def extract_features(self, inputs):
mean = tf.constant(
self.cfg['mean_pixel'], dtype=tf.float32, shape=[1, 1, 1, 3], name="img_mean"
)
im_centered = inputs - mean
if 'resnet' in self.cfg['net_type']:
# The next part of the code depends upon which tensorflow version you have.
vers = tf.__version__
vers = vers.split(
"."
) # Updated based on https://github.com/AlexEMG/DeepLabCut/issues/44
net_fun = net_funcs[self.cfg['net_type']]
if int(vers[0]) == 1 and int(vers[1]) < 4: # check if lower than version 1.4.
with slim.arg_scope(resnet_v1.resnet_arg_scope(False)):
net, end_points = net_fun(
im_centered, global_pool=False, output_stride=16
)
else:
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = net_fun(
im_centered, global_pool=False, output_stride=16, is_training=False
)
elif 'mobilenet' in self.cfg['net_type']:
net_fun = net_funcs[self.cfg['net_type']]
with slim.arg_scope(mobilenet_v2.training_scope()):
net, end_points = net_fun(im_centered)
elif 'efficientnet' in self.cfg['net_type']:
im_centered /= tf.constant(eff.STDDEV_RGB, shape=[1, 1, 3])
net, end_points = eff.build_model_base(im_centered,
self.cfg['net_type'],
use_batch_norm=self.cfg['use_batch_norm'],
drop_out=self.cfg['use_drop_out'])
return net, end_points
def extract_features(self,
inputs,
use_batch_norm=False,
use_drop_out=False):
mean = tf.constant(self.cfg.mean_pixel,
dtype=tf.float32,
shape=[1, 1, 1, 3],
name='img_mean')
im_centered = inputs - mean
im_centered /= tf.constant(eff.STDDEV_RGB, shape=[1, 1, 3])
with tf.variable_scope("efficientnet"):
eff_net_type = self.cfg.net_type.replace('_', '-')
net, end_points = eff.build_model_base(
im_centered,
eff_net_type,
use_batch_norm=use_batch_norm,
drop_out=use_drop_out)
return net, end_points