Remove inconsistent references to "type" in NNAPI
"Type" in the NNAPI refers both to "OperandCode" and
"ANeuralNetworksOperandType" throughout the NNAPI documentation.
To make the documentation more consistent and less ambiguous,
this CL uses documentation links to the name itself instead of "type".
Bug: 72650109
Test: None, only documentation changes
Change-Id: I6718063da98a9c7ad10c4bb0452bca9a8986270b
Merged-In: I6718063da98a9c7ad10c4bb0452bca9a8986270b
(cherry picked from commit a8cf6451c8e6602e91c694ab52357272f4970d48)
diff --git a/runtime/include/NeuralNetworks.h b/runtime/include/NeuralNetworks.h
index 4be3f0a..08d779a 100644
--- a/runtime/include/NeuralNetworks.h
+++ b/runtime/include/NeuralNetworks.h
@@ -93,8 +93,9 @@
typedef enum {
/** Adds two tensors, element-wise.
*
- * 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.
+ * Takes two input tensors of identical {@link OperandCode} and compatible dimensions.
+ * The output is the sum of both input tensors, optionally modified by an activation
+ * function.
*
* Two dimensions are compatible when:
* 1. they are equal, or
@@ -109,7 +110,7 @@
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -117,12 +118,12 @@
*
* 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 FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions as input0.
+ * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the {@link FuseCode}
+ * values. Specifies the activation to invoke on the result of each addition.
*
* Outputs:
- * * 0: The sum, a tensor of the same type as input0.
+ * * 0: The sum, a tensor of the same {@link OperandCode} as input0.
*/
ANEURALNETWORKS_ADD = 0,
@@ -135,7 +136,7 @@
* output[batch, row, col, channel] =
* sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -146,31 +147,37 @@
*
* 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
+ * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the left,
* in the ‘width’ dimension.
- * * 6: An INT32 value, specifying the stride when walking through input
+ * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the right,
+ * in the ‘width’ dimension.
+ * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the top,
* 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 FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the bottom,
+ * in the ‘height’ dimension.
+ * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘width’ dimension.
+ * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘height’ dimension.
+ * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter width.
+ * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter height.
+ * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
+ * {@link FuseCode} 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
- * {@link PaddingCode} values.
- * * 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 FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit padding scheme,
+ * has to be one of the {@link PaddingCode} values.
+ * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘width’ dimension.
+ * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘height’ dimension.
+ * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter width.
+ * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter height.
+ * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
+ * {@link FuseCode} 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].
@@ -179,10 +186,10 @@
/** Concatenates the input tensors along the given dimension.
*
- * The input tensors must have identical type and the same dimensions except the
- * dimension along the concatenation axis.
+ * The input tensors must have identical {@link OperandCode} and the same dimensions
+ * except the dimension along the concatenation axis.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -190,12 +197,12 @@
*
* Inputs:
* * 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm].
- * For inputs of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, all
+ * For inputs of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, all
* input tensors must have the same scale and zeroPoint.
- * * n: An INT32 value, specifying the concatenation axis.
+ * * n: An {@link ANEURALNETWORKS_INT32} scalar, specifying the concatenation axis.
*
* Outputs:
- * * 0: The output, a tensor of the same type as the input tensors.
+ * * 0: The output, a tensor of the same {@link OperandCode} as the input tensors.
* The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
*/
ANEURALNETWORKS_CONCATENATION = 2,
@@ -216,7 +223,7 @@
* bias[channel]
* )
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -229,44 +236,50 @@
* * 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 ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should
* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
- * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias
* should be of {@link ANEURALNETWORKS_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
+ * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the left,
* in the ‘width’ dimension.
- * * 8: An INT32 value, specifying the stride when walking through input
+ * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the right,
+ * in the ‘width’ dimension.
+ * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the top,
* in the ‘height’ dimension.
- * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the bottom,
+ * in the ‘height’ dimension.
+ * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘width’ dimension.
+ * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘height’ dimension.
+ * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
+ * {@link FuseCode} 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 ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should
* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
- * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias
* should be of {@link ANEURALNETWORKS_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
- * {@link PaddingCode} values.
- * * 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 FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit padding scheme,
+ * has to be one of the {@link PaddingCode} values.
+ * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘width’ dimension.
+ * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘height’ dimension.
+ * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
+ * {@link FuseCode} 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 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
+ * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following
* condition must be satisfied: output_scale > input_scale * filter_scale.
*/
ANEURALNETWORKS_CONV_2D = 3,
@@ -290,7 +303,7 @@
* filter[1, di, dj, k * channel_multiplier + q]
* )
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -303,46 +316,52 @@
* * 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 ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should
* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
- * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias
* should be of {@link ANEURALNETWORKS_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
+ * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the left,
* in the ‘width’ dimension.
- * * 8: An INT32 value, specifying the stride when walking through input
+ * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the right,
+ * in the ‘width’ dimension.
+ * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the top,
* in the ‘height’ dimension.
- * * 9: An INT32 value, specifying the depthwise multiplier.
- * * 10: An INT32 value, and has to be one of the {@link FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the bottom,
+ * in the ‘height’ dimension.
+ * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘width’ dimension.
+ * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘height’ dimension.
+ * * 9: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise multiplier.
+ * * 10: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
+ * {@link FuseCode} 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 ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should
* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
- * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias
* should be of {@link ANEURALNETWORKS_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
- * {@link PaddingCode} values.
- * * 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 FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit padding scheme,
+ * has to be one of the {@link PaddingCode} values.
+ * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘width’ dimension.
+ * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘height’ dimension.
+ * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise multiplier.
+ * * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
+ * {@link FuseCode} 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 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
+ * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following
* condition must be satisfied: output_scale > input_scale * filter_scale.
*/
ANEURALNETWORKS_DEPTHWISE_CONV_2D = 4,
@@ -360,7 +379,7 @@
* input_height * block_size.
* The depth of the input tensor must be divisible by block_size * block_size
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -368,8 +387,8 @@
*
* 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.
+ * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the block_size. block_size
+ * must be >=1 and block_size * block_size must be a divisor of the input depth.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
@@ -383,16 +402,16 @@
*
* output = (input - zeroPoint) * scale.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * * 0: A tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}.
+ * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}.
*
* Outputs:
- * * 0: The output tensor of same shape as input0, but with type
+ * * 0: The output tensor of same shape as input0, but with
* {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
*/
ANEURALNETWORKS_DEQUANTIZE = 6,
@@ -417,7 +436,7 @@
* and an error must be reported.
*
* Inputs:
- * * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32} type.
+ * * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32}.
* 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.
@@ -431,7 +450,7 @@
/** Computes element-wise floor() on the input tensor.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
@@ -440,7 +459,8 @@
* * 0: A tensor.
*
* Outputs:
- * * 0: The output tensor, of the same type and dimensions as the input tensor.
+ * * 0: The output tensor, of the same {@link OperandCode} and dimensions as the
+ * input tensor.
*/
ANEURALNETWORKS_FLOOR = 8,
@@ -451,7 +471,7 @@
*
* outputs = activation(inputs * weights’ + bias)
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -466,17 +486,18 @@
* * 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 ANEURALNETWORKS_TENSOR_FLOAT32} type, the bias should
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should
* also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
- * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias
* should be of {@link ANEURALNETWORKS_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 FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 3: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
+ * {@link FuseCode} values. Specifies the activation to invoke on the result of
+ * each addition.
*
* Outputs:
* * 0: The output tensor, of shape [batch_size, num_units].
- * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
+ * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following
* condition must be satisfied: output_scale > input_scale * filter_scale.
*/
ANEURALNETWORKS_FULLY_CONNECTED = 9,
@@ -533,12 +554,14 @@
* input[batch, row, col, channel] /
* sqrt(sum_{c} pow(input[batch, row, col, c], 2))
*
- * For input tensor 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:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
- * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels).
+ * 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].
@@ -557,7 +580,7 @@
* output[batch, row, col, channel] =
* sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" data layout.
@@ -566,31 +589,37 @@
*
* 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
+ * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the left,
* in the ‘width’ dimension.
- * * 6: An INT32 value, specifying the stride when walking through input
+ * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the right,
+ * in the ‘width’ dimension.
+ * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the top,
* 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 FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the bottom,
+ * in the ‘height’ dimension.
+ * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘width’ dimension.
+ * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘height’ dimension.
+ * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter width.
+ * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter height.
+ * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
+ * {@link FuseCode} 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
- * {@link PaddingCode} values.
- * * 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 FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit padding scheme,
+ * has to be one of the {@link PaddingCode} values.
+ * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘width’ dimension.
+ * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘height’ dimension.
+ * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter width.
+ * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter height.
+ * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
+ * {@link FuseCode} 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].
@@ -609,17 +638,18 @@
* 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:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_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.
+ * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the radius of the
+ * normalization window.
+ * * 2: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the bias, must not be zero.
+ * * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scale factor, alpha.
+ * * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the exponent, beta.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
@@ -632,7 +662,7 @@
*
* output = 1 / (1 + exp(-input))
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -643,7 +673,7 @@
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type,
+ * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*/
ANEURALNETWORKS_LOGISTIC = 14,
@@ -767,56 +797,74 @@
* http://arxiv.org/pdf/1503.04069.pdf
* Greff et al. "LSTM: A Search Space Odyssey"
*
- * Supported tensor types (type T):
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 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.
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [batch_size, input_size], where “batch_size” corresponds to the
+ * batching dimension, and “input_size” is the size of the input.
* * 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.
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units, input_size], where “num_units” corresponds to the
+ * number of cell units.
* * 2: The input-to-forget weights (\f$W_{xf}\f$).
- * A 2-D tensor of type T, of shape [num_units, input_size].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units, input_size].
* * 3: The input-to-cell weights (\f$W_{xc}\f$).
- * A 2-D tensor of type T, of shape [num_units, input_size].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units, input_size].
* * 4: The input-to-output weights (\f$W_{xo}\f$).
- * A 2-D tensor of type T, of shape [num_units, input_size].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units, input_size].
* * 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.
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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: The recurrent-to-forget weights (\f$W_{hf}\f$).
- * A 2-D tensor of type T, of shape [num_units, output_size].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units, output_size].
* * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
- * A 2-D tensor of type T, of shape [num_units, output_size].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units, output_size].
* * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
- * A 2-D tensor of type T, of shape [num_units, output_size].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units, output_size].
* * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 12:The input gate bias (\f$b_i\f$). Optional.
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 13:The forget gate bias (\f$b_f\f$).
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 14:The cell bias (\f$b_c\f$).
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 15:The output gate bias (\f$b_o\f$).
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 16:The projection weights (\f$W_{proj}\f$). Optional.
- * A 2-D tensor of type T, of shape [output_size, num_units].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [output_size, num_units].
* * 17:The projection bias (\f$b_{proj}\f$). Optional.
- * A 1-D tensor of type T, of shape [output_size].
+ * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [output_size].
* * 18:The output state (in) (\f$h_{t-1}\f$).
- * A 2-D tensor of type T, of shape [batch_size, output_size].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [batch_size, num_units].
* * 20:The activation function (\f$g\f$).
* A value indicating the activation function:
* <ul>
@@ -835,15 +883,19 @@
*
* Outputs:
* * 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.
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [batch_size, output_size].
* * 2: The cell state (out) (\f$C_t\f$).
- * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [batch_size, num_units].
* * 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 (out)” value.
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [batch_size, output_size]. This is effectively the same as the
+ * current “output state (out)” value.
*/
ANEURALNETWORKS_LSTM = 16,
@@ -856,7 +908,7 @@
* output[batch, row, col, channel] =
* max_{i, j} (input[batch, row + i, col + j, channel])
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -866,31 +918,37 @@
*
* 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
+ * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the left,
* in the ‘width’ dimension.
- * * 6: An INT32 value, specifying the stride when walking through input
+ * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the right,
+ * in the ‘width’ dimension.
+ * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the top,
* 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 FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on the bottom,
+ * in the ‘height’ dimension.
+ * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘width’ dimension.
+ * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘height’ dimension.
+ * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter width.
+ * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter height.
+ * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
+ * {@link FuseCode} 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
- * {@link PaddingCode} values.
- * * 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 FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit padding scheme,
+ * has to be one of the {@link PaddingCode} values.
+ * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘width’ dimension.
+ * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when walking
+ * through input in the ‘height’ dimension.
+ * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter width.
+ * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter height.
+ * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the
+ * {@link FuseCode} 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].
@@ -899,8 +957,9 @@
/** Multiplies two tensors, element-wise.
*
- * 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.
+ * Takes two input tensors of identical {@link OperandCode} and compatible dimensions.
+ * The output is the product of both input tensors, optionally modified by an
+ * activation function.
*
* Two dimensions are compatible when:
* 1. they are equal, or
@@ -909,7 +968,7 @@
* 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:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -917,13 +976,13 @@
*
* 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 FuseCode} values.
- * Specifies the activation to invoke on the result of each addition.
+ * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions as input0.
+ * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the {@link FuseCode}
+ * values. Specifies the activation to invoke on the result of each multiplication.
*
* Outputs:
- * * 0: The product, a tensor of the same type as input0.
- * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following
+ * * 0: The product, a tensor of the same {@link OperandCode} as input0.
+ * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following
* condition must be satisfied: output_scale > input1_scale * input2_scale.
*/
ANEURALNETWORKS_MUL = 18,
@@ -934,7 +993,7 @@
*
* output = max(0, input)
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -954,7 +1013,7 @@
*
* output = min(1.f, max(-1.f, input))
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -974,7 +1033,7 @@
*
* output = min(6, max(0, input))
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -993,7 +1052,7 @@
* Given tensor, this operation returns a tensor that has the same values as tensor,
* but with a newly specified shape.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -1001,7 +1060,7 @@
*
* Inputs:
* * 0: A tensor, specifying the tensor to be reshaped.
- * * 1: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32}, defining the shape
+ * * 1: A 1-D tensor of {@link ANEURALNETWORKS_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.
*
@@ -1016,15 +1075,17 @@
* input aspect ratio. The corner pixels of output may not be the same as
* corner pixels of input.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_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 height of the output tensor.
- * * 2: An INT32 value, specifying the output width of the output tensor.
+ * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output height of the
+ * output tensor.
+ * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output width of the
+ * output tensor.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
@@ -1046,24 +1107,28 @@
* * “activation” is the function passed as the “fused_activation_function”
* argument (if not “NONE”).
*
- * Supported tensor types (Type T):
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 0: input.
- * 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.
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} of shape
+ * [batch_size, input_size], where “batch_size” corresponds to the
+ * batching dimension, and “input_size” is the size of the input.
* * 1: weights.
- * A 2-D tensor of type T, of shape [num_units, input_size], where
- * “num_units” corresponds to the number of units.
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units, input_size], where “num_units” corresponds to the
+ * number of units.
* * 2: recurrent_weights.
- * A 2-D tensor of type T, of shape [num_units, num_units], with columns
- * corresponding to the weights from each unit.
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units, num_units], with columns corresponding to the weights
+ * from each unit.
* * 3: bias.
- * A 1-D tensor of type T, of shape [num_units].
+ * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units].
* * 4: hidden state (in).
- * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [batch_size, num_units].
* * 5: fused_activation_function.
* An optional {@link FuseCode} value indicating the activation
* function. If “NONE” is specified then it results in a linear
@@ -1071,11 +1136,13 @@
*
* Outputs:
* * 0: hidden state (out).
- * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [batch_size, num_units].
*
* * 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.
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [batch_size, num_units]. This is effectively the same as the
+ * current state value.
*/
ANEURALNETWORKS_RNN = 24,
@@ -1088,7 +1155,7 @@
* exp((input[batch, i] - max(input[batch, :])) * beta) /
* sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -1096,11 +1163,12 @@
*
* Inputs:
* * 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.
+ * * 1: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the positive
+ * scaling factor for the exponent, beta.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
- * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type,
+ * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*/
ANEURALNETWORKS_SOFTMAX = 25,
@@ -1117,7 +1185,7 @@
* 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:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
@@ -1125,8 +1193,8 @@
*
* 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.
+ * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the block_size. block_size
+ * must be >=1 and block_size must be a divisor of both the input height and width.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
@@ -1172,24 +1240,28 @@
* Each rank adds a dimension to the weights matrices by means of stacking
* the filters.
*
- * Supported tensor types (type T):
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Inputs:
* * 0: input.
- * 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.
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [batch_size, input_size], where “batch_size” corresponds to the
+ * batching dimension, and “input_size” is the size of the input.
* * 1: weights_feature.
- * A 2-D tensor of type T, of shape [num_units, input_size], where
- * “num_units” corresponds to the number of units.
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units, input_size], where “num_units” corresponds to the
+ * number of units.
* * 2: weights_time.
- * A 2-D tensor of type T, of shape [num_units, memory_size], where
- * “memory_size” corresponds to the fixed-size of the memory.
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [num_units, memory_size], where “memory_size” corresponds to the
+ * fixed-size of the memory.
* * 3: bias.
- * An optional 1-D tensor of type T, of shape [num_units].
+ * An optional 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32},
+ * of shape [num_units].
* * 4: state (in).
- * A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [batch_size, (memory_size - 1) * num_units * rank].
* * 5: rank.
* The rank of the SVD approximation.
* * 6: fused_activation_function.
@@ -1198,9 +1270,11 @@
*
* Outputs:
* * 0: state (out).
- * A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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].
+ * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape
+ * [batch_size, num_units].
*/
ANEURALNETWORKS_SVDF = 27,
@@ -1210,7 +1284,7 @@
*
* output = tanh(input)
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4.
@@ -1235,26 +1309,26 @@
*
* This is the reverse of SpaceToBatch.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4
*
* Inputs:
- * 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.
+ * * 0: An n-D tensor, specifying the tensor to be reshaped
+ * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block sizes
+ * for each spatial dimension of the input tensor. All values must be >= 1.
*
* Outputs:
- * 0: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandCode} as input0.
*/
ANEURALNETWORKS_BATCH_TO_SPACE_ND = 29,
/**
* Element-wise division of two tensors.
*
- * Takes two input tensors of identical type and compatible dimensions. The output
+ * Takes two input tensors of identical {@link OperandCode} and compatible dimensions. The output
* is the result of dividing the first input tensor by the second, optionally
* modified by an activation function.
*
@@ -1270,19 +1344,19 @@
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: An n-D tensor, specifying the first input.
- * 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: An n-D tensor, specifying the first input.
+ * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions as input0.
+ * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the {@link FuseCode}
+ * values. Specifies the activation to invoke on the result of each division.
*
* Outputs:
- * 0: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandCode} as input0.
*/
ANEURALNETWORKS_DIV = 30,
@@ -1296,20 +1370,22 @@
* If dimensions to reduce have no entries, all dimensions are reduced, and a tensor with
* a single element is returned.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: A tensor, specifying the input.
- * 1: A 1-D Tensor of type TENSOR_INT32. The dimensions to reduce. If None (the default),
- * reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
- * 2: An INT32 value, keep_dims. If positive, retains reduced dimensions with length 1.
+ * * 0: A tensor, specifying the input.
+ * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions to
+ * reduce. If None (the default), reduces all dimensions. Must be in the range
+ * [-rank(input_tensor), rank(input_tensor)).
+ * * 2: An {@link ANEURALNETWORKS_INT32} scalar, keep_dims. If positive, retains reduced
+ * dimensions with length 1.
*
* Outputs:
- * 0: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandCode} as input0.
*/
ANEURALNETWORKS_MEAN = 31,
@@ -1318,21 +1394,22 @@
*
* This operation pads a tensor according to the specified paddings.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 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.
+ * * 0: An n-D tensor, specifying the tensor to be padded.
+ * * 1: A 2-D Tensor of {@link ANEURALNETWORKS_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.
+ * * 0: A tensor of the same {@link OperandCode} as input0.
*/
ANEURALNETWORKS_PAD = 32,
@@ -1347,49 +1424,50 @@
* batch position. Prior to division into blocks, the spatial dimensions of the input are
* optionally zero padded according to paddings.
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_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. 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.
+ * * 0: An n-D tensor, specifying the input.
+ * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block sizes for each
+ * spatial dimension of the input tensor. All values must be >= 1.
+ * * 2: A 2-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the paddings for each spatial
+ * dimension of the 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.
+ * * 0: A tensor of the same {@link OperandCode} as input0.
*/
ANEURALNETWORKS_SPACE_TO_BATCH_ND = 33,
/**
* Removes dimensions of size 1 from the shape of a tensor.
*
- * 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 the axes (input1).
+ * Given a tensor input, this operation returns a tensor of the same {@link OperandCode}
+ * 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 the axes (input1).
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 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.
+ * * 0: An n-D tensor, the tensor to be squeezed.
+ * * 1: An optional 1-D tensor of {@link ANEURALNETWORKS_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
- * dimensions of size 1 removed.
+ * * 0: A tensor of the same {@link OperandCode} as input0. Contains the same data as input,
+ * but has one or more dimensions of size 1 removed.
*/
ANEURALNETWORKS_SQUEEZE = 34,
@@ -1401,39 +1479,41 @@
* 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:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 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. 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. 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. The length must be of rank(input0).
- * 4: An INT32 value, begin_mask. If the ith bit of begin_mask is set, begin[i] is ignored
- * and the fullest possible range in that dimension is used instead.
- * 5: An INT32 value, end_mask. If the ith bit of end_mask is set, end[i] is ignored and
- * the fullest possible range in that dimension is used instead.
- * 6: An INT32 value, shrink_axis_mask. An int32 mask. If the ith bit of shrink_axis_mask is
- * set, it implies that the ith specification shrinks the dimensionality by 1. A slice of
- * size 1 starting from begin[i] in the dimension must be preserved.
+ * * 0: An n-D tensor, specifying the tensor to be sliced.
+ * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the starts of the
+ * dimensions of the input tensor to be sliced. The length must be of rank(input0).
+ * * 2: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the ends of the
+ * dimensions of the input tensor to be sliced. The length must be of rank(input0).
+ * * 3: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the strides of the
+ * dimensions of the input tensor to be sliced. The length must be of rank(input0).
+ * * 4: An {@link ANEURALNETWORKS_INT32} scalar, begin_mask. If the ith bit of begin_mask
+ * is set, begin[i] is ignored and the fullest possible range in that dimension is
+ * used instead.
+ * * 5: An {@link ANEURALNETWORKS_INT32} scalar, end_mask. If the ith bit of end_mask is set,
+ * end[i] is ignored and the fullest possible range in that dimension is used instead.
+ * * 6: An {@link ANEURALNETWORKS_INT32} scalar, shrink_axis_mask. An int32 mask. If the ith
+ * bit of shrink_axis_mask is set, it implies that the ith specification shrinks the
+ * dimensionality by 1. A slice of size 1 starting from begin[i] in the dimension must
+ * be preserved.
*
* Outputs:
- * 0: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandCode} as input0.
*/
ANEURALNETWORKS_STRIDED_SLICE = 35,
/**
* 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
- * modified by an activation function.
+ * Takes two input tensors of identical {@link OperandCode} and compatible dimensions.
+ * The output is the result of subtracting the second input tensor from the first one,
+ * optionally modified by an activation function.
*
* Two dimensions are compatible when:
* 1. they are equal, or
@@ -1447,19 +1527,19 @@
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
- * Supported tensor types:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 0: An n-D tensor, specifying the first input.
- * 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: An n-D tensor, specifying the first input.
+ * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions as input0.
+ * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the {@link FuseCode}
+ * values. Specifies the activation to invoke on the result of each subtraction.
*
* Outputs:
- * 0: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandCode} as input0.
*/
ANEURALNETWORKS_SUB = 36,
@@ -1470,19 +1550,19 @@
* 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:
+ * Supported tensor {@link OperandCode}:
* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
- * 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.
+ * * 0: An n-D tensor, specifying the tensor to be transposed.
+ * * 1: An optional 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32},
+ * the permutation of the dimensions of the input tensor.
*
* Outputs:
- * 0: A tensor of the same type as input0.
+ * * 0: A tensor of the same {@link OperandCode} as input0.
*/
ANEURALNETWORKS_TRANSPOSE = 37,
#endif
@@ -1832,7 +1912,7 @@
*
* @param model The model to be modified.
* @param type The {@link ANeuralNetworksOperandType} that describes the shape
- * of the operand.
+ * of the operand.
*
* @return ANEURALNETWORKS_NO_ERROR if successful.
*/
@@ -1908,7 +1988,7 @@
* Add an operation to a model.
*
* @param model The model to be modified.
- * @param type The type of the operation.
+ * @param type The {@link ANeuralNetworksOperandType} of the operation.
* @param inputCount The number of entries in the inputs array.
* @param inputs An array of indexes identifying each operand.
* @param outputCount The number of entries in the outputs array.
@@ -2102,11 +2182,11 @@
* an index into the lists passed to
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
* the index associated with {@link ANeuralNetworksModel_addOperand}.
- * @param type The type of the operand. This should be used to specify the
- * dimensions that were set to 0 when the operand was added to the
- * model. All other properties of the type must be the same as
- * specified in the model. If the type is the same as specified
- * when the model was built, NULL can be passed.
+ * @param type The {@link ANeuralNetworksOperandType} of the operand. This should
+ * be used to specify the dimensions that were set to 0 when the
+ * operand was added to the model. All other properties of the type
+ * must be the same as specified in the model. If the type is the
+ * same as specified when the model was built, NULL can be passed.
* @param buffer The buffer containing the data.
* @param length The length in bytes of the buffer.
*
@@ -2134,11 +2214,11 @@
* an index into the lists passed to
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
* the index associated with {@link ANeuralNetworksModel_addOperand}.
- * @param type The type of the operand. This can be used to specify the
- * dimensions that were set to 0 when the operand was added to the
- * model. All other values must be the same as specified in the
- * model. If the type is the same as specified when the model
- * was built, NULL can be passed.
+ * @param type The {@link ANeuralNetworksOperandType} of the operand. This can be
+ * used to specify the dimensions that were set to 0 when the operand
+ * was added to the model. All other values must be the same as
+ * specified in the model. If the type is the same as specified when
+ * the model was built, NULL can be passed.
* @param memory The memory containing the data.
* @param offset This specifies the location of the data within the memory.
* The offset is in bytes from the start of memory.
@@ -2168,11 +2248,11 @@
* an index into the lists passed to
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
* the index associated with {@link ANeuralNetworksModel_addOperand}.
- * @param type The type of the operand. This can be used to specify the
- * dimensions that were set to 0 when the operand was added to the
- * model. All other values must be the same as specified in the
- * model. If the type is the same as specified when the model
- * was built, NULL can be passed.
+ * @param type The {@link ANeuralNetworksOperandType} of the operand. This can be
+ * used to specify the dimensions that were set to 0 when the operand
+ * was added to the model. All other values must be the same as
+ * specified in the model. If the type is the same as specified when
+ * the model was built, NULL can be passed.
* @param buffer The buffer where the data is to be written.
* @param length The length in bytes of the buffer.
*
@@ -2200,11 +2280,11 @@
* an index into the lists passed to
* {@link ANeuralNetworksModel_identifyInputsAndOutputs}. It is not
* the index associated with {@link ANeuralNetworksModel_addOperand}.
- * @param type The type of the operand. This can be used to specify the
- * dimensions that were set to 0 when the operand was added to the
- * model. All other values must be the same as specified in the
- * model. If the type is the same as specified when the model
- * was built, NULL can be passed.
+ * @param type The {@link ANeuralNetworksOperandType} of the operand. This can be
+ * used to specify the dimensions that were set to 0 when the operand
+ * was added to the model. All other values must be the same as
+ * specified in the model. If the type is the same as specified when
+ * the model was built, NULL can be passed.
* @param memory The memory where the data is to be stored.
* @param offset This specifies the location of the data within the memory.
* The offset is in bytes from the start of memory.
