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# Copyright (c) 2023-2024, 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 typing import Optional, Callable, Union, Tuple, Any, Dict, NamedTuple
import torch
from torch import nn
from timm.models import create_model, VisionTransformer
from .enable_cpe_support import enable_cpe
from .input_conditioner import InputConditioner
# Register extra models
from . import extra_timm_models
from .adaptor_base import AdaptorBase, RadioOutput, AdaptorInput
from . import eradio_model
class Resolution(NamedTuple):
height: int
width: int
class RADIOModel(nn.Module):
def __init__(
self,
model: nn.Module,
input_conditioner: InputConditioner,
patch_size: int,
max_resolution: int,
preferred_resolution: Resolution,
summary_idxs: Optional[torch.Tensor] = None,
window_size: int = None,
adaptors: Dict[str, AdaptorBase] = None,
):
super().__init__()
self.model = model
self.input_conditioner = input_conditioner
if summary_idxs is not None:
self.register_buffer('summary_idxs', summary_idxs)
else:
self.summary_idxs = None
self._preferred_resolution = preferred_resolution
self._patch_size = patch_size
self._max_resolution = max_resolution
self._window_size = window_size
adaptors = adaptors or dict()
self.adaptors = nn.ModuleDict(adaptors)
@property
def num_summary_tokens(self) -> int:
patch_gen = getattr(self.model, "patch_generator", None)
if patch_gen is not None:
return patch_gen.num_skip
elif self.model.global_pool == 'avg':
return 0
return 1
@property
def patch_size(self) -> int:
return self._patch_size
@property
def max_resolution(self) -> int:
return self._max_resolution
@property
def preferred_resolution(self) -> Resolution:
return self._preferred_resolution
@property
def window_size(self) -> int:
return self._window_size
@property
def min_resolution_step(self) -> int:
res = self.patch_size
if self.window_size is not None:
res *= self.window_size
return res
def make_preprocessor_external(self) -> Callable[[torch.Tensor], torch.Tensor]:
ret = self.input_conditioner
self.input_conditioner = nn.Identity()
return ret
def get_nearest_supported_resolution(self, height: int, width: int) -> Resolution:
height = int(round(height / self.min_resolution_step) * self.min_resolution_step)
width = int(round(width / self.min_resolution_step) * self.min_resolution_step)
height = max(height, self.min_resolution_step)
width = max(width, self.min_resolution_step)
return Resolution(height=height, width=width)
def switch_to_deploy(self):
fn = getattr(self.model, 'switch_to_deploy', None)
if fn is not None:
fn()
def forward(self, x: torch.Tensor) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
x = self.input_conditioner(x)
y = self.model.forward_features(x)
if isinstance(self.model, VisionTransformer):
patch_gen = getattr(self.model, "patch_generator", None)
if patch_gen is not None:
all_summary = y[:, : patch_gen.num_cls_tokens]
if self.summary_idxs is not None:
bb_summary = all_summary[:, self.summary_idxs]
else:
bb_summary = all_summary
all_feat = y[:, patch_gen.num_skip :]
elif self.model.global_pool == "avg":
all_summary = y[:, self.model.num_prefix_tokens :].mean(dim=1)
bb_summary = all_summary
all_feat = y
else:
all_summary = y[:, 0]
bb_summary = all_summary
all_feat = y[:, 1:]
elif isinstance(self.model, eradio_model.FasterViT):
_, f = y
all_feat = f.flatten(2).transpose(1, 2)
all_summary = all_feat.mean(dim=1)
bb_summary = all_summary
elif isinstance(y, (list, tuple)):
all_summary, all_feat = y
bb_summary = all_summary
else:
raise ValueError("Unsupported model type")
all_feat = all_feat.float()
ret = RadioOutput(bb_summary.flatten(1), all_feat).to(torch.float32)
if self.adaptors:
ret = dict(backbone=ret)
for name, adaptor in self.adaptors.items():
if all_summary.ndim == 3:
summary = all_summary[:, adaptor.head_idx]
else:
summary = all_summary
ada_input = AdaptorInput(images=x, summary=summary.float(), features=all_feat)
v = adaptor(ada_input).to(torch.float32)
ret[name] = v
return ret
def create_model_from_args(args) -> nn.Module:
in_chans = 3
if args.in_chans is not None:
in_chans = args.in_chans
elif args.input_size is not None:
in_chans = args.input_size[0]
# Skip weight initialization unless it's explicitly requested.
weight_init = args.model_kwargs.pop("weight_init", "skip")
model = create_model(
args.model,
pretrained=args.pretrained,
in_chans=in_chans,
num_classes=args.num_classes,
drop_rate=args.drop,
drop_path_rate=args.drop_path,
drop_block_rate=args.drop_block,
global_pool=args.gp,
bn_momentum=args.bn_momentum,
bn_eps=args.bn_eps,
scriptable=args.torchscript,
checkpoint_path=args.initial_checkpoint,
weight_init=weight_init,
**args.model_kwargs,
)
assert (
not args.cls_token_per_teacher or args.cpe_max_size is not None
), "CPE must be enabled for multiple CLS tokens!"
if args.cpe_max_size is not None:
enable_cpe(
model,
args.cpe_max_size,
num_cls_tokens=len(args.teachers) if args.cls_token_per_teacher else 1,
register_multiple=args.register_multiple,
)
return model
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