LaurentBerger
commented
1 year ago @berak @sturkmen72 @crackwitz
Please Can you run this code? My conclusion is : disable multithreading make opencv model adabins works
simplified model can be loaded here image is here
import onnx
import onnxruntime as rt
import numpy as np
import cv2 as cv
onnx_name = "AdaBins_kitti_sim.onnx"
image = cv.imread(cv.samples.findFile("classroom__rgb_00283.jpg"))
# DATA for ONNX and OPENCV
blob = np.transpose(image.astype(np.float32), [2, 0, 1])/255
blob = blob.reshape((1,3, 480, 640))
# ONNX inference
sess = rt.InferenceSession(onnx_name)
input_name = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name
predonnx = sess.run([sess.get_outputs()[0].name,
sess.get_outputs()[1].name,
],
{input_name: blob})
print("ONNX RESULT")
disparity_onnx = predonnx[1][0, 0, : ,:]
bins_onnx = predonnx[0]
print(disparity_onnx[100:105,25:43:3])
# OPENCV inference
net = cv.dnn.readNet(onnx_name)
net.setInput(blob)
pred_opencv = net.forward([sess.get_outputs()[0].name,
sess.get_outputs()[1].name,
])
print("OPENCV")
disparity_opencv = pred_opencv[1][0, 0, : ,:]
bins_opencv = pred_opencv[0]
print(disparity_opencv[100:105,25:43:3])
print("Quadratic error ", np.mean((disparity_onnx-disparity_opencv)**2))
print("Max error ", np.max((disparity_onnx-disparity_opencv)**2))
cv.setNumThreads(0)
print("DISABLE THREAD")
pred_opencv = net.forward([sess.get_outputs()[0].name,
sess.get_outputs()[1].name,
])
print("OPENCV")
disparity_opencv = pred_opencv[1][0, 0, : ,:]
bins_opencv = pred_opencv[0]
print(disparity_opencv[100:105,25:43:3])
print("Quadratic error ", np.mean((disparity_onnx-disparity_opencv)**2))
print("Max error ", np.max((disparity_onnx-disparity_opencv)**2))
crackwitz
zihaomu
System Information
Detailed description
pytorch and onnx gives same results opencv does not give good results
Results
ONNX RESULT [[4.446731 4.4490666 4.46463 4.4546375 4.4510665 4.456948 ] [4.4421244 4.4491835 4.4703193 4.460532 4.4576974 4.462741 ] [4.440228 4.4505563 4.4774194 4.4691973 4.4677935 4.470848 ] [4.435331 4.4522853 4.484742 4.480783 4.480365 4.4803877] [4.4323177 4.4522853 4.4912124 4.4923916 4.4934096 4.4903293]] OPENCV [[5.1145616 5.1145616 5.1145616 5.1145616 5.1145616 5.1145616] [5.115294 5.115294 5.115294 5.115294 5.115294 5.115294 ] [5.116197 5.116197 5.116197 5.116197 5.116197 5.116197 ] [5.1162567 5.1162567 5.1162567 5.1162567 5.1162567 5.1162567] [5.115356 5.115356 5.115356 5.115356 5.115356 5.115356 ]] 0.84839463 7.712089
Steps to reproduce
simplified model can be loaded here image is here
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