File size: 5,575 Bytes
da3ed5b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | # VGGT with custom trained DPT/SDT checkpoint (LoRA)
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, checkpoint: str, decoder: str, lora_rank: int, lora_alpha: int, num_tokens: int, device: Union[torch.device, str]):
# Create from repo
repo_path = os.path.abspath(repo_path)
training_path = os.path.join(repo_path, 'training')
if training_path not in sys.path:
sys.path.insert(0, training_path)
if repo_path not in sys.path:
sys.path.insert(0, repo_path)
if not Path(repo_path).exists():
raise FileNotFoundError(f'Cannot find the VGGT repository at {repo_path}.')
device = torch.device(device)
# Build model based on decoder type
if decoder == 'dpt':
from vggt.models.vggt import VGGT
model = VGGT(
enable_camera=True,
enable_depth=True,
enable_point=False,
enable_track=False,
)
elif decoder == 'sdt':
from vggt.models.vggt_sdt import VGGT_SDT
model = VGGT_SDT(
enable_camera=True,
enable_depth=True,
enable_point=False,
enable_track=False,
)
else:
raise ValueError(f"Unknown decoder: {decoder}")
# Apply LoRA
from lora import apply_lora
model = apply_lora(model, rank=lora_rank, alpha=lora_alpha)
print(f"Applied LoRA (rank={lora_rank}, alpha={lora_alpha})")
# Load checkpoint
if not os.path.exists(checkpoint):
raise FileNotFoundError(f'Cannot find checkpoint at {checkpoint}')
ckpt = torch.load(checkpoint, map_location='cpu')
if 'model' in ckpt:
state_dict = ckpt['model']
else:
state_dict = ckpt
# Remove 'module.' prefix if present
state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
missing, unexpected = model.load_state_dict(state_dict, strict=False)
print(f"Loaded checkpoint from {checkpoint}")
if missing:
print(f"Missing keys: {len(missing)}")
if unexpected:
print(f"Unexpected keys: {len(unexpected)}")
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='/home/ywan0794/vggt', help='Path to the VGGT repository.')
@click.option('--checkpoint', type=click.Path(), required=True, help='Path to trained checkpoint.')
@click.option('--decoder', type=click.Choice(['dpt', 'sdt']), default='dpt', help='Decoder type.')
@click.option('--lora_rank', type=int, default=8, help='LoRA rank.')
@click.option('--lora_alpha', type=int, default=16, help='LoRA alpha.')
@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, checkpoint: str, decoder: str, lora_rank: int, lora_alpha: int, num_tokens: int, device: torch.device = 'cuda'):
return Baseline(repo_path, checkpoint, decoder, lora_rank, lora_alpha, 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:]
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)
# VGGT expects [0, 1] range, not ImageNet normalized
image = image.to(self.device)
# VGGT expects sequence of images: [B, S, 3, H, W]
rgb_seq = image.unsqueeze(1).repeat(1, 2, 1, 1, 1)
# Forward pass
with torch.cuda.amp.autocast(dtype=torch.bfloat16):
output = self.model(images=rgb_seq)
# Extract depth from prediction
# pred["depth"] shape: [B, S, H, W, 1]
depth = output["depth"][0, 0, :, :, 0]
# Convert depth to disparity
disparity = 1.0 / (depth + 1e-6)
disparity = F.interpolate(disparity[None, None], size=(original_height, original_width), mode='bilinear', align_corners=False, antialias=False)[0, 0]
if omit_batch_dim:
pass # already squeezed
return {
'disparity_affine_invariant': disparity
}
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