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import tensorflow as tf
import numpy as np
FC_SIZE = 1024
DTYPE = tf .float32
def _weight_variable (name , shape ):
return tf .get_variable (name , shape , DTYPE , tf .truncated_normal_initializer (stddev = 0.1 ))
def _bias_variable (name , shape ):
return tf .get_variable (name , shape , DTYPE , tf .constant_initializer (0.1 , dtype = DTYPE ))
def inference (boxes , dataconfig ):
prev_layer = boxes
in_filters = dataconfig .num_props
with tf .variable_scope ('conv1' ) as scope :
out_filters = 16
kernel = _weight_variable ('weights' , [5 , 5 , 5 , in_filters , out_filters ])
conv = tf .nn .conv3d (prev_layer , kernel , [1 , 1 , 1 , 1 , 1 ], padding = 'SAME' )
biases = _bias_variable ('biases' , [out_filters ])
bias = tf .nn .bias_add (conv , biases )
conv1 = tf .nn .relu (bias , name = scope .name )
prev_layer = conv1
in_filters = out_filters
pool1 = tf .nn .max_pool3d (prev_layer , ksize = [1 , 3 , 3 , 3 , 1 ], strides = [1 , 2 , 2 , 2 , 1 ], padding = 'SAME' )
norm1 = pool1 # tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta = 0.75, name='norm1')
prev_layer = norm1
with tf .variable_scope ('conv2' ) as scope :
out_filters = 32
kernel = _weight_variable ('weights' , [5 , 5 , 5 , in_filters , out_filters ])
conv = tf .nn .conv3d (prev_layer , kernel , [1 , 1 , 1 , 1 , 1 ], padding = 'SAME' )
biases = _bias_variable ('biases' , [out_filters ])
bias = tf .nn .bias_add (conv , biases )
conv2 = tf .nn .relu (bias , name = scope .name )
prev_layer = conv2
in_filters = out_filters
# normalize prev_layer here
prev_layer = tf .nn .max_pool3d (prev_layer , ksize = [1 , 3 , 3 , 3 , 1 ], strides = [1 , 2 , 2 , 2 , 1 ], padding = 'SAME' )
with tf .variable_scope ('conv3_1' ) as scope :
out_filters = 64
kernel = _weight_variable ('weights' , [5 , 5 , 5 , in_filters , out_filters ])
conv = tf .nn .conv3d (prev_layer , kernel , [1 , 1 , 1 , 1 , 1 ], padding = 'SAME' )
biases = _bias_variable ('biases' , [out_filters ])
bias = tf .nn .bias_add (conv , biases )
prev_layer = tf .nn .relu (bias , name = scope .name )
in_filters = out_filters
with tf .variable_scope ('conv3_2' ) as scope :
out_filters = 64
kernel = _weight_variable ('weights' , [5 , 5 , 5 , in_filters , out_filters ])
conv = tf .nn .conv3d (prev_layer , kernel , [1 , 1 , 1 , 1 , 1 ], padding = 'SAME' )
biases = _bias_variable ('biases' , [out_filters ])
bias = tf .nn .bias_add (conv , biases )
prev_layer = tf .nn .relu (bias , name = scope .name )
in_filters = out_filters
with tf .variable_scope ('conv3_3' ) as scope :
out_filters = 32
kernel = _weight_variable ('weights' , [5 , 5 , 5 , in_filters , out_filters ])
conv = tf .nn .conv3d (prev_layer , kernel , [1 , 1 , 1 , 1 , 1 ], padding = 'SAME' )
biases = _bias_variable ('biases' , [out_filters ])
bias = tf .nn .bias_add (conv , biases )
prev_layer = tf .nn .relu (bias , name = scope .name )
in_filters = out_filters
# normalize prev_layer here
prev_layer = tf .nn .max_pool3d (prev_layer , ksize = [1 , 3 , 3 , 3 , 1 ], strides = [1 , 2 , 2 , 2 , 1 ], padding = 'SAME' )
with tf .variable_scope ('local3' ) as scope :
dim = np .prod (prev_layer .get_shape ().as_list ()[1 :])
prev_layer_flat = tf .reshape (prev_layer , [- 1 , dim ])
weights = _weight_variable ('weights' , [dim , FC_SIZE ])
biases = _bias_variable ('biases' , [FC_SIZE ])
local3 = tf .nn .relu (tf .matmul (prev_layer_flat , weights ) + biases , name = scope .name )
prev_layer = local3
with tf .variable_scope ('local4' ) as scope :
dim = np .prod (prev_layer .get_shape ().as_list ()[1 :])
prev_layer_flat = tf .reshape (prev_layer , [- 1 , dim ])
weights = _weight_variable ('weights' , [dim , FC_SIZE ])
biases = _bias_variable ('biases' , [FC_SIZE ])
local4 = tf .nn .relu (tf .matmul (prev_layer_flat , weights ) + biases , name = scope .name )
prev_layer = local4
with tf .variable_scope ('softmax_linear' ) as scope :
dim = np .prod (prev_layer .get_shape ().as_list ()[1 :])
weights = _weight_variable ('weights' , [dim , dataconfig .num_classes ])
biases = _bias_variable ('biases' , [dataconfig .num_classes ])
softmax_linear = tf .add (tf .matmul (prev_layer , weights ), biases , name = scope .name )
return softmax_linear
def loss (logits , labels ):
cross_entropy = tf .nn .softmax_cross_entropy_with_logits (
logits , labels , name = 'cross_entropy_per_example' )
return tf .reduce_mean (cross_entropy , name = 'xentropy_mean' )
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