| /hardware/qcom/neuralnetworks/hvxservice/1.0/ |
| D | HexagonModel.cpp | 149 std::vector<uint32_t> dims = getAlignedDimensions(operand.dimensions, 4); in addOperand() local
150 HEXAGON_SOFT_ASSERT_NE(0ul, dims.size(), "Rank must be at most 4"); in addOperand()
152 createTensorInternal(dims[0], dims[1], dims[2], dims[3], operand.buffer, operand.length); in addOperand()
202 std::vector<uint32_t> dims = getAlignedDimensions(mOperands[operand].dimensions, 4); in createConvFilterTensor() local
203 HEXAGON_SOFT_ASSERT_NE(0ul, dims.size(), "Need at most 4 dimensions"); in createConvFilterTensor()
207 transpose<float>(dims[0], dims[1] * dims[2] * dims[3], in createConvFilterTensor()
209 return createTensorInternal(dims[1], dims[2], dims[3], dims[0], in createConvFilterTensor()
214 transpose<uint8_t>(dims[0], dims[1] * dims[2] * dims[3], in createConvFilterTensor()
216 return createTensorInternal(dims[1], dims[2], dims[3], dims[0], in createConvFilterTensor()
224 std::vector<uint32_t> dims = getAlignedDimensions(mOperands[operand].dimensions, 4); in createDepthwiseFilterTensor() local
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| D | HexagonUtils.cpp | 109 std::vector<uint32_t> getAlignedDimensions(const std::vector<uint32_t>& dims, uint32_t N) { in getAlignedDimensions() argument
111 N, dims.size(), in getAlignedDimensions()
112 "Error: constant data dimensions " << dims.size() << " exceeds alignment of " << N); in getAlignedDimensions()
113 std::vector<uint32_t> dimensions(N - dims.size(), 1); in getAlignedDimensions()
114 dimensions.insert(dimensions.end(), dims.begin(), dims.end()); in getAlignedDimensions()
192 hexagon_nn_output make_hexagon_nn_output(const std::vector<uint32_t>& dims, uint32_t size) { in make_hexagon_nn_output() argument
193 std::vector<uint32_t> alignedDims = getAlignedDimensions(dims, 4); in make_hexagon_nn_output()
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| D | HexagonUtils.h | 72 std::vector<uint32_t> getAlignedDimensions(const std::vector<uint32_t>& dims, uint32_t N);
92 hexagon_nn_output make_hexagon_nn_output(const std::vector<uint32_t>& dims, uint32_t size);
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| D | HexagonOperationsPrepare.cpp | 115 const int32_t dims = model->getShape(ins[0]).dimensions.size(); in concatenation() local
116 inputs[0] = model->createScalar<int32_t>(axis + (4 - dims)); in concatenation()
574 const int32_t dims = model->getShape(ins[0]).dimensions.size(); in concatenation() local
575 inputs[0] = model->createScalar<int32_t>(axis + (4 - dims)); in concatenation()
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| /hardware/interfaces/renderscript/1.0/default/ |
| D | Context.h | 129 …uint32_t slot, const hidl_vec<uint8_t>& data, Element ve, const hidl_vec<uint32_t>& dims) override;
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| D | Context.cpp | 747 …ipt vs, uint32_t slot, const hidl_vec<uint8_t>& data, Element ve, const hidl_vec<uint32_t>& dims) { in scriptSetVarVE() argument
753 const uint32_t* _dimsPtr = dims.data(); in scriptSetVarVE()
754 size_t _dimLen = dims.size() * sizeof(uint32_t); in scriptSetVarVE()
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| /hardware/qcom/msm8x09/kernel-headers/linux/ |
| D | videodev2.h | 788 __u32 dims[V4L2_CTRL_MAX_DIMS]; member
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| /hardware/qcom/msm8996/kernel-headers/linux/ |
| D | videodev2.h | 975 __u32 dims[V4L2_CTRL_MAX_DIMS]; member
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| /hardware/qcom/msm8996/original-kernel-headers/linux/ |
| D | videodev2.h | 1397 __u32 dims[V4L2_CTRL_MAX_DIMS]; member
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| /hardware/qcom/msm8x09/original-kernel-headers/linux/ |
| D | videodev2.h | 1401 __u32 dims[V4L2_CTRL_MAX_DIMS]; member
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| /hardware/qcom/msm8998/kernel-headers/linux/ |
| D | videodev2.h | 1085 __u32 dims[V4L2_CTRL_MAX_DIMS]; member
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| /hardware/qcom/msm8998/original-kernel-headers/linux/ |
| D | videodev2.h | 1649 __u32 dims[V4L2_CTRL_MAX_DIMS]; member
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| /hardware/interfaces/renderscript/1.0/ |
| D | IContext.hal | 1144 * @param dims Collection of dimensions
1148 vec<uint32_t> dims);
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| /hardware/interfaces/neuralnetworks/1.2/ |
| D | types.hal | 4313 * times. The output tensor's i-th dimension has `input.dims(i) * multiples[i]`
|