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M3L / utils /init_utils.py

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	#!/usr/bin/env python3
	# encoding: utf-8
	# @Time : 2018/9/28 下午12:13
	# @Author : yuchangqian
	# @Contact : changqian_yu@163.com
	# @File : init_func.py.py
	import math
	import warnings
	import torch
	import torch.nn as nn
	from utils.seg_opr.conv_2_5d import Conv2_5D_depth, Conv2_5D_disp


	def __init_weight(feature, conv_init, norm_layer, bn_eps, bn_momentum,
	**kwargs):
	for name, m in feature.named_modules():
	if isinstance(m, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
	conv_init(m.weight, **kwargs)
	elif isinstance(m, Conv2_5D_depth):
	conv_init(m.weight_0, **kwargs)
	conv_init(m.weight_1, **kwargs)
	conv_init(m.weight_2, **kwargs)
	elif isinstance(m, Conv2_5D_disp):
	conv_init(m.weight_0, **kwargs)
	conv_init(m.weight_1, **kwargs)
	conv_init(m.weight_2, **kwargs)
	elif isinstance(m, norm_layer):
	m.eps = bn_eps
	m.momentum = bn_momentum
	nn.init.constant_(m.weight, 1)
	nn.init.constant_(m.bias, 0)


	def init_weight(module_list, conv_init, norm_layer, bn_eps, bn_momentum,
	**kwargs):
	if isinstance(module_list, list):
	for feature in module_list:
	__init_weight(feature, conv_init, norm_layer, bn_eps, bn_momentum,
	**kwargs)
	else:
	__init_weight(module_list, conv_init, norm_layer, bn_eps, bn_momentum,
	**kwargs)


	def group_weight(weight_group, module, norm_layer, lr):
	group_decay = []
	group_no_decay = []
	for m in module.modules():
	if isinstance(m, nn.Linear):
	group_decay.append(m.weight)
	if m.bias is not None:
	group_no_decay.append(m.bias)
	elif isinstance(m, (nn.Conv1d, nn.Conv2d, nn.Conv3d, nn.ConvTranspose2d, nn.ConvTranspose3d)):
	group_decay.append(m.weight)
	if m.bias is not None:
	group_no_decay.append(m.bias)
	elif isinstance(m, Conv2_5D_depth):
	group_decay.append(m.weight_0)
	group_decay.append(m.weight_1)
	group_decay.append(m.weight_2)
	if m.bias is not None:
	group_no_decay.append(m.bias)
	elif isinstance(m, Conv2_5D_disp):
	group_decay.append(m.weight_0)
	group_decay.append(m.weight_1)
	group_decay.append(m.weight_2)
	if m.bias is not None:
	group_no_decay.append(m.bias)
	elif isinstance(m, norm_layer) or isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d) \
	or isinstance(m, nn.BatchNorm3d) or isinstance(m, nn.GroupNorm):
	if m.weight is not None:
	group_no_decay.append(m.weight)
	if m.bias is not None:
	group_no_decay.append(m.bias)
	elif isinstance(m, nn.Parameter):
	group_decay.append(m)
	elif isinstance(m, nn.Embedding):
	group_decay.append(m)
	# else:
	# print(m, norm_layer)
	# print(module.modules)
	# print( len(list(module.parameters())) , 'HHHHHHHHHHHHHHHHH', len(group_decay) + len(
	# group_no_decay))
	assert len(list(module.parameters())) == len(group_decay) + len(
	group_no_decay)
	weight_group.append(dict(params=group_decay, lr=lr))
	weight_group.append(dict(params=group_no_decay, weight_decay=.0, lr=lr))
	return weight_group

	def _no_grad_trunc_normal_(tensor, mean, std, a, b):
	# Cut & paste from PyTorch official master until it's in a few official releases - RW
	# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
	def norm_cdf(x):
	# Computes standard normal cumulative distribution function
	return (1. + math.erf(x / math.sqrt(2.))) / 2.

	if (mean < a - 2 * std) or (mean > b + 2 * std):
	warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
	"The distribution of values may be incorrect.",
	stacklevel=2)

	with torch.no_grad():
	# Values are generated by using a truncated uniform distribution and
	# then using the inverse CDF for the normal distribution.
	# Get upper and lower cdf values
	l = norm_cdf((a - mean) / std)
	u = norm_cdf((b - mean) / std)

	# Uniformly fill tensor with values from [l, u], then translate to
	# [2l-1, 2u-1].
	tensor.uniform_(2 * l - 1, 2 * u - 1)

	# Use inverse cdf transform for normal distribution to get truncated
	# standard normal
	tensor.erfinv_()

	# Transform to proper mean, std
	tensor.mul_(std * math.sqrt(2.))
	tensor.add_(mean)

	# Clamp to ensure it's in the proper range
	tensor.clamp_(min=a, max=b)
	return tensor


	def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
	r"""Fills the input Tensor with values drawn from a truncated
	normal distribution. The values are effectively drawn from the
	normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
	with values outside :math:`[a, b]` redrawn until they are within
	the bounds. The method used for generating the random values works
	best when :math:`a \leq \text{mean} \leq b`.
	Args:
	tensor: an n-dimensional `torch.Tensor`
	mean: the mean of the normal distribution
	std: the standard deviation of the normal distribution
	a: the minimum cutoff value
	b: the maximum cutoff value
	Examples:
	>>> w = torch.empty(3, 5)
	>>> nn.init.trunc_normal_(w)
	"""
	return _no_grad_trunc_normal_(tensor, mean, std, a, b)