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| # %% | |
| import gradio.components as gc | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import torch | |
| from PIL import Image | |
| from transformers import CLIPModel, CLIPProcessor | |
| device = 'cpu' | |
| torch.no_grad().__enter__() | |
| torch.autocast('cuda').__enter__() | |
| # %% | |
| t = pd.read_pickle("clip_texts_1_fp16.pkl") | |
| words = t.reset_index().word | |
| wordsv = torch.tensor(t.values).to(device) | |
| # %% | |
| # %% | |
| model_name = "openai/clip-vit-large-patch14" | |
| mmm = CLIPModel.from_pretrained(model_name) | |
| mmm.eval() | |
| mmm.to(device) | |
| processor = CLIPProcessor.from_pretrained(model_name) | |
| # %% | |
| def slerp(t, v0, v1, DOT_THRESHOLD=0.9995): | |
| """ helper function to spherically interpolate two arrays v1 v2 """ | |
| inputs_are_torch = False | |
| if not isinstance(v0, np.ndarray): | |
| inputs_are_torch = True | |
| input_device = v0.device | |
| v0 = v0.cpu().numpy() | |
| v1 = v1.cpu().numpy() | |
| dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1))) | |
| if np.abs(dot) > DOT_THRESHOLD: | |
| v2 = (1 - t) * v0 + t * v1 | |
| else: | |
| theta_0 = np.arccos(dot) | |
| sin_theta_0 = np.sin(theta_0) | |
| theta_t = theta_0 * t | |
| sin_theta_t = np.sin(theta_t) | |
| s0 = np.sin(theta_0 - theta_t) / sin_theta_0 | |
| s1 = sin_theta_t / sin_theta_0 | |
| v2 = s0 * v0 + s1 * v1 | |
| if inputs_are_torch: | |
| v2 = torch.from_numpy(v2).to(input_device) | |
| return v2 | |
| def query(text: str, img: Image.Image, limit: int, score_threshold: float, slerp_degree: float): | |
| if text != '': | |
| inp = processor(text=text, return_tensors='pt').to(device) | |
| rout = mmm.get_text_features(**inp) | |
| tout = rout.detach().cpu().numpy()[0] | |
| out = tout | |
| if img is not None: | |
| inp = processor(images=[img], return_tensors="pt",).to(device) | |
| rout = mmm.get_image_features(**inp) | |
| iout = rout.detach().cpu().numpy()[0] | |
| out = iout | |
| if text != '' and img is not None: | |
| out = slerp(slerp_degree, tout, iout) | |
| if out is not None: | |
| # calculate cosine similarity | |
| scores = np.dot(out, wordsv.T) | |
| # sort by score | |
| topk = ( | |
| pd.concat( | |
| [words, pd.Series(scores, name='score')], | |
| axis=1 | |
| ) | |
| .sort_values('score', ascending=False) | |
| .query(f'score > {score_threshold}') | |
| .head(limit) | |
| ) | |
| topwords = "\n".join( | |
| f'{word}: {score:.2f} ' | |
| for _, word, score in topk.itertuples() | |
| ) | |
| return topwords | |
| searchtext = gc.Textbox(lines=2, placeholder="Search text") | |
| searchimage = gc.Image(shape=(224, 224), label="Search image", type='pil') | |
| inp_limit = gc.Slider(1, 50, 10, step=1, label='Limit') | |
| score_threshold = gc.Slider(0, 30, 0, step=.5, label='Score threshold') | |
| slerp_degree = gc.Slider( | |
| 0, 1, 0.5, step=.01, label='Slerp degree (if both text and image are provided)\nFinds a midpoint between image and text embeddings') | |
| dsurl = 'https://www.kaggle.com/datasets/yk1598/479k-english-words' | |
| gr.Interface( | |
| query, | |
| [searchtext, searchimage, inp_limit, score_threshold, slerp_degree], | |
| [gc.Textbox(label='Top words')], | |
| title="Initial Token Finder for Textual Inversion", | |
| description=f"find the closest single token word for a given text and/or image.\nbased on {model_name}.\n\nData: {dsurl}", | |
| analytics_enabled=False, | |
| allow_flagging='never', | |
| ).launch() | |