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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is based on https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
Ths copyright of pytorch/pytorch is a BSD-style license, as found in the LICENSE file.
"""
import math
import numpy as np
import paddle
import paddle.nn as nn
__all__ = [
"uniform_",
"normal_",
"constant_",
"ones_",
"zeros_",
"xavier_uniform_",
"xavier_normal_",
"kaiming_uniform_",
"kaiming_normal_",
"linear_init_",
"conv_init_",
"reset_initialized_parameter",
]
def _no_grad_uniform_(tensor, a, b):
with paddle.no_grad():
tensor.set_value(paddle.uniform(shape=tensor.shape, dtype=tensor.dtype, min=a, max=b))
return tensor
def _no_grad_normal_(tensor, mean=0.0, std=1.0):
with paddle.no_grad():
tensor.set_value(paddle.normal(mean=mean, std=std, shape=tensor.shape))
return tensor
def _no_grad_fill_(tensor, value=0.0):
with paddle.no_grad():
tensor.set_value(paddle.full_like(tensor, value, dtype=tensor.dtype))
return tensor
def uniform_(tensor, a, b):
"""
Modified tensor inspace using uniform_
Args:
tensor (paddle.Tensor): paddle Tensor
a (float|int): min value.
b (float|int): max value.
Return:
tensor
"""
return _no_grad_uniform_(tensor, a, b)
def normal_(tensor, mean=0.0, std=1.0):
"""
Modified tensor inspace using normal_
Args:
tensor (paddle.Tensor): paddle Tensor
mean (float|int): mean value.
std (float|int): std value.
Return:
tensor
"""
return _no_grad_normal_(tensor, mean, std)
def constant_(tensor, value=0.0):
"""
Modified tensor inspace using constant_
Args:
tensor (paddle.Tensor): paddle Tensor
value (float|int): value to fill tensor.
Return:
tensor
"""
return _no_grad_fill_(tensor, value)
def ones_(tensor):
"""
Modified tensor inspace using ones_
Args:
tensor (paddle.Tensor): paddle Tensor
Return:
tensor
"""
return _no_grad_fill_(tensor, 1)
def zeros_(tensor):
"""
Modified tensor inspace using zeros_
Args:
tensor (paddle.Tensor): paddle Tensor
Return:
tensor
"""
return _no_grad_fill_(tensor, 0)
def vector_(tensor, vector):
with paddle.no_grad():
tensor.set_value(paddle.to_tensor(vector, dtype=tensor.dtype))
return tensor
def _calculate_fan_in_and_fan_out(tensor, reverse=False):
"""
Calculate (fan_in, _fan_out) for tensor
Args:
tensor (Tensor): paddle.Tensor
reverse (bool: False): tensor data format order, False by default as [fout, fin, ...]. e.g. : conv.weight [cout, cin, kh, kw] is False; linear.weight [cin, cout] is True
Return:
Tuple[fan_in, fan_out]
"""
if tensor.ndim < 2:
raise ValueError("Fan in and fan out can not be computed for tensor with fewer than 2 dimensions")
if reverse:
num_input_fmaps, num_output_fmaps = tensor.shape[0], tensor.shape[1]
else:
num_input_fmaps, num_output_fmaps = tensor.shape[1], tensor.shape[0]
receptive_field_size = 1
if tensor.ndim > 2:
receptive_field_size = np.prod(tensor.shape[2:])
fan_in = num_input_fmaps * receptive_field_size
fan_out = num_output_fmaps * receptive_field_size
return fan_in, fan_out
def xavier_uniform_(tensor, gain=1.0, reverse=False):
"""
Modified tensor inspace using xavier_uniform_
Args:
tensor (paddle.Tensor): paddle Tensor
gain (float): super parameter, 1. default.
reverse (bool): reverse (bool: False): tensor data format order, False by default as [fout, fin, ...].
Return:
tensor
"""
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse)
std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
k = math.sqrt(3.0) * std
return _no_grad_uniform_(tensor, -k, k)
def xavier_normal_(tensor, gain=1.0, reverse=False):
"""
Modified tensor inspace using xavier_normal_
Args:
tensor (paddle.Tensor): paddle Tensor
gain (float): super parameter, 1. default.
reverse (bool): reverse (bool: False): tensor data format order, False by default as [fout, fin, ...].
Return:
tensor
"""
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse=reverse)
std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
return _no_grad_normal_(tensor, 0, std)
# reference: https://pytorch.org/docs/stable/_modules/torch/nn/init.html
def _calculate_correct_fan(tensor, mode, reverse=False):
mode = mode.lower()
valid_modes = ["fan_in", "fan_out"]
if mode not in valid_modes:
raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor, reverse)
return fan_in if mode == "fan_in" else fan_out
def _calculate_gain(nonlinearity, param=None):
linear_fns = ["linear", "conv1d", "conv2d", "conv3d", "conv_transpose1d", "conv_transpose2d", "conv_transpose3d"]
if nonlinearity in linear_fns or nonlinearity == "sigmoid":
return 1
elif nonlinearity == "tanh":
return 5.0 / 3
elif nonlinearity == "relu":
return math.sqrt(2.0)
elif nonlinearity == "leaky_relu":
if param is None:
negative_slope = 0.01
elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
# True/False are instances of int, hence check above
negative_slope = param
else:
raise ValueError("negative_slope {} not a valid number".format(param))
return math.sqrt(2.0 / (1 + negative_slope**2))
elif nonlinearity == "selu":
return 3.0 / 4
else:
raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
def kaiming_uniform_(tensor, a=0, mode="fan_in", nonlinearity="leaky_relu", reverse=False):
"""
Modified tensor inspace using kaiming_uniform method
Args:
tensor (paddle.Tensor): paddle Tensor
mode (str): ['fan_in', 'fan_out'], 'fin_in' defalut
nonlinearity (str): nonlinearity method name
reverse (bool): reverse (bool: False): tensor data format order, False by default as [fout, fin, ...].
Return:
tensor
"""
fan = _calculate_correct_fan(tensor, mode, reverse)
gain = _calculate_gain(nonlinearity, a)
std = gain / math.sqrt(fan)
k = math.sqrt(3.0) * std
return _no_grad_uniform_(tensor, -k, k)
def kaiming_normal_(tensor, a=0, mode="fan_in", nonlinearity="leaky_relu", reverse=False):
"""
Modified tensor inspace using kaiming_normal_
Args:
tensor (paddle.Tensor): paddle Tensor
mode (str): ['fan_in', 'fan_out'], 'fin_in' defalut
nonlinearity (str): nonlinearity method name
reverse (bool): reverse (bool: False): tensor data format order, False by default as [fout, fin, ...].
Return:
tensor
"""
fan = _calculate_correct_fan(tensor, mode, reverse)
gain = _calculate_gain(nonlinearity, a)
std = gain / math.sqrt(fan)
return _no_grad_normal_(tensor, 0, std)
def linear_init_(module):
bound = 1 / math.sqrt(module.weight.shape[0])
uniform_(module.weight, -bound, bound)
uniform_(module.bias, -bound, bound)
def conv_init_(module):
bound = 1 / np.sqrt(np.prod(module.weight.shape[1:]))
uniform_(module.weight, -bound, bound)
if module.bias is not None:
uniform_(module.bias, -bound, bound)
def bias_init_with_prob(prior_prob=0.01):
"""initialize conv/fc bias value according to a given probability value."""
bias_init = float(-np.log((1 - prior_prob) / prior_prob))
return bias_init
@paddle.no_grad()
def reset_initialized_parameter(model, include_self=True):
"""
Reset initialized parameter using following method for [conv, linear, embedding, bn]
Args:
model (paddle.Layer): paddle Layer
include_self (bool: False): include_self for Layer.named_sublayers method. Indicate whether including itself
Return:
None
"""
for _, m in model.named_sublayers(include_self=include_self):
if isinstance(m, nn.Conv2D):
k = float(m._groups) / (m._in_channels * m._kernel_size[0] * m._kernel_size[1])
k = math.sqrt(k)
_no_grad_uniform_(m.weight, -k, k)
if hasattr(m, "bias") and getattr(m, "bias") is not None:
_no_grad_uniform_(m.bias, -k, k)
elif isinstance(m, nn.Linear):
k = math.sqrt(1.0 / m.weight.shape[0])
_no_grad_uniform_(m.weight, -k, k)
if hasattr(m, "bias") and getattr(m, "bias") is not None:
_no_grad_uniform_(m.bias, -k, k)
elif isinstance(m, nn.Embedding):
_no_grad_normal_(m.weight, mean=0.0, std=1.0)
elif isinstance(m, (nn.BatchNorm2D, nn.LayerNorm)):
_no_grad_fill_(m.weight, 1.0)
if hasattr(m, "bias") and getattr(m, "bias") is not None:
_no_grad_fill_(m.bias, 0)
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