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
diff --git a/__pycache__/val.cpython-37.pyc b/__pycache__/val.cpython-37.pyc
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diff --git a/data/Argoverse.yaml b/data/Argoverse.yaml
new file mode 100644
index 0000000..3bf91ce
--- /dev/null
+++ b/data/Argoverse.yaml
@@ -0,0 +1,67 @@
+# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
+# Example usage: python train.py --data Argoverse.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── Argoverse ← downloads here
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Argoverse # dataset root dir
+train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
+val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
+test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
+
+# Classes
+nc: 8 # number of classes
+names: ['person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign'] # class names
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import json
+
+ from tqdm import tqdm
+ from utils.general import download, Path
+
+
+ def argoverse2yolo(set):
+ labels = {}
+ a = json.load(open(set, "rb"))
+ for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
+ img_id = annot['image_id']
+ img_name = a['images'][img_id]['name']
+ img_label_name = img_name[:-3] + "txt"
+
+ cls = annot['category_id'] # instance class id
+ x_center, y_center, width, height = annot['bbox']
+ x_center = (x_center + width / 2) / 1920.0 # offset and scale
+ y_center = (y_center + height / 2) / 1200.0 # offset and scale
+ width /= 1920.0 # scale
+ height /= 1200.0 # scale
+
+ img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
+ if not img_dir.exists():
+ img_dir.mkdir(parents=True, exist_ok=True)
+
+ k = str(img_dir / img_label_name)
+ if k not in labels:
+ labels[k] = []
+ labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
+
+ for k in labels:
+ with open(k, "w") as f:
+ f.writelines(labels[k])
+
+
+ # Download
+ dir = Path('../datasets/Argoverse') # dataset root dir
+ urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
+ download(urls, dir=dir, delete=False)
+
+ # Convert
+ annotations_dir = 'Argoverse-HD/annotations/'
+ (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
+ for d in "train.json", "val.json":
+ argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels
diff --git a/data/GlobalWheat2020.yaml b/data/GlobalWheat2020.yaml
new file mode 100644
index 0000000..de9c783
--- /dev/null
+++ b/data/GlobalWheat2020.yaml
@@ -0,0 +1,53 @@
+# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# Global Wheat 2020 dataset http://www.global-wheat.com/
+# Example usage: python train.py --data GlobalWheat2020.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── GlobalWheat2020 ← downloads here
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/GlobalWheat2020 # dataset root dir
+train: # train images (relative to 'path') 3422 images
+ - images/arvalis_1
+ - images/arvalis_2
+ - images/arvalis_3
+ - images/ethz_1
+ - images/rres_1
+ - images/inrae_1
+ - images/usask_1
+val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
+ - images/ethz_1
+test: # test images (optional) 1276 images
+ - images/utokyo_1
+ - images/utokyo_2
+ - images/nau_1
+ - images/uq_1
+
+# Classes
+nc: 1 # number of classes
+names: ['wheat_head'] # class names
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ from utils.general import download, Path
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
+ 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
+ download(urls, dir=dir)
+
+ # Make Directories
+ for p in 'annotations', 'images', 'labels':
+ (dir / p).mkdir(parents=True, exist_ok=True)
+
+ # Move
+ for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
+ 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
+ (dir / p).rename(dir / 'images' / p) # move to /images
+ f = (dir / p).with_suffix('.json') # json file
+ if f.exists():
+ f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
diff --git a/data/Objects365.yaml b/data/Objects365.yaml
new file mode 100644
index 0000000..457b9fd
--- /dev/null
+++ b/data/Objects365.yaml
@@ -0,0 +1,104 @@
+# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# Objects365 dataset https://www.objects365.org/
+# Example usage: python train.py --data Objects365.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── Objects365 ← downloads here
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Objects365 # dataset root dir
+train: images/train # train images (relative to 'path') 1742289 images
+val: images/val # val images (relative to 'path') 5570 images
+test: # test images (optional)
+
+# Classes
+nc: 365 # number of classes
+names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup',
+ 'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book',
+ 'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag',
+ 'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV',
+ 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle',
+ 'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird',
+ 'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
+ 'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning',
+ 'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife',
+ 'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock',
+ 'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish',
+ 'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan',
+ 'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard',
+ 'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign',
+ 'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
+ 'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard',
+ 'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry',
+ 'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
+ 'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors',
+ 'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape',
+ 'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck',
+ 'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette',
+ 'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket',
+ 'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine',
+ 'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine',
+ 'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon',
+ 'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse',
+ 'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball',
+ 'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin',
+ 'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts',
+ 'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
+ 'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD',
+ 'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder',
+ 'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips',
+ 'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab',
+ 'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
+ 'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart',
+ 'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French',
+ 'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell',
+ 'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil',
+ 'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis']
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ from pycocotools.coco import COCO
+ from tqdm import tqdm
+
+ from utils.general import download, Path
+
+ # Make Directories
+ dir = Path(yaml['path']) # dataset root dir
+ for p in 'images', 'labels':
+ (dir / p).mkdir(parents=True, exist_ok=True)
+ for q in 'train', 'val':
+ (dir / p / q).mkdir(parents=True, exist_ok=True)
+
+ # Download
+ url = "https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/train/"
+ download([url + 'zhiyuan_objv2_train.tar.gz'], dir=dir, delete=False) # annotations json
+ download([url + f for f in [f'patch{i}.tar.gz' for i in range(51)]], dir=dir / 'images' / 'train',
+ curl=True, delete=False, threads=8)
+
+ # Move
+ train = dir / 'images' / 'train'
+ for f in tqdm(train.rglob('*.jpg'), desc=f'Moving images'):
+ f.rename(train / f.name) # move to /images/train
+
+ # Labels
+ coco = COCO(dir / 'zhiyuan_objv2_train.json')
+ names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
+ for cid, cat in enumerate(names):
+ catIds = coco.getCatIds(catNms=[cat])
+ imgIds = coco.getImgIds(catIds=catIds)
+ for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
+ width, height = im["width"], im["height"]
+ path = Path(im["file_name"]) # image filename
+ try:
+ with open(dir / 'labels' / 'train' / path.with_suffix('.txt').name, 'a') as file:
+ annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
+ for a in coco.loadAnns(annIds):
+ x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
+ x, y = x + w / 2, y + h / 2 # xy to center
+ file.write(f"{cid} {x / width:.5f} {y / height:.5f} {w / width:.5f} {h / height:.5f}\n")
+
+ except Exception as e:
+ print(e)
diff --git a/data/SKU-110K.yaml b/data/SKU-110K.yaml
new file mode 100644
index 0000000..c85fa81
--- /dev/null
+++ b/data/SKU-110K.yaml
@@ -0,0 +1,52 @@
+# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
+# Example usage: python train.py --data SKU-110K.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── SKU-110K ← downloads here
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/SKU-110K # dataset root dir
+train: train.txt # train images (relative to 'path') 8219 images
+val: val.txt # val images (relative to 'path') 588 images
+test: test.txt # test images (optional) 2936 images
+
+# Classes
+nc: 1 # number of classes
+names: ['object'] # class names
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import shutil
+ from tqdm import tqdm
+ from utils.general import np, pd, Path, download, xyxy2xywh
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ parent = Path(dir.parent) # download dir
+ urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
+ download(urls, dir=parent, delete=False)
+
+ # Rename directories
+ if dir.exists():
+ shutil.rmtree(dir)
+ (parent / 'SKU110K_fixed').rename(dir) # rename dir
+ (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir
+
+ # Convert labels
+ names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names
+ for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
+ x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations
+ images, unique_images = x[:, 0], np.unique(x[:, 0])
+ with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
+ f.writelines(f'./images/{s}\n' for s in unique_images)
+ for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
+ cls = 0 # single-class dataset
+ with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
+ for r in x[images == im]:
+ w, h = r[6], r[7] # image width, height
+ xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance
+ f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label
diff --git a/data/VOC.yaml b/data/VOC.yaml
new file mode 100644
index 0000000..e59fb6a
--- /dev/null
+++ b/data/VOC.yaml
@@ -0,0 +1,80 @@
+# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC
+# Example usage: python train.py --data VOC.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── VOC ← downloads here
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VOC
+train: # train images (relative to 'path') 16551 images
+ - images/train2012
+ - images/train2007
+ - images/val2012
+ - images/val2007
+val: # val images (relative to 'path') 4952 images
+ - images/test2007
+test: # test images (optional)
+ - images/test2007
+
+# Classes
+nc: 20 # number of classes
+names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
+ 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import xml.etree.ElementTree as ET
+
+ from tqdm import tqdm
+ from utils.general import download, Path
+
+
+ def convert_label(path, lb_path, year, image_id):
+ def convert_box(size, box):
+ dw, dh = 1. / size[0], 1. / size[1]
+ x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
+ return x * dw, y * dh, w * dw, h * dh
+
+ in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
+ out_file = open(lb_path, 'w')
+ tree = ET.parse(in_file)
+ root = tree.getroot()
+ size = root.find('size')
+ w = int(size.find('width').text)
+ h = int(size.find('height').text)
+
+ for obj in root.iter('object'):
+ cls = obj.find('name').text
+ if cls in yaml['names'] and not int(obj.find('difficult').text) == 1:
+ xmlbox = obj.find('bndbox')
+ bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
+ cls_id = yaml['names'].index(cls) # class id
+ out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+ urls = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
+ url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
+ url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
+ download(urls, dir=dir / 'images', delete=False)
+
+ # Convert
+ path = dir / f'images/VOCdevkit'
+ for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
+ imgs_path = dir / 'images' / f'{image_set}{year}'
+ lbs_path = dir / 'labels' / f'{image_set}{year}'
+ imgs_path.mkdir(exist_ok=True, parents=True)
+ lbs_path.mkdir(exist_ok=True, parents=True)
+
+ image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').read().strip().split()
+ for id in tqdm(image_ids, desc=f'{image_set}{year}'):
+ f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
+ lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
+ f.rename(imgs_path / f.name) # move image
+ convert_label(path, lb_path, year, id) # convert labels to YOLO format
diff --git a/data/VisDrone.yaml b/data/VisDrone.yaml
new file mode 100644
index 0000000..fe6cb91
--- /dev/null
+++ b/data/VisDrone.yaml
@@ -0,0 +1,61 @@
+# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
+# Example usage: python train.py --data VisDrone.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── VisDrone ← downloads here
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VisDrone # dataset root dir
+train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
+val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
+test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
+
+# Classes
+nc: 10 # number of classes
+names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ from utils.general import download, os, Path
+
+ def visdrone2yolo(dir):
+ from PIL import Image
+ from tqdm import tqdm
+
+ def convert_box(size, box):
+ # Convert VisDrone box to YOLO xywh box
+ dw = 1. / size[0]
+ dh = 1. / size[1]
+ return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
+
+ (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
+ pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
+ for f in pbar:
+ img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
+ lines = []
+ with open(f, 'r') as file: # read annotation.txt
+ for row in [x.split(',') for x in file.read().strip().splitlines()]:
+ if row[4] == '0': # VisDrone 'ignored regions' class 0
+ continue
+ cls = int(row[5]) - 1
+ box = convert_box(img_size, tuple(map(int, row[:4])))
+ lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
+ with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
+ fl.writelines(lines) # write label.txt
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
+ 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
+ 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
+ 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
+ download(urls, dir=dir)
+
+ # Convert
+ for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
+ visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
diff --git a/data/coco.yaml b/data/coco.yaml
new file mode 100644
index 0000000..acf8e84
--- /dev/null
+++ b/data/coco.yaml
@@ -0,0 +1,44 @@
+# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# COCO 2017 dataset http://cocodataset.org
+# Example usage: python train.py --data coco.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── coco ← downloads here
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco # dataset root dir
+train: train2017.txt # train images (relative to 'path') 118287 images
+val: val2017.txt # train images (relative to 'path') 5000 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+
+# Classes
+nc: 80 # number of classes
+names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+ 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+ 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+ 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+ 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+ 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+ 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+ 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+ 'hair drier', 'toothbrush'] # class names
+
+
+# Download script/URL (optional)
+download: |
+ from utils.general import download, Path
+
+ # Download labels
+ segments = False # segment or box labels
+ dir = Path(yaml['path']) # dataset root dir
+ url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+ urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
+ download(urls, dir=dir.parent)
+
+ # Download data
+ urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
+ 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
+ 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
+ download(urls, dir=dir / 'images', threads=3)
diff --git a/data/coco128.yaml b/data/coco128.yaml
new file mode 100644
index 0000000..eda39dc
--- /dev/null
+++ b/data/coco128.yaml
@@ -0,0 +1,30 @@
+# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
+# Example usage: python train.py --data coco128.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── coco128 ← downloads here
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128 # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+nc: 80 # number of classes
+names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+ 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+ 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+ 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+ 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+ 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+ 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+ 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+ 'hair drier', 'toothbrush'] # class names
+
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
\ No newline at end of file
diff --git a/data/data_class_4.yaml b/data/data_class_4.yaml
new file mode 100644
index 0000000..482bc3d
--- /dev/null
+++ b/data/data_class_4.yaml
@@ -0,0 +1,37 @@
+# train: /home/sxkj/nyh/data/cupan_0803/train.txt # 128 images
+# val: /home/sxkj/nyh/data/cupan_0803/val.txt # 128 images
+train: /home/thsw/WJ/nyh/DATA/smogfire12_20230103/train.txt # 128 images
+val: /home/thsw/WJ/nyh/DATA/smogfire12_20230103/val.txt # 128 images
+
+
+
+# number of classes
+nc: 2
+names: [ 'smog','fire']
+
+#nc: 5 #
+# class names
+#names: ['ForestSpot','PestTree','pedestrian','fire','smog']
+
+
+
+#train: /home/test/Dataset_new2/data_dh3/train.txt
+#val: /home/test/Dataset_new2/data_dh3/val.txt
+#test: /home/test/Dataset_new2/data_dh3/test.txt
+#
+#nc: 2 # number of classes
+## class names
+#names: ['cigarette','phone']
+
+
+#train: E:\Pytorch\yolov5-master-revise\data\img
+#val: E:\Pytorch\yolov5-master-revise\data\img1
+#test: E:\Pytorch\yolov5-master-revise\data\img2
+#train: ../yolov5-revise-trainbolt/data/img/bolt/bolttrain/ # 128 images
+#val: ../yolov5-revise-trainbolt/data/img/bolt/boltval/ # 128 images
+
+
+#nc: 3 # number of classes
+# class names
+#names: ['crack1','crack2','crack3']
+#names: [ 'ExposedBar']
\ No newline at end of file
diff --git a/data/hyps/hyp.finetune.yaml b/data/hyps/hyp.finetune.yaml
new file mode 100644
index 0000000..237cd5b
--- /dev/null
+++ b/data/hyps/hyp.finetune.yaml
@@ -0,0 +1,39 @@
+# Hyperparameters for VOC finetuning
+# python train.py --batch 64 --weights yolov5m.pt --data VOC.yaml --img 512 --epochs 50
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+
+# Hyperparameter Evolution Results
+# Generations: 306
+# P R mAP.5 mAP.5:.95 box obj cls
+# Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146
+
+lr0: 0.0032
+lrf: 0.12
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 2.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+# anchors: 3.63
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
+copy_paste: 0.0
diff --git a/data/hyps/hyp.finetune_objects365.yaml b/data/hyps/hyp.finetune_objects365.yaml
new file mode 100644
index 0000000..435fa7a
--- /dev/null
+++ b/data/hyps/hyp.finetune_objects365.yaml
@@ -0,0 +1,29 @@
+lr0: 0.00258
+lrf: 0.17
+momentum: 0.779
+weight_decay: 0.00058
+warmup_epochs: 1.33
+warmup_momentum: 0.86
+warmup_bias_lr: 0.0711
+box: 0.0539
+cls: 0.299
+cls_pw: 0.825
+obj: 0.632
+obj_pw: 1.0
+iou_t: 0.2
+anchor_t: 3.44
+anchors: 3.2
+fl_gamma: 0.0
+hsv_h: 0.0188
+hsv_s: 0.704
+hsv_v: 0.36
+degrees: 0.0
+translate: 0.0902
+scale: 0.491
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
diff --git a/data/hyps/hyp.scratch-p6.yaml b/data/hyps/hyp.scratch-p6.yaml
new file mode 100644
index 0000000..fc1d8eb
--- /dev/null
+++ b/data/hyps/hyp.scratch-p6.yaml
@@ -0,0 +1,34 @@
+# Hyperparameters for COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.3 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 0.7 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3 # anchors per output layer (0 to ignore)
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0.1 # image translation (+/- fraction)
+scale: 0.9 # image scale (+/- gain)
+shear: 0.0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.5 # image flip left-right (probability)
+mosaic: 1.0 # image mosaic (probability)
+mixup: 0.0 # image mixup (probability)
+copy_paste: 0.0 # segment copy-paste (probability)
diff --git a/data/hyps/hyp.scratch.yaml b/data/hyps/hyp.scratch.yaml
new file mode 100644
index 0000000..a76bdbc
--- /dev/null
+++ b/data/hyps/hyp.scratch.yaml
@@ -0,0 +1,34 @@
+# Hyperparameters for COCO training from scratch
+# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.5 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 1.0 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3 # anchors per output layer (0 to ignore)
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 如果是0，则没用focal loss
+hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0.1 # image translation (+/- fraction)
+scale: 0.5 # image scale (+/- gain)
+shear: 0.0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.5 # image flip left-right (probability)
+mosaic: 1.0 # image mosaic (probability)
+mixup: 0.0 # image mixup (probability)
+copy_paste: 0.0 # segment copy-paste (probability)
diff --git a/data/img.cache b/data/img.cache
new file mode 100644
index 0000000..8502dcf
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diff --git a/data/img.cache.npy b/data/img.cache.npy
new file mode 100644
index 0000000..c500837
Binary files /dev/null and b/data/img.cache.npy differ
diff --git a/data/img1.cache b/data/img1.cache
new file mode 100644
index 0000000..1458e87
Binary files /dev/null and b/data/img1.cache differ
diff --git a/data/scripts/download_weights.sh b/data/scripts/download_weights.sh
new file mode 100644
index 0000000..a576c95
--- /dev/null
+++ b/data/scripts/download_weights.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# Download latest models from https://github.com/ultralytics/yolov5/releases
+# Example usage: bash path/to/download_weights.sh
+# parent
+# └── yolov5
+# ├── yolov5s.pt ← downloads here
+# ├── yolov5m.pt
+# └── ...
+
+python - <= cls >= 0, f'incorrect class index {cls}'
+
+ # Write YOLO label
+ if id not in shapes:
+ shapes[id] = Image.open(file).size
+ box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
+ with open((labels / id).with_suffix('.txt'), 'a') as f:
+ f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt
+ except Exception as e:
+ print(f'WARNING: skipping one label for {file}: {e}')
+
+
+ # Download manually from https://challenge.xviewdataset.org
+ dir = Path(yaml['path']) # dataset root dir
+ # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels
+ # 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images
+ # 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels)
+ # download(urls, dir=dir, delete=False)
+
+ # Convert labels
+ convert_labels(dir / 'xView_train.geojson')
+
+ # Move images
+ images = Path(dir / 'images')
+ images.mkdir(parents=True, exist_ok=True)
+ Path(dir / 'train_images').rename(dir / 'images' / 'train')
+ Path(dir / 'val_images').rename(dir / 'images' / 'val')
+
+ # Split
+ autosplit(dir / 'images' / 'train')
diff --git a/detect.py b/detect.py
new file mode 100644
index 0000000..a48d498
--- /dev/null
+++ b/detect.py
@@ -0,0 +1,239 @@
+"""Run inference with a YOLOv5 model on images, videos, directories, streams
+
+Usage:
+ $ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640
+"""
+
+import argparse
+import sys
+import time
+from pathlib import Path
+
+import cv2
+import torch
+import torch.backends.cudnn as cudnn
+
+FILE = Path(__file__).absolute()
+sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path
+
+from models.experimental import attempt_load
+from utils.datasets import LoadStreams, LoadImages
+from utils.general import check_img_size, check_requirements, check_imshow, colorstr, non_max_suppression, \
+ apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
+from utils.plots import colors, plot_one_box
+from utils.torch_utils import select_device, load_classifier, time_sync
+
+
+@torch.no_grad()
+def run(weights='yolov5s.pt', # model.pt path(s)
+ source='data/images', # file/dir/URL/glob, 0 for webcam
+ imgsz=640, # inference size (pixels)
+ conf_thres=0.25, # confidence threshold
+ iou_thres=0.45, # NMS IOU threshold
+ max_det=1000, # maximum detections per image
+ device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
+ view_img=False, # show results
+ save_txt=False, # save results to *.txt
+ save_conf=False, # save confidences in --save-txt labels
+ save_crop=False, # save cropped prediction boxes
+ nosave=False, # do not save images/videos
+ classes=None, # filter by class: --class 0, or --class 0 2 3
+ agnostic_nms=False, # class-agnostic NMS
+ augment=False, # augmented inference
+ visualize=False, # visualize features
+ update=False, # update all models
+ project='runs/detect', # save results to project/name
+ name='exp', # save results to project/name
+ exist_ok=False, # existing project/name ok, do not increment
+ line_thickness=3, # bounding box thickness (pixels)
+ hide_labels=False, # hide labels
+ hide_conf=False, # hide confidences
+ half=False, # use FP16 half-precision inference
+ ):
+ save_img = not nosave and not source.endswith('.txt') # save inference images
+ webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
+ ('rtsp://', 'rtmp://', 'http://', 'https://'))
+
+ # Directories
+ save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
+ (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
+
+ # Initialize
+ set_logging()
+ device = select_device(device)
+ half &= device.type != 'cpu' # half precision only supported on CUDA
+
+ # Load model
+ w = weights[0] if isinstance(weights, list) else weights
+ classify, pt, onnx = False, w.endswith('.pt'), w.endswith('.onnx') # inference type
+ stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults
+ if pt:
+ model = attempt_load(weights, map_location=device) # load FP32 model
+ stride = int(model.stride.max()) # model stride
+ names = model.module.names if hasattr(model, 'module') else model.names # get class names
+ if half:
+ model.half() # to FP16
+ if classify: # second-stage classifier
+ modelc = load_classifier(name='resnet50', n=2) # initialize
+ modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()
+ elif onnx:
+ check_requirements(('onnx', 'onnxruntime'))
+ import onnxruntime
+ session = onnxruntime.InferenceSession(w, None)
+ imgsz = check_img_size(imgsz, s=stride) # check image size
+
+ # Dataloader
+ if webcam:
+ view_img = check_imshow()
+ cudnn.benchmark = True # set True to speed up constant image size inference
+ dataset = LoadStreams(source, img_size=imgsz, stride=stride)
+ bs = len(dataset) # batch_size
+ else:
+ dataset = LoadImages(source, img_size=imgsz, stride=stride)
+ bs = 1 # batch_size
+ vid_path, vid_writer = [None] * bs, [None] * bs
+
+ # Run inference
+ if pt and device.type != 'cpu':
+ model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
+ t0 = time.time()
+ for path, img, im0s, vid_cap in dataset:
+ if pt:
+ img = torch.from_numpy(img).to(device)
+ img = img.half() if half else img.float() # uint8 to fp16/32
+ elif onnx:
+ img = img.astype('float32')
+ img /= 255.0 # 0 - 255 to 0.0 - 1.0
+ if len(img.shape) == 3:
+ img = img[None] # expand for batch dim
+
+ # Inference
+ t1 = time_sync()
+ if pt:
+ visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
+ pred = model(img, augment=augment, visualize=visualize)[0]
+ elif onnx:
+ pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
+
+ # NMS
+ pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
+ t2 = time_sync()
+
+ # Second-stage classifier (optional)
+ if classify:
+ pred = apply_classifier(pred, modelc, img, im0s)
+
+ # Process predictions
+ for i, det in enumerate(pred): # detections per image
+ if webcam: # batch_size >= 1
+ p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
+ else:
+ p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
+
+ p = Path(p) # to Path
+ save_path = str(save_dir / p.name) # img.jpg
+ txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
+ s += '%gx%g ' % img.shape[2:] # print string
+ gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
+ imc = im0.copy() if save_crop else im0 # for save_crop
+ if len(det):
+ # Rescale boxes from img_size to im0 size
+ det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
+
+ # Print results
+ for c in det[:, -1].unique():
+ n = (det[:, -1] == c).sum() # detections per class
+ s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
+
+ # Write results
+ for *xyxy, conf, cls in reversed(det):
+ if save_txt: # Write to file
+ xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
+ line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
+ with open(txt_path + '.txt', 'a') as f:
+ f.write(('%g ' * len(line)).rstrip() % line + '\n')
+
+ if save_img or save_crop or view_img: # Add bbox to image
+ c = int(cls) # integer class
+ label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
+ plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)
+ if save_crop:
+ save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
+
+ # Print time (inference + NMS)
+ print(f'{s}Done. ({t2 - t1:.3f}s)')
+
+ # Stream results
+ if view_img:
+ cv2.imshow(str(p), im0)
+ cv2.waitKey(1) # 1 millisecond
+
+ # Save results (image with detections)
+ if save_img:
+ if dataset.mode == 'image':
+ cv2.imwrite(save_path, im0)
+ else: # 'video' or 'stream'
+ if vid_path[i] != save_path: # new video
+ vid_path[i] = save_path
+ if isinstance(vid_writer[i], cv2.VideoWriter):
+ vid_writer[i].release() # release previous video writer
+ if vid_cap: # video
+ fps = vid_cap.get(cv2.CAP_PROP_FPS)
+ w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+ h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+ else: # stream
+ fps, w, h = 30, im0.shape[1], im0.shape[0]
+ save_path += '.mp4'
+ vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+ vid_writer[i].write(im0)
+
+ if save_txt or save_img:
+ s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
+ print(f"Results saved to {colorstr('bold', save_dir)}{s}")
+
+ if update:
+ strip_optimizer(weights) # update model (to fix SourceChangeWarning)
+
+ print(f'Done. ({time.time() - t0:.3f}s)')
+
+
+def parse_opt():
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--weights', nargs='+', type=str, default='weights/smogfire_20230105.pt', help='model.pt path(s)')
+ #parser.add_argument('--source', type=str, default='image', help='file/dir/URL/glob, 0 for webcam')
+ parser.add_argument('--source', type=str, default='/home/thsw/WJ/nyh_submission/0_smogfire/image_for_test', help='file/dir/URL/glob, 0 for webcam')
+ parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
+ parser.add_argument('--conf-thres', type=float, default=0.5, help='confidence threshold')
+ parser.add_argument('--iou-thres', type=float, default=0.7, help='NMS IoU threshold')
+ parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
+ parser.add_argument('--device', default='0,1', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+ parser.add_argument('--view-img', action='store_true', help='show results')
+ parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+ parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+ parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
+ parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
+ parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
+ parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
+ parser.add_argument('--augment', action='store_true', help='augmented inference')
+ parser.add_argument('--visualize', action='store_true', help='visualize features')
+ parser.add_argument('--update', action='store_true', help='update all models')
+ parser.add_argument('--project', default='runs/detect', help='save results to project/name')
+ parser.add_argument('--name', default='exp', help='save results to project/name')
+ parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+ parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
+ parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
+ parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
+ parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+ opt = parser.parse_args()
+ return opt
+
+
+def main(opt):
+ print(colorstr('detect: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
+ check_requirements(exclude=('tensorboard', 'thop'))
+ run(**vars(opt))
+
+
+if __name__ == "__main__":
+ opt = parse_opt()
+ main(opt)
diff --git a/detect1.py b/detect1.py
new file mode 100644
index 0000000..7a5c469
--- /dev/null
+++ b/detect1.py
@@ -0,0 +1,239 @@
+"""Run inference with a YOLOv5 model on images, videos, directories, streams
+
+Usage:
+ $ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640
+"""
+
+import argparse
+import sys
+import time
+from pathlib import Path
+
+import cv2
+import torch
+import torch.backends.cudnn as cudnn
+
+FILE = Path(__file__).absolute()
+sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path
+
+from models.experimental import attempt_load
+from utils.datasets import LoadStreams, LoadImages
+from utils.general import check_img_size, check_requirements, check_imshow, colorstr, non_max_suppression, \
+ apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
+from utils.plots import colors, plot_one_box
+from utils.torch_utils import select_device, load_classifier, time_sync
+
+
+@torch.no_grad()
+def run(weights='yolov5s.pt', # model.pt path(s)
+ source='data/images', # file/dir/URL/glob, 0 for webcam
+ imgsz=640, # inference size (pixels)
+ conf_thres=0.25, # confidence threshold
+ iou_thres=0.45, # NMS IOU threshold
+ max_det=1000, # maximum detections per image
+ device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
+ view_img=False, # show results
+ save_txt=False, # save results to *.txt
+ save_conf=False, # save confidences in --save-txt labels
+ save_crop=False, # save cropped prediction boxes
+ nosave=False, # do not save images/videos
+ classes=None, # filter by class: --class 0, or --class 0 2 3
+ agnostic_nms=False, # class-agnostic NMS
+ augment=False, # augmented inference
+ visualize=False, # visualize features
+ update=False, # update all models
+ project='runs/detect', # save results to project/name
+ name='exp', # save results to project/name
+ exist_ok=False, # existing project/name ok, do not increment
+ line_thickness=3, # bounding box thickness (pixels)
+ hide_labels=False, # hide labels
+ hide_conf=False, # hide confidences
+ half=False, # use FP16 half-precision inference
+ ):
+ save_img = not nosave and not source.endswith('.txt') # save inference images
+ webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
+ ('rtsp://', 'rtmp://', 'http://', 'https://'))
+
+ # Directories
+ save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
+ (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
+
+ # Initialize
+ set_logging()
+ device = select_device(device)
+ half &= device.type != 'cpu' # half precision only supported on CUDA
+
+ # Load model
+ w = weights[0] if isinstance(weights, list) else weights
+ classify, pt, onnx = False, w.endswith('.pt'), w.endswith('.onnx') # inference type
+ stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults
+ if pt:
+ model = attempt_load(weights, map_location=device) # load FP32 model
+ stride = int(model.stride.max()) # model stride
+ names = model.module.names if hasattr(model, 'module') else model.names # get class names
+ if half:
+ model.half() # to FP16
+ if classify: # second-stage classifier
+ modelc = load_classifier(name='resnet50', n=2) # initialize
+ modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()
+ elif onnx:
+ check_requirements(('onnx', 'onnxruntime'))
+ import onnxruntime
+ session = onnxruntime.InferenceSession(w, None)
+ imgsz = check_img_size(imgsz, s=stride) # check image size
+
+ # Dataloader
+ if webcam:
+ view_img = check_imshow()
+ cudnn.benchmark = True # set True to speed up constant image size inference
+ dataset = LoadStreams(source, img_size=imgsz, stride=stride)
+ bs = len(dataset) # batch_size
+ else:
+ dataset = LoadImages(source, img_size=imgsz, stride=stride)
+ bs = 1 # batch_size
+ vid_path, vid_writer = [None] * bs, [None] * bs
+
+ # Run inference
+ if pt and device.type != 'cpu':
+ model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
+ t0 = time.time()
+ for path, img, im0s, vid_cap in dataset:
+ if pt:
+ img = torch.from_numpy(img).to(device)
+ img = img.half() if half else img.float() # uint8 to fp16/32
+ elif onnx:
+ img = img.astype('float32')
+ img /= 255.0 # 0 - 255 to 0.0 - 1.0
+ if len(img.shape) == 3:
+ img = img[None] # expand for batch dim
+
+ # Inference
+ t1 = time_sync()
+ if pt:
+ visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
+ pred = model(img, augment=augment, visualize=visualize)[0]
+ elif onnx:
+ pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
+
+ # NMS
+ pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
+ t2 = time_sync()
+
+ # Second-stage classifier (optional)
+ if classify:
+ pred = apply_classifier(pred, modelc, img, im0s)
+
+ # Process predictions
+ for i, det in enumerate(pred): # detections per image
+ if webcam: # batch_size >= 1
+ p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
+ else:
+ p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
+
+ p = Path(p) # to Path
+ save_path = str(save_dir / p.name) # img.jpg
+ txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
+ s += '%gx%g ' % img.shape[2:] # print string
+ gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
+ imc = im0.copy() if save_crop else im0 # for save_crop
+ if len(det):
+ # Rescale boxes from img_size to im0 size
+ det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
+
+ # Print results
+ for c in det[:, -1].unique():
+ n = (det[:, -1] == c).sum() # detections per class
+ s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
+
+ # Write results
+ for *xyxy, conf, cls in reversed(det):
+ if save_txt: # Write to file
+ xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
+ line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
+ with open(txt_path + '.txt', 'a') as f:
+ f.write(('%g ' * len(line)).rstrip() % line + '\n')
+
+ if save_img or save_crop or view_img: # Add bbox to image
+ c = int(cls) # integer class
+ label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
+ plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)
+ if save_crop:
+ save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
+
+ # Print time (inference + NMS)
+ print(f'{s}Done. ({t2 - t1:.3f}s)')
+
+ # Stream results
+ if view_img:
+ cv2.imshow(str(p), im0)
+ cv2.waitKey(1) # 1 millisecond
+
+ # Save results (image with detections)
+ if save_img:
+ if dataset.mode == 'image':
+ cv2.imwrite(save_path, im0)
+ else: # 'video' or 'stream'
+ if vid_path[i] != save_path: # new video
+ vid_path[i] = save_path
+ if isinstance(vid_writer[i], cv2.VideoWriter):
+ vid_writer[i].release() # release previous video writer
+ if vid_cap: # video
+ fps = vid_cap.get(cv2.CAP_PROP_FPS)
+ w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+ h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+ else: # stream
+ fps, w, h = 30, im0.shape[1], im0.shape[0]
+ save_path += '.mp4'
+ vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+ vid_writer[i].write(im0)
+
+ if save_txt or save_img:
+ s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
+ print(f"Results saved to {colorstr('bold', save_dir)}{s}")
+
+ if update:
+ strip_optimizer(weights) # update model (to fix SourceChangeWarning)
+
+ print(f'Done. ({time.time() - t0:.3f}s)')
+
+
+def parse_opt():
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--weights', nargs='+', type=str, default='weights/yolov5s.pt', help='model.pt path(s)')
+ parser.add_argument('--source', type=str, default='image', help='file/dir/URL/glob, 0 for webcam')
+ parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
+ parser.add_argument('--conf-thres', type=float, default=0.5, help='confidence threshold')
+ parser.add_argument('--iou-thres', type=float, default=0.7, help='NMS IoU threshold')
+ parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
+ parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+ parser.add_argument('--view-img', action='store_true', help='show results')
+ parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+ parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+ parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
+ parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
+ parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
+ parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
+ parser.add_argument('--augment', action='store_true', help='augmented inference')
+ parser.add_argument('--visualize', action='store_true', help='visualize features')
+ parser.add_argument('--update', action='store_true', help='update all models')
+ parser.add_argument('--project', default='runs/detect', help='save results to project/name')
+ parser.add_argument('--name', default='exp', help='save results to project/name')
+ parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+ parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
+ parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
+ parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
+ parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+ opt = parser.parse_args()
+ return opt
+
+
+
+def main(opt):
+ print(colorstr('detect: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
+ check_requirements(exclude=('tensorboard', 'thop'))
+ run(**vars(opt))
+
+
+if __name__ == "__main__":
+ opt = parse_opt()
+ main(opt)
diff --git a/export.py b/export.py
new file mode 100644
index 0000000..cec8595
--- /dev/null
+++ b/export.py
@@ -0,0 +1,189 @@
+"""Export a YOLOv5 *.pt model to TorchScript, ONNX, CoreML formats
+
+Usage:
+ $ python path/to/export.py --weights yolov5s.pt --img 640 --batch 1
+"""
+
+import argparse
+import sys
+import time
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+from torch.utils.mobile_optimizer import optimize_for_mobile
+
+FILE = Path(__file__).absolute()
+sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path
+
+from models.common import Conv
+from models.yolo import Detect
+from models.experimental import attempt_load
+from utils.activations import Hardswish, SiLU
+from utils.general import colorstr, check_img_size, check_requirements, file_size, set_logging
+from utils.torch_utils import select_device
+
+
+def export_torchscript(model, img, file, optimize):
+ # TorchScript model export
+ prefix = colorstr('TorchScript:')
+ try:
+ print(f'\n{prefix} starting export with torch {torch.__version__}...')
+ f = file.with_suffix('.torchscript.pt')
+ ts = torch.jit.trace(model, img, strict=False)
+ (optimize_for_mobile(ts) if optimize else ts).save(f)
+ print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+ return ts
+ except Exception as e:
+ print(f'{prefix} export failure: {e}')
+
+
+def export_onnx(model, img, file, opset, train, dynamic, simplify):
+ # ONNX model export
+ prefix = colorstr('ONNX:')
+ try:
+ check_requirements(('onnx', 'onnx-simplifier'))
+ import onnx
+
+ print(f'\n{prefix} starting export with onnx {onnx.__version__}...')
+ f = file.with_suffix('.onnx')
+ torch.onnx.export(model, img, f, verbose=False, opset_version=opset,
+ training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
+ do_constant_folding=not train,
+ input_names=['images'],
+ output_names=['output'],
+ dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640)
+ 'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
+ } if dynamic else None)
+
+ # Checks
+ model_onnx = onnx.load(f) # load onnx model
+ onnx.checker.check_model(model_onnx) # check onnx model
+ # print(onnx.helper.printable_graph(model_onnx.graph)) # print
+
+ # Simplify
+ if simplify:
+ try:
+ import onnxsim
+
+ print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
+ model_onnx, check = onnxsim.simplify(
+ model_onnx,
+ dynamic_input_shape=dynamic,
+ input_shapes={'images': list(img.shape)} if dynamic else None)
+ assert check, 'assert check failed'
+ onnx.save(model_onnx, f)
+ except Exception as e:
+ print(f'{prefix} simplifier failure: {e}')
+ print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+ print(f"{prefix} run --dynamic ONNX model inference with detect.py: 'python detect.py --weights {f}'")
+ except Exception as e:
+ print(f'{prefix} export failure: {e}')
+
+
+def export_coreml(model, img, file):
+ # CoreML model export
+ prefix = colorstr('CoreML:')
+ try:
+ import coremltools as ct
+
+ print(f'\n{prefix} starting export with coremltools {ct.__version__}...')
+ f = file.with_suffix('.mlmodel')
+ model.train() # CoreML exports should be placed in model.train() mode
+ ts = torch.jit.trace(model, img, strict=False) # TorchScript model
+ model = ct.convert(ts, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
+ model.save(f)
+ print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+ except Exception as e:
+ print(f'\n{prefix} export failure: {e}')
+
+
+def run(weights='./yolov5s.pt', # weights path
+ img_size=(640, 640), # image (height, width)
+ batch_size=1, # batch size
+ device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu
+ include=('torchscript', 'onnx', 'coreml'), # include formats
+ half=False, # FP16 half-precision export
+ inplace=False, # set YOLOv5 Detect() inplace=True
+ train=False, # model.train() mode
+ optimize=False, # TorchScript: optimize for mobile
+ dynamic=False, # ONNX: dynamic axes
+ simplify=False, # ONNX: simplify model
+ opset=12, # ONNX: opset version
+ ):
+ t = time.time()
+ include = [x.lower() for x in include]
+ img_size *= 2 if len(img_size) == 1 else 1 # expand
+ file = Path(weights)
+
+ # Load PyTorch model
+ device = select_device(device)
+ assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
+ model = attempt_load(weights, map_location=device) # load FP32 model
+ names = model.names
+
+ # Input
+ gs = int(max(model.stride)) # grid size (max stride)
+ img_size = [check_img_size(x, gs) for x in img_size] # verify img_size are gs-multiples
+ img = torch.zeros(batch_size, 3, *img_size).to(device) # image size(1,3,320,192) iDetection
+
+ # Update model
+ if half:
+ img, model = img.half(), model.half() # to FP16
+ model.train() if train else model.eval() # training mode = no Detect() layer grid construction
+ for k, m in model.named_modules():
+ if isinstance(m, Conv): # assign export-friendly activations
+ if isinstance(m.act, nn.Hardswish):
+ m.act = Hardswish()
+ elif isinstance(m.act, nn.SiLU):
+ m.act = SiLU()
+ elif isinstance(m, Detect):
+ m.inplace = inplace
+ m.onnx_dynamic = dynamic
+ # m.forward = m.forward_export # assign forward (optional)
+
+ for _ in range(2):
+ y = model(img) # dry runs
+ print(f"\n{colorstr('PyTorch:')} starting from {weights} ({file_size(weights):.1f} MB)")
+
+ # Exports
+ if 'torchscript' in include:
+ export_torchscript(model, img, file, optimize)
+ if 'onnx' in include:
+ export_onnx(model, img, file, opset, train, dynamic, simplify)
+ if 'coreml' in include:
+ export_coreml(model, img, file)
+
+ # Finish
+ print(f'\nExport complete ({time.time() - t:.2f}s)'
+ f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
+ f'\nVisualize with https://netron.app')
+
+
+def parse_opt():
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')
+ parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image (height, width)')
+ parser.add_argument('--batch-size', type=int, default=1, help='batch size')
+ parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+ parser.add_argument('--include', nargs='+', default=['torchscript', 'onnx', 'coreml'], help='include formats')
+ parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
+ parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
+ parser.add_argument('--train', action='store_true', help='model.train() mode')
+ parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
+ parser.add_argument('--dynamic', action='store_true', help='ONNX: dynamic axes')
+ parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
+ parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version')
+ opt = parser.parse_args()
+ return opt
+
+
+def main(opt):
+ set_logging()
+ print(colorstr('export: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
+ run(**vars(opt))
+
+
+if __name__ == "__main__":
+ opt = parse_opt()
+ main(opt)
diff --git a/gen_wts.py b/gen_wts.py
new file mode 100644
index 0000000..789e5d7
--- /dev/null
+++ b/gen_wts.py
@@ -0,0 +1,59 @@
+import sys
+import argparse
+import os
+import struct
+import torch
+from utils.torch_utils import select_device
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(description='Convert .pt file to .wts')
+ parser.add_argument('-w', '--weights', required=True,
+ help='Input weights (.pt) file path (required)')
+ parser.add_argument(
+ '-o', '--output', help='Output (.wts) file path (optional)')
+ parser.add_argument(
+ '-t', '--type', type=str, default='detect', choices=['detect', 'cls', 'seg'],
+ help='determines the model is detection/classification')
+ args = parser.parse_args()
+ if not os.path.isfile(args.weights):
+ raise SystemExit('Invalid input file')
+ if not args.output:
+ args.output = os.path.splitext(args.weights)[0] + '.wts'
+ elif os.path.isdir(args.output):
+ args.output = os.path.join(
+ args.output,
+ os.path.splitext(os.path.basename(args.weights))[0] + '.wts')
+ return args.weights, args.output, args.type
+
+
+pt_file, wts_file, m_type = parse_args()
+print(f'Generating .wts for {m_type} model')
+
+# Load model
+print(f'Loading {pt_file}')
+device = select_device('cpu')
+model = torch.load(pt_file, map_location=device) # Load FP32 weights
+model = model['ema' if model.get('ema') else 'model'].float()
+
+if m_type in ['detect', 'seg']:
+ # update anchor_grid info
+ anchor_grid = model.model[-1].anchors * model.model[-1].stride[..., None, None]
+ # model.model[-1].anchor_grid = anchor_grid
+ delattr(model.model[-1], 'anchor_grid') # model.model[-1] is detect layer
+ # The parameters are saved in the OrderDict through the "register_buffer" method, and then saved to the weight.
+ model.model[-1].register_buffer("anchor_grid", anchor_grid)
+ model.model[-1].register_buffer("strides", model.model[-1].stride)
+
+model.to(device).eval()
+
+print(f'Writing into {wts_file}')
+with open(wts_file, 'w') as f:
+ f.write('{}\n'.format(len(model.state_dict().keys())))
+ for k, v in model.state_dict().items():
+ vr = v.reshape(-1).cpu().numpy()
+ f.write('{} {} '.format(k, len(vr)))
+ for vv in vr:
+ f.write(' ')
+ f.write(struct.pack('>f', float(vv)).hex())
+ f.write('\n')
diff --git a/images b/images
new file mode 100644
index 0000000..02cc755
--- /dev/null
+++ b/images
@@ -0,0 +1 @@
+../yolov3-spp/samples
\ No newline at end of file
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diff --git a/models/common.py b/models/common.py
new file mode 100644
index 0000000..62e2d36
--- /dev/null
+++ b/models/common.py
@@ -0,0 +1,457 @@
+# YOLOv5 common modules
+
+import logging
+import warnings
+from copy import copy
+from pathlib import Path
+
+import math
+import numpy as np
+import pandas as pd
+import requests
+import torch
+import torch.nn as nn
+from PIL import Image
+from torch.cuda import amp
+
+from utils.datasets import exif_transpose, letterbox
+from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box
+from utils.plots import colors, plot_one_box
+from utils.torch_utils import time_sync
+from functools import partial
+
+LOGGER = logging.getLogger(__name__)
+
+
+def autopad(k, p=None): # kernel, padding
+ # Pad to 'same'
+ if p is None:
+ p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
+ return p
+
+
+class Conv(nn.Module):
+ # Standard convolution
+ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
+ super().__init__()
+ self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
+ self.bn = nn.BatchNorm2d(c2)
+ self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
+
+ def forward(self, x):
+ return self.act(self.bn(self.conv(x)))
+
+ def forward_fuse(self, x):
+ return self.act(self.conv(x))
+
+
+class DWConv(Conv):
+ # Depth-wise convolution class if g = c1 = c2
+ def __init__(self, c1, c2, k=1, s=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
+ super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
+
+
+class TransformerLayer(nn.Module):
+ # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
+ def __init__(self, c, num_heads):
+ super().__init__()
+ self.q = nn.Linear(c, c, bias=False)
+ self.k = nn.Linear(c, c, bias=False)
+ self.v = nn.Linear(c, c, bias=False)
+ self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
+ self.fc1 = nn.Linear(c, c, bias=False)
+ self.fc2 = nn.Linear(c, c, bias=False)
+
+ def forward(self, x):
+ x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
+ x = self.fc2(self.fc1(x)) + x
+ return x
+
+
+class TransformerBlock(nn.Module):
+ # Vision Transformer https://arxiv.org/abs/2010.11929
+ def __init__(self, c1, c2, num_heads, num_layers):
+ super().__init__()
+ self.conv = None
+ if c1 != c2:
+ self.conv = Conv(c1, c2)
+ self.linear = nn.Linear(c2, c2) # learnable position embedding
+ self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)])
+ self.c2 = c2
+
+ def forward(self, x):
+ if self.conv is not None:
+ x = self.conv(x)
+ b, _, w, h = x.shape
+ p = x.flatten(2).unsqueeze(0).transpose(0, 3).squeeze(3)
+ return self.tr(p + self.linear(p)).unsqueeze(3).transpose(0, 3).reshape(b, self.c2, w, h)
+
+
+class Bottleneck(nn.Module):
+ # Res unit
+ # Standard bottleneck
+ def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = Conv(c_, c2, 3, 1, g=g)
+ self.add = shortcut and c1 == c2
+
+ def forward(self, x):
+ return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class BottleneckCSP(nn.Module):
+ #CSP1_x or CSP2_x
+ # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
+ self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
+ self.cv4 = Conv(2 * c_, c2, 1, 1)
+ self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
+ self.act = nn.LeakyReLU(0.1, inplace=True)
+ self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
+
+ def forward(self, x):
+ y1 = self.cv3(self.m(self.cv1(x)))
+ y2 = self.cv2(x)
+ return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
+
+
+class C3(nn.Module):
+ # CSP Bottleneck with 3 convolutions
+ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = Conv(c1, c_, 1, 1)
+ self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
+ self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
+ # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
+
+ def forward(self, x):
+ return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
+
+
+class C3TR(C3):
+ # C3 module with TransformerBlock()
+ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+ super().__init__(c1, c2, n, shortcut, g, e)
+ c_ = int(c2 * e)
+ self.m = TransformerBlock(c_, c_, 4, n)
+
+
+class C3SPP(C3):
+ # C3 module with SPP()
+ def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
+ super().__init__(c1, c2, n, shortcut, g, e)
+ c_ = int(c2 * e)
+ self.m = SPP(c_, c_, k)
+
+
+class C3Ghost(C3):
+ # C3 module with GhostBottleneck()
+ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+ super().__init__(c1, c2, n, shortcut, g, e)
+ c_ = int(c2 * e) # hidden channels
+ self.m = nn.Sequential(*[GhostBottleneck(c_, c_) for _ in range(n)])
+
+
+class SPP(nn.Module):
+ # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
+ def __init__(self, c1, c2, k=(5, 9, 13)):
+ super().__init__()
+ c_ = c1 // 2 # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
+ self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
+
+ def forward(self, x):
+ x = self.cv1(x)
+ with warnings.catch_warnings():
+ warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
+ return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
+
+
+class SPPF(nn.Module):
+ # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
+ def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
+ super().__init__()
+ c_ = c1 // 2 # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = Conv(c_ * 4, c2, 1, 1)
+ self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
+
+ def forward(self, x):
+ x = self.cv1(x)
+ with warnings.catch_warnings():
+ warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
+ y1 = self.m(x)
+ y2 = self.m(y1)
+ return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
+
+
+class Focus(nn.Module):
+ # Focus wh information into c-space
+ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
+ super().__init__()
+ self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
+ # self.contract = Contract(gain=2)
+
+ def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
+ return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
+ # return self.conv(self.contract(x))
+
+
+class GhostConv(nn.Module):
+ # Ghost Convolution https://github.com/huawei-noah/ghostnet
+ def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
+ super().__init__()
+ c_ = c2 // 2 # hidden channels
+ self.cv1 = Conv(c1, c_, k, s, None, g, act)
+ self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
+
+ def forward(self, x):
+ y = self.cv1(x)
+ return torch.cat([y, self.cv2(y)], 1)
+
+
+class GhostBottleneck(nn.Module):
+ # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
+ def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
+ super().__init__()
+ c_ = c2 // 2
+ self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
+ DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
+ GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
+ self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
+ Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
+
+ def forward(self, x):
+ return self.conv(x) + self.shortcut(x)
+
+
+class Contract(nn.Module):
+ # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
+ def __init__(self, gain=2):
+ super().__init__()
+ self.gain = gain
+
+ def forward(self, x):
+ b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
+ s = self.gain
+ x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2)
+ x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
+ return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40)
+
+
+class Expand(nn.Module):
+ # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
+ def __init__(self, gain=2):
+ super().__init__()
+ self.gain = gain
+
+ def forward(self, x):
+ b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
+ s = self.gain
+ x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80)
+ x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
+ return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160)
+
+
+class Concat(nn.Module):
+ # Concatenate a list of tensors along dimension
+ def __init__(self, dimension=1):
+ super().__init__()
+ self.d = dimension
+
+ def forward(self, x):
+ return torch.cat(x, self.d)
+
+
+class AutoShape(nn.Module):
+ # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+ conf = 0.25 # NMS confidence threshold
+ iou = 0.45 # NMS IoU threshold
+ classes = None # (optional list) filter by class
+ max_det = 1000 # maximum number of detections per image
+
+ def __init__(self, model):
+ super().__init__()
+ self.model = model.eval()
+
+ def autoshape(self):
+ LOGGER.info('AutoShape already enabled, skipping... ') # model already converted to model.autoshape()
+ return self
+
+ @torch.no_grad()
+ def forward(self, imgs, size=640, augment=False, profile=False):
+ # Inference from various sources. For height=640, width=1280, RGB images example inputs are:
+ # file: imgs = 'data/images/zidane.jpg' # str or PosixPath
+ # URI: = 'https://ultralytics.com/images/zidane.jpg'
+ # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
+ # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3)
+ # numpy: = np.zeros((640,1280,3)) # HWC
+ # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
+ # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
+
+ t = [time_sync()]
+ p = next(self.model.parameters()) # for device and type
+ if isinstance(imgs, torch.Tensor): # torch
+ with amp.autocast(enabled=p.device.type != 'cpu'):
+ return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
+
+ # Pre-process
+ n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
+ shape0, shape1, files = [], [], [] # image and inference shapes, filenames
+ for i, im in enumerate(imgs):
+ f = f'image{i}' # filename
+ if isinstance(im, (str, Path)): # filename or uri
+ im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
+ im = np.asarray(exif_transpose(im))
+ elif isinstance(im, Image.Image): # PIL Image
+ im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
+ files.append(Path(f).with_suffix('.jpg').name)
+ if im.shape[0] < 5: # image in CHW
+ im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
+ im = im[..., :3] if im.ndim == 3 else np.tile(im[..., None], 3) # enforce 3ch input
+ s = im.shape[:2] # HWC
+ shape0.append(s) # image shape
+ g = (size / max(s)) # gain
+ shape1.append([y * g for y in s])
+ imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
+ shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
+ x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
+ x = np.stack(x, 0) if n > 1 else x[0][None] # stack
+ x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
+ x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
+ t.append(time_sync())
+
+ with amp.autocast(enabled=p.device.type != 'cpu'):
+ # Inference
+ y = self.model(x, augment, profile)[0] # forward
+ t.append(time_sync())
+
+ # Post-process
+ y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det) # NMS
+ for i in range(n):
+ scale_coords(shape1, y[i][:, :4], shape0[i])
+
+ t.append(time_sync())
+ return Detections(imgs, y, files, t, self.names, x.shape)
+
+
+class Detections:
+ # YOLOv5 detections class for inference results
+ def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
+ super().__init__()
+ d = pred[0].device # device
+ gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations
+ self.imgs = imgs # list of images as numpy arrays
+ self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
+ self.names = names # class names
+ self.files = files # image filenames
+ self.xyxy = pred # xyxy pixels
+ self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
+ self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
+ self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
+ self.n = len(self.pred) # number of images (batch size)
+ self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms)
+ self.s = shape # inference BCHW shape
+
+ def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
+ for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
+ str = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '
+ if pred.shape[0]:
+ for c in pred[:, -1].unique():
+ n = (pred[:, -1] == c).sum() # detections per class
+ str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
+ if show or save or render or crop:
+ for *box, conf, cls in reversed(pred): # xyxy, confidence, class
+ label = f'{self.names[int(cls)]} {conf:.2f}'
+ if crop:
+ save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i])
+ else: # all others
+ plot_one_box(box, im, label=label, color=colors(cls))
+ else:
+ str += '(no detections)'
+
+ im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
+ if pprint:
+ LOGGER.info(str.rstrip(', '))
+ if show:
+ im.show(self.files[i]) # show
+ if save:
+ f = self.files[i]
+ im.save(save_dir / f) # save
+ if i == self.n - 1:
+ LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to '{save_dir}'")
+ if render:
+ self.imgs[i] = np.asarray(im)
+
+ def print(self):
+ self.display(pprint=True) # print results
+ LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
+ self.t)
+
+ def show(self):
+ self.display(show=True) # show results
+
+ def save(self, save_dir='runs/detect/exp'):
+ save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
+ self.display(save=True, save_dir=save_dir) # save results
+
+ def crop(self, save_dir='runs/detect/exp'):
+ save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
+ self.display(crop=True, save_dir=save_dir) # crop results
+ LOGGER.info(f'Saved results to {save_dir}\n')
+
+ def render(self):
+ self.display(render=True) # render results
+ return self.imgs
+
+ def pandas(self):
+ # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
+ new = copy(self) # return copy
+ ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
+ cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
+ for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
+ a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
+ setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
+ return new
+
+ def tolist(self):
+ # return a list of Detections objects, i.e. 'for result in results.tolist():'
+ x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)]
+ for d in x:
+ for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
+ setattr(d, k, getattr(d, k)[0]) # pop out of list
+ return x
+
+ def __len__(self):
+ return self.n
+
+
+class Classify(nn.Module):
+ # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
+ def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
+ super().__init__()
+ self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
+ self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
+ self.flat = nn.Flatten()
+
+ def forward(self, x):
+ z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
+ return self.flat(self.conv(z)) # flatten to x(b,c2)
+
+
+
+
+
+
+
+
+
diff --git a/models/common_1.py b/models/common_1.py
new file mode 100644
index 0000000..80f0b62
--- /dev/null
+++ b/models/common_1.py
@@ -0,0 +1,433 @@
+# YOLOv5 common modules
+
+import logging
+import warnings
+from copy import copy
+from pathlib import Path
+
+import math
+import numpy as np
+import pandas as pd
+import requests
+import torch
+import torch.nn as nn
+from PIL import Image
+from torch.cuda import amp
+
+from utils.datasets import exif_transpose, letterbox
+from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box
+from utils.plots import colors, plot_one_box
+from utils.torch_utils import time_sync
+
+LOGGER = logging.getLogger(__name__)
+
+#为卷积或池化后特征图大小不变，在输入特征图上做零填充。填充多少，由此函数计算。
+def autopad(k, p=None): # kernel, padding
+ # Pad to 'same'
+ if p is None:
+ p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad 如果k是int型的数，那就整除2，否则。。‘x // 2是x除2之后，取整数商。’
+ return p
+
+
+class Conv(nn.Module):#Conv类继承于nn.Module 他做了标准卷积+bn层+hardswish
+ # Standard convolution
+ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups #默认act为true
+ super().__init__()
+ self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)#bias为False因为，卷积2D和BN做完后，下面特征图融合时，偏置还会消掉。
+ self.bn = nn.BatchNorm2d(c2)
+ self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) #如果act为true时，，
+
+ def forward(self, x):#正向传播函数 网络执行的顺序是由forward来决定的，先输入x，得到conv，再得到bn，再得到act是激活函数
+ return self.act(self.bn(self.conv(x)))
+
+ def forward_fuse(self, x): #此处没有BN，只有卷积和激活
+ return self.act(self.conv(x))
+
+
+class DWConv(Conv): #dw卷积需要哪些参数，如下：输入、输出、卷积核大小、步长。将参数传给上面的Conv！
+ # Depth-wise convolution class
+ #在yolov5中没有真正使用，k=1是卷积核kenel，s=1是步长 #g是最大公约数，用于分组。这个缺失return返回conv。
+ def __init__(self, c1, c2, k=1, s=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
+ super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
+
+
+class TransformerLayer(nn.Module):
+ # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
+ def __init__(self, c, num_heads):
+ super().__init__()
+ self.q = nn.Linear(c, c, bias=False)
+ self.k = nn.Linear(c, c, bias=False)
+ self.v = nn.Linear(c, c, bias=False)
+ self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
+ self.fc1 = nn.Linear(c, c, bias=False)
+ self.fc2 = nn.Linear(c, c, bias=False)
+
+ def forward(self, x):
+ x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
+ x = self.fc2(self.fc1(x)) + x
+ return x
+
+
+class TransformerBlock(nn.Module):
+ # Vision Transformer https://arxiv.org/abs/2010.11929
+ def __init__(self, c1, c2, num_heads, num_layers):
+ super().__init__()
+ self.conv = None
+ if c1 != c2:
+ self.conv = Conv(c1, c2)
+ self.linear = nn.Linear(c2, c2) # learnable position embedding
+ self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)])
+ self.c2 = c2
+
+ def forward(self, x):
+ if self.conv is not None:
+ x = self.conv(x)
+ b, _, w, h = x.shape
+ p = x.flatten(2).unsqueeze(0).transpose(0, 3).squeeze(3)
+ return self.tr(p + self.linear(p)).unsqueeze(3).transpose(0, 3).reshape(b, self.c2, w, h)
+
+
+class Bottleneck(nn.Module):
+ # Standard bottleneck
+ def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion #shortcut默认True即为有短接。
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = Conv(c_, c2, 3, 1, g=g)
+ self.add = shortcut and c1 == c2 #输入与输出维度相同才能做相加运算
+
+ def forward(self, x):
+ return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) #x+两次卷积的值，否则只有两个卷积运算！！
+
+
+class BottleneckCSP(nn.Module):
+ # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks#一个分支是标准bottleneck堆叠，另一个分支是普通卷积层
+ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1) #Conv模块
+ self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) #卷积运算
+ self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)#卷积运算
+ self.cv4 = Conv(2 * c_, c2, 1, 1) #Conv模块 做拼接后，inchannel维度变大了
+ self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
+ self.act = nn.LeakyReLU(0.1, inplace=True)
+ self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) #*是解包，将list拆成很多独立元素
+ #用了n次的Bottleneck操作，得到后解包送入Sequential，给m
+
+ def forward(self, x):
+ y1 = self.cv3(self.m(self.cv1(x)))
+ y2 = self.cv2(x)
+ return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) #cv4是Conv模块
+
+
+class C3(nn.Module):
+ # CSP Bottleneck with 3 convolutions
+ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = Conv(c1, c_, 1, 1)
+ self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
+ self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
+ # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
+
+ def forward(self, x):
+ return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
+
+
+class C3TR(C3):
+ # C3 module with TransformerBlock()
+ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+ super().__init__(c1, c2, n, shortcut, g, e)
+ c_ = int(c2 * e)
+ self.m = TransformerBlock(c_, c_, 4, n)
+
+
+class C3SPP(C3):
+ # C3 module with SPP()
+ def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
+ super().__init__(c1, c2, n, shortcut, g, e)
+ c_ = int(c2 * e)
+ self.m = SPP(c_, c_, k)
+
+
+
+#空间金字塔池化
+class SPP(nn.Module):
+ # Spatial pyramid pooling layer used in YOLOv3-SPP
+ def __init__(self, c1, c2, k=(5, 9, 13)):#k是元祖
+ super().__init__()
+ c_ = c1 // 2 # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1) #Conv模块
+ self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) #Conv模块
+ self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])#最大池化 5,9,13都要做最大池化
+
+ def forward(self, x):
+ x = self.cv1(x)
+ with warnings.catch_warnings():
+ warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
+ return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) #叠加没做最大池化的输入+最大池化的
+
+
+class Focus(nn.Module):
+ # Focus wh information into c-space
+ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
+ super().__init__()
+ self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
+ # self.contract = Contract(gain=2)
+
+ def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) 特征图宽高都会减半
+ return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))# 这里slice
+ # return self.conv(self.contract(x))
+
+
+class Contract(nn.Module):
+ # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
+ def __init__(self, gain=2):
+ super().__init__()
+ self.gain = gain
+
+ def forward(self, x):
+ b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
+ s = self.gain
+ x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2)
+ x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
+ return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40)
+
+
+class Expand(nn.Module):
+ # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
+ def __init__(self, gain=2):
+ super().__init__()
+ self.gain = gain
+
+ def forward(self, x):
+ b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
+ s = self.gain
+ x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80)
+ x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
+ return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160)
+
+
+class Concat(nn.Module):#定义拼接的类
+ # Concatenate a list of tensors along dimension
+ def __init__(self, dimension=1):
+ super().__init__()
+ self.d = dimension #定义沿着哪个维度进行拼接
+
+ def forward(self, x):
+ return torch.cat(x, self.d)
+
+
+# def nms(self, mode=True): # add or remove NMS module 是不是和AutoShape是一个作用？？？？？？？不是！！！
+# present = type(self.model[-1]) is NMS # last layer is NMS
+# if mode and not present:
+# print('Adding NMS... ')
+# m = NMS() # module
+# m.f = -1 # from
+# m.i = self.model[-1].i + 1 # index
+# self.model.add_module(name='%s' % m.i, module=m) # add
+# self.eval()
+# elif not mode and present:
+# print('Removing NMS... ')
+# self.model = self.model[:-1] # remove
+# return self
+
+
+class NMS(nn.Module):
+#非极大值抑制模块
+ conf=0.25;
+ iou=0.45
+ classes=None
+
+ def __in__(self):
+ super(NMS,self).__init__()
+
+ def forward(self,x):
+ return non_max_suppression(x[0],conf_thres=self.conf,iou_thres=self.iou,classes=self.classes)
+
+
+
+class AutoShape(nn.Module): #图像来自不同文件，做一个预处理 在预处理、推理和非极大值抑制时要调整#在yolov5基本没有用？？
+ # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+ conf = 0.25 # NMS confidence threshold
+ iou = 0.45 # NMS IoU threshold
+ classes = None # (optional list) filter by class
+ max_det = 1000 # maximum number of detections per image
+
+ def __init__(self, model):
+ super().__init__()
+ self.model = model.eval()
+
+ def autoshape(self):
+ LOGGER.info('AutoShape already enabled, skipping... ') # model already converted to model.autoshape()
+ return self
+
+ @torch.no_grad()
+ def forward(self, imgs, size=640, augment=False, profile=False):
+ # Inference from various sources. For height=640, width=1280, RGB images example inputs are:
+ # file: imgs = 'data/images/zidane.jpg' # str or PosixPath
+ # URI: = 'https://ultralytics.com/images/zidane.jpg'
+ # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
+ # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3)
+ # numpy: = np.zeros((640,1280,3)) # HWC
+ # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
+ # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
+
+ t = [time_sync()]
+ p = next(self.model.parameters()) # for device and type
+ if isinstance(imgs, torch.Tensor): # torch
+ with amp.autocast(enabled=p.device.type != 'cpu'):
+ return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
+
+ # Pre-process
+ n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
+ shape0, shape1, files = [], [], [] # image and inference shapes, filenames
+ for i, im in enumerate(imgs):
+ f = f'image{i}' # filename
+ if isinstance(im, (str, Path)): # filename or uri
+ im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
+ im = np.asarray(exif_transpose(im))
+ elif isinstance(im, Image.Image): # PIL Image
+ im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
+ files.append(Path(f).with_suffix('.jpg').name)
+ if im.shape[0] < 5: # image in CHW
+ im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
+ im = im[..., :3] if im.ndim == 3 else np.tile(im[..., None], 3) # enforce 3ch input
+ s = im.shape[:2] # HWC
+ shape0.append(s) # image shape
+ g = (size / max(s)) # gain
+ shape1.append([y * g for y in s])
+ imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
+ shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
+ x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
+ x = np.stack(x, 0) if n > 1 else x[0][None] # stack
+ x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
+ x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
+ t.append(time_sync())
+
+ with amp.autocast(enabled=p.device.type != 'cpu'):
+ # Inference
+ y = self.model(x, augment, profile)[0] # forward
+ t.append(time_sync())
+
+ # Post-process
+ y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det) # NMS
+ for i in range(n):
+ scale_coords(shape1, y[i][:, :4], shape0[i])
+
+ t.append(time_sync())
+ return Detections(imgs, y, files, t, self.names, x.shape)
+
+
+class Flatten(nn.Module):
+ #U展平
+ @staticmethod
+ def forward(x):
+ return x.view(x.size(0),-1)
+
+
+
+
+class Detections:
+ # YOLOv5 detections class for inference results
+ def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
+ super().__init__()
+ d = pred[0].device # device
+ gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations
+ self.imgs = imgs # list of images as numpy arrays
+ self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
+ self.names = names # class names
+ self.files = files # image filenames
+ self.xyxy = pred # xyxy pixels
+ self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
+ self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
+ self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
+ self.n = len(self.pred) # number of images (batch size)
+ self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms)
+ self.s = shape # inference BCHW shape
+
+ def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
+ for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
+ str = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '
+ if pred.shape[0]:
+ for c in pred[:, -1].unique():
+ n = (pred[:, -1] == c).sum() # detections per class
+ str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
+ if show or save or render or crop:
+ for *box, conf, cls in reversed(pred): # xyxy, confidence, class
+ label = f'{self.names[int(cls)]} {conf:.2f}'
+ if crop:
+ save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i])
+ else: # all others
+ plot_one_box(box, im, label=label, color=colors(cls))
+ else:
+ str += '(no detections)'
+
+ im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
+ if pprint:
+ LOGGER.info(str.rstrip(', '))
+ if show:
+ im.show(self.files[i]) # show
+ if save:
+ f = self.files[i]
+ im.save(save_dir / f) # save
+ if i == self.n - 1:
+ LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to '{save_dir}'")
+ if render:
+ self.imgs[i] = np.asarray(im)
+
+ def print(self):
+ self.display(pprint=True) # print results
+ LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
+ self.t)
+
+ def show(self):
+ self.display(show=True) # show results
+
+ def save(self, save_dir='runs/detect/exp'):
+ save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
+ self.display(save=True, save_dir=save_dir) # save results
+
+ def crop(self, save_dir='runs/detect/exp'):
+ save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
+ self.display(crop=True, save_dir=save_dir) # crop results
+ LOGGER.info(f'Saved results to {save_dir}\n')
+
+ def render(self):
+ self.display(render=True) # render results
+ return self.imgs
+
+ def pandas(self):
+ # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
+ new = copy(self) # return copy
+ ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
+ cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
+ for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
+ a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
+ setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
+ return new
+
+ def tolist(self):
+ # return a list of Detections objects, i.e. 'for result in results.tolist():'
+ x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)]
+ for d in x:
+ for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
+ setattr(d, k, getattr(d, k)[0]) # pop out of list
+ return x
+
+ def __len__(self):
+ return self.n
+
+
+class Classify(nn.Module):#用于第二级分类
+ # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
+ def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
+ super().__init__()
+ self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) 自适应平均池化
+ self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
+ self.flat = nn.Flatten()
+
+ def forward(self, x):
+ z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
+ return self.flat(self.conv(z)) # flatten to x(b,c2)
diff --git a/models/experimental.py b/models/experimental.py
new file mode 100644
index 0000000..581c7b1
--- /dev/null
+++ b/models/experimental.py
@@ -0,0 +1,136 @@
+# YOLOv5 experimental modules
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from models.common import Conv, DWConv
+from utils.downloads import attempt_download
+
+
+class CrossConv(nn.Module):
+ # Cross Convolution Downsample
+ def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
+ # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, (1, k), (1, s))
+ self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
+ self.add = shortcut and c1 == c2
+
+ def forward(self, x):
+ return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class Sum(nn.Module):
+ # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+ def __init__(self, n, weight=False): # n: number of inputs
+ super().__init__()
+ self.weight = weight # apply weights boolean
+ self.iter = range(n - 1) # iter object
+ if weight:
+ self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights
+
+ def forward(self, x):
+ y = x[0] # no weight
+ if self.weight:
+ w = torch.sigmoid(self.w) * 2
+ for i in self.iter:
+ y = y + x[i + 1] * w[i]
+ else:
+ for i in self.iter:
+ y = y + x[i + 1]
+ return y
+
+
+class GhostConv(nn.Module):
+ # Ghost Convolution https://github.com/huawei-noah/ghostnet
+ def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
+ super().__init__()
+ c_ = c2 // 2 # hidden channels
+ self.cv1 = Conv(c1, c_, k, s, None, g, act)
+ self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
+
+ def forward(self, x):
+ y = self.cv1(x)
+ return torch.cat([y, self.cv2(y)], 1)
+
+
+class GhostBottleneck(nn.Module):
+ # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
+ def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
+ super().__init__()
+ c_ = c2 // 2
+ self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
+ DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
+ GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
+ self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
+ Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
+
+ def forward(self, x):
+ return self.conv(x) + self.shortcut(x)
+
+
+class MixConv2d(nn.Module):
+ # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
+ def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
+ super().__init__()
+ groups = len(k)
+ if equal_ch: # equal c_ per group
+ i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices
+ c_ = [(i == g).sum() for g in range(groups)] # intermediate channels
+ else: # equal weight.numel() per group
+ b = [c2] + [0] * groups
+ a = np.eye(groups + 1, groups, k=-1)
+ a -= np.roll(a, 1, axis=1)
+ a *= np.array(k) ** 2
+ a[0] = 1
+ c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
+
+ self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
+ self.bn = nn.BatchNorm2d(c2)
+ self.act = nn.LeakyReLU(0.1, inplace=True)
+
+ def forward(self, x):
+ return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
+
+
+class Ensemble(nn.ModuleList):
+ # Ensemble of models
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, x, augment=False, profile=False, visualize=False):
+ y = []
+ for module in self:
+ y.append(module(x, augment, profile, visualize)[0])
+ # y = torch.stack(y).max(0)[0] # max ensemble
+ # y = torch.stack(y).mean(0) # mean ensemble
+ y = torch.cat(y, 1) # nms ensemble
+ return y, None # inference, train output
+
+
+def attempt_load(weights, map_location=None, inplace=True):
+ from models.yolo import Detect, Model
+
+ # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
+ model = Ensemble()
+ for w in weights if isinstance(weights, list) else [weights]:
+ ckpt = torch.load(attempt_download(w), map_location=map_location) # load
+ model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model
+
+ # Compatibility updates
+ for m in model.modules():
+ if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
+ m.inplace = inplace # pytorch 1.7.0 compatibility
+ elif type(m) is Conv:
+ m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
+
+ if len(model) == 1:
+ return model[-1] # return model
+ else:
+ print(f'Ensemble created with {weights}\n')
+ for k in ['names']:
+ setattr(model, k, getattr(model[-1], k))
+ model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride
+ return model # return ensemble
diff --git a/models/hub/anchors.yaml b/models/hub/anchors.yaml
new file mode 100644
index 0000000..5751295
--- /dev/null
+++ b/models/hub/anchors.yaml
@@ -0,0 +1,58 @@
+# Default YOLOv5 anchors for COCO data
+
+
+# P5 -------------------------------------------------------------------------------------------------------------------
+# P5-640:
+anchors_p5_640:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+
+# P6 -------------------------------------------------------------------------------------------------------------------
+# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387
+anchors_p6_640:
+ - [9,11, 21,19, 17,41] # P3/8
+ - [43,32, 39,70, 86,64] # P4/16
+ - [65,131, 134,130, 120,265] # P5/32
+ - [282,180, 247,354, 512,387] # P6/64
+
+# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792
+anchors_p6_1280:
+ - [19,27, 44,40, 38,94] # P3/8
+ - [96,68, 86,152, 180,137] # P4/16
+ - [140,301, 303,264, 238,542] # P5/32
+ - [436,615, 739,380, 925,792] # P6/64
+
+# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187
+anchors_p6_1920:
+ - [28,41, 67,59, 57,141] # P3/8
+ - [144,103, 129,227, 270,205] # P4/16
+ - [209,452, 455,396, 358,812] # P5/32
+ - [653,922, 1109,570, 1387,1187] # P6/64
+
+
+# P7 -------------------------------------------------------------------------------------------------------------------
+# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372
+anchors_p7_640:
+ - [11,11, 13,30, 29,20] # P3/8
+ - [30,46, 61,38, 39,92] # P4/16
+ - [78,80, 146,66, 79,163] # P5/32
+ - [149,150, 321,143, 157,303] # P6/64
+ - [257,402, 359,290, 524,372] # P7/128
+
+# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818
+anchors_p7_1280:
+ - [19,22, 54,36, 32,77] # P3/8
+ - [70,83, 138,71, 75,173] # P4/16
+ - [165,159, 148,334, 375,151] # P5/32
+ - [334,317, 251,626, 499,474] # P6/64
+ - [750,326, 534,814, 1079,818] # P7/128
+
+# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227
+anchors_p7_1920:
+ - [29,34, 81,55, 47,115] # P3/8
+ - [105,124, 207,107, 113,259] # P4/16
+ - [247,238, 222,500, 563,227] # P5/32
+ - [501,476, 376,939, 749,711] # P6/64
+ - [1126,489, 801,1222, 1618,1227] # P7/128
diff --git a/models/hub/yolov3-spp.yaml b/models/hub/yolov3-spp.yaml
new file mode 100644
index 0000000..ddc0549
--- /dev/null
+++ b/models/hub/yolov3-spp.yaml
@@ -0,0 +1,49 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# darknet53 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Conv, [32, 3, 1]], # 0
+ [-1, 1, Conv, [64, 3, 2]], # 1-P1/2
+ [-1, 1, Bottleneck, [64]],
+ [-1, 1, Conv, [128, 3, 2]], # 3-P2/4
+ [-1, 2, Bottleneck, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 5-P3/8
+ [-1, 8, Bottleneck, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 7-P4/16
+ [-1, 8, Bottleneck, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
+ [-1, 4, Bottleneck, [1024]], # 10
+ ]
+
+# YOLOv3-SPP head
+head:
+ [[-1, 1, Bottleneck, [1024, False]],
+ [-1, 1, SPP, [512, [5, 9, 13]]],
+ [-1, 1, Conv, [1024, 3, 1]],
+ [-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
+
+ [-2, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 8], 1, Concat, [1]], # cat backbone P4
+ [-1, 1, Bottleneck, [512, False]],
+ [-1, 1, Bottleneck, [512, False]],
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
+
+ [-2, 1, Conv, [128, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P3
+ [-1, 1, Bottleneck, [256, False]],
+ [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
+
+ [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/models/hub/yolov3-tiny.yaml b/models/hub/yolov3-tiny.yaml
new file mode 100644
index 0000000..537ad75
--- /dev/null
+++ b/models/hub/yolov3-tiny.yaml
@@ -0,0 +1,39 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+ - [10,14, 23,27, 37,58] # P4/16
+ - [81,82, 135,169, 344,319] # P5/32
+
+# YOLOv3-tiny backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Conv, [16, 3, 1]], # 0
+ [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2
+ [-1, 1, Conv, [32, 3, 1]],
+ [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4
+ [-1, 1, Conv, [64, 3, 1]],
+ [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8
+ [-1, 1, Conv, [128, 3, 1]],
+ [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16
+ [-1, 1, Conv, [256, 3, 1]],
+ [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32
+ [-1, 1, Conv, [512, 3, 1]],
+ [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11
+ [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12
+ ]
+
+# YOLOv3-tiny head
+head:
+ [[-1, 1, Conv, [1024, 3, 1]],
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large)
+
+ [-2, 1, Conv, [128, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 8], 1, Concat, [1]], # cat backbone P4
+ [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)
+
+ [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5)
+ ]
diff --git a/models/hub/yolov3.yaml b/models/hub/yolov3.yaml
new file mode 100644
index 0000000..3adfc2c
--- /dev/null
+++ b/models/hub/yolov3.yaml
@@ -0,0 +1,49 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# darknet53 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Conv, [32, 3, 1]], # 0
+ [-1, 1, Conv, [64, 3, 2]], # 1-P1/2
+ [-1, 1, Bottleneck, [64]],
+ [-1, 1, Conv, [128, 3, 2]], # 3-P2/4
+ [-1, 2, Bottleneck, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 5-P3/8
+ [-1, 8, Bottleneck, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 7-P4/16
+ [-1, 8, Bottleneck, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
+ [-1, 4, Bottleneck, [1024]], # 10
+ ]
+
+# YOLOv3 head
+head:
+ [[-1, 1, Bottleneck, [1024, False]],
+ [-1, 1, Conv, [512, [1, 1]]],
+ [-1, 1, Conv, [1024, 3, 1]],
+ [-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
+
+ [-2, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 8], 1, Concat, [1]], # cat backbone P4
+ [-1, 1, Bottleneck, [512, False]],
+ [-1, 1, Bottleneck, [512, False]],
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
+
+ [-2, 1, Conv, [128, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P3
+ [-1, 1, Bottleneck, [256, False]],
+ [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
+
+ [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/models/hub/yolov5-bifpn.yaml b/models/hub/yolov5-bifpn.yaml
new file mode 100644
index 0000000..69f7b59
--- /dev/null
+++ b/models/hub/yolov5-bifpn.yaml
@@ -0,0 +1,46 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]]
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]],
+ [-1, 3, C3, [1024, False]], # 9
+ ]
+
+# YOLOv5 BiFPN head
+head:
+ [[-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 13
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 14, 6], 1, Concat, [1]], # cat P4
+ [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 10], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
+
+ [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/models/hub/yolov5-fpn.yaml b/models/hub/yolov5-fpn.yaml
new file mode 100644
index 0000000..217e4ca
--- /dev/null
+++ b/models/hub/yolov5-fpn.yaml
@@ -0,0 +1,40 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, Bottleneck, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, BottleneckCSP, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, BottleneckCSP, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]],
+ [-1, 6, BottleneckCSP, [1024]], # 9
+ ]
+
+# YOLOv5 FPN head
+head:
+ [[-1, 3, BottleneckCSP, [1024, False]], # 10 (P5/32-large)
+
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 1, Conv, [512, 1, 1]],
+ [-1, 3, BottleneckCSP, [512, False]], # 14 (P4/16-medium)
+
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 3, BottleneckCSP, [256, False]], # 18 (P3/8-small)
+
+ [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/models/hub/yolov5-p2.yaml b/models/hub/yolov5-p2.yaml
new file mode 100644
index 0000000..6a932a8
--- /dev/null
+++ b/models/hub/yolov5-p2.yaml
@@ -0,0 +1,52 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors: 3
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]],
+ [-1, 3, C3, [1024, False]], # 9
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 13
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+ [-1, 1, Conv, [128, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 2], 1, Concat, [1]], # cat backbone P2
+ [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall)
+
+ [-1, 1, Conv, [128, 3, 2]],
+ [[-1, 18], 1, Concat, [1]], # cat head P3
+ [-1, 3, C3, [256, False]], # 24 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 14], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 27 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 10], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [1024, False]], # 30 (P5/32-large)
+
+ [[24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/models/hub/yolov5-p6.yaml b/models/hub/yolov5-p6.yaml
new file mode 100644
index 0000000..58b86b0
--- /dev/null
+++ b/models/hub/yolov5-p6.yaml
@@ -0,0 +1,54 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors: 3
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
+ [-1, 3, C3, [768]],
+ [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
+ [-1, 1, SPP, [1024, [3, 5, 7]]],
+ [-1, 3, C3, [1024, False]], # 11
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [768, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 8], 1, Concat, [1]], # cat backbone P5
+ [-1, 3, C3, [768, False]], # 15
+
+ [-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 19
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 23 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 20], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 16], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [768, False]], # 29 (P5/32-large)
+
+ [-1, 1, Conv, [768, 3, 2]],
+ [[-1, 12], 1, Concat, [1]], # cat head P6
+ [-1, 3, C3, [1024, False]], # 32 (P5/64-xlarge)
+
+ [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
+ ]
diff --git a/models/hub/yolov5-p7.yaml b/models/hub/yolov5-p7.yaml
new file mode 100644
index 0000000..f6e8fc7
--- /dev/null
+++ b/models/hub/yolov5-p7.yaml
@@ -0,0 +1,65 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors: 3
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
+ [-1, 3, C3, [768]],
+ [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
+ [-1, 3, C3, [1024]],
+ [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128
+ [-1, 1, SPP, [1280, [3, 5]]],
+ [-1, 3, C3, [1280, False]], # 13
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [1024, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 10], 1, Concat, [1]], # cat backbone P6
+ [-1, 3, C3, [1024, False]], # 17
+
+ [-1, 1, Conv, [768, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 8], 1, Concat, [1]], # cat backbone P5
+ [-1, 3, C3, [768, False]], # 21
+
+ [-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 25
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 29 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 26], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 32 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 22], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [768, False]], # 35 (P5/32-large)
+
+ [-1, 1, Conv, [768, 3, 2]],
+ [[-1, 18], 1, Concat, [1]], # cat head P6
+ [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge)
+
+ [-1, 1, Conv, [1024, 3, 2]],
+ [[-1, 14], 1, Concat, [1]], # cat head P7
+ [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge)
+
+ [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7)
+ ]
diff --git a/models/hub/yolov5-panet.yaml b/models/hub/yolov5-panet.yaml
new file mode 100644
index 0000000..c5f3b48
--- /dev/null
+++ b/models/hub/yolov5-panet.yaml
@@ -0,0 +1,46 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, BottleneckCSP, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, BottleneckCSP, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, BottleneckCSP, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]],
+ [-1, 3, BottleneckCSP, [1024, False]], # 9
+ ]
+
+# YOLOv5 PANet head
+head:
+ [[-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, BottleneckCSP, [512, False]], # 13
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 14], 1, Concat, [1]], # cat head P4
+ [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 10], 1, Concat, [1]], # cat head P5
+ [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large)
+
+ [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/models/hub/yolov5l6.yaml b/models/hub/yolov5l6.yaml
new file mode 100644
index 0000000..d5afd7d
--- /dev/null
+++ b/models/hub/yolov5l6.yaml
@@ -0,0 +1,58 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+ - [19,27, 44,40, 38,94] # P3/8
+ - [96,68, 86,152, 180,137] # P4/16
+ - [140,301, 303,264, 238,542] # P5/32
+ - [436,615, 739,380, 925,792] # P6/64
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
+ [-1, 3, C3, [768]],
+ [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
+ [-1, 1, SPP, [1024, [3, 5, 7]]],
+ [-1, 3, C3, [1024, False]], # 11
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [768, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 8], 1, Concat, [1]], # cat backbone P5
+ [-1, 3, C3, [768, False]], # 15
+
+ [-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 19
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 23 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 20], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 16], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [768, False]], # 29 (P5/32-large)
+
+ [-1, 1, Conv, [768, 3, 2]],
+ [[-1, 12], 1, Concat, [1]], # cat head P6
+ [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
+
+ [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
+ ]
diff --git a/models/hub/yolov5m6.yaml b/models/hub/yolov5m6.yaml
new file mode 100644
index 0000000..16a841a
--- /dev/null
+++ b/models/hub/yolov5m6.yaml
@@ -0,0 +1,58 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 0.67 # model depth multiple
+width_multiple: 0.75 # layer channel multiple
+anchors:
+ - [19,27, 44,40, 38,94] # P3/8
+ - [96,68, 86,152, 180,137] # P4/16
+ - [140,301, 303,264, 238,542] # P5/32
+ - [436,615, 739,380, 925,792] # P6/64
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
+ [-1, 3, C3, [768]],
+ [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
+ [-1, 1, SPP, [1024, [3, 5, 7]]],
+ [-1, 3, C3, [1024, False]], # 11
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [768, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 8], 1, Concat, [1]], # cat backbone P5
+ [-1, 3, C3, [768, False]], # 15
+
+ [-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 19
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 23 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 20], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 16], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [768, False]], # 29 (P5/32-large)
+
+ [-1, 1, Conv, [768, 3, 2]],
+ [[-1, 12], 1, Concat, [1]], # cat head P6
+ [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
+
+ [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
+ ]
diff --git a/models/hub/yolov5s6.yaml b/models/hub/yolov5s6.yaml
new file mode 100644
index 0000000..2fb2450
--- /dev/null
+++ b/models/hub/yolov5s6.yaml
@@ -0,0 +1,58 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 0.33 # model depth multiple
+width_multiple: 0.50 # layer channel multiple
+anchors:
+ - [19,27, 44,40, 38,94] # P3/8
+ - [96,68, 86,152, 180,137] # P4/16
+ - [140,301, 303,264, 238,542] # P5/32
+ - [436,615, 739,380, 925,792] # P6/64
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
+ [-1, 3, C3, [768]],
+ [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
+ [-1, 1, SPP, [1024, [3, 5, 7]]],
+ [-1, 3, C3, [1024, False]], # 11
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [768, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 8], 1, Concat, [1]], # cat backbone P5
+ [-1, 3, C3, [768, False]], # 15
+
+ [-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 19
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 23 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 20], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 16], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [768, False]], # 29 (P5/32-large)
+
+ [-1, 1, Conv, [768, 3, 2]],
+ [[-1, 12], 1, Concat, [1]], # cat head P6
+ [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
+
+ [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
+ ]
diff --git a/models/hub/yolov5x6.yaml b/models/hub/yolov5x6.yaml
new file mode 100644
index 0000000..c518710
--- /dev/null
+++ b/models/hub/yolov5x6.yaml
@@ -0,0 +1,58 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.33 # model depth multiple
+width_multiple: 1.25 # layer channel multiple
+anchors:
+ - [19,27, 44,40, 38,94] # P3/8
+ - [96,68, 86,152, 180,137] # P4/16
+ - [140,301, 303,264, 238,542] # P5/32
+ - [436,615, 739,380, 925,792] # P6/64
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
+ [-1, 3, C3, [768]],
+ [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
+ [-1, 1, SPP, [1024, [3, 5, 7]]],
+ [-1, 3, C3, [1024, False]], # 11
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [768, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 8], 1, Concat, [1]], # cat backbone P5
+ [-1, 3, C3, [768, False]], # 15
+
+ [-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 19
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 23 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 20], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 26 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 16], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [768, False]], # 29 (P5/32-large)
+
+ [-1, 1, Conv, [768, 3, 2]],
+ [[-1, 12], 1, Concat, [1]], # cat head P6
+ [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
+
+ [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
+ ]
diff --git a/models/yolo.py b/models/yolo.py
new file mode 100644
index 0000000..ee5cb1d
--- /dev/null
+++ b/models/yolo.py
@@ -0,0 +1,300 @@
+"""YOLOv5-specific modules
+
+Usage:
+ $ python path/to/models/yolo.py --cfg yolov5s.yaml
+"""
+
+import argparse
+import sys
+from copy import deepcopy
+from pathlib import Path
+
+FILE = Path(__file__).absolute()
+sys.path.append(FILE.parents[1].as_posix()) # add yolov5/ to path
+
+from models.common import *
+from models.experimental import *
+from utils.autoanchor import check_anchor_order
+from utils.general import make_divisible, check_file, set_logging
+from utils.plots import feature_visualization
+from utils.torch_utils import time_sync, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
+ select_device, copy_attr
+
+try:
+ import thop # for FLOPs computation
+except ImportError:
+ thop = None
+
+LOGGER = logging.getLogger(__name__)
+
+
+class Detect(nn.Module):#对特征图进行检测的类
+ stride = None # strides computed during build
+ onnx_dynamic = False # ONNX export parameter
+
+ def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer
+ #ch()相应于每个特征图上卷积核的通道数
+ super().__init__()
+ self.nc = nc # number of classes
+ self.no = nc + 5 # number of outputs per anchor 每个anchor输出值的个数20个类别+4个坐标信息+得分
+ self.nl = len(anchors) # number of detection layers 做检测的特征图，相应的层数是4？？
+ self.na = len(anchors[0]) // 2 # number of anchors
+ self.grid = [torch.zeros(1)] * self.nl # init grid
+ a = torch.tensor(anchors).float().view(self.nl, -1, 2) #对变量a进行赋值
+ self.register_buffer('anchors', a) # shape(nl,na,2)
+ self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
+ self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv 输入通道是x，输出通道是self.no * self.na
+ #1x1卷积是将特征图通过此卷积运算得到我们预测后的值，包括预测框的坐标信息，目标性得分，分类概率。这里ch是得到通道的取值，分别为[192,192,384,768]??
+ self.inplace = inplace # use in-place ops (e.g. slice assignment)
+
+ def forward(self, x):
+ # x = x.copy() # for profiling
+ z = [] # inference output
+ for i in range(self.nl):#对nl进行迭代，即每个先验框进行迭代，
+ x[i] = self.m[i](x[i]) # conv 在detect层做卷积运算
+ bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
+ x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() #用permute调整前面的顺序，再用contiguout变成内存连续变量
+
+ if not self.training: # inference判断是否在做训练，不在做训练则是在做inference即推理
+ if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic:
+ self.grid[i] = self._make_grid(nx, ny).to(x[i].device) #调用make_grid函数调用网格
+
+ y = x[i].sigmoid() #调用sigmoid函数，求出预测框坐标信息，包括xy坐标信息以及wh坐标信息
+ if self.inplace:
+ y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
+ y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
+ else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
+ xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
+ wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].view(1, self.na, 1, 1, 2) # wh
+ y = torch.cat((xy, wh, y[..., 4:]), -1)
+ z.append(y.view(bs, -1, self.no))#预测框信息
+
+ return x if self.training else (torch.cat(z, 1), x) #如果是训练，则返回x即可。如果推理，返回预测框坐标，obj（目标性得分），cls（概率信息，这里是x）
+
+ @staticmethod
+ def _make_grid(nx=20, ny=20):#图像上划分网格，如果是640x640，则刚好32倍后是20x20
+ yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
+ return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
+
+
+#网络模型类 如何解析项目文件来构建网络结构
+class Model(nn.Module):
+ def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
+ super().__init__()
+ if isinstance(cfg, dict):
+ self.yaml = cfg # model dict
+ else: # is *.yaml
+ import yaml # for torch hub
+ self.yaml_file = Path(cfg).name #通过路径，将文件名取出，字符串型
+ with open(cfg,'r',encoding='utf-8') as f:
+ self.yaml = yaml.safe_load(f) # model dict #yaml变成字典
+
+ # Define model
+ ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
+ if nc and nc != self.yaml['nc']:
+ LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
+ self.yaml['nc'] = nc # override yaml value
+ if anchors:
+ LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
+ self.yaml['anchors'] = round(anchors) # override yaml value
+ self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist 通过parse_model来解析构建model
+ self.names = [str(i) for i in range(self.yaml['nc'])] # default names
+ self.inplace = self.yaml.get('inplace', True)
+ # LOGGER.info([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
+
+ # Build strides, anchors
+ m = self.model[-1] # Detect()
+ if isinstance(m, Detect):
+ s = 256 # 2x min stride
+ m.inplace = self.inplace
+ m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
+ m.anchors /= m.stride.view(-1, 1, 1)
+ check_anchor_order(m)
+ self.stride = m.stride
+ self._initialize_biases() # only run once
+ # LOGGER.info('Strides: %s' % m.stride.tolist())
+
+ # Init weights, biases
+ initialize_weights(self)
+ self.info()
+ LOGGER.info('')
+
+ def forward(self, x, augment=False, profile=False, visualize=False):
+ if augment:
+ return self.forward_augment(x) # augmented inference, None
+ return self.forward_once(x, profile, visualize) # single-scale inference, train
+
+ def forward_augment(self, x):
+ img_size = x.shape[-2:] # height, width
+ s = [1, 0.83, 0.67] # scales
+ f = [None, 3, None] # flips (2-ud, 3-lr)
+ y = [] # outputs
+ for si, fi in zip(s, f):
+ xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
+ yi = self.forward_once(xi)[0] # forward
+ # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
+ yi = self._descale_pred(yi, fi, si, img_size)
+ y.append(yi)
+ return torch.cat(y, 1), None # augmented inference, train
+
+ def forward_once(self, x, profile=False, visualize=False):
+ y, dt = [], [] # outputs
+ for m in self.model:
+ if m.f != -1: # if not from previous layer
+ x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
+
+ if profile:
+ o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
+ t = time_sync()
+ for _ in range(10):
+ _ = m(x)
+ dt.append((time_sync() - t) * 100)
+ if m == self.model[0]:
+ LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} {'module'}")
+ LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
+
+ x = m(x) # run
+ y.append(x if m.i in self.save else None) # save output
+
+ if visualize:
+ feature_visualization(x, m.type, m.i, save_dir=visualize)
+
+ if profile:
+ LOGGER.info('%.1fms total' % sum(dt))
+ return x
+
+ def _descale_pred(self, p, flips, scale, img_size):
+ # de-scale predictions following augmented inference (inverse operation)
+ if self.inplace:
+ p[..., :4] /= scale # de-scale
+ if flips == 2:
+ p[..., 1] = img_size[0] - p[..., 1] # de-flip ud
+ elif flips == 3:
+ p[..., 0] = img_size[1] - p[..., 0] # de-flip lr
+ else:
+ x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale
+ if flips == 2:
+ y = img_size[0] - y # de-flip ud
+ elif flips == 3:
+ x = img_size[1] - x # de-flip lr
+ p = torch.cat((x, y, wh, p[..., 4:]), -1)
+ return p
+
+ def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
+ # https://arxiv.org/abs/1708.02002 section 3.3
+ # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
+ m = self.model[-1] # Detect() module
+ for mi, s in zip(m.m, m.stride): # from
+ b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
+ b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
+ b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls
+ mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
+
+ def _print_biases(self):
+ m = self.model[-1] # Detect() module
+ for mi in m.m: # from
+ b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
+ LOGGER.info(
+ ('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
+
+ # def _print_weights(self):
+ # for m in self.model.modules():
+ # if type(m) is Bottleneck:
+ # LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
+
+ def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
+ LOGGER.info('Fusing layers... ')
+ for m in self.model.modules():
+ if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
+ m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
+ delattr(m, 'bn') # remove batchnorm
+ m.forward = m.forward_fuse # update forward
+ self.info()
+ return self
+
+ def autoshape(self): # add AutoShape module
+ LOGGER.info('Adding AutoShape... ')
+ m = AutoShape(self) # wrap model
+ copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes
+ return m
+
+ def info(self, verbose=False, img_size=640): # print model information
+ model_info(self, verbose, img_size)
+
+
+def parse_model(d, ch): # model_dict, input_channels(3)
+ LOGGER.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
+ anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
+ na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
+ no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
+
+ layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
+ for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
+ m = eval(m) if isinstance(m, str) else m # eval strings
+ for j, a in enumerate(args):
+ try:
+ args[j] = eval(a) if isinstance(a, str) else a # eval strings
+ except:
+ pass
+
+ n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
+ if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP,
+ C3, C3TR, C3SPP]:
+ c1, c2 = ch[f], args[0]
+ if c2 != no: # if not output
+ c2 = make_divisible(c2 * gw, 8)
+
+ args = [c1, c2, *args[1:]]
+ if m in [BottleneckCSP, C3, C3TR]:
+ args.insert(2, n) # number of repeats
+ n = 1
+ elif m is nn.BatchNorm2d:
+ args = [ch[f]]
+ elif m is Concat:
+ c2 = sum([ch[x] for x in f])
+ elif m is Detect:
+ args.append([ch[x] for x in f])
+ if isinstance(args[1], int): # number of anchors
+ args[1] = [list(range(args[1] * 2))] * len(f)
+ elif m is Contract:
+ c2 = ch[f] * args[0] ** 2
+ elif m is Expand:
+ c2 = ch[f] // args[0] ** 2
+ else:
+ c2 = ch[f]
+
+ m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module
+ t = str(m)[8:-2].replace('__main__.', '') # module type
+ np = sum([x.numel() for x in m_.parameters()]) # number params
+ m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
+ LOGGER.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n_, np, t, args)) # print
+ save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
+ layers.append(m_)
+ if i == 0:
+ ch = []
+ ch.append(c2)
+ return nn.Sequential(*layers), sorted(save)
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--cfg', type=str, default='yolov5m_add_detect.yaml', help='model.yaml')
+ parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+ opt = parser.parse_args()
+ opt.cfg = check_file(opt.cfg) # check file
+ set_logging()
+ device = select_device(opt.device)
+
+ # Create model
+ model = Model(opt.cfg).to(device)
+ model.train()
+
+ # Profile
+ # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 320, 320).to(device)
+ # y = model(img, profile=True)
+
+ # Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)
+ # from torch.utils.tensorboard import SummaryWriter
+ # tb_writer = SummaryWriter('.')
+ # LOGGER.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
+ # tb_writer.add_graph(torch.jit.trace(model, img, strict=False), []) # add model graph
diff --git a/models/yolov5l.yaml b/models/yolov5l.yaml
new file mode 100644
index 0000000..0c130c1
--- /dev/null
+++ b/models/yolov5l.yaml
@@ -0,0 +1,46 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+anchors:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]],
+ [-1, 3, C3, [1024, False]], # 9
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 13
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 14], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 10], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
+
+ [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/models/yolov5m-2transformer.yaml b/models/yolov5m-2transformer.yaml
new file mode 100644
index 0000000..ba95bb4
--- /dev/null
+++ b/models/yolov5m-2transformer.yaml
@@ -0,0 +1,46 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 0.67 # model depth multiple
+width_multiple: 0.75 # layer channel multiple
+anchors:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3TR, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]],
+ [-1, 3, C3TR, [1024, False]], # 9 <-------- C3TR() Transformer module
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 13
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 14], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 10], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
+
+ [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/models/yolov5m.yaml b/models/yolov5m.yaml
new file mode 100644
index 0000000..6ec604c
--- /dev/null
+++ b/models/yolov5m.yaml
@@ -0,0 +1,47 @@
+# Parameters
+#nc: 80 # number of classes
+nc: 3 # number of classes
+depth_multiple: 0.67 # model depth multiple
+width_multiple: 0.75 # layer channel multiple
+anchors:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]],
+ [-1, 3, C3, [1024, False]], # 9
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 13
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 14], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 10], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
+
+ [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/models/yolov5m_add_detect.yaml b/models/yolov5m_add_detect.yaml
new file mode 100644
index 0000000..ad77e47
--- /dev/null
+++ b/models/yolov5m_add_detect.yaml
@@ -0,0 +1,65 @@
+# parameters
+#nc: 80 # number of classes
+nc: 3 # number of classes
+depth_multiple: 0.67 # model depth multiple
+width_multiple: 0.75 # layer channel multiple 使卷积核个数变化
+
+# anchors
+anchors:
+ - [5,6, 8,14, 15,11] #P2/4 增加的锚点？？？？？ 增减检测层之后需要增加的 先验框的大小（4个尺度上的）
+ - [10,13, 16,30, 33,23] # P3/8 表示8倍下采样后的结果
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ #-1表示来自上一层输入；number表示本模块重复次数；
+
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2 功能层参数
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 功能层参数 128表示128个卷积核，3表示3x3卷积核，2表示步长是2
+ [-1, 3, C3, [128]], #160*160 瓶颈层是主要学习到特征，可增减瓶颈层的深度实现模型深度变化
+
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]], #80*80
+
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]], #40*40
+
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]], #spp也是功能层的参数
+ [-1, 3, C3, [1024, False]], # 9 20*20
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [512, 1, 1]], #20*20
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']], #40*40
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4 40*40
+ [-1, 3, C3, [512, False]], # 13 40*40
+
+ [-1, 1, Conv, [512, 1, 1]], #40*40
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3 80*80
+ [-1, 3, C3, [512, False]], # 17 (P3/8-small) 80*80
+
+ [-1, 1, Conv, [256, 1, 1]], #18 80*80
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']], #19 160*160
+ [[-1, 2], 1, Concat, [1]], #20 cat backbone p2 160*160
+ [-1, 3, C3, [256, False]], #21 160*160
+
+ [-1, 1, Conv, [256, 3, 2]], #22 80*80
+ [[-1, 18], 1, Concat, [1]], #23 80*80
+ [-1, 3, C3, [256, False]], #24 80*80
+
+ [-1, 1, Conv, [256, 3, 2]], #25 40*40
+ [[-1, 14], 1, Concat, [1]], # 26 cat head P4 40*40
+ [-1, 3, C3, [512, False]], # 27 (P4/16-medium) 40*40
+
+ [-1, 1, Conv, [512, 3, 2]], #28 20*20
+ [[-1, 10], 1, Concat, [1]], #29 cat head P5 #20*20
+ [-1, 3, C3, [1024, False]], # 30 (P5/32-large) 20*20
+
+ [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(p2, P3, P4, P5)
+ ]
+
diff --git a/models/yolov5m_s.yaml b/models/yolov5m_s.yaml
new file mode 100644
index 0000000..6498ad8
--- /dev/null
+++ b/models/yolov5m_s.yaml
@@ -0,0 +1,57 @@
+# parameters
+nc: 80 # number of classes
+depth_multiple: 0.67 # model depth multiple
+width_multiple: 0.75 # layer channel multiple
+
+# anchors
+anchors:
+ - [5,6, 8,14, 15,11] #P2/4
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]], #160*160
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]], #80*80
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]], #40*40
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]],
+ [-1, 3, C3, [1024, False]], # 9 20*20
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [512, 1, 1]], #20*20
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']], #40*40
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4 40*40
+ [-1, 3, C3, [512, False]], # 13 40*40
+
+ [-1, 1, Conv, [512, 1, 1]], #40*40
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3 80*80
+ [-1, 3, C3, [512, False]], # 17 (P3/8-small) 80*80
+
+ [-1, 1, Conv, [256, 1, 1]], #18 80*80
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']], #19 160*160
+ [[-1, 2], 1, Concat, [1]], #20 cat backbone p2 160*160
+ [-1, 3, C3, [256, False]], #21 160*160
+
+ [-1, 1, Conv, [256, 3, 2]], #22 80*80
+ [[-1, 18], 1, Concat, [1]], #23 80*80
+ [-1, 3, C3, [256, False]], #24 80*80
+
+ [-1, 1, Conv, [512, 3, 2]], #25 40*40
+ [[-1, 14], 1, Concat, [1]], # 26 cat head P4 40*40
+ [-1, 3, C3, [1024, False]], # 27 (P4/16-medium) 40*40
+
+
+
+ [[21, 24, 27], 1, Detect, [nc, anchors]], # Detect(p2, P3, P4)
+ ]
+
diff --git a/models/yolov5s-2transformer.yaml b/models/yolov5s-2transformer.yaml
new file mode 100644
index 0000000..f450c61
--- /dev/null
+++ b/models/yolov5s-2transformer.yaml
@@ -0,0 +1,46 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 0.33 # model depth multiple
+width_multiple: 0.50 # layer channel multiple
+anchors:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3TR, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]],
+ [-1, 3, C3TR, [1024, False]], # 9 <-------- C3TR() Transformer module
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 13
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 14], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 10], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
+
+ [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/models/yolov5s-transformer.yaml b/models/yolov5s-transformer.yaml
new file mode 100644
index 0000000..b999ebb
--- /dev/null
+++ b/models/yolov5s-transformer.yaml
@@ -0,0 +1,46 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 0.33 # model depth multiple
+width_multiple: 0.50 # layer channel multiple
+anchors:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]],
+ [-1, 3, C3TR, [1024, False]], # 9 <-------- C3TR() Transformer module
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 13
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 14], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 10], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
+
+ [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/models/yolov5s.yaml b/models/yolov5s.yaml
new file mode 100644
index 0000000..e85442d
--- /dev/null
+++ b/models/yolov5s.yaml
@@ -0,0 +1,46 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 0.33 # model depth multiple
+width_multiple: 0.50 # layer channel multiple
+anchors:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]],
+ [-1, 3, C3, [1024, False]], # 9
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 13
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 14], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 10], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
+
+ [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/models/yolov5x.yaml b/models/yolov5x.yaml
new file mode 100644
index 0000000..c7ca035
--- /dev/null
+++ b/models/yolov5x.yaml
@@ -0,0 +1,46 @@
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.33 # model depth multiple
+width_multiple: 1.25 # layer channel multiple
+anchors:
+ - [10,13, 16,30, 33,23] # P3/8
+ - [30,61, 62,45, 59,119] # P4/16
+ - [116,90, 156,198, 373,326] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, Focus, [64, 3]], # 0-P1/2
+ [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
+ [-1, 1, SPP, [1024, [5, 9, 13]]],
+ [-1, 3, C3, [1024, False]], # 9
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 6], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 13
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 17 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 14], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 10], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [1024, False]], # 23 (P5/32-large)
+
+ [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/plugin/yololayer.cu b/plugin/yololayer.cu
new file mode 100644
index 0000000..d80a9a4
--- /dev/null
+++ b/plugin/yololayer.cu
@@ -0,0 +1,280 @@
+#include "yololayer.h"
+#include "cuda_utils.h"
+
+#include
+#include
+#include
+
+namespace Tn {
+template
+void write(char*& buffer, const T& val) {
+ *reinterpret_cast(buffer) = val;
+ buffer += sizeof(T);
+}
+
+template
+void read(const char*& buffer, T& val) {
+ val = *reinterpret_cast(buffer);
+ buffer += sizeof(T);
+}
+}
+
+namespace nvinfer1 {
+YoloLayerPlugin::YoloLayerPlugin(int classCount, int netWidth, int netHeight, int maxOut, bool is_segmentation, const std::vector& vYoloKernel) {
+ mClassCount = classCount;
+ mYoloV5NetWidth = netWidth;
+ mYoloV5NetHeight = netHeight;
+ mMaxOutObject = maxOut;
+ is_segmentation_ = is_segmentation;
+ mYoloKernel = vYoloKernel;
+ mKernelCount = vYoloKernel.size();
+
+ CUDA_CHECK(cudaMallocHost(&mAnchor, mKernelCount * sizeof(void*)));
+ size_t AnchorLen = sizeof(float)* kNumAnchor * 2;
+ for (int ii = 0; ii < mKernelCount; ii++) {
+ CUDA_CHECK(cudaMalloc(&mAnchor[ii], AnchorLen));
+ const auto& yolo = mYoloKernel[ii];
+ CUDA_CHECK(cudaMemcpy(mAnchor[ii], yolo.anchors, AnchorLen, cudaMemcpyHostToDevice));
+ }
+}
+
+YoloLayerPlugin::~YoloLayerPlugin() {
+ for (int ii = 0; ii < mKernelCount; ii++) {
+ CUDA_CHECK(cudaFree(mAnchor[ii]));
+ }
+ CUDA_CHECK(cudaFreeHost(mAnchor));
+}
+
+// create the plugin at runtime from a byte stream
+YoloLayerPlugin::YoloLayerPlugin(const void* data, size_t length) {
+ using namespace Tn;
+ const char *d = reinterpret_cast(data), *a = d;
+ read(d, mClassCount);
+ read(d, mThreadCount);
+ read(d, mKernelCount);
+ read(d, mYoloV5NetWidth);
+ read(d, mYoloV5NetHeight);
+ read(d, mMaxOutObject);
+ read(d, is_segmentation_);
+ mYoloKernel.resize(mKernelCount);
+ auto kernelSize = mKernelCount * sizeof(YoloKernel);
+ memcpy(mYoloKernel.data(), d, kernelSize);
+ d += kernelSize;
+ CUDA_CHECK(cudaMallocHost(&mAnchor, mKernelCount * sizeof(void*)));
+ size_t AnchorLen = sizeof(float)* kNumAnchor * 2;
+ for (int ii = 0; ii < mKernelCount; ii++) {
+ CUDA_CHECK(cudaMalloc(&mAnchor[ii], AnchorLen));
+ const auto& yolo = mYoloKernel[ii];
+ CUDA_CHECK(cudaMemcpy(mAnchor[ii], yolo.anchors, AnchorLen, cudaMemcpyHostToDevice));
+ }
+ assert(d == a + length);
+}
+
+void YoloLayerPlugin::serialize(void* buffer) const TRT_NOEXCEPT {
+ using namespace Tn;
+ char* d = static_cast(buffer), *a = d;
+ write(d, mClassCount);
+ write(d, mThreadCount);
+ write(d, mKernelCount);
+ write(d, mYoloV5NetWidth);
+ write(d, mYoloV5NetHeight);
+ write(d, mMaxOutObject);
+ write(d, is_segmentation_);
+ auto kernelSize = mKernelCount * sizeof(YoloKernel);
+ memcpy(d, mYoloKernel.data(), kernelSize);
+ d += kernelSize;
+
+ assert(d == a + getSerializationSize());
+}
+
+size_t YoloLayerPlugin::getSerializationSize() const TRT_NOEXCEPT {
+ size_t s = sizeof(mClassCount) + sizeof(mThreadCount) + sizeof(mKernelCount);
+ s += sizeof(YoloKernel) * mYoloKernel.size();
+ s += sizeof(mYoloV5NetWidth) + sizeof(mYoloV5NetHeight);
+ s += sizeof(mMaxOutObject) + sizeof(is_segmentation_);
+ return s;
+}
+
+int YoloLayerPlugin::initialize() TRT_NOEXCEPT {
+ return 0;
+}
+
+Dims YoloLayerPlugin::getOutputDimensions(int index, const Dims* inputs, int nbInputDims) TRT_NOEXCEPT {
+ //output the result to channel
+ int totalsize = mMaxOutObject * sizeof(Detection) / sizeof(float);
+ return Dims3(totalsize + 1, 1, 1);
+}
+
+// Set plugin namespace
+void YoloLayerPlugin::setPluginNamespace(const char* pluginNamespace) TRT_NOEXCEPT {
+ mPluginNamespace = pluginNamespace;
+}
+
+const char* YoloLayerPlugin::getPluginNamespace() const TRT_NOEXCEPT {
+ return mPluginNamespace;
+}
+
+// Return the DataType of the plugin output at the requested index
+DataType YoloLayerPlugin::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const TRT_NOEXCEPT {
+ return DataType::kFLOAT;
+}
+
+// Return true if output tensor is broadcast across a batch.
+bool YoloLayerPlugin::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const TRT_NOEXCEPT {
+ return false;
+}
+
+// Return true if plugin can use input that is broadcast across batch without replication.
+bool YoloLayerPlugin::canBroadcastInputAcrossBatch(int inputIndex) const TRT_NOEXCEPT {
+ return false;
+}
+
+void YoloLayerPlugin::configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput) TRT_NOEXCEPT {}
+
+// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
+void YoloLayerPlugin::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) TRT_NOEXCEPT {}
+
+// Detach the plugin object from its execution context.
+void YoloLayerPlugin::detachFromContext() TRT_NOEXCEPT {}
+
+const char* YoloLayerPlugin::getPluginType() const TRT_NOEXCEPT {
+ return "YoloLayer_TRT";
+}
+
+const char* YoloLayerPlugin::getPluginVersion() const TRT_NOEXCEPT {
+ return "1";
+}
+
+void YoloLayerPlugin::destroy() TRT_NOEXCEPT {
+ delete this;
+}
+
+// Clone the plugin
+IPluginV2IOExt* YoloLayerPlugin::clone() const TRT_NOEXCEPT {
+ YoloLayerPlugin* p = new YoloLayerPlugin(mClassCount, mYoloV5NetWidth, mYoloV5NetHeight, mMaxOutObject, is_segmentation_, mYoloKernel);
+ p->setPluginNamespace(mPluginNamespace);
+ return p;
+}
+
+__device__ float Logist(float data) { return 1.0f / (1.0f + expf(-data)); };
+
+__global__ void CalDetection(const float *input, float *output, int noElements,
+ const int netwidth, const int netheight, int maxoutobject, int yoloWidth,
+ int yoloHeight, const float anchors[kNumAnchor * 2], int classes, int outputElem, bool is_segmentation) {
+
+ int idx = threadIdx.x + blockDim.x * blockIdx.x;
+ if (idx >= noElements) return;
+
+ int total_grid = yoloWidth * yoloHeight;
+ int bnIdx = idx / total_grid;
+ idx = idx - total_grid * bnIdx;
+ int info_len_i = 5 + classes;
+ if (is_segmentation) info_len_i += 32;
+ const float* curInput = input + bnIdx * (info_len_i * total_grid * kNumAnchor);
+
+ for (int k = 0; k < kNumAnchor; ++k) {
+ float box_prob = Logist(curInput[idx + k * info_len_i * total_grid + 4 * total_grid]);
+ if (box_prob < kIgnoreThresh) continue;
+ int class_id = 0;
+ float max_cls_prob = 0.0;
+ for (int i = 5; i < 5 + classes; ++i) {
+ float p = Logist(curInput[idx + k * info_len_i * total_grid + i * total_grid]);
+ if (p > max_cls_prob) {
+ max_cls_prob = p;
+ class_id = i - 5;
+ }
+ }
+ float *res_count = output + bnIdx * outputElem;
+ int count = (int)atomicAdd(res_count, 1);
+ if (count >= maxoutobject) return;
+ char *data = (char*)res_count + sizeof(float) + count * sizeof(Detection);
+ Detection *det = (Detection*)(data);
+
+ int row = idx / yoloWidth;
+ int col = idx % yoloWidth;
+
+ det->bbox[0] = (col - 0.5f + 2.0f * Logist(curInput[idx + k * info_len_i * total_grid + 0 * total_grid])) * netwidth / yoloWidth;
+ det->bbox[1] = (row - 0.5f + 2.0f * Logist(curInput[idx + k * info_len_i * total_grid + 1 * total_grid])) * netheight / yoloHeight;
+
+ det->bbox[2] = 2.0f * Logist(curInput[idx + k * info_len_i * total_grid + 2 * total_grid]);
+ det->bbox[2] = det->bbox[2] * det->bbox[2] * anchors[2 * k];
+ det->bbox[3] = 2.0f * Logist(curInput[idx + k * info_len_i * total_grid + 3 * total_grid]);
+ det->bbox[3] = det->bbox[3] * det->bbox[3] * anchors[2 * k + 1];
+ det->conf = box_prob * max_cls_prob;
+ det->class_id = class_id;
+
+ for (int i = 0; is_segmentation && i < 32; i++) {
+ det->mask[i] = curInput[idx + k * info_len_i * total_grid + (i + 5 + classes) * total_grid];
+ }
+ }
+}
+
+void YoloLayerPlugin::forwardGpu(const float* const* inputs, float *output, cudaStream_t stream, int batchSize) {
+ int outputElem = 1 + mMaxOutObject * sizeof(Detection) / sizeof(float);
+ for (int idx = 0; idx < batchSize; ++idx) {
+ CUDA_CHECK(cudaMemsetAsync(output + idx * outputElem, 0, sizeof(float), stream));
+ }
+ int numElem = 0;
+ for (unsigned int i = 0; i < mYoloKernel.size(); ++i) {
+ const auto& yolo = mYoloKernel[i];
+ numElem = yolo.width * yolo.height * batchSize;
+ if (numElem < mThreadCount) mThreadCount = numElem;
+
+ CalDetection << < (numElem + mThreadCount - 1) / mThreadCount, mThreadCount, 0, stream >> >
+ (inputs[i], output, numElem, mYoloV5NetWidth, mYoloV5NetHeight, mMaxOutObject, yolo.width, yolo.height, (float*)mAnchor[i], mClassCount, outputElem, is_segmentation_);
+ }
+}
+
+
+int YoloLayerPlugin::enqueue(int batchSize, const void* const* inputs, void* TRT_CONST_ENQUEUE* outputs, void* workspace, cudaStream_t stream) TRT_NOEXCEPT {
+ forwardGpu((const float* const*)inputs, (float*)outputs[0], stream, batchSize);
+ return 0;
+}
+
+PluginFieldCollection YoloPluginCreator::mFC{};
+std::vector YoloPluginCreator::mPluginAttributes;
+
+YoloPluginCreator::YoloPluginCreator() {
+ mPluginAttributes.clear();
+ mFC.nbFields = mPluginAttributes.size();
+ mFC.fields = mPluginAttributes.data();
+}
+
+const char* YoloPluginCreator::getPluginName() const TRT_NOEXCEPT {
+ return "YoloLayer_TRT";
+}
+
+const char* YoloPluginCreator::getPluginVersion() const TRT_NOEXCEPT {
+ return "1";
+}
+
+const PluginFieldCollection* YoloPluginCreator::getFieldNames() TRT_NOEXCEPT {
+ return &mFC;
+}
+
+IPluginV2IOExt* YoloPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc) TRT_NOEXCEPT {
+ assert(fc->nbFields == 2);
+ assert(strcmp(fc->fields[0].name, "netinfo") == 0);
+ assert(strcmp(fc->fields[1].name, "kernels") == 0);
+ int *p_netinfo = (int*)(fc->fields[0].data);
+ int class_count = p_netinfo[0];
+ int input_w = p_netinfo[1];
+ int input_h = p_netinfo[2];
+ int max_output_object_count = p_netinfo[3];
+ bool is_segmentation = (bool)p_netinfo[4];
+ std::vector kernels(fc->fields[1].length);
+ memcpy(&kernels[0], fc->fields[1].data, kernels.size() * sizeof(YoloKernel));
+ YoloLayerPlugin* obj = new YoloLayerPlugin(class_count, input_w, input_h, max_output_object_count, is_segmentation, kernels);
+ obj->setPluginNamespace(mNamespace.c_str());
+ return obj;
+}
+
+IPluginV2IOExt* YoloPluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength) TRT_NOEXCEPT {
+ // This object will be deleted when the network is destroyed, which will
+ // call YoloLayerPlugin::destroy()
+ YoloLayerPlugin* obj = new YoloLayerPlugin(serialData, serialLength);
+ obj->setPluginNamespace(mNamespace.c_str());
+ return obj;
+}
+}
+
diff --git a/plugin/yololayer.h b/plugin/yololayer.h
new file mode 100644
index 0000000..a73190b
--- /dev/null
+++ b/plugin/yololayer.h
@@ -0,0 +1,106 @@
+#pragma once
+
+#include "types.h"
+#include "macros.h"
+
+#include
+#include
+
+namespace nvinfer1 {
+class API YoloLayerPlugin : public IPluginV2IOExt {
+public:
+ YoloLayerPlugin(int classCount, int netWidth, int netHeight, int maxOut, bool is_segmentation, const std::vector& vYoloKernel);
+ YoloLayerPlugin(const void* data, size_t length);
+ ~YoloLayerPlugin();
+
+ int getNbOutputs() const TRT_NOEXCEPT override { return 1; }
+
+ Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) TRT_NOEXCEPT override;
+
+ int initialize() TRT_NOEXCEPT override;
+
+ virtual void terminate() TRT_NOEXCEPT override {};
+
+ virtual size_t getWorkspaceSize(int maxBatchSize) const TRT_NOEXCEPT override { return 0; }
+
+ virtual int enqueue(int batchSize, const void* const* inputs, void*TRT_CONST_ENQUEUE* outputs, void* workspace, cudaStream_t stream) TRT_NOEXCEPT override;
+
+ virtual size_t getSerializationSize() const TRT_NOEXCEPT override;
+
+ virtual void serialize(void* buffer) const TRT_NOEXCEPT override;
+
+ bool supportsFormatCombination(int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) const TRT_NOEXCEPT override {
+ return inOut[pos].format == TensorFormat::kLINEAR && inOut[pos].type == DataType::kFLOAT;
+ }
+
+ const char* getPluginType() const TRT_NOEXCEPT override;
+
+ const char* getPluginVersion() const TRT_NOEXCEPT override;
+
+ void destroy() TRT_NOEXCEPT override;
+
+ IPluginV2IOExt* clone() const TRT_NOEXCEPT override;
+
+ void setPluginNamespace(const char* pluginNamespace) TRT_NOEXCEPT override;
+
+ const char* getPluginNamespace() const TRT_NOEXCEPT override;
+
+ DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const TRT_NOEXCEPT override;
+
+ bool isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const TRT_NOEXCEPT override;
+
+ bool canBroadcastInputAcrossBatch(int inputIndex) const TRT_NOEXCEPT override;
+
+ void attachToContext(
+ cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) TRT_NOEXCEPT override;
+
+ void configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput) TRT_NOEXCEPT override;
+
+ void detachFromContext() TRT_NOEXCEPT override;
+
+ private:
+ void forwardGpu(const float* const* inputs, float *output, cudaStream_t stream, int batchSize = 1);
+ int mThreadCount = 256;
+ const char* mPluginNamespace;
+ int mKernelCount;
+ int mClassCount;
+ int mYoloV5NetWidth;
+ int mYoloV5NetHeight;
+ int mMaxOutObject;
+ bool is_segmentation_;
+ std::vector mYoloKernel;
+ void** mAnchor;
+};
+
+class API YoloPluginCreator : public IPluginCreator {
+ public:
+ YoloPluginCreator();
+
+ ~YoloPluginCreator() override = default;
+
+ const char* getPluginName() const TRT_NOEXCEPT override;
+
+ const char* getPluginVersion() const TRT_NOEXCEPT override;
+
+ const PluginFieldCollection* getFieldNames() TRT_NOEXCEPT override;
+
+ IPluginV2IOExt* createPlugin(const char* name, const PluginFieldCollection* fc) TRT_NOEXCEPT override;
+
+ IPluginV2IOExt* deserializePlugin(const char* name, const void* serialData, size_t serialLength) TRT_NOEXCEPT override;
+
+ void setPluginNamespace(const char* libNamespace) TRT_NOEXCEPT override {
+ mNamespace = libNamespace;
+ }
+
+ const char* getPluginNamespace() const TRT_NOEXCEPT override {
+ return mNamespace.c_str();
+ }
+
+ private:
+ std::string mNamespace;
+ static PluginFieldCollection mFC;
+ static std::vector mPluginAttributes;
+};
+REGISTER_TENSORRT_PLUGIN(YoloPluginCreator);
+};
+
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..f1629ea
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,31 @@
+# pip install -r requirements.txt
+
+# base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python>=4.1.2
+Pillow
+PyYAML>=5.3.1
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.41.0
+
+# logging -------------------------------------
+tensorboard>=2.4.1
+# wandb
+
+# plotting ------------------------------------
+seaborn>=0.11.0
+pandas
+
+# export --------------------------------------
+# coremltools>=4.1
+# onnx>=1.9.0
+# scikit-learn==0.19.2 # for coreml quantization
+
+# extras --------------------------------------
+# Cython # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
+# pycocotools>=2.0 # COCO mAP
+# albumentations>=1.0.3
+thop # FLOPs computation
diff --git a/src/calibrator.cpp b/src/calibrator.cpp
new file mode 100644
index 0000000..c339e63
--- /dev/null
+++ b/src/calibrator.cpp
@@ -0,0 +1,97 @@
+#include "calibrator.h"
+#include "cuda_utils.h"
+#include "utils.h"
+
+#include
+#include
+#include
+#include
+#include
+
+cv::Mat preprocess_img(cv::Mat& img, int input_w, int input_h) {
+ int w, h, x, y;
+ float r_w = input_w / (img.cols * 1.0);
+ float r_h = input_h / (img.rows * 1.0);
+ if (r_h > r_w) {
+ w = input_w;
+ h = r_w * img.rows;
+ x = 0;
+ y = (input_h - h) / 2;
+ } else {
+ w = r_h * img.cols;
+ h = input_h;
+ x = (input_w - w) / 2;
+ y = 0;
+ }
+ cv::Mat re(h, w, CV_8UC3);
+ cv::resize(img, re, re.size(), 0, 0, cv::INTER_LINEAR);
+ cv::Mat out(input_h, input_w, CV_8UC3, cv::Scalar(128, 128, 128));
+ re.copyTo(out(cv::Rect(x, y, re.cols, re.rows)));
+ return out;
+}
+
+Int8EntropyCalibrator2::Int8EntropyCalibrator2(int batchsize, int input_w, int input_h, const char* img_dir, const char* calib_table_name, const char* input_blob_name, bool read_cache)
+ : batchsize_(batchsize),
+ input_w_(input_w),
+ input_h_(input_h),
+ img_idx_(0),
+ img_dir_(img_dir),
+ calib_table_name_(calib_table_name),
+ input_blob_name_(input_blob_name),
+ read_cache_(read_cache) {
+ input_count_ = 3 * input_w * input_h * batchsize;
+ CUDA_CHECK(cudaMalloc(&device_input_, input_count_ * sizeof(float)));
+ read_files_in_dir(img_dir, img_files_);
+}
+
+Int8EntropyCalibrator2::~Int8EntropyCalibrator2() {
+ CUDA_CHECK(cudaFree(device_input_));
+}
+
+int Int8EntropyCalibrator2::getBatchSize() const TRT_NOEXCEPT {
+ return batchsize_;
+}
+
+bool Int8EntropyCalibrator2::getBatch(void* bindings[], const char* names[], int nbBindings) TRT_NOEXCEPT {
+ if (img_idx_ + batchsize_ > (int)img_files_.size()) {
+ return false;
+ }
+
+ std::vector input_imgs_;
+ for (int i = img_idx_; i < img_idx_ + batchsize_; i++) {
+ std::cout << img_files_[i] << " " << i << std::endl;
+ cv::Mat temp = cv::imread(img_dir_ + img_files_[i]);
+ if (temp.empty()) {
+ std::cerr << "Fatal error: image cannot open!" << std::endl;
+ return false;
+ }
+ cv::Mat pr_img = preprocess_img(temp, input_w_, input_h_);
+ input_imgs_.push_back(pr_img);
+ }
+ img_idx_ += batchsize_;
+ cv::Mat blob = cv::dnn::blobFromImages(input_imgs_, 1.0 / 255.0, cv::Size(input_w_, input_h_), cv::Scalar(0, 0, 0), true, false);
+
+ CUDA_CHECK(cudaMemcpy(device_input_, blob.ptr(0), input_count_ * sizeof(float), cudaMemcpyHostToDevice));
+ assert(!strcmp(names[0], input_blob_name_));
+ bindings[0] = device_input_;
+ return true;
+}
+
+const void* Int8EntropyCalibrator2::readCalibrationCache(size_t& length) TRT_NOEXCEPT {
+ std::cout << "reading calib cache: " << calib_table_name_ << std::endl;
+ calib_cache_.clear();
+ std::ifstream input(calib_table_name_, std::ios::binary);
+ input >> std::noskipws;
+ if (read_cache_ && input.good()) {
+ std::copy(std::istream_iterator(input), std::istream_iterator(), std::back_inserter(calib_cache_));
+ }
+ length = calib_cache_.size();
+ return length ? calib_cache_.data() : nullptr;
+}
+
+void Int8EntropyCalibrator2::writeCalibrationCache(const void* cache, size_t length) TRT_NOEXCEPT {
+ std::cout << "writing calib cache: " << calib_table_name_ << " size: " << length << std::endl;
+ std::ofstream output(calib_table_name_, std::ios::binary);
+ output.write(reinterpret_cast(cache), length);
+}
+
diff --git a/src/calibrator.h b/src/calibrator.h
new file mode 100644
index 0000000..9095110
--- /dev/null
+++ b/src/calibrator.h
@@ -0,0 +1,39 @@
+#pragma once
+
+#include "macros.h"
+#include
+#include
+#include
+
+cv::Mat preprocess_img(cv::Mat& img, int input_w, int input_h);
+
+//! \class Int8EntropyCalibrator2
+//!
+//! \brief Implements Entropy calibrator 2.
+//! CalibrationAlgoType is kENTROPY_CALIBRATION_2.
+//!
+class Int8EntropyCalibrator2 : public nvinfer1::IInt8EntropyCalibrator2 {
+ public:
+ Int8EntropyCalibrator2(int batchsize, int input_w, int input_h, const char* img_dir, const char* calib_table_name, const char* input_blob_name, bool read_cache = true);
+
+ virtual ~Int8EntropyCalibrator2();
+ int getBatchSize() const TRT_NOEXCEPT override;
+ bool getBatch(void* bindings[], const char* names[], int nbBindings) TRT_NOEXCEPT override;
+ const void* readCalibrationCache(size_t& length) TRT_NOEXCEPT override;
+ void writeCalibrationCache(const void* cache, size_t length) TRT_NOEXCEPT override;
+
+ private:
+ int batchsize_;
+ int input_w_;
+ int input_h_;
+ int img_idx_;
+ std::string img_dir_;
+ std::vector img_files_;
+ size_t input_count_;
+ std::string calib_table_name_;
+ const char* input_blob_name_;
+ bool read_cache_;
+ void* device_input_;
+ std::vector calib_cache_;
+};
+
diff --git a/src/config.h b/src/config.h
new file mode 100644
index 0000000..abf5cec
--- /dev/null
+++ b/src/config.h
@@ -0,0 +1,55 @@
+#pragma once
+
+/* --------------------------------------------------------
+ * These configs are related to tensorrt model, if these are changed,
+ * please re-compile and re-serialize the tensorrt model.
+ * --------------------------------------------------------*/
+
+// For INT8, you need prepare the calibration dataset, please refer to
+// https://github.com/wang-xinyu/tensorrtx/tree/master/yolov5#int8-quantization
+#define USE_FP16 // set USE_INT8 or USE_FP16 or USE_FP32
+
+// These are used to define input/output tensor names,
+// you can set them to whatever you want.
+const static char* kInputTensorName = "data";
+const static char* kOutputTensorName = "prob";
+
+// Detection model and Segmentation model' number of classes
+constexpr static int kNumClass = 80;
+
+// Classfication model's number of classes
+constexpr static int kClsNumClass = 1000;
+
+constexpr static int kBatchSize = 1;
+
+// Yolo's input width and height must by divisible by 32
+constexpr static int kInputH = 640;
+constexpr static int kInputW = 640;
+
+// Classfication model's input shape
+constexpr static int kClsInputH = 224;
+constexpr static int kClsInputW = 224;
+
+// Maximum number of output bounding boxes from yololayer plugin.
+// That is maximum number of output bounding boxes before NMS.
+constexpr static int kMaxNumOutputBbox = 1000;
+
+constexpr static int kNumAnchor = 3;
+
+// The bboxes whose confidence is lower than kIgnoreThresh will be ignored in yololayer plugin.
+constexpr static float kIgnoreThresh = 0.1f;
+
+/* --------------------------------------------------------
+ * These configs are NOT related to tensorrt model, if these are changed,
+ * please re-compile, but no need to re-serialize the tensorrt model.
+ * --------------------------------------------------------*/
+
+// NMS overlapping thresh and final detection confidence thresh
+const static float kNmsThresh = 0.45f;
+const static float kConfThresh = 0.5f;
+
+const static int kGpuId = 0;
+
+// If your image size is larger than 4096 * 3112, please increase this value
+const static int kMaxInputImageSize = 4096 * 3112;
+
diff --git a/src/cuda_utils.h b/src/cuda_utils.h
new file mode 100644
index 0000000..8fbd319
--- /dev/null
+++ b/src/cuda_utils.h
@@ -0,0 +1,18 @@
+#ifndef TRTX_CUDA_UTILS_H_
+#define TRTX_CUDA_UTILS_H_
+
+#include
+
+#ifndef CUDA_CHECK
+#define CUDA_CHECK(callstr)\
+ {\
+ cudaError_t error_code = callstr;\
+ if (error_code != cudaSuccess) {\
+ std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__;\
+ assert(0);\
+ }\
+ }
+#endif // CUDA_CHECK
+
+#endif // TRTX_CUDA_UTILS_H_
+
diff --git a/src/logging.h b/src/logging.h
new file mode 100644
index 0000000..6b79a8b
--- /dev/null
+++ b/src/logging.h
@@ -0,0 +1,504 @@
+/*
+ * Copyright (c) 2019, 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.
+ */
+
+#ifndef TENSORRT_LOGGING_H
+#define TENSORRT_LOGGING_H
+
+#include "NvInferRuntimeCommon.h"
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include "macros.h"
+
+using Severity = nvinfer1::ILogger::Severity;
+
+class LogStreamConsumerBuffer : public std::stringbuf
+{
+public:
+ LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
+ : mOutput(stream)
+ , mPrefix(prefix)
+ , mShouldLog(shouldLog)
+ {
+ }
+
+ LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
+ : mOutput(other.mOutput)
+ {
+ }
+
+ ~LogStreamConsumerBuffer()
+ {
+ // std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
+ // std::streambuf::pptr() gives a pointer to the current position of the output sequence
+ // if the pointer to the beginning is not equal to the pointer to the current position,
+ // call putOutput() to log the output to the stream
+ if (pbase() != pptr())
+ {
+ putOutput();
+ }
+ }
+
+ // synchronizes the stream buffer and returns 0 on success
+ // synchronizing the stream buffer consists of inserting the buffer contents into the stream,
+ // resetting the buffer and flushing the stream
+ virtual int sync()
+ {
+ putOutput();
+ return 0;
+ }
+
+ void putOutput()
+ {
+ if (mShouldLog)
+ {
+ // prepend timestamp
+ std::time_t timestamp = std::time(nullptr);
+ tm* tm_local = std::localtime(×tamp);
+ std::cout << "[";
+ std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/";
+ std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/";
+ std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-";
+ std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":";
+ std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
+ std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] ";
+ // std::stringbuf::str() gets the string contents of the buffer
+ // insert the buffer contents pre-appended by the appropriate prefix into the stream
+ mOutput << mPrefix << str();
+ // set the buffer to empty
+ str("");
+ // flush the stream
+ mOutput.flush();
+ }
+ }
+
+ void setShouldLog(bool shouldLog)
+ {
+ mShouldLog = shouldLog;
+ }
+
+private:
+ std::ostream& mOutput;
+ std::string mPrefix;
+ bool mShouldLog;
+};
+
+//!
+//! \class LogStreamConsumerBase
+//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
+//!
+class LogStreamConsumerBase
+{
+public:
+ LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
+ : mBuffer(stream, prefix, shouldLog)
+ {
+ }
+
+protected:
+ LogStreamConsumerBuffer mBuffer;
+};
+
+//!
+//! \class LogStreamConsumer
+//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages.
+//! Order of base classes is LogStreamConsumerBase and then std::ostream.
+//! This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field
+//! in LogStreamConsumer and then the address of the buffer is passed to std::ostream.
+//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
+//! Please do not change the order of the parent classes.
+//!
+class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream
+{
+public:
+ //! \brief Creates a LogStreamConsumer which logs messages with level severity.
+ //! Reportable severity determines if the messages are severe enough to be logged.
+ LogStreamConsumer(Severity reportableSeverity, Severity severity)
+ : LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)
+ , std::ostream(&mBuffer) // links the stream buffer with the stream
+ , mShouldLog(severity <= reportableSeverity)
+ , mSeverity(severity)
+ {
+ }
+
+ LogStreamConsumer(LogStreamConsumer&& other)
+ : LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)
+ , std::ostream(&mBuffer) // links the stream buffer with the stream
+ , mShouldLog(other.mShouldLog)
+ , mSeverity(other.mSeverity)
+ {
+ }
+
+ void setReportableSeverity(Severity reportableSeverity)
+ {
+ mShouldLog = mSeverity <= reportableSeverity;
+ mBuffer.setShouldLog(mShouldLog);
+ }
+
+private:
+ static std::ostream& severityOstream(Severity severity)
+ {
+ return severity >= Severity::kINFO ? std::cout : std::cerr;
+ }
+
+ static std::string severityPrefix(Severity severity)
+ {
+ switch (severity)
+ {
+ case Severity::kINTERNAL_ERROR: return "[F] ";
+ case Severity::kERROR: return "[E] ";
+ case Severity::kWARNING: return "[W] ";
+ case Severity::kINFO: return "[I] ";
+ case Severity::kVERBOSE: return "[V] ";
+ default: assert(0); return "";
+ }
+ }
+
+ bool mShouldLog;
+ Severity mSeverity;
+};
+
+//! \class Logger
+//!
+//! \brief Class which manages logging of TensorRT tools and samples
+//!
+//! \details This class provides a common interface for TensorRT tools and samples to log information to the console,
+//! and supports logging two types of messages:
+//!
+//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)
+//! - Test pass/fail messages
+//!
+//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is
+//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.
+//!
+//! In the future, this class could be extended to support dumping test results to a file in some standard format
+//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).
+//!
+//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger
+//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT
+//! library and messages coming from the sample.
+//!
+//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the
+//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
+//! object.
+
+class Logger : public nvinfer1::ILogger
+{
+public:
+ Logger(Severity severity = Severity::kWARNING)
+ : mReportableSeverity(severity)
+ {
+ }
+
+ //!
+ //! \enum TestResult
+ //! \brief Represents the state of a given test
+ //!
+ enum class TestResult
+ {
+ kRUNNING, //!< The test is running
+ kPASSED, //!< The test passed
+ kFAILED, //!< The test failed
+ kWAIVED //!< The test was waived
+ };
+
+ //!
+ //! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger
+ //! \return The nvinfer1::ILogger associated with this Logger
+ //!
+ //! TODO Once all samples are updated to use this method to register the logger with TensorRT,
+ //! we can eliminate the inheritance of Logger from ILogger
+ //!
+ nvinfer1::ILogger& getTRTLogger()
+ {
+ return *this;
+ }
+
+ //!
+ //! \brief Implementation of the nvinfer1::ILogger::log() virtual method
+ //!
+ //! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
+ //! inheritance from nvinfer1::ILogger
+ //!
+ void log(Severity severity, const char* msg) TRT_NOEXCEPT override
+ {
+ LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
+ }
+
+ //!
+ //! \brief Method for controlling the verbosity of logging output
+ //!
+ //! \param severity The logger will only emit messages that have severity of this level or higher.
+ //!
+ void setReportableSeverity(Severity severity)
+ {
+ mReportableSeverity = severity;
+ }
+
+ //!
+ //! \brief Opaque handle that holds logging information for a particular test
+ //!
+ //! This object is an opaque handle to information used by the Logger to print test results.
+ //! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
+ //! with Logger::reportTest{Start,End}().
+ //!
+ class TestAtom
+ {
+ public:
+ TestAtom(TestAtom&&) = default;
+
+ private:
+ friend class Logger;
+
+ TestAtom(bool started, const std::string& name, const std::string& cmdline)
+ : mStarted(started)
+ , mName(name)
+ , mCmdline(cmdline)
+ {
+ }
+
+ bool mStarted;
+ std::string mName;
+ std::string mCmdline;
+ };
+
+ //!
+ //! \brief Define a test for logging
+ //!
+ //! \param[in] name The name of the test. This should be a string starting with
+ //! "TensorRT" and containing dot-separated strings containing
+ //! the characters [A-Za-z0-9_].
+ //! For example, "TensorRT.sample_googlenet"
+ //! \param[in] cmdline The command line used to reproduce the test
+ //
+ //! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
+ //!
+ static TestAtom defineTest(const std::string& name, const std::string& cmdline)
+ {
+ return TestAtom(false, name, cmdline);
+ }
+
+ //!
+ //! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments
+ //! as input
+ //!
+ //! \param[in] name The name of the test
+ //! \param[in] argc The number of command-line arguments
+ //! \param[in] argv The array of command-line arguments (given as C strings)
+ //!
+ //! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
+ static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)
+ {
+ auto cmdline = genCmdlineString(argc, argv);
+ return defineTest(name, cmdline);
+ }
+
+ //!
+ //! \brief Report that a test has started.
+ //!
+ //! \pre reportTestStart() has not been called yet for the given testAtom
+ //!
+ //! \param[in] testAtom The handle to the test that has started
+ //!
+ static void reportTestStart(TestAtom& testAtom)
+ {
+ reportTestResult(testAtom, TestResult::kRUNNING);
+ assert(!testAtom.mStarted);
+ testAtom.mStarted = true;
+ }
+
+ //!
+ //! \brief Report that a test has ended.
+ //!
+ //! \pre reportTestStart() has been called for the given testAtom
+ //!
+ //! \param[in] testAtom The handle to the test that has ended
+ //! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
+ //! TestResult::kFAILED, TestResult::kWAIVED
+ //!
+ static void reportTestEnd(const TestAtom& testAtom, TestResult result)
+ {
+ assert(result != TestResult::kRUNNING);
+ assert(testAtom.mStarted);
+ reportTestResult(testAtom, result);
+ }
+
+ static int reportPass(const TestAtom& testAtom)
+ {
+ reportTestEnd(testAtom, TestResult::kPASSED);
+ return EXIT_SUCCESS;
+ }
+
+ static int reportFail(const TestAtom& testAtom)
+ {
+ reportTestEnd(testAtom, TestResult::kFAILED);
+ return EXIT_FAILURE;
+ }
+
+ static int reportWaive(const TestAtom& testAtom)
+ {
+ reportTestEnd(testAtom, TestResult::kWAIVED);
+ return EXIT_SUCCESS;
+ }
+
+ static int reportTest(const TestAtom& testAtom, bool pass)
+ {
+ return pass ? reportPass(testAtom) : reportFail(testAtom);
+ }
+
+ Severity getReportableSeverity() const
+ {
+ return mReportableSeverity;
+ }
+
+private:
+ //!
+ //! \brief returns an appropriate string for prefixing a log message with the given severity
+ //!
+ static const char* severityPrefix(Severity severity)
+ {
+ switch (severity)
+ {
+ case Severity::kINTERNAL_ERROR: return "[F] ";
+ case Severity::kERROR: return "[E] ";
+ case Severity::kWARNING: return "[W] ";
+ case Severity::kINFO: return "[I] ";
+ case Severity::kVERBOSE: return "[V] ";
+ default: assert(0); return "";
+ }
+ }
+
+ //!
+ //! \brief returns an appropriate string for prefixing a test result message with the given result
+ //!
+ static const char* testResultString(TestResult result)
+ {
+ switch (result)
+ {
+ case TestResult::kRUNNING: return "RUNNING";
+ case TestResult::kPASSED: return "PASSED";
+ case TestResult::kFAILED: return "FAILED";
+ case TestResult::kWAIVED: return "WAIVED";
+ default: assert(0); return "";
+ }
+ }
+
+ //!
+ //! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
+ //!
+ static std::ostream& severityOstream(Severity severity)
+ {
+ return severity >= Severity::kINFO ? std::cout : std::cerr;
+ }
+
+ //!
+ //! \brief method that implements logging test results
+ //!
+ static void reportTestResult(const TestAtom& testAtom, TestResult result)
+ {
+ severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
+ << testAtom.mCmdline << std::endl;
+ }
+
+ //!
+ //! \brief generate a command line string from the given (argc, argv) values
+ //!
+ static std::string genCmdlineString(int argc, char const* const* argv)
+ {
+ std::stringstream ss;
+ for (int i = 0; i < argc; i++)
+ {
+ if (i > 0)
+ ss << " ";
+ ss << argv[i];
+ }
+ return ss.str();
+ }
+
+ Severity mReportableSeverity;
+};
+
+namespace
+{
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
+//!
+//! Example usage:
+//!
+//! LOG_VERBOSE(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
+{
+ return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
+}
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO
+//!
+//! Example usage:
+//!
+//! LOG_INFO(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_INFO(const Logger& logger)
+{
+ return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
+}
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING
+//!
+//! Example usage:
+//!
+//! LOG_WARN(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_WARN(const Logger& logger)
+{
+ return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
+}
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR
+//!
+//! Example usage:
+//!
+//! LOG_ERROR(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_ERROR(const Logger& logger)
+{
+ return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
+}
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR
+// ("fatal" severity)
+//!
+//! Example usage:
+//!
+//! LOG_FATAL(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_FATAL(const Logger& logger)
+{
+ return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
+}
+
+} // anonymous namespace
+
+#endif // TENSORRT_LOGGING_H
diff --git a/src/macros.h b/src/macros.h
new file mode 100644
index 0000000..17339a2
--- /dev/null
+++ b/src/macros.h
@@ -0,0 +1,29 @@
+#ifndef __MACROS_H
+#define __MACROS_H
+
+#include
+
+#ifdef API_EXPORTS
+#if defined(_MSC_VER)
+#define API __declspec(dllexport)
+#else
+#define API __attribute__((visibility("default")))
+#endif
+#else
+
+#if defined(_MSC_VER)
+#define API __declspec(dllimport)
+#else
+#define API
+#endif
+#endif // API_EXPORTS
+
+#if NV_TENSORRT_MAJOR >= 8
+#define TRT_NOEXCEPT noexcept
+#define TRT_CONST_ENQUEUE const
+#else
+#define TRT_NOEXCEPT
+#define TRT_CONST_ENQUEUE
+#endif
+
+#endif // __MACROS_H
diff --git a/src/model.cpp b/src/model.cpp
new file mode 100644
index 0000000..339f663
--- /dev/null
+++ b/src/model.cpp
@@ -0,0 +1,629 @@
+#include "model.h"
+#include "calibrator.h"
+#include "config.h"
+#include "yololayer.h"
+
+#include
+#include
+#include