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android / platform / hardware / interfaces / refs/heads/main / . / neuralnetworks / 1.2 / types.hal
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/*
* Copyright (C) 2018 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@1.2;
import @1.0::DataLocation;
import @1.0::ErrorStatus;
import @1.0::OperandLifeTime;
import @1.0::OperandType;
import @1.0::PerformanceInfo;
import @1.1::OperationType;
import android.hidl.safe_union@1.0::Monostate;
enum Constant : uint32_t {
/**
* The byte size of the cache token.
*/
BYTE_SIZE_OF_CACHE_TOKEN = 32,
/**
* The maximum number of files for each type of cache in compilation caching.
*/
MAX_NUMBER_OF_CACHE_FILES = 32,
};
enum OperandType : @1.0::OperandType {
/**
* An 8 bit boolean scalar value.
*
* Values of this operand type are either true or false. A zero value
* represents false; any other value represents true.
*/
BOOL = 6,
/**
* A tensor of 16 bit signed integers that represent real numbers.
*
* Attached to this tensor is a number representing real value scale that is
* used to convert the 16 bit number to a real value in the following way:
* realValue = integerValue * scale.
*
* scale is a 32 bit floating point with value greater than zero.
*/
TENSOR_QUANT16_SYMM = 7,
/**
* A tensor of IEEE 754 16 bit floating point values.
*/
TENSOR_FLOAT16 = 8,
/**
* A tensor of 8 bit boolean values.
*
* Values of this operand type are either true or false. A zero value
* represents false; any other value represents true.
*/
TENSOR_BOOL8 = 9,
/**
* An IEEE 754 16 bit floating point scalar value.
*/
FLOAT16 = 10,
/**
* A tensor of 8 bit signed integers that represent real numbers.
*
* This tensor is associated with additional fields that can
* be used to convert the 8 bit signed integer to the real value and vice versa.
* These fields are:
* - channelDim: a 32 bit unsigned integer indicating channel dimension.
* - scales: an array of positive 32 bit floating point values.
* The size of the scales array must be equal to dimensions[channelDim].
*
* {@link SymmPerChannelQuantParams} must hold the parameters for an Operand of this type.
* The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0).
*
* The formula is:
* realValue[..., C, ...] =
* integerValue[..., C, ...] * scales[C]
* where C is an index in the Channel dimension.
*/
TENSOR_QUANT8_SYMM_PER_CHANNEL = 11,
/**
* A tensor of 16 bit unsigned integers that represent real numbers.
*
* Attached to this tensor are two numbers that can be used to convert the
* 16 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, 65535].
*
* The formula is:
* real_value = (integer_value - zeroPoint) * scale.
*/
TENSOR_QUANT16_ASYMM = 12,
/**
* A tensor of 8 bit signed integers that represent real numbers.
*
* Attached to this tensor is a number representing real value scale that is
* used to convert the 8 bit number to a real value in the following way:
* realValue = integerValue * scale.
*
* scale is a 32 bit floating point with value greater than zero.
*/
TENSOR_QUANT8_SYMM = 13,
/*
* DEPRECATED. Since HAL version 1.2, extensions are the preferred
* alternative to OEM operation and data types.
*
* OEM specific scalar value.
* OEM = 10000,
*/
/*
* DEPRECATED. Since HAL version 1.2, extensions are the preferred
* alternative to OEM operation and data types.
*
* A tensor of OEM specific values.
* TENSOR_OEM_BYTE = 10001,
*/
/* ADDING A NEW FUNDAMENTAL TYPE REQUIRES UPDATING THE VALUE OF
* OperandTypeRange::FUNDAMENTAL_MAX.
*/
/* ADDING A NEW OEM TYPE REQUIRES UPDATING THE VALUE OF
* OperandTypeRange::OEM_MAX.
*/
};
/**
* The range of operand values in the OperandType enum.
*/
enum OperandTypeRange : uint32_t {
BASE_MIN = 0,
FUNDAMENTAL_MIN = 0,
FUNDAMENTAL_MAX = 13,
OEM_MIN = 10000,
OEM_MAX = 10001,
BASE_MAX = 0xFFFF,
};
/**
* 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}
*
* Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
* dimension is only compatible with 0 or 1. The size of the output
* dimension is zero if either of corresponding input dimension is zero.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@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.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scales and zeroPoint can be different from input0 scale and zeroPoint.
* * 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.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
ADD = @1.1::OperationType:ADD,
/**
* 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[b, i, j, channel] =
* sum_{di, dj}(
* input[b, strides[1] * i + di, strides[2] * j + dj, channel]
* ) / sum(1)
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
* NCHW is supported since HAL version 1.2.
*
* 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.
* Since HAL version 1.2, zero batches is supported for this tensor.
* * 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.
* * 10: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* Since HAL version 1.2, zero batches is supported for this tensor.
* * 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.
* * 7: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
AVERAGE_POOL_2D = @1.1::OperationType:AVERAGE_POOL_2D,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
* (full support since HAL version 1.2, see the input section)
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0 ~ n-1: The list of n input tensors, of shape
* [D0, D1, ..., Daxis(i), ..., Dm].
* Before HAL version 1.2, all input tensors of
* {@link OperandType::TENSOR_QUANT8_ASYMM}
* must have the same scale and zeroPoint as the output tensor.
* Since HAL version 1.2, zero-sized tensors are supported.
* * 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].
* Since HAL version 1.2, for a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint values can be different from
* input tensors. Before HAL version 1.2 they have to be the same as for the
* input tensors.
*/
CONCATENATION = @1.1::OperationType:CONCATENATION,
/**
* Performs a 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[b, i, j, channel] =
* sum_{di, dj, k} (
* input[b, strides[1] * i + di, strides[2] * j + dj, k] *
* filter[channel, di, dj, k]
* ) + bias[channel]
*
* Supported tensor {@link OperandType} configurations:
* * 32 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
* * Quantized:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
* Available since HAL version 1.2:
* * 16 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
*
* * Quantized with symmetric per channel quantization for the filter:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
* * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
* * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
* NCHW is supported since HAL version 1.2.
*
* 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.
* Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_in], specifying the
* filter.
* For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
* the channel dimension (SymmPerChannelQuantParams::channelDim)
* must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32}
* or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same type.
* For filter 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.
* For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
* and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 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.
* * 10: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
* * 11: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 12 (dilation factor for height) must be specified as well.
* Available since HAL version 1.2.
* * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 11 (dilation factor for width) must be specified as well.
* Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
* specifying the input.
* Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_in], specifying the
* filter.
* For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
* the channel dimension (SymmPerChannelQuantParams::channelDim)
* must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32}
* or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
* type.
* For filter 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.
* For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
* and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 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.
* * 7: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
* * 8: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 9 (dilation factor for height) must be specified as well.
* Available since HAL version 1.2.
* * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 8 (dilation factor for width) must be specified as well.
* Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out].
* Before HAL version 1.2, for output tensor of
* {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition must
* be satisfied: output_scale > input_scale * filter_scale
*/
CONV_2D = @1.1::OperationType:CONV_2D,
/**
* 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]
* ) + bias[k * channel_multiplier + q]
*
* Supported tensor {@link OperandType} configurations:
* * 32 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
* * Quantized:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
* Available since HAL version 1.2:
* * 16 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
*
* * Quantized with symmetric per channel quantization for the filter:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
* * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
* * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
* NCHW is supported since HAL version 1.2.
*
* 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.
* For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
* the channel dimension (SymmPerChannelQuantParams::channelDim)
* must be set to 3.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32}
* or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same type.
* For filter 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.
* For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
* and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 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.
* * 11: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
* * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 13 (dilation factor for height) must be specified as well.
* Available since HAL version 1.2.
* * 13: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 12 (dilation factor for width) must be specified as well.
* Available since HAL version 1.2.
*
* 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 type {@link OperandType::TENSOR_FLOAT32}
* or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same type.
* For filter 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.
* For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
* and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 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.
* * 8: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
* * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on width dimension. If this input is set,
* input 10 (dilation factor for height) must be specified as well.
* Available since HAL version 1.2.
* * 10: An optional {@link OperandType::INT32} scalar, specifying the dilation
* factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
* cells between each filter element on height dimension. If this input is set,
* input 9 (dilation factor for width) must be specified as well.
* Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out]. Before HAL version 1.2, for
* output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the following condition must be satisfied:
* output_scale > input_scale * filter_scale
*/
DEPTHWISE_CONV_2D = @1.1::OperationType:DEPTHWISE_CONV_2D,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
* NCHW is supported since HAL version 1.2.
*
* 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.
* * 2: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batch, height*block_size,
* width*block_size, depth/(block_size*block_size)].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
DEPTH_TO_SPACE = @1.1::OperationType:DEPTH_TO_SPACE,
/**
* Dequantizes the input tensor.
*
* The formula is:
*
* output = (input - zeroPoint) * scale.
*
* Supported input tensor {@link OperandType}:
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
* * {@link OperandType::TENSOR_QUANT8_SYMM} (since HAL version 1.2)
* * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since HAL version 1.2)
*
* Supported output tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}.
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor.
* Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: A tensor with the same shape as input0.
*/
DEQUANTIZE = @1.1::OperationType:DEQUANTIZE,
/**
* 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.
*
* Supported value tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32} (since HAL version 1.2)
* * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported value tensor rank: from 2
*
* 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.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input1.
*/
EMBEDDING_LOOKUP = @1.1::OperationType:EMBEDDING_LOOKUP,
/**
* Computes element-wise floor() on the input tensor.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@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 = @1.1::OperationType:FLOOR,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@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".
* Since HAL version 1.2, zero batch_size is supported for this tensor.
* * 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]. Before HAL version 1.2, for
* output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
* condition must be satisfied: output_scale > input_scale * filter_scale.
*/
FULLY_CONNECTED = @1.1::OperationType:FULLY_CONNECTED,
/**
* 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.
*
* Supported value tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported value tensor rank: from 2
*
* 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 …].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input2.
* * 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 = @1.1::OperationType:HASHTABLE_LOOKUP,
/**
* Applies L2 normalization along the axis 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))
*
* By default the axis dimension is the last dimension of the input tensor.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported tensor rank: up to 4
* Tensors with rank less than 4 are only supported since HAL version 1.2.
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be normalized.
* * 1: An optional {@link OperandType::INT32} scalar, default to -1,
* specifying the dimension normalization would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
* the last axis). Must be in the range [-n, n).
* Available since HAL version 1.2.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} and same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 128 and the zeroPoint must be 128.
*/
L2_NORMALIZATION = @1.1::OperationType:L2_NORMALIZATION,
/**
* 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[b, i, j, c] =
* sqrt(sum_{di, dj} pow(input[b, strides[1] * i + di, strides[2] * j + dj, c], 2) /
* sum(1))
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
* NCHW is supported since HAL version 1.2.
*
* 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.
* Since HAL version 1.2, zero batches is supported for this tensor.
* * 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.
* * 10: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* Since HAL version 1.2, zero batches is supported for this tensor.
* * 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.
* * 7: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
*/
L2_POOL_2D = @1.1::OperationType:L2_POOL_2D,
/**
* 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)
*
* For input tensor with rank less than 4, independently normalizes each
* 1-D slice along specified dimension.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
* Tensors with rank less than 4 are only supported since HAL version 1.2.
*
* 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: A scalar, specifying the bias, must not be zero.
* For input tensor of {@link OperandType::TENSOR_FLOAT16}, the bias
* value must be of {@link OperandType::FLOAT16}.
* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
* value must be of {@link OperandType::FLOAT32}.
* * 3: A scalar, specifying the scale factor, alpha.
* For input tensor of {@link OperandType::TENSOR_FLOAT16}, the
* alpha value must be of {@link OperandType::FLOAT16}.
* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
* alpha value must be of {@link OperandType::FLOAT32}.
* * 4: A scalar, specifying the exponent, beta.
* For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta
* value must be of {@link OperandType::FLOAT16}.
* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta
* value must be of {@link OperandType::FLOAT32}.
* * 5: An optional {@link OperandType::INT32} scalar, default to -1,
* specifying the dimension normalization would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
* the last axis). Must be in the range [-n, n).
* Available since HAL version 1.2.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
LOCAL_RESPONSE_NORMALIZATION = @1.1::OperationType:LOCAL_RESPONSE_NORMALIZATION,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
* Since HAL version 1.2, this tensor may be zero-sized.
*
* 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 = @1.1::OperationType:LOGISTIC,
/**
* Projects an input to a bit vector via locality senstive hashing.
*
* Supported input tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported input tensor rank: from 1
*
* Inputs:
* * 0: Hash functions. Dim.size == 2, DataType: Float.
* Tensor[0].Dim[0]: Number of hash functions.
* Tensor[0].Dim[1]: Number of projected output bits generated by each
* hash function.
* If the projection type is Sparse:
* Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32
*
* * 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(=3) (since HAL version 1.2).
* Computed bit vector is considered to be sparse.
* Each output element is an int32 made up of multiple bits
* computed from hash functions.
*
* NOTE: To avoid collisions across hash functions, an offset value
* of k * (1 << Tensor[0].Dim[1]) will be added to each signature,
* where k is the index of the hash function.
*
* Value LSHProjectionType_SPARSE_DEPRECATED(=1).
* Legacy behavior that does not include the offset value.
*
* 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.
* The offset value for sparse projections was added in HAL version 1.2.
*/
LSH_PROJECTION = @1.1::OperationType:LSH_PROJECTION,
/**
* 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.
*
* Since HAL version 1.2 LSTM supports layer normalization.
* In case layer normalization is used, the inputs to internal activation
* functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered
* following an approach from section 3.1 from
* https://arxiv.org/pdf/1607.06450.pdf
*
* The operation has the following independently optional inputs:
* * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights
* (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all
* have values or neither of them have values (i.e., all set to null). If
* they have values, the peephole optimization is used.
* * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights
* (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values,
* or none of them have values. 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}
* In case peephole optimization is used and CIFG is not used
* cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the
* cell-to-input weights must have no value.
* * 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.
* * (HAL version 1.2 or later) The four layer normalization weights either all have
* values or none of them have values. Additionally, if CIFG is used,
* input layer normalization weights tensor is omitted and the other layer
* normalization weights either all have values or none of them have
* values. Layer normalization is used when the values of all the layer
* normalization weights are present.
*
* 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"
*
* The layer normalization is based on:
* https://arxiv.org/pdf/1607.06450.pdf
* Jimmy Ba et al. "Layer Normalization"
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* All input and output tensors must be of the same type.
*
* Inputs:
* * 0: The input (\f$x_t\f$).
* A 2-D tensor 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 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 shape [num_units, input_size].
* * 3: The input-to-cell weights (\f$W_{xc}\f$).
* A 2-D tensor of shape [num_units, input_size].
* * 4: The input-to-output weights (\f$W_{xo}\f$).
* A 2-D tensor of shape [num_units, input_size].
* * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
* A 2-D tensor 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 shape [num_units, output_size].
* * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
* A 2-D tensor of shape [num_units, output_size].
* * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
* A 2-D tensor of shape [num_units, output_size].
* * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
* A 1-D tensor of shape [num_units].
* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
* A 1-D tensor of shape [num_units].
* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
* A 1-D tensor of shape [num_units].
* * 12:The input gate bias (\f$b_i\f$). Optional.
* A 1-D tensor of shape [num_units].
* * 13:The forget gate bias (\f$b_f\f$).
* A 1-D tensor of shape [num_units].
* * 14:The cell bias (\f$b_c\f$).
* A 1-D tensor of shape [num_units].
* * 15:The output gate bias (\f$b_o\f$).
* A 1-D tensor of shape [num_units].
* * 16:The projection weights (\f$W_{proj}\f$). Optional.
* A 2-D tensor of shape [output_size, num_units].
* * 17:The projection bias (\f$b_{proj}\f$). Optional.
* A 1-D tensor of shape [output_size].
* * 18:The output state (in) (\f$h_{t-1}\f$).
* A 2-D tensor of shape [batch_size, output_size].
* * 19:The cell state (in) (\f$C_{t-1}\f$).
* A 2-D tensor 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.
* Until HAL version 1.2 this scalar must be of type {@link
* OperandType::FLOAT32}. Since HAL version 1.2, if all the input
* tensors have type {@link OperandType::TENSOR_FLOAT32}, this
* scalar must be of the type {@link OperandType::FLOAT32},
* otherwise if all the input tensors have the type {@link
* OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link
* OperandType::FLOAT16}.
* * 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.
* Until HAL version 1.2 this scalar must be of type {@link
* OperandType::FLOAT32}. Since HAL version 1.2, if all the input
* tensors have type {@link OperandType::TENSOR_FLOAT32}, this
* scalar must be of the type {@link OperandType::FLOAT32},
* otherwise if all the input tensors have the type {@link
* OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link
* OperandType::FLOAT16}.
* Since HAL version 1.2 there are additional inputs to this op:
* * 23:The input layer normalization weights.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at input gate.
* * 24:The forget layer normalization weights.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at forget gate.
* * 25:The cell layer normalization weights.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at cell gate.
* * 26:The output layer normalization weights.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at output gate.
*
* Outputs:
* * 0: The scratch buffer.
* A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or
* [batch_size, num_units * 4] without CIFG.
* * 1: The output state (out) (\f$h_t\f$).
* A 2-D tensor of shape [batch_size, output_size].
* * 2: The cell state (out) (\f$C_t\f$).
* A 2-D tensor of shape [batch_size, num_units].
* * 3: The output (\f$o_t\f$).
* A 2-D tensor of shape [batch_size, output_size]. This is effectively
* the same as the current “output state (out)” value.
*/
LSTM = @1.1::OperationType:LSTM,
/**
* 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[b, i, j, channel] =
* max_{di, dj} (
* input[b, strides[1] * i + di, strides[2] * j + dj, channel]
* )
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
* NCHW is supported since HAL version 1.2.
*
* 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.
* Since HAL version 1.2, zero batches is supported for this tensor.
* * 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.
* * 10: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* Since HAL version 1.2, zero batches is supported for this tensor.
* * 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.
* * 7: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
MAX_POOL_2D = @1.1::OperationType:MAX_POOL_2D,
/**
* 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.
*
* Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
* dimension is only compatible with 0 or 1. The size of the output
* dimension is zero if either of corresponding input dimension is zero.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@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 = @1.1::OperationType:MUL,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
* Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
RELU = @1.1::OperationType:RELU,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
* Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: The output tensor of the same shape as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
RELU1 = @1.1::OperationType:RELU1,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
* Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
RELU6 = @1.1::OperationType:RELU6,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@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.
*
* If one component of shape is the special value -1, the size of that
* dimension is computed so that the total size remains constant. In
* particular, a shape of [-1] flattens into 1-D. At most one component
* of shape can be -1.
*
* Outputs:
* * 0: The output tensor, of shape specified by the input shape.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
RESHAPE = @1.1::OperationType:RESHAPE,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
* NCHW is supported since HAL version 1.2.
*
* Both resizing by shape and resizing by scale are supported.
*
* Inputs (resizing by shape):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* Since HAL version 1.2, zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the output
* width of the output tensor.
* * 2: An {@link OperandType::INT32} scalar, specifying the output
* height of the output tensor.
* * 3: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
*
* Inputs (resizing by scale, since HAL version 1.2):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input. Zero batches is supported for this tensor.
* * 1: A scalar, specifying width_scale, the scaling factor of the width
* dimension from the input tensor to the output tensor. The output
* width is calculated as new_width = floor(width * width_scale).
* The scalar must be of {@link OperandType::FLOAT16} if input0 is
* of {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} otherwise.
* * 2: A scalar, specifying height_scale, the scaling factor of the height
* dimension from the input tensor to the output tensor. The output
* height is calculated as new_height = floor(height * height_scale).
* The scalar must be of {@link OperandType::FLOAT16} if input0 is
* of {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} otherwise.
* * 3: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, new_height, new_width, depth].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
RESIZE_BILINEAR = @1.1::OperationType:RESIZE_BILINEAR,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* The input tensors must all be the same type.
*
* Inputs:
* * 0: input.
* A 2-D tensor 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 shape [num_units, input_size], where “num_units”
* corresponds to the number of units.
* * 2: recurrent_weights.
* A 2-D tensor of shape [num_units, num_units], with columns
* corresponding to the weights from each unit.
* * 3: bias.
* A 1-D tensor of shape [num_units].
* * 4: hidden state (in).
* A 2-D tensor 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 shape [batch_size, num_units].
*
* * 1: output.
* A 2-D tensor of shape [batch_size, num_units]. This is effectively
* the same as the current state value.
*/
RNN = @1.1::OperationType:RNN,
/**
* 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)}
*
* For input tensor with rank other than 2, the activation will be applied
* independently on each 1-D slice along specified dimension.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4.
* Tensors with rank other than 2 or 4 are only supported since HAL version 1.2.
*
* Inputs:
* * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
* Since HAL version 1.2, this tensor may be zero-sized.
* * 1: A scalar, specifying the positive scaling factor for the exponent,
* beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of
* {@link OperandType::FLOAT32}.
* If input0 is of {@link OperandType::TENSOR_FLOAT16}, then the
* scalar must be of {@link OperandType::FLOAT16}.
* * 2: An optional {@link OperandType::INT32} scalar, default to -1,
* specifying the dimension the activation would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
* the last axis). Must be in the range [-n, n).
* Available since HAL version 1.2.
*
* 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 = @1.1::OperationType:SOFTMAX,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
* NCHW is supported since HAL version 1.2.
*
* 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.
* * 2: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
*
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, height/block_size,
* width/block_size, depth_in*block_size*block_size].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
SPACE_TO_DEPTH = @1.1::OperationType:SPACE_TO_DEPTH,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* All input tensors must be the same type.
*
* Inputs:
* * 0: input.
* A 2-D tensor 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 shape [num_units, input_size], where “num_units”
* corresponds to the number of units.
* * 2: weights_time.
* A 2-D tensor 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 shape [num_units].
* * 4: state (in).
* A 2-D tensor 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 the same {@link OperandType} as the inputs, with shape
* [batch_size, (memory_size - 1) * num_units * rank].
* * 1: output.
* A 2-D tensor of the same {@link OperandType} as the inputs, with shape
* [batch_size, num_units].
*/
SVDF = @1.1::OperationType:SVDF,
/**
* 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_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported tensor rank: up to 4.
*
* Inputs:
* * 0: A tensor, specifying the input.
* Since HAL version 1.2, this tensor may be zero-sized.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 128 and the zeroPoint must be 128.
*/
TANH = @1.1::OperationType:TANH,
/**
* BatchToSpace for N-dimensional tensors.
*
* This operation reshapes the batch dimension (dimension 0) into M + 1
* dimensions of shape block_shape + [batch], interleaves these blocks back
* into the grid defined by the spatial dimensions [1, ..., M], to obtain a
* result with the same rank as the input.
*
* This is the reverse of SpaceToBatch.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
* NCHW is supported since HAL version 1.2.
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be reshaped
* * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
* sizes for each spatial dimension of the input tensor. All values
* must be >= 1.
* * 2: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since API level 29.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
BATCH_TO_SPACE_ND = @1.1::OperationType:BATCH_TO_SPACE_ND,
/**
* Element-wise division of two tensors.
*
* Takes two input tensors of identical {@link OperandType} and compatible
* dimensions. The output is the result of dividing the first input tensor
* by the second, 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}
*
* Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
* dimension is only compatible with 0 or 1. The size of the output
* dimension is zero if either of corresponding input dimension is zero.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor, specifying the first input.
* * 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: A tensor of the same {@link OperandType} as input0.
*/
DIV = @1.1::OperationType:DIV,
/**
* Computes the mean of elements across dimensions of a tensor.
*
* Reduces the input tensor along the given dimensions to reduce. Unless
* keep_dims is true, the rank of the tensor is reduced by 1 for each entry
* in axis. If keep_dims is true, the reduced dimensions are retained with
* length 1.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor, specifying the input.
* * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}. The dimensions
* to reduce. Must be in the range
* [-rank(input_tensor), rank(input_tensor)).
*
* NOTE: When the operation was introduced, the documentation
* incorrectly stated that if dimensions were empty, the operation
* would reduce across all dimensions. This behavior was never
* implemented.
*
* * 2: An {@link OperandType::INT32} scalar, keep_dims. If positive,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
* If all dimensions are reduced and keep_dims is false, the output
* shape is [1].
*/
MEAN = @1.1::OperationType:MEAN,
/**
* Pads a tensor.
*
* This operation pads a tensor according to the specified paddings.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
* (full support since HAL version 1.2, see the output section)
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be padded.
* * 1: A 2-D Tensor of {@link OperandType::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 elements to be padded in the
* front of dimension i.
* padding[i, 1] specifies the number of elements to be padded after the
* end of dimension i.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0. The
* output tensor has the same rank as input0, and each
* dimension of the output tensor has the same size as the
* corresponding dimension of the input tensor plus the size
* of the padding:
* output0.dimension[i] =
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*
* NOTE: Before HAL version 1.2, the pad value for
* {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined.
* Since HAL version 1.2, the pad value is always the logical zero.
*/
PAD = @1.1::OperationType:PAD,
/**
* SpaceToBatch for N-Dimensional tensors.
*
* This operation divides "spatial" dimensions [1, ..., M] of the input into
* a grid of blocks of shape block_shape, and interleaves these blocks with
* the "batch" dimension (0) such that in the output, the spatial dimensions
* [1, ..., M] correspond to the position within the grid, and the batch
* dimension combines both the position within a spatial block and the
* original batch position. Prior to division into blocks, the spatial
* dimensions of the input are optionally zero padded according to paddings.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
* (full support since HAL version 1.2, see the output section)
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
* NCHW is supported since HAL version 1.2.
*
* Inputs:
* * 0: An n-D tensor, specifying the input.
* * 1: A 1-D Tensor of {@link OperandType::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 OperandType::TENSOR_INT32}, the paddings
* for each spatial dimension of the input tensor. All values must be
* >= 0. The shape of the tensor must be {M, 2}, where M is the number
* of spatial dimensions.
* 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.
* * 3: An optional {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
* Available since HAL version 1.2.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*
* NOTE: Before HAL version 1.2, the pad value for
* {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined.
* Since HAL version 1.2, the pad value is always the logical zero.
*/
SPACE_TO_BATCH_ND = @1.1::OperationType:SPACE_TO_BATCH_ND,
/**
* Removes dimensions of size 1 from the shape of a tensor.
*
* Given a tensor input, this operation returns a tensor of the same
* {@link OperandType} 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 {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::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 {@link OperandType::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 {@link OperandType} as input0. Contains the
* same data as input, but has one or more dimensions of size 1
* removed.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
* If all input dimensions are equal to 1 and are to be squeezed, the
* output shape is [1].
*/
SQUEEZE = @1.1::OperationType:SQUEEZE,
/**
* Extracts a strided slice of a tensor.
*
* Roughly speaking, this op extracts a slice of size (end - begin) / stride
* from the given input tensor. Starting at the location specified by begin
* the slice continues by adding stride to the index until all dimensions
* are not less than end. Note that a stride can be negative, which causes a
* reverse slice.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be sliced.
* * 1: begin, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
* starts of the dimensions of the input tensor to be sliced. The
* length must be of rank(input0).
* * 2: end, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
* ends of the dimensions of the input tensor to be sliced. The length
* must be of rank(input0).
* * 3: strides, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
* strides of the dimensions of the input tensor to be sliced. The
* length must be of rank(input0). The entries must be non-zero.
* * 4: begin_mask, an {@link OperandType::INT32} scalar. 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: end_mask, an {@link OperandType::INT32} scalar. 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: shrink_axis_mask, an {@link OperandType::INT32} scalar. If the
* ith bit of shrink_axis_mask is set, the ith dimension specification
* shrinks the dimensionality by 1, taking on the value at index
* begin[i]. In this case, the ith specification must define a
* slice of size 1, e.g. begin[i] = x, end[i] = x + 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k),
* where k is the number of bits set in shrink_axis_mask.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
* If shrink_axis_mask is true for all input dimensions, the output
* shape is [1].
*/
STRIDED_SLICE = @1.1::OperationType:STRIDED_SLICE,
/**
* Element-wise subtraction of two tensors.
*
* Takes two input tensors of identical {@link OperandType} 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
* 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}
*
* Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
* dimension is only compatible with 0 or 1. The size of the output
* dimension is zero if either of corresponding input dimension is zero.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor, specifying the first input.
* * 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: A tensor of the same {@link OperandType} as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
SUB = @1.1::OperationType:SUB,
/**
* Transposes the input tensor, permuting the dimensions according to the
* perm tensor.
*
* The returned tensor's dimension i corresponds to the input dimension
* perm[i]. If perm is not given, it is set to (n-1...0), where n is the
* rank of the input tensor. Hence by default, this operation performs a
* regular matrix transpose on 2-D input Tensors.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be transposed.
* Since HAL version 1.2, this tensor may be zero-sized.
* * 1: An optional 1-D Tensor of {@link OperandType::TENSOR_INT32},
* the permutation of the dimensions of the input tensor.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
TRANSPOSE = @1.1::OperationType:TRANSPOSE,
/**
* Computes the absolute value of a tensor, element-wise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
ABS = 38,
/**
* Returns the index of the largest element along an axis.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: An n-D tensor specifying the input. Must be non-empty.
* * 1: An {@link OperandType::INT32} scalar specifying the axis to
* reduce across. Negative index is used to specify axis from the
* end (e.g. -1 for the last axis). Must be in the range [-n, n).
*
* Outputs:
* * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
* If input is 1-dimensional, the output shape is [1].
*/
// There is no underscore in ARG_MAX to avoid name conflict with
// the macro defined in libc/kernel/uapi/linux/limits.h.
ARGMAX = 39,
/**
* Returns the index of the smallest element along an axis.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: An n-D tensor specifying the input. Must be non-empty.
* * 1: An {@link OperandType::INT32} scalar specifying the axis to
* reduce across. Negative index is used to specify axis from the
* end (e.g. -1 for the last axis). Must be in the range [-n, n).
*
* Outputs:
* * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
* If input is 1-dimensional, the output shape is [1].
*/
ARGMIN = 40, // See ARGMAX for naming discussion.
/**
* Transform axis-aligned bounding box proposals using bounding box deltas.
*
* Given the positions of bounding box proposals and the corresponding
* bounding box deltas for each class, return the refined bounding box
* regions. The resulting bounding boxes are cliped against the edges of
* the image.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT16_ASYMM}
*
* Inputs:
* * 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the
* bounding box proposals, each line with format [x1, y1, x2, y2].
* For tensor of type {@link OperandType::TENSOR_QUANT16_ASYMM},
* the zeroPoint must be 0 and the scale must be 0.125. Zero num_rois
* is supported for this tensor.
* * 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the
* bounding box delta for each region of interest and each class. The
* bounding box deltas are organized in the following order
* [dx, dy, dw, dh], where dx and dy is the relative correction factor
* for the center position of the bounding box with respect to the width
* and height, dw and dh is the log-scale relative correction factor
* for the width and height. For input0 of type
* {@link OperandType::TENSOR_QUANT16_ASYMM}, this tensor should be
* of {@link OperandType::TENSOR_QUANT8_ASYMM}. Zero num_rois is
* supported for this tensor.
* * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
* [num_rois], specifying the batch index of each box. Boxes with
* the same batch index are grouped together. Zero num_rois is
* supported for this tensor.
* * 3: A 2-D Tensor of shape [batches, 2], specifying the information of
* each image in the batch, each line with format
* [image_height, image_width].
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0, with shape
* [num_rois, num_classes * 4], specifying the coordinates of each
* output bounding box for each class, with format [x1, y1, x2, y2].
* For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
* scale must be 0.125 and the zero point must be 0.
*/
AXIS_ALIGNED_BBOX_TRANSFORM = 41,
/**
* A recurrent neural network layer that applies an LSTM cell to a
* sequence of inputs in forward and backward directions.
*
* The op supports cross-linking via an auxiliary input. Regular cell feeds
* one input into the two RNN cells in the following way:
*
* INPUT (INPUT_REVERSED)
* | |
* ---------------------
* | FW_LSTM BW_LSTM |
* ---------------------
* | |
* FW_OUT BW_OUT
*
* An op with cross-linking takes two inputs and feeds them into the RNN
* cells in the following way:
*
* AUX_INPUT (AUX_INPUT_REVERSED)
* | |
* INPUT | (INPUT_R'D.)|
* | | | |
* -----------------------
* | \ / \ / |
* | FW_LSTM BW_LSTM |
* -----------------------
* | |
* FW_OUT BW_OUT
*
* The cross-linking mode is enabled iff auxiliary input and auxiliary
* weights are present. While stacking this op on top of itself, this
* allows to connect both forward and backward outputs from previous cell
* to the next cell's input.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: 3, either time-major or batch-major.
*
* All input and output tensors must be of the same type.
*
* Inputs:
* * 0: The input.
* A 3-D tensor of shape:
* If time-major: [max_time, batch_size, input_size]
* If batch-major: [batch_size, max_time, input_size]
* where "max_time" is the number of timesteps (sequence length),
* "batch_size" corresponds to the batching dimension, and
* "input_size" is the size of the input.
* * 1: The forward input-to-input weights. Optional.
* A 2-D tensor of shape [fw_num_units, input_size], where “fw_num_units”
* corresponds to the number of forward cell units.
* * 2: The forward input-to-forget weights.
* A 2-D tensor of shape [fw_num_units, input_size].
* * 3: The forward input-to-cell weights.
* A 2-D tensor of shape [fw_num_units, input_size].
* * 4: The forward input-to-output weights.
* A 2-D tensor of shape [fw_num_units, input_size].
* * 5: The forward recurrent-to-input weights. Optional.
* A 2-D tensor of shape [fw_num_units, fw_output_size], where “fw_output_size”
* corresponds to either the number of cell units (i.e., fw_num_units),
* or the second dimension of the “fw_projection_weights”, if defined.
* * 6: The forward recurrent-to-forget weights.
* A 2-D tensor of shape [fw_num_units, fw_output_size].
* * 7: The forward recurrent-to-cell weights.
* A 2-D tensor of shape [fw_num_units, fw_output_size].
* * 8: The forward recurrent-to-output weights.
* A 2-D tensor of shape [fw_num_units, fw_output_size].
* * 9: The forward cell-to-input weights. Optional.
* A 1-D tensor of shape [fw_num_units].
* * 10: The forward cell-to-forget weights. Optional.
* A 1-D tensor of shape [fw_num_units].
* * 11: The forward cell-to-output weights. Optional.
* A 1-D tensor of shape [fw_num_units].
* * 12: The forward input gate bias. Optional.
* A 1-D tensor of shape [fw_num_units].
* * 13: The forward forget gate bias.
* A 1-D tensor of shape [fw_num_units].
* * 14: The forward cell gate bias.
* A 1-D tensor of shape [fw_num_units].
* * 15: The forward output gate bias.
* A 1-D tensor of shape [fw_num_units].
* * 16: The forward projection weights. Optional.
* A 2-D tensor of shape [fw_output_size, fw_num_units].
* * 17: The forward projection bias. Optional.
* A 1-D tensor of shape [fw_output_size].
* * 18: The backward input-to-input weights. Optional.
* A 2-D tensor of shape [bw_num_units, input_size], where “bw_num_units”
* corresponds to the number of backward cell units.
* * 19: The backward input-to-forget weights.
* A 2-D tensor of shape [bw_num_units, input_size].
* * 20: The backward input-to-cell weights.
* A 2-D tensor of shape [bw_num_units, input_size].
* * 21: The backward input-to-output weights.
* A 2-D tensor of shape [bw_num_units, input_size].
* * 22: The backward recurrent-to-input weights. Optional.
* A 2-D tensor of shape [bw_num_units, bw_output_size], where “bw_output_size”
* corresponds to either the number of cell units (i.e., “bw_num_units”),
* or the second dimension of the “bw_projection_weights”, if defined.
* * 23: The backward recurrent-to-forget weights.
* A 2-D tensor of shape [bw_num_units, bw_output_size].
* * 24: The backward recurrent-to-cell weights.
* A 2-D tensor of shape [bw_num_units, bw_output_size].
* * 25: The backward recurrent-to-output weights.
* A 2-D tensor of shape [bw_num_units, bw_output_size].
* * 26: The backward cell-to-input weights. Optional.
* A 1-D tensor of shape [bw_num_units].
* * 27: The backward cell-to-forget weights. Optional.
* A 1-D tensor of shape [bw_num_units].
* * 28: The backward cell-to-output weights. Optional.
* A 1-D tensor of shape [bw_num_units].
* * 29: The backward input gate bias. Optional.
* A 1-D tensor of shape [bw_num_units].
* * 30: The backward forget gate bias.
* A 1-D tensor of shape [bw_num_units].
* * 31: The backward cell gate bias.
* A 1-D tensor of shape [bw_num_units].
* * 32: The backward output gate bias.
* A 1-D tensor of shape [bw_num_units].
* * 33: The backward projection weights. Optional.
* A 2-D tensor of shape [bw_output_size, bw_num_units].
* * 34: The backward projection bias. Optional.
* A 1-D tensor of shape [bw_output_size].
* * 35: The forward input activation state.
* A 2-D tensor of shape [batch_size, bw_output_size].
* * 36: The forward input cell state.
* A 2-D tensor of shape [batch_size, bw_num_units].
* * 37: The backward input activation state.
* A 2-D tensor of shape [batch_size, bw_output_size].
* * 38: The backward input cell state.
* A 2-D tensor of shape [batch_size, bw_num_units].
* * 39: The auxiliary input. Optional.
* A 3-D tensor of shape [max_time, batch_size, input_size], where “batch_size”
* corresponds to the batching dimension, and “input_size” is the size
* of the input.
* * 40: The forward auxiliary input-to-input weights. Optional.
* A 2-D tensor of shape [fw_num_units, input_size].
* * 41: The forward auxiliary input-to-forget weights. Optional.
* A 2-D tensor of shape [fw_num_units, input_size].
* * 42: The forward auxiliary input-to-cell weights. Optional.
* A 2-D tensor of shape [fw_num_units, input_size].
* * 43: The forward auxiliary input-to-output weights. Optional.
* A 2-D tensor of shape [fw_num_units, input_size].
* * 44: The backward auxiliary input-to-input weights. Optional.
* A 2-D tensor of shape [bw_num_units, input_size].
* * 45: The backward auxiliary input-to-forget weights. Optional.
* A 2-D tensor of shape [bw_num_units, input_size].
* * 46: The backward auxiliary input-to-cell weights. Optional.
* A 2-D tensor of shape [bw_num_units, input_size].
* * 47: The backward auxiliary input-to-output weights. Optional.
* A 2-D tensor of shape [bw_num_units, input_size].
* * 48: The activation function.
* A value indicating the activation function:
* <ul>
* <li>0: None;
* <li>1: Relu;
* <li>3: Relu6;
* <li>4: Tanh;
* <li>6: Sigmoid.
* </ul>
* * 49: The clipping threshold for the cell state, such
* that values are bound within [-cell_clip, cell_clip]. If set to 0.0
* then clipping is disabled.
* If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
* this scalar must be of the type {@link OperandType::FLOAT32},
* otherwise if all the input tensors have the type
* {@link OperandType::TENSOR_FLOAT16}, this scalar must be
* of type {@link OperandType::FLOAT16}.
* * 50: The clipping threshold 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.
* If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
* this scalar must be of the type {@link OperandType::FLOAT32},
* otherwise if all the input tensors have the type
* {@link OperandType::TENSOR_FLOAT16}, this scalar must be
* of type {@link OperandType::FLOAT16}.
* * 51: merge_outputs
* An {@link OperandType::BOOL} scalar specifying if the outputs
* from forward and backward cells should be merged.
* * 52: time_major
* An {@link OperandType::BOOL} scalar specifying the shape format
* of input and output tensors.
* * 53: The forward input layer normalization weights. Optional.
* A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
* to activation at input gate.
* * 54: The forward forget layer normalization weights. Optional.
* A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
* to activation at forget gate.
* * 55: The forward cell layer normalization weights. Optional.
* A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
* to activation at cell gate.
* * 56: The forward output layer normalization weights. Optional.
* A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
* to activation at output gate.
* * 57: The backward input layer normalization weights. Optional.
* A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
* to activation at input gate.
* * 58: The backward forget layer normalization weights. Optional.
* A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
* to activation at forget gate.
* * 59: The backward cell layer normalization weights. Optional.
* A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
* to activation at cell gate.
* * 60: The backward output layer normalization weights. Optional.
* A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
* to activation at output gate.
*
* Outputs:
* * 0: The forward output.
* A 3-D tensor of shape:
* If time-major and not merge_outputs:
* [max_time, batch_size, fw_output_size]
* If time-major and merge_outputs:
* [max_time, batch_size, fw_output_size + bw_output_size]
* If batch-major and not merge_outputs:
* [batch_size, max_time, fw_output_size]
* If batch-major and merge_outputs:
* [batch_size, max_time, fw_output_size + bw_output_size]
* * 1: The backward output. Unused if merge_outputs is true.
* A 3-D tensor of shape:
* If time-major: [max_time, batch_size, bw_output_size]
* If batch-major: [batch_size, max_time, bw_output_size]
*/
BIDIRECTIONAL_SEQUENCE_LSTM = 42,
/**
* A recurrent neural network layer that applies a basic RNN cell to a
* sequence of inputs in forward and backward directions.
*
* This Op unrolls the input along the sequence dimension, and implements
* the following operation for each element in the sequence s =
* 1...sequence_length:
* fw_outputs[s] = fw_state = activation(inputs[s] * fw_input_weights’ +
* fw_state * fw_recurrent_weights’ + fw_bias)
*
* And for each element in sequence t = sequence_length : 1
* bw_outputs[t] = bw_state = activation(inputs[t] * bw_input_weights’ +
* bw_state * bw_recurrent_weights’ + bw_bias)
*
* Where:
* * “{fw,bw}_input_weights” is a weight matrix that multiplies the inputs;
* * “{fw,bw}_recurrent_weights” is a weight matrix that multiplies the
* current “state” which itself is the output from the previous time step
* computation;
* * “{fw,bw}_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”).
*
* The op supports cross-linking via an auxiliary input. Regular cell feeds
* one input into the two RNN cells in the following way:
*
* INPUT (INPUT_REVERSED)
* | |
* ---------------------
* | FW_RNN BW_RNN |
* ---------------------
* | |
* FW_OUT BW_OUT
*
* An op with cross-linking takes two inputs and feeds them into the RNN
* cells in the following way:
*
* AUX_INPUT (AUX_INPUT_REVERSED)
* | |
* INPUT | (INPUT_R'D.)|
* | | | |
* -----------------------
* | \ / \ / |
* | FW_RNN BW_RNN |
* -----------------------
* | |
* FW_OUT BW_OUT
*
* The cross-linking mode is enabled iff auxiliary input and auxiliary
* weights are present. While stacking this op on top of itself, this
* allows to connect both forward and backward outputs from previous cell
* to the next cell's input.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* The input tensors must all be the same type.
*
* Inputs:
* * 0: input.
* A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
* it is set to true, then the input has a shape [maxTime, batchSize,
* inputSize], otherwise the input has a shape [batchSize, maxTime,
* inputSize].
* * 1: fwWeights.
* A 2-D tensor of shape [fwNumUnits, inputSize].
* * 2: fwRecurrentWeights.
* A 2-D tensor of shape [fwNumUnits, fwNumUnits].
* * 3: fwBias.
* A 1-D tensor of shape [fwNumUnits].
* * 4: fwHiddenState.
* A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden
* state input for the first time step of the computation.
* * 5: bwWeights.
* A 2-D tensor of shape [bwNumUnits, inputSize].
* * 6: bwRecurrentWeights.
* A 2-D tensor of shape [bwNumUnits, bwNumUnits].
* * 7: bwBias.
* A 1-D tensor of shape [bwNumUnits].
* * 8: bwHiddenState
* A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden
* state input for the first time step of the computation.
* * 9: auxInput.
* A 3-D tensor. The shape is the same as of the input 0.
* * 10:fwAuxWeights.
* A 2-D tensor of shape [fwNumUnits, inputSize].
* * 11:bwAuxWeights.
* A 2-D tensor of shape [bwNumUnits, inputSize].
* * 12:fusedActivationFunction.
* A {@link FusedActivationFunc} value indicating the activation function. If
* “NONE” is specified then it results in a linear activation.
* * 13:timeMajor
* An {@link OperandType::BOOL} scalar specifying the shape format
* of input and output tensors.
* * 14:mergeOutputs
* An {@link OperandType::BOOL} scalar specifying if the outputs
* from forward and backward cells are separate (if set to false) or
* concatenated (if set to true).
* Outputs:
* * 0: fwOutput.
* A 3-D tensor. The first two dimensions of the shape are defined by
* the input 6 (timeMajor) and the third dimension is defined by the
* input 14 (mergeOutputs). If timeMajor is set to true, then the first
* two dimensions are [maxTime, batchSize], otherwise they are set to
* [batchSize, maxTime]. If mergeOutputs is set to true, then the third
* dimension is equal to (fwNumUnits + bwNumUnits), otherwise it is set
* to fwNumUnits.
* * 1: bwOutput.
* A 3-D tensor. If the input 14 (mergeOutputs) is set to true, then
* this tensor is not produced. The shape is defined by the input 6
* (timeMajor). If it is set to true, then the shape is set to
* [maxTime, batchSize, bwNumUnits], otherwise the shape is set to
* [batchSize, maxTime, bwNumUnits].
*/
BIDIRECTIONAL_SEQUENCE_RNN = 43,
/**
* Greedily selects a subset of bounding boxes in descending order of score.
*
* This op applies NMS algorithm to each class. In each loop of execution,
* the box with maximum score gets selected and removed from the pending set.
* The scores of the rest of boxes are lowered according to the
* intersection-over-union (IOU) overlapping with the previously selected
* boxes and a specified NMS kernel method. Any boxes with score less
* than a threshold are removed from the pending set.
*
* Three NMS kernels are supported:
* * Hard: score_new = score_old * (1 if IoU < threshold else 0)
* * Linear: score_new = score_old * (1 if IoU < threshold else 1 - IoU)
* * Gaussian: score_new = score_old * exp(- IoU^2 / sigma)
*
* Axis-aligned bounding boxes are represented by its upper-left corner
* coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
* bounding box should satisfy x1 <= x2 and y1 <= y2.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Inputs:
* * 0: A 2-D Tensor of shape [num_rois, num_classes], specifying the score
* of each bounding box proposal. The boxes are grouped by batches in the
* first dimension. Zero num_rois is supported for this tensor.
* * 1: A 2-D Tensor specifying the bounding boxes of shape
* [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2].
* The boxes are grouped by batches in the first dimension. The sequential
* order of the boxes corresponds with input0. For input0 of type
* {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of
* {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint of 0 and
* scale of 0.125.
* Zero num_rois is supported for this tensor.
* * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
* [num_rois], specifying the batch index of each box. Boxes with
* the same batch index are grouped together.
* * 3: An {@link OperandType::FLOAT32} scalar, score_threshold. Boxes
* with scores lower than the threshold are filtered before sending
* to the NMS algorithm.
* * 4: An {@link OperandType::INT32} scalar, specifying the maximum
* number of selected bounding boxes for each image. Set to a negative
* value for unlimited number of output bounding boxes.
* * 5: An {@link OperandType::INT32} scalar, specifying the NMS
* kernel method, options are 0:hard, 1:linear, 2:gaussian.
* * 6: An {@link OperandType::FLOAT32} scalar, specifying the IoU
* threshold in hard and linear NMS kernel. This field is ignored if
* gaussian kernel is selected.
* * 7: An {@link OperandType::FLOAT32} scalar, specifying the sigma in
* gaussian NMS kernel. This field is ignored if gaussian kernel is
* not selected.
* * 8: An {@link OperandType::FLOAT32} scalar, nms_score_threshold.
* Boxes with scores lower than the threshold are dropped during the
* score updating phase in soft NMS.
*
* Outputs:
* * 0: A 1-D Tensor of the same {@link OperandType} as input0, with shape
* [num_output_rois], specifying the score of each output box. The boxes
* are grouped by batches, but the sequential order in each batch is not
* guaranteed. For type of {@link OperandType::TENSOR_QUANT8_ASYMM},
* guaranteed. For type of {@link OperandType::TENSOR_QUANT8_ASYMM}
* the scale and zero point must be the same as input0.
* * 1: A 2-D Tensor of the same {@link OperandType} as input1, with shape
* [num_output_rois, 4], specifying the coordinates of each
* output bounding box with the same format as input1. The sequential
* order of the boxes corresponds with output0. For type of
* {@link OperandType::TENSOR_QUANT16_ASYMM}, the scale must be
* 0.125 and the zero point must be 0.
* * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
* [num_output_rois], specifying the class of each output box. The
* sequential order of the boxes corresponds with output0.
* * 3: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
* [num_output_rois], specifying the batch index of each box. Boxes
* with the same batch index are grouped together.
*/
BOX_WITH_NMS_LIMIT = 44,
/**
* Casts a tensor to a type.
*
* This operation ignores the scale and zeroPoint of quanized tensors,
* e.g. it treats a {@link OperandType::TENSOR_QUANT8_ASYMM} input
* as a tensor of uint8 values.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: A tensor with the same shape as input0.
*/
CAST = 45,
/**
* Shuffle the channels of the input tensor.
*
* Given an input tensor and a integer value of num_groups, CHANNEL_SHUFFLE
* divide the channel dimension into num_groups groups, and reorganize the
* channels by grouping channels with the same index in each group.
*
* Along the channel dimension, the output is calculated using this formula:
*
* output_channel[k * num_groups + g] = input_channel[g * group_size + k]
*
* where group_size = num_channels / num_groups
*
* The number of channels must be divisible by num_groups.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be shuffled.
* * 1: An {@link OperandType::INT32} scalar, specifying the number of
* groups.
* * 2: An {@link OperandType::INT32} scalar, specifying the dimension
* channel shuffle would be performed on. Negative index is used to
* specify axis from the end (e.g. -1 for the last axis). Must be in
* the range [-n, n).
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} and same shape as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
CHANNEL_SHUFFLE = 46,
/**
* Apply postprocessing steps to bounding box detections.
*
* Bounding box detections are generated by applying transformation on a set
* of predefined anchors with the bounding box deltas from bounding box
* regression. A final step of hard NMS is applied to limit the number of
* returned boxes.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Inputs:
* * 0: A 3-D Tensor of shape [batches, num_anchors, num_classes], specifying
* the score of each anchor with each class. Class 0 for each
* [batches, num_anchors, 0] is background and will be ignored.
* * 1: A 3-D Tensor of shape [batches, num_anchors, length_box_encoding], with
* the first four values in length_box_encoding specifying the bounding
* box deltas. The box deltas are encoded in the order of [dy, dx, dh, dw],
* where dy and dx is the linear-scale relative correction factor for the
* center position of the bounding box with respect to the width and height,
* dh and dw is the log-scale relative correction factor for the width and
* height. All the entries in length_box_encoding beyond the first four
* values are ignored in this operation.
* * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
* predefined anchor, with format [ctr_y, ctr_x, h, w], where ctr_y and
* ctr_x are the center position of the box, and h and w are the height
* and the width.
* * 3: An {@link OperandType::FLOAT32} scalar, specifying the scaling
* factor for dy in bounding box deltas.
* * 4: An {@link OperandType::FLOAT32} scalar, specifying the scaling
* factor for dx in bounding box deltas.
* * 5: An {@link OperandType::FLOAT32} scalar, specifying the scaling
* factor for dh in bounding box deltas.
* * 6: An {@link OperandType::FLOAT32} scalar, specifying the scaling
* factor for dw in bounding box deltas.
* * 7: An {@link OperandType::BOOL} scalar, set to true to use regular
* multi-class NMS algorithm that do NMS separately for each class,
* set to false for a faster algorithm that only do one single NMS
* using the highest class score..
* * 8: An {@link OperandType::INT32} scalar, max_num_detections, specifying
* the maximum number of boxes for the output. Boxes with the lowest
* scores are discarded to meet the limit.
* * 9: An {@link OperandType::INT32} scalar, only used when input7 is
* set to false, specifying the maximum number of classes per detection.
* * 10: An {@link OperandType::INT32} scalar, only used when input7 is
* set to true, specifying the maximum number of detections when
* applying NMS algorithm for each single class.
* * 11: A scalar, score_threshold. Boxes with scores lower than the
* threshold are filtered before sending to the NMS algorithm. The
* scalar must be of {@link OperandType::FLOAT16} if input0 is of
* {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} if input0 is of
* {@link OperandType::TENSOR_FLOAT32}.
* * 12: A scalar, specifying the IoU threshold for hard NMS. The scalar
* must be of {@link OperandType::FLOAT16} if input0 is of
* {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} if input0 is of
* {@link OperandType::TENSOR_FLOAT32}.
* * 13: An {@link OperandType::BOOL} scalar, set to true to include
* background class in the list of label map for the output, set
* to false to not include the background. When the background
* class is included, it has label 0 and the output classes start
* at 1 in the label map, otherwise, the output classes start at 0.
*
* Outputs:
* * 0: A 2-D tensor of the same {@link OperandType} as input0, with shape
* [batches, max_num_detections], specifying the score of each output
* detections.
* * 1: A 3-D tensor of shape [batches, max_num_detections, 4], specifying the
* coordinates of each output bounding box, with format
* [y1, x1, y2, x2].
* * 2: A 2-D {@link OperandType::TENSOR_INT32} tensor, of shape
* [batches, max_num_detections], specifying the class label for each
* output detection.
* * 3: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape [batches],
* specifying the number of valid output detections for each batch.
*/
DETECTION_POSTPROCESSING = 47,
/**
* For input tensors x and y, computes x == y elementwise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_BOOL8}
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType} and dimensions compatible
* with input0.
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
EQUAL = 48,
/**
* Computes exponential of x element-wise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
EXP = 49,
/**
* Inserts a dimension of 1 into a tensor's shape.
*
* Given a tensor input, this operation inserts a dimension of 1 at the
* given dimension index of input's shape. The dimension index starts at
* zero; if you specify a negative dimension index, it is counted backward
* from the end.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: An n-D tensor.
* * 1: An {@link OperandType::INT32} scalar specifying the dimension
* index to expand. Must be in the range [-(n + 1), (n + 1)).
*
* Outputs:
* * 0: An (n + 1)-D tensor with the same {@link OperandType} and data as
* input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
EXPAND_DIMS = 50,
/**
* Gathers values along an axis.
*
* Produces an output tensor with shape
* input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:]
* where:
* # Vector indices (output is rank(input0)).
* output[a_0, ..., a_n, i, b_0, ..., b_n] =
* input0[a_0, ..., a_n, indices[i], b_0, ..., b_n]
*
* # Higher rank indices (output is rank(input0) + rank(indices) - 1).
* output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
* input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: An n-D tensor from which to gather values.
* * 1: An {@link OperandType::INT32} scalar specifying the axis.
* Negative index is used to specify axis from the end
* (e.g. -1 for the last axis). Must be in the range [-n, n).
* * 2: A k-D tensor {@link OperandType::TENSOR_INT32} of indices.
* The values must be in the bounds of the corresponding dimensions
* of input0.
*
* Outputs:
* * 0: An (n + k - 1)-D tensor with the same {@link OperandType} as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
GATHER = 51,
/**
* Generate aixs-aligned bounding box proposals.
*
* Bounding box proposals are generated by applying transformation on a set
* of predefined anchors with the bounding box deltas from bounding box
* regression. A final step of hard NMS is applied to limit the number of
* returned boxes.
*
* Axis-aligned bounding boxes are represented by its upper-left corner
* coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
* bounding box should satisfy x1 <= x2 and y1 <= y2.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Inputs:
* * 0: A 4-D Tensor specifying the score of each anchor at each
* location. With "NHWC" data layout, the tensor shape is
* [batches, height, width, num_anchors]. With "NCHW" data layout,
* the tensor shape is [batches, num_anchors, height, width].
* * 1: A 4-D Tensor specifying the bounding box deltas. With "NHWC" data
* layout, the tensor shape is [batches, height, width, num_anchors * 4].
* With "NCHW" data layout, the tensor shape is
* [batches, num_anchors * 4, height, width]. The box deltas are encoded
* in the order of [dx, dy, dw, dh], where dx and dy is the linear-scale
* relative correction factor for the center position of the bounding box
* with respect to the width and height, dw and dh is the log-scale
* relative correction factor for the width and height. The last
* dimensions is the channel dimension.
* * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
* predefined anchor, with format [x1, y1, x2, y2]. For input0 of type
* {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of
* {@link OperandType::TENSOR_QUANT16_SYMM}, with scale of 0.125.
* * 3: A 2-D Tensor of shape [batches, 2], specifying the size of
* each image in the batch, with format [image_height, image_width].
* For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM}, this
* tensor should be of {@link OperandType::TENSOR_QUANT16_SYMM}, with
* scale of 0.125.
* * 4: An {@link OperandType::FLOAT32} scalar, specifying the ratio
* from the height of original image to the height of feature map.
* * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
* from the width of original image to the width of feature map.
* * 6: An {@link OperandType::INT32} scalar, specifying the maximum
* number of boxes before going into the hard NMS algorithm. Boxes
* with the lowest scores are discarded to meet the limit. Set to
* a non-positive value for unlimited number.
* * 7: An {@link OperandType::INT32} scalar, specifying the maximum
* number of boxes returning from the hard NMS algorithm. Boxes
* with the lowest scores are discarded to meet the limit. Set to
* a non-positive value for unlimited number.
* * 8: An {@link OperandType::FLOAT32} scalar, specifying the IoU
* threshold for hard NMS.
* * 9: An {@link OperandType::FLOAT32} scalar, min_size. Boxes with
* height or width lower than the absolute threshold are filtered out.
* * 10: An {@link OperandType::BOOL} scalar, set to true to specify
* NCHW data layout for input0 and input1. Set to false for NHWC.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0, of shape
* [num_output_rois], specifying the score of each output box.
* The boxes are grouped by batches, but the sequential order in
* each batch is not guaranteed. For type of
* {@link OperandType::TENSOR_QUANT8_ASYMM}, the scale and zero
* point must be the same as input0.
* * 1: A tensor of the same {@link OperandType} as input3, of shape
* [num_output_rois, 4], specifying the coordinates of each output
* bounding box for each class, with format [x1, y1, x2, y2].
* The sequential order of the boxes corresponds with output0.
* For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
* scale must be 0.125 and the zero point must be 0.
* * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
* [num_output_rois], specifying the batch index of each box. Boxes
* with the same batch index are grouped together.
*/
GENERATE_PROPOSALS = 52,
/**
* For input tensors x and y, computes x > y elementwise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_BOOL8}
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType} and dimensions compatible
* with input0.
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
GREATER = 53,
/**
* For input tensors x and y, computes x >= y elementwise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_BOOL8}
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType} and dimensions compatible
* with input0.
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
GREATER_EQUAL = 54,
/**
* Performs a grouped 2-D convolution operation.
*
* Given an input tensor of shape [batches, height, width, depth_in] and a
* filter tensor of shape [depth_out, filter_height, filter_width, depth_group]
* containing depth_out convolutional filters of depth depth_group, GROUPED_CONV
* applies a group of different filters to each input channel group, then
* concatenates the results together.
*
* Specifically, the input channels are divided into num_groups groups, each with
* depth depth_group, i.e. depth_in = num_groups * depth_group. The convolutional
* filters are also divided into num_groups groups, i.e. depth_out is divisible
* by num_groups. GROUPED_CONV applies each group of filters to the corresponding
* input channel group, and the result are concatenated together.
*
* 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, g * channel_multiplier + q] =
* sum_{di, dj, dk} (
* input[b, strides[1] * i + di, strides[2] * j + dj,
* g * depth_group + dk] *
* filter[g * channel_multiplier + q, di, dj, dk]
* ) + bias[channel]
*
* where channel_multiplier = depth_out / num_groups
*
* Supported tensor {@link OperandType} configurations:
* * 16 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
*
* * 32 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
* * Quantized:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
* * Quantized with symmetric per channel quantization for the filter:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
* * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
* * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* 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, where depth_in = num_groups * depth_group.
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_group], specifying
* the filter, where depth_out must be divisible by num_groups. For
* tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
* the channel dimension (channelDim at
* {@link SymmPerChannelQuantParams}) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same type.
* For filter 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. For filter tensor
* of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
* should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
* 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 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 number of
* groups.
* * 10: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
* * 11: An {@link OperandType::BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Inputs (implicit padding):
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
* specifying the input, where depth_in = num_groups * depth_group.
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_group], specifying
* the filter, where depth_out must be divisible by num_groups. For
* tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
* the channel dimension (SymmPerChannelQuantParams::channelDim)
* must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
* {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same type.
* For filter 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. For filter tensor
* of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
* should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
* 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 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 number of
* groups.
* * 7: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
* * 8: An {@link OperandType::BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
GROUPED_CONV_2D = 55,
/**
* Localize the maximum keypoints from heatmaps.
*
* This operation approximates the accurate maximum keypoint scores and
* indices after bicubic upscaling by using Taylor expansion up to the
* quadratic term.
*
* The bounding box is represented by its upper-left corner coordinate
* (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
* A valid bounding box should satisfy x1 <= x2 and y1 <= y2.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: A 4-D Tensor of shape
* [num_boxes, heatmap_size, heatmap_size, num_keypoints],
* specifying the heatmaps, the height and width of heatmaps should
* be the same, and must be greater than or equal to 2.
* * 1: A 2-D Tensor of shape [num_boxes, 4], specifying the bounding boxes,
* each with format [x1, y1, x2, y2]. For input0 of type
* {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should
* be of {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint
* of 0 and scale of 0.125.
* * 2: An {@link OperandType::BOOL} scalar, set to true to specify
* NCHW data layout for input0. Set to false for NHWC.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0, with shape
* [num_boxes, num_keypoints], specifying score of the keypoints.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint can be different from input0 scale and zeroPoint.
* * 1: A tensor of the same {@link OperandType} as input1, with shape
* [num_boxes, num_keypoints, 2], specifying the location of
* the keypoints, the second dimension is organized as
* [keypoint_x, keypoint_y].
* For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
* scale must be 0.125 and the zero point must be 0.
*/
HEATMAP_MAX_KEYPOINT = 56,
/**
* Applies instance normalization to the input tensor.
*
* The values in the output tensor are computed as:
*
* output[b, h, w, c] =
* (input[b, h, w, c] - mean[b, c]) * gamma /
* sqrt(var[b, c] + epsilon) + beta
*
* Where the mean and variance are computed across the spatial dimensions:
*
* mean[b, c] =
* sum_{h, w}(input[b, h, w, c]) / sum(1)
*
* var[b, c] =
* sum_{h, w}(pow(input[b, h, w, c] - mean[b, c], 2)) / sum(1)
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be normalized.
* * 1: A scalar, specifying gamma, the scale applied to the normalized
* tensor. The scalar must be of {@link OperandType::FLOAT16} if
* input0 is of {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} if input0 is of
* {@link OperandType::TENSOR_FLOAT32}.
* * 2: A scalar, specifying beta, the offset applied to the normalized
* tensor. The scalar must be of {@link OperandType::FLOAT16} if
* input0 is of {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} if input0 is of
* {@link OperandType::TENSOR_FLOAT32}.
* * 3: A scalar, specifying epsilon, the small value added to variance to
* avoid dividing by zero. The scalar must be of {@link OperandType::FLOAT16} if
* input0 is of {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} if input0 is of
* {@link OperandType::TENSOR_FLOAT32}.
* * 4: An {@link OperandType::BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} and same shape as input0.
*/
INSTANCE_NORMALIZATION = 57,
/**
* For input tensors x and y, computes x < y elementwise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_BOOL8}
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType} and dimensions compatible
* with input0.
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
LESS = 58,
/**
* For input tensors x and y, computes x <= y elementwise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_BOOL8}
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType} and dimensions compatible
* with input0.
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
LESS_EQUAL = 59,
/**
* Computes natural logarithm of x element-wise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
LOG = 60,
/**
* Returns the truth value of x AND y element-wise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_BOOL8}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
* * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions
* compatible with input0.
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
LOGICAL_AND = 61,
/**
* Computes the truth value of NOT x element-wise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_BOOL8}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
LOGICAL_NOT = 62,
/**
* Returns the truth value of x OR y element-wise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_BOOL8}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
* * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions
* compatible with input0.
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
LOGICAL_OR = 63,
/**
* Computes the log softmax activations given logits.
*
* The output is calculated using this formula:
*
* output = logits * beta - log(reduce_sum(exp(logits * beta), axis))
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor specifying the input logits.
* * 1: A scalar, specifying the positive scaling factor for the exponent,
* beta.
* For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta
* value must be of {@link OperandType::FLOAT16}.
* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta
* value must be of {@link OperandType::FLOAT32}.
* * 2: An {@link OperandType::INT32} scalar specifying the axis to
* reduce across. Negative index is used to specify axis from the
* end (e.g. -1 for the last axis). Must be in the range [-n, n).
*
* Outputs:
* * 0: The output tensor of the same {@link OperandType} and shape as
* input0.
*/
LOG_SOFTMAX = 64,
/**
* Returns the element-wise maximum of two tensors.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType} and compatible dimensions
* with input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scales and zeroPoint can be different from input0 scale and zeroPoint.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
MAXIMUM = 65,
/**
* Returns the element-wise minimum of two tensors.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType} and compatible dimensions
* with input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scales and zeroPoint can be different from input0 scale and zeroPoint.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
MINIMUM = 66,
/**
* Computes numerical negative value element-wise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
NEG = 67,
/**
* For input tensors x and y, computes x != y elementwise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_BOOL8}
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* This operation supports broadcasting.
*
* Inputs:
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType} and dimensions compatible
* with input0.
*
* Outputs:
* * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
*/
NOT_EQUAL = 68,
/**
* Pads a tensor with the given constant value according to the specified
* paddings.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::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 {@link OperandType::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 elements to be padded in the
* front of dimension i.
* padding[i, 1] specifies the number of elements to be padded after
* the end of dimension i.
* * 2: A scalar specifying the value to use for padding input0.
* For input tensor of {@link OperandType::TENSOR_FLOAT16}, the
* pad value must be of {@link OperandType::FLOAT16}.
* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
* pad value must be of {@link OperandType::FLOAT32}.
* For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the pad value must be of {@link OperandType::INT32}. The
* scale and zeroPoint are assumed to be the same as in input0.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0. The
* output tensor has the same rank as input0, and each
* dimension of the output tensor has the same size as the
* corresponding dimension of the input tensor plus the size
* of the padding:
* output0.dimension[i] =
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
PAD_V2 = 69,
/**
* Computes the power of one value to another.
*
* Given a tensor base and a tensor exponent, this operation computes
* base^exponent elementwise.
*
* This operations supports broadcasting. 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.
*
* For example:
* base.dimension = {4, 1, 2}
* exponent.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: A tensor specifying the base.
* * 1: A tensor specifying the exponent.
*
* Outputs:
* * 0: An output tensor.
*/
POW = 70,
/**
* Parametric Rectified Linear Unit.
*
* It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha
* is a learned array with the same {@link OperandType} and compatible
* dimensions as input x.
*
* 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:
* input.dimension = {4, 1, 2}
* alpha.dimension = {5, 4, 3, 1}
* output.dimension = {5, 4, 3, 2}
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: A tensor, specifying the input.
* * 1: A tensor of the same {@link OperandType}, and compatible dimensions
* as input0, specifying the alpha.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scales and zeroPoint can be different from input0 scale and zeroPoint.
*/
PRELU = 71,
/**
* Quantizes the input tensor.
*
* The formula for {@link OperandType::TENSOR_QUANT8_ASYMM} output tensor is:
*
* output = max(0, min(255, round(input / scale) + zeroPoint)
*
* Supported input tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported output tensor {@link OperandType}:
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: A tensor, may be zero-sized.
*
* Outputs:
* * 0: The output tensor of same shape as input0, but with
* {@link OperandType::TENSOR_QUANT8_ASYMM}.
*/
QUANTIZE = 72,
/**
* A version of quantized LSTM, using 16 bit quantization for internal
* state.
*
* There is no projection layer, so cell state size is equal to the output
* size.
*
* Inputs:
* * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
* and shape [numBatches, inputSize] specifying the input to the LSTM
* cell. Tensor is quantized with a fixed quantization range of
* [-1, 127/128] (scale = 1/128, zeroPoint = 128).
* * 1: The input-to-input weights.
* A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
* and shape [outputSize, inputSize] specifying input-to-input part of
* weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 2: The input-to-forget weights.
* A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
* and shape [outputSize, inputSize] specifying input-to-forget part of
* weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 3: The input-to-cell weights.
* A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
* and shape [outputSize, inputSize] specifying input-to-cell part of
* weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 4: The input-to-output weights.
* A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
* and shape [outputSize, inputSize] specifying input-to-output part of
* weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 5: The recurrent-to-input weights.
* A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
* and shape [outputSize, outputSize] specifying recurrent-to-input part
* of weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 6: The recurrent-to-forget weights.
* A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
* and shape [outputSize, outputSize] specifying recurrent-to-forget
* part of weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 7: The recurrent-to-cell weights.
* A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
* and shape [outputSize, outputSize] specifying recurrent-to-cell part
* of weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 8: The recurrent-to-output weights.
* A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
* and shape [outputSize, outputSize] specifying recurrent-to-output
* part of weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 9: The input gate bias.
* A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
* [outputSize] specifying the bias for the fully-connected layer
* inside the LSTM cell. Bias is quantized with scale being a product
* of input and weights scales and zeroPoint equal to 0.
* * 10:The forget gate bias.
* A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
* [outputSize] specifying the bias for the fully-connected layer
* inside the LSTM cell. Bias is quantized with scale being a product
* of input and weights scales and zeroPoint equal to 0.
* * 11:The cell bias.
* A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
* [outputSize] specifying the bias for the fully-connected layer
* inside the LSTM cell. Bias is quantized with scale being a product
* of input and weights scales and zeroPoint equal to 0.
* * 12:The output gate bias.
* A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
* [outputSize] specifying the bias for the fully-connected layer
* inside the LSTM cell. Bias is quantized with scale being a product
* of input and weights scales and zeroPoint equal to 0.
* * 13: A 2-D tensor of type {@link OperandType::TENSOR_QUANT16_SYMM}
* and shape [numBatches, outputSize] specifying the cell state from the
* previous time step of the LSTM cell. It is quantized using a
* quantization range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 /
* 32768, zeroPoint = 0).
* * 14: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
* and shape [numBathes, outputSize] specifying the output of the LSTM
* cell from previous time-step. Tensor is quantized with a fixed
* quantization range of [-1, 127/128] (scale = 1/128, zeroPoint =
* 128).
*
*
* Outputs:
* * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT16_SYMM}
* and shape [numBatches, outputSize] which contains a cell state from
* the current time step. Tensor is quantized using a quantization
* range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / 32768, zeroPoint =
* 0).
* * 1: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
* and shape [numBathes, outputSize] which contains the output value.
* Tensor is quantized with a fixed quantization range of [-1, 127/128]
* (scale = 1/128, zeroPoint = 128).
*/
QUANTIZED_16BIT_LSTM = 73,
/**
* Draws samples from a multinomial distribution.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Inputs:
* * 0: A 2-D tensor with shape [batches, classes], specifying the
* unnormalized log-probabilities for all classes.
* * 1: A scalar {@link OperandType::INT32}, specifying the number of
* independent samples to draw for each row slice.
* * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],
* specifying seeds used to initialize the random distribution. If both
* provided seeds are 0, both will be randomly generated.
* Outputs:
* * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape
* [batches, samples], containing the drawn samples.
*/
RANDOM_MULTINOMIAL = 74,
/**
* Reduces a tensor by computing the "logical and" of elements along given
* dimensions.
*
* If keep_dims is true, the reduced dimensions are
* retained with length 1. Otherwise, the rank of the tensor is reduced by
* 1 for each entry in dimensions.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_BOOL8}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor.
* * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
* to reduce. Dimension values must be in the range [-n, n).
* * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* If all dimensions are reduced and keep_dims is false, the output
* shape is [1].
*/
REDUCE_ALL = 75,
/**
* Reduces a tensor by computing the "logical or" of elements along given
* dimensions.
*
* If keep_dims is true, the reduced dimensions are
* retained with length 1. Otherwise, the rank of the tensor is reduced by
* 1 for each entry in dimensions.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_BOOL8}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor.
* * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
* to reduce. Dimension values must be in the range [-n, n).
* * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* If all dimensions are reduced and keep_dims is false, the output
* shape is [1].
*/
REDUCE_ANY = 76,
/**
* Reduces a tensor by computing the maximum of elements along given
* dimensions.
*
* If keep_dims is true, the reduced dimensions are
* retained with length 1. Otherwise, the rank of the tensor is reduced by
* 1 for each entry in dimensions.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor.
* * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
* to reduce. Dimension values must be in the range [-n, n).
* * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* If all dimensions are reduced and keep_dims is false, the output
* shape is [1].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
REDUCE_MAX = 77,
/**
* Reduces a tensor by computing the minimum of elements along given
* dimensions.
*
* If keep_dims is true, the reduced dimensions are
* retained with length 1. Otherwise, the rank of the tensor is reduced by
* 1 for each entry in dimensions.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor.
* * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
* to reduce. Dimension values must be in the range [-n, n).
* * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* If all dimensions are reduced and keep_dims is false, the output
* shape is [1].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
REDUCE_MIN = 78,
/**
* Reduces a tensor by multiplying elements along given dimensions.
*
* If keep_dims is true, the reduced dimensions are
* retained with length 1. Otherwise, the rank of the tensor is reduced by
* 1 for each entry in dimensions.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor.
* * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
* to reduce. Dimension values must be in the range [-n, n).
* * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* If all dimensions are reduced and keep_dims is false, the output
* shape is [1].
*/
REDUCE_PROD = 79,
/**
* Reduces a tensor by summing elements along given dimensions.
*
* If keep_dims is true, the reduced dimensions are
* retained with length 1. Otherwise, the rank of the tensor is reduced by
* 1 for each entry in dimensions.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: An n-D tensor.
* * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
* to reduce. Dimension values must be in the range [-n, n).
* * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
* retains reduced dimensions with length 1.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
* If all dimensions are reduced and keep_dims is false, the output
* shape is [1].
*/
REDUCE_SUM = 80,
/**
* Select and scale the feature map of each region of interest to a unified
* output size by average pooling sampling points from bilinear interpolation.
*
* The region of interest is represented by its upper-left corner coordinate
* (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
* A spatial scaling factor is applied to map into feature map coordinate.
* A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
*
* No rounding is applied in this operation. The sampling points are unified
* distributed in the pooling bin and their values are calculated by bilinear
* interpolation.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: A 4-D tensor, specifying the feature map.
* * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
* the regions of interest, each line with format [x1, y1, x2, y2].
* For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM},
* this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
* with zeroPoint of 0 and scale of 0.125. Zero num_rois is
* supported for this tensor.
* * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
* [num_rois], specifying the batch index of each box. Boxes with
* the same batch index are grouped together. Zero num_rois is
* supported for this tensor.
* * 3: An {@link OperandType::INT32} scalar, specifying the output
* height of the output tensor.
* * 4: An {@link OperandType::INT32} scalar, specifying the output
* width of the output tensor.
* * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
* from the height of original image to the height of feature map.
* * 6: An {@link OperandType::FLOAT32} scalar, specifying the ratio
* from the width of original image to the width of feature map.
* * 7: An {@link OperandType::INT32} scalar, specifying the number of
* sampling points in height dimension used to compute the output.
* Set to 0 for adaptive value of ceil(roi_height/out_height).
* * 8: An {@link OperandType::INT32} scalar, specifying the number of
* sampling points in width dimension used to compute the output.
* Set to 0 for adaptive value of ceil(roi_width/out_width).
* * 9: An {@link OperandType::BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0. The output
* shape is [num_rois, out_height, out_width, depth].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint can be different from the input0 scale and zeroPoint.
*/
ROI_ALIGN = 81,
/**
* Select and scale the feature map of each region of interest to a unified
* output size by max-pooling.
*
* The region of interest is represented by its upper-left corner coordinate
* (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
* A spatial scaling factor is applied to map into feature map coordinate.
* A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
*
* Rounding is applied in this operation to ensure integer boundary for
* regions of interest and pooling bins.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Inputs:
* * 0: A 4-D tensor, specifying the feature map.
* * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
* the regions of interest, each line with format [x1, y1, x2, y2].
* For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM},
* this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
* with zeroPoint of 0 and scale of 0.125.
* * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
* [num_rois], specifying the batch index of each box. Boxes with
* the same batch index are grouped together.
* * 3: An {@link OperandType::INT32} scalar, specifying the output
* height of the output tensor.
* * 4: An {@link OperandType::INT32} scalar, specifying the output
* width of the output tensor.
* * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
* from the height of original image to the height of feature map.
* * 6: An {@link OperandType::FLOAT32} scalar, specifying the ratio
* from the width of original image to the width of feature map.
* * 7: An {@link OperandType::BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0. The output
* shape is [num_rois, out_height, out_width, depth].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
ROI_POOLING = 82,
/**
* Computes reciprocal of square root of x element-wise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
RSQRT = 83,
/**
* Using a tensor of booleans c and input tensors x and y select values
* elementwise from both input tensors:
*
* O[i] = C[i] ? x[i] : y[i].
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: A tensor of type {@link OperandType::TENSOR_BOOL8} acting as a
* mask that chooses, based on the value at each element, whether the
* corresponding element in the output should be taken from input1 (if
* true) or input2 (if false).
* * 1: An input tensor of the same shape as input0.
* * 2: An input tensor of the same shape and type as input1.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scales and zeroPoint can be different from input1 scale and zeroPoint.
*
* Outputs:
* * 0: A tensor of the same type and shape as input1 and input2.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
SELECT = 84,
/**
* Computes sin of x element-wise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
SIN = 85,
/**
* Extracts a slice of specified size from the input tensor starting at a
* specified location.
*
* The starting location is specified as a 1-D tensor containing offsets
* for each dimension. The size is specified as a 1-D tensor containing
* either size of a slice along corresponding dimension or -1. In the latter
* case, all the remaining elements in dimension are included in the slice.
*
* A sum of begin offset and a size of a slice must not exceed size of a
* corresponding dimension.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: An n-D tensor to take slice from, may be zero-sized.
* * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying
* the beginning indices of the slice in each dimension.
* * 2: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying
* the size of the slice in each dimension.
*
* Outputs:
* * 0: An n-D tensor of the same type as the input containing the slice.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* its scale and zeroPoint has to be same as the input0 scale and zeroPoint.
*/
SLICE = 86,
/**
* Splits a tensor along a given axis into num_splits subtensors.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: An n-D tensor to split.
* * 1: An {@link OperandType::INT32} scalar specifying the axis along
* which to split.
* * 2: An {@link OperandType::INT32} scalar indicating the number of
* splits along given axis. Must evenly divide axis size.
*
* Outputs:
* * 0 ~ (num_splits - 1): Resulting subtensors.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
SPLIT = 87,
/**
* Computes square root of x element-wise.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: from 1.
*
* Inputs:
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*/
SQRT = 88,
/**
* Constructs a tensor by tiling a given tensor.
*
* This operation creates a new tensor by replicating `input` `multiples`
* times. The output tensor's i-th dimension has `input.dims(i) * multiples[i]`
* elements, and the values of `input` are replicated `multiples[i]` times
* along the i-th dimension.
* For example, tiling `[a b c d]` by `[2]` produces `[a b c d a b c d]`.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: input, an n-D tensor specifying the input.
* * 1: multiples, a 1-D tensor of {@link OperandType::TENSOR_INT32}.
* The length of multiples must be n.
*
* Outputs:
* * 0: A tiled tensor of the same {@link OperandType} and rank as `input`.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
TILE = 89,
/**
* Finds values and indices of the k largest entries for the last dimension.
*
* Resulting values in each dimensions are sorted in descending order. If
* two values are equal, the one with larger index appears first.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: from 1
*
* Inputs:
* * 0: input, an n-D tensor specifying the input.
* * 1: k, an {@link OperandType::INT32} scalar, specifying the number of
* top elements to look for along the last dimension.
*
* Outputs:
* * 0: An n-D tensor of the same type as the input, containing the k
* largest elements along each last dimensional slice.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
* * 1: An n-D tensor of type {@link OperandType::TENSOR_INT32}
* containing the indices of values within the last dimension of input.
*/
TOPK_V2 = 90,
/**
* Performs the transpose of 2-D convolution operation.
*
* This operation is sometimes called "deconvolution" after Deconvolutional
* Networks, but is actually the transpose (gradient) of
* {@link OperandType::CONV_2D} rather than an actual deconvolution.
*
* The output dimensions are functions of the filter dimensions, stride, and
* padding.
*
* Supported tensor {@link OperandType} configurations:
* * 16 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
*
* * 32 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
* * Quantized:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
* * Quantized with symmetric per channel quantization for the filter:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
* * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
* * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* 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. For tensor of type
* {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
* dimension (SymmPerChannelQuantParams::channelDim) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_FLOAT16}, the bias must be of the
* same type.
* For filter 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.
* For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
* the bias must be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
* and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 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.
* * 10: An {@link OperandType::BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* 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. For tensor of type
* {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
* dimension (SymmPerChannelQuantParams::channelDim) must be set to 0.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32} or
* {@link OperandType::TENSOR_FLOAT16}, the bias should be of the
* same type.
* For filter 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.
* For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
* the bias must be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
* and bias_scale of 0. The actual scale of each value 'i' is equal to
* bias_scale[i] = input_scale * filter_scale[i].
* * 3: An {@link OperandType::TENSOR_INT32} tensor, specifying the output
* tensor shape.
* * 4: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
* * 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, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
* * 8: An {@link OperandType::BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
TRANSPOSE_CONV_2D = 91,
/**
* A recurrent neural network specified by an LSTM cell.
*
* Performs (fully) dynamic unrolling of input.
*
* This Op unrolls the input along the time dimension, and implements the
* following operation for each element in the sequence
* s = 1...sequence_length:
* outputs[s] = projection(state = activation(LSTMOp(inputs[s])))
*
* Where LSTMOp is the LSTM op as in {@link OperandType::LSTM},
* the "projection" is an optional projection layer from state and output
* and the “activation” is the function passed as the
* “fused_activation_function” argument (if not “NONE”).
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: 3, either time-major or batch-major.
*
* All input and output tensors must be of the same type.
*
* Inputs:
* * 0: The input (\f$x_t\f$).
* A 3-D tensor of shape:
* If time-major: [max_time, batch_size, input_size]
* If batch-major: [batch_size, max_time, input_size]
* where “max_time” is the number of timesteps (sequence length),
* “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 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 shape [num_units, input_size].
* * 3: The input-to-cell weights (\f$W_{xc}\f$).
* A 2-D tensor of shape [num_units, input_size].
* * 4: The input-to-output weights (\f$W_{xo}\f$).
* A 2-D tensor of shape [num_units, input_size].
* * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
* A 2-D tensor 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 shape [num_units, output_size].
* * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
* A 2-D tensor of shape [num_units, output_size].
* * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
* A 2-D tensor of shape [num_units, output_size].
* * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
* A 1-D tensor of shape [num_units].
* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
* A 1-D tensor of shape [num_units].
* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
* A 1-D tensor of shape [num_units].
* * 12:The input gate bias (\f$b_i\f$). Optional.
* A 1-D tensor of shape [num_units].
* * 13:The forget gate bias (\f$b_f\f$).
* A 1-D tensor of shape [num_units].
* * 14:The cell bias (\f$b_c\f$).
* A 1-D tensor of shape [num_units].
* * 15:The output gate bias (\f$b_o\f$).
* A 1-D tensor of shape [num_units].
* * 16:The projection weights (\f$W_{proj}\f$). Optional.
* A 2-D tensor of shape [output_size, num_units].
* * 17:The projection bias (\f$b_{proj}\f$). Optional.
* A 1-D tensor of shape [output_size].
* * 18:The output state (in) (\f$h_{t-1}\f$).
* A 2-D tensor of shape [batch_size, output_size].
* * 19:The cell state (in) (\f$C_{t-1}\f$).
* A 2-D tensor 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.
* * 23:Time-major if true, batch-major if false.
* * 24:The input layer normalization weights. Optional.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at input gate.
* * 25:The forget layer normalization weights. Optional.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at forget gate.
* * 26:The cell layer normalization weights. Optional.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at cell gate.
* * 27:The output layer normalization weights. Optional.
* A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
* to activation at output gate.
*
* Outputs:
* * 0: The output (\f$o_t\f$).
* A 3-D tensor of shape:
* If time-major: [max_time, batch_size, output_size]
* If batch-major: [batch_size, max_time, output_size]
*/
UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
/**
* A recurrent neural network layer that applies a basic RNN cell to a
* sequence of inputs.
*
* This layer unrolls the input along the sequence dimension, and implements
* the following operation
* for each element in the sequence s = 1...sequence_length:
* outputs[s] = state = activation(inputs[s] * 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_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
*
* The input tensors must all be the same type.
*
* Inputs:
* * 0: input.
* A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
* it is set to 1, then the input has a shape [maxTime, batchSize,
* inputSize], otherwise the input has a shape [batchSize, maxTime,
* inputSize].
* * 1: weights.
* A 2-D tensor of shape [numUnits, inputSize].
* * 2: recurrent_weights.
* A 2-D tensor of shape [numUnits, numUnits].
* * 3: bias.
* A 1-D tensor of shape [numUnits].
* * 4: hidden state
* A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden
* state input for the first time step of the computation.
* * 5: fusedActivationFunction.
* A {@link FusedActivationFunc} value indicating the activation function. If
* “NONE” is specified then it results in a linear activation.
* * 6: timeMajor
* An {@link OperandType::INT32} scalar specifying the shape format
* of input and output tensors. Must be set to either 0 or 1.
* Outputs:
* * 0: output.
* A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
* it is set to 1, then the output has a shape [maxTime, batchSize,
* numUnits], otherwise the output has a shape [batchSize, maxTime,
* numUnits].
*/
UNIDIRECTIONAL_SEQUENCE_RNN = 93,
/**
* Resizes images to given size using the nearest neighbor 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_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
* be NCHW, the data storage order of: [batch, channels, height, width].
*
* Both resizing by shape and resizing by scale are supported.
*
* Inputs (resizing by shape):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input. Zero batches is supported for this tensor.
* * 1: An {@link OperandType::INT32} scalar, specifying the output
* width of the output tensor.
* * 2: An {@link OperandType::INT32} scalar, specifying the output
* height of the output tensor.
* * 3: An {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
*
* Inputs (resizing by scale):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input. Zero batches is supported for this tensor.
* * 1: A scalar, specifying width_scale, the scaling factor of the width
* dimension from the input tensor to the output tensor. The output
* width is calculated as new_width = floor(width * width_scale).
* The scalar must be of {@link OperandType::FLOAT16} if input0 is
* of {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} otherwise.
* * 2: A scalar, specifying height_scale, the scaling factor of the height
* dimension from the input tensor to the output tensor. The output
* height is calculated as new_height = floor(height * height_scale).
* The scalar must be of {@link OperandType::FLOAT16} if input0 is
* of {@link OperandType::TENSOR_FLOAT16} and of
* {@link OperandType::FLOAT32} otherwise.
* * 3: An {@link OperandType::BOOL} scalar, default to false.
* Set to true to specify NCHW data layout for input0 and output0.
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, new_height, new_width, depth].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
RESIZE_NEAREST_NEIGHBOR = 94,
/**
* DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
* OEM operation and data types.
*
* This operation is OEM specific. It should only be used for OEM
* applications.
*/
OEM_OPERATION = @1.1::OperationType:OEM_OPERATION,
/* ADDING A NEW FUNDAMENTAL OPERATION REQUIRES UPDATING THE VALUE OF
* OperationTypeRange::FUNDAMENTAL_MAX.
*/
/* ADDING A NEW OEM OPERATION REQUIRES UPDATING THE VALUE OF
* OperationTypeRange::OEM_MAX.
*/
};
/**
* The range of values in the OperationType enum.
*/
enum OperationTypeRange : uint32_t {
BASE_MIN = 0,
FUNDAMENTAL_MIN = 0,
FUNDAMENTAL_MAX = 94,
OEM_MIN = 10000,
OEM_MAX = 10000,
BASE_MAX = 0xFFFF,
};
/**
* Device types.
*
* The type of NNAPI device.
*/
enum DeviceType : int32_t {
// Leaving 0 unused as it means unknown type in NDK NNAPI. There is no
// HAL equivalent of unknown type and a 1.2 HAL implementation must belong
// to one of the categories below.
/** The device does not fall into any category below. */
OTHER = 1,
/** The device runs NNAPI models on single or multi-core CPU. */
CPU = 2,
/** The device can run NNAPI models and also accelerate graphics APIs such
* as OpenGL ES and Vulkan. */
GPU = 3,
/** Dedicated accelerator for Machine Learning workloads. */
ACCELERATOR = 4,
};
/**
* The capabilities of a driver.
*
* Performance of an operation comes from the type of its first operand.
* This represents performance for non extension operand types.
*/
struct Capabilities {
/**
* Driver performance when operating on float32 data but performing
* calculations with range and/or precision as low as that of the IEEE
* 754 16-bit floating-point format.
*/
PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
/**
* Driver performance when operating on a particular data type.
* In the case of float32 data, this is used when the calculations
* are not relaxed.
*/
struct OperandPerformance {
OperandType type;
PerformanceInfo info;
};
/**
* Performance by operand type. Must be sorted by OperandType.
* If a particular OperandType is not present in operandPerformance,
* its performance is treated as { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
*/
vec<OperandPerformance> operandPerformance;
};
/**
* Describes one operation of the model's graph.
*/
struct Operation {
/**
* The operation type.
*
* Besides the values listed in {@link OperationType}, any value above
* {@link OperationTypeRange::BASE_MAX} is possible and should be interpreted
* as an extension type according to {@link Model::extensionNameToPrefix}.
*/
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;
};
/**
* Parameters for TENSOR_QUANT8_SYMM_PER_CHANNEL operand.
*/
struct SymmPerChannelQuantParams {
/** Array of scaling values for each channel. Each value must be greater than zero. */
vec<float> scales;
/** Index of the channel dimension */
uint32_t channelDim;
};
/**
* Describes one operand of the model's graph.
*/
struct Operand {
/**
* The data type.
*
* Besides the values listed in {@link OperandType}, any value above
* {@link OperandTypeRange::BASE_MAX} is possible and should be interpreted
* as an extension type according to {@link Model::extensionNameToPrefix}.
*/
OperandType type;
/**
* Dimensions of the operand.
*
* For a scalar operand, dimensions.size() must be 0.
*
* 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. Fully
* specified dimensions must either be present in the
* Operand or they must be provided in the corresponding
* RequestArgument.
* EXCEPTION: If the input 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.
*
* A tensor operand with unspecified rank is represented by providing
* an empty dimensions vector.
*/
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.
*
* Must be 0 when not applicable to an operand type.
*
* See {@link OperandType}.
*/
float scale;
/**
* Quantized zero-point offset of the operand.
*
* Must be 0 when not applicable to an operand type.
*
* See {@link OperandType}.
*/
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;
/**
* Additional parameters specific to a particular operand type.
*/
safe_union ExtraParams {
/**
* No additional parameters.
*/
Monostate none;
/**
* Symmetric per-channel quantization parameters.
*
* Only applicable to operands of type TENSOR_QUANT8_SYMM_PER_CHANNEL.
*/
SymmPerChannelQuantParams channelQuant;
/**
* Extension operand parameters.
*
* The framework treats this as an opaque data blob.
* The format is up to individual extensions.
*/
vec<uint8_t> extension;
} extraParams;
};
/**
* 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
* may 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;
/**
* 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
* precision as low as that of the IEEE 754 16-bit floating-point format.
* 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
* range and precision of the IEEE 754 32-bit floating-point format.
*/
bool relaxComputationFloat32toFloat16;
/**
* The mapping between extension names and prefixes of operand and
* operation type values.
*
* An operand or operation whose numeric type value is above
* {@link OperandTypeRange::BASE_MAX} or
* {@link OperationTypeRange::BASE_MAX} respectively should be interpreted
* as an extension operand. The low
* {@link Model::ExtensionTypeEncoding::LOW_BITS_TYPE} bits of the value
* correspond to the type ID within the extension and the high
* {@link Model::ExtensionTypeEncoding::HIGH_BITS_PREFIX} bits encode
* the "prefix", which maps uniquely to the extension name.
*
* For example, if a model contains an operation whose value is
* 0xAAAABBBB and extensionNameToPrefix contains an entry with
* prefix=0xAAAA and name="vendor.test.test_extension", then
* the operation should be interpreted as the operation 0xBBBB
* of the extension named vendor.test.test_extension.
*
* This is a one-to-one correspondence. That is, there must be at most one
* prefix corresponding to each extension name and at most one extension
* name corresponding to each prefix.
*/
vec<ExtensionNameAndPrefix> extensionNameToPrefix;
/**
* A correspondence between an extension name and a prefix of operand and
* operation type values.
*/
struct ExtensionNameAndPrefix {
/**
* The extension name.
*
* See {@link Extension::name} for the format specification.
*/
string name;
/**
* The unique extension identifier within the model.
*
* See {@link Model::extensionNameToPrefix}.
*/
uint16_t prefix;
};
/**
* Numeric values of extension operand and operation types have the
* following structure:
* - 16 high bits represent the "prefix", which corresponds uniquely to the
* extension name.
* - 16 low bits represent the type ID within the extension.
*/
enum ExtensionTypeEncoding : uint8_t {
HIGH_BITS_PREFIX = 16,
LOW_BITS_TYPE = 16,
};
};
/**
* Describes the shape information of an output operand after execution.
*/
struct OutputShape {
/**
* Dimensions of the operand.
*/
vec<uint32_t> dimensions;
/**
* Whether the provided buffer size is sufficient for the output.
*/
bool isSufficient;
};
/**
* Specifies whether or not to measure timing information during execution.
*/
enum MeasureTiming : int32_t {
NO = 0,
YES = 1,
};
/**
* Timing information measured during execution. Each time is a duration from
* the beginning of some task to the end of that task, including time when that
* task is not active (for example, preempted by some other task, or
* waiting for some resource to become available).
*
* Times are measured in microseconds.
* When a time is not available, it must be reported as UINT64_MAX.
*/
struct Timing {
/** Execution time on device (not driver, which runs on host processor). */
uint64_t timeOnDevice;
/** Execution time in driver (including time on device). */
uint64_t timeInDriver;
};
/**
* FmqRequestDatum is a single element of a serialized representation of an
* execution request (a {@link @1.0::Request} object and a {@link MeasureTiming}
* value) which is sent across FastMessageQueue.
*
* The serialized representation for a particular execution is referred to later
* in these descriptions as a 'packet'.
*
* FastMessageQueue can only pass HIDL-defined types that do not involve nested
* buffers, handles, or interfaces.
*
* The request is serialized as follows:
* 1) 'packetInformation'
* 2) For each input operand:
* 2.1) 'inputOperandInformation'
* 2.2) For each dimension element of the operand:
* 2.2.1) 'inputOperandDimensionValue'
* 3) For each output operand:
* 3.1) 'outputOperandInformation'
* 3.2) For each dimension element of the operand:
* 3.2.1) 'outputOperandDimensionValue'
* 4) For each pool:
* 4.1) 'poolIdentifier'
* 5) 'measureTiming'
*/
safe_union FmqRequestDatum {
/**
* Type to describe the high-level layout of the packet.
*/
struct PacketInformation {
/**
* How many elements the packet contains, including the
* "packetInformation" datum.
*/
uint32_t packetSize;
/**
* Number of input operands.
*/
uint32_t numberOfInputOperands;
/**
* Number of output operands.
*/
uint32_t numberOfOutputOperands;
/**
* Number of pool identifiers.
*/
uint32_t numberOfPools;
};
/**
* Type representing the information for each operand.
*/
struct OperandInformation {
/**
* 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, 'numberOfDimensions' is set to 0, and the
* dimensions information is omitted from the serialization.
*/
bool hasNoValue;
/**
* The location within one of the memory pools passed in the Request.
*/
DataLocation location;
/**
* Number of subsequent elements that belong to the dimensions vector.
*/
uint32_t numberOfDimensions;
};
/**
* packetInformation is the first element of the packet and describes the
* remainder of the packet.
*/
PacketInformation packetInformation;
/**
* Information for each input operand.
*/
OperandInformation inputOperandInformation;
/**
* Element of the dimensions vector.
*/
uint32_t inputOperandDimensionValue;
/**
* Information for each output operand.
*/
OperandInformation outputOperandInformation;
/**
* Element of the dimensions vector.
*/
uint32_t outputOperandDimensionValue;
/**
* Unique identifier for a pool.
*
* A {@link @1.0::Request} passes across one or more pools of shared memory
* for the inputs and outputs of an execution. However, these memory pools
* are not able to be sent across FastMessageQueue directly. Instead, the
* producing side of the FMQ represents each different pool with a unique
* identifier, and sends this identifier across the FMQ. Whenever the
* consuming side of the FMQ needs the memory corresponding to this unique
* identifier, it can pass the identifier to
* {@link IBurstCallback::getMemories} to retreive the memory. Although this
* HIDL Binder call is expensive compared to communication across FMQ, it is
* only needed in the cases when the consumer does not recognize the unique
* identifier.
*/
int32_t poolIdentifier;
/**
* Specifies whether or not to measure duration of the execution. The
* duration runs from the time the driver dequeues the request from a
* FastMessageQueue to the time the driver enqueues results to a
* FastMessageQueue.
*/
MeasureTiming measureTiming;
};
/**
* FmqResultDatum is a single element of a serialized representation of the
* values returned from an execution ({@link @1.0::ErrorStatus},
* vec<{@link OutputShape}>, and {@link Timing}) which is returned via
* FastMessageQueue.
*
* The serialized representation for a particular execution is referred to later
* in these descriptions as a 'packet'.
*
* FastMessageQueue can only pass HIDL-defined types that do not involve nested
* buffers, handles, or interfaces.
*
* The execution return values ({@link @1.0::ErrorStatus} and
* vec<{@link OutputShape}>) are serialized as follows:
* 1) 'packetInformation'
* 2) For each returned operand:
* 2.1) 'operandInformation'
* 2.2) For each dimension element of the operand:
* 2.2.1) 'operandDimensionValue'
* 3) 'executionTiming'
*/
safe_union FmqResultDatum {
/**
* Type to describe the high-level layout of the packet.
*/
struct PacketInformation {
/**
* How many elements the packet contains, including the
* "packetInformation" datum.
*/
uint32_t packetSize;
/**
* Status of the execution.
*/
ErrorStatus errorStatus;
/**
* Number of returned operands.
*/
uint32_t numberOfOperands;
};
/**
* Type representing the information for each operand.
*/
struct OperandInformation {
/**
* Indicates whether the operand's output buffer is large enough to
* store the operand's result data.
*/
bool isSufficient;
/**
* Number of subsequent elements that belong to the dimensions vector.
*/
uint32_t numberOfDimensions;
};
/**
* packetInformation is the first element of the packet and describes the
* remainder of the packet. It additionally includes the status of the
* execution.
*/
PacketInformation packetInformation;
/**
* Information for each returned operand.
*/
OperandInformation operandInformation;
/**
* Element of the dimensions vector.
*/
uint32_t operandDimensionValue;
/**
* Duration of execution. Unless measurement was requested and execution
* succeeds, all times must be reported as UINT64_MAX. A driver may choose
* to report any time as UINT64_MAX, indicating that measurement is not
* available.
*/
Timing executionTiming;
};
/**
* Information about an extension.
*/
struct Extension {
/**
* The extension name.
*
* The name must consist of lowercase latin letters, numbers, periods, and
* underscore signs. The name must contain at least one period.
*
* The name must start with the reverse domain name of the vendor.
*
* Example: com.google.test_extension
*/
string name;
/**
* Information about an extension operand type.
*/
struct OperandTypeInformation {
/**
* The extension operand type.
*/
uint16_t type;
/**
* Indicates whether the extension operand type represents a tensor or
* a scalar.
*/
bool isTensor;
/**
* The byte size of the operand (if scalar) or of a single element (if
* tensor).
*/
uint32_t byteSize;
};
/**
* Information about operand types defined by the extension.
*/
vec<OperandTypeInformation> operandTypes;
};