Instructions to use nvidia/C-RADIOv2-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/C-RADIOv2-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="nvidia/C-RADIOv2-B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/C-RADIOv2-B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # NVIDIA CORPORATION and its licensors retain all intellectual property | |
| # and proprietary rights in and to this software, related documentation | |
| # and any modifications thereto. Any use, reproduction, disclosure or | |
| # distribution of this software and related documentation without an express | |
| # license agreement from NVIDIA CORPORATION is strictly prohibited. | |
| from argparse import Namespace | |
| from typing import NamedTuple, Optional | |
| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| class AdaptorInput(NamedTuple): | |
| images: torch.Tensor | |
| summary: torch.Tensor | |
| features: torch.Tensor | |
| feature_fmt: str | |
| patch_size: int | |
| class RadioOutput(NamedTuple): | |
| summary: torch.Tensor | |
| features: torch.Tensor | |
| def to(self, *args, **kwargs): | |
| return RadioOutput( | |
| self.summary.to(*args, **kwargs) if self.summary is not None else None, | |
| self.features.to(*args, **kwargs) if self.features is not None else None, | |
| ) | |
| class AdaptorBase(nn.Module): | |
| def forward(self, input: AdaptorInput) -> RadioOutput: | |
| raise NotImplementedError("Subclasses must implement this!") | |