Extract features from F-RCNN/Mask-RCNN

[spanev (Serge Panev)]

Hi @tomb88 ,

You can refer to this post:

How to access parameters in a certain layer of pretrained network?

Hi @pinata1337, An easy way to find out it is to print your model: >>> print(frcnn) FasterRCNN( (transform): GeneralizedRCNNTransform() (backbone): Sequential( (0): ConvBNReLU( (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU6(inplace=True) ) [...] You can always access the submodules of a module by it’s named attribute (or index when th…

So in MaskRCNN, you would define a forward hook (find more about it here):

mrcnn.roi_heads.box_roi_pool.register_forward_hook(hook_fn)

and your hook_fn would catpure the input.