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# Copyright (c) 2023, 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
from timm.models import VisionTransformer
import torch
from transformers import PretrainedConfig, PreTrainedModel
from .model import create_model_from_args
from .model import RADIOModel as RADIOModelBase
from .input_conditioner import get_default_conditioner, InputConditioner
class RADIOConfig(PretrainedConfig):
"""Pretrained Hugging Face configuration for RADIO models."""
def __init__(
self,
args: Optional[dict] = None,
version: Optional[str] = "v1",
return_summary: Optional[bool] = True,
return_spatial_features: Optional[bool] = True,
**kwargs,
):
self.args = args
self.version = version
self.return_summary = return_summary
self.return_spatial_features = return_spatial_features
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()
self.radio_model = RADIOModelBase(
model,
input_conditioner,
config.return_summary,
config.return_spatial_features,
)
@property
def model(self) -> VisionTransformer:
return self.radio_model.model
def forward(self, x: torch.Tensor):
return self.radio_model.forward(x)
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