def block_reduction_a(inputs, scope=None, is_train=False):
"""Builds Reduction-A block for Inception v4 network."""
# By default use stride=1 and SAME padding
with tf.variable_scope(scope, 'BlockReductionA', [inputs]):
with tf.variable_scope('Branch_0'):
branch_0, _ = conv_module(inputs,
n_out_channel=384,
filter_size=(3, 3),
strides=(2, 2),
padding='VALID',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
branch_1, _ = conv_module(inputs,
n_out_channel=192,
filter_size=(1, 1),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0a_1x1')
branch_1, _ = conv_module(branch_1,
n_out_channel=224,
filter_size=(3, 3),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0b_3x3')
branch_1, _ = conv_module(branch_1,
n_out_channel=256,
filter_size=(3, 3),
strides=(2, 2),
padding='VALID',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_1a_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = tl.layers.MaxPool2d(inputs, (3, 3),
strides=(2, 2),
padding='VALID',
name='MaxPool_1a_3x3')
return tl.layers.ConcatLayer([branch_0, branch_1, branch_2],
concat_dim=3,
name='concat_layer')
def block_reduction_a(inputs, scope=None, is_train=False):
"""Builds Reduction-A block for Inception v4 network."""
# By default use stride=1 and SAME padding
with tf.variable_scope(scope, 'BlockReductionA', [inputs]):
with tf.variable_scope('Branch_0'):
branch_0, _ = conv_module(
inputs, n_out_channel=384, filter_size=(3, 3), strides=(2, 2), padding='VALID', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_1a_3x3'
)
with tf.variable_scope('Branch_1'):
branch_1, _ = conv_module(
inputs, n_out_channel=192, filter_size=(1, 1), strides=(1, 1), padding='SAME', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_0a_1x1'
)
branch_1, _ = conv_module(
branch_1, n_out_channel=224, filter_size=(3, 3), strides=(1, 1), padding='SAME', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_0b_3x3'
)
branch_1, _ = conv_module(
branch_1, n_out_channel=256, filter_size=(3, 3), strides=(2, 2), padding='VALID', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_1a_3x3'
)
with tf.variable_scope('Branch_2'):
branch_2 = tl.layers.MaxPool2d(inputs, (3, 3), strides=(2, 2), padding='VALID', name='MaxPool_1a_3x3')
return tl.layers.ConcatLayer([branch_0, branch_1, branch_2], concat_dim=3, name='concat_layer')
def block_inception_a(inputs, scope=None, is_train=False):
"""Builds Inception-A block for Inception v4 network."""
# By default use stride=1 and SAME padding
with tf.variable_scope(name_or_scope=scope, default_name='BlockInceptionA', values=[inputs]):
with tf.variable_scope('Branch_0'):
branch_0, _ = conv_module(
inputs, n_out_channel=96, filter_size=(1, 1), strides=(1, 1), padding='SAME', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_0a_1x1'
)
with tf.variable_scope('Branch_1'):
branch_1, _ = conv_module(
inputs, n_out_channel=64, filter_size=(1, 1), strides=(1, 1), padding='SAME', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_0a_1x1'
)
branch_1, _ = conv_module(
branch_1, n_out_channel=96, filter_size=(3, 3), strides=(1, 1), padding='SAME', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_0b_3x3'
)
with tf.variable_scope('Branch_2'):
branch_2, _ = conv_module(
inputs, n_out_channel=64, filter_size=(1, 1), strides=(1, 1), padding='SAME', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_0a_1x1'
)
branch_2, _ = conv_module(
branch_2, n_out_channel=96, filter_size=(3, 3), strides=(1, 1), padding='SAME', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_0b_3x3'
)
branch_2, _ = conv_module(
branch_2, n_out_channel=96, filter_size=(3, 3), strides=(1, 1), padding='SAME', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_0c_3x3'
)
with tf.variable_scope('Branch_3'):
branch_3 = tl.layers.MeanPool2d(
inputs, filter_size=(3, 3), strides=(1, 1), padding='SAME', name='AvgPool_0a_3x3'
)
branch_3, _ = conv_module(
branch_3, n_out_channel=96, filter_size=(1, 1), strides=(1, 1), padding='SAME', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_0b_1x1'
)
return tl.layers.ConcatLayer([branch_0, branch_1, branch_2, branch_3], concat_dim=3, name='concat_layer')
def __call__(self, inputs, reuse=False, is_train=False):
with tf.variable_scope("InceptionV4", reuse=reuse):
preprocessed = inputs
with tf.variable_scope("preprocessing"):
max_val = tf.reduce_max(preprocessed)
min_val = tf.reduce_min(preprocessed)
need_int_rescale = tf.logical_and(
tf.greater(max_val, 1.0), tf.greater_equal(min_val, 0.0))
need_float_rescale = tf.logical_and(
tf.less_equal(max_val, 1.0),
tf.greater_equal(min_val, 0.0))
preprocessed = tf.cond(
pred=need_int_rescale,
true_fn=lambda: tf.subtract(tf.divide(preprocessed, 127.5),
1.0),
false_fn=lambda: preprocessed)
preprocessed = tf.cond(
pred=need_float_rescale,
true_fn=lambda: tf.multiply(tf.subtract(preprocessed, 0.5),
2.0),
false_fn=lambda: preprocessed)
# Input Layers
input_layer = tl.layers.InputLayer(preprocessed, name='input')
# 299 x 299 x 3
net, _ = conv_module(input_layer,
n_out_channel=32,
filter_size=(3, 3),
strides=(2, 2),
padding='VALID',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_1a_3x3')
# 149 x 149 x 32
net, _ = conv_module(net,
n_out_channel=32,
filter_size=(3, 3),
strides=(1, 1),
padding='VALID',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_2a_3x3')
# 147 x 147 x 32
net, _ = conv_module(net,
n_out_channel=64,
filter_size=(3, 3),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_2b_3x3')
# 147 x 147 x 64
with tf.variable_scope('Mixed_3a'):
with tf.variable_scope('Branch_0'):
branch_0 = tl.layers.MaxPool2d(net, (3, 3),
strides=(2, 2),
padding='VALID',
name='MaxPool_0a_3x3')
with tf.variable_scope('Branch_1'):
branch_1, _ = conv_module(net,
n_out_channel=96,
filter_size=(3, 3),
strides=(2, 2),
padding='VALID',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0a_3x3')
net = tl.layers.ConcatLayer([branch_0, branch_1], concat_dim=3)
# 73 x 73 x 160
with tf.variable_scope('Mixed_4a'):
with tf.variable_scope('Branch_0'):
branch_0, _ = conv_module(net,
n_out_channel=64,
filter_size=(1, 1),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0a_1x1')
branch_0, _ = conv_module(branch_0,
n_out_channel=96,
filter_size=(3, 3),
strides=(1, 1),
padding='VALID',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
branch_1, _ = conv_module(net,
n_out_channel=64,
filter_size=(1, 1),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0a_1x1')
branch_1, _ = conv_module(branch_1,
n_out_channel=64,
filter_size=(1, 7),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0b_1x7')
branch_1, _ = conv_module(branch_1,
n_out_channel=64,
filter_size=(7, 1),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0c_7x1')
branch_1, _ = conv_module(branch_1,
n_out_channel=96,
filter_size=(3, 3),
strides=(1, 1),
padding='VALID',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_1a_3x3')
net = tl.layers.ConcatLayer([branch_0, branch_1], concat_dim=3)
# 71 x 71 x 192
with tf.variable_scope('Mixed_5a'):
with tf.variable_scope('Branch_0'):
# 299 x 299 x 3
branch_0, _ = conv_module(net,
n_out_channel=192,
filter_size=(3, 3),
strides=(2, 2),
padding='VALID',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
branch_1 = tl.layers.MaxPool2d(net, (3, 3),
strides=(2, 2),
padding='VALID',
name='MaxPool_1a_3x3')
net = tl.layers.ConcatLayer([branch_0, branch_1], concat_dim=3)
# 35 x 35 x 384
# 4 x Inception-A blocks
for idx in range(4):
block_scope = 'Mixed_5' + chr(ord('b') + idx)
net = block_inception_a(net,
scope=block_scope,
is_train=is_train)
# 35 x 35 x 384
# Reduction-A block
net = block_reduction_a(net, scope='Mixed_6a', is_train=is_train)
# 17 x 17 x 1024
# 7 x Inception-B blocks
for idx in range(7):
block_scope = 'Mixed_6' + chr(ord('b') + idx)
net = block_inception_b(net,
scope=block_scope,
is_train=is_train)
# 17 x 17 x 1024
# Reduction-B block
net = block_reduction_b(net, scope='Mixed_7a', is_train=is_train)
# 8 x 8 x 1536
# 3 x Inception-C blocks
for idx in range(3):
block_scope = 'Mixed_7' + chr(ord('b') + idx)
net = block_inception_c(net,
scope=block_scope,
is_train=is_train)
if self.flatten_output and not self.include_FC_head:
net = tl.layers.FlattenLayer(net, name='flatten')
if self.include_FC_head:
with tf.variable_scope("Logits", reuse=reuse):
# 8 x 8 x 1536
net = tl.layers.MeanPool2d(
net,
filter_size=net.outputs.get_shape()[1:3],
strides=(1, 1),
padding='VALID',
name='AvgPool_1a')
# 1 x 1 x 1536
net = tl.layers.DropoutLayer(net,
keep=0.8,
is_fix=True,
is_train=is_train,
name='Dropout_1b')
net = tl.layers.FlattenLayer(net, name='PreLogitsFlatten')
# 1536
net, _ = dense_module(net,
n_units=1001,
activation_fn="softmax",
use_batchnorm=False,
batch_norm_init=None,
is_train=is_train,
name="Logits")
return net
def __call__(self, inputs, reuse=False, is_train=False):
with tf.variable_scope("InceptionV4", reuse=reuse):
preprocessed = inputs
with tf.variable_scope("preprocessing"):
max_val = tf.reduce_max(preprocessed)
min_val = tf.reduce_min(preprocessed)
need_int_rescale = tf.logical_and(tf.greater(max_val, 1.0), tf.greater_equal(min_val, 0.0))
need_float_rescale = tf.logical_and(tf.less_equal(max_val, 1.0), tf.greater_equal(min_val, 0.0))
preprocessed = tf.cond(
pred=need_int_rescale, true_fn=lambda: tf.subtract(tf.divide(preprocessed, 127.5), 1.0),
false_fn=lambda: preprocessed
)
preprocessed = tf.cond(
pred=need_float_rescale, true_fn=lambda: tf.multiply(tf.subtract(preprocessed, 0.5), 2.0),
false_fn=lambda: preprocessed
)
# Input Layers
input_layer = tl.layers.InputLayer(preprocessed, name='input')
# 299 x 299 x 3
net, _ = conv_module(
input_layer, n_out_channel=32, filter_size=(3, 3), strides=(2, 2), padding='VALID',
batch_norm_init=None, is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_1a_3x3'
)
# 149 x 149 x 32
net, _ = conv_module(
net, n_out_channel=32, filter_size=(3, 3), strides=(1, 1), padding='VALID', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_2a_3x3'
)
# 147 x 147 x 32
net, _ = conv_module(
net, n_out_channel=64, filter_size=(3, 3), strides=(1, 1), padding='SAME', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_2b_3x3'
)
# 147 x 147 x 64
with tf.variable_scope('Mixed_3a'):
with tf.variable_scope('Branch_0'):
branch_0 = tl.layers.MaxPool2d(net, (3, 3), strides=(2, 2), padding='VALID', name='MaxPool_0a_3x3')
with tf.variable_scope('Branch_1'):
branch_1, _ = conv_module(
net, n_out_channel=96, filter_size=(3, 3), strides=(2,
2), padding='VALID', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_0a_3x3'
)
net = tl.layers.ConcatLayer([branch_0, branch_1], concat_dim=3)
# 73 x 73 x 160
with tf.variable_scope('Mixed_4a'):
with tf.variable_scope('Branch_0'):
branch_0, _ = conv_module(
net, n_out_channel=64, filter_size=(1, 1), strides=(1, 1), padding='SAME', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_0a_1x1'
)
branch_0, _ = conv_module(
branch_0, n_out_channel=96, filter_size=(3, 3), strides=(1, 1), padding='VALID',
batch_norm_init=None, is_train=is_train, use_batchnorm=True, activation_fn='ReLU',
name='Conv2d_1a_3x3'
)
with tf.variable_scope('Branch_1'):
branch_1, _ = conv_module(
net, n_out_channel=64, filter_size=(1, 1), strides=(1, 1), padding='SAME', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_0a_1x1'
)
branch_1, _ = conv_module(
branch_1, n_out_channel=64, filter_size=(1, 7), strides=(1, 1), padding='SAME',
batch_norm_init=None, is_train=is_train, use_batchnorm=True, activation_fn='ReLU',
name='Conv2d_0b_1x7'
)
branch_1, _ = conv_module(
branch_1, n_out_channel=64, filter_size=(7, 1), strides=(1, 1), padding='SAME',
batch_norm_init=None, is_train=is_train, use_batchnorm=True, activation_fn='ReLU',
name='Conv2d_0c_7x1'
)
branch_1, _ = conv_module(
branch_1, n_out_channel=96, filter_size=(3, 3), strides=(1, 1), padding='VALID',
batch_norm_init=None, is_train=is_train, use_batchnorm=True, activation_fn='ReLU',
name='Conv2d_1a_3x3'
)
net = tl.layers.ConcatLayer([branch_0, branch_1], concat_dim=3)
# 71 x 71 x 192
with tf.variable_scope('Mixed_5a'):
with tf.variable_scope('Branch_0'):
# 299 x 299 x 3
branch_0, _ = conv_module(
net, n_out_channel=192, filter_size=(3, 3), strides=(2,
2), padding='VALID', batch_norm_init=None,
is_train=is_train, use_batchnorm=True, activation_fn='ReLU', name='Conv2d_1a_3x3'
)
with tf.variable_scope('Branch_1'):
branch_1 = tl.layers.MaxPool2d(net, (3, 3), strides=(2, 2), padding='VALID', name='MaxPool_1a_3x3')
net = tl.layers.ConcatLayer([branch_0, branch_1], concat_dim=3)
# 35 x 35 x 384
# 4 x Inception-A blocks
for idx in range(4):
block_scope = 'Mixed_5' + chr(ord('b') + idx)
net = block_inception_a(net, scope=block_scope, is_train=is_train)
# 35 x 35 x 384
# Reduction-A block
net = block_reduction_a(net, scope='Mixed_6a', is_train=is_train)
# 17 x 17 x 1024
# 7 x Inception-B blocks
for idx in range(7):
block_scope = 'Mixed_6' + chr(ord('b') + idx)
net = block_inception_b(net, scope=block_scope, is_train=is_train)
# 17 x 17 x 1024
# Reduction-B block
net = block_reduction_b(net, scope='Mixed_7a', is_train=is_train)
# 8 x 8 x 1536
# 3 x Inception-C blocks
for idx in range(3):
block_scope = 'Mixed_7' + chr(ord('b') + idx)
net = block_inception_c(net, scope=block_scope, is_train=is_train)
if self.flatten_output and not self.include_FC_head:
net = tl.layers.FlattenLayer(net, name='flatten')
if self.include_FC_head:
with tf.variable_scope("Logits", reuse=reuse):
# 8 x 8 x 1536
net = tl.layers.MeanPool2d(
net, filter_size=net.outputs.get_shape()[1:3], strides=(1, 1), padding='VALID',
name='AvgPool_1a'
)
# 1 x 1 x 1536
net = tl.layers.DropoutLayer(net, keep=0.8, is_fix=True, is_train=is_train, name='Dropout_1b')
net = tl.layers.FlattenLayer(net, name='PreLogitsFlatten')
# 1536
net, _ = dense_module(
net, n_units=1001, activation_fn="softmax", use_batchnorm=False, batch_norm_init=None,
is_train=is_train, name="Logits"
)
return net
def block_inception_c(inputs, scope=None, is_train=False):
"""Builds Inception-C block for Inception v4 network."""
# By default use stride=1 and SAME padding
with tf.variable_scope(scope, 'BlockInceptionC', [inputs]):
with tf.variable_scope('Branch_0'):
branch_0, _ = conv_module(inputs,
n_out_channel=256,
filter_size=(1, 1),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1, _ = conv_module(inputs,
n_out_channel=384,
filter_size=(1, 1),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0a_1x1')
branch_1a, _ = conv_module(branch_1,
n_out_channel=256,
filter_size=(1, 3),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0b_1x3')
branch_1b, _ = conv_module(branch_1,
n_out_channel=256,
filter_size=(3, 1),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0c_3x1')
branch_1 = tl.layers.ConcatLayer([branch_1a, branch_1b],
concat_dim=3,
name='concat_layer')
with tf.variable_scope('Branch_2'):
branch_2, _ = conv_module(inputs,
n_out_channel=384,
filter_size=(1, 1),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0a_1x1')
branch_2, _ = conv_module(branch_2,
n_out_channel=448,
filter_size=(3, 1),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0b_3x1')
branch_2, _ = conv_module(branch_2,
n_out_channel=512,
filter_size=(1, 3),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0c_1x3')
branch_2a, _ = conv_module(branch_2,
n_out_channel=256,
filter_size=(1, 3),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0d_1x3')
branch_2b, _ = conv_module(branch_2,
n_out_channel=256,
filter_size=(3, 1),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0e_3x1')
branch_2 = tl.layers.ConcatLayer([branch_2a, branch_2b],
concat_dim=3,
name='concat_layer')
with tf.variable_scope('Branch_3'):
branch_3 = tl.layers.MeanPool2d(inputs,
filter_size=(3, 3),
strides=(1, 1),
padding='SAME',
name='AvgPool_0a_3x3')
branch_3, _ = conv_module(branch_3,
n_out_channel=256,
filter_size=(1, 1),
strides=(1, 1),
padding='SAME',
batch_norm_init=None,
is_train=is_train,
use_batchnorm=True,
activation_fn='ReLU',
name='Conv2d_0b_1x1')
return tl.layers.ConcatLayer([branch_0, branch_1, branch_2, branch_3],
concat_dim=3,
name='concat_layer')
