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Searched refs:dimensions (Results 1 – 25 of 48) sorted by relevance

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/hardware/interfaces/neuralnetworks/1.3/
DIBuffer.hal45 * @param dimensions Updated dimensional information. If the dimensions of the IBuffer object
46 * are not fully specified, then the dimensions must be fully specified here. If the
47 * dimensions of the IBuffer object are fully specified, then the dimensions may be empty
48 * here. If dimensions.size() > 0, then all dimensions must be specified here, and any
54 * - INVALID_ARGUMENT if provided memory is invalid, or if the dimensions is invalid
56 copyFrom(memory src, vec<uint32_t> dimensions) generates (ErrorStatus status);
Dtypes.t255 * For a scalar operand, dimensions.size() must be 0.
257 * A tensor operand with all dimensions specified has "fully
258 * specified" dimensions. Whenever possible (i.e., whenever the
259 * dimensions are known at model construction time), a tensor
261 * specified dimensions, in order to enable the best possible
264 * If a tensor operand's dimensions are not fully specified, the
265 * dimensions of the operand are deduced from the operand
266 * dimensions and values of the operation for which that operand
268 * {@link OperationType::WHILE} operation input operand dimensions in the
271 * In the following situations, a tensor operand's dimensions must
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Dtypes.hal97 * dimensions. The output is the sum of both input tensors, optionally
100 * Two dimensions are compatible when:
105 * input operands. It starts with the trailing dimensions, and works its
129 * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
151 * The output dimensions are functions of the filter dimensions, stride, and
237 * dimensions except the dimension along the concatenation axis.
279 * The output dimensions are functions of the filter dimensions, stride, and
444 * The output dimensions are functions of the filter dimensions, stride, and
600 * and width dimensions. The value block_size indicates the input block size
727 * * 0: The output tensor, of the same {@link OperandType} and dimensions as
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/hardware/interfaces/neuralnetworks/1.3/vts/functional/
DMemoryDomainTests.cpp76 static_cast<OperandType>(operand.type), operand.dimensions); in createDummyData()
88 .dimensions = {}, in createInt32Scalar()
104 .dimensions = {operand.dimensions[3], 3, 3, operand.dimensions[3]}, in createConvModel()
112 .dimensions = {operand.dimensions[3]}, in createConvModel()
159 .dimensions = {}, in createSingleAddModel()
241 kTestOperand.dimensions)) {} in MemoryDomainTestBase()
277 .dimensions = {1, 32, 32, 8},
286 .dimensions = {1, 32, 32, 8},
295 .dimensions = {1, 32, 32, 8},
304 .dimensions = {1, 32, 32, 8},
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DUtils.cpp82 if (isTensor(operand.type) && operand.dimensions.size() == 0) return 0; in sizeOfData()
83 return std::accumulate(operand.dimensions.begin(), operand.dimensions.end(), dataSize, in sizeOfData()
DValidateModel.cpp106 .dimensions = {}, in addOperand()
227 size += sizeForBinder(operand.dimensions); in sizeForBinder()
437 model->main.operands[operand].dimensions = in mutateOperandRankTest()
779 newOperand.dimensions = hidl_vec<uint32_t>(); in mutateOperand()
786 newOperand.dimensions = in mutateOperand()
787 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
792 newOperand.dimensions = in mutateOperand()
793 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
800 newOperand.dimensions = in mutateOperand()
801 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
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DGeneratedTestHarness.cpp233 .dimensions = op.dimensions, in createSubgraph()
332 auto& dims = model->main.operands[i].dimensions; in makeOutputDimensionsUnspecified()
388 inputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}}; in createRequest()
398 inputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}}; in createRequest()
414 outputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}}; in createRequest()
432 outputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}}; in createRequest()
756 const auto& actual = outputShapes[i].dimensions; in EvaluatePreparedModel()
758 testModel.main.operands[testModel.main.outputIndexes[i]].dimensions; in EvaluatePreparedModel()
776 const auto& expect = testModel.main.operands[testModel.main.outputIndexes[i]].dimensions; in EvaluatePreparedModel()
777 const std::vector<uint32_t> actual = outputShapes[i].dimensions; in EvaluatePreparedModel()
/hardware/interfaces/neuralnetworks/1.1/
Dtypes.hal33 * dimensions of shape block_shape + [batch], interleaves these blocks back
34 * into the grid defined by the spatial dimensions [1, ..., M], to obtain a
63 * dimensions. The output is the result of dividing the first input tensor
66 * Two dimensions are compatible when:
71 * input operands. It starts with the trailing dimensions, and works its way
86 * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
98 * Computes the mean of elements across dimensions of a tensor.
100 * Reduces the input tensor along the given dimensions to reduce. Unless
102 * in axis. If keep_dims is true, the reduced dimensions are retained with
113 * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}. The dimensions
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/hardware/interfaces/neuralnetworks/1.0/vts/functional/
DBasicTests.cpp81 .dimensions = {1}, in TEST_P()
91 .dimensions = {1}, in TEST_P()
101 .dimensions = {}, in TEST_P()
111 .dimensions = {1}, in TEST_P()
121 .dimensions = {1}, in TEST_P()
131 .dimensions = {1}, in TEST_P()
DUtils.cpp118 inputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}}; in createRequest()
140 outputs[i] = {.hasNoValue = false, .location = loc, .dimensions = {}}; in createRequest()
213 if (isTensor(operand.type) && operand.dimensions.size() == 0) return 0; in sizeOfData()
214 return std::accumulate(operand.dimensions.begin(), operand.dimensions.end(), dataSize, in sizeOfData()
DValidateModel.cpp79 .dimensions = {}, in addOperand()
174 size += sizeForBinder(operand.dimensions); in sizeForBinder()
345 model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0); in mutateOperandRankTest()
649 newOperand.dimensions = hidl_vec<uint32_t>(); in mutateOperand()
654 newOperand.dimensions = in mutateOperand()
655 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
660 newOperand.dimensions = in mutateOperand()
661 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
665 newOperand.dimensions = in mutateOperand()
666 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
/hardware/interfaces/neuralnetworks/1.0/
Dtypes.t190 * For a scalar operand, dimensions.size() must be 0.
192 * For a tensor operand, dimensions.size() must be at least 1;
193 * however, any of the dimensions may be unspecified.
195 * A tensor operand with all dimensions specified has "fully
196 * specified" dimensions. Whenever possible (i.e., whenever the
197 * dimensions are known at model construction time), a tensor
199 * specified dimensions, in order to enable the best possible
202 * If a tensor operand's dimensions are not fully specified, the
203 * dimensions of the operand are deduced from the operand
204 * dimensions and values of the operation for which that operand
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Dtypes.hal26 * scalar values and must have no dimensions.
84 * dimensions. The output is the sum of both input tensors, optionally
87 * Two dimensions are compatible when:
92 * input operands. It starts with the trailing dimensions, and works its
109 * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
127 * The output dimensions are functions of the filter dimensions, stride, and
199 * dimensions except the dimension along the concatenation axis.
231 * The output dimensions are functions of the filter dimensions, stride, and
327 * The output dimensions are functions of the filter dimensions, stride, and
419 * and width dimensions. The value block_size indicates the input block size
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/hardware/interfaces/neuralnetworks/1.1/vts/functional/
DBasicTests.cpp88 .dimensions = {1}, in TEST_P()
98 .dimensions = {1}, in TEST_P()
108 .dimensions = {}, in TEST_P()
118 .dimensions = {1}, in TEST_P()
128 .dimensions = {1}, in TEST_P()
138 .dimensions = {1}, in TEST_P()
DValidateModel.cpp98 .dimensions = {}, in addOperand()
193 size += sizeForBinder(operand.dimensions); in sizeForBinder()
368 model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0); in mutateOperandRankTest()
681 newOperand.dimensions = hidl_vec<uint32_t>(); in mutateOperand()
686 newOperand.dimensions = in mutateOperand()
687 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
692 newOperand.dimensions = in mutateOperand()
693 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
697 newOperand.dimensions = in mutateOperand()
698 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
/hardware/qcom/neuralnetworks/hvxservice/1.0/
DHexagonModel.cpp38 .dimensions = operand.dimensions, in getOperandsInfo()
111 .dimensions = mOperands[operand].dimensions, in getShape()
121 mOperands[operand].dimensions = shape.dimensions; in setShape()
149 std::vector<uint32_t> dims = getAlignedDimensions(operand.dimensions, 4); in addOperand()
202 std::vector<uint32_t> dims = getAlignedDimensions(mOperands[operand].dimensions, 4); in createConvFilterTensor()
224 std::vector<uint32_t> dims = getAlignedDimensions(mOperands[operand].dimensions, 4); in createDepthwiseFilterTensor()
233 std::vector<uint32_t> dims = getAlignedDimensions(mOperands[operand].dimensions, 4); in createFullyConnectedWeightTensor()
298 outputs.push_back(make_hexagon_nn_output(operand.dimensions, getSize(operand.type))); in getHexagonOutputs()
429 make_hexagon_nn_output(mOperands[outputs[0]].dimensions, sizeof(uint8_t)); in addFusedQuant8Operation()
431 make_hexagon_nn_output(mOperands[outputs[0]].dimensions, sizeof(int32_t)); in addFusedQuant8Operation()
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DHexagonOperationsCheck.cpp86 HEXAGON_SOFT_ASSERT_NE(getPadding(inShape.dimensions[2], inShape.dimensions[1], in pool()
97 nn::calculateExplicitPadding(inShape.dimensions[2], stride_width, filter_width, in pool()
99 nn::calculateExplicitPadding(inShape.dimensions[1], stride_height, filter_height, in pool()
180 getPadding(inputShape.dimensions[2], inputShape.dimensions[1], stride_width, in conv_2d()
181 stride_height, filterShape.dimensions[2], filterShape.dimensions[1], in conv_2d()
189 nn::calculateExplicitPadding(inputShape.dimensions[2], stride_width, in conv_2d()
190 filterShape.dimensions[2], padding_implicit, &padding_left, in conv_2d()
192 nn::calculateExplicitPadding(inputShape.dimensions[1], stride_height, in conv_2d()
193 filterShape.dimensions[1], padding_implicit, &padding_top, in conv_2d()
240 getPadding(inputShape.dimensions[2], inputShape.dimensions[1], stride_width, in depthwise_conv_2d()
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DHexagonOperationsPrepare.cpp79 pad = getPadding(inputShape.dimensions[2], inputShape.dimensions[1], stride_width, in average_pool_2d()
115 const int32_t dims = model->getShape(ins[0]).dimensions.size(); in concatenation()
151 pad = getPadding(inputShape.dimensions[2], inputShape.dimensions[1], stride_width, in conv_2d()
152 stride_height, filterShape.dimensions[2], filterShape.dimensions[1], in conv_2d()
201 pad = getPadding(inputShape.dimensions[2], inputShape.dimensions[1], stride_width, in depthwise_conv_2d()
202 stride_height, filterShape.dimensions[2], filterShape.dimensions[1], in depthwise_conv_2d()
267 pad = getPadding(inputShape.dimensions[2], inputShape.dimensions[1], stride_width, in l2_pool_2d()
351 pad = getPadding(inputShape.dimensions[2], inputShape.dimensions[1], stride_width, in max_pool_2d()
534 pad = getPadding(inputShape.dimensions[2], inputShape.dimensions[1], stride_width, in average_pool_2d()
574 const int32_t dims = model->getShape(ins[0]).dimensions.size(); in concatenation()
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DHexagonUtils.cpp113 std::vector<uint32_t> dimensions(N - dims.size(), 1); in getAlignedDimensions() local
114 dimensions.insert(dimensions.end(), dims.begin(), dims.end()); in getAlignedDimensions()
115 return dimensions; in getAlignedDimensions()
281 ", .dimensions: " + toString(shape.dimensions.data(), shape.dimensions.size()) + in toString()
/hardware/interfaces/neuralnetworks/1.2/vts/functional/
DUtils.cpp78 if (isTensor(operand.type) && operand.dimensions.size() == 0) return 0; in sizeOfData()
79 return std::accumulate(operand.dimensions.begin(), operand.dimensions.end(), dataSize, in sizeOfData()
DBasicTests.cpp162 .dimensions = {1}, in TEST_P()
172 .dimensions = {1}, in TEST_P()
182 .dimensions = {}, in TEST_P()
192 .dimensions = {1}, in TEST_P()
202 .dimensions = {1}, in TEST_P()
212 .dimensions = {1}, in TEST_P()
DValidateModel.cpp99 .dimensions = {}, in addOperand()
220 size += sizeForBinder(operand.dimensions); in sizeForBinder()
418 model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0); in mutateOperandRankTest()
751 newOperand.dimensions = hidl_vec<uint32_t>(); in mutateOperand()
758 newOperand.dimensions = in mutateOperand()
759 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
764 newOperand.dimensions = in mutateOperand()
765 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
772 newOperand.dimensions = in mutateOperand()
773 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
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DGeneratedTestHarness.cpp104 .dimensions = op.dimensions, in createModel()
178 auto& dims = model->operands[i].dimensions; in makeOutputDimensionsUnspecified()
325 const auto& expect = testModel.main.operands[testModel.main.outputIndexes[i]].dimensions; in EvaluatePreparedModel()
326 const std::vector<uint32_t> actual = outputShapes[i].dimensions; in EvaluatePreparedModel()
/hardware/interfaces/neuralnetworks/1.2/
Dtypes.t205 * For a scalar operand, dimensions.size() must be 0.
207 * A tensor operand with all dimensions specified has "fully
208 * specified" dimensions. Whenever possible (i.e., whenever the
209 * dimensions are known at model construction time), a tensor
211 * specified dimensions, in order to enable the best possible
214 * If a tensor operand's dimensions are not fully specified, the
215 * dimensions of the operand are deduced from the operand
216 * dimensions and values of the operation for which that operand
219 * In the following situations, a tensor operand's dimensions must
226 * specified dimensions must either be present in the
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Dtypes.hal81 * The size of the scales array must be equal to dimensions[channelDim].
84 * The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0).
160 * dimensions. The output is the sum of both input tensors, optionally
163 * Two dimensions are compatible when:
168 * input operands. It starts with the trailing dimensions, and works its
190 * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
208 * The output dimensions are functions of the filter dimensions, stride, and
292 * dimensions except the dimension along the concatenation axis.
328 * The output dimensions are functions of the filter dimensions, stride, and
479 * The output dimensions are functions of the filter dimensions, stride, and
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