forked from huawei/mindspore2022
modify links from master to r1.5.
This commit is contained in:
zhangyi
parent
f31fc503d6
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f8aff5ec31
10
README.md
10
README.md
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@ -42,9 +42,9 @@ processor, and software hardware co-optimization. At the meantime MindSpore as
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a global AI open source community, aims to further advance the development and
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enrichment of the AI software/hardware application ecosystem.
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<img src="https://gitee.com/mindspore/mindspore/raw/master/docs/MindSpore-architecture.png" alt="MindSpore Architecture" width="600"/>
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<img src="https://gitee.com/mindspore/mindspore/raw/r1.5/docs/MindSpore-architecture.png" alt="MindSpore Architecture" width="600"/>
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For more details please check out our [Architecture Guide](https://www.mindspore.cn/docs/programming_guide/en/master/architecture.html).
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For more details please check out our [Architecture Guide](https://www.mindspore.cn/docs/programming_guide/en/r1.5/architecture.html).
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### Automatic Differentiation
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@ -58,7 +58,7 @@ TensorFlow adopted static calculation diagrams in the early days, whereas PyTorc
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But MindSpore finds another way, automatic differentiation based on source code conversion. On the one hand, it supports automatic differentiation of automatic control flow, so it is quite convenient to build models like PyTorch. On the other hand, MindSpore can perform static compilation optimization on neural networks to achieve great performance.
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<img src="https://gitee.com/mindspore/mindspore/raw/master/docs/Automatic-differentiation.png" alt="Automatic Differentiation" width="600"/>
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<img src="https://gitee.com/mindspore/mindspore/raw/r1.5/docs/Automatic-differentiation.png" alt="Automatic Differentiation" width="600"/>
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The implementation of MindSpore automatic differentiation can be understood as the symbolic differentiation of the program itself. Because MindSpore IR is a functional intermediate expression, it has an intuitive correspondence with the composite function in basic algebra. The derivation formula of the composite function composed of arbitrary basic functions can be derived. Each primitive operation in MindSpore IR can correspond to the basic functions in basic algebra, which can build more complex flow control.
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@ -66,7 +66,7 @@ The implementation of MindSpore automatic differentiation can be understood as t
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The goal of MindSpore automatic parallel is to build a training method that combines data parallelism, model parallelism, and hybrid parallelism. It can automatically select a least cost model splitting strategy to achieve automatic distributed parallel training.
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<img src="https://gitee.com/mindspore/mindspore/raw/master/docs/Automatic-parallel.png" alt="Automatic Parallel" width="600"/>
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<img src="https://gitee.com/mindspore/mindspore/raw/r1.5/docs/Automatic-parallel.png" alt="Automatic Parallel" width="600"/>
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At present, MindSpore uses a fine-grained parallel strategy of splitting operators, that is, each operator in the figure is split into a cluster to complete parallel operations. The splitting strategy during this period may be very complicated, but as a developer advocating Pythonic, you don't need to care about the underlying implementation, as long as the top-level API compute is efficient.
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@ -240,7 +240,7 @@ please check out [docker](https://gitee.com/mindspore/mindspore/blob/master/dock
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## Quickstart
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See the [Quick Start](https://www.mindspore.cn/tutorials/en/master/quick_start.html)
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See the [Quick Start](https://www.mindspore.cn/tutorials/en/r1.5/quick_start.html)
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to implement the image classification.
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## Docs
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10
README_CN.md
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README_CN.md
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@ -39,9 +39,9 @@ MindSpore提供了友好的设计和高效的执行，旨在提升数据科学
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同时，MindSpore作为全球AI开源社区，致力于进一步开发和丰富AI软硬件应用生态。
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<img src="https://gitee.com/mindspore/mindspore/raw/master/docs/MindSpore-architecture.png" alt="MindSpore Architecture" width="600"/>
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<img src="https://gitee.com/mindspore/mindspore/raw/r1.5/docs/MindSpore-architecture.png" alt="MindSpore Architecture" width="600"/>
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欲了解更多详情，请查看我们的[总体架构](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/architecture.html)。
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欲了解更多详情，请查看我们的[总体架构](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.5/architecture.html)。
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### 自动微分
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@ -55,7 +55,7 @@ TensorFlow早期采用的是静态计算图，PyTorch采用的是动态计算图
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MindSpore找到了另一种方法，即基于源代码转换的自动微分。一方面，它支持自动控制流的自动微分，因此像PyTorch这样的模型构建非常方便。另一方面，MindSpore可以对神经网络进行静态编译优化，以获得更好的性能。
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<img src="https://gitee.com/mindspore/mindspore/raw/master/docs/Automatic-differentiation.png" alt="Automatic Differentiation" width="600"/>
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<img src="https://gitee.com/mindspore/mindspore/raw/r1.5/docs/Automatic-differentiation.png" alt="Automatic Differentiation" width="600"/>
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MindSpore自动微分的实现可以理解为程序本身的符号微分。MindSpore IR是一个函数中间表达式，它与基础代数中的复合函数具有直观的对应关系。复合函数的公式由任意可推导的基础函数组成。MindSpore IR中的每个原语操作都可以对应基础代数中的基本功能，从而可以建立更复杂的流控制。
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@ -63,7 +63,7 @@ MindSpore自动微分的实现可以理解为程序本身的符号微分。MindS
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MindSpore自动并行的目的是构建数据并行、模型并行和混合并行相结合的训练方法。该方法能够自动选择开销最小的模型切分策略，实现自动分布并行训练。
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<img src="https://gitee.com/mindspore/mindspore/raw/master/docs/Automatic-parallel.png" alt="Automatic Parallel" width="600"/>
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<img src="https://gitee.com/mindspore/mindspore/raw/r1.5/docs/Automatic-parallel.png" alt="Automatic Parallel" width="600"/>
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目前MindSpore采用的是算子切分的细粒度并行策略，即图中的每个算子被切分为一个集群，完成并行操作。在此期间的切分策略可能非常复杂，但是作为一名Python开发者，您无需关注底层实现，只要顶层API计算是有效的即可。
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@ -236,7 +236,7 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore
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## 快速入门
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参考[快速入门](https://www.mindspore.cn/tutorials/zh-CN/master/quick_start.html)实现图片分类。
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参考[快速入门](https://www.mindspore.cn/tutorials/zh-CN/r1.5/quick_start.html)实现图片分类。
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## 文档
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63
RELEASE.md
63
RELEASE.md
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@ -10,44 +10,44 @@
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- [BETA] Add CV models on Ascend: midas_V2, attgan, FairMOT, CenterNet_resnet101, SEResNext, YOLOV3-tiny, RetinaFace
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- [STABLE] Add CV models on GPU: ssd_mobilenetv1_fpn, shufflenetv1, tinyDarkNet, CNN-CTC, unet++, DeepText, SqueezeNet
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- [STABLE] Add NLP models on GPU: GRU, GNMT2, Bert-Squad
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- [STABLE] Add recommand models on GPU: NCF
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- [STABLE] Add recommend models on GPU: NCF
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- [BETA] Add CV models on GPU: FaceAttribute, FaceDetection, FaceRecongnition SENet,
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- [BETA] Add Audio models on GPU: DeepSpeech2
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- [STABLE]`model_zoo` has been seperated to an individual repository`models`
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- [STABLE]`model_zoo` has been separated to an individual repository`models`
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#### FrontEnd
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* [STABLE] Support`while` and`break`,`continue` statements of training network in`GRAPH_MODE`.
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* [BETA] Support export MindIR file after model training in cloud side and evaluate in edge side by import the MindIR file.
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* [STABLE] Support forward mode auto-diff interface Jvp(Jacobian-Vector-Product).
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* [STABLE] Support backward mode auto-diff interface Vjp(Vector-Jacobian-Product).
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- [STABLE] Support`while` and`break`,`continue` statements of training network in`GRAPH_MODE`.
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- [BETA] Support export MindIR file after model training in cloud side and evaluate in edge side by import the MindIR file.
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- [STABLE] Support forward mode auto-diff interface Jvp(Jacobian-Vector-Product).
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- [STABLE] Support backward mode auto-diff interface Vjp(Vector-Jacobian-Product).
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#### Auto Parallel
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* [STABLE] Support distributed pipeline inference.
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* [STABLE] Add implementation of the sparse attention and its distributed operator.
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* [STABLE] Add implementations of distributed operator of Conv2d/Conv2dTranspose/Conv2dBackpropInput/Maxpool/Avgpool/Batchnorm/Gatherd.
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* [STABLE] Support configuring the dataset strategy on distributed training and inference mode.
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* [STABLE] Add high level API of the Transformer module.
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- [STABLE] Support distributed pipeline inference.
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- [STABLE] Add implementation of the sparse attention and its distributed operator.
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- [STABLE] Add implementations of distributed operator of Conv2d/Conv2dTranspose/Conv2dBackpropInput/Maxpool/Avgpool/Batchnorm/Gatherd.
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- [STABLE] Support configuring the dataset strategy on distributed training and inference mode.
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- [STABLE] Add high level API of the Transformer module.
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#### Executor
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* [STABLE] Support AlltoAll operator.
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* [STABLE] CPU operator (Adam) performance optimization increased by 50%.
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* [BETA] Support Adam offload feature, reduce the static memory usage of Pangu large model by 50%.
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* [STABLE] MindSpore Ascend backend supports configuration operator generation and loading cache path.
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* [STABLE] MindSpore Ascend backend supports lazy build in PyNaitve mode and compilation performance improved by 10 times.
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* [STABLE] The function or Cell decorated by ms_function supports gradient calculation in PyNative mode.
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* [STABLE] The outermost network supports parameters of non tensor type in PyNative mode.
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- [STABLE] Support AlltoAll operator.
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- [STABLE] CPU operator (Adam) performance optimization increased by 50%.
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- [BETA] Support Adam offload feature, reduce the static memory usage of Pangu large model by 50%.
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- [STABLE] MindSpore Ascend backend supports configuration operator generation and loading cache path.
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- [STABLE] MindSpore Ascend backend supports lazy build in PyNaitve mode and compilation performance improved by 10 times.
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- [STABLE] The function or Cell decorated by ms_function supports gradient calculation in PyNative mode.
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- [STABLE] The outermost network supports parameters of non tensor type in PyNative mode.
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#### DataSet
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* [BETA] Add a new method for class Model to support auto data preprocessing in scenario of Ascend 310 inference.
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* [STABLE] Add a new drawing tool to visualize detection/segmentation datasets.
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* [STABLE] Support a new tensor operaiton named ConvertColor to support color space transform of images.
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* [STABLE] Enhance the following tensor operations to handle multiple columns simultaneously: RandomCrop, RandomHorizontalFlip, RandomResize, RandomResizedCrop, RandomVerticalFlip.
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* [STABLE] Support electromagnetic simulation dataset loading and data augmentation.
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* [STABLE] Optimze the error logs of Dataset to make them more friendly to users.
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- [BETA] Add a new method for class Model to support auto data preprocessing in scenario of Ascend 310 inference.
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- [STABLE] Add a new drawing tool to visualize detection/segmentation datasets.
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- [STABLE] Support a new tensor operation named ConvertColor to support color space transform of images.
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- [STABLE] Enhance the following tensor operations to handle multiple columns simultaneously: RandomCrop, RandomHorizontalFlip, RandomResize, RandomResizedCrop, RandomVerticalFlip.
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- [STABLE] Support electromagnetic simulation dataset loading and data augmentation.
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- [STABLE] Optimize the error logs of Dataset to make them more friendly to users.
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#### Federated Learning
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@ -68,7 +68,6 @@
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Configuring the recomputation of the communication operations generated by the model parallel and optimizer parallel to save the memory on the
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devices. Users can pass `mp_comm_recompute` and `parallel_optimizer_comm_recompute` to enable the recomputation of the communication operations.
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### Bug fixes
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#### FrontEnd
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@ -77,9 +76,9 @@ devices. Users can pass `mp_comm_recompute` and `parallel_optimizer_comm_recompu
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#### Executor
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* RunTask failed when parameter_broadcast is enabled in PyNative mode. ([!23255](https://gitee.com/mindspore/mindspore/pulls/23255))
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* An illegal memory access was encountered in the dynamic shape net on GPU.
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* Fix tune failed for DynamicRnn. ([!21081](https://gitee.com/mindspore/mindspore/pulls/21081))
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- RunTask failed when parameter_broadcast is enabled in PyNative mode. ([!23255](https://gitee.com/mindspore/mindspore/pulls/23255))
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- An illegal memory access was encountered in the dynamic shape net on GPU.
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- Fix tune failed for DynamicRnn. ([!21081](https://gitee.com/mindspore/mindspore/pulls/21081))
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#### Dataset
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2. Support dynamic filter Convolution.
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3. Support serializing float32 weight into float16 weight for reducing size of model file.
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4. Provide unified runtime API for developer reusing their code between cloud side and end side.
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5. Now developer can configure build-in pass as custom passes.
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5. Now developer can configure built-in pass as custom passes.
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6. Now user can specify format and shape of model inputs while converting model.
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7. Support multiple devices inference, includeing CPU, NPU, GPU. User can set devices in mindspore::Context.
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8. Support mixed precision inference. User can set inference precision by LoadConfig API.
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@ -278,7 +277,7 @@ thor(net, learning_rate, damping, momentum, weight_decay=0.0, loss_scale=1.0, ba
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##### Dump Config
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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).
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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/r1.5/dump_in_graph_mode.html#dump).
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| 1.2.1 | 1.3.0 |
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| ------------------------------------------------------ | ------------------------------------------------------------------------------------------- |
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@ -662,7 +661,7 @@ However, currently MindSpore Parser cannot parse numpy.ndarray in JIT-graph. To
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###### mindspore.numpy interfaces remove support for keyword arguments `out` and `where`([!12726](https://gitee.com/mindspore/mindspore/pulls/12726))
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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)
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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/r1.5/api_python/numpy/mindspore.numpy.where.html#mindspore.numpy.where)
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<table>
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<tr>
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###### `nn.MatMul` is now deprecated in favor of `ops.matmul` ([!12817](https://gitee.com/mindspore/mindspore/pulls/12817))
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[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.
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[ops.matmul](https://www.mindspore.cn/docs/api/en/r1.5/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/r1.5/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.
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<table>
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<tr>
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set larger too, otherwise a `core dumped` exception may be raised because of system stack overflow.
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enable_sparse (bool): Whether to enable sparsity feature. Default: False.
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For details of sparsity and sparse tensor, please check
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`sparse tensor <https://www.mindspore.cn/docs/programming_guide/en/master/tensor.html#sparse-tensor>`_.
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`sparse tensor <https://www.mindspore.cn/docs/programming_guide/en/r1.5/tensor.html#sparse-tensor>`_.
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grad_for_scalar (bool): Whether to get gradient for scalar. Default: False.
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When grad_for_scalar is set to True, the function's scalar input can be derived.
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The default value is False. Because the back-end does not support scaling operations currently,
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We can enable cache in most of the dataset with its key arguments 'cache'. Please notice that cache is not supported
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on Windows platform yet. Do not use it while loading and processing data on Windows. More introductions and limitations
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can refer `Single-Node Tensor Cache <https://www.mindspore.cn/docs/programming_guide/en/master/cache.html>`_.
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can refer `Single-Node Tensor Cache <https://www.mindspore.cn/docs/programming_guide/en/r1.5/cache.html>`_.
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"""
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from .core import config
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@ -26,8 +26,8 @@ class DatasetCache:
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"""
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A client to interface with tensor caching service.
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For details, please check `Tutorial <https://www.mindspore.cn/docs/programming_guide/en/master/enable_cache.html>`_,
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`Programming guide <https://www.mindspore.cn/docs/programming_guide/en/master/cache.html>`_.
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For details, please check `Tutorial <https://www.mindspore.cn/docs/programming_guide/en/r1.5/enable_cache.html>`_,
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`Programming guide <https://www.mindspore.cn/docs/programming_guide/en/r1.5/cache.html>`_.
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Args:
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session_id (int): A user assigned session id for the current pipeline.
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<img src="../../docs/MindSpore-Lite-architecture.png" alt="MindSpore Lite Architecture" width="600"/>
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For more details please check out our [MindSpore Lite Architecture Guide](https://www.mindspore.cn/lite/docs/en/master/architecture_lite.html).
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For more details please check out our [MindSpore Lite Architecture Guide](https://www.mindspore.cn/lite/docs/en/r1.5/architecture_lite.html).
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### MindSpore Lite features
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2. Model converter and optimization
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If you use MindSpore or a third-party model, you need to use [MindSpore Lite Model Converter Tool](https://www.mindspore.cn/lite/docs/en/master/use/converter_tool.html) to convert the model into MindSpore Lite model. The MindSpore Lite model converter tool provides the converter of TensorFlow Lite, Caffe, ONNX to MindSpore Lite model, fusion and quantization could be introduced during convert procedure.
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If you use MindSpore or a third-party model, you need to use [MindSpore Lite Model Converter Tool](https://www.mindspore.cn/lite/docs/en/r1.5/use/converter_tool.html) to convert the model into MindSpore Lite model. The MindSpore Lite model converter tool provides the converter of TensorFlow Lite, Caffe, ONNX to MindSpore Lite model, fusion and quantization could be introduced during convert procedure.
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MindSpore also provides a tool to convert models running on IoT devices .
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@ -51,7 +51,7 @@ For more details please check out our [MindSpore Lite Architecture Guide](https:
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4. Inference
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|
||||
Load the model and perform inference. [Inference](https://www.mindspore.cn/lite/docs/en/master/use/runtime.html) is the process of running input data through the model to get output.
|
||||
Load the model and perform inference. [Inference](https://www.mindspore.cn/lite/docs/en/r1.5/use/runtime.html) is the process of running input data through the model to get output.
|
||||
|
||||
MindSpore provides pre-trained model that can be deployed on mobile device [example](https://www.mindspore.cn/lite/examples/en).
|
||||
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ MindSpore Lite是MindSpore推出的端云协同的、轻量化、高性能AI推
|
|||
|
||||
<img src="../../docs/MindSpore-Lite-architecture.png" alt="MindSpore Lite Architecture" width="600"/>
|
||||
|
||||
欲了解更多详情，请查看我们的[MindSpore Lite 总体架构](https://www.mindspore.cn/lite/docs/zh-CN/master/architecture_lite.html)。
|
||||
欲了解更多详情，请查看我们的[MindSpore Lite 总体架构](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/architecture_lite.html)。
|
||||
|
||||
## MindSpore Lite技术特点
|
||||
|
||||
|
|
@ -47,7 +47,7 @@ MindSpore Lite是MindSpore推出的端云协同的、轻量化、高性能AI推
|
|||
|
||||
2. 模型转换/优化
|
||||
|
||||
如果您使用MindSpore或第三方训练的模型，需要使用[MindSpore Lite模型转换工具](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html)转换成MindSpore Lite模型格式。MindSpore Lite模型转换工具不仅提供了将TensorFlow Lite、Caffe、ONNX等模型格式转换为MindSpore Lite模型格式，还提供了算子融合、量化等功能。
|
||||
如果您使用MindSpore或第三方训练的模型，需要使用[MindSpore Lite模型转换工具](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/converter_tool.html)转换成MindSpore Lite模型格式。MindSpore Lite模型转换工具不仅提供了将TensorFlow Lite、Caffe、ONNX等模型格式转换为MindSpore Lite模型格式，还提供了算子融合、量化等功能。
|
||||
|
||||
MindSpore还提供了将IoT设备上运行的模型转换成.C代码的生成工具。
|
||||
|
||||
|
|
@ -59,7 +59,7 @@ MindSpore Lite是MindSpore推出的端云协同的、轻量化、高性能AI推
|
|||
|
||||
4. 模型推理
|
||||
|
||||
主要完成模型推理工作，即加载模型，完成模型相关的所有计算。[推理](https://www.mindspore.cn/lite/docs/zh-CN/master/use/runtime.html)是通过模型运行输入数据，获取预测的过程。
|
||||
主要完成模型推理工作，即加载模型，完成模型相关的所有计算。[推理](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/runtime.html)是通过模型运行输入数据，获取预测的过程。
|
||||
|
||||
MindSpore提供了预训练模型部署在智能终端的[样例](https://www.mindspore.cn/lite/examples)。
|
||||
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@
|
|||
bash build.sh
|
||||
```
|
||||
|
||||
> 若MindSpore Lite推理框架下载失败，请手动下载硬件平台为CPU，操作系统为Ubuntu-x64的[MindSpore Lite 模型推理框架](https://www.mindspore.cn/lite/docs/zh-CN/master/use/downloads.html)，解压后将其拷贝对应到`mindspore/lite/examples/runtime_cpp/lib`目录。
|
||||
> 若MindSpore Lite推理框架下载失败，请手动下载硬件平台为CPU，操作系统为Ubuntu-x64的[MindSpore Lite 模型推理框架](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/downloads.html)，解压后将其拷贝对应到`mindspore/lite/examples/runtime_cpp/lib`目录。
|
||||
>
|
||||
> 若mobilenetv2模型下载失败，请手动下载相关模型文件[mobilenetv2](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ms)，并将其拷贝到`mindspore/lite/examples/runtime_cpp/model`目录。
|
||||
|
||||
|
|
|
|||
|
|
@ -46,8 +46,8 @@ mnist/
|
|||
- Server side
|
||||
- [MindSpore Framework](https://www.mindspore.cn/install/en): it is recommended to install a docker image
|
||||
- MindSpore ToD Framework
|
||||
- [Downloads](https://www.mindspore.cn/lite/docs/en/master/use/downloads.html)
|
||||
- [Build](https://www.mindspore.cn/lite/docs/en/master/use/build.html)
|
||||
- [Downloads](https://www.mindspore.cn/lite/docs/en/r1.5/use/downloads.html)
|
||||
- [Build](https://www.mindspore.cn/lite/docs/en/r1.5/use/build.html)
|
||||
- [Android NDK r20b](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip)
|
||||
- [Android SDK](https://developer.android.com/studio?hl=zh-cn#cmdline-tools)
|
||||
- A connected Android device
|
||||
|
|
|
|||
|
|
@ -45,8 +45,8 @@ mnist/
|
|||
|
||||
- 服务器或个人笔记本
|
||||
- [MindSpore Framework](https://www.mindspore.cn/install): 建议使用Docker安装
|
||||
- [MindSpore ToD Download](https://www.mindspore.cn/lite/docs/zh-CN/master/use/downloads.html)
|
||||
- [MindSpore ToD Build](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html)
|
||||
- [MindSpore ToD Download](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/downloads.html)
|
||||
- [MindSpore ToD Build](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/build.html)
|
||||
- [Android NDK r20b](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip)
|
||||
- [Android SDK](https://developer.android.com/studio?hl=zh-cn#cmdline-tools)
|
||||
- Android移动设备
|
||||
|
|
|
|||
|
|
@ -48,8 +48,8 @@ places
|
|||
- Server side
|
||||
- [MindSpore Framework](https://www.mindspore.cn/install/en) - it is recommended to install a docker image
|
||||
- MindSpore ToD Framework
|
||||
- [Downloads](https://www.mindspore.cn/lite/docs/en/master/use/downloads.html)
|
||||
- [Build](https://www.mindspore.cn/lite/docs/en/master/use/build.html)
|
||||
- [Downloads](https://www.mindspore.cn/lite/docs/en/r1.5/use/downloads.html)
|
||||
- [Build](https://www.mindspore.cn/lite/docs/en/r1.5/use/build.html)
|
||||
- [Android NDK r20b](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip)
|
||||
- [Android SDK](https://developer.android.com/studio?hl=zh-cn#cmdline-tools)
|
||||
- [ImageMagick convert tool](https://imagemagick.org/)
|
||||
|
|
|
|||
|
|
@ -47,8 +47,8 @@ places
|
|||
|
||||
- 服务端
|
||||
- [MindSpore Framework](https://www.mindspore.cn/install/en) - 建议使用安装docker环境
|
||||
- [MindSpore ToD Download](https://www.mindspore.cn/lite/docs/zh-CN/master/use/downloads.html)
|
||||
- [MindSpore ToD Build](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html)
|
||||
- [MindSpore ToD Download](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/downloads.html)
|
||||
- [MindSpore ToD Build](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/build.html)
|
||||
- [Android NDK r20b](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip)
|
||||
- [Android SDK](https://developer.android.com/studio?hl=zh-cn#cmdline-tools)
|
||||
- [ImageMagick convert tool](https://imagemagick.org/)
|
||||
|
|
|
|||
|
|
@ -46,8 +46,8 @@ mnist/
|
|||
- Server side
|
||||
- [MindSpore Framework](https://www.mindspore.cn/install/en): it is recommended to install a docker image
|
||||
- MindSpore ToD Framework
|
||||
- [Downloads](https://www.mindspore.cn/lite/docs/en/master/use/downloads.html)
|
||||
- [Build](https://www.mindspore.cn/lite/docs/en/master/use/build.html)
|
||||
- [Downloads](https://www.mindspore.cn/lite/docs/en/r1.5/use/downloads.html)
|
||||
- [Build](https://www.mindspore.cn/lite/docs/en/r1.5/use/build.html)
|
||||
- [Android NDK r20b](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip)
|
||||
- [Android SDK](https://developer.android.com/studio?hl=zh-cn#cmdline-tools)
|
||||
- A connected Android device
|
||||
|
|
|
|||
|
|
@ -45,8 +45,8 @@ mnist/
|
|||
|
||||
- 服务器或个人笔记本
|
||||
- [MindSpore Framework](https://www.mindspore.cn/install): 建议使用Docker安装
|
||||
- [MindSpore ToD Download](https://www.mindspore.cn/lite/docs/zh-CN/master/use/downloads.html)
|
||||
- [MindSpore ToD Build](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html)
|
||||
- [MindSpore ToD Download](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/downloads.html)
|
||||
- [MindSpore ToD Build](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/build.html)
|
||||
- [Android NDK r20b](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip)
|
||||
- [Android SDK](https://developer.android.com/studio?hl=zh-cn#cmdline-tools)
|
||||
- Android移动设备
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@
|
|||
|
||||
## 模型编译体验
|
||||
|
||||
用户可以使用脚本一键式编译生成MNIST分类模型的推理代码并执行推理，得到单次推理输出。下载[MindSpore源码](https://gitee.com/mindspore/mindspore)，进入[`mindspore/mindspore/lite/micro/examples/mnist_x86`](https://gitee.com/mindspore/mindspore/tree/master/mindspore/lite/micro/example/mnist_x86)目录，执行脚本`mnist.sh`自动生成模型推理代码并编译工程目录，即可得到单次推理输出。
|
||||
用户可以使用脚本一键式编译生成MNIST分类模型的推理代码并执行推理，得到单次推理输出。下载[MindSpore源码](https://gitee.com/mindspore/mindspore)，进入[`mindspore/mindspore/lite/micro/examples/mnist_x86`](https://gitee.com/mindspore/mindspore/tree/r1.5/mindspore/lite/micro/example/mnist_x86)目录，执行脚本`mnist.sh`自动生成模型推理代码并编译工程目录，即可得到单次推理输出。
|
||||
|
||||
```bash
|
||||
bash mnist.sh
|
||||
|
|
@ -47,7 +47,7 @@ name: Softmax-7, DataType: 43, Size: 40, Shape: [1 10], Data:
|
|||
|
||||
## 详细步骤
|
||||
|
||||
在编译此工程之前需要预先获取Ubuntu-x64 CPU平台的[Release包](https://www.mindspore.cn/lite/docs/zh-CN/master/use/downloads.html)，解压后得到`mindspore-lite-{version}-linux-x64`，将其拷贝到当前目录下。
|
||||
在编译此工程之前需要预先获取Ubuntu-x64 CPU平台的[Release包](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/downloads.html)，解压后得到`mindspore-lite-{version}-linux-x64`，将其拷贝到当前目录下。
|
||||
|
||||
> `{version}`为版本号字符串，如`1.2.0`。
|
||||
|
||||
|
|
@ -79,7 +79,7 @@ name: Softmax-7, DataType: 43, Size: 40, Shape: [1 10], Data:
|
|||
```
|
||||
|
||||
codegen在当前目录下将生成mnist目录，其中包含了可编译构建的mnist分类模型的代码。
|
||||
> 关于codegen的更多使用命令说明，可参见[codegen使用说明](https://www.mindspore.cn/lite/docs/zh-CN/master/use/micro.html#id4)。
|
||||
> 关于codegen的更多使用命令说明，可参见[codegen使用说明](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/micro.html#id4)。
|
||||
|
||||
### 部署应用
|
||||
|
||||
|
|
@ -176,7 +176,7 @@ codegen在当前目录下将生成mnist目录，其中包含了可编译构建
|
|||
|
||||
#### 编写推理代码示例
|
||||
|
||||
本教程中的`benchmark`内部实现主要用于指导用户如何编写以及调用codegen编译的模型推理代码接口。以下为接口调用的详细介绍，详情代码可以参见[examples/mnist_x86](https://gitee.com/mindspore/mindspore/tree/master/mindspore/lite/micro/example/mnist_x86)下的示例代码示例：
|
||||
本教程中的`benchmark`内部实现主要用于指导用户如何编写以及调用codegen编译的模型推理代码接口。以下为接口调用的详细介绍，详情代码可以参见[examples/mnist_x86](https://gitee.com/mindspore/mindspore/tree/r1.5/mindspore/lite/micro/example/mnist_x86)下的示例代码示例：
|
||||
|
||||
1. **构建推理的上下文以及会话**
|
||||
|
||||
|
|
@ -263,6 +263,6 @@ codegen在当前目录下将生成mnist目录，其中包含了可编译构建
|
|||
|
||||
## 更多详情
|
||||
|
||||
### [Android平台编译部署](https://gitee.com/mindspore/mindspore/blob/master/mindspore/lite/micro/example/mobilenetv2/README.md#)
|
||||
### [Android平台编译部署](https://gitee.com/mindspore/mindspore/blob/r1.5/mindspore/lite/micro/example/mobilenetv2/README.md#)
|
||||
|
||||
### [Arm Cortex-M平台编译部署](https://www.mindspore.cn/lite/docs/zh-CN/master/use/micro.html)
|
||||
### [Arm Cortex-M平台编译部署](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/micro.html)
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@
|
|||
|
||||
## Android编译部署
|
||||
|
||||
本教程以MobileNetv2在安卓手机编译部署为例，使用用户快速了解codegen在安卓平台生成代码、工程构建以及部署的一系列流程。关于converter、codegen的获取以及详细参数介绍可参考mindspore的[编译构建介绍](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html)。
|
||||
本教程以MobileNetv2在安卓手机编译部署为例，使用用户快速了解codegen在安卓平台生成代码、工程构建以及部署的一系列流程。关于converter、codegen的获取以及详细参数介绍可参考mindspore的[编译构建介绍](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/build.html)。
|
||||
|
||||
### 编译依赖
|
||||
|
||||
|
|
@ -42,7 +42,7 @@ codegen编译[MobileNetv2模型](https://download.mindspore.cn/model_zoo/officia
|
|||
./codegen --codePath=. --modelPath=mobilenetv2.ms --target=ARM64
|
||||
```
|
||||
|
||||
关于codegen的更多使用命令说明，可参见[codegen工具的详细介绍](https://www.mindspore.cn/lite/docs/zh-CN/master/use/downloads.html)
|
||||
关于codegen的更多使用命令说明，可参见[codegen工具的详细介绍](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/downloads.html)
|
||||
|
||||
#### 生成代码工程说明
|
||||
|
||||
|
|
@ -53,7 +53,7 @@ codegen编译[MobileNetv2模型](https://download.mindspore.cn/model_zoo/officia
|
|||
|
||||
##### 算子静态库目录说明
|
||||
|
||||
在编译此工程之前需要预先获取安卓平台对应的[Release包](https://www.mindspore.cn/lite/docs/zh-CN/master/use/downloads.html)。
|
||||
在编译此工程之前需要预先获取安卓平台对应的[Release包](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/downloads.html)。
|
||||
|
||||
安卓平台对应的Release包的目录如下:
|
||||
|
||||
|
|
@ -144,6 +144,6 @@ name: Softmax-65, ,DataType: 43, Size: 4004, Shape:1 1001, Data:
|
|||
|
||||
## 更多详情
|
||||
|
||||
### [Linux_x86_64编译部署](https://www.mindspore.cn/lite/docs/zh-CN/master/use/micro.html)
|
||||
### [Linux_x86_64编译部署](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/use/micro.html)
|
||||
|
||||
### [STM32F746编译部署](https://gitee.com/mindspore/mindspore/tree/master/mindspore/lite/micro/example/mnist_stm32f746)
|
||||
### [STM32F746编译部署](https://gitee.com/mindspore/mindspore/tree/r1.5/mindspore/lite/micro/example/mnist_stm32f746)
|
||||
|
|
|
|||
|
|
@ -15,24 +15,24 @@ The objective of MDP is to integrate deep learning with Bayesian learning. On th
|
|||
|
||||
### Layer 1-1: Statistical distributions classes used to generate stochastic tensors
|
||||
|
||||
- Distributions ([mindspore.nn.probability.distribution](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/distribution)): A large collection of probability distributions.
|
||||
- Bijectors([mindspore.nn.probability.bijectors](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/bijector)): Reversible and composable transformations of random variables.
|
||||
- Distributions ([mindspore.nn.probability.distribution](https://gitee.com/mindspore/mindspore/tree/r1.5/mindspore/nn/probability/distribution)): A large collection of probability distributions.
|
||||
- Bijectors([mindspore.nn.probability.bijectors](https://gitee.com/mindspore/mindspore/tree/r1.5/mindspore/nn/probability/bijector)): Reversible and composable transformations of random variables.
|
||||
|
||||
### Layer 1-2: Probabilistic inference algorithms
|
||||
|
||||
- SVI([mindspore.nn.probability.infer.variational](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/infer/variational)): A unified interface for stochastic variational inference.
|
||||
- SVI([mindspore.nn.probability.infer.variational](https://gitee.com/mindspore/mindspore/tree/r1.5/mindspore/nn/probability/infer/variational)): A unified interface for stochastic variational inference.
|
||||
- MC: Algorithms for approximating integrals via sampling.
|
||||
|
||||
## Layer 2: Deep Probabilistic Programming (DPP) aims to provide composable BNN modules
|
||||
|
||||
- Layers([mindspore.nn.probability.bnn_layers](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/bnn_layers)): BNN layers, which are used to construct BNN.
|
||||
- Dpn([mindspore.nn.probability.dpn](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/dpn)): A bunch of BNN models that allow to be integrated into DNN;
|
||||
- Transform([mindspore.nn.probability.transforms](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/transforms)): Interfaces for the transformation between BNN and DNN;
|
||||
- Layers([mindspore.nn.probability.bnn_layers](https://gitee.com/mindspore/mindspore/tree/r1.5/mindspore/nn/probability/bnn_layers)): BNN layers, which are used to construct BNN.
|
||||
- Dpn([mindspore.nn.probability.dpn](https://gitee.com/mindspore/mindspore/tree/r1.5/mindspore/nn/probability/dpn)): A bunch of BNN models that allow to be integrated into DNN;
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- Transform([mindspore.nn.probability.transforms](https://gitee.com/mindspore/mindspore/tree/r1.5/mindspore/nn/probability/transforms)): Interfaces for the transformation between BNN and DNN;
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- Context: context managers for models and layers.
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## Layer 3: Toolbox provides a set of BNN tools for some specific applications
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- Uncertainty Estimation([mindspore.nn.probability.toolbox.uncertainty_evaluation](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/toolbox/uncertainty_evaluation.py)): Interfaces to estimate epistemic uncertainty and aleatoric uncertainty.
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- Uncertainty Estimation([mindspore.nn.probability.toolbox.uncertainty_evaluation](https://gitee.com/mindspore/mindspore/tree/r1.5/mindspore/nn/probability/toolbox/uncertainty_evaluation.py)): Interfaces to estimate epistemic uncertainty and aleatoric uncertainty.
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- OoD detection: Interfaces to detect out of distribution samples.
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@ -42,7 +42,7 @@ MDP requires MindSpore version 0.7.0-beta or later. MDP is actively evolving. In
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### Bayesian Neural Network
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1. Process the required dataset. The MNIST dateset is used in the example. Data processing is consistent with [Implementing an Image Classification Application](https://www.mindspore.cn/tutorials/en/master/quick_start.html) in Tutorial.
|
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1. Process the required dataset. The MNIST dateset is used in the example. Data processing is consistent with [Implementing an Image Classification Application](https://www.mindspore.cn/tutorials/en/r1.5/quick_start.html) in Tutorial.
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2. Define a Bayesian Neural Network. The bayesian LeNet is used in this example.
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@ -227,7 +227,7 @@ optimizer = nn.Adam(params=vae.trainable_params(), learning_rate=0.001)
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net_with_loss = nn.WithLossCell(vae, net_loss)
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```
|
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|
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3. Process the required dataset. The MNIST dateset is used in the example. Data processing is consistent with [Implementing an Image Classification Application](https://www.mindspore.cn/tutorials/en/master/quick_start.html) in Tutorial.
|
||||
3. Process the required dataset. The MNIST dateset is used in the example. Data processing is consistent with [Implementing an Image Classification Application](https://www.mindspore.cn/tutorials/en/r1.5/quick_start.html) in Tutorial.
|
||||
4. Use SVI interface to train VAE network. vi.run can return the trained network, get_train_loss can get the loss after training.
|
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|
||||
```python
|
||||
|
|
@ -437,7 +437,7 @@ if __name__ == "__main__":
|
|||
|
||||
The uncertainty estimation toolbox is based on MindSpore Deep Probabilistic Programming (MDP), and it is suitable for mainstream deep learning models, such as regression, classification, target detection and so on. In the inference stage, with the uncertainy estimation toolbox, developers only need to pass in the trained model and training dataset, specify the task and the samples to be estimated, then can obtain the aleatoric uncertainty and epistemic uncertainty. Based the uncertainty information, developers can understand the model and the dataset better.
|
||||
|
||||
In classification task, for example, the model is lenet model. The MNIST dateset is used in the example. Data processing is consistent with [Implementing an Image Classification Application](https://www.mindspore.cn/tutorials/en/master/quick_start.html) in Tutorial. For evaluating the uncertainty of test examples, the use of the toolbox is as follows:
|
||||
In classification task, for example, the model is lenet model. The MNIST dateset is used in the example. Data processing is consistent with [Implementing an Image Classification Application](https://www.mindspore.cn/tutorials/en/r1.5/quick_start.html) in Tutorial. For evaluating the uncertainty of test examples, the use of the toolbox is as follows:
|
||||
|
||||
```python
|
||||
from mindspore.nn.probability.toolbox.uncertainty_evaluation import UncertaintyEvaluation
|
||||
|
|
@ -465,15 +465,15 @@ for eval_data in ds_eval.create_dict_iterator():
|
|||
|
||||
## Examples
|
||||
|
||||
Examples in [mindspore/tests/st/probability](https://gitee.com/mindspore/mindspore/blob/master/tests/st/probability) are as follows:
|
||||
Examples in [mindspore/tests/st/probability](https://gitee.com/mindspore/mindspore/blob/r1.5/tests/st/probability) are as follows:
|
||||
|
||||
- [Bayesian LeNet](https://gitee.com/mindspore/mindspore/blob/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/blob/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/blob/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/blob/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/blob/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/blob/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/blob/master/tests/st/probability/toolbox/test_uncertainty.py): Evaluate uncertainty of model and data.
|
||||
- [Bayesian LeNet](https://gitee.com/mindspore/mindspore/blob/r1.5/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/blob/r1.5/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/blob/r1.5/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/blob/r1.5/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/blob/r1.5/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/blob/r1.5/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/blob/r1.5/tests/st/probability/toolbox/test_uncertainty.py): Evaluate uncertainty of model and data.
|
||||
|
||||
## Community
|
||||
|
||||
|
|
|
|||
|
|
@ -400,7 +400,7 @@ def check_version_and_env_config():
|
|||
except OSError:
|
||||
logger.warning(
|
||||
"Pre-Load Lirary libgomp.so.1 failed, this might cause cannot allocate TLS memory problem, "
|
||||
"if so find solution in FAQ in https://www.mindspore.cn/docs/faq/en/master/index.html.")
|
||||
"if so find solution in FAQ in https://www.mindspore.cn/docs/faq/en/r1.5/index.html.")
|
||||
elif __package_name__.lower() == "mindspore-gpu":
|
||||
env_checker = GPUEnvChecker()
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@
|
|||
ResNet--layer2-SequentialCell--0-ResidualBlock--conv2-Conv2d--Cast-op954_input_0_shape_128_128_3_3_kNumberTypeFloat32_DefaultFormat.bin`.
|
||||
可观察到Cast-op954，说明该算子的op_num为op954, 如下图所示。
|
||||
|
||||
脚本名： **[map_file_to_code.py](https://gitee.com/mindspore/mindspore/blob/master/scripts/map_dump_file_to_code/map_file_to_code.py)**; 执行方式:
|
||||
脚本名： **[map_file_to_code.py](https://gitee.com/mindspore/mindspore/blob/r1.5/scripts/map_dump_file_to_code/map_file_to_code.py)**; 执行方式:
|
||||
|
||||
```ruby
|
||||
python3 map_file_to_code.py
|
||||
|
|
|
|||
Loading…
Reference in New Issue