add transforms to nn.probability

mindspore/nn/probability/transforms/__init__.py

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+# 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.
+# ============================================================================
+"""
+Transforms.
+
+The high-level components used to transform model between DNN and DNN.
+"""
+from . import transform_bnn
+from .transform_bnn import TransformToBNN
+
+__all__ = []
+__all__.extend(transform_bnn.__all__)

mindspore/nn/probability/transforms/bnn_loss/__init__.py

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+# 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.
+# ============================================================================
+"""
+bnn loss.
+"""
+from . import generate_kl_loss
+from .generate_kl_loss import gain_bnn_with_loss

mindspore/nn/probability/transforms/bnn_loss/generate_kl_loss.py

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+# 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.
+# ============================================================================
+"""Gain bnn_with_loss by rewrite WithLossCell as WithBNNLossCell to suit for BNN model"""
+import ast
+import importlib
+import os
+import sys
+import tempfile
+import astunparse
+import mindspore
+
+
+class _CodeTransformer(ast.NodeTransformer):
+    """
+    Add kl_loss computation by analyzing the python code structure with the help of the AST module.
+
+    Args:
+        layer_count (int): The number of kl loss to be generated, namely the number of Bayesian layers.
+    """
+
+    def __init__(self, layer_count):
+        self.layer_count = layer_count
+
+    def visit_FunctionDef(self, node):
+        """visit function and add kl_loss computation."""
+        self.generic_visit(node)
+        if node.name == 'compute_kl_loss':
+            for i in range(self.layer_count):
+                func = ast.Assign(targets=[ast.Name(id='loss', ctx=ast.Store())],
+                                  value=ast.BinOp(left=ast.Name(id='loss', ctx=ast.Load()), op=ast.Add(),
+                                                  right=ast.Call(func=ast.Name(id='self.kl_loss' + '[' + str(i) + ']',
+                                                                               ctx=ast.Load()),
+                                                                 args=[], keywords=[])))
+                node.body.insert(-1, func)
+        return node
+
+
+def _generate_kl_loss_func(layer_count):
+    """Rewrite WithLossCell as WithBNNLossCell to suit for BNN model."""
+    path = os.path.dirname(mindspore.__file__) + '/nn/probability/transforms/bnn_loss/withLossCell.py'
+    with open(path, 'r') as fp:
+        srclines = fp.readlines()
+    src = ''.join(srclines)
+    if src.startswith((' ', '\t')):
+        src = 'if 1:\n' + src
+    expr_ast = ast.parse(src, mode='exec')
+    transformer = _CodeTransformer(layer_count)
+    modify = transformer.visit(expr_ast)
+    modify = ast.fix_missing_locations(modify)
+    func = astunparse.unparse(modify)
+    return func
+
+
+def gain_bnn_with_loss(layer_count, backbone, loss_fn, dnn_factor, bnn_factor):
+    """
+    Gain bnn_with_loss, which wraps bnn network with loss function and kl loss of each bayesian layer.
+
+    Args:
+        layer_count (int): The number of kl loss to be generated, namely the number of Bayesian layers.
+        backbone (Cell): The target network to wrap.
+        loss_fn (Cell): The loss function used to compute loss.
+        dnn_factor ((int, float): The coefficient of backbone's loss, which is computed by loss function.
+        bnn_factor (int, float): The coefficient of kl loss, which is kl divergence of Bayesian layer.
+    """
+    bnn_loss_func = _generate_kl_loss_func(layer_count)
+    path = os.path.dirname(mindspore.__file__)
+    bnn_loss_file = tempfile.NamedTemporaryFile(mode='w+t', suffix='.py', delete=True,
+                                                dir=path + '/nn/probability/transforms/bnn_loss')
+    bnn_loss_file.write(bnn_loss_func)
+    bnn_loss_file.seek(0)
+
+    sys.path.append(path + '/nn/probability/transforms/bnn_loss')
+
+    module_name = os.path.basename(bnn_loss_file.name)[0:-3]
+    bnn_loss_module = importlib.import_module(module_name, __package__)
+    bnn_with_loss = bnn_loss_module.WithBNNLossCell(backbone, loss_fn, dnn_factor, bnn_factor)
+    return bnn_with_loss, bnn_loss_file

mindspore/nn/probability/transforms/bnn_loss/withLossCell.py

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+# 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.
+# ============================================================================
+"""Original WithBNNLossCell for ast to rewrite."""
+
+import mindspore.nn as nn
+from mindspore.nn.probability.bnn_layers.conv_variational import _ConvVariational
+from mindspore.nn.probability.bnn_layers.dense_variational import _DenseVariational
+
+
+class WithBNNLossCell(nn.Cell):
+    """
+    Cell with loss function.
+
+    Wraps the network with loss function. This Cell accepts data, label, backbone_factor and kl_factor as inputs and
+    the computed loss will be returned.
+    """
+    def __init__(self, backbone, loss_fn, backbone_factor=1, kl_factor=1):
+        super(WithBNNLossCell, self).__init__(auto_prefix=False)
+        self._backbone = backbone
+        self._loss_fn = loss_fn
+        self.backbone_factor = backbone_factor
+        self.kl_factor = kl_factor
+        self.kl_loss = []
+        self._add_kl_loss(self._backbone)
+
+    def construct(self, x, label):
+        y_pred = self._backbone(x)
+        backbone_loss = self._loss_fn(y_pred, label)
+        kl_loss = self.cal_kl_loss()
+        loss = backbone_loss*self.backbone_factor + kl_loss*self.kl_factor
+        return loss
+
+    def cal_kl_loss(self):
+        """Calculate kl loss."""
+        loss = 0.0
+        return loss
+
+    def _add_kl_loss(self, net):
+        """Collect kl loss of each Bayesian layer."""
+        for (_, layer) in net.name_cells().items():
+            if isinstance(layer, (_DenseVariational, _ConvVariational)):
+                self.kl_loss.append(layer.compute_kl_loss)
+            else:
+                self._add_kl_loss(layer)

mindspore/nn/probability/transforms/transform_bnn.py

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+# 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.
+# ============================================================================
+"""Transform DNN to BNN."""
+import mindspore.nn as nn
+from ...wrap.cell_wrapper import TrainOneStepCell
+from ....nn import optim
+from ....nn import layer
+from .bnn_loss.generate_kl_loss import gain_bnn_with_loss
+from ...probability import bnn_layers
+from ..bnn_layers.conv_variational import ConvReparam
+from ..bnn_layers.dense_variational import DenseReparam
+
+__all__ = ['TransformToBNN']
+
+
+class TransformToBNN:
+    r"""
+    Transform Deep Neural Network (DNN) model to Bayesian Neural Network (BNN) model.
+
+    Args:
+        trainable_dnn (Cell): A trainable DNN model (backbone) wrapped by TrainOneStepCell.
+        dnn_factor ((int, float): The coefficient of backbone's loss, which is computed by loss function.
+        bnn_factor (int, float): The coefficient of kl loss, which is kl divergence of Bayesian layer.
+
+    Examples:
+        >>> class Net(nn.Cell):
+        >>>     def __init__(self):
+        >>>         super(Net, self).__init__()
+        >>>         self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal')
+        >>>         self.bn = nn.BatchNorm2d(64)
+        >>>         self.relu = nn.ReLU()
+        >>>         self.flatten = nn.Flatten()
+        >>>         self.fc = nn.Dense(64*224*224, 12) # padding=0
+        >>>
+        >>>     def construct(self, x):
+        >>>         x = self.conv(x)
+        >>>         x = self.bn(x)
+        >>>         x = self.relu(x)
+        >>>         x = self.flatten(x)
+        >>>         out = self.fc(x)
+        >>>         return out
+        >>>
+        >>> net = Net()
+        >>> criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+        >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
+        >>> net_with_loss = WithLossCell(network, criterion)
+        >>> train_network = TrainOneStepCell(net_with_loss, optim)
+        >>> bnn_transformer = TransformToBNN(train_network, 60000, 0.1)
+    """
+
+    def __init__(self, trainable_dnn, dnn_factor=1, bnn_factor=1):
+        net_with_loss = trainable_dnn.network
+        self.optimizer = trainable_dnn.optimizer
+        self.backbone = net_with_loss.backbone_network
+        self.loss_fn = getattr(net_with_loss, "_loss_fn")
+        self.dnn_factor = dnn_factor
+        self.bnn_factor = bnn_factor
+        self.bnn_loss_file = None
+
+    def transform_to_bnn_model(self,
+                               get_dense_args=lambda dp: {"in_channels": dp.in_channels, "has_bias": dp.has_bias,
+                                                          "out_channels": dp.out_channels, "activation": dp.activation},
+                               get_conv_args=lambda dp: {"in_channels": dp.in_channels, "out_channels": dp.out_channels,
+                                                         "pad_mode": dp.pad_mode, "kernel_size": dp.kernel_size,
+                                                         "stride": dp.stride, "has_bias": dp.has_bias,
+                                                         "padding": dp.padding, "dilation": dp.dilation,
+                                                         "group": dp.group},
+                               add_dense_args=None,
+                               add_conv_args=None):
+        r"""
+        Transform the whole DNN model to BNN model, and wrap BNN model by TrainOneStepCell.
+
+        Args:
+            get_dense_args (function): The arguments gotten from the DNN full connection layer. Default: lambda dp:
+                {"in_channels": dp.in_channels, "out_channels": dp.out_channels, "has_bias": dp.has_bias}.
+            get_conv_args (function): The arguments gotten from the DNN convolutional layer. Default: lambda dp:
+                {"in_channels": dp.in_channels, "out_channels": dp.out_channels, "pad_mode": dp.pad_mode,
+                "kernel_size": dp.kernel_size, "stride": dp.stride, "has_bias": dp.has_bias}.
+            add_dense_args (dict): The new arguments added to BNN full connection layer. Default: {}.
+            add_conv_args (dict): The new arguments added to BNN convolutional layer. Default: {}.
+
+        Returns:
+            Cell, a trainable BNN model wrapped by TrainOneStepCell.
+
+        Examples:
+            >>> net = Net()
+            >>> criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+            >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
+            >>> net_with_loss = WithLossCell(network, criterion)
+            >>> train_network = TrainOneStepCell(net_with_loss, optim)
+            >>> bnn_transformer = TransformToBNN(train_network, 60000, 0.1)
+            >>> train_bnn_network = bnn_transformer.transform_to_bnn_model()
+        """
+        if not add_dense_args:
+            add_dense_args = {}
+        if not add_conv_args:
+            add_conv_args = {}
+
+        layer_count = self._replace_all_bnn_layers(self.backbone, get_dense_args, get_conv_args, add_dense_args,
+                                                   add_conv_args)
+
+        # rename layers of BNN model to prevent duplication of names
+        for value, param in self.backbone.parameters_and_names():
+            param.name = value
+
+        bnn_with_loss, self.bnn_loss_file = gain_bnn_with_loss(layer_count, self.backbone, self.loss_fn,
+                                                               self.dnn_factor, self.bnn_factor)
+        bnn_optimizer = self._create_optimizer_with_bnn_params()
+        train_bnn_network = TrainOneStepCell(bnn_with_loss, bnn_optimizer)
+        return train_bnn_network
+
+    def transform_to_bnn_layer(self, dnn_layer_type, bnn_layer_type, get_args=None, add_args=None):
+        r"""
+        Transform a specific type of layers in DNN model to corresponding BNN layer.
+
+        Args:
+            dnn_layer_type (Cell): The type of DNN layer to be transformed to BNN layer. The optional values are
+            nn.Dense, nn.Conv2d.
+            bnn_layer_type (Cell): The type of BNN layer to be transformed to. The optional values are
+                DenseReparameterization, ConvReparameterization.
+            get_args (dict): The arguments gotten from the DNN layer. Default: None.
+            add_args (dict): The new arguments added to BNN layer. Default: None.
+
+        Returns:
+            Cell, a trainable model wrapped by TrainOneStepCell, whose sprcific type of layer is transformed to the
+            corresponding bayesian layer.
+
+        Examples:
+            >>> net = Net()
+            >>> criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+            >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
+            >>> net_with_loss = WithLossCell(network, criterion)
+            >>> train_network = TrainOneStepCell(net_with_loss, optim)
+            >>> bnn_transformer = TransformToBNN(train_network, 60000, 0.1)
+            >>> train_bnn_network = bnn_transformer.transform_to_bnn_layer(Dense, DenseReparam)
+        """
+        if dnn_layer_type.__name__ not in ["Dense", "Conv2d"]:
+            raise ValueError(' \'dnn_layer\'' + str(dnn_layer_type) +
+                             ', should be one of values in \'nn.Dense\', \'nn.Conv2d\'.')
+
+        if bnn_layer_type.__name__ not in ["DenseReparam", "ConvReparam"]:
+            raise ValueError(' \'bnn_layer\'' + str(bnn_layer_type) +
+                             ', should be one of values in \'DenseReparam\', \'ConvReparam\'.')
+
+        dnn_layer_type = getattr(layer, dnn_layer_type.__name__)
+        bnn_layer_type = getattr(bnn_layers, bnn_layer_type.__name__)
+
+        if not get_args:
+            if dnn_layer_type.__name__ == "Dense":
+                get_args = self._get_dense_args
+            else:
+                get_args = self._get_conv_args
+
+        if not add_args:
+            add_args = {}
+
+        layer_count = self._replace_specified_dnn_layers(self.backbone, dnn_layer_type, bnn_layer_type, get_args,
+                                                         add_args)
+        for value, param in self.backbone.parameters_and_names():
+            param.name = value
+
+        bnn_with_loss, self.bnn_loss_file = gain_bnn_with_loss(layer_count, self.backbone, self.loss_fn,
+                                                               self.dnn_factor, self.bnn_factor)
+        bnn_optimizer = self._create_optimizer_with_bnn_params()
+
+        train_bnn_network = TrainOneStepCell(bnn_with_loss, bnn_optimizer)
+        return train_bnn_network
+
+    def _get_dense_args(self, dense_layer):
+        """Get arguments from dense layer."""
+        dense_args = {"in_channels": dense_layer.in_channels, "has_bias": dense_layer.has_bias,
+                      "out_channels": dense_layer.out_channels, "activation": dense_layer.activation}
+        return dense_args
+
+    def _get_conv_args(self, conv_layer):
+        """Get arguments from conv2d layer."""
+        conv_args = {"in_channels": conv_layer.in_channels, "out_channels": conv_layer.out_channels,
+                     "pad_mode": conv_layer.pad_mode, "kernel_size": conv_layer.kernel_size,
+                     "stride": conv_layer.stride, "has_bias": conv_layer.has_bias,
+                     "padding": conv_layer.padding, "dilation": conv_layer.dilation,
+                     "group": conv_layer.group}
+        return conv_args
+
+    def _create_optimizer_with_bnn_params(self):
+        """Create new optimizer that contains bnn trainable parameters."""
+        name = self.optimizer.__class__.__name__
+        modules = optim.__all__
+
+        if name not in modules:
+            raise TypeError('The optimizer can be {}, but got {}'.format(str(modules), name))
+
+        optimizer = getattr(optim, name)
+
+        args = {'params': self.backbone.trainable_params()}
+        params = optimizer.__init__.__code__.co_varnames
+        _params = self.optimizer.__dict__['_params']
+        for param in params:
+            if param in _params:
+                args[param] = self.optimizer.__getattr__(param).data.asnumpy().tolist()
+
+        new_optimizer = optimizer(**args)
+        return new_optimizer
+
+    def _replace_all_bnn_layers(self, backbone, get_dense_args, get_conv_args, add_dense_args, add_conv_args):
+        """Replace both dense layer and conv2d layer in DNN model to bayesian layers."""
+        count = 0
+        for name, cell in backbone.name_cells().items():
+            if isinstance(cell, nn.Dense):
+                dense_args = get_dense_args(cell)
+                new_layer = DenseReparam(**dense_args, **add_dense_args)
+                setattr(backbone, name, new_layer)
+                count += 1
+            elif isinstance(cell, nn.Conv2d):
+                conv_args = get_conv_args(cell)
+                new_layer = ConvReparam(**conv_args, **add_conv_args)
+                setattr(backbone, name, new_layer)
+                count += 1
+            else:
+                count += self._replace_all_bnn_layers(cell, get_dense_args, get_conv_args, add_dense_args,
+                                                      add_conv_args)
+        return count
+
+    def _replace_specified_dnn_layers(self, backbone, dnn_layer, bnn_layer, get_args, add_args):
+        """Convert a specific type of layers in DNN model to corresponding bayesian layers."""
+        count = 0
+        for name, cell in backbone.name_cells().items():
+            if isinstance(cell, dnn_layer):
+                args = get_args(cell)
+                new_layer = bnn_layer(**args, **add_args)
+                setattr(backbone, name, new_layer)
+                count += 1
+            else:
+                count += self._replace_specified_dnn_layers(cell, dnn_layer, bnn_layer, get_args, add_args)
+        return count