def build_loaders(config):
if config.corpus == "MSVD":
corpus = MSVD(config)
elif config.corpus == "MSR-VTT":
corpus = MSRVTT(config)
print('#vocabs: {} ({}), #words: {} ({}). Trim words which appear less than {} times.'.format(
corpus.vocab.n_vocabs, corpus.vocab.n_vocabs_untrimmed, corpus.vocab.n_words,
corpus.vocab.n_words_untrimmed, config.loader.min_count))
return corpus.train_data_loader, corpus.val_data_loader, corpus.test_data_loader, corpus.vocab
def run(ckpt_fpath):
checkpoint = torch.load(ckpt_fpath)
""" Load Config """
config = dict_to_cls(checkpoint['config'])
""" Build Data Loader """
if config.corpus == "MSVD":
corpus = MSVD(config)
elif config.corpus == "MSR-VTT":
corpus = MSRVTT(config)
train_iter, val_iter, test_iter, vocab = \
corpus.train_data_loader, corpus.val_data_loader, corpus.test_data_loader, corpus.vocab
print(
'#vocabs: {} ({}), #words: {} ({}). Trim words which appear less than {} times.'
.format(vocab.n_vocabs, vocab.n_vocabs_untrimmed, vocab.n_words,
vocab.n_words_untrimmed, config.loader.min_count))
""" Build Models """
decoder = Decoder(rnn_type=config.decoder.rnn_type,
num_layers=config.decoder.rnn_num_layers,
num_directions=config.decoder.rnn_num_directions,
feat_size=config.feat.size,
feat_len=config.loader.frame_sample_len,
embedding_size=config.vocab.embedding_size,
hidden_size=config.decoder.rnn_hidden_size,
attn_size=config.decoder.rnn_attn_size,
output_size=vocab.n_vocabs,
rnn_dropout=config.decoder.rnn_dropout)
decoder.load_state_dict(checkpoint['decoder'])
model = CaptionGenerator(decoder, config.loader.max_caption_len, vocab)
model = model.cuda()
""" Train Set """
"""
train_vid2pred = get_predicted_captions(train_iter, model, model.vocab, beam_width=5, beam_alpha=0.)
train_vid2GTs = get_groundtruth_captions(train_iter, model.vocab)
train_scores = score(train_vid2pred, train_vid2GTs)
print("[TRAIN] {}".format(train_scores))
"""
""" Validation Set """
"""
val_vid2pred = get_predicted_captions(val_iter, model, model.vocab, beam_width=5, beam_alpha=0.)
val_vid2GTs = get_groundtruth_captions(val_iter, model.vocab)
val_scores = score(val_vid2pred, val_vid2GTs)
print("[VAL] scores: {}".format(val_scores))
"""
""" Test Set """
test_vid2pred = get_predicted_captions(test_iter,
model,
model.vocab,
beam_width=5,
beam_alpha=0.)
test_vid2GTs = get_groundtruth_captions(test_iter, model.vocab)
test_scores = score(test_vid2pred, test_vid2GTs)
print("[TEST] {}".format(test_scores))
test_save_fpath = os.path.join(C.result_dpath,
"{}_{}.csv".format(config.corpus, 'test'))
save_result(test_vid2pred, test_vid2GTs, test_save_fpath)
def run(ckpt_fpath):
checkpoint = torch.load(ckpt_fpath)
""" Load Config """
config = dict_to_cls(checkpoint['config'])
""" Build Data Loader """
if config.corpus == "MSVD":
corpus = MSVD(config)
elif config.corpus == "MSR-VTT":
corpus = MSRVTT(config)
train_iter, val_iter, test_iter, vocab = \
corpus.train_data_loader, corpus.val_data_loader, corpus.test_data_loader, corpus.vocab
print(
'#vocabs: {} ({}), #words: {} ({}). Trim words which appear less than {} times.'
.format(vocab.n_vocabs, vocab.n_vocabs_untrimmed, vocab.n_words,
vocab.n_words_untrimmed, config.loader.min_count))
""" Build Models """
decoder = Decoder(rnn_type=config.decoder.rnn_type,
num_layers=config.decoder.rnn_num_layers,
num_directions=config.decoder.rnn_num_directions,
feat_size=config.feat.size,
feat_len=config.loader.frame_sample_len,
embedding_size=config.vocab.embedding_size,
hidden_size=config.decoder.rnn_hidden_size,
attn_size=config.decoder.rnn_attn_size,
output_size=vocab.n_vocabs,
rnn_dropout=config.decoder.rnn_dropout)
decoder.load_state_dict(checkpoint['decoder'])
if config.reconstructor.type == 'global':
reconstructor = GlobalReconstructor(
rnn_type=config.reconstructor.rnn_type,
num_layers=config.reconstructor.rnn_num_layers,
num_directions=config.reconstructor.rnn_num_directions,
decoder_size=config.decoder.rnn_hidden_size,
hidden_size=config.reconstructor.rnn_hidden_size,
rnn_dropout=config.reconstructor.rnn_dropout)
else:
reconstructor = LocalReconstructor(
rnn_type=config.reconstructor.rnn_type,
num_layers=config.reconstructor.rnn_num_layers,
num_directions=config.reconstructor.rnn_num_directions,
decoder_size=config.decoder.rnn_hidden_size,
hidden_size=config.reconstructor.rnn_hidden_size,
attn_size=config.reconstructor.rnn_attn_size,
rnn_dropout=config.reconstructor.rnn_dropout)
reconstructor.load_state_dict(checkpoint['reconstructor'])
model = CaptionGenerator(decoder, reconstructor,
config.loader.max_caption_len, vocab)
model = model.cuda()
'''
""" Train Set """
train_scores, train_refs, train_hypos = score(model, train_iter, vocab)
save_result(train_refs, train_hypos, C.result_dpath, config.corpus, 'train')
print("[TRAIN] {}".format(train_scores))
""" Validation Set """
val_scores, val_refs, val_hypos = score(model, val_iter, vocab)
save_result(val_refs, val_hypos, C.result_dpath, config.corpus, 'val')
print("[VAL] scores: {}".format(val_scores))
'''
""" Test Set """
test_scores, test_refs, test_hypos = score(model, test_iter, vocab)
save_result(test_refs, test_hypos, C.result_dpath, config.corpus, 'test')
print("[TEST] {}".format(test_scores))