models/utils/eg3d_superres.py

# python 3.7
"""Contains Super-Resolution Module described in EG3D."""

import numpy as np
import torch
from .official_stylegan2_model_helper import Conv2dLayer
from .official_stylegan2_model_helper import ToRGBLayer
from .official_stylegan2_model_helper import SynthesisLayer
from .official_stylegan2_model_helper import SynthesisBlock
from third_party.stylegan2_official_ops import upfirdn2d
from utils import eg3d_misc as misc

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

# for 512x512 generation
class SuperresolutionHybrid8X(torch.nn.Module):
    def __init__(self, channels, img_resolution, sr_num_fp16_res, sr_antialias,
                num_fp16_res=4, conv_clamp=None, channel_base=None, channel_max=None,# IGNORE
                **block_kwargs):
        super().__init__()
        assert img_resolution == 512

        use_fp16 = sr_num_fp16_res > 0
        self.input_resolution = 128
        self.sr_antialias = sr_antialias
        self.block0 = SynthesisBlock(channels, 128, w_dim=512, resolution=256,
                img_channels=3, is_last=False, use_fp16=use_fp16, conv_clamp=(256 if use_fp16 else None), **block_kwargs)
        self.block1 = SynthesisBlock(128, 64, w_dim=512, resolution=512,
                img_channels=3, is_last=True, use_fp16=use_fp16, conv_clamp=(256 if use_fp16 else None), **block_kwargs)
        self.register_buffer('resample_filter', upfirdn2d.setup_filter([1,3,3,1]))

    def forward(self, rgb, x, ws, **block_kwargs):
        ws = ws[:, -1:, :].repeat(1, 3, 1)

        if x.shape[-1] != self.input_resolution:
            x = torch.nn.functional.interpolate(x, size=(self.input_resolution, self.input_resolution),
                                                  mode='bilinear', align_corners=False)
            rgb = torch.nn.functional.interpolate(rgb, size=(self.input_resolution, self.input_resolution),
                                                  mode='bilinear', align_corners=False)

        x, rgb = self.block0(x, rgb, ws, **block_kwargs)
        x, rgb = self.block1(x, rgb, ws, **block_kwargs)
        return rgb

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

# for 256x256 generation
class SuperresolutionHybrid4X(torch.nn.Module):
    def __init__(self, channels, img_resolution, sr_num_fp16_res, sr_antialias,
                num_fp16_res=4, conv_clamp=None, channel_base=None, channel_max=None,# IGNORE
                **block_kwargs):
        super().__init__()
        assert img_resolution == 256
        use_fp16 = sr_num_fp16_res > 0
        self.sr_antialias = sr_antialias
        self.input_resolution = 128
        self.block0 = SynthesisBlockNoUp(channels, 128, w_dim=512, resolution=128,
                img_channels=3, is_last=False, use_fp16=use_fp16, conv_clamp=(256 if use_fp16 else None), **block_kwargs)
        self.block1 = SynthesisBlock(128, 64, w_dim=512, resolution=256,
                img_channels=3, is_last=True, use_fp16=use_fp16, conv_clamp=(256 if use_fp16 else None), **block_kwargs)
        self.register_buffer('resample_filter', upfirdn2d.setup_filter([1,3,3,1]))

    def forward(self, rgb, x, ws, **block_kwargs):
        ws = ws[:, -1:, :].repeat(1, 3, 1)

        if x.shape[-1] < self.input_resolution:
            x = torch.nn.functional.interpolate(x, size=(self.input_resolution, self.input_resolution),
                                                  mode='bilinear', align_corners=False)
            rgb = torch.nn.functional.interpolate(rgb, size=(self.input_resolution, self.input_resolution),
                                                  mode='bilinear', align_corners=False)

        x, rgb = self.block0(x, rgb, ws, **block_kwargs)
        x, rgb = self.block1(x, rgb, ws, **block_kwargs)
        return rgb

class SuperresolutionHybrid4X_conststyle(SuperresolutionHybrid4X):
    def forward(self, rgb, x, **block_kwargs):
        ws = torch.ones([x.shape[0], 3, 512]).float().to(x.device)
        if x.shape[-1] < self.input_resolution:
            x = torch.nn.functional.interpolate(x, size=(self.input_resolution, self.input_resolution),
                                                  mode='bilinear', align_corners=False)
            rgb = torch.nn.functional.interpolate(rgb, size=(self.input_resolution, self.input_resolution),
                                                  mode='bilinear', align_corners=False)

        x, rgb = self.block0(x, rgb, ws, **block_kwargs)
        x, rgb = self.block1(x, rgb, ws, **block_kwargs)
        return rgb

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

# for 128 x 128 generation
class SuperresolutionHybrid2X(torch.nn.Module):
    def __init__(self, channels, img_resolution, sr_num_fp16_res, sr_antialias,
                num_fp16_res=4, conv_clamp=None, channel_base=None, channel_max=None,# IGNORE
                **block_kwargs):
        super().__init__()
        assert img_resolution == 128

        use_fp16 = sr_num_fp16_res > 0
        self.input_resolution = 64
        self.sr_antialias = sr_antialias
        self.block0 = SynthesisBlockNoUp(channels, 128, w_dim=512, resolution=64,
                img_channels=3, is_last=False, use_fp16=use_fp16, conv_clamp=(256 if use_fp16 else None), **block_kwargs)
        self.block1 = SynthesisBlock(128, 64, w_dim=512, resolution=128,
                img_channels=3, is_last=True, use_fp16=use_fp16, conv_clamp=(256 if use_fp16 else None), **block_kwargs)
        self.register_buffer('resample_filter', upfirdn2d.setup_filter([1,3,3,1]))

    def forward(self, rgb, x, ws, **block_kwargs):
        ws = ws[:, -1:, :].repeat(1, 3, 1)

        if x.shape[-1] != self.input_resolution:
            x = torch.nn.functional.interpolate(x, size=(self.input_resolution, self.input_resolution),
                                                  mode='bilinear', align_corners=False)
            rgb = torch.nn.functional.interpolate(rgb, size=(self.input_resolution, self.input_resolution),
                                                  mode='bilinear', align_corners=False)

        x, rgb = self.block0(x, rgb, ws, **block_kwargs)
        x, rgb = self.block1(x, rgb, ws, **block_kwargs)
        return rgb

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

# TODO: Delete (here for backwards compatibility with old 256x256 models)
class SuperresolutionHybridDeepfp32(torch.nn.Module):
    def __init__(self, channels, img_resolution, sr_num_fp16_res,
                num_fp16_res=4, conv_clamp=None, channel_base=None, channel_max=None,# IGNORE
                **block_kwargs):
        super().__init__()
        assert img_resolution == 256
        use_fp16 = sr_num_fp16_res > 0

        self.input_resolution = 128
        self.block0 = SynthesisBlockNoUp(channels, 128, w_dim=512, resolution=128,
                img_channels=3, is_last=False, use_fp16=use_fp16, conv_clamp=(256 if use_fp16 else None), **block_kwargs)
        self.block1 = SynthesisBlock(128, 64, w_dim=512, resolution=256,
                img_channels=3, is_last=True, use_fp16=use_fp16, conv_clamp=(256 if use_fp16 else None), **block_kwargs)
        self.register_buffer('resample_filter', upfirdn2d.setup_filter([1,3,3,1]))

    def forward(self, rgb, x, ws, **block_kwargs):
        ws = ws[:, -1:, :].repeat(1, 3, 1)

        if x.shape[-1] < self.input_resolution:
            x = torch.nn.functional.interpolate(x, size=(self.input_resolution, self.input_resolution),
                                                  mode='bilinear', align_corners=False)
            rgb = torch.nn.functional.interpolate(rgb, size=(self.input_resolution, self.input_resolution),
                                                  mode='bilinear', align_corners=False)

        x, rgb = self.block0(x, rgb, ws, **block_kwargs)
        x, rgb = self.block1(x, rgb, ws, **block_kwargs)
        return rgb

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

class SynthesisBlockNoUp(torch.nn.Module):
    def __init__(self,
        in_channels,                            # Number of input channels, 0 = first block.
        out_channels,                           # Number of output channels.
        w_dim,                                  # Intermediate latent (W) dimensionality.
        resolution,                             # Resolution of this block.
        img_channels,                           # Number of output color channels.
        is_last,                                # Is this the last block?
        architecture            = 'skip',       # Architecture: 'orig', 'skip', 'resnet'.
        resample_filter         = [1,3,3,1],    # Low-pass filter to apply when resampling activations.
        conv_clamp              = 256,          # Clamp the output of convolution layers to +-X, None = disable clamping.
        use_fp16                = False,        # Use FP16 for this block?
        fp16_channels_last      = False,        # Use channels-last memory format with FP16?
        fused_modconv_default   = True,         # Default value of fused_modconv. 'inference_only' = True for inference, False for training.
        **layer_kwargs,                         # Arguments for SynthesisLayer.
    ):
        assert architecture in ['orig', 'skip', 'resnet']
        super().__init__()
        self.in_channels = in_channels
        self.w_dim = w_dim
        self.resolution = resolution
        self.img_channels = img_channels
        self.is_last = is_last
        self.architecture = architecture
        self.use_fp16 = use_fp16
        self.channels_last = (use_fp16 and fp16_channels_last)
        self.fused_modconv_default = fused_modconv_default
        self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
        self.num_conv = 0
        self.num_torgb = 0

        if in_channels == 0:
            self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution]))

        if in_channels != 0:
            self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution,
                conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
            self.num_conv += 1

        self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution,
            conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
        self.num_conv += 1

        if is_last or architecture == 'skip':
            self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim,
                conv_clamp=conv_clamp, channels_last=self.channels_last)
            self.num_torgb += 1

        if in_channels != 0 and architecture == 'resnet':
            self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2,
                resample_filter=resample_filter, channels_last=self.channels_last)

    def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs):
        _ = update_emas # unused
        misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim])
        w_iter = iter(ws.unbind(dim=1))
        if ws.device.type != 'cuda':
            force_fp32 = True
        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
        if fused_modconv is None:
            fused_modconv = self.fused_modconv_default
        if fused_modconv == 'inference_only':
            fused_modconv = (not self.training)

        # Input.
        if self.in_channels == 0:
            x = self.const.to(dtype=dtype, memory_format=memory_format)
            x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
        else:
            misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution])
            x = x.to(dtype=dtype, memory_format=memory_format)

        # Main layers.
        if self.in_channels == 0:
            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
        elif self.architecture == 'resnet':
            y = self.skip(x, gain=np.sqrt(0.5))
            x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs)
            x = y.add_(x)
        else:
            x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)

        # ToRGB.
        # if img is not None:
            # misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
            # img = upfirdn2d.upsample2d(img, self.resample_filter)
        if self.is_last or self.architecture == 'skip':
            y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv)
            y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format)
            img = img.add_(y) if img is not None else y

        assert x.dtype == dtype
        assert img is None or img.dtype == torch.float32
        return x, img

    def extra_repr(self):
        return f'resolution={self.resolution:d}, architecture={self.architecture:s}'


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

# for 512x512 generation
class SuperresolutionHybrid8XDC(torch.nn.Module):
    def __init__(self, channels, img_resolution, sr_num_fp16_res, sr_antialias,
                num_fp16_res=4, conv_clamp=None, channel_base=None, channel_max=None,# IGNORE
                **block_kwargs):
        super().__init__()
        assert img_resolution == 512

        use_fp16 = sr_num_fp16_res > 0
        self.input_resolution = 128
        self.sr_antialias = sr_antialias
        self.block0 = SynthesisBlock(channels, 256, w_dim=512, resolution=256,
                img_channels=3, is_last=False, use_fp16=use_fp16, conv_clamp=(256 if use_fp16 else None), **block_kwargs)
        self.block1 = SynthesisBlock(256, 128, w_dim=512, resolution=512,
                img_channels=3, is_last=True, use_fp16=use_fp16, conv_clamp=(256 if use_fp16 else None), **block_kwargs)

    def forward(self, rgb, x, ws, **block_kwargs):
        ws = ws[:, -1:, :].repeat(1, 3, 1)

        if x.shape[-1] != self.input_resolution:
            x = torch.nn.functional.interpolate(x, size=(self.input_resolution, self.input_resolution),
                                                  mode='bilinear', align_corners=False)
            rgb = torch.nn.functional.interpolate(rgb, size=(self.input_resolution, self.input_resolution),
                                                  mode='bilinear', align_corners=False)

        x, rgb = self.block0(x, rgb, ws, **block_kwargs)
        x, rgb = self.block1(x, rgb, ws, **block_kwargs)
        return rgb

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