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import os, sys, shutil |
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import numpy as np |
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from PIL import Image |
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import jax |
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from transformers import ViTFeatureExtractor |
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from transformers import GPT2Tokenizer |
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from huggingface_hub import hf_hub_download |
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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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from vit_gpt2.modeling_flax_vit_gpt2_lm import FlaxViTGPT2LMForConditionalGeneration |
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model_dir = './models/' |
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os.makedirs(model_dir, exist_ok=True) |
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filepath = hf_hub_download("flax-community/vit-gpt2", "checkpoints/ckpt_5/config.json") |
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shutil.copyfile(filepath, os.path.join(model_dir, 'config.json')) |
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filepath = hf_hub_download("flax-community/vit-gpt2", "checkpoints/ckpt_5/flax_model.msgpack") |
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shutil.copyfile(filepath, os.path.join(model_dir, 'flax_model.msgpack')) |
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flax_vit_gpt2_lm = FlaxViTGPT2LMForConditionalGeneration.from_pretrained(model_dir) |
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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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caption = caption.replace('<s>', '').replace('</s>', '').replace('<pad>', '') |
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caption.replace("à l'arrière-plan", '').replace("Une photo noire et blanche d'", '').replace("en arrière-plan", '') |
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while ' ' in caption: |
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caption = caption.replace(' ', ' ') |
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caption = caption.strip() |
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return caption |
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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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caption = predict(image) |
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image.close() |
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def predict_dummy(image): |
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return 'dummy caption!' |
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compile() |
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sample_dir = './samples/' |
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sample_fns = tuple([f"{int(f.replace('COCO_val2014_', '').replace('.jpg', ''))}.jpg" for f in os.listdir(sample_dir) if f.startswith('COCO_val2014_')]) |
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