def mean_iou(y_true, y_pred):
prec = []
for t in np.arange(0.5, 1.0, 0.05):
y_pred_ = tf.to_int32(y_pred > t)
score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 2)
K.get_session().run(tf.local_variables_initializer())
with tf.control_dependencies([up_opt]):
score = tf.identity(score)
prec.append(score)
return K.mean(K.stack(prec), axis=0)
model_dir = '../../100sw_model'
model_path = '../../100sw_model/AV_100sw.h5'
test_dataset = '../../pre_data/audio_video/tr_set.hdf5'
test_generator = data_generator(test_dataset, 1)
for i in range(1):
[input_spec_mix, input_face_1,
input_face_2], [output_spec_1, output_spec_2] = next(test_generator)
converter = MelConverter()
AV = load_model(model_path, custom_objects={'tf': tf, 'mean_iou': mean_iou})
#############################################################
import librosa
def spectrogram_separator(input_spec_mix, input_face_1, input_face_2,
output_spec_1, output_spec_2):
### predict
mask_pre_1, mask_pre_2 = AV.predict(
[input_spec_mix, input_face_1, input_face_2])
# mask_sum = mask_pre_1 + mask_pre_2
sess = tf.Session(config=config)
keras.backend.set_session(sess)
import keras.layers as layers
from keras.callbacks import EarlyStopping, ReduceLROnPlateau, TensorBoard
import h5py
from load_dataset import data_generator
import os
train_dataset = '../../new_dataset/audio_video/tr_set.hdf5'
val_dataset = '../../new_dataset/audio_video/val_set.hdf5'
test_dataset = '../../new_dataset/audio_video/test_set.hdf5'
batch_size = 10
epochs = 100
train_generator = data_generator(train_dataset, batch_size)
val_generator = data_generator(val_dataset, batch_size)
##V:video1 VV:video2
V1 = layers.Input(shape=(75, 512), name='Video1_input')
V2 = layers.Conv1D(256,
kernel_size=7,
dilation_rate=1,
padding='same',
activation='relu')(V1)
V3 = layers.BatchNormalization(axis=-1)(V2)
V4 = layers.Conv1D(256,
kernel_size=5,
dilation_rate=1,
padding='same',
activation='relu')(V3)
@author: macfa """ h5py_path = '../../Unetdata/dataset' tr_path = h5py_path + '/' + 'tr_set.hdf5' val_path = h5py_path + '/' + 'val_set.hdf5' test_path = h5py_path + '/' + 'test_set.hdf5' batch_size = 10 ################################DATA from keras.layers import Lambda import numpy as np from load_dataset import data_generator train_generator = data_generator(tr_path, batch_size) val_generator = data_generator(val_path, batch_size) test_generator = data_generator(test_path, batch_size) ##############################DATA ##############################Model import os from keras.models import Model, load_model from keras.layers import Input from keras.layers.core import Dropout, Lambda from keras.layers.convolutional import Conv2D, Conv2DTranspose from keras.layers.pooling import MaxPooling2D from keras.layers.merge import concatenate from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard from keras import backend as K import tensorflow as tf