# Copyright 2019 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.
""" Util for MindArmour. """
import numpy as np
from mindspore import Tensor
from mindspore.nn import Cell
from mindspore.ops.composite import GradOperation
from mindarmour.utils.logger import LogUtil
LOGGER = LogUtil.get_instance()
TAG = 'util'
def jacobian_matrix(grad_wrap_net, inputs, num_classes):
"""
Calculate the Jacobian matrix for inputs.
Args:
grad_wrap_net (Cell): A network wrapped by GradWrap.
inputs (numpy.ndarray): Input samples.
num_classes (int): Number of labels of model output.
Returns:
numpy.ndarray, the Jacobian matrix of inputs. (labels, batch_size, ...)
Raises:
ValueError: If grad_wrap_net is not a instance of class `GradWrap`.
"""
if not isinstance(grad_wrap_net, GradWrap):
msg = 'grad_wrap_net be and instance of class `GradWrap`.'
LOGGER.error(TAG, msg)
raise ValueError(msg)
grad_wrap_net.set_train()
grads_matrix = []
for idx in range(num_classes):
sens = np.zeros((inputs.shape[0], num_classes)).astype(np.float32)
sens[:, idx] = 1.0
grads = grad_wrap_net(Tensor(inputs), Tensor(sens))
grads_matrix.append(grads.asnumpy())
return np.asarray(grads_matrix)
class WithLossCell(Cell):
"""
Wrap the network with loss function.
Args:
network (Cell): The target network to wrap.
loss_fn (Function): The loss function is used for computing loss.
Examples:
>>> data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32)*0.01)
>>> label = Tensor(np.ones([1, 10]).astype(np.float32))
>>> net = NET()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
>>> loss_net = WithLossCell(net, loss_fn)
>>> loss_out = loss_net(data, label)
"""
def __init__(self, network, loss_fn):
super(WithLossCell, self).__init__()
self._network = network
self._loss_fn = loss_fn
def construct(self, data, label):
"""
Compute loss based on the wrapped loss cell.
Args:
data (Tensor): Tensor data to train.
label (Tensor): Tensor label data.
Returns:
Tensor, compute result.
"""
out = self._network(data)
return self._loss_fn(out, label)
[docs]class GradWrapWithLoss(Cell):
"""
Construct a network to compute the gradient of loss function in input space
and weighted by `weight`.
Args:
network (Cell): The target network to wrap.
Examples:
>>> data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32)*0.01)
>>> labels = Tensor(np.ones([1, 10]).astype(np.float32))
>>> net = NET()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
>>> loss_net = WithLossCell(net, loss_fn)
>>> grad_all = GradWrapWithLoss(loss_net)
>>> out_grad = grad_all(data, labels)
"""
def __init__(self, network):
super(GradWrapWithLoss, self).__init__()
self._grad_all = GradOperation(name="get_all",
get_all=True,
sens_param=False)
self._network = network
[docs] def construct(self, inputs, labels):
"""
Compute gradient of `inputs` with labels and weight.
Args:
inputs (Tensor): Inputs of network.
labels (Tensor): Labels of inputs.
Returns:
Tensor, gradient matrix.
"""
gout = self._grad_all(self._network)(inputs, labels)
return gout[0]
[docs]class GradWrap(Cell):
"""
Construct a network to compute the gradient of network outputs in input
space and weighted by `weight`, expressed as a jacobian matrix.
Args:
network (Cell): The target network to wrap.
Examples:
>>> data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32)*0.01)
>>> label = Tensor(np.ones([1, 10]).astype(np.float32))
>>> num_classes = 10
>>> sens = np.zeros((data.shape[0], num_classes)).astype(np.float32)
>>> sens[:, 1] = 1.0
>>> net = NET()
>>> wrap_net = GradWrap(net)
>>> wrap_net(data, Tensor(sens))
"""
def __init__(self, network):
super(GradWrap, self).__init__()
self.grad = GradOperation(name="grad", get_all=False,
sens_param=True)
self.network = network
[docs] def construct(self, inputs, weight):
"""
Compute jacobian matrix.
Args:
inputs (Tensor): Inputs of network.
weight (Tensor): Weight of each gradient, `weight` has the same
shape with labels.
Returns:
Tensor, Jacobian matrix.
"""
gout = self.grad(self.network)(inputs, weight)
return gout