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mmagic.models.editors.eg3d.dual_discriminator 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional

import numpy as np
import torch
import torch.nn.functional as F
from mmengine.runner.amp import autocast
from mmengine.utils import digit_version
from mmengine.utils.dl_utils import TORCH_VERSION
from torch import Tensor

from mmagic.registry import MODELS
from ..stylegan2 import StyleGAN2Discriminator


@MODELS.register_module('EG3DDiscriminator')
@MODELS.register_module()
[文档]class DualDiscriminator(StyleGAN2Discriminator): """Dual Discriminator for EG3D. DualDiscriminator shares the same network structure with StyleGAN2's Discriminator. However, DualDiscriminator take volume rendered low-resolution image and super-resolved image at the same time. The LR image will be upsampled and concatenate with SR ones, and then feed to the discriminator together. Args: img_channels (int): The number of the image channels. Defaults to 3. use_dual_disc (bool): Whether use dual discriminator as EG3D. If True, the input channel of the first conv block will be set as `2 * img_channels`. Defaults to True. disc_c_noise (float): The factor of noise's standard deviation add to conditional input before passed to mapping network. Defaults to 0. *args, **kwargs: Arguments for StyleGAN2Discriminator. """ def __init__(self, img_channels: int = 3, use_dual_disc: bool = True, disc_c_noise: float = 0, *args, **kwargs): if use_dual_disc: img_channels *= 2 self.use_dual_disc = use_dual_disc super().__init__(img_channels=img_channels, *args, **kwargs) self.disc_c_noise = disc_c_noise
[文档] def forward(self, img: Tensor, img_raw: Optional[Tensor] = None, cond: Optional[Tensor] = None): """Forward function. Args: img (torch.Tensor): Input high resoluation image tensor. img_raw (torch.Tensor): Input raw (low resolution) image tensor. Defaults to None. cond (torch.Tensor): The conditional input (camera-to-world matrix and intrinsics matrix). Defaults to None. Returns: torch.Tensor: Predict score for the input image. """ if self.use_dual_disc: assert img_raw is not None, ( '\'img_raw\' must be passed when \'use_dual_disc\' is True.') # This setting was used to finetune on converted weights if self.input_bgr2rgb: img = img[:, [2, 1, 0], ...] if img_raw is not None: img_raw = img_raw[:, [2, 1, 0], ...] if img_raw is not None: # the official implementation only use 'antialiased' upsampline, # therefore we only support 'antialiased' for torch >= 1.11.0 interpolation_kwargs = dict( size=(img.shape[-1], img.shape[-1]), mode='bilinear', align_corners=False) if digit_version(TORCH_VERSION) >= digit_version('1.11.0'): interpolation_kwargs['antialias'] = True img_raw_sr = F.interpolate(img_raw, **interpolation_kwargs) img = torch.cat([img, img_raw_sr], dim=1) # convs has own fp-16 controller, do not wrap here x = self.convs(img) x = self.mbstd_layer(x) fp16_enabled = ( self.final_conv.fp16_enabled or not self.convert_input_fp32) with autocast(enabled=fp16_enabled): if not fp16_enabled: x = x.to(torch.float32) x = self.final_conv(x) x = x.view(x.shape[0], -1) x = self.final_linear(x) # conditioning if cond is not None: assert self.mapping is not None, ( '\'mapping\' network must not be None when conditional ' 'input is passed.') # if self.disc_c_noise is not None and self.disc_c_noise > 0: if self.disc_c_noise is not None: cond = cond + torch.randn_like( cond) * cond.std() * self.disc_c_noise cmap = self.mapping(None, cond) x = (x * cmap).sum( dim=1, keepdim=True) * (1 / np.sqrt(cmap.shape[1])) return x
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