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architectures.py
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architectures.py
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import numpy as np
import tensorflow as tf
slim = tf.contrib.slim
class original_yolo_net():
def __init__(self,input,alpha,num_outputs,is_training,yolo_cell_size=7):
with tf.variable_scope('yolo'):
with slim.arg_scope([slim.conv2d, slim.fully_connected], #using scope to avoid mentioning the paramters repeatdely
activation_fn=self.lrelu(alpha),
weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
weights_regularizer=slim.l2_regularizer(0.0005)):
net = tf.pad(input, np.array([[0, 0], [3, 3], [3, 3], [0, 0]]), name='pad_1')
net = slim.conv2d(net, 64, 7, 2, padding='VALID', scope='conv_2')
net = slim.max_pool2d(net, 2, padding='SAME', scope='pool_3')
net = slim.conv2d(net, 192, 3, scope='conv_4')
net = slim.max_pool2d(net, 2, padding='SAME', scope='pool_5')
net = slim.conv2d(net, 128, 1, scope='conv_6')
net = slim.conv2d(net, 256, 3, scope='conv_7')
net = slim.conv2d(net, 256, 1, scope='conv_8')
net = slim.conv2d(net, 512, 3, scope='conv_9')
net = slim.max_pool2d(net, 2, padding='SAME', scope='pool_10')
net = slim.conv2d(net, 256, 1, scope='conv_11')
net = slim.conv2d(net, 512, 3, scope='conv_12')
net = slim.conv2d(net, 256, 1, scope='conv_13')
net = slim.conv2d(net, 512, 3, scope='conv_14')
net = slim.conv2d(net, 256, 1, scope='conv_15')
net = slim.conv2d(net, 512, 3, scope='conv_16')
net = slim.conv2d(net, 256, 1, scope='conv_17')
net = slim.conv2d(net, 512, 3, scope='conv_18')
net = slim.conv2d(net, 512, 1, scope='conv_19')
net = slim.conv2d(net, 1024, 3, scope='conv_20')
net = slim.max_pool2d(net, 2, padding='SAME', scope='pool_21')
net = slim.conv2d(net, 512, 1, scope='conv_22')
net = slim.conv2d(net, 1024, 3, scope='conv_23')
net = slim.conv2d(net, 512, 1, scope='conv_24')
net = slim.conv2d(net, 1024, 3, scope='conv_25')
net = slim.conv2d(net, 1024, 3, scope='conv_26')
net = tf.pad(net, np.array([[0, 0], [1, 1], [1, 1], [0, 0]]), name='pad_27')
net = slim.conv2d(net, 1024, 3, 2, padding='VALID', scope='conv_28')
net = slim.conv2d(net, 1024, 3, scope='conv_29')
net = slim.conv2d(net, 1024, 3, scope='conv_30')
net = tf.transpose(net, [0, 3, 1, 2], name='trans_31')
net = slim.flatten(net, scope='flat_32')
net = slim.fully_connected(net, 512, scope='fc_33')
net = slim.fully_connected(net, 4096, scope='fc_34')
net = slim.dropout(net,is_training=is_training, scope='dropout_35')
# net = slim.fully_connected(net, num_outputs=(yolo_cell_size*yolo_cell_size)*(20+2*5), #20 - num_classes,
#2 - boxes per cell
#5 - x,y,h,w,confidence
# activation_fn=None, scope='fc_36')
net = slim.fully_connected(net, num_outputs,
activation_fn=None, scope='fc_36')
self.net = net
def lrelu(self,alpha):
def op(inputs):
return tf.maximum(alpha * inputs, inputs, name='leaky_relu')
return op
class trial_model1():
def __init__(self,input,alpha,num_outputs,is_training,yolo_cell_size=7):
with tf.variable_scope('yolo'):
with slim.arg_scope([slim.conv2d, slim.fully_connected], #using scope to avoid mentioning the paramters repeatdely
activation_fn=self.lrelu(alpha),
weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
weights_regularizer=slim.l2_regularizer(0.0005)):
net = tf.pad(input, np.array([[0, 0], [3, 3], [3, 3], [0, 0]]), name='pad_1')
net = slim.conv2d(net, 64, 7, 2, padding='VALID', scope='conv_2')
net = slim.max_pool2d(net, 2, padding='SAME', scope='pool_3')
net = slim.conv2d(net, 192, 3, scope='conv_4')
net = slim.max_pool2d(net, 2, padding='SAME', scope='pool_5')
net = slim.conv2d(net, 128, 1, scope='conv_6')
net = slim.conv2d(net, 256, 3, scope='conv_7')
net = slim.conv2d(net, 256, 1, scope='conv_8')
net = slim.conv2d(net, 512, 3, scope='conv_9')
net = slim.max_pool2d(net, 2, padding='SAME', scope='pool_10')
net = slim.conv2d(net, 256, 1, scope='conv_11')
net = slim.conv2d(net, 512, 3, scope='conv_12')
net = slim.conv2d(net, 256, 1, scope='conv_13')
net = slim.conv2d(net, 512, 3, scope='conv_14')
net = slim.conv2d(net, 256, 1, scope='conv_15')
net = slim.conv2d(net, 512, 3, scope='conv_16')
net = slim.conv2d(net, 256, 1, scope='conv_17')
net = slim.conv2d(net, 512, 3, scope='conv_18')
net = slim.conv2d(net, 512, 1, scope='conv_19')
net = slim.conv2d(net, 1024, 3, scope='conv_20')
net = slim.max_pool2d(net, 2, padding='SAME', scope='pool_21')
net = slim.conv2d(net, 512, 1, scope='conv_22')
net = slim.conv2d(net, 1024, 3, scope='conv_23')
net = slim.conv2d(net, 512, 1, scope='conv_24')
net = slim.conv2d(net, 1024, 3, scope='conv_25')
net = slim.conv2d(net, 1024, 3, scope='trainable/conv_26')
net = tf.pad(net, np.array([[0, 0], [1, 1], [1, 1], [0, 0]]), name='trainable/pad_27')
net = slim.conv2d(net, 1024, 3, 2, padding='VALID', scope='trainable/conv_28')
net = slim.conv2d(net, 1024, 3, scope='trainable/conv_29')
net = slim.conv2d(net, 1024, 3, scope='trainable/conv_30')
net = tf.transpose(net, [0, 3, 1, 2], name='trainable/trans_31')
net = slim.flatten(net, scope='trainable/flat_32')
net = slim.fully_connected(net, 512, scope='trainable/fc_33_btsd')
net = slim.fully_connected(net, 4096, scope='trainable/fc_34_btsd')
net = slim.dropout(net,is_training=is_training, scope='trainable/dropout_35_btsd')
# net = slim.fully_connected(net, num_outputs=(yolo_cell_size*yolo_cell_size)*(20+2*5), #20 - num_classes,
#2 - boxes per cell
#5 - x,y,h,w,confidence
# activation_fn=None, scope='fc_36')
net = slim.fully_connected(net, num_outputs,
activation_fn=None, scope='trainable/fc_36_btsd')
self.net = net
def lrelu(self,alpha):
def op(inputs):
return tf.maximum(alpha * inputs, inputs, name='leaky_relu')
return op
if __name__ == '__main__':
inp = tf.placeholder(tf.float32, shape=(None,448,448,3))
obj = original_yolo_net(inp,0.004,True)
print obj.net