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我从 Huggingface 训练了一个 LayoutLMv2 模型，当我尝试在单个图像上推断它时，它会给出运行时错误。其代码如下：

query = '/Users/vaihabsaxena/Desktop/Newfolder/labeled/Others/Two.pdf26.png'
image = Image.open(query).convert("RGB")
encoded_inputs = processor(image, return_tensors="pt").to(device)
outputs = model(**encoded_inputs)
preds = torch.softmax(outputs.logits, dim=1).tolist()[0]
pred_labels = {label:pred for label, pred in zip(label2idx.keys(), preds)}
pred_labels

当我这样做时就会出现错误model(**encoded_inputs)。该processor目录在 Huggingface 中被称为目录，并与其他 API 一起按如下方式初始化：

feature_extractor = LayoutLMv2FeatureExtractor()
tokenizer = LayoutLMv2Tokenizer.from_pretrained("microsoft/layoutlmv2-base-uncased")
processor = LayoutLMv2Processor(feature_extractor, tokenizer)

该模型的定义和训练如下：

model = LayoutLMv2ForSequenceClassification.from_pretrained(
    "microsoft/layoutlmv2-base-uncased",  num_labels=len(label2idx)
)
model.to(device);


optimizer = AdamW(model.parameters(), lr=5e-5)
num_epochs = 3


for epoch in range(num_epochs):
    print("Epoch:", epoch)
    training_loss = 0.0
    training_correct = 0
    #put the …