CropAndResize

 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.