torchutil.py

# coding=utf-8
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init as init


def copyStateDict(state_dict):
    if list(state_dict.keys())[0].startswith("module"):
        start_idx = 1
    else:
        start_idx = 0
    new_state_dict = OrderedDict()
    for k, v in state_dict.items():
        name = ".".join(k.split(".")[start_idx:])
        new_state_dict[name] = v
    return new_state_dict


def init_weights(modules):
    for m in modules:
        if isinstance(m, nn.Conv2d):
            init.xavier_uniform_(m.weight.data)
            if m.bias is not None:
                m.bias.data.zero_()
        elif isinstance(m, nn.BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()
        elif isinstance(m, nn.Linear):
            m.weight.data.normal_(0, 0.01)
            m.bias.data.zero_()