mirror of https://github.com/alibaba/MNN.git
				
				
				
			
		
			
				
	
	
		
			66 lines
		
	
	
		
			2.4 KiB
		
	
	
	
		
			C++
		
	
	
	
			
		
		
	
	
			66 lines
		
	
	
		
			2.4 KiB
		
	
	
	
		
			C++
		
	
	
	
| //
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| //  ShapeCropAndResize.cpp
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| //  MNN
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| //
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| //  Created by MNN on 2019/01/10.
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| //  Copyright © 2018, Alibaba Group Holding Limited
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| //
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| 
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| #include "shape/SizeComputer.hpp"
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| #include "core/Macro.h"
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| 
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| namespace MNN {
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| 
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| class CropAndResizeComputer : public SizeComputer {
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|     virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
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|                                const std::vector<Tensor*>& outputs) const override {
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|         // The shape of 'image' is [batch_size, image_height, image_width,
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|         // channels].
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|         const Tensor* image = inputs[0];
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|         // The shape of 'boxes' is [num_boxes, 4].
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|         const Tensor* boxes = inputs[1];
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|         // The shape of 'box_index' is [num_boxes].
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|         const Tensor* box_index = inputs[2];
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|         // The shape of 'crop_size' is [2].
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|         Tensor* crop_size = inputs[3];
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| 
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|         MNN_ASSERT(4 == image->buffer().dimensions);
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| 
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|         const int image_height = image->buffer().dim[1].extent;
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|         const int image_width  = image->buffer().dim[2].extent;
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|         const int depth        = image->buffer().dim[3].extent;
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| 
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|         MNN_ASSERT(image_height > 0 && image_width > 0);
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|         MNN_ASSERT(1 == crop_size->buffer().dimensions && 2 == crop_size->buffer().dim[0].extent);
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| 
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|         int num_boxes = 0;
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|         if (boxes->length(0) == 0 && box_index->length(0) == 0) {
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|             num_boxes = 0;
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|         } else {
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|             num_boxes = boxes->buffer().dim[0].extent;
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|         }
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| 
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|         MNN_ASSERT(4 == boxes->buffer().dim[1].extent && 1 == box_index->buffer().dimensions &&
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|                    num_boxes == box_index->buffer().dim[0].extent);
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| 
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|         auto crop_size_vec = crop_size->host<int32_t>();
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| 
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|         const int32_t crop_height = crop_size_vec[0];
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|         const int32_t crop_width  = crop_size_vec[1];
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|         MNN_ASSERT(crop_height > 0 && crop_width > 0);
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| 
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|         outputs[0]->buffer().dimensions    = 4;
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|         outputs[0]->buffer().dim[0].extent = num_boxes;
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|         outputs[0]->buffer().dim[1].extent = crop_height;
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|         outputs[0]->buffer().dim[2].extent = crop_width;
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|         outputs[0]->buffer().dim[3].extent = depth;
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|         TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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|         outputs[0]->buffer().type = inputs[0]->getType();
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| 
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|         return true;
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|     }
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| };
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| 
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| REGISTER_SHAPE_INPUTS(CropAndResizeComputer, OpType_CropAndResize, {3});
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| } // namespace MNN
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