Added simple predict + submit script

crf.py

@@ -8,7 +8,6 @@ def dense_crf(img, output_probs):
 
     output_probs = np.expand_dims(output_probs, 0)
     output_probs = np.append(1 - output_probs, output_probs, axis=0)
-    print(output_probs.shape)
 
     d = dcrf.DenseCRF2D(w, h, 2)
     U = -np.log(output_probs)
@@ -19,10 +18,10 @@ def dense_crf(img, output_probs):
 
     d.setUnaryEnergy(U)
 
-    d.addPairwiseGaussian(sxy=10, compat=3)
+    d.addPairwiseGaussian(sxy=20, compat=3)
-    d.addPairwiseBilateral(sxy=50, srgb=20, rgbim=img, compat=10)
+    d.addPairwiseBilateral(sxy=30, srgb=20, rgbim=img, compat=10)
 
-    Q = d.inference(30)
+    Q = d.inference(5)
     Q = np.argmax(np.array(Q), axis=0).reshape((h, w))
 
     return Q

eval.py

@@ -32,7 +32,7 @@ def eval_net(net, dataset, gpu=False):
         dice = dice_coeff(y_pred, y.float()).data[0]
         tot += dice
 
-        if 0:
+        if 1:
             X = X.data.squeeze(0).cpu().numpy()
             X = np.transpose(X, axes=[1, 2, 0])
             y = y.data.squeeze(0).cpu().numpy()
@@ -45,12 +45,12 @@ def eval_net(net, dataset, gpu=False):
             ax2 = fig.add_subplot(1, 4, 2)
             ax2.imshow(y)
             ax3 = fig.add_subplot(1, 4, 3)
-            ax3.imshow((y_pred > 0.6))
+            ax3.imshow((y_pred > 0.5))
 
 
             Q = dense_crf(((X*255).round()).astype(np.uint8), y_pred)
             ax4 = fig.add_subplot(1, 4, 4)
             print(Q)
-            ax4.imshow(Q)
+            ax4.imshow(Q > 0.5)
             plt.show()
     return tot / i

load.py

@@ -40,3 +40,8 @@ def get_imgs_and_masks(ids, dir_img, dir_mask):
     masks = to_cropped_imgs(ids, dir_mask, '_mask.gif')
 
     return zip(imgs_normalized, masks)
+
+def get_full_img_and_mask(id, dir_img, dir_mask):
+    im = Image.open(dir_img + id + '.jpg')
+    mask = Image.open(dir_mask + id + '_mask.gif')
+    return np.array(im), np.array(mask)

predict.py

@@ -0,0 +1,41 @@
+import torch
+from utils import *
+import torch.nn.functional as F
+from PIL import Image
+from unet_model import UNet
+from torch.autograd import Variable
+import matplotlib.pyplot as plt
+from crf import dense_crf
+
+
+def predict_img(net, full_img, gpu=False):
+    img = resize_and_crop(full_img)
+
+    left = get_square(img, 0)
+    right = get_square(img, 1)
+
+    right = normalize(right)
+    left = normalize(left)
+
+    right = np.transpose(right, axes=[2, 0, 1])
+    left = np.transpose(left, axes=[2, 0, 1])
+
+    X_l = torch.FloatTensor(left).unsqueeze(0)
+    X_r = torch.FloatTensor(right).unsqueeze(0)
+
+    if gpu:
+        X_l = Variable(X_l, volatile=True).cuda()
+        X_r = Variable(X_r, volatile=True).cuda()
+    else:
+        X_l = Variable(X_l, volatile=True)
+        X_r = Variable(X_r, volatile=True)
+
+    y_l = F.sigmoid(net(X_l))
+    y_r = F.sigmoid(net(X_r))
+    y_l = F.upsample_bilinear(y_l, scale_factor=2).data[0][0].cpu().numpy()
+    y_r = F.upsample_bilinear(y_r, scale_factor=2).data[0][0].cpu().numpy()
+
+    y = merge_masks(y_l, y_r, 1918)
+    yy = dense_crf(np.array(full_img).astype(np.uint8), y)
+
+    return yy > 0.5

submit.py

@@ -0,0 +1,25 @@
+import os
+from PIL import Image
+from predict import *
+from utils import encode
+from unet_model import UNet
+
+def submit(net, gpu=False):
+    dir = 'data/test/'
+
+    N = len(list(os.listdir(dir)))
+    with open('SUBMISSION.csv', 'w') 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)
+            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'))
+    submit(net, True)

utils.py

@@ -53,3 +53,39 @@ def split_train_val(dataset, val_percent=0.05):
 
 def normalize(x):
     return x / 255
+
+
+def merge_masks(img1, img2, full_w):
+    w = img1.shape[1]
+    overlap = int(2 * w - full_w)
+    h = img1.shape[0]
+
+    new = np.zeros((h, full_w), np.float32)
+
+    margin = 0
+
+    new[:, :full_w//2+1] = img1[:, :full_w//2+1]
+    new[:, full_w//2+1:] = img2[:, -(full_w//2-1):]
+    #new[:, w-overlap+1+margin//2:-(w-overlap+margin//2)] = (img1[:, -overlap+margin:] +
+    #                                  img2[:, :overlap-margin])/2
+
+    return new
+
+
+def encode(mask):
+    """mask : HxW"""
+    flat = mask.transpose().reshape(-1)
+    enc = []
+    i = 0
+    while i < len(flat): # sorry python
+        if(flat[i]):
+            s = i
+            while(flat[i]):
+                i += 1
+            e = i-1
+            if(s != e):
+                enc.append(s)
+                enc.append(e - s + 1)
+        i += 1
+
+    return enc