问题
when I train the model,I have customized a loss function.The calculation of the loss value in this function requires the function of opencv.See the code,but I get a wrong.I don't know how to solve it,someone can help me?Thanks a lot.
#this is my loss function
def instance_loss_function(predict,label):
best_match_label_image=search_MaxPixelAccuracy_permutation(predict_convert_gray_image(predict),label)
predict_image=predict
loss_sum=0.0
best_match_label_image_contours_number=len(cv2.findContours( best_match_label_image.reshape(513,513), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[1])
predict_image_contours_number=len(cv2.findContours( predict_image.reshape(513,513), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[1])
counter_max=np.max([best_match_label_image_contours_number,predict_image_contours_number])
counter_min=np.min([best_match_label_image_contours_number,predict_image_contours_number])
for i in range(1,counter_min+1):
ith_instance_IoU=compute_oneClassIoU(predict_image,best_match_label_image,i)
if ith_instance_IoU!=0:
loss_sum=loss_sum+2*(1/(1+ith_instance_IoU)-1/2)
elif ith_instance_IoU==0:
loss_sum=loss_sum+2
if np.abs(counter_max-counter_min)!=0:
loss_sum=loss_sum+1*np.abs(counter_max-counter_min)
return loss_sum
and then I call the loss function like this:
loss=tf.py_func(instance_loss_function,[valid_logits,valid_labels],tf.float32)
train_op = optimizer.minimize(loss, global_step, var_list=train_var_list)
but it does not work, enter image description here
回答1:
To be able to train you network tensorflow needs to create a graph of differentiable operations. If you want to use OpenCV functions, Tensorflow has no idea of how to build derivatives for that. So you can't use arbitrary functions from different software packages, combine them and hope that it works.
来源：https://stackoverflow.com/questions/54456567/how-to-call-opencv-functions-in-tensorflowpython