cnn_model = Model(inputs=base_model.input, outputs=outputs)
return cnn_model
if __name__ == "__main__":
cnn_model = load_cnn_model()
seq_length = 16 # sequence length of frames to downsample each video to
dataset = DataSet(cnn_model)
# generate Xception features and time it
currtime = time.time()
for ind, sample in enumerate(dataset.data):
# save the sequences of frame features to npy files for eventual model training
path = os.path.join(
'data', 'sequences', sample[1],
sample[2] + '-' + str(seq_length) + '-Xception_features.npy')
if os.path.isfile(path):
print(sample)
print("Sequence: {} already exists".format(ind))
else:
print(sample)
print("Generating and saving sequence: {}".format(ind))
sequence = dataset.extract_seq_features(sample, Xception=True)
print("Time Elapsed: {}".format(time.time() - currtime))
# get the feature outputs of second-to-last layer (final FC layer)
outputs = base_model.get_layer('avg_pool').output
cnn_model = Model(inputs=base_model.input, outputs=outputs)
return cnn_model
if __name__ == "__main__":
cnn_model = load_cnn_model()
seq_length = 16 # sequence length of frames to downsample each video to
dataset = DataSet(cnn_model)
# generate InceptionV3 features and time it
currtime = time.time()
for ind, sample in enumerate(dataset.data):
# save the sequences of frame features to npy files for eventual model training
path = os.path.join('data', 'sequences', sample[1], sample[2] + '-' + str(seq_length) + '-features.npy')
if os.path.isfile(path):
print(sample)
print("Sequence: {} already exists".format(ind))
else:
print(sample)
print("Generating and saving sequence: {}".format(ind))
sequence = dataset.extract_seq_features(sample)
print("Time Elapsed: {}".format(time.time() - currtime))