datasets.py 文件源码

def sample_embeddings(self, embeddings, filenames, class_id, sample_num):
        if len(embeddings.shape) == 2 or embeddings.shape[1] == 1:
            return np.squeeze(embeddings)
        else:
            batch_size, embedding_num, _ = embeddings.shape
            # Take every sample_num captions to compute the mean vector
            sampled_embeddings = []
            sampled_captions = []
            for i in range(batch_size):
                randix = np.random.choice(embedding_num,
                                          sample_num, replace=False)
                if sample_num == 1:
                    randix = int(randix)
                    captions = self.readCaptions(filenames[i],
                                                 class_id[i])
                    sampled_captions.append(captions[randix])
                    sampled_embeddings.append(embeddings[i, randix, :])
                else:
                    e_sample = embeddings[i, randix, :]
                    e_mean = np.mean(e_sample, axis=0)
                    sampled_embeddings.append(e_mean)
            sampled_embeddings_array = np.array(sampled_embeddings)
            return np.squeeze(sampled_embeddings_array), sampled_captions