How to use create_kernels method in autotest

Best Python code snippet using autotest_python

KeyNet.py

Source:KeyNet.py

...20            core_layers.ConvLayer(shape=[5, 5, 24, 1])21        ]22        super().__init__(network)23        MSIP_sizes = [8, 16, 24, 32, 40]24        self.create_kernels(MSIP_sizes, "KeyNet")25    def create_kernels(self, MSIP_sizes, name_scope):26        # create_kernels27        self.kernels = {}28        # Grid Indexes for MSIP29        for ksize in MSIP_sizes:30            from KeyNet.model.keynet_architecture import ones_multiple_channels, grid_indexes, linear_upsample_weights31            ones_kernel = ones_multiple_channels(ksize, 1)32            indexes_kernel = grid_indexes(ksize)33            upsample_filter_np = linear_upsample_weights(int(ksize / 2), 1)34            self.ones_kernel = tf.constant(ones_kernel, name=name_scope +'_Ones_kernel_'+str(ksize))35            self.kernels['ones_kernel_'+str(ksize)] = self.ones_kernel36            self.upsample_filter_np = tf.constant(upsample_filter_np, name=name_scope+'_upsample_filter_np_'+str(ksize))37            self.kernels['upsample_filter_np_'+str(ksize)] = self.upsample_filter_np38            self.indexes_kernel = tf.constant(indexes_kernel, name=name_scope +'_indexes_kernel_'+str(ksize))39            self.kernels['indexes_kernel_'+str(ksize)] = self.indexes_kernel...

Q4.py

Source:Q4.py

1def create_kernels():2    kernel_45 = np.array([[2, 1, 0], [1, 0, -1], [0, -1, -2]])3    kernel_135 = np.rot90(kernel_45)4    return kernel_45, kernel_1355def get_45_edges(image):6    '''7    Returns the image which shows the 45-degree edges.8    9    Parameters:10        image (numpy.ndarray): The input image.11    12    Returns:13        edges_45 (numpy.ndarray): The 45-degree edges of input image.14    '''15    kernel, _ = create_kernels()16    17    edges_45 = cv2.filter2D(image, -1, kernel)18    return edges_4519def get_135_edges(image):20    '''21    Returns the image which shows the 135-degree edges.22    23    Parameters:24        image (numpy.ndarray): The input image.25    26    Returns:27        edges_135 (numpy.ndarray): The 135-degree edges of input image.28    '''29    _ , kernel = create_kernels()30    31    edges_135 = cv2.filter2D(image, -1, kernel)32    ...

generate

Source:generate

...4import sys5def main():6    kernels=get_kernels()7    #output_path=get_path() + "/kernels"8    create_kernels(kernels,sys.argv[1])9if __name__ == "__main__":...

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