UpdatedAugust 13, 2025

CropAndResize

Description

Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling (possibly with aspect ratio change) to a common output size specified by crop_height and crop_width. Returns a tensor with crops from the input image at positions defined at the bounding box locations in boxes. The cropped boxes are all resized (with bilinear or nearest neighbor interpolation) to a fixed size = [crop_height, crop_width]. The result is a 4-D tensor [num_boxes, crop_height, crop_width, depth]. The resizing is corner aligned.

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.

X (heterogeneous) – T1 : object, input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.
rois (heterogeneous) – T1 : object, roIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], …]. The RoIs’ coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the ‘batch_indices’ input.
batch_indices (heterogeneous) – T2 : object, 1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.
crop_size (heterogeneous) – T2 : object, 1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.

Parameters : cluster,

extrapolation_value : float, value used for extrapolation, when applicable.
Default value “0”.
mode : enum, the pooling method. Two modes are supported: ‘bilinear’ and ‘nearest’.
Default value “bilinear”.
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.

Output parameters

Y (heterogeneous) – T1 : object, roI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].

Type Constraints

T1 in (tensor(double), tensor(float), tensor(float16)) : Constrain types to float tensors.

T2 in (tensor(int32)) : Constrain types to int tensors.

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).

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