| |
| from torch import nn |
|
|
| from .registry import CONV_LAYERS |
|
|
| CONV_LAYERS.register_module('Conv1d', module=nn.Conv1d) |
| CONV_LAYERS.register_module('Conv2d', module=nn.Conv2d) |
| CONV_LAYERS.register_module('Conv3d', module=nn.Conv3d) |
| CONV_LAYERS.register_module('Conv', module=nn.Conv2d) |
|
|
|
|
| def build_conv_layer(cfg, *args, **kwargs): |
| """Build convolution layer. |
| |
| Args: |
| cfg (None or dict): The conv layer config, which should contain: |
| - type (str): Layer type. |
| - layer args: Args needed to instantiate an conv 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 conv layer. |
| """ |
| if cfg is None: |
| cfg_ = dict(type='Conv2d') |
| else: |
| if not isinstance(cfg, dict): |
| raise TypeError('cfg must be a dict') |
| if 'type' not in cfg: |
| raise KeyError('the cfg dict must contain the key "type"') |
| cfg_ = cfg.copy() |
|
|
| layer_type = cfg_.pop('type') |
| if layer_type not in CONV_LAYERS: |
| raise KeyError(f'Unrecognized norm type {layer_type}') |
| else: |
| conv_layer = CONV_LAYERS.get(layer_type) |
|
|
| layer = conv_layer(*args, **kwargs, **cfg_) |
|
|
| return layer |
|
|
