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ViT-B-32__openai/textual/ Runs with emulator now.

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This commit is contained in:
Yoni Yang 2024-12-01 16:42:53 +00:00
parent 7fddf282cf
commit bc849e2e9f
3 changed files with 14 additions and 7 deletions
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2
machine-learning/app/config.py
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@ -44,7 +44,7 @@ class Settings(BaseSettings):
ann: bool = True
ann_fp16_turbo: bool = False
ann_tuning_level: int = 2
rknn: bool = True
rknn: bool = False
preload: PreloadModelData | None = None
max_batch_size: MaxBatchSize | None = None

1
machine-learning/app/sessions/ort.py
View File

@ -46,6 +46,7 @@ class OrtSession:
input_feed: dict[str, NDArray[np.float32]] | dict[str, NDArray[np.int32]],
run_options: Any = None,
) -> list[NDArray[np.float32]]:
print(input_feed)
outputs: list[NDArray[np.float32]] = self.session.run(output_names, input_feed, run_options)
return outputs

18
machine-learning/app/sessions/rknn.py
View File

@ -17,15 +17,20 @@ class RknnSession:
def __init__(self, model_path: Path | str):
self.model_path = Path(model_path)
self.rknn = RKNN() # Initialize RKNN object
self.rknn.config(target_platform='rk3566')
# Load the RKNN model
log.info(f"Loading RKNN model from {self.model_path}")
self._load_model()
def _load_model(self) -> None:
ret = self.rknn.load_rknn(self.model_path.as_posix())
ret = self.rknn.load_onnx(self.model_path.as_posix())
if ret != 0:
raise RuntimeError("Failed to load RKNN model")
print('--> Building model')
ret = self.rknn.build(do_quantization=False)
if ret != 0:
print('Build model failed!')
exit(ret)
ret = self.rknn.init_runtime()
if ret != 0:
@ -41,15 +46,16 @@ class RknnSession:
def run(
self,
input_feed: dict[str, NDArray[np.float32] | NDArray[np.int32]],
output_names: list[str] | None,
input_feed: dict[str, NDArray[np.float32]] | dict[str, NDArray[np.int32]],
run_options: Any = None,
) -> List[NDArray[np.float32]]:
print(input_feed)
inputs = [v for v in input_feed.values()]
# Run inference
log.debug(f"Running inference on RKNN model")
ret, outputs = self.rknn.inference(inputs=inputs)
if ret != 0:
raise RuntimeError("Inference failed")
outputs = self.rknn.inference(inputs=inputs)
return outputs
def release(self) -> None: