parser.add_argument('--hyp', type=str, default='data/hyp.scratch.p5.yaml', help='hyperparameters path') # batch size,根据gpu配置设置 parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs') # 图像大小 parser.add_argument('--img-size', nargs...
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...
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size')) if rank in [-1, 0]: pbar = tqdm(pbar, total=nb) # progress bar optimizer.zero_grad() for i, (imgs, targets, paths, _) in pbar: # batch ...
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TABLE I: Training progress of YOLO in 100 epochs EpochGpu-memBoxObjClassTotalLabel 9411.8g0.01760.00970.000700.0280102 9511.8g0.01790.01040.000750.029176 9611.8g0.01810.00990.000710.028787 9711.8g0.01820.01000.000720.0289105 9811.8g0.01740.00940.000610.027490 ...
GPU: torch 1.13.1+cu116 CUDA:0 (Tesla T4, 15109.75MB) Error: Epoch gpu_mem box obj cls total labels img_size 0/54 11.6G 0.08637 0.07074 0.07379 0.2309 655 640: 3% 5/157 [00:17<08:40, 3.43s/it] Traceback (most recent call last): File "train.py", line 616, in train(hyp...
baI+p7PZLE0FMeMrSMxYijy0umswmxSfj45SWrK9scPjtHz5e898af8eXTr5kOW7z39hu8+UaP/pViomF0kLNW2SYefcvm2kPsNMTvCNa6bVrtFa4mVxgkew8ecdE/YppaVLVLrbeFIeS7r56ws73J1v0dLq8viIoqfnuFTm+PeJGSmoLRMiZsVdnZf0SzGTKffI0gxgsK3n/rPVbWV/nsq18TZwL8nAfvbXPn8dsML69IZ0teHDyjfzZncJ7w4p9O+PUvnlNZ6VBreL...
加载图片权重(可选),定义进度条,设置偏差Burn-in,使用多尺度,前向传播,损失函数,反向传播,优化器,打印进度条,保存训练参数至tensorboard,计算mAP,保存结果到results.txt,保存模型(最好和最后)。 for epoch in range(start_epoch, epochs): # epoch --- model.train() # Update image weights (optional) if ...
{'host': host_mem, 'device': device_mem}) def infer(self, img): self.inputs[0]['host'] = np.ravel(img) # transfer data to the gpu for inp in self.inputs: cuda.memcpy_htod_async(inp['device'], inp['host'], self.stream) # run inference self.context.execute_async_v2( ...
(binding)) # 分配GPU内存 host_mem = cuda.pagelocked_empty(size, dtype) device_mem = cuda.mem_alloc(host_mem.nbytes) # 绑定到输入或输出 bindings.append(int(device_mem)) # 如果是输入,打印输入尺寸 if engine.binding_is_input(binding): input_shape = engine.get_binding_shape(binding) print...