diff --git a/test.py b/test.py
index 881e4b8..6e2b9cd 100644
--- a/test.py
+++ b/test.py
@@ -1 +1,105 @@
-print('Hello 분리수거!')
\ No newline at end of file
+import os
+import json
+import warnings
+warnings.filterwarnings('ignore')
+
+import numpy as np
+import pandas as pd
+
+import torch
+from torch.utils.data import Dataset, DataLoader
+import segmentation_models_pytorch as smp
+from segmentation_models_pytorch.encoders import get_preprocessing_fn
+
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+
+from my_utils import *
+from dataloader import *
+#from loss import *
+
+from tqdm import tqdm
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def collate_fn(batch):
+    return tuple(zip(*batch))
+
+
+def test(model, data_loader, device):
+    size = 256
+    transform = A.Compose([A.Resize(256, 256)])
+    print('Start prediction.')
+    model.eval()
+    
+    file_name_list = []
+    preds_array = np.empty((0, size*size), dtype=np.long)
+    
+    with torch.no_grad():
+        for step, (imgs, image_infos) in tqdm(enumerate(data_loader)):
+
+            # inference (512 x 512)
+            outs = model(torch.stack(imgs).to(device))
+            oms = torch.argmax(outs, dim=1).detach().cpu().numpy()
+            # resize (256 x 256)
+            # temp_mask = []
+            # for img, mask in zip(np.stack(imgs), oms):
+            #     transformed = transform(image=img, mask=mask)
+            #     mask = transformed['mask']
+            #     temp_mask.append(mask)
+
+            # oms = np.array(temp_mask)
+            
+            oms = oms.reshape([oms.shape[0], size*size]).astype(int)
+            preds_array = np.vstack((preds_array, oms))
+            
+            file_name_list.append([i['file_name'] for i in image_infos])
+            print(f"step:{step+1:3d}/{len(data_loader)}")
+    print("End prediction.")
+    file_names = [y for x in file_name_list for y in x]
+    
+    return file_names, preds_array
+
+
+def main():
+    dataset_path = '../input/data'
+    test_path = dataset_path + '/test.json'
+
+    test_transform = A.Compose([
+                            A.Resize(256, 256),
+                            ToTensorV2()
+                            ])
+
+    test_dataset = CustomDataLoader(data_dir=test_path, mode='test', transform=test_transform)
+
+    test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
+                                            batch_size=16,
+                                            num_workers=4,
+                                            collate_fn=collate_fn)
+
+    model = smp.DeepLabV3Plus(
+        encoder_name='resnext50_32x4d',
+        encoder_weights='ssl', 
+        classes=12
+    )
+    load_model(model, device, saved_dir="models", file_name="deeplabv3plus_resnext50_32x4d.pt")
+    model.to(device)
+    #load_model(model, device, saved_dir, file_name)
+    # sample_submisson.csv 열기
+
+    submission = pd.read_csv('./submission/sample_submission.csv', index_col=None)
+
+    # test set에 대한 prediction
+    file_names, preds = test(model, test_loader, device)
+
+    # PredictionString 대입
+    for file_name, string in zip(file_names, preds):
+        submission = submission.append({"image_id" : file_name, "PredictionString" : ' '.join(str(e) for e in string.tolist())}, 
+                                    ignore_index=True)
+
+    # submission.csv로 저장
+    submission.to_csv("./submission/deeplabv3plus_resnext50_32x4d.csv", index=False)
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file