chore: import upstream snapshot with attribution

2026-07-13 13:33:03 +08:00
commit 5b57521aa1
8226 changed files with 3425766 additions and 0 deletions
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+//
+//  ShapeCropAndResize.cpp
+//  MNN
+//
+//  Created by MNN on 2019/01/10.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "shape/SizeComputer.hpp"
+#include "core/Macro.h"
+
+namespace MNN {
+
+class CropAndResizeComputer : public SizeComputer {
+    virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
+                               const std::vector<Tensor*>& outputs) const override {
+        // The shape of 'image' is [batch_size, image_height, image_width,
+        // channels].
+        const Tensor* image = inputs[0];
+        // The shape of 'boxes' is [num_boxes, 4].
+        const Tensor* boxes = inputs[1];
+        // The shape of 'box_index' is [num_boxes].
+        const Tensor* box_index = inputs[2];
+        // The shape of 'crop_size' is [2].
+        Tensor* crop_size = inputs[3];
+
+        MNN_ASSERT(4 == image->buffer().dimensions);
+
+        const int image_height = image->buffer().dim[1].extent;
+        const int image_width  = image->buffer().dim[2].extent;
+        const int depth        = image->buffer().dim[3].extent;
+
+        MNN_ASSERT(image_height > 0 && image_width > 0);
+        MNN_ASSERT(1 == crop_size->buffer().dimensions && 2 == crop_size->buffer().dim[0].extent);
+
+        int num_boxes = 0;
+        if (boxes->length(0) == 0 && box_index->length(0) == 0) {
+            num_boxes = 0;
+        } else {
+            num_boxes = boxes->buffer().dim[0].extent;
+        }
+
+        MNN_ASSERT(4 == boxes->buffer().dim[1].extent && 1 == box_index->buffer().dimensions &&
+                   num_boxes == box_index->buffer().dim[0].extent);
+
+        auto crop_size_vec = crop_size->host<int32_t>();
+
+        const int32_t crop_height = crop_size_vec[0];
+        const int32_t crop_width  = crop_size_vec[1];
+        MNN_ASSERT(crop_height > 0 && crop_width > 0);
+
+        outputs[0]->buffer().dimensions    = 4;
+        outputs[0]->buffer().dim[0].extent = num_boxes;
+        outputs[0]->buffer().dim[1].extent = crop_height;
+        outputs[0]->buffer().dim[2].extent = crop_width;
+        outputs[0]->buffer().dim[3].extent = depth;
+        TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
+        outputs[0]->buffer().type = inputs[0]->getType();
+
+        return true;
+    }
+};
+
+REGISTER_SHAPE_INPUTS(CropAndResizeComputer, OpType_CropAndResize, {3});
+} // namespace MNN