# 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.
# ============================================================================
"""Generate WithLossCell suitable for BNN."""
from .conv_variational import _ConvVariational
from .dense_variational import _DenseVariational
from ..transforms.bnn_loss.generate_kl_loss import gain_bnn_with_loss
__all__ = ['WithBNNLossCell']
class ClassWrap:
"""Decorator of WithBNNLossCell"""
def __init__(self, cls):
self._cls = cls
self.bnn_loss_file = None
self.__doc__ = cls.__doc__
self.__name__ = cls.__name__
self.__bases__ = cls.__bases__
def __call__(self, backbone, loss_fn, dnn_factor, bnn_factor):
obj = self._cls(backbone, loss_fn, dnn_factor, bnn_factor)
bnn_with_loss = obj()
self.bnn_loss_file = obj.bnn_loss_file
return bnn_with_loss
[docs]@ClassWrap
class WithBNNLossCell:
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:`()`.
Examples:
>>> net = Net()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
>>> net_with_criterion_object = WithBNNLossCell(net, loss_fn)
>>> net_with_criterion = net_with_criterion_object()
>>>
>>> batch_size = 2
>>> data = Tensor(np.ones([batch_size, 3, 64, 64]).astype(np.float32) * 0.01)
>>> label = Tensor(np.ones([batch_size, 1, 1, 1]).astype(np.int32))
>>>
>>> net_with_criterion(data, label)
"""
def __init__(self, backbone, loss_fn, 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')
self.backbone = backbone
self.loss_fn = loss_fn
self.dnn_factor = dnn_factor
self.bnn_factor = bnn_factor
self.bnn_loss_file = None
def _generate_loss_cell(self):
"""Generate WithBNNLossCell by ast."""
layer_count = self._kl_loss_count(self.backbone)
bnn_with_loss, self.bnn_loss_file = gain_bnn_with_loss(layer_count, self.backbone, self.loss_fn,
self.dnn_factor, self.bnn_factor)
return bnn_with_loss
def _kl_loss_count(self, net):
""" Calculate the number of Bayesian layers."""
count = 0
for (_, layer) in net.name_cells().items():
if isinstance(layer, (_DenseVariational, _ConvVariational)):
count += 1
else:
count += self._kl_loss_count(layer)
return count
def __call__(self):
return self._generate_loss_cell()