RuntimeError: CUDA out of memory. Tried to allocate 200.00 MiB (GPU 0; 7.77 GiB total capacity; 5.70 GiB already allocated; 177.62 MiB free; 5.92 GiB reserved in total by PyTorch) 1. 修改batch-size为16,可以运行,gpu_memory占用 修改为40,gpu_mem占用: 训练效果如下:(现在这样的参数--挺慢...
all 500 2575 0.926 0.524 0.584 0.338 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 3/20 3.85G 1.39 0.9464 1.199 134 640: 100%|██████████| 250/250 [00:18<00:00, 13.78it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 16...
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Epoch gpu_mem box obj cls labels img_size 2/200 20.8G 0.01578 0.01923 0.007006 22 1280: 100%|██████████| 849/849 [14:44<00:00, 1.04s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 213/213 [01:12<00:00, 2.95it/s] all 3395 173...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 1/10 2.61G 1.153 1.398 1.192 81 640: 1 Class Images Instances Box(P R mAP50 m all 128 929 0.688 0.506 0.61 0.446 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size ...
GPU环境安装 参考这个链接:点击 # 安装CUDA、CUDNN、Python、Pytorch、Torchvision 这里每个版本要相互对应pipinstall ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple 4.1 环境检测 下载yolov8n.pt和bus.jpg 然后命令行输入 yolo predictmodel=yolov8n.ptsource='ultralytics/data/images/bus.jpg' ...
Params GFLOPs GPU_mem (GB) forward (ms) backward (ms) input output 398554 0.6174 0.367 44.63 12.67 (1, 3, 320, 320) (1, 10) 398554 1.235 0.348 4.173 11.56 (2, 3, 320, 320) (2, 10) 398554 2.469 0.352 13.1 9.708 (4, 3, 320, 320) (4, 10) 398554 4.939 0.350 6.128 9.442 ...
im_gpu = im_gpu * inv_alpha_masks[-1] + mcs # 将混合后的图像乘以 255,并转为 numpy 数组 im_mask = im_gpu * 255 im_mask_np = im_mask.byte().cpu().numpy() # 根据 retina_masks 参数选择是否缩放图像 self.im[:] = im_mask_np if retina_masks else ops.scale_image(im_mask_np...
(_tune pid=62308) AutoBatch: CUDA:0 (NVIDIA GeForce RTX 4080 Laptop GPU) 11.99G total, 0.09G reserved, 0.08G allocated, 11.82G free (_tune pid=62308) Params GFLOPs GPU_mem (GB) forward (ms) backward (ms) input output (_tune pid=62308) 3011043 8.194 0.214 13 28.51 (1, 3, 640,...
3、图像数据copy到host buffer,迁移到GPU上进行推理 4、后处理首先获取所有的结果,并按照leterbox的方式进行还原,使用NMS去除低置信度的框,最终绘制到原图输出 需要注意的是推理前需要预热一下,以便推理的时间相对比较平稳 完整代码如下: """ An example that uses TensorRT's Python api to make inferences. ...