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87 lines
3.5 KiB
87 lines
3.5 KiB
""" Normalization + Activation Layers
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"""
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import torch
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from torch import nn as nn
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from torch.nn import functional as F
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from .create_act import get_act_layer
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class BatchNormAct2d(nn.BatchNorm2d):
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"""BatchNorm + Activation
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This module performs BatchNorm + Activation in a manner that will remain backwards
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compatible with weights trained with separate bn, act. This is why we inherit from BN
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instead of composing it as a .bn member.
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"""
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def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True,
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apply_act=True, act_layer=nn.ReLU, inplace=True, drop_block=None):
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super(BatchNormAct2d, self).__init__(
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num_features, eps=eps, momentum=momentum, affine=affine, track_running_stats=track_running_stats)
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if isinstance(act_layer, str):
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act_layer = get_act_layer(act_layer)
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if act_layer is not None and apply_act:
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act_args = dict(inplace=True) if inplace else {}
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self.act = act_layer(**act_args)
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else:
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self.act = None
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def _forward_jit(self, x):
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""" A cut & paste of the contents of the PyTorch BatchNorm2d forward function
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"""
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# exponential_average_factor is self.momentum set to
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# (when it is available) only so that if gets updated
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# in ONNX graph when this node is exported to ONNX.
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if self.momentum is None:
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exponential_average_factor = 0.0
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else:
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exponential_average_factor = self.momentum
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if self.training and self.track_running_stats:
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# TODO: if statement only here to tell the jit to skip emitting this when it is None
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if self.num_batches_tracked is not None:
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self.num_batches_tracked += 1
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if self.momentum is None: # use cumulative moving average
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exponential_average_factor = 1.0 / float(self.num_batches_tracked)
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else: # use exponential moving average
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exponential_average_factor = self.momentum
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x = F.batch_norm(
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x, self.running_mean, self.running_var, self.weight, self.bias,
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self.training or not self.track_running_stats,
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exponential_average_factor, self.eps)
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return x
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@torch.jit.ignore
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def _forward_python(self, x):
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return super(BatchNormAct2d, self).forward(x)
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def forward(self, x):
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# FIXME cannot call parent forward() and maintain jit.script compatibility?
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if torch.jit.is_scripting():
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x = self._forward_jit(x)
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else:
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x = self._forward_python(x)
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if self.act is not None:
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x = self.act(x)
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return x
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class GroupNormAct(nn.GroupNorm):
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# NOTE num_channel and num_groups order flipped for easier layer swaps / binding of fixed args
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def __init__(self, num_channels, num_groups, eps=1e-5, affine=True,
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apply_act=True, act_layer=nn.ReLU, inplace=True, drop_block=None):
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super(GroupNormAct, self).__init__(num_groups, num_channels, eps=eps, affine=affine)
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if isinstance(act_layer, str):
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act_layer = get_act_layer(act_layer)
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if act_layer is not None and apply_act:
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self.act = act_layer(inplace=inplace)
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else:
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self.act = None
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def forward(self, x):
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x = F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
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if self.act is not None:
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x = self.act(x)
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return x
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