import os, sys, shutil import numpy as np from PIL import Image import jax from transformers import ViTFeatureExtractor from transformers import GPT2Tokenizer from huggingface_hub import hf_hub_download from googletrans import Translator translator = Translator() current_path = os.path.dirname(os.path.abspath(__file__)) sys.path.append(current_path) # Main model - ViTGPT2LM from vit_gpt2.modeling_flax_vit_gpt2_lm import FlaxViTGPT2LMForConditionalGeneration # create target model directory model_dir = './models/' os.makedirs(model_dir, exist_ok=True) # copy config file filepath = hf_hub_download("flax-community/vit-gpt2", "checkpoints/ckpt_5/config.json") shutil.copyfile(filepath, os.path.join(model_dir, 'config.json')) # copy model file filepath = hf_hub_download("flax-community/vit-gpt2", "checkpoints/ckpt_5/flax_model.msgpack") shutil.copyfile(filepath, os.path.join(model_dir, 'flax_model.msgpack')) flax_vit_gpt2_lm = FlaxViTGPT2LMForConditionalGeneration.from_pretrained(model_dir) vit_model_name = 'google/vit-base-patch16-224-in21k' feature_extractor = ViTFeatureExtractor.from_pretrained(vit_model_name) gpt2_model_name = 'asi/gpt-fr-cased-small' tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name) max_length = 32 num_beams = 8 gen_kwargs = {"max_length": max_length, "num_beams": num_beams} @jax.jit def predict_fn(pixel_values): return flax_vit_gpt2_lm.generate(pixel_values, **gen_kwargs) def predict(image): # batch dim is added automatically encoder_inputs = feature_extractor(images=image, return_tensors="jax") pixel_values = encoder_inputs.pixel_values # generation generation = predict_fn(pixel_values) token_ids = np.array(generation.sequences)[0] caption = tokenizer.decode(token_ids) caption = caption.replace('', '').replace('', '').replace('', '') caption = caption.replace("à l'arrière-plan", '').replace("Une photo en noir et blanc d'", '').replace("Une photo noire et blanche d'", '').replace("en arrière-plan", '').replace("Un gros plan d'", '').replace("un gros plan d'", '').replace("Une image d'", '') while ' ' in caption: caption = caption.replace(' ', ' ') caption = caption.strip() if caption: caption = caption[0].upper() + caption[1:] return caption def compile(): image_path = 'samples/val_000000039769.jpg' image = Image.open(image_path) caption = predict(image) image.close() def predict_dummy(image): return 'dummy caption!' compile() sample_dir = './samples/' sample_fns = tuple([f"{int(f.replace('COCO_val2014_', '').replace('.jpg', ''))}.jpg" for f in os.listdir(sample_dir) if f.startswith('COCO_val2014_')])