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import json | |
import os, shutil | |
import random | |
from PIL import Image | |
import jax | |
from transformers import FlaxVisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer | |
from huggingface_hub import hf_hub_download | |
# create target model directory | |
model_dir = './models/' | |
os.makedirs(model_dir, exist_ok=True) | |
files_to_download = [ | |
"config.json", | |
"flax_model.msgpack", | |
"merges.txt", | |
"special_tokens_map.json", | |
"tokenizer.json", | |
"tokenizer_config.json", | |
"vocab.json", | |
"preprocessor_config.json", | |
] | |
# copy files from checkpoint hub: | |
for fn in files_to_download: | |
file_path = hf_hub_download("ydshieh/vit-gpt2-coco-en", f"ckpt_epoch_3_step_6900/{fn}") | |
shutil.copyfile(file_path, os.path.join(model_dir, fn)) | |
model = FlaxVisionEncoderDecoderModel.from_pretrained(model_dir) | |
feature_extractor = ViTFeatureExtractor.from_pretrained(model_dir) | |
tokenizer = AutoTokenizer.from_pretrained(model_dir) | |
max_length = 16 | |
num_beams = 4 | |
gen_kwargs = {"max_length": max_length, "num_beams": num_beams} | |
def generate(pixel_values): | |
output_ids = model.generate(pixel_values, **gen_kwargs).sequences | |
return output_ids | |
def predict(image): | |
pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values | |
output_ids = generate(pixel_values) | |
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) | |
preds = [pred.strip() for pred in preds] | |
return preds[0] | |
def _compile(): | |
image_path = 'samples/val_000000039769.jpg' | |
image = Image.open(image_path) | |
caption = predict(image) | |
image.close() | |
_compile() | |
sample_dir = './samples/' | |
sample_image_ids = tuple(["None"] + [int(f.replace('COCO_val2017_', '').replace('.jpg', '')) for f in os.listdir(sample_dir) if f.startswith('COCO_val2017_')]) | |
with open(os.path.join(sample_dir, "coco-val2017-img-ids.json"), "r", encoding="UTF-8") as fp: | |
coco_2017_val_image_ids = json.load(fp) | |
def get_random_image_id(): | |
image_id = random.sample(coco_2017_val_image_ids, k=1)[0] | |
return image_id | |