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resnet_v2.py
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resnet_v2.py
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from keras import Model
from keras.callbacks import ModelCheckpoint
from keras.layers import BatchNormalization, Input, Activation, Conv2D, MaxPooling2D, Add, GlobalAveragePooling2D, Dense
import os
import numpy as np
from lru import LRU
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
from keras_audio.library.utility.audio_utils import compute_melgram
def block(filters, inp):
inp = inp
layer_1 = BatchNormalization()(inp)
act_1 = Activation('relu')(layer_1)
conv_1 = Conv2D(filters, (3, 3), padding='same')(act_1)
layer_2 = BatchNormalization()(conv_1)
act_2 = Activation('relu')(layer_2)
conv_2 = Conv2D(filters, (3, 3), padding='same')(act_2)
return (conv_2)
def resnet(input_shape, classes):
filters = [32, 64, 128]
input_img = Input(input_shape)
x = Conv2D(filters[0], (3, 3), padding='same')(input_img)
y = MaxPooling2D(padding='same')(x)
x = Add()([block(filters[0], y), y])
y = Add()([block(filters[0], x), x])
x = Add()([block(filters[0], y), y])
x = Conv2D(filters[1], (3, 3), strides=(2, 2), padding='same',
activation='elu')(x)
y = Add()([block(filters[1], x), x])
x = Add()([block(filters[1], y), y])
y = Add()([block(filters[1], x), x])
y = Conv2D(filters[2], (3, 3), strides=(2, 2), padding='same',
activation='elu')(y)
x = Add()([block(filters[2], y), y])
y = Add()([block(filters[2], x), x])
x = Add()([block(filters[2], y), y])
x2 = GlobalAveragePooling2D()(x)
output = Dense(classes, activation='softmax')(x2)
model = Model(input_img, output)
return model
class ResNetV2AudioClassifier(object):
model_name = 'resnet-v2'
def __init__(self):
self.cache = LRU(400)
self.input_shape = None
self.nb_classes = None
self.model = None
self.config = None
def create_model(self):
self.model = resnet(input_shape=self.input_shape, classes=self.nb_classes)
self.model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
print(self.model.summary())
@staticmethod
def get_config_file_path(model_dir_path):
return os.path.join(model_dir_path, ResNetV2AudioClassifier.model_name + '-config.npy')
@staticmethod
def get_architecture_file_path(model_dir_path):
return os.path.join(model_dir_path, ResNetV2AudioClassifier.model_name + '-architecture.json')
@staticmethod
def get_weight_file_path(model_dir_path):
return os.path.join(model_dir_path, ResNetV2AudioClassifier.model_name + '-weights.h5')
def load_model(self, model_dir_path):
config_file_path = ResNetV2AudioClassifier.get_config_file_path(model_dir_path)
weight_file_path = ResNetV2AudioClassifier.get_weight_file_path(model_dir_path)
self.config = np.load(config_file_path).item()
self.input_shape = self.config['input_shape']
self.nb_classes = self.config['nb_classes']
self.create_model()
self.model.load_weights(weight_file_path)
def compute_melgram(self, audio_path):
if audio_path in self.cache:
return self.cache[audio_path]
else:
mg = compute_melgram(audio_path)
# mg = (mg + 100) / 200 # scale the values
self.cache[audio_path] = mg
return mg
def generate_batch(self, audio_paths, labels, batch_size):
num_batches = len(audio_paths) // batch_size
while True:
for batchIdx in range(0, num_batches):
start = batchIdx * batch_size
end = (batchIdx + 1) * batch_size
X = np.zeros(shape=(batch_size, self.input_shape[0], self.input_shape[1], self.input_shape[2]),
dtype=np.float32)
for i in range(start, end):
audio_path = audio_paths[i]
mg = compute_melgram(audio_path)
X[i - start, :, :, :] = mg
yield X, labels[start:end]
def fit(self, audio_path_label_pairs, model_dir_path, batch_size=None, epochs=None, test_size=None,
random_state=None, input_shape=None, nb_classes=None):
if batch_size is None:
batch_size = 64
if epochs is None:
epochs = 20
if test_size is None:
test_size = 0.2
if random_state is None:
random_state = 42
if input_shape is None:
input_shape = (96, 1366, 1)
if nb_classes is None:
nb_classes = 10
config_file_path = self.get_config_file_path(model_dir_path)
weight_file_path = self.get_weight_file_path(model_dir_path)
architecture_file_path = self.get_architecture_file_path(model_dir_path)
self.input_shape = input_shape
self.nb_classes = nb_classes
self.config = dict()
self.config['input_shape'] = input_shape
self.config['nb_classes'] = nb_classes
np.save(config_file_path, self.config)
self.create_model()
with open(architecture_file_path, 'wt') as file:
file.write(self.model.to_json())
checkpoint = ModelCheckpoint(weight_file_path)
X = []
Y = []
for audio_path, label in audio_path_label_pairs:
X.append(audio_path)
Y.append(label)
X = np.array(X)
Y = np.array(Y)
Y = np_utils.to_categorical(Y, self.nb_classes)
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=test_size, random_state=random_state)
train_gen = self.generate_batch(Xtrain, Ytrain, batch_size)
test_gen = self.generate_batch(Xtest, Ytest, batch_size)
train_num_batches = len(Xtrain) // batch_size
test_num_batches = len(Xtest) // batch_size
history = self.model.fit_generator(generator=train_gen, steps_per_epoch=train_num_batches,
epochs=epochs,
verbose=1, validation_data=test_gen, validation_steps=test_num_batches,
callbacks=[checkpoint])
self.model.save_weights(weight_file_path)
np.save(os.path.join(model_dir_path, ResNetV2AudioClassifier.model_name + '-history.npy'), history.history)
return history
def predict(self, audio_path):
mg = compute_melgram(audio_path)
mg = np.expand_dims(mg, axis=0)
return self.model.predict(mg)[0]
def predict_class(self, audio_path):
predicted = self.predict(audio_path)
return np.argmax(predicted)