{"action":"closed","issue":{"url":"https://api.github.com/repos/creafz/pytorch-cnn-finetune/issues/8","repository_url":"https://api.github.com/repos/creafz/pytorch-cnn-finetune","labels_url":"https://api.github.com/repos/creafz/pytorch-cnn-finetune/issues/8/labels{/name}","comments_url":"https://api.github.com/repos/creafz/pytorch-cnn-finetune/issues/8/comments","events_url":"https://api.github.com/repos/creafz/pytorch-cnn-finetune/issues/8/events","html_url":"https://github.com/creafz/pytorch-cnn-finetune/issues/8","id":352000549,"node_id":"MDU6SXNzdWUzNTIwMDA1NDk=","number":8,"title":"Error(s) in loading state_dict for Xception","user":{"login":"John1231983","id":24875971,"node_id":"MDQ6VXNlcjI0ODc1OTcx","avatar_url":"https://avatars3.githubusercontent.com/u/24875971?v=4","gravatar_id":"","url":"https://api.github.com/users/John1231983","html_url":"https://github.com/John1231983","followers_url":"https://api.github.com/users/John1231983/followers","following_url":"https://api.github.com/users/John1231983/following{/other_user}","gists_url":"https://api.github.com/users/John1231983/gists{/gist_id}","starred_url":"https://api.github.com/users/John1231983/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/John1231983/subscriptions","organizations_url":"https://api.github.com/users/John1231983/orgs","repos_url":"https://api.github.com/users/John1231983/repos","events_url":"https://api.github.com/users/John1231983/events{/privacy}","received_events_url":"https://api.github.com/users/John1231983/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2018-08-20T06:26:30Z","updated_at":"2018-08-22T17:53:08Z","closed_at":"2018-08-22T17:53:08Z","author_association":"NONE","body":"As your document, the xception has error from the issue \r\nhttps://github.com/Cadene/pretrained-models.pytorch/issues/62\r\nI am using your API and xception network size of 256. I got the error as the below log. Could you tell me how could I fix it using your API\r\nThis is my code\r\n```\r\nmodel = make_model(\r\n    'xception',\r\n    pretrained=True,\r\n    num_classes=100,\r\n    dropout_p=0.2,\r\n    input_size=(256, 256)\r\n)\r\n```\r\nThis is log\r\n```\r\nRuntimeError: Error(s) in loading state_dict for Xception:\r\n\tsize mismatch for block1.rep.0.pointwise.weight: copying a param of torch.Size([128, 64, 1, 1]) from checkpoint, where the shape is torch.Size([128, 64]) in current model.\r\n\tsize mismatch for block1.rep.3.pointwise.weight: copying a param of torch.Size([128, 128, 1, 1]) from checkpoint, where the shape is torch.Size([128, 128]) in current model.\r\n\tsize mismatch for block2.rep.1.pointwise.weight: copying a param of torch.Size([256, 128, 1, 1]) from checkpoint, where the shape is torch.Size([256, 128]) in current model.\r\n\tsize mismatch for block2.rep.4.pointwise.weight: copying a param of torch.Size([256, 256, 1, 1]) from checkpoint, where the shape is torch.Size([256, 256]) in current model.\r\n\tsize mismatch for block3.rep.1.pointwise.weight: copying a param of torch.Size([728, 256, 1, 1]) from checkpoint, where the shape is torch.Size([728, 256]) in current model.\r\n\tsize mismatch for block3.rep.4.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block4.rep.1.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block4.rep.4.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block4.rep.7.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block5.rep.1.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block5.rep.4.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block5.rep.7.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block6.rep.1.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block6.rep.4.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block6.rep.7.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block7.rep.1.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block7.rep.4.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block7.rep.7.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block8.rep.1.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block8.rep.4.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block8.rep.7.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block9.rep.1.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block9.rep.4.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block9.rep.7.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block10.rep.1.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block10.rep.4.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block10.rep.7.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block11.rep.1.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block11.rep.4.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block11.rep.7.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block12.rep.1.pointwise.weight: copying a param of torch.Size([728, 728, 1, 1]) from checkpoint, where the shape is torch.Size([728, 728]) in current model.\r\n\tsize mismatch for block12.rep.4.pointwise.weight: copying a param of torch.Size([1024, 728, 1, 1]) from checkpoint, where the shape is torch.Size([1024, 728]) in current model.\r\n\tsize mismatch for conv3.pointwise.weight: copying a param of torch.Size([1536, 1024, 1, 1]) from checkpoint, where the shape is torch.Size([1536, 1024]) in current model.\r\n\tsize mismatch for conv4.pointwise.weight: copying a param of torch.Size([2048, 1536, 1, 1]) from checkpoint, where the shape is torch.Size([2048, 1536]) in current model.\r\n```"}}
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