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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 .input_conditioner import get_default_conditioner, InputConditioner
resource_map = {
'radio_v1': 'https://huggingface.co./nvidia/RADIO/raw/main/radio_v1.pth.tar'
}
class RADIOConfig(PretrainedConfig):
"""Pretrained Hugging Face configuration for RADIO models."""
def __init__(
self,
args: Optional[dict] = None,
version: Optional[str]="v1",
**kwargs,
):
self.args = args
self.version = version
super().__init__(**kwargs)
class RADIOModel(PreTrainedModel):
"""Pretrained Hugging Face model for RADIO."""
def __init__(self, config):
super().__init__(config)
RADIOArgs = namedtuple("RADIOArgs", config.args.keys())
args = RADIOArgs(**config.args)
self.model = create_model_from_args(args)
self.input_conditioner: InputConditioner = get_default_conditioner()
#return RADIOModel(mod, conditioner, return_summary=return_summary, return_spatial_features=return_spatial_features)
def forward(self, x: torch.Tensor):
x = self.input_conditioner(x)
y = self.model.forward_features(x)
if isinstance(y, (list, tuple)):
summary, all_feat = y
elif isinstance(self.model, VisionTransformer):
patch_gen = getattr(self.model, 'patch_generator', None)
if patch_gen is not None:
summary = y[:, :patch_gen.num_cls_tokens].flatten(1)
all_feat = y[:, patch_gen.num_skip:]
elif self.model.global_pool == 'avg':
summary = y[:, self.model.num_prefix_tokens:].mean(dim=1)
all_feat = y
else:
summary = y[:, 0]
all_feat = y[:, 1:]
else:
raise ValueError("Unsupported model type")
if self.return_summary and self.return_spatial_features:
return summary, all_feat
elif self.return_summary:
return summary
return all_feat
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