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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+//
+//  ShapeGridSample.cpp
+//  MNN
+//
+//  Created by MNN on 2021/03/24.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "shape/SizeComputer.hpp"
+#include "core/Macro.h"
+
+namespace MNN {
+class GridSampleSizeComputer : public SizeComputer {
+    virtual bool onComputeSize(const MNN::Op *op, const std::vector<Tensor *> &inputs,
+                               const std::vector<Tensor *> &outputs) const override {
+        // https://pytorch.org/docs/1.7.1/nn.functional.html?highlight=grid_sample#torch.nn.functional.grid_sample
+        // inputs[0] is input, inputs[1] is grid
+        MNN_ASSERT(2 <= inputs.size());
+        MNN_ASSERT(1 == outputs.size());
+        auto &ibInput0 = inputs[0]->buffer();
+        auto &ob = outputs[0]->buffer();
+        ob.type = ibInput0.type;
+        TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(
+                inputs[0])->dimensionFormat;
+        if (inputs.size() > 2) {
+            // For Grad, just copy the shape
+            ob.dimensions = inputs[2]->length(0);
+            auto shapePtr = inputs[2]->host<int>();
+            for (int i=0; i<ob.dimensions; ++i) {
+                ob.dim[i].extent = shapePtr[i];
+            }
+            return true;
+        }
+
+        int input_dim = inputs[0]->buffer().dimensions;
+        int grid_dim = inputs[1]->buffer().dimensions;
+        MNN_ASSERT((4 == input_dim && 4 == grid_dim) || (5 == input_dim && 5 == grid_dim));
+        if (inputs[0]->buffer().dim[0].extent != inputs[1]->buffer().dim[0].extent) {
+            return false;
+        }
+        MNN_ASSERT(grid_dim - 2 == inputs[1]->buffer().dim[grid_dim - 1].extent);
+
+        auto &ibInput1 = inputs[1]->buffer();
+
+        ob.dimensions = ibInput1.dimensions;
+        ob.dim[0].extent = ibInput0.dim[0].extent;
+        ob.dim[1].extent = ibInput0.dim[1].extent;
+        ob.dim[2].extent = ibInput1.dim[1].extent;
+        ob.dim[3].extent = ibInput1.dim[2].extent;
+        if (grid_dim == 5) {
+            ob.dim[4].extent = ibInput1.dim[3].extent;
+        }
+
+        return true;
+    }
+
+    virtual float onComputeFlops(const MNN::Op *op, const std::vector<Tensor *> &inputs,
+                                 const std::vector<Tensor *> &outputs) const override {
+        auto gridSampleParam = op->main_as_GridSample();
+        if (gridSampleParam->mode() == MNN::SampleMode_BILINEAR) {
+            return 4 * SizeComputer::onComputeFlops(op, inputs, outputs);
+        }
+
+        return SizeComputer::onComputeFlops(op, inputs, outputs);
+    }
+};
+
+REGISTER_SHAPE_INPUTS(GridSampleSizeComputer, OpType_GridSample, {2});
+
+} // namespace MNN