def dump_errors(name,
ckpt,
fold,
output_filename,
method='full',
samples=0,
pooling='median'):
samples = int(samples)
with get_session() as sess:
kwargs = {'dataset_name': 'gehler', 'subset': 0, 'fold': fold}
fcn = FCN(sess=sess, name=name, kwargs=kwargs)
fcn.load(ckpt)
for i in range(4):
if method == 'full':
errors, t, _, _, _ = fcn.test(scales=[0.5])
elif method == 'resize':
errors, t = fcn.test_resize()
elif method == 'patches':
errors, t = fcn.test_patch_based(scale=0.5,
patches=samples,
pooling=pooling)
else:
assert False
utils.print_angular_errors(errors)
with open(output_filename, 'w') as f:
pickle.dump({'e': errors, 't': t}, f)
def test(name, ckpt, image_pack_name=None, output_filename=None):
external_image = image_pack_name.index('.') != -1
if image_pack_name is None:
data = None
elif not external_image:
data = load_data(image_pack_name.split(','))
with get_session() as sess:
fcn = FCN(sess=sess, name=name)
if ckpt != "-1":
fcn.load(ckpt)
else:
fcn.load_absolute(name)
if not external_image:
errors, _, _, _, ret, conf = fcn.test(scales=[0.5],
summary=True,
summary_key=123,
data=data,
eval_speed=False,
visualize=True)
if output_filename is not None:
with open('outputs/%s.pkl' % output_filename, 'wb') as f:
pickle.dump(ret, f)
with open('outputs/%s_err.pkl' % output_filename, 'wb') as f:
pickle.dump(errors, f)
with open('outputs/%s_conf.pkl' % output_filename, 'wb') as f:
pickle.dump(conf, f)
print ret
print 'results dumped'
else:
img = cv2.imread(image_pack_name)
# reverse gamma correction for sRGB
img = (img / 255.0)**2.2 * 16384
images = [img]
fcn.test_external(images=images, fns=[image_pack_name])
def dump_result(name, ckpt, image_pack_name=None):
if image_pack_name is None:
data = None
else:
data = load_data(image_pack_name.split(','))
outputs = []
gts = []
with get_session() as sess:
fcn = FCN(sess=sess, name=name)
fcn.load(ckpt)
_, _, outputs, gts = fcn.test(scales=[0.5],
summary=True,
summary_key=123,
data=data)
result = {
'outputs': np.array(outputs),
'gts': np.array(gts),
}
pickle.dump(
result,
open("outputs/%s-%s-%s.pkl" % (name, ckpt, image_pack_name), "wb"))
def test_input_gamma(name,
ckpt,
input_gamma,
image_pack_name=None,
output_filename=None):
config_set_input_gamma(float(input_gamma))
if image_pack_name is None:
data = None
else:
data = load_data(image_pack_name.split(','))
with get_session() as sess:
fcn = FCN(sess=sess, name=name)
fcn.load(ckpt)
_, _, _, _, ret = fcn.test(scales=[0.5],
summary=True,
summary_key=123,
data=data)
if output_filename is not None:
with open('outputs/%s.pkl' % output_filename, 'wb') as f:
pickle.dump(ret, f)
print ret
print 'results dumped'
