/frameworks/ml/nn/runtime/test/generated/models/ |
H A D | conv_quant8_overflow_weights_as_inputs.model.cpp | 14 auto stride = model->addOperand(&type3); local 22 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 23 model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4});
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H A D | conv_quant8_weights_as_inputs.model.cpp | 14 auto stride = model->addOperand(&type3); local 22 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 23 model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4});
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H A D | depthwise_conv2d_float_large_2_weights_as_inputs.model.cpp | 14 auto stride = model->addOperand(&type3); local 23 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 26 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
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H A D | depthwise_conv2d_float_large_2_weights_as_inputs_relaxed.model.cpp | 14 auto stride = model->addOperand(&type3); local 23 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 26 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
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H A D | depthwise_conv2d_float_large_weights_as_inputs.model.cpp | 13 auto stride = model->addOperand(&type2); local 22 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 25 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
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H A D | depthwise_conv2d_float_large_weights_as_inputs_relaxed.model.cpp | 13 auto stride = model->addOperand(&type2); local 22 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 25 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
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H A D | depthwise_conv2d_float_weights_as_inputs.model.cpp | 13 auto stride = model->addOperand(&type3); local 22 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 25 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
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H A D | depthwise_conv2d_float_weights_as_inputs_relaxed.model.cpp | 13 auto stride = model->addOperand(&type3); local 22 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 25 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
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H A D | depthwise_conv2d_quant8.model.cpp | 13 auto stride = model->addOperand(&type2); local 26 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 29 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
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H A D | depthwise_conv2d_quant8_large.model.cpp | 13 auto stride = model->addOperand(&type2); local 26 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 29 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
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H A D | depthwise_conv2d_quant8_large_weights_as_inputs.model.cpp | 13 auto stride = model->addOperand(&type2); local 22 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 25 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
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H A D | depthwise_conv2d_quant8_weights_as_inputs.model.cpp | 13 auto stride = model->addOperand(&type2); local 22 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 25 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
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H A D | depthwise_conv_2d.model.cpp | 12 auto stride = model->addOperand(&type2); local 21 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 24 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
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H A D | depthwise_conv_2d_quant8.model.cpp | 13 auto stride = model->addOperand(&type2); local 22 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 25 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
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H A D | max_pool_float_2.model.cpp | 8 auto stride = model->addOperand(&type1); local 15 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
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H A D | max_pool_float_2_relaxed.model.cpp | 8 auto stride = model->addOperand(&type1); local 15 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
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H A D | max_pool_float_3.model.cpp | 8 auto stride = model->addOperand(&type1); local 15 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu6_activation}, {output});
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H A D | max_pool_float_3_relaxed.model.cpp | 8 auto stride = model->addOperand(&type1); local 15 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu6_activation}, {output});
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H A D | max_pool_quant8_2.model.cpp | 8 auto stride = model->addOperand(&type1); local 15 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, activation}, {output});
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H A D | max_pool_quant8_3.model.cpp | 8 auto stride = model->addOperand(&type1); local 15 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 22 model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu1_activation}, {output});
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/frameworks/native/libs/vr/libbufferhub/ |
H A D | ion_buffer.cpp | 27 uint32_t stride, uint32_t format, uint64_t usage) 28 : IonBuffer(handle, width, height, kDefaultGraphicBufferLayerCount, stride, 32 uint32_t layer_count, uint32_t stride, uint32_t format, 37 "stride=%u format=%u usage=%" PRIx64, 38 handle, width, height, layer_count, stride, format, usage); 40 Import(handle, width, height, layer_count, stride, format, usage); 46 "IonBuffer::~IonBuffer: handle=%p width=%u height=%u stride=%u " 48 handle(), width(), height(), stride(), format(), usage()); 92 uint32_t layer_count, uint32_t stride, uint32_t format, 96 "stride 26 IonBuffer(buffer_handle_t handle, uint32_t width, uint32_t height, uint32_t stride, uint32_t format, uint64_t usage) argument 31 IonBuffer(buffer_handle_t handle, uint32_t width, uint32_t height, uint32_t layer_count, uint32_t stride, uint32_t format, uint64_t usage) argument 91 Reset(buffer_handle_t handle, uint32_t width, uint32_t height, uint32_t layer_count, uint32_t stride, uint32_t format, uint64_t usage) argument 101 Import(buffer_handle_t handle, uint32_t width, uint32_t height, uint32_t layer_count, uint32_t stride, uint32_t format, uint64_t usage) argument 122 Import(const int* fd_array, int fd_count, const int* int_array, int int_count, uint32_t width, uint32_t height, uint32_t layer_count, uint32_t stride, uint32_t format, uint64_t usage) argument [all...] |
/frameworks/native/opengl/libagl/ |
H A D | dxt.h | 29 void *surface, int stride, int format);
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/frameworks/rs/driver/runtime/ |
H A D | rs_sample.c | 84 getElementAt1(const uint8_t *p, size_t stride, int32_t x, int32_t y) { argument 85 p += y * stride; 91 getElementAt2(const uint8_t *p, size_t stride, int32_t x, int32_t y) { argument 92 p += y * stride; 99 getElementAt3(const uint8_t *p, size_t stride, int32_t x, int32_t y) { argument 100 p += y * stride; 107 getElementAt4(const uint8_t *p, size_t stride, int32_t x, int32_t y) { argument 108 p += y * stride; 115 getElementAt565(const uint8_t *p, size_t stride, int32_t x, int32_t y) { argument 116 p += y * stride; 188 getSample_A(const uint8_t *p, size_t stride, int locX, int locY, int nextX, int nextY, float w0, float w1, float w2, float w3) argument 201 getSample_L(const uint8_t *p, size_t stride, int locX, int locY, int nextX, int nextY, float w0, float w1, float w2, float w3) argument 214 getSample_LA(const uint8_t *p, size_t stride, int locX, int locY, int nextX, int nextY, float w0, float w1, float w2, float w3) argument 227 getSample_RGB(const uint8_t *p, size_t stride, int locX, int locY, int nextX, int nextY, float w0, float w1, float w2, float w3) argument 240 getSample_RGBA(const uint8_t *p, size_t stride, int locX, int locY, int nextX, int nextY, float w0, float w1, float w2, float w3) argument 252 getSample_565(const uint8_t *p, size_t stride, int locX, int locY, int nextX, int nextY, float w0, float w1, float w2, float w3) argument 323 size_t stride = alloc->mHal.drvState.lod[lod].stride; local 389 size_t stride = alloc->mHal.drvState.lod[lod].stride; local [all...] |
/frameworks/native/libs/vr/libbufferhub/include/private/dvr/ |
H A D | ion_buffer.h | 17 uint32_t stride, uint32_t format, uint64_t usage); 19 uint32_t layer_count, uint32_t stride, uint32_t format, 43 uint32_t layer_count, uint32_t stride, uint32_t format, 50 uint32_t layer_count, uint32_t stride, uint32_t format, 58 uint32_t layer_count, uint32_t stride, uint32_t format, 82 uint32_t stride() const { return buffer_.get() ? buffer_->getStride() : 0; } function in class:android::dvr::IonBuffer
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/frameworks/rs/driver/ |
H A D | rsdVertexArray.cpp | 47 stride = 0; 53 void RsdVertexArray::Attrib::set(uint32_t type, uint32_t size, uint32_t stride, argument 61 this->stride = stride; 69 ALOGV("va %i: slot=%i name=%s buf=%i ptr=%p size=%i type=0x%x stride=0x%x norm=%i offset=0x%p", 76 mAttribs[idx].stride, 113 mAttribs[ct].stride,
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