def init():
log.info('init called')
sess = tf.InteractiveSession()
loaded_model = MobileNet(input_shape=(
size, size, 1), alpha=1., weights=None, classes=NCATS)
loaded_model.load_weights('./doodle_classification/model/model.h5')
loaded_model.compile(optimizer=Adam(lr=0.002),
loss='categorical_crossentropy',
metrics=[categorical_crossentropy,
categorical_accuracy, top_3_accuracy])
# log.info(loaded_model.summary())
graph = tf.get_default_graph()
return loaded_model, sess, graph
def get_model():
from tensorflow.keras.applications import MobileNet
mobilenet = MobileNet(include_top=True,
weights=None,
input_shape=(224, 224, 3),
classes=1000)
mobilenet.load_weights("mobilenet_1_0_224_tf.h5")
inputs = {mobilenet.input_names[0]: ((1, 3, 224, 224), "float32")}
data = {}
np.random.seed(0)
for name, (shape, dtype) in inputs.items():
if dtype == "uint8":
low, high = 0, 1
else:
low, high = -1, 1
data[name] = np.random.uniform(low, high, shape).astype(dtype)
mod, params, ref_outputs = _get_keras_model(mobilenet, inputs, data)
return mod, params, inputs, data, ref_outputs
path = 'C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_rmsprop_1.h5'
mc = ModelCheckpoint(path, monitor='val_loss', verbose=1, save_best_only=True)
model.compile(optimizer=op,
loss="sparse_categorical_crossentropy",
metrics=['acc', f1_m, recall_m, precision_m])
history = model.fit(x_train,
y_train,
epochs=1000,
batch_size=batch_size,
validation_split=0.2,
callbacks=[es, lr, mc])
# 평가, 예측
# model = load_model('C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_rmsprop_1.h5')
model.load_weights('C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_rmsprop_1.h5')
result = model.evaluate(x_test, y_test, batch_size=8)
print("loss : {:.5f}".format(result[0]))
print("acc : {:.5f}".format(result[1]))
print("f1_score : {:.5f}".format(result[2]))
print("recall_m : {:.5f}".format(result[3]))
print("precision_m : {:.5f}".format(result[4]))
############################################ PREDICT ####################################
pred = ['C:/nmb/nmb_data/predict_04_26/F', 'C:/nmb/nmb_data/predict_04_26/M']
count_f = 0
count_m = 0
for pred_pathAudio in pred:
path = 'C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_nadam_1.h5'
mc = ModelCheckpoint(path, monitor='val_loss', verbose=1, save_best_only=True)
model.compile(optimizer=op,
loss="sparse_categorical_crossentropy",
metrics=['acc'])
history = model.fit(x_train,
y_train,
epochs=1000,
batch_size=batch_size,
validation_split=0.2,
callbacks=[es, lr, mc])
# 평가, 예측
# model = load_model('C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_nadam_1.h5')
model.load_weights('C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_nadam_1.h5')
result = model.evaluate(x_test, y_test, batch_size=8)
print("loss : {:.5f}".format(result[0]))
print("acc : {:.5f}".format(result[1]))
############################################ PREDICT ####################################
pred = ['C:/nmb/nmb_data/predict_04_26/F', 'C:/nmb/nmb_data/predict_04_26/M']
count_f = 0
count_m = 0
for pred_pathAudio in pred:
files = librosa.util.find_files(pred_pathAudio, ext=['wav'])
files = np.asarray(files)
for file in files:
SIZE = 64
BASE_SIZE = 256
N_LABELS = 340
sess = tf.Session()
graph = tf.get_default_graph()
set_session(sess)
model = MobileNet(input_shape=(SIZE, SIZE, 1),
alpha=1.,
weights=None,
classes=N_LABELS)
model.load_weights('model.h5')
with open('labels', 'rb') as fp:
labels = pickle.load(fp)
def draw(raw_strokes, size=256, lw=6, time_color=True):
img = np.zeros((BASE_SIZE, BASE_SIZE), np.uint8)
for t, stroke in enumerate(raw_strokes):
for i in range(len(stroke[0]) - 1):
color = 255 - min(t, 10) * 13 if time_color else 255
_ = cv2.line(img, (stroke[0][i], stroke[1][i]),
(stroke[0][i + 1], stroke[1][i + 1]), color, lw)
if size != BASE_SIZE:
return cv2.resize(img, (size, size))
else:
def mobilenet_seg_model(pretrained_weights=None, input_size=(256, 256, 3)):
model = MobileNet(input_shape=input_size,
weights='imagenet',
include_top=False)
x = model.output
encoder = Model(inputs=model.input, outputs=x)
skip_layer_1 = encoder.get_layer('conv_pw_1_relu').output
skip_layer_2 = encoder.get_layer('conv_pw_3_relu').output
skip_layer_3 = encoder.get_layer('conv_pw_5_relu').output
skip_layer_4 = encoder.get_layer('conv_pw_11_relu').output
up6 = Conv2D(
512,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(
x) #(UpSampling2D(size = (2,2), interpolation='bilinear')(x))
up6 = Conv2DTranspose(512,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up6)
merge6 = concatenate([skip_layer_4, up6], axis=3)
conv6 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv6)
up7 = Conv2D(
256,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(conv6) #(UpSampling2D(size = (2,2), interpolation='bilinear')(conv6))
up7 = Conv2DTranspose(256,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up7)
merge7 = concatenate([skip_layer_3, up7], axis=3)
conv7 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv7)
up8 = Conv2D(
128,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(conv7) #(UpSampling2D(size = (2,2), interpolation='bilinear')(conv7))
up8 = Conv2DTranspose(128,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up8)
merge8 = concatenate([skip_layer_2, up8], axis=3)
conv8 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv8)
up9 = Conv2D(
64,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(conv8) #(UpSampling2D(size = (2,2), interpolation='bilinear')(conv8))
up9 = Conv2DTranspose(64,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up9)
merge9 = concatenate([skip_layer_1, up9], axis=3)
conv9 = Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv9)
up10 = Conv2D(
32,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(conv9) #(UpSampling2D(size = (2,2), interpolation='bilinear')(conv9))
up10 = Conv2DTranspose(32,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up10)
#merge9 = concatenate([conv1,up9], axis = 3)
conv10 = Conv2D(32,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(up10)
conv10 = Conv2D(32,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv10)
conv10 = Conv2D(16,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv10)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv10)
model = Model(inputs=model.input, outputs=conv10)
#model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics=[my_IoU])
#model.compile(optimizer = Adam(lr = 1e-4), loss = dice_coef_loss, metrics=[my_IoU])
model.compile(optimizer=Adam(lr=1e-4),
loss=weighted_bce_dice_loss,
metrics=[my_IoU])
model.summary()
if (pretrained_weights is not None):
model.load_weights(pretrained_weights)
return model
NCATS = 340
DP_DIR = 'shuffled_csv/'
np.random.seed(seed=1987)
tf.set_random_seed(seed=1987)
STEPS = 1000
EPOCHS = 100
size = 128
batchsize = 512
print("Setting up MobileNet")
model = MobileNet(input_shape=(size, size, 1),
alpha=1.,
weights=None,
classes=NCATS)
model.load_weights('models/mobilenet-best-run.h5')
model.compile(
optimizer=Adam(lr=0.008),
loss='categorical_crossentropy',
metrics=[categorical_crossentropy, categorical_accuracy, top_3_accuracy])
valid_df = pd.read_csv(os.path.join(DP_DIR,
'train_k{}.csv.gz'.format(NCSVS - 1)),
nrows=35000)
x_valid = df_to_image_array_xd(valid_df, size)
y_valid = keras.utils.to_categorical(valid_df.y, num_classes=NCATS)
print(x_valid.shape, y_valid.shape)
print('Validation array memory {:.2f} GB'.format(x_valid.nbytes / 1024.**3))
train_datagen = image_generator_xd(size=size,
lr = ReduceLROnPlateau(monitor='val_loss', vactor=0.5, patience=10, verbose=1)
path = 'C:/data/modelcheckpoint/mobilenet_rmsprop_10s.h5'
mc = ModelCheckpoint(path, monitor='val_loss', verbose=1, save_best_only=True)
model.compile(optimizer=op,
loss="sparse_categorical_crossentropy",
metrics=['acc', f1_m, recall_m, precision_m])
history = model.fit(x_train,
y_train,
epochs=1000,
batch_size=batch_size,
validation_split=0.2,
callbacks=[es, lr, mc])
# 평가, 예측
model.load_weights('C:/data/modelcheckpoint/mobilenet_rmsprop_10s.h5')
result = model.evaluate(x_test, y_test, batch_size=8)
print("loss : {:.5f}".format(result[0]))
print("acc : {:.5f}".format(result[1]))
print("f1_score : {:.5f}".format(result[2]))
print("recall_m : {:.5f}".format(result[3]))
print("precision_m : {:.5f}".format(result[4]))
############################################ PREDICT ####################################
pred = ['D:/nmb/0602_10s/predict_10/10s_F', 'D:/nmb/0602_10s/predict_10/10s_M']
count_f = 0
count_m = 0
for pred_pathAudio in pred: