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/*
* Copyright (C) 2017 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package android.hardware.neuralnetworks@1.0;
/**
* Operand types.
*
* The type of an operand in a model.
*
* 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 {
/** 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,
/** 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.
*
* 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.
* - zeroPoint: a 32 bit integer, in range [0, 255].
*
* The formula is:
* real_value = (integer_value - zeroPoint) * scale.
*/
TENSOR_QUANT8_ASYMM = 5,
/** OEM specific scalar value. */
OEM = 10000,
/** A tensor of OEM specific values. */
TENSOR_OEM_BYTE = 10001,
};
/**
* Operation types.
*
* The type of an operation in a model.
*/
enum OperationType : int32_t {
/**
* Adds two tensors, element-wise.
*
* Takes two input tensors of identical {@link OperandType} 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
* 2. one of them is 1
*
* The size of the output is the maximum size along each dimension of the
* input operands. It starts with the trailing dimensions, and works its
* way forward.
*
* Example:
*
* input1.dimension = {4, 1, 2}
* input2.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor {@link OperandType}:
* * {@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 {@link OperandType}, and compatible dimensions
* as input0.
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The sum, a tensor of the same {@link OperandType} as input0.
*/
ADD = 0,
/**
* Performs a 2-D average pooling operation.
*
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
* 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 {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
* and Channels) data layout.
*
* 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 {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 2: An {@link OperandType::INT32} scalar, specifying the padding on
* the right, in the ‘width’ dimension.
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the top, in the ‘height’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
* the bottom, in the ‘height’ dimension.
* * 5: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 6: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 7: An {@link OperandType::INT32} scalar, specifying the filter
* width.
* * 8: An {@link OperandType::INT32} scalar, specifying the filter
* height.
* * 9: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* * 1: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
* * 2: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 3: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the filter
* width.
* * 5: An {@link OperandType::INT32} scalar, specifying the filter
* height.
* * 6: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
[batches, out_height, out_width, depth].
*/
AVERAGE_POOL_2D = 1,
/**
* Concatenates the input tensors along the given dimension.
*
* The input tensors must have identical {@link OperandType} and the same
* dimensions except the dimension along the concatenation axis.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0 ~ n-1: The list of n input tensors, of shape
* [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of
* {@link OperandType::TENSOR_QUANT8_ASYMM}, all input tensors
* must have the same scale and zeroPoint.
* * n: An {@link OperandType::INT32} scalar, specifying the
* concatenation axis.
*
* Outputs:
* * 0: The output, a tensor of the same {@link OperandType} as the input
* tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
*/
CONCATENATION = 2,
/**
* Performs an 2-D convolution operation.
*
* The CONV_2D op sweeps a 2-D filter that can mix channels together over a
* batch of images, applying the filter to each window of each image of the
* appropriate size.
*
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
* The values in the output tensor are computed as:
*
* output[batch, row, col, channel] =
* sum_{i, j} (
* input[batch, row + i, col + j, k] *
* filter[channel, row + i, col + j, k] +
* bias[channel]
* )
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Both explicit padding and implicit padding are supported.
*
* 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}, the bias
* should also be of {@link OperandType::TENSOR_FLOAT32}. For input
* tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias
* should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
* 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
* the right, in the ‘width’ dimension.
* * 5: An {@link OperandType::INT32} scalar, specifying the padding on
* the top, in the ‘height’ dimension.
* * 6: An {@link OperandType::INT32} scalar, specifying the padding on
* the bottom, in the ‘height’ dimension.
* * 7: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 8: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 9: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* 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}, the bias should
* also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
* of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
* of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
* * 4: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 5: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 6: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* 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}, the following condition
* must be satisfied: output_scale > input_scale * filter_scale.
*/
CONV_2D = 3,
/**
* Performs a depthwise 2-D convolution operation.
*
* Given an input tensor of shape [batches, height, width, depth_in] and a
* filter 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 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[1, di, dj, k * channel_multiplier + q]
* )
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* Both explicit padding and implicit padding are supported.
*
* 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}, the bias should
* also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
* of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
* of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
* the right, in the ‘width’ dimension.
* * 5: An {@link OperandType::INT32} scalar, specifying the padding on
* the top, in the ‘height’ dimension.
* * 6: An {@link OperandType::INT32} scalar, specifying the padding on
* the bottom, in the ‘height’ dimension.
* * 7: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 8: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 9: An {@link OperandType::INT32} scalar, specifying the depthwise
* multiplier.
* * 10: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* 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}, the bias should
* also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
* of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
* of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
* * 4: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 5: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 6: An {@link OperandType::INT32} scalar, specifying the depthwise
* multiplier.
* * 7: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* 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}, the following condition
* must be satisfied: output_scale > input_scale * filter_scale.
*/
DEPTHWISE_CONV_2D = 4,
/**
* Rearranges data from depth into blocks of spatial data.
*
* More specifically, this op outputs a copy of the input tensor where
* values from the depth dimension are moved in spatial blocks to the height
* and width dimensions. The value block_size indicates the input block size
* and how the data is moved.
*
* Chunks of data of size block_size * block_size from depth are rearranged
* into non-overlapping blocks of size block_size x block_size.
*
* The width of the output tensor is input_depth * block_size, whereas the
* height is input_height * block_size. The depth of the input tensor must
* be divisible by block_size * block_size
*
* Supported tensor {@link OperandType}:
* * {@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 {@link OperandType::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, depth/(block_size*block_size)].
*/
DEPTH_TO_SPACE = 5,
/**
* Dequantizes the input tensor.
*
* The formula is:
*
* output = (input - zeroPoint) * scale.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}.
*
* Outputs:
* * 0: The output tensor of same shape as input0, but with
* {@link OperandType::TENSOR_FLOAT32}.
*/
DEQUANTIZE = 6,
/**
* Looks up sub-tensors in the input tensor.
*
* 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: Lookups. A 1-D tensor of {@link OperandType::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.
*
* Output:
* * 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 {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor, of the same {@link OperandType} and dimensions as
* the input tensor.
*/
FLOOR = 8,
/**
* Denotes a fully (densely) connected layer, which connects all elements
* in the input tensor with each element in the output tensor.
*
* This layer implements the operation:
*
* outputs = activation(inputs * weights’ + bias)
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor of at least rank 2, specifying the input. If rank is
* greater than 2, then it gets flattened to a 2-D Tensor. The
* (flattened) 2-D Tensor is reshaped (if necessary) to
* [batch_size, input_size], where "input_size" corresponds to the
* number of inputs to the layer, matching the second dimension of
* weights, and "batch_size" is calculated by dividing the number of
* elements by "input_size".
* * 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}, the bias should
* also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
* of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
* of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The output tensor, of shape [batch_size, num_units]. For output
* tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
* condition must be satisfied:
* output_scale > input_scale * filter_scale.
*/
FULLY_CONNECTED = 9,
/**
* 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 {@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 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 the depth dimension.
*
* 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 input tensor with more dimensions, independently normalizes each 1-D
* slice along dimension dim.
*
* Supported tensor {@link OperandType}:
* * {@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].
*
* Outputs:
* * 0: The output 4-D tensor, of the same shape as input
* [batches, height, width, depth].
*/
L2_NORMALIZATION = 11,
/**
* Performs an 2-D L2 pooling operation.
*
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
* 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 {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* 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 {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 2: An {@link OperandType::INT32} scalar, specifying the padding on
* the right, in the ‘width’ dimension.
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the top, in the ‘height’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
* the bottom, in the ‘height’ dimension.
* * 5: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 6: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 7: An {@link OperandType::INT32} scalar, specifying the filter
* width.
* * 8: An {@link OperandType::INT32} scalar, specifying the filter
* height.
* * 9: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* * 1: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
* * 2: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 3: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the filter
* width.
* * 5: An {@link OperandType::INT32} scalar, specifying the filter
* height.
* * 6: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
*/
L2_POOL_2D = 12,
/**
* Applies Local Response Normalization along the depth dimension.
*
* The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the
* last 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.
*
* 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 {@link OperandType}:
* * {@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 {@link OperandType::INT32} scalar, specifying the radius of
* the normalization window.
* * 2: An {@link OperandType::FLOAT32} scalar, specifying the bias, must
* not be zero.
* * 3: An {@link OperandType::FLOAT32} scalar, specifying the scale
* factor, alpha.
* * 4: An {@link OperandType::FLOAT32} scalar, specifying the exponent,
* beta.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
LOCAL_RESPONSE_NORMALIZATION = 13,
/**
* Computes sigmoid activation on the input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = 1 / (1 + exp(-input))
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*/
LOGISTIC = 14,
/**
* Projects an input to a bit vector via locality senstive hashing.
*
* Inputs:
* * 0: Hash functions. Dim.size == 2, DataType: Float.
* Tensor[0].Dim[0]: Number of hash functions.
* Tensor[0].Dim[1]: Number of seeds per hash functions.
* Tensor[0].Dim[1] <= 32 in sparse case.
*
* * 1: Input. Dim.size >= 1, no restriction on DataType.
* * 2: Weight. Optional. Dim.size == 1, DataType: Float.
* If not set, each input element is considered to have the same weight
* of 1.0.
* Tensor[1].Dim[0] == Tensor[2].Dim[0]
* * 3: Type:
* Sparse: Value LSHProjectionType_SPARSE(=1).
* Computed bit vector is considered to be sparse.
* Each output element is an int32 made up of multiple bits
* computed from hash functions.
*
* Dense: Value LSHProjectionType_DENSE(=2).
* Computed bit vector is considered to be dense. Each output
* element represents a bit and can take the value of either
* 0 or 1.
*
* Outputs:
* * 0: If the projection type is sparse:
* Output.Dim == { Tensor[0].Dim[0] }
* A tensor of int32 that represents hash signatures.
* If the projection type is Dense:
* Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
* A flattened tensor that represents projected bit vectors.
*/
LSH_PROJECTION = 15,
/**
* Performs a single time step in a Long Short-Term Memory (LSTM) layer
*
* 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-forget weights (\f$W_{cf}\f$) and cell-to-output weights
* (\f$W_{co}\f$) either both have values or neither of them have values.
* If they have values, the peephole optimization is used. Additionally,
* if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also
* required to have values for peephole optimization.
* * 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 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"
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Inputs:
* * 0: The input (\f$x_t\f$).
* A 2-D tensor of {@link OperandType::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 {@link OperandType::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 {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, input_size].
* * 3: The input-to-cell weights (\f$W_{xc}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, input_size].
* * 4: The input-to-output weights (\f$W_{xo}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, input_size].
* * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
* A 2-D tensor of {@link OperandType::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 {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, output_size].
* * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, output_size].
* * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, output_size].
* * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* * 12:The input gate bias (\f$b_i\f$). Optional.
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* * 13:The forget gate bias (\f$b_f\f$).
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* * 14:The cell bias (\f$b_c\f$).
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* * 15:The output gate bias (\f$b_o\f$).
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* * 16:The projection weights (\f$W_{proj}\f$). Optional.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [output_size, num_units].
* * 17:The projection bias (\f$b_{proj}\f$). Optional.
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [output_size].
* * 18:The output state (in) (\f$h_{t-1}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, output_size].
* * 19:The cell state (in) (\f$C_{t-1}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, 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.
* * 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: The scratch buffer.
* A 2-D tensor of {@link OperandType::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 {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, output_size].
* * 2: The cell state (out) (\f$C_t\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, num_units].
* * 3: The output (\f$o_t\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, output_size]. This is effectively the same as the
* current “output state (out)” value.
*/
LSTM = 16,
/**
* Performs an 2-D max pooling operation.
*
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
* 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 {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
*
* 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 {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 2: An {@link OperandType::INT32} scalar, specifying the padding on
* the right, in the ‘width’ dimension.
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the top, in the ‘height’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
* the bottom, in the ‘height’ dimension.
* * 5: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 6: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 7: An {@link OperandType::INT32} scalar, specifying the filter
* width.
* * 8: An {@link OperandType::INT32} scalar, specifying the filter
* height.
* * 9: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* * 1: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
* * 2: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘width’ dimension.
* * 3: An {@link OperandType::INT32} scalar, specifying the stride when
* walking through input in the ‘height’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the filter
* width.
* * 5: An {@link OperandType::INT32} scalar, specifying the filter
* height.
* * 6: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
*/
MAX_POOL_2D = 17,
/**
* Multiplies two tensors, element-wise.
*
* Takes two input tensors of identical {@link OperandType} 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
* 2. one of them is 1
*
* 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 {@link OperandType}:
* * {@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 {@link OperandType}, and compatible dimensions
* as input0.
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The product, a tensor of the same {@link OperandType} as input0.
* For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the following condition must be satisfied:
* output_scale > input1_scale * input2_scale.
*/
MUL = 18,
/**
* Computes rectified linear activation on the input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = max(0, input)
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
RELU = 19,
/**
* Computes rectified linear 1 activation on the input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = min(1.f, max(-1.f, input))
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
RELU1 = 20,
/**
* Computes rectified linear 6 activation on the input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = min(6, max(0, input))
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
RELU6 = 21,
/**
* Reshapes a tensor.
*
* Given tensor, this operation returns a tensor that has the same values as
* tensor, but with a newly specified shape.
*
* Supported tensor {@link OperandType}:
* * {@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 {@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.
*
* 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 must be distorted if their output aspect ratio is not the
* same as input aspect ratio. The corner pixels of output may not be the
* same as corner pixels of input.
*
* Supported tensor {@link OperandType}:
* * {@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 {@link OperandType::INT32} scalar, specifying the output
* height of the output tensor.
* * 2: An {@link OperandType::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].
*/
RESIZE_BILINEAR = 23,
/**
* A basic recurrent neural network layer.
*
* This layer implements the operation:
* outputs = state = activation(inputs * input_weights +
* state * recurrent_weights + bias)
*
* Where:
* * “input_weights” is a weight matrix that multiplies the inputs;
* * “recurrent_weights” is a weight matrix that multiplies the current
* “state” which itself is the output from the previous time step
* computation;
* * “bias” is a bias vector (added to each output vector in the batch);
* * “activation” is the function passed as the “fused_activation_function”
* argument (if not “NONE”).
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Inputs:
* * 0: input.
* A 2-D tensor of {@link OperandType::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 {@link OperandType::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 {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, num_units], with columns corresponding to the weights
* from each unit.
* * 3: bias.
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* * 4: hidden state (in).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, 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.
*
* Outputs:
* * 0: hidden state (out).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, num_units].
*
* * 1: output.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, num_units]. This is effectively the same as the
* current state value.
*/
RNN = 24,
/**
* Computes the softmax activation on the input tensor element-wise, per
* batch, by normalizing the input vector so the maximum coefficient is
* zero.
*
* 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 {@link OperandType}:
* * {@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: An {@link OperandType::FLOAT32} scalar, specifying the positive
* scaling factor for the exponent, beta.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*/
SOFTMAX = 25,
/**
* Rearranges blocks of spatial data, into depth.
*
* More specifically, this op outputs a copy of the input tensor where
* values from the height and width dimensions are moved to the depth
* dimension. The value block_size indicates the input block size and how
* the data is moved.
*
* Chunks of data of size block_size * block_size from depth are rearranged
* into non-overlapping blocks of size block_size x block_size.
*
* 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 {@link OperandType}:
* * {@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 {@link OperandType::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 [batches, height/block_size,
* width/block_size, depth_in*block_size*block_size].
*/
SPACE_TO_DEPTH = 26,
/**
* SVDF op is a kind of stateful layer derived from the notion that a
* densely connected layer that's processing a sequence of input frames can
* be approximated by using a singular value decomposition of each of its
* nodes. The implementation is based on:
*
* https://research.google.com/pubs/archive/43813.pdf
*
* P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
* “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
* INTERSPEECH, 2015.
*
* It processes the incoming input using a 2-stage filtering mechanism:
* * stage 1 performs filtering on the "features" dimension, whose outputs
* get pushed into a memory of fixed-size memory_size.
* * stage 2 performs filtering on the "time" dimension of the memory_size
* memoized outputs of stage 1.
*
* Specifically, for rank 1, this layer implements the operation:
*
* 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
* convolving the input with every “feature filter”), and whose outputs
* get pushed, stacked in order, into the fixed-size “memory” (the oldest
* entry gets dropped);
* * “weights_time” is a weights matrix that processes the “memory” (by a
* batched matrix multiplication on the num_units);
* * “bias” is an optional bias vector (added to each output vector in the
* batch); and
* * “activation” is the function passed as the “fused_activation_function”
* argument (if not “NONE”).
*
* Each rank adds a dimension to the weights matrices by means of stacking
* the filters.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Inputs:
* * 0: input.
* A 2-D tensor of {@link OperandType::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 {@link OperandType::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 {@link OperandType::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 {@link OperandType::TENSOR_FLOAT32},
* of shape [num_units].
* * 4: state (in).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, (memory_size - 1) * num_units * rank].
* * 5: rank.
* The rank of the SVD approximation.
* * 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 (out).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, (memory_size - 1) * num_units * rank].
* * 1: output.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, num_units].
*/
SVDF = 27,
/**
* Computes hyperbolic tangent of input tensor element-wise.
*
* The output is calculated using this formula:
*
* output = tanh(input)
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
TANH = 28,
/**
* OEM specific operation.
*
* This operation is OEM specific. It should only be used for OEM
* applications.
*/
OEM_OPERATION = 10000,
};
/**
* Fused activation function types.
*/
enum FusedActivationFunc : int32_t {
NONE = 0,
RELU = 1,
RELU1 = 2,
RELU6 = 3,
};
/**
* How an operand is used.
*/
enum OperandLifeTime : int32_t {
/**
* The operand is internal to the model. It's created by an operation and
* consumed by other operations. It must be an output operand of
* exactly one operation.
*/
TEMPORARY_VARIABLE,
/**
* The operand is an input of the model. It must not be an output
* operand of any operation.
*
* An operand can't be both input and output of a model.
*/
MODEL_INPUT,
/**
* The operand is an output of the model. It must be an output
* operand of exactly one operation.
*
* An operand can't be both input and output of a model.
*/
MODEL_OUTPUT,
/**
* The operand is a constant found in Model.operandValues. It must
* not be an output operand of any operation.
*/
CONSTANT_COPY,
/**
* The operand is a constant that was specified via a Memory
* object. It must not be an output operand of any operation.
*/
CONSTANT_REFERENCE,
/**
* The operand does not have a value. This is valid only for optional
* arguments of operations.
*/
NO_VALUE,
};
/**
* Status of a device.
*/
enum DeviceStatus : int32_t {
AVAILABLE,
BUSY,
OFFLINE,
UNKNOWN,
};
/**
* Performance information for the reference workload.
*
* Used by a driver to report its performance characteristics.
*/
struct PerformanceInfo {
/**
* Ratio of the time taken by the driver to execute the
* workload compared to the time the CPU would take for the
* same workload. A lower number is better.
*/
float execTime;
/**
* Ratio of the energy used by the driver compared to what
* the CPU would use for doing the same workload. A lower number
* is better.
*/
float powerUsage;
};
/**
* The capabilities of a driver.
*/
struct Capabilities {
/**
* Driver performance when operating on float32 data.
*/
PerformanceInfo float32Performance;
/**
* Driver performance when operating on asymmetric 8-bit quantized data.
*/
PerformanceInfo quantized8Performance;
};
/**
* Describes the location of a data object.
*/
struct DataLocation {
/**
* The index of the memory pool where this location is found.
*/
uint32_t poolIndex;
/**
* Offset in bytes from the start of the pool.
*/
uint32_t offset;
/**
* The length of the data in bytes.
*/
uint32_t length;
};
/**
* Describes one operand of the model's graph.
*/
struct Operand {
/**
* Data type of the operand.
*/
OperandType type;
/**
* Dimensions of the operand.
*
* For a scalar operand, dimensions.size() must be 0.
*
* For a tensor operand, dimensions.size() must be at least 1;
* however, any of the dimensions may be unspecified.
*
* A tensor operand with all dimensions specified has "fully
* specified" dimensions. Whenever possible (i.e., whenever the
* dimensions are known at model construction time), a tensor
* operand should have (but is not required to have) fully
* specified dimensions, in order to enable the best possible
* performance.
*
* If a tensor operand's dimensions are not fully specified, the
* dimensions of the operand are deduced from the operand
* dimensions and values of the operation for which that operand
* is an output.
*
* In the following situations, a tensor operand's dimensions must
* be fully specified:
*
* . The operand has lifetime CONSTANT_COPY or
* CONSTANT_REFERENCE.
*
* . The operand has lifetime MODEL_INPUT or MODEL_OUTPUT. Fully
* specified dimensions must either be present in the
* Operand or they must be provided in the corresponding
* RequestArgument.
* EXCEPTION: If the input or output is optional and omitted
* (by setting the hasNoValue field of the corresponding
* RequestArgument to true) then it need not have fully
* specified dimensions.
*
* A tensor operand with some number of unspecified dimensions is
* represented by setting each unspecified dimension to 0.
*/
vec<uint32_t> dimensions;
/**
* The number of times this operand appears as an operation input.
*
* (For example, if this operand appears once in one operation's
* input list, and three times in another operation's input list,
* then numberOfConsumers = 4.)
*/
uint32_t numberOfConsumers;
/**
* Quantized scale of the operand.
*
* Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
* TENSOR_INT32.
*/
float scale;
/**
* Quantized zero-point offset of the operand.
*
* Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
*/
int32_t zeroPoint;
/**
* How the operand is used.
*/
OperandLifeTime lifetime;
/**
* Where to find the data for this operand.
* If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
* NO_VALUE:
* - 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.
* - location.length is set.
* If the lifetime is CONSTANT_REFERENCE:
* - location.poolIndex is set.
* - location.offset is the offset in bytes into the specified pool.
* - location.length is set.
*/
DataLocation location;
};
/**
* Describes one operation of the model's graph.
*/
struct Operation {
/**
* The operation type.
*/
OperationType type;
/**
* Describes the table that contains the indexes of the inputs of the
* operation. The offset is the index in the operandIndexes table.
*/
vec<uint32_t> inputs;
/**
* Describes the table that contains the indexes of the outputs of the
* operation. The offset is the index in the operandIndexes table.
*/
vec<uint32_t> outputs;
};
/**
* A Neural Network Model.
*
* This includes not only the execution graph, but also constant data such as
* weights or scalars added at construction time. The only information that
* might not be known is the shape of the input tensors.
*/
struct Model {
/**
* All operands included in the model.
*/
vec<Operand> operands;
/**
* All operations included in the model.
*
* The operations are sorted into execution order. Every operand
* with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
* written before it is read.
*/
vec<Operation> operations;
/**
* Input indexes of the model. There must be at least one.
*
* Each value corresponds to the index of the operand in "operands".
*/
vec<uint32_t> inputIndexes;
/**
* Output indexes of the model. There must be at least one.
*
* Each value corresponds to the index of the operand in "operands".
*/
vec<uint32_t> outputIndexes;
/**
* A byte buffer containing operand data that were copied into the model.
*
* An operand's value must be located here if and only if Operand::lifetime
* equals OperandLifeTime::CONSTANT_COPY.
*/
vec<uint8_t> operandValues;
/**
* A collection of shared memory pools containing operand values.
*
* An operand's value must be located here if and only if Operand::lifetime
* equals OperandLifeTime::CONSTANT_REFERENCE.
*/
vec<memory> pools;
};
/**
* Metadata information specifying the location of the input or output data and
* any updates to the input or output operand.
*/
struct RequestArgument {
/**
* If true, the argument does not have a value. This can be used for
* operations that take optional arguments. If true, the fields of location
* are set to 0 and the dimensions vector is left empty.
*/
bool hasNoValue;
/**
* The location within one of the memory pools passed in the Request.
*/
DataLocation location;
/**
* 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. This can't change the rank, just
* the value of the dimensions that were unspecified in the
* model. If dimensions.size() > 0, then all dimensions must be
* specified here; and any dimension that was specified in the
* model must have the same value here.
*
* If the dimensions in the model are not fully specified, then
* they must be fully specified here, unless hasNoValue is set to
* true. If the dimensions in the model are fully specified, then
* either dimensions.size() may be 0, or the dimensions in the
* model must be identical to the dimensions here.
*/
vec<uint32_t> dimensions;
};
/**
* Inputs to be sent to and outputs to be retrieved from a prepared model.
*
* A Request serves two primary tasks:
* 1) Provides the input and output data to be used when executing the model.
* 2) Specifies any updates to the input operand metadata that were left
* unspecified at model preparation time.
*
* An output must not overlap with any other output, with an input, or
* with an operand of lifetime CONSTANT_REFERENCE.
*/
struct Request {
/**
* Input data and information to be used in the execution of a prepared
* model.
*
* The index of the input corresponds to the index in Model.inputIndexes.
* E.g., input[i] corresponds to Model.inputIndexes[i].
*/
vec<RequestArgument> inputs;
/**
* Output data and information to be used in the execution of a prepared
* model.
*
* The index of the output corresponds to the index in Model.outputIndexes.
* E.g., output[i] corresponds to Model.outputIndexes[i].
*/
vec<RequestArgument> outputs;
/**
* A collection of shared memory pools containing operand data for both the
* inputs and the outputs to a model.
*/
vec<memory> pools;
};
/**
* Return status of a function.
*/
enum ErrorStatus : int32_t {
NONE,
DEVICE_UNAVAILABLE,
GENERAL_FAILURE,
OUTPUT_INSUFFICIENT_SIZE,
INVALID_ARGUMENT,
};