Hi: I have been trying to convert to a ONNX model to intermediate representation using model optimizer, and I have an error in the max pooling operation. This is the error: [ ERROR ] operands could not be broadcast together with shapes (2,) (3,) looking in the model optimizer code I see that is a numpy error, in pooling.py file line 116.
all variables sizes are 2 except pad_spatial_shape which is 3.
So my conclusion is that the pooling.py class is developed to use MaxPooling2D and the Pytorch operation makes 3D max pooling. https://pytorch.org/docs/stable/_modules/torch/nn/modules/pooling.html After digging in the ONNX conversor from Pytorch I see that the pooling3D operation is transformed to pooling operation but with the 3 dimensions of the pooling (window_x, window_y, window_z). its like MaxPool(stride_x, stride_y,stride_z). But in the code of pooling.py we see that is discarding this z variable.
I see that model optimizer can make custom operators. But no for ONNX.