"""
Defines networks.
@Encoder_resnet
@Encoder_fc3_dropout
Helper:
@get_encoder_fn_separate
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.contrib.layers.python.layers.initializers import variance_scaling_initializer
def Encoder_resnet(x, is_training=True, weight_decay=0.001, reuse=False):
"""
Resnet v2-50
Assumes input is [batch, height_in, width_in, channels]!!
Input:
- x: N x H x W x 3
- weight_decay: float
- reuse: bool->True if test
Outputs:
- cam: N x 3
- Pose vector: N x 72
- Shape vector: N x 10
- variables: tf variables
"""
from tensorflow.contrib.slim.python.slim.nets import resnet_v2
with tf.name_scope("Encoder_resnet", [x]):
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
net, end_points = resnet_v2.resnet_v2_50(
x,
num_classes=None,
is_training=is_training,
reuse=reuse,
scope='resnet_v2_50')
net = tf.squeeze(net, axis=[1, 2])
variables = tf.contrib.framework.get_variables('resnet_v2_50')
return net, variables
def Encoder_fc3_dropout(x,
num_output=85,
is_training=True,
reuse=False,
name="3D_module"):
"""
3D inference module. 3 MLP layers (last is the output)
With dropout on first 2.
Input:
- x: N x [|img_feat|, |3D_param|]
- reuse: bool
Outputs:
- 3D params: N x num_output
if orthogonal:
either 85: (3 + 24*3 + 10) or 109 (3 + 24*4 + 10) for factored axis-angle representation
if perspective:
86: (f, tx, ty, tz) + 24*3 + 10, or 110 for factored axis-angle.
- variables: tf variables
"""
if reuse:
print('Reuse is on!')
with tf.variable_scope(name, reuse=reuse) as scope:
net = slim.fully_connected(x, 1024, scope='fc1')
net = slim.dropout(net, 0.5, is_training=is_training, scope='dropout1')
net = slim.fully_connected(net, 1024, scope='fc2')
net = slim.dropout(net, 0.5, is_training=is_training, scope='dropout2')
small_xavier = variance_scaling_initializer(
factor=.01, mode='FAN_AVG', uniform=True)
net = slim.fully_connected(
net,
num_output,
activation_fn=None,
weights_initializer=small_xavier,
scope='fc3')
variables = tf.contrib.framework.get_variables(scope)
return net, variables
def get_encoder_fn_separate(model_type):
"""
Retrieves diff encoder fn for image and 3D
"""
encoder_fn = None
threed_fn = None
if 'resnet' in model_type:
encoder_fn = Encoder_resnet
else:
print('Unknown encoder %s!' % model_type)
exit(1)
if 'fc3_dropout' in model_type:
threed_fn = Encoder_fc3_dropout
if encoder_fn is None or threed_fn is None:
print('Dont know what encoder to use for %s' % model_type)
import ipdb
ipdb.set_trace()
return encoder_fn, threed_fn
