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import urllib.request |
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import modal |
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stub = modal.Stub("vit-gpt2-image-captioning") |
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volume = modal.SharedVolume().persist("shared_vol") |
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CACHE_PATH = "/root/model_cache" |
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@stub.function( |
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gpu="any", |
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image=modal.Image.debian_slim().pip_install("Pillow", "transformers", "torch"), |
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shared_volumes={CACHE_PATH: volume}, |
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retries=3, |
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) |
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def predict_step(image): |
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import io |
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from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer |
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import torch |
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from PIL import Image |
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model = VisionEncoderDecoderModel.from_pretrained( |
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"nlpconnect/vit-gpt2-image-captioning" |
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) |
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feature_extractor = ViTImageProcessor.from_pretrained( |
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"nlpconnect/vit-gpt2-image-captioning" |
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) |
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tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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max_length = 16 |
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num_beams = 4 |
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gen_kwargs = {"max_length": max_length, "num_beams": num_beams} |
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input_img = Image.open(io.BytesIO(image)) |
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pixel_values = feature_extractor( |
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images=[input_img], return_tensors="pt" |
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).pixel_values |
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pixel_values = pixel_values.to(device) |
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output_ids = model.generate(pixel_values, **gen_kwargs) |
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preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) |
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preds = [pred.strip() for pred in preds] |
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return preds |
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@stub.local_entrypoint() |
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def main(): |
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from pathlib import Path |
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image_filepath = Path(__file__).parent / "sample.png" |
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if image_filepath.exists(): |
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with open(image_filepath, "rb") as f: |
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image = f.read() |
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else: |
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try: |
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image = urllib.request.urlopen( |
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"https://drive.google.com/uc?id=0B0TjveMhQDhgLTlpOENiOTZ6Y00&export=download" |
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).read() |
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except urllib.error.URLError as e: |
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print(e.reason) |
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print(predict_step.call(image)[0]) |
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