LIB: Add mindspore backend

examples/lib-samples/backend/mindspore/ResNet50/README.md

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+# Resnet Example with Mindspore Backend
+This document describes how to use the mindspore backend to train Resnet-50 network with the cifar-10 dataset.
+
+## Script Description
+
+### Script and Sample Code
+```shell
+└──ResNet50
+  ├── README.md
+  ├── scripts
+    ├── run_eval.sh                        # launch ascend evaluation
+    ├── run_eval_cpu.sh                    # launch cpu evaluation
+    ├── run_infer.sh                       # launch cpu inference
+    ├── run_standalone_train.sh            # launch ascend standalone training
+    ├── run_standalone_train_cpu.sh        # launch cpu training
+  ├── src
+    ├── config.py                          # parameter configuration
+    ├── dataset.py                         # data preprocessing
+    ├── CrossEntropySmooth.py              # loss definition for ImageNet2012 dataset
+    ├── lr_generator.py                    # generate learning rate for each step
+    ├── resnet.py                          # resnet backbone, including resnet50 and resnet101 and se-resnet50
+  ├── inference.py                         # Entrance to inference
+  ├── interface.py                         # Implements class "Estimator"
+  ├── eval.py                              # Entrance to evaluation
+  ├── train.py                             # Entrance to training
+```
+
+## Script Parameters
+
+Parameters for both training and evaluation can be set in `config.py`.
+
+
+```bash
+"class_num": 10,                  # dataset class num
+"batch_size": 32,                 # batch size of input tensor
+"loss_scale": 1024,               # loss scale
+"momentum": 0.9,                  # momentum
+"weight_decay": 1e-4,             # weight decay
+"epoch_size": 90,                 # only valid for taining, which is always 1 for inference
+"pretrain_epoch_size": 0,         # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
+"save_checkpoint": True,          # whether save checkpoint or not
+"save_checkpoint_epochs": 5,      # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last step
+"keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint
+"warmup_epochs": 5,               # number of warmup epoch
+"lr_decay_mode": "poly"           # decay mode can be selected in steps, ploy and default
+"lr_init": 0.01,                  # initial learning rate
+"lr_end": 0.00001,                # final learning rate
+"lr_max": 0.1,                    # maximum learning rate
+```
+
+## Preparatory Stage
+### Prepare Dataset
+In this example, we need to prepare the cifar10 dataset in advance, and put it into `/home/sedna/examples/backend/mindspore/resnet/`.
+```bash
+cd /home/sedna/examples/lib-samples/backend/mindspore/ResNet50
+wget http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz
+tar -zxvf cifar-10-binary.tar.gz
+```
+### Parameters
+you can change the parameters of the model in `src/config.py`.
+
+## Modeling Stage
+This example support CPU and NPU, you can follow these steps for training, testing and inference.
+
+### Training
+* #### Running on CPU
+```bash
+ bash scripts/run_standalone_train_cpu.sh [DATASET_PATH] [MODEL_SAVE_PATH]
+ # model_save_path must be ABSOLUTE PATH
+ # The log message would be showed in the terminal
+ # The ckpt file would be saved in [MODEL_SAVE_PATH]
+```
+* #### Runing on NPU
+```bash
+ bash scripts/run_standalone_train.sh [DATASET_PATH] [MODEL_SAVE_PATH]
+ # [MODEL_SAVE_PATH] must be ABSOLUTE PATH
+ # The log message would be saved to scripts/train/log
+ # The ckpt file would be saved in [MODEL_SAVE_PATH]
+```
+
+### Evaluation
+* #### Running on CPU
+```bash 
+ bash scripts/run_eval_cpu.sh [DATASET_PATH] [CHECKPOINT_PATH]
+ # [CHECKPOINT_PATH] must be ABSOLUTE PATH
+ # The log message would be saved to scripts/test/log
+```
+* #### Running on NPU
+```bash
+ bash scripts/run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH]
+ # [CHECKPOINT_PATH] must be ABSOLUTE PATH
+ # The log message would be saved to scripts/test/log
+```
+
+### Inference
+```bash
+ bash scripts/run_infer.sh [IMAGE_PATH] [CHECKPOINT_PATH]
+ # [CHECKPOINT_PATH] must be ABSOLUTE PATH
+ # The log message would be saved to scripts/infer/log
+```

examples/lib-samples/backend/mindspore/ResNet50/eval.py

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+# 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.
+# ============================================================================
+"""train resnet."""
+import argparse
+from mindspore.common import set_seed
+from sedna.backend import set_backend
+from interface import Estimator
+
+parser = argparse.ArgumentParser(description='Image classification')
+parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
+parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
+parser.add_argument('--device_target', type=str, default='Ascend', choices=("Ascend", "GPU", "CPU"),
+                    help="Device target, support Ascend, GPU and CPU.")
+set_seed(1)
+
+
+def main():
+    args_opt = parser.parse_args()
+    valid_data_path = args_opt.dataset_path
+    instance = set_backend(estimator=Estimator)
+    return instance.evaluate(valid_data_path, args_opt=args_opt)
+
+
+if __name__ == '__main__':
+    main()

examples/lib-samples/backend/mindspore/ResNet50/inference.py

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+import argparse
+import mindspore as ms
+from mindspore import Tensor
+import mindspore.dataset.vision.c_transforms as C
+import numpy as np
+import cv2
+from sedna.backend import set_backend
+from interface import Estimator
+
+parser = argparse.ArgumentParser(description="resnet50 infer")
+parser.add_argument('--image_path', type=str, default="")
+parser.add_argument(
+    '--device_target',
+    type=str,
+    default="Ascend",
+    choices=(
+        "Ascend",
+        "CPU"),
+    help="Device target, support Ascend, CPU")
+parser.add_argument('--checkpoint_path', type=str)
+
+
+def preprocess():
+    resize = C.Resize((224, 224))
+    rescale = C.Rescale(1.0 / 255.0, 0.0)
+    normalize = C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
+    transpose = C.HWC2CHW()
+    return [resize, rescale, normalize, transpose]
+
+
+def main():
+    args = parser.parse_args()
+
+    # read image and preprocess
+    img = cv2.imread(args.image_path)
+    data_preprocess = preprocess()
+    for method in data_preprocess:
+        img = method(img)
+    img = np.expand_dims(img, 0)
+    data = Tensor(img, ms.float32)
+
+    model = set_backend(estimator=Estimator)
+    return model.predict(data)
+
+
+if __name__ == '__main__':
+    main()