def main():
args = params.getArgs()
x_train, x_test = get_data(args)
modelDict = model_IO.load_autoencoder(args)
encoder = modelDict.encoder
generator = modelDict.generator
indices = np.random.choice(len(x_train), 1000, replace=False)
images = x_train[indices, :]
z_sampled, z_mean, z_logvar = encode(encoder, images, args.batch_size)
visualize(z_sampled, z_mean, z_logvar)
from scipy.sparse import dok_matrix
import time
from itertools import accumulate
from scipy.stats import entropy
import params
from util import get_max_fea
# divide GPUs, by randomly selecting one for each process
gpus = os.environ["CUDA_VISIBLE_DEVICES"].split(',')
gpu_count = len(gpus)
gpu = gpus[np.random.randint(0, gpu_count)]
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# load parameters
args = params.getArgs()
# print("\n\n")
# for k in args.keys():
# print(k, ": ", args[k])
# print("\n\n")
if args.model_type in ("gnn", "nn"):
import tensorflow as tf
print("Num GPUs Available: ",
len(tf.config.experimental.list_physical_devices('GPU')))
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
except RuntimeError as e:
model.load_weights('model.h5')
wavs = model.predict(wavs)
wavs = wavs / config.multiplier
wavs = tf.reshape(wavs, [*wavs.shape[:2], 3, 10])
angles = tf.cast(tf.round(tf.reduce_sum(wavs, axis=(1, 2))), tf.int8)
classes = tf.cast(tf.round(tf.reduce_sum(wavs, axis=(1, 3))), tf.int8)
# import numpy as np
# from glob import glob
# num = len(sorted(glob('dataset/3rd_track3/*.wav')))
# angles = tf.convert_to_tensor(np.zeros((num, 10), dtype=np.int))
# classes = tf.convert_to_tensor(np.ones((num, 3), dtype=np.int) * 2)
data = {'track3_results': list()}
for idx, (ag, cl) in enumerate(zip(angles, classes)):
_data = {
'id': idx,
'angle': ag.numpy().tolist(),
'class': cl.numpy().tolist()
}
data['track3_results'].append(_data)
tojson(data)
if __name__ == "__main__":
import sys
main(getArgs(sys.argv[1:]))
