56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
import torch.nn as nn
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norm_cfg = {
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# format: layer_type: (abbreviation, module)
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'BN': ('bn', nn.BatchNorm2d),
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'SyncBN': ('bn', nn.SyncBatchNorm),
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'GN': ('gn', nn.GroupNorm),
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# and potentially 'SN'
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}
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def build_norm_layer(cfg, num_features, postfix=''):
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"""Build normalization layer.
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Args:
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cfg (dict): cfg should contain:
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type (str): identify norm layer type.
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layer args: args needed to instantiate a norm layer.
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requires_grad (bool): [optional] whether stop gradient updates
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num_features (int): number of channels from input.
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postfix (int, str): appended into norm abbreviation to
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create named layer.
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Returns:
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name (str): abbreviation + postfix
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layer (nn.Module): created norm layer
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"""
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assert isinstance(cfg, dict) and 'type' in 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 norm_cfg:
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raise KeyError('Unrecognized norm type {}'.format(layer_type))
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else:
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abbr, norm_layer = norm_cfg[layer_type]
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if norm_layer is None:
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raise NotImplementedError
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assert isinstance(postfix, (int, str))
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name = abbr + str(postfix)
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requires_grad = cfg_.pop('requires_grad', True)
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cfg_.setdefault('eps', 1e-5)
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if layer_type != 'GN':
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layer = norm_layer(num_features, **cfg_)
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if layer_type == 'SyncBN':
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layer._specify_ddp_gpu_num(1)
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else:
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assert 'num_groups' in cfg_
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layer = norm_layer(num_channels=num_features, **cfg_)
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for param in layer.parameters():
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param.requires_grad = requires_grad
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return name, layer
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