Spaces:
Running
Running
| import os | |
| import natsort | |
| from tqdm import tqdm | |
| import torch | |
| from jax import numpy as jnp | |
| from PIL import Image as PilImage | |
| class CustomDataSet(torch.utils.data.Dataset): | |
| def __init__(self, main_dir, transform): | |
| self.main_dir = main_dir | |
| self.transform = transform | |
| all_imgs = os.listdir(main_dir) | |
| self.total_imgs = natsort.natsorted(all_imgs) | |
| def __len__(self): | |
| return len(self.total_imgs) | |
| def get_image_name(self, idx): | |
| return self.total_imgs[idx] | |
| def __getitem__(self, idx): | |
| img_loc = os.path.join(self.main_dir, self.total_imgs[idx]) | |
| image = PilImage.open(img_loc).convert("RGB") | |
| tensor_image = self.transform(image) | |
| return tensor_image | |
| def text_encoder(text, model, tokenizer): | |
| inputs = tokenizer( | |
| [text], | |
| max_length=96, | |
| truncation=True, | |
| padding="max_length", | |
| return_tensors="np", | |
| ) | |
| embedding = model.get_text_features(inputs["input_ids"], inputs["attention_mask"])[ | |
| 0 | |
| ] | |
| embedding /= jnp.linalg.norm(embedding) | |
| return jnp.expand_dims(embedding, axis=0) | |
| def precompute_image_features(model, loader): | |
| image_features = [] | |
| for i, (images) in enumerate(tqdm(loader)): | |
| images = images.permute(0, 2, 3, 1).numpy() | |
| features = model.get_image_features( | |
| images, | |
| ) | |
| features /= jnp.linalg.norm(features, axis=-1, keepdims=True) | |
| image_features.extend(features) | |
| return jnp.array(image_features) | |
| def find_image(text_query, model, dataset, tokenizer, image_features, n=1): | |
| zeroshot_weights = text_encoder(text_query, model, tokenizer) | |
| zeroshot_weights /= jnp.linalg.norm(zeroshot_weights) | |
| distances = jnp.dot(image_features, zeroshot_weights.reshape(-1, 1)) | |
| file_paths = [] | |
| for i in range(1, n + 1): | |
| idx = jnp.argsort(distances, axis=0)[-i, 0] | |
| file_paths.append("photos/" + dataset.get_image_name(idx)) | |
| return file_paths | |