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import json |
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import os, shutil |
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import random |
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from PIL import Image |
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import jax |
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from transformers import FlaxVisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer |
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from huggingface_hub import hf_hub_download |
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model_dir = './models/' |
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os.makedirs(model_dir, exist_ok=True) |
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files_to_download = [ |
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"config.json", |
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"flax_model.msgpack", |
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"merges.txt", |
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"special_tokens_map.json", |
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"tokenizer.json", |
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"tokenizer_config.json", |
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"vocab.json", |
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"preprocessor_config.json", |
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] |
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for fn in files_to_download: |
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file_path = hf_hub_download("ydshieh/vit-gpt2-coco-en-ckpts", f"ckpt_epoch_3_step_6900/{fn}") |
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shutil.copyfile(file_path, os.path.join(model_dir, fn)) |
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model = FlaxVisionEncoderDecoderModel.from_pretrained(model_dir) |
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feature_extractor = ViTFeatureExtractor.from_pretrained(model_dir) |
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tokenizer = AutoTokenizer.from_pretrained(model_dir) |
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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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@jax.jit |
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def generate(pixel_values): |
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output_ids = model.generate(pixel_values, **gen_kwargs).sequences |
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return output_ids |
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def predict(image): |
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if image.mode != "RGB": |
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image = image.convert(mode="RGB") |
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pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values |
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output_ids = generate(pixel_values) |
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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[0] |
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def _compile(): |
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image_path = 'samples/val_000000039769.jpg' |
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image = Image.open(image_path) |
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predict(image) |
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image.close() |
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_compile() |
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sample_dir = './samples/' |
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sample_image_ids = tuple(["None"] + [int(f.replace('COCO_val2017_', '').replace('.jpg', '')) for f in os.listdir(sample_dir) if f.startswith('COCO_val2017_')]) |
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with open(os.path.join(sample_dir, "coco-val2017-img-ids.json"), "r", encoding="UTF-8") as fp: |
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coco_2017_val_image_ids = json.load(fp) |
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def get_random_image_id(): |
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image_id = random.sample(coco_2017_val_image_ids, k=1)[0] |
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return image_id |
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