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You are reading the documentation for MMEditing 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMEditing 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMEditing 1.0 for more details.

Source code for mmedit.models.components.discriminators.unet_disc

# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import load_checkpoint
from torch.nn.utils import spectral_norm

from mmedit.models.registry import COMPONENTS
from mmedit.utils import get_root_logger


[docs]@COMPONENTS.register_module() class UNetDiscriminatorWithSpectralNorm(nn.Module): """A U-Net discriminator with spectral normalization. Args: in_channels (int): Channel number of the input. mid_channels (int, optional): Channel number of the intermediate features. Default: 64. skip_connection (bool, optional): Whether to use skip connection. Default: True. """ def __init__(self, in_channels, mid_channels=64, skip_connection=True): super().__init__() self.skip_connection = skip_connection self.conv_0 = nn.Conv2d( in_channels, mid_channels, kernel_size=3, stride=1, padding=1) # downsample self.conv_1 = spectral_norm( nn.Conv2d(mid_channels, mid_channels * 2, 4, 2, 1, bias=False)) self.conv_2 = spectral_norm( nn.Conv2d(mid_channels * 2, mid_channels * 4, 4, 2, 1, bias=False)) self.conv_3 = spectral_norm( nn.Conv2d(mid_channels * 4, mid_channels * 8, 4, 2, 1, bias=False)) # upsample self.conv_4 = spectral_norm( nn.Conv2d(mid_channels * 8, mid_channels * 4, 3, 1, 1, bias=False)) self.conv_5 = spectral_norm( nn.Conv2d(mid_channels * 4, mid_channels * 2, 3, 1, 1, bias=False)) self.conv_6 = spectral_norm( nn.Conv2d(mid_channels * 2, mid_channels, 3, 1, 1, bias=False)) # final layers self.conv_7 = spectral_norm( nn.Conv2d(mid_channels, mid_channels, 3, 1, 1, bias=False)) self.conv_8 = spectral_norm( nn.Conv2d(mid_channels, mid_channels, 3, 1, 1, bias=False)) self.conv_9 = nn.Conv2d(mid_channels, 1, 3, 1, 1) self.upsample = nn.Upsample( scale_factor=2, mode='bilinear', align_corners=False) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
[docs] def forward(self, img): """Forward function. Args: img (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ feat_0 = self.lrelu(self.conv_0(img)) # downsample feat_1 = self.lrelu(self.conv_1(feat_0)) feat_2 = self.lrelu(self.conv_2(feat_1)) feat_3 = self.lrelu(self.conv_3(feat_2)) # upsample feat_3 = self.upsample(feat_3) feat_4 = self.lrelu(self.conv_4(feat_3)) if self.skip_connection: feat_4 = feat_4 + feat_2 feat_4 = self.upsample(feat_4) feat_5 = self.lrelu(self.conv_5(feat_4)) if self.skip_connection: feat_5 = feat_5 + feat_1 feat_5 = self.upsample(feat_5) feat_6 = self.lrelu(self.conv_6(feat_5)) if self.skip_connection: feat_6 = feat_6 + feat_0 # final layers out = self.lrelu(self.conv_7(feat_6)) out = self.lrelu(self.conv_8(out)) return self.conv_9(out)
[docs] def init_weights(self, pretrained=None, strict=True): """Init weights for models. Args: pretrained (str, optional): Path for pretrained weights. If given None, pretrained weights will not be loaded. Defaults to None. strict (boo, optional): Whether strictly load the pretrained model. Defaults to True. """ if isinstance(pretrained, str): logger = get_root_logger() load_checkpoint(self, pretrained, strict=strict, logger=logger) elif pretrained is not None: # Use PyTorch default initialization. raise TypeError(f'"pretrained" must be a str or None. ' f'But received {type(pretrained)}.')
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