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[200~Fix bugs and achieve higher FPS for KL720+KL520~

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Mason 2025-08-07 15:24:23 +08:00
parent 510628179e
commit d6c0b5eab5
1 changed files with 28 additions and 17 deletions
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35
multi_series_dongle_manager.py
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@ -26,8 +26,8 @@ class InferenceResult:
timestamp: float timestamp: float
class DongleSeriesSpec: class DongleSeriesSpec:
KL520_GOPS = 345 KL520_GOPS = 3
KL720_GOPS = 1425 KL720_GOPS = 28
SERIES_SPECS = { SERIES_SPECS = {
0x100: {"name": "KL520", "gops": KL520_GOPS}, 0x100: {"name": "KL520", "gops": KL520_GOPS},
@ -140,10 +140,8 @@ class MultiSeriesDongleManager:
) )
self.model_descriptors[product_id] = model_descriptor self.model_descriptors[product_id] = model_descriptor
# Prepare inference descriptor # Store model descriptor for later use
self.inference_descriptors[product_id] = kp.GenericImageInferenceDescriptor( # Note: inference descriptors will be created per task
model_id=model_descriptor.models[0].id
)
print(f"[{series_name}] Model upload success") print(f"[{series_name}] Model upload success")
# Create result queue for this dongle # Create result queue for this dongle
@ -244,6 +242,7 @@ class MultiSeriesDongleManager:
# Process image input # Process image input
if isinstance(image, str): if isinstance(image, str):
image_data = cv2.imread(image) image_data = cv2.imread(image)
image_data = cv2.cvtColor(image_data, cv2.COLOR_RGB2BGR565)
if image_data is None: if image_data is None:
raise FileNotFoundError(f"Image file not found: {image}") raise FileNotFoundError(f"Image file not found: {image}")
elif isinstance(image, np.ndarray): elif isinstance(image, np.ndarray):
@ -254,6 +253,8 @@ class MultiSeriesDongleManager:
# Convert format string to enum # Convert format string to enum
format_mapping = { format_mapping = {
'BGR565': kp.ImageFormat.KP_IMAGE_FORMAT_RGB565, 'BGR565': kp.ImageFormat.KP_IMAGE_FORMAT_RGB565,
# 'RGB888': kp.ImageFormat.KP_IMAGE_FORMAT_RGB888,
# 'BGR888': kp.ImageFormat.KP_IMAGE_FORMAT_RGB888, # OpenCV uses BGR by default
'RGB8888': kp.ImageFormat.KP_IMAGE_FORMAT_RGBA8888, 'RGB8888': kp.ImageFormat.KP_IMAGE_FORMAT_RGBA8888,
'YUYV': kp.ImageFormat.KP_IMAGE_FORMAT_YUYV, 'YUYV': kp.ImageFormat.KP_IMAGE_FORMAT_YUYV,
'RAW8': kp.ImageFormat.KP_IMAGE_FORMAT_RAW8 'RAW8': kp.ImageFormat.KP_IMAGE_FORMAT_RAW8
@ -341,7 +342,6 @@ class MultiSeriesDongleManager:
print(f"Send worker started for {series_name}") print(f"Send worker started for {series_name}")
device_group = self.dongle_groups[product_id] device_group = self.dongle_groups[product_id]
inference_descriptor = self.inference_descriptors[product_id]
result_queue = self.result_queues[product_id] result_queue = self.result_queues[product_id]
while not self.stop_event.is_set(): while not self.stop_event.is_set():
@ -350,22 +350,33 @@ class MultiSeriesDongleManager:
if task is None: if task is None:
continue continue
# Configure inference descriptor # print(f"Sending task {task.sequence_id} to {series_name}")
inference_descriptor = kp.GenericImageInferenceDescriptor(
model_id=self.model_descriptors[product_id].models[0].id,
)
# print(f"Using model ID: {inference_descriptor.model_id}")
inference_descriptor.inference_number = task.sequence_id inference_descriptor.inference_number = task.sequence_id
inference_descriptor.input_node_image_list = [kp.GenericInputNodeImage( # print(f"Task sequence ID: {task.sequence_id}")
# Create new inference descriptor for each task to avoid state issues
# print ("image data: ", task.image_data, ", format: ", task.image_format)
# print(device_group)
inference_descriptor.input_node_image_list = [
kp.GenericInputNodeImage(
image=task.image_data, image=task.image_data,
image_format=task.image_format, image_format=task.image_format,
resize_mode=kp.ResizeMode.KP_RESIZE_ENABLE, resize_mode=kp.ResizeMode.KP_RESIZE_ENABLE,
padding_mode=kp.PaddingMode.KP_PADDING_CORNER, padding_mode=kp.PaddingMode.KP_PADDING_CORNER,
normalize_mode=kp.NormalizeMode.KP_NORMALIZE_KNERON normalize_mode=kp.NormalizeMode.KP_NORMALIZE_KNERON
)] )
]
# print(f"Task image shape: {task.image_data.shape}, format: {task.image_format}")
# Send inference # Send inference
kp.inference.generic_image_inference_send( kp.inference.generic_image_inference_send(
device_group=device_group, device_group=device_group,
generic_inference_input_descriptor=inference_descriptor generic_inference_input_descriptor=inference_descriptor
) )
print(f"Task {task.sequence_id} sent successfully to {series_name}")
self.stats['dongle_stats'][product_id]['sent'] += 1 self.stats['dongle_stats'][product_id]['sent'] += 1
except queue.Empty: except queue.Empty: