wenyangchu edited a comment on issue #13750: mx.nd.Crop does not support FP16 and decpreciated but no direct alternative with central crop URL: https://github.com/apache/incubator-mxnet/issues/13750#issuecomment-450695334 One more comment: Crop function is depreciated but I don't see the alternative slice function provides the same central crop option. And it has been used for some people including me in the unet like implementation. It is especially important when padding is not desired in the use case of applying unet in an extremely large image to avoid edge effect caused by padding. I personally think the current Crop function is great and unique compared to tensorflow approach: https://tf-unet.readthedocs.io/en/latest/_modules/tf_unet/layers.html#crop_and_concat def crop_and_concat(x1,x2): with tf.name_scope("crop_and_concat"): x1_shape = tf.shape(x1) x2_shape = tf.shape(x2) # offsets for the top left corner of the crop offsets = [0, (x1_shape[1] - x2_shape[1]) // 2, (x1_shape[2] - x2_shape[2]) // 2, 0] size = [-1, x2_shape[1], x2_shape[2], -1] x1_crop = tf.slice(x1, offsets, size) return tf.concat([x1_crop, x2], 3) Mxnet approach: cropped = mx.nd.Crop(data,data_ref, num_args = 2, center_crop = True, name = 'cropped') After many search and trials I failed to implement the above crop_and_concat function in mxnet using a hybridBlock. I can only do this in the imperative mode. it is not desired because I prefer to export onnx for my deployment. If someone can teach me how to implement the above function right with hybrid block, it is be very appreciated :)
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