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Running
on
Zero
Running
on
Zero
File size: 1,993 Bytes
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# Copyright (2025) Bytedance Ltd. and/or its affiliates
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import numpy as np
import os
import torch
from extern.video_depth_anything.video_depth import VideoDepthAnything
class VDADemo:
def __init__(
self,
pre_train_path: str,
encoder: str = "vitl",
device: str = "cuda:0",
):
model_configs = {
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
}
self.video_depth_anything = VideoDepthAnything(**model_configs[encoder])
self.video_depth_anything.load_state_dict(torch.load(pre_train_path, map_location='cpu'), strict=True)
self.video_depth_anything = self.video_depth_anything.to(device).eval()
self.device = device
def infer(
self,
frames,
near,
far,
input_size = 518,
target_fps = -1,
):
if frames.max() < 2.:
frames = frames*255.
with torch.inference_mode():
depths, fps = self.video_depth_anything.infer_video_depth(frames, target_fps, input_size, self.device)
depths = torch.from_numpy(depths).unsqueeze(1) # 49 576 1024 ->
depths[depths < 1e-5] = 1e-5
depths = 10000. / depths
depths = depths.clip(near, far)
return depths
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