anirudhacharya commented on a change in pull request #14503: API to create
RecordIO files
URL: https://github.com/apache/incubator-mxnet/pull/14503#discussion_r269833603
##########
File path: python/mxnet/io/io.py
##########
@@ -966,6 +988,164 @@ def creator(*args, **kwargs):
creator.__doc__ = doc_str
return creator
+
+def _read_list(list_file, batch_size):
+ """
+ Helper function that reads the .lst file, binds it in
+ a generator and returns a batched version of the generator.
+ Parameters
+ ----------
+ list_file: input list file.
+ batch_size: batch size of the generator
+ Returns
+ -------
+ item iterator that contains information in .lst file
+ """
+ def get_generator():
+ """
+ wrap a generator around the list file
+ """
+ with open(list_file) as fin:
+ while True:
+ line = fin.readline()
+ if not line:
+ break
+ line = [i.strip() for i in line.strip().split('\t')]
+ line_len = len(line)
+ # check the data format of .lst file
+ if line_len < 3:
+ logging.info("lst should have at least has three parts, \
+ but only has {} parts for {}".format(line_len, line))
+ continue
+ try:
+ item = [int(line[0])] + [line[-1]] + [float(i) for i in
line[1:-1]]
+ except Exception:
+ logging.info('Parsing lst met error for {}, detail:
{}'.format(line))
+ continue
+ yield item
+ data_iter = iter(get_generator())
+ data_batch = list(itertools.islice(data_iter, batch_size))
+ while data_batch:
+ yield data_batch
+ data_batch = list(itertools.islice(data_iter, batch_size))
+
+def _read_worker(q_out, transformer, color, quality, encoding, data_record):
+ """
+ Helper function that will be run by the read workers
+ to fetch the image from the input queue apply
+ transformations and put it into output priority queue.
+ Parameters
+ ----------
+ q_out : queue
+ transformer : transformer object
+ color : color
+ quality : quality
+ encoding : encoding
+ data_record : image instance to work on.
+ """
+ i, item = data_record
+ img_path = item[1]
+ try:
+ # construct the header of the record
+ header = recordio.IRHeader(0, item[2:], item[0], 0)
+
+ img = cv2.imread(img_path, color)
Review comment:
will do.
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