| /frameworks/ml/nn/runtime/test/generated/models/ |
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| 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});
|
| /frameworks/native/libs/vr/libbufferhub/ |
| 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=%u format=%u usage=%" PRIx64
[all...] |
| /frameworks/native/opengl/libagl/ |
| dxt.h | 29 void *surface, int stride, int format);
|
| /external/libvpx/libvpx/vpx_dsp/ |
| fwd_txfm.c | 15 void vpx_fdct4x4_c(const int16_t *input, tran_low_t *output, int stride) {
36 in_high[0] = input[0 * stride] * 16;
37 in_high[1] = input[1 * stride] * 16;
38 in_high[2] = input[2 * stride] * 16;
39 in_high[3] = input[3 * stride] * 16;
81 void vpx_fdct4x4_1_c(const int16_t *input, tran_low_t *output, int stride) {
85 for (c = 0; c < 4; ++c) sum += input[r * stride + c];
90 void vpx_fdct8x8_c(const int16_t *input, tran_low_t *final_output, int stride) {
106 s0 = (input[0 * stride] + input[7 * stride]) * 4
[all...] |
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| conv1d_test.py | 44 for stride in [1, 2]:
46 c = nn_ops.conv1d(x, filters, stride, padding="VALID")
49 if stride == 1:
59 stride = 2
73 x, f, y_shape, stride=stride, padding="VALID")
85 # We add a case for locations divisible by the stride.
86 w_in = w % stride == 0 and w > pad and w < y_shape[1] - 1 - pad
|
| /external/tensorflow/tensorflow/core/kernels/ |
| mkl_pooling_ops_common.cc | 30 const std::vector<int32>& stride, Padding padding,
42 Init(context, ksize, stride, padding, data_format);
49 const std::vector<int32>& stride, Padding padding,
58 Init(context, ksize, stride, padding, data_format);
64 const std::vector<int32>& stride, Padding padding,
73 Init(context, ksize, stride, padding, data_format);
79 const std::vector<int32>& stride, Padding padding,
90 row_stride = GetTensorDim(stride, data_format, 'H');
91 col_stride = GetTensorDim(stride, data_format, 'W');
92 depth_stride = GetTensorDim(stride, data_format, 'C')
[all...] |
