text stringlengths 24 1.04M | metadata stringlengths 233 497 |
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### File: cmake/fetch_imagenette.cmake
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
function(fetch_imagenette DATA_DIR)
set(IMAGENETTE_DIR "${DATA_DIR}/imagenette2-160")
set(IMAGENETTE_DOWNLOAD_DIR "${DATA_DIR}/imagenette_download")
set(IMAGENETTE_DATA_URL
"https://s3.amazonaws.com/fast-ai-ima... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "cmake/fetch_imagenette.cmake", "license": "mit", "size": 1066} |
### File: cmake/fetch_libtorch.cmake
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
include(FetchContent)
set(CUDA_V "none" CACHE STRING "Determines libtorch CUDA version to download (11.8, 12.1 or none).")
if(${CUDA_V} STREQUAL "none")
set(LIBTORCH_DEVICE "cpu")
elseif(${CUDA_V} STREQUAL "11.8")
set(LIBTO... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "cmake/fetch_libtorch.cmake", "license": "mit", "size": 2506} |
### File: cmake/fetch_mnist.cmake
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
function(fetch_mnist DATA_DIR)
set(MNIST_DOWNLOAD_DIR "${DATA_DIR}/mnist/download")
set(MNIST_DIR "${DATA_DIR}/mnist")
set(MNIST_URL "https://ossci-datasets.s3.amazonaws.com/mnist")
set(MNIST_EXTRACTED_FILES
"t1... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "cmake/fetch_mnist.cmake", "license": "mit", "size": 1931} |
### File: cmake/fetch_neural_style_transfer_images.cmake
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
function(fetch_neural_style_transfer_imagers DATA_DIR)
set(NEURAL_STYLE_TRANSFER_IMAGES_DIR
"${DATA_DIR}/neural_style_transfer_images")
set(NEURAL_STYLE_TRANSFER_IMAGES_URL
"https://github.... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "cmake/fetch_neural_style_transfer_images.cmake", "license": "mit", "size": 1487} |
### File: cmake/fetch_penntreebank.cmake
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
function(fetch_penntreebank DATA_DIR)
set(PENNTREEBANK_DIR "${DATA_DIR}/penntreebank")
set(PENNTREEBANK_DOWNLOAD_NAME "train.txt")
set(PENNTREEBANK_URL
"https://raw.githubusercontent.com/wojzaremba/lstm/master... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "cmake/fetch_penntreebank.cmake", "license": "mit", "size": 895} |
### File: cmake/find_gz_extractor.cmake
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
# Find program that can extract .gz files.
# Based on: https://github.com/Amber-MD/cmake-buildscripts/blob/master/gzip.cmake
find_program(7ZIP_EXECUTABLE 7z 7za DOC "Path to 7zip executable")
find_program(GZIP_EXECUTABLE gzip DOC ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "cmake/find_gz_extractor.cmake", "license": "mit", "size": 839} |
### File: docker/docker-entrypoint.sh
#!/usr/bin/env bash
set -Eeo pipefail
cmake -B build -D CMAKE_BUILD_TYPE=Release \
-D CMAKE_PREFIX_PATH=/opt/conda/lib/python${PYTHON_VERSION}/site-packages/torch/share/cmake/Torch/ \
-D CREATE_SCRIPTMODULES=ON
case $1 in
basics|intermediate|advanced|popular)
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Shell", "repo_name": "minhanghuang/pytorch-cpp", "path": "docker/docker-entrypoint.sh", "license": "mit", "size": 865} |
### File: main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
int main() {
std::cout << "Welcome to PyTorch Tutorial in C++ for Deep Learning Researchers" << std::endl;
}
| {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "main.cpp", "license": "mit", "size": 205} |
### File: notebooks/pytorch_cpp_colab_notebook.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "VS2lCrJC55PX"
},
"source": [
"\n",
"\n",
"\n",
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Jupyter Notebook", "repo_name": "minhanghuang/pytorch-cpp", "path": "notebooks/pytorch_cpp_colab_notebook.ipynb", "license": "mit", "size": 30613} |
### File: notebooks/tensor_slicing.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"cling::DynamicLibraryManager::loadLibrary(): dlopen(/Users/prabhuomkar/Projects/PyTorch/flare/li... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Jupyter Notebook", "repo_name": "minhanghuang/pytorch-cpp", "path": "notebooks/tensor_slicing.ipynb", "license": "mit", "size": 11537} |
### File: tutorials/advanced/generative_adversarial_network/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(generative-adversarial-network VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME generative-adversarial-network)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIV... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/generative_adversarial_network/CMakeLists.txt", "license": "mit", "size": 980} |
### File: tutorials/advanced/generative_adversarial_network/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
#include "image_io.h"
using image_io::save_image;
int main() {
std::cout << "Generative Adversarial Network\n\n";
// Device
au... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/generative_adversarial_network/main.cpp", "license": "mit", "size": 6318} |
### File: tutorials/advanced/image_captioning/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(image-captioning VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME image-captioning)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE src/main.cpp
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/CMakeLists.txt", "license": "mit", "size": 2309} |
### File: tutorials/advanced/image_captioning/include/caption_dataset.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/data/datasets/base.h>
#include <torch/data/example.h>
#include <torch/types.h>
#include <cstddef>
#include <fstream>
#include <string>
#include <vector>
#include "vocabular... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/include/caption_dataset.h", "license": "mit", "size": 2595} |
### File: tutorials/advanced/image_captioning/include/data_utils.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <vector>
#include "vocabulary.h"
namespace data_utils {
struct CaptionData {
Vocabulary vocabulary;
std::unordered_map<std::string, std::vector<std::s... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/include/data_utils.h", "license": "mit", "size": 1198} |
### File: tutorials/advanced/image_captioning/include/decoder_rnn.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
/**
* "Soft" attention network.
*
* Based on the paper "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention"
* (see https://arxiv.org/pdf... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/include/decoder_rnn.h", "license": "mit", "size": 2139} |
### File: tutorials/advanced/image_captioning/include/encoder_cnn.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <utility>
class EncoderCNNImpl : public torch::nn::Module {
public:
EncoderCNNImpl(const std::string &backbone_scriptmodule_file_path, int64_t out_wh, i... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/include/encoder_cnn.h", "license": "mit", "size": 748} |
### File: tutorials/advanced/image_captioning/include/scheduler.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <vector>
#include <algorithm>
namespace scheduler {
template<typename TOptimizer>
struct OptimizerOptionsMap {
};
template<>
struct OptimizerOptionsMap<torch:... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/include/scheduler.h", "license": "mit", "size": 3038} |
### File: tutorials/advanced/image_captioning/include/score.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <vector>
#include <string>
#include <iterator>
namespace score {
struct CountFraction {
size_t numerator = 0;
size_t denominator = 0;
CountFraction &o... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/include/score.h", "license": "mit", "size": 1317} |
### File: tutorials/advanced/image_captioning/include/transform.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <random>
#include <vector>
#include "caption_dataset.h"
namespace transform {
double rand_double();
int64_t rand_int(int64_t max);
struct CaptionBatchTarget... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/include/transform.h", "license": "mit", "size": 2549} |
### File: tutorials/advanced/image_captioning/include/validate.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <vector>
#include <iostream>
#include "vocabulary.h"
#include "image_io.h"
template<typename TDataset>
using ValidationLoader =
std::unique_ptr<torch::data::Sta... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/include/validate.h", "license": "mit", "size": 7865} |
### File: tutorials/advanced/image_captioning/include/vocabulary.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <string>
#include <unordered_map>
#include <vector>
namespace data_utils {
class Vocabulary {
public:
Vocabulary();
int64_t add_word(const std::stri... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/include/vocabulary.h", "license": "mit", "size": 594} |
### File: tutorials/advanced/image_captioning/model/create_encoder_cnn_backbone_scriptmodule.py
import torch
import torchvision
import argparse
class EncoderCNNBackbone(torch.nn.Module):
def __init__(self):
super(EncoderCNNBackbone, self).__init__()
resnet_children = list(torchvision.model... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/model/create_encoder_cnn_backbone_scriptmodule.py", "license": "mit", "si... |
### File: tutorials/advanced/image_captioning/src/caption_dataset.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "caption_dataset.h"
#include <torch/torch.h>
#include <vector>
#include <algorithm>
#include <random>
#include "vocabulary.h"
#include "image_io.h"
using torch::indexing::Slice;
using torch::ind... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/src/caption_dataset.cpp", "license": "mit", "size": 3223} |
### File: tutorials/advanced/image_captioning/src/data_utils.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "data_utils.h"
#include <string>
#include <fstream>
#include <vector>
using torch::indexing::Slice;
using torch::indexing::None;
namespace data_utils {
namespace {
std::vector<std::string> tokenize_... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/src/data_utils.cpp", "license": "mit", "size": 4373} |
### File: tutorials/advanced/image_captioning/src/decoder_rnn.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "decoder_rnn.h"
#include <torch/types.h>
using torch::indexing::Slice;
using torch::indexing::Ellipsis;
using torch::indexing::None;
AttentionBlockImpl::AttentionBlockImpl(int64_t encoder_features,... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/src/decoder_rnn.cpp", "license": "mit", "size": 8605} |
### File: tutorials/advanced/image_captioning/src/encoder_cnn.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "encoder_cnn.h"
#include <torch/script.h>
EncoderCNNImpl::EncoderCNNImpl(const std::string &backbone_scriptmodule_file_path, int64_t out_wh, int64_t out_size)
: pool(torch::nn::AdaptiveAvgPo... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/src/encoder_cnn.cpp", "license": "mit", "size": 2002} |
### File: tutorials/advanced/image_captioning/src/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
#include <sstream>
#include <vector>
#include <chrono>
#include "cxxopts.hpp"
#include "vocabulary.h"
#include "data_utils.h"
#include "caption_datase... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/src/main.cpp", "license": "mit", "size": 15876} |
### File: tutorials/advanced/image_captioning/src/score.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "score.h"
#include <vector>
#include <cmath>
namespace score {
namespace {
torch::Tensor n_grams(const torch::Tensor &sequence, size_t n) {
return static_cast<size_t>(sequence.size(0)) < n ?
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/src/score.cpp", "license": "mit", "size": 4290} |
### File: tutorials/advanced/image_captioning/src/transform.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "transform.h"
#include <cmath>
#include <vector>
using torch::indexing::Slice;
using torch::indexing::Ellipsis;
using torch::indexing::None;
namespace transform {
double rand_double() {
return to... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/src/transform.cpp", "license": "mit", "size": 3190} |
### File: tutorials/advanced/image_captioning/src/vocabulary.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "vocabulary.h"
#include <algorithm>
#include <iostream>
namespace data_utils {
Vocabulary::Vocabulary() {
add_word("<pad>");
add_word("<start>");
add_word("<end>");
add_word("<unk>");... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/image_captioning/src/vocabulary.cpp", "license": "mit", "size": 798} |
### File: tutorials/advanced/neural_style_transfer/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(neural-style-transfer VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME neural-style-transfer)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE src/main.cpp
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/neural_style_transfer/CMakeLists.txt", "license": "mit", "size": 1422} |
### File: tutorials/advanced/neural_style_transfer/include/vggnet.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <utility>
#include <set>
enum class Layer { CONV64, CONV128, CONV256, CONV512, MAXPOOL };
class VGGNetImpl : public torch::nn::Module {
public:
VGGNetI... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/neural_style_transfer/include/vggnet.h", "license": "mit", "size": 1118} |
### File: tutorials/advanced/neural_style_transfer/model/create_vgg19_layers_scriptmodule.py
import torch
import torchvision
def main():
# Download and load the pretrained VGG19 layers.
vgg_19_layers = torchvision.models.vgg19(pretrained=True).features
for param in vgg_19_layers.parameters():
par... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/neural_style_transfer/model/create_vgg19_layers_scriptmodule.py", "license": "mit", "size"... |
### File: tutorials/advanced/neural_style_transfer/src/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
#include <fstream>
#include "vggnet.h"
#include "image_io.h"
using image_io::load_image;
using image_io::save_image;
void print_sizes(torch::Ten... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/neural_style_transfer/src/main.cpp", "license": "mit", "size": 5563} |
### File: tutorials/advanced/neural_style_transfer/src/vggnet.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "vggnet.h"
#include <utility>
namespace {
void initialize_weights(const torch::nn::Module& module) {
torch::NoGradGuard no_grad;
if (auto conv2d = module.as<torch::nn::Conv2d>()... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/neural_style_transfer/src/vggnet.cpp", "license": "mit", "size": 3633} |
### File: tutorials/advanced/variational_autoencoder/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(variational-autoencoder VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME variational-autoencoder)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE src/main.cpp
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/variational_autoencoder/CMakeLists.txt", "license": "mit", "size": 1184} |
### File: tutorials/advanced/variational_autoencoder/include/variational_autoencoder.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <utility>
struct VAEOutput {
torch::Tensor reconstruction;
torch::Tensor mu;
torch::Tensor log_var;
};
class VAEImpl : public... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/variational_autoencoder/include/variational_autoencoder.h", "license": "mit", "size": 722} |
### File: tutorials/advanced/variational_autoencoder/src/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
#include "variational_autoencoder.h"
#include "image_io.h"
using image_io::save_image;
int main() {
std::cout << "Variational Autoencoder\... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/variational_autoencoder/src/main.cpp", "license": "mit", "size": 4267} |
### File: tutorials/advanced/variational_autoencoder/src/variational_autoencoder.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "variational_autoencoder.h"
#include <utility>
VAEImpl::VAEImpl(int64_t image_size, int64_t h_dim, int64_t z_dim)
: fc1(image_size, h_dim),
fc2(h_dim, z_dim),
fc3(... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/advanced/variational_autoencoder/src/variational_autoencoder.cpp", "license": "mit", "size": 1540} |
### File: tutorials/basics/feedforward_neural_network/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(feedforward-neural-network VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME feedforward-neural-network)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE src/main.c... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/basics/feedforward_neural_network/CMakeLists.txt", "license": "mit", "size": 1010} |
### File: tutorials/basics/feedforward_neural_network/include/neural_net.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
class NeuralNetImpl : public torch::nn::Module {
public:
NeuralNetImpl(int64_t input_size, int64_t hidden_size, int64_t num_classes);
torch::Tensor f... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/basics/feedforward_neural_network/include/neural_net.h", "license": "mit", "size": 364} |
### File: tutorials/basics/feedforward_neural_network/src/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
#include "neural_net.h"
int main() {
std::cout << "FeedForward Neural Network\n\n";
// Device
auto cuda_available = torch::cuda::... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/basics/feedforward_neural_network/src/main.cpp", "license": "mit", "size": 4307} |
### File: tutorials/basics/feedforward_neural_network/src/neural_net.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "neural_net.h"
#include <torch/torch.h>
NeuralNetImpl::NeuralNetImpl(int64_t input_size, int64_t hidden_size, int64_t num_classes)
: fc1(input_size, hidden_size), fc2(hidden_size, num_cla... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/basics/feedforward_neural_network/src/neural_net.cpp", "license": "mit", "size": 464} |
### File: tutorials/basics/linear_regression/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(linear-regression VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME linear-regression)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE main.cpp)
target_link_libraries(${EX... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/basics/linear_regression/CMakeLists.txt", "license": "mit", "size": 721} |
### File: tutorials/basics/linear_regression/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
int main() {
std::cout << "Linear Regression\n\n";
// Device
auto cuda_available = torch::cuda::is_available();
torch::Device device(cuda_... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/basics/linear_regression/main.cpp", "license": "mit", "size": 1804} |
### File: tutorials/basics/logistic_regression/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(logistic-regression VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME logistic-regression)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE main.cpp)
target_link_librarie... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/basics/logistic_regression/CMakeLists.txt", "license": "mit", "size": 803} |
### File: tutorials/basics/logistic_regression/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
int main() {
std::cout << "Logistic Regression\n\n";
// Device
auto cuda_available = torch::cuda::is_available();
torch::Device device(c... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/basics/logistic_regression/main.cpp", "license": "mit", "size": 4305} |
### File: tutorials/basics/pytorch_basics/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(pytorch-basics VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME pytorch-basics)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE main.cpp)
target_link_libraries(${EXECUTABLE_... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/basics/pytorch_basics/CMakeLists.txt", "license": "mit", "size": 1197} |
### File: tutorials/basics/pytorch_basics/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <torch/script.h>
#include <iostream>
#include <iomanip>
void print_tensor_size(const torch::Tensor&);
void print_script_module(const torch::jit::script::Module& module, size_t spaces = 0);... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/basics/pytorch_basics/main.cpp", "license": "mit", "size": 11262} |
### File: tutorials/basics/pytorch_basics/model/create_resnet18_scriptmodule.py
import torch
import torchvision
def main():
# Source: https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/01-basics/pytorch_basics/main.py
# Download and load the pretrained ResNet-18.
model = torchvision.models.r... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/basics/pytorch_basics/model/create_resnet18_scriptmodule.py", "license": "mit", "size": 1116} |
### File: tutorials/intermediate/bidirectional_recurrent_neural_network/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(bidirectional-recurrent-neural-network VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME bidirectional-recurrent-neural-network)
add_executable(${EXECUTABLE_NAME})
target_so... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/bidirectional_recurrent_neural_network/CMakeLists.txt", "license": "mit", "size": 1026} |
### File: tutorials/intermediate/bidirectional_recurrent_neural_network/include/bi_rnn.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
class BiRNNImpl : public torch::nn::Module {
public:
BiRNNImpl(int64_t input_size, int64_t hidden_size, int64_t num_layers, int64_t num_clas... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/bidirectional_recurrent_neural_network/include/bi_rnn.h", "license": "mit", "size": 369} |
### File: tutorials/intermediate/bidirectional_recurrent_neural_network/src/bi_rnn.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "bi_rnn.h"
#include <torch/torch.h>
using torch::indexing::Slice;
BiRNNImpl::BiRNNImpl(int64_t input_size, int64_t hidden_size, int64_t num_layers, int64_t num_classes)
: l... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/bidirectional_recurrent_neural_network/src/bi_rnn.cpp", "license": "mit", "size": 1430} |
### File: tutorials/intermediate/bidirectional_recurrent_neural_network/src/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
#include "bi_rnn.h"
int main() {
std::cout << "Bidirectional Recurrent Neural Network\n\n";
// Device
auto cuda... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/bidirectional_recurrent_neural_network/src/main.cpp", "license": "mit", "size": 4508} |
### File: tutorials/intermediate/convolutional_neural_network/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(convolutional-neural-network VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME convolutional-neural-network)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVAT... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/convolutional_neural_network/CMakeLists.txt", "license": "mit", "size": 1164} |
### File: tutorials/intermediate/convolutional_neural_network/include/convnet.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
class ConvNetImpl : public torch::nn::Module {
public:
explicit ConvNetImpl(int64_t num_classes = 10);
torch::Tensor forward(torch::Tensor x);
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/convolutional_neural_network/include/convnet.h", "license": "mit", "size": 1067} |
### File: tutorials/intermediate/convolutional_neural_network/include/imagefolder_dataset.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/data/datasets/base.h>
#include <torch/data/example.h>
#include <torch/types.h>
#include <string>
#include <vector>
#include <unordered_map>
namespace d... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/convolutional_neural_network/include/imagefolder_dataset.h", "license": "mit", "size": 98... |
### File: tutorials/intermediate/convolutional_neural_network/src/convnet.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "convnet.h"
#include <torch/torch.h>
ConvNetImpl::ConvNetImpl(int64_t num_classes)
: fc(64 * 4 * 4, num_classes) {
register_module("layer1", layer1);
register_module("layer2"... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/convolutional_neural_network/src/convnet.cpp", "license": "mit", "size": 589} |
### File: tutorials/intermediate/convolutional_neural_network/src/imagefolder_dataset.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <imagefolder_dataset.h>
#include <torch/torch.h>
#include <vector>
#include <algorithm>
#include <filesystem>
#include <unordered_map>
#include "image_io.h"
namespace fs = st... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/convolutional_neural_network/src/imagefolder_dataset.cpp", "license": "mit", "size": 2271... |
### File: tutorials/intermediate/convolutional_neural_network/src/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
#include "convnet.h"
#include "imagefolder_dataset.h"
using dataset::ImageFolderDataset;
int main() {
std::cout << "Convolutional... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/convolutional_neural_network/src/main.cpp", "license": "mit", "size": 4496} |
### File: tutorials/intermediate/deep_residual_network/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(deep-residual-network VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME deep-residual-network)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE src/main.cpp
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/deep_residual_network/CMakeLists.txt", "license": "mit", "size": 1309} |
### File: tutorials/intermediate/deep_residual_network/include/cifar10.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/data/datasets/base.h>
#include <torch/data/example.h>
#include <torch/types.h>
#include <cstddef>
#include <fstream>
#include <string>
// CIFAR10 dataset
// based on: htt... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/deep_residual_network/include/cifar10.h", "license": "mit", "size": 1393} |
### File: tutorials/intermediate/deep_residual_network/include/residual_block.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
namespace resnet {
class ResidualBlockImpl : public torch::nn::Module {
public:
ResidualBlockImpl(int64_t in_channels, int64_t out_channels, int64_t ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/deep_residual_network/include/residual_block.h", "license": "mit", "size": 694} |
### File: tutorials/intermediate/deep_residual_network/include/resnet.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <vector>
#include "residual_block.h"
namespace resnet {
template<typename Block>
class ResNetImpl : public torch::nn::Module {
public:
explicit ResN... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/deep_residual_network/include/resnet.h", "license": "mit", "size": 2759} |
### File: tutorials/intermediate/deep_residual_network/include/transform.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <random>
#include <vector>
namespace transform {
class RandomHorizontalFlip : public torch::data::transforms::TensorTransform<torch::Tensor> {
public... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/deep_residual_network/include/transform.h", "license": "mit", "size": 1888} |
### File: tutorials/intermediate/deep_residual_network/src/cifar10.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "cifar10.h"
namespace {
// CIFAR10 dataset description can be found at https://www.cs.toronto.edu/~kriz/cifar.html.
constexpr uint32_t kTrainSize = 50000;
constexpr uint32_t kTestSize = 10000;
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/deep_residual_network/src/cifar10.cpp", "license": "mit", "size": 3396} |
### File: tutorials/intermediate/deep_residual_network/src/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
#include "resnet.h"
#include "cifar10.h"
#include "transform.h"
using resnet::ResNet;
using resnet::ResidualBlock;
using transform::ConstantP... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/deep_residual_network/src/main.cpp", "license": "mit", "size": 4936} |
### File: tutorials/intermediate/deep_residual_network/src/residual_block.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "residual_block.h"
#include <torch/torch.h>
namespace resnet {
ResidualBlockImpl::ResidualBlockImpl(int64_t in_channels, int64_t out_channels, int64_t stride,
torch::nn::Sequential d... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/deep_residual_network/src/residual_block.cpp", "license": "mit", "size": 1306} |
### File: tutorials/intermediate/deep_residual_network/src/transform.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "transform.h"
#include <torch/torch.h>
using torch::indexing::Slice;
using torch::indexing::Ellipsis;
namespace transform {
namespace {
double rand_double() {
return torch::rand(... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/deep_residual_network/src/transform.cpp", "license": "mit", "size": 1561} |
### File: tutorials/intermediate/language_model/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(language-model VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME language-model)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE src/main.cpp
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/language_model/CMakeLists.txt", "license": "mit", "size": 1371} |
### File: tutorials/intermediate/language_model/include/corpus.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <string>
#include "dictionary.h"
namespace data_utils {
class Corpus {
public:
explicit Corpus(const std::string& path) : path_(path) {}
torch::Tensor ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/language_model/include/corpus.h", "license": "mit", "size": 446} |
### File: tutorials/intermediate/language_model/include/dictionary.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <string>
#include <unordered_map>
#include <vector>
namespace data_utils {
class Dictionary {
public:
int64_t add_word(const std::string& word);
std::string word_at_index(i... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/language_model/include/dictionary.h", "license": "mit", "size": 488} |
### File: tutorials/intermediate/language_model/include/rnn_lm.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <tuple>
class RNNLMImpl : public torch::nn::Module {
public:
RNNLMImpl(int64_t vocab_size, int64_t embed_size, int64_t hidden_size, int64_t num_layers);
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/language_model/include/rnn_lm.h", "license": "mit", "size": 528} |
### File: tutorials/intermediate/language_model/src/corpus.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "corpus.h"
#include <torch/torch.h>
#include <fstream>
#include <sstream>
#include <exception>
#include <algorithm>
namespace data_utils {
torch::Tensor Corpus::get_data(int64_t batch_size) {
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/language_model/src/corpus.cpp", "license": "mit", "size": 1058} |
### File: tutorials/intermediate/language_model/src/dictionary.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "dictionary.h"
namespace data_utils {
int64_t Dictionary::add_word(const std::string& word) {
auto it = word2idx_.find(word);
if (it == word2idx_.end()) {
idx2word_... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/language_model/src/dictionary.cpp", "license": "mit", "size": 465} |
### File: tutorials/intermediate/language_model/src/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
#include <fstream>
#include <tuple>
#include "rnn_lm.h"
#include "corpus.h"
using data_utils::Corpus;
using torch::indexing::Slice;
int main() {
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/language_model/src/main.cpp", "license": "mit", "size": 5030} |
### File: tutorials/intermediate/language_model/src/rnn_lm.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "rnn_lm.h"
#include <torch/torch.h>
#include <tuple>
RNNLMImpl::RNNLMImpl(int64_t vocab_size, int64_t embed_size, int64_t hidden_size, int64_t num_layers)
: embed(vocab_size, embed_size),
lstm(... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/language_model/src/rnn_lm.cpp", "license": "mit", "size": 997} |
### File: tutorials/intermediate/recurrent_neural_network/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(recurrent-neural-network VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME recurrent-neural-network)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE src/main.c... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/recurrent_neural_network/CMakeLists.txt", "license": "mit", "size": 992} |
### File: tutorials/intermediate/recurrent_neural_network/include/rnn.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
class RNNImpl : public torch::nn::Module {
public:
RNNImpl(int64_t input_size, int64_t hidden_size, int64_t num_layers, int64_t num_classes);
torch::Tens... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/recurrent_neural_network/include/rnn.h", "license": "mit", "size": 363} |
### File: tutorials/intermediate/recurrent_neural_network/src/main.cpp
// Copyright 2020-present pytorch-cpp Authors
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
#include "rnn.h"
int main() {
std::cout << "Recurrent Neural Network\n\n";
// Device
auto cuda_available = torch::cuda::is_av... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/recurrent_neural_network/src/main.cpp", "license": "mit", "size": 4488} |
### File: tutorials/intermediate/recurrent_neural_network/src/rnn.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "rnn.h"
#include <torch/torch.h>
using torch::indexing::Slice;
RNNImpl::RNNImpl(int64_t input_size, int64_t hidden_size, int64_t num_layers, int64_t num_classes)
: lstm(torch::nn::LSTMOptio... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/intermediate/recurrent_neural_network/src/rnn.cpp", "license": "mit", "size": 575} |
### File: tutorials/popular/blitz/autograd/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(autograd VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME autograd)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE main.cpp)
target_link_libraries(${EXECUTABLE_NAME} ${TOR... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/autograd/CMakeLists.txt", "license": "mit", "size": 703} |
### File: tutorials/popular/blitz/autograd/main.cpp
// Copyright 2020-present pytorch-cpp Authors
// Original: https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
int main() {
std::cout << "Deep Learning with PyTorch: A 60 Minute Blitz... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/autograd/main.cpp", "license": "mit", "size": 2544} |
### File: tutorials/popular/blitz/neural_networks/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(neural-networks VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME neural-networks)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE src/main.cpp
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/neural_networks/CMakeLists.txt", "license": "mit", "size": 898} |
### File: tutorials/popular/blitz/neural_networks/include/nnet.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
class NetImpl : public torch::nn::Module {
public:
NetImpl();
torch::Tensor forward(torch::Tensor x);
torch::nn::Conv2d conv1;
torch::nn::Conv2d conv2;... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/neural_networks/include/nnet.h", "license": "mit", "size": 415} |
### File: tutorials/popular/blitz/neural_networks/src/main.cpp
// Copyright 2020-present pytorch-cpp Authors
// Original: https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
#include "nnet.h"
int main() {
std::cout << "Deep Lear... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/neural_networks/src/main.cpp", "license": "mit", "size": 2319} |
### File: tutorials/popular/blitz/neural_networks/src/nnet.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "nnet.h"
#include <torch/torch.h>
NetImpl::NetImpl() :
conv1(torch::nn::Conv2dOptions(1, 6, 3)),
conv2(torch::nn::Conv2dOptions(6, 16, 3)),
fc1(torch::nn::LinearOptions(16 * 6 * 6, 120)),
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/neural_networks/src/nnet.cpp", "license": "mit", "size": 1307} |
### File: tutorials/popular/blitz/tensors/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(tensors VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME tensors)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE main.cpp)
target_link_libraries(${EXECUTABLE_NAME} ${TORCH_... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/tensors/CMakeLists.txt", "license": "mit", "size": 701} |
### File: tutorials/popular/blitz/tensors/main.cpp
// Copyright 2020-present pytorch-cpp Authors
// Original: https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html
#include <torch/torch.h>
#include <iostream>
#include <iomanip>
int main() {
std::cout << "Deep Learning with PyTorch: A 60 Minute Blitz\n\... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/tensors/main.cpp", "license": "mit", "size": 2382} |
### File: tutorials/popular/blitz/training_a_classifier/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(training-a-classifier VERSION 1.0.0 LANGUAGES CXX)
set(EXECUTABLE_NAME training-a-classifier)
add_executable(${EXECUTABLE_NAME})
target_sources(${EXECUTABLE_NAME} PRIVATE src/main.cpp
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/training_a_classifier/CMakeLists.txt", "license": "mit", "size": 1108} |
### File: tutorials/popular/blitz/training_a_classifier/include/cifar10.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/data/datasets/base.h>
#include <torch/data/example.h>
#include <torch/types.h>
#include <cstddef>
#include <fstream>
#include <string>
// CIFAR10 dataset
// based on: ht... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/training_a_classifier/include/cifar10.h", "license": "mit", "size": 1393} |
### File: tutorials/popular/blitz/training_a_classifier/include/nnet.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
class NetImpl : public torch::nn::Module {
public:
NetImpl();
torch::Tensor forward(torch::Tensor x);
private:
torch::nn::Conv2d conv1;
torch::n... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/training_a_classifier/include/nnet.h", "license": "mit", "size": 401} |
### File: tutorials/popular/blitz/training_a_classifier/src/cifar10.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "cifar10.h"
namespace {
// CIFAR10 dataset description can be found at https://www.cs.toronto.edu/~kriz/cifar.html.
constexpr uint32_t kTrainSize = 50000;
constexpr uint32_t kTestSize = 10000;... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/training_a_classifier/src/cifar10.cpp", "license": "mit", "size": 3396} |
### File: tutorials/popular/blitz/training_a_classifier/src/main.cpp
// Copyright 2020-present pytorch-cpp Authors
// Original: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
#include <torch/torch.h>
#include <iostream>
#include <vector>
#include <iomanip>
#include "nnet.h"
#include "cifar10.h"
int... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/training_a_classifier/src/main.cpp", "license": "mit", "size": 4101} |
### File: tutorials/popular/blitz/training_a_classifier/src/nnet.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "nnet.h"
#include <torch/torch.h>
NetImpl::NetImpl() :
conv1(torch::nn::Conv2dOptions(3, 6, 5)),
pool(torch::nn::MaxPool2dOptions({2, 2})),
conv2(torch::nn::Conv2dOptions(6, 16, 5)),
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "tutorials/popular/blitz/training_a_classifier/src/nnet.cpp", "license": "mit", "size": 892} |
### File: utils/image_io/CMakeLists.txt
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
project(image-io VERSION 1.0.0 LANGUAGES CXX)
if(NOT Torch_FOUND)
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../cmake")
find_package(Torch REQUIRED PATHS "${CMAKE_CURRENT_SOURCE_DIR}/../../libtorch")
en... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/pytorch-cpp", "path": "utils/image_io/CMakeLists.txt", "license": "mit", "size": 680} |
### File: utils/image_io/include/image_io.h
// Copyright 2020-present pytorch-cpp Authors
#pragma once
#include <torch/torch.h>
#include <string>
namespace image_io {
enum class ImageFormat { PNG, JPG, BMP };
torch::Tensor load_image(const std::string& file_path,
torch::IntArrayRef shape = {},
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "utils/image_io/include/image_io.h", "license": "mit", "size": 722} |
### File: utils/image_io/src/image_io.cpp
// Copyright 2020-present pytorch-cpp Authors
#include "image_io.h"
#include <torch/torch.h>
#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
#define STB_IMAGE_WRITE_IMPLEMENTATION
#include "stb_image_write.h"
#define STB_IMAGE_RESIZE_IMPLEMENTATION
#include "stb_imag... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/pytorch-cpp", "path": "utils/image_io/src/image_io.cpp", "license": "mit", "size": 6096} |
### File: qt_dotgraph/CMakeLists.txt
cmake_minimum_required(VERSION 3.5)
project(qt_dotgraph)
find_package(ament_cmake REQUIRED)
find_package(ament_cmake_python REQUIRED)
ament_python_install_package(${PROJECT_NAME}
PACKAGE_DIR src/${PROJECT_NAME})
if(BUILD_TESTING)
find_package(ament_cmake_pytest REQUIRED)
fi... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/qt_gui_core", "path": "qt_dotgraph/CMakeLists.txt", "license": "bsd-3-clause", "size": 658} |
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