File size: 4,472 Bytes
201ab98 |
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 97 98 99 100 |
# Reference https://github.com/DepthAnything/Depth-Anything-V2/metric_depth
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
import cv2
from moge.test.baseline import MGEBaselineInterface
class Baseline(MGEBaselineInterface):
def __init__(self, repo_path: str, backbone: str, domain: str, num_tokens: int, device: str):
device = torch.device(device)
repo_path = os.path.abspath(repo_path)
if not Path(repo_path).exists():
raise FileNotFoundError(f'Cannot find the Depth-Anything repository at {repo_path}. Please clone the repository and provide the path to it using the --repo option.')
sys.path.append(os.path.join(repo_path, 'metric_depth'))
from depth_anything_v2.dpt import DepthAnythingV2
model_configs = {
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}
}
if domain == 'indoor':
dataset = 'hypersim'
max_depth = 20
elif domain == 'outdoor':
dataset = 'vkitti'
max_depth = 80
else:
raise ValueError(f"Invalid domain: {domain}")
model = DepthAnythingV2(**model_configs[backbone], max_depth=max_depth)
checkpoint_path = os.path.join(repo_path, f'checkpoints/depth_anything_v2_metric_{dataset}_{backbone}.pth')
if not os.path.exists(checkpoint_path):
raise FileNotFoundError(f'Cannot find the checkpoint file at {checkpoint_path}. Please download the checkpoint file and place it in the checkpoints directory.')
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu', weights_only=True))
model.eval().to(device)
self.model = model
self.num_tokens = num_tokens
self.device = device
@click.command()
@click.option('--repo', 'repo_path', type=click.Path(), default='../Depth-Anything-V2', help='Path to the Depth-Anything repository.')
@click.option('--backbone', type=click.Choice(['vits', 'vitb', 'vitl']), default='vitl', help='Backbone architecture.')
@click.option('--domain', type=click.Choice(['indoor', 'outdoor']), help='Domain of the dataset.')
@click.option('--num_tokens', type=int, default=None, help='Number of tokens for the ViT model')
@click.option('--device', type=str, default='cuda', help='Device to use for inference.')
@staticmethod
def load(repo_path: str, backbone: str, domain: str, num_tokens: int, device: str):
return Baseline(repo_path, backbone, domain, num_tokens, device)
@torch.inference_mode()
def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
original_height, original_width = image.shape[-2:]
assert intrinsics is None, "Depth-Anything-V2 does not support camera intrinsics input"
if image.ndim == 3:
image = image.unsqueeze(0)
omit_batch_dim = True
else:
omit_batch_dim = False
if self.num_tokens is None:
resize_factor = 518 / min(original_height, original_width)
expected_width = round(original_width * resize_factor / 14) * 14
expected_height = round(original_height * resize_factor / 14) * 14
else:
aspect_ratio = original_width / original_height
tokens_rows = round((self.num_tokens * aspect_ratio) ** 0.5)
tokens_cols = round((self.num_tokens / aspect_ratio) ** 0.5)
expected_width = tokens_cols * 14
expected_height = tokens_rows * 14
image = TF.resize(image, (expected_height, expected_width), interpolation=T.InterpolationMode.BICUBIC, antialias=True)
image = TF.normalize(image, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
depth = self.model(image)
depth = F.interpolate(depth[:, None], size=(original_height, original_width), mode='bilinear', align_corners=False, antialias=False)[:, 0]
if omit_batch_dim:
depth = depth.squeeze(0)
return {
'depth_metric': depth
}
|