add uncertainty toolbox
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zhangxinfeng3
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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Uncertainty toolbox.
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"""
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from .uncertainty_evaluation import UncertaintyEvaluation
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__all__ = ['UncertaintyEvaluation']
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Toolbox for Uncertainty Evaluation."""
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import numpy as np
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from mindspore._checkparam import check_int_positive
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from mindspore.train import Model
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from mindspore.train.callback import LossMonitor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from ...cell import Cell
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from ...layer.basic import Dense, Flatten, Dropout
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from ...layer.conv import Conv2d
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from ...layer.container import SequentialCell
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from ...loss import SoftmaxCrossEntropyWithLogits, MSELoss
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from ...metrics import Accuracy, MSE
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from ...optim import Adam
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class UncertaintyEvaluation:
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r"""
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Toolbox for Uncertainty Evaluation.
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Args:
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model (Cell): The model for uncertainty evaluation.
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train_dataset (Dataset): A dataset iterator.
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task_type (str): Option for the task types of model
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- regression: A regression model.
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- classification: A classification model.
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num_classes (int): The number of labels of classification.
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If the task type is classification, it must be set; if not classification, it need not to be set.
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Default: None.
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epochs (int): Total number of iterations on the data. Default: None.
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uncertainty_model_path (str): The save or read path of the uncertainty model.
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Examples:
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>>> network = LeNet()
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>>> param_dict = load_checkpoint('checkpoint_lenet.ckpt')
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>>> load_param_into_net(network, param_dict)
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>>> ds_train = create_dataset('workspace/mnist/train')
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>>> evaluation = UncertaintyEvaluation(model=network,
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>>> train_dataset=ds_train,
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>>> task_type='classification',
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>>> num_classes=10,
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>>> epochs=5,
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>>> uncertainty_model_path=None)
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>>> epistemic_uncertainty = evaluation.eval_epistemic(eval_data)
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>>> aleatoric_uncertainty = evaluation.eval_aleatoric(eval_data)
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"""
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def __init__(self, model, train_dataset, task_type, num_classes=None, epochs=None,
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uncertainty_model_path=None):
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self.model = model
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self.train_dataset = train_dataset
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self.task_type = task_type
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self.num_classes = check_int_positive(num_classes)
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self.epochs = epochs
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self.uncer_model_path = uncertainty_model_path
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self.uncer_model = None
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self.concat = P.Concat(axis=0)
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self.sum = P.ReduceSum()
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self.pow = P.Pow()
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if self.task_type not in ('regression', 'classification'):
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raise ValueError('The task should be regression or classification.')
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if self.task_type == 'classification':
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if self.num_classes is None:
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raise ValueError("Classification task needs to input labels.")
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def _uncertainty_normalize(self, data):
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area = np.max(data) - np.min(data)
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return (data - np.min(data)) / area
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def _get_epistemic_uncertainty_model(self):
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"""
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Get the model which can obtain the epistemic uncertainty.
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"""
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if self.uncer_model and self.uncer_model_path is None:
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self.uncer_model = EpistemicUncertaintyModel(self.model)
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if self.uncer_model.drop_count == 0:
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if self.task_type == 'classification':
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net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
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net_opt = Adam(self.uncer_model.trainable_params())
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model = Model(self.uncer_model, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
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else:
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net_loss = MSELoss()
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net_opt = Adam(self.uncer_model.trainable_params())
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model = Model(self.uncer_model, net_loss, net_opt, metrics={"MSE": MSE()})
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model.train(self.epochs, self.train_dataset, callbacks=[LossMonitor()])
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elif self.uncer_model is None:
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uncer_param_dict = load_checkpoint(self.uncer_model_path)
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load_param_into_net(self.uncer_model, uncer_param_dict)
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def _eval_epistemic_uncertainty(self, eval_data, mc=10):
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"""
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Evaluate the epistemic uncertainty of classification and regression models using MC dropout.
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"""
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self._get_epistemic_uncertainty_model()
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self.uncer_model.set_train(True)
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outputs = [None] * mc
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for i in range(mc):
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pred = self.uncer_model(eval_data)
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outputs[i] = pred.asnumpy()
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if self.task_type == 'classification':
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outputs = np.stack(outputs, axis=2)
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epi_uncertainty = outputs.var(axis=2)
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else:
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outputs = np.stack(outputs, axis=1)
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epi_uncertainty = outputs.var(axis=1)
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epi_uncertainty = self._uncertainty_normalize(np.array(epi_uncertainty))
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return epi_uncertainty
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def _get_aleatoric_uncertainty_model(self):
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"""
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Get the model which can obtain the aleatoric uncertainty.
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"""
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if self.uncer_model and self.uncer_model_path is None:
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self.uncer_model = AleatoricUncertaintyModel(self.model, self.num_classes, self.task_type)
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net_loss = AleatoricLoss(self.task_type)
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net_opt = Adam(self.uncer_model.trainable_params())
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if self.task_type == 'classification':
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model = Model(self.uncer_model, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
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else:
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model = Model(self.uncer_model, net_loss, net_opt, metrics={"MSE": MSE()})
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model.train(self.epochs, self.train_dataset, callbacks=[LossMonitor()])
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elif self.uncer_model is None:
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uncer_param_dict = load_checkpoint(self.uncer_model_path)
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load_param_into_net(self.uncer_model, uncer_param_dict)
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def _eval_aleatoric_uncertainty(self, eval_data):
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"""
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Evaluate the aleatoric uncertainty of classification and regression models.
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"""
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self._get_aleatoric_uncertainty_model()
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_, var = self.uncer_model(eval_data)
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ale_uncertainty = self.sum(self.pow(var, 2), 1)
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ale_uncertainty = self._uncertainty_normalize(ale_uncertainty.asnumpy())
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return ale_uncertainty
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def eval_epistemic(self, eval_data):
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"""
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Evaluate the epistemic uncertainty of inference results, which also called model uncertainty.
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Args:
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eval_data (Tensor): The data samples to be evaluated, the shape should be (N,C,H,W).
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Returns:
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numpy.dtype, the epistemic uncertainty of inference results of data samples.
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"""
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uncertainty = self._eval_aleatoric_uncertainty(eval_data)
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return uncertainty
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def eval_aleatoric(self, eval_data):
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"""
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Evaluate the aleatoric uncertainty of inference results, which also called data uncertainty.
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Args:
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eval_data (Tensor): The data samples to be evaluated, the shape should be (N,C,H,W).
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Returns:
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numpy.dtype, the aleatoric uncertainty of inference results of data samples.
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"""
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uncertainty = self._eval_epistemic_uncertainty(eval_data)
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return uncertainty
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class EpistemicUncertaintyModel(Cell):
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"""
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Using dropout during training and eval time which is approximate bayesian inference. In this way,
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we can obtain the epistemic uncertainty (also called model uncertainty).
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If the original model has Dropout layer, just use dropout when eval time, if not, add dropout layer
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after Dense layer or Conv layer, then use dropout during train and eval time.
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See more details in `Dropout as a Bayesian Approximation: Representing Model uncertainty in Deep Learning
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<https://arxiv.org/abs/1506.02142>`.
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"""
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def __init__(self, model):
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super(EpistemicUncertaintyModel, self).__init__()
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self.drop_count = 0
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self.model = self._make_epistemic(model)
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def construct(self, x):
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x = self.model(x)
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return x
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def _make_epistemic(self, model, dropout_rate=0.5):
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"""
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The dropout rate is set to 0.5 by default.
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"""
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for (name, layer) in model.name_cells().items():
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if isinstance(layer, Dropout):
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self.drop_count += 1
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return model
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for (name, layer) in model.name_cells().items():
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if isinstance(layer, (Conv2d, Dense)):
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uncertainty_layer = layer
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uncertainty_name = name
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drop = Dropout(keep_prob=dropout_rate)
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bnn_drop = SequentialCell([uncertainty_layer, drop])
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setattr(model, uncertainty_name, bnn_drop)
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return model
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raise ValueError("The model has not Dense Layer or Convolution Layer, "
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"it can not evaluate epistemic uncertainty so far.")
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class AleatoricUncertaintyModel(Cell):
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"""
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The aleatoric uncertainty (also called data uncertainty) is caused by input data, to obtain this
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uncertainty, the loss function should be modified in order to add variance into loss.
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See more details in `What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
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<https://arxiv.org/abs/1703.04977>`.
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"""
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def __init__(self, model, labels, task):
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super(AleatoricUncertaintyModel, self).__init__()
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self.task = task
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if task == 'classification':
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self.model = model
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self.var_layer = Dense(labels, labels)
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else:
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self.model, self.var_layer, self.pred_layer = self._make_aleatoric(model)
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def construct(self, x):
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if self.task == 'classification':
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pred = self.model(x)
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var = self.var_layer(pred)
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else:
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x = self.model(x)
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pred = self.pred_layer(x)
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var = self.var_layer(x)
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return pred, var
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def _make_aleatoric(self, model):
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"""
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In order to add variance into original loss, add var Layer after the original network.
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"""
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dense_layer = dense_name = None
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for (name, layer) in model.name_cells().items():
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if isinstance(layer, Dense):
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dense_layer = layer
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dense_name = name
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if dense_layer is None:
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raise ValueError("The model has not Dense Layer, "
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"it can not evaluate aleatoric uncertainty so far.")
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setattr(model, dense_name, Flatten())
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var_layer = Dense(dense_layer.in_channels, dense_layer.out_channels)
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return model, var_layer, dense_layer
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class AleatoricLoss(Cell):
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"""
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The loss function of aleatoric model, different modification methods are adopted for
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classification and regression.
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"""
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def __init__(self, task):
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super(AleatoricLoss, self).__init__()
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self.task = task
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if self.task == 'classification':
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self.sum = P.ReduceSum()
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self.exp = P.Exp()
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self.normal = C.normal
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self.to_tensor = P.ScalarToArray()
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self.entropy = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
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else:
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self.mean = P.ReduceMean()
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self.exp = P.Exp()
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self.pow = P.Pow()
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def construct(self, data_pred, y):
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y_pred, var = data_pred
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if self.task == 'classification':
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sample_times = 10
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epsilon = self.normal((1, sample_times), self.to_tensor(0.0), self.to_tensor(1.0), 0)
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total_loss = 0
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for i in range(sample_times):
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y_pred_i = y_pred + epsilon[0][i] * var
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loss = self.entropy(y_pred_i, y)
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total_loss += loss
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avg_loss = total_loss / sample_times
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return avg_loss
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loss = self.mean(0.5 * self.exp(-var) * self.pow(y - y_pred, 2) + 0.5 * var)
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return loss
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test uncertainty toolbox """
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.c_transforms as C
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import mindspore.dataset.transforms.vision.c_transforms as CV
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import mindspore.nn as nn
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from mindspore import context, Tensor
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from mindspore.common import dtype as mstype
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from mindspore.common.initializer import TruncatedNormal
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from mindspore.dataset.transforms.vision import Inter
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from mindspore.nn.probability.toolbox.uncertainty_evaluation import UncertaintyEvaluation
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
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"""weight initial for conv layer"""
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weight = weight_variable()
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding,
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weight_init=weight, has_bias=False, pad_mode="valid")
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def fc_with_initialize(input_channels, out_channels):
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"""weight initial for fc layer"""
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weight = weight_variable()
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bias = weight_variable()
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return nn.Dense(input_channels, out_channels, weight, bias)
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def weight_variable():
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"""weight initial"""
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return TruncatedNormal(0.02)
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class LeNet5(nn.Cell):
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def __init__(self, num_class=10, channel=1):
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super(LeNet5, self).__init__()
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self.num_class = num_class
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self.conv1 = conv(channel, 6, 5)
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self.conv2 = conv(6, 16, 5)
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self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
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self.fc2 = fc_with_initialize(120, 84)
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self.fc3 = fc_with_initialize(84, self.num_class)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = nn.Flatten()
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def construct(self, x):
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x = self.conv1(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.relu(x)
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x = self.fc3(x)
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return x
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def create_dataset(data_path, batch_size=32, repeat_size=1,
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num_parallel_workers=1):
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"""
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create dataset for train or test
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"""
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# define dataset
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mnist_ds = ds.MnistDataset(data_path)
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resize_height, resize_width = 32, 32
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rescale = 1.0 / 255.0
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shift = 0.0
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rescale_nml = 1 / 0.3081
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shift_nml = -1 * 0.1307 / 0.3081
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# define map operations
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resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
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rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
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rescale_op = CV.Rescale(rescale, shift)
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hwc2chw_op = CV.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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# apply map operations on images
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mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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buffer_size = 10000
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mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script
|
||||
mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
|
||||
mnist_ds = mnist_ds.repeat(repeat_size)
|
||||
|
||||
return mnist_ds
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# get trained model
|
||||
network = LeNet5()
|
||||
param_dict = load_checkpoint('checkpoint_lenet.ckpt')
|
||||
load_param_into_net(network, param_dict)
|
||||
# get train and eval dataset
|
||||
ds_train = create_dataset('workspace/mnist/train')
|
||||
ds_eval = create_dataset('workspace/mnist/test')
|
||||
evaluation = UncertaintyEvaluation(model=network,
|
||||
train_dataset=ds_train,
|
||||
task_type='classification',
|
||||
num_classes=10,
|
||||
epochs=5,
|
||||
uncertainty_model_path=None)
|
||||
for eval_data in ds_eval.create_dict_iterator():
|
||||
eval_data = Tensor(eval_data['image'], mstype.float32)
|
||||
epistemic_uncertainty = evaluation.eval_epistemic(eval_data)
|
||||
aleatoric_uncertainty = evaluation.eval_aleatoric(eval_data)
|
||||
Loading…
Reference in New Issue