forked from mindspore-Ecosystem/mindspore
概率编程toolbox应用接口删除
Signed-off-by: izayoi <dingyahao@huawei.com>
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mindspore.nn.probability.toolbox.UncertaintyEvaluation
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======================================================
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.. 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)
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包含数据不确定性和模型不确定性的评估工具箱。
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参数：
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- **model** (Cell) - 不确定性评估的模型。
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- **train_dataset** (Dataset) - 用于训练模型的数据集迭代器。
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- **task_type** (str) - 模型任务类型的选项。
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- regression：回归模型。
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- classification：分类模型。
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- **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。
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- **epochs** (int) - 数据的迭代总数。默认值：1。
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- **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。
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- **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。
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- **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。
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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。
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.. py:method:: eval_aleatoric_uncertainty(eval_data)
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评估推理结果的任意不确定性，也称为数据不确定性。
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参数：
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- **eval_data** (Tensor) - 要评估的数据样本，shape 必须是 (N,C,H,W)。
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返回：
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numpy.dtype，数据样本推断结果的任意不确定性。
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.. py:method:: eval_epistemic_uncertainty(eval_data)
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评估推理结果的认知不确定性，也称为模型不确定性。
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参数：
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- **eval_data** (Tensor) - 要评估的数据样本，shape 必须是 (N,C,H,W)。
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返回：
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numpy.dtype，数据样本推断结果的任意不确定性。
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mindspore.nn.probability.toolbox.VAEAnomalyDetection
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====================================================
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.. py:class:: mindspore.nn.probability.toolbox.VAEAnomalyDetection(encoder, decoder, hidden_size=400, latent_size=20)
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使用 VAE 进行异常检测的工具箱。
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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。
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参数：
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- **encoder** (Cell) - 定义为编码器的深度神经网络 (DNN) 模型。
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- **decoder** (Cell) - 定义为解码器的深度神经网络 (DNN) 模型。
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- **hidden_size** (int) - 编码器输出 Tensor 的大小。默认值：400。
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- **latent_size** (int) - 潜在空间的大小。默认值：20。
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.. py:method:: predict_outlier(sample_x, threshold=100.0)
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预测样本是否为异常值。
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参数：
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- **sample_x** (Tensor) - 待预测的样本，shape 为 (N, C, H, W)。
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- **threshold** (float) - 异常值的阈值。默认值：100.0。
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返回：
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bool，样本是否为异常值。
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.. py:method:: predict_outlier_score(sample_x)
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预测异常值分数。
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参数：
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- **sample_x** (Tensor) - 待预测的样本，shape 为 (N, C, H, W)。
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返回：
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float，样本的预测异常值分数。
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.. py:method:: train(train_dataset, epochs=5)
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训练 VAE 模型。
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参数：
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- **train_dataset** (Dataset) - 用于训练模型的数据集迭代器。
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- **epochs** (int) - 数据的迭代总数。默认值：5。
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返回：
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Cell，训练完的模型。
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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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from .anomaly_detection import VAEAnomalyDetection
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__all__ = ['UncertaintyEvaluation', 'VAEAnomalyDetection']
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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 anomaly detection by using VAE."""
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import numpy as np
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from mindspore._checkparam import Validator
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from mindspore.common.tensor import Tensor
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from ..dpn import VAE
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from ..infer import ELBO, SVI
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from ...optim import Adam
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from ...wrap.cell_wrapper import WithLossCell
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class VAEAnomalyDetection:
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r"""
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Toolbox for anomaly detection by using VAE.
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Variational Auto-Encoder(VAE) can be used for Unsupervised Anomaly Detection. The anomaly score is the error
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between the X and the reconstruction of X. If the score is high, the X is mostly outlier.
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Args:
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encoder(Cell): The Deep Neural Network (DNN) model defined as encoder.
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decoder(Cell): The DNN model defined as decoder.
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hidden_size(int): The size of encoder's output tensor. Default: 400.
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latent_size(int): The size of the latent space. Default: 20.
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Supported Platforms:
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``Ascend`` ``GPU``
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"""
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def __init__(self, encoder, decoder, hidden_size=400, latent_size=20):
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self.vae = VAE(encoder, decoder, hidden_size, latent_size)
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def train(self, train_dataset, epochs=5):
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"""
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Train the VAE model.
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Args:
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train_dataset (Dataset): A dataset iterator to train model.
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epochs (int): Total number of iterations on the data. Default: 5.
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Returns:
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Cell, the trained model.
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"""
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net_loss = ELBO()
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optimizer = Adam(params=self.vae.trainable_params(), learning_rate=0.001)
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net_with_loss = WithLossCell(self.vae, net_loss)
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vi = SVI(net_with_loss=net_with_loss, optimizer=optimizer)
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self.vae = vi.run(train_dataset, epochs)
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return self.vae
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def predict_outlier_score(self, sample_x):
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"""
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Predict the outlier score.
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Args:
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sample_x (Tensor): The sample to be predicted, the shape is (N, C, H, W).
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Returns:
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float, the predicted outlier score of the sample.
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"""
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if not isinstance(sample_x, Tensor):
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raise TypeError("The sample_x must be Tensor type.")
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reconstructed_sample = self.vae.reconstruct_sample(sample_x)
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return self._calculate_euclidean_distance(sample_x.asnumpy(), reconstructed_sample.asnumpy())
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def predict_outlier(self, sample_x, threshold=100.0):
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"""
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Predict whether the sample is an outlier.
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Args:
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sample_x (Tensor): The sample to be predicted, the shape is (N, C, H, W).
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threshold (float): the threshold of the outlier. Default: 100.0.
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Returns:
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Bool, whether the sample is an outlier.
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"""
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threshold = Validator.check_positive_float(threshold)
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score = self.predict_outlier_score(sample_x)
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return score >= threshold
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def _calculate_euclidean_distance(self, sample_x, reconstructed_sample):
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"""
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Calculate the euclidean distance of the sample_x and reconstructed_sample.
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"""
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return np.sqrt(np.sum(np.square(sample_x - reconstructed_sample)))
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# Copyright 2020-2021 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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from copy import deepcopy
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import numpy as np
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from mindspore._checkparam import Validator
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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, ModelCheckpoint, CheckpointConfig
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.common import dtype as mstype
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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.container import SequentialCell
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from ...layer.conv import Conv2d
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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 to train model.
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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; otherwise, it is not needed.
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Default: None.
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epochs (int): Total number of iterations on the data. Default: 1.
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epi_uncer_model_path (str): The save or read path of the epistemic uncertainty model. Default: None.
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ale_uncer_model_path (str): The save or read path of the aleatoric uncertainty model. Default: None.
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save_model (bool): Whether to save the uncertainty model or not, if true, the epi_uncer_model_path
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and ale_uncer_model_path must not be None. If false, the model to evaluate will be loaded from
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the the path of the uncertainty model; if the path is not given , it will not save or load the
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uncertainty model. Default: False.
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Supported Platforms:
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``Ascend`` ``GPU``
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Examples:
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>>> network = LeNet()
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>>> ds_train = create_dataset('workspace/mnist/train') # handle train data
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>>> ds_eval = create_dataset('workspace/mnist/test') # handle test data
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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=1,
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... epi_uncer_model_path=None,
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... ale_uncer_model_path=None,
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... save_model=False)
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>>> for eval_data in ds_eval.create_dict_iterator(output_numpy=True, num_epochs=1):
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... eval_data = Tensor(eval_data['image'], mstype.float32)
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... epistemic_uncertainty = evaluation.eval_epistemic_uncertainty(eval_data)
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... aleatoric_uncertainty = evaluation.eval_aleatoric_uncertainty(eval_data)
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>>> output = epistemic_uncertainty.shape
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>>> print(output)
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(32, 10)
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>>> output = aleatoric_uncertainty.shape
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>>> print(output)
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(32,)
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"""
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def __init__(self, model, train_dataset, task_type, num_classes=None, epochs=1,
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epi_uncer_model_path=None, ale_uncer_model_path=None, save_model=False):
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self.epi_model = deepcopy(model)
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self.ale_model = deepcopy(model)
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self.epi_train_dataset = train_dataset
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self.ale_train_dataset = deepcopy(train_dataset)
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self.task_type = task_type
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self.epochs = Validator.check_positive_int(epochs)
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self.epi_uncer_model_path = epi_uncer_model_path
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self.ale_uncer_model_path = ale_uncer_model_path
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self.save_model = Validator.check_bool(save_model)
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self.epi_uncer_model = None
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self.ale_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 not isinstance(model, Cell):
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raise TypeError('The model must be Cell type.')
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if task_type not in ('regression', 'classification'):
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raise ValueError(
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'The task should be regression or classification.')
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if task_type == 'classification':
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self.num_classes = Validator.check_positive_int(num_classes)
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else:
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self.num_classes = num_classes
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if save_model:
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if epi_uncer_model_path is None or ale_uncer_model_path is None:
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raise ValueError("If save_model is True, the epi_uncer_model_path and "
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"ale_uncer_model_path should not be None.")
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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.epi_uncer_model is None:
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self.epi_uncer_model = EpistemicUncertaintyModel(self.epi_model)
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if self.epi_uncer_model.drop_count == 0 and self.epi_train_dataset is not None:
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if self.task_type == 'classification':
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net_loss = SoftmaxCrossEntropyWithLogits(
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sparse=True, reduction="mean")
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net_opt = Adam(self.epi_uncer_model.trainable_params())
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model = Model(self.epi_uncer_model, net_loss,
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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.epi_uncer_model.trainable_params())
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model = Model(self.epi_uncer_model, net_loss,
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net_opt, metrics={"MSE": MSE()})
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if self.save_model:
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config_ck = CheckpointConfig(
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keep_checkpoint_max=self.epochs)
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ckpoint_cb = ModelCheckpoint(prefix='checkpoint_epi_uncer_model',
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directory=self.epi_uncer_model_path,
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config=config_ck)
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model.train(self.epochs, self.epi_train_dataset, dataset_sink_mode=False,
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callbacks=[ckpoint_cb, LossMonitor()])
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elif self.epi_uncer_model_path is None:
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model.train(self.epochs, self.epi_train_dataset, dataset_sink_mode=False,
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callbacks=[LossMonitor()])
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else:
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uncer_param_dict = load_checkpoint(
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self.epi_uncer_model_path)
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load_param_into_net(self.epi_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.epi_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.epi_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 = 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.ale_train_dataset is None:
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raise ValueError(
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'The train dataset should not be None when evaluating aleatoric uncertainty.')
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if self.ale_uncer_model is None:
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self.ale_uncer_model = AleatoricUncertaintyModel(
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self.ale_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.ale_uncer_model.trainable_params())
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if self.task_type == 'classification':
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model = Model(self.ale_uncer_model, net_loss,
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net_opt, metrics={"Accuracy": Accuracy()})
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else:
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model = Model(self.ale_uncer_model, net_loss,
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net_opt, metrics={"MSE": MSE()})
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if self.save_model:
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config_ck = CheckpointConfig(keep_checkpoint_max=self.epochs)
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ckpoint_cb = ModelCheckpoint(prefix='checkpoint_ale_uncer_model',
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directory=self.ale_uncer_model_path,
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config=config_ck)
|
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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
|
||||
<https://arxiv.org/abs/1506.02142>`_.
|
||||
"""
|
||||
|
||||
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?
|
||||
<https://arxiv.org/abs/1703.04977>`_.
|
||||
"""
|
||||
|
||||
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
|
||||
|
|
@ -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)
|
||||
Loading…
Reference in New Issue