2), (2, 1)],
        "resolution": [8, 16, 16],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 6)
        },
        "kernel1": [3, 3, 3],
        "kernel2": [3, 3, 1],
    }

    arch[17] = {
        "in_channels": [ch * item for item in [8, 4, 2]],
        "out_channels": [ch * item for item in [4, 2, 1]],
        "upsample": [(2, 2), (2, 2), (2, 1)],
        "resolution": [8, 16, 16],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 6)
        },
        "kernel1": [3, 3, 3],
        "kernel2": [3, 3, 1],
    }

    arch[20] = {
        "in_channels": [ch * item for item in [8, 4, 2]],
        "out_channels": [ch * item for item in [4, 2, 1]],
        "upsample": [(2, 2), (2, 2), (2, 1)],
        "resolution": [8, 16, 16],
        "attention": {
            2**i: (2 ** i in [int(item) for item in attention.split("_")])
            for i in range(3, 6)
        },
        "kernel1": [3, 3, 3],
        "kernel2": [3, 1, 1],
    }

    return arch


class Generator(nn.Module):
    def __init__(
        self,
        G_ch=64,
        dim_z=128,
        bottom_width=4,
        bottom_height=4,
        resolution=128,
        G_kernel_size=3,
        G_attn="64",
        n_classes=1000,
        num_G_SVs=1,
        num_G_SV_itrs=1,
        G_shared=True,
        shared_dim=0,
        no_hier=False,
        cross_replica=False,
        mybn=False,
        G_activation=nn.ReLU(inplace=False),
        BN_eps=1e-5,
        SN_eps=1e-12,
        G_fp16=False,
        G_init="ortho",
        skip_init=False,
        G_param="SN",
        norm_style="bn",
        gpu_ids=[],
        bn_linear="embed",
        input_nc=3,
        one_hot=False,
        first_layer=False,
        one_hot_k=1,
        **kwargs
    ):
        super(Generator, self).__init__()
        self.name = "G"
        # Use class only in first layer
        self.first_layer = first_layer
        # gpu-ids
        self.gpu_ids = gpu_ids
        # Use one hot vector representation for input class
        self.one_hot = one_hot
        # Use one hot k vector representation for input class if k is larger than 0. If it's 0, simly use the class number and not a k-hot encoding.
        self.one_hot_k = one_hot_k
        # Channel width mulitplier
        self.ch = G_ch
        # Dimensionality of the latent space
        self.dim_z = dim_z
        # The initial width dimensions
        self.bottom_width = bottom_width
        # The initial height dimension
        self.bottom_height = bottom_height
        # Resolution of the output
        self.resolution = resolution
        # Kernel size?
        self.kernel_size = G_kernel_size
        # Attention?
        self.attention = G_attn
        # number of classes, for use in categorical conditional generation
        self.n_classes = n_classes
        # Use shared embeddings?
        self.G_shared = G_shared
        # Dimensionality of the shared embedding? Unused if not using G_shared
        self.shared_dim = shared_dim if shared_dim > 0 else dim_z
        # Hierarchical latent space?
        self.hier = not no_hier
        # Cross replica batchnorm?
        self.cross_replica = cross_replica
        # Use my batchnorm?
        self.mybn = mybn
        # nonlinearity for residual blocks
        self.activation = G_activation
        # Initialization style
        self.init = G_init
        # Parameterization style
        self.G_param = G_param
        # Normalization style
        self.norm_style = norm_style
        # Epsilon for BatchNorm?
        self.BN_eps = BN_eps
        # Epsilon for Spectral Norm?
        self.SN_eps = SN_eps
        # fp16?
        self.fp16 = G_fp16
        # Architecture dict
        self.arch = G_arch(self.ch, self.attention)[resolution]
        self.bn_linear = bn_linear

        # self.transformer = Transformer(d_model = 2560)
        # self.input_proj = nn.Conv2d(512, 2560, kernel_size=1)
        self.linear_q = nn.Linear(512, 2048 * 2)

        self.DETR = build()
        self.DEC = Decoder(res_norm="in")
        # If using hierarchical latents, adjust z
        if self.hier:
            # Number of places z slots into
            self.num_slots = len(self.arch["in_channels"]) + 1
            self.z_chunk_size = self.dim_z // self.num_slots
            # Recalculate latent dimensionality for even splitting into chunks
            self.dim_z = self.z_chunk_size * self.num_slots
        else:
            self.num_slots = 1
            self.z_chunk_size = 0

        # Which convs, batchnorms, and linear layers to use
        if self.G_param == "SN":
            self.which_conv = functools.partial(
                layers.SNConv2d,
                kernel_size=3,
                padding=1,
                num_svs=num_G_SVs,
                num_itrs=num_G_SV_itrs,
                eps=self.SN_eps,
            )
            self.which_linear = functools.partial(
                layers.SNLinear,
                num_svs=num_G_SVs,
                num_itrs=num_G_SV_itrs,
                eps=self.SN_eps,
            )
        else:
            self.which_conv = functools.partial(nn.Conv2d, kernel_size=3, padding=1)
            self.which_linear = nn.Linear

        # We use a non-spectral-normed embedding here regardless;
        # For some reason applying SN to G's embedding seems to randomly cripple G
        if one_hot:
            self.which_embedding = functools.partial(
                layers.SNLinear,
                num_svs=num_G_SVs,
                num_itrs=num_G_SV_itrs,
                eps=self.SN_eps,
            )
        else:
            self.which_embedding = nn.Embedding

        bn_linear = (
            functools.partial(self.which_linear, bias=False)
            if self.G_shared
            else self.which_embedding
        )
        if self.bn_linear == "SN":
            bn_linear = functools.partial(self.which_linear, bias=False)
        if self.G_shared:
            input_size = self.shared_dim + self.z_chunk_size
        elif self.hier:
            if self.first_layer:
                input_size = self.z_chunk_size
            else:
                input_size = self.n_classes + self.z_chunk_size
            self.which_bn = functools.partial(
                layers.ccbn,
                which_linear=bn_linear,
                cross_replica=self.cross_replica,
                mybn=self.mybn,
                input_size=input_size,
                norm_style=self.norm_style,
                eps=self.BN_eps,
            )
        else:
            input_size = self.n_classes
            self.which_bn = functools.partial(
                layers.bn,
                cross_replica=self.cross_replica,
                mybn=self.mybn,
                eps=self.BN_eps,
            )

        # Prepare model
        # If not using shared embeddings, self.shared is just a passthrough
        self.shared = (
            self.which_embedding(n_classes, self.shared_dim)
            if G_shared
            else layers.identity()
        )
        # First linear layer
        # The parameters for the first linear layer depend on the different input variations.
        if self.first_layer:
            if self.one_hot:
                self.linear = self.which_linear(
                    self.dim_z // self.num_slots + self.n_classes,
                    self.arch["in_channels"][0]
                    * (self.bottom_width * self.bottom_height),
                )
            else:
                self.linear = self.which_linear(
                    self.dim_z // self.num_slots + 1,
                    self.arch["in_channels"][0]
                    * (self.bottom_width * self.bottom_height),
                )
            if self.one_hot_k == 1:
                self.linear = self.which_linear(
                    (self.dim_z // self.num_slots) * self.n_classes,
                    self.arch["in_channels"][0]
                    * (self.bottom_width * self.bottom_height),
                )
            if self.one_hot_k > 1:
                self.linear = self.which_linear(
                    self.dim_z // self.num_slots + self.n_classes * self.one_hot_k,
                    self.arch["in_channels"][0]
                    * (self.bottom_width * self.bottom_height),
                )

        else:
            self.linear = self.which_linear(
                self.dim_z // self.num_slots,
                self.arch["in_channels"][0] * (self.bottom_width * self.bottom_height),
            )
        # self.blocks is a doubly-nested list of modules, the outer loop intended
        # to be over blocks at a given resolution (resblocks and/or self-attention)
        # while the inner loop is over a given block
        self.blocks = []
        for index in range(len(self.arch["out_channels"])):
            if "kernel1" in self.arch.keys():
                padd1 = 1 if self.arch["kernel1"][index] > 1 else 0
                padd2 = 1 if self.arch["kernel2"][index] > 1 else 0
                conv1 = functools.partial(
                    layers.SNConv2d,
                    kernel_size=self.arch["kernel1"][index],
                    padding=padd1,
                    num_svs=num_G_SVs,
                    num_itrs=num_G_SV_itrs,
                    eps=self.SN_eps,
                )
                conv2 = functools.partial(
                    layers.SNConv2d,
                    kernel_size=self.arch["kernel2"][index],
                    padding=padd2,
                    num_svs=num_G_SVs,
                    num_itrs=num_G_SV_itrs,
                    eps=self.SN_eps,
                )
                self.blocks += [
                    [
                        layers.GBlock(
                            in_channels=self.arch["in_channels"][index],
                            out_channels=self.arch["out_channels"][index],
                            which_conv1=conv1,
                            which_conv2=conv2,
                            which_bn=self.which_bn,
                            activation=self.activation,
                            upsample=(
                                functools.partial(
                                    F.interpolate,
                                    scale_factor=self.arch["upsample"][index],
                                )
                                if index < len(self.arch["upsample"])
                                else None
                            ),
                        )
                    ]
                ]
            else:
                self.blocks += [
                    [
                        layers.GBlock(
                            in_channels=self.arch["in_channels"][index],
                            out_channels=self.arch["out_channels"][index],
                            which_conv1=self.which_conv,
                            which_conv2=self.which_conv,
                            which_bn=self.which_bn,
                            activation=self.activation,
                            upsample=(
                                functools.partial(
                                    F.interpolate,
                                    scale_factor=self.arch["upsample"][index],
                                )
                                if index < len(self.arch["upsample"])
                                else None
                            ),
                        )
                    ]
                ]

            # If attention on this block, attach it to the end
            if self.arch["attention"][self.arch["resolution"][index]]:
                print(
                    "Adding attention layer in G at resolution %d"
                    % self.arch["resolution"][index]
                )
                self.blocks[-1] += [
                    layers.Attention(self.arch["out_channels"][index], self.which_conv)
                ]

        # Turn self.blocks into a ModuleList so that it's all properly registered.
        self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks])

        # output layer: batchnorm-relu-conv.
        # Consider using a non-spectral conv here
        self.output_layer = nn.Sequential(
            layers.bn(
                self.arch["out_channels"][-1],
                cross_replica=self.cross_replica,
                mybn=self.mybn,
            ),
            self.activation,
            self.which_conv(self.arch["out_channels"][-1], input_nc),
        )

        # Initialize weights. Optionally skip init for testing.
        if not skip_init:
            self = init_weights(self, G_init)

    # Note on this forward function: we pass in a y vector which has
    # already been passed through G.shared to enable easy class-wise
    # interpolation later. If we passed in the one-hot and then ran it through
    # G.shared in this forward function, it would be harder to handle.
    def forward(self, x, y_ind, y):
        # If hierarchical, concatenate zs and ys

        h_all = self.DETR(x, y_ind)
        # h_all = torch.stack([h_all, h_all, h_all])

        # h_all_bs = torch.unbind(h_all[-1], 0)
        # y_bs = torch.unbind(y_ind, 0)

        # h = torch.stack([h_i[y_j] for h_i,y_j in zip(h_all_bs, y_bs)], 0)

        h = self.linear_q(h_all)

        h = h.contiguous()
        # Reshape - when y is not a single class value but rather an array of classes, the reshape is needed to create
        # a separate vertical patch for each input.
        if self.first_layer:
            # correct reshape
            h = h.view(h.size(0), h.shape[1] * 2, 4, -1)
            h = h.permute(0, 3, 2, 1)

        else:
            h = h.view(h.size(0), -1, self.bottom_width, self.bottom_height)

        # for index, blocklist in enumerate(self.blocks):
        # Second inner loop in case block has multiple layers
        #   for block in blocklist:
        #        h = block(h, ys[index])

        # Apply batchnorm-relu-conv-tanh at output
        # h = torch.tanh(self.output_layer(h))

        h = self.DEC(h)
        return h


# Discriminator architecture, same paradigm as G's above
def D_arch(ch=64, attention="64", input_nc=3, ksize="333333", dilation="111111"):
    arch = {}
    arch[256] = {
        "in_channels": [input_nc] + [ch * item for item in [1, 2, 4, 8, 8, 16]],
        "out_channels": [item * ch for item in [1, 2, 4, 8, 8, 16, 16]],
        "downsample": [True] * 6 + [False],
        "resolution": [128, 64, 32, 16, 8, 4, 4],
        "attention": {
            2**i: 2 ** i in [int(item) for item in attention.split("_")]
            for i in range(2, 8)
        },
    }
    arch[128] = {
        "in_channels": [input_nc] + [ch * item for item in [1, 2, 4, 8, 16]],
        "out_channels": [item * ch for item in [1, 2, 4, 8, 16, 16]],
        "downsample": [True] * 5 + [False],
        "resolution": [64, 32, 16, 8, 4, 4],
        "attention": {
            2**i: 2 ** i in [int(item) for item in attention.split("_")]
            for i in range(2, 8)
        },
    }
    arch[64] = {
        "in_channels": [input_nc] + [ch * item for item in [1, 2, 4, 8]],
        "out_channels": [item * ch for item in [1, 2, 4, 8, 16]],
        "downsample": [True] * 4 + [False],
        "resolution": [32, 16, 8, 4, 4],
        "attention": {
            2**i: 2 ** i in [int(item) for item in attention.split("_")]
            for i in range(2, 7)
        },
    }
    arch[63] = {
        "in_channels": [input_nc] + [ch * item for item in [1, 2, 4, 8]],
        "out_channels": [item * ch for item in [1, 2, 4, 8, 16]],
        "downsample": [True] * 4 + [False],
        "resolution": [32, 16, 8, 4, 4],
        "attention": {
            2**i: 2 ** i in [int(item) for item in attention.split("_")]
            for i in range(2, 7)
        },
    }
    arch[32] = {
        "in_channels": [input_nc] + [item * ch for item in [4, 4, 4]],
        "out_channels": [item * ch for item in [4, 4, 4, 4]],
        "downsample": [True, True, False, False],
        "resolution": [16, 16, 16, 16],
        "attention": {
            2**i: 2 ** i in [int(item) for item in attention.split("_")]
            for i in range(2, 6)
        },
    }
    arch[129] = {
        "in_channels": [input_nc] + [ch * item for item in [1, 2, 4, 8, 8, 16]],
        "out_channels": [item * ch for item in [1, 2, 4, 8, 8, 16, 16]],
        "downsample": [True] * 6 + [False],
        "resolution": [128, 64, 32, 16, 8, 4, 4],
        "attention": {
            2**i: 2 ** i in [int(item) for item in attention.split("_")]
            for i in range(2, 8)
        },
    }
    arch[33] = {
        "in_channels": [input_nc] + [ch * item for item in [1, 2, 4, 8, 16]],
        "out_channels": [item * ch for item in [1, 2, 4, 8, 16, 16]],
        "downsample": [True] * 5 + [False],
        "resolution": [64, 32, 16, 8, 4, 4],
        "attention": {
            2**i: 2 ** i in [int(item) for item in attention.split("_")]
            for i in range(2, 10)
        },
    }
    arch[31] = {
        "in_channels": [input_nc] + [ch * item for item in [1, 2, 4, 8, 16]],
        "out_channels": [item * ch for item in [1, 2, 4, 8, 16, 16]],
        "downsample": [True] * 5 + [False],
        "resolution": [64, 32, 16, 8, 4, 4],
        "attention": {
            2**i: 2 ** i in [int(item) for item in attention.split("_")]
            for i in range(2, 10)
        },
    }
    arch[16] = {
        "in_channels": [input_nc] + [ch * item for item in [1, 4, 8]],
        "out_channels": [item * ch for item in [1, 4, 8, 8]],
        "downsample": [True] * 3 + [False],
        "resolution": [8, 4, 4, 4],
        "attention": {
            2**i: 2 ** i in [int(item) for item in attention.split("_")]
            for i in range(2, 5)
        },
    }

    arch[17] = {
        "in_channels": [input_nc] + [ch * item for item in [1, 4]],
        "out_channels": [item * ch for item in [1, 4, 8]],
        "downsample": [True] * 3,
        "resolution": [16, 8, 4],
        "attention": {
            2**i: 2 ** i in [int(item) for item in attention.split("_")]
            for i in range(2, 5)
        },
    }

    arch[20] = {
        "in_channels": [input_nc] + [ch * item for item in [1, 8, 16]],
        "out_channels": [item * ch for item in [1, 8, 16, 16]],
        "downsample": [True] * 3 + [False],
        "resolution": [16, 8, 4, 4],
        "attention": {
            2**i: 2 ** i in [int(item) for item in attention.split("_")]
            for i in range(2, 5)
        },
    }
    return arch


class Discriminator(nn.Module):

    def __init__(
        self,
        D_ch=64,
        D_wide=True,
        resolution=resolution,
        D_kernel_size=3,
        D_attn="64",
        n_classes=VOCAB_SIZE,
        num_D_SVs=1,
        num_D_SV_itrs=1,
        D_activation=nn.ReLU(inplace=False),
        SN_eps=1e-8,
        output_dim=1,
        D_mixed_precision=False,
        D_fp16=False,
        D_init="N02",
        skip_init=False,
        D_param="SN",
        gpu_ids=[0],
        bn_linear="SN",
        input_nc=1,
        one_hot=False,
        **kwargs
    ):

        super(Discriminator, self).__init__()
        self.name = "D"
        # gpu_ids
        self.gpu_ids = gpu_ids
        # one_hot representation
        self.one_hot = one_hot
        # Width multiplier
        self.ch = D_ch
        # Use Wide D as in BigGAN and SA-GAN or skinny D as in SN-GAN?
        self.D_wide = D_wide
        # Resolution
        self.resolution = resolution
        # Kernel size
        self.kernel_size = D_kernel_size
        # Attention?
        self.attention = D_attn
        # Number of classes
        self.n_classes = n_classes
        # Activation
        self.activation = D_activation
        # Initialization style
        self.init = D_init
        # Parameterization style
        self.D_param = D_param
        # Epsilon for Spectral Norm?
        self.SN_eps = SN_eps
        # Fp16?
        self.fp16 = D_fp16
        # Architecture
        self.arch = D_arch(self.ch, self.attention, input_nc)[resolution]

        # Which convs, batchnorms, and linear layers to use
        # No option to turn off SN in D right now
        if self.D_param == "SN":
            self.which_conv = functools.partial(
                layers.SNConv2d,
                kernel_size=3,
                padding=1,
                num_svs=num_D_SVs,
                num_itrs=num_D_SV_itrs,
                eps=self.SN_eps,
            )
            self.which_linear = functools.partial(
                layers.SNLinear,
                num_svs=num_D_SVs,
                num_itrs=num_D_SV_itrs,
                eps=self.SN_eps,
            )
            self.which_embedding = functools.partial(
                layers.SNEmbedding,
                num_svs=num_D_SVs,
                num_itrs=num_D_SV_itrs,
                eps=self.SN_eps,
            )
            if bn_linear == "SN":
                self.which_embedding = functools.partial(
                    layers.SNLinear,
                    num_svs=num_D_SVs,
                    num_itrs=num_D_SV_itrs,
                    eps=self.SN_eps,
                )
        else:
            self.which_conv = functools.partial(nn.Conv2d, kernel_size=3, padding=1)
            self.which_linear = nn.Linear
            # We use a non-spectral-normed embedding here regardless;
            # For some reason applying SN to G's embedding seems to randomly cripple G
            self.which_embedding = nn.Embedding
        if one_hot:
            self.which_embedding = functools.partial(
                layers.SNLinear,
                num_svs=num_D_SVs,
                num_itrs=num_D_SV_itrs,
                eps=self.SN_eps,
            )
        # Prepare model
        # self.blocks is a doubly-nested list of modules, the outer loop intended
        # to be over blocks at a given resolution (resblocks and/or self-attention)
        self.blocks = []
        for index in range(len(self.arch["out_channels"])):
            self.blocks += [
                [
                    layers.DBlock(
                        in_channels=self.arch["in_channels"][index],
                        out_channels=self.arch["out_channels"][index],
                        which_conv=self.which_conv,
                        wide=self.D_wide,
                        activation=self.activation,
                        preactivation=(index > 0),
                        downsample=(
                            nn.AvgPool2d(2) if self.arch["downsample"][index] else None
                        ),
                    )
                ]
            ]
            # If attention on this block, attach it to the end
            if self.arch["attention"][self.arch["resolution"][index]]:
                print(
                    "Adding attention layer in D at resolution %d"
                    % self.arch["resolution"][index]
                )
                self.blocks[-1] += [
                    layers.Attention(self.arch["out_channels"][index], self.which_conv)
                ]
        # Turn self.blocks into a ModuleList so that it's all properly registered.
        self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks])
        # Linear output layer. The output dimension is typically 1, but may be
        # larger if we're e.g. turning this into a VAE with an inference output
        self.linear = self.which_linear(self.arch["out_channels"][-1], output_dim)
        # Embedding for projection discrimination
        self.embed = self.which_embedding(self.n_classes, self.arch["out_channels"][-1])

        # Initialize weights
        if not skip_init:
            self = init_weights(self, D_init)

    def forward(self, x, y=None, **kwargs):
        # Stick x into h for cleaner for loops without flow control
        h = x
        # Loop over blocks
        for index, blocklist in enumerate(self.blocks):
            for block in blocklist:
                h = block(h)
        # Apply global sum pooling as in SN-GAN
        h = torch.sum(self.activation(h), [2, 3])
        # Get initial class-unconditional output
        out = self.linear(h)
        # Get projection of final featureset onto class vectors and add to evidence
        if y is not None:
            out = out + torch.sum(self.embed(y) * h, 1, keepdim=True)
        return out

    def return_features(self, x, y=None):
        # Stick x into h for cleaner for loops without flow control
        h = x
        block_output = []
        # Loop over blocks
        for index, blocklist in enumerate(self.blocks):
            for block in blocklist:
                h = block(h)
                block_output.append(h)
        # Apply global sum pooling as in SN-GAN
        # h = torch.sum(self.activation(h), [2, 3])
        return block_output


class WDiscriminator(nn.Module):

    def __init__(
        self,
        D_ch=64,
        D_wide=True,
        resolution=resolution,
        D_kernel_size=3,
        D_attn="64",
        n_classes=VOCAB_SIZE,
        num_D_SVs=1,
        num_D_SV_itrs=1,
        D_activation=nn.ReLU(inplace=False),
        SN_eps=1e-8,
        output_dim=NUM_WRITERS,
        D_mixed_precision=False,
        D_fp16=False,
        D_init="N02",
        skip_init=False,
        D_param="SN",
        gpu_ids=[0],
        bn_linear="SN",
        input_nc=1,
        one_hot=False,
        **kwargs
    ):
        super(WDiscriminator, self).__init__()
        self.name = "D"
        # gpu_ids
        self.gpu_ids = gpu_ids
        # one_hot representation
        self.one_hot = one_hot
        # Width multiplier
        self.ch = D_ch
        # Use Wide D as in BigGAN and SA-GAN or skinny D as in SN-GAN?
        self.D_wide = D_wide
        # Resolution
        self.resolution = resolution
        # Kernel size
        self.kernel_size = D_kernel_size
        # Attention?
        self.attention = D_attn
        # Number of classes
        self.n_classes = n_classes
        # Activation
        self.activation = D_activation
        # Initialization style
        self.init = D_init
        # Parameterization style
        self.D_param = D_param
        # Epsilon for Spectral Norm?
        self.SN_eps = SN_eps
        # Fp16?
        self.fp16 = D_fp16
        # Architecture
        self.arch = D_arch(self.ch, self.attention, input_nc)[resolution]

        # Which convs, batchnorms, and linear layers to use
        # No option to turn off SN in D right now
        if self.D_param == "SN":
            self.which_conv = functools.partial(
                layers.SNConv2d,
                kernel_size=3,
                padding=1,
                num_svs=num_D_SVs,
                num_itrs=num_D_SV_itrs,
                eps=self.SN_eps,
            )
            self.which_linear = functools.partial(
                layers.SNLinear,
                num_svs=num_D_SVs,
                num_itrs=num_D_SV_itrs,
                eps=self.SN_eps,
            )
            self.which_embedding = functools.partial(
                layers.SNEmbedding,
                num_svs=num_D_SVs,
                num_itrs=num_D_SV_itrs,
                eps=self.SN_eps,
            )
            if bn_linear == "SN":
                self.which_embedding = functools.partial(
                    layers.SNLinear,
                    num_svs=num_D_SVs,
                    num_itrs=num_D_SV_itrs,
                    eps=self.SN_eps,
                )
        else:
            self.which_conv = functools.partial(nn.Conv2d, kernel_size=3, padding=1)
            self.which_linear = nn.Linear
            # We use a non-spectral-normed embedding here regardless;
            # For some reason applying SN to G's embedding seems to randomly cripple G
            self.which_embedding = nn.Embedding
        if one_hot:
            self.which_embedding = functools.partial(
                layers.SNLinear,
                num_svs=num_D_SVs,
                num_itrs=num_D_SV_itrs,
                eps=self.SN_eps,
            )
        # Prepare model
        # self.blocks is a doubly-nested list of modules, the outer loop intended
        # to be over blocks at a given resolution (resblocks and/or self-attention)
        self.blocks = []
        for index in range(len(self.arch["out_channels"])):
            self.blocks += [
                [
                    layers.DBlock(
                        in_channels=self.arch["in_channels"][index],
                        out_channels=self.arch["out_channels"][index],
                        which_conv=self.which_conv,
                        wide=self.D_wide,
                        activation=self.activation,
                        preactivation=(index > 0),
                        downsample=(
                            nn.AvgPool2d(2) if self.arch["downsample"][index] else None
                        ),
                    )
                ]
            ]
            # If attention on this block, attach it to the end
            if self.arch["attention"][self.arch["resolution"][index]]:
                print(
                    "Adding attention layer in D at resolution %d"
                    % self.arch["resolution"][index]
                )
                self.blocks[-1] += [
                    layers.Attention(self.arch["out_channels"][index], self.which_conv)
                ]
        # Turn self.blocks into a ModuleList so that it's all properly registered.
        self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks])
        # Linear output layer. The output dimension is typically 1, but may be
        # larger if we're e.g. turning this into a VAE with an inference output
        self.linear = self.which_linear(self.arch["out_channels"][-1], output_dim)
        # Embedding for projection discrimination
        self.embed = self.which_embedding(self.n_classes, self.arch["out_channels"][-1])
        self.cross_entropy = nn.CrossEntropyLoss()
        # Initialize weights
        if not skip_init:
            self = init_weights(self, D_init)

    def forward(self, x, y=None, **kwargs):
        # Stick x into h for cleaner for loops without flow control
        h = x
        # Loop over blocks
        for index, blocklist in enumerate(self.blocks):
            for block in blocklist:
                h = block(h)
        # Apply global sum pooling as in SN-GAN
        h = torch.sum(self.activation(h), [2, 3])
        # Get initial class-unconditional output
        out = self.linear(h)
        # Get projection of final featureset onto class vectors and add to evidence
        # if y is not None:
        # out = out + torch.sum(self.embed(y) * h, 1, keepdim=True)

        loss = self.cross_entropy(out, y.long())

        return loss

    def return_features(self, x, y=None):
        # Stick x into h for cleaner for loops without flow control
        h = x
        block_output = []
        # Loop over blocks
        for index, blocklist in enumerate(self.blocks):
            for block in blocklist:
                h = block(h)
                block_output.append(h)
        # Apply global sum pooling as in SN-GAN
        # h = torch.sum(self.activation(h), [2, 3])
        return block_output


class Encoder(Discriminator):
    def __init__(self, opt, output_dim, **kwargs):
        super(Encoder, self).__init__(**vars(opt))
        self.output_layer = nn.Sequential(
            self.activation,
            nn.Conv2d(
                self.arch["out_channels"][-1],
                output_dim,
                kernel_size=(4, 2),
                padding=0,
                stride=2,
            ),
        )

    def forward(self, x):
        # Stick x into h for cleaner for loops without flow control
        h = x
        # Loop over blocks
        for index, blocklist in enumerate(self.blocks):
            for block in blocklist:
                h = block(h)
        out = self.output_layer(h)
        return out


class BiDiscriminator(nn.Module):
    def __init__(self, opt):
        super(BiDiscriminator, self).__init__()
        self.infer_img = Encoder(opt, output_dim=opt.nimg_features)
        # self.infer_z = nn.Sequential(
        #     nn.Conv2d(opt.dim_z, 512, 1, stride=1, bias=False),
        #     nn.LeakyReLU(inplace=True),
        #     nn.Dropout2d(p=self.dropout),
        #     nn.Conv2d(512, opt.nz_features, 1, stride=1, bias=False),
        #     nn.LeakyReLU(inplace=True),
        #     nn.Dropout2d(p=self.dropout)
        # )
        self.infer_joint = nn.Sequential(
            nn.Conv2d(opt.dim_z + opt.nimg_features, 1024, 1, stride=1, bias=True),
            nn.ReLU(inplace=True),
            nn.Conv2d(1024, 1024, 1, stride=1, bias=True),
            nn.ReLU(inplace=True),
        )
        self.final = nn.Conv2d(1024, 1, 1, stride=1, bias=True)

    def forward(self, x, z, **kwargs):
        output_x = self.infer_img(x)
        # output_z = self.infer_z(z)
        if len(z.shape) == 2:
            z = z.unsqueeze(2).unsqueeze(2).repeat((1, 1, 1, output_x.shape[3]))
        output_features = self.infer_joint(torch.cat([output_x, z], dim=1))
        output = self.final(output_features)
        return output


# Parallelized G_D to minimize cross-gpu communication
# Without this, Generator outputs would get all-gathered and then rebroadcast.
class G_D(nn.Module):
    def __init__(self, G, D):
        super(G_D, self).__init__()
        self.G = G
        self.D = D

    def forward(
        self, z, gy, x=None, dy=None, train_G=False, return_G_z=False, split_D=False
    ):
        # If training G, enable grad tape
        with torch.set_grad_enabled(train_G):
            # Get Generator output given noise
            G_z = self.G(z, self.G.shared(gy))
            # Cast as necessary
            if self.G.fp16 and not self.D.fp16:
                G_z = G_z.float()
            if self.D.fp16 and not self.G.fp16:
                G_z = G_z.half()
        # Split_D means to run D once with real data and once with fake,
        # rather than concatenating along the batch dimension.
        if split_D:
            D_fake = self.D(G_z, gy)
            if x is not None:
                D_real = self.D(x, dy)
                return D_fake, D_real
            else:
                if return_G_z:
                    return D_fake, G_z
                else:
                    return D_fake
        # If real data is provided, concatenate it with the Generator's output
        # along the batch dimension for improved efficiency.
        else:
            D_input = torch.cat([G_z, x], 0) if x is not None else G_z
            D_class = torch.cat([gy, dy], 0) if dy is not None else gy
            # Get Discriminator output
            D_out = self.D(D_input, D_class)
            if x is not None:
                return torch.split(D_out, [G_z.shape[0], x.shape[0]])  # D_fake, D_real
            else:
                if return_G_z:
                    return D_out, G_z
                else:
                    return D_out