Create README.md

README.md

@@ -1,3 +1,62 @@
 ---
 license: mit
+language:
+- zh
+tags:
+- gpt2
+- vit
 ---
+
+
+
+# 推理代码
+# infer
+
+```python
+from transformers import (VisionEncoderDecoderModel, 
+                          AutoTokenizer,ViTImageProcessor)
+import torch
+from PIL import Image
+
+```
+
+
+```python
+vision_encoder_decoder_model_name_or_path = "yuanzhoulvpi/vit-gpt2-image-chinese-captioning"#"vit-gpt2-image-chinese-captioning/checkpoint-3200"
+
+processor = ViTImageProcessor.from_pretrained(vision_encoder_decoder_model_name_or_path)
+tokenizer = AutoTokenizer.from_pretrained(vision_encoder_decoder_model_name_or_path)
+model = VisionEncoderDecoderModel.from_pretrained(vision_encoder_decoder_model_name_or_path)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+```
+
+
+```python
+max_length = 16
+num_beams = 4
+gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
+
+
+def predict_step(image_paths):
+    images = []
+    for image_path in image_paths:
+        i_image = Image.open(image_path)
+        if i_image.mode != "RGB":
+            i_image = i_image.convert(mode="RGB")
+
+        images.append(i_image)
+
+    pixel_values = processor(images=images, return_tensors="pt").pixel_values
+    pixel_values = pixel_values.to(device)
+
+    output_ids = model.generate(pixel_values, **gen_kwargs)
+
+    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+    preds = [pred.strip() for pred in preds]
+    return preds
+
+
+predict_step(['bigdata/image_data/train-1000200.jpg'])
+
+```