Summer cleanup

Former-commit-id: f6185d67a4bc50aa7ec1b8168aab3f92721c4965

.gitignore

@@ -4,5 +4,5 @@ __pycache__/
 checkpoints/
 *.pth
 *.jpg
-SUBMISSION*
 venv/
+.idea/

Dockerfile

@@ -0,0 +1,9 @@
+FROM nvcr.io/nvidia/pytorch:21.06-py3
+
+RUN rm -rf /workspace/*
+WORKDIR /workspace/unet
+
+ADD requirements.txt .
+RUN pip install --no-cache-dir --upgrade --pre pip
+RUN pip install --no-cache-dir -r requirements.txt
+ADD . .

data_loading.py

@@ -0,0 +1,80 @@
+import logging
+from os import listdir
+from os.path import splitext
+from pathlib import Path
+
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import Dataset
+
+
+class BasicDataset(Dataset):
+    def __init__(self, images_dir: str, masks_dir: str, scale: float = 1.0, mask_suffix: str = ''):
+        self.images_dir = Path(images_dir)
+        self.masks_dir = Path(masks_dir)
+        assert 0 < scale <= 1, 'Scale must be between 0 and 1'
+        self.scale = scale
+        self.mask_suffix = mask_suffix
+
+        self.ids = [splitext(file)[0] for file in listdir(images_dir) if not file.startswith('.')]
+        if not self.ids:
+            raise RuntimeError(f'No input file found in {images_dir}, make sure you put your images there')
+        logging.info(f'Creating dataset with {len(self.ids)} examples')
+
+    def __len__(self):
+        return len(self.ids)
+
+    @classmethod
+    def preprocess(cls, pil_img, scale, is_mask):
+        w, h = pil_img.size
+        newW, newH = int(scale * w), int(scale * h)
+        assert newW > 0 and newH > 0, 'Scale is too small, resized images would have no pixel'
+        pil_img = pil_img.resize((newW, newH))
+        img_ndarray = np.asarray(pil_img)
+
+        if img_ndarray.ndim == 2 and not is_mask:
+            img_ndarray = img_ndarray[np.newaxis, ...]
+        elif not is_mask:
+            img_ndarray = img_ndarray.transpose((2, 0, 1))
+
+        if not is_mask:
+            img_ndarray = img_ndarray / 255
+
+        return img_ndarray
+
+    @classmethod
+    def load(cls, filename):
+        ext = splitext(filename)[1]
+        if ext in ['.npz', '.npy']:
+            return Image.fromarray(np.load(filename))
+        elif ext in ['.pt', '.pth']:
+            return Image.fromarray(torch.load(filename).numpy())
+        else:
+            return Image.open(filename)
+
+    def __getitem__(self, idx):
+        name = self.ids[idx]
+        mask_file = list(self.masks_dir.glob(name + self.mask_suffix + '.*'))
+        img_file = list(self.images_dir.glob(name + '.*'))
+
+        assert len(mask_file) == 1, f'Either no mask or multiple masks found for the ID {name}: {mask_file}'
+        assert len(img_file) == 1, f'Either no image or multiple images found for the ID {name}: {img_file}'
+        mask = self.load(mask_file[0])
+        img = self.load(img_file[0])
+
+        assert img.size == mask.size, \
+            'Image and mask {name} should be the same size, but are {img.size} and {mask.size}'
+
+        img = self.preprocess(img, self.scale, is_mask=False)
+        mask = self.preprocess(mask, self.scale, is_mask=True)
+
+        return {
+            'image': torch.as_tensor(img.copy()).float().contiguous(),
+            'mask': torch.as_tensor(mask.copy()).long().contiguous()
+        }
+
+
+class CarvanaDataset(BasicDataset):
+    def __init__(self, images_dir, masks_dir, scale=1):
+        super().__init__(images_dir, masks_dir, scale, mask_suffix='_mask')

dice_loss.py

@@ -1,42 +0,0 @@
-import torch
-from torch.autograd import Function
-
-
-class DiceCoeff(Function):
-    """Dice coeff for individual examples"""
-
-    def forward(self, input, target):
-        self.save_for_backward(input, target)
-        eps = 0.0001
-        self.inter = torch.dot(input.view(-1), target.view(-1))
-        self.union = torch.sum(input) + torch.sum(target) + eps
-
-        t = (2 * self.inter.float() + eps) / self.union.float()
-        return t
-
-    # This function has only a single output, so it gets only one gradient
-    def backward(self, grad_output):
-
-        input, target = self.saved_variables
-        grad_input = grad_target = None
-
-        if self.needs_input_grad[0]:
-            grad_input = grad_output * 2 * (target * self.union - self.inter) \
-                         / (self.union * self.union)
-        if self.needs_input_grad[1]:
-            grad_target = None
-
-        return grad_input, grad_target
-
-
-def dice_coeff(input, target):
-    """Dice coeff for batches"""
-    if input.is_cuda:
-        s = torch.FloatTensor(1).cuda().zero_()
-    else:
-        s = torch.FloatTensor(1).zero_()
-
-    for i, c in enumerate(zip(input, target)):
-        s = s + DiceCoeff().forward(c[0], c[1])
-
-    return s / (i + 1)

dice_score.py

@@ -0,0 +1,40 @@
+import torch
+from torch import Tensor
+
+
+def dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon=1e-6):
+    # Average of Dice coefficient for all batches, or for a single mask
+    assert input.size() == target.size()
+    if input.dim() == 2 and reduce_batch_first:
+        raise ValueError(f'Dice: asked to reduce batch but got tensor without batch dimension (shape {input.shape})')
+
+    if input.dim() == 2 or reduce_batch_first:
+        inter = torch.dot(input.view(-1), target.view(-1))
+        sets_sum = torch.sum(input) + torch.sum(target)
+        if sets_sum.item() == 0:
+            sets_sum = 2 * inter
+
+        return (2 * inter + epsilon) / (sets_sum + epsilon)
+    else:
+        # compute and average metric for each batch element
+        dice = 0
+        for i in range(input.shape[0]):
+            dice += dice_coeff(input[i, ...], target[i, ...])
+        return dice / input.shape[0]
+
+
+def multiclass_dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon=1e-6):
+    # Average of Dice coefficient for all classes
+    assert input.size() == target.size()
+    dice = 0
+    for channel in range(input.shape[1]):
+        dice += dice_coeff(input[:, channel, ...], target[:, channel, ...], reduce_batch_first, epsilon)
+
+    return dice / input.shape[1]
+
+
+def dice_loss(input: Tensor, target: Tensor, multiclass: bool = False):
+    # Dice loss (objective to minimize) between 0 and 1
+    assert input.size() == target.size()
+    fn = multiclass_dice_coeff if multiclass else dice_coeff
+    return 1 - fn(input, target, reduce_batch_first=True)

eval.py

@@ -1,33 +0,0 @@
-import torch
-import torch.nn.functional as F
-from tqdm import tqdm
-
-from dice_loss import dice_coeff
-
-
-def eval_net(net, loader, device):
-    """Evaluation without the densecrf with the dice coefficient"""
-    net.eval()
-    mask_type = torch.float32 if net.n_classes == 1 else torch.long
-    n_val = len(loader)  # the number of batch
-    tot = 0
-
-    with tqdm(total=n_val, desc='Validation round', unit='batch', leave=False) as pbar:
-        for batch in loader:
-            imgs, true_masks = batch['image'], batch['mask']
-            imgs = imgs.to(device=device, dtype=torch.float32)
-            true_masks = true_masks.to(device=device, dtype=mask_type)
-
-            with torch.no_grad():
-                mask_pred = net(imgs)
-
-            if net.n_classes > 1:
-                tot += F.cross_entropy(mask_pred, true_masks).item()
-            else:
-                pred = torch.sigmoid(mask_pred)
-                pred = (pred > 0.5).float()
-                tot += dice_coeff(pred, true_masks).item()
-            pbar.update()
-
-    net.train()
-    return tot / n_val

evaluate.py

@@ -0,0 +1,35 @@
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+
+from dice_score import multiclass_dice_coeff
+
+
+def evaluate(net, dataloader, device):
+    net.eval()
+    num_val_batches = len(dataloader)
+    dice_score = 0
+
+    # iterate over the validation set
+    for batch in tqdm(dataloader, total=num_val_batches, desc='Validation round', unit='batch', leave=False):
+        image, mask_true = batch['image'], batch['mask']
+        # move images and labels to correct device and type
+        image = image.to(device=device, dtype=torch.float32)
+        mask_true = mask_true.to(device=device, dtype=torch.long)
+        mask_true = F.one_hot(mask_true, net.n_classes).permute(0, 3, 1, 2).float()
+
+        with torch.no_grad():
+            # predict the mask
+            mask_pred = net(image)
+
+            # convert to one-hot format
+            if net.n_classes == 1:
+                mask_pred = (F.sigmoid(mask_pred) > 0).float()
+            else:
+                mask_pred = F.one_hot(mask_pred.argmax(dim=1), net.n_classes).permute(0, 3, 1, 2).float()
+
+            # compute the Dice score, ignoring background
+            dice_score += multiclass_dice_coeff(mask_pred[:, :1, ...], mask_true[:, :1, ...], reduce_batch_first=False)
+
+    net.train()
+    return dice_score / num_val_batches

predict.py

@@ -8,9 +8,9 @@ import torch.nn.functional as F
 from PIL import Image
 from torchvision import transforms
 
+from data_loading import BasicDataset
 from unet import UNet
-from utils.data_vis import plot_img_and_mask
-from utils.dataset import BasicDataset
+from utils import plot_img_and_mask
 
 
 def predict_img(net,
@@ -19,9 +19,7 @@ def predict_img(net,
                 scale_factor=1,
                 out_threshold=0.5):
     net.eval()
-
-    img = torch.from_numpy(BasicDataset.preprocess(full_img, scale_factor))
-
+    img = torch.from_numpy(BasicDataset.preprocess(full_img, scale_factor, is_mask=False))
     img = img.unsqueeze(0)
     img = img.to(device=device, dtype=torch.float32)
 
@@ -29,94 +27,75 @@ def predict_img(net,
         output = net(img)
 
         if net.n_classes > 1:
-            probs = F.softmax(output, dim=1)
+            probs = F.softmax(output, dim=1)[0]
         else:
-            probs = torch.sigmoid(output)
+            probs = torch.sigmoid(output)[0]
 
-        probs = probs.squeeze(0)
-
-        tf = transforms.Compose(
-            [
+        tf = transforms.Compose([
             transforms.ToPILImage(),
-                transforms.Resize(full_img.size[1]),
+            transforms.Resize((full_img.size[1], full_img.size[0])),
             transforms.ToTensor()
-            ]
-        )
+        ])
 
-        probs = tf(probs.cpu())
-        full_mask = probs.squeeze().cpu().numpy()
+        full_mask = tf(probs.cpu()).squeeze()
 
-    return full_mask > out_threshold
+    if net.n_classes == 1:
+        return (full_mask > out_threshold).numpy()
+    else:
+        return F.one_hot(full_mask.argmax(dim=0), net.n_classes).permute(2, 0, 1).numpy()
 
 
 def get_args():
-    parser = argparse.ArgumentParser(description='Predict masks from input images',
-                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--model', '-m', default='MODEL.pth',
-                        metavar='FILE',
-                        help="Specify the file in which the model is stored")
-    parser.add_argument('--input', '-i', metavar='INPUT', nargs='+',
-                        help='filenames of input images', required=True)
-
-    parser.add_argument('--output', '-o', metavar='INPUT', nargs='+',
-                        help='Filenames of ouput images')
+    parser = argparse.ArgumentParser(description='Predict masks from input images')
+    parser.add_argument('--model', '-m', default='MODEL.pth', metavar='FILE',
+                        help='Specify the file in which the model is stored')
+    parser.add_argument('--input', '-i', metavar='INPUT', nargs='+', help='Filenames of input images', required=True)
+    parser.add_argument('--output', '-o', metavar='INPUT', nargs='+', help='Filenames of output images')
     parser.add_argument('--viz', '-v', action='store_true',
-                        help="Visualize the images as they are processed",
-                        default=False)
-    parser.add_argument('--no-save', '-n', action='store_true',
-                        help="Do not save the output masks",
-                        default=False)
-    parser.add_argument('--mask-threshold', '-t', type=float,
-                        help="Minimum probability value to consider a mask pixel white",
-                        default=0.5)
-    parser.add_argument('--scale', '-s', type=float,
-                        help="Scale factor for the input images",
-                        default=0.5)
+                        help='Visualize the images as they are processed')
+    parser.add_argument('--no-save', '-n', action='store_true', help='Do not save the output masks')
+    parser.add_argument('--mask-threshold', '-t', type=float, default=0.5,
+                        help='Minimum probability value to consider a mask pixel white')
+    parser.add_argument('--scale', '-s', type=float, default=0.5,
+                        help='Scale factor for the input images')
 
     return parser.parse_args()
 
 
 def get_output_filenames(args):
-    in_files = args.input
-    out_files = []
+    def _generate_name(fn):
+        split = os.path.splitext(fn)
+        return f'{split[0]}_OUT{split[1]}'
 
-    if not args.output:
-        for f in in_files:
-            pathsplit = os.path.splitext(f)
-            out_files.append("{}_OUT{}".format(pathsplit[0], pathsplit[1]))
-    elif len(in_files) != len(args.output):
-        logging.error("Input files and output files are not of the same length")
-        raise SystemExit()
-    else:
-        out_files = args.output
-
-    return out_files
+    return args.output or list(map(_generate_name, args.input))
 
 
-def mask_to_image(mask):
+def mask_to_image(mask: np.ndarray):
+    if mask.ndim == 2:
         return Image.fromarray((mask * 255).astype(np.uint8))
+    elif mask.ndim == 3:
+        return Image.fromarray((np.argmax(mask, dim=0) * 255 / mask.shape[0]).astype(np.uint8))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     args = get_args()
     in_files = args.input
     out_files = get_output_filenames(args)
 
-    net = UNet(n_channels=3, n_classes=1)
-
-    logging.info("Loading model {}".format(args.model))
+    net = UNet(n_channels=3, n_classes=2)
 
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    logging.info(f'Loading model {args.model}')
     logging.info(f'Using device {device}')
+
     net.to(device=device)
     net.load_state_dict(torch.load(args.model, map_location=device))
 
-    logging.info("Model loaded !")
+    logging.info('Model loaded!')
 
-    for i, fn in enumerate(in_files):
-        logging.info("\nPredicting image {} ...".format(fn))
-
-        img = Image.open(fn)
+    for i, filename in enumerate(in_files):
+        logging.info(f'\nPredicting image {filename} ...')
+        img = Image.open(filename)
 
         mask = predict_img(net=net,
                            full_img=img,
@@ -125,12 +104,11 @@ if __name__ == "__main__":
                            device=device)
 
         if not args.no_save:
-            out_fn = out_files[i]
+            out_filename = out_files[i]
             result = mask_to_image(mask)
-            result.save(out_files[i])
-
-            logging.info("Mask saved to {}".format(out_files[i]))
+            result.save(out_filename)
+            logging.info(f'Mask saved to {out_filename}')
 
         if args.viz:
-            logging.info("Visualizing results for image {}, close to continue ...".format(fn))
+            logging.info(f'Visualizing results for image {filename}, close to continue...')
             plot_img_and_mask(img, mask)

requirements.txt

@@ -3,6 +3,5 @@ numpy
 Pillow
 torch
 torchvision
-tensorboard
-future
 tqdm
+wandb

submit.py

@@ -1,46 +0,0 @@
-""" Submit code specific to the kaggle challenge"""
-
-import os
-
-import torch
-from PIL import Image
-import numpy as np
-
-from predict import predict_img
-from unet import UNet
-
-# 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):
-    """Used for Kaggle submission: predicts and encode all test images"""
-    dir = 'data/test/'
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    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))
-
-            img = Image.open(dir + i)
-
-            mask = predict_img(net, img, device)
-            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('MODEL.pth'))
-    submit(net)

train.py

@@ -1,187 +1,193 @@
 import argparse
 import logging
-import os
 import sys
+from pathlib import Path
 
-import numpy as np
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
+import wandb
 from torch import optim
+from torch.utils.data import DataLoader, random_split
 from tqdm import tqdm
 
-from eval import eval_net
+from data_loading import BasicDataset, CarvanaDataset
+from dice_score import dice_loss
+from evaluate import evaluate
 from unet import UNet
 
-from torch.utils.tensorboard import SummaryWriter
-from utils.dataset import BasicDataset
-from torch.utils.data import DataLoader, random_split
-
-dir_img = 'data/imgs/'
-dir_mask = 'data/masks/'
-dir_checkpoint = 'checkpoints/'
+dir_img = Path('./data/imgs/')
+dir_mask = Path('./data/masks/')
+dir_checkpoint = Path('./checkpoints/')
 
 
 def train_net(net,
               device,
-              epochs=5,
-              batch_size=1,
-              lr=0.001,
-              val_percent=0.1,
-              save_cp=True,
-              img_scale=0.5):
-
+              epochs: int = 5,
+              batch_size: int = 1,
+              learning_rate: float = 0.001,
+              val_percent: float = 0.1,
+              save_checkpoint: bool = True,
+              img_scale: float = 0.5,
+              amp: bool = False):
+    # 1. Create dataset
+    try:
+        dataset = CarvanaDataset(dir_img, dir_mask, img_scale)
+    except (AssertionError, RuntimeError):
         dataset = BasicDataset(dir_img, dir_mask, img_scale)
+
+    # 2. Split into train / validation partitions
     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=8, pin_memory=True)
-    val_loader = DataLoader(val, batch_size=batch_size, shuffle=False, num_workers=8, pin_memory=True, drop_last=True)
+    train_set, val_set = random_split(dataset, [n_train, n_val], generator=torch.Generator().manual_seed(0))
 
-    writer = SummaryWriter(comment=f'LR_{lr}_BS_{batch_size}_SCALE_{img_scale}')
-    global_step = 0
+    # 3. Create data loaders
+    loader_args = dict(batch_size=batch_size, num_workers=4, pin_memory=True)
+    train_loader = DataLoader(train_set, shuffle=True, **loader_args)
+    val_loader = DataLoader(val_set, shuffle=False, drop_last=True, **loader_args)
+
+    # (Initialise logging)
+    experiment = wandb.init(project='U-Net', resume='allow', anonymous='must')
+    experiment.config.update(dict(epochs=epochs, batch_size=batch_size, learning_rate=learning_rate,
+                                  val_percent=val_percent, save_checkpoint=save_checkpoint, img_scale=img_scale,
+                                  amp=amp))
 
     logging.info(f'''Starting training:
         Epochs:          {epochs}
         Batch size:      {batch_size}
-        Learning rate:   {lr}
+        Learning rate:   {learning_rate}
         Training size:   {n_train}
         Validation size: {n_val}
-        Checkpoints:     {save_cp}
+        Checkpoints:     {save_checkpoint}
         Device:          {device.type}
         Images scaling:  {img_scale}
+        Mixed Precision: {amp}
     ''')
 
-    optimizer = optim.RMSprop(net.parameters(), lr=lr, weight_decay=1e-8, momentum=0.9)
-    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min' if net.n_classes > 1 else 'max', patience=2)
-    if net.n_classes > 1:
+    # 4. Set up the optimizer, the loss, the learning rate scheduler and the loss scaling for AMP
+    optimizer = optim.RMSprop(net.parameters(), lr=learning_rate, weight_decay=1e-8, momentum=0.9)
+    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'max', patience=2)  # goal: maximize Dice score
+    grad_scaler = torch.cuda.amp.GradScaler(enabled=amp)
     criterion = nn.CrossEntropyLoss()
-    else:
-        criterion = nn.BCEWithLogitsLoss()
+    global_step = 0
 
+    # 5. Begin training
     for epoch in range(epochs):
         net.train()
-
         epoch_loss = 0
         with tqdm(total=n_train, desc=f'Epoch {epoch + 1}/{epochs}', unit='img') as pbar:
             for batch in train_loader:
-                imgs = batch['image']
+                images = batch['image']
                 true_masks = batch['mask']
-                assert imgs.shape[1] == net.n_channels, \
+
+                assert images.shape[1] == net.n_channels, \
                     f'Network has been defined with {net.n_channels} input channels, ' \
-                    f'but loaded images have {imgs.shape[1]} channels. Please check that ' \
+                    f'but loaded images have {images.shape[1]} channels. Please check that ' \
                     'the images are loaded correctly.'
 
-                imgs = imgs.to(device=device, dtype=torch.float32)
-                mask_type = torch.float32 if net.n_classes == 1 else torch.long
-                true_masks = true_masks.to(device=device, dtype=mask_type)
+                images = images.to(device=device, dtype=torch.float32)
+                true_masks = true_masks.to(device=device, dtype=torch.long)
 
-                masks_pred = net(imgs)
-                loss = criterion(masks_pred, true_masks)
+                with torch.cuda.amp.autocast(enabled=amp):
+                    masks_pred = net(images)
+                    loss = criterion(masks_pred, true_masks) \
+                           + dice_loss(F.softmax(masks_pred, dim=1).float(),
+                                       F.one_hot(true_masks, net.n_classes).permute(0, 3, 1, 2).float(),
+                                       multiclass=True)
+
+                optimizer.zero_grad(set_to_none=True)
+                grad_scaler.scale(loss).backward()
+                grad_scaler.step(optimizer)
+                grad_scaler.update()
+
+                pbar.update(images.shape[0])
+                global_step += 1
                 epoch_loss += loss.item()
-                writer.add_scalar('Loss/train', loss.item(), global_step)
-
+                experiment.log({
+                    'train loss': loss.item(),
+                    'step': global_step,
+                    'epoch': epoch
+                })
                 pbar.set_postfix(**{'loss (batch)': loss.item()})
 
-                optimizer.zero_grad()
-                loss.backward()
-                nn.utils.clip_grad_value_(net.parameters(), 0.1)
-                optimizer.step()
-
-                pbar.update(imgs.shape[0])
-                global_step += 1
+                # Evaluation round
                 if global_step % (n_train // (10 * batch_size)) == 0:
+                    histograms = {}
                     for tag, value in net.named_parameters():
-                        tag = tag.replace('.', '/')
-                        writer.add_histogram('weights/' + tag, value.data.cpu().numpy(), global_step)
-                        writer.add_histogram('grads/' + tag, value.grad.data.cpu().numpy(), global_step)
-                    val_score = eval_net(net, val_loader, device)
+                        tag = tag.replace('/', '.')
+                        histograms['Weights/' + tag] = wandb.Histogram(value.data.cpu())
+                        histograms['Gradients/' + tag] = wandb.Histogram(value.grad.data.cpu())
+
+                    val_score = evaluate(net, val_loader, device)
                     scheduler.step(val_score)
-                    writer.add_scalar('learning_rate', optimizer.param_groups[0]['lr'], global_step)
 
-                    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)
+                    logging.info('Validation Dice score: {}'.format(val_score))
+                    experiment.log({
+                        'learning rate': optimizer.param_groups[0]['lr'],
+                        'validation Dice': val_score,
+                        'images': wandb.Image(images[0].cpu()),
+                        'masks': {
+                            'true': wandb.Image(true_masks[0].float().cpu()),
+                            'pred': wandb.Image(torch.softmax(masks_pred, dim=1)[0].float().cpu()),
+                        },
+                        'step': global_step,
+                        'epoch': epoch,
+                        **histograms
+                    })
 
-                    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:
-                os.mkdir(dir_checkpoint)
-                logging.info('Created checkpoint directory')
-            except OSError:
-                pass
-            torch.save(net.state_dict(),
-                       dir_checkpoint + f'CP_epoch{epoch + 1}.pth')
-            logging.info(f'Checkpoint {epoch + 1} saved !')
-
-    writer.close()
+        if save_checkpoint:
+            Path(dir_checkpoint).mkdir(parents=True, exist_ok=True)
+            torch.save(net.state_dict(), str(dir_checkpoint / 'checkpoint_epoch{}.pth'.format(epoch + 1)))
+            logging.info(f'Checkpoint {epoch + 1} saved!')
 
 
 def get_args():
-    parser = argparse.ArgumentParser(description='Train the UNet on images and target masks',
-                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('-e', '--epochs', metavar='E', type=int, default=5,
-                        help='Number of epochs', dest='epochs')
-    parser.add_argument('-b', '--batch-size', metavar='B', type=int, nargs='?', default=1,
-                        help='Batch size', dest='batchsize')
-    parser.add_argument('-l', '--learning-rate', metavar='LR', type=float, nargs='?', default=0.0001,
+    parser = argparse.ArgumentParser(description='Train the UNet on images and target masks')
+    parser.add_argument('--epochs', '-e', metavar='E', type=int, default=50, help='Number of epochs')
+    parser.add_argument('--batch-size', '-b', dest='batch_size', metavar='B', type=int, default=1, help='Batch size')
+    parser.add_argument('--learning-rate', '-l', metavar='LR', type=float, default=0.00001,
                         help='Learning rate', dest='lr')
-    parser.add_argument('-f', '--load', dest='load', type=str, default=False,
-                        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=10.0,
+    parser.add_argument('--load', '-f', type=str, default=False, help='Load model from a .pth file')
+    parser.add_argument('--scale', '-s', type=float, default=0.5, help='Downscaling factor of the images')
+    parser.add_argument('--validation', '-v', dest='val', type=float, default=10.0,
                         help='Percent of the data that is used as validation (0-100)')
+    parser.add_argument('--amp', action='store_true', default=False, help='Use mixed precision')
 
     return parser.parse_args()
 
 
 if __name__ == '__main__':
-    logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
     args = get_args()
+
+    logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
     logging.info(f'Using device {device}')
 
     # Change here to adapt to your data
     # n_channels=3 for RGB images
     # n_classes is the number of probabilities you want to get per pixel
-    #   - For 1 class and background, use n_classes=1
-    #   - For 2 classes, use n_classes=1
-    #   - For N > 2 classes, use n_classes=N
-    net = UNet(n_channels=3, n_classes=1, bilinear=True)
+    net = UNet(n_channels=3, n_classes=2, bilinear=True)
+
     logging.info(f'Network:\n'
                  f'\t{net.n_channels} input channels\n'
                  f'\t{net.n_classes} output channels (classes)\n'
                  f'\t{"Bilinear" if net.bilinear else "Transposed conv"} upscaling')
 
     if args.load:
-        net.load_state_dict(
-            torch.load(args.load, map_location=device)
-        )
+        net.load_state_dict(torch.load(args.load, map_location=device))
         logging.info(f'Model loaded from {args.load}')
 
     net.to(device=device)
-    # faster convolutions, but more memory
-    # cudnn.benchmark = True
-
     try:
         train_net(net=net,
                   epochs=args.epochs,
-                  batch_size=args.batchsize,
-                  lr=args.lr,
+                  batch_size=args.batch_size,
+                  learning_rate=args.lr,
                   device=device,
                   img_scale=args.scale,
-                  val_percent=args.val / 100)
+                  val_percent=args.val / 100,
+                  amp=args.amp)
     except KeyboardInterrupt:
         torch.save(net.state_dict(), 'INTERRUPTED.pth')
         logging.info('Saved interrupt')
-        try:
         sys.exit(0)
-        except SystemExit:
-            os._exit(0)

unet/unet_model.py

@@ -1,7 +1,5 @@
 """ Full assembly of the parts to form the complete network """
 
-import torch.nn.functional as F
-
 from .unet_parts import *
 
 

unet/unet_parts.py

@@ -50,10 +50,9 @@ class Up(nn.Module):
             self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
             self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
         else:
-            self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
+            self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
             self.conv = DoubleConv(in_channels, out_channels)
 
-
     def forward(self, x1, x2):
         x1 = self.up(x1)
         # input is CHW

utils.py

@@ -2,14 +2,14 @@ import matplotlib.pyplot as plt
 
 
 def plot_img_and_mask(img, mask):
-    classes = mask.shape[2] if len(mask.shape) > 2 else 1
+    classes = mask.shape[0] if len(mask.shape) > 2 else 1
     fig, ax = plt.subplots(1, classes + 1)
     ax[0].set_title('Input image')
     ax[0].imshow(img)
     if classes > 1:
         for i in range(classes):
-            ax[i+1].set_title(f'Output mask (class {i+1})')
-            ax[i+1].imshow(mask[:, :, i])
+            ax[i + 1].set_title(f'Output mask (class {i + 1})')
+            ax[i + 1].imshow(mask[:, :, i])
     else:
         ax[1].set_title(f'Output mask')
         ax[1].imshow(mask)

utils/dataset.py

@@ -1,71 +0,0 @@
-from os.path import splitext
-from os import listdir
-import numpy as np
-from glob import glob
-import torch
-from torch.utils.data import Dataset
-import logging
-from PIL import Image
-
-
-class BasicDataset(Dataset):
-    def __init__(self, imgs_dir, masks_dir, scale=1, mask_suffix=''):
-        self.imgs_dir = imgs_dir
-        self.masks_dir = masks_dir
-        self.scale = scale
-        self.mask_suffix = mask_suffix
-        assert 0 < scale <= 1, 'Scale must be between 0 and 1'
-
-        self.ids = [splitext(file)[0] for file in listdir(imgs_dir)
-                    if not file.startswith('.')]
-        logging.info(f'Creating dataset with {len(self.ids)} examples')
-
-    def __len__(self):
-        return len(self.ids)
-
-    @classmethod
-    def preprocess(cls, pil_img, scale):
-        w, h = pil_img.size
-        newW, newH = int(scale * w), int(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)
-
-        if len(img_nd.shape) == 2:
-            img_nd = np.expand_dims(img_nd, axis=2)
-
-        # HWC to CHW
-        img_trans = img_nd.transpose((2, 0, 1))
-        if img_trans.max() > 1:
-            img_trans = img_trans / 255
-
-        return img_trans
-
-    def __getitem__(self, i):
-        idx = self.ids[i]
-        mask_file = glob(self.masks_dir + idx + self.mask_suffix + '.*')
-        img_file = glob(self.imgs_dir + idx + '.*')
-
-        assert len(mask_file) == 1, \
-            f'Either no mask or multiple masks found for the ID {idx}: {mask_file}'
-        assert len(img_file) == 1, \
-            f'Either no image or multiple images found for the ID {idx}: {img_file}'
-        mask = Image.open(mask_file[0])
-        img = Image.open(img_file[0])
-
-        assert img.size == mask.size, \
-            f'Image and mask {idx} should be the same size, but are {img.size} and {mask.size}'
-
-        img = self.preprocess(img, self.scale)
-        mask = self.preprocess(mask, self.scale)
-
-        return {
-            'image': torch.from_numpy(img).type(torch.FloatTensor),
-            'mask': torch.from_numpy(mask).type(torch.FloatTensor)
-        }
-
-
-class CarvanaDataset(BasicDataset):
-    def __init__(self, imgs_dir, masks_dir, scale=1):
-        super().__init__(imgs_dir, masks_dir, scale, mask_suffix='_mask')