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PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils
utils
checkpoint
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. import logging import os import torch from maskrcnn_benchmark.utils.model_serialization import load_state_dict from maskrcnn_benchmark.utils.c2_model_loading import load_c2_format from maskrcnn_benchmark.utils.imports import import_file from maskrcnn_benchmark.utils.model_zoo import cache_url class Checkpointer(object): def __init__( self, model, optimizer=None, scheduler=None, save_dir="", save_to_disk=None, logger=None, ): self.model = model self.optimizer = optimizer self.scheduler = scheduler self.save_dir = save_dir self.save_to_disk = save_to_disk if logger is None: logger = logging.getLogger(__name__) self.logger = logger def save(self, name, **kwargs): if not self.save_dir: return if not self.save_to_disk: return data = {} data["model"] = self.model.state_dict() if self.optimizer is not None: data["optimizer"] = self.optimizer.state_dict() if self.scheduler is not None: data["scheduler"] = self.scheduler.state_dict() data.update(kwargs) save_file = os.path.join(self.save_dir, "{}.pth".format(name)) self.logger.info("Saving checkpoint to {}".format(save_file)) torch.save(data, save_file) self.tag_last_checkpoint(save_file) def load(self, f=None): if self.has_checkpoint(): # override argument with existing checkpoint f = self.get_checkpoint_file() if not f: # no checkpoint could be found self.logger.info("No checkpoint found. Initializing model from scratch") return {} self.logger.info("Loading checkpoint from {}".format(f)) checkpoint = self._load_file(f) self._load_model(checkpoint) if "optimizer" in checkpoint and self.optimizer: self.logger.info("Loading optimizer from {}".format(f)) self.optimizer.load_state_dict(checkpoint.pop("optimizer")) if "scheduler" in checkpoint and self.scheduler: self.logger.info("Loading scheduler from {}".format(f)) self.scheduler.load_state_dict(checkpoint.pop("scheduler")) # return any further checkpoint data return checkpoint def has_checkpoint(self): save_file = os.path.join(self.save_dir, "last_checkpoint") return os.path.exists(save_file) def get_checkpoint_file(self): save_file = os.path.join(self.save_dir, "last_checkpoint") try: with open(save_file, "r") as f: last_saved = f.read() last_saved = last_saved.strip() except IOError: # if file doesn't exist, maybe because it has just been # deleted by a separate process last_saved = "" return last_saved def tag_last_checkpoint(self, last_filename): save_file = os.path.join(self.save_dir, "last_checkpoint") with open(save_file, "w") as f: f.write(last_filename) def _load_file(self, f): return torch.load(f, map_location=torch.device("cpu")) def _load_model(self, checkpoint): load_state_dict(self.model, checkpoint.pop("model")) class DetectronCheckpointer(Checkpointer): def __init__( self, cfg, model, optimizer=None, scheduler=None, save_dir="", save_to_disk=None, logger=None, ): super(DetectronCheckpointer, self).__init__( model, optimizer, scheduler, save_dir, save_to_disk, logger ) self.cfg = cfg.clone() def _load_file(self, f): # catalog lookup if f.startswith("catalog://"): paths_catalog = import_file( "maskrcnn_benchmark.config.paths_catalog", self.cfg.PATHS_CATALOG, True ) catalog_f = paths_catalog.ModelCatalog.get(f[len("catalog://") :]) self.logger.info("{} points to {}".format(f, catalog_f)) f = catalog_f # download url files if f.startswith("http"): # if the file is a url path, download it and cache it cached_f = cache_url(f) self.logger.info("url {} cached in {}".format(f, cached_f)) f = cached_f # convert Caffe2 checkpoint from pkl if f.endswith(".pkl"): return load_c2_format(self.cfg, f) # load native detectron.pytorch checkpoint loaded = super(DetectronCheckpointer, self)._load_file(f) if "model" not in loaded: loaded = dict(model=loaded) return loaded
DGLPyTorch/DrugDiscovery/SE3Transformer/scripts
scripts
benchmark_inference
#!/usr/bin/env bash # Script to benchmark inference performance, without bases precomputation # CLI args with defaults BATCH_SIZE=${1:-240} AMP=${2:-true} CUDA_VISIBLE_DEVICES=0 python -m se3_transformer.runtime.inference \ --amp "$AMP" \ --batch_size "$BATCH_SIZE" \ --use_layer_norm \ --norm \ --task homo \ --seed 42 \ --benchmark
PyTorch/SpeechRecognition/wav2vec2/scripts
scripts
pretrain_base
#!/usr/bin/env bash # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. # # 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. # Pre-trains a BASE model on LibriSpeech set -a # IO : ${OUTPUT_DIR:="results/pretrain_base"} # Batching # To best utilize hw, increase batch size by increasing NUM_CONCAT_BATCHES, and lowering UPDATE_FREQ. # Keep NUM_NODES x $NUM_GPUS x $NUM_CONCAT_BATCHES x $UPDATE_FREQ = 64. # Note that this script does not control NUM_NODES. : ${NUM_GPUS:=8} : ${MAX_TOKENS:=1400000} : ${NUM_CONCAT_BATCHES:=8} : ${UPDATE_FREQ:=1} : ${MAX_SAMPLE_SIZE:=250000} # Training : ${MAX_UPDATE:=400000} : ${LOSS_WEIGHTS:="0.1 10.0"} : ${LEARNING_RATE:=0.0005} # Model : ${NORMALIZE:=false} : ${MASK_PROB:=0.65} : ${EXTRACTOR_MODE:="default"} : ${LAYER_NORM_FIRST:=false} : ${FINAL_DIM:=256} : ${LATENT_TEMP:="2.0 0.5 0.999995"} : ${ENCODER_LAYERDROP:=0.05} : ${DROPOUT_INPUT:=0.1} : ${DROPOUT_FEATURES:=0.1} : ${DROPOUT:=0.1} : ${ATTENTION_DROPOUT:=0.1} : ${CONV_BIAS:=false} : ${ENCODER_LAYERS:=12} : ${ENCODER_EMBED_DIM:=768} : ${ENCODER_FFN_EMBED_DIM:=3072} : ${ENCODER_ATTENTION_HEADS:=12} : ${FEATURE_GRAD_MULT:=0.1} : ${HOURGLASS_CONFIG="[2,(8,4),2]"} bash scripts/pretrain_large.sh "$@"
PyTorch/SpeechSynthesis/FastPitch/triton
triton
run_offline_performance_test_on_triton
#!/usr/bin/env python3 # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # 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. r""" For models with variable-sized inputs you must provide the --input-shape argument so that perf_analyzer knows what shape tensors to use. For example, for a model that has an input called IMAGE that has shape [ 3, N, M ], where N and M are variable-size dimensions, to tell perf_analyzer to send batch-size 4 requests of shape [ 3, 224, 224 ] `--shape IMAGE:3,224,224`. """ import argparse import csv import os import sys from pathlib import Path from typing import Dict, List, Optional # method from PEP-366 to support relative import in executed modules if __package__ is None: __package__ = Path(__file__).parent.name from .deployment_toolkit.report import save_results, show_results, sort_results from .deployment_toolkit.warmup import warmup def calculate_average_latency(r): avg_sum_fields = [ "Client Send", "Network+Server Send/Recv", "Server Queue", "Server Compute", "Server Compute Input", "Server Compute Infer", "Server Compute Output", "Client Recv", ] avg_latency = sum([int(r.get(f, 0)) for f in avg_sum_fields]) return avg_latency def update_performance_data(results: List, batch_size: int, performance_partial_file: str): row: Dict = {"batch_size": batch_size} with open(performance_partial_file, "r") as csvfile: reader = csv.DictReader(csvfile) for r in reader: avg_latency = calculate_average_latency(r) row = {**row, **r, "avg latency": avg_latency} results.append(row) def _parse_batch_sizes(batch_sizes: str): batches = batch_sizes.split(sep=",") return list(map(lambda x: int(x.strip()), batches)) def offline_performance( model_name: str, batch_sizes: List[int], result_path: str, input_shapes: Optional[List[str]] = None, profiling_data: str = "random", triton_instances: int = 1, server_url: str = "localhost", measurement_window: int = 10000, shared_memory: bool = False ): print("\n") print(f"==== Static batching analysis start ====") print("\n") input_shapes = " ".join(map(lambda shape: f" --shape {shape}", input_shapes)) if input_shapes else "" results: List[Dict] = list() for batch_size in batch_sizes: print(f"Running performance tests for batch size: {batch_size}") performance_partial_file = f"triton_performance_partial_{batch_size}.csv" exec_args = f"""-max-threads {triton_instances} \ -m {model_name} \ -x 1 \ -c {triton_instances} \ -t {triton_instances} \ -p {measurement_window} \ -v \ -i http \ -u {server_url}:8000 \ -b {batch_size} \ -f {performance_partial_file} \ --input-data {profiling_data} {input_shapes}""" if shared_memory: exec_args += " --shared-memory=cuda" result = os.system(f"perf_client {exec_args}") if result != 0: print(f"Failed running performance tests. Perf client failed with exit code {result}") sys.exit(1) update_performance_data(results, batch_size, performance_partial_file) os.remove(performance_partial_file) results = sort_results(results=results) save_results(filename=result_path, data=results) show_results(results=results) print("Performance results for static batching stored in: {0}".format(result_path)) print("\n") print(f"==== Analysis done ====") print("\n") def main(): parser = argparse.ArgumentParser() parser.add_argument("--model-name", type=str, required=True, help="Name of the model to test") parser.add_argument( "--input-data", type=str, required=False, default="random", help="Input data to perform profiling." ) parser.add_argument( "--input-shape", action="append", required=False, help="Input data shape in form INPUT_NAME:<full_shape_without_batch_axis>.", ) parser.add_argument("--batch-sizes", type=str, required=True, help="List of batch sizes to tests. Comma separated.") parser.add_argument("--result-path", type=str, required=True, help="Path where result file is going to be stored.") parser.add_argument("--triton-instances", type=int, default=1, help="Number of Triton Server instances") parser.add_argument("--server-url", type=str, required=False, default="localhost", help="Url to Triton server") parser.add_argument( "--measurement-window", required=False, help="Time which perf_analyzer will wait for results", default=10000 ) parser.add_argument("--shared-memory", help="Use shared memory for communication with Triton", action="store_true", default=False) args = parser.parse_args() warmup( server_url=args.server_url, model_name=args.model_name, batch_sizes=_parse_batch_sizes(args.batch_sizes), triton_instances=args.triton_instances, profiling_data=args.input_data, input_shapes=args.input_shape, measurement_window=args.measurement_window, shared_memory=args.shared_memory ) offline_performance( server_url=args.server_url, model_name=args.model_name, batch_sizes=_parse_batch_sizes(args.batch_sizes), triton_instances=args.triton_instances, profiling_data=args.input_data, input_shapes=args.input_shape, result_path=args.result_path, measurement_window=args.measurement_window, shared_memory=args.shared_memory ) if __name__ == "__main__": main()
PyTorch/SpeechSynthesis/Tacotron2/trtis_cpp/src/trt/plugins/taco2LSTMCellPlugin
taco2LSTMCellPlugin
taco2LSTMCellLayerPluginCreator
/* * Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in the * documentation and/or other materials provided with the distribution. * * Neither the name of the NVIDIA CORPORATION nor the * names of its contributors may be used to endorse or promote products * derived from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #include "taco2LSTMCellLayerPluginCreator.h" #include "taco2LSTMCellLayerPlugin.h" #include <stdexcept> #include <vector> using namespace nvinfer1; namespace nvinfer1 { namespace plugin { /****************************************************************************** * CONSTANTS ****************************************************************** *****************************************************************************/ namespace { constexpr const char* const LENGTH_STR = "Length"; constexpr const char* const DIMENSION_STR = "Dimension"; constexpr const char* const FP16_STR = "FP16"; constexpr const char* const INPUT_WEIGHTS_STR = "weight_ih"; constexpr const char* const HIDDEN_WEIGHTS_STR = "weight_hh"; constexpr const char* const INPUT_BIAS_STR = "bias_ih"; constexpr const char* const HIDDEN_BIAS_STR = "bias_hh"; } // namespace /****************************************************************************** * PUBLIC STATIC METHODS ****************************************************** *****************************************************************************/ PluginFieldCollection* Taco2LSTMCellLayerPluginCreator::getFields() { static PluginFieldCollection* pluginPtr = nullptr; static const std::vector<PluginField> fields{{LENGTH_STR, nullptr, PluginFieldType::kINT32, 0}, {DIMENSION_STR, nullptr, PluginFieldType::kINT32, 0}, {FP16_STR, nullptr, PluginFieldType::kINT32, 0}, {INPUT_WEIGHTS_STR, nullptr, PluginFieldType::kFLOAT32, 0}, {HIDDEN_WEIGHTS_STR, nullptr, PluginFieldType::kFLOAT32, 0}, {INPUT_BIAS_STR, nullptr, PluginFieldType::kFLOAT32, 0}, {HIDDEN_BIAS_STR, nullptr, PluginFieldType::kFLOAT32, 0}}; if (!pluginPtr) { pluginPtr = static_cast<PluginFieldCollection*>(malloc(sizeof(*pluginPtr) + fields.size() * sizeof(PluginField))); pluginPtr->nbFields = static_cast<int>(fields.size()); pluginPtr->fields = fields.data(); } return pluginPtr; } /****************************************************************************** * CONSTRUCTORS / DESTRUCTOR ************************************************** *****************************************************************************/ Taco2LSTMCellLayerPluginCreator::Taco2LSTMCellLayerPluginCreator() : mNamespace() { // do nothing } /****************************************************************************** * PUBLIC METHODS ************************************************************* *****************************************************************************/ const char* Taco2LSTMCellLayerPluginCreator::getPluginName() const { return Taco2LSTMCellLayerPlugin::getName(); } const char* Taco2LSTMCellLayerPluginCreator::getPluginVersion() const { return Taco2LSTMCellLayerPlugin::getVersion(); } const PluginFieldCollection* Taco2LSTMCellLayerPluginCreator::getFieldNames() { return getFields(); } IPluginV2* Taco2LSTMCellLayerPluginCreator::createPlugin(const char* const /*name*/, const PluginFieldCollection* fc) { int length = 0; int dimension = 0; bool fp16 = false; Weights inputWeights{DataType::kFLOAT, nullptr, 0}; Weights hiddenWeights{DataType::kFLOAT, nullptr, 0}; Weights inputBias{DataType::kFLOAT, nullptr, 0}; Weights hiddenBias{DataType::kFLOAT, nullptr, 0}; for (int i = 0; i < fc->nbFields; ++i) { const std::string name(fc->fields[i].name); if (name == LENGTH_STR) { length = static_cast<const int32_t*>(fc->fields[i].data)[0]; } else if (name == DIMENSION_STR) { dimension = static_cast<const int32_t*>(fc->fields[i].data)[0]; } else if (name == FP16_STR) { fp16 = static_cast<const int32_t*>(fc->fields[i].data)[0]; } else if (name == INPUT_WEIGHTS_STR) { inputWeights.values = fc->fields[i].data; inputWeights.count = fc->fields[i].length; } else if (name == HIDDEN_WEIGHTS_STR) { hiddenWeights.values = fc->fields[i].data; hiddenWeights.count = fc->fields[i].length; } else if (name == INPUT_BIAS_STR) { inputBias.values = fc->fields[i].data; inputBias.count = fc->fields[i].length; } else if (name == HIDDEN_BIAS_STR) { hiddenBias.values = fc->fields[i].data; hiddenBias.count = fc->fields[i].length; } else { throw std::runtime_error("Unknown plugin field: '" + name + "'"); } } return new Taco2LSTMCellLayerPlugin(inputWeights, hiddenWeights, inputBias, hiddenBias, length, dimension, fp16); } IPluginV2* Taco2LSTMCellLayerPluginCreator::deserializePlugin( const char* const /* layerName */, const void* const serialData, size_t const serialLength) { return new Taco2LSTMCellLayerPlugin(Taco2LSTMCellLayerPlugin::deserialize(serialData, serialLength)); } void Taco2LSTMCellLayerPluginCreator::setPluginNamespace(const char* pluginNamespace) { mNamespace = pluginNamespace; } const char* Taco2LSTMCellLayerPluginCreator::getPluginNamespace() const { return mNamespace.c_str(); } } // namespace plugin } // namespace nvinfer1
PyTorch/SpeechRecognition/Jasper/common/text/unidecoder
unidecoder
replacements
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # 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. # MIT License # # Copyright (c) Sindre Sorhus <sindresorhus@gmail.com> (https://sindresorhus.com) # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # # Based on: # https://github.com/sindresorhus/transliterate/blob/main/replacements.js # replacements = [ # German umlauts ['ß', 'ss'], ['ẞ', 'Ss'], ['ä', 'ae'], ['Ä', 'Ae'], ['ö', 'oe'], ['Ö', 'Oe'], ['ü', 'ue'], ['Ü', 'Ue'], # Latin ['À', 'A'], ['Á', 'A'], ['Â', 'A'], ['Ã', 'A'], ['Ä', 'Ae'], ['Å', 'A'], ['Æ', 'AE'], ['Ç', 'C'], ['È', 'E'], ['É', 'E'], ['Ê', 'E'], ['Ë', 'E'], ['Ì', 'I'], ['Í', 'I'], ['Î', 'I'], ['Ï', 'I'], ['Ð', 'D'], ['Ñ', 'N'], ['Ò', 'O'], ['Ó', 'O'], ['Ô', 'O'], ['Õ', 'O'], ['Ö', 'Oe'], ['Ő', 'O'], ['Ø', 'O'], ['Ù', 'U'], ['Ú', 'U'], ['Û', 'U'], ['Ü', 'Ue'], ['Ű', 'U'], ['Ý', 'Y'], ['Þ', 'TH'], ['ß', 'ss'], ['à', 'a'], ['á', 'a'], ['â', 'a'], ['ã', 'a'], ['ä', 'ae'], ['å', 'a'], ['æ', 'ae'], ['ç', 'c'], ['è', 'e'], ['é', 'e'], ['ê', 'e'], ['ë', 'e'], ['ì', 'i'], ['í', 'i'], ['î', 'i'], ['ï', 'i'], ['ð', 'd'], ['ñ', 'n'], ['ò', 'o'], ['ó', 'o'], ['ô', 'o'], ['õ', 'o'], ['ö', 'oe'], ['ő', 'o'], ['ø', 'o'], ['ù', 'u'], ['ú', 'u'], ['û', 'u'], ['ü', 'ue'], ['ű', 'u'], ['ý', 'y'], ['þ', 'th'], ['ÿ', 'y'], ['ẞ', 'SS'], # Vietnamese ['à', 'a'], ['À', 'A'], ['á', 'a'], ['Á', 'A'], ['â', 'a'], ['Â', 'A'], ['ã', 'a'], ['Ã', 'A'], ['è', 'e'], ['È', 'E'], ['é', 'e'], ['É', 'E'], ['ê', 'e'], ['Ê', 'E'], ['ì', 'i'], ['Ì', 'I'], ['í', 'i'], ['Í', 'I'], ['ò', 'o'], ['Ò', 'O'], ['ó', 'o'], ['Ó', 'O'], ['ô', 'o'], ['Ô', 'O'], ['õ', 'o'], ['Õ', 'O'], ['ù', 'u'], ['Ù', 'U'], ['ú', 'u'], ['Ú', 'U'], ['ý', 'y'], ['Ý', 'Y'], ['ă', 'a'], ['Ă', 'A'], ['Đ', 'D'], ['đ', 'd'], ['ĩ', 'i'], ['Ĩ', 'I'], ['ũ', 'u'], ['Ũ', 'U'], ['ơ', 'o'], ['Ơ', 'O'], ['ư', 'u'], ['Ư', 'U'], ['ạ', 'a'], ['Ạ', 'A'], ['ả', 'a'], ['Ả', 'A'], ['ấ', 'a'], ['Ấ', 'A'], ['ầ', 'a'], ['Ầ', 'A'], ['ẩ', 'a'], ['Ẩ', 'A'], ['ẫ', 'a'], ['Ẫ', 'A'], ['ậ', 'a'], ['Ậ', 'A'], ['ắ', 'a'], ['Ắ', 'A'], ['ằ', 'a'], ['Ằ', 'A'], ['ẳ', 'a'], ['Ẳ', 'A'], ['ẵ', 'a'], ['Ẵ', 'A'], ['ặ', 'a'], ['Ặ', 'A'], ['ẹ', 'e'], ['Ẹ', 'E'], ['ẻ', 'e'], ['Ẻ', 'E'], ['ẽ', 'e'], ['Ẽ', 'E'], ['ế', 'e'], ['Ế', 'E'], ['ề', 'e'], ['Ề', 'E'], ['ể', 'e'], ['Ể', 'E'], ['ễ', 'e'], ['Ễ', 'E'], ['ệ', 'e'], ['Ệ', 'E'], ['ỉ', 'i'], ['Ỉ', 'I'], ['ị', 'i'], ['Ị', 'I'], ['ọ', 'o'], ['Ọ', 'O'], ['ỏ', 'o'], ['Ỏ', 'O'], ['ố', 'o'], ['Ố', 'O'], ['ồ', 'o'], ['Ồ', 'O'], ['ổ', 'o'], ['Ổ', 'O'], ['ỗ', 'o'], ['Ỗ', 'O'], ['ộ', 'o'], ['Ộ', 'O'], ['ớ', 'o'], ['Ớ', 'O'], ['ờ', 'o'], ['Ờ', 'O'], ['ở', 'o'], ['Ở', 'O'], ['ỡ', 'o'], ['Ỡ', 'O'], ['ợ', 'o'], ['Ợ', 'O'], ['ụ', 'u'], ['Ụ', 'U'], ['ủ', 'u'], ['Ủ', 'U'], ['ứ', 'u'], ['Ứ', 'U'], ['ừ', 'u'], ['Ừ', 'U'], ['ử', 'u'], ['Ử', 'U'], ['ữ', 'u'], ['Ữ', 'U'], ['ự', 'u'], ['Ự', 'U'], ['ỳ', 'y'], ['Ỳ', 'Y'], ['ỵ', 'y'], ['Ỵ', 'Y'], ['ỷ', 'y'], ['Ỷ', 'Y'], ['ỹ', 'y'], ['Ỹ', 'Y'], # Arabic ['ء', 'e'], ['آ', 'a'], ['أ', 'a'], ['ؤ', 'w'], ['إ', 'i'], ['ئ', 'y'], ['ا', 'a'], ['ب', 'b'], ['ة', 't'], ['ت', 't'], ['ث', 'th'], ['ج', 'j'], ['ح', 'h'], ['خ', 'kh'], ['د', 'd'], ['ذ', 'dh'], ['ر', 'r'], ['ز', 'z'], ['س', 's'], ['ش', 'sh'], ['ص', 's'], ['ض', 'd'], ['ط', 't'], ['ظ', 'z'], ['ع', 'e'], ['غ', 'gh'], ['ـ', '_'], ['ف', 'f'], ['ق', 'q'], ['ك', 'k'], ['ل', 'l'], ['م', 'm'], ['ن', 'n'], ['ه', 'h'], ['و', 'w'], ['ى', 'a'], ['ي', 'y'], ['َ‎', 'a'], ['ُ', 'u'], ['ِ‎', 'i'], ['٠', '0'], ['١', '1'], ['٢', '2'], ['٣', '3'], ['٤', '4'], ['٥', '5'], ['٦', '6'], ['٧', '7'], ['٨', '8'], ['٩', '9'], # Persian / Farsi ['چ', 'ch'], ['ک', 'k'], ['گ', 'g'], ['پ', 'p'], ['ژ', 'zh'], ['ی', 'y'], ['۰', '0'], ['۱', '1'], ['۲', '2'], ['۳', '3'], ['۴', '4'], ['۵', '5'], ['۶', '6'], ['۷', '7'], ['۸', '8'], ['۹', '9'], # Pashto ['ټ', 'p'], ['ځ', 'z'], ['څ', 'c'], ['ډ', 'd'], ['ﺫ', 'd'], ['ﺭ', 'r'], ['ړ', 'r'], ['ﺯ', 'z'], ['ږ', 'g'], ['ښ', 'x'], ['ګ', 'g'], ['ڼ', 'n'], ['ۀ', 'e'], ['ې', 'e'], ['ۍ', 'ai'], # Urdu ['ٹ', 't'], ['ڈ', 'd'], ['ڑ', 'r'], ['ں', 'n'], ['ہ', 'h'], ['ھ', 'h'], ['ے', 'e'], # Russian ['А', 'A'], ['а', 'a'], ['Б', 'B'], ['б', 'b'], ['В', 'V'], ['в', 'v'], ['Г', 'G'], ['г', 'g'], ['Д', 'D'], ['д', 'd'], ['ъе', 'ye'], ['Ъе', 'Ye'], ['ъЕ', 'yE'], ['ЪЕ', 'YE'], ['Е', 'E'], ['е', 'e'], ['Ё', 'Yo'], ['ё', 'yo'], ['Ж', 'Zh'], ['ж', 'zh'], ['З', 'Z'], ['з', 'z'], ['И', 'I'], ['и', 'i'], ['ый', 'iy'], ['Ый', 'Iy'], ['ЫЙ', 'IY'], ['ыЙ', 'iY'], ['Й', 'Y'], ['й', 'y'], ['К', 'K'], ['к', 'k'], ['Л', 'L'], ['л', 'l'], ['М', 'M'], ['м', 'm'], ['Н', 'N'], ['н', 'n'], ['О', 'O'], ['о', 'o'], ['П', 'P'], ['п', 'p'], ['Р', 'R'], ['р', 'r'], ['С', 'S'], ['с', 's'], ['Т', 'T'], ['т', 't'], ['У', 'U'], ['у', 'u'], ['Ф', 'F'], ['ф', 'f'], ['Х', 'Kh'], ['х', 'kh'], ['Ц', 'Ts'], ['ц', 'ts'], ['Ч', 'Ch'], ['ч', 'ch'], ['Ш', 'Sh'], ['ш', 'sh'], ['Щ', 'Sch'], ['щ', 'sch'], ['Ъ', ''], ['ъ', ''], ['Ы', 'Y'], ['ы', 'y'], ['Ь', ''], ['ь', ''], ['Э', 'E'], ['э', 'e'], ['Ю', 'Yu'], ['ю', 'yu'], ['Я', 'Ya'], ['я', 'ya'], # Romanian ['ă', 'a'], ['Ă', 'A'], ['ș', 's'], ['Ș', 'S'], ['ț', 't'], ['Ț', 'T'], ['ţ', 't'], ['Ţ', 'T'], # Turkish ['ş', 's'], ['Ş', 'S'], ['ç', 'c'], ['Ç', 'C'], ['ğ', 'g'], ['Ğ', 'G'], ['ı', 'i'], ['İ', 'I'], # Armenian ['ա', 'a'], ['Ա', 'A'], ['բ', 'b'], ['Բ', 'B'], ['գ', 'g'], ['Գ', 'G'], ['դ', 'd'], ['Դ', 'D'], ['ե', 'ye'], ['Ե', 'Ye'], ['զ', 'z'], ['Զ', 'Z'], ['է', 'e'], ['Է', 'E'], ['ը', 'y'], ['Ը', 'Y'], ['թ', 't'], ['Թ', 'T'], ['ժ', 'zh'], ['Ժ', 'Zh'], ['ի', 'i'], ['Ի', 'I'], ['լ', 'l'], ['Լ', 'L'], ['խ', 'kh'], ['Խ', 'Kh'], ['ծ', 'ts'], ['Ծ', 'Ts'], ['կ', 'k'], ['Կ', 'K'], ['հ', 'h'], ['Հ', 'H'], ['ձ', 'dz'], ['Ձ', 'Dz'], ['ղ', 'gh'], ['Ղ', 'Gh'], ['ճ', 'tch'], ['Ճ', 'Tch'], ['մ', 'm'], ['Մ', 'M'], ['յ', 'y'], ['Յ', 'Y'], ['ն', 'n'], ['Ն', 'N'], ['շ', 'sh'], ['Շ', 'Sh'], ['ո', 'vo'], ['Ո', 'Vo'], ['չ', 'ch'], ['Չ', 'Ch'], ['պ', 'p'], ['Պ', 'P'], ['ջ', 'j'], ['Ջ', 'J'], ['ռ', 'r'], ['Ռ', 'R'], ['ս', 's'], ['Ս', 'S'], ['վ', 'v'], ['Վ', 'V'], ['տ', 't'], ['Տ', 'T'], ['ր', 'r'], ['Ր', 'R'], ['ց', 'c'], ['Ց', 'C'], ['ու', 'u'], ['ՈՒ', 'U'], ['Ու', 'U'], ['փ', 'p'], ['Փ', 'P'], ['ք', 'q'], ['Ք', 'Q'], ['օ', 'o'], ['Օ', 'O'], ['ֆ', 'f'], ['Ֆ', 'F'], ['և', 'yev'], # Georgian ['ა', 'a'], ['ბ', 'b'], ['გ', 'g'], ['დ', 'd'], ['ე', 'e'], ['ვ', 'v'], ['ზ', 'z'], ['თ', 't'], ['ი', 'i'], ['კ', 'k'], ['ლ', 'l'], ['მ', 'm'], ['ნ', 'n'], ['ო', 'o'], ['პ', 'p'], ['ჟ', 'zh'], ['რ', 'r'], ['ს', 's'], ['ტ', 't'], ['უ', 'u'], ['ფ', 'ph'], ['ქ', 'q'], ['ღ', 'gh'], ['ყ', 'k'], ['შ', 'sh'], ['ჩ', 'ch'], ['ც', 'ts'], ['ძ', 'dz'], ['წ', 'ts'], ['ჭ', 'tch'], ['ხ', 'kh'], ['ჯ', 'j'], ['ჰ', 'h'], # Czech ['č', 'c'], ['ď', 'd'], ['ě', 'e'], ['ň', 'n'], ['ř', 'r'], ['š', 's'], ['ť', 't'], ['ů', 'u'], ['ž', 'z'], ['Č', 'C'], ['Ď', 'D'], ['Ě', 'E'], ['Ň', 'N'], ['Ř', 'R'], ['Š', 'S'], ['Ť', 'T'], ['Ů', 'U'], ['Ž', 'Z'], # Dhivehi ['ހ', 'h'], ['ށ', 'sh'], ['ނ', 'n'], ['ރ', 'r'], ['ބ', 'b'], ['ޅ', 'lh'], ['ކ', 'k'], ['އ', 'a'], ['ވ', 'v'], ['މ', 'm'], ['ފ', 'f'], ['ދ', 'dh'], ['ތ', 'th'], ['ލ', 'l'], ['ގ', 'g'], ['ޏ', 'gn'], ['ސ', 's'], ['ޑ', 'd'], ['ޒ', 'z'], ['ޓ', 't'], ['ޔ', 'y'], ['ޕ', 'p'], ['ޖ', 'j'], ['ޗ', 'ch'], ['ޘ', 'tt'], ['ޙ', 'hh'], ['ޚ', 'kh'], ['ޛ', 'th'], ['ޜ', 'z'], ['ޝ', 'sh'], ['ޞ', 's'], ['ޟ', 'd'], ['ޠ', 't'], ['ޡ', 'z'], ['ޢ', 'a'], ['ޣ', 'gh'], ['ޤ', 'q'], ['ޥ', 'w'], ['ަ', 'a'], ['ާ', 'aa'], ['ި', 'i'], ['ީ', 'ee'], ['ު', 'u'], ['ޫ', 'oo'], ['ެ', 'e'], ['ޭ', 'ey'], ['ޮ', 'o'], ['ޯ', 'oa'], ['ް', ''], # Greek ['α', 'a'], ['β', 'v'], ['γ', 'g'], ['δ', 'd'], ['ε', 'e'], ['ζ', 'z'], ['η', 'i'], ['θ', 'th'], ['ι', 'i'], ['κ', 'k'], ['λ', 'l'], ['μ', 'm'], ['ν', 'n'], ['ξ', 'ks'], ['ο', 'o'], ['π', 'p'], ['ρ', 'r'], ['σ', 's'], ['τ', 't'], ['υ', 'y'], ['φ', 'f'], ['χ', 'x'], ['ψ', 'ps'], ['ω', 'o'], ['ά', 'a'], ['έ', 'e'], ['ί', 'i'], ['ό', 'o'], ['ύ', 'y'], ['ή', 'i'], ['ώ', 'o'], ['ς', 's'], ['ϊ', 'i'], ['ΰ', 'y'], ['ϋ', 'y'], ['ΐ', 'i'], ['Α', 'A'], ['Β', 'B'], ['Γ', 'G'], ['Δ', 'D'], ['Ε', 'E'], ['Ζ', 'Z'], ['Η', 'I'], ['Θ', 'TH'], ['Ι', 'I'], ['Κ', 'K'], ['Λ', 'L'], ['Μ', 'M'], ['Ν', 'N'], ['Ξ', 'KS'], ['Ο', 'O'], ['Π', 'P'], ['Ρ', 'R'], ['Σ', 'S'], ['Τ', 'T'], ['Υ', 'Y'], ['Φ', 'F'], ['Χ', 'X'], ['Ψ', 'PS'], ['Ω', 'O'], ['Ά', 'A'], ['Έ', 'E'], ['Ί', 'I'], ['Ό', 'O'], ['Ύ', 'Y'], ['Ή', 'I'], ['Ώ', 'O'], ['Ϊ', 'I'], ['Ϋ', 'Y'], # Disabled as it conflicts with German and Latin. # Hungarian # ['ä', 'a'], # ['Ä', 'A'], # ['ö', 'o'], # ['Ö', 'O'], # ['ü', 'u'], # ['Ü', 'U'], # ['ű', 'u'], # ['Ű', 'U'], # Latvian ['ā', 'a'], ['ē', 'e'], ['ģ', 'g'], ['ī', 'i'], ['ķ', 'k'], ['ļ', 'l'], ['ņ', 'n'], ['ū', 'u'], ['Ā', 'A'], ['Ē', 'E'], ['Ģ', 'G'], ['Ī', 'I'], ['Ķ', 'K'], ['Ļ', 'L'], ['Ņ', 'N'], ['Ū', 'U'], ['č', 'c'], ['š', 's'], ['ž', 'z'], ['Č', 'C'], ['Š', 'S'], ['Ž', 'Z'], # Lithuanian ['ą', 'a'], ['č', 'c'], ['ę', 'e'], ['ė', 'e'], ['į', 'i'], ['š', 's'], ['ų', 'u'], ['ū', 'u'], ['ž', 'z'], ['Ą', 'A'], ['Č', 'C'], ['Ę', 'E'], ['Ė', 'E'], ['Į', 'I'], ['Š', 'S'], ['Ų', 'U'], ['Ū', 'U'], # Macedonian ['Ќ', 'Kj'], ['ќ', 'kj'], ['Љ', 'Lj'], ['љ', 'lj'], ['Њ', 'Nj'], ['њ', 'nj'], ['Тс', 'Ts'], ['тс', 'ts'], # Polish ['ą', 'a'], ['ć', 'c'], ['ę', 'e'], ['ł', 'l'], ['ń', 'n'], ['ś', 's'], ['ź', 'z'], ['ż', 'z'], ['Ą', 'A'], ['Ć', 'C'], ['Ę', 'E'], ['Ł', 'L'], ['Ń', 'N'], ['Ś', 'S'], ['Ź', 'Z'], ['Ż', 'Z'], # Disabled as it conflicts with Vietnamese. # Serbian # ['љ', 'lj'], # ['њ', 'nj'], # ['Љ', 'Lj'], # ['Њ', 'Nj'], # ['đ', 'dj'], # ['Đ', 'Dj'], # ['ђ', 'dj'], # ['ј', 'j'], # ['ћ', 'c'], # ['џ', 'dz'], # ['Ђ', 'Dj'], # ['Ј', 'j'], # ['Ћ', 'C'], # ['Џ', 'Dz'], # Disabled as it conflicts with German and Latin. # Slovak # ['ä', 'a'], # ['Ä', 'A'], # ['ľ', 'l'], # ['ĺ', 'l'], # ['ŕ', 'r'], # ['Ľ', 'L'], # ['Ĺ', 'L'], # ['Ŕ', 'R'], # Disabled as it conflicts with German and Latin. # Swedish # ['å', 'o'], # ['Å', 'o'], # ['ä', 'a'], # ['Ä', 'A'], # ['ë', 'e'], # ['Ë', 'E'], # ['ö', 'o'], # ['Ö', 'O'], # Ukrainian ['Є', 'Ye'], ['І', 'I'], ['Ї', 'Yi'], ['Ґ', 'G'], ['є', 'ye'], ['і', 'i'], ['ї', 'yi'], ['ґ', 'g'], # Dutch ['IJ', 'IJ'], ['ij', 'ij'], # Danish # ['Æ', 'Ae'], # ['Ø', 'Oe'], # ['Å', 'Aa'], # ['æ', 'ae'], # ['ø', 'oe'], # ['å', 'aa'] # Currencies ['¢', 'c'], ['¥', 'Y'], ['߿', 'b'], ['৳', 't'], ['૱', 'Bo'], ['฿', 'B'], ['₠', 'CE'], ['₡', 'C'], ['₢', 'Cr'], ['₣', 'F'], ['₥', 'm'], ['₦', 'N'], ['₧', 'Pt'], ['₨', 'Rs'], ['₩', 'W'], ['₫', 's'], ['€', 'E'], ['₭', 'K'], ['₮', 'T'], ['₯', 'Dp'], ['₰', 'S'], ['₱', 'P'], ['₲', 'G'], ['₳', 'A'], ['₴', 'S'], ['₵', 'C'], ['₶', 'tt'], ['₷', 'S'], ['₸', 'T'], ['₹', 'R'], ['₺', 'L'], ['₽', 'P'], ['₿', 'B'], ['﹩', '$'], ['¢', 'c'], ['¥', 'Y'], ['₩', 'W'], # Latin ['𝐀', 'A'], ['𝐁', 'B'], ['𝐂', 'C'], ['𝐃', 'D'], ['𝐄', 'E'], ['𝐅', 'F'], ['𝐆', 'G'], ['𝐇', 'H'], ['𝐈', 'I'], ['𝐉', 'J'], ['𝐊', 'K'], ['𝐋', 'L'], ['𝐌', 'M'], ['𝐍', 'N'], ['𝐎', 'O'], ['𝐏', 'P'], ['𝐐', 'Q'], ['𝐑', 'R'], ['𝐒', 'S'], ['𝐓', 'T'], ['𝐔', 'U'], ['𝐕', 'V'], ['𝐖', 'W'], ['𝐗', 'X'], ['𝐘', 'Y'], ['𝐙', 'Z'], ['𝐚', 'a'], ['𝐛', 'b'], ['𝐜', 'c'], ['𝐝', 'd'], ['𝐞', 'e'], ['𝐟', 'f'], ['𝐠', 'g'], ['𝐡', 'h'], ['𝐢', 'i'], ['𝐣', 'j'], ['𝐤', 'k'], ['𝐥', 'l'], ['𝐦', 'm'], ['𝐧', 'n'], ['𝐨', 'o'], ['𝐩', 'p'], ['𝐪', 'q'], ['𝐫', 'r'], ['𝐬', 's'], ['𝐭', 't'], ['𝐮', 'u'], ['𝐯', 'v'], ['𝐰', 'w'], ['𝐱', 'x'], ['𝐲', 'y'], ['𝐳', 'z'], ['𝐴', 'A'], ['𝐵', 'B'], ['𝐶', 'C'], ['𝐷', 'D'], ['𝐸', 'E'], ['𝐹', 'F'], ['𝐺', 'G'], ['𝐻', 'H'], ['𝐼', 'I'], ['𝐽', 'J'], ['𝐾', 'K'], ['𝐿', 'L'], ['𝑀', 'M'], ['𝑁', 'N'], ['𝑂', 'O'], ['𝑃', 'P'], ['𝑄', 'Q'], ['𝑅', 'R'], ['𝑆', 'S'], ['𝑇', 'T'], ['𝑈', 'U'], ['𝑉', 'V'], ['𝑊', 'W'], ['𝑋', 'X'], ['𝑌', 'Y'], ['𝑍', 'Z'], ['𝑎', 'a'], ['𝑏', 'b'], ['𝑐', 'c'], ['𝑑', 'd'], ['𝑒', 'e'], ['𝑓', 'f'], ['𝑔', 'g'], ['𝑖', 'i'], ['𝑗', 'j'], ['𝑘', 'k'], ['𝑙', 'l'], ['𝑚', 'm'], ['𝑛', 'n'], ['𝑜', 'o'], ['𝑝', 'p'], ['𝑞', 'q'], ['𝑟', 'r'], ['𝑠', 's'], ['𝑡', 't'], ['𝑢', 'u'], ['𝑣', 'v'], ['𝑤', 'w'], ['𝑥', 'x'], ['𝑦', 'y'], ['𝑧', 'z'], ['𝑨', 'A'], ['𝑩', 'B'], ['𝑪', 'C'], ['𝑫', 'D'], ['𝑬', 'E'], ['𝑭', 'F'], ['𝑮', 'G'], ['𝑯', 'H'], ['𝑰', 'I'], ['𝑱', 'J'], ['𝑲', 'K'], ['𝑳', 'L'], ['𝑴', 'M'], ['𝑵', 'N'], ['𝑶', 'O'], ['𝑷', 'P'], ['𝑸', 'Q'], ['𝑹', 'R'], ['𝑺', 'S'], ['𝑻', 'T'], ['𝑼', 'U'], ['𝑽', 'V'], ['𝑾', 'W'], ['𝑿', 'X'], ['𝒀', 'Y'], ['𝒁', 'Z'], ['𝒂', 'a'], ['𝒃', 'b'], ['𝒄', 'c'], ['𝒅', 'd'], ['𝒆', 'e'], ['𝒇', 'f'], ['𝒈', 'g'], ['𝒉', 'h'], ['𝒊', 'i'], ['𝒋', 'j'], ['𝒌', 'k'], ['𝒍', 'l'], ['𝒎', 'm'], ['𝒏', 'n'], ['𝒐', 'o'], ['𝒑', 'p'], ['𝒒', 'q'], ['𝒓', 'r'], ['𝒔', 's'], ['𝒕', 't'], ['𝒖', 'u'], ['𝒗', 'v'], ['𝒘', 'w'], ['𝒙', 'x'], ['𝒚', 'y'], ['𝒛', 'z'], ['𝒜', 'A'], ['𝒞', 'C'], ['𝒟', 'D'], ['𝒢', 'g'], ['𝒥', 'J'], ['𝒦', 'K'], ['𝒩', 'N'], ['𝒪', 'O'], ['𝒫', 'P'], ['𝒬', 'Q'], ['𝒮', 'S'], ['𝒯', 'T'], ['𝒰', 'U'], ['𝒱', 'V'], ['𝒲', 'W'], ['𝒳', 'X'], ['𝒴', 'Y'], ['𝒵', 'Z'], ['𝒶', 'a'], ['𝒷', 'b'], ['𝒸', 'c'], ['𝒹', 'd'], ['𝒻', 'f'], ['𝒽', 'h'], ['𝒾', 'i'], ['𝒿', 'j'], ['𝓀', 'h'], ['𝓁', 'l'], ['𝓂', 'm'], ['𝓃', 'n'], ['𝓅', 'p'], ['𝓆', 'q'], ['𝓇', 'r'], ['𝓈', 's'], ['𝓉', 't'], ['𝓊', 'u'], ['𝓋', 'v'], ['𝓌', 'w'], ['𝓍', 'x'], ['𝓎', 'y'], ['𝓏', 'z'], ['𝓐', 'A'], ['𝓑', 'B'], ['𝓒', 'C'], ['𝓓', 'D'], ['𝓔', 'E'], ['𝓕', 'F'], ['𝓖', 'G'], ['𝓗', 'H'], ['𝓘', 'I'], ['𝓙', 'J'], ['𝓚', 'K'], ['𝓛', 'L'], ['𝓜', 'M'], ['𝓝', 'N'], ['𝓞', 'O'], ['𝓟', 'P'], ['𝓠', 'Q'], ['𝓡', 'R'], ['𝓢', 'S'], ['𝓣', 'T'], ['𝓤', 'U'], ['𝓥', 'V'], ['𝓦', 'W'], ['𝓧', 'X'], ['𝓨', 'Y'], ['𝓩', 'Z'], ['𝓪', 'a'], ['𝓫', 'b'], ['𝓬', 'c'], ['𝓭', 'd'], ['𝓮', 'e'], ['𝓯', 'f'], ['𝓰', 'g'], ['𝓱', 'h'], ['𝓲', 'i'], ['𝓳', 'j'], ['𝓴', 'k'], ['𝓵', 'l'], ['𝓶', 'm'], ['𝓷', 'n'], ['𝓸', 'o'], ['𝓹', 'p'], ['𝓺', 'q'], ['𝓻', 'r'], ['𝓼', 's'], ['𝓽', 't'], ['𝓾', 'u'], ['𝓿', 'v'], ['𝔀', 'w'], ['𝔁', 'x'], ['𝔂', 'y'], ['𝔃', 'z'], ['𝔄', 'A'], ['𝔅', 'B'], ['𝔇', 'D'], ['𝔈', 'E'], ['𝔉', 'F'], ['𝔊', 'G'], ['𝔍', 'J'], ['𝔎', 'K'], ['𝔏', 'L'], ['𝔐', 'M'], ['𝔑', 'N'], ['𝔒', 'O'], ['𝔓', 'P'], ['𝔔', 'Q'], ['𝔖', 'S'], ['𝔗', 'T'], ['𝔘', 'U'], ['𝔙', 'V'], ['𝔚', 'W'], ['𝔛', 'X'], ['𝔜', 'Y'], ['𝔞', 'a'], ['𝔟', 'b'], ['𝔠', 'c'], ['𝔡', 'd'], ['𝔢', 'e'], ['𝔣', 'f'], ['𝔤', 'g'], ['𝔥', 'h'], ['𝔦', 'i'], ['𝔧', 'j'], ['𝔨', 'k'], ['𝔩', 'l'], ['𝔪', 'm'], ['𝔫', 'n'], ['𝔬', 'o'], ['𝔭', 'p'], ['𝔮', 'q'], ['𝔯', 'r'], ['𝔰', 's'], ['𝔱', 't'], ['𝔲', 'u'], ['𝔳', 'v'], ['𝔴', 'w'], ['𝔵', 'x'], ['𝔶', 'y'], ['𝔷', 'z'], ['𝔸', 'A'], ['𝔹', 'B'], ['𝔻', 'D'], ['𝔼', 'E'], ['𝔽', 'F'], ['𝔾', 'G'], ['𝕀', 'I'], ['𝕁', 'J'], ['𝕂', 'K'], ['𝕃', 'L'], ['𝕄', 'M'], ['𝕆', 'N'], ['𝕊', 'S'], ['𝕋', 'T'], ['𝕌', 'U'], ['𝕍', 'V'], ['𝕎', 'W'], ['𝕏', 'X'], ['𝕐', 'Y'], ['𝕒', 'a'], ['𝕓', 'b'], ['𝕔', 'c'], ['𝕕', 'd'], ['𝕖', 'e'], ['𝕗', 'f'], ['𝕘', 'g'], ['𝕙', 'h'], ['𝕚', 'i'], ['𝕛', 'j'], ['𝕜', 'k'], ['𝕝', 'l'], ['𝕞', 'm'], ['𝕟', 'n'], ['𝕠', 'o'], ['𝕡', 'p'], ['𝕢', 'q'], ['𝕣', 'r'], ['𝕤', 's'], ['𝕥', 't'], ['𝕦', 'u'], ['𝕧', 'v'], ['𝕨', 'w'], ['𝕩', 'x'], ['𝕪', 'y'], ['𝕫', 'z'], ['𝕬', 'A'], ['𝕭', 'B'], ['𝕮', 'C'], ['𝕯', 'D'], ['𝕰', 'E'], ['𝕱', 'F'], ['𝕲', 'G'], ['𝕳', 'H'], ['𝕴', 'I'], ['𝕵', 'J'], ['𝕶', 'K'], ['𝕷', 'L'], ['𝕸', 'M'], ['𝕹', 'N'], ['𝕺', 'O'], ['𝕻', 'P'], ['𝕼', 'Q'], ['𝕽', 'R'], ['𝕾', 'S'], ['𝕿', 'T'], ['𝖀', 'U'], ['𝖁', 'V'], ['𝖂', 'W'], ['𝖃', 'X'], ['𝖄', 'Y'], ['𝖅', 'Z'], ['𝖆', 'a'], ['𝖇', 'b'], ['𝖈', 'c'], ['𝖉', 'd'], ['𝖊', 'e'], ['𝖋', 'f'], ['𝖌', 'g'], ['𝖍', 'h'], ['𝖎', 'i'], ['𝖏', 'j'], ['𝖐', 'k'], ['𝖑', 'l'], ['𝖒', 'm'], ['𝖓', 'n'], ['𝖔', 'o'], ['𝖕', 'p'], ['𝖖', 'q'], ['𝖗', 'r'], ['𝖘', 's'], ['𝖙', 't'], ['𝖚', 'u'], ['𝖛', 'v'], ['𝖜', 'w'], ['𝖝', 'x'], ['𝖞', 'y'], ['𝖟', 'z'], ['𝖠', 'A'], ['𝖡', 'B'], ['𝖢', 'C'], ['𝖣', 'D'], ['𝖤', 'E'], ['𝖥', 'F'], ['𝖦', 'G'], ['𝖧', 'H'], ['𝖨', 'I'], ['𝖩', 'J'], ['𝖪', 'K'], ['𝖫', 'L'], ['𝖬', 'M'], ['𝖭', 'N'], ['𝖮', 'O'], ['𝖯', 'P'], ['𝖰', 'Q'], ['𝖱', 'R'], ['𝖲', 'S'], ['𝖳', 'T'], ['𝖴', 'U'], ['𝖵', 'V'], ['𝖶', 'W'], ['𝖷', 'X'], ['𝖸', 'Y'], ['𝖹', 'Z'], ['𝖺', 'a'], ['𝖻', 'b'], ['𝖼', 'c'], ['𝖽', 'd'], ['𝖾', 'e'], ['𝖿', 'f'], ['𝗀', 'g'], ['𝗁', 'h'], ['𝗂', 'i'], ['𝗃', 'j'], ['𝗄', 'k'], ['𝗅', 'l'], ['𝗆', 'm'], ['𝗇', 'n'], ['𝗈', 'o'], ['𝗉', 'p'], ['𝗊', 'q'], ['𝗋', 'r'], ['𝗌', 's'], ['𝗍', 't'], ['𝗎', 'u'], ['𝗏', 'v'], ['𝗐', 'w'], ['𝗑', 'x'], ['𝗒', 'y'], ['𝗓', 'z'], ['𝗔', 'A'], ['𝗕', 'B'], ['𝗖', 'C'], ['𝗗', 'D'], ['𝗘', 'E'], ['𝗙', 'F'], ['𝗚', 'G'], ['𝗛', 'H'], ['𝗜', 'I'], ['𝗝', 'J'], ['𝗞', 'K'], ['𝗟', 'L'], ['𝗠', 'M'], ['𝗡', 'N'], ['𝗢', 'O'], ['𝗣', 'P'], ['𝗤', 'Q'], ['𝗥', 'R'], ['𝗦', 'S'], ['𝗧', 'T'], ['𝗨', 'U'], ['𝗩', 'V'], ['𝗪', 'W'], ['𝗫', 'X'], ['𝗬', 'Y'], ['𝗭', 'Z'], ['𝗮', 'a'], ['𝗯', 'b'], ['𝗰', 'c'], ['𝗱', 'd'], ['𝗲', 'e'], ['𝗳', 'f'], ['𝗴', 'g'], ['𝗵', 'h'], ['𝗶', 'i'], ['𝗷', 'j'], ['𝗸', 'k'], ['𝗹', 'l'], ['𝗺', 'm'], ['𝗻', 'n'], ['𝗼', 'o'], ['𝗽', 'p'], ['𝗾', 'q'], ['𝗿', 'r'], ['𝘀', 's'], ['𝘁', 't'], ['𝘂', 'u'], ['𝘃', 'v'], ['𝘄', 'w'], ['𝘅', 'x'], ['𝘆', 'y'], ['𝘇', 'z'], ['𝘈', 'A'], ['𝘉', 'B'], ['𝘊', 'C'], ['𝘋', 'D'], ['𝘌', 'E'], ['𝘍', 'F'], ['𝘎', 'G'], ['𝘏', 'H'], ['𝘐', 'I'], ['𝘑', 'J'], ['𝘒', 'K'], ['𝘓', 'L'], ['𝘔', 'M'], ['𝘕', 'N'], ['𝘖', 'O'], ['𝘗', 'P'], ['𝘘', 'Q'], ['𝘙', 'R'], ['𝘚', 'S'], ['𝘛', 'T'], ['𝘜', 'U'], ['𝘝', 'V'], ['𝘞', 'W'], ['𝘟', 'X'], ['𝘠', 'Y'], ['𝘡', 'Z'], ['𝘢', 'a'], ['𝘣', 'b'], ['𝘤', 'c'], ['𝘥', 'd'], ['𝘦', 'e'], ['𝘧', 'f'], ['𝘨', 'g'], ['𝘩', 'h'], ['𝘪', 'i'], ['𝘫', 'j'], ['𝘬', 'k'], ['𝘭', 'l'], ['𝘮', 'm'], ['𝘯', 'n'], ['𝘰', 'o'], ['𝘱', 'p'], ['𝘲', 'q'], ['𝘳', 'r'], ['𝘴', 's'], ['𝘵', 't'], ['𝘶', 'u'], ['𝘷', 'v'], ['𝘸', 'w'], ['𝘹', 'x'], ['𝘺', 'y'], ['𝘻', 'z'], ['𝘼', 'A'], ['𝘽', 'B'], ['𝘾', 'C'], ['𝘿', 'D'], ['𝙀', 'E'], ['𝙁', 'F'], ['𝙂', 'G'], ['𝙃', 'H'], ['𝙄', 'I'], ['𝙅', 'J'], ['𝙆', 'K'], ['𝙇', 'L'], ['𝙈', 'M'], ['𝙉', 'N'], ['𝙊', 'O'], ['𝙋', 'P'], ['𝙌', 'Q'], ['𝙍', 'R'], ['𝙎', 'S'], ['𝙏', 'T'], ['𝙐', 'U'], ['𝙑', 'V'], ['𝙒', 'W'], ['𝙓', 'X'], ['𝙔', 'Y'], ['𝙕', 'Z'], ['𝙖', 'a'], ['𝙗', 'b'], ['𝙘', 'c'], ['𝙙', 'd'], ['𝙚', 'e'], ['𝙛', 'f'], ['𝙜', 'g'], ['𝙝', 'h'], ['𝙞', 'i'], ['𝙟', 'j'], ['𝙠', 'k'], ['𝙡', 'l'], ['𝙢', 'm'], ['𝙣', 'n'], ['𝙤', 'o'], ['𝙥', 'p'], ['𝙦', 'q'], ['𝙧', 'r'], ['𝙨', 's'], ['𝙩', 't'], ['𝙪', 'u'], ['𝙫', 'v'], ['𝙬', 'w'], ['𝙭', 'x'], ['𝙮', 'y'], ['𝙯', 'z'], ['𝙰', 'A'], ['𝙱', 'B'], ['𝙲', 'C'], ['𝙳', 'D'], ['𝙴', 'E'], ['𝙵', 'F'], ['𝙶', 'G'], ['𝙷', 'H'], ['𝙸', 'I'], ['𝙹', 'J'], ['𝙺', 'K'], ['𝙻', 'L'], ['𝙼', 'M'], ['𝙽', 'N'], ['𝙾', 'O'], ['𝙿', 'P'], ['𝚀', 'Q'], ['𝚁', 'R'], ['𝚂', 'S'], ['𝚃', 'T'], ['𝚄', 'U'], ['𝚅', 'V'], ['𝚆', 'W'], ['𝚇', 'X'], ['𝚈', 'Y'], ['𝚉', 'Z'], ['𝚊', 'a'], ['𝚋', 'b'], ['𝚌', 'c'], ['𝚍', 'd'], ['𝚎', 'e'], ['𝚏', 'f'], ['𝚐', 'g'], ['𝚑', 'h'], ['𝚒', 'i'], ['𝚓', 'j'], ['𝚔', 'k'], ['𝚕', 'l'], ['𝚖', 'm'], ['𝚗', 'n'], ['𝚘', 'o'], ['𝚙', 'p'], ['𝚚', 'q'], ['𝚛', 'r'], ['𝚜', 's'], ['𝚝', 't'], ['𝚞', 'u'], ['𝚟', 'v'], ['𝚠', 'w'], ['𝚡', 'x'], ['𝚢', 'y'], ['𝚣', 'z'], # Dotless letters ['𝚤', 'l'], ['𝚥', 'j'], # Greek ['𝛢', 'A'], ['𝛣', 'B'], ['𝛤', 'G'], ['𝛥', 'D'], ['𝛦', 'E'], ['𝛧', 'Z'], ['𝛨', 'I'], ['𝛩', 'TH'], ['𝛪', 'I'], ['𝛫', 'K'], ['𝛬', 'L'], ['𝛭', 'M'], ['𝛮', 'N'], ['𝛯', 'KS'], ['𝛰', 'O'], ['𝛱', 'P'], ['𝛲', 'R'], ['𝛳', 'TH'], ['𝛴', 'S'], ['𝛵', 'T'], ['𝛶', 'Y'], ['𝛷', 'F'], ['𝛸', 'x'], ['𝛹', 'PS'], ['𝛺', 'O'], ['𝛻', 'D'], ['𝛼', 'a'], ['𝛽', 'b'], ['𝛾', 'g'], ['𝛿', 'd'], ['𝜀', 'e'], ['𝜁', 'z'], ['𝜂', 'i'], ['𝜃', 'th'], ['𝜄', 'i'], ['𝜅', 'k'], ['𝜆', 'l'], ['𝜇', 'm'], ['𝜈', 'n'], ['𝜉', 'ks'], ['𝜊', 'o'], ['𝜋', 'p'], ['𝜌', 'r'], ['𝜍', 's'], ['𝜎', 's'], ['𝜏', 't'], ['𝜐', 'y'], ['𝜑', 'f'], ['𝜒', 'x'], ['𝜓', 'ps'], ['𝜔', 'o'], ['𝜕', 'd'], ['𝜖', 'E'], ['𝜗', 'TH'], ['𝜘', 'K'], ['𝜙', 'f'], ['𝜚', 'r'], ['𝜛', 'p'], ['𝜜', 'A'], ['𝜝', 'V'], ['𝜞', 'G'], ['𝜟', 'D'], ['𝜠', 'E'], ['𝜡', 'Z'], ['𝜢', 'I'], ['𝜣', 'TH'], ['𝜤', 'I'], ['𝜥', 'K'], ['𝜦', 'L'], ['𝜧', 'M'], ['𝜨', 'N'], ['𝜩', 'KS'], ['𝜪', 'O'], ['𝜫', 'P'], ['𝜬', 'S'], ['𝜭', 'TH'], ['𝜮', 'S'], ['𝜯', 'T'], ['𝜰', 'Y'], ['𝜱', 'F'], ['𝜲', 'X'], ['𝜳', 'PS'], ['𝜴', 'O'], ['𝜵', 'D'], ['𝜶', 'a'], ['𝜷', 'v'], ['𝜸', 'g'], ['𝜹', 'd'], ['𝜺', 'e'], ['𝜻', 'z'], ['𝜼', 'i'], ['𝜽', 'th'], ['𝜾', 'i'], ['𝜿', 'k'], ['𝝀', 'l'], ['𝝁', 'm'], ['𝝂', 'n'], ['𝝃', 'ks'], ['𝝄', 'o'], ['𝝅', 'p'], ['𝝆', 'r'], ['𝝇', 's'], ['𝝈', 's'], ['𝝉', 't'], ['𝝊', 'y'], ['𝝋', 'f'], ['𝝌', 'x'], ['𝝍', 'ps'], ['𝝎', 'o'], ['𝝏', 'a'], ['𝝐', 'e'], ['𝝑', 'i'], ['𝝒', 'k'], ['𝝓', 'f'], ['𝝔', 'r'], ['𝝕', 'p'], ['𝝖', 'A'], ['𝝗', 'B'], ['𝝘', 'G'], ['𝝙', 'D'], ['𝝚', 'E'], ['𝝛', 'Z'], ['𝝜', 'I'], ['𝝝', 'TH'], ['𝝞', 'I'], ['𝝟', 'K'], ['𝝠', 'L'], ['𝝡', 'M'], ['𝝢', 'N'], ['𝝣', 'KS'], ['𝝤', 'O'], ['𝝥', 'P'], ['𝝦', 'R'], ['𝝧', 'TH'], ['𝝨', 'S'], ['𝝩', 'T'], ['𝝪', 'Y'], ['𝝫', 'F'], ['𝝬', 'X'], ['𝝭', 'PS'], ['𝝮', 'O'], ['𝝯', 'D'], ['𝝰', 'a'], ['𝝱', 'v'], ['𝝲', 'g'], ['𝝳', 'd'], ['𝝴', 'e'], ['𝝵', 'z'], ['𝝶', 'i'], ['𝝷', 'th'], ['𝝸', 'i'], ['𝝹', 'k'], ['𝝺', 'l'], ['𝝻', 'm'], ['𝝼', 'n'], ['𝝽', 'ks'], ['𝝾', 'o'], ['𝝿', 'p'], ['𝞀', 'r'], ['𝞁', 's'], ['𝞂', 's'], ['𝞃', 't'], ['𝞄', 'y'], ['𝞅', 'f'], ['𝞆', 'x'], ['𝞇', 'ps'], ['𝞈', 'o'], ['𝞉', 'a'], ['𝞊', 'e'], ['𝞋', 'i'], ['𝞌', 'k'], ['𝞍', 'f'], ['𝞎', 'r'], ['𝞏', 'p'], ['𝞐', 'A'], ['𝞑', 'V'], ['𝞒', 'G'], ['𝞓', 'D'], ['𝞔', 'E'], ['𝞕', 'Z'], ['𝞖', 'I'], ['𝞗', 'TH'], ['𝞘', 'I'], ['𝞙', 'K'], ['𝞚', 'L'], ['𝞛', 'M'], ['𝞜', 'N'], ['𝞝', 'KS'], ['𝞞', 'O'], ['𝞟', 'P'], ['𝞠', 'S'], ['𝞡', 'TH'], ['𝞢', 'S'], ['𝞣', 'T'], ['𝞤', 'Y'], ['𝞥', 'F'], ['𝞦', 'X'], ['𝞧', 'PS'], ['𝞨', 'O'], ['𝞩', 'D'], ['𝞪', 'av'], ['𝞫', 'g'], ['𝞬', 'd'], ['𝞭', 'e'], ['𝞮', 'z'], ['𝞯', 'i'], ['𝞰', 'i'], ['𝞱', 'th'], ['𝞲', 'i'], ['𝞳', 'k'], ['𝞴', 'l'], ['𝞵', 'm'], ['𝞶', 'n'], ['𝞷', 'ks'], ['𝞸', 'o'], ['𝞹', 'p'], ['𝞺', 'r'], ['𝞻', 's'], ['𝞼', 's'], ['𝞽', 't'], ['𝞾', 'y'], ['𝞿', 'f'], ['𝟀', 'x'], ['𝟁', 'ps'], ['𝟂', 'o'], ['𝟃', 'a'], ['𝟄', 'e'], ['𝟅', 'i'], ['𝟆', 'k'], ['𝟇', 'f'], ['𝟈', 'r'], ['𝟉', 'p'], ['𝟊', 'F'], ['𝟋', 'f'], ['⒜', '(a)'], ['⒝', '(b)'], ['⒞', '(c)'], ['⒟', '(d)'], ['⒠', '(e)'], ['⒡', '(f)'], ['⒢', '(g)'], ['⒣', '(h)'], ['⒤', '(i)'], ['⒥', '(j)'], ['⒦', '(k)'], ['⒧', '(l)'], ['⒨', '(m)'], ['⒩', '(n)'], ['⒪', '(o)'], ['⒫', '(p)'], ['⒬', '(q)'], ['⒭', '(r)'], ['⒮', '(s)'], ['⒯', '(t)'], ['⒰', '(u)'], ['⒱', '(v)'], ['⒲', '(w)'], ['⒳', '(x)'], ['⒴', '(y)'], ['⒵', '(z)'], ['Ⓐ', '(A)'], ['Ⓑ', '(B)'], ['Ⓒ', '(C)'], ['Ⓓ', '(D)'], ['Ⓔ', '(E)'], ['Ⓕ', '(F)'], ['Ⓖ', '(G)'], ['Ⓗ', '(H)'], ['Ⓘ', '(I)'], ['Ⓙ', '(J)'], ['Ⓚ', '(K)'], ['Ⓛ', '(L)'], ['Ⓝ', '(N)'], ['Ⓞ', '(O)'], ['Ⓟ', '(P)'], ['Ⓠ', '(Q)'], ['Ⓡ', '(R)'], ['Ⓢ', '(S)'], ['Ⓣ', '(T)'], ['Ⓤ', '(U)'], ['Ⓥ', '(V)'], ['Ⓦ', '(W)'], ['Ⓧ', '(X)'], ['Ⓨ', '(Y)'], ['Ⓩ', '(Z)'], ['ⓐ', '(a)'], ['ⓑ', '(b)'], ['ⓒ', '(b)'], ['ⓓ', '(c)'], ['ⓔ', '(e)'], ['ⓕ', '(f)'], ['ⓖ', '(g)'], ['ⓗ', '(h)'], ['ⓘ', '(i)'], ['ⓙ', '(j)'], ['ⓚ', '(k)'], ['ⓛ', '(l)'], ['ⓜ', '(m)'], ['ⓝ', '(n)'], ['ⓞ', '(o)'], ['ⓟ', '(p)'], ['ⓠ', '(q)'], ['ⓡ', '(r)'], ['ⓢ', '(s)'], ['ⓣ', '(t)'], ['ⓤ', '(u)'], ['ⓥ', '(v)'], ['ⓦ', '(w)'], ['ⓧ', '(x)'], ['ⓨ', '(y)'], ['ⓩ', '(z)'], # Numbers ['𝟎', '0'], ['𝟏', '1'], ['𝟐', '2'], ['𝟑', '3'], ['𝟒', '4'], ['𝟓', '5'], ['𝟔', '6'], ['𝟕', '7'], ['𝟖', '8'], ['𝟗', '9'], ['𝟘', '0'], ['𝟙', '1'], ['𝟚', '2'], ['𝟛', '3'], ['𝟜', '4'], ['𝟝', '5'], ['𝟞', '6'], ['𝟟', '7'], ['𝟠', '8'], ['𝟡', '9'], ['𝟢', '0'], ['𝟣', '1'], ['𝟤', '2'], ['𝟥', '3'], ['𝟦', '4'], ['𝟧', '5'], ['𝟨', '6'], ['𝟩', '7'], ['𝟪', '8'], ['𝟫', '9'], ['𝟬', '0'], ['𝟭', '1'], ['𝟮', '2'], ['𝟯', '3'], ['𝟰', '4'], ['𝟱', '5'], ['𝟲', '6'], ['𝟳', '7'], ['𝟴', '8'], ['𝟵', '9'], ['𝟶', '0'], ['𝟷', '1'], ['𝟸', '2'], ['𝟹', '3'], ['𝟺', '4'], ['𝟻', '5'], ['𝟼', '6'], ['𝟽', '7'], ['𝟾', '8'], ['𝟿', '9'], ['①', '1'], ['②', '2'], ['③', '3'], ['④', '4'], ['⑤', '5'], ['⑥', '6'], ['⑦', '7'], ['⑧', '8'], ['⑨', '9'], ['⑩', '10'], ['⑪', '11'], ['⑫', '12'], ['⑬', '13'], ['⑭', '14'], ['⑮', '15'], ['⑯', '16'], ['⑰', '17'], ['⑱', '18'], ['⑲', '19'], ['⑳', '20'], ['⑴', '1'], ['⑵', '2'], ['⑶', '3'], ['⑷', '4'], ['⑸', '5'], ['⑹', '6'], ['⑺', '7'], ['⑻', '8'], ['⑼', '9'], ['⑽', '10'], ['⑾', '11'], ['⑿', '12'], ['⒀', '13'], ['⒁', '14'], ['⒂', '15'], ['⒃', '16'], ['⒄', '17'], ['⒅', '18'], ['⒆', '19'], ['⒇', '20'], ['⒈', '1.'], ['⒉', '2.'], ['⒊', '3.'], ['⒋', '4.'], ['⒌', '5.'], ['⒍', '6.'], ['⒎', '7.'], ['⒏', '8.'], ['⒐', '9.'], ['⒑', '10.'], ['⒒', '11.'], ['⒓', '12.'], ['⒔', '13.'], ['⒕', '14.'], ['⒖', '15.'], ['⒗', '16.'], ['⒘', '17.'], ['⒙', '18.'], ['⒚', '19.'], ['⒛', '20.'], ['⓪', '0'], ['⓫', '11'], ['⓬', '12'], ['⓭', '13'], ['⓮', '14'], ['⓯', '15'], ['⓰', '16'], ['⓱', '17'], ['⓲', '18'], ['⓳', '19'], ['⓴', '20'], ['⓵', '1'], ['⓶', '2'], ['⓷', '3'], ['⓸', '4'], ['⓹', '5'], ['⓺', '6'], ['⓻', '7'], ['⓼', '8'], ['⓽', '9'], ['⓾', '10'], ['⓿', '0'], # Punctuation ['🙰', '&'], ['🙱', '&'], ['🙲', '&'], ['🙳', '&'], ['🙴', '&'], ['🙵', '&'], ['🙶', '"'], ['🙷', '"'], ['🙸', '"'], ['‽', '?!'], ['🙹', '?!'], ['🙺', '?!'], ['🙻', '?!'], ['🙼', '/'], ['🙽', '\\'], # Alchemy ['🜇', 'AR'], ['🜈', 'V'], ['🜉', 'V'], ['🜆', 'VR'], ['🜅', 'VF'], ['🜩', '2'], ['🜪', '5'], ['🝡', 'f'], ['🝢', 'W'], ['🝣', 'U'], ['🝧', 'V'], ['🝨', 'T'], ['🝪', 'V'], ['🝫', 'MB'], ['🝬', 'VB'], ['🝲', '3B'], ['🝳', '3B'], # Emojis ['💯', '100'], ['🔙', 'BACK'], ['🔚', 'END'], ['🔛', 'ON!'], ['🔜', 'SOON'], ['🔝', 'TOP'], ['🔞', '18'], ['🔤', 'abc'], ['🔠', 'ABCD'], ['🔡', 'abcd'], ['🔢', '1234'], ['🔣', 'T&@%'], ['#️⃣', '#'], ['*️⃣', '*'], ['0️⃣', '0'], ['1️⃣', '1'], ['2️⃣', '2'], ['3️⃣', '3'], ['4️⃣', '4'], ['5️⃣', '5'], ['6️⃣', '6'], ['7️⃣', '7'], ['8️⃣', '8'], ['9️⃣', '9'], ['🔟', '10'], ['🅰️', 'A'], ['🅱️', 'B'], ['🆎', 'AB'], ['🆑', 'CL'], ['🅾️', 'O'], ['🅿', 'P'], ['🆘', 'SOS'], ['🅲', 'C'], ['🅳', 'D'], ['🅴', 'E'], ['🅵', 'F'], ['🅶', 'G'], ['🅷', 'H'], ['🅸', 'I'], ['🅹', 'J'], ['🅺', 'K'], ['🅻', 'L'], ['🅼', 'M'], ['🅽', 'N'], ['🆀', 'Q'], ['🆁', 'R'], ['🆂', 'S'], ['🆃', 'T'], ['🆄', 'U'], ['🆅', 'V'], ['🆆', 'W'], ['🆇', 'X'], ['🆈', 'Y'], ['🆉', 'Z'], ]
Tools/PyTorch/TimeSeriesPredictionPlatform/models
models
trivial_model
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # 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. import torch import torch.nn as nn class TrivialModel(nn.Module): def __init__(self, config): super().__init__() self.bias = nn.Parameter(torch.zeros(1)) self.encoder_length = config.encoder_length self.example_length = config.example_length self.predict_steps = self.example_length - self.encoder_length self.output_dim = len(config.get("quantiles", [""])) def forward(self, batch): t = next(t for t in batch.values() if t is not None) bs = t.shape[0] return torch.ones([bs, self.example_length - self.encoder_length, self.output_dim]).to(device=t.device) + self.bias def predict(self, batch): targets = batch["target"].clone() prev_predictions = targets.roll(1, 1) return prev_predictions[:, -self.predict_steps :, :] # TODO: reenable usage of such functions def test_with_last(self, batch): bs = max([tensor.shape[0] if tensor is not None else 0 for tensor in batch.values()]) values = ( # TODO: this will become disfuntional after removing "targer_masked" from dataset. Seed comment in data_utils.py batch["target_masked"] .clone()[:, -1, :] .reshape((bs, 1, self.output_dim)) ) return torch.cat((self.example_length - self.encoder_length) * [values], dim=1) def test_with_previous_window(self, batch): targets = batch["target"].clone() prev_predictions = targets.roll(self.predict_steps, 1) return prev_predictions[:, -self.predict_steps :, :]
PyTorch/Segmentation/MaskRCNN/pytorch/maskrcnn_benchmark/utils
utils
c2_model_loading
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. import logging import pickle from collections import OrderedDict import torch from maskrcnn_benchmark.utils.model_serialization import load_state_dict from maskrcnn_benchmark.utils.registry import Registry def _rename_basic_resnet_weights(layer_keys): layer_keys = [k.replace("_", ".") for k in layer_keys] layer_keys = [k.replace(".w", ".weight") for k in layer_keys] layer_keys = [k.replace(".bn", "_bn") for k in layer_keys] layer_keys = [k.replace(".b", ".bias") for k in layer_keys] layer_keys = [k.replace("_bn.s", "_bn.scale") for k in layer_keys] layer_keys = [k.replace(".biasranch", ".branch") for k in layer_keys] layer_keys = [k.replace("bbox.pred", "bbox_pred") for k in layer_keys] layer_keys = [k.replace("cls.score", "cls_score") for k in layer_keys] layer_keys = [k.replace("res.conv1_", "conv1_") for k in layer_keys] # RPN / Faster RCNN layer_keys = [k.replace(".biasbox", ".bbox") for k in layer_keys] layer_keys = [k.replace("conv.rpn", "rpn.conv") for k in layer_keys] layer_keys = [k.replace("rpn.bbox.pred", "rpn.bbox_pred") for k in layer_keys] layer_keys = [k.replace("rpn.cls.logits", "rpn.cls_logits") for k in layer_keys] # Affine-Channel -> BatchNorm enaming layer_keys = [k.replace("_bn.scale", "_bn.weight") for k in layer_keys] # Make torchvision-compatible layer_keys = [k.replace("conv1_bn.", "bn1.") for k in layer_keys] layer_keys = [k.replace("res2.", "layer1.") for k in layer_keys] layer_keys = [k.replace("res3.", "layer2.") for k in layer_keys] layer_keys = [k.replace("res4.", "layer3.") for k in layer_keys] layer_keys = [k.replace("res5.", "layer4.") for k in layer_keys] layer_keys = [k.replace(".branch2a.", ".conv1.") for k in layer_keys] layer_keys = [k.replace(".branch2a_bn.", ".bn1.") for k in layer_keys] layer_keys = [k.replace(".branch2b.", ".conv2.") for k in layer_keys] layer_keys = [k.replace(".branch2b_bn.", ".bn2.") for k in layer_keys] layer_keys = [k.replace(".branch2c.", ".conv3.") for k in layer_keys] layer_keys = [k.replace(".branch2c_bn.", ".bn3.") for k in layer_keys] layer_keys = [k.replace(".branch1.", ".downsample.0.") for k in layer_keys] layer_keys = [k.replace(".branch1_bn.", ".downsample.1.") for k in layer_keys] # GroupNorm layer_keys = [k.replace("conv1.gn.s", "bn1.weight") for k in layer_keys] layer_keys = [k.replace("conv1.gn.bias", "bn1.bias") for k in layer_keys] layer_keys = [k.replace("conv2.gn.s", "bn2.weight") for k in layer_keys] layer_keys = [k.replace("conv2.gn.bias", "bn2.bias") for k in layer_keys] layer_keys = [k.replace("conv3.gn.s", "bn3.weight") for k in layer_keys] layer_keys = [k.replace("conv3.gn.bias", "bn3.bias") for k in layer_keys] layer_keys = [k.replace("downsample.0.gn.s", "downsample.1.weight") \ for k in layer_keys] layer_keys = [k.replace("downsample.0.gn.bias", "downsample.1.bias") \ for k in layer_keys] return layer_keys def _rename_fpn_weights(layer_keys, stage_names): for mapped_idx, stage_name in enumerate(stage_names, 1): suffix = "" if mapped_idx < 4: suffix = ".lateral" layer_keys = [ k.replace("fpn.inner.layer{}.sum{}".format(stage_name, suffix), "fpn_inner{}".format(mapped_idx)) for k in layer_keys ] layer_keys = [k.replace("fpn.layer{}.sum".format(stage_name), "fpn_layer{}".format(mapped_idx)) for k in layer_keys] layer_keys = [k.replace("rpn.conv.fpn2", "rpn.conv") for k in layer_keys] layer_keys = [k.replace("rpn.bbox_pred.fpn2", "rpn.bbox_pred") for k in layer_keys] layer_keys = [ k.replace("rpn.cls_logits.fpn2", "rpn.cls_logits") for k in layer_keys ] return layer_keys def _rename_weights_for_resnet(weights, stage_names): original_keys = sorted(weights.keys()) layer_keys = sorted(weights.keys()) # for X-101, rename output to fc1000 to avoid conflicts afterwards layer_keys = [k if k != "pred_b" else "fc1000_b" for k in layer_keys] layer_keys = [k if k != "pred_w" else "fc1000_w" for k in layer_keys] # performs basic renaming: _ -> . , etc layer_keys = _rename_basic_resnet_weights(layer_keys) # FPN layer_keys = _rename_fpn_weights(layer_keys, stage_names) # Mask R-CNN layer_keys = [k.replace("mask.fcn.logits", "mask_fcn_logits") for k in layer_keys] layer_keys = [k.replace(".[mask].fcn", "mask_fcn") for k in layer_keys] layer_keys = [k.replace("conv5.mask", "conv5_mask") for k in layer_keys] # Keypoint R-CNN layer_keys = [k.replace("kps.score.lowres", "kps_score_lowres") for k in layer_keys] layer_keys = [k.replace("kps.score", "kps_score") for k in layer_keys] layer_keys = [k.replace("conv.fcn", "conv_fcn") for k in layer_keys] # Rename for our RPN structure layer_keys = [k.replace("rpn.", "rpn.head.") for k in layer_keys] key_map = {k: v for k, v in zip(original_keys, layer_keys)} logger = logging.getLogger(__name__) logger.info("Remapping C2 weights") max_c2_key_size = max([len(k) for k in original_keys if "_momentum" not in k]) new_weights = OrderedDict() for k in original_keys: v = weights[k] if "_momentum" in k: continue # if 'fc1000' in k: # continue w = torch.from_numpy(v) # if "bn" in k: # w = w.view(1, -1, 1, 1) logger.info("C2 name: {: <{}} mapped name: {}".format(k, max_c2_key_size, key_map[k])) new_weights[key_map[k]] = w return new_weights def _load_c2_pickled_weights(file_path): with open(file_path, "rb") as f: data = pickle.load(f, encoding="latin1") if "blobs" in data: weights = data["blobs"] else: weights = data return weights _C2_STAGE_NAMES = { "R-50": ["1.2", "2.3", "3.5", "4.2"], "R-101": ["1.2", "2.3", "3.22", "4.2"], } C2_FORMAT_LOADER = Registry() @C2_FORMAT_LOADER.register("R-50-C4") @C2_FORMAT_LOADER.register("R-50-C5") @C2_FORMAT_LOADER.register("R-101-C4") @C2_FORMAT_LOADER.register("R-101-C5") @C2_FORMAT_LOADER.register("R-50-FPN") @C2_FORMAT_LOADER.register("R-101-FPN") def load_resnet_c2_format(cfg, f): state_dict = _load_c2_pickled_weights(f) conv_body = cfg.MODEL.BACKBONE.CONV_BODY arch = conv_body.replace("-C4", "").replace("-C5", "").replace("-FPN", "") stages = _C2_STAGE_NAMES[arch] state_dict = _rename_weights_for_resnet(state_dict, stages) return dict(model=state_dict) def load_c2_format(cfg, f): return C2_FORMAT_LOADER[cfg.MODEL.BACKBONE.CONV_BODY](cfg, f)
PyTorch/SpeechRecognition/QuartzNet/common/text/unidecoder
unidecoder
replacements
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # 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. # MIT License # # Copyright (c) Sindre Sorhus <sindresorhus@gmail.com> (https://sindresorhus.com) # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # # Based on: # https://github.com/sindresorhus/transliterate/blob/main/replacements.js # replacements = [ # German umlauts ['ß', 'ss'], ['ẞ', 'Ss'], ['ä', 'ae'], ['Ä', 'Ae'], ['ö', 'oe'], ['Ö', 'Oe'], ['ü', 'ue'], ['Ü', 'Ue'], # Latin ['À', 'A'], ['Á', 'A'], ['Â', 'A'], ['Ã', 'A'], ['Ä', 'Ae'], ['Å', 'A'], ['Æ', 'AE'], ['Ç', 'C'], ['È', 'E'], ['É', 'E'], ['Ê', 'E'], ['Ë', 'E'], ['Ì', 'I'], ['Í', 'I'], ['Î', 'I'], ['Ï', 'I'], ['Ð', 'D'], ['Ñ', 'N'], ['Ò', 'O'], ['Ó', 'O'], ['Ô', 'O'], ['Õ', 'O'], ['Ö', 'Oe'], ['Ő', 'O'], ['Ø', 'O'], ['Ù', 'U'], ['Ú', 'U'], ['Û', 'U'], ['Ü', 'Ue'], ['Ű', 'U'], ['Ý', 'Y'], ['Þ', 'TH'], ['ß', 'ss'], ['à', 'a'], ['á', 'a'], ['â', 'a'], ['ã', 'a'], ['ä', 'ae'], ['å', 'a'], ['æ', 'ae'], ['ç', 'c'], ['è', 'e'], ['é', 'e'], ['ê', 'e'], ['ë', 'e'], ['ì', 'i'], ['í', 'i'], ['î', 'i'], ['ï', 'i'], ['ð', 'd'], ['ñ', 'n'], ['ò', 'o'], ['ó', 'o'], ['ô', 'o'], ['õ', 'o'], ['ö', 'oe'], ['ő', 'o'], ['ø', 'o'], ['ù', 'u'], ['ú', 'u'], ['û', 'u'], ['ü', 'ue'], ['ű', 'u'], ['ý', 'y'], ['þ', 'th'], ['ÿ', 'y'], ['ẞ', 'SS'], # Vietnamese ['à', 'a'], ['À', 'A'], ['á', 'a'], ['Á', 'A'], ['â', 'a'], ['Â', 'A'], ['ã', 'a'], ['Ã', 'A'], ['è', 'e'], ['È', 'E'], ['é', 'e'], ['É', 'E'], ['ê', 'e'], ['Ê', 'E'], ['ì', 'i'], ['Ì', 'I'], ['í', 'i'], ['Í', 'I'], ['ò', 'o'], ['Ò', 'O'], ['ó', 'o'], ['Ó', 'O'], ['ô', 'o'], ['Ô', 'O'], ['õ', 'o'], ['Õ', 'O'], ['ù', 'u'], ['Ù', 'U'], ['ú', 'u'], ['Ú', 'U'], ['ý', 'y'], ['Ý', 'Y'], ['ă', 'a'], ['Ă', 'A'], ['Đ', 'D'], ['đ', 'd'], ['ĩ', 'i'], ['Ĩ', 'I'], ['ũ', 'u'], ['Ũ', 'U'], ['ơ', 'o'], ['Ơ', 'O'], ['ư', 'u'], ['Ư', 'U'], ['ạ', 'a'], ['Ạ', 'A'], ['ả', 'a'], ['Ả', 'A'], ['ấ', 'a'], ['Ấ', 'A'], ['ầ', 'a'], ['Ầ', 'A'], ['ẩ', 'a'], ['Ẩ', 'A'], ['ẫ', 'a'], ['Ẫ', 'A'], ['ậ', 'a'], ['Ậ', 'A'], ['ắ', 'a'], ['Ắ', 'A'], ['ằ', 'a'], ['Ằ', 'A'], ['ẳ', 'a'], ['Ẳ', 'A'], ['ẵ', 'a'], ['Ẵ', 'A'], ['ặ', 'a'], ['Ặ', 'A'], ['ẹ', 'e'], ['Ẹ', 'E'], ['ẻ', 'e'], ['Ẻ', 'E'], ['ẽ', 'e'], ['Ẽ', 'E'], ['ế', 'e'], ['Ế', 'E'], ['ề', 'e'], ['Ề', 'E'], ['ể', 'e'], ['Ể', 'E'], ['ễ', 'e'], ['Ễ', 'E'], ['ệ', 'e'], ['Ệ', 'E'], ['ỉ', 'i'], ['Ỉ', 'I'], ['ị', 'i'], ['Ị', 'I'], ['ọ', 'o'], ['Ọ', 'O'], ['ỏ', 'o'], ['Ỏ', 'O'], ['ố', 'o'], ['Ố', 'O'], ['ồ', 'o'], ['Ồ', 'O'], ['ổ', 'o'], ['Ổ', 'O'], ['ỗ', 'o'], ['Ỗ', 'O'], ['ộ', 'o'], ['Ộ', 'O'], ['ớ', 'o'], ['Ớ', 'O'], ['ờ', 'o'], ['Ờ', 'O'], ['ở', 'o'], ['Ở', 'O'], ['ỡ', 'o'], ['Ỡ', 'O'], ['ợ', 'o'], ['Ợ', 'O'], ['ụ', 'u'], ['Ụ', 'U'], ['ủ', 'u'], ['Ủ', 'U'], ['ứ', 'u'], ['Ứ', 'U'], ['ừ', 'u'], ['Ừ', 'U'], ['ử', 'u'], ['Ử', 'U'], ['ữ', 'u'], ['Ữ', 'U'], ['ự', 'u'], ['Ự', 'U'], ['ỳ', 'y'], ['Ỳ', 'Y'], ['ỵ', 'y'], ['Ỵ', 'Y'], ['ỷ', 'y'], ['Ỷ', 'Y'], ['ỹ', 'y'], ['Ỹ', 'Y'], # Arabic ['ء', 'e'], ['آ', 'a'], ['أ', 'a'], ['ؤ', 'w'], ['إ', 'i'], ['ئ', 'y'], ['ا', 'a'], ['ب', 'b'], ['ة', 't'], ['ت', 't'], ['ث', 'th'], ['ج', 'j'], ['ح', 'h'], ['خ', 'kh'], ['د', 'd'], ['ذ', 'dh'], ['ر', 'r'], ['ز', 'z'], ['س', 's'], ['ش', 'sh'], ['ص', 's'], ['ض', 'd'], ['ط', 't'], ['ظ', 'z'], ['ع', 'e'], ['غ', 'gh'], ['ـ', '_'], ['ف', 'f'], ['ق', 'q'], ['ك', 'k'], ['ل', 'l'], ['م', 'm'], ['ن', 'n'], ['ه', 'h'], ['و', 'w'], ['ى', 'a'], ['ي', 'y'], ['َ‎', 'a'], ['ُ', 'u'], ['ِ‎', 'i'], ['٠', '0'], ['١', '1'], ['٢', '2'], ['٣', '3'], ['٤', '4'], ['٥', '5'], ['٦', '6'], ['٧', '7'], ['٨', '8'], ['٩', '9'], # Persian / Farsi ['چ', 'ch'], ['ک', 'k'], ['گ', 'g'], ['پ', 'p'], ['ژ', 'zh'], ['ی', 'y'], ['۰', '0'], ['۱', '1'], ['۲', '2'], ['۳', '3'], ['۴', '4'], ['۵', '5'], ['۶', '6'], ['۷', '7'], ['۸', '8'], ['۹', '9'], # Pashto ['ټ', 'p'], ['ځ', 'z'], ['څ', 'c'], ['ډ', 'd'], ['ﺫ', 'd'], ['ﺭ', 'r'], ['ړ', 'r'], ['ﺯ', 'z'], ['ږ', 'g'], ['ښ', 'x'], ['ګ', 'g'], ['ڼ', 'n'], ['ۀ', 'e'], ['ې', 'e'], ['ۍ', 'ai'], # Urdu ['ٹ', 't'], ['ڈ', 'd'], ['ڑ', 'r'], ['ں', 'n'], ['ہ', 'h'], ['ھ', 'h'], ['ے', 'e'], # Russian ['А', 'A'], ['а', 'a'], ['Б', 'B'], ['б', 'b'], ['В', 'V'], ['в', 'v'], ['Г', 'G'], ['г', 'g'], ['Д', 'D'], ['д', 'd'], ['ъе', 'ye'], ['Ъе', 'Ye'], ['ъЕ', 'yE'], ['ЪЕ', 'YE'], ['Е', 'E'], ['е', 'e'], ['Ё', 'Yo'], ['ё', 'yo'], ['Ж', 'Zh'], ['ж', 'zh'], ['З', 'Z'], ['з', 'z'], ['И', 'I'], ['и', 'i'], ['ый', 'iy'], ['Ый', 'Iy'], ['ЫЙ', 'IY'], ['ыЙ', 'iY'], ['Й', 'Y'], ['й', 'y'], ['К', 'K'], ['к', 'k'], ['Л', 'L'], ['л', 'l'], ['М', 'M'], ['м', 'm'], ['Н', 'N'], ['н', 'n'], ['О', 'O'], ['о', 'o'], ['П', 'P'], ['п', 'p'], ['Р', 'R'], ['р', 'r'], ['С', 'S'], ['с', 's'], ['Т', 'T'], ['т', 't'], ['У', 'U'], ['у', 'u'], ['Ф', 'F'], ['ф', 'f'], ['Х', 'Kh'], ['х', 'kh'], ['Ц', 'Ts'], ['ц', 'ts'], ['Ч', 'Ch'], ['ч', 'ch'], ['Ш', 'Sh'], ['ш', 'sh'], ['Щ', 'Sch'], ['щ', 'sch'], ['Ъ', ''], ['ъ', ''], ['Ы', 'Y'], ['ы', 'y'], ['Ь', ''], ['ь', ''], ['Э', 'E'], ['э', 'e'], ['Ю', 'Yu'], ['ю', 'yu'], ['Я', 'Ya'], ['я', 'ya'], # Romanian ['ă', 'a'], ['Ă', 'A'], ['ș', 's'], ['Ș', 'S'], ['ț', 't'], ['Ț', 'T'], ['ţ', 't'], ['Ţ', 'T'], # Turkish ['ş', 's'], ['Ş', 'S'], ['ç', 'c'], ['Ç', 'C'], ['ğ', 'g'], ['Ğ', 'G'], ['ı', 'i'], ['İ', 'I'], # Armenian ['ա', 'a'], ['Ա', 'A'], ['բ', 'b'], ['Բ', 'B'], ['գ', 'g'], ['Գ', 'G'], ['դ', 'd'], ['Դ', 'D'], ['ե', 'ye'], ['Ե', 'Ye'], ['զ', 'z'], ['Զ', 'Z'], ['է', 'e'], ['Է', 'E'], ['ը', 'y'], ['Ը', 'Y'], ['թ', 't'], ['Թ', 'T'], ['ժ', 'zh'], ['Ժ', 'Zh'], ['ի', 'i'], ['Ի', 'I'], ['լ', 'l'], ['Լ', 'L'], ['խ', 'kh'], ['Խ', 'Kh'], ['ծ', 'ts'], ['Ծ', 'Ts'], ['կ', 'k'], ['Կ', 'K'], ['հ', 'h'], ['Հ', 'H'], ['ձ', 'dz'], ['Ձ', 'Dz'], ['ղ', 'gh'], ['Ղ', 'Gh'], ['ճ', 'tch'], ['Ճ', 'Tch'], ['մ', 'm'], ['Մ', 'M'], ['յ', 'y'], ['Յ', 'Y'], ['ն', 'n'], ['Ն', 'N'], ['շ', 'sh'], ['Շ', 'Sh'], ['ո', 'vo'], ['Ո', 'Vo'], ['չ', 'ch'], ['Չ', 'Ch'], ['պ', 'p'], ['Պ', 'P'], ['ջ', 'j'], ['Ջ', 'J'], ['ռ', 'r'], ['Ռ', 'R'], ['ս', 's'], ['Ս', 'S'], ['վ', 'v'], ['Վ', 'V'], ['տ', 't'], ['Տ', 'T'], ['ր', 'r'], ['Ր', 'R'], ['ց', 'c'], ['Ց', 'C'], ['ու', 'u'], ['ՈՒ', 'U'], ['Ու', 'U'], ['փ', 'p'], ['Փ', 'P'], ['ք', 'q'], ['Ք', 'Q'], ['օ', 'o'], ['Օ', 'O'], ['ֆ', 'f'], ['Ֆ', 'F'], ['և', 'yev'], # Georgian ['ა', 'a'], ['ბ', 'b'], ['გ', 'g'], ['დ', 'd'], ['ე', 'e'], ['ვ', 'v'], ['ზ', 'z'], ['თ', 't'], ['ი', 'i'], ['კ', 'k'], ['ლ', 'l'], ['მ', 'm'], ['ნ', 'n'], ['ო', 'o'], ['პ', 'p'], ['ჟ', 'zh'], ['რ', 'r'], ['ს', 's'], ['ტ', 't'], ['უ', 'u'], ['ფ', 'ph'], ['ქ', 'q'], ['ღ', 'gh'], ['ყ', 'k'], ['შ', 'sh'], ['ჩ', 'ch'], ['ც', 'ts'], ['ძ', 'dz'], ['წ', 'ts'], ['ჭ', 'tch'], ['ხ', 'kh'], ['ჯ', 'j'], ['ჰ', 'h'], # Czech ['č', 'c'], ['ď', 'd'], ['ě', 'e'], ['ň', 'n'], ['ř', 'r'], ['š', 's'], ['ť', 't'], ['ů', 'u'], ['ž', 'z'], ['Č', 'C'], ['Ď', 'D'], ['Ě', 'E'], ['Ň', 'N'], ['Ř', 'R'], ['Š', 'S'], ['Ť', 'T'], ['Ů', 'U'], ['Ž', 'Z'], # Dhivehi ['ހ', 'h'], ['ށ', 'sh'], ['ނ', 'n'], ['ރ', 'r'], ['ބ', 'b'], ['ޅ', 'lh'], ['ކ', 'k'], ['އ', 'a'], ['ވ', 'v'], ['މ', 'm'], ['ފ', 'f'], ['ދ', 'dh'], ['ތ', 'th'], ['ލ', 'l'], ['ގ', 'g'], ['ޏ', 'gn'], ['ސ', 's'], ['ޑ', 'd'], ['ޒ', 'z'], ['ޓ', 't'], ['ޔ', 'y'], ['ޕ', 'p'], ['ޖ', 'j'], ['ޗ', 'ch'], ['ޘ', 'tt'], ['ޙ', 'hh'], ['ޚ', 'kh'], ['ޛ', 'th'], ['ޜ', 'z'], ['ޝ', 'sh'], ['ޞ', 's'], ['ޟ', 'd'], ['ޠ', 't'], ['ޡ', 'z'], ['ޢ', 'a'], ['ޣ', 'gh'], ['ޤ', 'q'], ['ޥ', 'w'], ['ަ', 'a'], ['ާ', 'aa'], ['ި', 'i'], ['ީ', 'ee'], ['ު', 'u'], ['ޫ', 'oo'], ['ެ', 'e'], ['ޭ', 'ey'], ['ޮ', 'o'], ['ޯ', 'oa'], ['ް', ''], # Greek ['α', 'a'], ['β', 'v'], ['γ', 'g'], ['δ', 'd'], ['ε', 'e'], ['ζ', 'z'], ['η', 'i'], ['θ', 'th'], ['ι', 'i'], ['κ', 'k'], ['λ', 'l'], ['μ', 'm'], ['ν', 'n'], ['ξ', 'ks'], ['ο', 'o'], ['π', 'p'], ['ρ', 'r'], ['σ', 's'], ['τ', 't'], ['υ', 'y'], ['φ', 'f'], ['χ', 'x'], ['ψ', 'ps'], ['ω', 'o'], ['ά', 'a'], ['έ', 'e'], ['ί', 'i'], ['ό', 'o'], ['ύ', 'y'], ['ή', 'i'], ['ώ', 'o'], ['ς', 's'], ['ϊ', 'i'], ['ΰ', 'y'], ['ϋ', 'y'], ['ΐ', 'i'], ['Α', 'A'], ['Β', 'B'], ['Γ', 'G'], ['Δ', 'D'], ['Ε', 'E'], ['Ζ', 'Z'], ['Η', 'I'], ['Θ', 'TH'], ['Ι', 'I'], ['Κ', 'K'], ['Λ', 'L'], ['Μ', 'M'], ['Ν', 'N'], ['Ξ', 'KS'], ['Ο', 'O'], ['Π', 'P'], ['Ρ', 'R'], ['Σ', 'S'], ['Τ', 'T'], ['Υ', 'Y'], ['Φ', 'F'], ['Χ', 'X'], ['Ψ', 'PS'], ['Ω', 'O'], ['Ά', 'A'], ['Έ', 'E'], ['Ί', 'I'], ['Ό', 'O'], ['Ύ', 'Y'], ['Ή', 'I'], ['Ώ', 'O'], ['Ϊ', 'I'], ['Ϋ', 'Y'], # Disabled as it conflicts with German and Latin. # Hungarian # ['ä', 'a'], # ['Ä', 'A'], # ['ö', 'o'], # ['Ö', 'O'], # ['ü', 'u'], # ['Ü', 'U'], # ['ű', 'u'], # ['Ű', 'U'], # Latvian ['ā', 'a'], ['ē', 'e'], ['ģ', 'g'], ['ī', 'i'], ['ķ', 'k'], ['ļ', 'l'], ['ņ', 'n'], ['ū', 'u'], ['Ā', 'A'], ['Ē', 'E'], ['Ģ', 'G'], ['Ī', 'I'], ['Ķ', 'K'], ['Ļ', 'L'], ['Ņ', 'N'], ['Ū', 'U'], ['č', 'c'], ['š', 's'], ['ž', 'z'], ['Č', 'C'], ['Š', 'S'], ['Ž', 'Z'], # Lithuanian ['ą', 'a'], ['č', 'c'], ['ę', 'e'], ['ė', 'e'], ['į', 'i'], ['š', 's'], ['ų', 'u'], ['ū', 'u'], ['ž', 'z'], ['Ą', 'A'], ['Č', 'C'], ['Ę', 'E'], ['Ė', 'E'], ['Į', 'I'], ['Š', 'S'], ['Ų', 'U'], ['Ū', 'U'], # Macedonian ['Ќ', 'Kj'], ['ќ', 'kj'], ['Љ', 'Lj'], ['љ', 'lj'], ['Њ', 'Nj'], ['њ', 'nj'], ['Тс', 'Ts'], ['тс', 'ts'], # Polish ['ą', 'a'], ['ć', 'c'], ['ę', 'e'], ['ł', 'l'], ['ń', 'n'], ['ś', 's'], ['ź', 'z'], ['ż', 'z'], ['Ą', 'A'], ['Ć', 'C'], ['Ę', 'E'], ['Ł', 'L'], ['Ń', 'N'], ['Ś', 'S'], ['Ź', 'Z'], ['Ż', 'Z'], # Disabled as it conflicts with Vietnamese. # Serbian # ['љ', 'lj'], # ['њ', 'nj'], # ['Љ', 'Lj'], # ['Њ', 'Nj'], # ['đ', 'dj'], # ['Đ', 'Dj'], # ['ђ', 'dj'], # ['ј', 'j'], # ['ћ', 'c'], # ['џ', 'dz'], # ['Ђ', 'Dj'], # ['Ј', 'j'], # ['Ћ', 'C'], # ['Џ', 'Dz'], # Disabled as it conflicts with German and Latin. # Slovak # ['ä', 'a'], # ['Ä', 'A'], # ['ľ', 'l'], # ['ĺ', 'l'], # ['ŕ', 'r'], # ['Ľ', 'L'], # ['Ĺ', 'L'], # ['Ŕ', 'R'], # Disabled as it conflicts with German and Latin. # Swedish # ['å', 'o'], # ['Å', 'o'], # ['ä', 'a'], # ['Ä', 'A'], # ['ë', 'e'], # ['Ë', 'E'], # ['ö', 'o'], # ['Ö', 'O'], # Ukrainian ['Є', 'Ye'], ['І', 'I'], ['Ї', 'Yi'], ['Ґ', 'G'], ['є', 'ye'], ['і', 'i'], ['ї', 'yi'], ['ґ', 'g'], # Dutch ['IJ', 'IJ'], ['ij', 'ij'], # Danish # ['Æ', 'Ae'], # ['Ø', 'Oe'], # ['Å', 'Aa'], # ['æ', 'ae'], # ['ø', 'oe'], # ['å', 'aa'] # Currencies ['¢', 'c'], ['¥', 'Y'], ['߿', 'b'], ['৳', 't'], ['૱', 'Bo'], ['฿', 'B'], ['₠', 'CE'], ['₡', 'C'], ['₢', 'Cr'], ['₣', 'F'], ['₥', 'm'], ['₦', 'N'], ['₧', 'Pt'], ['₨', 'Rs'], ['₩', 'W'], ['₫', 's'], ['€', 'E'], ['₭', 'K'], ['₮', 'T'], ['₯', 'Dp'], ['₰', 'S'], ['₱', 'P'], ['₲', 'G'], ['₳', 'A'], ['₴', 'S'], ['₵', 'C'], ['₶', 'tt'], ['₷', 'S'], ['₸', 'T'], ['₹', 'R'], ['₺', 'L'], ['₽', 'P'], ['₿', 'B'], ['﹩', '$'], ['¢', 'c'], ['¥', 'Y'], ['₩', 'W'], # Latin ['𝐀', 'A'], ['𝐁', 'B'], ['𝐂', 'C'], ['𝐃', 'D'], ['𝐄', 'E'], ['𝐅', 'F'], ['𝐆', 'G'], ['𝐇', 'H'], ['𝐈', 'I'], ['𝐉', 'J'], ['𝐊', 'K'], ['𝐋', 'L'], ['𝐌', 'M'], ['𝐍', 'N'], ['𝐎', 'O'], ['𝐏', 'P'], ['𝐐', 'Q'], ['𝐑', 'R'], ['𝐒', 'S'], ['𝐓', 'T'], ['𝐔', 'U'], ['𝐕', 'V'], ['𝐖', 'W'], ['𝐗', 'X'], ['𝐘', 'Y'], ['𝐙', 'Z'], ['𝐚', 'a'], ['𝐛', 'b'], ['𝐜', 'c'], ['𝐝', 'd'], ['𝐞', 'e'], ['𝐟', 'f'], ['𝐠', 'g'], ['𝐡', 'h'], ['𝐢', 'i'], ['𝐣', 'j'], ['𝐤', 'k'], ['𝐥', 'l'], ['𝐦', 'm'], ['𝐧', 'n'], ['𝐨', 'o'], ['𝐩', 'p'], ['𝐪', 'q'], ['𝐫', 'r'], ['𝐬', 's'], ['𝐭', 't'], ['𝐮', 'u'], ['𝐯', 'v'], ['𝐰', 'w'], ['𝐱', 'x'], ['𝐲', 'y'], ['𝐳', 'z'], ['𝐴', 'A'], ['𝐵', 'B'], ['𝐶', 'C'], ['𝐷', 'D'], ['𝐸', 'E'], ['𝐹', 'F'], ['𝐺', 'G'], ['𝐻', 'H'], ['𝐼', 'I'], ['𝐽', 'J'], ['𝐾', 'K'], ['𝐿', 'L'], ['𝑀', 'M'], ['𝑁', 'N'], ['𝑂', 'O'], ['𝑃', 'P'], ['𝑄', 'Q'], ['𝑅', 'R'], ['𝑆', 'S'], ['𝑇', 'T'], ['𝑈', 'U'], ['𝑉', 'V'], ['𝑊', 'W'], ['𝑋', 'X'], ['𝑌', 'Y'], ['𝑍', 'Z'], ['𝑎', 'a'], ['𝑏', 'b'], ['𝑐', 'c'], ['𝑑', 'd'], ['𝑒', 'e'], ['𝑓', 'f'], ['𝑔', 'g'], ['𝑖', 'i'], ['𝑗', 'j'], ['𝑘', 'k'], ['𝑙', 'l'], ['𝑚', 'm'], ['𝑛', 'n'], ['𝑜', 'o'], ['𝑝', 'p'], ['𝑞', 'q'], ['𝑟', 'r'], ['𝑠', 's'], ['𝑡', 't'], ['𝑢', 'u'], ['𝑣', 'v'], ['𝑤', 'w'], ['𝑥', 'x'], ['𝑦', 'y'], ['𝑧', 'z'], ['𝑨', 'A'], ['𝑩', 'B'], ['𝑪', 'C'], ['𝑫', 'D'], ['𝑬', 'E'], ['𝑭', 'F'], ['𝑮', 'G'], ['𝑯', 'H'], ['𝑰', 'I'], ['𝑱', 'J'], ['𝑲', 'K'], ['𝑳', 'L'], ['𝑴', 'M'], ['𝑵', 'N'], ['𝑶', 'O'], ['𝑷', 'P'], ['𝑸', 'Q'], ['𝑹', 'R'], ['𝑺', 'S'], ['𝑻', 'T'], ['𝑼', 'U'], ['𝑽', 'V'], ['𝑾', 'W'], ['𝑿', 'X'], ['𝒀', 'Y'], ['𝒁', 'Z'], ['𝒂', 'a'], ['𝒃', 'b'], ['𝒄', 'c'], ['𝒅', 'd'], ['𝒆', 'e'], ['𝒇', 'f'], ['𝒈', 'g'], ['𝒉', 'h'], ['𝒊', 'i'], ['𝒋', 'j'], ['𝒌', 'k'], ['𝒍', 'l'], ['𝒎', 'm'], ['𝒏', 'n'], ['𝒐', 'o'], ['𝒑', 'p'], ['𝒒', 'q'], ['𝒓', 'r'], ['𝒔', 's'], ['𝒕', 't'], ['𝒖', 'u'], ['𝒗', 'v'], ['𝒘', 'w'], ['𝒙', 'x'], ['𝒚', 'y'], ['𝒛', 'z'], ['𝒜', 'A'], ['𝒞', 'C'], ['𝒟', 'D'], ['𝒢', 'g'], ['𝒥', 'J'], ['𝒦', 'K'], ['𝒩', 'N'], ['𝒪', 'O'], ['𝒫', 'P'], ['𝒬', 'Q'], ['𝒮', 'S'], ['𝒯', 'T'], ['𝒰', 'U'], ['𝒱', 'V'], ['𝒲', 'W'], ['𝒳', 'X'], ['𝒴', 'Y'], ['𝒵', 'Z'], ['𝒶', 'a'], ['𝒷', 'b'], ['𝒸', 'c'], ['𝒹', 'd'], ['𝒻', 'f'], ['𝒽', 'h'], ['𝒾', 'i'], ['𝒿', 'j'], ['𝓀', 'h'], ['𝓁', 'l'], ['𝓂', 'm'], ['𝓃', 'n'], ['𝓅', 'p'], ['𝓆', 'q'], ['𝓇', 'r'], ['𝓈', 's'], ['𝓉', 't'], ['𝓊', 'u'], ['𝓋', 'v'], ['𝓌', 'w'], ['𝓍', 'x'], ['𝓎', 'y'], ['𝓏', 'z'], ['𝓐', 'A'], ['𝓑', 'B'], ['𝓒', 'C'], ['𝓓', 'D'], ['𝓔', 'E'], ['𝓕', 'F'], ['𝓖', 'G'], ['𝓗', 'H'], ['𝓘', 'I'], ['𝓙', 'J'], ['𝓚', 'K'], ['𝓛', 'L'], ['𝓜', 'M'], ['𝓝', 'N'], ['𝓞', 'O'], ['𝓟', 'P'], ['𝓠', 'Q'], ['𝓡', 'R'], ['𝓢', 'S'], ['𝓣', 'T'], ['𝓤', 'U'], ['𝓥', 'V'], ['𝓦', 'W'], ['𝓧', 'X'], ['𝓨', 'Y'], ['𝓩', 'Z'], ['𝓪', 'a'], ['𝓫', 'b'], ['𝓬', 'c'], ['𝓭', 'd'], ['𝓮', 'e'], ['𝓯', 'f'], ['𝓰', 'g'], ['𝓱', 'h'], ['𝓲', 'i'], ['𝓳', 'j'], ['𝓴', 'k'], ['𝓵', 'l'], ['𝓶', 'm'], ['𝓷', 'n'], ['𝓸', 'o'], ['𝓹', 'p'], ['𝓺', 'q'], ['𝓻', 'r'], ['𝓼', 's'], ['𝓽', 't'], ['𝓾', 'u'], ['𝓿', 'v'], ['𝔀', 'w'], ['𝔁', 'x'], ['𝔂', 'y'], ['𝔃', 'z'], ['𝔄', 'A'], ['𝔅', 'B'], ['𝔇', 'D'], ['𝔈', 'E'], ['𝔉', 'F'], ['𝔊', 'G'], ['𝔍', 'J'], ['𝔎', 'K'], ['𝔏', 'L'], ['𝔐', 'M'], ['𝔑', 'N'], ['𝔒', 'O'], ['𝔓', 'P'], ['𝔔', 'Q'], ['𝔖', 'S'], ['𝔗', 'T'], ['𝔘', 'U'], ['𝔙', 'V'], ['𝔚', 'W'], ['𝔛', 'X'], ['𝔜', 'Y'], ['𝔞', 'a'], ['𝔟', 'b'], ['𝔠', 'c'], ['𝔡', 'd'], ['𝔢', 'e'], ['𝔣', 'f'], ['𝔤', 'g'], ['𝔥', 'h'], ['𝔦', 'i'], ['𝔧', 'j'], ['𝔨', 'k'], ['𝔩', 'l'], ['𝔪', 'm'], ['𝔫', 'n'], ['𝔬', 'o'], ['𝔭', 'p'], ['𝔮', 'q'], ['𝔯', 'r'], ['𝔰', 's'], ['𝔱', 't'], ['𝔲', 'u'], ['𝔳', 'v'], ['𝔴', 'w'], ['𝔵', 'x'], ['𝔶', 'y'], ['𝔷', 'z'], ['𝔸', 'A'], ['𝔹', 'B'], ['𝔻', 'D'], ['𝔼', 'E'], ['𝔽', 'F'], ['𝔾', 'G'], ['𝕀', 'I'], ['𝕁', 'J'], ['𝕂', 'K'], ['𝕃', 'L'], ['𝕄', 'M'], ['𝕆', 'N'], ['𝕊', 'S'], ['𝕋', 'T'], ['𝕌', 'U'], ['𝕍', 'V'], ['𝕎', 'W'], ['𝕏', 'X'], ['𝕐', 'Y'], ['𝕒', 'a'], ['𝕓', 'b'], ['𝕔', 'c'], ['𝕕', 'd'], ['𝕖', 'e'], ['𝕗', 'f'], ['𝕘', 'g'], ['𝕙', 'h'], ['𝕚', 'i'], ['𝕛', 'j'], ['𝕜', 'k'], ['𝕝', 'l'], ['𝕞', 'm'], ['𝕟', 'n'], ['𝕠', 'o'], ['𝕡', 'p'], ['𝕢', 'q'], ['𝕣', 'r'], ['𝕤', 's'], ['𝕥', 't'], ['𝕦', 'u'], ['𝕧', 'v'], ['𝕨', 'w'], ['𝕩', 'x'], ['𝕪', 'y'], ['𝕫', 'z'], ['𝕬', 'A'], ['𝕭', 'B'], ['𝕮', 'C'], ['𝕯', 'D'], ['𝕰', 'E'], ['𝕱', 'F'], ['𝕲', 'G'], ['𝕳', 'H'], ['𝕴', 'I'], ['𝕵', 'J'], ['𝕶', 'K'], ['𝕷', 'L'], ['𝕸', 'M'], ['𝕹', 'N'], ['𝕺', 'O'], ['𝕻', 'P'], ['𝕼', 'Q'], ['𝕽', 'R'], ['𝕾', 'S'], ['𝕿', 'T'], ['𝖀', 'U'], ['𝖁', 'V'], ['𝖂', 'W'], ['𝖃', 'X'], ['𝖄', 'Y'], ['𝖅', 'Z'], ['𝖆', 'a'], ['𝖇', 'b'], ['𝖈', 'c'], ['𝖉', 'd'], ['𝖊', 'e'], ['𝖋', 'f'], ['𝖌', 'g'], ['𝖍', 'h'], ['𝖎', 'i'], ['𝖏', 'j'], ['𝖐', 'k'], ['𝖑', 'l'], ['𝖒', 'm'], ['𝖓', 'n'], ['𝖔', 'o'], ['𝖕', 'p'], ['𝖖', 'q'], ['𝖗', 'r'], ['𝖘', 's'], ['𝖙', 't'], ['𝖚', 'u'], ['𝖛', 'v'], ['𝖜', 'w'], ['𝖝', 'x'], ['𝖞', 'y'], ['𝖟', 'z'], ['𝖠', 'A'], ['𝖡', 'B'], ['𝖢', 'C'], ['𝖣', 'D'], ['𝖤', 'E'], ['𝖥', 'F'], ['𝖦', 'G'], ['𝖧', 'H'], ['𝖨', 'I'], ['𝖩', 'J'], ['𝖪', 'K'], ['𝖫', 'L'], ['𝖬', 'M'], ['𝖭', 'N'], ['𝖮', 'O'], ['𝖯', 'P'], ['𝖰', 'Q'], ['𝖱', 'R'], ['𝖲', 'S'], ['𝖳', 'T'], ['𝖴', 'U'], ['𝖵', 'V'], ['𝖶', 'W'], ['𝖷', 'X'], ['𝖸', 'Y'], ['𝖹', 'Z'], ['𝖺', 'a'], ['𝖻', 'b'], ['𝖼', 'c'], ['𝖽', 'd'], ['𝖾', 'e'], ['𝖿', 'f'], ['𝗀', 'g'], ['𝗁', 'h'], ['𝗂', 'i'], ['𝗃', 'j'], ['𝗄', 'k'], ['𝗅', 'l'], ['𝗆', 'm'], ['𝗇', 'n'], ['𝗈', 'o'], ['𝗉', 'p'], ['𝗊', 'q'], ['𝗋', 'r'], ['𝗌', 's'], ['𝗍', 't'], ['𝗎', 'u'], ['𝗏', 'v'], ['𝗐', 'w'], ['𝗑', 'x'], ['𝗒', 'y'], ['𝗓', 'z'], ['𝗔', 'A'], ['𝗕', 'B'], ['𝗖', 'C'], ['𝗗', 'D'], ['𝗘', 'E'], ['𝗙', 'F'], ['𝗚', 'G'], ['𝗛', 'H'], ['𝗜', 'I'], ['𝗝', 'J'], ['𝗞', 'K'], ['𝗟', 'L'], ['𝗠', 'M'], ['𝗡', 'N'], ['𝗢', 'O'], ['𝗣', 'P'], ['𝗤', 'Q'], ['𝗥', 'R'], ['𝗦', 'S'], ['𝗧', 'T'], ['𝗨', 'U'], ['𝗩', 'V'], ['𝗪', 'W'], ['𝗫', 'X'], ['𝗬', 'Y'], ['𝗭', 'Z'], ['𝗮', 'a'], ['𝗯', 'b'], ['𝗰', 'c'], ['𝗱', 'd'], ['𝗲', 'e'], ['𝗳', 'f'], ['𝗴', 'g'], ['𝗵', 'h'], ['𝗶', 'i'], ['𝗷', 'j'], ['𝗸', 'k'], ['𝗹', 'l'], ['𝗺', 'm'], ['𝗻', 'n'], ['𝗼', 'o'], ['𝗽', 'p'], ['𝗾', 'q'], ['𝗿', 'r'], ['𝘀', 's'], ['𝘁', 't'], ['𝘂', 'u'], ['𝘃', 'v'], ['𝘄', 'w'], ['𝘅', 'x'], ['𝘆', 'y'], ['𝘇', 'z'], ['𝘈', 'A'], ['𝘉', 'B'], ['𝘊', 'C'], ['𝘋', 'D'], ['𝘌', 'E'], ['𝘍', 'F'], ['𝘎', 'G'], ['𝘏', 'H'], ['𝘐', 'I'], ['𝘑', 'J'], ['𝘒', 'K'], ['𝘓', 'L'], ['𝘔', 'M'], ['𝘕', 'N'], ['𝘖', 'O'], ['𝘗', 'P'], ['𝘘', 'Q'], ['𝘙', 'R'], ['𝘚', 'S'], ['𝘛', 'T'], ['𝘜', 'U'], ['𝘝', 'V'], ['𝘞', 'W'], ['𝘟', 'X'], ['𝘠', 'Y'], ['𝘡', 'Z'], ['𝘢', 'a'], ['𝘣', 'b'], ['𝘤', 'c'], ['𝘥', 'd'], ['𝘦', 'e'], ['𝘧', 'f'], ['𝘨', 'g'], ['𝘩', 'h'], ['𝘪', 'i'], ['𝘫', 'j'], ['𝘬', 'k'], ['𝘭', 'l'], ['𝘮', 'm'], ['𝘯', 'n'], ['𝘰', 'o'], ['𝘱', 'p'], ['𝘲', 'q'], ['𝘳', 'r'], ['𝘴', 's'], ['𝘵', 't'], ['𝘶', 'u'], ['𝘷', 'v'], ['𝘸', 'w'], ['𝘹', 'x'], ['𝘺', 'y'], ['𝘻', 'z'], ['𝘼', 'A'], ['𝘽', 'B'], ['𝘾', 'C'], ['𝘿', 'D'], ['𝙀', 'E'], ['𝙁', 'F'], ['𝙂', 'G'], ['𝙃', 'H'], ['𝙄', 'I'], ['𝙅', 'J'], ['𝙆', 'K'], ['𝙇', 'L'], ['𝙈', 'M'], ['𝙉', 'N'], ['𝙊', 'O'], ['𝙋', 'P'], ['𝙌', 'Q'], ['𝙍', 'R'], ['𝙎', 'S'], ['𝙏', 'T'], ['𝙐', 'U'], ['𝙑', 'V'], ['𝙒', 'W'], ['𝙓', 'X'], ['𝙔', 'Y'], ['𝙕', 'Z'], ['𝙖', 'a'], ['𝙗', 'b'], ['𝙘', 'c'], ['𝙙', 'd'], ['𝙚', 'e'], ['𝙛', 'f'], ['𝙜', 'g'], ['𝙝', 'h'], ['𝙞', 'i'], ['𝙟', 'j'], ['𝙠', 'k'], ['𝙡', 'l'], ['𝙢', 'm'], ['𝙣', 'n'], ['𝙤', 'o'], ['𝙥', 'p'], ['𝙦', 'q'], ['𝙧', 'r'], ['𝙨', 's'], ['𝙩', 't'], ['𝙪', 'u'], ['𝙫', 'v'], ['𝙬', 'w'], ['𝙭', 'x'], ['𝙮', 'y'], ['𝙯', 'z'], ['𝙰', 'A'], ['𝙱', 'B'], ['𝙲', 'C'], ['𝙳', 'D'], ['𝙴', 'E'], ['𝙵', 'F'], ['𝙶', 'G'], ['𝙷', 'H'], ['𝙸', 'I'], ['𝙹', 'J'], ['𝙺', 'K'], ['𝙻', 'L'], ['𝙼', 'M'], ['𝙽', 'N'], ['𝙾', 'O'], ['𝙿', 'P'], ['𝚀', 'Q'], ['𝚁', 'R'], ['𝚂', 'S'], ['𝚃', 'T'], ['𝚄', 'U'], ['𝚅', 'V'], ['𝚆', 'W'], ['𝚇', 'X'], ['𝚈', 'Y'], ['𝚉', 'Z'], ['𝚊', 'a'], ['𝚋', 'b'], ['𝚌', 'c'], ['𝚍', 'd'], ['𝚎', 'e'], ['𝚏', 'f'], ['𝚐', 'g'], ['𝚑', 'h'], ['𝚒', 'i'], ['𝚓', 'j'], ['𝚔', 'k'], ['𝚕', 'l'], ['𝚖', 'm'], ['𝚗', 'n'], ['𝚘', 'o'], ['𝚙', 'p'], ['𝚚', 'q'], ['𝚛', 'r'], ['𝚜', 's'], ['𝚝', 't'], ['𝚞', 'u'], ['𝚟', 'v'], ['𝚠', 'w'], ['𝚡', 'x'], ['𝚢', 'y'], ['𝚣', 'z'], # Dotless letters ['𝚤', 'l'], ['𝚥', 'j'], # Greek ['𝛢', 'A'], ['𝛣', 'B'], ['𝛤', 'G'], ['𝛥', 'D'], ['𝛦', 'E'], ['𝛧', 'Z'], ['𝛨', 'I'], ['𝛩', 'TH'], ['𝛪', 'I'], ['𝛫', 'K'], ['𝛬', 'L'], ['𝛭', 'M'], ['𝛮', 'N'], ['𝛯', 'KS'], ['𝛰', 'O'], ['𝛱', 'P'], ['𝛲', 'R'], ['𝛳', 'TH'], ['𝛴', 'S'], ['𝛵', 'T'], ['𝛶', 'Y'], ['𝛷', 'F'], ['𝛸', 'x'], ['𝛹', 'PS'], ['𝛺', 'O'], ['𝛻', 'D'], ['𝛼', 'a'], ['𝛽', 'b'], ['𝛾', 'g'], ['𝛿', 'd'], ['𝜀', 'e'], ['𝜁', 'z'], ['𝜂', 'i'], ['𝜃', 'th'], ['𝜄', 'i'], ['𝜅', 'k'], ['𝜆', 'l'], ['𝜇', 'm'], ['𝜈', 'n'], ['𝜉', 'ks'], ['𝜊', 'o'], ['𝜋', 'p'], ['𝜌', 'r'], ['𝜍', 's'], ['𝜎', 's'], ['𝜏', 't'], ['𝜐', 'y'], ['𝜑', 'f'], ['𝜒', 'x'], ['𝜓', 'ps'], ['𝜔', 'o'], ['𝜕', 'd'], ['𝜖', 'E'], ['𝜗', 'TH'], ['𝜘', 'K'], ['𝜙', 'f'], ['𝜚', 'r'], ['𝜛', 'p'], ['𝜜', 'A'], ['𝜝', 'V'], ['𝜞', 'G'], ['𝜟', 'D'], ['𝜠', 'E'], ['𝜡', 'Z'], ['𝜢', 'I'], ['𝜣', 'TH'], ['𝜤', 'I'], ['𝜥', 'K'], ['𝜦', 'L'], ['𝜧', 'M'], ['𝜨', 'N'], ['𝜩', 'KS'], ['𝜪', 'O'], ['𝜫', 'P'], ['𝜬', 'S'], ['𝜭', 'TH'], ['𝜮', 'S'], ['𝜯', 'T'], ['𝜰', 'Y'], ['𝜱', 'F'], ['𝜲', 'X'], ['𝜳', 'PS'], ['𝜴', 'O'], ['𝜵', 'D'], ['𝜶', 'a'], ['𝜷', 'v'], ['𝜸', 'g'], ['𝜹', 'd'], ['𝜺', 'e'], ['𝜻', 'z'], ['𝜼', 'i'], ['𝜽', 'th'], ['𝜾', 'i'], ['𝜿', 'k'], ['𝝀', 'l'], ['𝝁', 'm'], ['𝝂', 'n'], ['𝝃', 'ks'], ['𝝄', 'o'], ['𝝅', 'p'], ['𝝆', 'r'], ['𝝇', 's'], ['𝝈', 's'], ['𝝉', 't'], ['𝝊', 'y'], ['𝝋', 'f'], ['𝝌', 'x'], ['𝝍', 'ps'], ['𝝎', 'o'], ['𝝏', 'a'], ['𝝐', 'e'], ['𝝑', 'i'], ['𝝒', 'k'], ['𝝓', 'f'], ['𝝔', 'r'], ['𝝕', 'p'], ['𝝖', 'A'], ['𝝗', 'B'], ['𝝘', 'G'], ['𝝙', 'D'], ['𝝚', 'E'], ['𝝛', 'Z'], ['𝝜', 'I'], ['𝝝', 'TH'], ['𝝞', 'I'], ['𝝟', 'K'], ['𝝠', 'L'], ['𝝡', 'M'], ['𝝢', 'N'], ['𝝣', 'KS'], ['𝝤', 'O'], ['𝝥', 'P'], ['𝝦', 'R'], ['𝝧', 'TH'], ['𝝨', 'S'], ['𝝩', 'T'], ['𝝪', 'Y'], ['𝝫', 'F'], ['𝝬', 'X'], ['𝝭', 'PS'], ['𝝮', 'O'], ['𝝯', 'D'], ['𝝰', 'a'], ['𝝱', 'v'], ['𝝲', 'g'], ['𝝳', 'd'], ['𝝴', 'e'], ['𝝵', 'z'], ['𝝶', 'i'], ['𝝷', 'th'], ['𝝸', 'i'], ['𝝹', 'k'], ['𝝺', 'l'], ['𝝻', 'm'], ['𝝼', 'n'], ['𝝽', 'ks'], ['𝝾', 'o'], ['𝝿', 'p'], ['𝞀', 'r'], ['𝞁', 's'], ['𝞂', 's'], ['𝞃', 't'], ['𝞄', 'y'], ['𝞅', 'f'], ['𝞆', 'x'], ['𝞇', 'ps'], ['𝞈', 'o'], ['𝞉', 'a'], ['𝞊', 'e'], ['𝞋', 'i'], ['𝞌', 'k'], ['𝞍', 'f'], ['𝞎', 'r'], ['𝞏', 'p'], ['𝞐', 'A'], ['𝞑', 'V'], ['𝞒', 'G'], ['𝞓', 'D'], ['𝞔', 'E'], ['𝞕', 'Z'], ['𝞖', 'I'], ['𝞗', 'TH'], ['𝞘', 'I'], ['𝞙', 'K'], ['𝞚', 'L'], ['𝞛', 'M'], ['𝞜', 'N'], ['𝞝', 'KS'], ['𝞞', 'O'], ['𝞟', 'P'], ['𝞠', 'S'], ['𝞡', 'TH'], ['𝞢', 'S'], ['𝞣', 'T'], ['𝞤', 'Y'], ['𝞥', 'F'], ['𝞦', 'X'], ['𝞧', 'PS'], ['𝞨', 'O'], ['𝞩', 'D'], ['𝞪', 'av'], ['𝞫', 'g'], ['𝞬', 'd'], ['𝞭', 'e'], ['𝞮', 'z'], ['𝞯', 'i'], ['𝞰', 'i'], ['𝞱', 'th'], ['𝞲', 'i'], ['𝞳', 'k'], ['𝞴', 'l'], ['𝞵', 'm'], ['𝞶', 'n'], ['𝞷', 'ks'], ['𝞸', 'o'], ['𝞹', 'p'], ['𝞺', 'r'], ['𝞻', 's'], ['𝞼', 's'], ['𝞽', 't'], ['𝞾', 'y'], ['𝞿', 'f'], ['𝟀', 'x'], ['𝟁', 'ps'], ['𝟂', 'o'], ['𝟃', 'a'], ['𝟄', 'e'], ['𝟅', 'i'], ['𝟆', 'k'], ['𝟇', 'f'], ['𝟈', 'r'], ['𝟉', 'p'], ['𝟊', 'F'], ['𝟋', 'f'], ['⒜', '(a)'], ['⒝', '(b)'], ['⒞', '(c)'], ['⒟', '(d)'], ['⒠', '(e)'], ['⒡', '(f)'], ['⒢', '(g)'], ['⒣', '(h)'], ['⒤', '(i)'], ['⒥', '(j)'], ['⒦', '(k)'], ['⒧', '(l)'], ['⒨', '(m)'], ['⒩', '(n)'], ['⒪', '(o)'], ['⒫', '(p)'], ['⒬', '(q)'], ['⒭', '(r)'], ['⒮', '(s)'], ['⒯', '(t)'], ['⒰', '(u)'], ['⒱', '(v)'], ['⒲', '(w)'], ['⒳', '(x)'], ['⒴', '(y)'], ['⒵', '(z)'], ['Ⓐ', '(A)'], ['Ⓑ', '(B)'], ['Ⓒ', '(C)'], ['Ⓓ', '(D)'], ['Ⓔ', '(E)'], ['Ⓕ', '(F)'], ['Ⓖ', '(G)'], ['Ⓗ', '(H)'], ['Ⓘ', '(I)'], ['Ⓙ', '(J)'], ['Ⓚ', '(K)'], ['Ⓛ', '(L)'], ['Ⓝ', '(N)'], ['Ⓞ', '(O)'], ['Ⓟ', '(P)'], ['Ⓠ', '(Q)'], ['Ⓡ', '(R)'], ['Ⓢ', '(S)'], ['Ⓣ', '(T)'], ['Ⓤ', '(U)'], ['Ⓥ', '(V)'], ['Ⓦ', '(W)'], ['Ⓧ', '(X)'], ['Ⓨ', '(Y)'], ['Ⓩ', '(Z)'], ['ⓐ', '(a)'], ['ⓑ', '(b)'], ['ⓒ', '(b)'], ['ⓓ', '(c)'], ['ⓔ', '(e)'], ['ⓕ', '(f)'], ['ⓖ', '(g)'], ['ⓗ', '(h)'], ['ⓘ', '(i)'], ['ⓙ', '(j)'], ['ⓚ', '(k)'], ['ⓛ', '(l)'], ['ⓜ', '(m)'], ['ⓝ', '(n)'], ['ⓞ', '(o)'], ['ⓟ', '(p)'], ['ⓠ', '(q)'], ['ⓡ', '(r)'], ['ⓢ', '(s)'], ['ⓣ', '(t)'], ['ⓤ', '(u)'], ['ⓥ', '(v)'], ['ⓦ', '(w)'], ['ⓧ', '(x)'], ['ⓨ', '(y)'], ['ⓩ', '(z)'], # Numbers ['𝟎', '0'], ['𝟏', '1'], ['𝟐', '2'], ['𝟑', '3'], ['𝟒', '4'], ['𝟓', '5'], ['𝟔', '6'], ['𝟕', '7'], ['𝟖', '8'], ['𝟗', '9'], ['𝟘', '0'], ['𝟙', '1'], ['𝟚', '2'], ['𝟛', '3'], ['𝟜', '4'], ['𝟝', '5'], ['𝟞', '6'], ['𝟟', '7'], ['𝟠', '8'], ['𝟡', '9'], ['𝟢', '0'], ['𝟣', '1'], ['𝟤', '2'], ['𝟥', '3'], ['𝟦', '4'], ['𝟧', '5'], ['𝟨', '6'], ['𝟩', '7'], ['𝟪', '8'], ['𝟫', '9'], ['𝟬', '0'], ['𝟭', '1'], ['𝟮', '2'], ['𝟯', '3'], ['𝟰', '4'], ['𝟱', '5'], ['𝟲', '6'], ['𝟳', '7'], ['𝟴', '8'], ['𝟵', '9'], ['𝟶', '0'], ['𝟷', '1'], ['𝟸', '2'], ['𝟹', '3'], ['𝟺', '4'], ['𝟻', '5'], ['𝟼', '6'], ['𝟽', '7'], ['𝟾', '8'], ['𝟿', '9'], ['①', '1'], ['②', '2'], ['③', '3'], ['④', '4'], ['⑤', '5'], ['⑥', '6'], ['⑦', '7'], ['⑧', '8'], ['⑨', '9'], ['⑩', '10'], ['⑪', '11'], ['⑫', '12'], ['⑬', '13'], ['⑭', '14'], ['⑮', '15'], ['⑯', '16'], ['⑰', '17'], ['⑱', '18'], ['⑲', '19'], ['⑳', '20'], ['⑴', '1'], ['⑵', '2'], ['⑶', '3'], ['⑷', '4'], ['⑸', '5'], ['⑹', '6'], ['⑺', '7'], ['⑻', '8'], ['⑼', '9'], ['⑽', '10'], ['⑾', '11'], ['⑿', '12'], ['⒀', '13'], ['⒁', '14'], ['⒂', '15'], ['⒃', '16'], ['⒄', '17'], ['⒅', '18'], ['⒆', '19'], ['⒇', '20'], ['⒈', '1.'], ['⒉', '2.'], ['⒊', '3.'], ['⒋', '4.'], ['⒌', '5.'], ['⒍', '6.'], ['⒎', '7.'], ['⒏', '8.'], ['⒐', '9.'], ['⒑', '10.'], ['⒒', '11.'], ['⒓', '12.'], ['⒔', '13.'], ['⒕', '14.'], ['⒖', '15.'], ['⒗', '16.'], ['⒘', '17.'], ['⒙', '18.'], ['⒚', '19.'], ['⒛', '20.'], ['⓪', '0'], ['⓫', '11'], ['⓬', '12'], ['⓭', '13'], ['⓮', '14'], ['⓯', '15'], ['⓰', '16'], ['⓱', '17'], ['⓲', '18'], ['⓳', '19'], ['⓴', '20'], ['⓵', '1'], ['⓶', '2'], ['⓷', '3'], ['⓸', '4'], ['⓹', '5'], ['⓺', '6'], ['⓻', '7'], ['⓼', '8'], ['⓽', '9'], ['⓾', '10'], ['⓿', '0'], # Punctuation ['🙰', '&'], ['🙱', '&'], ['🙲', '&'], ['🙳', '&'], ['🙴', '&'], ['🙵', '&'], ['🙶', '"'], ['🙷', '"'], ['🙸', '"'], ['‽', '?!'], ['🙹', '?!'], ['🙺', '?!'], ['🙻', '?!'], ['🙼', '/'], ['🙽', '\\'], # Alchemy ['🜇', 'AR'], ['🜈', 'V'], ['🜉', 'V'], ['🜆', 'VR'], ['🜅', 'VF'], ['🜩', '2'], ['🜪', '5'], ['🝡', 'f'], ['🝢', 'W'], ['🝣', 'U'], ['🝧', 'V'], ['🝨', 'T'], ['🝪', 'V'], ['🝫', 'MB'], ['🝬', 'VB'], ['🝲', '3B'], ['🝳', '3B'], # Emojis ['💯', '100'], ['🔙', 'BACK'], ['🔚', 'END'], ['🔛', 'ON!'], ['🔜', 'SOON'], ['🔝', 'TOP'], ['🔞', '18'], ['🔤', 'abc'], ['🔠', 'ABCD'], ['🔡', 'abcd'], ['🔢', '1234'], ['🔣', 'T&@%'], ['#️⃣', '#'], ['*️⃣', '*'], ['0️⃣', '0'], ['1️⃣', '1'], ['2️⃣', '2'], ['3️⃣', '3'], ['4️⃣', '4'], ['5️⃣', '5'], ['6️⃣', '6'], ['7️⃣', '7'], ['8️⃣', '8'], ['9️⃣', '9'], ['🔟', '10'], ['🅰️', 'A'], ['🅱️', 'B'], ['🆎', 'AB'], ['🆑', 'CL'], ['🅾️', 'O'], ['🅿', 'P'], ['🆘', 'SOS'], ['🅲', 'C'], ['🅳', 'D'], ['🅴', 'E'], ['🅵', 'F'], ['🅶', 'G'], ['🅷', 'H'], ['🅸', 'I'], ['🅹', 'J'], ['🅺', 'K'], ['🅻', 'L'], ['🅼', 'M'], ['🅽', 'N'], ['🆀', 'Q'], ['🆁', 'R'], ['🆂', 'S'], ['🆃', 'T'], ['🆄', 'U'], ['🆅', 'V'], ['🆆', 'W'], ['🆇', 'X'], ['🆈', 'Y'], ['🆉', 'Z'], ]
PyTorch/SpeechRecognition/Jasper/triton/model_repo_configs/fp32/jasper-tensorrt
jasper-tensorrt
config
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of NVIDIA CORPORATION nor the names of its # contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY # OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. name: "jasper-tensorrt" platform: "tensorrt_plan" default_model_filename: "model.plan" max_batch_size: 8#MAX_BATCH input [ { name: "input__0" data_type: TYPE_FP32 dims: [64, -1] } ] output [ { name: "output__0" data_type: TYPE_FP32 dims: [-1, 29 ] } ] instance_group { count: 1#NUM_ENGINES gpus: 0 kind: KIND_GPU } #db#dynamic_batching { #db# preferred_batch_size: 8#MAX_BATCH #db# max_queue_delay_microseconds: #MAX_QUEUE #db#}
PyTorch/SpeechRecognition/QuartzNet/common
common
filter_warnings
# Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved. # # 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. """Mutes known and unrelated PyTorch warnings. The warnings module keeps a list of filters. Importing it as late as possible prevents its filters from being overriden. """ import warnings # NGC 22.04-py3 container (PyTorch 1.12.0a0+bd13bc6) warnings.filterwarnings( "ignore", message='positional arguments and argument "destination" are deprecated.' ' nn.Module.state_dict will not accept them in the future.') # 22.08-py3 container warnings.filterwarnings( "ignore", message="is_namedtuple is deprecated, please use the python checks")
TensorFlow2/LanguageModeling/BERT/official/nlp/modeling/layers
layers
masked_softmax_test
# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # 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. # ============================================================================== """Tests for Keras-based masked softmax layer.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow as tf from tensorflow.python.keras import keras_parameterized # pylint: disable=g-direct-tensorflow-import from official.nlp.modeling.layers import masked_softmax # This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It # guarantees forward compatibility of this code for the V2 switchover. @keras_parameterized.run_all_keras_modes class MaskedSoftmaxLayerTest(keras_parameterized.TestCase): def test_non_masked_softmax(self): test_layer = masked_softmax.MaskedSoftmax() input_tensor = tf.keras.Input(shape=(4, 8)) output = test_layer(input_tensor) model = tf.keras.Model(input_tensor, output) input_data = 10 * np.random.random_sample((3, 4, 8)) output_data = model.predict(input_data) expected_data = tf.nn.softmax(input_data) self.assertAllClose(expected_data, output_data) def test_masked_softmax(self): test_layer = masked_softmax.MaskedSoftmax() input_tensor = tf.keras.Input(shape=(4, 8)) mask_tensor = tf.keras.Input(shape=(4, 8)) output = test_layer([input_tensor, mask_tensor]) model = tf.keras.Model([input_tensor, mask_tensor], output) input_data = 10 * np.random.random_sample((3, 4, 8)) mask_data = np.random.randint(2, size=(3, 4, 8)) output_data = model.predict([input_data, mask_data]) expected_zeros = np.greater(mask_data, 0) is_zeros = np.greater(output_data, 0) self.assertAllEqual(expected_zeros, is_zeros) def test_masked_softmax_with_none_mask(self): test_layer = masked_softmax.MaskedSoftmax() input_tensor = tf.keras.Input(shape=(4, 8)) output = test_layer([input_tensor, None]) model = tf.keras.Model(input_tensor, output) input_data = 10 * np.random.random_sample((3, 4, 8)) output_data = model.predict(input_data) expected_data = tf.nn.softmax(input_data) self.assertAllClose(expected_data, output_data) def test_softmax_with_axes_expansion(self): test_layer = masked_softmax.MaskedSoftmax(mask_expansion_axes=[1]) input_tensor = tf.keras.Input(shape=(4, 8)) mask_tensor = tf.keras.Input(shape=(8)) output = test_layer([input_tensor, mask_tensor]) model = tf.keras.Model([input_tensor, mask_tensor], output) input_data = 10 * np.random.random_sample((3, 4, 8)) mask_data = np.random.randint(2, size=(3, 8)) output_data = model.predict([input_data, mask_data]) expanded_mask = np.expand_dims(mask_data, axis=1) * np.ones_like(input_data) expected_zeros = np.greater(expanded_mask, 0) is_zeros = np.greater(output_data, 0) self.assertAllEqual(expected_zeros, is_zeros) if __name__ == '__main__': tf.test.main()
PyTorch/SpeechSynthesis/FastPitch/triton
triton
run_inference_on_fw
#!/usr/bin/env python3 # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # 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. r""" To infer the model on framework runtime, you can use `run_inference_on_fw.py` script. It infers data obtained from pointed data loader locally and saves received data into npz files. Those files are stored in directory pointed by `--output-dir` argument. Example call: ```shell script python ./triton/run_inference_on_fw.py \ --input-path /models/exported/model.onnx \ --input-type onnx \ --dataloader triton/dataloader.py \ --data-dir /data/imagenet \ --batch-size 32 \ --output-dir /results/dump_local \ --dump-labels ``` """ import argparse import logging import os from pathlib import Path os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" os.environ["TF_ENABLE_DEPRECATION_WARNINGS"] = "0" from tqdm import tqdm # method from PEP-366 to support relative import in executed modules if __package__ is None: __package__ = Path(__file__).parent.name from .deployment_toolkit.args import ArgParserGenerator from .deployment_toolkit.core import DATALOADER_FN_NAME, BaseLoader, BaseRunner, Format, load_from_file from .deployment_toolkit.dump import NpzWriter from .deployment_toolkit.extensions import loaders, runners LOGGER = logging.getLogger("run_inference_on_fw") def _verify_and_format_dump(args, ids, x, y_pred, y_real): data = {"outputs": y_pred, "ids": {"ids": ids}} if args.dump_inputs: data["inputs"] = x if args.dump_labels: if not y_real: raise ValueError( "Found empty label values. Please provide labels in dataloader_fn or do not use --dump-labels argument" ) data["labels"] = y_real return data def _parse_and_validate_args(): supported_inputs = set(runners.supported_extensions) & set(loaders.supported_extensions) parser = argparse.ArgumentParser(description="Dump local inference output of given model", allow_abbrev=False) parser.add_argument("--input-path", help="Path to input model", required=True) parser.add_argument("--input-type", help="Input model type", choices=supported_inputs, required=True) parser.add_argument("--dataloader", help="Path to python file containing dataloader.", required=True) parser.add_argument("--output-dir", help="Path to dir where output files will be stored", required=True) parser.add_argument("--dump-labels", help="Dump labels to output dir", action="store_true", default=False) parser.add_argument("--dump-inputs", help="Dump inputs to output dir", action="store_true", default=False) parser.add_argument("-v", "--verbose", help="Verbose logs", action="store_true", default=False) args, *_ = parser.parse_known_args() get_dataloader_fn = load_from_file(args.dataloader, label="dataloader", target=DATALOADER_FN_NAME) ArgParserGenerator(get_dataloader_fn).update_argparser(parser) Loader: BaseLoader = loaders.get(args.input_type) ArgParserGenerator(Loader, module_path=args.input_path).update_argparser(parser) Runner: BaseRunner = runners.get(args.input_type) ArgParserGenerator(Runner).update_argparser(parser) args = parser.parse_args() types_requiring_io_params = [] if args.input_type in types_requiring_io_params and not all(p for p in [args.inputs, args.outputs]): parser.error(f"For {args.input_type} input provide --inputs and --outputs parameters") return args def main(): args = _parse_and_validate_args() log_level = logging.INFO if not args.verbose else logging.DEBUG log_format = "%(asctime)s %(levelname)s %(name)s %(message)s" logging.basicConfig(level=log_level, format=log_format) LOGGER.info(f"args:") for key, value in vars(args).items(): LOGGER.info(f" {key} = {value}") Loader: BaseLoader = loaders.get(args.input_type) Runner: BaseRunner = runners.get(args.input_type) loader = ArgParserGenerator(Loader, module_path=args.input_path).from_args(args) runner = ArgParserGenerator(Runner).from_args(args) LOGGER.info(f"Loading {args.input_path}") model = loader.load(args.input_path) with runner.init_inference(model=model) as runner_session, NpzWriter(args.output_dir) as writer: get_dataloader_fn = load_from_file(args.dataloader, label="dataloader", target=DATALOADER_FN_NAME) dataloader_fn = ArgParserGenerator(get_dataloader_fn).from_args(args) LOGGER.info(f"Data loader initialized; Running inference") for ids, x, y_real in tqdm(dataloader_fn(), unit="batch", mininterval=10): y_pred = runner_session(x) data = _verify_and_format_dump(args, ids=ids, x=x, y_pred=y_pred, y_real=y_real) writer.write(**data) LOGGER.info(f"Inference finished") if __name__ == "__main__": main()
PyTorch/Classification/ConvNets/image_classification
image_classification
quantization
from tqdm import tqdm import torch import contextlib import time import logging from pytorch_quantization import quant_modules from pytorch_quantization import nn as quant_nn from pytorch_quantization import calib from pytorch_quantization.tensor_quant import QuantDescriptor from . import logger as log from .utils import calc_ips import dllogger initialize = quant_modules.initialize deactivate = quant_modules.deactivate IPS_METADATA = {"unit": "img/s", "format": ":.2f"} TIME_METADATA = {"unit": "s", "format": ":.5f"} def select_default_calib_method(calib_method='histogram'): """Set up selected calibration method in whole network""" quant_desc_input = QuantDescriptor(calib_method=calib_method) quant_nn.QuantConv1d.set_default_quant_desc_input(quant_desc_input) quant_nn.QuantConv2d.set_default_quant_desc_input(quant_desc_input) quant_nn.QuantLinear.set_default_quant_desc_input(quant_desc_input) quant_nn.QuantAdaptiveAvgPool2d.set_default_quant_desc_input(quant_desc_input) def quantization_setup(calib_method='histogram'): """Change network into quantized version "automatically" and selects histogram as default quantization method""" select_default_calib_method(calib_method) initialize() def disable_calibration(model): """Disables calibration in whole network. Should be run always before running interference.""" for name, module in model.named_modules(): if isinstance(module, quant_nn.TensorQuantizer): if module._calibrator is not None: module.enable_quant() module.disable_calib() else: module.enable() def collect_stats(model, data_loader, logger, num_batches): """Feed data to the network and collect statistic""" if logger is not None: logger.register_metric( f"calib.total_ips", log.PERF_METER(), verbosity=dllogger.Verbosity.DEFAULT, metadata=IPS_METADATA, ) logger.register_metric( f"calib.data_time", log.PERF_METER(), verbosity=dllogger.Verbosity.DEFAULT, metadata=TIME_METADATA, ) logger.register_metric( f"calib.compute_latency", log.PERF_METER(), verbosity=dllogger.Verbosity.DEFAULT, metadata=TIME_METADATA, ) # Enable calibrators data_iter = enumerate(data_loader) if logger is not None: data_iter = logger.iteration_generator_wrapper(data_iter, mode='calib') for name, module in model.named_modules(): if isinstance(module, quant_nn.TensorQuantizer): if module._calibrator is not None: module.disable_quant() module.enable_calib() else: module.disable() end = time.time() if logger is not None: logger.start_calibration() for i, (image, _) in data_iter: bs = image.size(0) data_time = time.time() - end model(image.cuda()) it_time = time.time() - end if logger is not None: logger.log_metric(f"calib.total_ips", calc_ips(bs, it_time)) logger.log_metric(f"calib.data_time", data_time) logger.log_metric(f"calib.compute_latency", it_time - data_time) if i >= num_batches: time.sleep(5) break end = time.time() if logger is not None: logger.end_calibration() logging.disable(logging.WARNING) disable_calibration(model) def compute_amax(model, **kwargs): """Loads statistics of data and calculates quantization parameters in whole network""" for name, module in model.named_modules(): if isinstance(module, quant_nn.TensorQuantizer) and module._calibrator is not None: if isinstance(module._calibrator, calib.MaxCalibrator): module.load_calib_amax() else: module.load_calib_amax(**kwargs) model.cuda() def calibrate(model, train_loader, logger, calib_iter=1, percentile=99.99): """Calibrates whole network i.e. gathers data for quantization and calculates quantization parameters""" model.eval() with torch.no_grad(): collect_stats(model, train_loader, logger, num_batches=calib_iter) compute_amax(model, method="percentile", percentile=percentile) logging.disable(logging.NOTSET) @contextlib.contextmanager def switch_on_quantization(do_quantization=True): """Context manager for quantization activation""" if do_quantization: initialize() try: yield finally: if do_quantization: deactivate()
DGLPyTorch/DrugDiscovery/SE3Transformer/se3_transformer/runtime
runtime
callbacks
# Copyright (c) 2021-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. # # SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES # SPDX-License-Identifier: MIT import logging import time from abc import ABC, abstractmethod from typing import Optional import numpy as np import torch from se3_transformer.runtime.loggers import Logger from se3_transformer.runtime.metrics import MeanAbsoluteError class BaseCallback(ABC): def on_fit_start(self, optimizer, args, start_epoch): pass def on_fit_end(self): pass def on_epoch_end(self): pass def on_batch_start(self): pass def on_validation_step(self, input, target, pred): pass def on_validation_end(self, epoch=None): pass def on_checkpoint_load(self, checkpoint): pass def on_checkpoint_save(self, checkpoint): pass class LRSchedulerCallback(BaseCallback): def __init__(self, logger: Optional[Logger] = None): self.logger = logger self.scheduler = None @abstractmethod def get_scheduler(self, optimizer, args, last_epoch): pass def on_fit_start(self, optimizer, args, start_epoch): self.scheduler = self.get_scheduler(optimizer, args, start_epoch - 1) if hasattr(self, 'state_dict'): self.scheduler.load_state_dict(self.state_dict) def on_checkpoint_load(self, checkpoint): self.state_dict = checkpoint['scheduler_state_dict'] def on_checkpoint_save(self, checkpoint): checkpoint['scheduler_state_dict'] = self.scheduler.state_dict() def on_epoch_end(self): if self.logger is not None: self.logger.log_metrics({'learning rate': self.scheduler.get_last_lr()[0]}, step=self.scheduler.last_epoch) self.scheduler.step() class QM9MetricCallback(BaseCallback): """ Logs the rescaled mean absolute error for QM9 regression tasks """ def __init__(self, logger, targets_std, prefix=''): self.mae = MeanAbsoluteError() self.logger = logger self.targets_std = targets_std self.prefix = prefix self.best_mae = float('inf') self.last_mae = None def on_validation_step(self, input, target, pred): self.mae(pred.detach(), target.detach()) def on_validation_end(self, epoch=None): mae = self.mae.compute() * self.targets_std logging.info(f'{self.prefix} MAE: {mae}') self.logger.log_metrics({f'{self.prefix} MAE': mae}, epoch) self.best_mae = min(self.best_mae, mae) self.last_mae = mae def on_fit_end(self): if self.best_mae != float('inf'): self.logger.log_metrics({f'{self.prefix} best MAE': self.best_mae}) self.logger.log_metrics({f'{self.prefix} loss': self.last_mae / self.targets_std}) class QM9LRSchedulerCallback(LRSchedulerCallback): def __init__(self, logger, epochs): super().__init__(logger) self.epochs = epochs def get_scheduler(self, optimizer, args, last_epoch): min_lr = args.min_learning_rate if args.min_learning_rate else args.learning_rate / 10.0 return torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, self.epochs, eta_min=min_lr, last_epoch=last_epoch) class PerformanceCallback(BaseCallback): def __init__(self, logger, batch_size: int, warmup_epochs: int = 1, mode: str = 'train'): self.batch_size = batch_size self.warmup_epochs = warmup_epochs self.epoch = 0 self.timestamps = [] self.mode = mode self.logger = logger def on_batch_start(self): if self.epoch >= self.warmup_epochs: torch.cuda.synchronize() self.timestamps.append(time.time() * 1000.0) def _log_perf(self): stats = self.process_performance_stats() for k, v in stats.items(): logging.info(f'performance {k}: {v}') self.logger.log_metrics(stats) def on_epoch_end(self): self.epoch += 1 def on_fit_end(self): if self.epoch > self.warmup_epochs: self._log_perf() self.timestamps = [] def process_performance_stats(self): timestamps = np.asarray(self.timestamps) deltas = np.diff(timestamps) throughput = self.batch_size / deltas.mean() stats = { f"throughput_{self.mode}": throughput, f"latency_{self.mode}_mean": deltas.mean(), f"total_time_{self.mode}": timestamps[-1] - timestamps[0], } for level in [90, 95, 99]: stats.update({f"latency_{self.mode}_{level}": np.percentile(deltas, level)}) return stats
TensorFlow2/Segmentation/nnUNet/data_loading
data_loading
dali_loader
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # 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. import itertools import horovod.tensorflow as hvd import numpy as np import nvidia.dali.fn as fn import nvidia.dali.ops as ops import nvidia.dali.plugin.tf as dali_tf import nvidia.dali.types as types import tensorflow as tf from nvidia.dali.pipeline import Pipeline def get_numpy_reader(files, shard_id, num_shards, seed, shuffle): return ops.readers.Numpy( seed=seed, files=files, device="cpu", read_ahead=True, shard_id=shard_id, pad_last_batch=True, num_shards=num_shards, dont_use_mmap=True, shuffle_after_epoch=shuffle, ) def random_augmentation(probability, augmented, original): condition = fn.cast(fn.random.coin_flip(probability=probability), dtype=types.DALIDataType.BOOL) neg_condition = condition ^ True return condition * augmented + neg_condition * original class GenericPipeline(Pipeline): def __init__( self, batch_size, num_threads, shard_id, seed, num_gpus, dim, shuffle_input=True, input_x_files=None, input_y_files=None, ): super().__init__( batch_size=batch_size, num_threads=num_threads, device_id=hvd.rank(), seed=seed, ) if input_x_files is not None: self.input_x = get_numpy_reader( files=input_x_files, shard_id=shard_id, seed=seed, num_shards=num_gpus, shuffle=shuffle_input, ) if input_y_files is not None: self.input_y = get_numpy_reader( files=input_y_files, shard_id=shard_id, seed=seed, num_shards=num_gpus, shuffle=shuffle_input, ) self.dim = dim self.internal_seed = seed class TrainPipeline(GenericPipeline): def __init__(self, imgs, lbls, oversampling, patch_size, batch_size_2d=None, **kwargs): super().__init__(input_x_files=imgs, input_y_files=lbls, shuffle_input=True, **kwargs) self.oversampling = oversampling self.patch_size = patch_size if self.dim == 2 and batch_size_2d is not None: self.patch_size = [batch_size_2d] + self.patch_size self.crop_shape = types.Constant(np.array(self.patch_size), dtype=types.INT64) self.crop_shape_float = types.Constant(np.array(self.patch_size), dtype=types.FLOAT) def load_data(self): img, lbl = self.input_x(name="ReaderX"), self.input_y(name="ReaderY") img, lbl = fn.reshape(img, layout="DHWC"), fn.reshape(lbl, layout="DHWC") return img, lbl @staticmethod def slice_fn(img): return fn.slice(img, 1, 3, axes=[0]) def biased_crop_fn(self, img, lbl): roi_start, roi_end = fn.segmentation.random_object_bbox( lbl, format="start_end", foreground_prob=self.oversampling, k_largest=2, device="cpu", cache_objects=True, ) anchor = fn.roi_random_crop( lbl, roi_start=roi_start, roi_end=roi_end, crop_shape=[*self.patch_size, 1], ) anchor = fn.slice(anchor, 0, 3, axes=[0]) img, lbl = fn.slice( [img, lbl], anchor, self.crop_shape, axis_names="DHW", out_of_bounds_policy="pad", device="cpu", ) img, lbl = img.gpu(), lbl.gpu() return img, lbl def zoom_fn(self, img, lbl): scale = random_augmentation(0.15, fn.random.uniform(range=(0.7, 1.0)), 1.0) d, h, w = [scale * x for x in self.patch_size] if self.dim == 2: d = self.patch_size[0] img, lbl = fn.crop(img, crop_h=h, crop_w=w, crop_d=d), fn.crop(lbl, crop_h=h, crop_w=w, crop_d=d) img = fn.resize( img, interp_type=types.DALIInterpType.INTERP_CUBIC, size=self.crop_shape_float, ) lbl = fn.resize(lbl, interp_type=types.DALIInterpType.INTERP_NN, size=self.crop_shape_float) return img, lbl def noise_fn(self, img): img_noised = fn.noise.gaussian(img, stddev=fn.random.uniform(range=(0.0, 0.3))) return random_augmentation(0.15, img_noised, img) def blur_fn(self, img): img_blurred = fn.gaussian_blur(img, sigma=fn.random.uniform(range=(0.5, 1.5))) return random_augmentation(0.15, img_blurred, img) def brightness_contrast_fn(self, img): img_transformed = fn.brightness_contrast( img, brightness=fn.random.uniform(range=(0.7, 1.3)), contrast=fn.random.uniform(range=(0.65, 1.5)) ) return random_augmentation(0.15, img_transformed, img) def flips_fn(self, img, lbl): kwargs = { "horizontal": fn.random.coin_flip(probability=0.5), "vertical": fn.random.coin_flip(probability=0.5), } if self.dim == 3: kwargs.update({"depthwise": fn.random.coin_flip(probability=0.5)}) return fn.flip(img, **kwargs), fn.flip(lbl, **kwargs) def define_graph(self): img, lbl = self.load_data() img, lbl = self.biased_crop_fn(img, lbl) img, lbl = self.zoom_fn(img, lbl) img, lbl = self.flips_fn(img, lbl) img = self.noise_fn(img) img = self.blur_fn(img) img = self.brightness_contrast_fn(img) return img, lbl class EvalPipeline(GenericPipeline): def __init__(self, imgs, lbls, patch_size, **kwargs): super().__init__(input_x_files=imgs, input_y_files=lbls, shuffle_input=False, **kwargs) self.patch_size = patch_size def define_graph(self): img, lbl = self.input_x(name="ReaderX").gpu(), self.input_y(name="ReaderY").gpu() img, lbl = fn.reshape(img, layout="DHWC"), fn.reshape(lbl, layout="DHWC") return img, lbl class TestPipeline(GenericPipeline): def __init__(self, imgs, meta, **kwargs): super().__init__(input_x_files=imgs, input_y_files=meta, shuffle_input=False, **kwargs) def define_graph(self): img, meta = self.input_x(name="ReaderX").gpu(), self.input_y(name="ReaderY").gpu() img = fn.reshape(img, layout="DHWC") return img, meta class BenchmarkPipeline(GenericPipeline): def __init__(self, imgs, lbls, patch_size, batch_size_2d=None, **kwargs): super().__init__(input_x_files=imgs, input_y_files=lbls, shuffle_input=False, **kwargs) self.patch_size = patch_size if self.dim == 2 and batch_size_2d is not None: self.patch_size = [batch_size_2d] + self.patch_size def crop_fn(self, img, lbl): img = fn.crop(img, crop=self.patch_size, out_of_bounds_policy="pad") lbl = fn.crop(lbl, crop=self.patch_size, out_of_bounds_policy="pad") return img, lbl def define_graph(self): img, lbl = self.input_x(name="ReaderX").gpu(), self.input_y(name="ReaderY").gpu() img, lbl = self.crop_fn(img, lbl) img, lbl = fn.reshape(img, layout="DHWC"), fn.reshape(lbl, layout="DHWC") return img, lbl def fetch_dali_loader(imgs, lbls, batch_size, mode, **kwargs): assert len(imgs) > 0, "No images found" if lbls is not None: assert len(imgs) == len(lbls), f"Got {len(imgs)} images but {len(lbls)} lables" gpus = hvd.size() device_id = hvd.rank() if kwargs["benchmark"]: # Just to make sure the number of examples is large enough for benchmark run. nbs = kwargs["bench_steps"] if kwargs["dim"] == 3: nbs *= batch_size imgs = list(itertools.chain(*(100 * [imgs])))[: nbs * gpus] lbls = list(itertools.chain(*(100 * [lbls])))[: nbs * gpus] pipe_kwargs = { "dim": kwargs["dim"], "num_gpus": gpus, "seed": kwargs["seed"], "batch_size": batch_size, "num_threads": kwargs["num_workers"], "shard_id": device_id, } if kwargs["dim"] == 2: if kwargs["benchmark"]: pipe_kwargs.update({"batch_size_2d": batch_size}) batch_size = 1 elif mode == "train": pipe_kwargs.update({"batch_size_2d": batch_size // kwargs["nvol"]}) batch_size = kwargs["nvol"] if mode == "eval": # Validation data is manually sharded beforehand. pipe_kwargs["shard_id"] = 0 pipe_kwargs["num_gpus"] = 1 output_dtypes = (tf.float32, tf.uint8) if kwargs["benchmark"]: pipeline = BenchmarkPipeline(imgs, lbls, kwargs["patch_size"], **pipe_kwargs) elif mode == "train": pipeline = TrainPipeline(imgs, lbls, kwargs["oversampling"], kwargs["patch_size"], **pipe_kwargs) elif mode == "eval": pipeline = EvalPipeline(imgs, lbls, kwargs["patch_size"], **pipe_kwargs) else: pipeline = TestPipeline(imgs, kwargs["meta"], **pipe_kwargs) output_dtypes = (tf.float32, tf.int64) tf_pipe = dali_tf.DALIDataset(pipeline, batch_size=batch_size, device_id=device_id, output_dtypes=output_dtypes) return tf_pipe
PyTorch/Classification/ConvNets/triton/scripts
scripts
download_data
#!/usr/bin/env bash # Copyright (c) 2021 NVIDIA CORPORATION. All rights reserved. # # 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. # Download checkpoint if [ -f "${CHECKPOINT_DIR}/nvidia_resnet50_200821.pth.tar" ]; then echo "Checkpoint already downloaded." else echo "Downloading checkpoint ..." wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/resnet50_pyt_amp/versions/20.06.0/zip -O \ resnet50_pyt_amp_20.06.0.zip || { echo "ERROR: Failed to download checkpoint from NGC" exit 1 } unzip resnet50_pyt_amp_20.06.0.zip -d ${CHECKPOINT_DIR} rm resnet50_pyt_amp_20.06.0.zip echo "ok" fi
TensorFlow2/Classification/ConvNets/runtime
runtime
runner_utils
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # 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. from __future__ import print_function import os import math import tensorflow as tf import horovod.tensorflow as hvd from utils import hvd_utils from utils import callbacks from dataloader import dataset_factory __all__ = ['get_optimizer_params', 'get_metrics', 'get_learning_rate_params', 'build_model_params', 'get_models', 'build_augmenter_params', \ 'get_image_size_from_model', 'get_dataset_builders', 'build_stats', 'parse_inference_input', 'preprocess_image_files'] def get_optimizer_params(name, decay, epsilon, momentum, moving_average_decay, nesterov, beta_1, beta_2): return { 'name': name, 'decay': decay, 'epsilon': epsilon, 'momentum': momentum, 'moving_average_decay': moving_average_decay, 'nesterov': nesterov, 'beta_1': beta_1, 'beta_2': beta_2 } def get_metrics(one_hot: bool): """Get a dict of available metrics to track.""" if one_hot: return { # (name, metric_fn) 'acc': tf.keras.metrics.CategoricalAccuracy(name='accuracy'), 'accuracy': tf.keras.metrics.CategoricalAccuracy(name='accuracy'), 'top_1': tf.keras.metrics.CategoricalAccuracy(name='accuracy'), 'top_5': tf.keras.metrics.TopKCategoricalAccuracy( k=5, name='top_5_accuracy'), } else: return { # (name, metric_fn) 'acc': tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'), 'accuracy': tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'), 'top_1': tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'), 'top_5': tf.keras.metrics.SparseTopKCategoricalAccuracy( k=5, name='top_5_accuracy'), } def get_learning_rate_params(name, initial_lr, decay_epochs, decay_rate, warmup_epochs): return { 'name':name, 'initial_lr': initial_lr, 'decay_epochs': decay_epochs, 'decay_rate': decay_rate, 'warmup_epochs': warmup_epochs, 'examples_per_epoch': None, 'boundaries': None, 'multipliers': None, 'scale_by_batch_size': 1./128., 'staircase': True } def build_augmenter_params(augmenter_name, cutout_const, translate_const, num_layers, magnitude, autoaugmentation_name): if augmenter_name is None or augmenter_name not in ['randaugment', 'autoaugment']: return {} augmenter_params = {} if cutout_const is not None: augmenter_params['cutout_const'] = cutout_const if translate_const is not None: augmenter_params['translate_const'] = translate_const if augmenter_name == 'randaugment': if num_layers is not None: augmenter_params['num_layers'] = num_layers if magnitude is not None: augmenter_params['magnitude'] = magnitude if augmenter_name == 'autoaugment': if autoaugmentation_name is not None: augmenter_params['autoaugmentation_name'] = autoaugmentation_name return augmenter_params # def get_image_size_from_model(arch): # """If the given model has a preferred image size, return it.""" # if 'efficientnet_v1' in arch: # if arch in efficientnet_model_v1.MODEL_CONFIGS: # return efficientnet_model_v1.MODEL_CONFIGS[arch]['resolution'] # elif 'efficientnet_v2' in arch: # if arch in efficientnet_model_v2.MODEL_CONFIGS: # return efficientnet_model_v2.MODEL_CONFIGS[arch]['resolution'] # return None def get_dataset_builders(params, one_hot, hvd_size=None): """Create and return train and validation dataset builders.""" builders = [] validation_dataset_builder = None train_dataset_builder = None if "train" in params.mode: img_size = params.train_img_size print("Image size {} used for training".format(img_size)) print("Train batch size {}".format(params.train_batch_size)) train_dataset_builder = dataset_factory.Dataset(data_dir=params.data_dir, index_file_dir=params.index_file, split='train', num_classes=params.num_classes, image_size=img_size, batch_size=params.train_batch_size, one_hot=one_hot, use_dali=params.train_use_dali, augmenter=params.augmenter_name, augmenter_params=build_augmenter_params(params.augmenter_name, params.cutout_const, params.translate_const, params.raug_num_layers, params.raug_magnitude, params.autoaugmentation_name), mixup_alpha=params.mixup_alpha, cutmix_alpha=params.cutmix_alpha, defer_img_mixing=params.defer_img_mixing, mean_subtract=params.mean_subtract_in_dpipe, standardize=params.standardize_in_dpipe, hvd_size=hvd_size, disable_map_parallelization=params.disable_map_parallelization ) if "eval" in params.mode: img_size = params.eval_img_size print("Image size {} used for evaluation".format(img_size)) validation_dataset_builder = dataset_factory.Dataset(data_dir=params.data_dir, index_file_dir=params.index_file, split='validation', num_classes=params.num_classes, image_size=img_size, batch_size=params.eval_batch_size, one_hot=one_hot, use_dali=params.eval_use_dali, hvd_size=hvd_size) builders.append(train_dataset_builder) builders.append(validation_dataset_builder) return builders def build_stats(history, validation_output, train_callbacks, eval_callbacks, logger, comment=''): stats = {} stats['comment'] = comment if validation_output: stats['eval_loss'] = float(validation_output[0]) stats['eval_accuracy_top_1'] = float(validation_output[1]) stats['eval_accuracy_top_5'] = float(validation_output[2]) #This part is train loss on GPU_0 if history and history.history: train_hist = history.history #Gets final loss from training. stats['training_loss'] = float(hvd.allreduce(tf.constant(train_hist['loss'][-1], dtype=tf.float32), average=True)) # Gets top_1 training accuracy. if 'categorical_accuracy' in train_hist: stats['training_accuracy_top_1'] = float(hvd.allreduce(tf.constant(train_hist['categorical_accuracy'][-1], dtype=tf.float32), average=True)) elif 'sparse_categorical_accuracy' in train_hist: stats['training_accuracy_top_1'] = float(hvd.allreduce(tf.constant(train_hist['sparse_categorical_accuracy'][-1], dtype=tf.float32), average=True)) elif 'accuracy' in train_hist: stats['training_accuracy_top_1'] = float(hvd.allreduce(tf.constant(train_hist['accuracy'][-1], dtype=tf.float32), average=True)) stats['training_accuracy_top_5'] = float(hvd.allreduce(tf.constant(train_hist['top_5_accuracy'][-1], dtype=tf.float32), average=True)) # Look for the time history callback which was used during keras.fit if train_callbacks: for callback in train_callbacks: if isinstance(callback, callbacks.TimeHistory): if callback.epoch_runtime_log: stats['avg_exp_per_second_training'] = callback.average_examples_per_second stats['avg_exp_per_second_training_per_GPU'] = callback.average_examples_per_second / hvd.size() if eval_callbacks: for eval_callback in eval_callbacks: if not isinstance(eval_callback, callbacks.EvalTimeHistory): continue stats['avg_exp_per_second_eval'] = float(eval_callback.average_examples_per_second) # * hvd.size(), performing one-gpu evluation now stats['avg_exp_per_second_eval_per_GPU'] = float(eval_callback.average_examples_per_second) stats['avg_time_per_exp_eval'] = 1000./stats['avg_exp_per_second_eval'] batch_time = eval_callback.batch_time batch_time.sort() latency_pct_per_batch = sum( batch_time[:-1] ) / int( len(batch_time) - 1 ) stats['latency_pct'] = 1000.0 * latency_pct_per_batch latency_90pct_per_batch = sum( batch_time[:int( 0.9 * len(batch_time) )] ) / int( 0.9 * len(batch_time) ) stats['latency_90pct'] = 1000.0 * latency_90pct_per_batch latency_95pct_per_batch = sum( batch_time[:int( 0.95 * len(batch_time) )] ) / int( 0.95 * len(batch_time) ) stats['latency_95pct'] = 1000.0 * latency_95pct_per_batch latency_99pct_per_batch = sum( batch_time[:int( 0.99 * len(batch_time) )] ) / int( 0.99 * len(batch_time) ) stats['latency_99pct'] = 1000.0 * latency_99pct_per_batch if not hvd_utils.is_using_hvd() or hvd.rank() == 0: logger.log(step=(), data=stats) def preprocess_image_files(directory_name, img_size, batch_size, dtype): # data format should always be channels_last. If need be, it will be adjusted in the model module. data_format = "channels_last" datagen = tf.keras.preprocessing.image.ImageDataGenerator(data_format=data_format, dtype=dtype) images = datagen.flow_from_directory(directory_name, class_mode=None, batch_size=batch_size, target_size=(img_size, img_size), shuffle=False) return images def parse_inference_input(to_predict): filenames = [] image_formats = ['.jpg', '.jpeg', '.JPEG', '.JPG', '.png', '.PNG'] if os.path.isdir(to_predict): filenames = [f for f in os.listdir(to_predict) if os.path.isfile(os.path.join(to_predict, f)) and os.path.splitext(f)[1] in image_formats] elif os.path.isfile(to_predict): filenames.append(to_predict) return filenames @tf.function def train_step(self, data): """[summary] custom training step, which is used in case the user requests gradient accumulation. """ # params use_amp = self.config.use_amp grad_accum_steps = self.config.grad_accum_steps hvd_fp16_compression = self.config.hvd_fp16_compression grad_clip_norm = self.config.grad_clip_norm #Forward and Backward pass x,y = data with tf.GradientTape() as tape: y_pred = self(x, training=True) loss = self.compiled_loss(y, y_pred, regularization_losses=self.losses) if use_amp: loss = self.optimizer.get_scaled_loss(loss) # Update metrics (includes the metric that tracks the loss) self.compiled_metrics.update_state(y, y_pred) #Backprop gradients # tape = hvd.DistributedGradientTape(tape, compression=hvd.Compression.fp16 if use_amp and hvd_fp16_compression else hvd.Compression.none) gradients = tape.gradient(loss, self.trainable_variables) #Get unscaled gradients if AMP if use_amp: gradients = self.optimizer.get_unscaled_gradients(gradients) #Accumulate gradients self.grad_accumulator(gradients) if self.local_step % grad_accum_steps == 0: gradients = [None if g is None else hvd.allreduce(g / tf.cast(grad_accum_steps, g.dtype), compression=hvd.Compression.fp16 if use_amp and hvd_fp16_compression else hvd.Compression.none) for g in self.grad_accumulator.gradients] if grad_clip_norm > 0: (gradients, gradients_gnorm) = tf.clip_by_global_norm(gradients, clip_norm=grad_clip_norm) self.gradients_gnorm.assign(gradients_gnorm) # this will later appear on tensorboard #Weight update & iteration update self.optimizer.apply_gradients(zip(gradients, self.trainable_variables)) self.grad_accumulator.reset() # update local counter self.local_step.assign_add(1) # Return a dict mapping metric names to current value return {m.name: m.result() for m in self.metrics}
Tools/PyTorch/TimeSeriesPredictionPlatform/conf/model_dataset
model_dataset
cuml_auto_arima_electricity
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. # # 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. dataset: config: stride: 400
PyTorch/Translation/Transformer/fairseq
fairseq
meters
# Copyright (c) 2017-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the LICENSE file in # the root directory of this source tree. An additional grant of patent rights # can be found in the PATENTS file in the same directory. import time import torch class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count class TimeMeter(object): """Computes the average occurrence of some event per second""" def __init__(self, init=0): self.reset(init) def reset(self, init=0): self.init = init torch.cuda.synchronize() self.start = time.time() self.n = 0 self.last_update = time.time() def update(self, val=1): self.n += val torch.cuda.synchronize() self.last_update = time.time() @property def avg(self): return self.n / self.elapsed_time @property def elapsed_time(self): torch.cuda.synchronize() return self.init + (time.time() - self.start) @property def u_avg(self): return self.n / (self.last_update - self.start) class StopwatchMeter(object): """Computes the sum/avg duration of some event in seconds""" def __init__(self): self.reset() self.intervals = [] def start(self): torch.cuda.synchronize() self.start_time = time.time() def stop(self, n=1): torch.cuda.synchronize() if self.start_time is not None: delta = time.time() - self.start_time self.intervals.append(delta) self.sum += delta self.n += n self.start_time = None def reset(self): self.sum = 0 self.n = 0 self.start_time = None self.intervals = [] @property def avg(self): return self.sum / self.n def p(self, i): assert i <= 100 idx = int(len(self.intervals) * i / 100) return sorted(self.intervals)[idx]
PyTorch/Classification/ConvNets/resnet50v1.5/training/AMP
AMP
DGXA100_resnet50_AMP_250E
python ./multiproc.py --nproc_per_node 8 ./launch.py --model resnet50 --precision AMP --mode convergence --platform DGXA100 /imagenet --workspace ${1:-./} --raport-file raport.json
TensorFlow/Detection/SSD/models/research/slim/nets
nets
inception_resnet_v2_test
# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # 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. # ============================================================================== """Tests for slim.inception_resnet_v2.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from nets import inception class InceptionTest(tf.test.TestCase): def testBuildLogits(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, endpoints = inception.inception_resnet_v2(inputs, num_classes) self.assertTrue('AuxLogits' in endpoints) auxlogits = endpoints['AuxLogits'] self.assertTrue( auxlogits.op.name.startswith('InceptionResnetV2/AuxLogits')) self.assertListEqual(auxlogits.get_shape().as_list(), [batch_size, num_classes]) self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) def testBuildWithoutAuxLogits(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, endpoints = inception.inception_resnet_v2(inputs, num_classes, create_aux_logits=False) self.assertTrue('AuxLogits' not in endpoints) self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) def testBuildNoClasses(self): batch_size = 5 height, width = 299, 299 num_classes = None with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) net, endpoints = inception.inception_resnet_v2(inputs, num_classes) self.assertTrue('AuxLogits' not in endpoints) self.assertTrue('Logits' not in endpoints) self.assertTrue( net.op.name.startswith('InceptionResnetV2/Logits/AvgPool')) self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1536]) def testBuildEndPoints(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = inception.inception_resnet_v2(inputs, num_classes) self.assertTrue('Logits' in end_points) logits = end_points['Logits'] self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) self.assertTrue('AuxLogits' in end_points) aux_logits = end_points['AuxLogits'] self.assertListEqual(aux_logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Conv2d_7b_1x1'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 8, 8, 1536]) def testBuildBaseNetwork(self): batch_size = 5 height, width = 299, 299 inputs = tf.random_uniform((batch_size, height, width, 3)) net, end_points = inception.inception_resnet_v2_base(inputs) self.assertTrue(net.op.name.startswith('InceptionResnetV2/Conv2d_7b_1x1')) self.assertListEqual(net.get_shape().as_list(), [batch_size, 8, 8, 1536]) expected_endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1'] self.assertItemsEqual(end_points.keys(), expected_endpoints) def testBuildOnlyUptoFinalEndpoint(self): batch_size = 5 height, width = 299, 299 endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1'] for index, endpoint in enumerate(endpoints): with tf.Graph().as_default(): inputs = tf.random_uniform((batch_size, height, width, 3)) out_tensor, end_points = inception.inception_resnet_v2_base( inputs, final_endpoint=endpoint) if endpoint != 'PreAuxLogits': self.assertTrue(out_tensor.op.name.startswith( 'InceptionResnetV2/' + endpoint)) self.assertItemsEqual(endpoints[:index+1], end_points.keys()) def testBuildAndCheckAllEndPointsUptoPreAuxLogits(self): batch_size = 5 height, width = 299, 299 inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = inception.inception_resnet_v2_base( inputs, final_endpoint='PreAuxLogits') endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32], 'Conv2d_2a_3x3': [5, 147, 147, 32], 'Conv2d_2b_3x3': [5, 147, 147, 64], 'MaxPool_3a_3x3': [5, 73, 73, 64], 'Conv2d_3b_1x1': [5, 73, 73, 80], 'Conv2d_4a_3x3': [5, 71, 71, 192], 'MaxPool_5a_3x3': [5, 35, 35, 192], 'Mixed_5b': [5, 35, 35, 320], 'Mixed_6a': [5, 17, 17, 1088], 'PreAuxLogits': [5, 17, 17, 1088] } self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) for endpoint_name in endpoints_shapes: expected_shape = endpoints_shapes[endpoint_name] self.assertTrue(endpoint_name in end_points) self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), expected_shape) def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithAlignedFeatureMaps(self): batch_size = 5 height, width = 299, 299 inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = inception.inception_resnet_v2_base( inputs, final_endpoint='PreAuxLogits', align_feature_maps=True) endpoints_shapes = {'Conv2d_1a_3x3': [5, 150, 150, 32], 'Conv2d_2a_3x3': [5, 150, 150, 32], 'Conv2d_2b_3x3': [5, 150, 150, 64], 'MaxPool_3a_3x3': [5, 75, 75, 64], 'Conv2d_3b_1x1': [5, 75, 75, 80], 'Conv2d_4a_3x3': [5, 75, 75, 192], 'MaxPool_5a_3x3': [5, 38, 38, 192], 'Mixed_5b': [5, 38, 38, 320], 'Mixed_6a': [5, 19, 19, 1088], 'PreAuxLogits': [5, 19, 19, 1088] } self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) for endpoint_name in endpoints_shapes: expected_shape = endpoints_shapes[endpoint_name] self.assertTrue(endpoint_name in end_points) self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), expected_shape) def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithOutputStrideEight(self): batch_size = 5 height, width = 299, 299 inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = inception.inception_resnet_v2_base( inputs, final_endpoint='PreAuxLogits', output_stride=8) endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32], 'Conv2d_2a_3x3': [5, 147, 147, 32], 'Conv2d_2b_3x3': [5, 147, 147, 64], 'MaxPool_3a_3x3': [5, 73, 73, 64], 'Conv2d_3b_1x1': [5, 73, 73, 80], 'Conv2d_4a_3x3': [5, 71, 71, 192], 'MaxPool_5a_3x3': [5, 35, 35, 192], 'Mixed_5b': [5, 35, 35, 320], 'Mixed_6a': [5, 33, 33, 1088], 'PreAuxLogits': [5, 33, 33, 1088] } self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) for endpoint_name in endpoints_shapes: expected_shape = endpoints_shapes[endpoint_name] self.assertTrue(endpoint_name in end_points) self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), expected_shape) def testVariablesSetDevice(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) # Force all Variables to reside on the device. with tf.variable_scope('on_cpu'), tf.device('/cpu:0'): inception.inception_resnet_v2(inputs, num_classes) with tf.variable_scope('on_gpu'), tf.device('/gpu:0'): inception.inception_resnet_v2(inputs, num_classes) for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'): self.assertDeviceEqual(v.device, '/cpu:0') for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'): self.assertDeviceEqual(v.device, '/gpu:0') def testHalfSizeImages(self): batch_size = 5 height, width = 150, 150 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, end_points = inception.inception_resnet_v2(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Conv2d_7b_1x1'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 3, 3, 1536]) def testGlobalPool(self): batch_size = 1 height, width = 330, 400 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, end_points = inception.inception_resnet_v2(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Conv2d_7b_1x1'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 8, 11, 1536]) def testGlobalPoolUnknownImageShape(self): batch_size = 1 height, width = 330, 400 num_classes = 1000 with self.test_session() as sess: inputs = tf.placeholder(tf.float32, (batch_size, None, None, 3)) logits, end_points = inception.inception_resnet_v2( inputs, num_classes, create_aux_logits=False) self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Conv2d_7b_1x1'] images = tf.random_uniform((batch_size, height, width, 3)) sess.run(tf.global_variables_initializer()) logits_out, pre_pool_out = sess.run([logits, pre_pool], {inputs: images.eval()}) self.assertTupleEqual(logits_out.shape, (batch_size, num_classes)) self.assertTupleEqual(pre_pool_out.shape, (batch_size, 8, 11, 1536)) def testUnknownBatchSize(self): batch_size = 1 height, width = 299, 299 num_classes = 1000 with self.test_session() as sess: inputs = tf.placeholder(tf.float32, (None, height, width, 3)) logits, _ = inception.inception_resnet_v2(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) self.assertListEqual(logits.get_shape().as_list(), [None, num_classes]) images = tf.random_uniform((batch_size, height, width, 3)) sess.run(tf.global_variables_initializer()) output = sess.run(logits, {inputs: images.eval()}) self.assertEquals(output.shape, (batch_size, num_classes)) def testEvaluation(self): batch_size = 2 height, width = 299, 299 num_classes = 1000 with self.test_session() as sess: eval_inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = inception.inception_resnet_v2(eval_inputs, num_classes, is_training=False) predictions = tf.argmax(logits, 1) sess.run(tf.global_variables_initializer()) output = sess.run(predictions) self.assertEquals(output.shape, (batch_size,)) def testTrainEvalWithReuse(self): train_batch_size = 5 eval_batch_size = 2 height, width = 150, 150 num_classes = 1000 with self.test_session() as sess: train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) inception.inception_resnet_v2(train_inputs, num_classes) eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) logits, _ = inception.inception_resnet_v2(eval_inputs, num_classes, is_training=False, reuse=True) predictions = tf.argmax(logits, 1) sess.run(tf.global_variables_initializer()) output = sess.run(predictions) self.assertEquals(output.shape, (eval_batch_size,)) def testNoBatchNormScaleByDefault(self): height, width = 299, 299 num_classes = 1000 inputs = tf.placeholder(tf.float32, (1, height, width, 3)) with tf.contrib.slim.arg_scope(inception.inception_resnet_v2_arg_scope()): inception.inception_resnet_v2(inputs, num_classes, is_training=False) self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), []) def testBatchNormScale(self): height, width = 299, 299 num_classes = 1000 inputs = tf.placeholder(tf.float32, (1, height, width, 3)) with tf.contrib.slim.arg_scope( inception.inception_resnet_v2_arg_scope(batch_norm_scale=True)): inception.inception_resnet_v2(inputs, num_classes, is_training=False) gamma_names = set( v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$')) self.assertGreater(len(gamma_names), 0) for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'): self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names) if __name__ == '__main__': tf.test.main()
CUDA-Optimized/FastSpeech/fastspeech/dataset
dataset
ljspeech_dataset
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the NVIDIA CORPORATION nor the # names of its contributors may be used to endorse or promote products # derived from this software without specific prior written permission. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import csv import pprint import librosa from torch.utils.data import Dataset import pandas as pd from fastspeech.text_norm import text_to_sequence from fastspeech import audio from fastspeech.utils.logging import tprint import os import pathlib import fire import numpy as np from tqdm import tqdm from fastspeech import hparam as hp pp = pprint.PrettyPrinter(indent=4, width=1000) class LJSpeechDataset(Dataset): def __init__(self, root_path, meta_file="metadata.csv", sr=22050, n_fft=1024, win_len=1024, hop_len=256, n_mels=80, mel_fmin=0.0, mel_fmax=8000.0, exclude_mels=False, mels_path=None, aligns_path=None, text_cleaner=['english_cleaners'], sort_by_length=False): self.root_path = root_path self.meta_file = meta_file self.text_cleaner = text_cleaner self.sr = sr self.n_fft = n_fft self.win_len = win_len self.hop_len = hop_len self.n_mels = n_mels self.mel_fmin = mel_fmin self.mel_fmax = mel_fmax self.aligns_path = aligns_path self.mels_path = mels_path self.exclude_mels = exclude_mels self.sort_by_length = sort_by_length # Read metadata file. # - column: <name, transcription, normalized_transcription> self.metas = pd.read_csv(os.path.join(root_path, meta_file), sep="|", header=None, keep_default_na=False, quoting=csv.QUOTE_NONE, names=["name", "transcription", "normalized_transcription"], ) if sort_by_length: self.metas.insert(3, 'length', self.metas['normalized_transcription'].str.len()) self.metas.sort_values('length', ascending=True, inplace=True) def __len__(self): return len(self.metas) def __getitem__(self, idx): name = self.metas.iloc[idx, 0] path = "{}/wavs/{}.wav".format(self.root_path, name) # Text normalization text = self.metas.iloc[idx, 1] text_norm = self.metas.iloc[idx, 2] text_encoded = np.array(text_to_sequence(text_norm, self.text_cleaner)) text_pos = np.array([idx+1 for idx, _ in enumerate(text_encoded)]) data = { "name": name, "text": text, "text_norm": text_norm, "text_encoded": text_encoded, "text_pos": text_pos, "text_len": text_encoded.shape[-1], "sr": self.sr } if not self.exclude_mels: wav, sr = librosa.load(path, sr=self.sr) # wav is [-1.0, 1.0] if sr != self.sr: raise ValueError("{} SR doesn't match target {} SR".format(sr, self.sr)) # Audio processing wav, _ = librosa.effects.trim(wav, frame_length=self.win_len, hop_length=self.hop_len) if self.mels_path: mel = np.load(os.path.join(self.mels_path, name + ".mel.npy")) else: mel = librosa.feature.melspectrogram(wav, sr=sr, n_fft=self.n_fft, win_length=self.win_len, hop_length=self.hop_len, n_mels=self.n_mels, fmin=self.mel_fmin, fmax=self.mel_fmax, power=1.0) mel = audio.dynamic_range_compression(mel) data_mel = { "wav": wav, "mel": mel, "mel_len": mel.shape[-1], } data.update(data_mel) if self.aligns_path: aligns = np.load(os.path.join(self.aligns_path, name + ".align.npy")) data['align'] = aligns return data def preprocess_mel(hparam="base.yaml", **kwargs): """The script for preprocessing mel-spectrograms from the dataset. By default, this script assumes to load parameters in the default config file, fastspeech/hparams/base.yaml. Besides the flags, you can also set parameters in the config file via the command-line. For examples, --dataset_path=DATASET_PATH Path to dataset directory. --mels_path=MELS_PATH Path to output preprocessed mels directory. Refer to fastspeech/hparams/base.yaml to see more parameters. Args: hparam (str, optional): Path to default config file. Defaults to "base.yaml". """ hp.set_hparam(hparam, kwargs) tprint("Hparams:\n{}".format(pp.pformat(hp))) pathlib.Path(hp.mels_path).mkdir(parents=True, exist_ok=True) dataset = LJSpeechDataset(hp.dataset_path, mels_path=None) for data in tqdm(dataset): name = data["name"] mel = data["mel"] save_path = os.path.join(hp.mels_path, name + ".mel.npy") if os.path.exists(save_path): continue # print(name, mel) np.save(save_path, mel) if __name__ == '__main__': fire.Fire(preprocess_mel)