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Reference for pyronn/ct_reconstruction/layers/tensorflow/projection_2d.py


pyronn.ct_reconstruction.layers.tensorflow.projection_2d.parallel_projection2d 

parallel_projection2d(volume, geometry)

Wrapper function for making the layer call. Args: volume: Input volume to project. geometry: Corresponding GeometryParallel2D Object defining parameters. Returns: Initialized lme_custom_ops.parallel_projection2d layer.

Source code in pyronn/ct_reconstruction/layers/tensorflow/projection_2d.py 
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def parallel_projection2d(volume, geometry):
    """
    Wrapper function for making the layer call.
    Args:
        volume:     Input volume to project.
        geometry:   Corresponding GeometryParallel2D Object defining parameters.
    Returns:
            Initialized lme_custom_ops.parallel_projection2d layer.
    """
    batch = np.shape(volume)[0]
    return pyronn_layers.parallel_projection2d(volume,
                                               projection_shape=geometry.sinogram_shape,
                                               volume_origin=np.broadcast_to(geometry.volume_origin, [batch, *np.shape(geometry.volume_origin)]),
                                               detector_origin=np.broadcast_to(geometry.detector_origin, [batch, *np.shape(geometry.detector_origin)]),
                                               volume_spacing=np.broadcast_to(geometry.volume_spacing, [batch, *np.shape(geometry.volume_spacing)]),
                                               detector_spacing=np.broadcast_to(geometry.detector_spacing, [batch, *np.shape(geometry.detector_spacing)]),
                                               ray_vectors=np.broadcast_to(geometry.trajectory, [batch, *np.shape(geometry.trajectory)]))




pyronn.ct_reconstruction.layers.tensorflow.projection_2d._project_grad 

_project_grad(op, grad)

Compute the gradient of the projection op by invoking the backprojector.

Source code in pyronn/ct_reconstruction/layers/tensorflow/projection_2d.py 
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@ops.RegisterGradient("FanProjection2D")
def _project_grad(op, grad):
    # TODO: Document this function on your own. Could not be documented by the model.
    # TODO: Document this function on your own. Could not be documented by the model.
    '''
        Compute the gradient of the projection op by invoking the backprojector.
    '''
    reco = pyronn_layers.fan_backprojection2d(
        sinogram=grad,
        volume_shape=op.inputs[0].shape[1:],
        volume_origin=op.inputs[2],
        detector_origin=op.inputs[3],
        volume_spacing=op.inputs[4],
        detector_spacing=op.inputs[5],
        source_2_isocenter_distance=op.inputs[6],
        source_2_detector_distance=op.inputs[7],
        central_ray_vectors=op.inputs[8],
    )
    return [reco, tf.stop_gradient(op.inputs[1]), tf.stop_gradient(op.inputs[2]), tf.stop_gradient(op.inputs[3]), tf.stop_gradient(op.inputs[4]), tf.stop_gradient(op.inputs[5]), tf.stop_gradient(op.inputs[6]),
            tf.stop_gradient(op.inputs[7]), tf.stop_gradient(op.inputs[8])]




pyronn.ct_reconstruction.layers.tensorflow.projection_2d.fan_projection2d 

fan_projection2d(volume, geometry)

Wrapper function for making the layer call. Args: volume: Input volume to project. geometry: Corresponding GeometryFan2D Object defining parameters. Returns: Initialized lme_custom_ops.fan_projection2d layer.

Source code in pyronn/ct_reconstruction/layers/tensorflow/projection_2d.py 
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def fan_projection2d(volume, geometry):
    """
    Wrapper function for making the layer call.
    Args:
        volume:     Input volume to project.
        geometry:   Corresponding GeometryFan2D Object defining parameters.
    Returns:
            Initialized lme_custom_ops.fan_projection2d layer.
    """
    batch = np.shape(volume)[0]
    return pyronn_layers.fan_projection2d(volume,
                                          projection_shape=geometry.sinogram_shape,
                                          volume_origin=np.broadcast_to(geometry.volume_origin, [batch, *np.shape(geometry.volume_origin)]),
                                          detector_origin=np.broadcast_to(geometry.detector_origin, [batch, *np.shape(geometry.detector_origin)]),
                                          volume_spacing=np.broadcast_to(geometry.volume_spacing, [batch, *np.shape(geometry.volume_spacing)]),
                                          detector_spacing=np.broadcast_to(geometry.detector_spacing, [batch, *np.shape(geometry.detector_spacing)]),
                                          source_2_isocenter_distance=np.broadcast_to(geometry.source_isocenter_distance, [batch, *np.shape(geometry.source_isocenter_distance)]),
                                          source_2_detector_distance=np.broadcast_to(geometry.source_detector_distance, [batch, *np.shape(geometry.source_detector_distance)]),
                                          central_ray_vectors=np.broadcast_to(geometry.trajectory, [batch, *np.shape(geometry.trajectory)]))




pyronn.ct_reconstruction.layers.tensorflow.projection_2d._project_grad 

_project_grad(op, grad)

Compute the gradient of the projection op by invoking the backprojector.

Source code in pyronn/ct_reconstruction/layers/tensorflow/projection_2d.py 
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@ops.RegisterGradient("FanProjection2D")
def _project_grad(op, grad):
    # TODO: Document this function on your own. Could not be documented by the model.
    # TODO: Document this function on your own. Could not be documented by the model.
    '''
        Compute the gradient of the projection op by invoking the backprojector.
    '''
    reco = pyronn_layers.fan_backprojection2d(
        sinogram=grad,
        volume_shape=op.inputs[0].shape[1:],
        volume_origin=op.inputs[2],
        detector_origin=op.inputs[3],
        volume_spacing=op.inputs[4],
        detector_spacing=op.inputs[5],
        source_2_isocenter_distance=op.inputs[6],
        source_2_detector_distance=op.inputs[7],
        central_ray_vectors=op.inputs[8],
    )
    return [reco, tf.stop_gradient(op.inputs[1]), tf.stop_gradient(op.inputs[2]), tf.stop_gradient(op.inputs[3]), tf.stop_gradient(op.inputs[4]), tf.stop_gradient(op.inputs[5]), tf.stop_gradient(op.inputs[6]),
            tf.stop_gradient(op.inputs[7]), tf.stop_gradient(op.inputs[8])]