About the crop size for training and testing

class BaseNet(nn.Module):
    def __init__(self, nclass, backbone, aux, se_loss, dilated=True, norm_layer=None,
                 base_size=576, crop_size=608, mean=[.485, .456, .406],
                 std=[.229, .224, .225], root='~/.encoding/models'):

Previously, I noticed that you choose base_size=520, crop_size=480 and now you change them to base_size=576, crop_size=608.

However, I still have a concern about it, the 608 should be larger than the largest highth or width in your dataset, so we should increase this parameter if we are dealing with larger datasets.

Besides, I noticed that you mentioned that your MultiEvalModule only support single image evalution, but you also provide the batch size parameter, which is contradictory… I guess we can only set the batch size as 1 during testing phase.

  def forward(self, image):
        """Mult-size Evaluation"""
        # only single image is supported for evaluation

[huanghoujing]

Well, the author has carefully considered this case as this, which I just found. Just ignore my ignorance.

[huanghoujing]

Well, it has been solved, which I just found. So there is no problem now.