# 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,
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# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Generate WithLossCell suitable for BNN."""
from .conv_variational import _ConvVariational
from .dense_variational import _DenseVariational
from ...cell import Cell
__all__ = ['WithBNNLossCell']
[docs]class WithBNNLossCell(Cell):
r"""
Generate a suitable WithLossCell for BNN to wrap the bayesian network with loss function.
Args:
backbone (Cell): The target network.
loss_fn (Cell): The loss function used to compute loss.
dnn_factor(int, float): The coefficient of backbone's loss, which is computed by the loss function. Default: 1.
bnn_factor(int, float): The coefficient of KL loss, which is the KL divergence of Bayesian layer. Default: 1.
Inputs:
- **data** (Tensor) - Tensor of shape :math:`(N, \ldots)`.
- **label** (Tensor) - Tensor of shape :math:`(N, \ldots)`.
Outputs:
Tensor, a scalar tensor with shape :math:`()`.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> net = Net()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
>>> net_with_criterion = WithBNNLossCell(net, loss_fn)
>>>
>>> batch_size = 2
>>> data = Tensor(np.ones([batch_size, 16]).astype(np.float32) * 0.01)
>>> label = Tensor(np.ones([batch_size, 1]).astype(np.float32))
>>>
>>> net_with_criterion(data, label)
"""
def __init__(self, backbone, loss_fn, dnn_factor=1, bnn_factor=1):
super(WithBNNLossCell, self).__init__(auto_prefix=False)
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')
self._backbone = backbone
self._loss_fn = loss_fn
self.dnn_factor = dnn_factor
self.bnn_factor = bnn_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 = 0
for i in range(len(self.kl_loss)):
kl_loss += self.kl_loss[i]()
loss = backbone_loss * self.dnn_factor + kl_loss * self.bnn_factor
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)
@property
def backbone_network(self):
"""
Returns the backbone network.
Returns:
Cell, the backbone network.
"""
return self._backbone