|
|
|
|
|
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, backbone: str, num_tokens: int, device: Union[torch.device, str]): |
|
|
|
|
|
repo_path = os.path.abspath(repo_path) |
|
|
if repo_path not in sys.path: |
|
|
sys.path.append(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.') |
|
|
from depth_anything_v2.dpt import DepthAnythingV2 |
|
|
|
|
|
device = torch.device(device) |
|
|
|
|
|
|
|
|
model = DepthAnythingV2(encoder=backbone, features=256, out_channels=[256, 512, 1024, 1024]) |
|
|
|
|
|
|
|
|
checkpoint_path = os.path.join(repo_path, f'checkpoints/depth_anything_v2_{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.') |
|
|
checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=True) |
|
|
model.load_state_dict(checkpoint) |
|
|
|
|
|
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-V2', help='Path to the Depth-Anything repository.') |
|
|
@click.option('--backbone', type=click.Choice(['vits', 'vitb', 'vitl']), default='vitl', help='Encoder architecture.') |
|
|
@click.option('--num_tokens', type=int, default=None, help='Number of tokens to use for the input image.') |
|
|
@click.option('--device', type=str, default='cuda', help='Device to use for inference.') |
|
|
@staticmethod |
|
|
def load(repo_path: str, backbone, num_tokens: int, device: torch.device = 'cuda'): |
|
|
return Baseline(repo_path, backbone, 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]) |
|
|
|
|
|
disparity = self.model(image) |
|
|
|
|
|
disparity = F.interpolate(disparity[:, None], size=(original_height, original_width), mode='bilinear', align_corners=False, antialias=False)[:, 0] |
|
|
|
|
|
if omit_batch_dim: |
|
|
disparity = disparity.squeeze(0) |
|
|
|
|
|
return { |
|
|
'disparity_affine_invariant': disparity |
|
|
} |
|
|
|
|
|
|
