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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-ckpts", 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}
@jax.jit
def generate(pixel_values):
output_ids = model.generate(pixel_values, **gen_kwargs).sequences
return output_ids
def predict(image):
if image.mode != "RGB":
image = image.convert(mode="RGB")
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)
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 |