def load_codec(self, weights_prefix):
encoder_weight_filename = weights_prefix + "_encoder.h5"
decoder_weight_filename = weights_prefix + "_decoder.h5"
if not os.path.isfile(encoder_weight_filename):
raise Exception("The file for encoder weights does not exist:{}".format(encoder_weight_filename))
self.encoder.load_weights(encoder_weight_filename)
if not os.path.isfile(decoder_weight_filename):
raise Exception("The file for decoder weights does not exist:{}".format(decoder_weight_filename))
self.decoder.load_weights(decoder_weight_filename)
print("Encoder summaries")
self.encoder.summary()
_, encode_H, encode_W, numChannels = self.encoder.output_shape
config = self.decoder.get_config()
config2 = config[1::]
config2[0]['config']['batch_input_shape'] = (None, encode_H, encode_W, numChannels)
decoder_temp = Sequential.from_config(config2, custom_objects={"tf": tf})
# set weights
cnt = -1
for l in self.decoder.layers:
cnt += 1
if cnt == 0:
continue
weights = l.get_weights()
decoder_temp.layers[cnt - 1].set_weights(weights)
self.decoder = decoder_temp
print("Decoder summaries")
self.decoder.summary()
def load_codec(codec_prefix, print_summary=False):
# load data
saveFilePrefix = codec_prefix + '_'
# load models
encoder_model_filename = saveFilePrefix + "encoder.json"
decoder_model_filename = saveFilePrefix + "decoder.json"
encoder_weight_filename = saveFilePrefix + "encoder.h5"
decoder_weight_filename = saveFilePrefix + "decoder.h5"
if not os.path.isfile(encoder_model_filename):
raise Exception("The file for encoder model does not exist:{}".format(
encoder_model_filename))
json_file = open(encoder_model_filename, 'r')
encoder = model_from_json(json_file.read(), custom_objects={"tf": tf})
json_file.close()
if not os.path.isfile(encoder_weight_filename):
raise Exception(
"The file for encoder weights does not exist:{}".format(
encoder_weight_filename))
encoder.load_weights(encoder_weight_filename)
if not os.path.isfile(decoder_model_filename):
raise Exception("The file for decoder model does not exist:{}".format(
decoder_model_filename))
json_file = open(decoder_model_filename, 'r')
decoder_temp = model_from_json(json_file.read(), custom_objects={"tf": tf})
json_file.close()
if not os.path.isfile(decoder_weight_filename):
raise Exception(
"The file for decoder weights does not exist:{}".format(
decoder_weight_filename))
decoder_temp.load_weights(decoder_weight_filename)
if print_summary:
print("Encoder summaries")
encoder.summary()
_, encode_H, encode_W, numChannels = encoder.output_shape
# the workaround
# use config to construct the decoder model
# and then load the weights for each layer
# Note that the information in config[0::] is the sequential model for encoder
# thus, we need to exclude the first element
config = decoder_temp.get_config()
config2 = config[1::]
config2[0]['config']['batch_input_shape'] = (None, encode_H, encode_W,
numChannels)
decoder = Sequential.from_config(config2, custom_objects={"tf": tf})
# set weights
cnt = -1
for l in decoder_temp.layers:
cnt += 1
if cnt == 0:
continue
weights = l.get_weights()
decoder.layers[cnt - 1].set_weights(weights)
if print_summary:
print("Decoder summaries")
decoder.summary()
return encoder, decoder
