Hello,
I am trying to compile a model from model-zoo (ONNX model)using edgeai-tidl script (I made certain changes to it).
"""
Script to run onnx file and compile it.
"""
from scripts.utils import loggerWritter, plot_TI_performance_data, plot_TI_DDRBW_data, get_benchmark_output
import os
os.environ["TIDL_TOOLS_PATH"] = "/home/xyz/edgeai-tidl-tools/tidl_tools"
os.environ["LD_LIBRARY_PATH"] = "/home/xyz/edgeai-tidl-tools/tidl_tools"
import tqdm
import numpy as np
import onnxruntime as rt
import shutil
import matplotlib.pyplot as plt
from pathlib import Path
import onnx
import cv2
def preprocess(img):
"""
To do:implement resizing for image
"""
#img = cv2.imread(image_path)
#img = img[:,:,::-1] # convert to RGB
print("Reshaping the image", img.shape)
img = cv2.resize(img, (224, 224))
img = img.astype("float32")
img = np.expand_dims(img, axis=0)
img = np.transpose(img, (0, 3, 1, 2))
return img
calib_images = ["/home/xyz/TestCode/sample-images/elephant.bmp",
"/home/xyz/TestCode/sample-images/bus.bmp",
"/home/xyz/TestCode/sample-images/bicycle.bmp",
"/home/xyz/TestCode/sample-images/zebra.bmp"] # here goes list of images to be used for calibration
output_dir = "/home/xyz/TestCode/output_dir"
onnx_model_path = "/home/xyz/TestCode/mobilenetv2-1.0.onnx"
onnx.shape_inference.infer_shapes_path(onnx_model_path, onnx_model_path)
num_bits = 8
accuracy = 1
log_dir = Path("logs").mkdir(parents=True, exist_ok=True)
with loggerWritter("logs/custom-model-onnx"):
compile_options = {
"tidl_tools_path": os.environ["TIDL_TOOLS_PATH"],
"artifacts_folder" : output_dir,
"tensor_bits": num_bits,
"accuracy_level": accuracy,
"advanced_options:calibration_frames": 2,#len(calib_images),
"advanced_options:calibration_iterations": 1,
"debug_level" : 1,
"deny_list": "MaxPool, Gemm, Transpose, Pad, GlobalAveragePool, Squeeze"
}
os.makedirs(output_dir, exist_ok = True)
for root, dirs, files in os.walk(output_dir, topdown=False):
[os.remove(os.path.join(root, f)) for f in files]
[os.rmdir(os.path.join(root, d)) for d in dirs]
so = rt.SessionOptions()
EP_list = ["TIDLCompilationProvider", "CPUExecutionProvider"]
sess = rt.InferenceSession(onnx_model_path, providers=EP_list, provider_options=[compile_options, {}], sess_options=so)
print("********************Model compiled successfully*******************************")
input_details = sess.get_inputs()
for i, num in enumerate(calib_images):
image = cv2.imread(calib_images[i])
output = list(sess.run(None, {input_details[0].name:preprocess(image)}))[0]
EP_list = ["TIDLExecutionProvider", "CPUExecutionProvider"]
sess = rt.InferenceSession(onnx_model_path, providers=EP_list, provider_options=[compile_options, {}], sess_options=so)
print("********************Model Loaded**********************************************")
for i in range(5):
img = cv2.imread('/home/xyz/TestCode/sample-images/elephant.bmp')
output = list(sess.run(None, {input_details[0].name : preprocess(img)}))
root_src_dir = output_dir
root_dst_dir = "cusom-artifacts/onnx/converted_model.onnx"
for src_dir, dirs, files in os.walk(root_src_dir):
dst_dir = src_dir.replace(root_src_dir, root_dst_dir, 1)
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
for file_ in files:
src_file = os.path.join(src_dir, file_)
dst_file = os.path.join(dst_dir, file_)
if os.path.exists(dst_file):
os.remove(dst_file)
shutil.copy(src_file, dst_dir)
However, I encounter failure while doing so. Please find attached the logs for the same.
tidl_tools_path = /home/xyz/edgeai-tidl-tools/tidl_tools
artifacts_folder = /home/xyz/TestCode/output_dir
tidl_tensor_bits = 8
debug_level = 1
num_tidl_subgraphs = 16
tidl_denylist = MaxPool Gemm Transpose Pad GlobalAveragePool Squeeze
tidl_denylist_layer_name =
tidl_denylist_layer_type =
tidl_allowlist_layer_name =
model_type =
tidl_calibration_accuracy_level = 7
tidl_calibration_options:num_frames_calibration = 2
tidl_calibration_options:bias_calibration_iterations = 1
mixed_precision_factor = -1,000000
model_group_id = 0
power_of_2_quantization = 2
enable_high_resolution_optimization = 0
pre_batchnorm_fold = 1
add_data_convert_ops = 0
output_feature_16bit_names_list =
m_params_16bit_names_list =
reserved_compile_constraints_flag = 1601
ti_internal_reserved_1 =
****** WARNING : Network not identified as Object Detection network : (1) Ignore if network is not Object Detection network (2) If network is Object Detection network, please specify "model_type":"OD" as part of OSRT compilation options******
Supported TIDL layer type --- Conv -- mobilenetv20_features_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck0_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck0_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck0_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck0_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck0_conv2_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck1_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck1_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck1_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck1_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck1_conv2_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck2_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck2_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck2_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck2_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck2_conv2_fwd
Supported TIDL layer type --- Add -- mobilenetv20_features_linearbottleneck2_elemwise_add0
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck3_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck3_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck3_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck3_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck3_conv2_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck4_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck4_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck4_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck4_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck4_conv2_fwd
Supported TIDL layer type --- Add -- mobilenetv20_features_linearbottleneck4_elemwise_add0
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck5_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck5_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck5_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck5_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck5_conv2_fwd
Supported TIDL layer type --- Add -- mobilenetv20_features_linearbottleneck5_elemwise_add0
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck6_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck6_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck6_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck6_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck6_conv2_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck7_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck7_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck7_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck7_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck7_conv2_fwd
Supported TIDL layer type --- Add -- mobilenetv20_features_linearbottleneck7_elemwise_add0
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck8_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck8_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck8_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck8_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck8_conv2_fwd
Supported TIDL layer type --- Add -- mobilenetv20_features_linearbottleneck8_elemwise_add0
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck9_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck9_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck9_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck9_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck9_conv2_fwd
Supported TIDL layer type --- Add -- mobilenetv20_features_linearbottleneck9_elemwise_add0
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck10_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck10_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck10_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck10_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck10_conv2_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck11_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck11_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck11_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck11_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck11_conv2_fwd
Supported TIDL layer type --- Add -- mobilenetv20_features_linearbottleneck11_elemwise_add0
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck12_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck12_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck12_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck12_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck12_conv2_fwd
Supported TIDL layer type --- Add -- mobilenetv20_features_linearbottleneck12_elemwise_add0
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck13_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck13_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck13_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck13_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck13_conv2_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck14_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck14_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck14_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck14_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck14_conv2_fwd
Supported TIDL layer type --- Add -- mobilenetv20_features_linearbottleneck14_elemwise_add0
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck15_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck15_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck15_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck15_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck15_conv2_fwd
Supported TIDL layer type --- Add -- mobilenetv20_features_linearbottleneck15_elemwise_add0
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck16_conv0_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck16_relu0_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck16_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_linearbottleneck16_relu1_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_linearbottleneck16_conv2_fwd
Supported TIDL layer type --- Conv -- mobilenetv20_features_conv1_fwd
Supported TIDL layer type --- Relu -- mobilenetv20_features_relu1_fwd
Op type 'GlobalAveragePool' added to unsupported nodes as specified in deny list
Supported TIDL layer type --- Conv -- mobilenetv20_output_pred_fwd
Supported TIDL layer type --- Reshape -- mobilenetv20_output_flatten0_reshape0
Preliminary subgraphs created = 2
Final number of subgraphs created are : 2, - Offloaded Nodes - 101, Total Nodes - 102
Node in deny list...delegated to ARM --- layer type - GlobalAveragePool, Node name - mobilenetv20_features_pool0_fwd
Running runtimes graphviz - /home/xyz/edgeai-tidl-tools/tidl_tools/tidl_graphVisualiser_runtimes.out /home/xyz/TestCode/output_dir/allowedNode.txt /home/xyz/TestCode/output_dir/tempDir/graphvizInfo.txt /home/xyz/TestCode/output_dir/tempDir/runtimes_visualization.svg
*** In TIDL_createStateImportFunc ***
Compute on node : TIDLExecutionProvider_TIDL_0_0
0, Conv, 3, 1, data, mobilenetv20_features_batchnorm0_fwd
1, Relu, 1, 1, mobilenetv20_features_batchnorm0_fwd, mobilenetv20_features_relu0_fwd
2, Conv, 3, 1, mobilenetv20_features_relu0_fwd, mobilenetv20_features_linearbottleneck0_batchnorm0_fwd
3, Relu, 1, 1, mobilenetv20_features_linearbottleneck0_batchnorm0_fwd, mobilenetv20_features_linearbottleneck0_relu0_fwd
4, Conv, 3, 1, mobilenetv20_features_linearbottleneck0_relu0_fwd, mobilenetv20_features_linearbottleneck0_batchnorm1_fwd
5, Relu, 1, 1, mobilenetv20_features_linearbottleneck0_batchnorm1_fwd, mobilenetv20_features_linearbottleneck0_relu1_fwd
6, Conv, 3, 1, mobilenetv20_features_linearbottleneck0_relu1_fwd, mobilenetv20_features_linearbottleneck0_batchnorm2_fwd
7, Conv, 3, 1, mobilenetv20_features_linearbottleneck0_batchnorm2_fwd, mobilenetv20_features_linearbottleneck1_batchnorm0_fwd
8, Relu, 1, 1, mobilenetv20_features_linearbottleneck1_batchnorm0_fwd, mobilenetv20_features_linearbottleneck1_relu0_fwd
9, Conv, 3, 1, mobilenetv20_features_linearbottleneck1_relu0_fwd, mobilenetv20_features_linearbottleneck1_batchnorm1_fwd
10, Relu, 1, 1, mobilenetv20_features_linearbottleneck1_batchnorm1_fwd, mobilenetv20_features_linearbottleneck1_relu1_fwd
11, Conv, 3, 1, mobilenetv20_features_linearbottleneck1_relu1_fwd, mobilenetv20_features_linearbottleneck1_batchnorm2_fwd
12, Conv, 3, 1, mobilenetv20_features_linearbottleneck1_batchnorm2_fwd, mobilenetv20_features_linearbottleneck2_batchnorm0_fwd
13, Relu, 1, 1, mobilenetv20_features_linearbottleneck2_batchnorm0_fwd, mobilenetv20_features_linearbottleneck2_relu0_fwd
14, Conv, 3, 1, mobilenetv20_features_linearbottleneck2_relu0_fwd, mobilenetv20_features_linearbottleneck2_batchnorm1_fwd
15, Relu, 1, 1, mobilenetv20_features_linearbottleneck2_batchnorm1_fwd, mobilenetv20_features_linearbottleneck2_relu1_fwd
16, Conv, 3, 1, mobilenetv20_features_linearbottleneck2_relu1_fwd, mobilenetv20_features_linearbottleneck2_batchnorm2_fwd
17, Add, 2, 1, mobilenetv20_features_linearbottleneck2_batchnorm2_fwd, mobilenetv20_features_linearbottleneck2_elemwise_add0
18, Conv, 3, 1, mobilenetv20_features_linearbottleneck2_elemwise_add0, mobilenetv20_features_linearbottleneck3_batchnorm0_fwd
19, Relu, 1, 1, mobilenetv20_features_linearbottleneck3_batchnorm0_fwd, mobilenetv20_features_linearbottleneck3_relu0_fwd
20, Conv, 3, 1, mobilenetv20_features_linearbottleneck3_relu0_fwd, mobilenetv20_features_linearbottleneck3_batchnorm1_fwd
21, Relu, 1, 1, mobilenetv20_features_linearbottleneck3_batchnorm1_fwd, mobilenetv20_features_linearbottleneck3_relu1_fwd
22, Conv, 3, 1, mobilenetv20_features_linearbottleneck3_relu1_fwd, mobilenetv20_features_linearbottleneck3_batchnorm2_fwd
23, Conv, 3, 1, mobilenetv20_features_linearbottleneck3_batchnorm2_fwd, mobilenetv20_features_linearbottleneck4_batchnorm0_fwd
24, Relu, 1, 1, mobilenetv20_features_linearbottleneck4_batchnorm0_fwd, mobilenetv20_features_linearbottleneck4_relu0_fwd
25, Conv, 3, 1, mobilenetv20_features_linearbottleneck4_relu0_fwd, mobilenetv20_features_linearbottleneck4_batchnorm1_fwd
26, Relu, 1, 1, mobilenetv20_features_linearbottleneck4_batchnorm1_fwd, mobilenetv20_features_linearbottleneck4_relu1_fwd
27, Conv, 3, 1, mobilenetv20_features_linearbottleneck4_relu1_fwd, mobilenetv20_features_linearbottleneck4_batchnorm2_fwd
28, Add, 2, 1, mobilenetv20_features_linearbottleneck4_batchnorm2_fwd, mobilenetv20_features_linearbottleneck4_elemwise_add0
29, Conv, 3, 1, mobilenetv20_features_linearbottleneck4_elemwise_add0, mobilenetv20_features_linearbottleneck5_batchnorm0_fwd
30, Relu, 1, 1, mobilenetv20_features_linearbottleneck5_batchnorm0_fwd, mobilenetv20_features_linearbottleneck5_relu0_fwd
31, Conv, 3, 1, mobilenetv20_features_linearbottleneck5_relu0_fwd, mobilenetv20_features_linearbottleneck5_batchnorm1_fwd
32, Relu, 1, 1, mobilenetv20_features_linearbottleneck5_batchnorm1_fwd, mobilenetv20_features_linearbottleneck5_relu1_fwd
33, Conv, 3, 1, mobilenetv20_features_linearbottleneck5_relu1_fwd, mobilenetv20_features_linearbottleneck5_batchnorm2_fwd
34, Add, 2, 1, mobilenetv20_features_linearbottleneck5_batchnorm2_fwd, mobilenetv20_features_linearbottleneck5_elemwise_add0
35, Conv, 3, 1, mobilenetv20_features_linearbottleneck5_elemwise_add0, mobilenetv20_features_linearbottleneck6_batchnorm0_fwd
36, Relu, 1, 1, mobilenetv20_features_linearbottleneck6_batchnorm0_fwd, mobilenetv20_features_linearbottleneck6_relu0_fwd
37, Conv, 3, 1, mobilenetv20_features_linearbottleneck6_relu0_fwd, mobilenetv20_features_linearbottleneck6_batchnorm1_fwd
38, Relu, 1, 1, mobilenetv20_features_linearbottleneck6_batchnorm1_fwd, mobilenetv20_features_linearbottleneck6_relu1_fwd
39, Conv, 3, 1, mobilenetv20_features_linearbottleneck6_relu1_fwd, mobilenetv20_features_linearbottleneck6_batchnorm2_fwd
40, Conv, 3, 1, mobilenetv20_features_linearbottleneck6_batchnorm2_fwd, mobilenetv20_features_linearbottleneck7_batchnorm0_fwd
41, Relu, 1, 1, mobilenetv20_features_linearbottleneck7_batchnorm0_fwd, mobilenetv20_features_linearbottleneck7_relu0_fwd
42, Conv, 3, 1, mobilenetv20_features_linearbottleneck7_relu0_fwd, mobilenetv20_features_linearbottleneck7_batchnorm1_fwd
43, Relu, 1, 1, mobilenetv20_features_linearbottleneck7_batchnorm1_fwd, mobilenetv20_features_linearbottleneck7_relu1_fwd
44, Conv, 3, 1, mobilenetv20_features_linearbottleneck7_relu1_fwd, mobilenetv20_features_linearbottleneck7_batchnorm2_fwd
45, Add, 2, 1, mobilenetv20_features_linearbottleneck7_batchnorm2_fwd, mobilenetv20_features_linearbottleneck7_elemwise_add0
46, Conv, 3, 1, mobilenetv20_features_linearbottleneck7_elemwise_add0, mobilenetv20_features_linearbottleneck8_batchnorm0_fwd
47, Relu, 1, 1, mobilenetv20_features_linearbottleneck8_batchnorm0_fwd, mobilenetv20_features_linearbottleneck8_relu0_fwd
48, Conv, 3, 1, mobilenetv20_features_linearbottleneck8_relu0_fwd, mobilenetv20_features_linearbottleneck8_batchnorm1_fwd
49, Relu, 1, 1, mobilenetv20_features_linearbottleneck8_batchnorm1_fwd, mobilenetv20_features_linearbottleneck8_relu1_fwd
50, Conv, 3, 1, mobilenetv20_features_linearbottleneck8_relu1_fwd, mobilenetv20_features_linearbottleneck8_batchnorm2_fwd
51, Add, 2, 1, mobilenetv20_features_linearbottleneck8_batchnorm2_fwd, mobilenetv20_features_linearbottleneck8_elemwise_add0
52, Conv, 3, 1, mobilenetv20_features_linearbottleneck8_elemwise_add0, mobilenetv20_features_linearbottleneck9_batchnorm0_fwd
53, Relu, 1, 1, mobilenetv20_features_linearbottleneck9_batchnorm0_fwd, mobilenetv20_features_linearbottleneck9_relu0_fwd
54, Conv, 3, 1, mobilenetv20_features_linearbottleneck9_relu0_fwd, mobilenetv20_features_linearbottleneck9_batchnorm1_fwd
55, Relu, 1, 1, mobilenetv20_features_linearbottleneck9_batchnorm1_fwd, mobilenetv20_features_linearbottleneck9_relu1_fwd
56, Conv, 3, 1, mobilenetv20_features_linearbottleneck9_relu1_fwd, mobilenetv20_features_linearbottleneck9_batchnorm2_fwd
57, Add, 2, 1, mobilenetv20_features_linearbottleneck9_batchnorm2_fwd, mobilenetv20_features_linearbottleneck9_elemwise_add0
58, Conv, 3, 1, mobilenetv20_features_linearbottleneck9_elemwise_add0, mobilenetv20_features_linearbottleneck10_batchnorm0_fwd
59, Relu, 1, 1, mobilenetv20_features_linearbottleneck10_batchnorm0_fwd, mobilenetv20_features_linearbottleneck10_relu0_fwd
60, Conv, 3, 1, mobilenetv20_features_linearbottleneck10_relu0_fwd, mobilenetv20_features_linearbottleneck10_batchnorm1_fwd
61, Relu, 1, 1, mobilenetv20_features_linearbottleneck10_batchnorm1_fwd, mobilenetv20_features_linearbottleneck10_relu1_fwd
62, Conv, 3, 1, mobilenetv20_features_linearbottleneck10_relu1_fwd, mobilenetv20_features_linearbottleneck10_batchnorm2_fwd
63, Conv, 3, 1, mobilenetv20_features_linearbottleneck10_batchnorm2_fwd, mobilenetv20_features_linearbottleneck11_batchnorm0_fwd
64, Relu, 1, 1, mobilenetv20_features_linearbottleneck11_batchnorm0_fwd, mobilenetv20_features_linearbottleneck11_relu0_fwd
65, Conv, 3, 1, mobilenetv20_features_linearbottleneck11_relu0_fwd, mobilenetv20_features_linearbottleneck11_batchnorm1_fwd
66, Relu, 1, 1, mobilenetv20_features_linearbottleneck11_batchnorm1_fwd, mobilenetv20_features_linearbottleneck11_relu1_fwd
67, Conv, 3, 1, mobilenetv20_features_linearbottleneck11_relu1_fwd, mobilenetv20_features_linearbottleneck11_batchnorm2_fwd
68, Add, 2, 1, mobilenetv20_features_linearbottleneck11_batchnorm2_fwd, mobilenetv20_features_linearbottleneck11_elemwise_add0
69, Conv, 3, 1, mobilenetv20_features_linearbottleneck11_elemwise_add0, mobilenetv20_features_linearbottleneck12_batchnorm0_fwd
70, Relu, 1, 1, mobilenetv20_features_linearbottleneck12_batchnorm0_fwd, mobilenetv20_features_linearbottleneck12_relu0_fwd
71, Conv, 3, 1, mobilenetv20_features_linearbottleneck12_relu0_fwd, mobilenetv20_features_linearbottleneck12_batchnorm1_fwd
72, Relu, 1, 1, mobilenetv20_features_linearbottleneck12_batchnorm1_fwd, mobilenetv20_features_linearbottleneck12_relu1_fwd
73, Conv, 3, 1, mobilenetv20_features_linearbottleneck12_relu1_fwd, mobilenetv20_features_linearbottleneck12_batchnorm2_fwd
74, Add, 2, 1, mobilenetv20_features_linearbottleneck12_batchnorm2_fwd, mobilenetv20_features_linearbottleneck12_elemwise_add0
75, Conv, 3, 1, mobilenetv20_features_linearbottleneck12_elemwise_add0, mobilenetv20_features_linearbottleneck13_batchnorm0_fwd
76, Relu, 1, 1, mobilenetv20_features_linearbottleneck13_batchnorm0_fwd, mobilenetv20_features_linearbottleneck13_relu0_fwd
77, Conv, 3, 1, mobilenetv20_features_linearbottleneck13_relu0_fwd, mobilenetv20_features_linearbottleneck13_batchnorm1_fwd
78, Relu, 1, 1, mobilenetv20_features_linearbottleneck13_batchnorm1_fwd, mobilenetv20_features_linearbottleneck13_relu1_fwd
79, Conv, 3, 1, mobilenetv20_features_linearbottleneck13_relu1_fwd, mobilenetv20_features_linearbottleneck13_batchnorm2_fwd
80, Conv, 3, 1, mobilenetv20_features_linearbottleneck13_batchnorm2_fwd, mobilenetv20_features_linearbottleneck14_batchnorm0_fwd
81, Relu, 1, 1, mobilenetv20_features_linearbottleneck14_batchnorm0_fwd, mobilenetv20_features_linearbottleneck14_relu0_fwd
82, Conv, 3, 1, mobilenetv20_features_linearbottleneck14_relu0_fwd, mobilenetv20_features_linearbottleneck14_batchnorm1_fwd
83, Relu, 1, 1, mobilenetv20_features_linearbottleneck14_batchnorm1_fwd, mobilenetv20_features_linearbottleneck14_relu1_fwd
84, Conv, 3, 1, mobilenetv20_features_linearbottleneck14_relu1_fwd, mobilenetv20_features_linearbottleneck14_batchnorm2_fwd
85, Add, 2, 1, mobilenetv20_features_linearbottleneck14_batchnorm2_fwd, mobilenetv20_features_linearbottleneck14_elemwise_add0
86, Conv, 3, 1, mobilenetv20_features_linearbottleneck14_elemwise_add0, mobilenetv20_features_linearbottleneck15_batchnorm0_fwd
87, Relu, 1, 1, mobilenetv20_features_linearbottleneck15_batchnorm0_fwd, mobilenetv20_features_linearbottleneck15_relu0_fwd
88, Conv, 3, 1, mobilenetv20_features_linearbottleneck15_relu0_fwd, mobilenetv20_features_linearbottleneck15_batchnorm1_fwd
89, Relu, 1, 1, mobilenetv20_features_linearbottleneck15_batchnorm1_fwd, mobilenetv20_features_linearbottleneck15_relu1_fwd
90, Conv, 3, 1, mobilenetv20_features_linearbottleneck15_relu1_fwd, mobilenetv20_features_linearbottleneck15_batchnorm2_fwd
91, Add, 2, 1, mo********************Model compiled successfully*******************************
Reshaping the image (462, 620, 3)
Shape of image is:********************************************** (1, 3, 224, 224) ****************************************
bilenetv20_features_linearbottleneck15_batchnorm2_fwd, mobilenetv20_features_linearbottleneck15_elemwise_add0
92, Conv, 3, 1, mobilenetv20_features_linearbottleneck15_elemwise_add0, mobilenetv20_features_linearbottleneck16_batchnorm0_fwd
93, Relu, 1, 1, mobilenetv20_features_linearbottleneck16_batchnorm0_fwd, mobilenetv20_features_linearbottleneck16_relu0_fwd
94, Conv, 3, 1, mobilenetv20_features_linearbottleneck16_relu0_fwd, mobilenetv20_features_linearbottleneck16_batchnorm1_fwd
95, Relu, 1, 1, mobilenetv20_features_linearbottleneck16_batchnorm1_fwd, mobilenetv20_features_linearbottleneck16_relu1_fwd
96, Conv, 3, 1, mobilenetv20_features_linearbottleneck16_relu1_fwd, mobilenetv20_features_linearbottleneck16_batchnorm2_fwd
97, Conv, 3, 1, mobilenetv20_features_linearbottleneck16_batchnorm2_fwd, mobilenetv20_features_batchnorm1_fwd
98, Relu, 1, 1, mobilenetv20_features_batchnorm1_fwd, mobilenetv20_features_relu1_fwd
Input tensor name - data
Output tensor name - mobilenetv20_features_relu1_fwd
*** In TIDL_createStateImportFunc ***
Compute on node : TIDLExecutionProvider_TIDL_1_1
0, Conv, 2, 1, mobilenetv20_features_pool0_fwd, mobilenetv20_output_pred_fwd
1, Reshape, 2, 1, mobilenetv20_output_pred_fwd, mobilenetv20_output_flatten0_reshape0
Input tensor name - mobilenetv20_features_pool0_fwd
Output tensor name - mobilenetv20_output_flatten0_reshape0
In TIDL_onnxRtImportInit subgraph_name=mobilenetv20_features_relu1_fwd
Layer 0, subgraph id mobilenetv20_features_relu1_fwd, name=mobilenetv20_features_relu1_fwd
Layer 1, subgraph id mobilenetv20_features_relu1_fwd, name=data
In TIDL_runtimesOptimizeNet: LayerIndex = 101, dataIndex = 100
************** Frame index 1 : Running float import *************
In TIDL_runtimesPostProcessNet
****************************************************
** ALL MODEL CHECK PASSED **
****************************************************
************ in TIDL_subgraphRtCreate ************
The soft limit is 2048
The hard limit is 2048
MEM: Init ... !!!
MEM: Init ... Done !!!
0.0s: VX_ZONE_INIT:Enabled
0.8s: VX_ZONE_ERROR:Enabled
0.9s: VX_ZONE_WARNING:Enabled
0.1397s: VX_ZONE_INIT:[tivxInit:184] Initialization Done !!!
************ TIDL_subgraphRtCreate done ************
******* In TIDL_subgraphRtInvoke ********
Layer, Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger, paddingWait,LayerWithoutPad,LayerHandleCopy, BackupCycles, RestoreCycles,
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Sum of Layer Cycles 0
Sub Graph Stats 109,000000 5653879,000000 222,000000
******* TIDL_subgraphRtInvoke done ********
********** Frame Index 1 : Running float inference **********
In TIDL_onnxRtImportInit subgraph_name=mobilenetv20_output_flatten0_reshape0
Layer 0, subgraph id mobilenetv20_output_flatten0_reshape0, name=mobilenetv20_output_flatten0_reshape0
Layer 1, subgraph id mobilenetv20_output_flatten0_reshape0, name=mobilenetv20_features_pool0_fwd
In TIDL_runtimesOptimizeNet: LayerIndex = 4, dataIndex = 3
************** Frame index 1 : Running float import *************
In TIDL_runtimesPostProcessNet
****************************************************
** ALL MODEL CHECK PASSED **
******************************Reshaping the image (421, 571, 3)
Shape of image is:********************************************** (1, 3, 224, 224) ****************************************
**********************
************ in TIDL_subgraphRtCreate ************
************ TIDL_subgraphRtCreate done ************
Warning : Couldn't find corresponding ioBuf tensor for onnx tensor with matching name
******* In TIDL_subgraphRtInvoke ********
Layer, Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger, paddingWait,LayerWithoutPad,LayerHandleCopy, BackupCycles, RestoreCycles,
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4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
Sum of Layer Cycles 0
Sub Graph Stats 20,000000 19820,000000 50,000000
******* TIDL_subgraphRtInvoke done ********
********** Frame Index 1 : Running float inference **********
******* In TIDL_subgraphRtInvoke ********
Layer, Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger, paddingWait,LayerWithoutPad,LayerHandleCopy, BackupCycles, RestoreCycles,
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Processing config file #0 : /home/xyz/TestCode/output_dir/tempDir/mobilenetv20_features_relu1_fwd_tidl_io_.qunat_stats_config.txt
Parameter value parse error for 'quantRangeUpdateFactor' = '-1,000000' (Conversion error)...
Parser Failed
Processing config file #0 : /home/xyz/TestCode/output_dir/tempDir/mobilenetv20_features_relu1_fwd_tidl_io_.qunat_stats_config.txt
Parameter value parse error for 'quantRangeUpdateFactor' = '-1,000000' (Conversion error)...
Parser Failed
------------------ Network Compiler Traces -----------------------------
NC running for device: 1
Running with OTF buffer optimizations
successful Memory allocation
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Sum of Layer Cycles 0
Sub Graph Stats 122,000000 5577737,000000 404,000000
******* TIDL_subgraphRtInvoke done ********
********** Frame Index 2 : Running fixed point mode for calibration **********
In TIDL_runtimesPostProcessNet
~~~~~Running TIDL in PC emulation mode to collect Activations range for each layer~~~~~
~~~~~Running TIDL in PC emulation mode to collect Activations range for each layer~~~~~
***************** Calibration iteration number 0 completed ************************
TIDL ALLOWLISTING LAYER CHECK: TIDL_E_QUANT_STATS_NOT_AVAILABLE] tidl_quant_stats_tool.out fails to collect dynamic range. Please look into quant stats log. This model will get fault on target.
****************************************************
** 0 WARNINGS 1 ERRORS **
****************************************************
Warning : Couldn't find corresponding ioBuf tensor for onnx tensor with matching name
******* In TIDL_subgraphRtInvoke ********
Layer, Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger, paddingWait,LayerWithoutPad,LayerHandleCopy, BackupCycles, RestoreCycles,
1, 0, 0, 0,
Processing config file #0 : /home/xyz/TestCode/output_dir/tempDir/mobilenetv20_output_flatten0_reshape0_tidl_io_.qunat_stats_config.txt
Parameter value parse error for 'quantRangeUpdateFactor' = '-1,000000' (Conversion error)...
Parser Failed
Processing config file #0 : /home/xyz/TestCode/output_dir/tempDir/mobilenetv20_output_flatten0_reshape0_tidl_io_.qunat_stats_config.txt
Parameter value parse error for 'quantRangeUpdateFactor' = '-1,000000' (Conversion error)...
Parser Failed
------------------ Network Compiler Traces -----------------------------
NC running for device: 1
Running with OTF buffer optimizations
successful Memory allocation
------------------ Network Compiler Traces -----------------------------
NC running for device: 1
Running with OTF buffer optimizations
successful Memory allocation
Reshaping the image (360, 581, 3)
Shape of image is:********************************************** (1, 3, 224, 224) ****************************************
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
Sum of Layer Cycles 0
Sub Graph Stats 12,000000 17753,000000 80,000000
******* TIDL_subgraphRtInvoke done ********
********** Frame Index 2 : Running fixed point mode for calibration **********
In TIDL_runtimesPostProcessNet
~~~~~Running TIDL in PC emulation mode to collect Activations range for each layer~~~~~
~~~~~Running TIDL in PC emulation mode to collect Activations range for each layer~~~~~
***************** Calibration iteration number 0 completed ************************
Rerunning network compiler for reshape
TIDL ALLOWLISTING LAYER CHECK: TIDL_E_QUANT_STATS_NOT_AVAILABLE] tidl_quant_stats_tool.out fails to collect dynamic range. Please look into quant stats log. This model will get fault on target.
****************************************************
** 0 WARNINGS 1 ERRORS **
****************************************************
******* In TIDL_subgraphRtInvoke ********
Layer, Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger, paddingWait,LayerWithoutPad,LayerHandleCopy, BackupCycles, RestoreCycles,
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2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
31, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
46, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
53, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
54, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
56, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
57, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
59, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
60, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
61, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
62, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
63, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
Sum of Layer Cycles 0
Sub Graph Stats 100,000000 5481190,000000 420,000000
******* TIDL_subgraphRtInvoke done ********
********** Frame Index 3 Running inference - currFrameIdx > numFramesCalibration **********
Warning : Couldn't find corresponding ioBuf tensor for onnx tensor with matching name
******* In TIDL_subgraphRtInvoke ********
Layer, Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger, paddingWait,LayerWithoutPad,LayerHandleCopy, BackupCycles, RestoreCycles,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 0, 0, 0, Reshaping the image (427, 640, 3)
Shape of image is:********************************************** (1, 3, 224, 224) ****************************************
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
Sum of Layer Cycles 0
Sub Graph Stats 9,000000 16440,000000 59,000000
******* TIDL_subgraphRtInvoke done ********
********** Frame Index 3 Running inference - currFrameIdx > numFramesCalibration **********
******* In TIDL_subgraphRtInvoke ********
Layer, Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger, paddingWait,LayerWithoutPad,LayerHandleCopy, BackupCycles, RestoreCycles,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
22, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
26, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
29, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
41, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
46, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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56, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
57, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
59, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
60, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
61, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
62, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
63, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
Sum of Layer Cycles 0
Sub Graph Stats 96,000000 5568327,000000 378,000000
******* TIDL_subgraphRtInvoke done ********
********** Frame Index 4 Running inference - currFrameIdx > numFramesCalibration **********
Warning : Couldn't find corresponding ioBuf tensor for onnx tensor with matching name
******* In TIDL_subgraphRtInvoke ********
Layer, Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger, paddingWait,LayerWithoutPad,LayerHandleCopy, BackupCycles, RestoreCycles,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
Sum of Layer Cycles 0
Sub Graph Stats 8,000000 16152,000000 13,000000
******* TIDL_subgraphRtInvoke done ********
********** Frame Index 4 Running inference - currFrameIdx > numFramesCalibration **********
libtidl_onnxrt_EP loaded 0x890fd30
artifacts_folder = /home/xyz/TestCode/output_dir
debug_level = 1
target_priority = 0
max_pre_empt_delay = 340282346638528859811704183484516925440,000000
Final number of subgraphs created are : 2, - Offloaded Nodes - 101, Total Nodes - 102
In TIDL_createStateInfer
Compute on node : TIDLExecutionProvider_TIDL_0_0
************ in TIDL_subgraphRtCreate ************ ********************Model Loaded**********************************************
Reshaping the image (462, 620, 3)
Shape of image is:********************************************** (1, 3, 224, 224) ****************************************
25.413871s: VX_ZONE_ERROR:[tivxAlgiVisionCreate:363] Calling ialg.algAlloc failed with status = -1115
25.413890s: VX_ZONE_ERROR:[tivxKernelTIDLCreate:720] tivxAlgiVisionCreate returned NULL
25.414465s: VX_ZONE_ERROR:[ownContextSendCmd:801] Command ack message returned failure cmd_status: -1
25.414467s: VX_ZONE_ERROR:[ownContextSendCmd:835] tivxEventWait() failed.
25.414469s: VX_ZONE_ERROR:[ownNodeKernelInit:527] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
25.414469s: VX_ZONE_ERROR:[ownNodeKernelInit:528] Please be sure the target callbacks have been registered for this core
25.414470s: VX_ZONE_ERROR:[ownNodeKernelInit:529] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
25.414472s: VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl:1:1 ... failed !!!
25.414474s: VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
25.414475s: VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
TIDL_RT_OVX: ERROR: Verifying TIDL graph ... Failed !!!
TIDL_RT_OVX: ERROR: Verify OpenVX graph failed
************ TIDL_subgraphRtCreate done ************
In TIDL_createStateInfer
Compute on node : TIDLExecutionProvider_TIDL_1_1
************ in TIDL_subgraphRtCreate ************
25.417636s: VX_ZONE_ERROR:[tivxAlgiVisionCreate:363] Calling ialg.algAlloc failed with status = -1115
25.417660s: VX_ZONE_ERROR:[tivxKernelTIDLCreate:720] tivxAlgiVisionCreate returned NULL
25.418007s: VX_ZONE_ERROR:[ownContextSendCmd:801] Command ack message returned failure cmd_status: -1
25.418009s: VX_ZONE_ERROR:[ownContextSendCmd:835] tivxEventWait() failed.
25.418010s: VX_ZONE_ERROR:[ownNodeKernelInit:527] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
25.418011s: VX_ZONE_ERROR:[ownNodeKernelInit:528] Please be sure the target callbacks have been registered for this core
25.418012s: VX_ZONE_ERROR:[ownNodeKernelInit:529] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
25.418014s: VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl:1:1 ... failed !!!
25.418015s: VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
25.418016s: VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
TIDL_RT_OVX: ERROR: Verifying TIDL graph ... Failed !!!
TIDL_RT_OVX: ERROR: Verify OpenVX graph failed
************ TIDL_subgraphRtCreate done ************
******* In TIDL_subgraphRtInvoke ********
25.422042s: VX_ZONE_ERROR:[tivxAlgiVisionCreate:363] Calling ialg.algAlloc failed with status = -1115
25.422057s: VX_ZONE_ERROR:[tivxKernelTIDLCreate:720] tivxAlgiVisionCreate returned NULL
25.422713s: VX_ZONE_ERROR:[ownContextSendCmd:801] Command ack message returned failure cmd_status: -1
25.422715s: VX_ZONE_ERROR:[ownContextSendCmd:835] tivxEventWait() failed.
25.422717s: VX_ZONE_ERROR:[ownNodeKernelInit:527] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
25.422718s: VX_ZONE_ERROR:[ownNodeKernelInit:528] Please be sure the target callbacks have been registered for this core
25.422719s: VX_ZONE_ERROR:[ownNodeKernelInit:529] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
25.422720s: VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl:1:1 ... failed !!!
25.422722s: VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
25.422723s: VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
25.422923s: VX_ZONE_ERROR:[ownGraphScheduleGraphWrapper:799] graph is not in a state required to be scheduled
25.422924s: VX_ZONE_ERROR:[vxProcessGraph:734] schedule graph failed
25.422925s: VX_ZONE_ERROR:[vxProcessGraph:739] wait graph failed
ERROR: Running TIDL graph ... Failed !!!
Sub Graph Stats 263,000000 3148,000000 17581775893973498,000000
******* TIDL_subgraphRtInvoke done ********
Warning : Couldn't find corresponding ioBuf tensor forReshaping the image (462, 620, 3)
Shape of image is:********************************************** (1, 3, 224, 224) ****************************************
onnx tensor with matching name
******* In TIDL_subgraphRtInvoke ********
25.424394s: VX_ZONE_ERROR:[tivxAlgiVisionCreate:363] Calling ialg.algAlloc failed with status = -1115
25.424407s: VX_ZONE_ERROR:[tivxKernelTIDLCreate:720] tivxAlgiVisionCreate returned NULL
25.424835s: VX_ZONE_ERROR:[ownContextSendCmd:801] Command ack message returned failure cmd_status: -1
25.424837s: VX_ZONE_ERROR:[ownContextSendCmd:835] tivxEventWait() failed.
25.424838s: VX_ZONE_ERROR:[ownNodeKernelInit:527] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
25.424839s: VX_ZONE_ERROR:[ownNodeKernelInit:528] Please be sure the target callbacks have been registered for this core
25.424840s: VX_ZONE_ERROR:[ownNodeKernelInit:529] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
25.424842s: VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl:1:1 ... failed !!!
25.424843s: VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
25.424844s: VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
25.424923s: VX_ZONE_ERROR:[ownGraphScheduleGraphWrapper:799] graph is not in a state required to be scheduled
25.424924s: VX_ZONE_ERROR:[vxProcessGraph:734] schedule graph failed
25.424925s: VX_ZONE_ERROR:[vxProcessGraph:739] wait graph failed
ERROR: Running TIDL graph ... Failed !!!
Sub Graph Stats 16,000000 1857,000000 17581775893973498,000000
******* TIDL_subgraphRtInvoke done ********
******* In TIDL_subgraphRtInvoke ********
25.428447s: VX_ZONE_ERROR:[tivxAlgiVisionCreate:363] Calling ialg.algAlloc failed with status = -1115
25.428460s: VX_ZONE_ERROR:[tivxKernelTIDLCreate:720] tivxAlgiVisionCreate returned NULL
25.428951s: VX_ZONE_ERROR:[ownContextSendCmd:801] Command ack message returned failure cmd_status: -1
25.428952s: VX_ZONE_ERROR:[ownContextSendCmd:835] tivxEventWait() failed.
25.428954s: VX_ZONE_ERROR:[ownNodeKernelInit:527] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
25.428955s: VX_ZONE_ERROR:[ownNodeKernelInit:528] Please be sure the target callbacks have been registered for this core
25.428955s: VX_ZONE_ERROR:[ownNodeKernelInit:529] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
25.428957s: VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl:1:1 ... failed !!!
25.428958s: VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
25.428959s: VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
25.429033s: VX_ZONE_ERROR:[ownGraphScheduleGraphWrapper:799] graph is not in a state required to be scheduled
25.429034s: VX_ZONE_ERROR:[vxProcessGraph:734] schedule graph failed
25.429035s: VX_ZONE_ERROR:[vxProcessGraph:739] wait graph failed
ERROR: Running TIDL graph ... Failed !!!
Sub Graph Stats 206,000000 2712,000000 17581775893973498,000000
******* TIDL_subgraphRtInvoke done ********
Warning : Couldn't find corresponding ioBuf tensor for onnx tensor with matching name
******* In TIDL_subgraphRtInvoke ********
25.430469s: VX_ZONE_ERROR:[tivxAlgiVisionCreate:363] Calling ialg.algAlloc failed with status = -1115
25.430479s: VX_ZONE_ERROR:[tivxKernelTIDLCreate:720] tivxAlgiVisionCreate returned NULL
25.430965s: VX_ZONE_ERROR:[ownContextSendCmd:801] Command ack message returned failure cmd_status: -1
25.430967s: VX_ZONE_ERROR:[ownContextSendCmd:835] tivxEventWait() failed.
25.430968s: VX_ZONE_ERROR:[ownNodeKernelInit:527] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
25.430969s: VX_ZONE_ERROR:[ownNodeKernelInit:528] Please be sure the target callbacks have been registered for this core
25.430970s: VX_ZONE_ERROR:[ownNodeKernelInit:529] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
25.430971s: VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl:1:1 ... failed !!!
25.430986s: VX_ZONE_ERROR:Reshaping the image (462, 620, 3)
Shape of image is:********************************************** (1, 3, 224, 224) ****************************************
Reshaping the image (462, 620, 3)
Shape of image is:********************************************** (1, 3, 224, 224) ****************************************
[vxVerifyGraph:2055] Node kernel init failed
25.431008s: VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
25.431086s: VX_ZONE_ERROR:[ownGraphScheduleGraphWrapper:799] graph is not in a state required to be scheduled
25.431087s: VX_ZONE_ERROR:[vxProcessGraph:734] schedule graph failed
25.431087s: VX_ZONE_ERROR:[vxProcessGraph:739] wait graph failed
ERROR: Running TIDL graph ... Failed !!!
Sub Graph Stats 7,000000 2005,000000 17581775893973498,000000
******* TIDL_subgraphRtInvoke done ********
******* In TIDL_subgraphRtInvoke ********
25.434487s: VX_ZONE_ERROR:[tivxAlgiVisionCreate:363] Calling ialg.algAlloc failed with status = -1115
25.434498s: VX_ZONE_ERROR:[tivxKernelTIDLCreate:720] tivxAlgiVisionCreate returned NULL
25.435273s: VX_ZONE_ERROR:[ownContextSendCmd:801] Command ack message returned failure cmd_status: -1
25.435275s: VX_ZONE_ERROR:[ownContextSendCmd:835] tivxEventWait() failed.
25.435276s: VX_ZONE_ERROR:[ownNodeKernelInit:527] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
25.435276s: VX_ZONE_ERROR:[ownNodeKernelInit:528] Please be sure the target callbacks have been registered for this core
25.435276s: VX_ZONE_ERROR:[ownNodeKernelInit:529] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
25.435277s: VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl:1:1 ... failed !!!
25.435278s: VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
25.435279s: VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
25.435303s: VX_ZONE_ERROR:[ownGraphScheduleGraphWrapper:799] graph is not in a state required to be scheduled
25.435303s: VX_ZONE_ERROR:[vxProcessGraph:734] schedule graph failed
25.435304s: VX_ZONE_ERROR:[vxProcessGraph:739] wait graph failed
ERROR: Running TIDL graph ... Failed !!!
Sub Graph Stats 258,000000 2874,000000 17581775893973498,000000
******* TIDL_subgraphRtInvoke done ********
Warning : Couldn't find corresponding ioBuf tensor for onnx tensor with matching name
******* In TIDL_subgraphRtInvoke ********
25.436668s: VX_ZONE_ERROR:[tivxAlgiVisionCreate:363] Calling ialg.algAlloc failed with status = -1115
25.436680s: VX_ZONE_ERROR:[tivxKernelTIDLCreate:720] tivxAlgiVisionCreate returned NULL
25.437088s: VX_ZONE_ERROR:[ownContextSendCmd:801] Command ack message returned failure cmd_status: -1
25.437090s: VX_ZONE_ERROR:[ownContextSendCmd:835] tivxEventWait() failed.
25.437091s: VX_ZONE_ERROR:[ownNodeKernelInit:527] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
25.437092s: VX_ZONE_ERROR:[ownNodeKernelInit:528] Please be sure the target callbacks have been registered for this core
25.437093s: VX_ZONE_ERROR:[ownNodeKernelInit:529] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
25.437094s: VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl:1:1 ... failed !!!
25.437096s: VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
25.437097s: VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
25.437178s: VX_ZONE_ERROR:[ownGraphScheduleGraphWrapper:799] graph is not in a state required to be scheduled
25.437179s: VX_ZONE_ERROR:[vxProcessGraph:734] schedule graph failed
25.437180s: VX_ZONE_ERROR:[vxProcessGraph:739] wait graph failed
ERROR: Running TIDL graph ... Failed !!!
Sub Graph Stats 5,000000 1833,000000 17581775893973498,000000
******* TIDL_subgraphRtInvoke done ********
******* In TIDL_subgraphRtInvoke ********
25.440859s: VX_ZONE_ERROR:[tivxAlgiVisionCreate:363] Calling ialg.algAlloc failed with status = -1115
25.440871s: VX_ZONE_ERROR:[tivxKernelTIDLCreate:720] tivxAlgiVisionCreate returned NULL
25.441401s: VX_ZONE_ERROR:[ownContextSendCmd:801] Command ack message returned failure cmd_status: -1
25.441403s: VX_ZONE_ERROR:[ownContextSendCmd:835] tivxEventWait() failed.
25.441405s: VX_ZONE_ERROR:[ownNodeKernelInit:527] Target kernel, TIVX_CMD_NODEReshaping the image (462, 620, 3)
Shape of image is:********************************************** (1, 3, 224, 224) ****************************************
_CREATE failed for node TIDLNode
25.441427s: VX_ZONE_ERROR:[ownNodeKernelInit:528] Please be sure the target callbacks have been registered for this core
25.441428s: VX_ZONE_ERROR:[ownNodeKernelInit:529] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
25.441430s: VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl:1:1 ... failed !!!
25.441431s: VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
25.441432s: VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
25.441514s: VX_ZONE_ERROR:[ownGraphScheduleGraphWrapper:799] graph is not in a state required to be scheduled
25.441515s: VX_ZONE_ERROR:[vxProcessGraph:734] schedule graph failed
25.441516s: VX_ZONE_ERROR:[vxProcessGraph:739] wait graph failed
ERROR: Running TIDL graph ... Failed !!!
Sub Graph Stats 235,000000 2952,000000 17581775893973498,000000
******* TIDL_subgraphRtInvoke done ********
Warning : Couldn't find corresponding ioBuf tensor for onnx tensor with matching name
******* In TIDL_subgraphRtInvoke ********
25.443032s: VX_ZONE_ERROR:[tivxAlgiVisionCreate:363] Calling ialg.algAlloc failed with status = -1115
25.443043s: VX_ZONE_ERROR:[tivxKernelTIDLCreate:720] tivxAlgiVisionCreate returned NULL
25.443448s: VX_ZONE_ERROR:[ownContextSendCmd:801] Command ack message returned failure cmd_status: -1
25.443451s: VX_ZONE_ERROR:[ownContextSendCmd:835] tivxEventWait() failed.
25.443452s: VX_ZONE_ERROR:[ownNodeKernelInit:527] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
25.443453s: VX_ZONE_ERROR:[ownNodeKernelInit:528] Please be sure the target callbacks have been registered for this core
25.443454s: VX_ZONE_ERROR:[ownNodeKernelInit:529] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
25.443455s: VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl:1:1 ... failed !!!
25.443457s: VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
25.443458s: VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
25.443542s: VX_ZONE_ERROR:[ownGraphScheduleGraphWrapper:799] graph is not in a state required to be scheduled
25.443543s: VX_ZONE_ERROR:[vxProcessGraph:734] schedule graph failed
25.443544s: VX_ZONE_ERROR:[vxProcessGraph:739] wait graph failed
ERROR: Running TIDL graph ... Failed !!!
Sub Graph Stats 7,000000 1983,000000 17581775893973498,000000
******* TIDL_subgraphRtInvoke done ********
******* In TIDL_subgraphRtInvoke ********
25.447230s: VX_ZONE_ERROR:[tivxAlgiVisionCreate:363] Calling ialg.algAlloc failed with status = -1115
25.447243s: VX_ZONE_ERROR:[tivxKernelTIDLCreate:720] tivxAlgiVisionCreate returned NULL
25.447812s: VX_ZONE_ERROR:[ownContextSendCmd:801] Command ack message returned failure cmd_status: -1
25.447814s: VX_ZONE_ERROR:[ownContextSendCmd:835] tivxEventWait() failed.
25.447815s: VX_ZONE_ERROR:[ownNodeKernelInit:527] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
25.447816s: VX_ZONE_ERROR:[ownNodeKernelInit:528] Please be sure the target callbacks have been registered for this core
25.447816s: VX_ZONE_ERROR:[ownNodeKernelInit:529] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
25.447818s: VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl:1:1 ... failed !!!
25.447819s: VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
25.447820s: VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
25.447894s: VX_ZONE_ERROR:[ownGraphScheduleGraphWrapper:799] graph is not in a state required to be scheduled
25.447895s: VX_ZONE_ERROR:[vxProcessGraph:734] schedule graph failed
25.447896s: VX_ZONE_ERROR:[vxProcessGraph:739] wait graph failed
ERROR: Running TIDL graph ... Failed !!!
Sub Graph Stats 259,000000 2907,000000 17581775893973498,000000
******* TIDL_subgraphRtInvoke done ********
Warning : Couldn't find corresponding ioBuf tensor for onnx tensor with matching name
******* In TIDL_subgraphRtInvoke ********
25.449203s: VX_ZONE_ERROR:[tivxAlgiVisionCreate:363] Calling ialg.algAlloc failed with status = -1115
25.449213s: VX_ZONE_ERROR:[tivxKernelTIDLCreate:720] tivxAlgiVisionCreate returned NULL
25.449627s: VX_ZONE_ERROR:[ownContextSendCmd:801] Command ack message returned failure cmd_status: -1
25.449630s: VX_ZONE_ERROR:[ownContextSendCmd:835] tivxEventWait() failed.
25.449631s: VX_ZONE_ERROR:[ownNodeKernelInit:527] Target kernel, TIVX_CMD_NODE_CREATE failed for node TIDLNode
25.449632s: VX_ZONE_ERROR:[ownNodeKernelInit:528] Please be sure the target callbacks have been registered for this core
25.449632s: VX_ZONE_ERROR:[ownNodeKernelInit:529] If the target callbacks have been registered, please ensure no errors are occurring within the create callback of this kernel
25.449634s: VX_ZONE_ERROR:[ownGraphNodeKernelInit:583] kernel init for node 0, kernel com.ti.tidl:1:1 ... failed !!!
25.449635s: VX_ZONE_ERROR:[vxVerifyGraph:2055] Node kernel init failed
25.449636s: VX_ZONE_ERROR:[vxVerifyGraph:2109] Graph verify failed
25.449710s: VX_ZONE_ERROR:[ownGraphScheduleGraphWrapper:799] graph is not in a state required to be scheduled
25.449711s: VX_ZONE_ERROR:[vxProcessGraph:734] schedule graph failed
25.449712s: VX_ZONE_ERROR:[vxProcessGraph:739] wait graph failed
ERROR: Running TIDL graph ... Failed !!!
Sub Graph Stats 7,000000 1748,000000 17581775893973498,000000
******* TIDL_subgraphRtInvoke done ********
************ in TIDL_subgraphRtDelete ************
************ in TIDL_subgraphRtDelete ************
************ in TIDL_subgraphRtDelete ************
************ in TIDL_subgraphRtDelete ************
MEM: Deinit ... !!!
MEM: Alloc's: 102 alloc's of 162827725 bytes
MEM: Free's : 102 free's of 162827725 bytes
MEM: Open's : 0 allocs of 0 bytes
MEM: Deinit ... Done !!!
Any pointers as to how to proceed?
Best Regards
Ashay
