for device in devices[1:]: new_state_dict = torch.utils._pytree.tree_map(lambda x: x.clone().to(device), state_dict) state_dicts.append(new_state_dict) # call forward in devices separately ys = [] for t, state_dict in zip(xs, state_dicts): output = torch.func.functional_call...
通过jax.tree_util.tree_map(lambda x: x[0], loss)来取第一个device中的loss值。 总结+ 源码 总结下,使用pmap进行数据并行的要点: batch_size扩充为原始batch_size * device数量 使用jax.lax.pmean计算所有device的梯度均值 pmap别忘了设置axis_name,随便一个有意义的字符串就行 pmap转换后的函数传参,x和...
x):"""Computes gradients and loss for a single batch."""logits=state.apply_fn({"params":state.params,"batch_stats":state.batch_stats},x,train=False)returnjnp.argmax(logits,-1)defeval_model(state,loader):total_acc=0.total_num=0.forxs,ysinloader:xs=jax.tree_map(lambdax...
WRITING DISTRIBUTED APPLICATIONS WITH PYTORCH https://github.com/pytorch/examples/tree/master/imagenet 这里,常规的操作就不多叙述了,主要讲一下和分布式相关的代码部分。 代码语言:javascript 复制 parser.add_argument('--world-size', default=2, type=int, help='number of distributed processes') parser.a...
Put each case left out in the construction of the kth tree down the kth tree to get a classification. In this way, a test set classification is obtained for each case in about one-third of the trees. At the end of the run, take j to be the class that got most of the votes ever...
实现参考了pytorch官方的语义分割实例,https://github.com/pytorch/android-demo-app/tree/master/ImageSegmentation。其中有两个主要修改: public static String assetFilePath(Context context, String assetName) throws IOException { File file = new File(context.getFilesDir(), assetName); ...
可视化一个 feature map (将 feature map 所有通道的值取平均数) 代码语言:javascript 复制 importtimeimportmatplotlib.pyplotasplt item=torch.mean(x,dim=1).detach().cpu().numpy().transpose((1,2,0))plt.figure()plt.axis('off')plt.imshow(item,cmap='jet')plt.savefig(f'fig{time.time()}.png...
quanto 基准测试https://github.com/huggingface/quanto/tree/main/bench/ 集成进 transformers 我们已将 quanto 无缝集成至 Hugging Face transformers 库中。你可以通过给 from_pretrained API 传 QuantoConfig 参数来对任何模型进行量化！transformershttps://github.com/huggingface/transformers 目前，你需要使用最新版本...
MAP是衡量object dectection算法的重要criteria,然而一直没有仔细阅读相关代码,今天就好好看一下: 1. 测试test过程是由FRCN/tools/test_net.py中调用的test_net()完成#from model.test import test_net test_net()定义在FRCN/lib/model/test.py(193-194行):调用了imdb.evaluate_detections ...
Along with 2.0, we are also releasing a series of beta updates to the PyTorch domain libraries, including those that are in-tree, and separate libraries including TorchAudio, TorchVision, and TorchText. An update for TorchX is also being released as it moves to community supported mode. More...