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import sys, os, datasets, json |
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current_path = os.path.dirname(os.path.abspath(__file__)) |
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sys.path.append(current_path) |
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import jax |
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from vit_gpt2.modeling_flax_vit_gpt2_lm import FlaxViTGPT2LMForConditionalGeneration |
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from transformers import ViTFeatureExtractor |
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from PIL import Image |
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import requests |
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import numpy as np |
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from transformers import ViTFeatureExtractor, GPT2Tokenizer |
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ckpt_no = 5 |
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model_name_or_path = f'./outputs/ckpt_{ckpt_no}/' |
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flax_vit_gpt2_lm = FlaxViTGPT2LMForConditionalGeneration.from_pretrained(model_name_or_path) |
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vit_model_name = 'google/vit-base-patch16-224-in21k' |
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feature_extractor = ViTFeatureExtractor.from_pretrained(vit_model_name) |
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gpt2_model_name = 'asi/gpt-fr-cased-small' |
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tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name) |
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max_length = 32 |
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num_beams = 8 |
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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 predict_fn(pixel_values): |
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return flax_vit_gpt2_lm.generate(pixel_values, **gen_kwargs) |
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def predict(image): |
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encoder_inputs = feature_extractor(images=image, return_tensors="jax") |
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pixel_values = encoder_inputs.pixel_values |
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generation = predict_fn(pixel_values) |
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token_ids = np.array(generation.sequences)[0] |
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caption = tokenizer.decode(token_ids) |
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return caption, token_ids |
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if __name__ == '__main__': |
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from datetime import datetime |
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split = 'val' |
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image_id = 322141 |
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p = f'/home/33611/caption/{split}2014/COCO_{split}2014_{str(image_id).zfill(12)}.jpg' |
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image = Image.open(p) |
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caption, token_ids = predict(image) |
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image.close() |
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print(f'token_ids: {token_ids}') |
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print(f'caption: {caption}') |
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ds = datasets.load_dataset('./coco_dataset_script.py', data_dir='/home/33611/caption/') |
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ds = ds['validation'] |
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ds = ds.select(range(100)) |
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predictions = [] |
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for ex in ds: |
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p = ex['image_file'] |
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image = Image.open(p) |
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s = datetime.now() |
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caption, token_ids = predict(image) |
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caption = caption.replace('<s>', '').replace('</s>', '').replace('<pad>', '').strip() |
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image.close() |
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e = datetime.now() |
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e = (e - s).total_seconds() |
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print(f' timing: {e}') |
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print(f' caption: {ex["fr"]}') |
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print(f'prediction: {caption}') |
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print('-' * 20) |
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ex['pred'] = caption |
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predictions.append(ex) |
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with open(f'ckpt_{ckpt_no}_preds.json', 'w', encoding='UTF-8') as fp: |
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json.dump(predictions, fp, ensure_ascii=False, indent=4) |
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