!6155 modify path of ut and st of mdp

Merge pull request !6155 from byweng/master

mindspore/nn/probability/README.md

@@ -452,13 +452,13 @@ for eval_data in ds_eval.create_dict_iterator():
 
 ### Examples
 Examples in [mindspore/tests/st/probability](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability) are as follows:
-- [Bayesian LeNet](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/test_bnn_layer.py). How to construct and train a LeNet by bnn layers.
+- [Bayesian LeNet](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/bnn_layers/test_bnn_layer.py). How to construct and train a LeNet by bnn layers.
-- [Transform whole DNN model to BNN](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/test_transform_bnn_model.py): How to transform whole DNN model to BNN.
+- [Transform whole DNN model to BNN](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/transforms/test_transform_bnn_model.py): How to transform whole DNN model to BNN.
-- [Transform DNN layer to BNN](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/test_transform_bnn_layer.py): How to transform one certainty type of layer in DNN model to corresponding Bayesian layer.
+- [Transform DNN layer to BNN](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/transforms/test_transform_bnn_layer.py): How to transform one certainty type of layer in DNN model to corresponding Bayesian layer.
-- [Variational Auto-Encoder](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/test_gpu_svi_vae.py): Variational Auto-Encoder (VAE) model trained with MNIST to generate sample images.
+- [Variational Auto-Encoder](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/dpn/test_gpu_svi_vae.py): Variational Auto-Encoder (VAE) model trained with MNIST to generate sample images.
-- [Conditional Variational Auto-Encoder](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/test_gpu_svi_cvae.py): Conditional Variational Auto-Encoder (CVAE) model trained with MNIST to generate sample images.
+- [Conditional Variational Auto-Encoder](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/dpn/test_gpu_svi_cvae.py): Conditional Variational Auto-Encoder (CVAE) model trained with MNIST to generate sample images.
-- [VAE-GAN](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/test_gpu_vae_gan.py): VAE-GAN model trained with MNIST to generate sample images.
+- [VAE-GAN](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/dpn/test_gpu_vae_gan.py): VAE-GAN model trained with MNIST to generate sample images.
-- [Uncertainty Estimation](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/test_uncertainty.py): Evaluate uncertainty of model and data..
+- [Uncertainty Estimation](https://gitee.com/mindspore/mindspore/tree/master/tests/st/probability/toobox/test_uncertainty.py): Evaluate uncertainty of model and data..
 
 ### Community
 As part of MindSpore, we are committed to creating an open and friendly environment.

tests/st/probability/transforms/dataset.py

@@ -0,0 +1,60 @@
+# 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.
+# ============================================================================
+"""
+Produce the dataset
+"""
+
+import mindspore.dataset as ds
+import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore.dataset.vision import Inter
+from mindspore.common import dtype as mstype
+
+
+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