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utils2.py
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utils2.py
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from __future__ import print_function
import errno
import os
import numpy as np
# from PIL import Image
import torch
import torch.nn as nn
from vqa_eval.PythonEvaluationTools.vqaEvaluation.vqaEval import VQAEval
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 pil_loader(path):
# with open(path, 'rb') as f:
# with Image.open(f) as img:
# return img.convert('RGB')
def weights_init(m):
"""custom weights initialization."""
cname = m.__class__
if cname == nn.Linear or cname == nn.Conv2d or cname == nn.ConvTranspose2d:
m.weight.data.normal_(0.0, 0.02)
elif cname == nn.BatchNorm2d:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
else:
print('%s is not initialized.' % cname)
def init_net(net, net_file):
if net_file:
net.load_state_dict(torch.load(net_file))
else:
net.apply(weights_init)
def create_dir(path):
if not os.path.exists(path):
try:
os.makedirs(path)
except OSError as exc:
if exc.errno != errno.EEXIST:
raise
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)
def preprocess_answer(answer):
""" Mimicing the answer pre-processing with evaluation server. """
dummy_vqa = lambda: None
dummy_vqa.getQuesIds = lambda: None
vqa_eval = VQAEval(dummy_vqa, None)
answer = vqa_eval.processDigitArticle(
vqa_eval.processPunctuation(answer))
answer = answer.replace(',', '')
return answer