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Updated each source file in the repo with the existing license.

* Upgrade to cppcheck 2.8

As described in #1196, the ONNX mean parser does not work correctly for integral types. This update fixes the issue by handling integral types separately, where summation is performed before division. Additional test cases have also been added for handling integral types.

Add ref and gpu implementations for ONNX op GatherND

Resolves #1032

Implements the ReverseSequence ONNX operator as a parser.

This parser can only handle a constant sequence_lens input. This is the same as what is handled for TensorRT as far as I can tell.
We could handle a variable sequence_lens input; that would require ref and GPU implementations of the operator.
The ONNX backend tests are disabled because this does not handle variable sequence_lens.

Refactored the reference implementation of pooling to something like what was done for roialign. Moved the reference implementation of pooling from targets/ref/lowering.cpp to pooling.hpp.
Removed cpu_pooling, instead using reference pooling in pooling.hpp
Added reference implementation of Lp Norm pooling and the global version
Added tests for the Lp Norm Pooling

Change the "scatter" struct and op to a base/child set of three: scatter_none, scatter_add, scatter_mul to mirror Onnx' ScatterElements op. and its three reduction options. (Onnx Scatter op is deprecated and is equivalent to scatter_none.)

Provides both a reference op. and update to Onnx parsing. Tests updated and new test case added.

* LpNormalization ONNX parser

Add Celu ONNX operator

* Implement size ONNX operator and tests

add_common_op for parse_clip
Should fix #1119

Adds EyeLike ONNX parser and unit tests.

Changed the pooling values for two structures from strings to specialized enum classes. Many test and operator parsing changes to support this. Introduces one new source file, op_enums.cpp.

Add onnx parser and ref and gpu implementations of ONNX op ScatterND

Implements the IsNaN operator, ref, gpu, and onnx parser.

* use the parse_resize to parse the upsample operator

* Add mean op onnx parser and unit tests
* Refactor parse_mean to use add_broadcastable_binary_op

Add HardSwish to HardSigmoid parser

HardSwish formula is y = x * HardSigmoid<alpha=1/6, beta=0.5>(x)
HardSigmoid parser sets alpha to 1/6 and adds the mul instruction if op name is HardSwish

Resolves #1062

Add onnx parser for operator GreaterOrEqual

Add onnx parser and unit tests for Softsign

* Add onnx parser and unit test

Add HardSigmoid onnx parser and unit tests
Produces mathematical equivalent to ONNX operator through combination of existing pointwise ops.
Resolves #1028

Fix bug caused by casting time seed to float

This PR is the ref implementation of the nonmaxsuppression operator. It always returns the max possible output shape, which is the problem tracked in issue #948.

Implementation of the roialign operator. For now, we have only the ref implementation. When we run a model on the GPU, we fall back the execution to use the ref implementation.

Inverse of DepthToSpace op
Co-authored-by: Shucai Xiao <shucai@gmail.com>

This PR is for the nonzero operator with static output shape.  
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>
Co-authored-by: mvermeulen <5479696+mvermeulen@users.noreply.github.com>

Previously dot operator was defined as C = alpha * A . B + beta * C where * is scalar multiplication and . is dot product or matrix multiplication depending on dimension of the inputs.

Aim is to have the definition of dot operator as C = A . B without having alpha or beta.

In order to achieve the same effect as alpha and beta (1) it multiplies the one of the inputs to the dot operator with alpha value. (2) if beta is present then, multiplies the C with beta and then adds into the output from step 1.

Add multinomial op to onnx parser with ref and GPU implementations.

The onnx parser inserts a literal of shape {batch_size, sample_size} with random values in the range [0, 1) and inserts existing ops to compute the cumulative density function. The multinomial operator multiplies the random values by the sum of the CDF and returns the index of the first element of the CDF that is greater than the result, representing samples randomly drawn from [0, class_size) that follow the log-probability distribution.

Resolves #821
Co-authored-by: Shucai Xiao <shucai@gmail.com>

Add RandomNormal, RandomNormalLike, RandomUniform, and RandomUniformLike to onnx parser and onnx tests

Each pair of Random*/Random*Like is implemented using a single op_parser because the ops share the same essential attributes and algorithm with the difference that Random*Like get the output type and/or shape from an input argument and Random* take both from attributes.

Resolves #907
Resolves #959

Supports 1,11,13 ONNX Operator Set

This reverts commit 9e43cb8b.

This PR aims to remove alpha and beta attributes from dot operator completely.

Previously dot operator was defined as C = alpha * A . B + beta * C where * is scalar multiplication and . is dot product or matrix multiplication depending on dimension of the inputs.

Aim is to have the definition of dot operator as C = A . B without having alpha or beta.

In order to achieve the same effect as alpha and beta (1) it multiplies the one of the inputs to the dot operator with alpha value. (2) if beta is present then, multiplies the C with beta and then adds into the output from step 1.

Add Loop operator for opset version 13.
Notes: 1) Default max iteration number is 10 if no max iteration number is provided
2) To change the max iter number, a user can set the max_loop_iterations in the onnx_option struct when parsing a model.
3) The returned shape of the scan output is from the max_loop_iterations even the actual loop num is less than that. This issue also applies to other operators like NonZero and NonMaxSuppression. A issue #948 is created to track this and to be resolved later.
Co-authored-by: Paul <pfultz2@yahoo.com>
Co-authored-by: mvermeulen <5479696+mvermeulen@users.noreply.github.com>

Add ability to parse ThresholdedRelu ONNX operator.

Resolves #888
Co-authored-by: Shucai Xiao <shucai@gmail.com>

Implement the Where operator for the CPU and GPU.  This is for better performance.

* add topk operator doe ref, cpu and gpu
* Hash modules for quicker lookup of modules
* add onnx unit test
* add unit tests for the topk operator
Co-authored-by: Paul <pfultz2@yahoo.com>
Co-authored-by: mvermeulen <5479696+mvermeulen@users.noreply.github.com>

* fix two asserts for debug build

* add unit test for copy parameters

* clang format

* add a unit test for reorder_dims

* change tranpose to always require perm not be empty

* clang format

* remove an unnecessary line

* fix tidy error

* fix review comments

* rename broadcast and multibroadcast output_lens attribute to out_lens attribute, and change tests and source code to reflect the same

* change the reshape attribute from dims to out_lens

* change transpose attribute's name from dims to perm to reflect better meaning

* use permutation instead of perm for transpose

clang formaating

* use dims instead of out_lens for reshape

clang formatting

* Add operators, refactor parsers, add rewrite passes, add tests

* Add ref implementations

* Move broadcasting of scales and zero points to onnx parser

* Allow for x and zero_point to have different types in quantizelinear; fix zero_point default type

* Switch certain variables to int64_t

* Fix overflow in implicit constant conversion

* Remove operators.hpp from includes in tf_test.cpp

* Add conversion for int32 input to quantizelinear and add test case; remove operators.hpp from onnx_test.cpp includes

* Switch dequantizelinear math from int32 to float

* Remove changes to operators.hpp

* Simplify apply_quantizelinear

* Add verify test for int32 data

* Add rewrite_quantization back to CMakeLists

* Add passes to insert qdq after add_bias is applied, replace quant_ops, and remove remaining qdq pairs

* Renaming, refactoring, cleaning up code, adding formal test, and adding passes to targets

* Renaming, review comments, begin adding more specific tests

* Add more specific unit tests

* Fix failing test on CI

* Correct matcher and update qop rewriting, update tests and add more tests

* Update matcher, clean up simplify_qdq, tweak tests

* Add tests, remove pass from CPU target, update dot parameters, clean up simplify_qdq

* Fix correctness bug in ref q/dq implementations; edit gemm parser to make beta always 0.0

* Remove unused variables in onnx gemm tests