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| import streamlit as st | |
| from huggingface_hub import snapshot_download | |
| from PIL import Image | |
| import argparse | |
| import json | |
| import os | |
| from typing import Any, Dict, List | |
| from loguru import logger | |
| import torch | |
| import torchvision | |
| from torch.utils.data import DataLoader | |
| from tqdm import tqdm | |
| import wordsegment as ws | |
| from virtex.config import Config | |
| from virtex.data import ImageDirectoryDataset | |
| from virtex.factories import TokenizerFactory, PretrainingModelFactory | |
| from virtex.utils.checkpointing import CheckpointManager | |
| from virtex.utils.common import common_parser | |
| CONFIG_PATH = "config.yaml" | |
| MODEL_PATH = "checkpoint_last5.pth" | |
| # x = st.slider("Select a value") | |
| # st.write(x, "squared is", x * x) | |
| class ImageLoader(): | |
| def __init__(self): | |
| self.transformer = torchvision.transforms.Compose([torchvision.transforms.Resize(256), | |
| torchvision.transforms.CenterCrop(224), | |
| torchvision.transforms.ToTensor()]) | |
| def load(self, im_path, prompt): | |
| im = torch.FloatTensor(self.transformer(Image.open(im_path))).unsqueeze(0) | |
| return {"image": im, "decode_prompt": prompt} | |
| class VirTexModel(): | |
| def __init__(self): | |
| self.config = Config(CONFIG_PATH) | |
| ws.load() | |
| self.device = 'cpu' | |
| self.tokenizer = TokenizerFactory.from_config(self.config) | |
| self.model = PretrainingModelFactory.from_config(self.config).to(self.device) | |
| CheckpointManager(model=self.model).load("./checkpoint_last5.pth") | |
| self.model.eval() | |
| self.loader = ImageLoader() | |
| def predict(self, im_path): | |
| subreddit_tokens = torch.tensor([self.model.sos_index], device=self.device).long() | |
| predictions: List[Dict[str, Any]] = [] | |
| image = self.loader.load(im_path, subreddit_tokens) # should be of shape 1, 3, 224, 224 | |
| output_dict = self.model(image) | |
| caption = output_dict["predictions"][0] #only one prediction | |
| caption = caption.tolist() | |
| if self.tokenizer.token_to_id("[SEP]") in caption: # this is just the 0 index actually | |
| sos_index = caption.index(self.tokenizer.token_to_id("[SEP]")) | |
| caption[sos_index] = self.tokenizer.token_to_id("::") | |
| caption = self.tokenizer.decode(caption) | |
| # Separate out subreddit from the rest of caption. | |
| if "⁇" in caption: # "⁇" is the token decode equivalent of "::" | |
| subreddit, rest_of_caption = caption.split("⁇") | |
| subreddit = "".join(subreddit.split()) | |
| rest_of_caption = rest_of_caption.strip() | |
| else: | |
| subreddit, rest_of_caption = "", caption | |
| return subreddit, rest_of_caption | |
| def load_models(): | |
| #download model files | |
| download_files = [CONFIG_PATH, MODEL_PATH] | |
| for f in download_files: | |
| fp = cached_download(hf_hub_url("zamborg/redcaps", filename=f)) | |
| os.system(f"cp {fp} ./{f}") | |
| # load a virtex model | |
| from huggingface_hub import hf_hub_url, cached_download | |
| # #download model files | |
| download_files = [CONFIG_PATH, MODEL_PATH] | |
| for f in download_files: | |
| fp = cached_download(hf_hub_url("zamborg/redcaps", filename=f)) | |
| os.system(f"cp {fp} ./{f}") | |
| #inference on test.jpg | |
| virtexModel = VirTexModel() | |
| subreddit, caption = virtexModel.predict("./test.jpg") | |
| print(subreddit) | |
| print(caption) | |