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import re
import sys
sys.path.insert(0, "../../python")
import time
import logging
import os.path
import mxnet as mx
import numpy as np
from lstm_proj import lstm_unroll
from io_util import BucketSentenceIter, TruncatedSentenceIter, SimpleIter, DataReadStream
from config_util import parse_args, get_checkpoint_path, parse_contexts
from io_func.feat_readers.writer_kaldi import KaldiWriteOut
# some constants
METHOD_BUCKETING = 'bucketing'
METHOD_TBPTT = 'truncated-bptt'
METHOD_SIMPLE = 'simple'
def prepare_data(args):
batch_size = args.config.getint('train', 'batch_size')
num_hidden = args.config.getint('arch', 'num_hidden')
num_lstm_layer = args.config.getint('arch', 'num_lstm_layer')
init_c = [('l%d_init_c' % l, (batch_size, num_hidden)) for l in range(num_lstm_layer)]
init_h = [('l%d_init_h' % l, (batch_size, num_hidden)) for l in range(num_lstm_layer)]
init_states = init_c + init_h
file_test = args.config.get('data', 'train')
file_format = args.config.get('data', 'format')
feat_dim = args.config.getint('data', 'xdim')
test_data_args = {
"gpu_chunk": 32768,
"lst_file": file_test,
"file_format": file_format,
"separate_lines": True,
"has_labels": True
test_sets = DataReadStream(test_data_args, feat_dim)
return (init_states, test_sets)
if __name__ == '__main__':
args = parse_args()
decoding_method = args.config.get('train', 'method')
contexts = parse_contexts(args)
init_states, test_sets = prepare_data(args)
state_names = [x[0] for x in init_states]
batch_size = args.config.getint('train', 'batch_size')
num_hidden = args.config.getint('arch', 'num_hidden')
num_lstm_layer = args.config.getint('arch', 'num_lstm_layer')
feat_dim = args.config.getint('data', 'xdim')
label_dim = args.config.getint('data', 'ydim')
out_file = args.config.get('data', 'out_file')
num_epoch = args.config.getint('train', 'num_epoch')
model_name = get_checkpoint_path(args)
logging.basicConfig(level=logging.DEBUG, format='%(asctime)-15s %(message)s')
# load the model
label_mean = np.zeros((label_dim,1), dtype='float32')
data_test = TruncatedSentenceIter(test_sets, batch_size, init_states,
20, feat_dim=feat_dim,
do_shuffling=False, pad_zeros=True, has_label=True)
for i, batch in enumerate(data_test.labels):
hist, edges = np.histogram(batch.flat, bins=range(0,label_dim+1))
label_mean += hist.reshape(label_dim,1)
kaldiWriter = KaldiWriteOut(None, out_file)
kaldiWriter.write("label_mean", label_mean)