feat(semseg): allow model customization (Lightning-AI#1371)

* feat(semantic_segmentation): allow customization of unet

* feat(semseg): allow model customization

* style(semseg): format to PEP8

* fix(semseg): rename logger

* docs(changelog): updated semantic segmentation example

* suggestions

* suggestions

* flake8

Co-authored-by: J. Borovec <jirka.borovec@seznam.cz>

CHANGELOG.md

@@ -17,14 +17,14 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Changed the default behaviour to no longer include a NaN check with each training iteration. ([#1475](https://github.com/PyTorchLightning/pytorch-lightning/pull/1475))
 
+- Updated semantic segmentation example with custom u-net and logging ([#1371](https://github.com/PyTorchLightning/pytorch-lightning/pull/1371))
+
 - 
 
 ### Deprecated
 
 - 
 
-- 
-
 
 ### Removed
 

pl_examples/domain_templates/semantic_segmentation.py

@@ -7,9 +7,14 @@
 import torchvision.transforms as transforms
 from PIL import Image
 from torch.utils.data import DataLoader, Dataset
+import random
 
 import pytorch_lightning as pl
 from pl_examples.models.unet import UNet
+from pytorch_lightning.loggers import WandbLogger
+
+DEFAULT_VOID_LABELS = (0, 1, 2, 3, 4, 5, 6, 9, 10, 14, 15, 16, 18, 29, 30, -1)
+DEFAULT_VALID_LABELS = (7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33)
 
 
 class KITTI(Dataset):
@@ -34,37 +39,40 @@ class KITTI(Dataset):
     encoded using `encode_segmap`, and given `transform` (if any) are applied to the image only
     (mask does not usually require transforms, but they can be implemented in a similar way).
     """
+    IMAGE_PATH = os.path.join('training', 'image_2')
+    MASK_PATH = os.path.join('training', 'semantic')
 
     def __init__(
         self,
-        root_path,
-        split='test',
-        img_size=(1242, 376),
-        void_labels=[0, 1, 2, 3, 4, 5, 6, 9, 10, 14, 15, 16, 18, 29, 30, -1],
-        valid_labels=[7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33],
+        data_path: str,
+        split: str,
+        img_size: tuple = (1242, 376),
+        void_labels: list = DEFAULT_VOID_LABELS,
+        valid_labels: list = DEFAULT_VALID_LABELS,
         transform=None
     ):
         self.img_size = img_size
         self.void_labels = void_labels
         self.valid_labels = valid_labels
         self.ignore_index = 250
         self.class_map = dict(zip(self.valid_labels, range(len(self.valid_labels))))
-        self.split = split
-        self.root = root_path
-        if self.split == 'train':
-            self.img_path = os.path.join(self.root, 'training/image_2')
-            self.mask_path = os.path.join(self.root, 'training/semantic')
-        else:
-            self.img_path = os.path.join(self.root, 'testing/image_2')
-            self.mask_path = None
-
         self.transform = transform
 
+        self.split = split
+        self.data_path = data_path
+        self.img_path = os.path.join(self.data_path, self.IMAGE_PATH)
+        self.mask_path = os.path.join(self.data_path, self.MASK_PATH)
         self.img_list = self.get_filenames(self.img_path)
+        self.mask_list = self.get_filenames(self.mask_path)
+
+        # Split between train and valid set (80/20)
+        random_inst = random.Random(12345)  # for repeatability
+        n_items = len(self.img_list)
+        idxs = random_inst.sample(range(n_items), n_items // 5)
         if self.split == 'train':
-            self.mask_list = self.get_filenames(self.mask_path)
-        else:
-            self.mask_list = None
+            idxs = [idx for idx in range(n_items) if idx not in idxs]
+        self.img_list = [self.img_list[i] for i in idxs]
+        self.mask_list = [self.mask_list[i] for i in idxs]
 
     def __len__(self):
         return len(self.img_list)
@@ -74,19 +82,15 @@ def __getitem__(self, idx):
         img = img.resize(self.img_size)
         img = np.array(img)
 
-        if self.split == 'train':
-            mask = Image.open(self.mask_list[idx]).convert('L')
-            mask = mask.resize(self.img_size)
-            mask = np.array(mask)
-            mask = self.encode_segmap(mask)
+        mask = Image.open(self.mask_list[idx]).convert('L')
+        mask = mask.resize(self.img_size)
+        mask = np.array(mask)
+        mask = self.encode_segmap(mask)
 
         if self.transform:
             img = self.transform(img)
 
-        if self.split == 'train':
-            return img, mask
-        else:
-            return img
+        return img, mask
 
     def encode_segmap(self, mask):
         """
@@ -96,6 +100,8 @@ def encode_segmap(self, mask):
             mask[mask == voidc] = self.ignore_index
         for validc in self.valid_labels:
             mask[mask == validc] = self.class_map[validc]
+        # remove extra idxs from updated dataset
+        mask[mask > 18] = self.ignore_index
         return mask
 
     def get_filenames(self, path):
@@ -124,17 +130,19 @@ class SegModel(pl.LightningModule):
 
     def __init__(self, hparams):
         super().__init__()
-        self.root_path = hparams.root
+        self.hparams = hparams
+        self.data_path = hparams.data_path
         self.batch_size = hparams.batch_size
         self.learning_rate = hparams.lr
-        self.net = UNet(num_classes=19)
+        self.net = UNet(num_classes=19, num_layers=hparams.num_layers,
+                        features_start=hparams.features_start, bilinear=hparams.bilinear)
         self.transform = transforms.Compose([
             transforms.ToTensor(),
             transforms.Normalize(mean=[0.35675976, 0.37380189, 0.3764753],
                                  std=[0.32064945, 0.32098866, 0.32325324])
         ])
-        self.trainset = KITTI(self.root_path, split='train', transform=self.transform)
-        self.testset = KITTI(self.root_path, split='test', transform=self.transform)
+        self.trainset = KITTI(self.data_path, split='train', transform=self.transform)
+        self.validset = KITTI(self.data_path, split='valid', transform=self.transform)
 
     def forward(self, x):
         return self.net(x)
@@ -145,7 +153,21 @@ def training_step(self, batch, batch_nb):
         mask = mask.long()
         out = self(img)
         loss_val = F.cross_entropy(out, mask, ignore_index=250)
-        return {'loss': loss_val}
+        log_dict = {'train_loss': loss_val}
+        return {'loss': loss_val, 'log': log_dict, 'progress_bar': log_dict}
+
+    def validation_step(self, batch, batch_idx):
+        img, mask = batch
+        img = img.float()
+        mask = mask.long()
+        out = self(img)
+        loss_val = F.cross_entropy(out, mask, ignore_index=250)
+        return {'val_loss': loss_val}
+
+    def validation_epoch_end(self, outputs):
+        loss_val = sum(output['val_loss'] for output in outputs) / len(outputs)
+        log_dict = {'val_loss': loss_val}
+        return {'log': log_dict, 'val_loss': log_dict['val_loss'], 'progress_bar': log_dict}
 
     def configure_optimizers(self):
         opt = torch.optim.Adam(self.net.parameters(), lr=self.learning_rate)
@@ -155,8 +177,8 @@ def configure_optimizers(self):
     def train_dataloader(self):
         return DataLoader(self.trainset, batch_size=self.batch_size, shuffle=True)
 
-    def test_dataloader(self):
-        return DataLoader(self.testset, batch_size=self.batch_size, shuffle=False)
+    def val_dataloader(self):
+        return DataLoader(self.validset, batch_size=self.batch_size, shuffle=False)
 
 
 def main(hparams):
@@ -166,24 +188,49 @@ def main(hparams):
     model = SegModel(hparams)
 
     # ------------------------
-    # 2 INIT TRAINER
+    # 2 SET LOGGER
+    # ------------------------
+    logger = False
+    if hparams.log_wandb:
+        logger = WandbLogger()
+
+        # optional: log model topology
+        logger.watch(model.net)
+
+    # ------------------------
+    # 3 INIT TRAINER
     # ------------------------
     trainer = pl.Trainer(
-        gpus=hparams.gpus
+        gpus=hparams.gpus,
+        logger=logger,
+        max_epochs=hparams.epochs,
+        accumulate_grad_batches=hparams.grad_batches,
+        distributed_backend=hparams.distributed_backend,
+        precision=16 if hparams.use_amp else 32,
     )
 
     # ------------------------
-    # 3 START TRAINING
+    # 5 START TRAINING
     # ------------------------
     trainer.fit(model)
 
 
 if __name__ == '__main__':
     parser = ArgumentParser()
-    parser.add_argument("--root", type=str, help="path where dataset is stored")
-    parser.add_argument("--gpus", type=int, help="number of available GPUs")
+    parser.add_argument("--data_path", type=str, help="path where dataset is stored")
+    parser.add_argument("--gpus", type=int, default=-1, help="number of available GPUs")
+    parser.add_argument('--distributed-backend', type=str, default='dp', choices=('dp', 'ddp', 'ddp2'),
+                        help='supports three options dp, ddp, ddp2')
+    parser.add_argument('--use_amp', action='store_true', help='if true uses 16 bit precision')
     parser.add_argument("--batch_size", type=int, default=4, help="size of the batches")
     parser.add_argument("--lr", type=float, default=0.001, help="adam: learning rate")
+    parser.add_argument("--num_layers", type=int, default=5, help="number of layers on u-net")
+    parser.add_argument("--features_start", type=float, default=64, help="number of features in first layer")
+    parser.add_argument("--bilinear", action='store_true', default=False,
+                        help="whether to use bilinear interpolation or transposed")
+    parser.add_argument("--grad_batches", type=int, default=1, help="number of batches to accumulate")
+    parser.add_argument("--epochs", type=int, default=20, help="number of epochs to train")
+    parser.add_argument("--log_wandb", action='store_true', help="log training on Weights & Biases")
 
     hparams = parser.parse_args()
 

pl_examples/models/unet.py

@@ -9,39 +9,46 @@ class UNet(nn.Module):
     Link - https://arxiv.org/abs/1505.04597
 
     Parameters:
-        num_classes (int): Number of output classes required (default 19 for KITTI dataset)
-        bilinear (bool): Whether to use bilinear interpolation or transposed
+        num_classes: Number of output classes required (default 19 for KITTI dataset)
+        num_layers: Number of layers in each side of U-net
+        features_start: Number of features in first layer
+        bilinear: Whether to use bilinear interpolation or transposed
             convolutions for upsampling.
     """
 
-    def __init__(self, num_classes=19, bilinear=False):
+    def __init__(
+            self, num_classes: int = 19,
+            num_layers: int = 5,
+            features_start: int = 64,
+            bilinear: bool = False
+    ):
         super().__init__()
-        self.layer1 = DoubleConv(3, 64)
-        self.layer2 = Down(64, 128)
-        self.layer3 = Down(128, 256)
-        self.layer4 = Down(256, 512)
-        self.layer5 = Down(512, 1024)
+        self.num_layers = num_layers
 
-        self.layer6 = Up(1024, 512, bilinear=bilinear)
-        self.layer7 = Up(512, 256, bilinear=bilinear)
-        self.layer8 = Up(256, 128, bilinear=bilinear)
-        self.layer9 = Up(128, 64, bilinear=bilinear)
+        layers = [DoubleConv(3, features_start)]
 
-        self.layer10 = nn.Conv2d(64, num_classes, kernel_size=1)
+        feats = features_start
+        for _ in range(num_layers - 1):
+            layers.append(Down(feats, feats * 2))
+            feats *= 2
 
-    def forward(self, x):
-        x1 = self.layer1(x)
-        x2 = self.layer2(x1)
-        x3 = self.layer3(x2)
-        x4 = self.layer4(x3)
-        x5 = self.layer5(x4)
+        for _ in range(num_layers - 1):
+            layers.append(Up(feats, feats // 2), bilinear)
+            feats //= 2
+
+        layers.append(nn.Conv2d(feats, num_classes, kernel_size=1))
 
-        x6 = self.layer6(x5, x4)
-        x6 = self.layer7(x6, x3)
-        x6 = self.layer8(x6, x2)
-        x6 = self.layer9(x6, x1)
+        self.layers = nn.ModuleList(layers)
 
-        return self.layer10(x6)
+    def forward(self, x):
+        xi = [self.layers[0](x)]
+        # Down path
+        for layer in self.layers[1:self.num_layers]:
+            xi.append(layer(xi[-1]))
+        # Up path
+        for i, layer in enumerate(self.layers[self.num_layers:-1]):
+            xi[-1] = layer(xi[-1], xi[-2 - i])
+        return self.layers[-1](xi[-1])
 
 
 class DoubleConv(nn.Module):
@@ -50,7 +57,7 @@ class DoubleConv(nn.Module):
     (3x3 conv -> BN -> ReLU) ** 2
     """
 
-    def __init__(self, in_ch, out_ch):
+    def __init__(self, in_ch: int, out_ch: int):
         super().__init__()
         self.net = nn.Sequential(
             nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
@@ -70,7 +77,7 @@ class Down(nn.Module):
     Combination of MaxPool2d and DoubleConv in series
     """
 
-    def __init__(self, in_ch, out_ch):
+    def __init__(self, in_ch: int, out_ch: int):
         super().__init__()
         self.net = nn.Sequential(
             nn.MaxPool2d(kernel_size=2, stride=2),
@@ -88,7 +95,7 @@ class Up(nn.Module):
     followed by double 3x3 convolution.
     """
 
-    def __init__(self, in_ch, out_ch, bilinear=False):
+    def __init__(self, in_ch: int, out_ch: int, bilinear: bool = False):
         super().__init__()
         self.upsample = None
         if bilinear: