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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+//
+//  ShapeSqueeze.cpp
+//  MNN
+//
+//  Created by MNN on 2019/01/10.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "shape/SizeComputer.hpp"
+#include "core/Macro.h"
+#include "core/TensorUtils.hpp"
+
+namespace MNN {
+class UnSqueezeSizeComputer : public SizeComputer {
+    virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
+                               const std::vector<Tensor*>& outputs) const override {
+        MNN_ASSERT(1 == outputs.size());
+
+        const int* squeezeDim = nullptr;
+        int squeezeDimSize    = 0;
+        if (nullptr != op->main_as_SqueezeParam()->squeezeDims()) {
+            squeezeDim     = op->main_as_SqueezeParam()->squeezeDims()->data();
+            squeezeDimSize = op->main_as_SqueezeParam()->squeezeDims()->size();
+        } else if (inputs.size() > 1) {
+            squeezeDim     = inputs[1]->host<int>();
+            squeezeDimSize = inputs[1]->elementSize();
+        }
+        auto& ob = outputs[0]->buffer();
+        auto& ib  = inputs[0]->buffer();
+        ob.dimensions = ib.dimensions + squeezeDimSize;
+        if (ob.dimensions > MNN_MAX_TENSOR_DIM || ob.dimensions < 0) {
+            return false;
+        }
+        uint32_t mask[MNN_MAX_TENSOR_DIM];
+        ::memset(mask, 0, sizeof(mask));
+        for (int i = 0; i < squeezeDimSize; i++) {
+            int axis = squeezeDim[i];
+            if (axis < 0) {
+                axis += ob.dimensions;
+            }
+            if (axis < 0 || axis >= ob.dimensions) {
+                return false;
+            }
+            mask[axis] = 1;
+        }
+        int oDim      = 0;
+        for (int i = 0; i < ob.dimensions; i++) {
+            ob.dim[i].extent = 1;
+            if (mask[i] == 0) {
+                ob.dim[i].extent = ib.dim[oDim].extent;
+                oDim++;
+            }
+        }
+        ob.type                                               = inputs[0]->buffer().type;
+        TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
+
+        return true;
+    }
+};
+class SqueezeSizeComputer : public SizeComputer {
+    virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
+                               const std::vector<Tensor*>& outputs) const override {
+        MNN_ASSERT(1 == outputs.size());
+
+        const int* squeezeDim = nullptr;
+        int squeezeDimSize    = 0;
+        if (nullptr != op->main_as_SqueezeParam()->squeezeDims()) {
+            squeezeDim     = op->main_as_SqueezeParam()->squeezeDims()->data();
+            squeezeDimSize = op->main_as_SqueezeParam()->squeezeDims()->size();
+        } else if (inputs.size() > 1) {
+            squeezeDim     = inputs[1]->host<int>();
+            squeezeDimSize = inputs[1]->elementSize();
+        }
+        uint32_t mask[MNN_MAX_TENSOR_DIM];
+        ::memset(mask, 0, sizeof(mask));
+        auto& ob = outputs[0]->buffer();
+        auto& ib = inputs[0]->buffer();
+        for (int i = 0; i < squeezeDimSize; i++) {
+            int axis = squeezeDim[i];
+            if (axis < 0) {
+                axis += ib.dimensions;
+            }
+            if (axis < 0 || axis >= ib.dimensions) {
+                return false;
+            }
+            if (1 != ib.dim[axis].extent) {
+                MNN_ERROR("Cannot Squeeze dim[%d], 1 is expected, %d is got. input shape:", axis, ib.dim[axis].extent);
+                inputs[0]->printShape();
+                return false;
+            }
+            mask[axis] = 1;
+        }
+        if (squeezeDimSize == 0) {
+            for (int i = 0; i < ib.dimensions; ++i) {
+                if (ib.dim[i].extent == 1) {
+                    mask[i] = 1;
+                }
+            }
+        }
+        // Count actual unique squeezed dimensions from mask
+        int actualSqueeze = 0;
+        for (int i = 0; i < ib.dimensions; i++) {
+            if (mask[i]) {
+                actualSqueeze++;
+            }
+        }
+
+        ob.dimensions = ib.dimensions - actualSqueeze;
+        int oDim      = 0;
+        for (int i = 0; i < ib.dimensions; i++) {
+            if (mask[i] == 0) {
+                ob.dim[oDim].extent = ib.dim[i].extent;
+                oDim++;
+            }
+        }
+        ob.type                                               = inputs[0]->buffer().type;
+        TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
+        return true;
+    }
+};
+
+REGISTER_SHAPE(SqueezeSizeComputer, OpType_Squeeze);
+REGISTER_SHAPE(UnSqueezeSizeComputer, OpType_Unsqueeze);
+} // namespace MNN