add st

tests/st/probability/dataset.py

@@ -0,0 +1,60 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# 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.
+# ============================================================================
+"""
+Produce the dataset
+"""
+
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as CV
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore.dataset.transforms.vision import Inter
+from mindspore.common import dtype as mstype
+
+
+def create_dataset(data_path, batch_size=32, repeat_size=1,
+                   num_parallel_workers=1):
+    """
+    create dataset for train or test
+    """
+    # define dataset
+    mnist_ds = ds.MnistDataset(data_path)
+
+    resize_height, resize_width = 32, 32
+    rescale = 1.0 / 255.0
+    shift = 0.0
+    rescale_nml = 1 / 0.3081
+    shift_nml = -1 * 0.1307 / 0.3081
+
+    # define map operations
+    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Bilinear mode
+    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
+    rescale_op = CV.Rescale(rescale, shift)
+    hwc2chw_op = CV.HWC2CHW()
+    type_cast_op = C.TypeCast(mstype.int32)
+
+    # apply map operations on images
+    mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
+
+    # apply DatasetOps
+    buffer_size = 10000
+    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
+    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
+    mnist_ds = mnist_ds.repeat(repeat_size)
+
+    return mnist_ds

tests/st/probability/test_bnn_layer.py

@@ -0,0 +1,145 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# 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.
+# ============================================================================
+"""test bnn layers"""
+
+import numpy as np
+from mindspore import Tensor
+from mindspore.common.initializer import TruncatedNormal
+import mindspore.nn as nn
+from mindspore.nn import TrainOneStepCell
+from mindspore.nn.probability import bnn_layers
+from mindspore.ops import operations as P
+from mindspore import context
+from dataset import create_dataset
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="GPU")
+
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+    """weight initial for conv layer"""
+    weight = weight_variable()
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     weight_init=weight, has_bias=False, pad_mode="valid")
+
+
+def fc_with_initialize(input_channels, out_channels):
+    """weight initial for fc layer"""
+    weight = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+
+def weight_variable():
+    """weight initial"""
+    return TruncatedNormal(0.02)
+
+
+class BNNLeNet5(nn.Cell):
+    """
+    bayesian Lenet network
+
+    Args:
+        num_class (int): Num classes. Default: 10.
+
+    Returns:
+        Tensor, output tensor
+    Examples:
+        >>> BNNLeNet5(num_class=10)
+
+    """
+    def __init__(self, num_class=10):
+        super(BNNLeNet5, self).__init__()
+        self.num_class = num_class
+        self.conv1 = bnn_layers.ConvReparam(1, 6, 5, stride=1, padding=0, has_bias=False, pad_mode="valid")
+        self.conv2 = conv(6, 16, 5)
+        self.fc1 = bnn_layers.DenseReparam(16 * 5 * 5, 120)
+        self.fc2 = fc_with_initialize(120, 84)
+        self.fc3 = fc_with_initialize(84, self.num_class)
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+        self.reshape = P.Reshape()
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        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 = BNNLeNet5()
+
+    criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    optimizer = nn.AdamWeightDecay(params=network.trainable_params(), learning_rate=0.0001)
+
+    net_with_loss = bnn_layers.WithBNNLossCell(network, criterion, 60000, 0.000001)
+    train_bnn_network = TrainOneStepCell(net_with_loss, optimizer)
+    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 = 100
+
+    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))

tests/st/probability/test_transform_bnn_layer.py

@@ -0,0 +1,150 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# 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.
+# ============================================================================
+"""test transform_to_bnn_layer"""
+
+import numpy as np
+from mindspore import Tensor
+from mindspore.common.initializer import TruncatedNormal
+import mindspore.nn as nn
+from mindspore.nn import TrainOneStepCell, WithLossCell
+from mindspore.nn.probability import transforms, bnn_layers
+from mindspore.ops import operations as P
+from mindspore import context
+from dataset import create_dataset
+
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="GPU")
+
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+    """weight initial for conv layer"""
+    weight = weight_variable()
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     weight_init=weight, has_bias=False, pad_mode="valid")
+
+
+def fc_with_initialize(input_channels, out_channels):
+    """weight initial for fc layer"""
+    weight = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+
+def weight_variable():
+    """weight initial"""
+    return TruncatedNormal(0.02)
+
+
+class LeNet5(nn.Cell):
+    """
+    Lenet network
+
+    Args:
+        num_class (int): Num classes. Default: 10.
+
+    Returns:
+        Tensor, output tensor
+    Examples:
+        >>> LeNet5(num_class=10)
+
+    """
+    def __init__(self, num_class=10):
+        super(LeNet5, self).__init__()
+        self.num_class = num_class
+        self.conv1 = conv(1, 6, 5)
+        self.conv2 = conv(6, 16, 5)
+        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
+        self.fc2 = fc_with_initialize(120, 84)
+        self.fc3 = fc_with_initialize(84, self.num_class)
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+        self.reshape = P.Reshape()
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        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_layer(nn.Conv2d, bnn_layers.ConvReparam)
+    # train_bnn_network = bnn_transformer.transform_to_bnn_layer(nn.Dense, bnn_layers.DenseReparam)
+    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 = 100
+
+    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))

tests/st/probability/test_transform_bnn_model.py

@@ -0,0 +1,149 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# 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.
+# ============================================================================
+"""test transform_to_bnn_model"""
+import numpy as np
+from mindspore import Tensor
+from mindspore.common.initializer import TruncatedNormal
+import mindspore.nn as nn
+from mindspore.nn import WithLossCell, TrainOneStepCell
+from mindspore.nn.probability import transforms
+from mindspore.ops import operations as P
+from mindspore import context
+from dataset import create_dataset
+
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="GPU")
+
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+    """weight initial for conv layer"""
+    weight = weight_variable()
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     weight_init=weight, has_bias=False, pad_mode="valid")
+
+
+def fc_with_initialize(input_channels, out_channels):
+    """weight initial for fc layer"""
+    weight = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+
+def weight_variable():
+    """weight initial"""
+    return TruncatedNormal(0.02)
+
+
+class LeNet5(nn.Cell):
+    """
+    Lenet network
+
+    Args:
+        num_class (int): Num classes. Default: 10.
+
+    Returns:
+        Tensor, output tensor
+    Examples:
+        >>> LeNet5(num_class=10)
+
+    """
+    def __init__(self, num_class=10):
+        super(LeNet5, self).__init__()
+        self.num_class = num_class
+        self.conv1 = conv(1, 6, 5)
+        self.conv2 = conv(6, 16, 5)
+        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
+        self.fc2 = fc_with_initialize(120, 84)
+        self.fc3 = fc_with_initialize(84, self.num_class)
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+        self.reshape = P.Reshape()
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        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))