def main(args):
# Load the models
conf = getattr(configs, 'config_'+args.model)()
model=torch.load(f='./output/{}/{}/{}/models/model_epo{}.pckl'.format(args.model, args.expname, args.dataset, args.reload_from))
model.eval()
# Set the random seed manually for reproducibility.
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(args.seed)
else:
print("Note that our pre-trained models require CUDA to evaluate.")
# Load data
data_path=args.data_path+args.dataset+'/'
glove_path = args.data_path+'glove.twitter.27B.200d.txt'
corpus = getattr(data, args.dataset+'Corpus')(data_path, wordvec_path=glove_path, wordvec_dim=conf['emb_size'])
dials, metas = corpus.get_dialogs(), corpus.get_metas()
test_dial, test_meta = dials.get("test"), metas.get("test")
# convert to numeric input outputs that fits into TF models
test_loader = getattr(data, args.dataset+'DataLoader')("Test", test_dial, test_meta, conf['maxlen'])
test_loader.epoch_init(1, conf['diaglen'], 1, shuffle=False)
ivocab = corpus.vocab
vocab = corpus.ivocab
metrics=Metrics(corpus.word2vec)
f_eval = open("./output/{}/{}/{}/results.txt".format(args.model, args.expname, args.dataset), "w")
repeat = args.n_samples
evaluate(model, metrics, test_loader, vocab, ivocab, f_eval, repeat)
"valid"), metas.get("test")
train_loader = getattr(data, args.dataset + 'DataLoader')("Train", train_dial,
train_meta,
config['maxlen'])
valid_loader = getattr(data, args.dataset + 'DataLoader')("Valid", valid_dial,
valid_meta,
config['maxlen'])
test_loader = getattr(data,
args.dataset + 'DataLoader')("Test", test_dial,
test_meta, config['maxlen'])
vocab = corpus.ivocab
ivocab = corpus.vocab
n_tokens = len(ivocab)
metrics = Metrics(corpus.word2vec)
print("Loaded data!")
###############################################################################
# Define the models
###############################################################################
model = getattr(models, args.model)(
config, n_tokens) if args.reload_from < 0 else load_model(args.reload_from)
if use_cuda:
model = model.cuda()
if corpus.word2vec is not None and args.reload_from < 0:
print("Loaded word2vec")
model.embedder.weight.data.copy_(torch.from_numpy(corpus.word2vec))
"valid"), metas.get("test")
train_loader = getattr(data, args.dataset + 'DataLoader')("Train", train_dial,
train_meta,
config['maxlen'])
valid_loader = getattr(data, args.dataset + 'DataLoader')("Valid", valid_dial,
valid_meta,
config['maxlen'])
test_loader = getattr(data,
args.dataset + 'DataLoader')("Test", test_dial,
test_meta, config['maxlen'])
vocab = corpus.ivocab
ivocab = corpus.vocab
n_tokens = len(ivocab)
metrics = Metrics(corpus.word2vec) # 利用glove计算avg, ext, greedy
print("Loaded data!")
###############################################################################
# Define the models
###############################################################################
model = getattr(models, args.model)(
config, n_tokens) if args.reload_from < 0 else load_model(args.reload_from)
if use_cuda:
model = model.cuda()
if corpus.word2vec is not None and args.reload_from < 0:
print("Loaded word2vec")
model.embedder.weight.data.copy_(torch.from_numpy(corpus.word2vec))