.. _l-onnx-doccom.microsoft-CropAndResize:
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com.microsoft - CropAndResize
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.. contents::
:local:
.. _l-onnx-opcom-microsoft-cropandresize-1:
CropAndResize - 1 (com.microsoft)
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**Version**
* **name**: `CropAndResize (GitHub) `_
* **domain**: **com.microsoft**
* **since_version**: **1**
* **function**:
* **support_level**:
* **shape inference**:
This version of the operator has been available
**since version 1 of domain com.microsoft**.
**Summary**
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.
**Attributes**
* **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 ``?``.
**Inputs**
* **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.
**Outputs**
* **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].
**Examples**