图像分类：对google/vit-large-patch32-384模型进行微调
背景：
图像分类是很常见的场景，比如日常的物体识别，可很多时候我们还需要根据自己的数据来训练自己的分类，大模型出现以后，我们不需要再重头来训练我们的模型，直接根据已经训练好的大模型进行微调即可，本文就针对google/vit-large-patch32-384模型进行微调。
数据准备：
数据准备参考huggingface如何加载本地数据集进行大模型训练-CSDN博客
代码：
import json
import os
from PIL import Image
from datasets import Dataset
from sklearn.metrics import accuracy_score,f1_score, recall_score
from transformers import AutoImageProcessor
from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor
from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
from transformers import DefaultDataCollator
path = '/data/dataset/image'
def gen(path):
image_json = os.path.join(path, "image.json")
with open(image_json, 'r') as f:
# 读取JSON数据
data = json.load(f)
for key, value in data.items():
imagePath = os.path.join(path, "image")
imagePath = os.path.join(imagePath, key)
image = Image.open(imagePath)
yield {'image': image, 'label': value}
def get_label(path):
label_json = os.path.join(path, "label.json")
with open(label_json, 'r') as f:
# 读取JSON数据
data = json.load(f)
label2id, id2label = dict(), dict()
for key, value in data.items():
label2id[key] = str(value)
id2label[str(value)] = key
return label2id, id2label
ds = Dataset.from_generator(gen, gen_kwargs={"path": path})
ds = ds.train_test_split(test_size=0.2)
label2id, id2label = get_label(path)
checkpoint = "/data/model/vit-large-patch32-384"
image_processor = AutoImageProcessor.from_pretrained(checkpoint)
normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
size = (
image_processor.size["shortest_edge"]
if "shortest_edge" in image_processor.size
else (image_processor.size["height"], image_processor.size["width"])
)
_transforms = Compose([RandomResizedCrop(size), ToTensor(), normalize])
def transforms(examples):
examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]]
del examples["image"]
return examples
ds = ds.with_transform(transforms)
def compute_metrics(pred):
labels = pred.label_ids
preds = pred.predictions.argmax(-1)
f1 = f1_score(labels,preds,average="weighted")
acc = accuracy_score(labels,preds)
recall = recall_score(labels,preds,average="weighted")
return {"accuracy":acc,"f1":f1, "recall": recall}
model = AutoModelForImageClassification.from_pretrained(
checkpoint,
num_labels=5,
id2label=id2label,
label2id=label2id,
ignore_mismatched_sizes=True)
training_args = TrainingArguments(
output_dir="my_awesome_food_model",
remove_unused_columns=False,
evaluation_strategy="epoch",
save_strategy="epoch",
learning_rate=5e-5,
per_device_train_batch_size=16,
gradient_accumulation_steps=4,
per_device_eval_batch_size=16,
num_train_epochs=3,
warmup_ratio=0.1,
logging_steps=10,
greater_is_better=True,
load_best_model_at_end=True,
metric_for_best_model="accuracy"
)
data_collator = DefaultDataCollator()
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=ds["train"],
eval_dataset=ds["test"],
tokenizer=image_processor,
compute_metrics=compute_metrics,
)
trainer.train()训练结果：
{
"best_metric": 1.0,
"best_model_checkpoint": "my_awesome_food_model/checkpoint-2",
"epoch": 3.0,
"eval_steps": 500,
"global_step": 3,
"is_hyper_param_search": false,
"is_local_process_zero": true,
"is_world_process_zero": true,
"log_history": [
{
"epoch": 1.0,
"eval_accuracy": 0.0,
"eval_f1": 0.0,
"eval_loss": 1.8605551719665527,
"eval_recall": 0.0,
"eval_runtime": 0.1864,
"eval_samples_per_second": 10.727,
"eval_steps_per_second": 5.363,
"step": 1
},
{
"epoch": 2.0,
"eval_accuracy": 1.0,
"eval_f1": 1.0,
"eval_loss": 1.2016913890838623,
"eval_recall": 1.0,
"eval_runtime": 0.175,
"eval_samples_per_second": 11.43,
"eval_steps_per_second": 5.715,
"step": 2
},
{
"epoch": 3.0,
"eval_accuracy": 1.0,
"eval_f1": 1.0,
"eval_loss": 0.8268076181411743,
"eval_recall": 1.0,
"eval_runtime": 0.1774,
"eval_samples_per_second": 11.271,
"eval_steps_per_second": 5.635,
"step": 3
}
],
"logging_steps": 10,
"max_steps": 3,
"num_train_epochs": 3,
"save_steps": 500,
"total_flos": 1.946783884640256e+16,
"trial_name": null,
"trial_params": null
}注意事项：
第一次进行训练的时候，控制台报了如下的异常：
(.env) (base) [ipa_sudo@comm-agi image]$ python vit.py
Traceback (most recent call last):
File "/data/image/vit.py", line 72, in <module>
model = AutoModelForImageClassification.from_pretrained(checkpoint, num_labels=5, id2label=id2label, label2id=label2id)
File "/data/.env/lib/python3.9/site-packages/transformers/models/auto/auto_factory.py", line 516, in from_pretrained
return model_class.from_pretrained(
File "/data/.env/lib/python3.9/site-packages/transformers/modeling_utils.py", line 3091, in from_pretrained
) = cls._load_pretrained_model(
File "/data/.env/lib/python3.9/site-packages/transformers/modeling_utils.py", line 3532, in _load_pretrained_model
raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
RuntimeError: Error(s) in loading state_dict for ViTForImageClassification:
size mismatch for classifier.weight: copying a param with shape torch.Size([1000, 1024]) from checkpoint, the shape in current model is torch.Size([5, 1024]).
size mismatch for classifier.bias: copying a param with shape torch.Size([1000]) from checkpoint, the shape in current model is torch.Size([5]).
You may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method.从这个提示可以看出来，原来的模型支持的是1000个分类，而我当前传入的是5个分类，所以向量的维度不一致。
怎么解决这个问题呢，其实它已经给了解决方案，那就是在方法AutoModelForImageClassification.from_pretrained()里增加一个ignore_mismatched_sizes=True参数即可。
model = AutoModelForImageClassification.from_pretrained(
checkpoint,
num_labels=5,
id2label=id2label,
label2id=label2id,
ignore_mismatched_sizes=True)总结：
1、比较繁琐的是图片数据的准备，既需要下载图片，还需要标注图片
2、图片数据需要进行变换，这样能增加模型的鲁棒性
3、训练参数中需要增加remove_unused_columns=False，否则Trainer会删除image这样，然后后面就没法计算pixel_values字段了。