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94 lines
3.2 KiB
94 lines
3.2 KiB
# 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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"""
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Function:
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test network
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Usage:
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python test_predict_save_model.py --path ./
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"""
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import os
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import argparse
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import numpy as np
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import mindspore.nn as nn
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import mindspore.ops.operations as P
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import mindspore.context as context
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from mindspore.common.tensor import Tensor
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from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
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class LeNet(nn.Cell):
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def __init__(self):
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super(LeNet, self).__init__()
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self.relu = P.ReLU()
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self.batch_size = 32
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self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
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self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
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self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
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self.reshape = P.Reshape()
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self.fc1 = nn.Dense(400, 120)
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self.fc2 = nn.Dense(120, 84)
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self.fc3 = nn.Dense(84, 10)
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def construct(self, input_x):
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output = self.conv1(input_x)
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output = self.relu(output)
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output = self.pool(output)
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output = self.conv2(output)
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output = self.relu(output)
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output = self.pool(output)
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output = self.reshape(output, (self.batch_size, -1))
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output = self.fc1(output)
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output = self.relu(output)
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output = self.fc2(output)
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output = self.relu(output)
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output = self.fc3(output)
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return output
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parser = argparse.ArgumentParser(description='MindSpore Model Save')
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parser.add_argument('--path', default='./lenet_model.ms', type=str, help='model save path')
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if __name__ == '__main__':
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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context.set_context(enable_task_sink=True)
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print("test lenet predict start")
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seed = 0
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np.random.seed(seed)
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batch = 1
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channel = 1
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input_h = 32
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input_w = 32
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origin_data = np.random.uniform(low=0, high=255, size=(batch, channel, input_h, input_w)).astype(np.float32)
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origin_data.tofile("lenet_input_data.bin")
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input_data = Tensor(origin_data)
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print(input_data.asnumpy())
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net = LeNet()
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ckpt_file_path = "./tests/ut/python/predict/checkpoint_lenet.ckpt"
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predict_args = parser.parse_args()
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model_path_name = predict_args.path
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is_ckpt_exist = os.path.exists(ckpt_file_path)
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if is_ckpt_exist:
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param_dict = load_checkpoint(ckpoint_file_name=ckpt_file_path)
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load_param_into_net(net, param_dict)
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export(net, input_data, file_name=model_path_name, file_format='LITE')
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print("test lenet predict success.")
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else:
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print("checkpoint file is not exist.")
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