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- # 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 file into air, onnx or mindir model#################
- python export.py
- """
-
- import numpy as np
- from mindspore import Tensor, load_checkpoint, load_param_into_net, export, context
- from mindspore import dtype as mstype
-
- from src.args import args
- from src.models.NFNet.std_conv import ScaledStdConv2dUnit
- from src.tools.cell import cast_amp
- from src.tools.criterion import get_criterion, NetWithLoss
- from src.tools.get_misc import get_model
-
- 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__':
- net = get_model(args)
- criterion = get_criterion(args)
- cast_amp(net)
- net_with_loss = NetWithLoss(net, criterion)
- assert args.pretrained is not None, "checkpoint_path is None."
-
- param_dict = load_checkpoint(args.pretrained)
- load_param_into_net(net, param_dict)
-
- net.set_train(False)
- net.to_float(mstype.float32)
- for name, cell in net.cells_and_names():
- if isinstance(cell, ScaledStdConv2dUnit):
- cell.weight.set_data(cell.wise_normalize())
- print(f"=> uniwise {name}'s weight for inference")
-
- input_arr = Tensor(np.zeros([1, 3, args.test_input_size, args.test_input_size], np.float32))
- export(net, input_arr, file_name=args.arch, file_format=args.file_format)
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