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dataloader_human_study.py
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dataloader_human_study.py
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import os
import json
import h5py
import numpy as np
import torch
from six import iteritems
from six.moves import range
from sklearn.preprocessing import normalize
from torch.utils.data import Dataset
from typing import Dict, List, Union
class VisDialDatasetHumanStudy(Dataset):
def __init__(self, params, subsets):
'''
Initialize the dataset with splits given by 'subsets', where
subsets is taken from ['train', 'val', 'test']
Notation:
'dtype' is a split taking values from ['train', 'val', 'test']
'stype' is a sqeuence type from ['ques', 'ans']
'''
# By default, load Q-Bot, A-Bot and dialog options for A-Bot
self.useQuestion = True
self.useAnswer = True
self.useOptions = True
self.useHistory = True
self.useIm = True
self.useNDCG = params["useNDCG"]
# Absorb parameters
for key, value in iteritems(params):
setattr(self, key, value)
self.subsets = tuple(subsets)
self.numRounds = params['numRounds']
print('\nDataloader loading json file: ' + self.inputJson)
with open(self.inputJson, 'r') as fileId:
info = json.load(fileId)
# Absorb values
for key, value in iteritems(info):
setattr(self, key, value)
if 'val' in subsets and self.useNDCG:
with open(self.inputDenseJson, 'r') as fileId:
dense_annotation = json.load(fileId)
self.dense_annotation = dense_annotation
wordCount = len(self.word2ind)
# Add <START> and <END> to vocabulary
self.word2ind['<START>'] = wordCount + 1
self.word2ind['<END>'] = wordCount + 2
self.startToken = self.word2ind['<START>']
self.endToken = self.word2ind['<END>']
# Padding token is at index 0
self.vocabSize = wordCount + 3
print('Vocab size with <START>, <END>: %d' % self.vocabSize)
# Construct the reverse map
self.ind2word = {
int(ind): word
for word, ind in iteritems(self.word2ind)
}
# Read questions, answers and options
print('Dataloader loading h5 file: ' + self.inputQues)
quesFile = h5py.File(self.inputQues, 'r')
if self.useIm:
# Read images
print('Dataloader loading h5 file: ' + self.inputImg)
imgFile = h5py.File(self.inputImg, 'r')
# Number of data points in each split (train/val/test)
self.numDataPoints = {}
self.data = {}
# map from load to save labels
ioMap = {
'ques_%s': '%s_ques',
'ques_length_%s': '%s_ques_len',
'ans_%s': '%s_ans',
'ans_length_%s': '%s_ans_len',
'ans_index_%s': '%s_ans_ind',
'img_pos_%s': '%s_img_pos',
'opt_%s': '%s_opt',
'opt_length_%s': '%s_opt_len',
'opt_list_%s': '%s_opt_list'
}
# Processing every split in subsets
for dtype in subsets: # dtype is in [train, val, test]
print("\nProcessing split [%s]..." % dtype)
if ('ques_%s' % dtype) not in quesFile:
self.useQuestion = False
if ('ans_%s' % dtype) not in quesFile:
self.useAnswer = False
if ('opt_%s' % dtype) not in quesFile:
self.useOptions = False
# read the question, answer, option related information
for loadLabel, saveLabel in iteritems(ioMap):
if loadLabel % dtype not in quesFile:
continue
dataMat = np.array(quesFile[loadLabel % dtype], dtype='int64')
self.data[saveLabel % dtype] = torch.from_numpy(dataMat)
# Read image features, if needed
if self.useIm:
print('Reading image features...')
imgFeats = np.array(imgFile['images_' + dtype])
if not self.imgNorm:
continue
# normalize, if needed
print('Normalizing image features..')
imgFeats = normalize(imgFeats, axis=1, norm='l2')
# save img features
self.data['%s_img_fv' % dtype] = torch.FloatTensor(imgFeats)
# Visdial
if hasattr(self, 'unique_img_train') and params['cocoDir']:
coco_dir = params['cocoDir']
with open(params['cocoInfo'], 'r') as f:
coco_info = json.load(f)
id_to_fname = {
im['id']: im['file_path']
for im in coco_info['images']
}
cocoids = getattr(self, 'unique_img_%s'%dtype)
if '.jpg' not in cocoids[0]:
img_fnames = [
os.path.join(coco_dir, id_to_fname[int(cocoid)])
for cocoid in cocoids
]
else:
img_fnames = cocoids
self.data['%s_img_fnames' % dtype] = img_fnames
# read the history, if needed
if self.useHistory:
captionMap = {
'cap_%s': '%s_cap',
'cap_length_%s': '%s_cap_len'
}
for loadLabel, saveLabel in iteritems(captionMap):
mat = np.array(quesFile[loadLabel % dtype], dtype='int32')
self.data[saveLabel % dtype] = torch.from_numpy(mat)
# Number of data points
self.numDataPoints[dtype] = self.data[dtype + '_cap'].size(0)
# Prepare dataset for training
for dtype in subsets:
print("\nSequence processing for [%s]..." % dtype)
self.prepareDataset(dtype)
print("")
# Default pytorch loader dtype is set to train
# load image indices in the test set on which the human study is done.
if 'train' in subsets:
self._split = 'train'
else:
self._split = subsets[0]
self.rand_idx = np.loadtxt('data/human_study/human_study_indices.csv')
self.NUM_SAMPLES = self.rand_idx.shape[0]
@property
def split(self):
return self._split
@split.setter
def split(self, split):
assert split in self.subsets # ['train', 'val', 'test']
self._split = split
#----------------------------------------------------------------------------
# Dataset preprocessing
#----------------------------------------------------------------------------
def prepareDataset(self, dtype):
if self.useHistory:
self.processCaption(dtype)
# prefix/postfix with <START> and <END>
if self.useOptions:
self.processOptions(dtype)
# options are 1-indexed, changed to 0-indexed
self.data[dtype + '_opt'] -= 1
# process answers and questions
if self.useAnswer:
self.processSequence(dtype, stype='ans')
# 1 indexed to 0 indexed
if dtype != 'test':
self.data[dtype + '_ans_ind'] -= 1
if self.useQuestion:
self.processSequence(dtype, stype='ques')
def processSequence(self, dtype, stype='ans'):
'''
Add <START> and <END> token to answers or questions.
Arguments:
'dtype' : Split to use among ['train', 'val', 'test']
'sentType' : Sequence type, either 'ques' or 'ans'
'''
assert stype in ['ques', 'ans']
prefix = dtype + "_" + stype
seq = self.data[prefix]
seqLen = self.data[prefix + '_len']
numConvs, numRounds, maxAnsLen = seq.size()
newSize = torch.Size([numConvs, numRounds, maxAnsLen + 2])
sequence = torch.LongTensor(newSize).fill_(0)
# decodeIn begins with <START>
sequence[:, :, 0] = self.word2ind['<START>']
endTokenId = self.word2ind['<END>']
for thId in range(numConvs):
for rId in range(numRounds):
length = seqLen[thId, rId]
if length == 0:
print('Warning: Skipping empty %s sequence at (%d, %d)'\
%(stype, thId, rId))
continue
sequence[thId, rId, 1:length + 1] = seq[thId, rId, :length]
sequence[thId, rId, length + 1] = endTokenId
# Sequence length is number of tokens + 1
self.data[prefix + "_len"] = seqLen + 1
self.data[prefix] = sequence
def processCaption(self, dtype):
'''
Add <START> and <END> token to caption.
Arguments:
'dtype' : Split to use among ['train', 'val', 'test']
'''
prefix = dtype + '_cap'
seq = self.data[prefix]
seqLen = self.data[prefix + '_len']
numConvs, maxCapLen = seq.size()
newSize = torch.Size([numConvs, maxCapLen + 2])
sequence = torch.LongTensor(newSize).fill_(0)
# decodeIn begins with <START>
sequence[:, 0] = self.word2ind['<START>']
endTokenId = self.word2ind['<END>']
for thId in range(numConvs):
length = seqLen[thId]
if length == 0:
print('Warning: Skipping empty %s sequence at (%d)' % (stype,
thId))
continue
sequence[thId, 1:length + 1] = seq[thId, :length]
sequence[thId, length + 1] = endTokenId
# Sequence length is number of tokens + 1
self.data[prefix + "_len"] = seqLen + 1
self.data[prefix] = sequence
def processOptions(self, dtype):
ans = self.data[dtype + '_opt_list']
ansLen = self.data[dtype + '_opt_len']
ansListLen, maxAnsLen = ans.size()
newSize = torch.Size([ansListLen, maxAnsLen + 2])
options = torch.LongTensor(newSize).fill_(0)
# decodeIn begins with <START>
options[:, 0] = self.word2ind['<START>']
endTokenId = self.word2ind['<END>']
for ansId in range(ansListLen):
length = ansLen[ansId]
if length == 0:
print('Warning: Skipping empty option answer list at (%d)'\
%ansId)
continue
options[ansId, 1:length + 1] = ans[ansId, :length]
options[ansId, length + 1] = endTokenId
self.data[dtype + '_opt_len'] = ansLen + 1
self.data[dtype + '_opt_seq'] = options
#----------------------------------------------------------------------------
# Dataset helper functions for PyTorch's datalaoder
#----------------------------------------------------------------------------
def __len__(self):
# Assert that loader_dtype is in subsets ['train', 'val', 'test']
return self.NUM_SAMPLES
def __getitem__(self, idx):
print(type(self.rand_idx[idx]))
idx = int(self.rand_idx[idx])
item = self.getIndexItem(self._split, idx)
return item
def collate_fn(self, batch):
out = {}
mergedBatch = {key: [d[key] for d in batch] for key in batch[0]}
for key in mergedBatch:
if key == 'img_fname' or key == 'index':
out[key] = mergedBatch[key]
elif key == 'cap_len':
# 'cap_lens' are single integers, need special treatment
out[key] = torch.LongTensor(mergedBatch[key])
else:
out[key] = torch.stack(mergedBatch[key], 0)
# Dynamic shaping of padded batch
if 'ques' in out.keys():
quesLen = out['ques_len'] + 1
out['ques'] = out['ques'][:, :, :torch.max(quesLen)].contiguous()
if 'ans' in out.keys():
ansLen = out['ans_len'] + 1
out['ans'] = out['ans'][:, :, :torch.max(ansLen)].contiguous()
if 'cap' in out.keys():
capLen = out['cap_len'] + 1
out['cap'] = out['cap'][:, :torch.max(capLen)].contiguous()
if 'opt' in out.keys():
optLen = out['opt_len'] + 1
out['opt'] = out['opt'][:, :, :, :torch.max(optLen) + 2].contiguous()
return out
#----------------------------------------------------------------------------
# Dataset indexing
#----------------------------------------------------------------------------
def getIndexItem(self, dtype, idx):
item = {'index': idx}
# get question
if self.useQuestion:
ques = self.data[dtype + '_ques'][idx]
quesLen = self.data[dtype + '_ques_len'][idx]
item['ques'] = ques
item['ques_len'] = quesLen
# get answer
if self.useAnswer:
ans = self.data[dtype + '_ans'][idx]
ansLen = self.data[dtype + '_ans_len'][idx]
item['ans_len'] = ansLen
item['ans'] = ans
# get caption
if self.useHistory:
cap = self.data[dtype + '_cap'][idx]
capLen = self.data[dtype + '_cap_len'][idx]
item['cap'] = cap
item['cap_len'] = capLen
if self.useOptions and dtype != 'test':
optInds = self.data[dtype + '_opt'][idx]
ansId = self.data[dtype + '_ans_ind'][idx]
optSize = list(optInds.size())
newSize = torch.Size(optSize + [-1])
indVector = optInds.view(-1)
optLens = self.data[dtype + '_opt_len'].index_select(0, indVector)
optLens = optLens.view(optSize)
opts = self.data[dtype + '_opt_seq'].index_select(0, indVector)
item['opt'] = opts.view(newSize)
item['opt_len'] = optLens
item['ans_id'] = ansId
# if image needed
if self.useIm:
item['img_feat'] = self.data[dtype + '_img_fv'][idx]
# item['img_fname'] = self.data[dtype + '_img_fnames'][idx]
if dtype + '_img_labels' in self.data:
item['img_label'] = self.data[dtype + '_img_labels'][idx]
# dense annotations if val set
if dtype == 'val' and self.useNDCG:
round_id = self.dense_annotation[idx]['round_id']
gt_relevance = self.dense_annotation[idx]['gt_relevance']
image_id = self.dense_annotation[idx]['image_id']
item["round_id"] = torch.LongTensor([round_id])
item["gt_relevance"] = torch.FloatTensor(gt_relevance)
item["image_id"] = torch.LongTensor([image_id])
return item