payload: {"action":"created","issue":{"url":"https://api.github.com/repos/rusty1s/pytorch_geometric/issues/331","repository_url":"https://api.github.com/repos/rusty1s/pytorch_geometric","labels_url":"https://api.github.com/repos/rusty1s/pytorch_geometric/issues/331/labels{/name}","comments_url":"https://api.github.com/repos/rusty1s/pytorch_geometric/issues/331/comments","events_url":"https://api.github.com/repos/rusty1s/pytorch_geometric/issues/331/events","html_url":"https://github.com/rusty1s/pytorch_geometric/issues/331","id":447068036,"node_id":"MDU6SXNzdWU0NDcwNjgwMzY=","number":331,"title":"Issue reproducing the results of the original ecc implementation. Pooling layer and conv layer are giving different results of the original implementation","user":{"login":"dhorka","id":39189267,"node_id":"MDQ6VXNlcjM5MTg5MjY3","avatar_url":"https://avatars0.githubusercontent.com/u/39189267?v=4","gravatar_id":"","url":"https://api.github.com/users/dhorka","html_url":"https://github.com/dhorka","followers_url":"https://api.github.com/users/dhorka/followers","following_url":"https://api.github.com/users/dhorka/following{/other_user}","gists_url":"https://api.github.com/users/dhorka/gists{/gist_id}","starred_url":"https://api.github.com/users/dhorka/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dhorka/subscriptions","organizations_url":"https://api.github.com/users/dhorka/orgs","repos_url":"https://api.github.com/users/dhorka/repos","events_url":"https://api.github.com/users/dhorka/events{/privacy}","received_events_url":"https://api.github.com/users/dhorka/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":78,"created_at":"2019-05-22T10:55:26Z","updated_at":"2019-06-06T09:24:17Z","closed_at":"2019-06-05T12:44:46Z","author_association":"NONE","body":"As I mentioned in #319  I have problems to reproduce the [ecc implemenation](https://github.com/mys007/ecc) using pytorch_geometric. I found some differences between the results obtained, first one is that the results of both convolution operations using the same weights have different results. Moreover, the results of the pooling layers are also different. \r\n\r\nI created a test that checks this things. Basically, the scripts load the same weights to both implementations. These weights are obtained from train a network using the ecc_implementation. Below you can see the output of my test.\r\n\r\n```\r\nECC Weights and PyGeometric weights are equal: True #I am only doing a re-check in order to be sure that both weights are equal before to load to the models.\r\nLoading weights \r\nStarting validation:\r\necc features conv1:  (997, 16) #Shape of the output of first conv in ecc implementation\r\npygeometric features conv1:  (997, 16) #Shape of the output of first conv in pygeometric implementation\r\nMax difference between features of first conv 2.549824\r\nOutput of ecc pooling:  (398, 32)\r\nOutput of PyGeometric pooling:  (385, 32)\r\nPygeomtric Acc:  41.51982378854625  Ecc accuracy:  63.65638766519823\r\nPygeomtric Loss:  2.435516586519023  Ecc Loss:  0.9878960176974138\r\n```\r\nAs you can observe this difference has an impact to the accuracy using the same weights. You can find the source code [here](https://github.com/dhorka/eccvspygeometric.git). One important thing, the data used for this tests is obtained from the original code of the ecc.\r\n"},"comment":{"url":"https://api.github.com/repos/rusty1s/pytorch_geometric/issues/comments/499419257","html_url":"https://github.com/rusty1s/pytorch_geometric/issues/331#issuecomment-499419257","issue_url":"https://api.github.com/repos/rusty1s/pytorch_geometric/issues/331","id":499419257,"node_id":"MDEyOklzc3VlQ29tbWVudDQ5OTQxOTI1Nw==","user":{"login":"dhorka","id":39189267,"node_id":"MDQ6VXNlcjM5MTg5MjY3","avatar_url":"https://avatars0.githubusercontent.com/u/39189267?v=4","gravatar_id":"","url":"https://api.github.com/users/dhorka","html_url":"https://github.com/dhorka","followers_url":"https://api.github.com/users/dhorka/followers","following_url":"https://api.github.com/users/dhorka/following{/other_user}","gists_url":"https://api.github.com/users/dhorka/gists{/gist_id}","starred_url":"https://api.github.com/users/dhorka/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dhorka/subscriptions","organizations_url":"https://api.github.com/users/dhorka/orgs","repos_url":"https://api.github.com/users/dhorka/repos","events_url":"https://api.github.com/users/dhorka/events{/privacy}","received_events_url":"https://api.github.com/users/dhorka/received_events","type":"User","site_admin":false},"created_at":"2019-06-06T09:24:17Z","updated_at":"2019-06-06T09:24:17Z","author_association":"NONE","body":"But, if any previous node is assigned to this cluster (using the nearest algorithm I mean), this new point doesn't have any feature and I think it is not interesting to have this node on my graph. How can I know which node has no representation? Because far as I understood the dim_size is adding 0 padding to the end of the feature vector, right?"}}
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