Removed unused function and general cleanup

Former-commit-id: c34a455f1722e0b899e9e92c7766b83a9a641980

eval.py

@@ -1,11 +1,11 @@
-import torch
-from myloss import dice_coeff
-import numpy as np
-from torch.autograd import Variable
 import matplotlib.pyplot as plt
+import numpy as np
+import torch
 import torch.nn.functional as F
+from torch.autograd import Variable
 
-from utils import dense_crf, plot_img_mask
+from myloss import dice_coeff
+from utils import dense_crf
 
 
 def eval_net(net, dataset, gpu=False):
@@ -47,7 +47,7 @@ def eval_net(net, dataset, gpu=False):
             ax3 = fig.add_subplot(1, 4, 3)
             ax3.imshow((y_pred > 0.5))
 
-            Q = dense_crf(((X*255).round()).astype(np.uint8), y_pred)
+            Q = dense_crf(((X * 255).round()).astype(np.uint8), y_pred)
             ax4 = fig.add_subplot(1, 4, 4)
             print(Q)
             ax4.imshow(Q > 0.5)

myloss.py

@@ -1,23 +1,20 @@
-
 #
 # myloss.py : implementation of the Dice coeff and the associated loss
 #
 
 import torch
-import torch.nn.functional as F
-
-from torch.nn.modules.loss import _Loss
 from torch.autograd import Function, Variable
 
 
 class DiceCoeff(Function):
     """Dice coeff for individual examples"""
+
     def forward(self, input, target):
         self.save_for_backward(input, target)
         self.inter = torch.dot(input, target) + 0.0001
         self.union = torch.sum(input) + torch.sum(target) + 0.0001
 
-        t = 2*self.inter.float()/self.union.float()
+        t = 2 * self.inter.float() / self.union.float()
         return t
 
     # This function has only a single output, so it gets only one gradient
@@ -45,9 +42,4 @@ def dice_coeff(input, target):
     for i, c in enumerate(zip(input, target)):
         s = s + DiceCoeff().forward(c[0], c[1])
 
-    return s / (i+1)
+    return s / (i + 1)
-
-
-class DiceLoss(_Loss):
-    def forward(self, input, target):
-        return 1 - dice_coeff(F.sigmoid(input), target)

predict.py

@@ -1,15 +1,12 @@
+import argparse
+
+import numpy
 import torch
 import torch.nn.functional as F
 from torch.autograd import Variable
-import matplotlib.pyplot as plt
-import numpy
-from PIL import Image
-import argparse
-import os
-
-from utils import *
 
 from unet import UNet
+from utils import *
 
 
 def predict_img(net, full_img, gpu=False):

submit.py

@@ -1,17 +1,14 @@
 # used to predict all test images and encode results in a csv file
 
-import os
-from PIL import Image
 from predict import *
-from utils import encode
 from unet import UNet
 
+
 def submit(net, gpu=False):
     dir = 'data/test/'
 
     N = len(list(os.listdir(dir)))
     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))

train.py

@@ -1,17 +1,16 @@
+import sys
+from optparse import OptionParser
+
 import torch
 import torch.backends.cudnn as cudnn
-import torch.nn.functional as F
 import torch.nn as nn
+import torch.nn.functional as F
+from torch import optim
+from torch.autograd import Variable
 
-from utils import *
-from myloss import DiceLoss
 from eval import eval_net
 from unet import UNet
-from torch.autograd import Variable
+from utils import *
-from torch import optim
-from optparse import OptionParser
-import sys
-import os
 
 
 def train_net(net, epochs=5, batch_size=2, lr=0.1, val_percent=0.05,
@@ -39,15 +38,14 @@ def train_net(net, epochs=5, batch_size=2, lr=0.1, val_percent=0.05,
 
     N_train = len(iddataset['train'])
 
-    train = get_imgs_and_masks(iddataset['train'], dir_img, dir_mask)
-    val = get_imgs_and_masks(iddataset['val'], dir_img, dir_mask)
-
     optimizer = optim.SGD(net.parameters(),
                           lr=lr, momentum=0.9, weight_decay=0.0005)
     criterion = nn.BCELoss()
 
     for epoch in range(epochs):
-        print('Starting epoch {}/{}.'.format(epoch+1, epochs))
+        print('Starting epoch {}/{}.'.format(epoch + 1, epochs))
+
+        # reset the generators
         train = get_imgs_and_masks(iddataset['train'], dir_img, dir_mask)
         val = get_imgs_and_masks(iddataset['val'], dir_img, dir_mask)
 
@@ -80,7 +78,7 @@ def train_net(net, epochs=5, batch_size=2, lr=0.1, val_percent=0.05,
             loss = criterion(probs_flat, y_flat.float())
             epoch_loss += loss.data[0]
 
-            print('{0:.4f} --- loss: {1:.6f}'.format(i*batch_size/N_train,
+            print('{0:.4f} --- loss: {1:.6f}'.format(i * batch_size / N_train,
                                                      loss.data[0]))
 
             optimizer.zero_grad()
@@ -89,13 +87,13 @@ def train_net(net, epochs=5, batch_size=2, lr=0.1, val_percent=0.05,
 
             optimizer.step()
 
-        print('Epoch finished ! Loss: {}'.format(epoch_loss/i))
+        print('Epoch finished ! Loss: {}'.format(epoch_loss / i))
 
         if cp:
             torch.save(net.state_dict(),
-                       dir_checkpoint + 'CP{}.pth'.format(epoch+1))
+                       dir_checkpoint + 'CP{}.pth'.format(epoch + 1))
 
-            print('Checkpoint {} saved !'.format(epoch+1))
+            print('Checkpoint {} saved !'.format(epoch + 1))
 
 
 if __name__ == '__main__':

utils/load.py

@@ -1,13 +1,13 @@
-
 #
 # load.py : utils on generators / lists of ids to transform from strings to
 #           cropped images and masks
 
 import os
-import numpy as np
-
-from PIL import Image
 from functools import partial
+
+import numpy as np
+from PIL import Image
+
 from .utils import resize_and_crop, get_square, normalize
 
 
@@ -41,6 +41,7 @@ def get_imgs_and_masks(ids, dir_img, dir_mask):
 
     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')

utils/utils.py

@@ -1,7 +1,7 @@
-import PIL
-import numpy as np
 import random
 
+import numpy as np
+
 
 def get_square(img, pos):
     """Extract a left or a right square from PILimg shape : (H, W, C))"""
@@ -34,7 +34,7 @@ def batch(iterable, batch_size):
     b = []
     for i, t in enumerate(iterable):
         b.append(t)
-        if (i+1) % batch_size == 0:
+        if (i + 1) % batch_size == 0:
             yield b
             b = []
 
@@ -46,7 +46,6 @@ def split_train_val(dataset, val_percent=0.05):
     dataset = list(dataset)
     length = len(dataset)
     n = int(length * val_percent)
-    random.seed(42)
     random.shuffle(dataset)
     return {'train': dataset[:-n], 'val': dataset[-n:]}
 
@@ -56,58 +55,16 @@ def normalize(x):
 
 
 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[:, :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
 
 
-import matplotlib.pyplot as plt
-
-def encode(mask):
-    """mask : HxW"""
-    plt.imshow(mask.transpose())
-    plt.show()
-    flat = mask.transpose().reshape(-1)
-    enc = []
-    i = 1
-
-    while i <= len(flat):
-        if(flat[i-1]):
-            s = i
-            while(flat[i-1]):
-                i += 1
-            e = i-1
-            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
@@ -119,7 +76,3 @@ def rle_encode(mask_image):
     runs = np.where(pixels[1:] != pixels[:-1])[0] + 2
     runs[1::2] = runs[1::2] - runs[:-1:2]
     return runs
-
-def full_process(filename):
-    im = PIL.Image.open(filename)
-    im = resize_and_crop(im)