CenterCropPad
Description
Center crop or pad an input to given dimensions.
Documentation illustration (illustration unavailable in the archive).
The crop/pad dimensions can be specified for a subset of the axes; unspecified dimensions will remain unchanged.
If the input dimensions are larger than the target crop dimensions, a centered cropping window will be extracted from the input. The starting value for the cropping window is rounded down, which means that if the difference between the input shape and the crop shape is odd, the cropping window will be shifted half a pixel to the left of the input center.
If the input dimensions are smaller than the target crop dimensions, the input will be padded equally on both sides to center it in the output. In cases where the total number of padding pixels is odd, an additional pixel will be added to the right side. The padding value used is zero.
Input parameters
specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.
Graphs in : cluster, ONNX model architecture.
input_data (heterogeneous) – T : object, input to extract the centered crop from. shape (heterogeneous) – Tind : object, 1-D tensor representing the cropping window dimensions.
Documentation illustration (illustration unavailable in the archive).
Parameters : cluster,
axes : array, if provided, it specifies a subset of axes that ‘shape’ refer to. If not provided, all axes are assumed [0, 1, …, r-1], where r = rank(data). Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.
Default value “empty”.
training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
lda coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.
name (optional) : string, name of the node.
Documentation illustration (illustration unavailable in the archive).
Output parameters
output_data (heterogeneous) – T : object, output data.
Type Constraints
T in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16),tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)) : Constrain input and output types to all tensor types.
Tind in (tensor(int32), tensor(int64)) : Constrain indices to integer types
Example
All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).