!4831 Add test of bnn_layers and transforms
Merge pull request !4831 from byweng/add_test
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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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"""
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Produce the dataset
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"""
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.vision.c_transforms as CV
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import mindspore.dataset.transforms.c_transforms as C
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from mindspore.dataset.transforms.vision import Inter
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from mindspore.common import dtype as mstype
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def create_dataset(data_path, batch_size=32, repeat_size=1,
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num_parallel_workers=1):
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"""
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create dataset for train or test
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"""
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# define dataset
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mnist_ds = ds.MnistDataset(data_path)
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resize_height, resize_width = 32, 32
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rescale = 1.0 / 255.0
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shift = 0.0
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rescale_nml = 1 / 0.3081
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shift_nml = -1 * 0.1307 / 0.3081
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# define map operations
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resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
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rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
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rescale_op = CV.Rescale(rescale, shift)
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hwc2chw_op = CV.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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# apply map operations on images
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mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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buffer_size = 10000
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mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script
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mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
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mnist_ds = mnist_ds.repeat(repeat_size)
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return mnist_ds
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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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"""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()):
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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()):
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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(is_grad=False, 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, test_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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# 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 transform_to_bnn_layer"""
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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, WithLossCell
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from mindspore.nn.probability import transforms, 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 LeNet5(nn.Cell):
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"""
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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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>>> LeNet5(num_class=10)
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"""
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def __init__(self, num_class=10):
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super(LeNet5, self).__init__()
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self.num_class = num_class
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self.conv1 = conv(1, 6, 5)
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self.conv2 = conv(6, 16, 5)
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self.fc1 = fc_with_initialize(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()):
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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()):
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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 = LeNet5()
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criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, 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 = WithLossCell(network, criterion)
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train_network = TrainOneStepCell(net_with_loss, optimizer)
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bnn_transformer = transforms.TransformToBNN(train_network, 60000, 0.000001)
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train_bnn_network = bnn_transformer.transform_to_bnn_layer(nn.Conv2d, bnn_layers.ConvReparam)
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# train_bnn_network = bnn_transformer.transform_to_bnn_layer(nn.Dense, bnn_layers.DenseReparam)
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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, test_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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@ -0,0 +1,149 @@
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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
|
||||
#
|
||||
# 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.
|
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# ============================================================================
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"""test transform_to_bnn_model"""
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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 WithLossCell, TrainOneStepCell
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from mindspore.nn.probability import transforms
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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 LeNet5(nn.Cell):
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"""
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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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>>> LeNet5(num_class=10)
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"""
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def __init__(self, num_class=10):
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super(LeNet5, self).__init__()
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self.num_class = num_class
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self.conv1 = conv(1, 6, 5)
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self.conv2 = conv(6, 16, 5)
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self.fc1 = fc_with_initialize(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)
|
||||
x = self.fc1(x)
|
||||
x = self.relu(x)
|
||||
x = self.fc2(x)
|
||||
x = self.relu(x)
|
||||
x = self.fc3(x)
|
||||
return x
|
||||
|
||||
|
||||
def train_model(train_net, net, dataset):
|
||||
accs = []
|
||||
loss_sum = 0
|
||||
for _, data in enumerate(dataset.create_dict_iterator()):
|
||||
train_x = Tensor(data['image'].astype(np.float32))
|
||||
label = Tensor(data['label'].astype(np.int32))
|
||||
loss = train_net(train_x, label)
|
||||
output = net(train_x)
|
||||
log_output = P.LogSoftmax(axis=1)(output)
|
||||
acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
|
||||
accs.append(acc)
|
||||
loss_sum += loss.asnumpy()
|
||||
|
||||
loss_sum = loss_sum / len(accs)
|
||||
acc_mean = np.mean(accs)
|
||||
return loss_sum, acc_mean
|
||||
|
||||
|
||||
def validate_model(net, dataset):
|
||||
accs = []
|
||||
for _, data in enumerate(dataset.create_dict_iterator()):
|
||||
train_x = Tensor(data['image'].astype(np.float32))
|
||||
label = Tensor(data['label'].astype(np.int32))
|
||||
output = net(train_x)
|
||||
log_output = P.LogSoftmax(axis=1)(output)
|
||||
acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
|
||||
accs.append(acc)
|
||||
|
||||
acc_mean = np.mean(accs)
|
||||
return acc_mean
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
network = LeNet5()
|
||||
|
||||
criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
|
||||
optimizer = nn.AdamWeightDecay(params=network.trainable_params(), learning_rate=0.0001)
|
||||
|
||||
net_with_loss = WithLossCell(network, criterion)
|
||||
train_network = TrainOneStepCell(net_with_loss, optimizer)
|
||||
|
||||
bnn_transformer = transforms.TransformToBNN(train_network, 60000, 0.000001)
|
||||
|
||||
train_bnn_network = bnn_transformer.transform_to_bnn_model()
|
||||
train_bnn_network.set_train()
|
||||
|
||||
train_set = create_dataset('/home/workspace/mindspore_dataset/mnist_data/train', 64, 1)
|
||||
test_set = create_dataset('/home/workspace/mindspore_dataset/mnist_data/test', 64, 1)
|
||||
|
||||
epoch = 500
|
||||
|
||||
for i in range(epoch):
|
||||
train_loss, train_acc = train_model(train_bnn_network, network, test_set)
|
||||
|
||||
valid_acc = validate_model(network, test_set)
|
||||
|
||||
print('Epoch: {} \tTraining Loss: {:.4f} \tTraining Accuracy: {:.4f} \tvalidation Accuracy: {:.4f}'.format(
|
||||
i, train_loss, train_acc, valid_acc))
|
||||
Loading…
Reference in New Issue