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7df8df8 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | # 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) |