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# coding: utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import h5py
import numpy as np
import random
from utils.util import batch_indexer, token_indexer
class Dataset(object):
def __init__(self,
params,
img_file,
src_file,
tgt_file,
max_len=100,
max_img_len=512,
batch_or_token='batch'):
self.source = src_file
self.target = tgt_file
self.image = img_file
self.max_len = max_len
self.max_img_len = max_img_len
self.batch_or_token = batch_or_token
self.src_vocab = params.src_vocab
self.tgt_vocab = params.tgt_vocab
self.data_leak_ratio = params.data_leak_ratio
self.img_feature_size = params.img_feature_size
self.p = params
self.leak_buffer = []
# We save the sign video features in h5py
# and dynamically load the features
if isinstance(self.image, str):
self.img_reader = h5py.File(self.image, 'r')
else:
assert isinstance(self.image, dict)
self.img_reader = self.image
def load_data(self, is_training=False):
with open(self.source, 'r') as src_reader, \
open(self.target, 'r') as tgt_reader: \
while True:
# src_line: [feature index] [(<aug>)] [source text/glosses]
# tgt_line: target text
# feature index -> sign video feature index in h5py, -1: not sign video features
# <aug> -> optional, if it appears, the sample is from machine translation
# source text/glosses -> machine translation source or sign gloss sequence
src_line = src_reader.readline()
tgt_line = tgt_reader.readline()
if src_line == "" or tgt_line == "":
break
src_line = src_line.strip()
tgt_line = tgt_line.strip()
if src_line == "" or tgt_line == "":
continue
src_line_tokens = src_line.strip().split()
img_index = int(src_line_tokens[0])
src_tokens = src_line_tokens[1:]
if src_tokens and src_tokens[0].isdigit():
src_tokens = src_tokens[1:]
src_line = ' '.join(src_tokens)
tgt_tokens = tgt_line.strip().split()
if tgt_tokens and tgt_tokens[0].isdigit():
tgt_tokens = tgt_tokens[1:]
tgt_line = ' '.join(tgt_tokens)
# apply stochastic BPE dropout
if is_training and random.random() < self.p.bpe_dropout_stochastic_rate:
src_line = src_line.strip().replace('@@ ', '')
tgt_line = tgt_line.strip().replace('@@ ', '')
# apply dropout
aug = False
if '<aug>' in src_line:
aug = True
src_line = ' '.join(src_line.strip().split()[1:])
src_line = self.p.src_bpe.process_line(src_line, dropout=self.p.src_bpe_dropout)
tgt_line = self.p.tgt_bpe.process_line(tgt_line, dropout=self.p.tgt_bpe_dropout)
if aug:
src_line = '<aug> ' + src_line
yield (
self.src_vocab.to_id(src_line.strip().split()[:self.max_len]),
self.tgt_vocab.to_id(tgt_line.strip().split()[:self.max_len]),
img_index,
)
def get_reader(self, is_train=False):
# We randomly crop and flip images to get 11 duplicated features
# during training, we randomly sample one feature to simulate data augmentation for sign videos
range = self.p.img_aug_size if is_train else 1
return random.randint(0, range-1)
def to_matrix(self, batch, is_train=False):
# perform batching
batch_size = len(batch)
src_lens = [len(sample[1]) for sample in batch]
tgt_lens = [len(sample[2]) for sample in batch]
src_len = min(self.max_len, max(src_lens))
tgt_len = min(self.max_len, max(tgt_lens))
s = np.zeros([batch_size, src_len], dtype=np.int32)
t = np.zeros([batch_size, tgt_len], dtype=np.int32)
x = []
for eidx, sample in enumerate(batch):
x.append(sample[0])
src_ids, tgt_ids = sample[1], sample[2]
s[eidx, :min(src_len, len(src_ids))] = src_ids[:src_len]
t[eidx, :min(tgt_len, len(tgt_ids))] = tgt_ids[:tgt_len]
images_indices = [sample[3] for sample in batch]
images = [] # feature sequence
img_idx = [] # indicators -> whether this sample is sign example
dummy = np.zeros([1, self.img_feature_size], dtype=np.float32)
for image_index in images_indices:
if image_index < 0:
img_idx.append(0.0)
images.append(dummy)
continue
else:
img_idx.append(1.0)
i = self.get_reader(is_train)
if is_train:
candidate_keys = [f"{image_index}_{i}", f"{image_index}"]
else:
candidate_keys = [f"{image_index}"]
if isinstance(self.img_reader, dict):
new_image = None
for key in candidate_keys:
if key in self.img_reader:
new_image = self.img_reader[key]
break
if new_image is None:
raise KeyError("Image feature {} not found in feature dict".format(candidate_keys[0]))
else:
reader = self.img_reader
new_image = None
for key in candidate_keys:
if key in reader:
new_image = reader[key][()]
break
if new_image is None:
raise KeyError("Image feature {} not found in feature file".format(candidate_keys[0]))
images.append(new_image)
img_lens = [len(img) for img in images]
img_len = min(max(img_lens), self.max_img_len)
m = np.zeros([batch_size, img_len, self.img_feature_size], dtype=np.float32)
mask = np.zeros([batch_size, img_len], dtype=np.float32)
img_idx = np.asarray(img_idx, dtype=np.float32)
for eidx, img in enumerate(images):
m[eidx, :min(img_len, len(img))] = img[:img_len]
mask[eidx, :min(img_len, len(img))] = 1.0
# construct sparse label sequence, for ctc training
seq_indexes = []
seq_values = []
for n, sample in enumerate(batch):
sequence = sample[1][:src_len]
seq_indexes.extend(zip([n] * len(sequence), range(len(sequence))))
seq_values.extend(sequence)
seq_indexes = np.asarray(seq_indexes, dtype=np.int64)
seq_values = np.asarray(seq_values, dtype=np.int32)
seq_shape = np.asarray([batch_size, src_len], dtype=np.int64)
return x, s, t, m, mask, (seq_indexes, seq_values, seq_shape), img_idx
def batcher(self, size, buffer_size=1000, shuffle=True, train=True):
def _handle_buffer(_buffer):
sorted_buffer = sorted(
_buffer, key=lambda xx: max(len(xx[1]), len(xx[2])))
if self.batch_or_token == 'batch':
buffer_index = batch_indexer(len(sorted_buffer), size)
else:
buffer_index = token_indexer(
[[len(sample[1]), len(sample[2])] for sample in sorted_buffer], size)
index_over_index = batch_indexer(len(buffer_index), 1)
if shuffle: np.random.shuffle(index_over_index)
for ioi in index_over_index:
index = buffer_index[ioi[0]]
batch = [sorted_buffer[ii] for ii in index]
x, s, t, m, mask, spar, img_idx = self.to_matrix(batch, train)
yield {
'src': s,
'tgt': t,
'img': m,
'is_img': img_idx,
'mask': mask,
'spar': spar,
'index': x,
'raw': batch,
}
buffer = self.leak_buffer
self.leak_buffer = []
for i, (src_ids, tgt_ids, img_index) in enumerate(self.load_data(train)):
buffer.append((i, src_ids, tgt_ids, img_index))
if len(buffer) >= buffer_size:
for data in _handle_buffer(buffer):
# check whether the data is tailed
batch_size = len(data['raw']) if self.batch_or_token == 'batch' \
else max(np.sum(data['tgt'] > 0), np.sum(data['src'] > 0))
if batch_size < size * self.data_leak_ratio:
self.leak_buffer += data['raw']
else:
yield data
buffer = self.leak_buffer
self.leak_buffer = []
# deal with data in the buffer
if len(buffer) > 0:
for data in _handle_buffer(buffer):
# check whether the data is tailed
batch_size = len(data['raw']) if self.batch_or_token == 'batch' \
else max(np.sum(data['tgt'] > 0), np.sum(data['src'] > 0))
if train and batch_size < size * self.data_leak_ratio:
self.leak_buffer += data['raw']
else:
yield data
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