Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| from huggingface_hub import hf_hub_url, cached_download | |
| from PIL import Image | |
| import os | |
| import json | |
| import glob | |
| import random | |
| from typing import Any, Dict, List | |
| import torch | |
| import torchvision | |
| import wordsegment as ws | |
| from virtex.config import Config | |
| from virtex.factories import TokenizerFactory, PretrainingModelFactory, ImageTransformsFactory | |
| from virtex.utils.checkpointing import CheckpointManager | |
| CONFIG_PATH = "config.yaml" | |
| MODEL_PATH = "checkpoint_last5.pth" | |
| VALID_SUBREDDITS_PATH = "subreddit_list.json" | |
| SAMPLES_PATH = "./samples/*.jpg" | |
| class ImageLoader(): | |
| def __init__(self): | |
| self.image_transform = torchvision.transforms.Compose([ | |
| torchvision.transforms.ToTensor(), | |
| torchvision.transforms.Resize(256), | |
| torchvision.transforms.CenterCrop(224), | |
| torchvision.transforms.Normalize((.485, .456, .406), (.229, .224, .225))]) | |
| self.show_size=500 | |
| def load(self, im_path): | |
| im = torch.FloatTensor(self.image_transform(Image.open(im_path))).unsqueeze(0) | |
| return {"image": im} | |
| def raw_load(self, im_path): | |
| im = torch.FloatTensor(Image.open(im_path)) | |
| return {"image": im} | |
| def transform(self, image): | |
| im = torch.FloatTensor(self.image_transform(image)).unsqueeze(0) | |
| return {"image": im} | |
| def text_transform(self, text): | |
| # at present just lowercasing: | |
| return text.lower() | |
| def show_resize(self, image): | |
| # ugh we need to do this manually cuz this is pytorch==0.8 not 1.9 lol | |
| image = torchvision.transforms.functional.to_tensor(image) | |
| x,y = image.shape[-2:] | |
| ratio = float(self.show_size/max((x,y))) | |
| image = torchvision.transforms.functional.resize(image, [int(x * ratio), int(y * ratio)]) | |
| return torchvision.transforms.functional.to_pil_image(image) | |
| 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.valid_subs = json.load(open(VALID_SUBREDDITS_PATH)) | |
| def predict(self, image_dict, sub_prompt = None, prompt = ""): | |
| if sub_prompt is None: | |
| subreddit_tokens = torch.tensor([self.model.sos_index], device=self.device).long() | |
| else: | |
| subreddit_tokens = " ".join(ws.segment(ws.clean(sub_prompt))) | |
| subreddit_tokens = ( | |
| [self.model.sos_index] + | |
| self.tokenizer.encode(subreddit_tokens) + | |
| [self.tokenizer.token_to_id("[SEP]")] | |
| ) | |
| subreddit_tokens = torch.tensor(subreddit_tokens, device=self.device).long() | |
| if prompt is not "": | |
| # at present prompts without subreddits will break without this change | |
| # TODO FIX | |
| cap_tokens = self.tokenizer.encode(prompt) | |
| cap_tokens = torch.tensor(cap_tokens, device=self.device).long() | |
| subreddit_tokens = subreddit_tokens if sub_prompt is not None else torch.tensor( | |
| ( | |
| [self.model.sos_index] + | |
| self.tokenizer.encode("pics") + | |
| [self.tokenizer.token_to_id("[SEP]")] | |
| ), device = self.device).long() | |
| subreddit_tokens = torch.cat( | |
| [ | |
| subreddit_tokens, | |
| torch.tensor([self.tokenizer.token_to_id("[SEP]")], device=self.device).long(), | |
| cap_tokens | |
| ]) | |
| predictions: List[Dict[str, Any]] = [] | |
| is_valid_subreddit = False | |
| subreddit, rest_of_caption = "", "" | |
| image_dict["decode_prompt"] = subreddit_tokens | |
| while not is_valid_subreddit: | |
| with torch.no_grad(): | |
| caption = self.model(image_dict)["predictions"][0].tolist() | |
| if self.tokenizer.token_to_id("[SEP]") in caption: | |
| sep_index = caption.index(self.tokenizer.token_to_id("[SEP]")) | |
| caption[sep_index] = self.tokenizer.token_to_id("://") | |
| caption = self.tokenizer.decode(caption) | |
| if "://" in caption: | |
| subreddit, rest_of_caption = caption.split("://") | |
| subreddit = "".join(subreddit.split()) | |
| rest_of_caption = rest_of_caption.strip() | |
| else: | |
| subreddit, rest_of_caption = "", caption | |
| is_valid_subreddit = subreddit in self.valid_subs | |
| return subreddit, rest_of_caption | |
| def download_files(): | |
| #download model files | |
| download_files = [CONFIG_PATH, MODEL_PATH, VALID_SUBREDDITS_PATH] | |
| for f in download_files: | |
| fp = cached_download(hf_hub_url("zamborg/redcaps", filename=f)) | |
| os.system(f"cp {fp} ./{f}") | |
| def get_samples(): | |
| return glob.glob(SAMPLES_PATH) | |
| def get_rand_idx(samples): | |
| return random.randint(0,len(samples)-1) | |
| def create_objects(): | |
| sample_images = get_samples() | |
| virtexModel = VirTexModel() | |
| imageLoader = ImageLoader() | |
| valid_subs = json.load(open(VALID_SUBREDDITS_PATH)) | |
| valid_subs.insert(0, None) | |
| return virtexModel, imageLoader, sample_images, valid_subs | |