def predict_two_stream18_test():
spatial_weights_dir = '/home/changan/ActionRecognition/models/finetuned_resnet_RGB_65.h5'
temporal_weights_dir = '/home/changan/ActionRecognition/models/temporal_cnn_42.h5'
model = two_stream_model(spatial_weights_dir=spatial_weights_dir,
temporal_weights_dir=temporal_weights_dir)
print(model.summary())
print('Start to predict two stream model')
data_dir = '/home/changan/ActionRecognition/data'
list_dir = os.path.join(data_dir, 'ucfTrainTestlist')
test_list = os.path.join(list_dir, 'testlist.txt')
frames_dir = '/home/changan/ActionRecognition/data/UCF-Preprocessed-OF/test'
flow_dir = '/home/changan/ActionRecognition/data/OF_data/test'
N_CLASSES = 101
spatial_shape = (216, 216, 3)
temporal_shape = (216, 216, 18)
generator = two_stream18_generator(test_list, frames_dir, flow_dir, 1,
spatial_shape, temporal_shape,
N_CLASSES)
steps = 3000
correct_num = 0
for i in range(steps):
x, y = next(generator)
prediction = model.predict(x)
if y[0][np.argmax(prediction)] == 1:
correct_num += 1
print('test accuracy on', steps, 'examples is', float(correct_num) / steps)
def predict_two_stream3_test():
spatial_weights_dir = '/home/changan/ActionRecognition/models/finetuned_resnet_RGB_65.h5'
temporal_weights_dir = '/home/changan/ActionRecognition/models/finetuned_resnet_flow.h5'
model = two_stream_model(spatial_weights_dir=spatial_weights_dir,
temporal_weights_dir=temporal_weights_dir)
print(model.summary())
print('Start to predict two stream model')
data_dir = '/home/changan/ActionRecognition/data'
list_dir = os.path.join(data_dir, 'ucfTrainTestlist')
test_list = os.path.join(list_dir, 'testlist.txt')
frames_dir = '/home/changan/ActionRecognition/data/frames'
flow_images_dir = '/home/changan/ActionRecognition/data/flow_images'
input_shape = (216, 216, 3)
generator = two_stream3_generator(test_list,
frames_dir,
flow_images_dir,
1,
input_shape,
N_CLASSES,
mean_sub=True,
normalization=True,
random_crop=False,
horizontal_flip=False)
steps = 300
correct_num = 0
for i in range(steps):
x, y = next(generator)
prediction = model.predict(x)
if y[0][np.argmax(prediction)] == 1:
correct_num += 1
print('test accuracy on', steps, 'examples is', float(correct_num) / steps)