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/*
* SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// This file contains all bindings related to TensorRT INetworkDefinition.
#include "ForwardDeclarations.h"
#include "impl/NvInferPythonPlugin.h"
#include "utils.h"
#include <pybind11/stl.h>
#include <tuple>
#if ENABLE_INETWORK_SERIALIZE
#include "NvInferSerialize.h"
#endif
#include <vector>
#include "infer/pyGraphDoc.h"
// clang-format off
namespace tensorrt
{
using namespace nvinfer1;
// Long lambda functions should go here rather than being inlined into the bindings (1 liners are OK).
namespace lambdas
{
Weights optionalWeights(Weights* weights, DataType dtype)
{
if (weights)
{
return *weights;
}
return Weights{dtype, nullptr, 0};
}
// For permutation
static const auto permutation_vector_constructor = [] (std::vector<int32_t> const& in) {
// Static casts are required here, so that MAX_DIMS is resolved at compile/link time.
int32_t const maxDims{static_cast<int32_t>(Dims::MAX_DIMS)};
PY_ASSERT_VALUE_ERROR(in.size() <= maxDims,
"Invalid input length. Max expected length is " + std::to_string(maxDims));
Permutation* self = new Permutation{};
for (size_t i = 0; i < in.size(); ++i)
self->order[i] = static_cast<int32_t>(in[i]);
return self;
};
static const auto permutation_to_str = [] (Permutation const& self) {
int32_t const maxDims = static_cast<int32_t>(Dims::MAX_DIMS);
std::string temp = "(";
for (int32_t i = 0; i < maxDims - 1; ++i)
temp += std::to_string(self.order[i]) + ", ";
temp += std::to_string(self.order[maxDims - 1]) + ")";
return temp;
};
// TODO: Add slicing support?
static const auto permutation_getter = [] (Permutation const& self, int32_t const pyIndex) {
PY_ASSERT_INDEX_ERROR(pyIndex < static_cast<int32_t>(Dims::MAX_DIMS));
int32_t const index{(pyIndex < 0) ? static_cast<int32_t>(Dims::MAX_DIMS) + pyIndex : pyIndex};
// Static cast is REQUIRED here, or chaos ensues as MAX_DIMS is not pulled in at link time.
PY_ASSERT_INDEX_ERROR(index >= 0 && index < static_cast<int32_t>(Dims::MAX_DIMS));
return self.order[index];
};
static const auto permutation_setter = [] (Permutation& self, int32_t const pyIndex, int32_t const item) {
PY_ASSERT_INDEX_ERROR(pyIndex < static_cast<int32_t>(Dims::MAX_DIMS));
int32_t const index = (pyIndex < 0) ? static_cast<int32_t>(Dims::MAX_DIMS) + pyIndex : pyIndex;
// Static cast is REQUIRED here, or chaos ensues as MAX_DIMS is not pulled in at link time.
PY_ASSERT_INDEX_ERROR(index >= 0 && index < static_cast<int32_t>(Dims::MAX_DIMS));
self.order[index] = item;
};
static const auto permutation_len = [] (Permutation const& self) {
return static_cast<int32_t>(Dims::MAX_DIMS);
};
// For INetworkDefinition
// Need a ptr to const-ptr to ITensor.
static const auto add_concatenation = [] (INetworkDefinition& self, std::vector<ITensor*> const& inputs) {
return self.addConcatenation(inputs.data(), inputs.size());
};
// Need a ptr to const-ptr to ITensor.
static const auto add_plugin_v2 = [] (INetworkDefinition& self, std::vector<ITensor*> const& inputs, IPluginV2& plugin) {
return self.addPluginV2(inputs.data(), inputs.size(), plugin);
};
static const auto add_plugin_v3 = [] (INetworkDefinition& self, std::vector<ITensor*> const& inputs, std::vector<ITensor*> const& shapeInputs, IPluginV3& plugin)
{
return self.addPluginV3(inputs.data(), inputs.size(), shapeInputs.data(), shapeInputs.size(), plugin);
};
static const auto add_plugin = [] (INetworkDefinition& self, py::tuple pyTuple)
{
auto const tupleInput = pyTuple.cast<std::tuple<std::vector<ITensor*>, std::vector<ITensor*>, IPluginV3*>>();
std::vector<ITensor*> const& inputs = std::get<0>(tupleInput);
std::vector<ITensor*> const& shapeInputs = std::get<1>(tupleInput);
IPluginV3* plugin = std::get<2>(tupleInput);
return self.addPluginV3(inputs.data(), inputs.size(), shapeInputs.data(), shapeInputs.size(), *plugin);
};
static const auto add_plugin_default_func = [] (INetworkDefinition& self, py::function func)
{
auto const preferAOT = self.getFlag(NetworkDefinitionCreationFlag::kPREFER_AOT_PYTHON_PLUGINS);
auto const preferJIT = self.getFlag(NetworkDefinitionCreationFlag::kPREFER_JIT_PYTHON_PLUGINS);
PY_ASSERT_VALUE_ERROR(!preferAOT || !preferJIT, "Both NetworkDefinitionCreationFlag.PREFER_AOT_PYTHON_PLUGINS and NetworkDefinitionCreationFlag.PREFER_JIT_PYTHON_PLUGINS cannot be specified at the same time.");
// If none of these flags are specified, it is fine as long as only AOT or only JIT implementation is defined;
// we defer checking that to the Python backend
// - If both are defined, the plugin creator will raise an error.
auto const request = preferJIT
? QuickPluginCreationRequest::kPREFER_JIT
: (preferAOT ? QuickPluginCreationRequest::kPREFER_AOT : QuickPluginCreationRequest::kUNKNOWN);
return add_plugin(self, func(request));
};
static const auto add_plugin_func = [] (INetworkDefinition& self, py::function func, bool aot)
{
auto const request = aot ? QuickPluginCreationRequest::kSTRICT_AOT : QuickPluginCreationRequest::kSTRICT_JIT;
py::tuple pyResult = func(request);
return add_plugin(self, pyResult);
};
static const auto add_convolution_nd = [](INetworkDefinition& self, ITensor& input, int32_t numOutputMaps, Dims kernelSize, Weights kernel, Weights* bias)
{
return self.addConvolutionNd(input, numOutputMaps, kernelSize, kernel, optionalWeights(bias, input.getType()));
};
IGridSampleLayer* add_grid_sample(INetworkDefinition& self, ITensor& input, ITensor& grid)
{
return self.addGridSample(input, grid);
}
static const auto add_scale = [](INetworkDefinition& self, ITensor& input, ScaleMode mode, Weights* shift, Weights* scale, Weights* power)
{
return self.addScale(input, mode, optionalWeights(shift, input.getType()), optionalWeights(scale, input.getType()), optionalWeights(power, input.getType()));
};
static const auto add_scale_nd = [](INetworkDefinition& self, ITensor& input, ScaleMode mode, Weights* shift, Weights* scale, Weights* power, int32_t channelAxis)
{
return self.addScaleNd(input, mode, optionalWeights(shift, input.getType()), optionalWeights(scale, input.getType()), optionalWeights(power, input.getType()), channelAxis);
};
static const auto add_deconvolution_nd = [](INetworkDefinition& self, ITensor& input, int32_t numOutputMaps, Dims kernelSize, Weights kernel, Weights* bias)
{
return self.addDeconvolutionNd(input, numOutputMaps, kernelSize, kernel, optionalWeights(bias, input.getType()));
};
static const auto add_einsum = [] (INetworkDefinition& self, const std::vector<ITensor*>& inputs, const char* equation) {
return self.addEinsum(inputs.data(), inputs.size(), equation);
};
#if ENABLE_INETWORK_SERIALIZE
// Serialization
static const auto network_serialize = [] (INetworkDefinition& self) {
return serialize::serializeNetwork(self);
};
#endif
// TODO: Need to ensure that these are returning by reference rather than by copy.
// NumPy getters for layers.
static const auto conv_get_kernel = [](IConvolutionLayer& self) { auto w = self.getKernelWeights(); return utils::weights_to_numpy(w); };
static const auto conv_get_bias = [](IConvolutionLayer& self) { auto w = self.getBiasWeights(); return utils::weights_to_numpy(w); };
static const auto scale_get_shift = [](IScaleLayer& self) { auto w = self.getShift(); return utils::weights_to_numpy(w); };
static const auto scale_get_scale = [](IScaleLayer& self) { auto w = self.getScale(); return utils::weights_to_numpy(w); };
static const auto scale_get_power = [](IScaleLayer& self) { auto w = self.getPower(); return utils::weights_to_numpy(w); };
static const auto deconv_get_kernel = [](IDeconvolutionLayer& self) { auto w = self.getKernelWeights(); return utils::weights_to_numpy(w); };
static const auto deconv_get_bias = [](IDeconvolutionLayer& self) { auto w = self.getBiasWeights(); return utils::weights_to_numpy(w); };
static const auto constant_get_weights = [](IConstantLayer& self) { auto w = self.getWeights(); return utils::weights_to_numpy(w); };
// TODO: Add slicing support?
static const auto network_getitem = [](INetworkDefinition& self, int32_t pyIndex) {
// Support python's negative indexing
int32_t index = (pyIndex < 0) ? self.getNbLayers() + pyIndex : pyIndex;
PY_ASSERT_INDEX_ERROR(index < self.getNbLayers());
return self.getLayer(index);
};
static const auto resize_set_scales = [](IResizeLayer& self, const std::vector<float>& scales) { self.setScales(scales.data(), scales.size()); };
static const auto resize_get_scales = [](IResizeLayer& self)
{
int32_t nbScales = self.getScales(0, nullptr);
// nbScales of -1 signifies that scales are unused for resize caluclation. Return an empty vector here.
if (nbScales == -1)
{
return std::vector<float>();
}
std::vector<float> scales(nbScales, 1.0f);
self.getScales(nbScales, scales.data());
return scales;
};
// For Fill layer
static auto set_alpha = [](IFillLayer& self, py::object alpha) {
try
{
double alphaDouble = alpha.cast<double>();
self.setAlpha(alphaDouble);
}
catch (py::cast_error const&) {}
try
{
int64_t alphaInt64 = alpha.cast<int64_t>();
self.setAlphaInt64(alphaInt64);
}
catch (py::cast_error const&) {}
};
static auto get_alpha = [](IFillLayer& self) {
if (self.isAlphaBetaInt64())
return py::cast(self.getAlphaInt64());
else
return py::cast(self.getAlpha());
};
static auto set_beta = [](IFillLayer& self, py::object beta) {
try
{
double betaDouble = beta.cast<double>();
self.setBeta(betaDouble);
}
catch (py::cast_error const&) {}
try
{
int64_t betaInt64 = beta.cast<int64_t>();
self.setBetaInt64(betaInt64);
}
catch (py::cast_error const&) {}
};
static auto get_beta = [](IFillLayer& self) {
if (self.isAlphaBetaInt64())
return py::cast(self.getBetaInt64());
else
return py::cast(self.getBeta());
};
static auto add_dist_collective_adapter = [](
INetworkDefinition& net,
ITensor& inp,
CollectiveOperation collective_op,
py::object reduce_op,
py::object root,
py::object pyGroups
)
{
std::vector<int64_t> grp = pyGroups.is_none() ? std::vector<int64_t>() : pyGroups.cast<std::vector<int64_t>>();
return net.addDistCollective(inp, collective_op, reduce_op.cast<ReduceOperation>(), root.cast<int64_t>(), grp.data(), grp.size());
};
} /* lambdas */
static void cumulative_layer_set_operation(ICumulativeLayer& self, CumulativeOperation op)
{
bool const status = self.setOperation(op);
PY_ASSERT_RUNTIME_ERROR(status, "Failed to set CumulativeLayer's CumulativeOperation");
}
static void attention_set_operation(IAttention& self, AttentionNormalizationOp op)
{
bool const status = self.setNormalizationOperation(op);
PY_ASSERT_RUNTIME_ERROR(status, "Failed to set Attention's AttentionNormalizationOp");
}
namespace
{
void attention_set_causal_kind(IAttention& self, CausalMaskKind kind)
{
bool const status = self.setCausalKind(kind);
PY_ASSERT_RUNTIME_ERROR(status, "Failed to set Attention's CausalMaskKind");
}
} // namespace
static void kv_cache_update_layer_set_cache_mode(IKVCacheUpdateLayer& self, KVCacheMode mode)
{
bool const status = self.setCacheMode(mode);
PY_ASSERT_RUNTIME_ERROR(status, "Failed to set KVCacheUpdateLayer's KVCacheMode");
}
namespace
{
void attention_set_query_form(IAttention& self, AttentionIOForm form)
{
PY_ASSERT_RUNTIME_ERROR(self.setQueryForm(form), "Failed to set Attention's query form");
}
void attention_set_key_value_form(IAttention& self, AttentionIOForm form)
{
PY_ASSERT_RUNTIME_ERROR(self.setKeyValueForm(form), "Failed to set Attention's key-value form");
}
void attention_set_query_lengths(IAttention& self, ITensor* lengths)
{
PY_ASSERT_RUNTIME_ERROR(self.setQueryLengths(lengths), "Failed to set Attention's query lengths");
}
void attention_set_key_value_lengths(IAttention& self, ITensor* lengths)
{
PY_ASSERT_RUNTIME_ERROR(self.setKeyValueLengths(lengths), "Failed to set Attention's key-value lengths");
}
void kv_cache_update_layer_set_update_form(IKVCacheUpdateLayer& self, AttentionIOForm form)
{
PY_ASSERT_RUNTIME_ERROR(self.setUpdateForm(form), "Failed to set KVCacheUpdateLayer's update form");
}
void kv_cache_update_layer_set_update_lengths(IKVCacheUpdateLayer& self, ITensor* lengths)
{
PY_ASSERT_RUNTIME_ERROR(self.setUpdateLengths(lengths), "Failed to set KVCacheUpdateLayer's update lengths");
}
} // namespace
void bindGraph(py::module& m)
{
// Bind to a Python enum called LayerType.
py::enum_<LayerType>(m, "LayerType", LayerTypeDoc::descr, py::module_local())
.value("CONVOLUTION", LayerType::kCONVOLUTION, LayerTypeDoc::CONVOLUTION)
.value("GRID_SAMPLE", LayerType::kGRID_SAMPLE, LayerTypeDoc::GRID_SAMPLE)
.value("NMS", LayerType::kNMS, LayerTypeDoc::NMS)
.value("ACTIVATION", LayerType::kACTIVATION, LayerTypeDoc::ACTIVATION)
.value("POOLING", LayerType::kPOOLING, LayerTypeDoc::POOLING)
.value("LRN", LayerType::kLRN, LayerTypeDoc::LRN)
.value("SCALE", LayerType::kSCALE, LayerTypeDoc::SCALE)
.value("SOFTMAX", LayerType::kSOFTMAX, LayerTypeDoc::SOFTMAX)
.value("DECONVOLUTION", LayerType::kDECONVOLUTION, LayerTypeDoc::DECONVOLUTION)
.value("CONCATENATION", LayerType::kCONCATENATION, LayerTypeDoc::CONCATENATION)
.value("ELEMENTWISE", LayerType::kELEMENTWISE, LayerTypeDoc::ELEMENTWISE)
.value("PLUGIN", LayerType::kPLUGIN, LayerTypeDoc::PLUGIN)
.value("UNARY", LayerType::kUNARY, LayerTypeDoc::UNARY)
.value("PADDING", LayerType::kPADDING, LayerTypeDoc::PADDING)
.value("SHUFFLE", LayerType::kSHUFFLE, LayerTypeDoc::SHUFFLE)
.value("REDUCE", LayerType::kREDUCE, LayerTypeDoc::REDUCE)
.value("TOPK", LayerType::kTOPK, LayerTypeDoc::TOPK)
.value("GATHER", LayerType::kGATHER, LayerTypeDoc::GATHER)
.value("MATRIX_MULTIPLY", LayerType::kMATRIX_MULTIPLY, LayerTypeDoc::MATRIX_MULTIPLY)
.value("RAGGED_SOFTMAX", LayerType::kRAGGED_SOFTMAX, LayerTypeDoc::RAGGED_SOFTMAX)
.value("CONSTANT", LayerType::kCONSTANT, LayerTypeDoc::CONSTANT)
.value("IDENTITY", LayerType::kIDENTITY, LayerTypeDoc::IDENTITY)
.value("CAST", LayerType::kCAST, LayerTypeDoc::CAST)
.value("PLUGIN_V2", LayerType::kPLUGIN_V2, LayerTypeDoc::PLUGIN_V2)
.value("SLICE", LayerType::kSLICE, LayerTypeDoc::SLICE)
.value("SHAPE", LayerType::kSHAPE, LayerTypeDoc::SHAPE)
.value("PARAMETRIC_RELU", LayerType::kPARAMETRIC_RELU, LayerTypeDoc::PARAMETRIC_RELU)
.value("RESIZE", LayerType::kRESIZE, LayerTypeDoc::RESIZE)
.value("TRIP_LIMIT", LayerType::kTRIP_LIMIT, LayerTypeDoc::TRIP_LIMIT)
.value("RECURRENCE", LayerType::kRECURRENCE, LayerTypeDoc::RECURRENCE)
.value("ITERATOR", LayerType::kITERATOR, LayerTypeDoc::ITERATOR)
.value("LOOP_OUTPUT", LayerType::kLOOP_OUTPUT, LayerTypeDoc::LOOP_OUTPUT)
.value("SELECT", LayerType::kSELECT, LayerTypeDoc::SELECT)
.value("ASSERTION", LayerType::kASSERTION, LayerTypeDoc::ASSERTION)
.value("FILL", LayerType::kFILL, LayerTypeDoc::FILL)
.value("QUANTIZE", LayerType::kQUANTIZE, LayerTypeDoc::QUANTIZE)
.value("DEQUANTIZE", LayerType::kDEQUANTIZE, LayerTypeDoc::DEQUANTIZE)
.value("CONDITION", LayerType::kCONDITION, LayerTypeDoc::CONDITION)
.value("CONDITIONAL_INPUT", LayerType::kCONDITIONAL_INPUT, LayerTypeDoc::CONDITIONAL_INPUT)
.value("CONDITIONAL_OUTPUT", LayerType::kCONDITIONAL_OUTPUT, LayerTypeDoc::CONDITIONAL_OUTPUT)
.value("SCATTER", LayerType::kSCATTER, LayerTypeDoc::SCATTER)
.value("EINSUM", LayerType::kEINSUM, LayerTypeDoc::EINSUM)
.value("ONE_HOT", LayerType::kONE_HOT, LayerTypeDoc::ONE_HOT)
.value("NON_ZERO", LayerType::kNON_ZERO, LayerTypeDoc::NON_ZERO)
.value("REVERSE_SEQUENCE", LayerType::kREVERSE_SEQUENCE, LayerTypeDoc::REVERSE_SEQUENCE)
.value("NORMALIZATION", LayerType::kNORMALIZATION, LayerTypeDoc::NORMALIZATION)
.value("PLUGIN_V3", LayerType::kPLUGIN_V3, LayerTypeDoc::PLUGIN_V3)
.value("SQUEEZE", LayerType::kSQUEEZE, LayerTypeDoc::SQUEEZE)
.value("UNSQUEEZE", LayerType::kUNSQUEEZE, LayerTypeDoc::UNSQUEEZE)
.value("CUMULATIVE", LayerType::kCUMULATIVE, LayerTypeDoc::CUMULATIVE)
.value("DYNAMIC_QUANTIZE", LayerType::kDYNAMIC_QUANTIZE, LayerTypeDoc::DYNAMIC_QUANTIZE)
.value("ATTENTION_INPUT", LayerType::kATTENTION_INPUT, LayerTypeDoc::ATTENTION_INPUT)
.value("ATTENTION_OUTPUT", LayerType::kATTENTION_OUTPUT, LayerTypeDoc::ATTENTION_OUTPUT)
.value("ROTARY_EMBEDDING", LayerType::kROTARY_EMBEDDING, LayerTypeDoc::ROTARY_EMBEDDING)
.value("KV_CACHE_UPDATE", LayerType::kKVCACHE_UPDATE, LayerTypeDoc::KV_CACHE_UPDATE)
.value("MOE", LayerType::kMOE, LayerTypeDoc::MOE)
.value("DIST_COLLECTIVE", LayerType::kDIST_COLLECTIVE, LayerTypeDoc::DIST_COLLECTIVE)
; // LayerType
py::enum_<TensorFormat>(m, "TensorFormat", TensorFormatDoc::descr, py::arithmetic{}, py::module_local())
.value("LINEAR", TensorFormat::kLINEAR, TensorFormatDoc::LINEAR)
.value("CHW2", TensorFormat::kCHW2, TensorFormatDoc::CHW2)
.value("HWC8", TensorFormat::kHWC8, TensorFormatDoc::HWC8)
.value("CHW4", TensorFormat::kCHW4, TensorFormatDoc::CHW4)
.value("CHW16", TensorFormat::kCHW16, TensorFormatDoc::CHW16)
.value("CHW32", TensorFormat::kCHW32, TensorFormatDoc::CHW32)
.value("DHWC8", TensorFormat::kDHWC8, TensorFormatDoc::DHWC8)
.value("CDHW32", TensorFormat::kCDHW32, TensorFormatDoc::CDHW32)
.value("HWC", TensorFormat::kHWC, TensorFormatDoc::HWC)
.value("DLA_LINEAR", TensorFormat::kDLA_LINEAR, TensorFormatDoc::DLA_LINEAR)
.value("DLA_HWC4", TensorFormat::kDLA_HWC4, TensorFormatDoc::DLA_HWC4)
.value("HWC16", TensorFormat::kHWC16, TensorFormatDoc::HWC16)
.value("DHWC", TensorFormat::kDHWC, TensorFormatDoc::DHWC)
; // TensorFormat
// ITensor
py::class_<ITensor, std::unique_ptr<ITensor, py::nodelete>>(m, "ITensor", ITensorDoc::descr, py::module_local())
.def_property("name", &ITensor::getName, &ITensor::setName)
.def_property("shape", utils::throwingDimsGetter(&ITensor::getDimensions, "tensor shape"),
&ITensor::setDimensions)
.def_property_readonly("dtype", &ITensor::getType)
.def_property("broadcast_across_batch", utils::deprecateMember(&ITensor::getBroadcastAcrossBatch, "Implicit batch dimensions support has been removed"), utils::deprecateMember(&ITensor::setBroadcastAcrossBatch, "Implicit batch dimensions support has been removed"))
.def_property("location", &ITensor::getLocation, &ITensor::setLocation)
.def_property("allowed_formats", &ITensor::getAllowedFormats, &ITensor::setAllowedFormats)
.def_property_readonly("is_network_input", &ITensor::isNetworkInput)
.def_property_readonly("is_network_output", &ITensor::isNetworkOutput)
.def_property_readonly("is_shape_tensor", &ITensor::isShapeTensor)
.def_property_readonly("is_execution_tensor", &ITensor::isExecutionTensor)
.def_property("allowed_formats", &ITensor::getAllowedFormats, &ITensor::setAllowedFormats)
.def("set_dimension_name", &ITensor::setDimensionName, "index"_a, "name"_a, ITensorDoc::set_dimension_name)
.def("get_dimension_name", &ITensor::getDimensionName, "index"_a, ITensorDoc::get_dimension_name)
;
py::class_<ILayer, std::unique_ptr<ILayer, py::nodelete>>(m, "ILayer", ILayerDoc::descr, py::module_local())
.def_property("name", &ILayer::getName, &ILayer::setName)
.def_property("metadata", &ILayer::getMetadata, &ILayer::setMetadata)
.def_property("num_ranks", &ILayer::getNbRanks, [](ILayer& self, int32_t nbRanks) {
PY_ASSERT_RUNTIME_ERROR(self.setNbRanks(nbRanks),
"Failed to set num_ranks. Only IDistCollectiveLayer supports num_ranks > 1 via ILayer.");
}, ILayerDoc::num_ranks)
.def_property_readonly("type", &ILayer::getType)
.def_property_readonly("num_inputs", &ILayer::getNbInputs)
.def_property_readonly("num_outputs", &ILayer::getNbOutputs)
.def("set_input", &ILayer::setInput, "index"_a, "tensor"_a, ILayerDoc::set_input)
.def("get_input", &ILayer::getInput, "index"_a, ILayerDoc::get_input)
.def("get_output", &ILayer::getOutput, "index"_a, ILayerDoc::get_output)
.def("get_output_type", &ILayer::getOutputType, "index"_a, ILayerDoc::get_output_type)
;
py::enum_<PaddingMode>(m, "PaddingMode", PaddingModeDoc::descr, py::module_local())
.value("EXPLICIT_ROUND_DOWN", PaddingMode::kEXPLICIT_ROUND_DOWN, PaddingModeDoc::EXPLICIT_ROUND_DOWN)
.value("EXPLICIT_ROUND_UP", PaddingMode::kEXPLICIT_ROUND_UP, PaddingModeDoc::EXPLICIT_ROUND_UP)
.value("SAME_UPPER", PaddingMode::kSAME_UPPER, PaddingModeDoc::SAME_UPPER)
.value("SAME_LOWER", PaddingMode::kSAME_LOWER, PaddingModeDoc::SAME_LOWER)
;
py::class_<IConvolutionLayer, ILayer, std::unique_ptr<IConvolutionLayer, py::nodelete>>(m, "IConvolutionLayer", IConvolutionLayerDoc::descr, py::module_local())
.def_property("num_output_maps", &IConvolutionLayer::getNbOutputMaps, &IConvolutionLayer::setNbOutputMaps)
.def_property("pre_padding", utils::throwingDimsGetter(&IConvolutionLayer::getPrePadding, "pre_padding"),
&IConvolutionLayer::setPrePadding)
.def_property("post_padding", utils::throwingDimsGetter(&IConvolutionLayer::getPostPadding, "post_padding"),
&IConvolutionLayer::setPostPadding)
.def_property("padding_mode", &IConvolutionLayer::getPaddingMode, &IConvolutionLayer::setPaddingMode)
.def_property("num_groups", &IConvolutionLayer::getNbGroups, &IConvolutionLayer::setNbGroups)
// Return numpy arrays instead of weights.
.def_property("kernel", lambdas::conv_get_kernel, py::cpp_function(&IConvolutionLayer::setKernelWeights, py::keep_alive<1, 2>{}))
.def_property("bias", lambdas::conv_get_bias, py::cpp_function(&IConvolutionLayer::setBiasWeights, py::keep_alive<1, 2>{}))
.def_property("kernel_size_nd",
utils::throwingDimsGetter(&IConvolutionLayer::getKernelSizeNd, "kernel_size_nd"),
&IConvolutionLayer::setKernelSizeNd)
.def_property("stride_nd", utils::throwingDimsGetter(&IConvolutionLayer::getStrideNd, "stride_nd"),
&IConvolutionLayer::setStrideNd)
.def_property("padding_nd", utils::throwingDimsGetter(&IConvolutionLayer::getPaddingNd, "padding_nd"),
&IConvolutionLayer::setPaddingNd)
.def_property("dilation_nd", utils::throwingDimsGetter(&IConvolutionLayer::getDilationNd, "dilation_nd"),
&IConvolutionLayer::setDilationNd)
;
// Bind to a Python enum called ActivationType.
py::enum_<ActivationType>(m, "ActivationType", ActivationTypeDoc::descr, py::module_local())
.value("RELU", ActivationType::kRELU, ActivationTypeDoc::RELU)
.value("SIGMOID", ActivationType::kSIGMOID, ActivationTypeDoc::SIGMOID)
.value("TANH", ActivationType::kTANH, ActivationTypeDoc::TANH)
.value("LEAKY_RELU", ActivationType::kLEAKY_RELU, ActivationTypeDoc::LEAKY_RELU)
.value("ELU", ActivationType::kELU, ActivationTypeDoc::ELU)
.value("SELU", ActivationType::kSELU, ActivationTypeDoc::SELU)
.value("SOFTSIGN", ActivationType::kSOFTSIGN, ActivationTypeDoc::SOFTSIGN)
.value("SOFTPLUS", ActivationType::kSOFTPLUS, ActivationTypeDoc::SOFTPLUS)
.value("CLIP", ActivationType::kCLIP, ActivationTypeDoc::CLIP)
.value("HARD_SIGMOID", ActivationType::kHARD_SIGMOID, ActivationTypeDoc::HARD_SIGMOID)
.value("SCALED_TANH", ActivationType::kSCALED_TANH, ActivationTypeDoc::SCALED_TANH)
.value("THRESHOLDED_RELU", ActivationType::kTHRESHOLDED_RELU, ActivationTypeDoc::THRESHOLDED_RELU)
.value("GELU_ERF", ActivationType::kGELU_ERF, ActivationTypeDoc::GELU_ERF)
.value("GELU_TANH", ActivationType::kGELU_TANH, ActivationTypeDoc::GELU_TANH)
; // ActivationType
py::class_<IActivationLayer, ILayer, std::unique_ptr<IActivationLayer, py::nodelete>>(m, "IActivationLayer", IActivationLayerDoc::descr, py::module_local())
.def_property("type", &IActivationLayer::getActivationType, &IActivationLayer::setActivationType)
.def_property("alpha", &IActivationLayer::getAlpha, &IActivationLayer::setAlpha)
.def_property("beta", &IActivationLayer::getBeta, &IActivationLayer::setBeta)
;
// Bind to a Python enum called PoolingType.
py::enum_<PoolingType>(m, "PoolingType", PoolingTypeDoc::descr, py::module_local())
.value("MAX", PoolingType::kMAX, PoolingTypeDoc::MAX)
.value("AVERAGE", PoolingType::kAVERAGE, PoolingTypeDoc::AVERAGE)
.value("MAX_AVERAGE_BLEND", PoolingType::kMAX_AVERAGE_BLEND, PoolingTypeDoc::MAX_AVERAGE_BLEND)
; // PoolingType
py::class_<IPoolingLayer, ILayer, std::unique_ptr<IPoolingLayer, py::nodelete>>(m, "IPoolingLayer", IPoolingLayerDoc::descr, py::module_local())
.def_property("type", &IPoolingLayer::getPoolingType, &IPoolingLayer::setPoolingType)
.def_property("pre_padding", utils::throwingDimsGetter(&IPoolingLayer::getPrePadding, "pre_padding"),
&IPoolingLayer::setPrePadding)
.def_property("post_padding", utils::throwingDimsGetter(&IPoolingLayer::getPostPadding, "post_padding"),
&IPoolingLayer::setPostPadding)
.def_property("padding_mode", &IPoolingLayer::getPaddingMode, &IPoolingLayer::setPaddingMode)
.def_property("blend_factor", &IPoolingLayer::getBlendFactor, &IPoolingLayer::setBlendFactor)
.def_property("average_count_excludes_padding", &IPoolingLayer::getAverageCountExcludesPadding, &IPoolingLayer::setAverageCountExcludesPadding)
.def_property("window_size_nd", utils::throwingDimsGetter(&IPoolingLayer::getWindowSizeNd, "window_size_nd"),
&IPoolingLayer::setWindowSizeNd)
.def_property("stride_nd", utils::throwingDimsGetter(&IPoolingLayer::getStrideNd, "stride_nd"),
&IPoolingLayer::setStrideNd)
.def_property("padding_nd", utils::throwingDimsGetter(&IPoolingLayer::getPaddingNd, "padding_nd"),
&IPoolingLayer::setPaddingNd)
;
py::class_<ILRNLayer, ILayer, std::unique_ptr<ILRNLayer, py::nodelete>>(m, "ILRNLayer", ILRNLayerDoc::descr, py::module_local())
.def_property("window_size", &ILRNLayer::getWindowSize, &ILRNLayer::setWindowSize)
.def_property("alpha", &ILRNLayer::getAlpha, &ILRNLayer::setAlpha)
.def_property("beta", &ILRNLayer::getBeta, &ILRNLayer::setBeta)
.def_property("k", &ILRNLayer::getK, &ILRNLayer::setK)
;
// Bind to a Python enum called ScaleMode.
py::enum_<ScaleMode>(m, "ScaleMode", ScaleModeDoc::descr, py::module_local())
.value("UNIFORM", ScaleMode::kUNIFORM, ScaleModeDoc::UNIFORM)
.value("CHANNEL", ScaleMode::kCHANNEL, ScaleModeDoc::CHANNEL)
.value("ELEMENTWISE", ScaleMode::kELEMENTWISE, ScaleModeDoc::ELEMENTWISE)
; // ScaleMode
py::class_<IScaleLayer, ILayer, std::unique_ptr<IScaleLayer, py::nodelete>>(m, "IScaleLayer", IScaleLayerDoc::descr, py::module_local())
.def_property("mode", &IScaleLayer::getMode, &IScaleLayer::setMode)
.def_property("shift", lambdas::scale_get_shift, py::cpp_function(&IScaleLayer::setShift, py::keep_alive<1, 2>{}))
.def_property("scale", lambdas::scale_get_scale, py::cpp_function(&IScaleLayer::setScale, py::keep_alive<1, 2>{}))
.def_property("power", lambdas::scale_get_power, py::cpp_function(&IScaleLayer::setPower, py::keep_alive<1, 2>{}))
.def_property("channel_axis", &IScaleLayer::getChannelAxis, &IScaleLayer::setChannelAxis)
;
py::class_<IQuantizeLayer, ILayer, std::unique_ptr<IQuantizeLayer, py::nodelete>>(m, "IQuantizeLayer", IQuantizeLayerDoc::descr, py::module_local())
.def_property("axis", &IQuantizeLayer::getAxis, &IQuantizeLayer::setAxis)
.def_property("block_shape", utils::throwingDimsGetter(&IQuantizeLayer::getBlockShape, "block_shape"),
&IQuantizeLayer::setBlockShape)
.def_property("to_type", &IQuantizeLayer::getToType, &IQuantizeLayer::setToType)
;
py::class_<IDequantizeLayer, ILayer, std::unique_ptr<IDequantizeLayer, py::nodelete>>(m, "IDequantizeLayer", IDequantizeLayerDoc::descr, py::module_local())
.def_property("axis", &IDequantizeLayer::getAxis, &IDequantizeLayer::setAxis)
.def_property("block_shape", utils::throwingDimsGetter(&IDequantizeLayer::getBlockShape, "block_shape"),
&IDequantizeLayer::setBlockShape)
.def_property("to_type", &IDequantizeLayer::getToType, &IDequantizeLayer::setToType)
;
py::class_<IDynamicQuantizeLayer, ILayer, std::unique_ptr<IDynamicQuantizeLayer, py::nodelete>>(m, "IDynamicQuantizeLayer", IDynamicQuantizeLayerDoc::descr, py::module_local())
.def_property("axis", &IDynamicQuantizeLayer::getAxis, &IDynamicQuantizeLayer::setAxis)
.def_property("block_size", &IDynamicQuantizeLayer::getBlockSize, &IDynamicQuantizeLayer::setBlockSize)
.def_property("block_shape",
utils::throwingDimsGetter(&IDynamicQuantizeLayer::getBlockShape, "block_shape"),
&IDynamicQuantizeLayer::setBlockShape)
.def_property("to_type", &IDynamicQuantizeLayer::getToType, &IDynamicQuantizeLayer::setToType)
.def_property("scale_type", &IDynamicQuantizeLayer::getScaleType, &IDynamicQuantizeLayer::setScaleType)
;
py::class_<ISoftMaxLayer, ILayer, std::unique_ptr<ISoftMaxLayer, py::nodelete>>(m, "ISoftMaxLayer", ISoftMaxLayerDoc::descr, py::module_local())
.def_property("axes", &ISoftMaxLayer::getAxes, &ISoftMaxLayer::setAxes)
;
py::class_<IConcatenationLayer, ILayer, std::unique_ptr<IConcatenationLayer, py::nodelete>>(m, "IConcatenationLayer", IConcatenationLayerDoc::descr, py::module_local())
.def_property("axis", &IConcatenationLayer::getAxis, &IConcatenationLayer::setAxis)
;
py::class_<IDeconvolutionLayer, ILayer, std::unique_ptr<IDeconvolutionLayer, py::nodelete>>(m, "IDeconvolutionLayer", IDeconvolutionLayerDoc::descr, py::module_local())
.def_property("num_output_maps", &IDeconvolutionLayer::getNbOutputMaps, &IDeconvolutionLayer::setNbOutputMaps)
.def_property("pre_padding", utils::throwingDimsGetter(&IDeconvolutionLayer::getPrePadding, "pre_padding"),
&IDeconvolutionLayer::setPrePadding)
.def_property("post_padding",
utils::throwingDimsGetter(&IDeconvolutionLayer::getPostPadding, "post_padding"),
&IDeconvolutionLayer::setPostPadding)
.def_property("padding_mode", &IDeconvolutionLayer::getPaddingMode, &IDeconvolutionLayer::setPaddingMode)
.def_property("num_groups", &IDeconvolutionLayer::getNbGroups, &IDeconvolutionLayer::setNbGroups)
.def_property("kernel", lambdas::deconv_get_kernel, py::cpp_function(&IDeconvolutionLayer::setKernelWeights, py::keep_alive<1, 2>{}))
.def_property("bias", lambdas::deconv_get_bias, py::cpp_function(&IDeconvolutionLayer::setBiasWeights, py::keep_alive<1, 2>{}))
.def_property("kernel_size_nd",
utils::throwingDimsGetter(&IDeconvolutionLayer::getKernelSizeNd, "kernel_size_nd"),
&IDeconvolutionLayer::setKernelSizeNd)
.def_property("stride_nd", utils::throwingDimsGetter(&IDeconvolutionLayer::getStrideNd, "stride_nd"),
&IDeconvolutionLayer::setStrideNd)
.def_property("padding_nd", utils::throwingDimsGetter(&IDeconvolutionLayer::getPaddingNd, "padding_nd"),
&IDeconvolutionLayer::setPaddingNd)
.def_property("dilation_nd", utils::throwingDimsGetter(&IDeconvolutionLayer::getDilationNd, "dilation_nd"),
&IDeconvolutionLayer::setDilationNd)
;
// Bind to a Python enum called ElementWiseOperation.
py::enum_<ElementWiseOperation>(m, "ElementWiseOperation", ElementWiseOperationDoc::descr, py::module_local())
.value("SUM", ElementWiseOperation::kSUM, ElementWiseOperationDoc::SUM)
.value("PROD", ElementWiseOperation::kPROD, ElementWiseOperationDoc::PROD)
.value("MAX", ElementWiseOperation::kMAX, ElementWiseOperationDoc::MAX)
.value("MIN", ElementWiseOperation::kMIN, ElementWiseOperationDoc::MIN)
.value("SUB", ElementWiseOperation::kSUB, ElementWiseOperationDoc::SUB)
.value("DIV", ElementWiseOperation::kDIV, ElementWiseOperationDoc::DIV)
.value("POW", ElementWiseOperation::kPOW, ElementWiseOperationDoc::POW)
.value("FLOOR_DIV", ElementWiseOperation::kFLOOR_DIV, ElementWiseOperationDoc::FLOOR_DIV)
.value("AND", ElementWiseOperation::kAND, ElementWiseOperationDoc::AND)
.value("OR", ElementWiseOperation::kOR, ElementWiseOperationDoc::OR)
.value("XOR", ElementWiseOperation::kXOR, ElementWiseOperationDoc::XOR)
.value("EQUAL", ElementWiseOperation::kEQUAL, ElementWiseOperationDoc::EQUAL)
.value("GREATER", ElementWiseOperation::kGREATER, ElementWiseOperationDoc::GREATER)
.value("LESS", ElementWiseOperation::kLESS, ElementWiseOperationDoc::LESS)
; // ElementWiseOperation
py::class_<IElementWiseLayer, ILayer, std::unique_ptr<IElementWiseLayer, py::nodelete>>(m, "IElementWiseLayer", IElementWiseLayerDoc::descr, py::module_local())
.def_property("op", &IElementWiseLayer::getOperation, &IElementWiseLayer::setOperation)
;
// Bind to a Python enum called ScatterMode.
py::enum_<ScatterMode>(m, "ScatterMode", ScatterModeDoc::descr, py::module_local())
.value("ELEMENT", ScatterMode::kELEMENT, ScatterModeDoc::ELEMENT)
.value("ND", ScatterMode::kND, ScatterModeDoc::ND)
; // ScatterMode
py::class_<IScatterLayer, ILayer, std::unique_ptr<IScatterLayer,py::nodelete>>(m, "IScatterLayer", IScatterLayerDoc::descr, py::module_local())
.def_property("axis", &IScatterLayer::getAxis, &IScatterLayer::setAxis)
.def_property("mode", &IScatterLayer::getMode, &IScatterLayer::setMode);
py::class_<IGatherLayer, ILayer, std::unique_ptr<IGatherLayer, py::nodelete>>(m, "IGatherLayer", IGatherLayerDoc::descr, py::module_local())
.def_property("axis", &IGatherLayer::getGatherAxis, &IGatherLayer::setGatherAxis)
.def_property("num_elementwise_dims", &IGatherLayer::getNbElementWiseDims, &IGatherLayer::setNbElementWiseDims)
.def_property("mode", &IGatherLayer::getMode, &IGatherLayer::setMode)
;
py::enum_<GatherMode>(m, "GatherMode", GatherModeDoc::descr, py::module_local())
.value("DEFAULT", GatherMode::kDEFAULT, GatherModeDoc::DEFAULT)
.value("ELEMENT", GatherMode::kELEMENT, GatherModeDoc::ELEMENT)
.value("ND", GatherMode::kND, GatherModeDoc::ND)
;
py::class_<IPluginV2Layer, ILayer, std::unique_ptr<IPluginV2Layer, py::nodelete>>(m, "IPluginV2Layer", IPluginV2LayerDoc::descr, py::module_local())
.def_property_readonly("plugin", &IPluginV2Layer::getPlugin)
;
py::class_<IPluginV3Layer, ILayer, std::unique_ptr<IPluginV3Layer, py::nodelete>>(m, "IPluginV3Layer", IPluginV3LayerDoc::descr, py::module_local())
.def_property_readonly("plugin", &IPluginV3Layer::getPlugin)
;
py::enum_<UnaryOperation>(m, "UnaryOperation", UnaryOperationDoc::descr, py::module_local())
.value("EXP", UnaryOperation::kEXP, UnaryOperationDoc::EXP)
.value("LOG", UnaryOperation::kLOG, UnaryOperationDoc::LOG)
.value("SQRT", UnaryOperation::kSQRT, UnaryOperationDoc::SQRT)
.value("RECIP", UnaryOperation::kRECIP, UnaryOperationDoc::RECIP)
.value("ABS", UnaryOperation::kABS, UnaryOperationDoc::ABS)
.value("NEG", UnaryOperation::kNEG, UnaryOperationDoc::NEG)
.value("SIN", UnaryOperation::kSIN, UnaryOperationDoc::SIN)
.value("COS", UnaryOperation::kCOS, UnaryOperationDoc::COS)
.value("TAN", UnaryOperation::kTAN, UnaryOperationDoc::TAN)
.value("SINH", UnaryOperation::kSINH, UnaryOperationDoc::SINH)
.value("COSH", UnaryOperation::kCOSH, UnaryOperationDoc::COSH)
.value("ASIN", UnaryOperation::kASIN, UnaryOperationDoc::ASIN)
.value("ACOS", UnaryOperation::kACOS, UnaryOperationDoc::ACOS)
.value("ATAN", UnaryOperation::kATAN, UnaryOperationDoc::ATAN)
.value("ASINH", UnaryOperation::kASINH, UnaryOperationDoc::ASINH)
.value("ACOSH", UnaryOperation::kACOSH, UnaryOperationDoc::ACOSH)
.value("ATANH", UnaryOperation::kATANH, UnaryOperationDoc::ATANH)
.value("CEIL", UnaryOperation::kCEIL, UnaryOperationDoc::CEIL)
.value("FLOOR", UnaryOperation::kFLOOR, UnaryOperationDoc::FLOOR)
.value("ERF", UnaryOperation::kERF, UnaryOperationDoc::ERF)
.value("NOT", UnaryOperation::kNOT, UnaryOperationDoc::NOT)
.value("SIGN", UnaryOperation::kSIGN, UnaryOperationDoc::SIGN)
.value("ROUND", UnaryOperation::kROUND, UnaryOperationDoc::ROUND)
.value("ISINF", UnaryOperation::kISINF, UnaryOperationDoc::ISINF)
.value("ISNAN", UnaryOperation::kISNAN, UnaryOperationDoc::ISNAN)
;
py::class_<IUnaryLayer, ILayer, std::unique_ptr<IUnaryLayer, py::nodelete>>(m, "IUnaryLayer", IUnaryLayerDoc::descr, py::module_local())
.def_property("op", &IUnaryLayer::getOperation, &IUnaryLayer::setOperation)
;
py::enum_<ReduceOperation>(m, "ReduceOperation", ReduceOperationDoc::descr, py::module_local())
.value("SUM", ReduceOperation::kSUM, ReduceOperationDoc::SUM)
.value("PROD", ReduceOperation::kPROD, ReduceOperationDoc::PROD)
.value("MAX", ReduceOperation::kMAX, ReduceOperationDoc::MAX)
.value("MIN", ReduceOperation::kMIN, ReduceOperationDoc::MIN)
.value("AVG", ReduceOperation::kAVG, ReduceOperationDoc::AVG)
.value("NONE", ReduceOperation::kNONE, ReduceOperationDoc::NONE)
;
py::class_<IReduceLayer, ILayer, std::unique_ptr<IReduceLayer, py::nodelete>>(m, "IReduceLayer", IReduceLayerDoc::descr, py::module_local())
.def_property("op", &IReduceLayer::getOperation, &IReduceLayer::setOperation)
.def_property("axes", &IReduceLayer::getReduceAxes, &IReduceLayer::setReduceAxes)
.def_property("keep_dims", &IReduceLayer::getKeepDimensions, &IReduceLayer::setKeepDimensions)
;
py::class_<IPaddingLayer, ILayer, std::unique_ptr<IPaddingLayer, py::nodelete>>(m, "IPaddingLayer", IPaddingLayerDoc::descr, py::module_local())
.def_property("pre_padding_nd",
utils::throwingDimsGetter(&IPaddingLayer::getPrePaddingNd, "pre_padding_nd"),
&IPaddingLayer::setPrePaddingNd)
.def_property("post_padding_nd",
utils::throwingDimsGetter(&IPaddingLayer::getPostPaddingNd, "post_padding_nd"),
&IPaddingLayer::setPostPaddingNd)
;
py::class_<Permutation>(m, "Permutation", PermutationDoc::descr, py::module_local())
.def(py::init<>())
.def(py::init(lambdas::permutation_vector_constructor))
// Allow for string representations (displays like a python tuple).
.def("__str__", lambdas::permutation_to_str)
.def("__repr__", lambdas::permutation_to_str)
// Allows for iteration.
.def("__getitem__", lambdas::permutation_getter)
.def("__setitem__", lambdas::permutation_setter)
.def("__len__", lambdas::permutation_len)
;
// Make it possible to use tuples/lists in Python in place of Permutation.
py::implicitly_convertible<std::vector<int32_t>, Permutation>();
py::class_<IShuffleLayer, ILayer, std::unique_ptr<IShuffleLayer, py::nodelete>>(m, "IShuffleLayer", IShuffleLayerDoc::descr, py::module_local())
.def_property("first_transpose", &IShuffleLayer::getFirstTranspose, &IShuffleLayer::setFirstTranspose)
.def_property("reshape_dims", utils::optionalDimsGetter(&IShuffleLayer::getReshapeDimensions),
&IShuffleLayer::setReshapeDimensions)
.def_property("second_transpose", &IShuffleLayer::getSecondTranspose, &IShuffleLayer::setSecondTranspose)
.def_property("zero_is_placeholder", &IShuffleLayer::getZeroIsPlaceholder, &IShuffleLayer::setZeroIsPlaceholder)
.def("set_input", &IShuffleLayer::setInput, "index"_a, "tensor"_a, IShuffleLayerDoc::set_input)
;
py::class_<ISliceLayer, ILayer, std::unique_ptr<ISliceLayer, py::nodelete>>(m, "ISliceLayer", ISliceLayerDoc::descr, py::module_local())
.def_property("start", utils::optionalDimsGetter(&ISliceLayer::getStart), &ISliceLayer::setStart)
.def_property("shape", utils::optionalDimsGetter(&ISliceLayer::getSize), &ISliceLayer::setSize)
.def_property("stride", utils::optionalDimsGetter(&ISliceLayer::getStride), &ISliceLayer::setStride)
.def_property("mode", &ISliceLayer::getMode, &ISliceLayer::setMode)
.def("set_input", &ISliceLayer::setInput, "index"_a, "tensor"_a, ISliceLayerDoc::set_input)
.def_property("axes", utils::optionalDimsGetter(&ISliceLayer::getAxes), &ISliceLayer::setAxes)
;
py::enum_<InterpolationMode>(m, "InterpolationMode", InterpolationModeDoc::descr, py::module_local())
.value("NEAREST", InterpolationMode::kNEAREST, InterpolationModeDoc::NEAREST)
.value("LINEAR", InterpolationMode::kLINEAR, InterpolationModeDoc::LINEAR)
.value("CUBIC", InterpolationMode::kCUBIC, InterpolationModeDoc::CUBIC)
;
py::enum_<SampleMode>(m, "SampleMode", SampleModeDoc::descr, py::module_local())
.value("STRICT_BOUNDS", SampleMode::kSTRICT_BOUNDS, SampleModeDoc::STRICT_BOUNDS)
.value("WRAP", SampleMode::kWRAP, SampleModeDoc::WRAP)
.value("CLAMP", SampleMode::kCLAMP, SampleModeDoc::CLAMP)
.value("FILL", SampleMode::kFILL, SampleModeDoc::FILL)
.value("REFLECT", SampleMode::kREFLECT, SampleModeDoc::REFLECT)
;
py::class_<IShapeLayer, ILayer, std::unique_ptr<IShapeLayer, py::nodelete>>(m, "IShapeLayer", IShapeLayerDoc::descr, py::module_local());
py::enum_<TopKOperation>(m, "TopKOperation", TopKOperationDoc::descr, py::module_local())
.value("MAX", TopKOperation::kMAX, TopKOperationDoc::MAX)
.value("MIN", TopKOperation::kMIN, TopKOperationDoc::MIN)
;
py::class_<ITopKLayer, ILayer, std::unique_ptr<ITopKLayer, py::nodelete>>(m, "ITopKLayer", ITopKLayerDoc::descr, py::module_local())
.def_property("op", &ITopKLayer::getOperation, &ITopKLayer::setOperation)
.def_property("k", &ITopKLayer::getK, &ITopKLayer::setK)
.def_property("axes", &ITopKLayer::getReduceAxes, &ITopKLayer::setReduceAxes)
.def("set_input", &ITopKLayer::setInput, "index"_a, "tensor"_a, ITopKLayerDoc::set_input)
.def_property("indices_type", &ITopKLayer::getIndicesType, &ITopKLayer::setIndicesType)
;
py::enum_<MatrixOperation>(m, "MatrixOperation", MatrixOperationDoc::descr, py::module_local())
.value("NONE", MatrixOperation::kNONE, MatrixOperationDoc::NONE)
.value("TRANSPOSE", MatrixOperation::kTRANSPOSE, MatrixOperationDoc::TRANSPOSE)
.value("VECTOR", MatrixOperation::kVECTOR, MatrixOperationDoc::VECTOR)
;
py::class_<IMatrixMultiplyLayer, ILayer, std::unique_ptr<IMatrixMultiplyLayer, py::nodelete>>(m, "IMatrixMultiplyLayer", IMatrixMultiplyLayerDoc::descr, py::module_local())
.def_property("op0", [](IMatrixMultiplyLayer& self) {return self.getOperation(0);}, [](IMatrixMultiplyLayer& self, MatrixOperation op) {return self.setOperation(0, op);})
.def_property("op1", [](IMatrixMultiplyLayer& self) {return self.getOperation(1);}, [](IMatrixMultiplyLayer& self, MatrixOperation op) {return self.setOperation(1, op);})
;
py::enum_<CollectiveOperation>(m, "CollectiveOperation", CollectiveOperationDoc::descr, py::module_local())
.value("ALL_REDUCE", CollectiveOperation::kALL_REDUCE, CollectiveOperationDoc::ALL_REDUCE)
.value("ALL_GATHER", CollectiveOperation::kALL_GATHER, CollectiveOperationDoc::ALL_GATHER)
.value("BROADCAST", CollectiveOperation::kBROADCAST, CollectiveOperationDoc::BROADCAST)
.value("REDUCE", CollectiveOperation::kREDUCE, CollectiveOperationDoc::REDUCE)
.value("REDUCE_SCATTER", CollectiveOperation::kREDUCE_SCATTER, CollectiveOperationDoc::REDUCE_SCATTER)
.value("ALL_TO_ALL", CollectiveOperation::kALL_TO_ALL, CollectiveOperationDoc::ALL_TO_ALL)
.value("GATHER", CollectiveOperation::kGATHER, CollectiveOperationDoc::GATHER)
.value("SCATTER", CollectiveOperation::kSCATTER, CollectiveOperationDoc::SCATTER)
;
py::class_<IRaggedSoftMaxLayer, ILayer, std::unique_ptr<IRaggedSoftMaxLayer, py::nodelete>>(m, "IRaggedSoftMaxLayer", IRaggedSoftMaxLayerDoc::descr, py::module_local());
py::class_<IIdentityLayer, ILayer, std::unique_ptr<IIdentityLayer, py::nodelete>>(m, "IIdentityLayer", IIdentityLayerDoc::descr, py::module_local());
py::class_<ICastLayer, ILayer, std::unique_ptr<ICastLayer, py::nodelete>>(m, "ICastLayer", ICastLayerDoc::descr, py::module_local())
.def_property("to_type", &ICastLayer::getToType, &ICastLayer::setToType)
;
py::class_<IConstantLayer, ILayer, std::unique_ptr<IConstantLayer, py::nodelete>>(m, "IConstantLayer", IConstantLayerDoc::descr, py::module_local())
.def_property("weights", lambdas::constant_get_weights, py::cpp_function(&IConstantLayer::setWeights, py::keep_alive<1, 2>{}))
.def_property("shape", utils::throwingDimsGetter(&IConstantLayer::getDimensions, "constant shape"),
&IConstantLayer::setDimensions)
;
py::class_<IParametricReLULayer, ILayer, std::unique_ptr<IParametricReLULayer, py::nodelete>>(m, "IParametricReLULayer", IParametricReLULayerDoc::descr, py::module_local());
py::enum_<ResizeCoordinateTransformation>(m, "ResizeCoordinateTransformation", ResizeCoordinateTransformationDoc::descr, py::module_local())
.value("ALIGN_CORNERS", ResizeCoordinateTransformation::kALIGN_CORNERS, ResizeCoordinateTransformationDoc::ALIGN_CORNERS)
.value("ASYMMETRIC", ResizeCoordinateTransformation::kASYMMETRIC, ResizeCoordinateTransformationDoc::ASYMMETRIC)
.value("HALF_PIXEL", ResizeCoordinateTransformation::kHALF_PIXEL, ResizeCoordinateTransformationDoc::HALF_PIXEL)
; // ResizeCoordinateTransformation
py::enum_<ResizeSelector>(m, "ResizeSelector", ResizeSelectorDoc::descr, py::module_local())
.value("FORMULA", ResizeSelector::kFORMULA,ResizeSelectorDoc::FORMULA)
.value("UPPER", ResizeSelector::kUPPER, ResizeSelectorDoc::UPPER)
; // ResizeSelector
py::enum_<ResizeRoundMode>(m, "ResizeRoundMode", ResizeRoundModeDoc::descr, py::module_local())
.value("HALF_UP", ResizeRoundMode::kHALF_UP,ResizeRoundModeDoc::HALF_UP)
.value("HALF_DOWN", ResizeRoundMode::kHALF_DOWN, ResizeRoundModeDoc::HALF_DOWN)
.value("FLOOR", ResizeRoundMode::kFLOOR,ResizeRoundModeDoc::FLOOR)
.value("CEIL", ResizeRoundMode::kCEIL, ResizeRoundModeDoc::CEIL)
; // ResizeRoundMode
py::class_<IResizeLayer, ILayer, std::unique_ptr<IResizeLayer, py::nodelete>>(m, "IResizeLayer", IResizeLayerDoc::descr, py::module_local())
.def_property("shape", utils::optionalDimsGetter(&IResizeLayer::getOutputDimensions),
&IResizeLayer::setOutputDimensions)
.def_property("scales", lambdas::resize_get_scales, lambdas::resize_set_scales)
.def_property("resize_mode", &IResizeLayer::getResizeMode, &IResizeLayer::setResizeMode)
.def_property("coordinate_transformation", &IResizeLayer::getCoordinateTransformation, &IResizeLayer::setCoordinateTransformation)
.def_property("selector_for_single_pixel", &IResizeLayer::getSelectorForSinglePixel, &IResizeLayer::setSelectorForSinglePixel)
.def_property("nearest_rounding", &IResizeLayer::getNearestRounding, &IResizeLayer::setNearestRounding)
.def_property("exclude_outside", &IResizeLayer::getExcludeOutside, &IResizeLayer::setExcludeOutside)
.def_property("cubic_coeff", &IResizeLayer::getCubicCoeff, &IResizeLayer::setCubicCoeff)
.def("set_input", &IResizeLayer::setInput, "index"_a, "tensor"_a, IResizeLayerDoc::set_input)
;
py::enum_<LoopOutput>(m, "LoopOutput", LoopOutputDoc::descr, py::module_local())
.value("LAST_VALUE", LoopOutput::kLAST_VALUE, LoopOutputDoc::LAST_VALUE)
.value("CONCATENATE", LoopOutput::kCONCATENATE, LoopOutputDoc::CONCATENATE)
.value("REVERSE", LoopOutput::kREVERSE, LoopOutputDoc::REVERSE)
;
py::enum_<TripLimit>(m, "TripLimit", TripLimitDoc::descr, py::module_local())
.value("COUNT", TripLimit::kCOUNT, TripLimitDoc::COUNT)
.value("WHILE", TripLimit::kWHILE, TripLimitDoc::WHILE)
;
py::class_<ILoopBoundaryLayer, ILayer, std::unique_ptr<ILoopBoundaryLayer, py::nodelete>>(m, "ILoopBoundaryLayer", ILoopBoundaryLayerDoc::descr, py::module_local())
.def_property_readonly("loop", &ILoopBoundaryLayer::getLoop)
;
py::class_<IRecurrenceLayer, ILoopBoundaryLayer, std::unique_ptr<IRecurrenceLayer, py::nodelete>>(m, "IRecurrenceLayer", IRecurrenceLayerDoc::descr, py::module_local())
.def("set_input", &IRecurrenceLayer::setInput, "index"_a, "tensor"_a, IRecurrenceLayerDoc::set_input)
;
py::class_<ILoopOutputLayer, ILoopBoundaryLayer, std::unique_ptr<ILoopOutputLayer, py::nodelete>>(m, "ILoopOutputLayer", ILoopOutputLayerDoc::descr, py::module_local())
.def("set_input", &ILoopOutputLayer::setInput, "index"_a, "tensor"_a, ILoopOutputLayerDoc::set_input)
.def_property("axis", &ILoopOutputLayer::getAxis, &ILoopOutputLayer::setAxis)
.def_property_readonly("kind", &ILoopOutputLayer::getLoopOutput)
;
py::class_<ITripLimitLayer, ILoopBoundaryLayer, std::unique_ptr<ITripLimitLayer, py::nodelete>>(m, "ITripLimitLayer", ITripLimitLayerDoc::descr, py::module_local())
.def_property_readonly("kind", &ITripLimitLayer::getTripLimit)
;
py::class_<IIteratorLayer, ILoopBoundaryLayer, std::unique_ptr<IIteratorLayer, py::nodelete>>(m, "IIteratorLayer", IIteratorLayerDoc::descr, py::module_local())
.def_property("axis", &IIteratorLayer::getAxis, &IIteratorLayer::setAxis)
.def_property("reverse", &IIteratorLayer::getReverse, &IIteratorLayer::setReverse)
;
py::class_<ILoop, std::unique_ptr<ILoop, py::nodelete>>(m, "ILoop", ILoopDoc::descr, py::module_local())
.def("add_recurrence", &ILoop::addRecurrence, "initial_value"_a, ILoopDoc::add_recurrence)
.def("add_trip_limit", &ILoop::addTripLimit, "tensor"_a, "kind"_a, ILoopDoc::add_trip_limit)
// cppcheck-suppress assignBoolToPointer
.def("add_iterator", &ILoop::addIterator, "tensor"_a, "axis"_a = 0, "reverse"_a = false, ILoopDoc::add_iterator)
.def("add_loop_output", &ILoop::addLoopOutput, "tensor"_a, "kind"_a, "axis"_a = 0, ILoopDoc::add_loop_output)
.def_property("name", &ILoop::getName, &ILoop::setName)
;
py::class_<ISelectLayer, ILayer, std::unique_ptr<ISelectLayer, py::nodelete>>(m, "ISelectLayer", ISelectLayerDoc::descr, py::module_local())
;
py::class_<IAssertionLayer, ILayer, std::unique_ptr<IAssertionLayer, py::nodelete>>(m, "IAssertionLayer", IAssertionLayerDoc::descr, py::module_local())
.def_property("message", &IAssertionLayer::getMessage, &IAssertionLayer::setMessage);
;
py::class_<IGridSampleLayer, ILayer, std::unique_ptr<IGridSampleLayer, py::nodelete>>(m, "IGridSampleLayer", IGridSampleLayerDoc::descr, py::module_local())
.def_property("interpolation_mode", &IGridSampleLayer::getInterpolationMode, &IGridSampleLayer::setInterpolationMode)
.def_property("align_corners", &IGridSampleLayer::getAlignCorners, &IGridSampleLayer::setAlignCorners)
.def_property("sample_mode", &IGridSampleLayer::getSampleMode, &IGridSampleLayer::setSampleMode)
;
py::enum_<BoundingBoxFormat>(m, "BoundingBoxFormat", BoundingBoxFormatDoc::descr, py::module_local())
.value("CORNER_PAIRS", BoundingBoxFormat::kCORNER_PAIRS, BoundingBoxFormatDoc::CORNER_PAIRS)
.value("CENTER_SIZES", BoundingBoxFormat::kCENTER_SIZES, BoundingBoxFormatDoc::CENTER_SIZES)
;
py::class_<INMSLayer, ILayer, std::unique_ptr<INMSLayer, py::nodelete>>(m, "INMSLayer", INMSLayerDoc::descr, py::module_local())
.def_property("bounding_box_format", &INMSLayer::getBoundingBoxFormat, &INMSLayer::setBoundingBoxFormat)
.def_property("topk_box_limit", &INMSLayer::getTopKBoxLimit, &INMSLayer::setTopKBoxLimit)
.def("set_input", &INMSLayer::setInput, "index"_a, "tensor"_a, INMSLayerDoc::set_input)
.def_property("indices_type", &INMSLayer::getIndicesType, &INMSLayer::setIndicesType)
;
py::enum_<FillOperation>(m, "FillOperation", FillOperationDoc::descr, py::module_local())
.value("LINSPACE", FillOperation::kLINSPACE, FillOperationDoc::LINSPACE)
.value("RANDOM_UNIFORM", FillOperation::kRANDOM_UNIFORM, FillOperationDoc::RANDOM_UNIFORM)
.value("RANDOM_NORMAL", FillOperation::kRANDOM_NORMAL, FillOperationDoc::RANDOM_NORMAL)
; // FillOperation
py::class_<IFillLayer, ILayer, std::unique_ptr<IFillLayer, py::nodelete>>(m, "IFillLayer", IFillLayerDoc::descr, py::module_local())
.def_property("shape", utils::optionalDimsGetter(&IFillLayer::getDimensions), &IFillLayer::setDimensions)
.def_property("operation", &IFillLayer::getOperation, &IFillLayer::setOperation)
.def_property("alpha", lambdas::get_alpha, lambdas::set_alpha)
.def_property("beta", lambdas::get_beta, lambdas::set_beta)
.def_property("to_type", &IFillLayer::getToType, &IFillLayer::setToType)
.def("set_input", &IFillLayer::setInput, "index"_a, "tensor"_a, IFillLayerDoc::set_input)
.def("is_alpha_beta_int64", &IFillLayer::isAlphaBetaInt64)
;
py::class_<IIfConditionalBoundaryLayer, ILayer, std::unique_ptr<IIfConditionalBoundaryLayer, py::nodelete>>(m, "IIfConditionalBoundaryLayer", IIfConditionalBoundaryLayerDoc::descr, py::module_local())
.def_property_readonly("conditional", &IIfConditionalBoundaryLayer::getConditional)
;
py::class_<IIfConditionalOutputLayer, IIfConditionalBoundaryLayer, std::unique_ptr<IIfConditionalOutputLayer, py::nodelete>>(m, "IIfConditionalOutputLayer", IIfConditionalOutputLayerDoc::descr, py::module_local())
;
py::class_<IIfConditionalInputLayer, IIfConditionalBoundaryLayer, std::unique_ptr<IIfConditionalInputLayer, py::nodelete>>(m, "IIfConditionalInputLayer", IIfConditionalInputLayerDoc::descr, py::module_local())
;
py::class_<IConditionLayer, IIfConditionalBoundaryLayer, std::unique_ptr<IConditionLayer, py::nodelete>>(m, "IConditionLayer", IConditionLayerDoc::descr, py::module_local())
;
py::class_<IIfConditional, std::unique_ptr<IIfConditional, py::nodelete>>(m, "IIfConditional", IIfConditionalDoc::descr, py::module_local())
.def("set_condition", &IIfConditional::setCondition, "condition"_a, IIfConditionalDoc::set_condition)
.def("add_output", &IIfConditional::addOutput, "true_subgraph_output"_a, "false_subgraph_output"_a, IIfConditionalDoc::add_output)
.def("add_input", &IIfConditional::addInput, "input"_a, IIfConditionalDoc::add_input)
.def_property("name", &IIfConditional::getName, &IIfConditional::setName)
;
py::class_<IEinsumLayer, ILayer, std::unique_ptr<IEinsumLayer, py::nodelete>>(m, "IEinsumLayer", IEinsumLayerDoc::descr, py::module_local())
.def_property("equation", &IEinsumLayer::getEquation, &IEinsumLayer::setEquation)
;
py::class_<IOneHotLayer, ILayer, std::unique_ptr<IOneHotLayer,py::nodelete>>(m, "IOneHotLayer", IOneHotLayerDoc::descr, py::module_local())
.def_property("axis", &IOneHotLayer::getAxis, &IOneHotLayer::setAxis)
;
py::class_<INonZeroLayer, ILayer, std::unique_ptr<INonZeroLayer,py::nodelete>>(m, "INonZeroLayer", INonZeroLayerDoc::descr, py::module_local())
.def_property("indices_type", &INonZeroLayer::getIndicesType, &INonZeroLayer::setIndicesType)
;
py::class_<IReverseSequenceLayer, ILayer, std::unique_ptr<IReverseSequenceLayer, py::nodelete>>(m, "IReverseSequenceLayer", IReverseSequenceLayerDoc::descr, py::module_local())
.def_property("batch_axis", &IReverseSequenceLayer::getBatchAxis, &IReverseSequenceLayer::setBatchAxis)
.def_property("sequence_axis", &IReverseSequenceLayer::getSequenceAxis, &IReverseSequenceLayer::setSequenceAxis)
;
py::class_<INormalizationLayer, ILayer, std::unique_ptr<INormalizationLayer, py::nodelete>>(m, "INormalizationLayer", INormalizationLayerDoc::descr, py::module_local())
.def_property("epsilon", &INormalizationLayer::getEpsilon, &INormalizationLayer::setEpsilon)
.def_property("axes", &INormalizationLayer::getAxes, &INormalizationLayer::setAxes)
.def_property("num_groups", &INormalizationLayer::getNbGroups, &INormalizationLayer::setNbGroups)
.def_property_readonly("is_v2", &INormalizationLayer::isV2)
;
py::class_<ISqueezeLayer, ILayer, std::unique_ptr<ISqueezeLayer, py::nodelete>>(m, "ISqueezeLayer", ISqueezeLayerDoc::descr, py::module_local())
.def("set_input", &ISqueezeLayer::setInput, "index"_a, "tensor"_a, ISqueezeLayerDoc::set_input)
;
py::class_<IUnsqueezeLayer, ILayer, std::unique_ptr<IUnsqueezeLayer, py::nodelete>>(m, "IUnsqueezeLayer", IUnsqueezeLayerDoc::descr, py::module_local())
.def("set_input", &IUnsqueezeLayer::setInput, "index"_a, "tensor"_a, IUnsqueezeLayerDoc::set_input)
;
py::class_<IDistCollectiveLayer, ILayer, std::unique_ptr<IDistCollectiveLayer, py::nodelete>>(m, "IDistCollectiveLayer", IDistCollectiveLayerDoc::descr, py::module_local());
py::enum_<CumulativeOperation>(m, "CumulativeOperation", CumulativeOperationDoc::descr, py::module_local())
.value("SUM", CumulativeOperation::kSUM, CumulativeOperationDoc::SUM)
;
py::class_<ICumulativeLayer, ILayer, std::unique_ptr<ICumulativeLayer, py::nodelete>>(m, "ICumulativeLayer", ICumulativeLayerDoc::descr, py::module_local())
.def_property("op", &ICumulativeLayer::getOperation, &cumulative_layer_set_operation)
.def_property("exclusive", &ICumulativeLayer::getExclusive, &ICumulativeLayer::setExclusive)
.def_property("reverse", &ICumulativeLayer::getReverse, &ICumulativeLayer::setReverse)
;
py::enum_<AttentionNormalizationOp>(m, "AttentionNormalizationOp", AttentionNormalizationOpDoc::descr, py::module_local())
.value("NONE", AttentionNormalizationOp::kNONE, AttentionNormalizationOpDoc::NONE)
.value("SOFTMAX", AttentionNormalizationOp::kSOFTMAX, AttentionNormalizationOpDoc::SOFTMAX)
;
py::enum_<AttentionIOForm>(m, "AttentionIOForm", AttentionIOFormDoc::descr, py::module_local())
.value("PADDED_BHND", AttentionIOForm::kPADDED_BHND, AttentionIOFormDoc::PADDED_BHND)
.value("PACKED_NHD", AttentionIOForm::kPACKED_NHD, AttentionIOFormDoc::PACKED_NHD)
;
py::enum_<CausalMaskKind>(m, "CausalMaskKind", CausalMaskKindDoc::descr, py::module_local())
.value("NONE", CausalMaskKind::kNONE, CausalMaskKindDoc::NONE)
.value("UPPER_LEFT", CausalMaskKind::kUPPER_LEFT, CausalMaskKindDoc::UPPER_LEFT)
.value("LOWER_RIGHT", CausalMaskKind::kLOWER_RIGHT, CausalMaskKindDoc::LOWER_RIGHT)
;
py::class_<IAttention, std::unique_ptr<IAttention, py::nodelete>>(m, "IAttention", IAttentionDoc::descr, py::module_local())
.def_property("mask", &IAttention::getMask, &IAttention::setMask)
.def_property("norm_op", &IAttention::getNormalizationOperation, &attention_set_operation)
.def_property("decomposable", &IAttention::getDecomposable, &IAttention::setDecomposable)
.def_property("causal", &IAttention::getCausal, &IAttention::setCausal)
.def_property("causal_kind", &IAttention::getCausalKind, &attention_set_causal_kind)
.def_property("name", &IAttention::getName, &IAttention::setName)