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# 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 dataclasses import dataclass
from typing import Optional, NamedTuple, Union, List, Dict
from transformers import PretrainedConfig
class Resolution(NamedTuple):
height: int
width: int
@dataclass
class RadioResource:
url: str
patch_size: int
max_resolution: int
preferred_resolution: Resolution
vitdet_num_windowed: Optional[int] = None
vitdet_num_global: Optional[int] = None
RESOURCE_MAP = {
# RADIOv2.5
"radio_v2.5-b": RadioResource(
"https://huggingface.co/nvidia/RADIO/resolve/main/radio-v2.5-b_half.pth.tar?download=true",
patch_size=16,
max_resolution=2048,
preferred_resolution=(768, 768),
vitdet_num_global=4,
),
"radio_v2.5-l": RadioResource(
"https://huggingface.co/nvidia/RADIO/resolve/main/radio-v2.5-l_half.pth.tar?download=true",
patch_size=16,
max_resolution=2048,
preferred_resolution=(768, 768),
vitdet_num_global=4,
),
"radio_v2.5-h": RadioResource(
"https://huggingface.co/nvidia/RADIO/resolve/main/radio_v2.5-h.pth.tar?download=true",
patch_size=16,
max_resolution=2048,
preferred_resolution=(768, 768),
vitdet_num_global=4,
),
"radio_v2.5-h-norm": RadioResource(
"https://huggingface.co/nvidia/RADIO/resolve/main/radio_v2.5-h-norm.pth.tar?download=true",
patch_size=16,
max_resolution=2048,
preferred_resolution=(768, 768),
vitdet_num_global=4,
),
"radio_v2.5-g": RadioResource(
"https://huggingface.co/nvidia/RADIO/resolve/main/radio_v2.5-g.pth.tar?download=true",
patch_size=14,
max_resolution=1792,
preferred_resolution=(896, 896),
vitdet_num_global=8,
),
# RADIO
"radio_v2.1": RadioResource(
"https://huggingface.co/nvidia/RADIO/resolve/main/radio_v2.1_bf16.pth.tar?download=true",
patch_size=16,
max_resolution=2048,
preferred_resolution=Resolution(432, 432),
vitdet_num_windowed=5,
),
"radio_v2": RadioResource(
"https://huggingface.co/nvidia/RADIO/resolve/main/radio_v2.pth.tar?download=true",
patch_size=16,
max_resolution=2048,
preferred_resolution=Resolution(432, 432),
vitdet_num_windowed=5,
),
"radio_v1": RadioResource(
"https://huggingface.co/nvidia/RADIO/resolve/main/radio_v1.pth.tar?download=true",
patch_size=14,
max_resolution=1050,
preferred_resolution=Resolution(378, 378),
),
# E-RADIO
"e-radio_v2": RadioResource(
"https://huggingface.co/nvidia/RADIO/resolve/main/eradio_v2.pth.tar?download=true",
patch_size=16,
max_resolution=2048,
preferred_resolution=Resolution(512, 512),
),
# C-RADIO
"c-radio_v2.5-g": RadioResource(
"https://huggingface.co/nvidia/C-RADIOv2-g/resolve/main/c-radio_v2-g_half.pth.tar",
patch_size=16,
max_resolution=2048,
preferred_resolution=(768, 768),
vitdet_num_global=8,
),
"c-radio_v3-l": RadioResource(
# NOTE: Currently, this model cannot be loaded via TorchHub. Instead, use the transformers API at https://huggingface.co/nvidia/C-RADIOv3-L
# and accept the license terms.
"https://huggingface.co/nvidia/C-RADIOv3-L/resolve/main/c-radio-v3_l_half.pth.tar?download=true",
patch_size=16,
max_resolution=2048,
preferred_resolution=Resolution(512, 512),
),
}
DEFAULT_VERSION = "radio_v2.5-h"
class RADIOConfig(PretrainedConfig):
"""Pretrained Hugging Face configuration for RADIO models."""
def __init__(
self,
args: Optional[dict] = None,
version: Optional[str] = DEFAULT_VERSION,
patch_size: Optional[int] = None,
max_resolution: Optional[int] = None,
preferred_resolution: Optional[Resolution] = None,
adaptor_names: Union[str, List[str]] = None,
adaptor_configs: Dict[str, Dict[str, int]] = None,
vitdet_window_size: Optional[int] = None,
feature_normalizer_config: Optional[dict] = None,
inter_feature_normalizer_config: Optional[dict] = None,
**kwargs,
):
self.args = args
for field in ["dtype", "amp_dtype"]:
if self.args is not None and field in self.args:
# Convert to a string in order to make it serializable.
# For example for torch.float32 we will store "float32",
# for "bfloat16" we will store "bfloat16".
self.args[field] = str(args[field]).split(".")[-1]
self.version = version
resource = RESOURCE_MAP[version]
self.patch_size = patch_size or resource.patch_size
self.max_resolution = max_resolution or resource.max_resolution
self.preferred_resolution = (
preferred_resolution or resource.preferred_resolution
)
self.adaptor_names = adaptor_names
self.adaptor_configs = adaptor_configs
self.vitdet_window_size = vitdet_window_size
self.feature_normalizer_config = feature_normalizer_config
self.inter_feature_normalizer_config = inter_feature_normalizer_config
super().__init__(**kwargs) |