else:
for i in range(config.n_layers):
# out = tf.keras.layers.Dropout(0.1)(out)
out = tf.keras.layers.Dense(config.n_dim)(out)
out = tf.keras.layers.Activation('sigmoid')(out) * out
out = tf.keras.layers.Dense(config.n_classes, activation='relu')(out)
model = tf.keras.models.Model(inputs=model.input, outputs=out)
specs = None
for name in config.name.split(','):
NAME = name if name.endswith('.h5') else name + '.h5'
model.load_weights(NAME)
if specs is None:
specs = model.predict(wavs, verbose=True)
else:
specs += model.predict(wavs, verbose=True)
specs /= len(config.name.split(','))
specs = specs / config.multiplier
specs = tf.reshape(specs, [*specs.shape[:2], 3, 10])
angles = tf.cast(tf.round(tf.reduce_sum(specs, axis=(1, 2))), tf.int32)
classes = tf.cast(tf.round(tf.reduce_sum(specs, axis=(1, 3))), tf.int32)
d_dir = D_direction(tf.cast(gt_angle, tf.float32),
tf.cast(angles, tf.float32))
d_cls = D_class(tf.cast(gt_class, tf.float32),
tf.cast(classes, tf.float32))
opt = SGD(config.lr, momentum=0.9)
else:
opt = RMSprop(config.lr, momentum=0.9)
if config.l2 > 0:
model = apply_kernel_regularizer(
model, tf.keras.regularizers.l2(config.l2))
model.compile(optimizer=opt,
loss='binary_crossentropy',
metrics=['AUC'])
model.summary()
model.load_weights(NAME)
print('loaded pretrained model')
""" DATA """
# wavs = glob.glob('/codes/2020_track3/t3_audio/*.wav')
wavs = glob.glob(
'/media/data1/datasets/ai_challenge/2020_track3/t3_audio/*.wav')
wavs.sort()
to_mel = magphase_to_mel(config.n_mels)
for wav in wavs:
sample = load_wav(wav)[None, :] # [1, freq, time, chan2]
sample = complex_to_magphase(sample)
sample = to_mel(sample)
sample = minmax_log_on_mel(sample)
# PREDICT
output = model.predict(sample)[0] # [time', 30]
plt.imshow(output)
plt.savefig(os.path.split(wav)[-1].replace('.wav', '.png'))
pdb.set_trace()
wavs = wavs.numpy().transpose((2, 0, 1))
wavs = np.concatenate([np.array(list(map(_filt, wavs[:2]))), wavs[2:]])
return tf.convert_to_tensor(wavs)
wavs = tf.map_fn(filt, wavs)
wavs = complex_to_magphase(wavs)
wavs = magphase_to_mel(config.n_mels)(wavs)
wavs = minmax_log_on_mel(wavs)
wavs = tf.concat([wavs, tf.reverse(wavs, axis=[-1])], axis=0)
gt_angle = tf.concat([gt_angle, tf.reverse(gt_angle, axis=[-1])], axis=0)
gt_class = tf.concat([gt_class, gt_class], axis=0)
wavs = model.predict(wavs)
wavs = wavs / config.multiplier
wavs = tf.reshape(wavs, [*wavs.shape[:2], 3, 10])
angles = tf.round(tf.reduce_sum(wavs, axis=(1, 2)))
classes = tf.round(tf.reduce_sum(wavs, axis=(1, 3)))
d_dir = D_direction(tf.cast(gt_angle, tf.float32),
tf.cast(angles, tf.float32))
d_cls = D_class(tf.cast(gt_class, tf.float32),
tf.cast(classes, tf.float32))
d_total = (d_dir * 0.8 + d_cls * 0.2).numpy()
print('total')
print(d_total, d_total.mean())
],
axis=0)
eval_y = np.concatenate([
np.load(os.path.join(PATH, 'test_y.npy')),
np.load(os.path.join(PATH, 'noise_test_y.npy'))
],
axis=0)
n_chan = eval_x.shape[-1] // 2
if config.norm:
eval_x = minmax_norm_magphase(eval_x)
eval_x = log_magphase(eval_x)
eval_y = degree_to_class(eval_y, one_hot=False)
# 3. predict
pred_y = model.predict(eval_x)
if config.verbose:
print(pred_y[:5])
print(np.max(pred_y, axis=1))
n_classes = pred_y.shape[-1]
pred_y = np.argmax(pred_y, axis=-1)
print("GROUND TRUTH\n", eval_y)
print("PREDICTIONS\n", pred_y)
print("Accuracy:", Accuracy()(eval_y, pred_y).numpy())
print("SCORE:",
score(class_to_degree(eval_y), class_to_degree(pred_y)).numpy())
print(confusion_matrix(eval_y, pred_y))
print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)
print('끝')
# 2. 모델
model = Sequential()
model.add(EfficientNetB0(include_top=False))
model.add(Dense(20, activation='softmax',name='s1'))
model.summary()
# 3. 훈련
model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['acc'])
model.fit(x_train, y_train, batch_size=12, epochs=2, validation_split=0.1)
# 4. 평가, 예측
loss, acc = model.evaluate(x_train, y_train)
y_predict = model.predict(x_test)
print("loss : ", loss)
print("acc : ", acc)
print(y_predict)
print('진짜 끝')
def main(config):
wavs = load_wav(config)
import tensorflow as tf
import efficientnet.model as model
x = tf.keras.layers.Input(shape=(config.n_mels, None, 2))
model = getattr(model, config.model)(
include_top=False,
weights=None,
input_tensor=x,
backend=tf.keras.backend,
layers=tf.keras.layers,
models=tf.keras.models,
utils=tf.keras.utils,
)
out = tf.transpose(model.output, perm=[0, 2, 1, 3])
out = tf.keras.layers.Reshape([-1, out.shape[-1] * out.shape[-2]])(out)
if config.n_layers > 0:
if config.mode == 'GRU':
out = tf.keras.layers.Dense(config.n_dim)(out)
for i in range(config.n_layers):
# out = transformer_layer(config.n_dim, config.n_heads)(out)
out = tf.keras.layers.Bidirectional(
tf.keras.layers.GRU(config.n_dim, return_sequences=True),
backward_layer=tf.keras.layers.GRU(config.n_dim,
return_sequences=True,
go_backwards=True))(out)
elif config.mode == 'transformer':
out = tf.keras.layers.Dense(config.n_dim)(out)
out = encoder(config.n_layers, config.n_dim, config.n_heads)(out)
out = tf.keras.layers.Flatten()(out)
out = tf.keras.layers.ReLU()(out)
out = tf.keras.layers.Dense(config.n_classes, activation='relu')(out)
model = tf.keras.models.Model(inputs=model.input, outputs=out)
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)