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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.backbones.generation_backbones.unet_generator

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

from mmedit.models.common import (UnetSkipConnectionBlock,
                                  generation_init_weights)
from mmedit.models.registry import BACKBONES
from mmedit.utils import get_root_logger


[docs]@BACKBONES.register_module() class UnetGenerator(nn.Module): """Construct the Unet-based generator from the innermost layer to the outermost layer, which is a recursive process. Args: in_channels (int): Number of channels in input images. out_channels (int): Number of channels in output images. num_down (int): Number of downsamplings in Unet. If `num_down` is 8, the image with size 256x256 will become 1x1 at the bottleneck. Default: 8. base_channels (int): Number of channels at the last conv layer. Default: 64. norm_cfg (dict): Config dict to build norm layer. Default: `dict(type='BN')`. use_dropout (bool): Whether to use dropout layers. Default: False. init_cfg (dict): Config dict for initialization. `type`: The name of our initialization method. Default: 'normal'. `gain`: Scaling factor for normal, xavier and orthogonal. Default: 0.02. """ def __init__(self, in_channels, out_channels, num_down=8, base_channels=64, norm_cfg=dict(type='BN'), use_dropout=False, init_cfg=dict(type='normal', gain=0.02)): super().__init__() # We use norm layers in the unet generator. assert isinstance(norm_cfg, dict), ("'norm_cfg' should be dict, but" f'got {type(norm_cfg)}') assert 'type' in norm_cfg, "'norm_cfg' must have key 'type'" # add the innermost layer unet_block = UnetSkipConnectionBlock( base_channels * 8, base_channels * 8, in_channels=None, submodule=None, norm_cfg=norm_cfg, is_innermost=True) # add intermediate layers with base_channels * 8 filters for _ in range(num_down - 5): unet_block = UnetSkipConnectionBlock( base_channels * 8, base_channels * 8, in_channels=None, submodule=unet_block, norm_cfg=norm_cfg, use_dropout=use_dropout) # gradually reduce the number of filters # from base_channels * 8 to base_channels unet_block = UnetSkipConnectionBlock( base_channels * 4, base_channels * 8, in_channels=None, submodule=unet_block, norm_cfg=norm_cfg) unet_block = UnetSkipConnectionBlock( base_channels * 2, base_channels * 4, in_channels=None, submodule=unet_block, norm_cfg=norm_cfg) unet_block = UnetSkipConnectionBlock( base_channels, base_channels * 2, in_channels=None, submodule=unet_block, norm_cfg=norm_cfg) # add the outermost layer self.model = UnetSkipConnectionBlock( out_channels, base_channels, in_channels=in_channels, submodule=unet_block, is_outermost=True, norm_cfg=norm_cfg) self.init_type = 'normal' if init_cfg is None else init_cfg.get( 'type', 'normal') self.init_gain = 0.02 if init_cfg is None else init_cfg.get( 'gain', 0.02)
[docs] def forward(self, x): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ return self.model(x)
[docs] def init_weights(self, pretrained=None, strict=True): """Initialize weights for the model. Args: pretrained (str, optional): Path for pretrained weights. If given None, pretrained weights will not be loaded. Default: None. strict (bool, optional): Whether to allow different params for the model and checkpoint. Default: True. """ if isinstance(pretrained, str): logger = get_root_logger() load_checkpoint(self, pretrained, strict=strict, logger=logger) elif pretrained is None: generation_init_weights( self, init_type=self.init_type, init_gain=self.init_gain) else: raise TypeError("'pretrained' must be a str or None. " f'But received {type(pretrained)}.')
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