def main(): if len(sys.argv) < 2: print_usage() command = sys.argv[1] args = sys.argv[2:] if command in HELP_OPTIONS or command == 'help': if args: print_usage(command=args[0]) else: print_usage() if command not in COMMANDS: print_usage(error="%s: unrecognized command" % command) mod = COMMANDS[command] try: options, args = parse_options(getattr(mod, 'OPTIONS', {}), args) if 'help' in options: print_usage(command=command) mod.command(options, args) except CommandInvocationError as exc: print_usage(command=command, error=str(exc)) except CommandExecutionError as exc: print(str(exc))
import tvm from PIL import Image from tvm import te from tvm.contrib import graph_runtime from tvm import relay from tvm.runtime import container from tvm.runtime import vm as vm_rt from tvm.relay import testing from tvm.relay import vm from tvm.relay.op.contrib import arm_compute_lib from tvm.contrib.download import download_testdata from util import load_test_image, build_module, update_lib, get_cpu_op_count, download_model_zoo, parse_options, get_device_arch, get_device_attributes, get_device_type, get_tvm_target import sys argv = sys.argv[1:] device = parse_options(argv) model_dir = '/mobilenet_v2_1.0_224/' model_name = 'mobilenet_v2_1.0_224.tflite' model_dir = download_model_zoo(model_dir, model_name) tflite_model_file = os.path.join(model_dir, model_name) tflite_model_buf = open(tflite_model_file, "rb").read() # Get TFLite model from buffer try: import tflite tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0) except AttributeError: import tflite.Model
import os import file_io import util def convert_to_hdf5(data_folder, tgt=None): if tgt is None: tgt = os.path.join(data_folder, "scene.h5") scene = dict() scene["LF"] = file_io.read_lightfield(data_folder) params = file_io.read_parameters(data_folder) if params["category"] != "test": scene["disp_highres"] = file_io.read_disparity(data_folder, highres=True) scene["disp_lowres"] = file_io.read_disparity(data_folder, highres=False) scene["depth_highres"] = file_io.read_depth(data_folder, highres=True) scene["depth_lowres"] = file_io.read_depth(data_folder, highres=False) file_io.write_hdf5(scene, tgt) if __name__ == '__main__': data_folders = util.parse_options() for data_folder in data_folders: print "converting: %s" % data_folder convert_to_hdf5(data_folder)