com.microsoft - CropAndResize#
CropAndResize - 1 (com.microsoft)#
name: CropAndResize (GitHub)
This version of the operator has been available since version 1 of domain com.microsoft.
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.
extrapolation_value: Value used for extrapolation, when applicable. Default is 0.0f. Default value is
mode: The pooling method. Two modes are supported: ‘bilinear’ and ‘nearest’. Default is ‘bilinear’. Default value is
X (heterogeneous) - T1: 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: 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: 1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.
crop_size (heterogeneous) - T2: 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.
Y (heterogeneous) - T1: 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].