# Copyright 2024 The HuggingFace Team. 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. import os from collections import OrderedDict from typing import Dict, List, Optional, Union import numpy as np import timm import torch from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE from packaging import version from timm.layers.config import set_fused_attn from timm.models._hub import load_model_config_from_hf from transformers import PretrainedConfig, PreTrainedModel from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from transformers.image_transforms import resize, to_channel_dimension_format from transformers.image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageFeatureExtractionMixin, ImageInput, PILImageResampling, make_list_of_images, to_numpy_array, valid_images, ) from transformers.modeling_outputs import ImageClassifierOutput from transformers.utils import TensorType from optimum.exporters.onnx.config import VisionOnnxConfig from optimum.utils import NormalizedVisionConfig from .utils import _is_timm_ov_dir set_fused_attn(False, False) class TimmConfig(PretrainedConfig): model_type = "timm" @classmethod def from_pretrained( cls, pretrained_model_name_or_path: Union[str, os.PathLike], cache_dir: str = HUGGINGFACE_HUB_CACHE, force_download: bool = False, local_files_only: bool = False, token: Optional[Union[str, bool]] = None, revision: str = "main", **kwargs, ) -> "PretrainedConfig": if _is_timm_ov_dir(pretrained_model_name_or_path): config_path = os.path.join(pretrained_model_name_or_path, "config.json") return cls.from_json_file(config_path) kwargs["cache_dir"] = cache_dir kwargs["force_download"] = force_download kwargs["local_files_only"] = local_files_only kwargs["revision"] = revision config_dict = load_model_config_from_hf(pretrained_model_name_or_path)[0] config_dict["num_labels"] = config_dict.pop("num_classes") config_dict["image_size"] = config_dict.get("input_size")[-1] return cls.from_dict(config_dict, **kwargs) class TimmOnnxConfig(VisionOnnxConfig): DEFAULT_TIMM_ONNX_OPSET = 13 outputs = OrderedDict([("logits", {0: "batch_size"})]) NORMALIZED_CONFIG_CLASS = NormalizedVisionConfig MIN_TORCH_VERSION = version.parse("1.11") @property def inputs(self) -> Dict[str, Dict[int, str]]: return {"pixel_values": {0: "batch_size", 1: "num_channels", 2: "height", 3: "width"}} class TimmForImageClassification(PreTrainedModel): def __init__(self, config: TimmConfig, num_labels: int = None, **kwargs) -> None: super().__init__(config, **kwargs) if num_labels: config.num_labels = num_labels self.model = timm.create_model( "hf-hub:" + self.config.hf_hub_id, num_classes=self.config.num_labels, pretrained=True, in_chans=3, ) self.model.eval() @classmethod def from_pretrained(cls, model_name_or_path, **kwargs): config = TimmConfig.from_pretrained(model_name_or_path, **kwargs) return cls(config, **kwargs) def forward(self, pixel_values: Optional[torch.Tensor] = None): logits = self.model(pixel_values) return ImageClassifierOutput(logits=logits) # Adapted from ViTImageProcessor - https://github.com/huggingface/transformers/blob/main/src/transformers/models/vit/image_processing_vit.py class TimmImageProcessor(BaseImageProcessor, ImageFeatureExtractionMixin): r""" Constructs a ViT image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `(size["height"], size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`dict`, *optional*, defaults to `{"height": 224, "width": 224}`): Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Optional[Dict[str, int]] = None, resample: PILImageResampling = PILImageResampling.BILINEAR, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) size = size if size is not None else {"height": 224, "width": 224} size = get_size_dict(size) self.do_resize = do_resize self.do_rescale = do_rescale self.do_normalize = do_normalize self.size = size self.resample = resample self.rescale_factor = rescale_factor self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD @classmethod def from_pretrained( cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs, ): timm_config_dict = load_model_config_from_hf(pretrained_model_name_or_path)[0] _, im_h, im_w = timm_config_dict.get("input_size", [3, 224, 224]) image_preprocess_config_dict = { "crop_size": {"height": im_h, "width": im_w}, "do_center_crop": True if timm_config_dict.get("crop_mode") == "center" else False, "do_normalize": True, "do_reduce_labels": False, "do_rescale": True, "do_resize": True, "image_mean": timm_config_dict.get("mean", IMAGENET_STANDARD_MEAN), "image_processor_type": "TimmImageProcessor", "image_std": timm_config_dict.get("std", IMAGENET_STANDARD_STD), "resample": 3, "rescale_factor": 0.00392156862745098, "size": {"height": im_h, "width": im_w}, } return cls.from_dict(image_preprocess_config_dict, **kwargs) def resize( self, image: np.ndarray, size: Dict[str, int], resample: PILImageResampling = PILImageResampling.BILINEAR, data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """ Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. resample: `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. Returns: `np.ndarray`: The resized image. """ size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}") if image.ndim == 2: image = np.stack([image] * 3, axis=-1) return resize( image, size=(size["height"], size["width"]), resample=resample, data_format=data_format, **kwargs ) def preprocess( self, images: ImageInput, do_resize: Optional[bool] = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST, **kwargs, ): """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Dictionary in the format `{"height": h, "width": w}` specifying the size of the output image after resizing. resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`): `PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image values between [0 - 1]. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean to use if `do_normalize` is set to `True`. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use if `do_normalize` is set to `True`. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input image. """ do_resize = do_resize if do_resize is not None else self.do_resize do_rescale = do_rescale if do_rescale is not None else self.do_rescale do_normalize = do_normalize if do_normalize is not None else self.do_normalize resample = resample if resample is not None else self.resample rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std size = size if size is not None else self.size size_dict = get_size_dict(size) images = make_list_of_images(images) if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) if do_resize and size is None: raise ValueError("Size must be specified if do_resize is True.") if do_rescale and rescale_factor is None: raise ValueError("Rescale factor must be specified if do_rescale is True.") # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] if do_resize: images = [self.resize(image=image, size=size_dict, resample=resample) for image in images] if do_rescale: images = [self.rescale(image=image, scale=rescale_factor) for image in images] if do_normalize: images = [self.normalize(image=image, mean=image_mean, std=image_std) for image in images] images = [to_channel_dimension_format(image, data_format) for image in images] data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors)