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我正在尝试使用他们自己的教程从他们的页面微调 Pytorch 模型。我在来自 Kaggle 的数据集 StaVer 上进行了尝试：rtatman/stamp-verification-staver-dataset。

他们代码的唯一变化是数据集的路径。训练模型部分出现的错误：

# let's train it for 10 epochs
from torch.optim.lr_scheduler import StepLR

num_epochs = 20

for epoch in range(num_epochs):
    # train for one epoch, printing every 10 iterations
    train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=1)
    # update the learning rate
    lr_scheduler.step()
    # evaluate on the test dataset
    evaluate(model, data_loader_test, device=device)

.

只需运行以下代码即可复制相同的错误：

model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
dataset = StaverDataset("", get_transform(train=True))
data_loader = torch.utils.data.DataLoader(
    dataset, batch_size=2, shuffle=False, num_workers=0, collate_fn=utils.collate_fn
)
a = iter(data_loader)
for i in range(len(a)):
  images, targets = next(a)
  images = list(image for image in images)
  targets = [{k: v for k, v in t.items()} for t in targets]
  output = model(images, targets)

奇怪的是它在 image_382 之前工作正常。如果我删除所有以前的图像并在最后大约运行模型。50张图片也可以。似乎问题与图像无关，而与图像数量有关。通过模型（）运行的图像数量。

这是错误：

scans/scans/stampDS-00380.png
scans/scans/stampDS-00381.png
scans/scans/stampDS-00382.png
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-40-6ccb0398c1aa> in <module>()
      4   images = list(image for image in images)
      5   targets = [{k: v for k, v in t.items()} for t in targets]
----> 6   output = model(images, targets)
      7   model.eval()

6 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in interpolate(input, size, scale_factor, mode, align_corners, recompute_scale_factor)
   3710         return torch._C._nn.upsample_nearest1d(input, output_size, scale_factors)
   3711     if input.dim() == 4 and mode == "nearest":
-> 3712         return torch._C._nn.upsample_nearest2d(input, output_size, scale_factors)
   3713     if input.dim() == 5 and mode == "nearest":
   3714         return torch._C._nn.upsample_nearest3d(input, output_size, scale_factors)

RuntimeError: Input and output sizes should be greater than 0, but got input (H: 1605, W: 2) output (H: 799, W: 0)

有谁知道错误与什么有关？先感谢您。迈克尔

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1 回答 1

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似乎问题出在图像的大小上。将图像的大小减小 2 倍使其工作。但坦率地说，我不知道为什么。

于 2022-02-22T19:57:19.310 回答