Final tweaks

Former-commit-id: 547d4580c776afa5782a08fc5288526da82a0972

submit.py

@@ -1,3 +1,4 @@
+
 import os
 from PIL import Image
 from predict import *
@@ -8,18 +9,20 @@ def submit(net, gpu=False):
     dir = 'data/test/'
 
     N = len(list(os.listdir(dir)))
-    with open('SUBMISSION.csv', 'w') as f:
+    with open('SUBMISSION.csv', 'a') as f:
+
         f.write('img,rle_mask\n')
         for index, i in enumerate(os.listdir(dir)):
             print('{}/{}'.format(index, N))
+
             img = Image.open(dir + i)
 
             mask = predict_img(net, img, gpu)
-            enc = encode(mask)
+            enc = rle_encode(mask)
             f.write('{},{}\n'.format(i, ' '.join(map(str, enc))))
 
 
 if __name__ == '__main__':
     net = UNet(3, 1).cuda()
-    net.load_state_dict(torch.load('INTERRUPTED.pth'))
+    net.load_state_dict(torch.load('MODEL.pth'))
     submit(net, True)

utils.py

@@ -72,20 +72,50 @@ def merge_masks(img1, img2, full_w):
     return new
 
 
+import matplotlib.pyplot as plt
+
 def encode(mask):
     """mask : HxW"""
+    plt.imshow(mask.transpose())
+    plt.show()
     flat = mask.transpose().reshape(-1)
     enc = []
-    i = 0
-    while i < len(flat): # sorry python
-        if(flat[i]):
+    i = 1
+
+    while i <= len(flat):
+        if(flat[i-1]):
             s = i
-            while(flat[i]):
+            while(flat[i-1]):
                 i += 1
             e = i-1
-            if(s != e):
             enc.append(s)
             enc.append(e - s + 1)
         i += 1
 
+    plt.imshow(decode(enc))
+    plt.show()
     return enc
+
+def decode(list):
+    mask = np.zeros((1280*1920), np.bool)
+
+    for i, e in enumerate(list):
+        if(i%2 == 0):
+            mask[e-1:e-2+list[i+1]] = True
+
+    mask = mask.reshape(1920, 1280).transpose()
+
+    return mask
+
+
+def rle_encode(mask_image):
+    pixels = mask_image.flatten()
+    # We avoid issues with '1' at the start or end (at the corners of
+    # the original image) by setting those pixels to '0' explicitly.
+    # We do not expect these to be non-zero for an accurate mask,
+    # so this should not harm the score.
+    pixels[0] = 0
+    pixels[-1] = 0
+    runs = np.where(pixels[1:] != pixels[:-1])[0] + 2
+    runs[1::2] = runs[1::2] - runs[:-1:2]
+    return runs