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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 Callable, Optional, List, Union
from timm.models import VisionTransformer
import torch
from torch import nn
from transformers import PretrainedConfig, PreTrainedModel
from .common import RESOURCE_MAP, DEFAULT_VERSION
# Force import of eradio_model in order to register it.
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
# 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
@input_conditioner.setter
def input_conditioner(self, v: InputConditioner):
self.radio_model.input_conditioner = v
def make_preprocessor_external(self) -> Callable[[torch.Tensor], torch.Tensor]:
ret = self.input_conditioner
self.input_conditioner = nn.Identity()
return ret
def forward(self, x: torch.Tensor):
return self.radio_model.forward(x)
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