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dataloader.py
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dataloader.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import h5py
import os
import numpy as np
import random
from models.ass_fun import *
import torch
import torch.utils.data as data
import multiprocessing
class DataLoader(data.Dataset):
def reset_iterator(self, split):
del self._prefetch_process[split]
self._prefetch_process[split] = BlobFetcher(split, self, split == 'train')
self.iterators[split] = 0
def get_vocab_size(self):
return self.vocab_size
def get_rela_dict_size(self):
return self.rela_dict_size
def get_vocab(self):
return self.ix_to_word
def get_seq_length(self):
return self.seq_length
def __init__(self, opt):
self.opt = opt
self.batch_size = self.opt.batch_size
self.seq_per_img = opt.seq_per_img
# feature related options
self.use_att = getattr(opt, 'use_att', True)
self.use_box = getattr(opt, 'use_box', 0)
# whether use relationship info or not
self.use_rela = getattr(opt, 'use_rela', 0)
self.use_ssg = getattr(opt, 'use_ssg', 0)
self.norm_att_feat = getattr(opt, 'norm_att_feat', 0)
self.norm_box_feat = getattr(opt, 'norm_box_feat', 0)
self.senti_coco = getattr(opt, 'senti_coco', 0)
self.senti_dict_path = getattr(opt, 'senti_dict_path', '0')
if self.use_rela:
self.input_rela_dir = self.opt.input_rela_dir
self.rela_dict_dir = self.opt.rela_dict_dir
rela_dict_info = np.load(self.rela_dict_dir)
rela_dict = rela_dict_info[()]['rela_dict']
self.rela_dict_size = len(rela_dict)
print('rela dict size is {0}'.format(self.rela_dict_size))
# load the json file which contains additional information about the dataset
print('DataLoader loading json file: ', opt.input_json)
self.info = json.load(open(self.opt.input_json))
self.ix_to_word = self.info['ix_to_word']
if self.senti_coco:
senti_dict = np.load(self.senti_dict_path)
self.ix_to_word = senti_dict[()]['i2w']
self.vocab_size = len(self.ix_to_word)
if self.use_ssg:
print('using sentence scene graph info')
#ssg_dict_info = np.load(self.opt.ssg_dict_path)['spice_dict'][()]
ssg_dict_info = np.load(self.opt.ssg_dict_path,allow_pickle=True)[()]
self.ix_to_word = ssg_dict_info['ix_to_word']
self.vocab_size = len(self.ix_to_word)
self.input_ssg_dir = self.opt.input_ssg_dir
print('vocab size is ', self.vocab_size)
# open the hdf5 file
print('DataLoader loading h5 file: ', opt.input_fc_dir, opt.input_att_dir, opt.input_box_dir,
opt.input_label_h5)
self.h5_label_file = h5py.File(self.opt.input_label_h5, 'r', driver='core')
self.input_fc_dir = self.opt.input_fc_dir
self.input_att_dir = self.opt.input_att_dir
self.input_box_dir = self.opt.input_box_dir
# load in the sequence data
seq_size = self.h5_label_file['labels'].shape
print("seq_size:{0}".format(seq_size))
self.seq_length = seq_size[1]
print('max sequence length in data is', self.seq_length)
# load the pointers in full to RAM (should be small enough)
self.label_start_ix = self.h5_label_file['label_start_ix'][:]
self.label_end_ix = self.h5_label_file['label_end_ix'][:]
self.num_images = self.label_start_ix.shape[0]
print('read %d image features' % (self.num_images))
# separate out indexes for each of the provided splits
self.split_ix = {'train': [], 'val': [], 'test': [], 'train_sg': [], 'val_sg': [], 'test_sg': []}
for ix in range(len(self.info['images'])):
img = self.info['images'][ix]
if img['split'] == 'train':
self.split_ix['train'].append(ix)
elif img['split'] == 'val':
self.split_ix['val'].append(ix)
elif img['split'] == 'test':
self.split_ix['test'].append(ix)
elif opt.train_only == 0: # restval
self.split_ix['train'].append(ix)
print('assigned %d images to split train' % len(self.split_ix['train']))
print('assigned %d images to split val' % len(self.split_ix['val']))
print('assigned %d images to split test' % len(self.split_ix['test']))
print('assigned %d images to split train_sg' % len(self.split_ix['train_sg']))
print('assigned %d images to split val_sg' % len(self.split_ix['val_sg']))
print('assigned %d images to split test_sg' % len(self.split_ix['test_sg']))
self.max_train_num = len(self.split_ix['train'])
self.iterators = {'train': 0, 'val': 0, 'test': 0, 'train_sg': 0, 'val_sg': 0, 'test_sg': 0}
self._prefetch_process = {} # The three prefetch process
for split in self.iterators.keys():
self._prefetch_process[split] = BlobFetcher(split, self, split == 'train')
# Terminate the child process when the parent exists
def cleanup():
print('Terminating BlobFetcher')
for split in self.iterators.keys():
del self._prefetch_process[split]
import atexit
atexit.register(cleanup)
def get_captions(self, ix, seq_per_img):
# fetch the sequence labels
ix1 = self.label_start_ix[ix] - 1 # label_start_ix starts from 1
ix2 = self.label_end_ix[ix] - 1
ncap = ix2 - ix1 + 1 # number of captions available for this image
assert ncap > 0, 'an image does not have any label. this can be handled but right now isn\'t'
if ncap < seq_per_img:
# we need to subsample (with replacement)
seq = np.zeros([seq_per_img, self.seq_length], dtype='int')
for q in range(seq_per_img):
ixl = random.randint(ix1, ix2)
seq[q, :] = self.h5_label_file['labels'][ixl, :self.seq_length]
else:
ixl = random.randint(ix1, ix2 - seq_per_img + 1)
seq = self.h5_label_file['labels'][ixl: ixl + seq_per_img, :self.seq_length]
return seq
def get_batch(self, split, batch_size=None, seq_per_img=None):
batch_size = batch_size or self.batch_size
seq_per_img = seq_per_img or self.seq_per_img
fc_batch = [] # np.ndarray((batch_size * seq_per_img, self.opt.fc_feat_size), dtype = 'float32')
att_batch = [] # np.ndarray((batch_size * seq_per_img, 14, 14, self.opt.att_feat_size), dtype = 'float32')
rela_batch = []
rela_attr_batch = []
ssg_rela_batch = []
ssg_obj_batch = []
ssg_attr_batch = []
label_batch = np.zeros([batch_size * seq_per_img, self.seq_length + 2], dtype='int')
mask_batch = np.zeros([batch_size * seq_per_img, self.seq_length + 2], dtype='float32')
wrapped = False
infos = []
gts = []
for i in range(batch_size):
# fetch image
tmp_fc, tmp_att, tmp_rela, tmp_ssg, ix, tmp_wrapped = self._prefetch_process[split].get()
fc_batch.append(tmp_fc)
att_batch.append(tmp_att)
rela_batch.append(tmp_rela['rela_matrix'])
rela_attr_batch.append(tmp_rela['attr_matrix'])
ssg_rela_batch.append(tmp_ssg['ssg_rela_matrix'])
ssg_attr_batch.append(tmp_ssg['ssg_attr'])
ssg_obj_batch.append(tmp_ssg['ssg_obj'])
label_batch[i * seq_per_img: (i + 1) * seq_per_img, 1: self.seq_length + 1] = self.get_captions(ix, seq_per_img)
if tmp_wrapped:
wrapped = True
# Used for reward evaluation
gts.append(self.h5_label_file['labels'][self.label_start_ix[ix] - 1: self.label_end_ix[ix]])
# record associated info as well
info_dict = {}
info_dict['ix'] = ix
info_dict['id'] = self.info['images'][ix]['id']
info_dict['file_path'] = self.info['images'][ix]['file_path']
infos.append(info_dict)
# #sort by att_feat length
# fc_batch, att_batch, label_batch, gts, infos = \
# zip(*sorted(zip(fc_batch, att_batch, np.vsplit(label_batch, batch_size), gts, infos), key=lambda x: len(x[1]), reverse=True))
fc_batch, att_batch, label_batch, gts, infos = zip(*sorted(zip(fc_batch, att_batch, np.vsplit(label_batch, batch_size), gts, infos), key=lambda x: 0, reverse=True))
data = {}
data['fc_feats'] = np.stack(reduce(lambda x, y: x + y, [[_] * seq_per_img for _ in fc_batch]))
max_att_len = max([_.shape[0] for _ in att_batch])
# merge att_feats
data['att_feats'] = np.zeros([len(att_batch) * seq_per_img, max_att_len, att_batch[0].shape[1]], dtype='float32')
for i in range(len(att_batch)):
data['att_feats'][i * seq_per_img:(i + 1) * seq_per_img, :att_batch[i].shape[0]] = att_batch[i]
data['att_masks'] = np.zeros(data['att_feats'].shape[:2], dtype='float32')
for i in range(len(att_batch)):
data['att_masks'][i * seq_per_img:(i + 1) * seq_per_img, :att_batch[i].shape[0]] = 1
# set att_masks to None if attention features have same length
if data['att_masks'].sum() == data['att_masks'].size:
data['att_masks'] = None
if self.use_rela:
max_rela_len = max([_.shape[0] for _ in rela_batch])
data['rela_matrix'] = np.zeros([len(att_batch) * seq_per_img, max_rela_len, 3])
for i in range(len(rela_batch)):
data['rela_matrix'][i * seq_per_img:(i + 1) * seq_per_img, 0:len(rela_batch[i]), :] = rela_batch[i]
data['rela_masks'] = np.zeros(data['rela_matrix'].shape[:2], dtype='float32')
for i in range(len(rela_batch)):
data['rela_masks'][i * seq_per_img:(i + 1) * seq_per_img, :rela_batch[i].shape[0]] = 1
max_attr_obj_len = max(_.shape[0] for _ in rela_attr_batch)
max_attr_each_len = max(_.shape[1] for _ in rela_attr_batch)
data['attr_masks'] = np.zeros([len(att_batch) * seq_per_img, max_attr_obj_len, max_attr_each_len], dtype='float32')
data['attr_matrix'] = np.zeros([len(att_batch) * seq_per_img, max_attr_obj_len, max_attr_each_len], dtype='float32')
for i in range(len(rela_attr_batch)):
data['attr_matrix'][i * seq_per_img:(i + 1) * seq_per_img, 0:len(rela_attr_batch[i]), :rela_attr_batch[i].shape[1]] = rela_attr_batch[i]
for i in range(len(rela_attr_batch)):
attr_obj_len = rela_attr_batch[i].shape[0]
for j in range(attr_obj_len):
attr_each_len = np.sum(rela_attr_batch[i][j, :] >= 0)
data['attr_masks'][i * seq_per_img:(i + 1) * seq_per_img, j, :attr_each_len] = 1
if self.use_ssg:
max_rela_len = max([_.shape[0] for _ in ssg_rela_batch])
data['ssg_rela_matrix'] = np.ones([len(att_batch) * seq_per_img, max_rela_len, 3]) * -1
for i in range(len(ssg_rela_batch)):
data['ssg_rela_matrix'][i * seq_per_img:(i + 1) * seq_per_img, 0:len(ssg_rela_batch[i]), :] = \
ssg_rela_batch[i]
data['ssg_rela_masks'] = np.zeros(data['ssg_rela_matrix'].shape[:2], dtype='float32')
for i in range(len(ssg_rela_batch)):
data['ssg_rela_masks'][i * seq_per_img:(i + 1) * seq_per_img, :ssg_rela_batch[i].shape[0]] = 1
max_obj_len = max([_.shape[0] for _ in ssg_obj_batch])
data['ssg_obj'] = np.ones([len(att_batch) * seq_per_img, max_obj_len]) * -1
for i in range(len(ssg_obj_batch)):
data['ssg_obj'][i * seq_per_img:(i + 1) * seq_per_img, 0:len(ssg_obj_batch[i])] = ssg_obj_batch[i]
data['ssg_obj_masks'] = np.zeros(data['ssg_obj'].shape, dtype='float32')
for i in range(len(ssg_obj_batch)):
data['ssg_obj_masks'][i * seq_per_img:(i + 1) * seq_per_img, :ssg_obj_batch[i].shape[0]] = 1
max_attr_len = max([_.shape[1] for _ in ssg_attr_batch])
data['ssg_attr'] = np.ones([len(att_batch) * seq_per_img, max_obj_len, max_attr_len]) * -1
for i in range(len(ssg_obj_batch)):
data['ssg_attr'][i * seq_per_img:(i + 1) * seq_per_img, 0:len(ssg_obj_batch[i]),
0:ssg_attr_batch[i].shape[1]] = ssg_attr_batch[i]
data['ssg_attr_masks'] = np.zeros(data['ssg_attr'].shape, dtype='float32')
for i in range(len(ssg_attr_batch)):
for j in range(len(ssg_attr_batch[i])):
N_attr_temp = np.sum(ssg_attr_batch[i][j, :] >= 0)
data['ssg_attr_masks'][i * seq_per_img: (i + 1) * seq_per_img, j, 0:int(N_attr_temp)] = 1
data['labels'] = np.vstack(label_batch)
# generate mask
nonzeros = np.array(list(map(lambda x: (x != 0).sum() + 2, data['labels'])))
for ix, row in enumerate(mask_batch):
row[:nonzeros[ix]] = 1
data['masks'] = mask_batch
data['gts'] = gts # all ground truth captions of each images
data['bounds'] = {'it_pos_now': self.iterators[split], 'it_max': len(self.split_ix[split]), 'wrapped': wrapped}
data['infos'] = infos
return data
# It's not coherent to make DataLoader a subclass of Dataset, but essentially, we only need to implement the following to functions,
# so that the torch.utils.data.DataLoader can load the data according the index.
# However, it's minimum change to switch to pytorch data loading.
def __getitem__(self, index):
"""This function returns a tuple that is further passed to collate_fn
"""
ix = index # self.split_ix[index]
rela_data = {}
rela_data['rela_matrix'] = []
rela_data['attr_matrix'] = []
ssg_data = {}
ssg_data['ssg_rela_matrix'] = {}
ssg_data['ssg_attr'] = {}
ssg_data['ssg_obj'] = {}
if self.use_att:
att_feat = np.load(os.path.join(self.input_att_dir, str(self.info['images'][ix]['id']) + '.npz'),allow_pickle=True)['feat']
# Reshape to K x C
att_feat = att_feat.reshape(-1, att_feat.shape[-1])
if self.norm_att_feat:
att_feat = att_feat / np.linalg.norm(att_feat, 2, 1, keepdims=True)
if self.use_box:
box_feat = np.load(os.path.join(self.input_box_dir, str(self.info['images'][ix]['id']) + '.npy'))
# devided by image width and height
x1, y1, x2, y2 = np.hsplit(box_feat, 4)
h, w = self.info['images'][ix]['height'], self.info['images'][ix]['width']
box_feat = np.hstack((x1 / w, y1 / h, x2 / w, y2 / h, (x2 - x1) * (y2 - y1) / (w * h))) # question? x2-x1+1??
if self.norm_box_feat:
box_feat = box_feat / np.linalg.norm(box_feat, 2, 1, keepdims=True)
att_feat = np.hstack([att_feat, box_feat])
# sort the features by the size of boxes
att_feat = np.stack(sorted(att_feat, key=lambda x: x[-1], reverse=True))
if self.use_rela:
path_temp = os.path.join(self.input_rela_dir, str(self.info['images'][ix]['id']) + '.npy')
if os.path.isfile(path_temp):
rela_info = np.load(os.path.join(path_temp))
rela_data['rela_matrix'] = rela_info[()]['rela_matrix']
rela_data['attr_matrix'] = rela_info[()]['obj_attr']
else:
# if we do not have rela_matrix, this matrix is set to be [0,3] zero matrix
rela_data = {}
rela_data['rela_matrix'] = []
rela_data['attr_matrix'] = []
if self.use_ssg:
path_temp = os.path.join(self.input_ssg_dir, str(self.info['images'][ix]['id']) + '.npy')
if os.path.isfile(path_temp):
ssg_info = np.load(os.path.join(path_temp),allow_pickle=True)
ssg_rela_matrix = ssg_info[()]['rela_info']
ssg_obj_att_info = ssg_info[()]['obj_info']
len_obj = len(ssg_obj_att_info)
ssg_obj = np.zeros([len_obj, ])
if len_obj == 0:
ssg_rela_matrix = np.zeros([0, 3])
ssg_attr = np.zeros([0, 1])
ssg_obj = np.zeros([0, ])
else:
max_attr_len = max([len(_) for _ in ssg_obj_att_info])
ssg_attr = np.ones([len_obj, max_attr_len - 1]) * -1
for i in range(len_obj):
ssg_obj[i] = ssg_obj_att_info[i][0]
for j in range(1, len(ssg_obj_att_info[i])):
ssg_attr[i, j - 1] = ssg_obj_att_info[i][j]
ssg_data = {}
ssg_data['ssg_rela_matrix'] = ssg_rela_matrix
ssg_data['ssg_attr'] = ssg_attr
ssg_data['ssg_obj'] = ssg_obj
else:
ssg_data = {}
ssg_data['ssg_rela_matrix'] = np.zeros([0, 3])
ssg_data['ssg_attr'] = np.zeros([0, 1])
ssg_data['ssg_obj'] = np.zeros([0, ])
else:
att_feat = np.zeros((1, 1, 1))
return (np.load(os.path.join(self.input_fc_dir, str(self.info['images'][ix]['id'][5:]) + '.npy'),allow_pickle=True),
att_feat,
rela_data,
ssg_data,
ix)
def __len__(self):
return len(self.info['images'])
class SubsetSampler(torch.utils.data.sampler.Sampler):
r"""Samples elements randomly from a given list of indices, without replacement.
Arguments:
indices (list): a list of indices
"""
def __init__(self, indices):
self.indices = indices
def __iter__(self):
return (self.indices[i] for i in range(len(self.indices)))
def __len__(self):
return len(self.indices)
class BlobFetcher():
"""Experimental class for prefetching blobs in a separate process."""
def __init__(self, split, dataloader, if_shuffle=False):
"""
db is a list of tuples containing: imcrop_name, caption, bbox_feat of gt box, imname
"""
self.split = split
self.dataloader = dataloader
self.if_shuffle = if_shuffle
# Add more in the queue
def reset(self):
"""
Two cases for this function to be triggered:
1. not hasattr(self, 'split_loader'): Resume from previous training. Create the dataset given the saved split_ix and iterator
2. wrapped: a new epoch, the split_ix and iterator have been updated in the get_minibatch_inds already.
"""
# batch_size is 1, the merge is done in DataLoader class
self.split_loader = iter(data.DataLoader(dataset=self.dataloader,
batch_size=1,
sampler=SubsetSampler(self.dataloader.split_ix[self.split][
self.dataloader.iterators[self.split]:]),
shuffle=False,
pin_memory=True,
num_workers=4, # 4 is usually enough
collate_fn=lambda x: x[0]))
def _get_next_minibatch_inds(self):
max_index = len(self.dataloader.split_ix[self.split])
wrapped = False
ri = self.dataloader.iterators[self.split]
ix = self.dataloader.split_ix[self.split][ri]
ri_next = ri + 1
if ri_next >= max_index:
ri_next = 0
if self.if_shuffle:
random.shuffle(self.dataloader.split_ix[self.split])
wrapped = True
self.dataloader.iterators[self.split] = ri_next
return ix, wrapped
def get(self):
if not hasattr(self, 'split_loader'):
self.reset()
ix, wrapped = self._get_next_minibatch_inds()
tmp = self.split_loader.next()
if wrapped:
self.reset()
assert tmp[4] == ix, "ix not equal"
return tmp + [wrapped]