93d2bae95a
* Move Mul node before Reshape in GroupConvolutionMultiplyFusion
In following scenario:
+-----------+
| weights |
+-----------+
|
v
+-----------+
| Reshape |
.... +-----------+
\ /
\ /
v v
+--------------------+
| GroupConvolution |
+--------------------+
|
v
+-----------+ +----------+
| Multiply | <--- | Constant |
+-----------+ +----------+
if a second input (weights) to GroupConvolution is a Reshape,
we should apply Multiply node directly to weights rather than Reshape node:
+-----------+
| weights |
+-----------+
|
v
+-----------+ +----------+
| Multiply | <--- | Constant |
+-----------+ +----------+
|
v
+-----------+
| Reshape |
.... +-----------+
\ /
\ /
v v
+--------------------+
| GroupConvolution |
+--------------------+
|
v
That approach has no side effects in the usual scenario when weights are constant,
but it's necessary for models with FakeQuantize since it gives possibility to perform
GroupConvolutionTransformation from LPT transformations.
* remove unnecessary new lines
* simplify code
* check if input to reshape has shape that starts with G * O
* add pattern::has_static_dims({0, 1}) to weights
* Use K = 3 in WeightsWithFakeQuantizeAndReshape tests
* Add LPT tests for groupconvolution_qdq_transformation
* fix PullReshapeThroughDequantizationTransformation tests
|
||
|---|---|---|
| .ci | ||
| .github | ||
| cmake | ||
| docs | ||
| inference-engine | ||
| licensing | ||
| model-optimizer | ||
| ngraph | ||
| openvino | ||
| scripts | ||
| tests | ||
| thirdparty | ||
| tools | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| CMakeLists.txt | ||
| CODEOWNERS | ||
| install_build_dependencies.sh | ||
| Jenkinsfile | ||
| LICENSE | ||
| README.md | ||
| SECURITY.md | ||
OpenVINO™ Toolkit
This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.
This open source version includes several components: namely Model Optimizer, nGraph and Inference Engine, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as Caffe*, TensorFlow*, MXNet* and ONNX*.
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