191 lines
6.6 KiB
Python
191 lines
6.6 KiB
Python
import os
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from argparse import ArgumentParser
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torchvision.transforms as transforms
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from PIL import Image
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from torch.utils.data import DataLoader, Dataset
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import pytorch_lightning as pl
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from pl_examples.models.unet import UNet
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class KITTI(Dataset):
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"""
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Class for KITTI Semantic Segmentation Benchmark dataset
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Dataset link - http://www.cvlibs.net/datasets/kitti/eval_semseg.php?benchmark=semantics2015
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There are 34 classes in the given labels. However, not all of them are useful for training
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(like railings on highways, road dividers, etc.).
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So, these useless classes (the pixel values of these classes) are stored in the `void_labels`.
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The useful classes are stored in the `valid_labels`.
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The `encode_segmap` function sets all pixels with any of the `void_labels` to `ignore_index`
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(250 by default). It also sets all of the valid pixels to the appropriate value between 0 and
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`len(valid_labels)` (since that is the number of valid classes), so it can be used properly by
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the loss function when comparing with the output.
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The `get_filenames` function retrieves the filenames of all images in the given `path` and
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saves the absolute path in a list.
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In the `get_item` function, images and masks are resized to the given `img_size`, masks are
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encoded using `encode_segmap`, and given `transform` (if any) are applied to the image only
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(mask does not usually require transforms, but they can be implemented in a similar way).
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"""
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def __init__(
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self,
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root_path,
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split='test',
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img_size=(1242, 376),
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void_labels=[0, 1, 2, 3, 4, 5, 6, 9, 10, 14, 15, 16, 18, 29, 30, -1],
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valid_labels=[7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33],
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transform=None
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):
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self.img_size = img_size
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self.void_labels = void_labels
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self.valid_labels = valid_labels
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self.ignore_index = 250
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self.class_map = dict(zip(self.valid_labels, range(len(self.valid_labels))))
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self.split = split
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self.root = root_path
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if self.split == 'train':
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self.img_path = os.path.join(self.root, 'training/image_2')
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self.mask_path = os.path.join(self.root, 'training/semantic')
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else:
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self.img_path = os.path.join(self.root, 'testing/image_2')
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self.mask_path = None
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self.transform = transform
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self.img_list = self.get_filenames(self.img_path)
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if self.split == 'train':
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self.mask_list = self.get_filenames(self.mask_path)
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else:
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self.mask_list = None
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def __len__(self):
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return len(self.img_list)
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def __getitem__(self, idx):
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img = Image.open(self.img_list[idx])
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img = img.resize(self.img_size)
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img = np.array(img)
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if self.split == 'train':
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mask = Image.open(self.mask_list[idx]).convert('L')
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mask = mask.resize(self.img_size)
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mask = np.array(mask)
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mask = self.encode_segmap(mask)
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if self.transform:
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img = self.transform(img)
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if self.split == 'train':
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return img, mask
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else:
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return img
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def encode_segmap(self, mask):
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"""
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Sets void classes to zero so they won't be considered for training
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"""
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for voidc in self.void_labels:
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mask[mask == voidc] = self.ignore_index
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for validc in self.valid_labels:
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mask[mask == validc] = self.class_map[validc]
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return mask
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def get_filenames(self, path):
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"""
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Returns a list of absolute paths to images inside given `path`
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"""
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files_list = list()
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for filename in os.listdir(path):
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files_list.append(os.path.join(path, filename))
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return files_list
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class SegModel(pl.LightningModule):
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"""
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Semantic Segmentation Module
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This is a basic semantic segmentation module implemented with Lightning.
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It uses CrossEntropyLoss as the default loss function. May be replaced with
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other loss functions as required.
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It is specific to KITTI dataset i.e. dataloaders are for KITTI
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and Normalize transform uses the mean and standard deviation of this dataset.
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It uses the FCN ResNet50 model as an example.
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Adam optimizer is used along with Cosine Annealing learning rate scheduler.
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"""
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def __init__(self, hparams):
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super().__init__()
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self.root_path = hparams.root
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self.batch_size = hparams.batch_size
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self.learning_rate = hparams.lr
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self.net = UNet(num_classes=19)
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self.transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.35675976, 0.37380189, 0.3764753],
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std=[0.32064945, 0.32098866, 0.32325324])
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])
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self.trainset = KITTI(self.root_path, split='train', transform=self.transform)
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self.testset = KITTI(self.root_path, split='test', transform=self.transform)
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def forward(self, x):
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return self.net(x)
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def training_step(self, batch, batch_nb):
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img, mask = batch
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img = img.float()
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mask = mask.long()
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out = self(img)
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loss_val = F.cross_entropy(out, mask, ignore_index=250)
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return {'loss': loss_val}
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def configure_optimizers(self):
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opt = torch.optim.Adam(self.net.parameters(), lr=self.learning_rate)
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sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=10)
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return [opt], [sch]
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def train_dataloader(self):
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return DataLoader(self.trainset, batch_size=self.batch_size, shuffle=True)
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def test_dataloader(self):
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return DataLoader(self.testset, batch_size=self.batch_size, shuffle=False)
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def main(hparams):
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# ------------------------
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# 1 INIT LIGHTNING MODEL
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# ------------------------
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model = SegModel(hparams)
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# ------------------------
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# 2 INIT TRAINER
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# ------------------------
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trainer = pl.Trainer(
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gpus=hparams.gpus
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)
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# ------------------------
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# 3 START TRAINING
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# ------------------------
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trainer.fit(model)
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if __name__ == '__main__':
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parser = ArgumentParser()
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parser.add_argument("--root", type=str, help="path where dataset is stored")
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parser.add_argument("--gpus", type=int, help="number of available GPUs")
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parser.add_argument("--batch_size", type=int, default=4, help="size of the batches")
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parser.add_argument("--lr", type=float, default=0.001, help="adam: learning rate")
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hparams = parser.parse_args()
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main(hparams)
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