/examples/pytorch/gcmc/utils.py

import csv
import re
import torch as th
import numpy as np
import torch.nn as nn
import torch.optim as optim
from collections import OrderedDict

class MetricLogger(object):
    def __init__(self, attr_names, parse_formats, save_path):
        self._attr_format_dict = OrderedDict(zip(attr_names, parse_formats))
        self._file = open(save_path, 'w')
        self._csv = csv.writer(self._file)
        self._csv.writerow(attr_names)
        self._file.flush()

    def log(self, **kwargs):
        self._csv.writerow([parse_format % kwargs[attr_name]
                            for attr_name, parse_format in self._attr_format_dict.items()])
        self._file.flush()

    def close(self):
        self._file.close()


def torch_total_param_num(net):
    return sum([np.prod(p.shape) for p in net.parameters()])


def torch_net_info(net, save_path=None):
    info_str = 'Total Param Number: {}\n'.format(torch_total_param_num(net)) +\
               'Params:\n'
    for k, v in net.named_parameters():
        info_str += '\t{}: {}, {}\n'.format(k, v.shape, np.prod(v.shape))
    info_str += str(net)
    if save_path is not None:
        with open(save_path, 'w') as f:
            f.write(info_str)
    return info_str


def get_activation(act):
    """Get the activation based on the act string

    Parameters
    ----------
    act: str or callable function

    Returns
    -------
    ret: callable function
    """
    if act is None:
        return lambda x: x
    if isinstance(act, str):
        if act == 'leaky':
            return nn.LeakyReLU(0.1)
        elif act == 'relu':
            return nn.ReLU()
        elif act == 'tanh':
            return nn.Tanh()
        elif act == 'sigmoid':
            return nn.Sigmoid()
        elif act == 'softsign':
            return nn.Softsign()
        else:
            raise NotImplementedError
    else:
        return act


def get_optimizer(opt):
    if opt == 'sgd':
        return optim.SGD
    elif opt == 'adam':
        return optim.Adam
    else:
        raise NotImplementedError