try: tf.train.import_meta_graph(args.meta, clear_devices=True) except KeyError: print( "If your graph contains non-standard ops, you need to import the relevant library first." ) raise # loading... if args.input.endswith('.npz'): dic = np.load(args.input) else: dic = varmanip.load_chkpt_vars(args.input) dic = {get_op_tensor_name(k)[1]: v for k, v in six.iteritems(dic)} # save variables that are GLOBAL, and either TRAINABLE or MODEL var_to_dump = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) var_to_dump.extend(tf.get_collection(tf.GraphKeys.MODEL_VARIABLES)) if len(set(var_to_dump)) != len(var_to_dump): print("TRAINABLE and MODEL variables have duplication!") var_to_dump = list(set(var_to_dump)) globvarname = set([k.name for k in tf.global_variables()]) var_to_dump = set([k.name for k in var_to_dump if k.name in globvarname]) for name in var_to_dump: assert name in dic, "Variable {} not found in the model!".format(name) dic_to_dump = {k: v for k, v in six.iteritems(dic) if k in var_to_dump} varmanip.save_chkpt_vars(dic_to_dump, args.output)
parser.add_argument(dest='input', help='input model file, has to be a TF checkpoint') parser.add_argument(dest='output', help='output model file, can be npz or TF checkpoint') args = parser.parse_args() # this script does not need GPU os.environ['CUDA_VISIBLE_DEVICES'] = '' tf.train.import_meta_graph(args.meta, clear_devices=True) # loading... if args.input.endswith('.npz'): dic = np.load(args.input) else: dic = varmanip.load_chkpt_vars(args.input) dic = {get_op_tensor_name(k)[1]: v for k, v in six.iteritems(dic)} # save variables that are GLOBAL, and either TRAINABLE or MODEL var_to_dump = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) var_to_dump.extend(tf.get_collection(tf.GraphKeys.MODEL_VARIABLES)) if len(set(var_to_dump)) != len(var_to_dump): print("TRAINABLE and MODEL variables have duplication!") var_to_dump = list(set(var_to_dump)) globvarname = set([k.name for k in tf.global_variables()]) var_to_dump = set([k.name for k in var_to_dump if k.name in globvarname]) for name in var_to_dump: assert name in dic, "Variable {} not found in the model!".format(name) dic_to_dump = {k: v for k, v in six.iteritems(dic) if k in var_to_dump} varmanip.save_chkpt_vars(dic_to_dump, args.output)