def __init__(self, squad_json, vocab_file, cache_file, batch_size,
max_seq_length, num_inputs):
# Whenever you specify a custom constructor for a TensorRT class,
# you MUST call the constructor of the parent explicitly.
forward.IPyBatchStream.__init__(self)
self.cache_file = cache_file
# Every time get_batch is called, the next batch of size batch_size will be copied to the device and returned.
self.data = dp.read_squad_json(squad_json)
self.max_seq_length = max_seq_length
self.batch_size = batch_size
self.current_index = 0
self.num_inputs = num_inputs
self.tokenizer = tokenization.BertTokenizer(vocab_file=vocab_file,
do_lower_case=True)
self.doc_stride = 128
self.max_query_length = 64
self.maxbatch = 500
def __init__(self, squad_json, vocab_file, cache_file, batch_size, max_seq_length, num_inputs):
# Whenever you specify a custom constructor for a TensorRT class,
# you MUST call the constructor of the parent explicitly.
trt.IInt8LegacyCalibrator.__init__(self)
self.cache_file = cache_file
# Every time get_batch is called, the next batch of size batch_size will be copied to the device and returned.
self.data = dp.read_squad_json(squad_json)
self.max_seq_length = max_seq_length
self.batch_size = batch_size
self.current_index = 0
self.num_inputs = num_inputs
self.tokenizer = tokenization.BertTokenizer(vocab_file=vocab_file, do_lower_case=True)
self.doc_stride = 128
self.max_query_length = 64
# Allocate enough memory for a whole batch.
self.device_inputs = [cuda.mem_alloc(self.max_seq_length * trt.int32.itemsize * self.batch_size) for binding in range(3)]
if __name__ == '__main__':
args = parse_args()
paragraph_text = None
squad_examples = None
output_prediction_file = None
if not args.passage == '':
paragraph_text = ' '.join(args.passage)
elif not args.passage_file == '':
f = open(args.passage_file, 'r')
paragraph_text = f.read()
elif not args.squad_json == '':
squad_examples = dp.read_squad_json(args.squad_json)
output_prediction_file = args.output_prediction_file
else:
paragraph_text = input("Paragraph: ")
question_text = None
if not args.question == '':
question_text = ' '.join(args.question)
elif not args.question_file == '':
f = open(args.question_file, 'r')
question_text = f.read()
tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file,
do_lower_case=True)
# When splitting up a long document into chunks, how much stride to take between chunks.
doc_stride = 128
