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你好,我在centernet.py line153下面打印up_fmap的尺寸(使用test_net.py)
up_fmap = self.upsample(features) print(up_fmap.size())
,发现输出为 torch.Size([1, 64, 128, 168]) torch.Size([1, 64, 128, 168]) torch.Size([1, 64, 112, 168]) torch.Size([1, 64, 128, 168]) torch.Size([1, 64, 168, 96]) torch.Size([1, 64, 168, 160]) torch.Size([1, 64, 168, 128]) torch.Size([1, 64, 112, 168]) torch.Size([1, 64, 136, 168]) torch.Size([1, 64, 168, 112]) torch.Size([1, 64, 168, 112])
这部分尺寸输出不应该是128吗,我没有找到输入resize为512那部分代码?请问这个复现不是基于固定尺寸(输入512,输出128)的吗?
The text was updated successfully, but these errors were encountered:
这个复现是固定尺寸(512, 512)的,改变size的code在这里
Sorry, something went wrong.
非常感谢你的及时回复,但是我追踪output_size为centernet.py inference函数中的new_h, new_w = (h | 31) + 1, (w | 31) + 1 计算得到的,其中的h和w是 batch input images的尺寸,我又看了代码,训练时候是从512到128,测试时候是否是原图进行测试的呢?
new_h, new_w = (h | 31) + 1, (w | 31) + 1
here
谢谢,我发现是测试的时候没有transform(因为我的图片比较大,所以inference time过长,所以才产生了困惑)。麻烦了,新年快乐。
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你好,我在centernet.py line153下面打印up_fmap的尺寸(使用test_net.py)
,发现输出为
torch.Size([1, 64, 128, 168])
torch.Size([1, 64, 128, 168])
torch.Size([1, 64, 112, 168])
torch.Size([1, 64, 128, 168])
torch.Size([1, 64, 168, 96])
torch.Size([1, 64, 168, 160])
torch.Size([1, 64, 168, 128])
torch.Size([1, 64, 112, 168])
torch.Size([1, 64, 136, 168])
torch.Size([1, 64, 168, 112])
torch.Size([1, 64, 168, 112])
这部分尺寸输出不应该是128吗,我没有找到输入resize为512那部分代码?请问这个复现不是基于固定尺寸(输入512,输出128)的吗?
The text was updated successfully, but these errors were encountered: