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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, mindir models#################
- python export.py
- """
- import argparse
- import numpy as np
- import mindspore.common.dtype as mstype
- from mindspore import context, Tensor, nn, load_checkpoint, load_param_into_net, export
- from src.simclr_model import SimCLR, SimCLR_Classifier
- from src.resnet import resnet50 as resnet
-
- parser = argparse.ArgumentParser(description='SimCLR')
- parser.add_argument("--device_id", type=int, default=0, help="Device id")
- parser.add_argument("--batch_size", type=int, default=1, help="batch size")
- parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['cifar10'],
- help='Dataset, Currently only cifar10 is supported.')
- parser.add_argument('--device_target', type=str, default="Ascend",
- choices=['Ascend'],
- help='Device target, Currently only Ascend is supported.')
- parser.add_argument("--ckpt_simclr_encoder", type=str, required=True, help="Simclr encoder checkpoint file path.")
- parser.add_argument("--ckpt_linear_classifier", type=str, required=True, help="Linear classifier checkpoint file path.")
- parser.add_argument("--file_name", type=str, default="simclr_classifier", help="output file name.")
- parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="MINDIR", help="file format")
- args_opt = parser.parse_args()
-
- context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
- if args_opt.device_target == "Ascend":
- context.set_context(device_id=args_opt.device_id)
-
-
- if __name__ == '__main__':
- if args_opt.dataset_name != 'cifar10':
- raise ValueError("dataset is not support.")
- width_multiplier = 1
- cifar_stem = True
- projection_dimension = 128
- class_num = 10
- image_height = 32
- image_width = 32
-
- encoder = resnet(1, width_multiplier=width_multiplier, cifar_stem=cifar_stem)
- classifier = nn.Dense(encoder.end_point.in_channels, class_num)
-
- simclr = SimCLR(encoder, projection_dimension, encoder.end_point.in_channels)
- param_simclr = load_checkpoint(args_opt.ckpt_simclr_encoder)
- load_param_into_net(simclr, param_simclr)
-
- param_classifier = load_checkpoint(args_opt.ckpt_linear_classifier)
- load_param_into_net(classifier, param_classifier)
-
- # export SimCLR_Classifier network
- simclr_classifier = SimCLR_Classifier(simclr.encoder, classifier)
- input_data = Tensor(np.zeros([args_opt.batch_size, 3, image_height, image_width]), mstype.float32)
- export(simclr_classifier, input_data, file_name=args_opt.file_name, file_format=args_opt.file_format)
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