Merge "Sync NNAPI Operand and Operation documentation fixes" into pi-dev
diff --git a/current.txt b/current.txt
index ddbca36..e79e2d6 100644
--- a/current.txt
+++ b/current.txt
@@ -241,11 +241,11 @@
86ba9c03978b79a742e990420bc5ced0673d25a939f82572996bef92621e2014 android.hardware.cas@1.0::IMediaCasService
503da837d1a67cbdb7c08a033e927e5430ae1b159d98bf72c6336b4dcc5e76f5 android.hardware.cas.native@1.0::types
619600109232ed64b827c8a11beed8070b1827ae464547d7aa146cf0473b4bca android.hardware.cas.native@1.0::IDescrambler
-246a56d37d57a47224562c9d077b4a2886ce6242b9311bd98a17325944c280d7 android.hardware.neuralnetworks@1.0::types
93eb3757ceaf21590fa4cd1d4a7dfe3b3794af5396100a6d25630879352abce9 android.hardware.neuralnetworks@1.0::IDevice
f66f9a38541bf92001d3adcce678cd7e3da2262124befb460b1c9aea9492813b android.hardware.neuralnetworks@1.0::IExecutionCallback
953607822954435874f4b81686440a604e2a88cdd2d9164c6293f3d5772510d7 android.hardware.neuralnetworks@1.0::IPreparedModel
73e03573494ba96f0e711ab7f1956c5b2d54c3da690cd7ecf4d6d0f287447730 android.hardware.neuralnetworks@1.0::IPreparedModelCallback
+246a56d37d57a47224562c9d077b4a2886ce6242b9311bd98a17325944c280d7 android.hardware.neuralnetworks@1.0::types
f4945e397b5dea41bb64518dfde59be71245d8a125fd1e0acffeb57ac7b08fed android.hardware.thermal@1.1::IThermal
c8bc853546dd55584611def2a9fa1d99f657e3366c976d2f60fe6b8aa6d2cb87 android.hardware.thermal@1.1::IThermalCallback
@@ -259,7 +259,8 @@
251594ea9b27447bfa005ebd806e58fb0ae4aad84a69938129c9800ec0c64eda android.hardware.gnss@1.0::IGnssMeasurementCallback
4e7169919d24fbe5573e5bcd683d0bd7abf553a4e6c34c41f9dfc1e12050db07 android.hardware.gnss@1.0::IGnssNavigationMessageCallback
5804ca86611d72e5481f022b3a0c1b334217f2e4988dad25730c42af2d1f4d1c android.hardware.neuralnetworks@1.0::IDevice
-6721fc5b64d997f3eda15b762a0dd9f3fa414926219dbca58312972d565b4bee android.hardware.neuralnetworks@1.0::types
+12e8dca4ab7d8aadd0ef8f1b438021938e2396139e85db2ed65783b08800aa52 android.hardware.neuralnetworks@1.0::IExecutionCallback
+702f9a4cd3b7486a4b04f7155b737757ac2ca4b3548976d5782ad3cae9ff9780 android.hardware.neuralnetworks@1.0::types
d4840db8efabdf1e4b344fc981cd36e5fe81a39aff6e199f6d06c1c8da413efd android.hardware.radio@1.0::types
b280c4704dfcc548a9bf127b59b7c3578f460c50cce70a06b66fe0df8b27cff0 android.hardware.wifi@1.0::types
@@ -338,7 +339,7 @@
4a2c0dc82780e6c90731725a103feab8ab6ecf85a64e049b9cbd2b2c61620fe1 android.hardware.media.bufferpool@1.0::IConnection
6aef1218e5949f867b0104752ac536c1b707222a403341720de90141df129e3e android.hardware.media.bufferpool@1.0::types
3e4d8e0085ebe8549efb8ad4b8b400a141a3fa3f47ae23696b3e05a1612eb003 android.hardware.neuralnetworks@1.1::IDevice
-e808a6f61cd7b47887c599d8843e67a2dcbf4ec5aadd5d22fdce93020070ef1b android.hardware.neuralnetworks@1.1::types
+50db076b03a6760557fc60ef433ba9dd2ff983cf3305eeb504b0fff3eaa604ff android.hardware.neuralnetworks@1.1::types
8d3d86da0bfa4bf070970d8303c659f67f35d670c287d45a3f542e4fedadd578 android.hardware.nfc@1.1::INfc
e85f566698d2a2c28100e264fcf2c691a066756ddf8dd341d009ff50cfe10614 android.hardware.nfc@1.1::INfcClientCallback
5e278fcaa3287d397d8eebe1c22aaa28150f5caae1cf9381cd6dc32cb37899c5 android.hardware.nfc@1.1::types
diff --git a/neuralnetworks/1.0/IExecutionCallback.hal b/neuralnetworks/1.0/IExecutionCallback.hal
index ef0f454..9c06166 100644
--- a/neuralnetworks/1.0/IExecutionCallback.hal
+++ b/neuralnetworks/1.0/IExecutionCallback.hal
@@ -28,7 +28,7 @@
* ErrorStatus resulting from the execution. If the asynchronous task
* is not launched, notify must be invoked with the appropriate error.
*
- * @return param Error status returned from launching the asynchronous task
+ * @param status Error status returned from launching the asynchronous task
* (if the launch fails) or from the asynchronous task itself
* (if the launch succeeds). Must be:
* - NONE if the asynchronous execution was successful
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 5b8f22c..8c07fcc 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -24,38 +24,40 @@
* Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors
* with at least one dimension). Types not prefaced by TENSOR_* represent
* scalar values and must have no dimensions.
+ *
+ * Although many types are defined, most operators accept just a few
+ * types. Most used are {@link OperandType::TENSOR_FLOAT32},
+ * {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * and {@link OperandType::INT32}.
*/
enum OperandType : int32_t {
- /**
- * The following entries are used to declare scalars.
- */
+ /** A 32 bit floating point scalar value. */
FLOAT32 = 0,
+ /** A signed 32 bit integer scalar value. */
INT32 = 1,
+ /** An unsigned 32 bit integer scalar value. */
UINT32 = 2,
- /**
- * The following entries are used to declare tensors.
- */
+ /** A tensor of 32 bit floating point values. */
TENSOR_FLOAT32 = 3,
+ /** A tensor of 32 bit integer values. */
TENSOR_INT32 = 4,
-
- /**
- * A tensor of 8 bit integers that represent real numbers.
+ /** A tensor of 8 bit integers that represent real numbers.
*
* Attached to this tensor are two numbers that can be used to convert the
* 8 bit integer to the real value and vice versa. These two numbers are:
- * - scale: a 32 bit floating point value greater than zero
- * - zero_value: a 32 bit integer
+ * - scale: a 32 bit floating point value greater than zero.
+ * - zeroPoint: a 32 bit integer, in range [0, 255].
*
* The formula is:
- * real_value = (integer_value - zero_value) * scale.
+ * real_value = (integer_value - zeroPoint) * scale.
*/
TENSOR_QUANT8_ASYMM = 5,
- /**
- * The following entries are OEM specific operand types.
- */
+ /** OEM specific scalar value. */
OEM = 10000,
+
+ /** A tensor of OEM specific values. */
TENSOR_OEM_BYTE = 10001,
};
@@ -66,9 +68,9 @@
*/
enum OperationType : int32_t {
/**
- * Adds two tensors, elment-wise.
+ * Adds two tensors, element-wise.
*
- * Takes two input tensors of identical type and compatible dimensions. The output
+ * Takes two input tensors of identical type and compatible dimensions. The output
* is the sum of both input tensors, optionally modified by an activation function.
*
* Two dimensions are compatible when:
@@ -79,22 +81,25 @@
* It starts with the trailing dimensions, and works its way forward.
*
* Example:
- * input1.dimension = {4, 1, 2}
+ *
+ * input1.dimension = {4, 1, 2}
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: A tensor.
- * 1: A tensor of the same type, and compatible dimensions as input0.
- * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 0: A tensor.
+ * * 1: A tensor of the same type, and compatible dimensions as input0.
+ * * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
*
- * Ouputs:
- * 0: The sum, a tensor of the same type as input0.
+ * Outputs:
+ * * 0: The sum, a tensor of the same type as input0.
*/
ADD = 0,
@@ -103,29 +108,50 @@
*
* The output dimensions are functions of the filter dimensions, stride, and padding.
*
- * The values in output Tensor is computed as:
+ * The values in the output tensor are computed as:
+ *
* output[batch, row, col, channel] =
* sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
- * Inputs:
- * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
- * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
- * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
- * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
- * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
- * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
- * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
- * 7: An INT32 value, specifying the filter width.
- * 8: An INT32 value, specifying the filter height.
- * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels)
+ * data layout.
*
- * Ouputs:
- * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+ * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+ * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+ * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+ * * 5: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 6: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 7: An INT32 value, specifying the filter width.
+ * * 8: An INT32 value, specifying the filter height.
+ * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 2: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 3: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 4: An INT32 value, specifying the filter width.
+ * * 5: An INT32 value, specifying the filter height.
+ * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
*/
AVERAGE_POOL_2D = 1,
@@ -135,19 +161,21 @@
* The input tensors must have identical type and the same dimensions except the
* dimension along the concatenation axis.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
- * 0 ~ n: The list on n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]
- * n+1: An INT32 value, specifying the concatenation axis.
- * n+2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm].
+ * For inputs of {@link OperandType::TENSOR_QUANT8_ASYMM} type, all
+ * input tensors must have the same scale and zeroPoint.
+ * * n: An INT32 value, specifying the concatenation axis.
*
- * Ouputs:
- * 0: The output, a tensor of the same type as the input tensors.
- The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+ * Outputs:
+ * * 0: The output, a tensor of the same type as the input tensors.
+ * The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
*/
CONCATENATION = 2,
@@ -159,7 +187,8 @@
*
* The output dimensions are functions of the filter dimensions, stride, and padding.
*
- * The values in output Tensor is computed as:
+ * The values in the output tensor are computed as:
+ *
* output[batch, row, col, channel] =
* sum_{i, j} (
* input[batch, row + i, col + j, k] *
@@ -167,77 +196,135 @@
* bias[channel]
* )
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: 4, with "NHWC" data layout.
*
- * Inputs:
- * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
- * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
- * specifying the filter.
- * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
- * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}.
- * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
- * should be of {@link OperandType::TENSOR_INT32}.
- * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
- * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
- * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
- * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
- * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
- * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
- * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * Both explicit padding and implicit padding are supported.
*
- * Ouputs:
- * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
+ * specifying the filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+ * also be of {@link OperandType::TENSOR_FLOAT32}.
+ * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+ * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+ * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+ * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+ * * 7: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 8: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
+ * specifying the filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+ * also be of {@link OperandType::TENSOR_FLOAT32}.
+ * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 4: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 5: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+ * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+ * condition must be satisfied: output_scale > input_scale * filter_scale.
*/
CONV_2D = 3,
/**
- * Performs an depthwise 2-D convolution operation.
+ * Performs a depthwise 2-D convolution operation.
*
* Given an input tensor of shape [batches, height, width, depth_in] and a filter
- * tensor of shape [depth_out, filter_height, filter_width, depth_in] containing
- * in_channels convolutional filters of depth 1, DEPTHWISE_CONV applies a different
+ * tensor of shape [1, filter_height, filter_width, depth_out] containing
+ * depth_out convolutional filters of depth 1, DEPTHWISE_CONV applies a different
* filter to each input channel (expanding from 1 channel to channel_multiplier channels
* for each), then concatenates the results together.
*
* The output has depth_out = depth_in * depth_multiplier channels.
* The output dimensions are functions of the filter dimensions, stride, and padding.
*
- * The values in output Tensor is computed as:
+ * The values in the output tensor are computed as:
+ *
* output[b, i, j, k * channel_multiplier + q] =
* sum_{di, dj} (
* input[b, strides[1] * i + di, strides[2] * j + dj, k] *
- * filter[di, dj, k, q]
+ * filter[1, di, dj, k * channel_multiplier + q]
* )
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: 4, with "NHWC" data layout.
*
- * Inputs:
- * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
- * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
- * specifying the filter.
- * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
- * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}.
- * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
- * should be of {@link OperandType::TENSOR_INT32}.
- * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
- * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
- * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
- * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
- * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
- * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
- * 9: An INT32 value, specifying the depthwise multiplier.
- * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * Both explicit padding and implicit padding are supported.
*
- * Ouputs:
- * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+ * specifying the filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+ * also be of {@link OperandType::TENSOR_FLOAT32}.
+ * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+ * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+ * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+ * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+ * * 7: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 8: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 9: An INT32 value, specifying the depthwise multiplier.
+ * * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+ * specifying the filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+ * also be of {@link OperandType::TENSOR_FLOAT32}.
+ * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 4: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 5: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 6: An INT32 value, specifying the depthwise multiplier.
+ * * 7: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+ * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+ * condition must be satisfied: output_scale > input_scale * filter_scale.
*/
DEPTHWISE_CONV_2D = 4,
@@ -255,18 +342,20 @@
* input_height * block_size.
* The depth of the input tensor must be divisible by block_size * block_size
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
- * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
- * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
- * block_size * block_size must be a divisor of the input depth.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+ * block_size * block_size must be a divisor of the input depth.
*
- * Ouputs:
- * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
- * depth/(block_size*block_size)].
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
+ * depth/(block_size*block_size)].
*/
DEPTH_TO_SPACE = 5,
@@ -274,53 +363,69 @@
* Dequantizes the input tensor.
*
* The formula is:
- * output = (input - zero_value) * scale.
*
- * Supported tensor types: {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * output = (input - zeroPoint) * scale.
+ *
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}.
+ * * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}.
*
- * Ouputs:
- * 0: The output tensor of same shape as input0, but with type
- {@link OperandType::TENSOR_FLOAT32}.
+ * Outputs:
+ * * 0: The output tensor of same shape as input0, but with type
+ * {@link OperandType::TENSOR_FLOAT32}.
*/
DEQUANTIZE = 6,
/**
- * Looks up items from a given tensor.
+ * Looks up sub-tensors in the input tensor.
*
- * Each item in the output is a raw copy of the corresponding item in
- * the input “values”. If the the given “lookup” indices are out of bounds,
- * the op will fail and an error will be reported.
+ * This operator takes for input a tensor of values (Values) and
+ * a one-dimensional tensor of selection indices (Lookups).
+ * The output tensor is the concatenation of sub-tensors of Values as
+ * selected by Lookups.
+ *
+ * Think of Values as being sliced along its first dimension:
+ * The entries in Lookups select which slices are concatenated together
+ * to create the output tensor.
+ *
+ * For example, if Values has shape of [40, 200, 300] and
+ * Lookups has shape of [3], all three values found in Lookups are
+ * expected to be between 0 and 39. The resulting tensor must
+ * have shape of [3, 200, 300].
+ *
+ * If a value in Lookups is out of bounds, the operation must fail
+ * and an error must be reported.
*
* Inputs:
- * * 0: Values. An n-D tensor of any type X (where n >= 2). E.g., if n is 2,
- * then the shape would be [lookup_dimension, values_dimension], where
- * “lookup_dimension” corresponds to the indexing dimension in the lookup
- * table, and “values_dimension” to the contents.
- * * 1: Lookups. An 1-D tensor of type T, of shape [lookup_size], where
- * “lookup_size” is the number of elements to look for, and each entry
- * corresponds to the first dimension of the “values” tensor.
+ * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32} type.
+ * The values are indices into the first dimension of Values.
+ * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are
+ * extracted.
*
* Output:
- * * 0: A n-D tensor of type X and the same rank and shape as the “values”
- * tensor, except for the first dimension which has size “lookup_size”.
+ * * 0: A n-D tensor with the same rank and shape as the Values
+ * tensor, except for the first dimension which has the same size
+ * as Lookups' only dimension.
*/
EMBEDDING_LOOKUP = 7,
/**
* Computes element-wise floor() on the input tensor.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: A tensor.
+ * * 0: A tensor.
*
- * Ouputs:
- * 0: The output, a tensor of the same type and dimensions as input0.
+ * Outputs:
+ * * 0: The output tensor, of the same type and dimensions as the input tensor.
*/
FLOOR = 8,
@@ -329,66 +434,104 @@
* tensor with each element in the output tensor.
*
* This layer implements the operation:
+ *
* outputs = activation(inputs * weights’ + bias)
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4.
*
* Inputs:
- * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
- * a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
- * [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
- * and “input_size” is the size of the input.
- * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where “num_units”
- * corresponds to the number of output nodes.
- * 2: A 1-D tensor, of shape [num_units], specifying the bias.
- * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
- * also be of {@link OperandType::TENSOR_FLOAT32}.
- * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
- * should be of {@link OperandType::TENSOR_INT32}.
- * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
+ * a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
+ * [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
+ * and “input_size” is the size of the input.
+ * * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where
+ * "num_units" corresponds to the number of output nodes.
+ * * 2: A 1-D tensor, of shape [num_units], specifying the bias.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+ * also be of {@link OperandType::TENSOR_FLOAT32}.
+ * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+ * bias_scale == input_scale * filter_scale.
+ * * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
*
- * Ouputs:
- * 0: The output tensor, of shape [batch_size, num_units].
+ * Outputs:
+ * * 0: The output tensor, of shape [batch_size, num_units].
+ * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+ * condition must be satisfied: output_scale > input_scale * filter_scale.
*/
FULLY_CONNECTED = 9,
/**
- * Looks up values of a hash table with given keys.
+ * Looks up sub-tensors in the input tensor using a key-value map.
+ *
+ * This operator takes for input a tensor of values (Values),
+ * a one-dimensional tensor of selection values (Lookups) and
+ * a one-dimensional tensor that maps these values to Values
+ * indexes. The output tensor is the concatenation of sub-tensors of
+ * Values as selected by Lookups via Keys.
+ *
+ * Think of Values as being sliced along its outer-most dimension.
+ * The output is a concatenation of selected slices, with one slice
+ * for each entry of Lookups. The slice selected is the one at the
+ * same index as the Maps entry that matches the value in Lookups.
+ *
+ * For a hit, the corresponding sub-tensor of Values is included
+ * in the Output tensor. For a miss, the corresponding sub-tensor in
+ * Output must have zero values.
+ *
+ * For example, if Values has shape of [40, 200, 300],
+ * Keys should have a shape of [40]. If Lookups tensor has shape
+ * of [3], three slices are being concatenated, so the resulting tensor
+ * must have the shape of [3, 200, 300]. If the first entry in Lookups
+ * has the value 123456, that value must be located in Keys tensor.
+ * If the sixth entry of Keys contains 123456, the sixth slice of Values
+ * must be selected. If no entry in Keys has 123456, a slice of zeroes
+ * must be concatenated.
*
* Inputs:
- * * 0: Lookups. A 1-D int32 tensor with shape [ k ].
- * * 1: Keys. A 1-D int32 tensor with shape [ n ], *MUST* be sorted in
- * ascending order.
- * * 2: Values. A tensor with shape [ n … ].
+ * * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [ k ].
+ * * 1: Keys. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [ n ];
+ * Keys and Values pair represent a map, i.e., the ith element
+ * in Keys (Keys[i]) is the key to select the ith sub-tensor
+ * in Values (Values[i]), where 0 <= i <= n-1.
+ * Keys tensor *MUST* be sorted in ascending order.
+ * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension must be n.
*
* Outputs:
* * 0: Output. A tensor with shape [ k …].
- * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup
- * hits or not.
+ * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
+ * hits (True) or not (False).
+ * Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0 and scale 1.0f.
+ * A non-zero byte represents True, a hit. A zero indicates otherwise.
*/
HASHTABLE_LOOKUP = 10,
/**
- * Applies L2 normalization along a the depth dimension.
+ * Applies L2 normalization along the depth dimension.
*
- * The values in output Tensor is computed as:
+ * The values in the output tensor are computed as:
+ *
* output[batch, row, col, channel] =
* input[batch, row, col, channel] /
* sqrt(sum_{c} pow(input[batch, row, col, c], 2))
*
- * For x with more dimensions, independently normalizes each 1-D slice along dimension dim.
+ * For input tensor with more dimensions, independently normalizes each 1-D slice along dimension dim.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * Supported tensor rank: 4, with "NHWC" data layout.
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels).
*
* Inputs:
- * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth].
*
- * Ouputs:
- * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
*/
L2_NORMALIZATION = 11,
@@ -397,28 +540,48 @@
*
* The output dimensions are functions of the filter dimensions, stride, and padding.
*
- * The values in output Tensor is computed as:
+ * The values in the output tensor are computed as:
+ *
* output[batch, row, col, channel] =
* sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
* Supported tensor rank: 4, with "NHWC" data layout.
*
- * Inputs:
- * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
- * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
- * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
- * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
- * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
- * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
- * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
- * 7: An INT32 value, specifying the filter width.
- * 8: An INT32 value, specifying the filter height.
- * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * Both explicit padding and implicit padding are supported.
*
- * Ouputs:
- * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+ * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+ * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+ * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+ * * 5: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 6: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 7: An INT32 value, specifying the filter width.
+ * * 8: An INT32 value, specifying the filter height.
+ * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 2: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 3: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 4: An INT32 value, specifying the filter width.
+ * * 5: An INT32 value, specifying the filter height.
+ * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
*/
L2_POOL_2D = 12,
@@ -429,41 +592,49 @@
* dimension), and each vector is normalized independently. Within a given vector,
* each component is divided by the weighted, squared sum of inputs within depth_radius.
*
- * In details:
+ * The output is calculated using this formula:
+ *
* sqr_sum[a, b, c, d] =
* sum(pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2)
* output = input / pow((bias + alpha * sqr_sum), beta)
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
- * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
- * 1: An INT32 value, specifying the radius of the normalization window.
- * 2: A FLOAT32 value, specifying the bias, must not be zero.
- * 3: A FLOAT32 value, specifying the scale factor, alpha.
- * 4: A FLOAT32 value, specifying the exponent, beta.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the radius of the normalization window.
+ * * 2: A FLOAT32 value, specifying the bias, must not be zero.
+ * * 3: A FLOAT32 value, specifying the scale factor, alpha.
+ * * 4: A FLOAT32 value, specifying the exponent, beta.
*
- * Ouputs:
- * 0: The output tensor of same shape as input0.
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
*/
LOCAL_RESPONSE_NORMALIZATION = 13,
/**
* Computes sigmoid activation on the input tensor element-wise.
*
- * In details:
+ * The output is calculated using this formula:
+ *
* output = 1 / (1 + exp(-input))
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4.
*
* Inputs:
- * 0: A tensor, specifying the input.
+ * * 0: A tensor, specifying the input.
*
- * Ouputs:
- * 0: The output tensor of same shape as input0.
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM} type,
+ * the scale must be 1.f / 256 and the zeroPoint must be 0.
*/
LOGISTIC = 14,
@@ -502,102 +673,165 @@
LSH_PROJECTION = 15,
/**
- * Long short-term memory unit (LSTM) recurrent network layer.
+ * Performs a single time step in a Long Short-Term Memory (LSTM) layer
*
- * The default non-peephole implementation is based on:
- * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+ * The LSTM operation is described by the following equations.
+ *
+ * \f{eqnarray*}{
+ * i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\
+ * f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\
+ * C_t =& clip(f_t \odot C_{t-1} + i_t \odot g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell})& \\
+ * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o)& \\
+ * & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj}) & if\ there\ is\ a\ projection; \\
+ * h_t =& & \\
+ * & o_t \odot g(C_t) & otherwise. \\
+ * \f}
+ * Where:
+ * * \f$x_t\f$ is the input,
+ * * \f$i_t\f$ is the input gate,
+ * * \f$f_t\f$ is the forget gate,
+ * * \f$C_t\f$ is the cell state,
+ * * \f$o_t\f$ is the output,
+ * * \f$h_t\f$ is the output state,
+ * * \f$\sigma\f$ is the logistic sigmoid function,
+ * * \f$g\f$ is the cell input and cell output activation function, usually \f$tahn\f$,
+ * * \f$W_{xi}\f$ is the input-to-input weight matrix,
+ * * \f$W_{hi}\f$ is the recurrent to input weight matrix,
+ * * \f$W_{ci}\f$ is the cell-to-input weight matrix,
+ * * \f$b_i\f$ is the input gate bias,
+ * * \f$W_{xf}\f$ is the input-to-forget weight matrix,
+ * * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix,
+ * * \f$W_{cf}\f$ is the cell-to-forget weight matrix,
+ * * \f$b_f\f$ is the forget gate bias,
+ * * \f$W_{xc}\f$ is the input-to-cell weight matrix,
+ * * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix,
+ * * \f$b_c\f$ is the cell bias,
+ * * \f$W_{xo}\f$ is the input-to-output weight matrix,
+ * * \f$W_{ho}\f$ is the recurrent-to-output weight matrix,
+ * * \f$W_{co}\f$ is the cell-to-output weight matrix,
+ * * \f$b_o\f$ is the output gate bias,
+ * * \f$W_{proj}\f$ is the projection weight matrix,
+ * * \f$b_{proj}\f$ is the projection bias,
+ * * \f$t_{cell}\f$ is the threshold for clipping the cell state, and
+ * * \f$t_{proj}\f$ is the threshold for clipping the projected output.
+ * * \f$\odot\f$ is the <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)">
+ * Hadamard product</a> that takes two matrices and produces another
+ * matrix, each element of which is the product of the corresponding
+ * elements of the input matrices.
+ *
+ * The operation has the following independently optional inputs:
+ * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights (\f$W_{hi}\f$),
+ * cell-to-input (\f$W_{ci}\f$) weights, and input gate bias (\f$b_i\f$) either all have values,
+ * or none of them have values (i.e., all set to null). If they have no
+ * values, coupling of input and forget gates (CIFG) is used, in which case
+ * the input gate (\f$i_t\f$) is calculated using the following equation instead.
+ * \f{eqnarray*}{
+ * i_t = 1 - f_t
+ * \f}
+ * * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights (\f$W_{cf}\f$), and cell-to-output
+ * weights (\f$W_{co}\f$) either all have values or none of them have values.
+ * If they have values, the peephole optimization is used.
+ * * The projection weights (\f$W_{proj}\f$) is required only for the recurrent projection
+ * layer, and should otherwise have no value.
+ * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a value if the
+ * recurrent projection layer exists, and should otherwise have no value.
+ *
+ * References:
+ *
+ * The default non-peephole non-CIFG implementation is based on:
+ * http://www.bioinf.jku.at/publications/older/2604.pdf
* S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
* Computation, 9(8):1735-1780, 1997.
*
- * The peephole implementation is based on:
+ * The peephole implementation and projection layer is based on:
* https://research.google.com/pubs/archive/43905.pdf
* Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
* recurrent neural network architectures for large scale acoustic modeling."
* INTERSPEECH, 2014.
+ * (However, the concept of peephole optimization was introduced in work
+ * prior to this paper.)
*
* The coupling of input and forget gate (CIFG) is based on:
* http://arxiv.org/pdf/1503.04069.pdf
* Greff et al. "LSTM: A Search Space Odyssey"
*
- * The class has the following independently optional inputs:
- * * If input gate (if CIFG): “input_to_forget_weights”,
- * “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”.
- * * If no peephole connections: “cell_to_input_weights”,
- * “cell_to_forget_weights”, “cell_to_output_weights”.
- * * If no projection layer: “projection_weights” and “projection_bias”.
- * * If no projection bias: “projection_bias”.
- *
- * Supported tensor types:
+ * Supported tensor types (type T):
* * {@link OperandType::TENSOR_FLOAT32}
*
* Inputs:
- * * 0: Input.
+ * * 0: The input (\f$x_t\f$).
* A 2-D tensor of type T, of shape [batch_size, input_size], where
* “batch_size” corresponds to the batching dimension, and “input_size”
* is the size of the input.
- * * 1: input_to_input_weights.
+ * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
* A 2-D tensor of type T, of shape [num_units, input_size], where
* “num_units” corresponds to the number of cell units.
- * * 2: input_to_forget_weights.
+ * * 2: The input-to-forget weights (\f$W_{xf}\f$).
* A 2-D tensor of type T, of shape [num_units, input_size].
- * * 3: input_to_cell_weights.
+ * * 3: The input-to-cell weights (\f$W_{xc}\f$).
* A 2-D tensor of type T, of shape [num_units, input_size].
- * * 4: input_to_output_weights.
+ * * 4: The input-to-output weights (\f$W_{xo}\f$).
* A 2-D tensor of type T, of shape [num_units, input_size].
- * * 5: recurrent_to_input_weights.
+ * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
* A 2-D tensor of type T, of shape [num_units, output_size], where
* “output_size” corresponds to either the number of cell units (i.e.,
* “num_units”), or the second dimension of the “projection_weights”, if
* defined.
- * * 6: recurrent_to_forget_weights.
+ * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
* A 2-D tensor of type T, of shape [num_units, output_size].
- * * 7: recurrent_to_cell_weights.
+ * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
* A 2-D tensor of type T, of shape [num_units, output_size].
- * * 8: recurrent_to_output_weights.
+ * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
* A 2-D tensor of type T, of shape [num_units, output_size].
- * * 9: cell_to_input_weights.
+ * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
* A 1-D tensor of type T, of shape [num_units].
- * * 10:cell_to_forget_weights.
+ * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
* A 1-D tensor of type T, of shape [num_units].
- * * 11:cell_to_output_weights.
+ * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
* A 1-D tensor of type T, of shape [num_units].
- * * 12:input_gate_bias.
+ * * 12:The input gate bias (\f$b_i\f$). Optional.
* A 1-D tensor of type T, of shape [num_units].
- * * 13:forget_gate_bias.
+ * * 13:The forget gate bias (\f$b_f\f$).
* A 1-D tensor of type T, of shape [num_units].
- * * 14:cell_bias.
+ * * 14:The cell bias (\f$b_c\f$).
* A 1-D tensor of type T, of shape [num_units].
- * * 15:output_gate_bias.
+ * * 15:The output gate bias (\f$b_o\f$).
* A 1-D tensor of type T, of shape [num_units].
- * * 16:projection_weights.
+ * * 16:The projection weights (\f$W_{proj}\f$). Optional.
* A 2-D tensor of type T, of shape [output_size, num_units].
- * * 17:projection_bias.
+ * * 17:The projection bias (\f$b_{proj}\f$). Optional.
* A 1-D tensor of type T, of shape [output_size].
- *
- * Parameters:
- * * 18:fused_activation_function.
- * An (optional) ActivationFunctionType indicating the activation
- * function.
- * If “NONE” is specified then it results in a linear activation.
- * * 19:cell_clip.
- * A clipping threshold for the cell state, such that values are bound
+ * * 18:The output state (in) (\f$h_{t-1}\f$).
+ * A 2-D tensor of type T, of shape [batch_size, output_size].
+ * * 19:The cell state (in) (\f$C_{t-1}\f$).
+ * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * * 20:The activation function (\f$g\f$).
+ * A value indicating the activation function:
+ * <ul>
+ * <li>0: None;
+ * <li>1: Relu;
+ * <li>3: Relu6;
+ * <li>4: Tanh;
+ * <li>6: Sigmoid.
+ * </ul>
+ * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such that values are bound
* within [-cell_clip, cell_clip]. If set to 0.0 then clipping is
* disabled.
- * * 20:proj_clip.
- * A clipping threshold for the output from the projection layer, such
+ * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the projection layer, such
* that values are bound within [-proj_clip, proj_clip]. If set to 0.0
* then clipping is disabled.
*
* Outputs:
- * * 0: scratch_buffer.
- * A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
- * * 1: output_state.
+ * * 0: The scratch buffer.
+ * A 2-D tensor of type T, of shape [batch_size, num_units * 4] with
+ * CIFG, or [batch_size, num_units * 3] without CIFG.
+ * * 1: The output state (out) (\f$h_t\f$).
* A 2-D tensor of type T, of shape [batch_size, output_size].
- * * 2: cell_state.
+ * * 2: The cell state (out) (\f$C_t\f$).
* A 2-D tensor of type T, of shape [batch_size, num_units].
- * * 3: output.
+ * * 3: The output (\f$o_t\f$).
* A 2-D tensor of type T, of shape [batch_size, output_size]. This is
- * effectively the same as the current “output_state” value.
+ * effectively the same as the current “output state (out)” value.
*/
LSTM = 16,
@@ -606,36 +840,56 @@
*
* The output dimensions are functions of the filter dimensions, stride, and padding.
*
- * The values in output Tensor is computed as:
+ * The values in the output tensor are computed as:
+ *
* output[batch, row, col, channel] =
* max_{i, j} (input[batch, row + i, col + j, channel])
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: 4, with "NHWC" data layout.
*
- * Inputs:
- * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
- * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
- * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
- * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
- * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
- * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
- * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
- * 7: An INT32 value, specifying the filter width.
- * 8: An INT32 value, specifying the filter height.
- * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * Both explicit padding and implicit padding are supported.
*
- * Ouputs:
- * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+ * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+ * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+ * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+ * * 5: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 6: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 7: An INT32 value, specifying the filter width.
+ * * 8: An INT32 value, specifying the filter height.
+ * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 2: An INT32 value, specifying the stride when walking through input
+ * in the ‘width’ dimension.
+ * * 3: An INT32 value, specifying the stride when walking through input
+ * in the ‘height’ dimension.
+ * * 4: An INT32 value, specifying the filter width.
+ * * 5: An INT32 value, specifying the filter height.
+ * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
*/
MAX_POOL_2D = 17,
/**
- * Multiplies two tensors, elment-wise.
+ * Multiplies two tensors, element-wise.
*
- * Takes two input tensors of identical type and compatible dimensions. The output
+ * Takes two input tensors of identical type and compatible dimensions. The output
* is the product of both input tensors, optionally modified by an activation function.
*
* Two dimensions are compatible when:
@@ -645,72 +899,85 @@
* The size of the resulting output is the maximum size along each dimension of the
* input operands. It starts with the trailing dimensions, and works its way forward.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: A tensor.
- * 1: A tensor of the same type, and compatible dimensions as input0.
- * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 0: A tensor.
+ * * 1: A tensor of the same type, and compatible dimensions as input0.
+ * * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+ * Specifies the activation to invoke on the result of each addition.
*
- * Ouputs:
- * 0: The product, a tensor of the same type as input0.
+ * Outputs:
+ * * 0: The product, a tensor of the same type as input0.
+ * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+ * condition must be satisfied: output_scale > input1_scale * input2_scale.
*/
MUL = 18,
/**
* Computes rectified linear activation on the input tensor element-wise.
*
- * In details:
+ * The output is calculated using this formula:
+ *
* output = max(0, input)
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4.
*
* Inputs:
- * 0: A tensor, specifying the input.
+ * * 0: A tensor, specifying the input.
*
- * Ouputs:
- * 0: The output tensor of same shape as input0.
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
*/
RELU = 19,
/**
* Computes rectified linear 1 activation on the input tensor element-wise.
*
- * In details:
+ * The output is calculated using this formula:
+ *
* output = min(1.f, max(-1.f, input))
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4.
*
* Inputs:
- * 0: A tensor, specifying the input.
+ * * 0: A tensor, specifying the input.
*
- * Ouputs:
- * 0: The output tensor of same shape as input0.
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
*/
RELU1 = 20,
/**
* Computes rectified linear 6 activation on the input tensor element-wise.
*
- * In details:
+ * The output is calculated using this formula:
+ *
* output = min(6, max(0, input))
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4.
*
* Inputs:
- * 0: A tensor, specifying the input.
+ * * 0: A tensor, specifying the input.
*
- * Ouputs:
- * 0: The output tensor of same shape as input0.
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
*/
RELU6 = 21,
@@ -720,36 +987,41 @@
* Given tensor, this operation returns a tensor that has the same values as tensor,
* but with a newly specified shape.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4.
*
* Inputs:
- * 0: A tensor, specifying the tensor to be reshaped.
- * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32}, defining the shape
- * of the output tensor. The number of elements implied by shape must be the same
- * as the number of elements in the input tensor.
+ * * 0: A tensor, specifying the tensor to be reshaped.
+ * * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32}, defining the shape
+ * of the output tensor. The number of elements implied by shape must be the same
+ * as the number of elements in the input tensor.
*
- * Ouputs:
- * 0: The output tensor, of shape specified by the input shape.
+ * Outputs:
+ * * 0: The output tensor, of shape specified by the input shape.
*/
RESHAPE = 22,
/**
* Resizes images to given size using the bilinear interpretation.
*
- * Resized images will be distorted if their original aspect ratio is not the same as input.
+ * Resized images must be distorted if their output aspect ratio is not the same as
+ * input aspect ratio.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
- * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
- * 1: An INT32 value, specifying the output width of the output tensor.
- * 2: An INT32 value, specifying the output height of the output tensor.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+ * * 1: An INT32 value, specifying the output height of the output tensor.
+ * * 2: An INT32 value, specifying the output width of the output tensor.
*
- * Ouputs:
- * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
*/
RESIZE_BILINEAR = 23,
@@ -768,7 +1040,7 @@
* * “activation” is the function passed as the “fused_activation_function”
* argument (if not “NONE”).
*
- * Supported tensor types:
+ * Supported tensor types (Type T):
* * {@link OperandType::TENSOR_FLOAT32}
*
* Inputs:
@@ -784,21 +1056,18 @@
* corresponding to the weights from each unit.
* * 3: bias.
* A 1-D tensor of type T, of shape [num_units].
- *
- * For FLOAT32 input tensor, bias must also be FLOAT32.
- * For UINT8 input tensor, bias must be INT32.
- *
- * Parameters
- * * 4: fused_activation_function.
- * An (optional) ActivationFunctionType indicating the activation
+ * * 4: hidden state (in).
+ * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * * 5: fused_activation_function.
+ * An optional {@link FusedActivationFunc} value indicating the activation
* function. If “NONE” is specified then it results in a linear
* activation.
*
- * * 5: Hidden state.
+ * Outputs:
+ * * 0: hidden state (out).
* A 2-D tensor of type T, of shape [batch_size, num_units].
*
- * Outputs:
- * * 0: output.
+ * * 1: output.
* A 2-D tensor of type T, of shape [batch_size, num_units]. This is
* effectively the same as the current state value.
*/
@@ -808,21 +1077,26 @@
* Computes the softmax activation on the input tensor element-wise, per batch, by
* normalizing the input vector so the maximum coefficient is zero.
*
- * In details:
+ * The output is calculated using this formula:
+ *
* output[batch, i] =
* exp((input[batch, i] - max(input[batch, :])) * beta) /
* sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: 2 or 4.
*
* Inputs:
- * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
- * 1: A FLOAT32 value, specifying the scaling factor for the exponent, beta.
+ * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+ * * 1: A FLOAT32 value, specifying the positive scaling factor for the exponent, beta.
*
- * Ouputs:
- * 0: The output tensor of same shape as input0.
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM} type,
+ * the scale must be 1.f / 256 and the zeroPoint must be 0.
*/
SOFTMAX = 25,
@@ -839,18 +1113,20 @@
* The depth of the output tensor is input_depth * block_size * block_size.
* The input tensor's height and width must be divisible by block_size.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Inputs:
- * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
- * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
- * block_size must be a divisor of both the input height and width.
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+ * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+ * block_size must be a divisor of both the input height and width.
*
- * Ouputs:
- * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
- * depth*block_size*block_size].
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
+ * depth*block_size*block_size].
*/
SPACE_TO_DEPTH = 26,
@@ -874,8 +1150,8 @@
*
* Specifically, for rank 1, this layer implements the operation:
*
- * memory = push(conv1d(inputs, weights_feature, feature_dim, "VALID"));
- * outputs = activation(memory * weights_time + bias);
+ * memory = push(conv1d(inputs, weights_feature, feature_dim, "PADDING_VALID"));
+ * outputs = activation(memory * weights_time + bias);
*
* Where:
* * “weights_feature” is a weights matrix that processes the inputs (by
@@ -892,7 +1168,7 @@
* Each rank adds a dimension to the weights matrices by means of stacking
* the filters.
*
- * Supported tensor types:
+ * Supported tensor types (type T):
* * {@link OperandType::TENSOR_FLOAT32}
*
* Inputs:
@@ -907,20 +1183,17 @@
* A 2-D tensor of type T, of shape [num_units, memory_size], where
* “memory_size” corresponds to the fixed-size of the memory.
* * 3: bias.
- * A optional 1-D tensor of type T, of shape [num_units].
- *
- * For FLOAT32 input tensor, bias must also be FLOAT32.
- * For UINT8 input tensor, bias must be INT32.
- *
- * Parameters:
- * * 4: rank.
+ * An optional 1-D tensor of type T, of shape [num_units].
+ * * 4: state (in).
+ * A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+ * * 5: rank.
* The rank of the SVD approximation.
- * * 5: fused_activation_function.
- * An (optional) ActivationFunctionType indicating the activation function.
+ * * 6: fused_activation_function.
+ * An optional {@link FusedActivationFunc} value indicating the activation function.
* If “NONE” is specified then it results in a linear activation.
*
* Outputs:
- * * 0: state.
+ * * 0: state (out).
* A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
* * 1: output.
* A 2-D tensor of type T, of shape [batch_size, num_units].
@@ -930,17 +1203,20 @@
/**
* Computes hyperbolic tangent of input tensor element-wise.
*
- * In details:
+ * The output is calculated using this formula:
+ *
* output = tanh(input)
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
* Supported tensor rank: up to 4.
*
* Inputs:
- * 0: A tensor, specifying the input.
+ * * 0: A tensor, specifying the input.
*
- * Ouputs:
- * 0: The output tensor of same shape as input0.
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
*/
TANH = 28,
@@ -967,8 +1243,8 @@
*/
enum OperandLifeTime : int32_t {
/**
- * The operand is internal to the model. It's created by an operation
- * and consumed by other operations.
+ * The operand is internal to the model. It's created by an operation and
+ * consumed by other operations.
*/
TEMPORARY_VARIABLE,
@@ -1112,7 +1388,7 @@
/**
* Where to find the data for this operand.
* If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or NO_VALUE:
- * - All the fields will be 0.
+ * - All the fields must be 0.
* If the lifetime is CONSTANT_COPY:
* - location.poolIndex is 0.
* - location.offset is the offset in bytes into Model.operandValues.
@@ -1220,7 +1496,7 @@
* Updated dimension information.
*
* If dimensions.size() > 0, dimension information was provided along with the
- * argument. This can be the case for models that accept inputs of varying size.
+ * argument. This can be the case for models that accept inputs of varying size.
* This can't change the rank, just the value of the dimensions that were
* unspecified in the model.
*/
diff --git a/neuralnetworks/1.1/types.hal b/neuralnetworks/1.1/types.hal
index 1d470d6..b4fccae 100644
--- a/neuralnetworks/1.1/types.hal
+++ b/neuralnetworks/1.1/types.hal
@@ -27,25 +27,24 @@
*/
enum OperationType : @1.0::OperationType {
/**
- * BatchToSpace for N-D tensors.
+ * BatchToSpace for N-dimensional tensors.
*
- * This operation reshapes the "batch" dimension 0 into M + 1 dimensions of shape
+ * This operation reshapes the batch dimension (dimension 0) into M + 1 dimensions of shape
* block_shape + [batch], interleaves these blocks back into the grid defined by the
* spatial dimensions [1, ..., M], to obtain a result with the same rank as the input.
- * The spatial dimensions of this intermediate result are then optionally cropped
- * according to the amount to crop to produce the output.
+ *
* This is the reverse of SpaceToBatch.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
- * Supported tensor rank: up to 4
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4
*
* Inputs:
- * 0: An n-D tensor, specifying the input.
+ * 0: An n-D tensor, specifying the tensor to be reshaped
* 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
* input tensor. All values must be >= 1.
- * 2: A 1-D Tensor of type TENSOR_INT32, the amount to crop for each spatial diemension of the
- * input tensor. All values must be >= 0.
*
* Outputs:
* 0: A tensor of the same type as input0.
@@ -53,9 +52,9 @@
BATCH_TO_SPACE_ND = 29,
/**
- * Divides the second tensor from the first tensor, element-wise.
+ * Element-wise division of two tensors.
*
- * Takes two input tensors of identical OperandType and compatible dimensions. The output
+ * Takes two input tensors of identical type and compatible dimensions. The output
* is the result of dividing the first input tensor by the second, optionally
* modified by an activation function.
*
@@ -71,7 +70,9 @@
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
@@ -88,15 +89,17 @@
/**
* Computes the mean of elements across dimensions of a tensor.
*
- * Reduces input tensor along the dimensions given in axis. Unless keep_dims is true,
- * the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is
- * true, the reduced dimensions are retained with length 1.
+ * Reduces the input tensor along the given dimensions to reduce. Unless keep_dims
+ * is true, the rank of the tensor is reduced by 1 for each entry in axis.
+ * If keep_dims is true, the reduced dimensions are retained with length 1.
*
- * If axis has no entries, all dimensions are reduced, and a tensor with a single
- * element is returned.
+ * If dimensions to reduce have no entries, all dimensions are reduced, and a tensor with
+ * a single element is returned.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
@@ -115,14 +118,18 @@
*
* This operation pads a tensor according to the specified paddings.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: An n-D tensor, specifying the input.
- * 1: A 2-D Tensor of type TENSOR_INT32. The paddings, before and after for each spatial dimension
- * of the input tensor.
+ * 0: An n-D tensor, specifying the tensor to be padded.
+ * 1: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial dimension of the
+ * input tensor. The shape of the tensor must be {rank(input0), 2}.
+ * padding[i, 0] specifies the number of element to be padded in the front of dimension i.
+ * padding[i, 1] specifies the number of element to be padded after the end of dimension i.
*
* Outputs:
* 0: A tensor of the same type as input0.
@@ -130,7 +137,7 @@
PAD = 32,
/**
- * SpaceToBatch for N-D tensors.
+ * SpaceToBatch for N-Dimensional tensors.
*
* This operation divides "spatial" dimensions [1, ..., M] of the input into a grid of blocks
* of shape block_shape, and interleaves these blocks with the "batch" dimension (0) such that
@@ -139,16 +146,20 @@
* batch position. Prior to division into blocks, the spatial dimensions of the input are
* optionally zero padded according to paddings.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
- * Supported tensor rank: up to 4
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4
*
* Inputs:
* 0: An n-D tensor, specifying the input.
* 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
* input tensor. All values must be >= 1.
* 2: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial diemension of the
- * input tensor. All values must be >= 0.
+ * input tensor. All values must be >= 0. The shape of the tensor must be {rank(input0), 2}.
+ * padding[i, 0] specifies the number of element to be padded in the front of dimension i.
+ * padding[i, 1] specifies the number of element to be padded after the end of dimension i.
*
* Outputs:
* 0: A tensor of the same type as input0.
@@ -160,17 +171,20 @@
*
* Given a tensor input, this operation returns a tensor of the same type with all
* dimensions of size 1 removed. If you don't want to remove all size 1 dimensions,
- * you can remove specific size 1 dimensions by specifying axis.
+ * you can remove specific size 1 dimensions by specifying the axes (input1).
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: An n-D tensor, specifying the input.
- * 1: An 1-D Tensor of type TENSOR_INT32. The dimensions to squeeze. If None (the default),
- * squeezes all dimensions. If specified, only squeezes the dimensions listed. The dimension
- * index starts at 0. It is an error to squeeze a dimension that is not 1.
+ * 0: An n-D tensor, the tensor to be squeezed.
+ * 1: An optional 1-D tensor of type TENSOR_INT32. The dimensions to squeeze. If specified
+ * only squeezes the dimensions listed. Otherwise, squeezes all dimensions.
+ * The dimension index starts at 0. An error must be reported if squeezing a dimension that
+ * is not 1.
*
* Outputs:
* 0: A tensor of the same type as input0. Contains the same data as input, but has one or more
@@ -181,23 +195,25 @@
/**
* Extracts a strided slice of a tensor.
*
- * This op extracts a slice of size (end-begin)/stride from the given input tensor.
- * Starting at the location specified by begin the slice continues by adding
+ * Roughly speaking, this op extracts a slice of size (end - begin) / stride from the given
+ * input tensor. Starting at the location specified by begin the slice continues by adding
* stride to the index until all dimensions are not less than end. Note that a stride can
* be negative, which causes a reverse slice.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: An n-D tensor, specifying the input.
+ * 0: An n-D tensor, specifying the tensor to be sliced.
* 1: A 1-D Tensor of type TENSOR_INT32, the starts of the dimensions of the input
- * tensor to be sliced.
+ * tensor to be sliced. The length must be of rank(input0).
* 2: A 1-D Tensor of type TENSOR_INT32, the ends of the dimensions of the input
- * tensor to be sliced.
+ * tensor to be sliced. The length must be of rank(input0).
* 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input
- * tensor to be sliced.
+ * tensor to be sliced. The length must be of rank(input0).
*
* Outputs:
* 0: A tensor of the same type as input0.
@@ -205,7 +221,7 @@
STRIDED_SLICE = 35,
/**
- * Subtracts the second tensor from the first tensor, element-wise.
+ * Element-wise subtraction of two tensors.
*
* Takes two input tensors of identical type and compatible dimensions. The output
* is the result of subtracting the second input tensor from the first one, optionally
@@ -223,7 +239,9 @@
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
@@ -240,18 +258,20 @@
/**
* Transposes the input tensor, permuting the dimensions according to the perm tensor.
*
- * The returned tensor's dimension i must correspond to the input dimension perm[i].
+ * The returned tensor's dimension i corresponds to the input dimension perm[i].
* If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor.
* Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors.
*
- * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
- * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Supported tensor types:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: An n-D tensor, specifying the input.
- * 1: A 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the input
- * tensor.
+ * 0: An n-D tensor, specifying the tensor to be transposed.
+ * 1: An optional 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the
+ * input tensor.
*
* Outputs:
* 0: A tensor of the same type as input0.