def run_test_musicnet():
global BATCH_SIZE
BATCH_SIZE = cnf.batch_size
prepare_data_for_test()
data_gen.test_counters = np.zeros(cnf.bin_max_len, dtype=np.int32) # reset test counters
data_supplier = data_feeder.create_data_supplier()
for test_length in cnf.bins:
with tf.Graph().as_default():
tester = create_tester(test_length)
with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto()) as sess:
sess.run(tf.compat.v1.global_variables_initializer())
saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables())
saver.restore(sess, cnf.model_file)
predictions = []
labels = []
full_test_len = len(data_gen.test_set[cnf.task][test_length])
# rounds it up to the next size divisible by batch size:
n_test_inputs = (full_test_len // BATCH_SIZE) * BATCH_SIZE + BATCH_SIZE
print("Testing on {} test inputs: ".format(n_test_inputs), end="")
threshold = 0
for i in range(n_test_inputs // BATCH_SIZE):
if i > threshold:
print("{},".format(i * BATCH_SIZE), end="", flush=True)
threshold += 1000 // BATCH_SIZE
batch_xs, batch_ys = data_supplier.supply_test_data(test_length, BATCH_SIZE)
pred_flat = (tester.get_result(sess, batch_xs, batch_ys)).flatten()
stride_labels = 128
n_frames = cnf.musicnet_window_size // stride_labels - 1
labels_pre = np.array(batch_ys[0])[:,
stride_labels * (n_frames // 2):stride_labels * (n_frames // 2) + 128]
labels_flat = (labels_pre - 1).flatten() # gets 0/1 labels on 128 notes
predictions += list(pred_flat)
labels += list(labels_flat)
predictions = np.array(predictions)
labels = np.array(labels)
n_overshoot = n_test_inputs - full_test_len # inputs more than test len
if n_overshoot > 0: # removes duplicates
predictions = predictions[:-(128 * n_overshoot)]
labels = labels[:-(128 * n_overshoot)]
avg_prec_score = average_precision_score(labels, predictions)
print("\n")
print("Cutting {} input duplicates".format(n_overshoot))
print("Done testing on all {} test inputs".format(len(labels) / 128))
print("AVERAGE PRECISION SCORE on all test data = {0:.7f}".format(avg_prec_score))
data_gen.init()
if cnf.task in cnf.language_tasks:
task = data_gen.find_data_task(cnf.task)
task.prepare_data()
data_gen.collect_bins()
data_gen.print_bin_usage()
else:
for length in range(1, max_length + 1):
n_examples = cnf.data_size
data_gen.init_data(cnf.task, length, n_examples, cnf.n_input)
data_gen.collect_bins()
if len(data_gen.train_set[cnf.task][cnf.forward_max]) == 0:
data_gen.init_data(cnf.task, cnf.forward_max, cnf.test_data_size, cnf.n_input)
data_supplier = data_feeder.create_data_supplier()
# Perform training
with tf.Graph().as_default():
learner = RSE(cnf.n_hidden, cnf.bins, cnf.n_input, countList, cnf.n_output, cnf.dropout_keep_prob,
create_translation_model=cnf.task in cnf.language_tasks, use_two_gpus=cnf.use_two_gpus)
learner.create_graph()
learner.variable_summaries = tf.compat.v1.summary.merge_all()
tf.compat.v1.get_variable_scope().reuse_variables()
learner.create_test_graph(cnf.forward_max)
saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables())
with tf.compat.v1.Session(config=cnf.tf_config) as sess:
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.compat.v1.local_variables_initializer())