# 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, Dict, 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 # Import all required modules. from .adaptor_base import AdaptorBase, RadioOutput, AdaptorInput from .adaptor_generic import GenericAdaptor, AdaptorBase from .adaptor_mlp import create_mlp_from_config from .adaptor_registry import adaptor_registry from .cls_token import ClsToken from .dinov2_arch import dinov2_vitg14_reg from .enable_cpe_support import enable_cpe from .enable_spectral_reparam import configure_spectral_reparam_from_args from .eradio_model import eradio from .feature_normalizer import FeatureNormalizer, IntermediateFeatureNormalizer from .forward_intermediates import forward_intermediates 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 * from .extra_models import * def rename_all_gamma_to_weight_with_proxy(module): """ Renames all parameters named 'gamma' in a module (including submodules) to 'weight' and sets up a property so that accesses to 'gamma' still work. """ # Recursively iterate through submodules for submodule_name, submodule in module.named_modules(): # Get all parameters within the current submodule for param_name, param in list(submodule.named_parameters(recurse=False)): if 'gamma' in param_name: # Generate the new name by replacing 'gamma' with 'weight' new_name = param_name.replace('gamma', 'weight') print("In submodule {}: Renaming '{}' to '{}'".format(submodule_name, param_name, new_name)) # Remove the old parameter and assign it with the new name delattr(submodule, param_name) setattr(submodule, new_name, nn.Parameter(param.data)) # Define a property to proxy access to the renamed parameter def make_property(old_name, new_name): return property(lambda self: getattr(self, new_name), lambda self, value: setattr(self, new_name, value)) # Add the property to the submodule to proxy access to 'gamma' setattr(submodule.__class__, param_name, make_property(param_name, new_name)) 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, adaptor_configs: Dict[str, Dict[str, int]] = None, vitdet_window_size: Optional[int] = None, feature_normalizer_config: Optional[dict] = None, inter_feature_normalizer_config: Optional[dict] = None, rename_gamma_to_weight: bool = False, **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.adaptor_configs = adaptor_configs self.vitdet_window_size = vitdet_window_size self.feature_normalizer_config = feature_normalizer_config self.inter_feature_normalizer_config = inter_feature_normalizer_config self.rename_gamma_to_weight = rename_gamma_to_weight 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: RADIOConfig): 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_configs = config.adaptor_configs adaptor_names = config.adaptor_names or [] adaptors = dict() for adaptor_name in adaptor_names: mlp_config = adaptor_configs[adaptor_name] adaptor = GenericAdaptor(args, None, None, mlp_config) adaptor.head_idx = mlp_config["head_idx"] adaptors[adaptor_name] = adaptor feature_normalizer = None if config.feature_normalizer_config is not None: # Actual normalization values will be restored when loading checkpoint weights. feature_normalizer = FeatureNormalizer(config.feature_normalizer_config["embed_dim"]) inter_feature_normalizer = None if config.inter_feature_normalizer_config is not None: inter_feature_normalizer = IntermediateFeatureNormalizer( config.inter_feature_normalizer_config["num_intermediates"], config.inter_feature_normalizer_config["embed_dim"], rot_per_layer=config.inter_feature_normalizer_config["rot_per_layer"], dtype=dtype) 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, feature_normalizer=feature_normalizer, inter_feature_normalizer=inter_feature_normalizer, ) if config.rename_gamma_to_weight: rename_all_gamma_to_weight_with_proxy(self.radio_model) @property def adaptors(self) -> nn.ModuleDict: return self.radio_model.adaptors @property def model(self) -> VisionTransformer: return self.radio_model.model @property def input_conditioner(self) -> InputConditioner: return self.radio_model.input_conditioner @property def num_summary_tokens(self) -> int: return self.radio_model.num_summary_tokens @property def patch_size(self) -> int: return self.radio_model.patch_size @property def max_resolution(self) -> int: return self.radio_model.max_resolution @property def preferred_resolution(self) -> Resolution: return self.radio_model.preferred_resolution @property def window_size(self) -> int: return self.radio_model.window_size @property def min_resolution_step(self) -> int: return self.radio_model.min_resolution_step def make_preprocessor_external(self) -> Callable[[torch.Tensor], torch.Tensor]: return self.radio_model.make_preprocessor_external() def get_nearest_supported_resolution(self, height: int, width: int) -> Resolution: return self.radio_model.get_nearest_supported_resolution(height, width) def switch_to_deploy(self): return self.radio_model.switch_to_deploy() def forward(self, x: torch.Tensor): return self.radio_model.forward(x)