From 8236d8818bca21c692d5c4508fee2af835ec1dbe Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Tue, 25 Oct 2022 18:13:48 +0200 Subject: [PATCH] Created using Colaboratory --- tutorial.ipynb | 141 +++---------------------------------------------- 1 file changed, 6 insertions(+), 135 deletions(-) diff --git a/tutorial.ipynb b/tutorial.ipynb index 63abebc..10e14b9 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -954,7 +954,7 @@ "source": [ "# Appendix\n", "\n", - "Additional content below for PyTorch Hub, CI, reproducing results, profiling speeds, VOC training, classification training and TensorRT example." + "Additional content below." ] }, { @@ -963,145 +963,16 @@ "id": "GMusP4OAxFu6" }, "source": [ + "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", "import torch\n", "\n", - "# PyTorch Hub Model\n", - "model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom\n", - "\n", - "# Images\n", - "img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list\n", - "\n", - "# Inference\n", - "results = model(img)\n", - "\n", - "# Results\n", + "model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # yolov5n - yolov5x6 or custom\n", + "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n", + "results = model(im) # inference\n", "results.print() # or .show(), .save(), .crop(), .pandas(), etc." ], "execution_count": null, "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "FGH0ZjkGjejy" - }, - "source": [ - "# YOLOv5 CI\n", - "%%shell\n", - "rm -rf runs # remove runs/\n", - "m=yolov5n # official weights\n", - "b=runs/train/exp/weights/best # best.pt checkpoint\n", - "python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device 0 # train\n", - "for d in 0 cpu; do # devices\n", - " for w in $m $b; do # weights\n", - " python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val\n", - " python detect.py --imgsz 64 --weights $w.pt --device $d # detect\n", - " done\n", - "done\n", - "python hubconf.py --model $m # hub\n", - "python models/tf.py --weights $m.pt # build TF model\n", - "python models/yolo.py --cfg $m.yaml # build PyTorch model\n", - "python export.py --weights $m.pt --img 64 --include torchscript # export" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "mcKoSIK2WSzj" - }, - "source": [ - "# Reproduce\n", - "for x in (f'yolov5{x}' for x in 'nsmlx'):\n", - " !python val.py --weights {x}.pt --data coco.yaml --img 640 --task speed # speed\n", - " !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAP" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "gogI-kwi3Tye" - }, - "source": [ - "# Profile\n", - "from utils.torch_utils import profile\n", - "\n", - "m1 = lambda x: x * torch.sigmoid(x)\n", - "m2 = torch.nn.SiLU()\n", - "results = profile(input=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "BSgFCAcMbk1R" - }, - "source": [ - "# VOC\n", - "for b, m in zip([64, 64, 64, 32, 16], [f'yolov5{x}' for x in 'nsmlx']): # batch, model\n", - " !python train.py --batch {b} --weights {m}.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.VOC.yaml --project VOC --name {m} --cache" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Classification train\n", - "for m in [*(f'yolov5{x}-cls.pt' for x in 'nsmlx'), 'resnet50.pt', 'resnet101.pt', 'efficientnet_b0.pt', 'efficientnet_b1.pt']:\n", - " for d in 'mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'imagenette160', 'imagenette320', 'imagenette', 'imagewoof160', 'imagewoof320', 'imagewoof':\n", - " !python classify/train.py --model {m} --data {d} --epochs 10 --project YOLOv5-cls --name {m}-{d}" - ], - "metadata": { - "id": "UWGH7H6yakVl" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Classification val\n", - "!bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G - 50000 images)\n", - "!python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate" - ], - "metadata": { - "id": "yYgOiFNHZx-1" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Validate on COCO test. Zip results.json and submit to eval server at https://competitions.codalab.org/competitions/20794\n", - "!bash data/scripts/get_coco.sh --test # download COCO test-dev2017 (7G - 40000 images, test 20000)\n", - "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half --task test" - ], - "metadata": { - "id": "aq4DPWGu0Bl1" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "VTRwsvA9u7ln" - }, - "source": [ - "# TensorRT \n", - "!pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # install\n", - "!python export.py --weights yolov5s.pt --include engine --imgsz 640 --device 0 # export\n", - "!python detect.py --weights yolov5s.engine --imgsz 640 --device 0 # inference" - ], - "execution_count": null, - "outputs": [] } ] -} +} \ No newline at end of file