Hi, I have a question about round1 result. I got email in 25/12.
Yesterday some inference submissions to the Digital Mammography challenge were erroneously marked invalid. We caught and corrected the error and requeued the submissions to be run and scored, but you may have received an unintended email message saying your submission was invalid. The IDs of the affected submissions are below. There is no action that you need to take.
...
7905339,
7905544,
7901603,
**7902117,**
7902649,
7904011,
7904012,
...
7902117 and other submissions which we submitted have not been received.
Also, there is no our team score on the leaderboard.
Can you tell me what's going on?
Created by MinHwan Yu minhwan90 Regarding submission 7898060, we sent this message on 12/23:
```
Dear DMIS_mammo:
Your Submission to the Digital Mammography challenge (submission ID 7898060) has failed to complete its scoring phase. The message is:
Error encountered during prediction. Last few lines of logs are shown below.
Please direct any questions to the challenge forum, https://www.synapse.org/#!Synapse:syn4224222/discussion.
Sincerely,
Challenge Administration
Logs:
STDOUT: -batchSize mini-batch size (1 = pure stochastic) [32]
STDOUT: -display_iter display of training iteration [15]
STDOUT: -top5_display display top5 accuracy [false]
STDOUT: -testOnly Run on validation set only [false]
STDOUT: -tenCrop Ten-crop testing [false]
STDOUT: -save Directory in which to save checkpoints [/scratch]
STDOUT: -resume Resume from the latest checkpoint in this directory [/modelState]
STDOUT: -modelState Directory for saving model state [/modelState]
STDOUT: -saveLatest Resume from the latest checkpoint [false]
STDOUT: -saveCut Save by CPU every 10 epochs [true]
STDOUT: -LR initial learning rate [0.1]
STDOUT: -momentum momentum [0.9]
STDOUT: -weightDecay weight decay [0.0005]
STDOUT: -netType Options: resnet | wide-resnet [resnet]
STDOUT: -depth ResNet depth: 6n+4 [50]
STDOUT: -widen_factor Wide-Resnet width [2]
STDOUT: -dropout Dropout rate [0]
STDOUT: -shortcutType Options: A | B | C []
STDOUT: -retrain fine-tuning, Path to model to retrain with [none]
STDOUT: -optimState Path to an optimState to reload from [none]
STDOUT: -shareGradInput Share gradInput tensors to reduce memory usage [true]
STDOUT: -optnet Use optnet to reduce memory usage [false]
STDOUT: -resetClassifier Reset the fully connected layer for fine-tuning [false]
STDOUT: -nClasses Number of classes in the dataset [0]
STDOUT:
```
"Error encountered during prediction" means that we ran your container but it terminated with a non-zero exit code. The final lines that it printed before exiting are shown in the email.
7901582 suffered the same fate:
```
Dear DMIS_mammo:
Your Submission to the Digital Mammography challenge (submission ID 7901582) has failed to complete its scoring phase. The message is:
Error encountered during prediction. Last few lines of logs are shown below.
Please direct any questions to the challenge forum, https://www.synapse.org/#!Synapse:syn4224222/discussion.
Sincerely,
Challenge Administration
Logs:
STDOUT: -batchSize mini-batch size (1 = pure stochastic) [32]
STDOUT: -display_iter display of training iteration [15]
STDOUT: -top5_display display top5 accuracy [false]
STDOUT: -testOnly Run on validation set only [false]
STDOUT: -tenCrop Ten-crop testing [false]
STDOUT: -save Directory in which to save checkpoints [/scratch]
STDOUT: -resume Resume from the latest checkpoint in this directory [/modelState]
STDOUT: -modelState Directory for saving model state [/modelState]
STDOUT: -saveLatest Resume from the latest checkpoint [false]
STDOUT: -saveCut Save by CPU every 10 epochs [true]
STDOUT: -LR initial learning rate [0.1]
STDOUT: -momentum momentum [0.9]
STDOUT: -weightDecay weight decay [0.0005]
STDOUT: -netType Options: resnet | wide-resnet [resnet]
STDOUT: -depth ResNet depth: 6n+4 [50]
STDOUT: -widen_factor Wide-Resnet width [2]
STDOUT: -dropout Dropout rate [0]
STDOUT: -shortcutType Options: A | B | C []
STDOUT: -retrain fine-tuning, Path to model to retrain with [none]
STDOUT: -optimState Path to an optimState to reload from [none]
STDOUT: -shareGradInput Share gradInput tensors to reduce memory usage [true]
STDOUT: -optnet Use optnet to reduce memory usage [false]
STDOUT: -resetClassifier Reset the fully connected layer for fine-tuning [false]
STDOUT: -nClasses Number of classes in the dataset [0]
STDOUT:
```
... and 7901603
```
Dear DMIS_mammo:
Your Submission to the Digital Mammography challenge (submission ID 7901603) has failed to complete its scoring phase. The message is:
Error encountered during prediction. Last few lines of logs are shown below.
Please direct any questions to the challenge forum, https://www.synapse.org/#!Synapse:syn4224222/discussion.
Sincerely,
Challenge Administration
Logs:
STDOUT: -batchSize mini-batch size (1 = pure stochastic) [32]
STDOUT: -display_iter display of training iteration [15]
STDOUT: -top5_display display top5 accuracy [false]
STDOUT: -testOnly Run on validation set only [false]
STDOUT: -tenCrop Ten-crop testing [false]
STDOUT: -save Directory in which to save checkpoints [/scratch]
STDOUT: -resume Resume from the latest checkpoint in this directory [/modelState]
STDOUT: -modelState Directory for saving model state [/modelState]
STDOUT: -saveLatest Resume from the latest checkpoint [false]
STDOUT: -saveCut Save by CPU every 10 epochs [true]
STDOUT: -LR initial learning rate [0.1]
STDOUT: -momentum momentum [0.9]
STDOUT: -weightDecay weight decay [0.0005]
STDOUT: -netType Options: resnet | wide-resnet [resnet]
STDOUT: -depth ResNet depth: 6n+4 [50]
STDOUT: -widen_factor Wide-Resnet width [2]
STDOUT: -dropout Dropout rate [0]
STDOUT: -shortcutType Options: A | B | C []
STDOUT: -retrain fine-tuning, Path to model to retrain with [none]
STDOUT: -optimState Path to an optimState to reload from [none]
STDOUT: -shareGradInput Share gradInput tensors to reduce memory usage [true]
STDOUT: -optnet Use optnet to reduce memory usage [false]
STDOUT: -resetClassifier Reset the fully connected layer for fine-tuning [false]
STDOUT: -nClasses Number of classes in the dataset [0]
STDOUT:
```
... and 7902117:
```
Dear DMIS_mammo:
Your Submission to the Digital Mammography challenge (submission ID 7902117) has failed to complete its scoring phase. The message is:
Error encountered during prediction. Last few lines of logs are shown below.
Please direct any questions to the challenge forum, https://www.synapse.org/#!Synapse:syn4224222/discussion.
Sincerely,
Challenge Administration
Logs:
STDOUT: -batchSize mini-batch size (1 = pure stochastic) [32]
STDOUT: -display_iter display of training iteration [15]
STDOUT: -top5_display display top5 accuracy [false]
STDOUT: -testOnly Run on validation set only [false]
STDOUT: -tenCrop Ten-crop testing [false]
STDOUT: -save Directory in which to save checkpoints [/scratch]
STDOUT: -resume Resume from the latest checkpoint in this directory [/modelState]
STDOUT: -modelState Directory for saving model state [/modelState]
STDOUT: -saveLatest Resume from the latest checkpoint [false]
STDOUT: -saveCut Save by CPU every 10 epochs [true]
STDOUT: -LR initial learning rate [0.1]
STDOUT: -momentum momentum [0.9]
STDOUT: -weightDecay weight decay [0.0005]
STDOUT: -netType Options: resnet | wide-resnet [resnet]
STDOUT: -depth ResNet depth: 6n+4 [50]
STDOUT: -widen_factor Wide-Resnet width [2]
STDOUT: -dropout Dropout rate [0]
STDOUT: -shortcutType Options: A | B | C []
STDOUT: -retrain fine-tuning, Path to model to retrain with [none]
STDOUT: -optimState Path to an optimState to reload from [none]
STDOUT: -shareGradInput Share gradInput tensors to reduce memory usage [true]
STDOUT: -optnet Use optnet to reduce memory usage [false]
STDOUT: -resetClassifier Reset the fully connected layer for fine-tuning [false]
STDOUT: -nClasses Number of classes in the dataset [0]
STDOUT:
```
7900699:
```
Dear DMIS_mammo:
Your Submission to the Digital Mammography challenge (submission ID 7900699) has failed to complete its scoring phase. The message is:
Error encountered during prediction. Last few lines of logs are shown below.
Please direct any questions to the challenge forum, https://www.synapse.org/#!Synapse:syn4224222/discussion.
Sincerely,
Challenge Administration
Logs:
STDOUT: -batchSize mini-batch size (1 = pure stochastic) [32]
STDOUT: -display_iter display of training iteration [15]
STDOUT: -top5_display display top5 accuracy [false]
STDOUT: -testOnly Run on validation set only [false]
STDOUT: -tenCrop Ten-crop testing [false]
STDOUT: -save Directory in which to save checkpoints [/scratch]
STDOUT: -resume Resume from the latest checkpoint in this directory [/modelState]
STDOUT: -modelState Directory for saving model state [/modelState]
STDOUT: -saveLatest Resume from the latest checkpoint [false]
STDOUT: -saveCut Save by CPU every 10 epochs [true]
STDOUT: -LR initial learning rate [0.1]
STDOUT: -momentum momentum [0.9]
STDOUT: -weightDecay weight decay [0.0005]
STDOUT: -netType Options: resnet | wide-resnet [resnet]
STDOUT: -depth ResNet depth: 6n+4 [50]
STDOUT: -widen_factor Wide-Resnet width [2]
STDOUT: -dropout Dropout rate [0]
STDOUT: -shortcutType Options: A | B | C []
STDOUT: -retrain fine-tuning, Path to model to retrain with [none]
STDOUT: -optimState Path to an optimState to reload from [none]
STDOUT: -shareGradInput Share gradInput tensors to reduce memory usage [true]
STDOUT: -optnet Use optnet to reduce memory usage [false]
STDOUT: -resetClassifier Reset the fully connected layer for fine-tuning [false]
STDOUT: -nClasses Number of classes in the dataset [0]
STDOUT:
```
and 7902116:
```
Dear DMIS_mammo:
Your Submission to the Digital Mammography challenge (submission ID 7902116) has failed to complete its scoring phase. The message is:
Error encountered during prediction. Last few lines of logs are shown below.
Please direct any questions to the challenge forum, https://www.synapse.org/#!Synapse:syn4224222/discussion.
Sincerely,
Challenge Administration
Logs:
STDOUT: -batchSize mini-batch size (1 = pure stochastic) [32]
STDOUT: -display_iter display of training iteration [15]
STDOUT: -top5_display display top5 accuracy [false]
STDOUT: -testOnly Run on validation set only [false]
STDOUT: -tenCrop Ten-crop testing [false]
STDOUT: -save Directory in which to save checkpoints [/scratch]
STDOUT: -resume Resume from the latest checkpoint in this directory [/modelState]
STDOUT: -modelState Directory for saving model state [/modelState]
STDOUT: -saveLatest Resume from the latest checkpoint [false]
STDOUT: -saveCut Save by CPU every 10 epochs [true]
STDOUT: -LR initial learning rate [0.1]
STDOUT: -momentum momentum [0.9]
STDOUT: -weightDecay weight decay [0.0005]
STDOUT: -netType Options: resnet | wide-resnet [resnet]
STDOUT: -depth ResNet depth: 6n+4 [50]
STDOUT: -widen_factor Wide-Resnet width [2]
STDOUT: -dropout Dropout rate [0]
STDOUT: -shortcutType Options: A | B | C []
STDOUT: -retrain fine-tuning, Path to model to retrain with [none]
STDOUT: -optimState Path to an optimState to reload from [none]
STDOUT: -shareGradInput Share gradInput tensors to reduce memory usage [true]
STDOUT: -optnet Use optnet to reduce memory usage [false]
STDOUT: -resetClassifier Reset the fully connected layer for fine-tuning [false]
STDOUT: -nClasses Number of classes in the dataset [0]
STDOUT:
```
I believe that accounts for all your inference submissions.
The lists below show a total of six submissions from your team. Is this the complete list of submissions you are asking about?
###Leaderboard Inference Submissions: Sub-Challenge 1
${leaderboard?path=%2Fevaluation%2Fsubmission%2Fquery%3Fquery%3Dselect%2B%2A%2Bfrom%2Bevaluation%5F7453778%2Bwhere%2BSUBMITTER%253D%253D%25223347757%2522&paging=true&queryTableResults=true&showIfLoggedInOnly=false&pageSize=100&showRowNumber=false&jsonResultsKeyName=rows&columnConfig0=none%2CSubmission ID%2CobjectId%3B%2CDESC&columnConfig1=none%2CStatus%2Cstatus%3B%2CNONE&columnConfig2=none%2CStatus Detail%2CSTATUS%5FDESCRIPTION%3B%2CNONE&columnConfig3=epochdate%2CSubmitted On%2CcreatedOn%3B%2CNONE&columnConfig4=epochdate%2CLast Updated%2CmodifiedOn%3B%2CNONE&columnConfig5=synapseid%2CSubmitted Repository or File%2CentityId%3B%2CNONE&columnConfig6=none%2CFile Version%2CversionNumber%3B%2CNONE&columnConfig7=userid%2CParticipant%2CuserId%3B%2CNONE}
###Leaderboard Inference Submissions: Sub-Challenge 2
${leaderboard?path=%2Fevaluation%2Fsubmission%2Fquery%3Fquery%3Dselect%2B%2A%2Bfrom%2Bevaluation%5F7453793%2Bwhere%2BSUBMITTER%253D%253D%25223347757%2522&paging=true&queryTableResults=true&showIfLoggedInOnly=false&pageSize=100&showRowNumber=false&jsonResultsKeyName=rows&columnConfig0=none%2CSubmission ID%2CobjectId%3B%2CDESC&columnConfig1=none%2CStatus%2Cstatus%3B%2CNONE&columnConfig2=none%2CStatus Detail%2CSTATUS%5FDESCRIPTION%3B%2CNONE&columnConfig3=epochdate%2CSubmitted On%2CcreatedOn%3B%2CNONE&columnConfig4=epochdate%2CLast Updated%2CmodifiedOn%3B%2CNONE&columnConfig5=synapseid%2CSubmitted Repository or File%2CentityId%3B%2CNONE&columnConfig6=none%2CFile Version%2CversionNumber%3B%2CNONE&columnConfig7=userid%2CParticipant%2CuserId%3B%2CNONE}