- Optimize thread monitoring to solve the problem of running multiple multiprocessesing on Windwos. ([!23232](https://gitee.com/mindspore/mindspore/pulls/23232))
- Fix bugs of Dataset tensor operations in lite mode. ([!21999](https://gitee.com/mindspore/mindspore/pulls/21999))
- Fix memory increasing when using create_dict_iterator in for loop. ([!22529](https://gitee.com/mindspore/mindspore/pulls/22529))([!22529](https://gitee.com/mindspore/mindspore/pulls/22529))
## MindSpore Lite
### Major Features and Improvements
#### Converter and runtime
1. Optimize TDNN-like streaming model by reusing the result of last inference.
2. Support dynamic filter Convolution.
3. Support serializing float32 weight into float16 weight for reducing size of model file.
4. Provide unified runtime API for developer reusing their code between cloud side and end side.
6. Now user can specify format and shape of model inputs while converting model.
7. Support multiple devices inference, includeing CPU, NPU, GPU. User can set devices in mindspore::Context.
8. Support mixed precision inference. User can set inference precision by LoadConfig API.
9. Support custom operator registration and enable inference on third-party hardware.
#### ARM backend optimization
1. Support the nchw data format of some Operators, such as Conv, InstanceNorm, etc. The performance of some models convertered from onnx and caffe is greatly improved.
2. Fix bugs of memory leak on NPU.
#### Post quantization
1. Weight quantization supports mixed bit quantization.
2. Full quantization supports data pre-processing.
3. Adjust the quantization parameters from the command line to the configuration file.
#### Training on Device
1. Unify lite external api with MindSpore.
2. Implement static memory allocator and common workspace for TOD，save memory 10-20%.
3. Provide getgradients and setgradients interface，get and set optimizer params interfaces to support MOE Model.
4. Support user specified output node when export IOD Model.
5. Support more text networks (tinybert,albert) and operators.
#### Codegen
1. Support kernel register for custom op. Third-party hardware like NNIE can be accessed through it.
- [BETA] Add EPP-MVSNet: a novel deep learning network for 3D reconstruction from multi-view stereo, which has won the first place in Tanks & Temples leaderboard(until April 1, 2021)(GPU).
- [STABLE] MindSpore GPU inference performance optimization by integrating TensorRT.
- [STABLE] MindSpore built on one Linux distribution can now be used on multiple Linux distributions with the same CPU architecture (e.g. EulerOS, Ubuntu, CentOS).
- [STABLE] MindSpore now supports Ascend310 and Ascend910 environments with one single wheel package and provides an alternate binary package for Ascend310 specifically.
- [STABLE] MindSpore Lite Device-side Inference & Training Interconnection with FL-Client.
#### Running Data Recorder
- [STABLE] Provide records of multi-stage computational graphs, memory allocation information and graph execution order when a "Launch kernel failed" occurs. (CPU)
#### GraphKernel Fusion
- [STABLE] Add options to control the optimization level.
- [STABLE] Enhance the generalization ability on GPU. GraphKernel is enabled by default in 40+ networks which cover the field of NLP, CV, Recommender, NAS and Audio. The result shows their throughput is significantly improved, and you are Recommended enabling GraphKernel in your network.
Previously, we have a parameter `prefetch_size` in `device_que` to define the prefetch number of records ahead of the user's request. But indeed this parameter is never used which means it is an ineffective parameter. Therefore, we remove this parameter in 1.3.0 and users can set this configuration by [mindspore.dataset.config.set_prefetch_size](https://www.mindspore.cn/docs/api/en/r1.3/api_python/mindspore.dataset.config.html#mindspore.dataset.config.set_prefetch_size).
###### `mindspore.nn.optim.thor` interface changes to lowercase `thor` and adds two parameters `enable_clip_grad` and `frequency`([!17212](https://gitee.com/mindspore/mindspore/pulls/17212))
The parameter `enable_clip_grad` is used for gradient clipping and another parameter `frequency` is used to control the update interval of second order information matrix.
Previously, we could only dump tensor data for one or all steps. To make the dump feature easier to use, we changed the dump configuration format and dump structure. View the [New Dump Tutorial](https://www.mindspore.cn/docs/programming_guide/en/master/dump_in_graph_mode.html#dump).
- Fix the exception of 'exceeding limit call depth' in compile graph process when using while expression with grad operation. ([!18662](https://e.gitee.com/mind_spore/repos/mindspore/mindspore/pulls/18662))
- Fix the build error when multiple python versions are installed in the environment. ([!19165](https://gitee.com/mindspore/mindspore/pulls/19165))
- The warning when the te/topi/hccl version does not match is optimized, and fix the repeated warning. ([!18704](https://gitee.com/mindspore/mindspore/pulls/18704))
- Fix the error in a cluster with more than 8 pcs in pynative mode. ([!16376](https://gitee.com/mindspore/mindspore/pulls/16376))
- Fix an out-of-memory error when ImagefolderDataset gets an illegal directory. ([!16196](https://gitee.com/mindspore/mindspore/pulls/16196))
- Fix bugs of vision transformations in lite mode. ([!14722](https://gitee.com/mindspore/mindspore/pulls/14722),[!14774](https://gitee.com/mindspore/mindspore/pulls/14774),[!15050](https://gitee.com/mindspore/mindspore/pulls/15050))
- Fix default numbers of parallel workers of MindData for those CPUs with fewer cores. ([!15921](https://gitee.com/mindspore/mindspore/pulls/15921))
- Fix MindRecord writing failed probabilistically in multiprocessing. ([!15242](https://gitee.com/mindspore/mindspore/pulls/15242))
## MindSpore Lite
### Major Features and Improvements
#### Converter and runtime
1. Support Caffe model running on Hi3516D.
2. Support delegate mechanism to run your models(part or whole) on user specified executor.
1. Support fp32 training model export to quantization model after training process end.
2. Unify APIs and output package name of training and inference.
3. Simplify implementation of Train Session.
4. Optimize train and infer compile, reduce libmindspore-lite-train.so memory.
5. Training memory optimization: memory reduce 10-50% compare with r1.2.
6. Training performance optimization: for 1*1 special input shape Cov2DGradInput and SparseSoftmaxCrossEntropyWithLogits operator optimization, improved 10%-20%.
###### Unify LiteSession and TrainSession, Merge LiteSession And TrainSession.([!17356](https://gitee.com/mindspore/mindspore/pulls/17356))
Previously, Training on Device use TrainSession while Inference on Device use LiteSession. To simplify implementation, we move TrainSession functions to LiteSession as virtual function. and move APIs previous defined in train_session.h to lite_session.h.
Previously, Training on Device uses SaveToFile API to save the training model to file. Export API was added in this release to support more format, more model type(train or interface part of the model), and save weight quant model of train.
New createSession method is an API that integrates four old APIs: LiteSession.init, Model.loadModel, LiteSession.compileGraph and model.free. It is simple and efficient as it reduces one modelBuffer copy operation.
- [BETA] Add TPRR: Thinking Path Re-Ranker, an original ranked-base framework for Multi-Hop Question Answering which has won the first place in HotpotQA leaderboard.(Ascend)
- [STABLE] Add SPONGE modules for molecular dynamics simulation, including Bond, Angle, Dihedral, Non Bond 14, NeighborList, Particle Mesh Ewald, Langevin MD and LIUJIAN MD.(GPU)
- [STABLE] If the libnuma library is installed in the environment, you can run `export DATASET_ENABLE_NUMA=True` to configure NUMA binding. In multi-card training scenarios, the training data processing speed can be improved, thereby improving the network training efficiency.
- [STABLE] Unify API Tensor structure of Training/Inference interfaces in C++ SDK.
- [STABLE] Optimize duplicated Decode in data preprocess using cache, improve preprocess efficiency.
- [STABLE] Support eager mode to run data augmentation in Python & C++.
- [STABLE] Support more data augmentation operators(e.g. Affine, Perspective) in MindSpore-Lite.
- [STABLE] Support light pipeline to process MindData in MindSpore-Lite training.
- [STABLE] Support more data preprossing operators based on DVPP hardware module and can be used on on Ascend310 platform.
- [STABLE] Support copy-free property for data in Ascend310 inference process scenarios.
- [STABLE] Support running data recorder (RDR) for exception demarcation.
- [STABLE] Provide records of multi-stage computational graphs, memory allocation information, graph execution order, stream execution order and task debug information when a "run task error" or "distribute task failed" occurs. (Ascend)
- [STABLE] Provide records of multi-stage computational graphs, memory allocation information and graph execution order when a "SyncStream error" occurs. (GPU)
- [STABLE] Support RMSELoss loss function, MAELoss loss function, FocalLoss loss function, DiceLoss binary loss function, and MultiClassDiceLoss multi-type loss function for 2D/3D network.
###### `mindspore.numpy.array()`, `mindspore.numpy.asarray()`, `mindspore.numpy.asfarray()`, `mindspore.numpy.copy()` now support GRAPH mode, but cannot accept `numpy.ndarray` as input arguments anymore([!12726](https://gitee.com/mindspore/mindspore/pulls/12726))
Previously, these interfaces can accept numpy.ndarray as arguments and convert numpy.ndarray to Tensor, but cannot be used in GRAPH mode.
However, currently MindSpore Parser cannot parse numpy.ndarray in JIT-graph. To support these interfaces in graph mode, we have to remove `numpy.ndarray` support. With that being said, users can still use `Tensor` to convert `numpy.ndarray` to tensors.
Previously, we have incomplete support for keyword arguments `out` and `where` in mindspore.numpy interfaces, however, the `out` argument is only functional when `where` argument is also provided, and `out` cannot be used to pass reference to numpy functions. Therefore, we have removed these two arguments to avoid any confusion users may have. Their original functionality can be found in [np.where](https://www.mindspore.cn/docs/api/en/master/api_python/numpy/mindspore.numpy.where.html#mindspore.numpy.where)
###### Turn `ops.MakeRefKey` into an internal interface ([!12010](https://gitee.com/mindspore/mindspore/pulls/12010))
Previously MakeRefKey is an external interface that is not used, now make it an internal interface with the same usage. We do not recommend users to use this interface, and we will remove the relevant introduction of this interface from the official website.
###### `ops.ApplyFtrl`, `ops.ApplyMomentum`, `ops.ApplyRMSProp`, `ops.ApplyCenteredRMSProp` change the output on Ascend backend from multiple to a single. ([!11895](https://gitee.com/mindspore/mindspore/pulls/11895))
Previously the number of outputs of these operator is different on different backends. To unify their definition we change their output on Ascend backend from multiple to a single.
###### `ControlDepend` is deleted, use `Depend` instead. The decorator `@C.add_flags(has_effect=True)` does not work. ([!13793](https://gitee.com/mindspore/mindspore/pulls/13793))
Previously, we used ControlDepend to control the execution order of multiple operators. In version 1.2.0, mindspore introduces the auto-monad side effects expression to ensure that the perform order of user's semantics is correct. Therefore, ControlDepend is deleted and Depend is recommended.
In most scenarios, if operators have IO side effects (such as print) or memory side effects (such as assign), they will be executed according to the user's semantics. In some scenarios, if the two operators A and B have no order dependency, and A must be executed before B, we recommend using Depend to specify their execution order. See the API documentation of the Depend operator for specific usage.
After the introduction of the auto-monad side effect expression feature, the decorator `@C.add_flags(has_effect=True)` does not work. If the decorator is used in the script, please modify. Take the overflow identification operator (without side effects) as an example, the modification method is as follows:
[ops.matmul](https://www.mindspore.cn/docs/api/en/master/api_python/ops/mindspore.ops.matmul.html#mindspore.ops.matmul) follows the API of [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html) as closely as possible. As a function interface, [ops.matmul](https://www.mindspore.cn/docs/api/en/master/api_python/ops/mindspore.ops.matmul.html#mindspore.ops.matmul) is applied without instantiation, as opposed to `nn.MatMul`, which should only be used as a class instance.
- add passes to support frontend IR unification, including following operations: SliceGrad([!11783](https://gitee.com/mindspore/mindspore/pulls/11783)), ApplyFtrl, ApplyMomentum, ApplyRMSProp, CenteredRMSProp([!11895](https://gitee.com/mindspore/mindspore/pulls/11895)), AvgPoolGrad([!12813](https://gitee.com/mindspore/mindspore/pulls/12813)), BatchNorm([!12115](https://gitee.com/mindspore/mindspore/pulls/12115))
- Fix getter functions(e.g. GetDatasetSize) terminated abnormally when use python multi-processing. ([!13571](https://gitee.com/mindspore/mindspore/pulls/13571), [!13823](https://gitee.com/mindspore/mindspore/pulls/13823))
- Fix unclear error log of data augmentation operators. ([!12398](https://gitee.com/mindspore/mindspore/pulls/12398), [!12883](https://gitee.com/mindspore/mindspore/pulls/12883), [!13176](https://gitee.com/mindspore/mindspore/pulls/13176))
- Fix profiling performs abnormally when sink_size = False, as saving data is later than profiling analysis. ([!13944](https://gitee.com/mindspore/mindspore/pulls/13944))
1. Support TensorFlow model in Converter except aware-training model.
2. Add fusion pattern for same horizontal operators in Converter.
3. Support Jar in x86_64 system for integrating into server with Java backend conveniently.
4. Provide unified runtime API for developer reusing their code between cloud side and end side.[BETA]
5. Improve control-flow capabilities continually: Support GRU fusion in Converter; Support weight-quant for control-flow model; Support control-flow model inference with half precision; Support nested control-flow model.[BETA]
###### Add header file named lite_types.h for some common data structs. ([!12262](https://gitee.com/mindspore/mindspore/pulls/12262))
Previously, some common data structs such as `CpuBindMode` and `DeviceType` are in context.h, this may cause cross-dependency between headers. So we create a new header named lite_types.h for some common data structs and move `CpuBindMode` and `DeviceType` from context.h into lite_types.h.
<table>
<tr>
<tdstyle="text-align:center"> lite_types.h </td>
</tr>
<tr>
<td>
```cpp
namespace mindspore::lite {
/// \brief CpuBindMode defined for holding bind cpu strategy argument.
typedef enum {
NO_BIND, /**<nobind*/
HIGHER_CPU, /**<bindhighercpufirst*/
MID_CPU /**<bindmiddlecpufirst*/
} CpuBindMode;
/// \brief DeviceType defined for holding user's preferred backend.
typedef enum {
DT_CPU, /**<CPUdevicetype*/
DT_GPU, /**<GPUdevicetype*/
DT_NPU /**<NPUdevicetype*/
} DeviceType;
} // namespace mindspore::lite
```
</td>
</tr>
</table>
###### Add some new interfaces in ms_tensor.h for unified runtime API.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))
Previously, users could not create `MSTensor` or modify ``MSTensor, all `MSTensor` are created and managed by framework. However users need to create or modify MSTensor sometimes such as pre-processing input data. So we provide two new interfaces in ms_tensor.h: `CreateTensor` interface for creating `MSTensor` by user and `set_shape` interface for modifying the shape of `MSTensor`.
<table>
<tr>
<tdstyle="text-align:center"> CreateTensor </td>
</tr>
<tr>
<td>
```cpp
/// \brief Create a MSTensor.
///
/// \return Pointer to an instance of MindSpore Lite MSTensor.
Previously, users could access to data of `MSTensor` by interface named `MutableData`. However `MutableData` is not only returning data of tensor but also allocating data for tensor if its data is nullptr. So we provide a new interfaces in ms_tensor.h named `data` for returning data of tensor without allocating automatically.
<table>
<tr>
<tdstyle="text-align:center"> data </td>
</tr>
<tr>
<td>
```cpp
/// \brief Get the pointer of data in MSTensor.
///
/// \note The data pointer can be used to both write and read data in MSTensor. No memory buffer will be
/// allocated.
///
/// \return the pointer points to data in MSTensor.
virtual void *data() = 0;
```
</td>
</tr>
</table>
###### Delete `DimensionSize()` in ms_tensor.h.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))
The interface named `DimensionSize` is fuinctionally overlapped with the interface named `shape`. For the simplicity of the interface, we delete `DimensionSize` and recommend users to use the new interface named `shape` instead.
/// \brief Get size of the dimension of the MindSpore Lite MSTensor index by the parameter index.
///
/// \param[in] index Define index of dimension returned.
///
/// \return Size of dimension of the MindSpore Lite MSTensor.
virtual int DimensionSize(size_t index) const = 0;
```
</td>
</tr>
</table>
###### Move allocator from namespace mindspore::lite to namespace lite for unified runtime API.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))
Previously, class `Allocator` is in namespace mindspore::lite. Considering unified allocator interface for unified runtime API, we move `Allocator` to namespace mindspore.
- [STABLE] BGCF: a Bayesian Graph Collaborative Filtering(BGCF) framework used to model the uncertainty in the user-item interaction graph and thus recommend accurate and diverse items on Amazon recommendation dataset.(Ascend)
- [STABLE] GRU: a recurrent neural network architecture like the LSTM(Long-Short Term Memory) on Multi30K dataset.(Ascend)
- [STABLE] FastText: a simple and efficient text classification algorithm on AG's news topic classification dataset, DBPedia Ontology classification dataset and Yelp Review Polarity dataset.(Ascend)
- [STABLE] LSTM: a recurrent neural network architecture used to learn word vectors for sentiment analysis on aclImdb_v1 dataset.(Ascend)
- [STABLE] SimplePoseNet: a convolution-based neural network for the task of human pose estimation and tracking on COCO2017 dataset.(Ascend)
##### `ops.AvgPool`, `ops.MaxPool`, `ops.MaxPoolWithArgmax` change attr name from 'ksize', 'padding' to 'kernel_size', 'pad_mode' ([!11350](https://gitee.com/mindspore/mindspore/pulls/11350))
Previously the kernel size and pad mode attrs of pooling ops are named "ksize" and "padding", which is a little puzzling and inconsistent with convolution ops. So they are rename to "kernel_size" and "pad_mode".
The operator name TensorAdd is not standardized, it is changed to Add. The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.
##### `ops.Gelu`, `ops.GeluGrad`, `ops.FastGelu`, `ops.FastGeluGrad`, change API name to `ops.GeLU`, `ops.GeLUGrad`, `ops.FastGeLU`, `ops.FastGeLUGrad` ([!11603](https://gitee.com/mindspore/mindspore/pulls/11603))
Gelu, GeluGrad, FastGelu, and FastGeluGrad names are unified into ReLU naming rules, "lu" is changed to the uppercase "LU". The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.
GatherV2 is changed to Gather. The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.
Pack is changed to Stack, and Unpack is changed to Unstack. The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.
##### `ops.Depend`, add operator description and use case ([!11815](https://gitee.com/mindspore/mindspore/pulls/11815)), ([!11879](https://gitee.com/mindspore/mindspore/pulls/11879))
##### `ops.SpaceToBatch`, `ops.BatchToSpace` are deprecated in favor of `ops.SpaceToBatchND`, `ops.BatchToSpaceND`([!11527](https://gitee.com/mindspore/mindspore/pulls/11527))
The `ops.SpaceToBatchND`, `ops.BatchToSpaceND` are more general and have same behavior as `ops.SpaceToBatch`, `ops.BatchToSpace` when `block_shape` is a int.
##### `ops.DepthwiseConv2dNative` is deprecated in favor of `nn.Conv2D`([!11702](https://gitee.com/mindspore/mindspore/pulls/11702))
The `ops.DepthwiseConv2dNative` is only supported by Ascend, it is recommended to directly use `nn.Conv2D`. If `group` is equal to `in_ channels` and `out_channels`, the 2D convolution layer is also a 2D depthwise convolution layer.
- [STABLE] GNMT v2: similar to the model described in Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, which is mainly used for corpus translation, on WMT Englis-German dataset.(Ascend)
- [STABLE] MaskRCNN: a conceptually simple, flexible, and general framework for object instance segmentation on COCO2017 dataset.(Ascend)
- [STABLE] YOLOv4: a state-of-the-art detector which is faster and more accurate than all available alternative detectors on MS COCO dataset.(Ascend)
- [STABLE] Openpose: proposes a bottom-up human attitude estimation algorithm using Part Affinity Fields on COCO2017 dataset.(Ascend)
- [STABLE] CNN-CTC: proposes three major contributions to addresses scene text recognition (STR) on MJSynth and SynthText dataset.(Ascend)
- [STABLE] CenterFace: a practical anchor-free face detection and alignment method for edge devices on WiderFace dataset.(Ascend)
- [STABLE] ShuffleNetV2: a much faster and more accurate network than the previous networks on ImageNet 2012 dataset.(GPU)
- [STABLE] EfficientNet-B0: a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient on ImageNet 2012 dataset.(GPU)
- [BETA] SSD-GhostNet: based on an Ghost module structure which generate more features from cheap operations on Oxford-IIIT Pet dataset.(Ascend)
- [STABLE] Refactor the MINDIR to support 310 inference(Ascend).
- [STABLE] The execution backend of sparse operations in optimizer can be set through 'target'. (Ascend/GPU/CPU)
- [STABLE] Support saving specified network to checkpoint and filtering parameters according to prefix when load checkpoint. (Ascend/GPU/CPU)
- [STABLE] Allow users choose whether to load parameter into network strictly.(Ascend/GPU/CPU)
- [STABLE] Before training, in graph mode, in order to have the same network initialization parameter values for all devices, broadcast the parameters on device 0 to other devices. (Ascend/GPU)
- [STABLE] Support if by if of control flow subgraph. (Ascend/GPU)
- [STABLE] Support the judgment that whether a tensor is in a list. (Ascend/GPU/CPU)
- [STABLE] Support to get a value by using the corresponding key in a dictionary in the network; Support to get keys and values of a dictionary in the network. (Ascend/GPU/CPU)
- [STABLE] Support Tensor in enumerate. (Ascend/GPU/CPU)
- [STABLE] Support multilevel index assignment. (Ascend/GPU/CPU)
- [STABLE] Support the 'expand_as','view','abs','mean' method of Tensor. (Ascend/GPU/CPU)
- [STABLE] Support ResizeBilinear operation transfer ratio. (Ascend)
- [STABLE] Support modelzoo net in PyNative mode(Ascend 29, GPU 23, CPU 2).(Ascend/GPU/CPU)
- [STABLE] Support PyNative mode on CPU.(CPU)
- [STABLE] Optimize performance in PyNative mode.(Ascend/GPU/CPU)
- [STABLE] Support Safe Optimized Memory Allocation Solver (SOMAS) on Ascend to improve the memory-reuse, the batch size of Bert large model (128 sequence length) is increased from 160 to 208.(Ascend)
- [BETA] Support second order differentiation in PyNative mode.(Ascend/GPU)
- [DEMO] Add distributed trainning in PyNative mode.(Ascend/GPU)
###### Delete shape and dtype of class Initializer ([!7373](https://gitee.com/mindspore/mindspore/pulls/7373/files))
Delete shape and dtype attributes of Initializer class.
###### Modify the return type of initializer ([!7373](https://gitee.com/mindspore/mindspore/pulls/7373/files))
Previously, the return type of initializer function may be string, number, instance of class Tensor or subclass of class Initializer.
After modification, initializer function will return instance of class MetaTensor, class Tensor or subclass of class Initializer.
Noted that the MetaTensor is forbidden to initialize parameters, so we recommend that use str, number or subclass of Initializer for parameters initialization rather than the initializer functions.
###### `nn.LinSpace` ([!9494](https://gitee.com/mindspore/mindspore/pulls/9494)) has been removed and modify `ops.LinSpace` ([!8920](https://gitee.com/mindspore/mindspore/pulls/8920))
The `nn.LinSpace` interface only support passing the value by args previously. For the convenience, we provided enhancive `ops.LinSpace` interface, which support passing the value by the inputs at the latest version. So there is no need for `nn.LinSpace`.
###### `export` Modify the input parameters and export's file name ([!7385](https://gitee.com/mindspore/mindspore/pulls/7385)， [!9057](https://gitee.com/mindspore/mindspore/pulls/9057/files))
###### `Dense`, `Conv2dBnAct`, `DenseBnAct`, `DenseQuant` support setting the activation attribute as an instance of a class derived from `nn.Cell` or `Primtive` ([!7581](https://gitee.com/mindspore/mindspore/pulls/7581))
activation (Union[str, Cell, Primitive]): activate function applied to the output of the fully connected layer
Previously, tensor.size() and tensor.dim() were used for checking the total number of elements/dimensions in the tensor.
However, from a user's perspective, tensor.size and tensor.ndim (methods -> properties) are better choices, since they follow the numpy naming convention.
>>> result = EmbeddingLookup(4,2)(input_indices, sparse=False)
>>> print(result.shape)
(2, 2, 2)
```
</td>
</tr>
</table>
###### `nn.probability.bijector` change types of attributes from (int, float) to (float, list, numpy.ndarray, Tensor) ([!8191](https://gitee.com/mindspore/mindspore/pulls/8191))
Attributes Type change: (int, float) -> (float, list, numpy.ndarray, Tensor).
Int type is not supported anymore. Parameters of all bijectors should be type float, list, numpy.ndarray or Tensor.
>>> import mindspore.nn.probability.bijector as msb
>>>
>>> bijector = msb.GumbelCDF(loc=0.0, scale=1.0)
```
</td>
</tr>
</table>
###### `nn.layer.combined.Conv2dBnAct`, `nn.layer.combined.DenseBnAct` move from nn.layer.quant to nn.layer.combined ([!8187](https://gitee.com/mindspore/mindspore/pulls/8187))
Previously Conv2dBnAct and DenseBnAct are in nn.layer.quant, since they are not quant cells, now they are moved to nn.layer.combined. If you import Conv2dBnAct, DenseBnAct from mindspore.nn, then your code doesn't need any change.
>>> from mindspore.nn.layer.quant import Conv2dBnAct, DenseBnAct
```
</td>
<td>
```python
>>> from mindspore.nn import Conv2dBnAct, DenseBnAct
```
</td>
</tr>
</table>
###### `nn.layer.conv.Conv2D`, `nn.layer.quant.Conv2dBnFoldQuant`, `nn.layer.quant.Conv2dBnWithoutFoldQuant` change weight shape when group > 1 in Ascend platform ([!9723](https://gitee.com/mindspore/mindspore/pulls/9723))
In Ascend platform, if group > 1, the weight shape of Conv2D change from [in_channels//group, out_channels, kernel_size, kernel_size] to [out_channels, in_channels//group, kernel_size, kernel_size]. Previously, checkpoints of the networks are used, which use Conv2D with group > 1, such as MobileNet, can not be directly used now, need to transpose the first and second axis of the weight.
1. Support dynamic shape in MindSpore Lite Converter.
2. Optimize sub-graph mechanism by dynamically splitting the entire graph into multiple subgraphs based on the operator supported, backend hardware and user configuration.
3. Support TensorList and TensorList operators such as TensorListFromTensor, TensorListGetItem and so on.
4. Support BatchMatMul fusion and LSTM fusion in MindSpore Lite Converter.
5. Support converting model and run inference on Windows operator system.
6. Support Model(.ms) visualization on Netron.
7. Support Tensorflow model in MindSpore Lite Converter
8. Add 86 converter parsers.
9. Convert aware training model without user’s awareness
10. Support scalar tensor in MindSpore Lite Converter and Runtime
11. Support NPU backend on HUAWEI Kirin SoC.[BETA]
1. Add 50+ new operators, including new Op type(like Adder, Gru).
2. Enhanced performance on armv8.2 supported platform. For example, utilizing sdot instruction more efficiently.
3. Optimize all operators(fp32, fp16, int8) by implementing multi-thread, SIMD tech as much as possible. Model inference time can reduce at least 20% after these optimizations.
4. Extending to support operators for x86_64 platform based on SSE/AVX instruction set.
2. Performance optimization: by memory layout optimize, Winograd Convolution select strategyoptimize, SIMT local size optimize, local cache optimize, GPU performance improvement up to 20+% vs MSLITE Version1.0
3. Add Online Graph optimzation: by fusion Convolution/Matmul/Fullconnection and add/mul/pad/reshape, improve performance up to 50+% for some networks;
4. Add auto tuning: by online tuning in the graph compilation phase, optimize performance up to 10%;
MindSpore Lite supports both weight quantization and full quantization. Currently, Weights can be quantized into 1 ~ 16 bits according to user configuration. In internal testing, quantization of networks, such as classification, detection, segmentation and transformer are well supported. To ensure high accuracy of quantized models, MindSpore Lite uses a pipeline quantization method. In the first phase, the weight and activation value are quantized using linear quantization methods, such as MIN-MAX. In the second phase, the quantization error is analyzed, and uses statistical methods to compensate loss caused by fp32 quantization to a fixed point such as Int8 to quantized models. The features of Post-training quantization are:
1. perchannel asymmetric quantization for weights, such as MAX_MIN and KMEANS
2. Perlayer symmetric quantization for activation, such as KL and MAX_MIN.
3. perlayer asymmetrical quantization for activation, such as, RemoveOutlier.
4. accuracy loss compensation, such as BiasCorrection
| mobilenet_v2 | ACC (ImageNet) |
|---|---|
| FP32 | 71.56% |
|A8W8 | 71.16% |
| A8W8(without BiasCorrection) | 70.74% |
| A8W7 | 71.06% |
| A7W7 | 70.78% |
The above table uses the mobilenet_v2 model from TF official website. Using MindSpore Lite quantization, the precision of A8W8 (8-bit activation value quantization and 8-bit weight quantization) decreases from 0.82% to 0.4% after accuracy loss compensation, for 7-bit quantization, the precision loss is still no more than 1%.
Within MindSpore 1.1 release, the MindSpore Lite provides the following Training-on-Device (ToD) capabilities:
1. Learning from scratch and Transfer Learning strategies are supported
2. MindSpore based models can be converted and used in training on the device. (Third-party models such as TensorFlow and PyTorch for now cannot be directly imported to the framework)
3. Grad operations are supported for more than 30 operators such as Dense layers, Convolutions and Batch Normalizations. Momentum, SGD, and ADAM optimizers are supported.
4. Supports networks such as LeNet, Alexnet, Resnet, MobileNetV1/V2/V3, and EffectiveNet, and provides complete model loading, conversion, and Python training scripts on the device side.
The MindSpore Lite ToD framework is already in use in the newest Huawei Smart TV, providing a unique and personalized user experience as a family entertainment center.
- DenseNet121: a dense convolutional neural network, which connects each layer to every other layer in a feed-forward fashion for object recognition on ImageNet dataset.
- UNet2D-Medical: Unet Medical model for 2D image segmentation, Convolutional Networks for Biomedical Image Segmentation on ISBI Challenge database.
- Frontend and user interface
- Second-Order Optimization
- Enable second-order optimization for Bert on Ascend 910, which can achieve a masked lm accuracy of 71.3% in 800 seconds using 8 Ascend 910 (Bert-Large @MLPerf v0.7 dataset).
- New GNN model BGCF
- Bayesian Graph Convolutional Filtering network which naturally incorporate the uncertainty in the user-item interaction graph shows excellent recommendation performance on Amazon-Beauty dataset.
- Add append interface for SequentialCell.
- Add a level `auto` for AMP.
- Executor and performance optimization
- Support quantitative network (Resnet50 & YoloV3 & MobileNetV2).
- Project ease of use optimization: project compilation time optimization, CMakelist regularization, cudnn, cuda independent compilation and installation independent.
- Enable second-order optimization for resnet50 on GPU, which achieve 30% improvement on training time compared to SGD with Momentum (Resnet50 @ImageNet).
- Remove useless API dataset.set_dataset_size（[!5806](https://gitee.com/mindspore/mindspore/pulls/5806))
- Some of Dataset API add usage parameter（[!5605](https://gitee.com/mindspore/mindspore/pulls/5605))
- Change the import path, such as from mindspore.dataset.transforms.vision to mindspore.dataset.vision.transforms（[!5384](https://gitee.com/mindspore/mindspore/pulls/5384))
- Rename ImageFolderDatasetV2 to ImageFolderDataset（[!5384](https://gitee.com/mindspore/mindspore/pulls/5384))
- fix the constant folding problem in multiply.([!6092](https://gitee.com/mindspore/mindspore/pulls/6092))
- move batch_size from bert_net_cfg to cfg in bert scripts.([!6233](https://gitee.com/mindspore/mindspore/pulls/6233))
- modify the checkpoint file path.([!6137](https://gitee.com/mindspore/mindspore/pulls/6137))
- Python API
- fix semi auto parallel parameter of reshape has another user([!5722](https://gitee.com/mindspore/mindspore/pulls/5722))
- raise ValueError when call hook function in graph mode([!5831](https://gitee.com/mindspore/mindspore/pulls/5831))
- Executor
- fix pynative mode to build temporary nn objects.（[!6189](https://gitee.com/mindspore/mindspore/pulls/6189))
- fix the accuracy problem of multiple inputs of multi-card communication operator broadcast.([!6522](https://gitee.com/mindspore/mindspore/pulls/5622))
- fix the problem that the sample distribution interface categorical does not support graph mode.([!5772](https://gitee.com/mindspore/mindspore/pulls/5772))
- fix the random seed failure problem of the polynomial downsampling distribution operator.([!5948](https://gitee.com/mindspore/mindspore/pulls/5948))
- fix unnecessary address binding issues in GPU heterogeneous scenarios.([!6232](https://gitee.com/mindspore/mindspore/pulls/6232))
- GPU platform
- fix for kernel resource leak([!5315](https://gitee.com/mindspore/mindspore/pulls/5315))
- fix for insufficient memory for continuous unit test running([!5617](https://gitee.com/mindspore/mindspore/pulls/5617))
- fix for the memory leak in the sparse slicer([!5578](https://gitee.com/mindspore/mindspore/pulls/5578))
- Data processing
- fix hang when use pyfunc([!6346](https://gitee.com/mindspore/mindspore/pulls/6346))
- fix GPU device queue does not release GIL during resource clean up([!5964](https://gitee.com/mindspore/mindspore/pulls/5964))
- fix hang if scripte exit unnormally([!6441](https://gitee.com/mindspore/mindspore/pulls/6441))
- Third party
- Sqlite : Update sqlite to 3.32.2 to handle [CVE-2020-11656](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11656), [CVE-2020-13871](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13871), [CVE-2020-11655](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655), [CVE-2020-9327](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-9327), [CVE-2020-13630](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13630), [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15358), [CVE-2020-13631](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13631), [CVE-2020-13632](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13632), [CVE-2020-13434](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13434), [CVE-2020-13435](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13435), and [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655).
- Libjpeg-turbo : Update libjpeg-turbo to 2.0.4 to handle [CVE-2020-13790](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13790).
- TinyBert: a smaller and faster version of BERT using transformer distillation for natural language understanding on GLUE benchmark.
- SE-ResNet50: add Squeeze-and-Excitation blocks(SE-Blocks) to the resnet50 network to improve channel interdependencies for image classification on ImageNet 2012 dataset.
- Inception V3: the third version of Inception convolutional architectures for image classification on ImageNet 2012 dataset.
- Frontend and user interface
- Embedding operator high-level packaging to support segmented by field for Wide&Deep.
- Load multi-node checkpoint into single-process to support host-device hybrid inference.
- Support Concat/Tile/Strideslice distributed operators.
- Support cumulative gradient and batch training split.
- Support variable parameter input for Cell object.
- Parameter mixed calculation optimization for pynative mode.
- Deep Probabilistic Programming
- Support statistical distributions classes used to generate stochastic tensors.
- Support probabilistic inference algorithms.
- Support BNN layers used to construct BNN in Graph mode.
- Support interfaces for the transformation between BNN and DNN in Graph mode.
- Support uncertainty estimation to estimate epistemic uncertainty and aleatoric uncertainty.
- User interfaces change log
- change base class of parameter([!3473](https://gitee.com/mindspore/mindspore/pulls/3473))
- change binary to mindir([!4258](https://gitee.com/mindspore/mindspore/pulls/4258))
- change export from geir to air([!4269](https://gitee.com/mindspore/mindspore/pulls/4269))
- Init parameter data by default([!3967](https://gitee.com/mindspore/mindspore/pulls/3967))
- change IndexedSlices to RowTensor([!4031](https://gitee.com/mindspore/mindspore/pulls/4031))
- Must set or change parallel mode before any Initializer created([!4801](https://gitee.com/mindspore/mindspore/pulls/4801))
- Executor and performance optimization
- MindSpore graph compilation process performance improved by 20%.
- Decoupling C++ and Python modules to achieve separate compilation of core modules.
- fix bug of cast dtype when using mix_presion in pynative mode([!3730](https://gitee.com/mindspore/mindspore/pulls/3730))
- Executor
- fix etsnet train error when UnsegmentSum's first input shape is (1,) ([!4573](https://gitee.com/mindspore/mindspore/pulls/4573))
- fix bug of result error in while control flow because of unsupporting for value reference ([!4103](https://gitee.com/mindspore/mindspore/pulls/4103))
- fix bug of the output tensor does not carry device data type ([!3774](https://gitee.com/mindspore/mindspore/pulls/3774))
- fix bug of avoiding multi attr value are eliminated in pynative mode ([!4225](https://gitee.com/mindspore/mindspore/pulls/4225))
- fix bug of AssignAdd unable to work normally in multi-cases ([!5171](https://gitee.com/mindspore/mindspore/pulls/5171))
- GPU platform
- improve the environment variable checking for nvcc compiler path ([!5140](https://gitee.com/mindspore/mindspore/pulls/5140))
- fix bug of error in cast operator conversion from fp16 to fp32 ([!4147](https://gitee.com/mindspore/mindspore/pulls/4147))
- fix bug of the array out of bound in case of make_tuple operator ([!5219](https://gitee.com/mindspore/mindspore/pulls/5219))
- Data processing and Pro
- fix GeneratorDataset time out([!3624](https://gitee.com/mindspore/mindspore/pulls/3624))
- ResNext50: a simple, highly modularized network architecture using aggregated resdiual transformations for image classification on ImageNet 2012 dataset.
- MASS: a pre-training method for sequence to sequence based language generation tasks on Text Summarization and Conversational Response Generation using News Crawls 2007-2017 dataset, Gigaword corpus and Cornell movie dialog corpus.
- Transformer: a neural network architecture for language understanding on WMT 2014 English-German dataset.
- GCN：Graph Convolutional Networks for the task of classification of nodes in a graph on Cora and Citeseer datasets.
- GAT：an attention-based graph neural network for node classification on Cora and CiteSeer dataset.
- Frontend and user interface
- Support tensor value and assignment of mixed tensor index in graph mode.
- Support tensor comparison, len operator, constexpr syntax, value and assignment of tensor index in pynative mode.
- Support converting MindSpore IR to pb format for infer model.
- Support print operator to write data directly on the hard disk.
- Add the double recursive programming solution for very high speed parallel strategy search in automatic parallel.
- User interfaces change log
- Allow the learning rate of AdamWeightDecayDynamicLR and Lamb to be 0([!1826](https://gitee.com/mindspore/mindspore/pulls/1826))
- Restricting the entire network input parameter is Tensor([!1967](https://gitee.com/mindspore/mindspore/pulls/1967))
- Turn shape and dtype into attributes instead of interfaces([!1919](https://gitee.com/mindspore/mindspore/pulls/1919))
- Refactor the callback module in an encapsulated way, use _CallbackManager instead of_build_callbacks([!2236](https://gitee.com/mindspore/mindspore/pulls/2236))
- Bert, Move Bert from `example` to `model_zoo`, optimize network for better performance. ([!1902](https://gitee.com/mindspore/mindspore/pulls/1902))
- VGG16, Move VGG16 from `example` to `model_zoo`, optimize network for better accuracy. ([!2645](https://gitee.com/mindspore/mindspore/pulls/2645))
- Alexnet, modify parameter setting to improve accuracy ([!1364](https://gitee.com/mindspore/mindspore/pulls/2370))
- Wide&Deep, Move Wide&Deep from `example` to `model_zoo`, optimize network for better performance. ([!2221](https://gitee.com/mindspore/mindspore/pulls/2221))
- Python API
- Fix bug in auto cast([!1766](https://gitee.com/mindspore/mindspore/pulls/1766))
- Fix bug of register_backward_hook([!2148](https://gitee.com/mindspore/mindspore/pulls/2148))
- Fix bug of tuple args in pynative mode([!1878](https://gitee.com/mindspore/mindspore/pulls/1878))
- Fix bug of checking numbers of arguments and graph parameters([!1701](https://gitee.com/mindspore/mindspore/pulls/1701))
- Executor
- Fix bug of loading input data repeatedly in pynative mode([!1966](https://gitee.com/mindspore/mindspore/pulls/1966))
- Fix bug of list cannot be used as input in pynative mode([!1765](https://gitee.com/mindspore/mindspore/pulls/1765))
- Fix bug of kernel select ([!2103](https://gitee.com/mindspore/mindspore/pulls/2103))
- Fix bug of pattern matching for batchnorm fusion in the case of auto mix precision.([!1851](https://gitee.com/mindspore/mindspore/pulls/1851))
- Fix bug of generate hccl's kernel info.([!2393](https://gitee.com/mindspore/mindspore/pulls/2393))
- GPU platform
- Fix bug of summary feature invalid([!2173](https://gitee.com/mindspore/mindspore/pulls/2173))
- Data processing
- Fix bug of Cifar dataset reading([!2096](https://gitee.com/mindspore/mindspore/pulls/2096))
- Fix bug of C++ behavior in RandomCropAndResize([!2026](https://gitee.com/mindspore/mindspore/pulls/2026))
- Fix the bug of mindrecord shuffle([!2420](https://gitee.com/mindspore/mindspore/pulls/2420))
- Third party
- Sqlite : Update sqlite to 3.32.2 to handle [CVE-2020-11656](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11656), [CVE-2020-13871](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13871), [CVE-2020-11655](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655), [CVE-2020-9327](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-9327), [CVE-2020-13630](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13630), [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15358), [CVE-2020-13631](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13631), [CVE-2020-13632](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13632), [CVE-2020-13434](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13434), [CVE-2020-13435](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13435), and [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655).
- DeepFM: a factorization-machine based neural network for CTR prediction on Criteo dataset.
- DeepLabV3: significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2007 semantic image segmentation benchmark.
- Faster-RCNN: towards real-time object detection with region proposal networks on COCO 2017 dataset.
- SSD: a single stage object detection methods on COCO 2017 dataset.
- GoogLeNet: a deep convolutional neural network architecture codenamed Inception V1 for classification and detection on CIFAR-10 dataset.
- Wide&Deep: jointly trained wide linear models and deep neural networks for recommender systems on Criteo dataset.
- Frontend and User Interface
- Complete numpy advanced indexing method. Supports value and assignment through tensor index.
- Some optimizers support separating parameter groups. Different parameter groups can set different `learning_rate` and `weight_decay`.
- Support setting submodule's logging level independently, e.g. you can set logging level of module `A` to warning and set logging level of module `B` to info.
- Support weights to be compiled according to shape to solve the problem of large memory overhead.
- Add some operators implement and grammar support in pynative mode. To be consistent with graph mode.
- User interfaces change log
- Learning rate and weight decay making group params([!637](https://gitee.com/mindspore/mindspore/pulls/637))
- Support weights to be compiled according to shape([!1015](https://gitee.com/mindspore/mindspore/pulls/1015))
- delete some context param([!1100](https://gitee.com/mindspore/mindspore/pulls/1100))
- Fix dropout，topK and addn errors in PyNative mode ([!1285](https://gitee.com/mindspore/mindspore/pulls/1285), [!1138](https://gitee.com/mindspore/mindspore/pulls/1138), [!1033](https://gitee.com/mindspore/mindspore/pulls/1033)).
- Fix memory leaks after execution in PyNatvie mode ([!1201](https://gitee.com/mindspore/mindspore/pulls/1201)).
- Fix HCCL failure in some special scenes ([!1204](https://gitee.com/mindspore/mindspore/pulls/1204), [!1252](https://gitee.com/mindspore/mindspore/pulls/1252)).
- Fix Topk operator selection strategy bug between aicore and aicpu([!1367](https://gitee.com/mindspore/mindspore/pulls/1367)).
- Fix input memory size of 'assign' op unequal in control sink mode when assigning a data from one child graph to another child graph([!802](https://gitee.com/mindspore/mindspore/pulls/802)).
- Fix allreduce ir inconsistency([!989](https://gitee.com/mindspore/mindspore/pulls/989)).
- GPU platform
- Fix summary for gradient collection ([!1364](https://gitee.com/mindspore/mindspore/pulls/1364))
- Fix the slice operator ([!1489](https://gitee.com/mindspore/mindspore/pulls/1489))
- Data processing
- Fix memory problems of GeneratorDataset of sub-process ([!907](https://gitee.com/mindspore/mindspore/pulls/907))
- Fix getting data timeout when training the cifar10 dataset under the lenet([!1391](https://gitee.com/mindspore/mindspore/pulls/1391))
- Recommended OS: Ubuntu 16.04 (or later) or EulerOS 2.5 or EulerOS 2.8
- Python version: 3.7.5
- Preset models
- ResNet-50: residual structure-based convolutional neural network (CNN) for image classification, which is widely used.
- AlexNet: classic CNN for image classification, achieving historical results in ImageNet LSVRC-2012.
- LeNet: classic CNN for image classification, which was proposed by Yann LeCun.
- VGG16: classic CNN for image classification, which was proposed by Oxford Visual Geometry Group.
- YoloV3: real-time object detection network.
- NEZHA: BERT-based Chinese pre-training network produced by Huawei Noah's Ark Laboratory.
- Execution modes
- Graph mode: provides graph optimization methods such as memory overcommitment, IR fusion, and buffer fusion to achieve optimal execution performance.
- PyNative mode: single-step execution mode, facilitating process debugging.
- Debugging capability and methods
- Save CheckPoints and Summary data during training.
- Support asynchronous printing.
- Dump the computing data.
- Support profiling analysis of the execution process performance.
- Distributed execution
- Support AllReduce, AllGather, and BroadCast collective communication.
- AllReduce data parallel: Each device obtains different training data, which accelerates the overall training process.
- Collective communication-based layerwise parallel: Models are divided and allocated to different devices to solve the problem of insufficient memory for large model processing and improve the training speed.
- Automatic parallel mode: The better data and model parallel mode can be predicted based on the cost model. It is recommended that this mode be used on ResNet series networks.
- Automatic differentiation
- Implement automatic differentiation based on Source to Source.
- Support distributed scenarios and automatic insertion of reverse communication operators.
- Data processing, augmentation, and save format
- Load common datasets such as ImageNet, MNIST, CIFAR-10, and CIFAR-100.
- Support common data loading pipeline operations, such as shuffle, repeat, batch, map, and sampler.
- Provide basic operator libraries to cover common CV scenarios.
- Support users to customize Python data augmentation operators through the Pyfunc mechanism.
- Support the access of user-defined datasets through the GeneratorDataset mechanism.
- Provide the MindSpore data format, data aggregation and storage, random access example, data partition, efficient parallel read, user-defined index, and dataset search.
- Convert user datasets to the MindSpore data format.
- After data processing and augmentation, provide training applications in feed and graph modes.
- FP32/16 mixed precision computation, supporting automatic and manual configuration
- Provide common operators such as nn, math, and array, which can be customized.
