with open("%s/args.pkl" % exp_dir, "rb") as f:
print "load hparams from %s/args.pkl" % exp_dir
hparams = cPickle.load(f)
print hparams
used_labs = hparams.facs.split(':')
c_n = OrderedDict([(used_labs[0], 5), (used_labs[1], 3)]) # hack
used_talabs = hparams.talab_facs.split(':')
b_n = OrderedDict([(used_talabs[0], 48)]) # hack
if dataset == None:
dataset = hparams.dataset
dt_iterator, dt_iterator_by_seqs, dt_seqs, dt_seq2lab_d = \
load_data(dataset, set_name, hparams.is_numpy, seqlist)
FHVAE = load_model(hparams.model)
if hasattr(hparams, "nmu2"):
print "model trained with hierarchical sampling, nmu2=%s" % hparams.nmu2
nmu2 = hparams.nmu2
else:
print "model trained with normal training, nmu2=%s" % hparams.tr_nseqs
nmu2 = hparams.tr_nseqs
tf.reset_default_graph()
xin = tf.placeholder(tf.float32, shape=(None,)+hparams.tr_shape, name="xin")
xout = tf.placeholder(tf.float32, shape=(None,)+hparams.tr_shape, name="xout")
y = tf.placeholder(tf.int64, shape=(None,), name="y")
n = tf.placeholder(tf.float32, shape=(None,), name="n")
#cReg = tf.placeholder(tf.int64, shape=(None,len(used_labs)), name="cReg")
parser.add_argument("--seqlist", type=str, default=None,
help="specify a list of sequences to evaluate; randomly sample 10 by default")
parser.add_argument("--step", type=int, default=-1,
help="step of the model to load. -1 for the best")
args = parser.parse_args()
exp_dir, set_name, seqlist, step = args.exp_dir, args.set_name, args.seqlist, args.step
with open("%s/args.pkl" % exp_dir, "rb") as f:
print "load arguments from %s/args.pkl" % exp_dir
args = cPickle.load(f)
print args
dt_iterator, dt_iterator_by_seqs, dt_seqs, dt_seq2lab_d = \
load_data(args.dataset, set_name, args.is_numpy, seqlist)
FHVAE = load_model(args.model)
if hasattr(args, "nmu2"):
print "model trained with hierarchical sampling, nmu2=%s" % args.nmu2
nmu2 = args.nmu2
else:
print "model trained with normal training, nmu2=%s" % args.tr_nseqs
nmu2 = args.tr_nseqs
tf.reset_default_graph()
xin = tf.placeholder(tf.float32, shape=(None,)+args.tr_shape, name="xin")
xout = tf.placeholder(tf.float32, shape=(None,)+args.tr_shape, name="xout")
y = tf.placeholder(tf.int64, shape=(None,), name="y")
n = tf.placeholder(tf.float32, shape=(None,), name="n")
model = FHVAE(xin, xout, y, n, nmu2)