mirror of
https://github.com/Evolution-X/hardware_interfaces
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Merge changes Iab38ef29,I85b66ab5 am: 5bb1721f6e
Change-Id: I76a4400b4bb378a1c3a37d6b028f43c2ce00c171
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@@ -584,11 +584,11 @@ cfa81f229b69f9011c58f48264fcb552447430fe68610eac514e811e65bc306a android.hardwar
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# ABI preserving changes to HALs during Android R
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b69a7615c508acf5c5201efd1bfa3262167874fc3594e2db5a3ff93addd8ac75 android.hardware.keymaster@4.0::IKeymasterDevice
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eb2fa0c883c2185d514be0b84c179b283753ef0c1b77b45b4f359bd23bba8b75 android.hardware.neuralnetworks@1.0::IPreparedModel
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8eac60e1f724d141c71c69f06d4544acb720a55dfbbcd97fa01bb3d25ee4e2f5 android.hardware.neuralnetworks@1.0::types
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92e101b30e47bdf526a01c52cecfbe730def5997b8260ab497eb949eb2a6dcdf android.hardware.neuralnetworks@1.0::types
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5f6d3097ba84cb63c430787123f4de1b31c11f90b531b98eae9a8623a5ae962a android.hardware.neuralnetworks@1.1::types
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fb382e986c10b8fbb797a8546e8f9ea6d1107bfe6f3fb7e57f6bbbf1f807a906 android.hardware.neuralnetworks@1.2::IDevice
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40e71cd693de5b832325c5d8f081f2ff20a7ba2b89d401cee5b4b3eb0e241681 android.hardware.neuralnetworks@1.2::IPreparedModel
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00649d29680f2c47edf60000c3ae7ae906ba638f0616947147e3676a83cf36fa android.hardware.neuralnetworks@1.2::types
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ee1a0dee5be00a6fe2d4d3270068c78016dcb194d768fe07ed894ea20904037f android.hardware.neuralnetworks@1.2::types
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a785a57447a81e9c130eef6904c3a5c256076c6a04588c40620ebd6fa2660d77 android.hardware.radio@1.2::types
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1a6e2bd289f22931c526b21916910f1d4c436b7acb9556e4243de4ce8e6cc2e4 android.hardware.soundtrigger@2.0::ISoundTriggerHwCallback
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fd65298e1e09e0e3c781ab18305920d757dbe55a3b459ce17814ec5cf6dfee99 android.hardware.wifi@1.0::IWifiP2pIface
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@@ -639,7 +639,7 @@ ddcf89cd8ee2df0d32aee55050826446fb64f7aafde0a7cd946c64f61b1a364c android.hardwar
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6e904be0ddca5ae1de8eba020e6c38ed935ea7d80cd08f47787f137a0ca58555 android.hardware.neuralnetworks@1.3::IFencedExecutionCallback
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2b0b10d2ea7a18a4048cd0eb83d35c19a817aeee95f65807fc31f4ef21381397 android.hardware.neuralnetworks@1.3::IPreparedModel
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eee3430cc86c97c7b407495863d8fb61da6f1a64b7721e77b9b4909b11b174e9 android.hardware.neuralnetworks@1.3::IPreparedModelCallback
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e442ab1b440327fe4e8a3b0b8ac6874e9bc6342e91fe976eb9fea77c63961ec8 android.hardware.neuralnetworks@1.3::types
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acf84925f8ee0a651f2ec547ac334034de266479b93af5434f6c1f25e66aba96 android.hardware.neuralnetworks@1.3::types
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3e01d4446cd69fd1c48f8572efd97487bc179564b32bd795800b97bbe10be37b android.hardware.wifi@1.4::IWifi
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a64467bae843569f0d465c5be7f0c7a5b987985b55a3ef4794dd5afc68538650 android.hardware.wifi.supplicant@1.3::ISupplicant
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44445b8a03d7b9e68b2fbd954672c18a8fce9e32851b0692f4f4ab3407f86ecb android.hardware.wifi.supplicant@1.3::ISupplicantStaIface
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@@ -261,7 +261,7 @@ enum OperationType : int32_t {
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* filter.
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* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
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* tensor of type {@link OperandType::TENSOR_FLOAT32}
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* the bias must be of the same type.
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* the bias must be of the same type.
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* For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
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* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
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* of 0 and bias_scale == input_scale * filter_scale.
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@@ -289,7 +289,7 @@ enum OperationType : int32_t {
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* filter.
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* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
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* tensor of type {@link OperandType::TENSOR_FLOAT32}
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* the bias must be of the same
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* the bias must be of the same
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* type.
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* For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
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* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
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@@ -356,7 +356,7 @@ enum OperationType : int32_t {
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* specifying the filter.
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* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
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* tensor of type {@link OperandType::TENSOR_FLOAT32}
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* the bias must be of the same type.
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* the bias must be of the same type.
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* For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
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* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
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* of 0 and bias_scale == input_scale * filter_scale.
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@@ -385,7 +385,7 @@ enum OperationType : int32_t {
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* specifying the filter.
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* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
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* tensor of type {@link OperandType::TENSOR_FLOAT32}
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* the bias must be of the same type.
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* the bias must be of the same type.
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* For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
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* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
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* of 0 and bias_scale == input_scale * filter_scale.
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@@ -628,7 +628,7 @@ enum OperationType : int32_t {
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HASHTABLE_LOOKUP = 10,
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/**
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* Applies L2 normalization along the depth dimension.
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* Applies L2 normalization along the axis dimension.
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*
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* The values in the output tensor are computed as:
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*
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@@ -846,7 +846,7 @@ enum OperationType : int32_t {
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HASHTABLE_LOOKUP = @1.1::OperationType:HASHTABLE_LOOKUP,
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/**
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* Applies L2 normalization along the depth dimension.
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* Applies L2 normalization along the axis dimension.
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*
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* The values in the output tensor are computed as:
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*
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@@ -854,8 +854,7 @@ enum OperationType : int32_t {
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* input[batch, row, col, channel] /
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* sqrt(sum_{c} pow(input[batch, row, col, c], 2))
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*
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* For input tensor with rank less than 4, independently normalizes each
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* 1-D slice along dimension dim.
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* By default the axis dimension is the last dimension of the input tensor.
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*
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* Supported tensor {@link OperandType}:
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* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
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@@ -3843,7 +3842,8 @@ enum OperationType : int32_t {
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* * 1: A scalar {@link OperandType::INT32}, specifying the number of
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* independent samples to draw for each row slice.
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* * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],
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* specifying seeds used to initialize the random distribution.
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* specifying seeds used to initialize the random distribution. If both
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* provided seeds are 0, both will be randomly generated.
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* Outputs:
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* * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape
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* [batches, samples], containing the drawn samples.
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@@ -833,7 +833,7 @@ enum OperationType : int32_t {
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HASHTABLE_LOOKUP = @1.2::OperationType:HASHTABLE_LOOKUP,
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/**
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* Applies L2 normalization along the depth dimension.
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* Applies L2 normalization along the axis dimension.
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*
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* The values in the output tensor are computed as:
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*
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@@ -841,8 +841,7 @@ enum OperationType : int32_t {
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* input[batch, row, col, channel] /
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* sqrt(sum_{c} pow(input[batch, row, col, c], 2))
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*
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* For input tensor with rank less than 4, independently normalizes each
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* 1-D slice along dimension dim.
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* By default the axis dimension is the last dimension of the input tensor.
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*
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* Supported tensor {@link OperandType}:
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* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
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@@ -867,6 +866,10 @@ enum OperationType : int32_t {
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* the scale must be 1.f / 128 and the zeroPoint must be 128.
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* For {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
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* the scale must be 1.f / 128 and the zeroPoint must be 0.
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*
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* NOTE: Before HAL version 1.3, if the elements along an axis are all zeros,
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* the result is undefined. Since HAL version 1.3, if the elements along an axis
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* are all zeros, the result is logical zero.
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*/
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L2_NORMALIZATION = @1.2::OperationType:L2_NORMALIZATION,
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@@ -4063,7 +4066,8 @@ enum OperationType : int32_t {
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* * 1: A scalar {@link OperandType::INT32}, specifying the number of
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* independent samples to draw for each row slice.
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* * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],
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* specifying seeds used to initialize the random distribution.
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* specifying seeds used to initialize the random distribution. If both
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* provided seeds are 0, both will be randomly generated.
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* Outputs:
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* * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape
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* [batches, samples], containing the drawn samples.
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