forked from mindspore-Ecosystem/mindspore
146 lines
4.9 KiB
Python
146 lines
4.9 KiB
Python
# 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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"""test bnn layers"""
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import numpy as np
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from mindspore import Tensor
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from mindspore.common.initializer import TruncatedNormal
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import mindspore.nn as nn
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from mindspore.nn import TrainOneStepCell
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from mindspore.nn.probability import bnn_layers
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from mindspore.ops import operations as P
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from mindspore import context
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from dataset import create_dataset
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context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="GPU")
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def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
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"""weight initial for conv layer"""
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weight = weight_variable()
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding,
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weight_init=weight, has_bias=False, pad_mode="valid")
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def fc_with_initialize(input_channels, out_channels):
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"""weight initial for fc layer"""
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weight = weight_variable()
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bias = weight_variable()
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return nn.Dense(input_channels, out_channels, weight, bias)
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def weight_variable():
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"""weight initial"""
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return TruncatedNormal(0.02)
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class BNNLeNet5(nn.Cell):
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"""
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bayesian Lenet network
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Args:
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num_class (int): Num classes. Default: 10.
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Returns:
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Tensor, output tensor
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Examples:
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>>> BNNLeNet5(num_class=10)
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"""
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def __init__(self, num_class=10):
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super(BNNLeNet5, self).__init__()
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self.num_class = num_class
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self.conv1 = bnn_layers.ConvReparam(1, 6, 5, stride=1, padding=0, has_bias=False, pad_mode="valid")
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self.conv2 = conv(6, 16, 5)
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self.fc1 = bnn_layers.DenseReparam(16 * 5 * 5, 120)
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self.fc2 = fc_with_initialize(120, 84)
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self.fc3 = fc_with_initialize(84, self.num_class)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = nn.Flatten()
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self.reshape = P.Reshape()
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def construct(self, x):
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x = self.conv1(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.relu(x)
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x = self.fc3(x)
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return x
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def train_model(train_net, net, dataset):
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accs = []
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loss_sum = 0
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for _, data in enumerate(dataset.create_dict_iterator(output_numpy=True, num_epochs=1)):
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train_x = Tensor(data['image'].astype(np.float32))
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label = Tensor(data['label'].astype(np.int32))
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loss = train_net(train_x, label)
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output = net(train_x)
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log_output = P.LogSoftmax(axis=1)(output)
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acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
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accs.append(acc)
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loss_sum += loss.asnumpy()
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loss_sum = loss_sum / len(accs)
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acc_mean = np.mean(accs)
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return loss_sum, acc_mean
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def validate_model(net, dataset):
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accs = []
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for _, data in enumerate(dataset.create_dict_iterator(output_numpy=True, num_epochs=1)):
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train_x = Tensor(data['image'].astype(np.float32))
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label = Tensor(data['label'].astype(np.int32))
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output = net(train_x)
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log_output = P.LogSoftmax(axis=1)(output)
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acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
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accs.append(acc)
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acc_mean = np.mean(accs)
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return acc_mean
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if __name__ == "__main__":
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network = BNNLeNet5()
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criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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optimizer = nn.AdamWeightDecay(params=network.trainable_params(), learning_rate=0.0001)
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net_with_loss = bnn_layers.WithBNNLossCell(network, criterion, 60000, 0.000001)
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train_bnn_network = TrainOneStepCell(net_with_loss, optimizer)
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train_bnn_network.set_train()
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train_set = create_dataset('/home/workspace/mindspore_dataset/mnist_data/train', 64, 1)
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test_set = create_dataset('/home/workspace/mindspore_dataset/mnist_data/test', 64, 1)
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epoch = 100
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for i in range(epoch):
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train_loss, train_acc = train_model(train_bnn_network, network, train_set)
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valid_acc = validate_model(network, test_set)
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print('Epoch: {} \tTraining Loss: {:.4f} \tTraining Accuracy: {:.4f} \tvalidation Accuracy: {:.4f}'.format(
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i, train_loss, train_acc, valid_acc))
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