nsr/dual_discriminator.py

# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
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# documentation and any modifications thereto. Any use, reproduction,
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"""Discriminator architectures from the paper
"Efficient Geometry-aware 3D Generative Adversarial Networks"."""

import numpy as np
import torch
from torch_utils import persistence
from torch_utils.ops import upfirdn2d
from .networks_stylegan2 import DiscriminatorBlock, MappingNetwork, DiscriminatorEpilogue
from pdb import set_trace as st


@persistence.persistent_class
class SingleDiscriminator(torch.nn.Module):
    def __init__(
            self,
            c_dim,  # Conditioning label (C) dimensionality.
            img_resolution,  # Input resolution.
            img_channels,  # Number of input color channels.
            architecture='resnet',  # Architecture: 'orig', 'skip', 'resnet'.
            channel_base=32768,  # Overall multiplier for the number of channels.
            channel_max=512,  # Maximum number of channels in any layer.
            num_fp16_res=4,  # Use FP16 for the N highest resolutions.
            conv_clamp=256,  # Clamp the output of convolution layers to +-X, None = disable clamping.
            cmap_dim=None,  # Dimensionality of mapped conditioning label, None = default.
            sr_upsample_factor=1,  # Ignored for SingleDiscriminator
            block_kwargs={},  # Arguments for DiscriminatorBlock.
            mapping_kwargs={},  # Arguments for MappingNetwork.
            epilogue_kwargs={},  # Arguments for DiscriminatorEpilogue.
    ):
        super().__init__()
        self.c_dim = c_dim
        self.img_resolution = img_resolution
        self.img_resolution_log2 = int(np.log2(img_resolution))
        self.img_channels = img_channels
        self.block_resolutions = [
            2**i for i in range(self.img_resolution_log2, 2, -1)
        ]
        channels_dict = {
            res: min(channel_base // res, channel_max)
            for res in self.block_resolutions + [4]
        }
        fp16_resolution = max(2**(self.img_resolution_log2 + 1 - num_fp16_res),
                              8)

        if cmap_dim is None:
            cmap_dim = channels_dict[4]
        if c_dim == 0:
            cmap_dim = 0

        common_kwargs = dict(img_channels=img_channels,
                             architecture=architecture,
                             conv_clamp=conv_clamp)
        cur_layer_idx = 0
        for res in self.block_resolutions:
            in_channels = channels_dict[res] if res < img_resolution else 0
            tmp_channels = channels_dict[res]
            out_channels = channels_dict[res // 2]
            use_fp16 = (res >= fp16_resolution)
            block = DiscriminatorBlock(in_channels,
                                       tmp_channels,
                                       out_channels,
                                       resolution=res,
                                       first_layer_idx=cur_layer_idx,
                                       use_fp16=use_fp16,
                                       **block_kwargs,
                                       **common_kwargs)
            setattr(self, f'b{res}', block)
            cur_layer_idx += block.num_layers
        if c_dim > 0:
            self.mapping = MappingNetwork(z_dim=0,
                                          c_dim=c_dim,
                                          w_dim=cmap_dim,
                                          num_ws=None,
                                          w_avg_beta=None,
                                          **mapping_kwargs)
        self.b4 = DiscriminatorEpilogue(channels_dict[4],
                                        cmap_dim=cmap_dim,
                                        resolution=4,
                                        **epilogue_kwargs,
                                        **common_kwargs)

    def forward(self, img, c, update_emas=False, **block_kwargs):
        img = img['image']

        _ = update_emas  # unused
        x = None
        for res in self.block_resolutions:
            block = getattr(self, f'b{res}')
            x, img = block(x, img, **block_kwargs)

        cmap = None
        if self.c_dim > 0:
            cmap = self.mapping(None, c)
        x = self.b4(x, img, cmap)
        return x

    def extra_repr(self):
        return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'


#----------------------------------------------------------------------------


def filtered_resizing(image_orig_tensor, size, f, filter_mode='antialiased'):
    if filter_mode == 'antialiased':
        ada_filtered_64 = torch.nn.functional.interpolate(image_orig_tensor,
                                                          size=(size, size),
                                                          mode='bilinear',
                                                          align_corners=False,
                                                          antialias=True)
    elif filter_mode == 'classic':
        ada_filtered_64 = upfirdn2d.upsample2d(image_orig_tensor, f, up=2)
        ada_filtered_64 = torch.nn.functional.interpolate(ada_filtered_64,
                                                          size=(size * 2 + 2,
                                                                size * 2 + 2),
                                                          mode='bilinear',
                                                          align_corners=False)
        ada_filtered_64 = upfirdn2d.downsample2d(ada_filtered_64,
                                                 f,
                                                 down=2,
                                                 flip_filter=True,
                                                 padding=-1)
    elif filter_mode == 'none':
        ada_filtered_64 = torch.nn.functional.interpolate(image_orig_tensor,
                                                          size=(size, size),
                                                          mode='bilinear',
                                                          align_corners=False)
    elif type(filter_mode) == float:
        assert 0 < filter_mode < 1

        filtered = torch.nn.functional.interpolate(image_orig_tensor,
                                                   size=(size, size),
                                                   mode='bilinear',
                                                   align_corners=False,
                                                   antialias=True)
        aliased = torch.nn.functional.interpolate(image_orig_tensor,
                                                  size=(size, size),
                                                  mode='bilinear',
                                                  align_corners=False,
                                                  antialias=False)
        ada_filtered_64 = (1 -
                           filter_mode) * aliased + (filter_mode) * filtered

    return ada_filtered_64


#----------------------------------------------------------------------------


@persistence.persistent_class
class DualDiscriminator(torch.nn.Module):
    def __init__(
            self,
            c_dim,  # Conditioning label (C) dimensionality.
            img_resolution,  # Input resolution.
            img_channels,  # Number of input color channels.
            architecture='resnet',  # Architecture: 'orig', 'skip', 'resnet'.
            channel_base=32768,  # Overall multiplier for the number of channels.
            channel_max=512,  # Maximum number of channels in any layer.
            num_fp16_res=4,  # Use FP16 for the N highest resolutions.
            conv_clamp=256,  # Clamp the output of convolution layers to +-X, None = disable clamping.
            cmap_dim=None,  # Dimensionality of mapped conditioning label, None = default.
            disc_c_noise=0,  # Corrupt camera parameters with X std dev of noise before disc. pose conditioning.
            block_kwargs={},  # Arguments for DiscriminatorBlock.
            mapping_kwargs={},  # Arguments for MappingNetwork.
            epilogue_kwargs={},  # Arguments for DiscriminatorEpilogue.
    ):
        super().__init__()
        # img_channels *= 2
        if img_channels == 3:
            img_channels *= 2

        self.c_dim = c_dim
        self.img_resolution = img_resolution
        self.img_resolution_log2 = int(np.log2(img_resolution))
        self.img_channels = img_channels
        self.block_resolutions = [
            2**i for i in range(self.img_resolution_log2, 2, -1)
        ]
        channels_dict = {
            res: min(channel_base // res, channel_max)
            for res in self.block_resolutions + [4]
        }
        fp16_resolution = max(2**(self.img_resolution_log2 + 1 - num_fp16_res),
                              8)

        if cmap_dim is None:
            cmap_dim = channels_dict[4]
        if c_dim == 0:
            cmap_dim = 0

        common_kwargs = dict(img_channels=img_channels,
                             architecture=architecture,
                             conv_clamp=conv_clamp)
        cur_layer_idx = 0
        for res in self.block_resolutions:
            in_channels = channels_dict[res] if res < img_resolution else 0
            tmp_channels = channels_dict[res]
            out_channels = channels_dict[res // 2]
            use_fp16 = (res >= fp16_resolution)
            block = DiscriminatorBlock(in_channels,
                                       tmp_channels,
                                       out_channels,
                                       resolution=res,
                                       first_layer_idx=cur_layer_idx,
                                       use_fp16=use_fp16,
                                       **block_kwargs,
                                       **common_kwargs)
            setattr(self, f'b{res}', block)
            cur_layer_idx += block.num_layers
        if c_dim > 0:
            self.mapping = MappingNetwork(z_dim=0,
                                          c_dim=c_dim,
                                          w_dim=cmap_dim,
                                          num_ws=None,
                                          w_avg_beta=None,
                                          **mapping_kwargs)
        self.b4 = DiscriminatorEpilogue(channels_dict[4],
                                        cmap_dim=cmap_dim,
                                        resolution=4,
                                        **epilogue_kwargs,
                                        **common_kwargs)
        self.register_buffer('resample_filter',
                             upfirdn2d.setup_filter([1, 3, 3, 1]))
        self.disc_c_noise = disc_c_noise

    def forward(self, img, c, update_emas=False, **block_kwargs):
        image_raw = filtered_resizing(img['image_raw'],
                                    #   size=img['image'].shape[-1],
                                      size=img['image_sr'].shape[-1],
                                      f=self.resample_filter)
        # img = torch.cat([img['image'], image_raw], 1)
        img = torch.cat([img['image_sr'], image_raw], 1)

        _ = update_emas  # unused
        x = None
        for res in self.block_resolutions:
            block = getattr(self, f'b{res}')
            x, img = block(x, img, **block_kwargs)

        cmap = None
        if self.c_dim > 0:
            if self.disc_c_noise > 0:
                c += torch.randn_like(c) * c.std(0) * self.disc_c_noise
            cmap = self.mapping(None, c)
        x = self.b4(x, img, cmap)
        return x

    def extra_repr(self):
        return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'


@persistence.persistent_class
class GeoDualDiscriminator(DualDiscriminator):
    def __init__(self, c_dim, img_resolution, img_channels, architecture='resnet', channel_base=32768, channel_max=512, num_fp16_res=4, conv_clamp=256, cmap_dim=None, disc_c_noise=0, block_kwargs={}, mapping_kwargs={}, epilogue_kwargs={}, normal_condition=False):
        super().__init__(c_dim, img_resolution, img_channels, architecture, channel_base, channel_max, num_fp16_res, conv_clamp, cmap_dim, disc_c_noise, block_kwargs, mapping_kwargs, epilogue_kwargs)
        self.normal_condition = normal_condition

    def forward(self, img, c, update_emas=False, **block_kwargs):
        image= img['image']
        image_raw = filtered_resizing(img['image_raw'],
                                      size=img['image'].shape[-1],
                                      f=self.resample_filter)
        D_input_img = torch.cat([image, image_raw], 1)

        image_depth = filtered_resizing(img['image_depth'], size=img['image'].shape[-1], f=self.resample_filter)
        if self.normal_condition and 'normal' in img:
            image_normal = filtered_resizing(img['normal'], size=img['image'].shape[-1], f=self.resample_filter)
            D_input_img = torch.cat([D_input_img, image_depth, image_normal], 1)
        else:
            D_input_img = torch.cat([D_input_img, image_depth], 1)

        img = D_input_img

        _ = update_emas  # unused
        x = None
        for res in self.block_resolutions:
            block = getattr(self, f'b{res}')
            x, img = block(x, img, **block_kwargs)

        cmap = None
        if self.c_dim > 0:
            if self.disc_c_noise > 0:
                c += torch.randn_like(c) * c.std(0) * self.disc_c_noise
            cmap = self.mapping(None, c)
        x = self.b4(x, img, cmap)
        return x

#----------------------------------------------------------------------------


@persistence.persistent_class
class DummyDualDiscriminator(torch.nn.Module):
    def __init__(
            self,
            c_dim,  # Conditioning label (C) dimensionality.
            img_resolution,  # Input resolution.
            img_channels,  # Number of input color channels.
            architecture='resnet',  # Architecture: 'orig', 'skip', 'resnet'.
            channel_base=32768,  # Overall multiplier for the number of channels.
            channel_max=512,  # Maximum number of channels in any layer.
            num_fp16_res=4,  # Use FP16 for the N highest resolutions.
            conv_clamp=256,  # Clamp the output of convolution layers to +-X, None = disable clamping.
            cmap_dim=None,  # Dimensionality of mapped conditioning label, None = default.
            block_kwargs={},  # Arguments for DiscriminatorBlock.
            mapping_kwargs={},  # Arguments for MappingNetwork.
            epilogue_kwargs={},  # Arguments for DiscriminatorEpilogue.
    ):
        super().__init__()
        img_channels *= 2

        self.c_dim = c_dim
        self.img_resolution = img_resolution
        self.img_resolution_log2 = int(np.log2(img_resolution))
        self.img_channels = img_channels
        self.block_resolutions = [
            2**i for i in range(self.img_resolution_log2, 2, -1)
        ]
        channels_dict = {
            res: min(channel_base // res, channel_max)
            for res in self.block_resolutions + [4]
        }
        fp16_resolution = max(2**(self.img_resolution_log2 + 1 - num_fp16_res),
                              8)

        if cmap_dim is None:
            cmap_dim = channels_dict[4]
        if c_dim == 0:
            cmap_dim = 0

        common_kwargs = dict(img_channels=img_channels,
                             architecture=architecture,
                             conv_clamp=conv_clamp)
        cur_layer_idx = 0
        for res in self.block_resolutions:
            in_channels = channels_dict[res] if res < img_resolution else 0
            tmp_channels = channels_dict[res]
            out_channels = channels_dict[res // 2]
            use_fp16 = (res >= fp16_resolution)
            block = DiscriminatorBlock(in_channels,
                                       tmp_channels,
                                       out_channels,
                                       resolution=res,
                                       first_layer_idx=cur_layer_idx,
                                       use_fp16=use_fp16,
                                       **block_kwargs,
                                       **common_kwargs)
            setattr(self, f'b{res}', block)
            cur_layer_idx += block.num_layers
        if c_dim > 0:
            self.mapping = MappingNetwork(z_dim=0,
                                          c_dim=c_dim,
                                          w_dim=cmap_dim,
                                          num_ws=None,
                                          w_avg_beta=None,
                                          **mapping_kwargs)
        self.b4 = DiscriminatorEpilogue(channels_dict[4],
                                        cmap_dim=cmap_dim,
                                        resolution=4,
                                        **epilogue_kwargs,
                                        **common_kwargs)
        self.register_buffer('resample_filter',
                             upfirdn2d.setup_filter([1, 3, 3, 1]))

        self.raw_fade = 1

    def forward(self, img, c, update_emas=False, **block_kwargs):
        self.raw_fade = max(0, self.raw_fade - 1 / (500000 / 32))

        image_raw = filtered_resizing(img['image_raw'],
                                      size=img['image'].shape[-1],
                                      f=self.resample_filter) * self.raw_fade
        img = torch.cat([img['image'], image_raw], 1)

        _ = update_emas  # unused
        x = None
        for res in self.block_resolutions:
            block = getattr(self, f'b{res}')
            x, img = block(x, img, **block_kwargs)

        cmap = None
        if self.c_dim > 0:
            cmap = self.mapping(None, c)
        x = self.b4(x, img, cmap)
        return x

    def extra_repr(self):
        return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'


#----------------------------------------------------------------------------

# panohead
# Tri-discriminator: upsampled image, super-resolved image, and segmentation mask
# V2: first concatenate imgs and seg mask, using only one conv block
@persistence.persistent_class
class MaskDualDiscriminatorV2(torch.nn.Module):
    def __init__(self,
        c_dim,                          # Conditioning label (C) dimensionality.
        img_resolution,                 # Input resolution.
        img_channels,                   # Number of input color channels.
        seg_resolution,                 # Input resolution.
        seg_channels,                   # Number of input color channels.
        architecture        = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
        channel_base        = 32768,    # Overall multiplier for the number of channels.
        channel_max         = 512,      # Maximum number of channels in any layer.
        num_fp16_res        = 4,        # Use FP16 for the N highest resolutions.
        conv_clamp          = 256,      # Clamp the output of convolution layers to +-X, None = disable clamping.
        cmap_dim            = None,     # Dimensionality of mapped conditioning label, None = default.
        disc_c_noise        = 0,        # Corrupt camera parameters with X std dev of noise before disc. pose conditioning.
        block_kwargs        = {},       # Arguments for DiscriminatorBlock.
        mapping_kwargs      = {},       # Arguments for MappingNetwork.
        epilogue_kwargs     = {},       # Arguments for DiscriminatorEpilogue.
    ):
        super().__init__()
        img_channels = img_channels * 2 + seg_channels

        self.c_dim = c_dim
        self.img_resolution = img_resolution
        self.img_resolution_log2 = int(np.log2(img_resolution))
        self.img_channels = img_channels
        self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)]
        channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]}
        fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)

        if cmap_dim is None:
            cmap_dim = channels_dict[4]
        if c_dim == 0:
            cmap_dim = 0

        common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp)
        cur_layer_idx = 0
        for res in self.block_resolutions:
            in_channels = channels_dict[res] if res < img_resolution else 0
            tmp_channels = channels_dict[res]
            out_channels = channels_dict[res // 2]
            use_fp16 = (res >= fp16_resolution)
            block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res,
                first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs)
            setattr(self, f'b{res}', block)
            cur_layer_idx += block.num_layers
        if c_dim > 0:
            self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs)
        self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs)
        self.register_buffer('resample_filter', upfirdn2d.setup_filter([1,3,3,1]))
        self.disc_c_noise = disc_c_noise

    def forward(self, img, c, update_emas=False, **block_kwargs):
        image_raw = filtered_resizing(img['image_raw'], size=img['image'].shape[-1], f=self.resample_filter)
        seg = filtered_resizing(img['image_mask'], size=img['image'].shape[-1], f=self.resample_filter)
        seg = 2 * seg - 1 # normalize to [-1,1]
        img = torch.cat([img['image'], image_raw, seg], 1)

        _ = update_emas # unused
        x = None
        for res in self.block_resolutions:
            block = getattr(self, f'b{res}')
            x, img = block(x, img, **block_kwargs)

        cmap = None
        if self.c_dim > 0:
            if self.disc_c_noise > 0: c += torch.randn_like(c) * c.std(0) * self.disc_c_noise
            cmap = self.mapping(None, c)
        x = self.b4(x, img, cmap)
        return x

    def extra_repr(self):
        return ' '.join([
            f'c_dim={self.c_dim:d},',
            f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
            f'seg_resolution={self.seg_resolution:d}, seg_channels={self.seg_channels:d}'])