mirror of https://github.com/alibaba/EasyCV.git
114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import torch
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import torch.nn as nn
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from easycv.framework.errors import KeyError, NotImplementedError
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class SyncIBN(nn.Module):
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r"""Instance-Batch Normalization layer from
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`"Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net"
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<https://arxiv.org/pdf/1807.09441.pdf>`
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Args:
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planes (int): Number of channels for the input tensor
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ratio (float): Ratio of instance normalization in the IBN layer
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"""
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def __init__(self, planes, ratio=0.5, eps=1e-5):
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super(SyncIBN, self).__init__()
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self.half = int(planes * ratio)
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self.IN = nn.InstanceNorm2d(self.half, affine=True)
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self.BN = nn.SyncBatchNorm(planes - self.half, eps=eps)
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def forward(self, x):
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split = torch.split(x, self.half, 1)
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out1 = self.IN(split[0].contiguous())
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out2 = self.BN(split[1].contiguous())
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out = torch.cat((out1, out2), 1)
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return out
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class IBN(nn.Module):
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r"""Instance-Batch Normalization layer from
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`"Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net"
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<https://arxiv.org/pdf/1807.09441.pdf>`
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Args:
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planes (int): Number of channels for the input tensor
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ratio (float): Ratio of instance normalization in the IBN layer
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"""
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def __init__(self, planes, ratio=0.5, eps=1e-5):
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super(IBN, self).__init__()
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self.half = int(planes * ratio)
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self.IN = nn.InstanceNorm2d(self.half, affine=True)
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self.BN = nn.BatchNorm2d(planes - self.half, eps=eps)
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def forward(self, x):
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split = torch.split(x, self.half, 1)
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out1 = self.IN(split[0].contiguous())
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out2 = self.BN(split[1].contiguous())
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out = torch.cat((out1, out2), 1)
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return out
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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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'IBN': ('ibn', IBN),
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'SyncIBN': ('ibn', SyncIBN),
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'IN': ('in', nn.InstanceNorm2d),
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'LN': ('ln', nn.LayerNorm)
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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' and hasattr(layer, '_specify_ddp_gpu_num'):
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layer._specify_ddp_gpu_num(1)
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elif layer_type == 'SyncIBN' and hasattr(layer,
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'_specify_ddp_gpu_num'):
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layer.BN._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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