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D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction

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请问如何用两块GPU训练呢??应该怎么改代码呢?

Why the batch_size for test is 4. I see that you concatenate the image together. Can I use the batchsize equal 1?

Traceback (most recent call last): File "test.py", line 149, in mask = solver.test_one_img_from_path(source+name) File "test.py", line 32, in test_one_img_from_path return self.test_one_img_from_path_2(path) File "test.py", line 94, in test_one_img_from_path_2 img90 = np.array(np.rot90(img))...

when i run python train.py using python virtual environment it shows the error. how to solve the error,anyone solved it? thanks a lot . Another, i have tried to use...

请问您在训练过程中有没有遇到推理结构全白或者全黑的情形?这可能是什么原因导致的呢?分析了很多原因,做了很多修改和尝试,但推理结果依然是全白全黑,该怎么处理呢

DLink原文是8,看到代码里加了一个16的版本,是否有做过相应的精度对比实验呢?

我使用的是readme给模型,在运行test.py文件之后生成对于验证集的预测mask,发现好多mask都是空白的,并且其他的有黑色mask的结果并不是卫星图片当中的道路,这个是我代码改错了吗?我仅仅resize了一下图片的结果 def test_one_img_from_path_4(self, path): img = cv2.imread(path)#.transpose(2,0,1)[None] img=cv2.resize(img,(1024,1024),interpolation=cv2.INTER_LINEAR) img90 = np.array(np.rot90(img)) img90=cv2.resize(img90,(1024,1024),interpolation=cv2.INTER_LINEAR) img1 = np.concatenate([img[None],img90[None]])

错误出现在train.py中低34行左右函数dataloader中data_loader = torch.utils.data.DataLoader( dataset, batch_size=batchsize, shuffle=True, num_workers=4, ) ValueError: num_samples should be a positive integer value, but got num_samples=0 我的运行环境为:python3.6 pytorch1.3.1 打扰您了,期待您的回复,祝您在新的一年心想事成,平安喜乐~~