| |
| import inspect |
|
|
| import torch.nn as nn |
|
|
| from annotator.mmpkg.mmcv.utils import is_tuple_of |
| from annotator.mmpkg.mmcv.utils.parrots_wrapper import SyncBatchNorm, _BatchNorm, _InstanceNorm |
| from .registry import NORM_LAYERS |
|
|
| NORM_LAYERS.register_module('BN', module=nn.BatchNorm2d) |
| NORM_LAYERS.register_module('BN1d', module=nn.BatchNorm1d) |
| NORM_LAYERS.register_module('BN2d', module=nn.BatchNorm2d) |
| NORM_LAYERS.register_module('BN3d', module=nn.BatchNorm3d) |
| NORM_LAYERS.register_module('SyncBN', module=SyncBatchNorm) |
| NORM_LAYERS.register_module('GN', module=nn.GroupNorm) |
| NORM_LAYERS.register_module('LN', module=nn.LayerNorm) |
| NORM_LAYERS.register_module('IN', module=nn.InstanceNorm2d) |
| NORM_LAYERS.register_module('IN1d', module=nn.InstanceNorm1d) |
| NORM_LAYERS.register_module('IN2d', module=nn.InstanceNorm2d) |
| NORM_LAYERS.register_module('IN3d', module=nn.InstanceNorm3d) |
|
|
|
|
| def infer_abbr(class_type): |
| """Infer abbreviation from the class name. |
| |
| When we build a norm layer with `build_norm_layer()`, we want to preserve |
| the norm type in variable names, e.g, self.bn1, self.gn. This method will |
| infer the abbreviation to map class types to abbreviations. |
| |
| Rule 1: If the class has the property "_abbr_", return the property. |
| Rule 2: If the parent class is _BatchNorm, GroupNorm, LayerNorm or |
| InstanceNorm, the abbreviation of this layer will be "bn", "gn", "ln" and |
| "in" respectively. |
| Rule 3: If the class name contains "batch", "group", "layer" or "instance", |
| the abbreviation of this layer will be "bn", "gn", "ln" and "in" |
| respectively. |
| Rule 4: Otherwise, the abbreviation falls back to "norm". |
| |
| Args: |
| class_type (type): The norm layer type. |
| |
| Returns: |
| str: The inferred abbreviation. |
| """ |
| if not inspect.isclass(class_type): |
| raise TypeError( |
| f'class_type must be a type, but got {type(class_type)}') |
| if hasattr(class_type, '_abbr_'): |
| return class_type._abbr_ |
| if issubclass(class_type, _InstanceNorm): |
| return 'in' |
| elif issubclass(class_type, _BatchNorm): |
| return 'bn' |
| elif issubclass(class_type, nn.GroupNorm): |
| return 'gn' |
| elif issubclass(class_type, nn.LayerNorm): |
| return 'ln' |
| else: |
| class_name = class_type.__name__.lower() |
| if 'batch' in class_name: |
| return 'bn' |
| elif 'group' in class_name: |
| return 'gn' |
| elif 'layer' in class_name: |
| return 'ln' |
| elif 'instance' in class_name: |
| return 'in' |
| else: |
| return 'norm_layer' |
|
|
|
|
| def build_norm_layer(cfg, num_features, postfix=''): |
| """Build normalization layer. |
| |
| Args: |
| cfg (dict): The norm layer config, which should contain: |
| |
| - type (str): Layer type. |
| - layer args: Args needed to instantiate a norm layer. |
| - requires_grad (bool, optional): Whether stop gradient updates. |
| num_features (int): Number of input channels. |
| postfix (int | str): The postfix to be appended into norm abbreviation |
| to create named layer. |
| |
| Returns: |
| (str, nn.Module): The first element is the layer name consisting of |
| abbreviation and postfix, e.g., bn1, gn. The second element is the |
| created norm layer. |
| """ |
| 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 NORM_LAYERS: |
| raise KeyError(f'Unrecognized norm type {layer_type}') |
|
|
| norm_layer = NORM_LAYERS.get(layer_type) |
| abbr = infer_abbr(norm_layer) |
|
|
| assert isinstance(postfix, (int, str)) |
| name = abbr + str(postfix) |
|
|
| requires_grad = cfg_.pop('requires_grad', True) |
| cfg_.setdefault('eps', 1e-5) |
| if layer_type != 'GN': |
| layer = norm_layer(num_features, **cfg_) |
| if layer_type == 'SyncBN' and hasattr(layer, '_specify_ddp_gpu_num'): |
| layer._specify_ddp_gpu_num(1) |
| else: |
| assert 'num_groups' in cfg_ |
| layer = norm_layer(num_channels=num_features, **cfg_) |
|
|
| for param in layer.parameters(): |
| param.requires_grad = requires_grad |
|
|
| return name, layer |
|
|
|
|
| def is_norm(layer, exclude=None): |
| """Check if a layer is a normalization layer. |
| |
| Args: |
| layer (nn.Module): The layer to be checked. |
| exclude (type | tuple[type]): Types to be excluded. |
| |
| Returns: |
| bool: Whether the layer is a norm layer. |
| """ |
| if exclude is not None: |
| if not isinstance(exclude, tuple): |
| exclude = (exclude, ) |
| if not is_tuple_of(exclude, type): |
| raise TypeError( |
| f'"exclude" must be either None or type or a tuple of types, ' |
| f'but got {type(exclude)}: {exclude}') |
|
|
| if exclude and isinstance(layer, exclude): |
| return False |
|
|
| all_norm_bases = (_BatchNorm, _InstanceNorm, nn.GroupNorm, nn.LayerNorm) |
| return isinstance(layer, all_norm_bases) |
|
|
