Using AdamW, 1cycle policy. norm per channel. CV: 45.6%, LB: 35.2%

turbulent0 · Jul 5, 2019 · f5b87e5 · f5b87e5
1 parent 4b58ab1
commit f5b87e5
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Showing 3 changed files with 6 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/se_resnext50_32x4d/
+LOGDIR=/raid/bac/kaggle/logs/recursion_cell/test/c123_s1_1cycle_adamw_norm_per_channel/se_resnext50_32x4d/
 catalyst-dl run \
     --config=./configs/${RUN_CONFIG} \
     --logdir=$LOGDIR \

diff --git a/src/dataset.py b/src/dataset.py
@@ -262,11 +262,11 @@ def __getitem__(self, idx):
 
         image = load_images_as_tensor(channel_paths, dtype=np.float32)
         # image = convert_tensor_to_rgb(image)
-        image = image / 255
+        # image = image / 255
         if self.transform:
             image = self.transform(image=image)['image']
 
-        # image = normalize(image, std=std_arr, mean=mean_arr, max_pixel_value=255)
+        image = normalize(image, std=std_arr, mean=mean_arr, max_pixel_value=255)
         image = np.transpose(image, (2, 0, 1)).astype(np.float32)
 
         if self.mode == 'train':

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/se_resnext50_32x4d/"
+    log_dir = "/raid/bac/kaggle/logs/recursion_cell/test/c123_s1_1cycle_adamw_norm_per_channel/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.csv', index=False, columns=['id_code', 'sirna'])
-    np.save("./submission/se_resnext50_32x4d_c123_s1_1cycle_adamw.npy", pred)
+    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)
 
 
 if __name__ == '__main__':