File size: 4,356 Bytes
45b0ed8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | # Reference: https://github.com/ByteDance-Seed/Depth-Anything-3
# Variant of `baselines/da3.py` that loads DA3's *monocular* preset(s).
# DA3 README: "DA3 Monocular Series (DA3Mono-Large). A dedicated model for high-quality
# relative monocular depth estimation. Unlike disparity-based models (e.g. Depth Anything 2),
# it directly predicts depth, resulting in superior geometric accuracy."
#
# Strictly follows the same Python API as da3.py:
# model = DepthAnything3.from_pretrained(<hf_id>)
# output = model(image) # image shape [B, N, 3, H, W]
# depth = output['depth'][:, 0] # [B, H, W]
#
# NOTE on output key: DA3-Mono outputs depth directly (per README), not disparity.
# We therefore return `depth_scale_invariant` instead of `disparity_affine_invariant`.
import os
import sys
from typing import *
from pathlib import Path
import click
import torch
import torch.nn.functional as F
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from moge.test.baseline import MGEBaselineInterface
class Baseline(MGEBaselineInterface):
def __init__(self, repo_path: str, hf_id: str, num_tokens: Optional[int], device: Union[torch.device, str]):
repo_path = os.path.abspath(repo_path)
if not Path(repo_path).exists():
raise FileNotFoundError(
f"Cannot find Depth-Anything-3 repo at {repo_path}. Clone "
f"https://github.com/ByteDance-Seed/Depth-Anything-3."
)
src_path = os.path.join(repo_path, 'src')
if src_path not in sys.path:
sys.path.insert(0, src_path)
# Silence DA3's verbose per-image INFO logs (DA3_LOG_LEVEL is read at logger init)
os.environ.setdefault('DA3_LOG_LEVEL', 'WARN')
from depth_anything_3.api import DepthAnything3
device = torch.device(device)
model = DepthAnything3.from_pretrained(hf_id)
model.to(device).eval()
self.model = model
self.num_tokens = num_tokens
self.device = device
@click.command()
@click.option('--repo', 'repo_path', type=click.Path(), default='../Depth-Anything-3',
help='Path to the ByteDance-Seed/Depth-Anything-3 repository.')
@click.option('--hf_id', type=str, default='depth-anything/DA3MONO-LARGE',
help='HF repo id of the DA3-Mono variant (e.g. depth-anything/DA3MONO-LARGE).')
@click.option('--num_tokens', type=int, default=None,
help='Number of tokens; None uses 518 / min(H, W) factor as in da3.py.')
@click.option('--device', type=str, default='cuda')
@staticmethod
def load(repo_path: str, hf_id: str, num_tokens: Optional[int], device: str = 'cuda'):
return Baseline(repo_path, hf_id, num_tokens, device)
@torch.inference_mode()
def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
# Same input pipeline as baselines/da3.py to keep apples-to-apples.
assert intrinsics is None, "DA3-Mono does not consume intrinsics."
original_height, original_width = image.shape[-2:]
if image.ndim == 3:
image = image.unsqueeze(0)
omit_batch_dim = True
else:
omit_batch_dim = False
# Use DA3's high-level `model.inference()` API per README. Direct `model(x)`
# goes through `forward(... export_feat_layers=None)` and the DA3-Mono backbone
# (DINOv2 fork) crashes inside `_get_intermediate_layers_not_chunked` because it
# tries `i in export_feat_layers` on None. `inference()` handles processing,
# autocast, and post-processing correctly.
import numpy as np
np_img = (image[0].cpu().permute(1, 2, 0).clamp(0, 1).numpy() * 255).astype(np.uint8)
prediction = self.model.inference([np_img])
# prediction.depth: [N, H, W] float32
depth_t = torch.as_tensor(prediction.depth[0], device=self.device, dtype=torch.float32)
if depth_t.shape != (original_height, original_width):
depth_t = F.interpolate(depth_t[None, None], size=(original_height, original_width),
mode='bilinear', align_corners=False)[0, 0]
if not omit_batch_dim:
depth_t = depth_t.unsqueeze(0)
return {'depth_scale_invariant': depth_t}
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