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data.py
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data.py
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# -----------------------------------------------------------
# Position Focused Attention Network (PFAN) implementation based on
# another network Stacked Cross Attention Network (https://arxiv.org/abs/1803.08024)
# the code of SCAN: https://github.com/kuanghuei/SCAN
# ---------------------------------------------------------------
"""Data provider"""
import torch
import torch.utils.data as data
import torchvision.transforms as transforms
import os
import nltk
from PIL import Image
import numpy as np
import json as jsonmod
class PrecompDataset(data.Dataset):
"""
Load precomputed captions and image features
Possible options: f30k_precomp, coco_precomp
"""
def __init__(self, data_path, data_split, vocab):
self.vocab = vocab
loc = data_path + '/'
self.data_split = data_split
# Captions
self.captions = []
file_name = loc+'%s_caps.txt' % data_split
if os.path.exists(file_name):
with open(file_name, 'rb') as f:
for line in f:
self.captions.append(line.strip())
# Image features
print "Image path", loc+'%s_ims.npy' % data_split
self.images = np.load(loc+'%s_ims.npy' % data_split)
self.boxes = np.load(loc+'%s_boxes.npy' % data_split)
self.length = len(self.captions)
print "Len in captions", self.length
self.public_data = True
# True means len(self.captions) > self.images.shape[0] (public data)
# False means len(self.captions) <= self.images.shape[0] (Our own data)
if self._get_data_split(data_split):
if len(self.captions) > self.images.shape[0]:
self.length = len(self.captions)
self.im_div = len(self.captions) / self.images.shape[0]
else:
self.length = self.images.shape[0]
self.im_div = self.images.shape[0] / len(self.captions)
self.public_data = False
# rkiros data has redundancy in images, we divide by 5, 10crop doesn't
print("Image shape in data_loader", self.images.shape, self.length)
print("Boxes shape in data_loader", self.boxes.shape)
if not self._get_data_split(data_split):
if self.images.shape[0] != self.length:
self.im_div = 5
else:
self.im_div = 1
# the development set for coco is large and so validation would be slow
#if data_split == 'dev':
# self.length = 5000
def _get_data_split(self, data_split):
if data_split.startswith("test"):
return True
return False
def __getitem__(self, index):
# handle the image redundancy
img_id = index/self.im_div
cap_id = index
if not self.public_data:
img_id = index
cap_id = index/self.im_div
#print("___", self.data_split, img_id, index, self.im_div)
image = torch.Tensor(self.images[img_id])
box = torch.Tensor(self.boxes[img_id])
caption = self.captions[cap_id]
vocab = self.vocab
# Convert caption (string) to word ids.
tokens = nltk.tokenize.word_tokenize(
str(caption).lower().decode('utf-8'))
caption = []
caption.append(vocab('<start>'))
caption.extend([vocab(token) for token in tokens])
caption.append(vocab('<end>'))
#print "Caption id", caption
target = torch.Tensor(caption)
return image, box, target, index, img_id
def __len__(self):
return self.length
def collate_fn(data):
"""Build mini-batch tensors from a list of (image, box, caption) tuples.
Args:
data: list of (image, box, caption) tuple.
- image: torch tensor of shape (3, 256, 256).
- box: torch tensor of shape
- caption: torch tensor of shape (?); variable length.
Returns:
images: torch tensor of shape (batch_size, 3, 256, 256).
targets: torch tensor of shape (batch_size, padded_length).
lengths: list; valid length for each padded caption.
"""
# Sort a data list by caption length
data.sort(key=lambda x: len(x[2]), reverse=True)
images, boxes, captions, ids, img_ids = zip(*data)
# Merge images (convert tuple of 3D tensor to 4D tensor)
images = torch.stack(images, 0)
boxes = torch.stack(boxes, 0)
# Merget captions (convert tuple of 1D tensor to 2D tensor)
lengths = [len(cap) for cap in captions]
targets = torch.zeros(len(captions), max(lengths)).long()
for i, cap in enumerate(captions):
end = lengths[i]
targets[i, :end] = cap[:end]
return images, boxes, targets, lengths, ids
def get_precomp_loader(data_path, data_split, vocab, opt, batch_size=100,
shuffle=True, num_workers=2):
"""Returns torch.utils.data.DataLoader for custom coco dataset."""
dset = PrecompDataset(data_path, data_split, vocab)
data_loader = torch.utils.data.DataLoader(dataset=dset,
batch_size=batch_size,
shuffle=shuffle,
pin_memory=True,
collate_fn=collate_fn)
return data_loader
def get_loaders(data_name, vocab, batch_size, workers, opt):
dpath = os.path.join(opt.data_path, data_name)
train_loader = get_precomp_loader(dpath, 'train', vocab, opt,
batch_size, True, workers)
val_loader = get_precomp_loader(dpath, 'dev', vocab, opt,
batch_size, False, workers)
return train_loader, val_loader
def get_test_loader(split_name, data_name, vocab, batch_size,
workers, opt):
dpath = os.path.join(opt.data_path, data_name)
test_loader = get_precomp_loader(dpath, split_name, vocab, opt,
batch_size, False, workers)
return test_loader