utils.py

"""
This code is extended from Hengyuan Hu's repository.
https://github.com/hengyuan-hu/bottom-up-attention-vqa
"""
from __future__ import print_function

import errno
import os
import re
import collections
import numpy as np
import operator
import functools
import torch
import torch.nn.functional as F
from torch._six import string_classes
from torch.utils.data.dataloader import default_collate


EPS = 1e-7


def assert_eq(real, expected):
    assert real == expected, '%s (true) vs %s (expected)' % (real, expected)


def assert_array_eq(real, expected):
    assert (np.abs(real-expected) < EPS).all(), \
        '%s (true) vs %s (expected)' % (real, expected)


def load_folder(folder, suffix):
    imgs = []
    for f in sorted(os.listdir(folder)):
        if f.endswith(suffix):
            imgs.append(os.path.join(folder, f))
    return imgs


def load_imageid(folder):
    images = load_folder(folder, 'jpg')
    img_ids = set()
    for img in images:
        img_id = int(img.split('/')[-1].split('.')[0].split('_')[-1])
        img_ids.add(img_id)
    return img_ids


def create_dir(path):
    if not os.path.exists(path):
        try:
            os.makedirs(path)
        except OSError as exc:
            if exc.errno != errno.EEXIST:
                raise


def print_model(model, logger):
    print(model)
    nParams = 0
    for w in model.parameters():
        if w.requires_grad:
            nParams += functools.reduce(operator.mul, w.size(), 1)
    if logger:
        logger.write('nParams=\t'+str(nParams))


def save_model(path, model, epoch, optimizer=None):
    model_dict = {
            'epoch': epoch,
            'model_state': model.state_dict()
        }
    if optimizer is not None:
        model_dict['optimizer_state'] = optimizer.state_dict()

    torch.save(model_dict, path)


def trim_collate(batch):
    "Puts each data field into a tensor with outer dimension batch size"
    _use_shared_memory = True
    error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
    elem_type = type(batch[0])
    if torch.is_tensor(batch[0]):
        out = None
        if 2 == batch[0].dim(): # image features
            max_num_boxes = max([x.size(0) for x in batch])
            if _use_shared_memory:
                # If we're in a background process, concatenate directly into a
                # shared memory tensor to avoid an extra copy
                numel = len(batch) * max_num_boxes * batch[0].size(-1)
                storage = batch[0].storage()._new_shared(numel)
                out = batch[0].new(storage)
            # warning: F.pad returns Variable!
            return torch.stack([F.pad(x, (0,0,0,max_num_boxes-x.size(0))).data for x in batch], 0, out=out)
        else:
            if _use_shared_memory:
                # If we're in a background process, concatenate directly into a
                # shared memory tensor to avoid an extra copy
                numel = sum([x.numel() for x in batch])
                storage = batch[0].storage()._new_shared(numel)
                out = batch[0].new(storage)
            return torch.stack(batch, 0, out=out)
    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
            and elem_type.__name__ != 'string_':
        elem = batch[0]
        if elem_type.__name__ == 'ndarray':
            # array of string classes and object
            if re.search('[SaUO]', elem.dtype.str) is not None:
                raise TypeError(error_msg.format(elem.dtype))

            return torch.stack([torch.from_numpy(b) for b in batch], 0)
        if elem.shape == ():  # scalars
            py_type = float if elem.dtype.name.startswith('float') else int
            return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
    elif isinstance(batch[0], int):
        return torch.LongTensor(batch)
    elif isinstance(batch[0], float):
        return torch.DoubleTensor(batch)
    elif isinstance(batch[0], string_classes):
        return batch
    elif isinstance(batch[0], collections.Mapping):
        return {key: default_collate([d[key] for d in batch]) for key in batch[0]}
    elif isinstance(batch[0], collections.Sequence):
        transposed = zip(*batch)
        return [trim_collate(samples) for samples in transposed]

    raise TypeError((error_msg.format(type(batch[0]))))


class Logger(object):
    def __init__(self, output_name):
        dirname = os.path.dirname(output_name)
        if not os.path.exists(dirname):
            os.mkdir(dirname)

        self.log_file = open(output_name, 'w')
        self.infos = {}

    def append(self, key, val):
        vals = self.infos.setdefault(key, [])
        vals.append(val)

    def log(self, extra_msg=''):
        msgs = [extra_msg]
        for key, vals in self.infos.iteritems():
            msgs.append('%s %.6f' % (key, np.mean(vals)))
        msg = '\n'.join(msgs)
        self.log_file.write(msg + '\n')
        self.log_file.flush()
        self.infos = {}
        return msg

    def write(self, msg):
        self.log_file.write(msg + '\n')
        self.log_file.flush()
        print(msg)