Other Parts Discussed in Thread: AM69A
Tool/software:
WHILE RUNNING INFERENCE ON CPUEXECUTIONPROVIDER KEYPOINT AND BBOX ARE CORRECT BUT WHEN I USE TIDLEXECUTIONPROVIDER THE OUTPUTS ARE INCORRECT IE INCORRECT DETECTION LOW ACCURACY
PLS HELP I FEEL THE ISSUE WITH MODEL COMPILE AND ARTIFACTS GENERATION
PLS HELP I FEEL THE ISSUE WITH MODEL COMPILE AND ARTIFACTS GENERATION
import os
import sys
import shutil
import onnxruntime as rt
import onnx
import onnx.shape_inference
import numpy as np
from PIL import Image
os.environ["TIDL_RT_PERFSTATS"] = "1"
if __name__ == "__main__":
_, model_path, calibration_images_path, out_dir_path = sys.argv
tidl_tools_path = os.environ["TIDL_TOOLS_PATH"]
os.makedirs(out_dir_path, exist_ok=True)
out_model_name = os.path.splitext(os.path.basename(model_path))[0] + "_with_shapes.onnx"
out_model_path = os.path.join(out_dir_path, out_model_name)
onnx.shape_inference.infer_shapes_path(model_path, out_model_path)
artifacts_dir = os.path.join(out_dir_path, "tidl_output")
try:
shutil.rmtree(artifacts_dir)
except FileNotFoundError:
pass
os.makedirs(artifacts_dir, exist_ok=False)
so = rt.SessionOptions()
print("Available execution providers : ", rt.get_available_providers())
calibration_images = [ os.path.join(calibration_images_path, name) for name in os.listdir(calibration_images_path) ]
num_calibration_frames = len(calibration_images)
num_calibration_iterations = 10
compilation_options = {
"platform": "AM69A",
"tidl_tools_path": tidl_tools_path,
"artifacts_folder": artifacts_dir,
"tensor_bits": 8,
"model_type": "OD",
'object_detection:meta_arch_type': 6,
'object_detection:meta_layers_names_list': os.path.splitext(model_path)[0] + ".prototxt",
"debug_level": 300,
"advanced_options:calibration_frames": num_calibration_frames,
"advanced_options:calibration_iterations": num_calibration_iterations,
}
desired_eps = ['TIDLCompilationProvider','CPUExecutionProvider']
sess = rt.InferenceSession(
out_model_path,
providers=desired_eps,
provider_options=[compilation_options, {}],
sess_options=so
)
input_details, = sess.get_inputs()
batch_size, channel, height, width = input_details.shape
print(f"Input shape: {input_details.shape}")
assert isinstance(batch_size, str) or batch_size == 1
assert channel == 3
input_name = input_details.name
input_type = input_details.type
print(f'Input "{input_name}": {input_type}')
assert input_type == 'tensor(float)'
for image_path in calibration_images:
img = Image.open(image_path).convert("RGB").resize((640, 640))
input_data = np.asarray(img).astype(np.float32).transpose((2, 0, 1))
input_data = np.expand_dims(input_data, 0)
sess.run(None, {input_name: input_data})
MODEL IS yoloxpose_s_8xb32-300e_coco-640 TRAINED ON CUSTOM DATA WITH 4 KEYPOINTS
name: "yolox"
tidl_yolo {
yolo_param {
input: "276"
anchor_width: 8.0
anchor_height: 8.0
}
yolo_param {
input: "308"
anchor_width: 16.0
anchor_height: 16.0
}
yolo_param {
input: "339"
anchor_width: 32.0
anchor_height: 32.0
}
detection_output_param {
num_classes: 1
share_location: true
background_label_id: -1
nms_param {
nms_threshold: 0.45
top_k: 200
}
code_type: CODE_TYPE_YOLO_X
keep_top_k: 200
confidence_threshold: 0.3
num_keypoint: 4
keypoint_confidence: true
}
name: "yolox"
in_width: 640
in_height: 640
output: "dets"
output: "labels"
}
DEPLOY CONFIG
_base_ = ['./pose-detection_static.py', '../_base_/backends/onnxruntime.py']
onnx_config = dict(
output_names=['detections'],
codebase_config = dict(
post_processing=dict(
score_threshold=0.05,
iou_threshold=0.5,
max_output_boxes_per_class=200,
pre_top_k=5000,
keep_top_k=100,
background_label_id=-1,
))
