|
- # Copyright 2021 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.
- # ============================================================================
- """Export checkpoint into mindir or air for inference."""
- import argparse
- import numpy as np
-
- import mindspore.nn as nn
- import mindspore.ops as ops
- from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
-
- from src.seg_hrnet import get_seg_model
- from src.config import hrnetw48_config as model_config
- from src.dataset.dataset_generator import create_seg_dataset
-
-
- class InferModel(nn.Cell):
- """Add resize and exp behind HRNet."""
- def __init__(self, num_classes):
- super(InferModel, self).__init__()
- self.model = get_seg_model(model_config, num_classes)
- self.resize = ops.ResizeBilinear((1024, 2048))
- self.exp = ops.Exp()
-
- def construct(self, x):
- """Model construction."""
- out = self.model(x)
- out = self.resize(out)
- out = self.exp(out)
- return out
-
-
- def main():
- """Export mindir for 310 inference."""
- parser = argparse.ArgumentParser("HRNet Semantic Segmentation exporting.")
- parser.add_argument("--device_id", type=int, default=0, help="Device ID. ")
- parser.add_argument("--checkpoint_file", type=str, help="Checkpoint file path. ")
- parser.add_argument("--file_name", type=str, help="Output file name. ")
- parser.add_argument("--file_format", type=str, default="MINDIR",
- choices=["AIR", "MINDIR"], help="Output file format. ")
- parser.add_argument("--device_target", type=str, default="Ascend",
- choices=["Ascend", "GPU", "CPU"], help="Device target.")
- parser.add_argument("--dataset", type=str, default="cityscapes")
-
- 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)
- image_size, num_classes = create_seg_dataset(args.dataset, is_train=False)
- net = InferModel(num_classes)
- pd = load_checkpoint(args.checkpoint_file)
- params_dict = {}
- for k, v in pd.items():
- params_dict["model." + k] = v
- load_param_into_net(net, params_dict, strict_load=True)
- net.set_train(False)
- height, width = image_size
- input_data = Tensor(np.zeros([1, 3, height, width], dtype=np.float32))
- export(net, input_data, file_name=args.file_name, file_format=args.file_format)
-
-
- if __name__ == "__main__":
- main()
|