Cleanup + now using tensorboard

Former-commit-id: 79928c84cdf990ef6fe1043a3e4f74b9cc252642

eval.py

@@ -10,20 +10,22 @@ def eval_net(net, loader, device, n_val):
     net.eval()
     tot = 0
 
-    for i, b in tqdm(enumerate(loader), desc='Validation round', unit='img'):
-        imgs = b['image']
-        true_masks = b['mask']
+    with tqdm(total=n_val, desc='Validation round', unit='img', leave=False) as pbar:
+        for batch in loader:
+            imgs = batch['image']
+            true_masks = batch['mask']
 
-        imgs = imgs.to(device=device, dtype=torch.float32)
-        true_masks = true_masks.to(device=device, dtype=torch.float32)
+            imgs = imgs.to(device=device, dtype=torch.float32)
+            true_masks = true_masks.to(device=device, dtype=torch.float32)
 
-        mask_pred = net(imgs)
+            mask_pred = net(imgs)
 
-        for true_mask in true_masks:
-            mask_pred = (mask_pred > 0.5).float()
-            if net.n_classes > 1:
-                tot += F.cross_entropy(mask_pred.unsqueeze(dim=0), true_mask.unsqueeze(dim=0)).item()
-            else:
-                tot += dice_coeff(mask_pred, true_mask.squeeze(dim=1)).item()
+            for true_mask in true_masks:
+                mask_pred = (mask_pred > 0.5).float()
+                if net.n_classes > 1:
+                    tot += F.cross_entropy(mask_pred.unsqueeze(dim=0), true_mask.unsqueeze(dim=0)).item()
+                else:
+                    tot += dice_coeff(mask_pred, true_mask.squeeze(dim=1)).item()
+            pbar.update(imgs.shape[0])
 
     return tot / n_val

predict.py

@@ -9,8 +9,10 @@ from torchvision import transforms
 import torch.nn.functional as F
 
 from unet import UNet
-from utils import plot_img_and_mask
+from utils.data_vis import plot_img_and_mask
 from utils.dataset import BasicDataset
+from utils.crf import dense_crf
+
 
 def predict_img(net,
                 full_img,

submit.py

@@ -4,10 +4,23 @@ import os
 
 import torch
 from PIL import Image
+import numpy as np
 
 from predict import predict_img
 from unet import UNet
-from utils import rle_encode
+
+# credits to https://stackoverflow.com/users/6076729/manuel-lagunas
+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
 
 
 def submit(net, gpu=False):

train.py

@@ -11,8 +11,8 @@ from tqdm import tqdm
 
 from eval import eval_net
 from unet import UNet
-from utils import get_ids, split_train_val, get_imgs_and_masks, batch
 
+from torch.utils.tensorboard import SummaryWriter
 from utils.dataset import BasicDataset
 from torch.utils.data import DataLoader, random_split
 
@@ -26,7 +26,7 @@ def train_net(net,
               epochs=5,
               batch_size=1,
               lr=0.1,
-              val_percent=0.15,
+              val_percent=0.1,
               save_cp=True,
               img_scale=0.5):
 
@@ -34,8 +34,11 @@ def train_net(net,
     n_val = int(len(dataset) * val_percent)
     n_train = len(dataset) - n_val
     train, val = random_split(dataset, [n_train, n_val])
-    train_loader = DataLoader(train, batch_size=batch_size, shuffle=True, num_workers=4)
-    val_loader = DataLoader(val, batch_size=batch_size, shuffle=False, num_workers=4)
+    train_loader = DataLoader(train, batch_size=batch_size, shuffle=True, num_workers=8, pin_memory=True)
+    val_loader = DataLoader(val, batch_size=batch_size, shuffle=False, num_workers=8, pin_memory=True)
+
+    writer = SummaryWriter(comment=f'LR_{lr}_BS_{batch_size}_SCALE_{img_scale}')
+    global_step = 0
 
     logging.info(f'''Starting training:
         Epochs:          {epochs}
@@ -48,7 +51,7 @@ def train_net(net,
         Images scaling:  {img_scale}
     ''')
 
-    optimizer = optim.Adam(net.parameters(), lr=lr)
+    optimizer = optim.RMSprop(net.parameters(), lr=lr, weight_decay=1e-8)
     if net.n_classes > 1:
         criterion = nn.CrossEntropyLoss()
     else:
@@ -78,6 +81,7 @@ def train_net(net,
                 masks_pred = net(imgs)
                 loss = criterion(masks_pred, true_masks)
                 epoch_loss += loss.item()
+                writer.add_scalar('Loss/train', loss.item(), global_step)
 
                 pbar.set_postfix(**{'loss (batch)': loss.item()})
 
@@ -85,7 +89,22 @@ def train_net(net,
                 loss.backward()
                 optimizer.step()
 
-                pbar.update(batch_size)
+                pbar.update(imgs.shape[0])
+                global_step += 1
+                if global_step % (len(dataset) // (10 * batch_size)) == 0:
+                    val_score = eval_net(net, val_loader, device, n_val)
+                    if net.n_classes > 1:
+                        logging.info('Validation cross entropy: {}'.format(val_score))
+                        writer.add_scalar('Loss/test', val_score, global_step)
+
+                    else:
+                        logging.info('Validation Dice Coeff: {}'.format(val_score))
+                        writer.add_scalar('Dice/test', val_score, global_step)
+
+                    writer.add_images('images', imgs, global_step)
+                    if net.n_classes == 1:
+                        writer.add_images('masks/true', true_masks, global_step)
+                        writer.add_images('masks/pred', torch.sigmoid(masks_pred) > 0.5, global_step)
 
         if save_cp:
             try:
@@ -97,12 +116,7 @@ def train_net(net,
                        dir_checkpoint + f'CP_epoch{epoch + 1}.pth')
             logging.info(f'Checkpoint {epoch + 1} saved !')
 
-        val_score = eval_net(net, val_loader, device, n_val)
-        if net.n_classes > 1:
-            logging.info('Validation cross entropy: {}'.format(val_score))
-
-        else:
-            logging.info('Validation Dice Coeff: {}'.format(val_score))
+    writer.close()
 
 
 def get_args():
@@ -118,7 +132,7 @@ def get_args():
                         help='Load model from a .pth file')
     parser.add_argument('-s', '--scale', dest='scale', type=float, default=0.5,
                         help='Downscaling factor of the images')
-    parser.add_argument('-v', '--validation', dest='val', type=float, default=15.0,
+    parser.add_argument('-v', '--validation', dest='val', type=float, default=10.0,
                         help='Percent of the data that is used as validation (0-100)')
 
     return parser.parse_args()

utils/__init__.py

@@ -1,4 +0,0 @@
-from .crf import *
-from .load import *
-from .utils import *
-from .data_vis import *

utils/dataset.py

@@ -25,6 +25,7 @@ class BasicDataset(Dataset):
     def preprocess(self, pil_img):
         w, h = pil_img.size
         newW, newH = int(self.scale * w), int(self.scale * h)
+        assert newW > 0 and newH > 0, 'Scale is too small'
         pil_img = pil_img.resize((newW, newH))
 
         img_nd = np.array(pil_img)

utils/utils.py

@@ -1,14 +0,0 @@
-import numpy as np
-
-# credits to https://stackoverflow.com/users/6076729/manuel-lagunas
-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