| |
| import inspect |
| from typing import Dict |
|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from mmengine.model import xavier_init |
| from mmengine.registry import MODELS |
|
|
| MODELS.register_module('nearest', module=nn.Upsample) |
| MODELS.register_module('bilinear', module=nn.Upsample) |
|
|
|
|
| @MODELS.register_module(name='pixel_shuffle') |
| 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: int, out_channels: int, scale_factor: int, |
| upsample_kernel: int): |
| super().__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: torch.Tensor) -> torch.Tensor: |
| x = self.upsample_conv(x) |
| x = F.pixel_shuffle(x, self.scale_factor) |
| return x |
|
|
|
|
| def build_upsample_layer(cfg: Dict, *args, **kwargs) -> nn.Module: |
| """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 inspect.isclass(layer_type): |
| upsample = layer_type |
| |
| |
| |
| else: |
| with MODELS.switch_scope_and_registry(None) as registry: |
| upsample = registry.get(layer_type) |
| if upsample is None: |
| raise KeyError(f'Cannot find {upsample} in registry under scope ' |
| f'name {registry.scope}') |
| if upsample is nn.Upsample: |
| cfg_['mode'] = layer_type |
| layer = upsample(*args, **kwargs, **cfg_) |
| return layer |
|
|
