forked from mindspore-Ecosystem/mindspore
41 lines
2.0 KiB
Python
41 lines
2.0 KiB
Python
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import os
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import numpy as np
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from resnet_torch import resnet50
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from mindspore.train.callback import Callback
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore import Tensor
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.train.serialization import save, load, save_checkpoint, load_checkpoint,\
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load_param_into_net, _exec_save_checkpoint,\
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_check_filedir_or_create, _chg_model_file_name_if_same_exist, \
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_read_file_last_line, context, export
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
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enable_task_sink=True,enable_loop_sink=True,enable_ir_fusion=True)
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def test_resnet50_export(batch_size=1, num_classes=5):
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context.set_context(enable_ir_fusion=False)
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input_np = np.random.uniform(0.0, 1.0, size=[batch_size, 3, 224, 224]).astype(np.float32)
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net = resnet50(batch_size, num_classes)
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#param_dict = load_checkpoint("./resnet50-1_103.ckpt")
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#load_param_into_net(net, param_dict)
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export(net, Tensor(input_np), file_name="./me_resnet50.pb", file_format="GEIR")
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