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vgg_simple.py
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vgg_simple.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
slim = tf.contrib.slim
def vgg_arg_scope(weight_decay=0.0005):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_regularizer=slim.l2_regularizer(weight_decay),
biases_initializer=tf.zeros_initializer()):
with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc:
return arg_sc
def vgg_a(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_a',
fc_conv_padding='VALID',
global_pool=False):
with tf.variable_scope(scope, 'vgg_a', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, slim.max_pool2d],
outputs_collections=end_points_collection):
net = slim.repeat(inputs, 1, slim.conv2d, 64, [3, 3], scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.repeat(net, 1, slim.conv2d, 128, [3, 3], scope='conv2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.repeat(net, 2, slim.conv2d, 256, [3, 3], scope='conv3')
net = slim.max_pool2d(net, [2, 2], scope='pool3')
net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv4')
net = slim.max_pool2d(net, [2, 2], scope='pool4')
net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv5')
net = slim.max_pool2d(net, [2, 2], scope='pool5')
net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
scope='dropout6')
net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
end_points = slim.utils.convert_collection_to_dict(end_points_collection)
if global_pool:
net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
end_points['global_pool'] = net
if num_classes:
net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
scope='dropout7')
net = slim.conv2d(net, num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='fc8')
if spatial_squeeze:
net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
def vgg_16(inputs,
num_classes=1000,
is_training=False,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_16',
fc_conv_padding='VALID',
global_pool=False,
reuse=False):
with tf.variable_scope(scope, 'vgg_16', [inputs], reuse=reuse) as sc:
out = []
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
outputs_collections=end_points_collection):
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
out1 = net
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
out2 = net
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
out3 = net
net = slim.max_pool2d(net, [2, 2], scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
out4 = net
net = slim.max_pool2d(net, [2, 2], scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
out5 = net
exclude = ['vgg_16/fc6', 'vgg_16/pool5','vgg_16/fc7','vgg_16/global_pool','vgg_16/fc8/squeezed','vgg_16/fc8']
return out1, out2, out3, out4, out5, exclude
def vgg_19(inputs,
num_classes=1000,
is_training=False,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_19',
fc_conv_padding='VALID',
global_pool=False,
reuse=False):
with tf.variable_scope(scope, 'vgg_19', [inputs], reuse=reuse) as sc:
out = []
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
outputs_collections=end_points_collection):
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
out1 = net
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
out2 = net
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.repeat(net, 4, slim.conv2d, 256, [3, 3], scope='conv3')
out3 = net
net = slim.max_pool2d(net, [2, 2], scope='pool3')
net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4')
out4 = net
net = slim.max_pool2d(net, [2, 2], scope='pool4')
net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5')
out5 = net
exclude = ['vgg_19/fc6','vgg_19/pool5','vgg_19/fc7','vgg_19/global_pool','vgg_19/fc8/squeezed','vgg_19/fc8']
return out1, out2, out3, out4, out5, exclude
def vgg_191(inputs,
num_classes=1000,
is_training=False,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_19',
fc_conv_padding='VALID',
global_pool=False,
reuse=False):
with tf.variable_scope(scope, 'vgg_19', [inputs], reuse=reuse) as sc:
out = []
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
outputs_collections=end_points_collection):
#net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
net = slim.conv2d(inputs, 64, [3,3], scope='conv1/conv1_1')
out1 = net
net = slim.conv2d(net, 64, [3,3], scope='conv1/conv1_2')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
#net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
net = slim.conv2d(net, 128, [3,3], scope='conv2/conv2_1')
out2 = net
net = slim.conv2d(net, 128, [3,3], scope='conv2/conv2_2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
#net = slim.repeat(net, 4, slim.conv2d, 256, [3, 3], scope='conv3')
net = slim.conv2d(net, 256, [3,3], scope='conv3/conv3_1')
out3 = net
net = slim.conv2d(net, 256, [3,3], scope='conv3/conv3_2')
net = slim.conv2d(net, 256, [3,3], scope='conv3/conv3_3')
net = slim.conv2d(net, 256, [3,3], scope='conv3/conv3_4')
outc = net
net = slim.max_pool2d(net, [2, 2], scope='pool3')
#net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4')
net = slim.conv2d(net, 512, [3,3], scope='conv4/conv4_1')
out4 = net
net = slim.conv2d(net, 512, [3,3], scope='conv4/conv4_2')
net = slim.conv2d(net, 512, [3,3], scope='conv4/conv4_3')
net = slim.conv2d(net, 512, [3,3], scope='conv4/conv4_4')
net = slim.max_pool2d(net, [2, 2], scope='pool4')
#net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5')
net = slim.conv2d(net, 512, [3,3], scope='conv5/conv5_1')
out5 = net
net = slim.conv2d(net, 512, [3,3], scope='conv5/conv5_2')
net = slim.conv2d(net, 512, [3,3], scope='conv5/conv5_3')
net = slim.conv2d(net, 512, [3,3], scope='conv5/conv5_4')
exclude = ['vgg_19/fc6','vgg_19/pool5','vgg_19/fc7','vgg_19/global_pool','vgg_19/fc8/squeezed','vgg_19/fc8']
return out1, out2, out3, outc, out4, out5, exclude