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92 lines
3.5 KiB
92 lines
3.5 KiB
# Copyright 2019 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 numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.nn import TrainOneStepCell, WithLossCell
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from mindspore.nn.optim import Momentum
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from mindspore.ops import operations as P
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import ms_service_pb2
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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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def train(net, data, label):
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learning_rate = 0.01
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momentum = 0.9
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optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
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criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
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net_with_criterion = WithLossCell(net, criterion)
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train_network = TrainOneStepCell(net_with_criterion, optimizer) # optimizer
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train_network.set_train()
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res = train_network(data, label)
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print("+++++++++Loss+++++++++++++")
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print(res)
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print("+++++++++++++++++++++++++++")
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assert res
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return res
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def test_lenet(data, label):
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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net = LeNet()
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return train(net, data, label)
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if __name__ == '__main__':
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tensor = ms_service_pb2.Tensor()
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tensor.tensor_shape.dim.extend([32, 1, 32, 32])
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# tensor.tensor_shape.dim.add() = 1
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# tensor.tensor_shape.dim.add() = 32
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# tensor.tensor_shape.dim.add() = 32
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tensor.tensor_type = ms_service_pb2.MS_FLOAT32
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tensor.data = np.ones([32, 1, 32, 32]).astype(np.float32).tobytes()
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data_from_buffer = np.frombuffer(tensor.data, dtype=np.float32)
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print(tensor.tensor_shape.dim)
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data_from_buffer = data_from_buffer.reshape(tensor.tensor_shape.dim)
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print(data_from_buffer.shape)
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input_data = Tensor(data_from_buffer * 0.01)
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input_label = Tensor(np.ones([32]).astype(np.int32))
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test_lenet(input_data, input_label)
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