StreetHazards with Entropic Loss

We train a Feature Pyramid Segmentation model with a ResNet-50 backbone pre-trained on the ImageNet on the StreetHazards test set using the supervised EntropicOpenSetLoss.

We then use the Entropy OOD detector.

This setup is merely made to demonstrate how to train a supervised anomaly segmentation model with this loss function.

Warning

We train on the test set, as it contains examples of anomalies. The results will not be meaningful.

Note

Training with a batch-size of 4 requires slightly more than 12 GB of GPU memory. However, the models tend to also converge to reasonable performance with a smaller batch-size.

import segmentation_models_pytorch as smp
import torch
from segmentation_models_pytorch.encoders import get_preprocessing_fn
from segmentation_models_pytorch.metrics import iou_score
from torch.utils.data import DataLoader
from torchvision.transforms.functional import pad, to_tensor

from pytorch_ood.dataset.img import StreetHazards
from pytorch_ood.detector import Entropy
from pytorch_ood.loss import EntropicOpenSetLoss
from pytorch_ood.utils import OODMetrics, fix_random_seed

device = "cuda:0"
batch_size = 4
num_epochs = 1

fix_random_seed(12345)
g = torch.Generator()
g.manual_seed(0)

Setup preprocessing

preprocess_input = get_preprocessing_fn("resnet50", pretrained="imagenet")


def my_transform(img, target):
    img = to_tensor(img)[:3, :, :]  # drop 4th channel
    img = torch.moveaxis(img, 0, -1)
    img = preprocess_input(img)
    img = torch.moveaxis(img, -1, 0)

    # size must be divisible by 32, so we pad the image.
    img = pad(img, [0, 8]).float()
    target = pad(target, [0, 8])
    return img, target

Setup datasets, train on ood images for demonstration purposes.

dataset = StreetHazards(root="data", subset="test", transform=my_transform, download=True)
dataset_test = StreetHazards(root="data", subset="test", transform=my_transform, download=True)

Setup model

model = smp.FPN(
    encoder_name="resnet50",
    encoder_weights="imagenet",
    in_channels=3,
    classes=13,
).to(device)

Train model for some epochs

criterion = EntropicOpenSetLoss()
optimizer = torch.optim.Adam(params=model.parameters(), lr=0.0001)
loader = DataLoader(
    dataset,
    batch_size=batch_size,
    shuffle=True,
    num_workers=10,
    worker_init_fn=fix_random_seed,
    generator=g,
)

ious = []
loss_ema = 0
ioe_ema = 0

for epoch in range(num_epochs):
    for n, (x, y) in enumerate(loader):
        optimizer.zero_grad()
        y, x = y.to(device), x.to(device)

        y_hat = model(x)
        loss = criterion(y_hat, y)
        loss.backward()
        optimizer.step()

        tp, fp, fn, tn = smp.metrics.get_stats(
            y_hat.softmax(dim=1).max(dim=1).indices.long(),
            y.long(),
            mode="multiclass",
            num_classes=13,
        )
        iou = iou_score(tp, fp, fn, tn)

        loss_ema = 0.8 * loss_ema + 0.2 * loss.item()
        ioe_ema = 0.8 * ioe_ema + 0.2 * iou.mean().item()

        if n % 10 == 0:
            print(
                f"Epoch {epoch:03d} [{n:05d}/{len(loader):05d}] \t Loss: {loss_ema:02.2f} \t IoU: {ioe_ema:02.2f}"
            )

Evaluate

print("Evaluating")
model.eval()
loader = DataLoader(dataset_test, batch_size=4, worker_init_fn=fix_random_seed, generator=g)
detector = Entropy(model)
metrics = OODMetrics(mode="segmentation")

with torch.no_grad():
    for n, (x, y) in enumerate(loader):
        y, x = y.to(device), x.to(device)
        o = detector(x)

        # undo padding
        o = pad(o, [-8, -8])
        y = pad(y, [-8, -8])

        metrics.update(o, y)

print(metrics.compute())

Output:

{‘AUROC’: 0.9705050587654114, ‘AUPR-IN’: 0.3917403519153595, ‘AUPR-OUT’: 0.9997314214706421, ‘FPR95TPR’: 0.10926716774702072}