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