RADIO / hf_model.py

Un-fix double conditioning. It wasn't a problem before. (#7)

42f66ec verified about 1 month ago

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5.65 kB

	# 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: 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_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 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)