Update README.md

README.md

@@ -9,4 +9,90 @@ See the dataset in the huggingface format [here](https://huggingface.co/datasets
 
 Note that all images in these webpages are replaced by a placeholder image (rick.jpg)
 
-Please refer to our [project page](https://salt-nlp.github.io/Design2Code/) and paper for more information.
+Please refer to our [project page](https://salt-nlp.github.io/Design2Code/) and paper for more information.
+
+# Example Usage
+
+For example, you can generate predictions using [HuggingFaceM4/VLM_WebSight_finetuned](https://huggingface.co/HuggingFaceM4/VLM_WebSight_finetuned).
+
+```
+import torch
+from PIL import Image
+from transformers import AutoModelForCausalLM, AutoProcessor
+from transformers.image_utils import to_numpy_array, PILImageResampling, ChannelDimension
+from transformers.image_transforms import resize, to_channel_dimension_format
+from gpt4v_utils import cleanup_response
+from tqdm import tqdm 
+import os
+
+DEVICE = torch.device("cuda")
+HF_TOKEN = "..." # Your HF_TOKEN
+
+PROCESSOR = AutoProcessor.from_pretrained(
+    "HuggingFaceM4/VLM_WebSight_finetuned",
+    token=HF_TOKEN
+)
+MODEL = AutoModelForCausalLM.from_pretrained(
+    "HuggingFaceM4/VLM_WebSight_finetuned",
+    token=HF_TOKEN,
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+).to(DEVICE)
+
+print ("parameter count: ", MODEL.num_parameters())
+
+image_seq_len = MODEL.config.perceiver_config.resampler_n_latents
+BOS_TOKEN = PROCESSOR.tokenizer.bos_token
+BAD_WORDS_IDS = PROCESSOR.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
+
+def convert_to_rgb(image):
+    # `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
+    # for transparent images. The call to `alpha_composite` handles this case
+    if image.mode == "RGB":
+        return image
+
+    image_rgba = image.convert("RGBA")
+    background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
+    alpha_composite = Image.alpha_composite(background, image_rgba)
+    alpha_composite = alpha_composite.convert("RGB")
+    return alpha_composite
+
+# The processor is the same as the Idefics processor except for the BILINEAR interpolation,
+# so this is a hack in order to redefine ONLY the transform method
+def custom_transform(x):
+    x = convert_to_rgb(x)
+    x = to_numpy_array(x)
+    x = resize(x, (960, 960), resample=PILImageResampling.BILINEAR)
+    x = PROCESSOR.image_processor.rescale(x, scale=1 / 255)
+    x = PROCESSOR.image_processor.normalize(
+        x,
+        mean=PROCESSOR.image_processor.image_mean,
+        std=PROCESSOR.image_processor.image_std
+    )
+    x = to_channel_dimension_format(x, ChannelDimension.FIRST)
+    x = torch.tensor(x)
+    return x
+
+inputs = PROCESSOR.tokenizer(
+    f"{BOS_TOKEN}<fake_token_around_image>{'<image>' * image_seq_len}<fake_token_around_image>",
+    return_tensors="pt",
+    add_special_tokens=False,
+)
+
+
+test_data_dir = "/path/to/Design2Code"
+predictions_dir = "/path/to/Design2Code_predictions"
+
+for filename in tqdm(os.listdir(test_data_dir)):
+    if filename.endswith(".png"):
+        image_path = os.path.join(test_data_dir, filename)
+        with Image.open(image_path) as image:
+            inputs["pixel_values"] = PROCESSOR.image_processor([image], transform=custom_transform)
+        inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
+        generated_ids = MODEL.generate(**inputs, bad_words_ids=BAD_WORDS_IDS, max_length=4096)
+        generated_text = PROCESSOR.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        generated_text = cleanup_response(generated_text)
+
+        with open(os.path.join(predictions_dir, filename.replace(".png", ".html")), "w", encoding='utf-8') as f:
+            f.write(generated_text)
+```