print("-- Loading index")
with open(args.model_filename + ".index", mode="rb") as in_file:
index = pickle.load(in_file)
token2id = index["token2id"]
id2token = index["id2token"]
label2id = index["label2id"]
id2label = index["id2label"]
num_tokens = len(token2id)
num_labels = len(label2id)
print("-- Loading test set")
test_labels, test_padded_premises, test_padded_hypotheses, test_img_names, test_original_premises, test_original_hypotheses = \
load_vte_dataset(
args.test_filename,
token2id,
label2id
)
print("-- Loading images")
image_reader = ImageReader(args.img_names_filename, args.img_features_filename)
print("-- Restoring model")
premise_input = tf.placeholder(tf.int32, (None, None), name="premise_input")
hypothesis_input = tf.placeholder(tf.int32, (None, None), name="hypothesis_input")
img_features_input = tf.placeholder(tf.float32, (None, params["num_img_features"], params["img_features_size"]),
name="img_features_input")
label_input = tf.placeholder(tf.int32, (None,), name="label_input")
dropout_input = tf.placeholder(tf.float32, name="dropout_input")
logits = build_bottom_up_top_down_vte_model_hi(
premise_input,
print("-- Loading index")
with open(args.model_filename + ".index", mode="rb") as in_file:
index = pickle.load(in_file)
token2id = index["token2id"]
id2token = index["id2token"]
label2id = index["label2id"]
id2label = index["id2label"]
num_tokens = len(token2id)
num_labels = len(label2id)
print("-- Loading test set")
test_labels, test_padded_premises, test_padded_hypotheses, test_img_names,\
test_original_premises, test_original_hypotheses = load_vte_dataset(
args.test_filename,
token2id,
label2id
)
print("-- Loading images")
image_reader = ImageReader(args.img_names_filename,
args.img_features_filename)
print("-- Restoring model")
premise_input = tf.placeholder(tf.int32, (None, None),
name="premise_input")
hypothesis_input = tf.placeholder(tf.int32, (None, None),
name="hypothesis_input")
img_features_input = tf.placeholder(tf.float32,
(None, params["img_features_size"]),
name="img_features_input")
with open(args.model_save_filename + ".index", mode="wb") as out_file:
pickle.dump(
{
"token2id": token2id,
"id2token": id2token,
"label2id": label2id,
"id2label": id2label
}, out_file)
print("Index saved to: {}".format(args.model_save_filename +
".index"))
print("-- Loading training set")
train_labels, train_premises, train_hypotheses, train_img_names, _, _ =\
load_vte_dataset(
args.train_filename,
token2id,
label2id
)
print("-- Loading development set")
dev_labels, dev_premises, dev_hypotheses, dev_img_names, _, _ =\
load_vte_dataset(
args.dev_filename,
token2id,
label2id
)
print("-- Loading images")
image_reader = ImageReader(args.img_names_filename,
args.img_features_filename)
print("Params saved to: {}".format(args.model_save_filename +
".params"))
with open(args.model_save_filename + ".index", mode="wb") as out_file:
pickle.dump(
{
"token2id": token2id,
"id2token": id2token,
"label2id": label2id,
"id2label": id2label
}, out_file)
print("Index saved to: {}".format(args.model_save_filename +
".index"))
print("-- Loading training set")
train_labels, train_premises, train_hypotheses, train_img_names, _, _ = load_vte_dataset(
args.train_filename, token2id, label2id)
print("-- Loading development set")
dev_labels, dev_premises, dev_hypotheses, dev_img_names, _, _ = load_vte_dataset(
args.dev_filename, token2id, label2id)
print("-- Loading images")
image_reader = ImageReader(args.img_names_filename,
args.img_features_filename)
print("-- Building model")
premise_input = tf.placeholder(tf.int32, (None, None),
name="premise_input")
hypothesis_input = tf.placeholder(tf.int32, (None, None),
name="hypothesis_input")
img_features_input = tf.placeholder(tf.float32,
"vte_id2label": vte_id2label,
"ic_label2id": ic_label2id,
"ic_id2label": ic_id2label
}, out_file)
print("Index saved to: {}".format(args.model_save_filename +
".index"))
print("-- Loading training set")
ic_train_labels, ic_train_sentences, ic_train_img_names, _ = load_ic_dataset(
args.ic_train_filename, token2id, ic_label2id)
print("-- Loading training set")
vte_train_labels, vte_train_premises, vte_train_hypotheses, vte_train_img_names, _, _ =\
load_vte_dataset(
args.vte_train_filename,
token2id,
vte_label2id
)
print("-- Loading development set")
vte_dev_labels, vte_dev_premises, vte_dev_hypotheses, vte_dev_img_names, _, _ =\
load_vte_dataset(
args.vte_dev_filename,
token2id,
vte_label2id
)
print("-- Loading images")
ic_image_reader = ImageReader(args.ic_img_names_filename,
args.ic_img_features_filename)
