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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from ..utils import xavier_init | |
| from .registry import UPSAMPLE_LAYERS | |
| UPSAMPLE_LAYERS.register_module('nearest', module=nn.Upsample) | |
| UPSAMPLE_LAYERS.register_module('bilinear', module=nn.Upsample) | |
| class PixelShufflePack(nn.Module): | |
| """Pixel Shuffle upsample layer. | |
| This module packs `F.pixel_shuffle()` and a nn.Conv2d module together to | |
| achieve a simple upsampling with pixel shuffle. | |
| Args: | |
| in_channels (int): Number of input channels. | |
| out_channels (int): Number of output channels. | |
| scale_factor (int): Upsample ratio. | |
| upsample_kernel (int): Kernel size of the conv layer to expand the | |
| channels. | |
| """ | |
| def __init__(self, in_channels, out_channels, scale_factor, | |
| upsample_kernel): | |
| super(PixelShufflePack, self).__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.scale_factor = scale_factor | |
| self.upsample_kernel = upsample_kernel | |
| self.upsample_conv = nn.Conv2d( | |
| self.in_channels, | |
| self.out_channels * scale_factor * scale_factor, | |
| self.upsample_kernel, | |
| padding=(self.upsample_kernel - 1) // 2) | |
| self.init_weights() | |
| def init_weights(self): | |
| xavier_init(self.upsample_conv, distribution='uniform') | |
| def forward(self, x): | |
| x = self.upsample_conv(x) | |
| x = F.pixel_shuffle(x, self.scale_factor) | |
| return x | |
| def build_upsample_layer(cfg, *args, **kwargs): | |
| """Build upsample layer. | |
| Args: | |
| cfg (dict): The upsample layer config, which should contain: | |
| - type (str): Layer type. | |
| - scale_factor (int): Upsample ratio, which is not applicable to | |
| deconv. | |
| - layer args: Args needed to instantiate a upsample layer. | |
| args (argument list): Arguments passed to the ``__init__`` | |
| method of the corresponding conv layer. | |
| kwargs (keyword arguments): Keyword arguments passed to the | |
| ``__init__`` method of the corresponding conv layer. | |
| Returns: | |
| nn.Module: Created upsample layer. | |
| """ | |
| if not isinstance(cfg, dict): | |
| raise TypeError(f'cfg must be a dict, but got {type(cfg)}') | |
| if 'type' not in cfg: | |
| raise KeyError( | |
| f'the cfg dict must contain the key "type", but got {cfg}') | |
| cfg_ = cfg.copy() | |
| layer_type = cfg_.pop('type') | |
| if layer_type not in UPSAMPLE_LAYERS: | |
| raise KeyError(f'Unrecognized upsample type {layer_type}') | |
| else: | |
| upsample = UPSAMPLE_LAYERS.get(layer_type) | |
| if upsample is nn.Upsample: | |
| cfg_['mode'] = layer_type | |
| layer = upsample(*args, **kwargs, **cfg_) | |
| return layer | |