A brief introduction for the purpose of each file.

The updated receptive field for each kernel. It contains a python dictionary with the following structure.

Resolution -> Layer -> Row

Resolution: SphereH[h]Ks[k], where h is the height of the input image and k is the resolution for 65.5° FOV.

Layer: {1_1,...,5_3}

Row: {0,...,h/2}, because the receptive field is symmetric about the equator.

Utility class / function for sphere to tangent plane projection.

Caffe layers for spherical convolution.

SplitRowLayer: split the 2D image to multiple rows for each kernel.

MergeRowLayer: merge the convolution output for each kernel.

The other layers are for SphConv specific data fetching and loss.

Functions for computing and loading rf.yaml

Several parameters are hand coded in this files, including

kernel_sizes: the receptive field for the kernels in each layer

top_down: order of the layers, with (key, val) := (top layer, bottom layer).

strides: pixel size (in terms of FOV) relative to the input image for each layer. For example, 4 indicates that the pixel in the current layer is 4x larger than the input pixels. The structure is: Ks -> SphereH -> Layer

Generate the target values for spherical convolution. It will compute the exact convolution result for each pixel and stores the outputs in HDF5 binary.

Similar to generate_sources.py, but only compute the values for a subset of pixels. It stores the spherical convolution target value and pixel location in pkl file.

Generate the training data for each layer by reading the output of generate_sources.py and generate_targets.py. It reads the spherical convolution target values from the output of generate_targets.py and crops the corresponding input values from the output of generate_sources.py. The output is a HDF5 file containing the target and source value for the kernel.

Generate the Caffe prototxt file of each kernel for kernel-wise training.

Generate the Caffe prototxt file for the entire spherical convolution network.

Perform kernel-wise training. It requires the output of crop_srcs.py and generate_proto.py.

Training full spherical convolution network.