Instructions to use RGBD-SOD/dptdepth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RGBD-SOD/dptdepth with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RGBD-SOD/dptdepth", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RGBD-SOD/dptdepth", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 367 Bytes
dcacd5e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | import torch
class BaseModel(torch.nn.Module):
def load(self, path):
"""Load model from file.
Args:
path (str): file path
"""
parameters = torch.load(path, map_location=torch.device("cpu"))
if "optimizer" in parameters:
parameters = parameters["model"]
self.load_state_dict(parameters)
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