# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """evaluate_imagenet""" import argparse import numpy as np import mindspore as ms from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export from src.config import config_gpu as cfg from src.shufflenetv2 import ShuffleNetV2 parser = argparse.ArgumentParser(description='checkpoint export') parser.add_argument("--device_id", type=int, default=0, help="Device id") parser.add_argument("--batch_size", type=int, default=128, help="batch size") parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.") parser.add_argument('--width', type=int, default=224, help='input width') parser.add_argument('--height', type=int, default=224, help='input height') parser.add_argument("--file_name", type=str, default="shufflenetv2", help="output file name.") parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format") parser.add_argument("--device_target", type=str, default="GPU", choices=["Ascend", "GPU", "CPU"], help="device where the code will be implemented (default: GPU)") args = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) if args.device_target == "Ascend": context.set_context(device_id=args.device_id) if __name__ == '__main__': if args.device_target != 'GPU': raise ValueError("Only supported GPU now.") net = ShuffleNetV2(n_class=cfg.num_classes) ckpt = load_checkpoint(args.ckpt_file) load_param_into_net(net, ckpt) net.set_train(False) input_data = Tensor(np.ones([args.batch_size, 3, args.height, args.width]), ms.float32) export(net, input_data, file_name=args.file_name, file_format=args.file_format)