add code for generating manual score

ApplyModel.py

@@ -1,11 +1,14 @@
+import pickle
+
 import numpy as np
 import torch
 from PIL import Image
 
+import Compress
 import DataLoader
 import ex4
 from ImageImpaint import get_train_device
-from netio import load_model
+from netio import load_model, eval_evalset, write_to_pickle
 
 
 def apply_model(filepath: str):
@@ -26,6 +29,37 @@ def apply_model(filepath: str):
     im = Image.fromarray(out)
     im.save("filename.jpg", format="jpeg")
 
+def test():
+    # read the provided testing pickle file
+    print("Generating pickle file with privided test data")
+    PICKEL_PATH = "test"
+
+    model = load_model()
+    model.eval()
+
+    loader,_ = DataLoader.get_image_loader("training/", np.float32)
+    outarr = np.zeros(dtype=np.uint8, shape=(8663, 3, 100, 100))
+    targetarr = np.zeros(dtype=np.uint8, shape=(8663, 3, 100, 100))
+
+    i = 0
+    for input, target in loader:
+        out = model(input)
+        out = DataLoader.postprocess(out.cpu().detach().numpy())
+        outarr[i] = out
+        targetarr[i] = DataLoader.postprocess(target.cpu().detach().numpy())
+        print(f'\rApplying model [{i}/{len(loader)}]', end='')
+        i += 1
+        if i==8663:
+            break
+    write_to_pickle(PICKEL_PATH + "_pred.pkl", list(outarr))
+    # compress the generated pickle arr
+    Compress.compress(PICKEL_PATH + "_pred.pkl")
+
+    write_to_pickle(PICKEL_PATH + "_target.pkl", list(targetarr))
+    # compress the generated pickle arr
+    Compress.compress(PICKEL_PATH + "_target.pkl")
 
 if __name__ == '__main__':
     apply_model("training/000/000017.jpg")
+    eval_evalset()
+    # test()

ImageImpaint.py

@@ -115,8 +115,8 @@ def plot(inputs, targets, predictions, path, update, epoch):
         for ax, data, title in zip(axes, [inputs, targets, predictions], ["Input", "Target", "Prediction"]):
             ax.clear()
             ax.set_title(title)
-            # ax.imshow(DataLoader.postprocess(np.transpose(data[i], (1, 2, 0))), interpolation="none")
-            ax.imshow(np.transpose((data[i]), (1, 2, 0)), interpolation="none")
+            ax.imshow(DataLoader.postprocess(np.transpose(data[i], (1, 2, 0))), interpolation="none")
+            # ax.imshow(np.transpose((data[i]), (1, 2, 0)), interpolation="none")
             ax.set_axis_off()
         fig.savefig(os.path.join(path, f"{epoch:02d}_{update:07d}_{i:02d}.png"), dpi=100)
 

Scoring.py

@@ -27,8 +27,6 @@ import zipfile
 
 import dill as pkl
 import numpy as np
-import onnx
-import onnxruntime
 
 TEST_DATA_PATH = r"/daten/challenge/django/data/datasets/image_inpainting_2022/test.zip"