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Tensor and Sample

The runtime data model has two primary types:

  • Tensor: typed numeric payload (shape, dtype, layout, storage, device).
  • Sample: envelope around tensor payload with media/runtime metadata.

Tensor​

Use Tensor when you only need numeric data and shape/type semantics.

Typical metadata in tensor:

  • shape / dtype / layout
  • storage and mapping behavior
  • optional image/audio/encoded semantic tags

Related references:

NumPy and PyTorch interop​

For Python developers familiar with NumPy and PyTorch, Tensor supports DLPack-based interop:

  • Tensor.from_numpy(...)
  • Tensor.to_numpy(...)
  • Tensor.from_torch(...)
  • Tensor.to_torch(...)
  • Tensor.from_dlpack(...)
  • Tensor.__dlpack__()

This keeps interop paths explicit and enables zero-copy where backend data layout permits it.

import numpy as np
import pyneat as neat

# HWC uint8 image-like tensor
arr = np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8)

t = neat.Tensor.from_numpy(arr, copy=False, image_format=neat.PixelFormat.RGB)
arr_back = t.to_numpy(copy=False)

# If you already have a model, NumPy can be passed directly.
# out = model.run(arr, timeout_ms=2000)

Sample​

Use Sample when you need pipeline metadata in addition to tensor bytes.

Typical sample fields:

  • caps_string, media_type, payload_tag
  • pts_ns, dts_ns, duration_ns
  • stream_id, frame_id, port_name
  • fields for bundle outputs

Related references:

#include "neat/session.h"
#include "neat/nodes.h"

simaai::neat::Session session;
session.add(simaai::neat::nodes::Input({}));
session.add(simaai::neat::nodes::Output({}));

auto run = session.build(simaai::neat::Sample{}, simaai::neat::RunMode::Async);

simaai::neat::Sample in;
in.kind = simaai::neat::SampleKind::Tensor;
in.stream_id = "cam-0";
in.frame_id = 42;
// in.tensor = ...;

run.push(in);
auto out = run.pull(1000);
if (out) {
  std::cout << "stream=" << out->stream_id
            << " frame=" << out->frame_id
            << " media_type=" << out->media_type << "\n";
}

Runtime handle​

Both types flow through Run (push, pull, push_and_pull, run).

See also​

Tutorials​