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Searched refs:dimensions (Results 1 – 25 of 29) sorted by relevance

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/hardware/interfaces/neuralnetworks/1.1/
Dtypes.hal34 * dimensions of shape block_shape + [batch], interleaves these blocks back
35 * 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
100 * Computes the mean of elements across dimensions of a tensor.
102 * Reduces the input tensor along the given dimensions to reduce. Unless
104 * in axis. If keep_dims is true, the reduced dimensions are retained with
115 * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}. The dimensions
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/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()
DHexagonModel.h47 std::vector<uint32_t> dimensions; member
/hardware/interfaces/neuralnetworks/1.2/vts/functional/
DValidateModel.cpp114 .dimensions = {}, in addOperand()
186 model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0); in mutateOperandRankTest()
287 newOperand.dimensions = hidl_vec<uint32_t>(); in mutateOperand()
294 newOperand.dimensions = in mutateOperand()
295 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
300 newOperand.dimensions = in mutateOperand()
301 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
308 newOperand.dimensions = in mutateOperand()
309 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
313 newOperand.dimensions = in mutateOperand()
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DValidateRequest.cpp201 .dimensions = {}, in createRequests()
223 .dimensions = {}, in createRequests()
DCompilationCachingTests.cpp158 .dimensions = {}, in createLargeTestModelImpl()
195 .dimensions = {1}, in createLargeTestModelImpl()
207 .dimensions = {1}, in createLargeTestModelImpl()
233 .dimensions = {1}, in createLargeTestModelImpl()
/hardware/interfaces/neuralnetworks/1.0/vts/functional/
DValidateModel.cpp98 .dimensions = {}, in addOperand()
159 model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0); in mutateOperandRankTest()
239 newOperand.dimensions = hidl_vec<uint32_t>(); in mutateOperand()
244 newOperand.dimensions = in mutateOperand()
245 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
250 newOperand.dimensions = in mutateOperand()
251 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
255 newOperand.dimensions = in mutateOperand()
256 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
DValidateRequest.cpp125 .dimensions = {}, in createRequests()
147 .dimensions = {}, in createRequests()
DGeneratedTestHarness.cpp167 .dimensions = {}, in EvaluatePreparedModel()
205 .dimensions = {}, in EvaluatePreparedModel()
349 dim = outputShapes[idx].dimensions; in EvaluatePreparedModel()
/hardware/interfaces/neuralnetworks/1.1/vts/functional/
DValidateModel.cpp114 .dimensions = {}, in addOperand()
175 model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0); in mutateOperandRankTest()
255 newOperand.dimensions = hidl_vec<uint32_t>(); in mutateOperand()
260 newOperand.dimensions = in mutateOperand()
261 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
266 newOperand.dimensions = in mutateOperand()
267 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
271 newOperand.dimensions = in mutateOperand()
272 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1}); in mutateOperand()
DValidateRequest.cpp125 .dimensions = {}, in createRequests()
147 .dimensions = {}, in createRequests()
/hardware/interfaces/neuralnetworks/1.0/
Dtypes.hal26 * scalar values and must have no dimensions.
86 * dimensions. The output is the sum of both input tensors, optionally
89 * Two dimensions are compatible when:
94 * input operands. It starts with the trailing dimensions, and works its
111 * * 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.
230 * The output dimensions are functions of the filter dimensions, stride, and
321 * The output dimensions are functions of the filter dimensions, stride, and
408 * and width dimensions. The value block_size indicates the input block size
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DIPreparedModel.hal46 * tensor operands have fully specified dimensions, and the inputs
/hardware/interfaces/neuralnetworks/1.2/
Dtypes.hal91 * The size of the scales array must be equal to dimensions[channelDim].
93 * The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0).
173 * dimensions. The output is the sum of both input tensors, optionally
176 * Two dimensions are compatible when:
181 * input operands. It starts with the trailing dimensions, and works its
203 * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
219 * The output dimensions are functions of the filter dimensions, stride, and
302 * dimensions except the dimension along the concatenation axis.
337 * The output dimensions are functions of the filter dimensions, stride, and
487 * The output dimensions are functions of the filter dimensions, stride, and
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DIPreparedModel.hal51 * tensor operands have fully specified dimensions, and the inputs
94 * operands have fully specified dimensions, and the inputs to the function
/hardware/interfaces/camera/device/1.0/
DICameraDevicePreviewCallback.hal67 * Set the dimensions and format of future preview buffers.
/hardware/interfaces/graphics/bufferqueue/1.0/
DIGraphicBufferProducer.hal257 * An error due to invalid dimensions might not be reported until
335 * 1) It is unnecessary to know the dimensions, format, or usage of the
425 * * crop rect is out of bounds of the buffer dimensions
573 * Allocates buffers based on the given dimensions/format.
577 * given format, dimensions, and usage bits, which are interpreted in the
/hardware/interfaces/graphics/bufferqueue/2.0/
DIGraphicBufferProducer.hal125 * glGetIntegerv()). An error due to invalid dimensions may not be reported
128 * If `width` and `height` are both zero, the default dimensions shall be
532 * Allocates buffers based on the given dimensions, format and usage.
536 * given format, dimensions, and usage bits, which are interpreted in the
/hardware/interfaces/graphics/composer/2.1/
DIComposerClient.hal354 * height equal to the active display configuration dimensions,
952 * origin is the top-left corner. They must not exceed the dimensions of
1010 * of the given layer. This frame must not exceed the display dimensions.
1050 * exceed the dimensions of the latched buffer.
1083 * and must not exceed the dimensions of the screen.
/hardware/interfaces/renderscript/1.0/
DIContext.hal40 * the shape of the window. Any dimensions present in the type must be
41 * equal to or smaller than the dimensions in the source allocation. A
186 * dimensions of the Allocation.
1144 * @param dims Collection of dimensions
/hardware/interfaces/input/common/1.0/
Dtypes.hal687 * The array is a 2-D row-major matrix with dimensions (height, width).
714 * change), the frame's dimensions will become 6 x 10
/hardware/interfaces/graphics/composer/2.2/
DIComposerClient.hal300 * height equal to the active display configuration dimensions,

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