train_epochs_loss = [] valid_epochs_loss = [] def train_loop(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) for batch, (X, y) in enumerate(dataloader): # Compute prediction and loss pred = model(X) loss = loss_fn(pred, y) # Backpropagation optimizer.zero_...
predict_y = torch.max(outputs, dim=1)[1] acc += torch.eq(predict_y, val_labels.to(device)).sum().item() val_accurate = acc / val_num print('[epoch %d] train_loss: %.3f val_accuracy: %.3f' % (epoch + 1, running_loss / train_steps, val_accurate)) if val_accurate > be...
我们可以将一个可迭代对象转换成迭代器,所谓迭代器,就是内部含有 __iter__ 和__next__ 方法的对象,它可以记住遍历位置,不会像列表那样一次性全部加载。 迭代器有什么好处呢,正如前面所言,因为不用一次性全部加载对象,所以可以节约内存,我们可以通过 next() 方法来逐个访问对象中的元素。 我们可以使用 iter() ...
return train_loader def get_eval_loader(self, batch_size, is_distributed, testdev=False): from yolox.data import VOCDetection, ValTransform valdataset = VOCDetection( data_dir='/data/Datasets/VOCdevkit', image_sets=[('2007', 'test')], img_size=self.test_size, preproc=ValTransform( rgb...
show() # get some random training images dataiter = iter(trainloader) images, labels = dataiter.next() # show images imshow(torchvision.utils.make_grid(images)) # print labels print(' '.join('%5s' % classes[labels[j]] for j in range(4))) 输出: cat plane ship frog 定义一个...
在第一个例子中，你在循环中反复调用iter。当你调用iter(s)时，它为字符串创建了一个新的迭代器，...
(self): for i in range(len(self)): yield next(self.iterator) class _RepeatSampler(object): """ Sampler that repeats forever 这部分是进行持续采样 Args: sampler (Sampler) """ def __init__(self, sampler): self.sampler = sampler def __iter__(self): while True: yield from iter(self...
在第一个例子中，你在循环中反复调用iter。当你调用iter(s)时，它为字符串创建了一个新的迭代器，...
deftrain_one_iter(self):iter_start_time = time.time()# 迭代开始时间# inps:([bs, 3, 640, 640]),targets:torch.Size([bs, 120, 5])inps, targets = self.prefetcher.next()inps = inps.to(self.data_type)targets = targets.to(self.data_type)targets.requires_grad =False# targets不需要梯度...
train_data_loader()): 330 if nranks > 1: 331 outputs = self.run(ddp_net, data, mode='train') /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py in __next__(self) 695 696 if in_dygraph_mode(): --> 697 data = self._...