I have a custom trained yolov8n.pt that works fine when converted to openvino.
I did the steps detaild in the hailomz user guide:
I first created the .har file:
hailomz parse --hw-arch hailo8 --ckpt …/yolov8/yolov8n.onnx yolov8n
then ran:
hailomz optimize yolov8n --har ./yolov8n.har --calib-path …/yolov8/valid/images/
But I get the following error output.
Any help?
Start run for network yolov8n …
Initializing the hailo8 runner…
Preparing calibration data…
[info] Loading model script commands to yolov8n from /home/a/Hailo/hailo_ai_sw_suite/sources/model_zoo/hailo_model_zoo/cfg/alls/generic/yolov8n.alls
[info] Starting Model Optimization
[warning] Reducing optimization level to 0 (the accuracy won’t be optimized and compression won’t be used) because there’s no available GPU
[warning] Running model optimization with zero level of optimization is not recommended for production use and might lead to suboptimal accuracy results
[info] Model received quantization params from the hn
Traceback (most recent call last):
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/bin/hailomz”, line 33, in
sys.exit(load_entry_point(‘hailo-model-zoo’, ‘console_scripts’, ‘hailomz’)())
File “/home/a/Hailo/hailo_ai_sw_suite/sources/model_zoo/hailo_model_zoo/main.py”, line 122, in main
run(args)
File “/home/a/Hailo/hailo_ai_sw_suite/sources/model_zoo/hailo_model_zoo/main.py”, line 111, in run
return handlersargs.command
File “/home/a/Hailo/hailo_ai_sw_suite/sources/model_zoo/hailo_model_zoo/main_driver.py”, line 227, in optimize
optimize_model(
File “/home/a/Hailo/hailo_ai_sw_suite/sources/model_zoo/hailo_model_zoo/core/main_utils.py”, line 326, in optimize_model
runner.optimize(calib_feed_callback)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_common/states/states.py”, line 16, in wrapped_func
return func(self, *args, **kwargs)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_client/runner/client_runner.py”, line 2093, in optimize
self.optimize(calib_data, data_type=data_type, work_dir=work_dir)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_common/states/states.py”, line 16, in wrapped_func
return func(self, *args, **kwargs)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_client/runner/client_runner.py”, line 1935, in optimize
self.sdk_backend.full_quantization(calib_data, data_type=data_type, work_dir=work_dir)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_client/sdk_backend/sdk_backend.py”, line 1045, in full_quantization
self.full_acceleras_run(self.calibration_data, data_type)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_client/sdk_backend/sdk_backend.py”, line 1229, in full_acceleras_run
optimization_flow.run()
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_model_optimization/tools/orchestator.py”, line 306, in wrapper
return func(self, *args, **kwargs)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_model_optimization/flows/optimization_flow.py”, line 326, in run
step_func()
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_model_optimization/tools/orchestator.py”, line 250, in wrapped
result = method(*args, **kwargs)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_model_optimization/tools/subprocess_wrapper.py”, line 123, in parent_wrapper
self.build_model()
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_model_optimization/tools/orchestator.py”, line 250, in wrapped
result = method(*args, **kwargs)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_model_optimization/flows/optimization_flow.py”, line 240, in build_model
model.compute_output_shape(shapes)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_model_optimization/acceleras/model/hailo_model/hailo_model.py”, line 1039, in compute_output_shape
return self.compute_and_verify_output_shape(input_shape, verify_layer_inputs_shape=False)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_model_optimization/acceleras/model/hailo_model/hailo_model.py”, line 1073, in compute_and_verify_output_shape
layer_output_shape = layer.compute_output_shape(layer_input_shapes)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/keras/engine/base_layer.py”, line 917, in compute_output_shape
outputs = self(inputs, training=False)
File “/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/keras/utils/traceback_utils.py”, line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/tmp/autograph_generated_file2140zhur.py", line 41, in tf__call
outputs = ag.converted_call(ag.ld(self).call_core, (ag.ld(inputs), ag_.ld(training)), dict(**ag__.ld(kwargs)), fscope)
File "/tmp/autograph_generated_fileiihquo7b.py", line 90, in tf__call_core
ag.if_stmt(ag__.ld(self).postprocess_type in [ag__.ld(PostprocessType).NMS, ag__.ld(PostprocessType).BBOX_DECODER], if_body_3, else_body_3, get_state_3, set_state_3, (‘do_return’, ‘retval_’), 2)
File "/tmp/autograph_generated_fileiihquo7b.py", line 22, in if_body_3
retval = ag_.converted_call(ag__.ld(self).bbox_decoding_and_nms_call, (ag__.ld(inputs),), dict(is_bbox_decoding_only=ag__.ld(self).postprocess_type == ag__.ld(PostprocessType).BBOX_DECODER), fscope)
File "/tmp/autograph_generated_file5z7j20ih.py", line 99, in tf__bbox_decoding_and_nms_call
ag.if_stmt(ag__.ld(self).meta_arch in [ag__.ld(NMSOnCpuMetaArchitectures).YOLOV5, ag__.ld(NMSOnCpuMetaArchitectures).YOLOX], if_body_4, else_body_4, get_state_4, set_state_4, (‘decoded_bboxes’, ‘detection_score’, ‘do_return’, ‘retval_’, ‘inputs’), 4)
File "/tmp/autograph_generated_file5z7j20ih.py", line 96, in else_body_4
ag.if_stmt(ag__.ld(self).meta_arch == ag__.ld(NMSOnCpuMetaArchitectures).YOLOV5_SEG, if_body_3, else_body_3, get_state_3, set_state_3, (‘decoded_bboxes’, ‘detection_score’, ‘do_return’, ‘retval_’), 4)
File "/tmp/autograph_generated_file5z7j20ih.py", line 93, in else_body_3
ag.if_stmt(ag__.ld(self).meta_arch == ag__.ld(NMSOnCpuMetaArchitectures).YOLOV8, if_body_2, else_body_2, get_state_2, set_state_2, (‘decoded_bboxes’, ‘detection_score’), 2)
File "/tmp/autograph_generated_file5z7j20ih.py", line 69, in if_body_2
(decoded_bboxes, detection_score) = ag.converted_call(ag__.ld(self).yolov8_decoding_call, (ag__.ld(inputs),), None, fscope)
File "/tmp/autograph_generated_filem03g9hrs.py", line 82, in tf__yolov8_decoding_call
decoded_bboxes = ag.converted_call(ag__.ld(tf).expand_dims, (ag__.ld(decoded_bboxes),), dict(axis=2), fscope)
ValueError: Exception encountered when calling layer “yolov8_nms_postprocess” (type HailoPostprocess).
in user code:
File "/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_model_optimization/acceleras/hailo_layers/base_hailo_none_nn_core_layer.py", line 45, in call *
outputs = self.call_core(inputs, training, **kwargs)
File "/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_model_optimization/acceleras/hailo_layers/hailo_postprocess.py", line 123, in call_core *
is_bbox_decoding_only=self.postprocess_type == PostprocessType.BBOX_DECODER,
File "/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_model_optimization/acceleras/hailo_layers/hailo_postprocess.py", line 157, in bbox_decoding_and_nms_call *
decoded_bboxes, detection_score = self.yolov8_decoding_call(inputs)
File "/home/a/Hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_model_optimization/acceleras/hailo_layers/hailo_postprocess.py", line 367, in yolov8_decoding_call *
decoded_bboxes = tf.expand_dims(decoded_bboxes, axis=2)
ValueError: Tried to convert 'input' to a tensor and failed. Error: None values not supported.
Call arguments received by layer “yolov8_nms_postprocess” (type HailoPostprocess):
• inputs=[‘tf.Tensor(shape=(None, 100, 100, 64), dtype=float32)’, ‘tf.Tensor(shape=(None, 100, 100, 8), dtype=float32)’, ‘tf.Tensor(shape=(None, 50, 50, 64), dtype=float32)’, ‘tf.Tensor(shape=(None, 50, 50, 8), dtype=float32)’, ‘tf.Tensor(shape=(None, 25, 25, 64), dtype=float32)’, ‘tf.Tensor(shape=(None, 25, 25, 8), dtype=float32)’]
• training=False
• kwargs=<class ‘inspect._empty’>
