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Remove list of the supported ops by ONNX Importer (#2061)

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This commit is contained in:
Tomasz Socha
2020-09-07 10:50:55 +02:00
committed by GitHub
parent 75b4e193f7
commit a5389010a9
6 changed files with 23 additions and 219 deletions
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2
docs/IE_DG/Integrate_with_customer_application_new_API.md
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@@ -36,7 +36,7 @@ InferenceEngine::Core core;
```cpp
auto network = core.ReadNetwork("Model.xml");
```
**Or read the model from ONNX format** (.onnx and .prototxt are supported formats). You can find more information about the ONNX format support in the document [ONNX format support in the OpenVINO™](./ONNX_Supported_Ops.md).
**Or read the model from ONNX format** (.onnx and .prototxt are supported formats). You can find more information about the ONNX format support in the document [ONNX format support in the OpenVINO™](./ONNX_Support.md).
```cpp
auto network = core.ReadNetwork("model.onnx");
```

2
docs/IE_DG/Introduction.md
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@@ -117,7 +117,7 @@ Please refer to the [Overview of nGraph Flow](nGraph_Flow.md) describing the det
Inference Engine is a runtime that delivers a unified API to integrate the inference with application logic:
* Takes a model as an input. The model can be presented in [the native ONNX format](./ONNX_Supported_Ops.md) or in the specific form of [Intermediate Representation (IR)](../MO_DG/IR_and_opsets.md)
* Takes a model as an input. The model can be presented in [the native ONNX format](./ONNX_Support.md) or in the specific form of [Intermediate Representation (IR)](../MO_DG/IR_and_opsets.md)
produced by Model Optimizer.
* Optimizes inference execution for target hardware.
* Delivers inference solution with reduced footprint on embedded inference platforms.

2
docs/IE_DG/Migration_CoreAPI.md
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@@ -45,7 +45,7 @@ read networks using the Core class:
```cpp
CNNNetwork network = core.ReadNetwork(input_model);
```
The Core class also allows reading models from the ONNX format (more information is [here](./ONNX_Supported_Ops.md)):
The Core class also allows reading models from the ONNX format (more information is [here](./ONNX_Support.md)):
```cpp
CNNNetwork network = core.ReadNetwork("model.onnx");
```

19
docs/IE_DG/ONNX_Support.md Normal file
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@@ -0,0 +1,19 @@
# ONNX format support in the OpenVINO™ {#openvino_docs_IE_DG_ONNX_Support}
Starting from the 2020.4 release, OpenVINO™ supports reading native ONNX models.
`Core::ReadNetwork()` method provides a uniform way to read models from IR or ONNX format, it is a recommended approach to reading models.
Example:
```cpp
InferenceEngine::Core core;
auto network = core.ReadNetwork("model.onnx");
```
OpenVINO™ doesn't provide a mechanism to specify pre-processing (like mean values subtraction, reverse input channels) for the ONNX format.
If an ONNX model contains dynamic shapes for input, please use the `CNNNetwork::reshape` method for shape specialization.
Unsupported types of tensors:
* `string`,
* `complex64`,
* `complex128`.

215
docs/IE_DG/ONNX_Supported_Ops.md
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@@ -1,215 +0,0 @@
# ONNX format support in the OpenVINO™ {#openvino_docs_IE_DG_ONNX_Supported_Ops}
Starting from the 2020.4 release, OpenVINO™ supports reading native ONNX models.
`Core::ReadNetwork()` method provides a uniform way to read models from IR or ONNX format, it is a recommended approach to reading models.
Example:
```cpp
InferenceEngine::Core core;
auto network = core.ReadNetwork("model.onnx");
```
This document describes the list of supported ONNX operations and known limitations of this functionality.
OpenVINO™ doesn't provide a mechanism to specify pre-processing (like mean values subtraction, reverse input channels) for the ONNX format.
If an ONNX model contains dynamic shapes for input, please use the `CNNNetwork::reshape` method for shape specialization.
Generally nGraph doesn't support tensors of types:
* `string`,
* `complex64`,
* `complex128`.
Value in `()` _parenthesis_ indicates that this op was introduced since the specific
ONNX Standard opset version.
Values seperated by `-` _dash_ indicate the changes were made to that op definition
in the ONNX Standard. If there were minor changes they are usually supported by single
implementation, otherwise there are multiple versions, each appropriate for specific opset
version range.
For example, with the schema represented below the operator `Abs` is supported in all
opset versions starting from `1` to `6` and to the latest opset version.
## Supported Ops:
| Name | ONNX Opset supported | nGraph opset support | Comment |
|------|----------------------------|---------|-----|
| Abs | 1-6- | 0,1 |
| Acos | 7- | 0,1 |
| Acosh | 9- | 0, | Have to change to only v1 ops (NGONNX-1015)
| Add | 1-6-7- | 0,1 |
| And | 1-7- | 0,1 |
| ArgMax | 1- | 0,1 |
| ArgMin | 1- | 0,1 |
| Asin | 7- | 0,1 |
| Asinh | 9- | 0, | Have to change to only v1 ops (NGONNX-1015)
| Atan | 7 - | 0,1 |
| Atanh | 9- | 0, | Have to change to only v1 ops (NGONNX-1015)
| AveragePool | 1-7- | 0,1 |
| BatchNormalization | 1-6-7- | 0,1 |
| Cast | 1-6-9- | 0,1 |
| Ceil | 1-6- | 0,1 |
| Clip | 1-6- | 0,1 |
| Concat | 1-4- | 0,1 |
| Constant | 1- | 0,1 |
| Conv | 1- | 0,1 |
| ConvInteger | 10- | 0, |
| ConvTranspose | 1- | 0,1 |
| Cos | 7- | 0,1 |
| Cosh | 9- | 0,1 | Have to change to only v1 ops (NGONNX-1015)
| CumSum | 11- | 0, | NGONNX-944
| DepthToSpace | 1-11- | 0,1 |
| DequantizeLinear | 10- | 0, |
| Div | 1-6-7- | 0,1 |
| Dropout | (1-6-7)-10- | 0,1 | Only for inference.
| Elu | 1-6- | 0,1 |
| Equal | 1-7 | 0,1 |
| Erf | 9- | 0,1 |
| Exp | 1-6- | 0,1 |
| Expand | 8- | 0,1 | Only static version
| EyeLike | 9- | 0,1 |
| Flatten | 1-9- | 0,1 |
| Floor | 1-6- | 0,1 |
| Gather | 11- | 0,1 |
| GatherND | 11- | 0, |
| Gemm | 1-6-7-9-11 | 0, | Some tests failing (NGONNX-494), Have to change to only v1 ops (NGONNX-1015)
| GlobalAveragePool | 1- | 0,1 |
| GlobalLpPool | 1-2- | 0, (1) | Not fully v1, need `lp_norm` expressed with v1 ops (NGONNX-1018)
| GlobalMaxPool | 1- | 0,1 |
| Greater | 1-7-9 | 0,1 |
| HardSigmoid | 1-6- | 0,1 |
| Hardmax | 11- | 0, (1) | GOE
| Identity | 1- | 0,1 |
| InstanceNormalization | 1- | 0, (1) | Have to change to only v1 ops (NGONNX-1015)
| LRN | 1- | 0,1 |
| LSTM | 1-7- | 0,(1) |
| LeakyRelu | 1-6- | 0,(1) | (NGONNX-1015)
| Less | 1-7-9 | 0,1 |
| Log | 1-6- | 0,1 |
| LogSoftmax | 1- | 0,1 |
| LpNormalization | 1- | 0, | (NGONNX-1018) need to update some builders
| MatMul | 1-9 | 0,(1) | Uses `v0::Dot`, v0 broadcasts and reshapes, update builders
| MatMulInteger | 10- | 0, | `v0::QuantizedDot`
| Max | 1-6-8- | 0, 1 |
| MaxPool | 1-8- | 0, 1 |
| Mean | 1-6-8- | 0, 1 |
| Min | 1-6-8- | 0,1 |
| Mod | 10- | 1 |
| Mul | (1-6-)7- | 0,1 |
| Neg | 1-6- | 0,1 |
| NonMaxSuppression | 11- | 1 |
| Not | 1- | 0,1 | (aka `v1::LogicalNot`)
| OneHot | (9) | 0, (1) | (NGONNX-1015)
| Or | 1-7- | 0,1 | (aka `v1::LogicalOr`)
| PRelu | 1-6-7-9 | 0, 1 | fused op uses arithmetic and broadcasting from v0
| Pad | 1-2-11- | 0, (1) | (NGONNX-1015)
| Pow | 1-7- | 0,1 |
| QLinearConv | 10- | 0 | `opset0::QuantizedConvolution`
| QLinearMatMul | 10- | 0 | `v0::QuantizedDot`
| QuantizeLinear | 10- | 0 | `opset0::Quantize`
| Reciprocal | 1-6- | 0, 1|
| ReduceL1 | 1- | 0, | (NGONNX-1018)
| ReduceL2 | 1- | 0,1 |
| ReduceLogSum | 1- | 0,1 |
| ReduceLogSumExp | 1- | 0,1 |
| ReduceMax | 1- | 0,1 |
| ReduceMean | 1- | 0,1 |
| ReduceMin | 1- | 0,1 |
| ReduceProd | 1- | 0,1 |
| ReduceSum | 1- | 0,1 |
| ReduceSumSquare | 1- | 0,1 |
| Relu | 1-6- | 0,1 |
| Reshape | 1-5- | (0,1) | v1 supports dynamic target shape, but only as Constant?
| ReverseSequence | 10- | 0,1 |
| ScatterND | 11- | 0, |
| Selu | 1-6- | 0, 1 |
| Shape | 1- | 0,1 |
| Shrink | 1- | 0,1 |
| Sigmoid | 1-6- | 0,1 |
| Sign | 9- | 0,1 |
| Sin | 7- | 0,1 |
| Sinh | 9- | 0,1 |
| Size | 1- | 0,1 |
| Slice | 1- | 0,1 |
| Softmax | 1- | 0,1 |
| Softplus | 1- | 0,1 |
| Softsign | 1- | 0,(1) | (NGONNX-1015)
| SpaceToDepth | 1- | 0,1 |
| Split | 1-2- | 0,1 |
| Sqrt | 1-6- | 0,1 |
| Squeeze | 1- | 0,(1) |
| Sub | (1-6-)7- | 0,1 |
| Sum | 1-6-8- | 0,1 |
| Tan | 7- | 0,1 |
| Tanh | 1-6- | 0,1 |
| ThresholdedRelu | 10- | 0,1 |
| TopK | 1- | 0,(1) | Need v0::GOE
| Transpose | 1- | 0,1 |
| Unsqueeze | 1- | 0,1 |
| Xor | 1-7- | 0,1 |
| Where | 9- | 0,1 |
### Able to implement or WIP
| Name | Opset supported | NGCORE | NGONNX | Comment |
|------|-----------------|--------|--------|---------|
| BitShift | (11)- | | 1014 |
| ConstantOfShape | (9) | 286 | 445 | Dynamic shape input. WIP |
| DynamicQuantizeLinear | (11) | | 786 |
| GRU | - | | 325, 177 | There is no `GRUCell` nor `GRU` in v1 |
| RNN | - | | 323, 287 | `v1::RNNCell`|
| Round | (11)- | | 1008 | `v0::Round`
| Tile | - | NGRAPH-3292 | 368 | Dynamic op. WIP |
| Cast | 1-6- | 290 | 452 | Float16 unsupported. |
## Unsupported Ops:
### Lack of support in nGraph
| Name | Opset supported | NGCORE | NGONNX | Comment |
|------|-----------------|--------|--------|---------|
| MaxUnpool | (9) | 286, 289 | 447 | |
| LpPool | - | 291 | 488 | Unsupported by nGraph - only max/avg pooling ops. Need separate kernel. |
| Multinomial | - | 199 | 435 | Lack of PRNG in nGraph. |
| RandomNormal | - | 199 | 434 | Lack of PRNG in nGraph. |
| RandomNormalLike | - | 199 | 434 | Lack of PRNG in nGraph. |
| IsInf | (10) | | 528 |
| StringNormalizer | (10) | | 600 | Need support for `string` data type.
| TfIdfVectorizer | (9) | | 523 |
| Det | (11) | | 754 | |
### Futher analysis needed
| Name | Opset supported | NGCORE | NGONNX | Comment |
|------|-----------------|--------|--------|---------|
| If | - | | 432 | At this moment probably impossible. |
| IsNaN | (9) | | 440 | Hacky way is to generate constant nodes with representations of NaN and compare with them. |
| Loop | - | | 432 | Static loops with some preconditions may be possible, however no idea how to pass graph (proto?) as a _body_ attribute. (what about graph contains `Loop`?) |
| Scan | - | | 433 | Further analysis needed. - determine whether it is possible to import graph passed by op attribute. |
| Einsum | (12) | | | User can define in a language the operation to perform |
| NonZero | (9) | | 472 | Maybe we can leverage TopK here? First count NonZero elements with logic ops and reduction and then TopK.
| Resize | (10-11)- | | 782 | Look like Interpolation over ROIs. Very specialized types of interpolation.
| ScatterElements | (11) | | 977 |
| ScatterND | (11) | | 1020 |
| Unique | (11) | | 761 |
### Dynamic operators
| Name | Opset supported | NGCORE | NGONNX | Comment |
|------|-----------------|--------|--------|---------|
| Compress | (9-11) | 285 | 438 | Dynamically selected indices |
| Expand | - | NGRAPH-3289 | 367 | Dynamic op. |
| GatherElements | - | | 757 | |
| OneHot | (9) | NGCORE-339 | 486 | Dynamic output shape
| Upsample | (7-9-10-) | 287 | 441 | Dynamic op. **Deprecated** from opset 10 |
| MaxRoiPool | - | 288 | 487 | Dynamic op - Need dynamic slicing. Beside just use _slice/op/concat_ pattern. |
| Reshape | 1-5- | NGRAPH-3290 | 357 | Lack of support for dynamic shape input. Only as a Constant or as an Initializer. |
| Scatter | (9) | 289 | 446 | Dynamic indices input. **Deprecated** in ONNX standard |
| RoiAlign | (10) | | 601 |
### Sequence* ops
| Name | Opset supported | NGCORE | NGONNX | Comment |
|------|-----------------|--------|--------|---------|
| ConcatFromSequence | (11)- | | 1016 |
| SequenceAt | (11) | | 1021 | need further analysis |
| SequenceConstruct | (11) | | 1021 | need further analysis |
| SequenceEmpty | (11) | | 1021 | need further analysis |
| SequenceErase | (11) | | 1021 | need further analysis |
| SequenceInsert | (11) | | 1021 | need further analysis |
| SequenceLength | (11) | | 1021 | need further analysis |
| SplitToSequence | (11) | | 1021 | need further analysis |

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docs/doxygen/ie_docs.xml
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@@ -267,7 +267,7 @@
<tab type="user" title="Introduction to Performance Topics" url="@ref openvino_docs_IE_DG_Intro_to_Performance"/>
<tab type="user" title="Inference Engine Python* API Overview" url="@ref openvino_inference_engine_ie_bridges_python_docs_api_overview"/>
<tab type="user" title="Build a Model with nGraph" url="@ref openvino_docs_IE_DG_nGraphTutorial"/>
<tab type="user" title="Read an ONNX model" url="@ref openvino_docs_IE_DG_ONNX_Supported_Ops"/>
<tab type="user" title="Read an ONNX model" url="@ref openvino_docs_IE_DG_ONNX_Support"/>
<tab type="user" title="[DEPRECATED] Import an ONNX model" url="@ref openvino_docs_IE_DG_OnnxImporterTutorial"/>
<tab type="user" title="Graph Debug Capabilities" url="@ref openvino_docs_IE_DG_Graph_debug_capabilities"/>
<tab type="user" title="Using Dynamic Batching Feature" url="@ref openvino_docs_IE_DG_DynamicBatching"/>