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已解决 #20
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注意model.py的输出维度
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主题: [wfs123456/CCTrans] 你好,在运行train.py时,报了AssertionError,指向的是以下这一行 assert self.output_size == normed_density.size(2),求解答 (Issue #20)
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感谢回复,代码现已正常运行。想再请教您一个问题,在train_helper.py文件的val_epoch函数内,crop_pred = (F.interpolate())中的h8,w8以及除以64,是和输出特征的大小有关吗?由于输出特征图大小为原始图像下采样8倍,因此要h和w都乘8。如若输出特征图大小为原始图像下采样4倍,则是将代码改为h4,w4以及除以16,请问我的理解是否有误呢?期待您的回复 |
预测密度图的积分和代表预测人数,,但预测密度图的大小为1/8,那每个像素值就会相应的缩小,为了保证积分和不变,,需要对每个像素值放大 8*8 ,也就是64倍,,,,你的输出为1/4,,那确实是放大4*4,即16倍
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主题: Re: [wfs123456/CCTrans] 已解决 (Issue #20)
注意model.py的输出维度
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感谢回复,代码现已正常运行。想再请教您一个问题,在train_helper.py文件的val_epoch函数内,crop_pred = (F.interpolate())中的h8,w8以及除以64,是和输出特征的大小有关吗?由于输出特征图大小为原始图像下采样8倍,因此要h和w都乘8。如若输出特征图大小为原始图像下采样4倍,则是将代码改为h4,w4以及除以16,请问我的理解是否有误呢?期待您的回复
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@lijinStudy 请问这个问题你怎么解决的 |
注意把model.py的输出预测密度图size和真实密度图size对齐,比果你的output_size是input_size的1/4,那么就要将train_helper.py的downsample_ratio设置为4。 |
谢谢了 |
大佬你好,再打扰你一下,请问你有复现过Paper的损失函数吗。 |
没有 |
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