Source code for mindspore.nn.probability.transforms.transform_bnn
# Copyright 2020 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ============================================================================
"""Transform DNN to BNN."""
import mindspore.nn as nn
from ...wrap.cell_wrapper import TrainOneStepCell
from ....nn import optim
from ....nn import layer
from ...probability import bnn_layers
from ..bnn_layers.bnn_cell_wrapper import WithBNNLossCell
from ..bnn_layers.conv_variational import ConvReparam
from ..bnn_layers.dense_variational import DenseReparam
__all__ = ['TransformToBNN']
[docs]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. Default: 1.
bnn_factor (int, float): The coefficient of KL loss, which is KL divergence of Bayesian layer. Default: 1.
Supported Platforms:
``Ascend`` ``GPU``
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(sparse=True)
>>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> net_with_loss = WithLossCell(net, criterion)
>>> train_network = TrainOneStepCell(net_with_loss, optim)
>>> bnn_transformer = TransformToBNN(train_network, 60000, 0.0001)
"""
def __init__(self, trainable_dnn, dnn_factor=1, bnn_factor=1):
if isinstance(dnn_factor, bool) or not isinstance(dnn_factor, (int, float)):
raise TypeError('The type of `dnn_factor` should be `int` or `float`')
if dnn_factor < 0:
raise ValueError('The value of `dnn_factor` should >= 0')
if isinstance(bnn_factor, bool) or not isinstance(bnn_factor, (int, float)):
raise TypeError('The type of `bnn_factor` should be `int` or `float`')
if bnn_factor < 0:
raise ValueError('The value of `bnn_factor` should >= 0')
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
[docs] 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: 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: 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. Note that the arguments in
`add_dense_args` must not duplicate arguments in `get_dense_args`. Default: None.
add_conv_args (dict): The new arguments added to BNN convolutional layer. Note that the arguments in
`add_conv_args` must not duplicate arguments in `get_conv_args`. Default: None.
Returns:
Cell, a trainable BNN model wrapped by TrainOneStepCell.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> net = Net()
>>> criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> net_with_loss = WithLossCell(net, 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 = {}
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 = WithBNNLossCell(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
[docs] 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 and nn.Conv2d.
bnn_layer_type (Cell): The type of BNN layer to be transformed to. The optional values are
DenseReparam and ConvReparam.
get_args: The arguments gotten from the DNN layer. Default: None.
add_args (dict): The new arguments added to BNN layer. Note that the arguments in `add_args` must not
duplicate arguments in `get_args`. Default: None.
Returns:
Cell, a trainable model wrapped by TrainOneStepCell, whose specific type of layer is transformed to the
corresponding bayesian layer.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> net = Net()
>>> criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> net_with_loss = WithLossCell(net, 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 = {}
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 = WithBNNLossCell(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."""
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)
elif isinstance(cell, nn.Conv2d):
conv_args = get_conv_args(cell)
new_layer = ConvReparam(**conv_args, **add_conv_args)
setattr(backbone, name, new_layer)
else:
self._replace_all_bnn_layers(cell, get_dense_args, get_conv_args, add_dense_args,
add_conv_args)
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."""
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)
else:
self._replace_specified_dnn_layers(cell, dnn_layer, bnn_layer, get_args, add_args)