mirror of
https://gitlab.freedesktop.org/gstreamer/gstreamer.git
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Daniel Morin
parent
ab9bc34384
commit
ddf81294d3
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@ -251,10 +251,42 @@ specific to machine-learning techniques and can also be used to store analysis
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results from computer-vision, heuristics or other techniques. It can be used as
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a bridge between different techniques.
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##### Storing Tensors Into Analytics Meta
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To be able to describe more precisely analytics results, an analytics pipeline
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where the output tensor of the first inference stage is directly pushed, without tensor decoding,
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into a second inference stage. It would be useful to store those tensors using
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analytics-meta because we could communicate the relation between tensor of first
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inference and tensor of second inference. With the relation description a
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tensor-decoder of second inference would be able to retrieve associated tensor of
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of first inference and extract potentially useful information that is not
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available the tensor of the second inference.
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### Semantically-Agnostic Tensor Processing
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Not all tensor processing is model dependent. Sometime the processing can be
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done uniformly on all tensor's values. For example normalization, range
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adjustment, offset adjustment, quantization are examples of operations that do
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not require knowledge of how the information is encoded in the tensor. To the
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contrary of tensor-decoder, elements implementing these types of processing
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don't need to know how information is encoded in the tensor but need to know
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general information about the tensor like: cardinality, dimension and data type.
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Note GStreamer already does a lot of semantically-agnostic tensor processing,
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remember image/frame are also a form of tensor, processing like scaling,
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cropping, color-space conversion, ...
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#### Semantically-Agnostic Tensor Processing With Graph-Computing Framework
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Graph computing frameworks, like ONNX, can also be used this type of operation.
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#### Tensor-Decoder Bin And Auto-Plugging
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Since tensor-decoders are model specific, we expect that many will be created
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and one way to simplify analytics pipeline creation an promote re-usability is to provide
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tensor-decoder bin able to identify the correct tensor-decoder for the tensor to
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be decoded. It's possible to have multiple tensor-decoder able to decode the
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exact same decoder, but making use of specific acceleration. Rank can be used to
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identify the ideal tensor-decoder for a specific platform.
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### Tensor Transport Mode
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Two transport mode are envisioned as Meta or as Media. Both mode have pros and
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cons which justify supporting both mode. Currently tensor are only transported
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as meta.
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cons which justify supporting both mode.
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#### Tensor Transport As Meta
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In this mode tensor is attached to the buffer (the media) on which the analysis
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@ -266,13 +298,150 @@ advantage is the ability to keep a relation description between tensors in a
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refinement context On the other hand this mode of transporting analytics result
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make negotiation of tensor-decoder in particular difficult.
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#### Tensor Transport As Media
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In this mode tensor is a media, potentially referring to buffer (original media on
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which the analysis was performed) using a Meta (idea behind OriginalBufferMeta MR).
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The advantage of this mode is tensor-decoder negotiation is simple, but
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association of the analytics result with the original buffer on which the
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analysis was performed is more difficult.
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*Also note this the mode of transport used by NNStreamer.*
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### Negotiation
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Allowing to negotiate the required analysis pre/post-processing and automatically
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injecting the required elements to able to perform them would be very valuable and
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minimize effort of porting an analytics pipeline between different platforms and
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making use of acceleration available. Tensor-decoders bin, auto-plugging of
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pre-processing (considering acceleration available), auto-plugging of inference
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element (optimized of the platform), post-processing, tensor-decoder bin
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selecting required tensor-decoders potentially from multiple functionally
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equivalent, but more adapted to the platform are all aspect to consider when
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designing negotiation involved in analytics-pipeline.
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#### Negotiating Tensor-Decoder
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As described above tensor-decoder need to know 4 attributes about a tensor to
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know if it can handle it:
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1. Tensor dimension cardinality ( not required explicitly in some cases)
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2. Tensor dimension
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3. Tensor datatype
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4. Tensor type (identifier of analytics-result encoding semantic)
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Note 1, 2, 3 could be encoded into 4, but this is not desirable because 1,2,3
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are useful for selection semantically-agnostic tensor processor.
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Tensor-decoder can handle multiple tensor types. This could be expressed in the
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sinkpad(s) template by a list of arrays where each combination of tensor types
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it can handle would be expressed. This would make the sinkpad(s) caps difficult
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to read. To avoid this problem when a tensor-decoder handle multiple tensors the
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tensor type is a category the encapsulate all tensor type it can handle.
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Referring again to YOLOv3's 3 tensors: small, medium large, all 3 would have the
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same tensor-type identifier, ex YOLOv3, and each tensors themselves would have
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sub-type field distinguishing them ('small', 'medium', 'large'). Same also
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applies to FastSAM 2 tensors ('FastSAM-masks', 'FastSAM-logits') where both
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would be represented by the same tensor type ('FastSAM') in pad capability level.
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When tensor is stored as a meta, allocation query need to be used to negotiate
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tensor-decoder. TODO: expand how this would work.
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##### Tensor-Decoder Sinkpad Caps Examples
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Examples assuming object-detection on video frame
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```
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PadTemplates:
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SINK template: 'vsink' // Tensor attached on to buffer
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Avaiability: 'always'
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Capabilities:
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video/x-raw
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format: {...}
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width: {...}
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height: {...}
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framerate: {...}
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SINK template: 'tsink' // Tensor
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Avaiability: 'always'
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Capabilities:
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tensor/x-raw
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shape:{<a, b, ...z>} // This represent a x b x ... x z
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datatype: {(enum) "int8", "float32", ...}
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type: { (string)"YOLOv3", (string)"YOLOv4", (string)"SSD", ...)}
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```
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##### Tensor-Decoder Srcpad(s)
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Typically will be the same as sinkpad but could be different. In general
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tensor-decoder only attach an analytics-meta to buffer. Analytics-meta
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consumption is left to other downstream elements. It's also possible for
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tensor-decoder to have very different caps on srcpad. This can be the case when
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analytics-result is difficult to represent like text-to-speech or
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super-resolution. In these case the tensor-decoder could be producing a media
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directly. audio for TTS or image for super-resolution.
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### Inference Sinkpad(s) Capabilities
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Sinkpad capability, before been constrained based on model, can be any
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media type.
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media type, including `tensor` . Note that multiple sinkpads can be present.
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#### Batch Inference
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To support batch inference `tensor` media type need to be used. Batching is
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a method used to spread the fixed time cost of scheduling work on some
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accelerator (GPU). Multiple samples (buffers) are aggregated into a batch that
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is pushed to the inference. When batching is used, output tensor will also contain
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analytics results in the form of a batch. Un-batching require information on how
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the batch was formed: buffer timestamp, buffer source, media type, buffer caps.
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A batch can be formed from a single source, forming the batch with time multiplexing
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of from multiple sources, time and source multiplexing. Once multiplexed, the
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batch can be pushed to the inference element as a tensor media.
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TODO: Describe TensorBatchMeta
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### Inference Srcpad(s) Capabilities
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Srcpads capabilities, will be identical to sinkpads capabilities.
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Srcpads capabilities, will be identical to sinkpads capabilities or a
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```tensor```.
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```
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PadTemplates:
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SRC template: 'vsrc_%u' // Tensor attached on to buffer
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Avaiability: 'always'
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Capabilities:
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video/x-raw
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format: {...}
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width: {...}
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height: {...}
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framerate: {...}
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SRC template: 'asrc_%u' // Tensor attached on to buffer
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Avaiability: 'always'
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Capabilities:
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audio/x-raw
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format: {...}
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layout: {...}
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rate: [...]
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channels: [...]
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SRC template: 'tsrc_%u' // Tensor attached on to buffer
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Avaiability: 'always'
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Capabilities:
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text/x-raw
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format: {...}
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SRC template: 'src_%u' // Tensor
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Avaiability: 'always'
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Capabilities:
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tensor/x-raw
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shape:{<a, b, ...z>} // This represent a x b x ... x z
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datatype: {(enum) "int8", "float32", ...}
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type: { (string)"YOLOv3", (string)"YOLOv4", (string)"SSD", ...)}
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```
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### New Video Format
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TODO
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- We need to add floating point video formats
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### Batch Aggregator Element
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TODO
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# Reference
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- [Onnx-Refactor-MR](https://gitlab.freedesktop.org/gstreamer/gstreamer/-/merge_requests/4916)
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