Using smooth label. CV: 48%, LB: 38.5%

ngxbac · Jul 5, 2019 · a49d90e · a49d90e
1 parent f5b87e5
commit a49d90e
Show file tree

Hide file tree

Showing 3 changed files with 8 additions and 6 deletions.
diff --git a/bin/train.sh b/bin/train.sh
@@ -4,7 +4,7 @@ export CUDA_VISIBLE_DEVICES=2,3
 RUN_CONFIG=config.yml
 
 
-LOGDIR=/raid/bac/kaggle/logs/recursion_cell/test/c123_s1_1cycle_adamw_norm_per_channel/se_resnext50_32x4d/
+LOGDIR=/raid/bac/kaggle/logs/recursion_cell/test/c123_s1_1cycle_adamw_norm_per_channel_smooth/se_resnext50_32x4d/
 catalyst-dl run \
     --config=./configs/${RUN_CONFIG} \
     --logdir=$LOGDIR \

diff --git a/configs/config.yml b/configs/config.yml
@@ -19,7 +19,8 @@ stages:
     minimize_metric: False
 
   criterion_params:
-    criterion: CrossEntropyLoss
+#    criterion: CrossEntropyLoss
+    criterion: LabelSmoothingCrossEntropy
 
   optimizer_params:
     optimizer: AdamW
@@ -52,7 +53,8 @@ stages:
 
     callbacks_params: &callback_params
       loss:
-        callback: CriterionCallback
+#        callback: CriterionCallback
+        callback: LabelSmoothCriterionCallback
       optimizer:
         callback: OptimizerCallback
         accumulation_steps: 2

diff --git a/src/make_submission.py b/src/make_submission.py
@@ -35,7 +35,7 @@ def predict(model, loader):
 def predict_all():
     test_csv = '/raid/data/kaggle/recursion-cellular-image-classification/test.csv'
     # test_csv = './csv/valid_0.csv'
-    log_dir = "/raid/bac/kaggle/logs/recursion_cell/test/c123_s1_1cycle_adamw_norm_per_channel/se_resnext50_32x4d/"
+    log_dir = "/raid/bac/kaggle/logs/recursion_cell/test/c123_s1_1cycle_adamw_norm_per_channel_smooth/se_resnext50_32x4d/"
     root = "/raid/data/kaggle/recursion-cellular-image-classification/"
     site = 1
     channels = [1,2,3]
@@ -75,8 +75,8 @@ def predict_all():
     submission = df.copy()
     submission['sirna'] = all_preds.astype(int)
     os.makedirs("submission", exist_ok=True)
-    submission.to_csv('./submission/se_resnext50_32x4d_c123_s1_1cycle_adamw_norm_per_channel.csv', index=False, columns=['id_code', 'sirna'])
-    np.save("./submission/se_resnext50_32x4d_c123_s1_1cycle_adamw_norm_per_channel.npy", pred)
+    submission.to_csv('./submission/se_resnext50_32x4d_c123_s1_1cycle_adamw_norm_per_channel_smooth.csv', index=False, columns=['id_code', 'sirna'])
+    np.save("./submission/se_resnext50_32x4d_c123_s1_1cycle_adamw_norm_per_channel_smooth.npy", pred)
 
 
 if __name__ == '__main__':