From 20fa03cbaefffa8afd0f49b53e664720287f14cb Mon Sep 17 00:00:00 2001 From: izayoi Date: Fri, 17 Feb 2023 17:07:20 +0800 Subject: [PATCH] =?UTF-8?q?=E6=A6=82=E7=8E=87=E7=BC=96=E7=A8=8Btoolbox?= =?UTF-8?q?=E5=BA=94=E7=94=A8=E6=8E=A5=E5=8F=A3=E5=88=A0=E9=99=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: izayoi --- ...bability.toolbox.UncertaintyEvaluation.rst | 39 -- ...robability.toolbox.VAEAnomalyDetection.rst | 46 --- .../nn/probability/toolbox/__init__.py | 22 -- .../probability/toolbox/anomaly_detection.py | 99 ----- .../toolbox/uncertainty_evaluation.py | 364 ------------------ .../probability/toolbox/test_uncertainty.py | 135 ------- 6 files changed, 705 deletions(-) delete mode 100644 docs/api/api_python/probability/mindspore.nn.probability.toolbox.UncertaintyEvaluation.rst delete mode 100644 docs/api/api_python/probability/mindspore.nn.probability.toolbox.VAEAnomalyDetection.rst delete mode 100644 mindspore/python/mindspore/nn/probability/toolbox/__init__.py delete mode 100644 mindspore/python/mindspore/nn/probability/toolbox/anomaly_detection.py delete mode 100644 mindspore/python/mindspore/nn/probability/toolbox/uncertainty_evaluation.py delete mode 100644 tests/st/probability/toolbox/test_uncertainty.py diff --git a/docs/api/api_python/probability/mindspore.nn.probability.toolbox.UncertaintyEvaluation.rst b/docs/api/api_python/probability/mindspore.nn.probability.toolbox.UncertaintyEvaluation.rst deleted file mode 100644 index 5a61bf24248..00000000000 --- a/docs/api/api_python/probability/mindspore.nn.probability.toolbox.UncertaintyEvaluation.rst +++ /dev/null @@ -1,39 +0,0 @@ -mindspore.nn.probability.toolbox.UncertaintyEvaluation -====================================================== - -.. py:class:: mindspore.nn.probability.toolbox.UncertaintyEvaluation(model, train_dataset, task_type, num_classes=None, epochs=1, epi_uncer_model_path=None, ale_uncer_model_path=None, save_model=False) - - 包含数据不确定性和模型不确定性的评估工具箱。 - - 参数： - - **model** (Cell) - 不确定性评估的模型。 - - **train_dataset** (Dataset) - 用于训练模型的数据集迭代器。 - - **task_type** (str) - 模型任务类型的选项。 - - regression：回归模型。 - - classification：分类模型。 - - **num_classes** (int) - 分类标签的数量。如果任务类型为分类，则必须设置；否则，它是不需要的。默认值：None。 - - **epochs** (int) - 数据的迭代总数。默认值：1。 - - **epi_uncer_model_path** (str) - 认知不确定性模型的保存或读取路径。默认值：None。 - - **ale_uncer_model_path** (str) - 任意不确定性模型的保存或读取路径。默认值：None。 - - **save_model** (bool) - 是否保存不确定性模型，如果为 true，`epi_uncer_model_path` 和 `ale_uncer_model_path` 不能为 None。 - 如果为 false，则从不确定性模型的路径中加载要评估的模型；如果未给出路径，则不会保存或加载不确定性模型。默认值：false。 - - .. py:method:: eval_aleatoric_uncertainty(eval_data) - - 评估推理结果的任意不确定性，也称为数据不确定性。 - - 参数： - - **eval_data** (Tensor) - 要评估的数据样本，shape 必须是 (N,C,H,W)。 - - 返回： - numpy.dtype，数据样本推断结果的任意不确定性。 - - .. py:method:: eval_epistemic_uncertainty(eval_data) - - 评估推理结果的认知不确定性，也称为模型不确定性。 - - 参数： - - **eval_data** (Tensor) - 要评估的数据样本，shape 必须是 (N,C,H,W)。 - - 返回： - numpy.dtype，数据样本推断结果的任意不确定性。 \ No newline at end of file diff --git a/docs/api/api_python/probability/mindspore.nn.probability.toolbox.VAEAnomalyDetection.rst b/docs/api/api_python/probability/mindspore.nn.probability.toolbox.VAEAnomalyDetection.rst deleted file mode 100644 index a74572669fc..00000000000 --- a/docs/api/api_python/probability/mindspore.nn.probability.toolbox.VAEAnomalyDetection.rst +++ /dev/null @@ -1,46 +0,0 @@ -mindspore.nn.probability.toolbox.VAEAnomalyDetection -==================================================== - -.. py:class:: mindspore.nn.probability.toolbox.VAEAnomalyDetection(encoder, decoder, hidden_size=400, latent_size=20) - - 使用 VAE 进行异常检测的工具箱。 - - 变分自动编码器（VAE）可用于无监督异常检测。异常分数是 sample_x 与重建 sample_x 之间的误差。如果分数高，则 X 大多是异常值。 - - 参数： - - **encoder** (Cell) - 定义为编码器的深度神经网络 (DNN) 模型。 - - **decoder** (Cell) - 定义为解码器的深度神经网络 (DNN) 模型。 - - **hidden_size** (int) - 编码器输出 Tensor 的大小。默认值：400。 - - **latent_size** (int) - 潜在空间的大小。默认值：20。 - - .. py:method:: predict_outlier(sample_x, threshold=100.0) - - 预测样本是否为异常值。 - - 参数： - - **sample_x** (Tensor) - 待预测的样本，shape 为 (N, C, H, W)。 - - **threshold** (float) - 异常值的阈值。默认值：100.0。 - - 返回： - bool，样本是否为异常值。 - - .. py:method:: predict_outlier_score(sample_x) - - 预测异常值分数。 - - 参数： - - **sample_x** (Tensor) - 待预测的样本，shape 为 (N, C, H, W)。 - - 返回： - float，样本的预测异常值分数。 - - .. py:method:: train(train_dataset, epochs=5) - - 训练 VAE 模型。 - - 参数： - - **train_dataset** (Dataset) - 用于训练模型的数据集迭代器。 - - **epochs** (int) - 数据的迭代总数。默认值：5。 - - 返回： - Cell，训练完的模型。 \ No newline at end of file diff --git a/mindspore/python/mindspore/nn/probability/toolbox/__init__.py b/mindspore/python/mindspore/nn/probability/toolbox/__init__.py deleted file mode 100644 index 867a00a7245..00000000000 --- a/mindspore/python/mindspore/nn/probability/toolbox/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -# 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. -# ============================================================================ -""" -Uncertainty toolbox. -""" - -from .uncertainty_evaluation import UncertaintyEvaluation -from .anomaly_detection import VAEAnomalyDetection - -__all__ = ['UncertaintyEvaluation', 'VAEAnomalyDetection'] diff --git a/mindspore/python/mindspore/nn/probability/toolbox/anomaly_detection.py b/mindspore/python/mindspore/nn/probability/toolbox/anomaly_detection.py deleted file mode 100644 index b58885d18c6..00000000000 --- a/mindspore/python/mindspore/nn/probability/toolbox/anomaly_detection.py +++ /dev/null @@ -1,99 +0,0 @@ -# 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. -# ============================================================================ -"""Toolbox for anomaly detection by using VAE.""" -import numpy as np - -from mindspore._checkparam import Validator -from mindspore.common.tensor import Tensor -from ..dpn import VAE -from ..infer import ELBO, SVI -from ...optim import Adam -from ...wrap.cell_wrapper import WithLossCell - - -class VAEAnomalyDetection: - r""" - Toolbox for anomaly detection by using VAE. - - Variational Auto-Encoder(VAE) can be used for Unsupervised Anomaly Detection. The anomaly score is the error - between the X and the reconstruction of X. If the score is high, the X is mostly outlier. - - Args: - encoder(Cell): The Deep Neural Network (DNN) model defined as encoder. - decoder(Cell): The DNN model defined as decoder. - hidden_size(int): The size of encoder's output tensor. Default: 400. - latent_size(int): The size of the latent space. Default: 20. - - Supported Platforms: - ``Ascend`` ``GPU`` - - """ - - def __init__(self, encoder, decoder, hidden_size=400, latent_size=20): - self.vae = VAE(encoder, decoder, hidden_size, latent_size) - - def train(self, train_dataset, epochs=5): - """ - Train the VAE model. - - Args: - train_dataset (Dataset): A dataset iterator to train model. - epochs (int): Total number of iterations on the data. Default: 5. - - Returns: - Cell, the trained model. - """ - net_loss = ELBO() - optimizer = Adam(params=self.vae.trainable_params(), learning_rate=0.001) - net_with_loss = WithLossCell(self.vae, net_loss) - vi = SVI(net_with_loss=net_with_loss, optimizer=optimizer) - self.vae = vi.run(train_dataset, epochs) - return self.vae - - def predict_outlier_score(self, sample_x): - """ - Predict the outlier score. - - Args: - sample_x (Tensor): The sample to be predicted, the shape is (N, C, H, W). - - Returns: - float, the predicted outlier score of the sample. - """ - if not isinstance(sample_x, Tensor): - raise TypeError("The sample_x must be Tensor type.") - reconstructed_sample = self.vae.reconstruct_sample(sample_x) - return self._calculate_euclidean_distance(sample_x.asnumpy(), reconstructed_sample.asnumpy()) - - def predict_outlier(self, sample_x, threshold=100.0): - """ - Predict whether the sample is an outlier. - - Args: - sample_x (Tensor): The sample to be predicted, the shape is (N, C, H, W). - threshold (float): the threshold of the outlier. Default: 100.0. - - Returns: - Bool, whether the sample is an outlier. - """ - threshold = Validator.check_positive_float(threshold) - score = self.predict_outlier_score(sample_x) - return score >= threshold - - def _calculate_euclidean_distance(self, sample_x, reconstructed_sample): - """ - Calculate the euclidean distance of the sample_x and reconstructed_sample. - """ - return np.sqrt(np.sum(np.square(sample_x - reconstructed_sample))) diff --git a/mindspore/python/mindspore/nn/probability/toolbox/uncertainty_evaluation.py b/mindspore/python/mindspore/nn/probability/toolbox/uncertainty_evaluation.py deleted file mode 100644 index 91ad1fe24f2..00000000000 --- a/mindspore/python/mindspore/nn/probability/toolbox/uncertainty_evaluation.py +++ /dev/null @@ -1,364 +0,0 @@ -# Copyright 2020-2021 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. -# ============================================================================ -"""Toolbox for Uncertainty Evaluation.""" -from copy import deepcopy - -import numpy as np -from mindspore._checkparam import Validator -from mindspore.ops import composite as C -from mindspore.ops import operations as P -from mindspore.train import Model -from mindspore.train.callback import LossMonitor, ModelCheckpoint, CheckpointConfig -from mindspore.train.serialization import load_checkpoint, load_param_into_net -from mindspore.common import dtype as mstype - -from ...cell import Cell -from ...layer.basic import Dense, Flatten, Dropout -from ...layer.container import SequentialCell -from ...layer.conv import Conv2d -from ...loss import SoftmaxCrossEntropyWithLogits, MSELoss -from ...metrics import Accuracy, MSE -from ...optim import Adam - - -class UncertaintyEvaluation: - r""" - Toolbox for Uncertainty Evaluation. - - Args: - model (Cell): The model for uncertainty evaluation. - train_dataset (Dataset): A dataset iterator to train model. - task_type (str): Option for the task types of model - - - regression: A regression model. - - classification: A classification model. - - num_classes (int): The number of labels of classification. - If the task type is classification, it must be set; otherwise, it is not needed. - Default: None. - epochs (int): Total number of iterations on the data. Default: 1. - epi_uncer_model_path (str): The save or read path of the epistemic uncertainty model. Default: None. - ale_uncer_model_path (str): The save or read path of the aleatoric uncertainty model. Default: None. - save_model (bool): Whether to save the uncertainty model or not, if true, the epi_uncer_model_path - and ale_uncer_model_path must not be None. If false, the model to evaluate will be loaded from - the the path of the uncertainty model; if the path is not given , it will not save or load the - uncertainty model. Default: False. - - Supported Platforms: - ``Ascend`` ``GPU`` - - Examples: - >>> network = LeNet() - >>> ds_train = create_dataset('workspace/mnist/train') # handle train data - >>> ds_eval = create_dataset('workspace/mnist/test') # handle test data - >>> evaluation = UncertaintyEvaluation(model=network, - ... train_dataset=ds_train, - ... task_type='classification', - ... num_classes=10, - ... epochs=1, - ... epi_uncer_model_path=None, - ... ale_uncer_model_path=None, - ... save_model=False) - >>> for eval_data in ds_eval.create_dict_iterator(output_numpy=True, num_epochs=1): - ... eval_data = Tensor(eval_data['image'], mstype.float32) - ... epistemic_uncertainty = evaluation.eval_epistemic_uncertainty(eval_data) - ... aleatoric_uncertainty = evaluation.eval_aleatoric_uncertainty(eval_data) - >>> output = epistemic_uncertainty.shape - >>> print(output) - (32, 10) - >>> output = aleatoric_uncertainty.shape - >>> print(output) - (32,) - """ - - def __init__(self, model, train_dataset, task_type, num_classes=None, epochs=1, - epi_uncer_model_path=None, ale_uncer_model_path=None, save_model=False): - self.epi_model = deepcopy(model) - self.ale_model = deepcopy(model) - self.epi_train_dataset = train_dataset - self.ale_train_dataset = deepcopy(train_dataset) - self.task_type = task_type - self.epochs = Validator.check_positive_int(epochs) - self.epi_uncer_model_path = epi_uncer_model_path - self.ale_uncer_model_path = ale_uncer_model_path - self.save_model = Validator.check_bool(save_model) - self.epi_uncer_model = None - self.ale_uncer_model = None - self.concat = P.Concat(axis=0) - self.sum = P.ReduceSum() - self.pow = P.Pow() - if not isinstance(model, Cell): - raise TypeError('The model must be Cell type.') - if task_type not in ('regression', 'classification'): - raise ValueError( - 'The task should be regression or classification.') - if task_type == 'classification': - self.num_classes = Validator.check_positive_int(num_classes) - else: - self.num_classes = num_classes - if save_model: - if epi_uncer_model_path is None or ale_uncer_model_path is None: - raise ValueError("If save_model is True, the epi_uncer_model_path and " - "ale_uncer_model_path should not be None.") - - def _get_epistemic_uncertainty_model(self): - """ - Get the model which can obtain the epistemic uncertainty. - """ - if self.epi_uncer_model is None: - self.epi_uncer_model = EpistemicUncertaintyModel(self.epi_model) - if self.epi_uncer_model.drop_count == 0 and self.epi_train_dataset is not None: - if self.task_type == 'classification': - net_loss = SoftmaxCrossEntropyWithLogits( - sparse=True, reduction="mean") - net_opt = Adam(self.epi_uncer_model.trainable_params()) - model = Model(self.epi_uncer_model, net_loss, - net_opt, metrics={"Accuracy": Accuracy()}) - else: - net_loss = MSELoss() - net_opt = Adam(self.epi_uncer_model.trainable_params()) - model = Model(self.epi_uncer_model, net_loss, - net_opt, metrics={"MSE": MSE()}) - if self.save_model: - config_ck = CheckpointConfig( - keep_checkpoint_max=self.epochs) - ckpoint_cb = ModelCheckpoint(prefix='checkpoint_epi_uncer_model', - directory=self.epi_uncer_model_path, - config=config_ck) - model.train(self.epochs, self.epi_train_dataset, dataset_sink_mode=False, - callbacks=[ckpoint_cb, LossMonitor()]) - elif self.epi_uncer_model_path is None: - model.train(self.epochs, self.epi_train_dataset, dataset_sink_mode=False, - callbacks=[LossMonitor()]) - else: - uncer_param_dict = load_checkpoint( - self.epi_uncer_model_path) - load_param_into_net(self.epi_uncer_model, uncer_param_dict) - - def _eval_epistemic_uncertainty(self, eval_data, mc=10): - """ - Evaluate the epistemic uncertainty of classification and regression models using MC dropout. - """ - self._get_epistemic_uncertainty_model() - self.epi_uncer_model.set_train(True) - outputs = [None] * mc - for i in range(mc): - pred = self.epi_uncer_model(eval_data) - outputs[i] = pred.asnumpy() - if self.task_type == 'classification': - outputs = np.stack(outputs, axis=2) - epi_uncertainty = outputs.var(axis=2) - else: - outputs = np.stack(outputs, axis=1) - epi_uncertainty = outputs.var(axis=1) - epi_uncertainty = np.array(epi_uncertainty) - return epi_uncertainty - - def _get_aleatoric_uncertainty_model(self): - """ - Get the model which can obtain the aleatoric uncertainty. - """ - if self.ale_train_dataset is None: - raise ValueError( - 'The train dataset should not be None when evaluating aleatoric uncertainty.') - if self.ale_uncer_model is None: - self.ale_uncer_model = AleatoricUncertaintyModel( - self.ale_model, self.num_classes, self.task_type) - net_loss = AleatoricLoss(self.task_type) - net_opt = Adam(self.ale_uncer_model.trainable_params()) - if self.task_type == 'classification': - model = Model(self.ale_uncer_model, net_loss, - net_opt, metrics={"Accuracy": Accuracy()}) - else: - model = Model(self.ale_uncer_model, net_loss, - net_opt, metrics={"MSE": MSE()}) - if self.save_model: - config_ck = CheckpointConfig(keep_checkpoint_max=self.epochs) - ckpoint_cb = ModelCheckpoint(prefix='checkpoint_ale_uncer_model', - directory=self.ale_uncer_model_path, - config=config_ck) - model.train(self.epochs, self.ale_train_dataset, dataset_sink_mode=False, - callbacks=[ckpoint_cb, LossMonitor()]) - elif self.ale_uncer_model_path is None: - model.train(self.epochs, self.ale_train_dataset, dataset_sink_mode=False, - callbacks=[LossMonitor()]) - else: - uncer_param_dict = load_checkpoint(self.ale_uncer_model_path) - load_param_into_net(self.ale_uncer_model, uncer_param_dict) - - def _eval_aleatoric_uncertainty(self, eval_data): - """ - Evaluate the aleatoric uncertainty of classification and regression models. - """ - self._get_aleatoric_uncertainty_model() - _, var = self.ale_uncer_model(eval_data) - ale_uncertainty = self.sum(self.pow(var, 2), 1) - ale_uncertainty = ale_uncertainty.asnumpy() - return ale_uncertainty - - def eval_epistemic_uncertainty(self, eval_data): - """ - Evaluate the epistemic uncertainty of inference results, which also called model uncertainty. - - Args: - eval_data (Tensor): The data samples to be evaluated, the shape must be (N,C,H,W). - - Returns: - numpy.dtype, the epistemic uncertainty of inference results of data samples. - """ - uncertainty = self._eval_epistemic_uncertainty(eval_data) - return uncertainty - - def eval_aleatoric_uncertainty(self, eval_data): - """ - Evaluate the aleatoric uncertainty of inference results, which also called data uncertainty. - - Args: - eval_data (Tensor): The data samples to be evaluated, the shape must be (N,C,H,W). - - Returns: - numpy.dtype, the aleatoric uncertainty of inference results of data samples. - """ - uncertainty = self._eval_aleatoric_uncertainty(eval_data) - return uncertainty - - -class EpistemicUncertaintyModel(Cell): - """ - Using dropout during training and eval time which is approximate bayesian inference. In this way, - we can obtain the epistemic uncertainty (also called model uncertainty). - - If the original model has Dropout layer, just use dropout when eval time, if not, add dropout layer - after Dense layer or Conv layer, then use dropout during train and eval time. - - See more details in `Dropout as a Bayesian Approximation: Representing Model uncertainty in Deep Learning - `_. - """ - - def __init__(self, epi_model): - super(EpistemicUncertaintyModel, self).__init__() - self.drop_count = 0 - if not self._make_epistemic(epi_model): - raise ValueError("The model has not Dense Layer or Convolution Layer, " - "it can not evaluate epistemic uncertainty so far.") - self.epi_model = self._make_epistemic(epi_model) - - def construct(self, x): - x = self.epi_model(x) - return x - - def _make_epistemic(self, epi_model, keep_prob=0.5): - """ - The dropout rate is set to 0.5 by default. - """ - for (name, layer) in epi_model.name_cells().items(): - if isinstance(layer, (Conv2d, Dense, Dropout)): - if isinstance(layer, Dropout): - self.drop_count += 1 - return epi_model - uncertainty_layer = layer - uncertainty_name = name - drop = Dropout(keep_prob=keep_prob) - bnn_drop = SequentialCell([uncertainty_layer, drop]) - setattr(epi_model, uncertainty_name, bnn_drop) - return epi_model - if self._make_epistemic(layer): - return epi_model - return None - - -class AleatoricUncertaintyModel(Cell): - """ - The aleatoric uncertainty (also called data uncertainty) is caused by input data, to obtain this - uncertainty, the loss function must be modified in order to add variance into loss. - - See more details in `What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? - `_. - """ - - def __init__(self, ale_model, num_classes, task): - super(AleatoricUncertaintyModel, self).__init__() - self.task = task - if task == 'classification': - self.ale_model = ale_model - self.var_layer = Dense(num_classes, num_classes) - else: - self.ale_model, self.var_layer, self.pred_layer = self._make_aleatoric( - ale_model) - - def construct(self, x): - if self.task == 'classification': - pred = self.ale_model(x) - var = self.var_layer(pred) - else: - x = self.ale_model(x) - pred = self.pred_layer(x) - var = self.var_layer(x) - return pred, var - - def _make_aleatoric(self, ale_model): - """ - In order to add variance into original loss, add var Layer after the original network. - """ - dense_layer = dense_name = None - for (name, layer) in ale_model.name_cells().items(): - if isinstance(layer, Dense): - dense_layer = layer - dense_name = name - if dense_layer is None: - raise ValueError("The model has not Dense Layer, " - "it can not evaluate aleatoric uncertainty so far.") - setattr(ale_model, dense_name, Flatten()) - var_layer = Dense(dense_layer.in_channels, dense_layer.out_channels) - return ale_model, var_layer, dense_layer - - -class AleatoricLoss(Cell): - """ - The loss function of aleatoric model, different modification methods are adopted for - classification and regression. - """ - - def __init__(self, task): - super(AleatoricLoss, self).__init__() - self.task = task - if self.task == 'classification': - self.sum = P.ReduceSum() - self.exp = P.Exp() - self.normal = C.normal - self.to_tensor = P.ScalarToTensor() - self.entropy = SoftmaxCrossEntropyWithLogits( - sparse=True, reduction="mean") - else: - self.mean = P.ReduceMean() - self.exp = P.Exp() - self.pow = P.Pow() - - def construct(self, data_pred, y): - y_pred, var = data_pred - if self.task == 'classification': - sample_times = 10 - epsilon = self.normal((1, sample_times), self.to_tensor(0.0, mstype.float32), - self.to_tensor(1.0, mstype.float32), 0) - total_loss = 0 - for i in range(sample_times): - y_pred_i = y_pred + epsilon[0][i] * var - loss = self.entropy(y_pred_i, y) - total_loss += loss - avg_loss = total_loss / sample_times - return avg_loss - loss = self.mean(0.5 * self.exp(-var) * - self.pow(y - y_pred, 2) + 0.5 * var) - return loss diff --git a/tests/st/probability/toolbox/test_uncertainty.py b/tests/st/probability/toolbox/test_uncertainty.py deleted file mode 100644 index b5e0cb1ff6c..00000000000 --- a/tests/st/probability/toolbox/test_uncertainty.py +++ /dev/null @@ -1,135 +0,0 @@ -# Copyright 2020-2022 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. -# ============================================================================ -""" test uncertainty toolbox """ -import mindspore.dataset as ds -import mindspore.dataset.transforms as C -import mindspore.dataset.vision as CV -import mindspore.nn as nn -from mindspore import context, Tensor -from mindspore import dtype as mstype -from mindspore.common.initializer import TruncatedNormal -from mindspore.dataset.vision import Inter -from mindspore.nn.probability.toolbox.uncertainty_evaluation import UncertaintyEvaluation -from mindspore.train import load_checkpoint, load_param_into_net - -context.set_context(mode=context.GRAPH_MODE, device_target="GPU") - - -def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): - """weight initial for conv layer""" - weight = weight_variable() - return nn.Conv2d(in_channels, out_channels, - kernel_size=kernel_size, stride=stride, padding=padding, - weight_init=weight, has_bias=False, pad_mode="valid") - - -def fc_with_initialize(input_channels, out_channels): - """weight initial for fc layer""" - weight = weight_variable() - bias = weight_variable() - return nn.Dense(input_channels, out_channels, weight, bias) - - -def weight_variable(): - """weight initial""" - return TruncatedNormal(0.02) - - -class LeNet5(nn.Cell): - def __init__(self, num_class=10, channel=1): - super(LeNet5, self).__init__() - self.num_class = num_class - self.conv1 = conv(channel, 6, 5) - self.conv2 = conv(6, 16, 5) - self.fc1 = fc_with_initialize(16 * 5 * 5, 120) - self.fc2 = fc_with_initialize(120, 84) - self.fc3 = fc_with_initialize(84, self.num_class) - self.relu = nn.ReLU() - self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) - self.flatten = nn.Flatten() - - def construct(self, x): - x = self.conv1(x) - x = self.relu(x) - x = self.max_pool2d(x) - x = self.conv2(x) - x = self.relu(x) - x = self.max_pool2d(x) - x = self.flatten(x) - x = self.fc1(x) - x = self.relu(x) - x = self.fc2(x) - x = self.relu(x) - x = self.fc3(x) - return x - - -def create_dataset(data_path, batch_size=32, repeat_size=1, - num_parallel_workers=1): - """ - create dataset for train or test - """ - # define dataset - mnist_ds = ds.MnistDataset(data_path) - - resize_height, resize_width = 32, 32 - rescale = 1.0 / 255.0 - shift = 0.0 - rescale_nml = 1 / 0.3081 - shift_nml = -1 * 0.1307 / 0.3081 - - # define map operations - resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode - rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) - rescale_op = CV.Rescale(rescale, shift) - hwc2chw_op = CV.HWC2CHW() - type_cast_op = C.TypeCast(mstype.int32) - - # apply map operations on images - mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers) - mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers) - mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers) - mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers) - mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers) - - # apply DatasetOps - buffer_size = 10000 - 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=1, - epi_uncer_model_path=None, - ale_uncer_model_path=None, - save_model=False) - for eval_data in ds_eval.create_dict_iterator(output_numpy=True, num_epochs=1): - eval_data = Tensor(eval_data['image'], mstype.float32) - epistemic_uncertainty = evaluation.eval_epistemic_uncertainty(eval_data) - aleatoric_uncertainty = evaluation.eval_aleatoric_uncertainty(eval_data)