und3rstand-dust3r-512-dpt

import requests
from PIL import Image
import torchvision.transforms as T
from src.models.probes import PointmapProbes

model, probes = PointmapProbes.load_backbone_and_probe(
    "jgaubil/und3rstand-dust3r-512-dpt"
)
model.eval()
probes.eval()

view1_path = "https://raw.githubusercontent.com/JulienGaubil/und3rstand/main/assets/samples/example_view1.jpg"
view2_path = "https://raw.githubusercontent.com/JulienGaubil/und3rstand/main/assets/samples/example_view2.jpg"
transform = T.Compose([
    T.Resize(512),
    T.CenterCrop(512),
    T.ToTensor(),
    T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
view1_images = transform(
    Image.open(requests.get(view1_path, stream=True).raw).convert("RGB")
).unsqueeze(0)
view2_images = transform(
    Image.open(requests.get(view2_path, stream=True).raw).convert("RGB")
).unsqueeze(0)

feat_list = model(view1_images, view2_images)
outputs = probes(feat_list)

for layer_id, (pred1, pred2) in zip(model.probed_layers.layer_ids, outputs):
    print(f"{layer_id}: pts3d={pred1['pts3d'].shape}, conf={pred1['conf'].shape}")