Help getting started with VIM3 NPU Usage

VIM3
21

Thanks for the info. I think I’m doing things correctly but the output doesn’t make sense to me. The code I used to run the model against a single input image (found here) looks like this:

import numpy as np
import os
import argparse
import sys
from ksnn.api import KSNN
from ksnn.types import *
import cv2 as cv
import time

mean = [128,128,128]
var = [0.0078125]


model = KSNN('VIM3')
print(' |--- KSNN Version: {} +---| '.format(model.get_nn_version()))

model.nn_init(library='model/libnn_pose_densenet121_body.so', model='model/pose_densenet121_body.nb', level=1)

with open("poses.txt", "r") as file:
  for line in file:
    # Process each line here
    print(line.strip())  # Print the line after removing leading/trailing whitespace

    cv_img=[]
    orig_img = cv.imread(line.strip(), cv.IMREAD_COLOR)
    img = cv.resize(orig_img, (256, 256)).astype(np.float32)
    img[:, :, 0] = img[:, :, 0] - mean[0]
    img[:, :, 1] = img[:, :, 1] - mean[1]
    img[:, :, 2] = img[:, :, 2] - mean[2]
    img = img * var[0]

    img = cv.cvtColor(img, cv.COLOR_BGR2RGB)

    img = img.transpose(2, 0, 1)
    cv_img.append(img)

    start = time.time()
    outputs = model.nn_inference(cv_img, platform = 'ONNX', reorder='2 1 0', output_format=output_format.OUT_FORMAT_FLOAT32)
    end = time.time()
    print('Done. inference time: ', end - start)
    print(outputs)
    print(len(outputs[0]))

And when I run the program I get:


 |--- KSNN Version: v1.4 +---|
Create Neural Network: 22ms or 22830us
./inputs/pose1.jpg
Start run graph [1] times...
Run the 1 time: 63.00ms or 63809.00us
vxProcessGraph execution time:
Total   63.00ms or 63885.00us
Average 63.88ms or 63885.00us
Done. inference time:  0.2368161678314209
[array([ 1.17176925e-04,  5.85884627e-05,  0.00000000e+00, ...,
        1.17176925e-04, -2.34353851e-04, -4.10119246e-04], dtype=float32)]
73728

The problem is that there are 2 output layers, and the post processing code for this model that I’m porting from the Jetson platform is looking for both output layer results (it refers to the outputs[0] as the cmap outputs and outputs[1] as the paf outputs). This output data, though, is only a single dimension array. So it appears as if the outputs of one of the output layers is missing.

Just to be sure, I re-ran the convert tool with the input size, input layer name, and output layers arguments. The results were the same.

Am I misunderstanding something or is this output not what a 2 output layer model should look like?

22

Just some extra information, when I run the model on the original Jetson platform it gives me the following information about the model:

[TRT]       binding 0
                -- index   0
                -- name    'input'
                -- type    FP32
                -- in/out  INPUT
                -- # dims  4
                -- dim #0  1
                -- dim #1  3
                -- dim #2  256
                -- dim #3  256
[TRT]       binding 1
                -- index   1
                -- name    'cmap'
                -- type    FP32
                -- in/out  OUTPUT
                -- # dims  4
                -- dim #0  1
                -- dim #1  18
                -- dim #2  64
                -- dim #3  64
[TRT]       binding 2
                -- index   2
                -- name    'paf'
                -- type    FP32
                -- in/out  OUTPUT
                -- # dims  4
                -- dim #0  1
                -- dim #1  42
                -- dim #2  64
                -- dim #3  64

The cmap layer output dimensions is 18,64,64, which is 73,728 values. That corresponds to the length of the output and confirms that the output from the model only contains the output from the cmap layer, not the paf layer as well. There is a missing output when I run this model.

23

Hello @mjasner ,

Add the parameter output_tensor at nn_inference like this.

image
image1474×220 7.28 KB

24

Really dumb question, but I assume the value I set it to should be the number of output layers, right?

25

Hello @mjasner ,

Yes, the number of your outputs.

26

Thanks, that got me what I wanted. When I run my model I’m seeing outputs of the correct size but the model clearly isn’t detecting anything. When I run the exact same image through the converted model on the Khadas board and the original model on a Jetson board the outputs look DRASTICALLY different and so the converted model does not get any valid detections. I’m thinking perhaps my pre-processing for the image is incorrect, so I’m attempting to figure out what the original Jetson code that I’ve been asked to port to the Khadas board is doing. Hopefully this is the missing step to make it work! Thanks again!

27

While going through and debugging the code from the Jetson to make sure my pre-processing was correct I found this output from the Jetson TensorRT engine:

[TRT]    completed loading NVIDIA plugins.
[TRT]    detected model format - ONNX  (extension '.onnx')
[TRT]    desired precision specified for GPU: FASTEST
[TRT]    requested fasted precision for device GPU without providing valid calibrator, disabling INT8
[TRT]    [MemUsageChange] Init CUDA: CPU +225, GPU +0, now: CPU 255, GPU 2807 (MiB)
[TRT]    [MemUsageSnapshot] Begin constructing builder kernel library: CPU 255 MiB, GPU 2836 MiB
[TRT]    [MemUsageSnapshot] End constructing builder kernel library: CPU 284 MiB, GPU 2866 MiB
[TRT]    native precisions detected for GPU:  FP32, FP16
[TRT]    selecting fastest native precision for GPU:  FP16
[TRT]    found engine cache file /usr/local/bin/networks/Pose-DenseNet121-Body/pose_densenet121_body.onnx.1.1.8201.GPU.FP16.engine
[TRT]    found model checksum /usr/local/bin/networks/Pose-DenseNet121-Body/pose_densenet121_body.onnx.sha256sum
[TRT]    echo "$(cat /usr/local/bin/networks/Pose-DenseNet121-Body/pose_densenet121_body.onnx.sha256sum) /usr/local/bin/networks/Pose-DenseNet121-Body/pose_densenet121_body.onnx" | sha256sum --check --status
[TRT]    model matched checksum /usr/local/bin/networks/Pose-DenseNet121-Body/pose_densenet121_body.onnx.sha256sum
[TRT]    loading network plan from engine cache... /usr/local/bin/networks/Pose-DenseNet121-Body/pose_densenet121_body.onnx.1.1.8201.GPU.FP16.engine

Is this saying the original ONNX model is not int8, but is actually FP16? Should I have converted the model with qtype=FP16, not INT8? How does that change the mean values?

28

Hello @mjasner ,

Original ONNX model is FP32 normally if you do not quantify it. Convert parameter qtype int8 means convert tool will quantify model to int8 and then convert it in nb model.

About the mean values, it is the parameter of model normalization. Each model has itself mean values. Are you sure your model mean values is ‘128 128 128 0.0078125’ ?

29

So youre saying int8 is the correct type to use? I’m confused about that. The model is float32 and the preprocessing code I got working with the original onnx model via onnxruntime was:

image = cv2.resize(image, (224, 224)) # Resize to expected input dimensions
image = image.astype(np.float32) / 255.0 # Normalize pixel values
image = image.transpose((2, 0, 1)) # Change channel order to (C, H, W)
input_data = np.expand_dims(image, axis=0) # Add batch dimension

You’re saying that code won’t work because it uses float32 and the model needs to be quantized for int8 for the Amlogic NPU? I’m sorry, I’m still learning neural networks and so I’m not completely familiar with what I need to do to the image then.

As for the mean values, I’ll be honest I have no idea what the mean values should be or what they represent. This is not a model I developed, it’s as model I was given. I believe it’s a community available model. How do I know what the mean values should be? The values I’m using are what I thought I understood the values should be based on reading the documentation for the conversion tools.

30

Hello @mjasner ,

You misunderstand. Convert tool will automatically help you quantify the model. The nb model has been quantified.

Your problem is setting wrong mean values. Before push input to model, you need to do this normalization. The calculation method is as follows.

image[:, :, 0] = image[:, :, 0] - mean[0]
image[:, :, 1] = image[:, :, 1] - mean[1]
image[:, :, 2] = image[:, :, 2] - mean[2]
image = image * var

If mean values is ‘128 128 128 0.0078125’.

image[:, :, 0] = image[:, :, 0] - 128
image[:, :, 1] = image[:, :, 1] - 128
image[:, :, 2] = image[:, :, 2] - 128
image = image / 128 # 0.0078125=1/128

From your preprocess code, your model mean value is ‘0 0 0 255’.

image
image1072×202 23.7 KB

Try to reconvert model by mean value ‘0 0 0 255’ and remember modify the mean and var in demo codes.

31

I will try that. I also assume i need to change the preprocess code to change the line

image = image.astype(np.float32) / 255.0 #

To

image = image.astype(np.int8) / 255.0 #

Is that correct?

32

Should the mean value parameter be

0 0 0 255

Or

0 0 0 0.0039215686

Should the scale parameter be (1/255), or just 255?

33

Hello @mjasner ,

Right is image = image.astype(np.float32) / 255.0. KSNN will automatically help you change to int8 input data.

Sorry, i wrote a mistake. Right is ‘0 0 0 0.0039215686’.

34

Ok, that makes sense, but it still doesn’t make any detections. I have a sample test image that I know should generate a single detection from the model.

I first reconverted the model with the new mean values with this command

./convert --model-name pose_densenet121_body --platform onnx --model ~/src/khadas/pose_densenet121_body.onnx --mean-values '0 0 0 0.0039215686' --quantized-dtype asymmetric_affine --source-files mjdataset.txt --batch-size 1 --iterations 1 --kboard VIM3 --print-level 0 --inputs input --outputs "'cmap paf'" --input-size-list "'3, 256, 256'"

I ran the original ONNX model with the onnxruntime using the following python code:

#!/usr/bin/python3
import onnxruntime
import cv2
import numpy as np

def postprocess_pose(output_heatmaps, output_pafs, threshold=0.1):
    """
    Postprocesses the output of a pose estimation model to extract keypoints and skeletons.

    Args:
        output_heatmaps (np.ndarray): Heatmaps output from the model.
        output_pafs (np.ndarray): PAFs output from the model.
        threshold (float): Confidence threshold for keypoint detection.

    Returns:
        list: List of detected poses, where each pose is a list of keypoints 
              (x, y, confidence).
    """

    num_keypoints = output_heatmaps.shape[1]
    heatmaps = np.squeeze(output_heatmaps)
    pafs = np.squeeze(output_pafs)

    poses = []
    for kpt_idx in range(num_keypoints):
        # Find peaks in heatmap
        heatmap = heatmaps[kpt_idx]
        _, thresh = cv2.threshold(heatmap, threshold, 1, cv2.THRESH_BINARY)
        contours, _ = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        for cnt in contours:
            M = cv2.moments(cnt)
            if M["m00"] != 0:
                cX = int(M["m10"] / M["m00"])
                cY = int(M["m01"] / M["m00"])
                confidence = heatmap[cY, cX]
                if confidence > threshold:
                    poses.append((cX, cY, confidence))
    return poses

session = onnxruntime.InferenceSession("pose_densenet121_body.onnx")
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
output_names = [output.name for output in session.get_outputs()]

print(f"{input_name} {output_names}")

image = cv2.imread("pose1.jpg")
image = cv2.resize(image, (256, 256)) # Resize to expected input dimensions
image = image.astype(np.float32) / 255.0 # Normalize pixel values
image = image.transpose((2, 0, 1)) # Change channel order to (C, H, W)
input_data = np.expand_dims(image, axis=0) # Add batch dimension

output = session.run(output_names, {input_name: input_data})

print(f"Output type={type(output)}")
print(f"Output Len={len(output)}")
print(output)

print(f"CMAP output shape={output[0].shape}")
print(f"PAFS output shape={output[1].shape}")

poses=postprocess_pose(output[0], output[1])
print(poses)

The output is:

input ['cmap', 'paf']
Output type=<class 'list'>
Output Len=2
[array([[[[-4.38258547e-04,  1.42941071e-05, -2.96796425e-05, ...,
          -2.43663235e-05,  6.50559377e-05, -1.20143573e-04],
         [ 5.52342226e-06,  4.34457361e-05, -5.59325690e-06, ...,
          -4.82940595e-06,  6.06977210e-06,  6.93790425e-05],
         [ 5.79954103e-05,  3.37156598e-05, -5.81956465e-06, ...,
          -4.12151849e-06, -2.94739402e-06,  3.60250306e-05],
         ...,
         [ 6.78446231e-05,  3.65443157e-05, -4.69990391e-06, ...,
          -1.19122296e-06,  5.85721973e-05,  1.94626555e-04],
         [ 2.72795907e-04,  2.05694487e-05, -1.18826492e-05, ...,
          -3.42658495e-07,  4.78150287e-05,  2.60828179e-04],
         [ 6.22820808e-04,  3.06129623e-05, -4.34198446e-05, ...,
           2.43719078e-05,  2.06580531e-04,  6.61183672e-04]],

        [[ 2.16633816e-05,  5.72611862e-05,  6.70433201e-06, ...,
           7.77542391e-06,  8.08852201e-05, -5.52270736e-04],
         [ 7.29129242e-05,  5.66046729e-05,  1.87763198e-05, ...,
           2.18868518e-05,  2.58310865e-05,  3.64165135e-06],
         [ 1.17881602e-04,  6.13801458e-05,  2.01291659e-05, ...,
           2.17817633e-05,  2.15128603e-05,  3.94502968e-05],
         ...,
         [ 1.62543962e-04,  4.55523623e-05,  1.97590944e-05, ...,
           2.77979834e-05,  6.63646060e-05,  1.27297215e-04],
         [ 3.54883523e-04,  3.64387306e-05,  1.74369889e-05, ...,
           3.36093799e-05,  1.02052887e-04,  4.54059831e-04],
         [ 3.91395035e-04,  4.44106518e-05, -7.40098621e-05, ...,
           1.81446958e-05,  3.65421467e-04,  2.58796616e-04]],

        [[-5.33516752e-04, -2.25007443e-05,  1.26433151e-05, ...,
          -2.39886522e-05, -2.10368253e-05, -3.48687026e-05],
         [ 6.13946668e-06,  4.81417592e-05,  1.75952955e-05, ...,
           1.72941909e-05,  2.23786301e-05,  2.42734295e-05],
         [ 5.27131197e-05,  3.40496881e-05,  1.68342776e-05, ...,
           1.84594974e-05,  1.72041164e-05,  1.61031421e-04],
         ...,
         [ 1.77219234e-04,  4.54067231e-05,  1.67203416e-05, ...,
           1.88760223e-05,  4.59752409e-05, -9.34354830e-07],
         [ 2.02952404e-04,  4.55703121e-05,  1.56401275e-05, ...,
           3.07885421e-05,  6.52645540e-05,  2.62622663e-04],
         [-8.19856214e-05,  9.59621684e-05, -1.20737765e-04, ...,
          -6.67629720e-05,  1.82195854e-05, -4.52700770e-04]],

        ...,

        [[-3.16275720e-04,  6.69998553e-05,  8.68840725e-05, ...,
           1.36585004e-04,  1.88858510e-04,  5.95541671e-04],
         [-4.13454909e-05,  3.95883799e-05,  4.08163542e-05, ...,
           1.56171227e-05,  8.02815484e-05,  6.98445074e-05],
         [-8.97708378e-06, -8.77945513e-06, -7.17432704e-06, ...,
          -1.33917192e-05, -3.48519916e-06,  5.40822548e-05],
         ...,
         [-6.49168651e-05,  1.66437894e-05, -1.42672479e-05, ...,
          -3.47545210e-07,  3.73968105e-05, -7.99871050e-05],
         [ 1.17308555e-05, -1.44123187e-05, -1.26414243e-05, ...,
           1.20882942e-06, -3.43281499e-06, -8.54631508e-05],
         [-3.88355169e-04, -1.82456461e-06, -3.74960800e-05, ...,
           6.66807864e-06, -7.68914324e-05, -8.76843755e-04]],

        [[-4.45454469e-04,  8.64812027e-05,  1.85020526e-05, ...,
           7.76505476e-05,  1.58696843e-04, -3.87071341e-05],
         [ 1.63681194e-04,  3.12959310e-05, -6.69378096e-06, ...,
          -1.20192735e-05,  3.32272539e-05,  4.21607052e-04],
         [-2.73062524e-05, -2.17704564e-05, -2.32534476e-05, ...,
          -2.47958815e-05, -9.16034332e-06, -5.18101297e-06],
         ...,
         [-1.98033867e-05, -1.57800860e-05, -2.73654259e-05, ...,
          -2.35437237e-05, -1.54106074e-05, -1.97953545e-04],
         [-9.70092660e-05, -2.32342300e-05, -2.63364309e-05, ...,
          -2.03803193e-05, -1.10458896e-05, -9.41137550e-05],
         [-2.49945442e-04, -6.91625100e-05, -7.54482098e-05, ...,
          -6.88704458e-05, -1.54247991e-05, -6.87016174e-04]],

        [[ 1.25692226e-04,  2.84862144e-05,  2.96489961e-05, ...,
           5.83393085e-05,  7.65087025e-05, -3.30271178e-05],
         [ 1.98257796e-04,  5.98743754e-05, -4.33488367e-06, ...,
          -4.91459014e-06, -5.74451406e-06,  6.39905047e-05],
         [ 5.48749776e-05,  5.82853863e-05, -6.03009175e-06, ...,
           1.65152778e-06, -1.01725109e-05,  3.11455078e-05],
         ...,
         [ 4.54043147e-05,  1.41492310e-05, -2.35322659e-07, ...,
          -1.29708405e-05,  1.82312506e-05,  6.47778070e-05],
         [ 2.58017099e-04,  1.07355561e-04,  1.02606755e-05, ...,
           1.31050074e-05,  1.26958239e-05, -5.21557631e-05],
         [ 5.81798551e-04,  2.13615094e-05, -7.83945579e-05, ...,
           1.19052245e-04, -6.44724205e-05,  3.56668716e-05]]]],
      dtype=float32), array([[[[ 6.81468926e-04,  8.30375939e-06, -7.16405484e-05, ...,
           1.28261527e-05, -7.01578028e-05,  6.41877239e-04],
         [-3.26606387e-04, -2.69025040e-04,  1.62706157e-04, ...,
           4.02966834e-05, -2.34022096e-04, -3.54795193e-04],
         [-6.93092516e-05, -6.89344888e-05, -6.78861034e-05, ...,
          -8.85761910e-05, -1.13957940e-04, -5.51639496e-05],
         ...,
         [-3.07946393e-05, -1.08405038e-05, -4.56886846e-05, ...,
          -3.58929246e-05,  1.40899327e-04,  1.01967671e-05],
         [-4.30289656e-05, -6.48865389e-05, -6.48854402e-05, ...,
          -7.41075055e-05, -6.41715960e-05, -2.24139163e-04],
         [-1.14201056e-03,  3.34838842e-06,  3.15008656e-05, ...,
          -2.31472986e-05, -4.53194953e-05,  9.57547629e-04]],

        [[-2.92814511e-04, -3.31833435e-05, -8.33570539e-06, ...,
           1.02068771e-06, -1.57782670e-05,  7.47047452e-05],
         [-2.45611045e-05, -2.11868391e-05,  1.96279943e-05, ...,
           6.99556949e-06, -9.04299486e-06, -3.34338656e-05],
         [-1.53204310e-05, -3.82930284e-06, -2.91912284e-06, ...,
           3.43414149e-06,  1.06314628e-05,  5.68490213e-06],
         ...,
         [ 2.66103280e-06,  1.43032867e-06, -1.97261170e-06, ...,
          -8.95269295e-07,  1.67372418e-05,  4.88224759e-06],
         [-2.51190468e-05, -8.68001553e-06, -5.41173813e-06, ...,
          -8.03459898e-07, -1.07922842e-05, -2.34125946e-05],
         [-2.80420849e-04, -7.76202069e-06, -7.35341837e-06, ...,
          -9.16753925e-06, -4.62116077e-05, -3.62043502e-04]],

        [[-6.11959433e-04, -2.79179105e-04, -2.24991498e-04, ...,
          -1.00047422e-04, -1.03330549e-05, -5.51681151e-04],
         [-1.91685467e-04, -2.98391333e-05, -2.74827980e-05, ...,
          -2.99587628e-05, -8.94658806e-05, -4.12231602e-05],
         [-5.25284122e-05, -2.37871373e-05, -3.44281107e-05, ...,
          -8.88461727e-05, -1.51471002e-04, -9.60764592e-05],
         ...,
         [-1.00278696e-04, -2.83212557e-05, -2.85250153e-05, ...,
          -2.83314548e-05, -2.78153602e-05, -2.28077897e-05],
         [ 1.51940967e-05, -1.70032108e-05, -2.53161688e-05, ...,
          -7.14475827e-05, -1.52784978e-05, -2.35756932e-04],
         [-6.27330563e-04,  1.06265179e-06,  1.44480164e-05, ...,
          -3.42344341e-04, -5.26957738e-04, -4.86333825e-04]],

        ...,

        [[-4.83533251e-04,  1.46470426e-04, -2.49106688e-06, ...,
          -4.12518875e-06, -4.98606423e-05, -2.26729717e-05],
         [ 2.63934809e-04,  1.19048609e-05,  7.57502539e-06, ...,
           1.14361837e-05,  1.99962378e-05, -7.52096603e-05],
         [-8.64601461e-05,  1.78132832e-05,  1.46616476e-05, ...,
           1.92745574e-05,  2.67112064e-05,  1.55227579e-04],
         ...,
         [ 1.60837211e-04,  1.20368513e-05,  1.21119265e-05, ...,
           1.17347981e-05,  1.05395648e-05, -5.07147015e-05],
         [-5.24510921e-04,  1.31911547e-05,  1.33498015e-05, ...,
           1.29748814e-05,  1.39230124e-05, -2.21359165e-04],
         [ 8.05606076e-04,  1.06572596e-04,  1.28883417e-04, ...,
           3.73878465e-05,  3.11715237e-04,  5.61388268e-04]],

        [[-4.52847380e-05,  1.16553507e-03, -2.00211332e-04, ...,
           1.73310807e-04, -5.23059105e-04, -2.21532700e-03],
         [ 1.72093336e-04,  4.24333120e-06,  2.48589204e-05, ...,
          -3.30561525e-05,  9.56717777e-05, -4.14871174e-04],
         [-1.68121711e-04, -5.97650796e-07, -3.12069023e-05, ...,
           1.13616443e-04,  3.23493790e-04,  5.98082028e-04],
         ...,
         [-1.55922840e-04, -4.02975929e-05, -4.58366339e-05, ...,
          -4.52321910e-05, -2.90891203e-05, -1.46856910e-04],
         [-3.81018908e-04, -4.94033084e-05, -4.17494521e-05, ...,
          -4.52931126e-05, -2.60552624e-05,  1.11810550e-04],
         [-1.42595312e-03, -4.17845469e-04,  1.84095552e-04, ...,
          -1.09522475e-03, -1.00946857e-03, -9.14987701e-04]],

        [[-4.52920329e-04,  3.13173368e-05, -4.23565216e-05, ...,
          -6.42458544e-05,  1.92667048e-05,  4.91063809e-04],
         [-8.21880676e-05, -2.51202764e-05, -2.43366740e-05, ...,
          -3.22823485e-06, -3.70852249e-05,  3.09521056e-05],
         [ 5.64269476e-06, -5.71718601e-06,  2.08367805e-06, ...,
          -3.31687916e-05, -1.10432135e-04,  3.49664842e-05],
         ...,
         [-1.20726647e-04,  4.25169532e-07, -2.70589936e-07, ...,
          -5.78260824e-07, -3.74405090e-06, -5.50971345e-05],
         [-5.06536511e-04, -8.04895535e-07,  1.71349734e-06, ...,
          -8.92751905e-06, -5.16608861e-06, -5.02192881e-04],
         [-1.13720278e-04, -2.44772295e-04, -2.25195021e-04, ...,
          -1.42401348e-06,  1.24353595e-04, -1.15157120e-04]]]],
      dtype=float32)]
CMAP output shape=(1, 18, 64, 64)
PAFS output shape=(1, 42, 64, 64)
[(31, 11, 0.6139334), (32, 10, 0.7885559), (31, 10, 0.6215377), (32, 11, 0.43233508), (30, 11, 0.57079566), (34, 18, 0.6081526), (29, 18, 0.66460234), (36, 26, 0.7507798), (28, 25, 0.64223593), (33, 30, 0.2270495), (29, 30, 0.56159514), (33, 33, 0.35248762), (30, 33, 0.5204712), (34, 44, 0.44216946), (29, 45, 0.47740406), (35, 57, 0.32568738), (28, 58, 0.5527475), (32, 17, 0.52170956)]

and you can see a single pose detected at the end that has been extracted from the model output. I then take the converted model and run it with KSNN using the following python code:

#!/usr/bin/python3
import numpy as np
import os
import argparse
import sys
from ksnn.api import KSNN
from ksnn.types import *
import cv2
import time
import ctypes

def postprocess_pose(output_heatmaps, output_pafs, threshold=0.1):
    """
    Postprocesses the output of a pose estimation model to extract keypoints and skeletons.

    Args:
        output_heatmaps (np.ndarray): Heatmaps output from the model.
        output_pafs (np.ndarray): PAFs output from the model.
        threshold (float): Confidence threshold for keypoint detection.

    Returns:
        list: List of detected poses, where each pose is a list of keypoints
              (x, y, confidence).
    """

    num_keypoints = output_heatmaps.shape[1]
    heatmaps = np.squeeze(output_heatmaps)
    pafs = np.squeeze(output_pafs)

    poses = []
    for kpt_idx in range(num_keypoints):
        # Find peaks in heatmap
        heatmap = heatmaps[kpt_idx]
        _, thresh = cv2.threshold(heatmap, threshold, 1, cv2.THRESH_BINARY)
        contours, _ = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        for cnt in contours:
            M = cv2.moments(cnt)
            if M["m00"] != 0:
                cX = int(M["m10"] / M["m00"])
                cY = int(M["m01"] / M["m00"])
                confidence = heatmap[cY, cX]
                if confidence > threshold:
                    poses.append((cX, cY, confidence))
    return poses



model = KSNN('VIM3')
print(' |--- KSNN Version: {} +---| '.format(model.get_nn_version()))

model.nn_init(library='model/libnn_pose_densenet121_body.so', model='model/pose_densenet121_body.nb', level=1)

image = cv2.imread("pose1.jpg")
image = cv2.resize(image, (256, 256)) # Resize to expected input dimensions
image = image.astype(np.float32) / 255.0 # Normalize pixel values
image = image.transpose((2, 0, 1)) # Change channel order to (C, H, W)
input_data = np.expand_dims(image, axis=0) # Add batch dimension

start = time.time()
outputs = model.nn_inference(input_data, platform = 'ONNX', reorder='2 1 0', output_tensor=2, output_format=output_format.OUT_FORMAT_FLOAT32)
end = time.time()
print('Done. inference time: ', end - start)

print(f"Output type={type(outputs)}")
print(f"Output Len={len(outputs)}")
print(outputs)

print(f"CMAP output shape={outputs[0].shape}")
print(f"PAFS output shape={outputs[1].shape}")


heatmaps=outputs[0].reshape((1,18,64,64))
pafs=outputs[1].reshape((1,42,64,64))
print(f"Reshaped CMAP output={heatmaps.shape}")
print(f"Reshaped PAFS output={pafs.shape}")

poses=postprocess_pose(heatmaps,pafs)
print(poses)

and the output is

 |--- KSNN Version: v1.4 +---| 
Create Neural Network: 22ms or 22191us
Start run graph [1] times...
Run the 1 time: 63.00ms or 63055.00us
vxProcessGraph execution time:
Total   63.00ms or 63129.00us
Average 63.13ms or 63129.00us
Done. inference time:  0.15249395370483398
Output type=<class 'list'>
Output Len=2
[array([-0.00026079,  0.00013039,  0.        , ...,  0.        ,
       -0.00039118, -0.00013039], dtype=float32), array([ 0.        ,  0.        ,  0.        , ...,  0.        ,
        0.        , -0.00281812], dtype=float32)]
CMAP output shape=(73728,)
PAFS output shape=(172032,)
Reshaped CMAP output=(1, 18, 64, 64)
Reshaped PAFS output=(1, 42, 64, 64)
[]

The pre and post processing code is the same. The only real difference is that the KSNN outputs from the converted model are not in the right shape. They come out as a single dimension array, so I reshape the outputs into the expected shape and then pass them to the post processing code that is the same as in the onnx script. The output from the model is noticeably different and the detections list is empty.

The mean values should now be correct, the pre and post processing code work, as you can see them work with the original onnx format model, but the KSNN converted model still cannot make any detections.

Do you know what else I should be checking? I really appreciate the help so far.

35

Hello @mjasner ,

Could you provide your ONNX model and some quantified images? I ask our engineer for help.

36

I’m still a little unclear on what “quantified images” are. These are just images from the input source (a camera) that will be fed to the network, correct? There is no special processing, just direct captures that will be pre-processed and passed to the inputs of the neural network? If so, I have uploaded 3 sample images and 1 additional image that I used in my testing of the converted model.

The model can be found here: https://www.dropbox.com/scl/fi/9ef9dc033w7aloohc8zbo/densenet121_body.tgz?rlkey=2czvhvwey517pi77mcwh2k83b&st=o0jobzrz&dl=0

The images can be found at these links:
Image 1
Image 2
Image 3

Image for Testing

37

Hello @mjasner ,

Quantified images are used to infer the range of each parameter when quantify. It is best to use actual scene images, and then is test images. And the number is around 200-500.

How many Quantified images you use? We use your ONNX model with 500 images. The nb model can infer right result.

2
21830×404 78.5 KB

38

So the images I sent were good to use then? What was the convert command you used?

My dataset contained roughly 600 images. I can try a different dataset to see if I get any different results. Is it possible to get a cooy of your converted model and dataset for comparison to what i have?

39

Hello @mjasner ,

We do not use your images to quantify. We simply choose some images from COCO dataset to quantify.

Have you changed the parameter iterations? batch-size × iterations is the number of quantified images used.

The command we use.

./convert --model-name pose_densenet121_body \
          --platform onnx \
          --model pose_densenet121_body.onnx \
          --mean-values '0 0 0 0.0039215686' \
          --quantized-dtype asymmetric_affine \
          --source-files dataset.txt \
          --batch-size 1 \
          --iterations 500 \
          --kboard VIM3 \
          --print-level 0
40

Ill try it with some of the coco dataset images.

You’re right though, I had the iterations argument wrong. I had it at 1 so I guess I was only using a single image instead of the whole dataset! I’ll fix that and try it again!