Upload processor

image_processing_resnet.py

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+import os
+
+import albumentations as A
+from albumentations.pytorch.transforms import ToTensorV2
+import PIL.Image
+import numpy as np
+from functools import partial
+from typing import Dict, List, Optional, Union
+from datasets import load_dataset, DatasetDict, Image  # pip install datasets
+from torch.utils.data import DataLoader
+import torch
+
+# 自定义 ImageProcessor 为了与 pipeline使用
+from transformers import ViTImageProcessor
+from transformers.image_utils import PILImageResampling, ChannelDimension
+from transformers.image_processing_utils import get_size_dict
+
+class ResnetImageProcessor(ViTImageProcessor):
+    """
+    >>> # tfs = A.Compose([A.Resize(256, 256), A.CenterCrop(224, 224)])
+    >>> # 如果传入 参数 tfs=tfs 在调用save_pretrained会报错
+    >>> # 本地使用
+    >>> mean = [0.485, 0.456, 0.406];std = [0.229, 0.224, 0.225]
+    >>> image_processor = ResnetImageProcessor(size=(224, 224), image_mean=mean, image_std=std)
+    >>> image_processor.save_pretrained("custom-resnet")
+    >>> image_processor = ResnetImageProcessor.from_pretrained("custom-resnet")
+
+    >>> # push_to_hub
+    >>> # hub登录
+    >>> from huggingface_hub import notebook_login;notebook_login()
+    >>> # or huggingface-cli login
+
+    >>> ResnetImageProcessor.register_for_auto_class()
+    >>> mean = [0.485, 0.456, 0.406];std = [0.229, 0.224, 0.225]
+    >>> image_processor = ResnetImageProcessor(size=(224, 224), image_mean=mean, image_std=std)
+    >>> image_processor.save_pretrained("custom-resnet")
+    >>> # image_processor = ResnetImageProcessor.from_pretrained("custom-resnet")
+    >>> # 如果要执行 push_to_hub 需要将 custom-resnet/preprocessor_config.json 中的 "image_processor_type" 改成 "ViTImageProcessor"
+    >>> # 默认的 ResnetImageProcessor 没有注册到 AutoImageProcessor
+    >>> # 否则从 使用 AutoImageProcessor 加载 会报错了
+    >>> image_processor.push_to_hub('custom-resnet')
+
+    >>> # 从 huggingface hub 加载
+    >>> from transformers import AutoImageProcessor
+    >>> AutoImageProcessor.register(config_class='wucng/custom-resnet/config.json',image_processor_class=ResnetImageProcessor)
+    >>> image_processor = AutoImageProcessor.from_pretrained('wucng/custom-resnet', trust_remote_code=True)
+    """
+
+    def resize(
+            self,
+            image: np.ndarray,
+            size: Dict[str, int],
+            resample: PILImageResampling = PILImageResampling.BILINEAR,
+            data_format: Optional[Union[str, ChannelDimension]] = None,
+            input_data_format: Optional[Union[str, ChannelDimension]] = None,
+            **kwargs,
+    ) -> np.ndarray:
+        size = get_size_dict(size)
+        output_size = (size["height"], size["width"])
+        height, width = size["height"], size["width"]
+
+        tfs = kwargs.get('tfs', None)
+        if tfs is None:
+            ratio = 256 / 224
+            tfs = A.Compose([A.Resize(int(ratio * height), int(ratio * width)), A.CenterCrop(height, width)])
+        return tfs(image=image)['image']

preprocessor_config.json

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+{
+  "auto_map": {
+    "AutoImageProcessor": "image_processing_resnet.ResnetImageProcessor"
+  },
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "ResnetImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}