added initial semantic segmentation example

pl_examples/full_examples/semantic_segmentation/semseg.py

@@ -0,0 +1,193 @@
+import os
+from argparse import ArgumentParser
+from collections import OrderedDict
+from PIL import Image
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader, Dataset
+from torchvision.models.segmentation import fcn_resnet50
+
+import pytorch_lightning as pl
+
+
+class KITTI(Dataset):
+    '''
+    Dataset Class for KITTI Semantic Segmentation Benchmark dataset
+    Dataset link - http://www.cvlibs.net/datasets/kitti/eval_semseg.php?benchmark=semantics2015
+
+    There are 34 classes in the given labels. However, not all of them are useful for training
+    (like railings on highways, road dividers, etc.).
+    So, these useless classes (the pixel values of these classes) are stored in the `void_labels`.
+    The useful classes are stored in the `valid_labels`.
+
+    The `encode_segmap` function sets all pixels with any of the `void_labels` to `ignore_index`
+    (250 by default). It also sets all of the valid pixels to the appropriate value between 0 and
+    `len(valid_labels)` (since that is the number of valid classes), so it can be used properly by
+    the loss function when comparing with the output.
+
+    The `get_filenames` function retrieves the filenames of all images in the given `path` and
+    saves the absolute path in a list.
+
+    In the `get_item` function, images and masks are resized to the given `img_size`, masks are
+    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).
+    '''
+    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],
+        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.img_list = self.get_filenames(self.img_path)
+        if self.split == 'train':
+            self.mask_list = self.get_filenames(self.mask_path)
+        else:
+            self.mask_list = None
+
+    def __len__(self):
+        return(len(self.img_list))
+
+    def __getitem__(self, idx):
+        img = Image.open(self.img_list[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)
+
+        if self.transform:
+            img = self.transform(img)
+
+        if self.split == 'train':
+            return img, mask
+        else:
+            return img
+
+    def encode_segmap(self, mask):
+        '''
+        Sets void classes to zero so they won't be considered for training
+        '''
+        for voidc in self.void_labels:
+            mask[mask == voidc] = self.ignore_index
+        for validc in self.valid_labels:
+            mask[mask == validc] = self.class_map[validc]
+        return mask
+
+    def get_filenames(self, path):
+        '''
+        Returns a list of absolute paths to images inside given `path`
+        '''
+        files_list = list()
+        for filename in os.listdir(path):
+            files_list.append(os.path.join(path, filename))
+        return files_list
+
+
+class SegModel(pl.LightningModule):
+    '''
+    Semantic Segmentation Module
+
+    This is a basic semantic segmentation module implemented with Lightning.
+    It uses CrossEntropyLoss as the default loss function. May be replaced with
+    other loss functions as required.
+    It is specific to KITTI dataset i.e. dataloaders are for KITTI
+    and Normalize transform uses the mean and standard deviation of this dataset.
+    It uses the FCN ResNet50 model as an example.
+
+    Adam optimizer is used along with Cosine Annealing learning rate scheduler.
+    '''
+    def __init__(self, hparams):
+        super(SegModel, self).__init__()
+        self.root_path = hparams.root
+        self.batch_size = hparams.batch_size
+        self.learning_rate = hparams.lr
+        self.net = torchvision.models.segmentation.fcn_resnet50(pretrained=False,
+                                                                progress=True,
+                                                                num_classes=19)
+        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)
+
+    def forward(self, x):
+        return self.net(x)
+
+    def training_step(self, batch, batch_nb):
+        img, mask = batch
+        img = img.float()
+        mask = mask.long()
+        out = self.forward(img)
+        loss_val = F.cross_entropy(out['out'], mask, ignore_index=250)
+        return {'loss': loss_val}
+
+    def configure_optimizers(self):
+        opt = torch.optim.Adam(self.net.parameters(), lr=self.learning_rate)
+        sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=10)
+        return [opt], [sch]
+
+    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 main(hparams):
+    # ------------------------
+    # 1 INIT LIGHTNING MODEL
+    # ------------------------
+    model = SegModel(hparams)
+
+    # ------------------------
+    # 2 INIT TRAINER
+    # ------------------------
+    trainer = pl.Trainer(
+        gpus=hparams.gpus
+    )
+
+    # ------------------------
+    # 3 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("--batch_size", type=int, default=4, help="size of the batches")
+    parser.add_argument("--lr", type=float, default=0.001, help="adam: learning rate")
+
+    hparams = parser.parse_args()
+
+    main(hparams)