INNER CODE UNIT · Python

pbar

patrickjohncyh/fashion-clip · fashion_clip/fashion_clip.py:209

        pbar = tqdm(total=len(text) // batch_size, position=0)
        with torch.no_grad():
            for batch in dataloader:
                batch = {k: v.to(self.device) for k, v in batch.items()}
                text_embeddings.extend(self.model.get_text_features(**batch).detach().cpu().numpy())
                pbar.update(1)
            pbar.close()
        return np.stack(text_embeddings)

    def _cosine_similarity(self, key_vectors: np.ndarray, space_vectors: np.ndarray, normalize=True):
        if normalize:
            key_vectors = key_vectors / np.linalg.norm(key_vectors, ord=2, axis=-1, keepdims=True)
        return np.matmul(key_vectors, space_vectors.T)

    def _nearest_neighbours(self, k, key_vectors, space_vectors, normalize=True, debug=False):
        if type(key_vectors) == List:
            key_vectors = np.array(key_vectors)
        if type(space_vectors) == List:

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