def aux_logit_layer( inputs, num_classes, is_training ):
with tf.variable_scope("pool2d"):
pooled = layers.avg_pool2d(inputs, [ 5, 5 ], stride = 3 )
with tf.variable_scope("conv11"):
conv11 = layers.conv2d( pooled, 128, [1, 1] )
with tf.variable_scope("flatten"):
flat = tf.reshape( conv11, [-1, 2048] )
with tf.variable_scope("fc"):
fc = layers.fully_connected( flat, 1024, activation_fn=None )
with tf.variable_scope("drop"):
drop = layers.dropout( fc, 0.3, is_training = is_training )
with tf.variable_scope( "linear" ):
linear = layers.fully_connected( drop, num_classes, activation_fn=None )
with tf.variable_scope("soft"):
soft = tf.nn.softmax( linear )
return soft
python类avg_pool2d()的实例源码
def _block_a(net, scope='BlockA'):
# 35 x 35 x 384 grid
# default padding = SAME
# default stride = 1
with tf.variable_scope(scope):
with tf.variable_scope('Br1_Pool'):
br1 = layers.avg_pool2d(net, [3, 3], scope='Pool1_3x3')
br1 = layers.conv2d(br1, 96, [1, 1], scope='Conv1_1x1')
with tf.variable_scope('Br2_1x1'):
br2 = layers.conv2d(net, 96, [1, 1], scope='Conv1_1x1')
with tf.variable_scope('Br3_3x3'):
br3 = layers.conv2d(net, 64, [1, 1], scope='Conv1_1x1')
br3 = layers.conv2d(br3, 96, [3, 3], scope='Conv2_3x3')
with tf.variable_scope('Br4_3x3Dbl'):
br4 = layers.conv2d(net, 64, [1, 1], scope='Conv1_1x1')
br4 = layers.conv2d(br4, 96, [3, 3], scope='Conv2_3x3')
br4 = layers.conv2d(br4, 96, [3, 3], scope='Conv3_3x3')
net = tf.concat(3, [br1, br2, br3, br4], name='Concat1')
# 35 x 35 x 384
return net
def _block_b(net, scope='BlockB'):
# 17 x 17 x 1024 grid
# default padding = SAME
# default stride = 1
with tf.variable_scope(scope):
with tf.variable_scope('Br1_Pool'):
br1 = layers.avg_pool2d(net, [3, 3], scope='Pool1_3x3')
br1 = layers.conv2d(br1, 128, [1, 1], scope='Conv1_1x1')
with tf.variable_scope('Br2_1x1'):
br2 = layers.conv2d(net, 384, [1, 1], scope='Conv1_1x1')
with tf.variable_scope('Br3_7x7'):
br3 = layers.conv2d(net, 192, [1, 1], scope='Conv1_1x1')
br3 = layers.conv2d(br3, 224, [1, 7], scope='Conv2_1x7')
br3 = layers.conv2d(br3, 256, [7, 1], scope='Conv3_7x1')
with tf.variable_scope('Br4_7x7Dbl'):
br4 = layers.conv2d(net, 192, [1, 1], scope='Conv1_1x1')
br4 = layers.conv2d(br4, 192, [1, 7], scope='Conv2_1x7')
br4 = layers.conv2d(br4, 224, [7, 1], scope='Conv3_7x1')
br4 = layers.conv2d(br4, 224, [1, 7], scope='Conv4_1x7')
br4 = layers.conv2d(br4, 256, [7, 1], scope='Conv5_7x1')
net = tf.concat(3, [br1, br2, br3, br4], name='Concat1')
# 17 x 17 x 1024
return net
def _block_c(net, scope='BlockC'):
# 8 x 8 x 1536 grid
# default padding = SAME
# default stride = 1
with tf.variable_scope(scope):
with tf.variable_scope('Br1_Pool'):
br1 = layers.avg_pool2d(net, [3, 3], scope='Pool1_3x3')
br1 = layers.conv2d(br1, 256, [1, 1], scope='Conv1_1x1')
with tf.variable_scope('Br2_1x1'):
br2 = layers.conv2d(net, 256, [1, 1], scope='Conv1_1x1')
with tf.variable_scope('Br3_3x3'):
br3 = layers.conv2d(net, 384, [1, 1], scope='Conv1_1x1')
br3a = layers.conv2d(br3, 256, [1, 3], scope='Conv2_1x3')
br3b = layers.conv2d(br3, 256, [3, 1], scope='Conv3_3x1')
with tf.variable_scope('Br4_7x7Dbl'):
br4 = layers.conv2d(net, 384, [1, 1], scope='Conv1_1x1')
br4 = layers.conv2d(br4, 448, [1, 7], scope='Conv2_1x7')
br4 = layers.conv2d(br4, 512, [7, 1], scope='Conv3_7x1')
br4a = layers.conv2d(br4, 256, [1, 7], scope='Conv4a_1x7')
br4b = layers.conv2d(br4, 256, [7, 1], scope='Conv4b_7x1')
net = tf.concat(3, [br1, br2, br3a, br3b, br4a, br4b], name='Concat1')
# 8 x 8 x 1536
return net
def __call__(self, inputs, reuse = True):
with tf.variable_scope(self.name) as vs:
tf.get_variable_scope()
if reuse:
vs.reuse_variables()
conv1 = tcl.conv2d(inputs,
num_outputs = 64,
kernel_size = (7, 7),
stride = (2, 2),
padding = 'SAME')
conv1 = tcl.batch_norm(conv1)
conv1 = tf.nn.relu(conv1)
conv1 = tcl.max_pool2d(conv1,
kernel_size = (3, 3),
stride = (2, 2),
padding = 'SAME')
x = conv1
filters = 64
first_layer = True
for i, r in enumerate(self.repetitions):
x = _residual_block(self.block_fn,
filters = filters,
repetition = r,
is_first_layer = first_layer)(x)
filters *= 2
if first_layer:
first_layer = False
_, h, w, ch = x.shape.as_list()
outputs = tcl.avg_pool2d(x,
kernel_size = (h, w),
stride = (1, 1))
outputs = tcl.flatten(outputs)
logits = tcl.fully_connected(outputs, num_outputs = self.num_output,
activation_fn = None)
return logits
def _output(net, endpoints, num_classes, pre_act=False):
with tf.variable_scope('Output'):
if pre_act:
net = layers.batch_norm(net, activation_fn=tf.nn.relu)
shape = net.get_shape()
net = layers.avg_pool2d(net, shape[1:3], scope='Pool1_Global')
endpoints['OutputPool1'] = net
net = layers.flatten(net)
net = layers.fully_connected(net, num_classes, activation_fn=None, scope='Logits')
endpoints['Logits'] = net
# num_classes
return net
def _block_a_reduce(net, endpoints, k=192, l=224, m=256, n=384, scope='BlockReduceA'):
# 35 x 35 -> 17 x 17 reduce
# inception-v4: k=192, l=224, m=256, n=384
# inception-resnet-v1: k=192, l=192, m=256, n=384
# inception-resnet-v2: k=256, l=256, m=384, n=384
# default padding = VALID
# default stride = 1
with arg_scope([layers.conv2d, layers.max_pool2d, layers.avg_pool2d], padding='VALID'):
with tf.variable_scope(scope):
with tf.variable_scope('Br1_Pool'):
br1 = layers.max_pool2d(net, [3, 3], stride=2, scope='Pool1_3x3/2')
# 17 x 17 x input
with tf.variable_scope('Br2_3x3'):
br2 = layers.conv2d(net, n, [3, 3], stride=2, scope='Conv1_3x3/2')
# 17 x 17 x n
with tf.variable_scope('Br3_3x3Dbl'):
br3 = layers.conv2d(net, k, [1, 1], padding='SAME', scope='Conv1_1x1')
br3 = layers.conv2d(br3, l, [3, 3], padding='SAME', scope='Conv2_3x3')
br3 = layers.conv2d(br3, m, [3, 3], stride=2, scope='Conv3_3x3/2')
# 17 x 17 x m
net = tf.concat(3, [br1, br2, br3], name='Concat1')
# 17 x 17 x input + n + m
# 1024 for v4 (384 + 384 + 256)
# 896 for res-v1 (256 + 384 +256)
# 1152 for res-v2 (384 + 384 + 384)
endpoints[scope] = net
print('%s output shape: %s' % (scope, net.get_shape()))
return net
def _block_stem_res(net, endpoints, scope='Stem'):
# Simpler _stem for inception-resnet-v1 network
# NOTE observe endpoints of first 3 layers
# default padding = VALID
# default stride = 1
with arg_scope([layers.conv2d, layers.max_pool2d, layers.avg_pool2d], padding='VALID'):
with tf.variable_scope(scope):
# 299 x 299 x 3
net = layers.conv2d(net, 32, [3, 3], stride=2, scope='Conv1_3x3/2')
endpoints[scope + '/Conv1'] = net
# 149 x 149 x 32
net = layers.conv2d(net, 32, [3, 3], scope='Conv2_3x3')
endpoints[scope + '/Conv2'] = net
# 147 x 147 x 32
net = layers.conv2d(net, 64, [3, 3], padding='SAME', scope='Conv3_3x3')
endpoints[scope + '/Conv3'] = net
# 147 x 147 x 64
net = layers.max_pool2d(net, [3, 3], stride=2, scope='Pool1_3x3/2')
# 73 x 73 x 64
net = layers.conv2d(net, 80, [1, 1], padding='SAME', scope='Conv4_1x1')
# 73 x 73 x 80
net = layers.conv2d(net, 192, [3, 3], scope='Conv5_3x3')
# 71 x 71 x 192
net = layers.conv2d(net, 256, [3, 3], stride=2, scope='Conv6_3x3/2')
# 35 x 35 x 256
endpoints[scope] = net
print('%s output shape: %s' % (scope, net.get_shape()))
return net
def _block_b_reduce_res(net, endpoints, ver=2, scope='BlockReduceB'):
# 17 x 17 -> 8 x 8 reduce
# configure branch filter numbers
br3_num = 256
br4_num = 256
if ver == 1:
br3_inc = 0
br4_inc = 0
else:
br3_inc = 32
br4_inc = 32
with arg_scope([layers.conv2d, layers.max_pool2d, layers.avg_pool2d], padding='VALID'):
with tf.variable_scope(scope):
with tf.variable_scope('Br1_Pool'):
br1 = layers.max_pool2d(net, [3, 3], stride=2, scope='Pool1_3x3/2')
with tf.variable_scope('Br2_3x3'):
br2 = layers.conv2d(net, 256, [1, 1], padding='SAME', scope='Conv1_1x1')
br2 = layers.conv2d(br2, 384, [3, 3], stride=2, scope='Conv2_3x3/2')
with tf.variable_scope('Br3_3x3'):
br3 = layers.conv2d(net, br3_num, [1, 1], padding='SAME', scope='Conv1_1x1')
br3 = layers.conv2d(br3, br3_num + br3_inc, [3, 3], stride=2, scope='Conv2_3x3/2')
with tf.variable_scope('Br4_3x3Dbl'):
br4 = layers.conv2d(net, br4_num, [1, 1], padding='SAME', scope='Conv1_1x1')
br4 = layers.conv2d(br4, br4_num + 1*br4_inc, [3, 3], padding='SAME', scope='Conv2_3x3')
br4 = layers.conv2d(br4, br4_num + 2*br4_inc, [3, 3], stride=2, scope='Conv3_3x3/2')
net = tf.concat(3, [br1, br2, br3, br4], name='Concat1')
# 8 x 8 x 1792 v1, 2144 v2 (paper indicates 2048 but only get this if we use a v1 config for this block)
endpoints[scope] = net
print('%s output shape: %s' % (scope, net.get_shape()))
return net
def _block_output(net, endpoints, num_classes=1000, dropout_keep_prob=0.5, scope='Output'):
with tf.variable_scope(scope):
# 8 x 8 x 1536
shape = net.get_shape()
net = layers.avg_pool2d(net, shape[1:3], padding='VALID', scope='Pool1_Global')
endpoints['Output/Pool1'] = net
# 1 x 1 x 1536
net = layers.dropout(net, dropout_keep_prob)
net = layers.flatten(net)
# 1536
net = layers.fully_connected(net, num_classes, activation_fn=None, scope='Logits')
# num classes
endpoints['Logits'] = net
return net
def _block_stem(net, endpoints, scope='Stem'):
# Stem shared by inception-v4 and inception-resnet-v2 (resnet-v1 uses simpler _stem below)
# NOTE observe endpoints of first 3 layers
with arg_scope([layers.conv2d, layers.max_pool2d, layers.avg_pool2d], padding='VALID'):
with tf.variable_scope(scope):
# 299 x 299 x 3
net = layers.conv2d(net, 32, [3, 3], stride=2, scope='Conv1_3x3/2')
endpoints[scope + '/Conv1'] = net
# 149 x 149 x 32
net = layers.conv2d(net, 32, [3, 3], scope='Conv2_3x3')
endpoints[scope + '/Conv2'] = net
# 147 x 147 x 32
net = layers.conv2d(net, 64, [3, 3], padding='SAME', scope='Conv3_3x3')
endpoints[scope + '/Conv3'] = net
# 147 x 147 x 64
with tf.variable_scope('Br1A_Pool'):
br1a = layers.max_pool2d(net, [3, 3], stride=2, scope='Pool1_3x3/2')
with tf.variable_scope('Br1B_3x3'):
br1b = layers.conv2d(net, 96, [3, 3], stride=2, scope='Conv4_3x3/2')
net = tf.concat(3, [br1a, br1b], name='Concat1')
endpoints[scope + '/Concat1'] = net
# 73 x 73 x 160
with tf.variable_scope('Br2A_3x3'):
br2a = layers.conv2d(net, 64, [1, 1], padding='SAME', scope='Conv5_1x1')
br2a = layers.conv2d(br2a, 96, [3, 3], scope='Conv6_3x3')
with tf.variable_scope('Br2B_7x7x3'):
br2b = layers.conv2d(net, 64, [1, 1], padding='SAME', scope='Conv5_1x1')
br2b = layers.conv2d(br2b, 64, [7, 1], padding='SAME', scope='Conv6_7x1')
br2b = layers.conv2d(br2b, 64, [1, 7], padding='SAME', scope='Conv7_1x7')
br2b = layers.conv2d(br2b, 96, [3, 3], scope='Conv8_3x3')
net = tf.concat(3, [br2a, br2b], name='Concat2')
endpoints[scope + '/Concat2'] = net
# 71 x 71 x 192
with tf.variable_scope('Br3A_3x3'):
br3a = layers.conv2d(net, 192, [3, 3], stride=2, scope='Conv9_3x3/2')
with tf.variable_scope('Br3B_Pool'):
br3b = layers.max_pool2d(net, [3, 3], stride=2, scope='Pool2_3x3/2')
net = tf.concat(3, [br3a, br3b], name='Concat3')
endpoints[scope + '/Concat3'] = net
print('%s output shape: %s' % (scope, net.get_shape()))
# 35x35x384
return net
def _build_inception_v4(
inputs,
stack_counts=[4, 7, 3],
dropout_keep_prob=0.8,
num_classes=1000,
is_training=True,
scope=''):
"""Inception v4 from http://arxiv.org/abs/
Args:
inputs: a tensor of size [batch_size, height, width, channels].
dropout_keep_prob: dropout keep_prob.
num_classes: number of predicted classes.
is_training: whether is training or not.
scope: Optional scope for op_scope.
Returns:
a list containing 'logits' Tensors and a dict of Endpoints.
"""
# endpoints will collect relevant activations for external use, for example, summaries or losses.
endpoints = {}
name_scope_net = tf.name_scope(scope, 'Inception_v4', [inputs])
arg_scope_train = arg_scope([layers.batch_norm, layers.dropout], is_training=is_training)
arg_scope_conv = arg_scope([layers.conv2d, layers.max_pool2d, layers.avg_pool2d], stride=1, padding='SAME')
with name_scope_net, arg_scope_train, arg_scope_conv:
net = _block_stem(inputs, endpoints)
# 35 x 35 x 384
with tf.variable_scope('Scale1'):
net = _stack(net, endpoints, fn=_block_a, count=stack_counts[0], scope='BlockA')
# 35 x 35 x 384
with tf.variable_scope('Scale2'):
net = _block_a_reduce(net, endpoints)
# 17 x 17 x 1024
net = _stack(net, endpoints, fn=_block_b, count=stack_counts[1], scope='BlockB')
# 17 x 17 x 1024
with tf.variable_scope('Scale3'):
net = _block_b_reduce(net, endpoints)
# 8 x 8 x 1536
net = _stack(net, endpoints, fn=_block_c, count=stack_counts[2], scope='BlockC')
# 8 x 8 x 1536
logits = _block_output(net, endpoints, num_classes, dropout_keep_prob, scope='Output')
endpoints['Predictions'] = tf.nn.softmax(logits, name='Predictions')
return logits, endpoints
