dougsouza / efficient-t2i Goto Github PK

How do you evaluate the IS on oxford-102? Did you use the inception_finetuned_models provided by stackGAN? But eval.py looks only write for CUB-birds.

Hi.

Thank you for opening codes of efficiet-t2i. I've been interested in this since the paper was uploaded to arxiv. I would like to try on my dataset. I predict this file(CUB_200_2011/birds/train/attn_embeddings.npy) was made by the AttnGAN's prepare_DAMSM.py, is it correct?

I look forward to hearing from you.

Hi.

I'm Haruka.

I would like to ask about attn_embeddings.npy again. I checked that the shape of CUB's attn_embeddings.npy is (8855, 10, 256). I thiught that shpe is (data_length, seq_size, nef). And, I tried to generate the embeddings from my dataset, but I got the shape (data_length, 14, 17, 17).

I created the following code by appropriating the following from AttnGAN and created it, is it wrong? I treat the attn is a attn_embeddings.

    list_attn_map       = []
    list_class_info     = []
    list_keys           = []
    with torch.no_grad():
        for step, data in enumerate(dataloader, 0):
            real_imgs, captions, cap_lens, \
                    class_ids, keys = prepare_data(data)

            # words_emb: batch_size x nef x seq_len
            # sent_emb: batch_size x nef
            words_features, sent_code = cnn_model(real_imgs[-1])

            # nef, att_sze = words_features.size(1), words_features.size(2)
            # _words_features = words_features.view(batch_size, nef, -1)

            hidden = rnn_model.init_hidden(batch_size)
            words_emb, sent_emb = rnn_model(captions, cap_lens, hidden)

            w_loss0, w_loss1, attn = words_loss(words_features, words_emb, labels,
                                                cap_lens, class_ids, batch_size)

            for j, (key, attn_map, cap_len, class_id) in enumerate(zip(keys, attn, cap_lens, class_ids)):
                print( "[%d/%d] [%d/%d]\t%s" % (j+1, len(keys), step, len(dataloader), key) )
                np_attn = attn_map.to('cpu').detach().numpy().copy()[0]
                list_attn_map.append(np_attn)
                list_class_info.append(class_id)
                list_keys.append(key)

    return np.array(list_attn_map), list_keys, list_class_info

Thank you.

There is no download_flowers.sh in scripts, could you upload it?

Pretrained birds model link in scripts/download_pretrained_birds_model.sh gives an access denied response:

Access denied with the following error:

    Cannot retrieve the public link of the file. You may need to change
    the permission to 'Anyone with the link', or have had many accesses.

You may still be able to access the file from the browser:

     https://drive.google.com/uc?id=1YLqAkHuyPWof64amelOie2t2NwRdmsWk

Opening the link in the browser gives a 403 error.