/*
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* Copyright (C) 2017 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package android.hardware.neuralnetworks@1.0;
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/**
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* Operand types.
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*
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* The type of an operand in a model.
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*
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* Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors
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* with at least one dimension). Types not prefaced by TENSOR_* represent
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* scalar values and must have no dimensions.
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*
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* Although many types are defined, most operators accept just a few
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* types. Most used are {@link OperandType::TENSOR_FLOAT32},
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* {@link OperandType::TENSOR_QUANT8_ASYMM},
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* and {@link OperandType::INT32}.
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*/
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enum OperandType : int32_t {
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/** A 32 bit floating point scalar value. */
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FLOAT32 = 0,
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/** A signed 32 bit integer scalar value. */
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INT32 = 1,
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/** An unsigned 32 bit integer scalar value. */
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UINT32 = 2,
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/** A tensor of 32 bit floating point values. */
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TENSOR_FLOAT32 = 3,
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/** A tensor of 32 bit integer values. */
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TENSOR_INT32 = 4,
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/**
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* A tensor of 8 bit integers that represent real numbers.
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*
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* Attached to this tensor are two numbers that can be used to convert the
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* 8 bit integer to the real value and vice versa. These two numbers are:
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* - scale: a 32 bit floating point value greater than zero.
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* - zeroPoint: a 32 bit integer, in range [0, 255].
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*
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* The formula is:
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* real_value = (integer_value - zeroPoint) * scale.
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*/
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TENSOR_QUANT8_ASYMM = 5,
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/**
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* DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
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* OEM operation and data types.
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*
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* OEM specific scalar value.
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*/
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OEM = 10000,
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/**
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* DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
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* OEM operation and data types.
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*
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* A tensor of OEM specific values.
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*/
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TENSOR_OEM_BYTE = 10001,
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};
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/**
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* Operation types.
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*
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* The type of an operation in a model.
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*/
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enum OperationType : int32_t {
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/**
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* Adds two tensors, element-wise.
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*
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* Takes two input tensors of identical {@link OperandType} and compatible
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* dimensions. The output is the sum of both input tensors, optionally
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* modified by an activation function.
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*
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* Two dimensions are compatible when:
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* 1. they are equal, or
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* 2. one of them is 1
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*
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* The size of the output is the maximum size along each dimension of the
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* input operands. It starts with the trailing dimensions, and works its
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* way forward.
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*
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* Example:
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*
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* input1.dimension = {4, 1, 2}
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* input2.dimension = {5, 4, 3, 1}
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* output.dimension = {5, 4, 3, 2}
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*
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* Supported tensor {@link OperandType}:
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* * {@link OperandType::TENSOR_FLOAT32}
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* * {@link OperandType::TENSOR_QUANT8_ASYMM}
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*
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* Supported tensor rank: up to 4
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*
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* Inputs:
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* * 0: A tensor.
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* * 1: A tensor of the same {@link OperandType}, and compatible dimensions
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* as input0.
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* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
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* {@link FusedActivationFunc} values. Specifies the activation to
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* invoke on the result.
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*
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* Outputs:
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* * 0: The sum, a tensor of the same {@link OperandType} as input0.
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*
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* Available since API level 27.
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*/
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ADD = 0,
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/**
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* Performs a 2-D average pooling operation.
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*
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* The output dimensions are functions of the filter dimensions, stride, and
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* padding.
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*
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* The values in the output tensor are computed as:
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*
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* output[b, i, j, channel] =
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* sum_{di, dj}(
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* input[b, strides[1] * i + di, strides[2] * j + dj, channel]
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* ) / sum(1)
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*
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* Supported tensor {@link OperandType}:
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* * {@link OperandType::TENSOR_FLOAT32}
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* * {@link OperandType::TENSOR_QUANT8_ASYMM}
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*
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* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
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* and Channels) data layout.
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*
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* Both explicit padding and implicit padding are supported.
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*
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* Inputs (explicit padding):
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* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
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* the input.
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* * 1: An {@link OperandType::INT32} scalar, specifying the padding on
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* the left, in the ‘width’ dimension.
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* * 2: An {@link OperandType::INT32} scalar, specifying the padding on
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* the right, in the ‘width’ dimension.
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* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
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* the top, in the ‘height’ dimension.
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* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
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* the bottom, in the ‘height’ dimension.
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* * 5: An {@link OperandType::INT32} scalar, specifying the stride when
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* walking through input in the ‘width’ dimension.
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* * 6: An {@link OperandType::INT32} scalar, specifying the stride when
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* walking through input in the ‘height’ dimension.
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* * 7: An {@link OperandType::INT32} scalar, specifying the filter
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* width.
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* * 8: An {@link OperandType::INT32} scalar, specifying the filter
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* height.
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* * 9: An {@link OperandType::INT32} scalar, and has to be one of the
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* {@link FusedActivationFunc} values. Specifies the activation to
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* invoke on the result.
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*
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* Inputs (implicit padding):
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* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
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* the input.
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* * 1: An {@link OperandType::INT32} scalar, specifying the implicit
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* padding scheme, has to be one of the
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* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
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* * 2: An {@link OperandType::INT32} scalar, specifying the stride when
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* walking through input in the ‘width’ dimension.
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* * 3: An {@link OperandType::INT32} scalar, specifying the stride when
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* walking through input in the ‘height’ dimension.
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* * 4: An {@link OperandType::INT32} scalar, specifying the filter
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* width.
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* * 5: An {@link OperandType::INT32} scalar, specifying the filter
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* height.
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* * 6: An {@link OperandType::INT32} scalar, and has to be one of the
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* {@link FusedActivationFunc} values. Specifies the activation to
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* invoke on the result.
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*
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* Outputs:
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* * 0: The output 4-D tensor, of shape
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* [batches, out_height, out_width, depth].
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*
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* Available since API level 27.
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*/
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AVERAGE_POOL_2D = 1,
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/**
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* Concatenates the input tensors along the given dimension.
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*
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* The input tensors must have identical {@link OperandType} and the same
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* dimensions except the dimension along the concatenation axis.
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*
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* Supported tensor {@link OperandType}:
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* * {@link OperandType::TENSOR_FLOAT32}
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* * {@link OperandType::TENSOR_QUANT8_ASYMM}
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*
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* Supported tensor rank: up to 4
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*
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* Inputs:
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* * 0 ~ n-1: The list of n input tensors, of shape
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* [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of
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* {@link OperandType::TENSOR_QUANT8_ASYMM}, all input tensors
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* must have the same scale and zeroPoint.
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* * n: An {@link OperandType::INT32} scalar, specifying the
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* concatenation axis.
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*
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* Outputs:
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* * 0: The output, a tensor of the same {@link OperandType} as the input
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* tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
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*
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* Available since API level 27.
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*/
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CONCATENATION = 2,
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/**
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* Performs an 2-D convolution operation.
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*
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* The CONV_2D op sweeps a 2-D filter that can mix channels together over a
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* batch of images, applying the filter to each window of each image of the
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* appropriate size.
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*
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* The output dimensions are functions of the filter dimensions, stride, and
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* padding.
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*
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* The values in the output tensor are computed as:
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*
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* output[b, i, j, channel] =
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* sum_{di, dj, k} (
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* input[b, strides[1] * i + di, strides[2] * j + dj, k] *
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* filter[channel, di, dj, k]
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* ) + bias[channel]
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*
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* Supported tensor {@link OperandType}:
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* * {@link OperandType::TENSOR_FLOAT32}
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* * {@link OperandType::TENSOR_QUANT8_ASYMM}
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*
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* Supported tensor rank: 4, with "NHWC" data layout.
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*
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* Both explicit padding and implicit padding are supported.
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*
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* Inputs (explicit padding):
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* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
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* specifying the input.
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* * 1: A 4-D tensor, of shape
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* [depth_out, filter_height, filter_width, depth_in], specifying the
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* filter.
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* * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
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* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
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* should also be of {@link OperandType::TENSOR_FLOAT32}. For input
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* tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias
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* should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
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* 0 and bias_scale == input_scale * filter_scale.
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* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
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* the left, in the ‘width’ dimension.
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* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
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* the right, in the ‘width’ dimension.
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* * 5: An {@link OperandType::INT32} scalar, specifying the padding on
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* the top, in the ‘height’ dimension.
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* * 6: An {@link OperandType::INT32} scalar, specifying the padding on
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* the bottom, in the ‘height’ dimension.
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* * 7: An {@link OperandType::INT32} scalar, specifying the stride when
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* walking through input in the ‘width’ dimension.
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* * 8: An {@link OperandType::INT32} scalar, specifying the stride when
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* walking through input in the ‘height’ dimension.
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* * 9: An {@link OperandType::INT32} scalar, and has to be one of the
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* {@link FusedActivationFunc} values. Specifies the activation to
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* invoke on the result.
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*
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* Inputs (implicit padding):
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* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
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* specifying the input.
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* * 1: A 4-D tensor, of shape
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* [depth_out, filter_height, filter_width, depth_in], specifying the
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* filter.
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* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
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* tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
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* also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
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* of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
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* of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
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* bias_scale == input_scale * filter_scale.
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* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
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* padding scheme, has to be one of the
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* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
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* * 4: An {@link OperandType::INT32} scalar, specifying the stride when
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* walking through input in the ‘width’ dimension.
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* * 5: An {@link OperandType::INT32} scalar, specifying the stride when
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* walking through input in the ‘height’ dimension.
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* * 6: An {@link OperandType::INT32} scalar, and has to be one of the
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* {@link FusedActivationFunc} values. Specifies the activation to
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* invoke on the result.
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*
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* Outputs:
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* * 0: The output 4-D tensor, of shape
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* [batches, out_height, out_width, depth_out]. For output tensor of
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* {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
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* must be satisfied: output_scale > input_scale * filter_scale.
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*
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* Available since API level 27.
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*/
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CONV_2D = 3,
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/**
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* Performs a depthwise 2-D convolution operation.
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*
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* Given an input tensor of shape [batches, height, width, depth_in] and a
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* filter tensor of shape [1, filter_height, filter_width, depth_out]
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* containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV
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* applies a different filter to each input channel (expanding from 1
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* channel to channel_multiplier channels for each), then concatenates the
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* results together.
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*
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* The output has depth_out = depth_in * depth_multiplier channels.
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* The output dimensions are functions of the filter dimensions, stride, and
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* padding.
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*
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* The values in the output tensor are computed as:
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*
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* output[b, i, j, k * channel_multiplier + q] =
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* sum_{di, dj} (
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* input[b, strides[1] * i + di, strides[2] * j + dj, k] *
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* filter[1, di, dj, k * channel_multiplier + q]
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* ) + bias[k * channel_multiplier + q]
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*
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* Supported tensor {@link OperandType}:
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* * {@link OperandType::TENSOR_FLOAT32}
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* * {@link OperandType::TENSOR_QUANT8_ASYMM}
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*
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* Supported tensor rank: 4, with "NHWC" data layout.
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*
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* Both explicit padding and implicit padding are supported.
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*
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* Inputs (explicit padding):
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* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
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* specifying the input.
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* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
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* specifying the filter.
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* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
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* tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
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* also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
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* of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
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* of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
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* bias_scale == input_scale * filter_scale.
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* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
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* the left, in the ‘width’ dimension.
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* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
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* the right, in the ‘width’ dimension.
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* * 5: An {@link OperandType::INT32} scalar, specifying the padding on
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* the top, in the ‘height’ dimension.
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* * 6: An {@link OperandType::INT32} scalar, specifying the padding on
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* the bottom, in the ‘height’ dimension.
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* * 7: An {@link OperandType::INT32} scalar, specifying the stride when
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* walking through input in the ‘width’ dimension.
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* * 8: An {@link OperandType::INT32} scalar, specifying the stride when
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* walking through input in the ‘height’ dimension.
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* * 9: An {@link OperandType::INT32} scalar, specifying the depthwise
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* multiplier.
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* * 10: An {@link OperandType::INT32} scalar, and has to be one of the
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* {@link FusedActivationFunc} values. Specifies the activation to
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* invoke on the result.
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*
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* Inputs (implicit padding):
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* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
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* specifying the input.
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* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
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* specifying the filter.
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* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
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* tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
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* also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
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* of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
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* of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
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* bias_scale == input_scale * filter_scale.
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* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
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* padding scheme, has to be one of the
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* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
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* * 4: An {@link OperandType::INT32} scalar, specifying the stride when
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* walking through input in the ‘width’ dimension.
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* * 5: An {@link OperandType::INT32} scalar, specifying the stride when
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* walking through input in the ‘height’ dimension.
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* * 6: An {@link OperandType::INT32} scalar, specifying the depthwise
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* multiplier.
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* * 7: An {@link OperandType::INT32} scalar, and has to be one of the
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* {@link FusedActivationFunc} values. Specifies the activation to
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* invoke on the result.
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*
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* Outputs:
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* * 0: The output 4-D tensor, of shape
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* [batches, out_height, out_width, depth_out]. For output tensor of
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* {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
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* must be satisfied: output_scale > input_scale * filter_scale.
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*
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* Available since API level 27.
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*/
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DEPTHWISE_CONV_2D = 4,
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/**
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* Rearranges data from depth into blocks of spatial data.
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*
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* More specifically, this op outputs a copy of the input tensor where
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* values from the depth dimension are moved in spatial blocks to the height
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* and width dimensions. The value block_size indicates the input block size
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* and how the data is moved.
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*
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* Chunks of data of size block_size * block_size from depth are rearranged
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* into non-overlapping blocks of size block_size x block_size.
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*
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* The width of the output tensor is input_depth * block_size, whereas the
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* height is input_height * block_size. The depth of the input tensor must
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* be divisible by block_size * block_size
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*
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* Supported tensor {@link OperandType}:
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* * {@link OperandType::TENSOR_FLOAT32}
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* * {@link OperandType::TENSOR_QUANT8_ASYMM}
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*
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* Supported tensor rank: 4, with "NHWC" data layout.
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*
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* Inputs:
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* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
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* specifying the input.
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* * 1: An {@link OperandType::INT32} scalar, specifying the block_size.
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* block_size must be >=1 and block_size * block_size must be a divisor
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* of the input depth.
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*
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* Outputs:
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* * 0: The output 4-D tensor, of shape [batch, height*block_size,
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* width*block_size, depth/(block_size*block_size)].
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*
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* Available since API level 27.
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*/
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DEPTH_TO_SPACE = 5,
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/**
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* Dequantizes the input tensor.
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*
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* The formula is:
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*
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* output = (input - zeroPoint) * scale.
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*
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* Supported tensor {@link OperandType}:
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* * {@link OperandType::TENSOR_QUANT8_ASYMM}
|
*
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* Supported tensor rank: up to 4
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*
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* Inputs:
|
* * 0: A tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}.
|
*
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* Outputs:
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* * 0: The output tensor of same shape as input0, but with
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* {@link OperandType::TENSOR_FLOAT32}.
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*
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* Available since API level 27.
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*/
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DEQUANTIZE = 6,
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/**
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* Looks up sub-tensors in the input tensor.
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*
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* This operator takes for input a tensor of values (Values) and
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* a one-dimensional tensor of selection indices (Lookups).
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* The output tensor is the concatenation of sub-tensors of Values as
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* selected by Lookups.
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*
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* 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.
|
*
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* 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
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* have shape of [3, 200, 300].
|
*
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* If a value in Lookups is out of bounds, the operation must fail
|
* and an error must be reported.
|
*
|
* Inputs:
|
* * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32}.
|
* The values are indices into the first dimension of Values.
|
* * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are
|
* extracted.
|
*
|
* Output:
|
* * 0: A n-D tensor with the same rank and shape as the Values
|
* tensor, except for the first dimension which has the same size
|
* as Lookups' only dimension.
|
*
|
* Available since API level 27.
|
*/
|
EMBEDDING_LOOKUP = 7,
|
|
/**
|
* Computes element-wise floor() on the input tensor.
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
*
|
* Supported tensor rank: up to 4
|
*
|
* Inputs:
|
* * 0: A tensor.
|
*
|
* Outputs:
|
* * 0: The output tensor, of the same {@link OperandType} and dimensions as
|
* the input tensor.
|
*
|
* Available since API level 27.
|
*/
|
FLOOR = 8,
|
|
/**
|
* Denotes a fully (densely) connected layer, which connects all elements
|
* in the input tensor with each element in the output tensor.
|
*
|
* This layer implements the operation:
|
*
|
* outputs = activation(inputs * weights’ + bias)
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
|
*
|
* Supported tensor rank: up to 4.
|
*
|
* Inputs:
|
* * 0: A tensor of at least rank 2, specifying the input. If rank is
|
* greater than 2, then it gets flattened to a 2-D Tensor. The
|
* (flattened) 2-D Tensor is reshaped (if necessary) to
|
* [batch_size, input_size], where "input_size" corresponds to the
|
* number of inputs to the layer, matching the second dimension of
|
* weights, and "batch_size" is calculated by dividing the number of
|
* elements by "input_size".
|
* * 1: A 2-D tensor, specifying the weights, of shape
|
* [num_units, input_size], where "num_units" corresponds to the number
|
* of output nodes.
|
* * 2: A 1-D tensor, of shape [num_units], specifying the bias. For input
|
* tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
|
* also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
|
* of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
|
* of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
|
* bias_scale == input_scale * filter_scale.
|
* * 3: An {@link OperandType::INT32} scalar, and has to be one of the
|
* {@link FusedActivationFunc} values. Specifies the activation to
|
* invoke on the result.
|
*
|
* Outputs:
|
* * 0: The output tensor, of shape [batch_size, num_units]. For output
|
* tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
|
* condition must be satisfied:
|
* output_scale > input_scale * filter_scale.
|
*
|
* Available since API level 27.
|
*/
|
FULLY_CONNECTED = 9,
|
|
/**
|
* Looks up sub-tensors in the input tensor using a key-value map.
|
*
|
* This operator takes for input a tensor of values (Values),
|
* a one-dimensional tensor of selection values (Lookups) and
|
* a one-dimensional tensor that maps these values to Values
|
* indexes. The output tensor is the concatenation of sub-tensors of
|
* Values as selected by Lookups via Keys.
|
*
|
* Think of Values as being sliced along its outer-most dimension.
|
* The output is a concatenation of selected slices, with one slice
|
* for each entry of Lookups. The slice selected is the one at the
|
* same index as the Maps entry that matches the value in Lookups.
|
*
|
* For a hit, the corresponding sub-tensor of Values is included
|
* in the Output tensor. For a miss, the corresponding sub-tensor in
|
* Output must have zero values.
|
*
|
* For example, if Values has shape of [40, 200, 300],
|
* Keys should have a shape of [40]. If Lookups tensor has shape
|
* of [3], three slices are being concatenated, so the resulting tensor
|
* must have the shape of [3, 200, 300]. If the first entry in Lookups
|
* has the value 123456, that value must be located in Keys tensor.
|
* If the sixth entry of Keys contains 123456, the sixth slice of Values
|
* must be selected. If no entry in Keys has 123456, a slice of zeroes
|
* must be concatenated.
|
*
|
* Inputs:
|
* * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with
|
* shape [ k ].
|
* * 1: Keys. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape
|
* [ n ]; Keys and Values pair represent a map, i.e., the ith element
|
* in Keys (Keys[i]) is the key to select the ith sub-tensor in Values
|
* (Values[i]), where 0 <= i <= n-1. Keys tensor *MUST* be sorted in
|
* ascending order.
|
* * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension
|
* must be n.
|
*
|
* Outputs:
|
* * 0: Output. A tensor with shape [ k …].
|
* * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
|
* hits (True) or not (False).
|
* Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0
|
* and scale 1.0f.
|
* A non-zero byte represents True, a hit. A zero indicates otherwise.
|
*
|
* Available since API level 27.
|
*/
|
HASHTABLE_LOOKUP = 10,
|
|
/**
|
* Applies L2 normalization along the depth dimension.
|
*
|
* The values in the output tensor are computed as:
|
*
|
* output[batch, row, col, channel] =
|
* input[batch, row, col, channel] /
|
* sqrt(sum_{c} pow(input[batch, row, col, c], 2))
|
*
|
* For input tensor with more dimensions, independently normalizes each 1-D
|
* slice along dimension dim.
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
*
|
* Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples,
|
* Height, Width, and Channels).
|
*
|
* Inputs:
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth].
|
*
|
* Outputs:
|
* * 0: The output 4-D tensor, of the same shape as input
|
* [batches, height, width, depth].
|
*
|
* Available since API level 27.
|
*/
|
L2_NORMALIZATION = 11,
|
|
/**
|
* Performs an 2-D L2 pooling operation.
|
*
|
* The output dimensions are functions of the filter dimensions, stride, and
|
* padding.
|
*
|
* The values in the output tensor are computed as:
|
*
|
* output[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_FLOAT32}
|
*
|
* Supported tensor rank: 4, with "NHWC" data layout.
|
*
|
* Both explicit padding and implicit padding are supported.
|
*
|
* Inputs (explicit padding):
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
* the input.
|
* * 1: An {@link OperandType::INT32} scalar, specifying the padding on
|
* the left, in the ‘width’ dimension.
|
* * 2: An {@link OperandType::INT32} scalar, specifying the padding on
|
* the right, in the ‘width’ dimension.
|
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
|
* the top, in the ‘height’ dimension.
|
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
|
* the bottom, in the ‘height’ dimension.
|
* * 5: An {@link OperandType::INT32} scalar, specifying the stride when
|
* walking through input in the ‘width’ dimension.
|
* * 6: An {@link OperandType::INT32} scalar, specifying the stride when
|
* walking through input in the ‘height’ dimension.
|
* * 7: An {@link OperandType::INT32} scalar, specifying the filter
|
* width.
|
* * 8: An {@link OperandType::INT32} scalar, specifying the filter
|
* height.
|
* * 9: An {@link OperandType::INT32} scalar, and has to be one of the
|
* {@link FusedActivationFunc} values. Specifies the activation to
|
* invoke on the result.
|
*
|
* Inputs (implicit padding):
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
* the input.
|
* * 1: An {@link OperandType::INT32} scalar, specifying the implicit
|
* padding scheme, has to be one of the
|
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
|
* * 2: An {@link OperandType::INT32} scalar, specifying the stride when
|
* walking through input in the ‘width’ dimension.
|
* * 3: An {@link OperandType::INT32} scalar, specifying the stride when
|
* walking through input in the ‘height’ dimension.
|
* * 4: An {@link OperandType::INT32} scalar, specifying the filter
|
* width.
|
* * 5: An {@link OperandType::INT32} scalar, specifying the filter
|
* height.
|
* * 6: An {@link OperandType::INT32} scalar, and has to be one of the
|
* {@link FusedActivationFunc} values. Specifies the activation to
|
* invoke on the result.
|
*
|
* Outputs:
|
* * 0: The output 4-D tensor, of shape
|
* [batches, out_height, out_width, depth].
|
*
|
* Available since API level 27.
|
*/
|
L2_POOL_2D = 12,
|
|
/**
|
* Applies Local Response Normalization along the depth dimension.
|
*
|
* The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the
|
* last dimension), and each vector is normalized independently. Within a
|
* given vector, each component is divided by the weighted, squared sum of
|
* inputs within depth_radius.
|
*
|
* The output is calculated using this formula:
|
*
|
* sqr_sum[a, b, c, d] = sum(
|
* pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2))
|
* output = input / pow((bias + alpha * sqr_sum), beta)
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
*
|
* Supported tensor rank: 4, with "NHWC" data layout.
|
*
|
* Inputs:
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
* the input.
|
* * 1: An {@link OperandType::INT32} scalar, specifying the radius of
|
* the normalization window.
|
* * 2: An {@link OperandType::FLOAT32} scalar, specifying the bias, must
|
* not be zero.
|
* * 3: An {@link OperandType::FLOAT32} scalar, specifying the scale
|
* factor, alpha.
|
* * 4: An {@link OperandType::FLOAT32} scalar, specifying the exponent,
|
* beta.
|
*
|
* Outputs:
|
* * 0: The output tensor of same shape as input0.
|
*
|
* Available since API level 27.
|
*/
|
LOCAL_RESPONSE_NORMALIZATION = 13,
|
|
/**
|
* Computes sigmoid activation on the input tensor element-wise.
|
*
|
* The output is calculated using this formula:
|
*
|
* output = 1 / (1 + exp(-input))
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
|
*
|
* Supported tensor rank: up to 4.
|
*
|
* Inputs:
|
* * 0: A tensor, specifying the input.
|
*
|
* Outputs:
|
* * 0: The output tensor of same shape as input0.
|
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
|
* the scale must be 1.f / 256 and the zeroPoint must be 0.
|
*
|
* Available since API level 27.
|
*/
|
LOGISTIC = 14,
|
|
/**
|
* Projects an input to a bit vector via locality senstive hashing.
|
*
|
* Inputs:
|
* * 0: Hash functions. Dim.size == 2, DataType: Float.
|
* Tensor[0].Dim[0]: Number of hash functions.
|
* Tensor[0].Dim[1]: Number of seeds per hash functions.
|
* Tensor[0].Dim[1] <= 32 in sparse case.
|
*
|
* * 1: Input. Dim.size >= 1, no restriction on DataType.
|
* * 2: Weight. Optional. Dim.size == 1, DataType: Float.
|
* If not set, each input element is considered to have the same weight
|
* of 1.0.
|
* Tensor[1].Dim[0] == Tensor[2].Dim[0]
|
* * 3: Type:
|
* Sparse: Value LSHProjectionType_SPARSE(=1).
|
* Computed bit vector is considered to be sparse.
|
* Each output element is an int32 made up of multiple bits
|
* computed from hash functions.
|
*
|
* Dense: Value LSHProjectionType_DENSE(=2).
|
* Computed bit vector is considered to be dense. Each output
|
* element represents a bit and can take the value of either
|
* 0 or 1.
|
*
|
* Outputs:
|
* * 0: If the projection type is sparse:
|
* Output.Dim == { Tensor[0].Dim[0] }
|
* A tensor of int32 that represents hash signatures.
|
* If the projection type is Dense:
|
* Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
|
* A flattened tensor that represents projected bit vectors.
|
*
|
* Available since API level 27.
|
*/
|
LSH_PROJECTION = 15,
|
|
/**
|
* Performs a single time step in a Long Short-Term Memory (LSTM) layer
|
*
|
* The LSTM operation is described by the following equations.
|
*
|
* \f{eqnarray*}{
|
* i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\
|
* f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\
|
* C_t =& clip(f_t \odot C_{t-1} + i_t \odot
|
* g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell}) & \\
|
* o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o) & \\
|
* & & \\
|
* & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj})
|
* & if\ there\ is\ a\ projection; \\
|
* h_t =& & \\
|
* & o_t \odot g(C_t) & otherwise. \\
|
* \f}
|
* Where:
|
* * \f$x_t\f$ is the input,
|
* * \f$i_t\f$ is the input gate,
|
* * \f$f_t\f$ is the forget gate,
|
* * \f$C_t\f$ is the cell state,
|
* * \f$o_t\f$ is the output,
|
* * \f$h_t\f$ is the output state,
|
* * \f$\sigma\f$ is the logistic sigmoid function,
|
* * \f$g\f$ is the cell input and cell output activation function, usually
|
* \f$tahn\f$,
|
* * \f$W_{xi}\f$ is the input-to-input weight matrix,
|
* * \f$W_{hi}\f$ is the recurrent to input weight matrix,
|
* * \f$W_{ci}\f$ is the cell-to-input weight matrix,
|
* * \f$b_i\f$ is the input gate bias,
|
* * \f$W_{xf}\f$ is the input-to-forget weight matrix,
|
* * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix,
|
* * \f$W_{cf}\f$ is the cell-to-forget weight matrix,
|
* * \f$b_f\f$ is the forget gate bias,
|
* * \f$W_{xc}\f$ is the input-to-cell weight matrix,
|
* * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix,
|
* * \f$b_c\f$ is the cell bias,
|
* * \f$W_{xo}\f$ is the input-to-output weight matrix,
|
* * \f$W_{ho}\f$ is the recurrent-to-output weight matrix,
|
* * \f$W_{co}\f$ is the cell-to-output weight matrix,
|
* * \f$b_o\f$ is the output gate bias,
|
* * \f$W_{proj}\f$ is the projection weight matrix,
|
* * \f$b_{proj}\f$ is the projection bias,
|
* * \f$t_{cell}\f$ is the threshold for clipping the cell state, and
|
* * \f$t_{proj}\f$ is the threshold for clipping the projected output.
|
* * \f$\odot\f$ is the
|
* <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)">
|
* Hadamard product</a> that takes two matrices and produces another
|
* matrix, each element of which is the product of the corresponding
|
* elements of the input matrices.
|
*
|
* The operation has the following independently optional inputs:
|
* * The 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.
|
*
|
* References:
|
*
|
* The default non-peephole non-CIFG implementation is based on:
|
* http://www.bioinf.jku.at/publications/older/2604.pdf
|
* S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
|
* Computation, 9(8):1735-1780, 1997.
|
*
|
* The peephole implementation and projection layer is based on:
|
* https://research.google.com/pubs/archive/43905.pdf
|
* Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
|
* recurrent neural network architectures for large scale acoustic
|
* modeling." INTERSPEECH, 2014.
|
* (However, the concept of peephole optimization was introduced in work
|
* prior to this paper.)
|
*
|
* The coupling of input and forget gate (CIFG) is based on:
|
* http://arxiv.org/pdf/1503.04069.pdf
|
* Greff et al. "LSTM: A Search Space Odyssey"
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
*
|
* Inputs:
|
* * 0: The input (\f$x_t\f$).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, input_size], where “batch_size” corresponds to the
|
* batching dimension, and “input_size” is the size of the input.
|
* * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units, input_size], where “num_units” corresponds to the
|
* number of cell units.
|
* * 2: The input-to-forget weights (\f$W_{xf}\f$).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units, input_size].
|
* * 3: The input-to-cell weights (\f$W_{xc}\f$).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units, input_size].
|
* * 4: The input-to-output weights (\f$W_{xo}\f$).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units, input_size].
|
* * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units, output_size], where “output_size” corresponds to either
|
* the number of cell units (i.e., “num_units”), or the second
|
* dimension of the “projection_weights”, if defined.
|
* * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units, output_size].
|
* * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units, output_size].
|
* * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units, output_size].
|
* * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
|
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units].
|
* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
|
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units].
|
* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
|
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units].
|
* * 12:The input gate bias (\f$b_i\f$). Optional.
|
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units].
|
* * 13:The forget gate bias (\f$b_f\f$).
|
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units].
|
* * 14:The cell bias (\f$b_c\f$).
|
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units].
|
* * 15:The output gate bias (\f$b_o\f$).
|
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units].
|
* * 16:The projection weights (\f$W_{proj}\f$). Optional.
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [output_size, num_units].
|
* * 17:The projection bias (\f$b_{proj}\f$). Optional.
|
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [output_size].
|
* * 18:The output state (in) (\f$h_{t-1}\f$).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, output_size].
|
* * 19:The cell state (in) (\f$C_{t-1}\f$).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, num_units].
|
* * 20:The activation function (\f$g\f$).
|
* A value indicating the activation function:
|
* <ul>
|
* <li>0: None;
|
* <li>1: Relu;
|
* <li>3: Relu6;
|
* <li>4: Tanh;
|
* <li>6: Sigmoid.
|
* </ul>
|
* * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such
|
* that values are bound within [-cell_clip, cell_clip]. If set to 0.0
|
* then clipping is disabled.
|
* * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the
|
* projection layer, such that values are bound within
|
* [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
|
*
|
* Outputs:
|
* * 0: The scratch buffer.
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, num_units * 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 {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, output_size].
|
* * 2: The cell state (out) (\f$C_t\f$).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, num_units].
|
* * 3: The output (\f$o_t\f$).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, output_size]. This is effectively the same as the
|
* current “output state (out)” value.
|
*
|
* Available since API level 27.
|
*/
|
LSTM = 16,
|
|
/**
|
* Performs an 2-D max pooling operation.
|
*
|
* The output dimensions are functions of the filter dimensions, stride, and
|
* padding.
|
*
|
* The values in the output tensor are computed as:
|
*
|
* output[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_FLOAT32}
|
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
|
*
|
* Supported tensor rank: 4, with "NHWC" data layout.
|
*
|
* Both explicit padding and implicit padding are supported.
|
*
|
* Inputs (explicit padding):
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
* the input.
|
* * 1: An {@link OperandType::INT32} scalar, specifying the padding on
|
* the left, in the ‘width’ dimension.
|
* * 2: An {@link OperandType::INT32} scalar, specifying the padding on
|
* the right, in the ‘width’ dimension.
|
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
|
* the top, in the ‘height’ dimension.
|
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
|
* the bottom, in the ‘height’ dimension.
|
* * 5: An {@link OperandType::INT32} scalar, specifying the stride when
|
* walking through input in the ‘width’ dimension.
|
* * 6: An {@link OperandType::INT32} scalar, specifying the stride when
|
* walking through input in the ‘height’ dimension.
|
* * 7: An {@link OperandType::INT32} scalar, specifying the filter
|
* width.
|
* * 8: An {@link OperandType::INT32} scalar, specifying the filter
|
* height.
|
* * 9: An {@link OperandType::INT32} scalar, and has to be one of the
|
* {@link FusedActivationFunc} values. Specifies the activation to
|
* invoke on the result.
|
*
|
* Inputs (implicit padding):
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
* the input.
|
* * 1: An {@link OperandType::INT32} scalar, specifying the implicit
|
* padding scheme, has to be one of the
|
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
|
* * 2: An {@link OperandType::INT32} scalar, specifying the stride when
|
* walking through input in the ‘width’ dimension.
|
* * 3: An {@link OperandType::INT32} scalar, specifying the stride when
|
* walking through input in the ‘height’ dimension.
|
* * 4: An {@link OperandType::INT32} scalar, specifying the filter
|
* width.
|
* * 5: An {@link OperandType::INT32} scalar, specifying the filter
|
* height.
|
* * 6: An {@link OperandType::INT32} scalar, and has to be one of the
|
* {@link FusedActivationFunc} values. Specifies the activation to
|
* invoke on the result.
|
*
|
* Outputs:
|
* * 0: The output 4-D tensor, of shape
|
* [batches, out_height, out_width, depth].
|
*
|
* Available since API level 27.
|
*/
|
MAX_POOL_2D = 17,
|
|
/**
|
* Multiplies two tensors, element-wise.
|
*
|
* Takes two input tensors of identical {@link OperandType} and compatible
|
* dimensions. The output is the product of both input tensors, optionally
|
* modified by an activation function.
|
*
|
* Two dimensions are compatible when:
|
* 1. they are equal, or
|
* 2. one of them is 1
|
*
|
* The size of the resulting output is the maximum size along each dimension
|
* of the input operands. It starts with the trailing dimensions, and works
|
* its way forward.
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
|
*
|
* Supported tensor rank: up to 4
|
*
|
* Inputs:
|
* * 0: A tensor.
|
* * 1: A tensor of the same {@link OperandType}, and compatible dimensions
|
* as input0.
|
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
|
* {@link FusedActivationFunc} values. Specifies the activation to
|
* invoke on the result.
|
*
|
* Outputs:
|
* * 0: The product, a tensor of the same {@link OperandType} as input0.
|
* For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
|
* the following condition must be satisfied:
|
* output_scale > input1_scale * input2_scale.
|
*
|
* Available since API level 27.
|
*/
|
MUL = 18,
|
|
/**
|
* Computes rectified linear activation on the input tensor element-wise.
|
*
|
* The output is calculated using this formula:
|
*
|
* output = max(0, input)
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
|
*
|
* Supported tensor rank: up to 4.
|
*
|
* Inputs:
|
* * 0: A tensor, specifying the input.
|
*
|
* Outputs:
|
* * 0: The output tensor of same shape as input0.
|
*
|
* Available since API level 27.
|
*/
|
RELU = 19,
|
|
/**
|
* Computes rectified linear 1 activation on the input tensor element-wise.
|
*
|
* The output is calculated using this formula:
|
*
|
* output = min(1.f, max(-1.f, input))
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
|
*
|
* Supported tensor rank: up to 4.
|
*
|
* Inputs:
|
* * 0: A tensor, specifying the input.
|
*
|
* Outputs:
|
* * 0: The output tensor of same shape as input0.
|
*
|
* Available since API level 27.
|
*/
|
RELU1 = 20,
|
|
/**
|
* Computes rectified linear 6 activation on the input tensor element-wise.
|
*
|
* The output is calculated using this formula:
|
*
|
* output = min(6, max(0, input))
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
|
*
|
* Supported tensor rank: up to 4.
|
*
|
* Inputs:
|
* * 0: A tensor, specifying the input.
|
*
|
* Outputs:
|
* * 0: The output tensor of same shape as input0.
|
*
|
* Available since API level 27.
|
*/
|
RELU6 = 21,
|
|
/**
|
* Reshapes a tensor.
|
*
|
* Given tensor, this operation returns a tensor that has the same values as
|
* tensor, but with a newly specified shape.
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
|
*
|
* Supported tensor rank: up to 4.
|
*
|
* Inputs:
|
* * 0: A tensor, specifying the tensor to be reshaped.
|
* * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}, defining the
|
* shape of the output tensor. The number of elements implied by shape
|
* must be the same as the number of elements in the input tensor.
|
*
|
* 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.
|
*
|
* Available since API level 27.
|
*/
|
RESHAPE = 22,
|
|
/**
|
* Resizes images to given size using the bilinear interpretation.
|
*
|
* Resized images must be distorted if their output aspect ratio is not the
|
* same as input aspect ratio. The corner pixels of output may not be the
|
* same as corner pixels of input.
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
*
|
* Supported tensor rank: 4, with "NHWC" data layout.
|
*
|
* Inputs:
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
|
* the input.
|
* * 1: An {@link OperandType::INT32} scalar, specifying the output
|
* width of the output tensor.
|
* * 2: An {@link OperandType::INT32} scalar, specifying the output
|
* height of the output tensor.
|
*
|
* Outputs:
|
* * 0: The output 4-D tensor, of shape
|
* [batches, new_height, new_width, depth].
|
*
|
* Available since API level 27.
|
*/
|
RESIZE_BILINEAR = 23,
|
|
/**
|
* A basic recurrent neural network layer.
|
*
|
* This layer implements the operation:
|
* outputs = state = activation(inputs * input_weights +
|
* state * recurrent_weights + bias)
|
*
|
* Where:
|
* * “input_weights” is a weight matrix that multiplies the inputs;
|
* * “recurrent_weights” is a weight matrix that multiplies the current
|
* “state” which itself is the output from the previous time step
|
* computation;
|
* * “bias” is a bias vector (added to each output vector in the batch);
|
* * “activation” is the function passed as the “fused_activation_function”
|
* argument (if not “NONE”).
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
*
|
* Inputs:
|
* * 0: input.
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32} of shape
|
* [batch_size, input_size], where “batch_size” corresponds to the
|
* batching dimension, and “input_size” is the size of the input.
|
* * 1: weights.
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units, input_size], where “num_units” corresponds to the
|
* number of units.
|
* * 2: recurrent_weights.
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units, num_units], with columns corresponding to the weights
|
* from each unit.
|
* * 3: bias.
|
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units].
|
* * 4: hidden state (in).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, num_units].
|
* * 5: fused_activation_function.
|
* An optional {@link FusedActivationFunc} value indicating the
|
* activation function. If “NONE” is specified then it results in a
|
* linear activation.
|
*
|
* Outputs:
|
* * 0: hidden state (out).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, num_units].
|
*
|
* * 1: output.
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, num_units]. This is effectively the same as the
|
* current state value.
|
*
|
* Available since API level 27.
|
*/
|
RNN = 24,
|
|
/**
|
* Computes the softmax activation on the input tensor element-wise, per
|
* batch, by normalizing the input vector so the maximum coefficient is
|
* zero.
|
*
|
* The output is calculated using this formula:
|
*
|
* output[batch, i] =
|
* exp((input[batch, i] - max(input[batch, :])) * beta) /
|
* sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
|
*
|
* Supported tensor rank: 2 or 4.
|
*
|
* Inputs:
|
* * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
|
* * 1: An {@link OperandType::FLOAT32} scalar, specifying the positive
|
* scaling factor for the exponent, beta.
|
*
|
* Outputs:
|
* * 0: The output tensor of same shape as input0.
|
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
|
* the scale must be 1.f / 256 and the zeroPoint must be 0.
|
*
|
* Available since API level 27.
|
*/
|
SOFTMAX = 25,
|
|
/**
|
* Rearranges blocks of spatial data, into depth.
|
*
|
* More specifically, this op outputs a copy of the input tensor where
|
* values from the height and width dimensions are moved to the depth
|
* dimension. The value block_size indicates the input block size and how
|
* the data is moved.
|
*
|
* Chunks of data of size block_size * block_size from depth are rearranged
|
* into non-overlapping blocks of size block_size x block_size.
|
*
|
* The depth of the output tensor is input_depth * block_size * block_size.
|
* The input tensor's height and width must be divisible by block_size.
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
|
*
|
* Supported tensor rank: 4, with "NHWC" data layout.
|
*
|
* Inputs:
|
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
|
* specifying the input.
|
* * 1: An {@link OperandType::INT32} scalar, specifying the block_size.
|
* block_size must be >=1 and block_size must be a divisor of both the
|
* input height and width.
|
*
|
* Outputs:
|
* * 0: The output 4-D tensor, of shape [batches, height/block_size,
|
* width/block_size, depth_in*block_size*block_size].
|
*
|
* Available since API level 27.
|
*/
|
SPACE_TO_DEPTH = 26,
|
|
/**
|
* SVDF op is a kind of stateful layer derived from the notion that a
|
* densely connected layer that's processing a sequence of input frames can
|
* be approximated by using a singular value decomposition of each of its
|
* nodes. The implementation is based on:
|
*
|
* https://research.google.com/pubs/archive/43813.pdf
|
*
|
* P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
|
* “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
|
* INTERSPEECH, 2015.
|
*
|
* It processes the incoming input using a 2-stage filtering mechanism:
|
* * stage 1 performs filtering on the "features" dimension, whose outputs
|
* get pushed into a memory of fixed-size memory_size.
|
* * stage 2 performs filtering on the "time" dimension of the memory_size
|
* memoized outputs of stage 1.
|
*
|
* Specifically, for rank 1, this layer implements the operation:
|
*
|
* memory = push(conv1d(inputs, weights_feature, feature_dim,
|
* "PADDING_VALID"));
|
* outputs = activation(memory * weights_time + bias);
|
*
|
* Where:
|
* * “weights_feature” is a weights matrix that processes the inputs (by
|
* convolving the input with every “feature filter”), and whose outputs
|
* get pushed, stacked in order, into the fixed-size “memory” (the oldest
|
* entry gets dropped);
|
* * “weights_time” is a weights matrix that processes the “memory” (by a
|
* batched matrix multiplication on the num_units);
|
* * “bias” is an optional bias vector (added to each output vector in the
|
* batch); and
|
* * “activation” is the function passed as the “fused_activation_function”
|
* argument (if not “NONE”).
|
*
|
* Each rank adds a dimension to the weights matrices by means of stacking
|
* the filters.
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
*
|
* Inputs:
|
* * 0: input.
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, input_size], where “batch_size” corresponds to the
|
* batching dimension, and “input_size” is the size of the input.
|
* * 1: weights_feature.
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units, input_size], where “num_units” corresponds to the
|
* number of units.
|
* * 2: weights_time.
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [num_units, memory_size], where “memory_size” corresponds to the
|
* fixed-size of the memory.
|
* * 3: bias.
|
* An optional 1-D tensor of {@link OperandType::TENSOR_FLOAT32},
|
* of shape [num_units].
|
* * 4: state (in).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, (memory_size - 1) * num_units * rank].
|
* * 5: rank.
|
* The rank of the SVD approximation.
|
* * 6: fused_activation_function.
|
* An optional {@link FusedActivationFunc} value indicating the
|
* activation function. If “NONE” is specified then it results in a
|
* linear activation.
|
*
|
* Outputs:
|
* * 0: state (out).
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, (memory_size - 1) * num_units * rank].
|
* * 1: output.
|
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
|
* [batch_size, num_units].
|
*
|
* Available since API level 27.
|
*/
|
SVDF = 27,
|
|
/**
|
* Computes hyperbolic tangent of input tensor element-wise.
|
*
|
* The output is calculated using this formula:
|
*
|
* output = tanh(input)
|
*
|
* Supported tensor {@link OperandType}:
|
* * {@link OperandType::TENSOR_FLOAT32}
|
*
|
* Supported tensor rank: up to 4.
|
*
|
* Inputs:
|
* * 0: A tensor, specifying the input.
|
*
|
* Outputs:
|
* * 0: The output tensor of same shape as input0.
|
*
|
* Available since API level 27.
|
*/
|
TANH = 28,
|
|
/**
|
* 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 = 10000,
|
};
|
|
/**
|
* Fused activation function types.
|
*/
|
enum FusedActivationFunc : int32_t {
|
NONE = 0,
|
RELU = 1,
|
RELU1 = 2,
|
RELU6 = 3,
|
};
|
|
/**
|
* How an operand is used.
|
*/
|
enum OperandLifeTime : int32_t {
|
/**
|
* The operand is internal to the model. It's created by an operation and
|
* consumed by other operations. It must be an output operand of
|
* exactly one operation.
|
*/
|
TEMPORARY_VARIABLE,
|
|
/**
|
* The operand is an input of the model. It must not be an output
|
* operand of any operation.
|
*
|
* An operand can't be both input and output of a model.
|
*/
|
MODEL_INPUT,
|
|
/**
|
* The operand is an output of the model. It must be an output
|
* operand of exactly one operation.
|
*
|
* An operand can't be both input and output of a model.
|
*/
|
MODEL_OUTPUT,
|
|
/**
|
* The operand is a constant found in Model.operandValues. It must
|
* not be an output operand of any operation.
|
*/
|
CONSTANT_COPY,
|
|
/**
|
* The operand is a constant that was specified via a Memory
|
* object. It must not be an output operand of any operation.
|
*/
|
CONSTANT_REFERENCE,
|
|
/**
|
* The operand does not have a value. This is valid only for optional
|
* arguments of operations.
|
*/
|
NO_VALUE,
|
};
|
|
/**
|
* Status of a device.
|
*/
|
enum DeviceStatus : int32_t {
|
AVAILABLE,
|
BUSY,
|
OFFLINE,
|
UNKNOWN,
|
};
|
|
/**
|
* Performance information for the reference workload.
|
*
|
* Used by a driver to report its performance characteristics.
|
*/
|
struct PerformanceInfo {
|
/**
|
* Ratio of the time taken by the driver to execute the
|
* workload compared to the time the CPU would take for the
|
* same workload. A lower number is better.
|
*/
|
float execTime;
|
|
/**
|
* Ratio of the energy used by the driver compared to what
|
* the CPU would use for doing the same workload. A lower number
|
* is better.
|
*/
|
float powerUsage;
|
};
|
|
/**
|
* The capabilities of a driver.
|
*/
|
struct Capabilities {
|
/**
|
* Driver performance when operating on float32 data.
|
*/
|
PerformanceInfo float32Performance;
|
|
/**
|
* Driver performance when operating on asymmetric 8-bit quantized data.
|
*/
|
PerformanceInfo quantized8Performance;
|
};
|
|
/**
|
* Describes the location of a data object.
|
*/
|
struct DataLocation {
|
/**
|
* The index of the memory pool where this location is found.
|
*/
|
uint32_t poolIndex;
|
|
/**
|
* Offset in bytes from the start of the pool.
|
*/
|
uint32_t offset;
|
|
/**
|
* The length of the data in bytes.
|
*/
|
uint32_t length;
|
};
|
|
/**
|
* Describes one operand of the model's graph.
|
*/
|
struct Operand {
|
/**
|
* Data type of the operand.
|
*/
|
OperandType type;
|
|
/**
|
* Dimensions of the operand.
|
*
|
* For a scalar operand, dimensions.size() must be 0.
|
*
|
* For a tensor operand, dimensions.size() must be at least 1;
|
* however, any of the dimensions may be unspecified.
|
*
|
* A tensor operand with all dimensions specified has "fully
|
* specified" dimensions. Whenever possible (i.e., whenever the
|
* dimensions are known at model construction time), a tensor
|
* operand should have (but is not required to have) fully
|
* specified dimensions, in order to enable the best possible
|
* performance.
|
*
|
* If a tensor operand's dimensions are not fully specified, the
|
* dimensions of the operand are deduced from the operand
|
* dimensions and values of the operation for which that operand
|
* is an output.
|
*
|
* In the following situations, a tensor operand's dimensions must
|
* be fully specified:
|
*
|
* . The operand has lifetime CONSTANT_COPY or
|
* CONSTANT_REFERENCE.
|
*
|
* . The operand has lifetime MODEL_INPUT or MODEL_OUTPUT. Fully
|
* specified dimensions must either be present in the
|
* Operand or they must be provided in the corresponding
|
* RequestArgument.
|
* EXCEPTION: If the input or output is optional and omitted
|
* (by setting the hasNoValue field of the corresponding
|
* RequestArgument to true) then it need not have fully
|
* specified dimensions.
|
*
|
* A tensor operand with some number of unspecified dimensions is
|
* represented by setting each unspecified dimension to 0.
|
*/
|
vec<uint32_t> dimensions;
|
|
/**
|
* The number of times this operand appears as an operation input.
|
*
|
* (For example, if this operand appears once in one operation's
|
* input list, and three times in another operation's input list,
|
* then numberOfConsumers = 4.)
|
*/
|
uint32_t numberOfConsumers;
|
|
/**
|
* Quantized scale of the operand.
|
*
|
* Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
|
* TENSOR_INT32.
|
*/
|
float scale;
|
|
/**
|
* Quantized zero-point offset of the operand.
|
*
|
* Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
|
*/
|
int32_t zeroPoint;
|
|
/**
|
* How the operand is used.
|
*/
|
OperandLifeTime lifetime;
|
|
/**
|
* Where to find the data for this operand.
|
* If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
|
* NO_VALUE:
|
* - All the fields must be 0.
|
* If the lifetime is CONSTANT_COPY:
|
* - location.poolIndex is 0.
|
* - location.offset is the offset in bytes into Model.operandValues.
|
* - location.length is set.
|
* If the lifetime is CONSTANT_REFERENCE:
|
* - location.poolIndex is set.
|
* - location.offset is the offset in bytes into the specified pool.
|
* - location.length is set.
|
*/
|
DataLocation location;
|
};
|
|
/**
|
* Describes one operation of the model's graph.
|
*/
|
struct Operation {
|
/**
|
* The operation type.
|
*/
|
OperationType type;
|
|
/**
|
* Describes the table that contains the indexes of the inputs of the
|
* operation. The offset is the index in the operandIndexes table.
|
*/
|
vec<uint32_t> inputs;
|
|
/**
|
* Describes the table that contains the indexes of the outputs of the
|
* operation. The offset is the index in the operandIndexes table.
|
*/
|
vec<uint32_t> outputs;
|
};
|
|
/**
|
* A Neural Network Model.
|
*
|
* This includes not only the execution graph, but also constant data such as
|
* weights or scalars added at construction time. The only information that
|
* might not be known is the shape of the input tensors.
|
*/
|
struct Model {
|
/**
|
* All operands included in the model.
|
*/
|
vec<Operand> operands;
|
|
/**
|
* All operations included in the model.
|
*
|
* The operations are sorted into execution order. Every operand
|
* with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
|
* written before it is read.
|
*/
|
vec<Operation> operations;
|
|
/**
|
* Input indexes of the model. There must be at least one.
|
*
|
* Each value corresponds to the index of the operand in "operands".
|
*/
|
vec<uint32_t> inputIndexes;
|
|
/**
|
* Output indexes of the model. There must be at least one.
|
*
|
* Each value corresponds to the index of the operand in "operands".
|
*/
|
vec<uint32_t> outputIndexes;
|
|
/**
|
* A byte buffer containing operand data that were copied into the model.
|
*
|
* An operand's value must be located here if and only if Operand::lifetime
|
* equals OperandLifeTime::CONSTANT_COPY.
|
*/
|
vec<uint8_t> operandValues;
|
|
/**
|
* A collection of shared memory pools containing operand values.
|
*
|
* An operand's value must be located here if and only if Operand::lifetime
|
* equals OperandLifeTime::CONSTANT_REFERENCE.
|
*/
|
vec<memory> pools;
|
};
|
|
/**
|
* Metadata information specifying the location of the input or output data and
|
* any updates to the input or output operand.
|
*/
|
struct RequestArgument {
|
/**
|
* If true, the argument does not have a value. This can be used for
|
* operations that take optional arguments. If true, the fields of location
|
* are set to 0 and the dimensions vector is left empty.
|
*/
|
bool hasNoValue;
|
|
/**
|
* The location within one of the memory pools passed in the Request.
|
*/
|
DataLocation location;
|
|
/**
|
* Updated dimension information.
|
*
|
* If dimensions.size() > 0, dimension information was provided
|
* along with the argument. This can be the case for models that
|
* accept inputs of varying size. This can't change the rank, just
|
* the value of the dimensions that were unspecified in the
|
* model. If dimensions.size() > 0, then all dimensions must be
|
* specified here; and any dimension that was specified in the
|
* model must have the same value here.
|
*
|
* If the dimensions in the model are not fully specified, then
|
* they must be fully specified here, unless hasNoValue is set to
|
* true. If the dimensions in the model are fully specified, then
|
* either dimensions.size() may be 0, or the dimensions in the
|
* model must be identical to the dimensions here.
|
*/
|
vec<uint32_t> dimensions;
|
};
|
|
/**
|
* Inputs to be sent to and outputs to be retrieved from a prepared model.
|
*
|
* A Request serves two primary tasks:
|
* 1) Provides the input and output data to be used when executing the model.
|
* 2) Specifies any updates to the input operand metadata that were left
|
* unspecified at model preparation time.
|
*
|
* An output must not overlap with any other output, with an input, or
|
* with an operand of lifetime CONSTANT_REFERENCE.
|
*/
|
struct Request {
|
/**
|
* Input data and information to be used in the execution of a prepared
|
* model.
|
*
|
* The index of the input corresponds to the index in Model.inputIndexes.
|
* E.g., input[i] corresponds to Model.inputIndexes[i].
|
*/
|
vec<RequestArgument> inputs;
|
|
/**
|
* Output data and information to be used in the execution of a prepared
|
* model.
|
*
|
* The index of the output corresponds to the index in Model.outputIndexes.
|
* E.g., output[i] corresponds to Model.outputIndexes[i].
|
*/
|
vec<RequestArgument> outputs;
|
|
/**
|
* A collection of shared memory pools containing operand data for both the
|
* inputs and the outputs to a model.
|
*/
|
vec<memory> pools;
|
};
|
|
/**
|
* Return status of a function.
|
*/
|
enum ErrorStatus : int32_t {
|
NONE,
|
DEVICE_UNAVAILABLE,
|
GENERAL_FAILURE,
|
OUTPUT_INSUFFICIENT_SIZE,
|
INVALID_ARGUMENT,
|
};
|
