使用 keras 和变压器进行图像分类时应用增量时出现错误“img”

Question

我想应用 VIT 进行图像分类。 但我有一个问题，我不知道如何解决它。 我的错误是这个“KeyError：'img'”。 当我应用最后一个命令时显示错误，我不知道我的错误在哪里。 数据集中的图像是 in.png，但我不认为这是错误的。 下面是脚本：

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_addons as tfa
import os
import cv2
import matplotlib.pyplot as plt
from transformers import ViTFeatureExtractor, ViTForImageClassification
from transformers import TrainingArguments, Trainer
from tensorflow import keras 
from tensorflow.keras import layers
from datasets import load_metric
from PIL import Image as img
from IPython.display import Image, display
from datasets import load_dataset 
import torch
dataset = load_dataset("imagefolder", data_dir="Datasets")

dataset
example = dataset["train"][10]
example
dataset["train"].features
example['image']
example['image'].resize((200, 200))
example['label']
dataset["train"].features["label"]


img_class_labels = dataset["train"].features["label"].names

from transformers import ViTFeatureExtractor
from tensorflow import keras
from tensorflow.keras import layers


model_id = "google/vit-base-patch16-224-in21k"
feature_extractor = ViTFeatureExtractor.from_pretrained(model_id)

# learn more about data augmentation here: https://www.tensorflow.org/tutorials/images/data_augmentation
data_augmentation = keras.Sequential(
    [
        layers.Resizing(feature_extractor.size, feature_extractor.size),
        layers.Rescaling(1./255),
        layers.RandomFlip("horizontal"),
        layers.RandomRotation(factor=0.02),
        layers.RandomZoom(
            height_factor=0.2, width_factor=0.2
        ),
    ],
    name="data_augmentation",
)
# use keras image data augementation processing
def augmentation(examples):
    # print(examples["img"])
    examples["pixel_values"] = [data_augmentation(image) for image in examples["img"]]
    return examples


# basic processing (only resizing)
def process(examples):
    examples.update(feature_extractor(examples['img'], ))
    return examples

# we are also renaming our label col to labels to use `.to_tf_dataset` later
dataset_ds = dataset["train"].rename_column("label", "labels")

processed_dataset = dataset_ds.map(augmentation, batched=True)
processed_dataset

Answer 1

我想错误在这里：

def augmentation(examples):
    # print(examples["img"])
    examples["pixel_values"] = [data_augmentation(image) for image in examples["img"]]
    return examples

您正在尝试使用“img”键访问“examples”字典。 从上面的一些代码看来，关键应该是“图像”：

    examples["pixel_values"] = [data_augmentation(image) for image in examples["image"]]