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# Copyright (c) 2023-2024, NVIDIA CORPORATION. All rights reserved.
#
# 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.
from collections import namedtuple
from typing import Optional, List, Union
from timm.models import VisionTransformer
import torch
from transformers import PretrainedConfig, PreTrainedModel
from .common import RESOURCE_MAP, DEFAULT_VERSION
# Import all required modules.
from .adaptor_base import AdaptorBase, RadioOutput, AdaptorInput
from .adaptor_generic import GenericAdaptor, AdaptorBase
from .adaptor_mlp import create_mlp_from_state
from .adaptor_registry import adaptor_registry
from .cls_token import ClsToken
from .enable_cpe_support import enable_cpe
from .enable_spectral_reparam import configure_spectral_reparam_from_args
from .eradio_model import eradio
from .radio_model import create_model_from_args
from .radio_model import RADIOModel as RADIOModelBase, Resolution
from .input_conditioner import get_default_conditioner, InputConditioner
from .open_clip_adaptor import OpenCLIP_RADIO
from .vit_patch_generator import ViTPatchGenerator
from .vitdet import apply_vitdet_arch, VitDetArgs
# Register extra models
from .extra_timm_models import *
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,
vitdet_window_size: Optional[int] = 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.vitdet_window_size = vitdet_window_size
super().__init__(**kwargs)
class RADIOModel(PreTrainedModel):
"""Pretrained Hugging Face model for RADIO.
This class inherits from PreTrainedModel, which provides
HuggingFace's functionality for loading and saving models.
"""
config_class = RADIOConfig
def __init__(self, config):
super().__init__(config)
RADIOArgs = namedtuple("RADIOArgs", config.args.keys())
args = RADIOArgs(**config.args)
self.config = config
model = create_model_from_args(args)
input_conditioner: InputConditioner = get_default_conditioner()
dtype = getattr(args, "dtype", torch.float32)
if isinstance(dtype, str):
# Convert the dtype's string representation back to a dtype.
dtype = getattr(torch, dtype)
model.to(dtype=dtype)
input_conditioner.dtype = dtype
summary_idxs = torch.tensor(
[i for i, t in enumerate(args.teachers) if t.get("use_summary", True)],
dtype=torch.int64,
)
adaptor_names = config.adaptor_names
if adaptor_names is not None:
raise NotImplementedError(
f"Adaptors are not yet supported in Hugging Face models. Adaptor names: {adaptor_names}"
)
adaptors = dict()
self.radio_model = RADIOModelBase(
model,
input_conditioner,
summary_idxs=summary_idxs,
patch_size=config.patch_size,
max_resolution=config.max_resolution,
window_size=config.vitdet_window_size,
preferred_resolution=config.preferred_resolution,
adaptors=adaptors,
)
@property
def model(self) -> VisionTransformer:
return self.radio_model.model
@property
def input_conditioner(self) -> InputConditioner:
return self.radio_model.input_conditioner
def forward(self, x: torch.Tensor):
return self.radio_model.forward(x)
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