out_file = r'/home/oanhnt/thainh/data/database/train-opt{}.pickle'.format(opt_size)
else:
out_file = r'/home/oanhnt/thainh/data/database/test-opt{}.pickle'.format(opt_size)
valid_file = r'/home/oanhnt/thainh/data/database/test-opt{}.pickle'.format(opt_size)
else:
if train:
out_file = '/mnt/smalldata/database/train-opt2.pickle'
else:
out_file = '/mnt/smalldata/database/test-opt2.pickle'
# Spatial
input_x = Input(shape=(224,224,3))
if train:
x = mobilenet.mobilenet_by_me(
name='spatial',
inputs=input_x,
input_shape=(224,224,3),
classes=classes,
weight='weights/mobilenet_spatial_{}e.h5'.format(spa_epochs))
else:
x = mobilenet.mobilenet_by_me(
name='spatial',
inputs=input_x,
input_shape=(224,224,3),
classes=classes)
# Temporal
input_y = Input(shape=(224,224,20))
if train:
y = mobilenet.mobilenet_by_me(
name='temporal',
inputs=input_y,
classes = int(sys.argv[7])
server = config.server()
if server:
if train:
out_file = r'/home/oanhnt/thainh/data/database/train-all.pickle'
else:
out_file = r'/home/oanhnt/thainh/data/database/test-all.pickle'
valid_file = r'/home/oanhnt/thainh/data/database/test-all.pickle'
# Spatial
input_x = Input(shape=(224, 224, 3))
if train:
x = mobilenet.mobilenet_by_me(
name='spatial',
inputs=input_x,
input_shape=(224, 224, 3),
classes=classes,
weight='weights/mobilenet_spatial_{}e.h5'.format(spa_epochs))
else:
x = mobilenet.mobilenet_by_me(name='spatial',
inputs=input_x,
input_shape=(224, 224, 3),
classes=classes)
# Temporal 1
input_y1 = Input(shape=(224, 224, 20))
if train:
y1 = mobilenet.mobilenet_by_me(
name='temporal1',
inputs=input_y1,
input_shape=(224, 224, 20),
valid_file = r'{}database/test-opt{}.pickle'.format(
data_output_path, opt_size)
else:
out_file = r'{}database/test-opt{}.pickle'.format(
data_output_path, opt_size)
else:
out_file = r'{}database/cross-opt{}.pickle'.format(data_output_path,
opt_size)
# Spatial
input_x = Input(shape=(224, 224, 3))
if train:
x = mobilenet.mobilenet_by_me(name='spatial',
inputs=input_x,
input_shape=(224, 224, 3),
classes=classes,
weight='weights/spatial_{}e_cr{}.h5'.format(
spa_epochs, cross_index),
non_train=True)
else:
x = mobilenet.mobilenet_by_me(name='spatial',
inputs=input_x,
input_shape=(224, 224, 3),
classes=classes)
# Temporal
input_y = Input(shape=(224, 224, 20))
if train:
y = mobilenet.mobilenet_by_me(
name='temporal',
inputs=input_y,
out_file = r'{}database/test-all.pickle'.format(data_output_path)
else:
out_file = r'{}database/cross-all.pickle'.format(data_output_path)
inputs = []
outputs = []
for i in range(len(multi_opt_size)):
opt = multi_opt_size[i]
if opt == 0:
# Spatial
input_x = Input(shape=(224,224,3))
inputs.append(input_x)
if train:
x = mobilenet.mobilenet_by_me(
name='spatial',
inputs=input_x,
input_shape=(224,224,3),
classes=classes,
weight='weights/spatial_{}e_cr{}.h5'.format(pretrains[i],cross_index))
else:
x = mobilenet.mobilenet_by_me(
name='spatial',
inputs=input_x,
input_shape=(224,224,3),
classes=classes)
outputs.append(x)
else:
# Temporal
input_y = Input(shape=(224,224,20))
inputs.append(input_y)
if train:
[K.count_params(p) for p in set(model.non_trainable_weights)])
total_memory = 4.0 * batch_size * (shapes_mem_count + trainable_count +
non_trainable_count)
gbytes = np.round(total_memory / (1024.0**3), 3)
return gbytes
classes = 11
depth = 20
drop_rate = 0.5
input_x = Input(shape=(224, 224, 3))
x = mobilenet.mobilenet_by_me(name='spatial',
inputs=input_x,
input_shape=(224, 224, 3),
classes=classes)
# Temporal
input_y1 = Input(shape=(224, 224, 20))
input_y2 = Input(shape=(224, 224, 20))
input_y3 = Input(shape=(224, 224, 20))
inputs = [input_y1, input_y2, input_y3]
y = mobilenet.mobilenet_new(name='temporal',
inputs=inputs,
input_shape=(224, 224, 20),
classes=classes)
z = Concatenate()([x, y])
z = GlobalAveragePooling2D()(z)