feature_network.mode.set_value(np.uint8(1))
print "instantiating model..."
model = RATM(name='RATM', imsize=imsize,
patchsize=patchsize, nhid=nhid,
numpy_rng=numpy_rng, eps=1e-4,
hids_scale=1.,
feature_network=feature_network,
input_feature_layer_name=input_feature_layer_name,
metric_feature_layer_name=metric_feature_layer_name,
nchannels=1,
weight_decay=weight_decay)
print "done (with instantiating model)"
model.load(
'attention_model_kth_{0:02d}left_out_val_best.h5'.format(
args.test_person))
def compute_avg_IoU(inputs, targets, masks):
bbs = targets
vids = inputs
max_nframes = np.max(np.where(masks > .5)[1])
N = vids.shape[0]
Xs = (bbs[:, :, 1::2] + bbs[:, :, ::2]) / 2.
# left, right, top, bottom (w/h = r-l, b-t)
width_height = bbs[:, :, 1::2] - bbs[:, :, ::2]
vids = vids.astype(np.float32)
print "instantiating model..."
model = RATM(name='RATM',
imsize=imsize,
patchsize=patchsize,
nhid=nhid,
numpy_rng=numpy_rng,
eps=1e-4,
hids_scale=1.,
feature_network=feature_network,
input_feature_layer_name=input_feature_layer_name,
metric_feature_layer_name=metric_feature_layer_name,
nchannels=1,
weight_decay=weight_decay)
print "done (with instantiating model)"
model.load('attention_model_kth_{0:02d}left_out_val_best.h5'.format(
args.test_person))
def compute_avg_IoU(inputs, targets, masks):
bbs = targets
vids = inputs
max_nframes = np.max(np.where(masks > .5)[1])
N = vids.shape[0]
Xs = (bbs[:, :, 1::2] + bbs[:, :, ::2]) / 2.
# left, right, top, bottom (w/h = r-l, b-t)
width_height = bbs[:, :, 1::2] - bbs[:, :, ::2]
vids = vids.astype(np.float32)
