feat(semseg): allow model customization

pl_examples/full_examples/semantic_segmentation/models/unet/model.py

@@ -1,6 +1,6 @@
 import torch.nn as nn
 
-from models.unet.parts import DoubleConv, Down, Up
+from .parts import DoubleConv, Down, Up
 
 
 class UNet(nn.Module):
@@ -10,35 +10,37 @@ class UNet(nn.Module):
 
     Parameters:
         num_classes (int) - Number of output classes required (default 19 for KITTI dataset)
+        num_layers (int) - Number of layers in each side of U-net
+        features_start (int) - Number of features in first layer
         bilinear (bool) - Whether to use bilinear interpolation or transposed
-        convolutions for upsampling.
+            convolutions for upsampling.
     '''
 
-    def __init__(self, num_classes=19, bilinear=False):
+    def __init__(self, num_classes=19, num_layers=5, features_start=64, bilinear=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
+
+        for _ in range(num_layers - 1):
+            layers.append(Up(feats, feats // 2))
+            feats //= 2
+
+        layers.append(nn.Conv2d(feats, num_classes, kernel_size=1))
+
+        self.layers = nn.ModuleList(layers)
 
     def forward(self, x):
-        x1 = self.layer1(x)
-        x2 = self.layer2(x1)
-        x3 = self.layer3(x2)
-        x4 = self.layer4(x3)
-        x5 = self.layer5(x4)
-
-        x6 = self.layer6(x5, x4)
-        x6 = self.layer7(x6, x3)
-        x6 = self.layer8(x6, x2)
-        x6 = self.layer9(x6, x1)
-
-        return self.layer10(x6)
+        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])

pl_examples/full_examples/semantic_segmentation/semseg.py

@@ -8,8 +8,10 @@
 from PIL import Image
 from models.unet.model import UNet
 from torch.utils.data import DataLoader, Dataset
+import random
 
 import pytorch_lightning as pl
+from pytorch_lightning.loggers import WandbLogger
 
 
 class KITTI(Dataset):
@@ -37,8 +39,8 @@ class KITTI(Dataset):
 
     def __init__(
         self,
-        root_path,
-        split='test',
+        data_path,
+        split,
         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],
@@ -49,22 +51,23 @@ def __init__(
         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, 'training/image_2')
+        self.mask_path = os.path.join(self.data_path, 'training/semantic')
         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 +77,15 @@ 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 +95,8 @@ 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 +125,19 @@ 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 +148,21 @@ 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 +172,8 @@ 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 +183,50 @@ 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,
+        use_amp=hparams.use_16bit
     )
 
     # ------------------------
-    # 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-16bit', dest='use_16bit', 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", type=float, 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()