187 lines
6.9 KiB
Python
187 lines
6.9 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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# _no_grad_trunc_normal_ & trunc_normal_ & variance_scaling_ & lecun_normal_
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# Modified from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py.
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# Copyright 2019 Ross Wightman
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# _init_transformer_weights & c2_msra_fill & _init_convnet_weights
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# Modified from https://github.com/facebookresearch/SlowFast/blob/main/slowfast/utils/weight_init_helper.py.
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# Copyright 2019, Facebook, Inc
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import warnings
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import torch
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import torch.nn as nn
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from torch.nn.init import _calculate_fan_in_and_fan_out
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def _no_grad_trunc_normal_(tensor, mean, std, a, b):
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# Cut & paste from PyTorch official master until it's in a few official releases - RW
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# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
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def norm_cdf(x):
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# Computes standard normal cumulative distribution function
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return (1. + math.erf(x / math.sqrt(2.))) / 2.
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if (mean < a - 2 * std) or (mean > b + 2 * std):
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warnings.warn(
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'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
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'The distribution of values may be incorrect.',
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stacklevel=2)
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with torch.no_grad():
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# Values are generated by using a truncated uniform distribution and
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# then using the inverse CDF for the normal distribution.
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# Get upper and lower cdf values
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le = norm_cdf((a - mean) / std)
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u = norm_cdf((b - mean) / std)
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# Uniformly fill tensor with values from [l, u], then translate to
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# [2l-1, 2u-1].
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tensor.uniform_(2 * le - 1, 2 * u - 1)
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# Use inverse cdf transform for normal distribution to get truncated
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# standard normal
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tensor.erfinv_()
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# Transform to proper mean, std
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tensor.mul_(std * math.sqrt(2.))
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tensor.add_(mean)
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# Clamp to ensure it's in the proper range
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tensor.clamp_(min=a, max=b)
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return tensor
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def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
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# type: (torch.Tensor, float, float, float, float) -> torch.Tensor
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r"""Fills the input Tensor with values drawn from a truncated
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normal distribution. The values are effectively drawn from the
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normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
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with values outside :math:`[a, b]` redrawn until they are within
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the bounds. The method used for generating the random values works
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best when :math:`a \leq \text{mean} \leq b`.
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Args:
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tensor: an n-dimensional `torch.Tensor`
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mean: the mean of the normal distribution
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std: the standard deviation of the normal distribution
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a: the minimum cutoff value
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b: the maximum cutoff value
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Examples:
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>>> w = torch.empty(3, 5)
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>>> nn.init.trunc_normal_(w)
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"""
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return _no_grad_trunc_normal_(tensor, mean, std, a, b)
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def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
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fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
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if mode == 'fan_in':
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denom = fan_in
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elif mode == 'fan_out':
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denom = fan_out
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elif mode == 'fan_avg':
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denom = (fan_in + fan_out) / 2
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variance = scale / denom
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if distribution == 'truncated_normal':
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# constant is stddev of standard normal truncated to (-2, 2)
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trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
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elif distribution == 'normal':
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tensor.normal_(std=math.sqrt(variance))
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elif distribution == 'uniform':
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bound = math.sqrt(3 * variance)
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tensor.uniform_(-bound, bound)
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else:
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raise ValueError(f'invalid distribution {distribution}')
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def lecun_normal_(tensor):
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variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')
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def _init_transformer_weights(m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if m.bias is not None:
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.LayerNorm):
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nn.init.zeros_(m.bias)
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nn.init.ones_(m.weight)
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def c2_msra_fill(module: nn.Module) -> None:
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"""
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Initialize `module.weight` using the "MSRAFill" implemented in Caffe2.
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Also initializes `module.bias` to 0.
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Args:
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module (torch.nn.Module): module to initialize.
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"""
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# pyre-ignore
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nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
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if module.bias is not None: # pyre-ignore
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nn.init.constant_(module.bias, 0)
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def _init_convnet_weights(model, fc_init_std=0.01, zero_init_final_bn=True):
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"""
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Performs ResNet style weight initialization.
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Args:
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fc_init_std (float): the expected standard deviation for fc layer.
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zero_init_final_bn (bool): if True, zero initialize the final bn for
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every bottleneck.
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"""
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for m in model.modules():
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if hasattr(m, 'skip_init'):
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continue
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if isinstance(m, nn.Conv3d) and not hasattr(m, 'linear'):
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"""
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Follow the initialization method proposed in:
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{He, Kaiming, et al.
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"Delving deep into rectifiers: Surpassing human-level
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performance on imagenet classification."
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arXiv preprint arXiv:1502.01852 (2015)}
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"""
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c2_msra_fill(m)
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elif isinstance(m, nn.BatchNorm3d):
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if (hasattr(m, 'transform_final_bn') and m.transform_final_bn
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and zero_init_final_bn):
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batchnorm_weight = 0.0
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else:
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batchnorm_weight = 1.0
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if m.weight is not None:
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m.weight.data.fill_(batchnorm_weight)
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if m.bias is not None:
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m.bias.data.zero_()
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if isinstance(m, nn.Linear) or hasattr(m, 'linear'):
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m.weight.data.normal_(mean=0.0, std=fc_init_std)
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if m.bias is not None:
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m.bias.data.zero_()
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