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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
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