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mmagic.models.editors.lsgan.lsgan_discriminator 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule

from mmagic.registry import MODELS


@MODELS.register_module()
[文档]class LSGANDiscriminator(BaseModule): """Discriminator for LSGAN. Implementation Details for LSGAN architecture: #. Adopt convolution in the discriminator; #. Use batchnorm in the discriminator except for the input and final \ output layer; #. Use LeakyReLU in the discriminator in addition to the output layer; #. Use fully connected layer in the output layer; #. Use 5x5 conv rather than 4x4 conv in DCGAN. Args: input_scale (int, optional): The scale of the input image. Defaults to 128. output_scale (int, optional): The final scale of the convolutional feature. Defaults to 8. out_channels (int, optional): The channel number of the final output layer. Defaults to 1. in_channels (int, optional): The channel number of the input image. Defaults to 3. base_channels (int, optional): The basic channel number of the generator. The other layers contains channels based on this number. Defaults to 128. conv_cfg (dict, optional): Config for the convolution module used in this discriminator. Defaults to dict(type='Conv2d'). default_norm_cfg (dict, optional): Norm config for all of layers except for the final output layer. Defaults to ``dict(type='BN')``. default_act_cfg (dict, optional): Activation config for all of layers except for the final output layer. Defaults to ``dict(type='LeakyReLU', negative_slope=0.2)``. out_act_cfg (dict, optional): Activation config for the final output layer. Defaults to ``dict(type='Tanh')``. init_cfg (dict, optional): Initialization config dict. """ def __init__(self, input_scale=128, output_scale=8, out_channels=1, in_channels=3, base_channels=64, conv_cfg=dict(type='Conv2d'), default_norm_cfg=dict(type='BN'), default_act_cfg=dict(type='LeakyReLU', negative_slope=0.2), out_act_cfg=None, init_cfg=None): super().__init__(init_cfg=init_cfg) assert input_scale % output_scale == 0 assert input_scale // output_scale >= 2 self.input_scale = input_scale self.output_scale = output_scale self.out_channels = out_channels self.base_channels = base_channels self.with_out_activation = out_act_cfg is not None self.conv_blocks = nn.ModuleList() self.conv_blocks.append( ConvModule( in_channels, base_channels, kernel_size=5, stride=2, padding=2, conv_cfg=conv_cfg, norm_cfg=None, act_cfg=default_act_cfg)) # the number of times for downsampling self.num_downsamples = int(np.log2(input_scale // output_scale)) - 1 # build up downsampling backbone (excluding the output layer) curr_channels = base_channels for _ in range(self.num_downsamples): self.conv_blocks.append( ConvModule( curr_channels, curr_channels * 2, kernel_size=5, stride=2, padding=2, conv_cfg=conv_cfg, norm_cfg=default_norm_cfg, act_cfg=default_act_cfg)) curr_channels = curr_channels * 2 # output layer self.decision = nn.Sequential( nn.Linear(output_scale * output_scale * curr_channels, out_channels)) if self.with_out_activation: self.out_activation = MODELS.build(out_act_cfg)
[文档] def forward(self, x): """Forward function. Args: x (torch.Tensor): Fake or real image tensor. Returns: torch.Tensor: Prediction for the reality of the input image. """ n = x.shape[0] for conv in self.conv_blocks: x = conv(x) x = x.reshape(n, -1) x = self.decision(x) if self.with_out_activation: x = self.out_activation(x) return x
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