utils/init_utils.py

#!/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)