From 9ceae167c103ddb70424c31bdfd8e93c32ae8ea7 Mon Sep 17 00:00:00 2001
From: NYH <175484793@qq.com>
Date: Wed, 27 Dec 2023 15:00:04 +0800
Subject: [PATCH] V1.0
---
CMakeLists.txt | 52 +
README.md | 158 ++-
__pycache__/val.cpython-37.pyc | Bin 0 -> 12122 bytes
__pycache__/val.cpython-38.pyc | Bin 0 -> 12077 bytes
data/Argoverse.yaml | 67 ++
data/GlobalWheat2020.yaml | 53 +
data/Objects365.yaml | 104 ++
data/SKU-110K.yaml | 52 +
data/VOC.yaml | 80 ++
data/VisDrone.yaml | 61 ++
data/coco.yaml | 44 +
data/coco128.yaml | 30 +
data/data_class_4.yaml | 37 +
data/hyps/hyp.finetune.yaml | 39 +
data/hyps/hyp.finetune_objects365.yaml | 29 +
data/hyps/hyp.scratch-p6.yaml | 34 +
data/hyps/hyp.scratch.yaml | 34 +
data/img.cache | Bin 0 -> 94802 bytes
data/img.cache.npy | Bin 0 -> 113581 bytes
data/img1.cache | Bin 0 -> 8820 bytes
data/scripts/download_weights.sh | 17 +
data/scripts/get_coco.sh | 27 +
data/scripts/get_coco128.sh | 17 +
data/xView.yaml | 102 ++
detect.py | 239 +++++
detect1.py | 239 +++++
export.py | 189 ++++
gen_wts.py | 59 ++
images | 1 +
models/__init__.py | 0
models/__pycache__/__init__.cpython-37.pyc | Bin 0 -> 143 bytes
models/__pycache__/__init__.cpython-38.pyc | Bin 0 -> 142 bytes
models/__pycache__/common.cpython-37.pyc | Bin 0 -> 20039 bytes
models/__pycache__/common.cpython-38.pyc | Bin 0 -> 21210 bytes
.../__pycache__/experimental.cpython-37.pyc | Bin 0 -> 6042 bytes
.../__pycache__/experimental.cpython-38.pyc | Bin 0 -> 5942 bytes
models/__pycache__/yolo.cpython-37.pyc | Bin 0 -> 10833 bytes
models/__pycache__/yolo.cpython-38.pyc | Bin 0 -> 10863 bytes
models/common.py | 457 ++++++++
models/common_1.py | 433 ++++++++
models/experimental.py | 136 +++
models/hub/anchors.yaml | 58 +
models/hub/yolov3-spp.yaml | 49 +
models/hub/yolov3-tiny.yaml | 39 +
models/hub/yolov3.yaml | 49 +
models/hub/yolov5-bifpn.yaml | 46 +
models/hub/yolov5-fpn.yaml | 40 +
models/hub/yolov5-p2.yaml | 52 +
models/hub/yolov5-p6.yaml | 54 +
models/hub/yolov5-p7.yaml | 65 ++
models/hub/yolov5-panet.yaml | 46 +
models/hub/yolov5l6.yaml | 58 +
models/hub/yolov5m6.yaml | 58 +
models/hub/yolov5s6.yaml | 58 +
models/hub/yolov5x6.yaml | 58 +
models/yolo.py | 300 ++++++
models/yolov5l.yaml | 46 +
models/yolov5m-2transformer.yaml | 46 +
models/yolov5m.yaml | 47 +
models/yolov5m_add_detect.yaml | 65 ++
models/yolov5m_s.yaml | 57 +
models/yolov5s-2transformer.yaml | 46 +
models/yolov5s-transformer.yaml | 46 +
models/yolov5s.yaml | 46 +
models/yolov5x.yaml | 46 +
plugin/yololayer.cu | 280 +++++
plugin/yololayer.h | 106 ++
requirements.txt | 31 +
src/calibrator.cpp | 97 ++
src/calibrator.h | 39 +
src/config.h | 55 +
src/cuda_utils.h | 18 +
src/logging.h | 504 +++++++++
src/macros.h | 29 +
src/model.cpp | 629 +++++++++++
src/model.h | 16 +
src/postprocess.cpp | 189 ++++
src/postprocess.h | 16 +
src/preprocess.cu | 153 +++
src/preprocess.h | 15 +
src/types.h | 17 +
src/utils.h | 70 ++
train.py | 620 +++++++++++
utils/__init__.py | 0
utils/__pycache__/__init__.cpython-37.pyc | Bin 0 -> 142 bytes
utils/__pycache__/__init__.cpython-38.pyc | Bin 0 -> 141 bytes
.../__pycache__/augmentations.cpython-37.pyc | Bin 0 -> 8848 bytes
.../__pycache__/augmentations.cpython-38.pyc | Bin 0 -> 8843 bytes
utils/__pycache__/autoanchor.cpython-37.pyc | Bin 0 -> 6252 bytes
utils/__pycache__/autoanchor.cpython-38.pyc | Bin 0 -> 6208 bytes
utils/__pycache__/callbacks.cpython-37.pyc | Bin 0 -> 6428 bytes
utils/__pycache__/callbacks.cpython-38.pyc | Bin 0 -> 6277 bytes
utils/__pycache__/datasets.cpython-37.pyc | Bin 0 -> 34193 bytes
utils/__pycache__/datasets.cpython-38.pyc | Bin 0 -> 34153 bytes
utils/__pycache__/downloads.cpython-37.pyc | Bin 0 -> 3801 bytes
utils/__pycache__/downloads.cpython-38.pyc | Bin 0 -> 3846 bytes
utils/__pycache__/general.cpython-37.pyc | Bin 0 -> 26277 bytes
utils/__pycache__/general.cpython-38.pyc | Bin 0 -> 26373 bytes
utils/__pycache__/loss.cpython-37.pyc | Bin 0 -> 6640 bytes
utils/__pycache__/loss.cpython-38.pyc | Bin 0 -> 6532 bytes
utils/__pycache__/metrics.cpython-37.pyc | Bin 0 -> 10639 bytes
utils/__pycache__/metrics.cpython-38.pyc | Bin 0 -> 10573 bytes
utils/__pycache__/plots.cpython-37.pyc | Bin 0 -> 15994 bytes
utils/__pycache__/plots.cpython-38.pyc | Bin 0 -> 15859 bytes
utils/__pycache__/torch_utils.cpython-37.pyc | Bin 0 -> 12057 bytes
utils/__pycache__/torch_utils.cpython-38.pyc | Bin 0 -> 12115 bytes
utils/activations.py | 98 ++
utils/augmentations.py | 271 +++++
utils/autoanchor.py | 161 +++
utils/aws/__init__.py | 0
utils/aws/mime.sh | 26 +
utils/aws/resume.py | 37 +
utils/aws/userdata.sh | 27 +
utils/callbacks.py | 175 ++++
utils/datasets.py | 989 ++++++++++++++++++
utils/downloads.py | 146 +++
utils/flask_rest_api/README.md | 68 ++
utils/flask_rest_api/example_request.py | 13 +
utils/flask_rest_api/restapi.py | 37 +
utils/general.py | 721 +++++++++++++
utils/google_app_engine/Dockerfile | 25 +
.../additional_requirements.txt | 4 +
utils/google_app_engine/app.yaml | 14 +
utils/loggers/__init__.py | 141 +++
.../__pycache__/__init__.cpython-37.pyc | Bin 0 -> 6269 bytes
.../__pycache__/__init__.cpython-38.pyc | Bin 0 -> 6278 bytes
utils/loggers/wandb/README.md | 140 +++
utils/loggers/wandb/__init__.py | 0
.../wandb/__pycache__/__init__.cpython-37.pyc | Bin 0 -> 156 bytes
.../wandb/__pycache__/__init__.cpython-38.pyc | Bin 0 -> 155 bytes
.../__pycache__/wandb_utils.cpython-37.pyc | Bin 0 -> 18644 bytes
.../__pycache__/wandb_utils.cpython-38.pyc | Bin 0 -> 18704 bytes
utils/loggers/wandb/log_dataset.py | 23 +
utils/loggers/wandb/sweep.py | 33 +
utils/loggers/wandb/sweep.yaml | 143 +++
utils/loggers/wandb/wandb_utils.py | 510 +++++++++
utils/loss.py | 245 +++++
utils/metrics.py | 332 ++++++
utils/plots.py | 432 ++++++++
utils/torch_utils.py | 327 ++++++
val.py | 352 +++++++
yolov5_cls.cpp | 288 +++++
yolov5_cls_trt.py | 248 +++++
yolov5_det.cpp | 233 +++++
yolov5_det_cuda_python.py | 453 ++++++++
yolov5_det_trt.py | 452 ++++++++
yolov5_seg.cpp | 245 +++++
yolov5_seg_trt.py | 561 ++++++++++
148 files changed, 15117 insertions(+), 2 deletions(-)
create mode 100644 CMakeLists.txt
create mode 100644 __pycache__/val.cpython-37.pyc
create mode 100644 __pycache__/val.cpython-38.pyc
create mode 100644 data/Argoverse.yaml
create mode 100644 data/GlobalWheat2020.yaml
create mode 100644 data/Objects365.yaml
create mode 100644 data/SKU-110K.yaml
create mode 100644 data/VOC.yaml
create mode 100644 data/VisDrone.yaml
create mode 100644 data/coco.yaml
create mode 100644 data/coco128.yaml
create mode 100644 data/data_class_4.yaml
create mode 100644 data/hyps/hyp.finetune.yaml
create mode 100644 data/hyps/hyp.finetune_objects365.yaml
create mode 100644 data/hyps/hyp.scratch-p6.yaml
create mode 100644 data/hyps/hyp.scratch.yaml
create mode 100644 data/img.cache
create mode 100644 data/img.cache.npy
create mode 100644 data/img1.cache
create mode 100644 data/scripts/download_weights.sh
create mode 100644 data/scripts/get_coco.sh
create mode 100644 data/scripts/get_coco128.sh
create mode 100644 data/xView.yaml
create mode 100644 detect.py
create mode 100644 detect1.py
create mode 100644 export.py
create mode 100644 gen_wts.py
create mode 100644 images
create mode 100644 models/__init__.py
create mode 100644 models/__pycache__/__init__.cpython-37.pyc
create mode 100644 models/__pycache__/__init__.cpython-38.pyc
create mode 100644 models/__pycache__/common.cpython-37.pyc
create mode 100644 models/__pycache__/common.cpython-38.pyc
create mode 100644 models/__pycache__/experimental.cpython-37.pyc
create mode 100644 models/__pycache__/experimental.cpython-38.pyc
create mode 100644 models/__pycache__/yolo.cpython-37.pyc
create mode 100644 models/__pycache__/yolo.cpython-38.pyc
create mode 100644 models/common.py
create mode 100644 models/common_1.py
create mode 100644 models/experimental.py
create mode 100644 models/hub/anchors.yaml
create mode 100644 models/hub/yolov3-spp.yaml
create mode 100644 models/hub/yolov3-tiny.yaml
create mode 100644 models/hub/yolov3.yaml
create mode 100644 models/hub/yolov5-bifpn.yaml
create mode 100644 models/hub/yolov5-fpn.yaml
create mode 100644 models/hub/yolov5-p2.yaml
create mode 100644 models/hub/yolov5-p6.yaml
create mode 100644 models/hub/yolov5-p7.yaml
create mode 100644 models/hub/yolov5-panet.yaml
create mode 100644 models/hub/yolov5l6.yaml
create mode 100644 models/hub/yolov5m6.yaml
create mode 100644 models/hub/yolov5s6.yaml
create mode 100644 models/hub/yolov5x6.yaml
create mode 100644 models/yolo.py
create mode 100644 models/yolov5l.yaml
create mode 100644 models/yolov5m-2transformer.yaml
create mode 100644 models/yolov5m.yaml
create mode 100644 models/yolov5m_add_detect.yaml
create mode 100644 models/yolov5m_s.yaml
create mode 100644 models/yolov5s-2transformer.yaml
create mode 100644 models/yolov5s-transformer.yaml
create mode 100644 models/yolov5s.yaml
create mode 100644 models/yolov5x.yaml
create mode 100644 plugin/yololayer.cu
create mode 100644 plugin/yololayer.h
create mode 100644 requirements.txt
create mode 100644 src/calibrator.cpp
create mode 100644 src/calibrator.h
create mode 100644 src/config.h
create mode 100644 src/cuda_utils.h
create mode 100644 src/logging.h
create mode 100644 src/macros.h
create mode 100644 src/model.cpp
create mode 100644 src/model.h
create mode 100644 src/postprocess.cpp
create mode 100644 src/postprocess.h
create mode 100644 src/preprocess.cu
create mode 100644 src/preprocess.h
create mode 100644 src/types.h
create mode 100644 src/utils.h
create mode 100644 train.py
create mode 100644 utils/__init__.py
create mode 100644 utils/__pycache__/__init__.cpython-37.pyc
create mode 100644 utils/__pycache__/__init__.cpython-38.pyc
create mode 100644 utils/__pycache__/augmentations.cpython-37.pyc
create mode 100644 utils/__pycache__/augmentations.cpython-38.pyc
create mode 100644 utils/__pycache__/autoanchor.cpython-37.pyc
create mode 100644 utils/__pycache__/autoanchor.cpython-38.pyc
create mode 100644 utils/__pycache__/callbacks.cpython-37.pyc
create mode 100644 utils/__pycache__/callbacks.cpython-38.pyc
create mode 100644 utils/__pycache__/datasets.cpython-37.pyc
create mode 100644 utils/__pycache__/datasets.cpython-38.pyc
create mode 100644 utils/__pycache__/downloads.cpython-37.pyc
create mode 100644 utils/__pycache__/downloads.cpython-38.pyc
create mode 100644 utils/__pycache__/general.cpython-37.pyc
create mode 100644 utils/__pycache__/general.cpython-38.pyc
create mode 100644 utils/__pycache__/loss.cpython-37.pyc
create mode 100644 utils/__pycache__/loss.cpython-38.pyc
create mode 100644 utils/__pycache__/metrics.cpython-37.pyc
create mode 100644 utils/__pycache__/metrics.cpython-38.pyc
create mode 100644 utils/__pycache__/plots.cpython-37.pyc
create mode 100644 utils/__pycache__/plots.cpython-38.pyc
create mode 100644 utils/__pycache__/torch_utils.cpython-37.pyc
create mode 100644 utils/__pycache__/torch_utils.cpython-38.pyc
create mode 100644 utils/activations.py
create mode 100644 utils/augmentations.py
create mode 100644 utils/autoanchor.py
create mode 100644 utils/aws/__init__.py
create mode 100644 utils/aws/mime.sh
create mode 100644 utils/aws/resume.py
create mode 100644 utils/aws/userdata.sh
create mode 100644 utils/callbacks.py
create mode 100644 utils/datasets.py
create mode 100644 utils/downloads.py
create mode 100644 utils/flask_rest_api/README.md
create mode 100644 utils/flask_rest_api/example_request.py
create mode 100644 utils/flask_rest_api/restapi.py
create mode 100644 utils/general.py
create mode 100644 utils/google_app_engine/Dockerfile
create mode 100644 utils/google_app_engine/additional_requirements.txt
create mode 100644 utils/google_app_engine/app.yaml
create mode 100644 utils/loggers/__init__.py
create mode 100644 utils/loggers/__pycache__/__init__.cpython-37.pyc
create mode 100644 utils/loggers/__pycache__/__init__.cpython-38.pyc
create mode 100644 utils/loggers/wandb/README.md
create mode 100644 utils/loggers/wandb/__init__.py
create mode 100644 utils/loggers/wandb/__pycache__/__init__.cpython-37.pyc
create mode 100644 utils/loggers/wandb/__pycache__/__init__.cpython-38.pyc
create mode 100644 utils/loggers/wandb/__pycache__/wandb_utils.cpython-37.pyc
create mode 100644 utils/loggers/wandb/__pycache__/wandb_utils.cpython-38.pyc
create mode 100644 utils/loggers/wandb/log_dataset.py
create mode 100644 utils/loggers/wandb/sweep.py
create mode 100644 utils/loggers/wandb/sweep.yaml
create mode 100644 utils/loggers/wandb/wandb_utils.py
create mode 100644 utils/loss.py
create mode 100644 utils/metrics.py
create mode 100644 utils/plots.py
create mode 100644 utils/torch_utils.py
create mode 100644 val.py
create mode 100644 yolov5_cls.cpp
create mode 100644 yolov5_cls_trt.py
create mode 100644 yolov5_det.cpp
create mode 100644 yolov5_det_cuda_python.py
create mode 100644 yolov5_det_trt.py
create mode 100644 yolov5_seg.cpp
create mode 100644 yolov5_seg_trt.py
diff --git a/CMakeLists.txt b/CMakeLists.txt
new file mode 100644
index 0000000..a1522ce
--- /dev/null
+++ b/CMakeLists.txt
@@ -0,0 +1,52 @@
+cmake_minimum_required(VERSION 3.10)
+
+project(yolov5)
+
+add_definitions(-std=c++11)
+add_definitions(-DAPI_EXPORTS)
+option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
+set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_BUILD_TYPE Debug)
+
+# TODO(Call for PR): make cmake compatible with Windows
+set(CMAKE_CUDA_COMPILER /usr/local/cuda/bin/nvcc)
+enable_language(CUDA)
+
+# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
+# cuda
+include_directories(/usr/local/cuda/include)
+link_directories(/usr/local/cuda/lib64)
+# tensorrt
+# TODO(Call for PR): make TRT path configurable from command line
+include_directories(/home/nvidia/TensorRT-8.2.5.1/include/)
+link_directories(/home/nvidia/TensorRT-8.2.5.1/lib/)
+
+include_directories(${PROJECT_SOURCE_DIR}/src/)
+include_directories(${PROJECT_SOURCE_DIR}/plugin/)
+file(GLOB_RECURSE SRCS ${PROJECT_SOURCE_DIR}/src/*.cpp ${PROJECT_SOURCE_DIR}/src/*.cu)
+file(GLOB_RECURSE PLUGIN_SRCS ${PROJECT_SOURCE_DIR}/plugin/*.cu)
+
+add_library(myplugins SHARED ${PLUGIN_SRCS})
+target_link_libraries(myplugins nvinfer cudart)
+
+find_package(OpenCV)
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+add_executable(yolov5_det yolov5_det.cpp ${SRCS})
+target_link_libraries(yolov5_det nvinfer)
+target_link_libraries(yolov5_det cudart)
+target_link_libraries(yolov5_det myplugins)
+target_link_libraries(yolov5_det ${OpenCV_LIBS})
+
+add_executable(yolov5_cls yolov5_cls.cpp ${SRCS})
+target_link_libraries(yolov5_cls nvinfer)
+target_link_libraries(yolov5_cls cudart)
+target_link_libraries(yolov5_cls myplugins)
+target_link_libraries(yolov5_cls ${OpenCV_LIBS})
+
+add_executable(yolov5_seg yolov5_seg.cpp ${SRCS})
+target_link_libraries(yolov5_seg nvinfer)
+target_link_libraries(yolov5_seg cudart)
+target_link_libraries(yolov5_seg myplugins)
+target_link_libraries(yolov5_seg ${OpenCV_LIBS})
+
diff --git a/README.md b/README.md
index edc6200..6d53628 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,157 @@
-# TensorRT_Transform
+# YOLOv5
+
+TensorRTx inference code base for [ultralytics/yolov5](https://github.com/ultralytics/yolov5).
+
+## Contributors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Different versions of yolov5
+
+Currently, we support yolov5 v1.0, v2.0, v3.0, v3.1, v4.0, v5.0, v6.0, v6.2, v7.0
+
+- For yolov5 v7.0, download .pt from [yolov5 release v7.0](https://github.com/ultralytics/yolov5/releases/tag/v7.0), `git clone -b v7.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v7.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v7.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v7.0/yolov5)
+- For yolov5 v6.2, download .pt from [yolov5 release v6.2](https://github.com/ultralytics/yolov5/releases/tag/v6.2), `git clone -b v6.2 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v6.2 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v6.2](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v6.2/yolov5)
+- For yolov5 v6.0, download .pt from [yolov5 release v6.0](https://github.com/ultralytics/yolov5/releases/tag/v6.0), `git clone -b v6.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v6.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v6.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v6.0/yolov5).
+- For yolov5 v5.0, download .pt from [yolov5 release v5.0](https://github.com/ultralytics/yolov5/releases/tag/v5.0), `git clone -b v5.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v5.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v5.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v5.0/yolov5).
+- For yolov5 v4.0, download .pt from [yolov5 release v4.0](https://github.com/ultralytics/yolov5/releases/tag/v4.0), `git clone -b v4.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v4.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v4.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v4.0/yolov5).
+- For yolov5 v3.1, download .pt from [yolov5 release v3.1](https://github.com/ultralytics/yolov5/releases/tag/v3.1), `git clone -b v3.1 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v3.1 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v3.1](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v3.1/yolov5).
+- For yolov5 v3.0, download .pt from [yolov5 release v3.0](https://github.com/ultralytics/yolov5/releases/tag/v3.0), `git clone -b v3.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v3.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v3.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v3.0/yolov5).
+- For yolov5 v2.0, download .pt from [yolov5 release v2.0](https://github.com/ultralytics/yolov5/releases/tag/v2.0), `git clone -b v2.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v2.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v2.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v2.0/yolov5).
+- For yolov5 v1.0, download .pt from [yolov5 release v1.0](https://github.com/ultralytics/yolov5/releases/tag/v1.0), `git clone -b v1.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v1.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v1.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v1.0/yolov5).
+
+## Config
+
+- Choose the YOLOv5 sub-model n/s/m/l/x/n6/s6/m6/l6/x6 from command line arguments.
+- Other configs please check [src/config.h](src/config.h)
+
+## Build and Run
+
+### Detection
+
+1. generate .wts from pytorch with .pt, or download .wts from model zoo
+
+```
+git clone -b v7.0 https://github.com/ultralytics/yolov5.git
+git clone -b yolov5-v7.0 https://github.com/wang-xinyu/tensorrtx.git
+cd yolov5/
+wget https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt
+cp [PATH-TO-TENSORRTX]/yolov5/gen_wts.py .
+python gen_wts.py -w yolov5s.pt -o yolov5s.wts
+# A file 'yolov5s.wts' will be generated.
+```
+
+2. build tensorrtx/yolov5 and run
+
+```
+cd [PATH-TO-TENSORRTX]/yolov5/
+# Update kNumClass in src/config.h if your model is trained on custom dataset
+mkdir build
+cd build
+cp [PATH-TO-ultralytics-yolov5]/yolov5s.wts .
+cmake ..
+make
+
+./yolov5_det -s [.wts] [.engine] [n/s/m/l/x/n6/s6/m6/l6/x6 or c/c6 gd gw] // serialize model to plan file
+./yolov5_det -d [.engine] [image folder] // deserialize and run inference, the images in [image folder] will be processed.
+
+# For example yolov5s
+./yolov5_det -s yolov5s.wts yolov5s.engine s
+./yolov5_det -d yolov5s.engine ../images
+
+# For example Custom model with depth_multiple=0.17, width_multiple=0.25 in yolov5.yaml
+./yolov5_det -s yolov5_custom.wts yolov5.engine c 0.17 0.25
+./yolov5_det -d yolov5.engine ../images
+```
+
+3. Check the images generated, _zidane.jpg and _bus.jpg
+
+4. Optional, load and run the tensorrt model in Python
+
+```
+// Install python-tensorrt, pycuda, etc.
+// Ensure the yolov5s.engine and libmyplugins.so have been built
+python yolov5_det_trt.py
+
+// Another version of python script, which is using CUDA Python instead of pycuda.
+python yolov5_det_trt_cuda_python.py
+```
+
+
+
+
+
+### Classification
+
+```
+# Download ImageNet labels
+wget https://github.com/joannzhang00/ImageNet-dataset-classes-labels/blob/main/imagenet_classes.txt
+
+# Build and serialize TensorRT engine
+./yolov5_cls -s yolov5s-cls.wts yolov5s-cls.engine s
+
+# Run inference
+./yolov5_cls -d yolov5s-cls.engine ../images
+```
+
+### Instance Segmentation
+
+```
+# Build and serialize TensorRT engine
+./yolov5_seg -s yolov5s-seg.wts yolov5s-seg.engine s
+
+# Download the labels file
+wget -O coco.txt https://raw.githubusercontent.com/amikelive/coco-labels/master/coco-labels-2014_2017.txt
+
+# Run inference with labels file
+./yolov5_seg -d yolov5s-seg.engine ../images coco.txt
+```
+
+
+
+
+
+# INT8 Quantization
+
+1. Prepare calibration images, you can randomly select 1000s images from your train set. For coco, you can also download my calibration images `coco_calib` from [GoogleDrive](https://drive.google.com/drive/folders/1s7jE9DtOngZMzJC1uL307J2MiaGwdRSI?usp=sharing) or [BaiduPan](https://pan.baidu.com/s/1GOm_-JobpyLMAqZWCDUhKg) pwd: a9wh
+
+2. unzip it in yolov5/build
+
+3. set the macro `USE_INT8` in src/config.h and make
+
+4. serialize the model and test
+
+
+## More Information
+
+See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)
-TensorRT转化代码
\ No newline at end of file
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