def model(is_training, resue, num_classes=5):
common_args = common_layer_args(is_training, reuse)
conv_args = make_args(
untie_biases=True, batch_norm=batch_norm, **common_args)
logit_args = make_args(activation=prelu, **common_args)
inputs = input((None, crop_size[1], crop_size[0], 3), **common_args)
net = conv2d(inputs, 32, filter_size=(3, 3), stride=(
2, 2), name='conv1', **conv_params)
net = conv2d(net, 64, name='conv2', **conv_params)
net = bottleneck_v1(net, num_unit=128, name='block_v1_1', **conv_args)
net = bottleneck_v1(net, num_unit=256, name='block_v1_2', **conv_args)
net = bottleneck_v1(net, num_unit=728, name='block_v1_3', **conv_args)
for i in range(8):
prefix = 'block_v2_' + str(i + 5)
net = bottleneck_v2(net, num_unit=728, name=prefix, **kwargs)
net = bottleneck_v1(net, num_unit=1024, name='block_v1_4', **conv_args)
net = separable_conv2d(net, 1536, filter_size=(3, 3), stride=(1, 1),
name='sconv1', **kwargs)
net = separable_conv2d(net, 2048, filter_size=(3, 3), stride=(1, 1),
name='sconv2', **kwargs)
with tf.variable_scope('Logits'):
net = avg_pool_2d(net, net.get_shape()[1:3], name='AvgPool_1a')
net = dropout(
net, is_training, drop_p=1 - dropout_keep_prob, name='Dropout_1b')
logits = fully_connected(net, num_classes,
name='logits', **logit_args)
predictions = softmax(logits, name='predictions', **common_args)
return end_points(is_training)
def model(is_training, reuse, dropout_keep_prob=0.5):
common_args = common_layer_args(is_training, reuse)
conv_args = make_args(batch_norm=True, activation=prelu, w_init=initz.he_normal(
scale=1), untie_biases=False, **common_args)
pool_args = make_args(padding='SAME', **common_args)
inputs = input((None, crop_size[1], crop_size[0], 3), **common_args)
with tf.variable_scope('squeezenet', values=[inputs]):
net = conv2d(inputs, 96, stride=(2, 2), name='conv1', **conv_args)
net = max_pool(net, name='maxpool1', **pool_args)
net = fire_module(net, 16, 64, name='fire2', **conv_args)
net = fire_module(net, 16, 64, name='fire3', **conv_args)
net = fire_module(net, 32, 128, name='fire4', **conv_args)
net = max_pool(net, name='maxpool4', **pool_args)
net = fire_module(net, 32, 128, name='fire5', **conv_args)
net = fire_module(net, 48, 192, name='fire6', **conv_args)
net = fire_module(net, 48, 192, name='fire7', **conv_args)
net = fire_module(net, 64, 256, name='fire8', **conv_args)
net = max_pool(net, name='maxpool8', **pool_args)
net = fire_module(net, 64, 256, name='fire9', **conv_args)
# Reversed avg and conv layers per 'Network in Network'
net = dropout(net, drop_p=1 - dropout_keep_prob,
name='dropout6', **common_args)
net = conv2d(net, 10, filter_size=(1, 1), name='conv10', **conv_args)
logits = global_avg_pool(net, name='logits', **pool_args)
predictions = softmax(logits, name='predictions', **common_args)
return end_points(is_training)
def model(is_training, reuse):
common_args = common_layer_args(is_training, reuse)
fc_args = make_args(activation=relu, **common_args)
logit_args = make_args(activation=None, **common_args)
x = input((None, height * width), **common_args)
x = fully_connected(x, n_output=100, name='fc1', **fc_args)
logits = fully_connected(x, n_output=10, name="logits", **logit_args)
predictions = softmax(logits, name='predictions', **common_args)
return end_points(is_training)
def model(is_training, reuse):
common_args = common_layer_args(is_training, reuse)
x = input((None, 7, 7, 512), **common_args)
# x = batch_norm_tf(x, **common_args)
x = fully_connected(x, 512, activation=relu, name='fc1', **common_args)
x = dropout(x, drop_p=0.5, name='dropout1', **common_args)
logits = fully_connected(x,
6,
activation=None,
name='logits',
**common_args)
predictions = softmax(logits, name='predictions', **common_args)
return end_points(is_training)
def model(is_training, reuse):
common_args = common_layer_args(is_training, reuse)
conv_args = make_args(batch_norm=None, activation=prelu, **common_args)
fc_args = make_args(activation=prelu, **common_args)
logit_args = make_args(activation=None, **common_args)
x = input((None, crop_size[1], crop_size[0], 1), **common_args)
x = conv2d(x, 32, name='conv1_1', **conv_args)
x = conv2d(x, 32, name='conv1_2', **conv_args)
x = max_pool(x, name='pool1', **common_args)
x = dropout(x, drop_p=0.25, name='dropout1', **common_args)
x = fully_connected(x, n_output=128, name='fc1', **fc_args)
x = dropout(x, drop_p=0.5, name='dropout2', **common_args)
logits = fully_connected(x, n_output=36, name="logits", **logit_args)
predictions = softmax(logits, name='predictions', **common_args)
return end_points(is_training)
def model(is_training, reuse):
common_args = common_layer_args(is_training, reuse)
conv_args = make_args(activation=relu, **common_args)
pool_args = make_args(filter_size=(2, 2), **common_args)
fc_args = make_args(activation=relu, **common_args)
logit_args = make_args(activation=None, **common_args)
x = input((None, crop_size[1], crop_size[0], 3), **common_args)
x = conv2d(x, 64, name='conv1_1', **conv_args)
x = conv2d(x, 64, name='conv1_2', **conv_args)
x = max_pool(x, name='maxpool1', **pool_args)
x = conv2d(x, 128, name='conv2_1', **conv_args)
x = conv2d(x, 128, name='conv2_2', **conv_args)
x = max_pool(x, name='maxpool2', **pool_args)
x = conv2d(x, 256, name='conv3_1', **conv_args)
x = conv2d(x, 256, name='conv3_2', **conv_args)
x = conv2d(x, 256, name='conv3_3', **conv_args)
x = max_pool(x, name='maxpool3', **pool_args)
x = conv2d(x, 512, name='conv4_1', **conv_args)
x = conv2d(x, 512, name='conv4_2', **conv_args)
x = conv2d(x, 512, name='conv4_3', **conv_args)
x = max_pool(x, name='maxpool4', **pool_args)
x = conv2d(x, 512, name='conv5_1', **conv_args)
x = conv2d(x, 512, name='conv5_2', **conv_args)
x = conv2d(x, 512, name='conv5_3', **conv_args)
x = max_pool(x, name='maxpool5', **pool_args)
x = fully_connected(x, n_output=4096, name='fc6', **fc_args)
x = dropout(x, drop_p=0.5, name='dropout1', **common_args)
x = fully_connected(x, n_output=4096, name='fc7', **fc_args)
x = dropout(x, drop_p=0.5, name='dropout2', **common_args)
logits = fully_connected(x, n_output=1000, name="logits", **logit_args)
predictions = softmax(logits, name='predictions', **common_args)
return end_points(is_training)
def model(is_training, reuse, flexi_inputs=False):
common_args = common_layer_args(is_training, reuse)
conv_args = make_args(activation=relu, **common_args)
pool_args = make_args(filter_size=(2, 2), **common_args)
logit_args = make_args(activation=None, **common_args)
if flexi_inputs:
inputs_shape = (None, None, None, 3)
else:
inputs_shape = (None, crop_size[1], crop_size[0], 3)
net_inputs = input(inputs_shape, **common_args)
x = net_inputs
with tf.variable_scope('vgg_16', reuse=reuse):
mean_rgb = tf.get_variable(name='mean_rgb',
initializer=tf.truncated_normal(shape=[3]),
trainable=False)
x = x - mean_rgb
with tf.variable_scope('conv1'):
x = conv2d(x, 64, name='conv1_1', **conv_args)
x = conv2d(x, 64, name='conv1_2', **conv_args)
x = max_pool(x, name='maxpool1', **pool_args)
with tf.variable_scope('conv2'):
x = conv2d(x, 128, name='conv2_1', **conv_args)
x = conv2d(x, 128, name='conv2_2', **conv_args)
x = max_pool(x, name='maxpool2', **pool_args)
with tf.variable_scope('conv3'):
x = conv2d(x, 256, name='conv3_1', **conv_args)
x = conv2d(x, 256, name='conv3_2', **conv_args)
x = conv2d(x, 256, name='conv3_3', **conv_args)
x = max_pool(x, name='maxpool3', **pool_args)
with tf.variable_scope('conv4'):
x = conv2d(x, 512, name='conv4_1', **conv_args)
x = conv2d(x, 512, name='conv4_2', **conv_args)
x = conv2d(x, 512, name='conv4_3', **conv_args)
x = max_pool(x, name='maxpool4', **pool_args)
with tf.variable_scope('conv5'):
x = conv2d(x, 512, name='conv5_1', **conv_args)
x = conv2d(x, 512, name='conv5_2', **conv_args)
x = conv2d(x, 512, name='conv5_3', **conv_args)
x = max_pool(x, name='maxpool5', **pool_args)
x = conv2d(x,
4096,
name='fc6',
filter_size=(7, 7),
padding='VALID',
**conv_args)
x = dropout(x, drop_p=0.5, name='dropout6', **common_args)
x = conv2d(x, 4096, name='fc7', filter_size=(1, 1), **conv_args)
x = dropout(x, drop_p=0.5, name='dropout7', **common_args)
x = conv2d(x, 1000, name='fc8', filter_size=(1, 1), **logit_args)
if flexi_inputs:
logits = alias(x, name='logits', **common_args)
else:
logits = squeeze(x, axis=[1, 2], name='logits', **common_args)
predictions = softmax(logits, name='predictions', **common_args)
return end_points(is_training)
def vgg_16(is_training, reuse,
num_classes=1000,
dropout_keep_prob=0.5,
spatial_squeeze=True,
name='vgg_16'):
"""Oxford Net VGG 16-Layers version D Example.
Note: All the fully_connected layers have been transformed to conv2d layers.
To use in classification mode, resize input to 224x224.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether or not the model is being trained.
dropout_keep_prob: the probability that activations are kept in the dropout
layers during training.
spatial_squeeze: whether or not should squeeze the spatial dimensions of the
outputs. Useful to remove unnecessary dimensions for classification.
name: Optional name for the variables.
Returns:
the last op containing the log predictions and end_points dict.
"""
common_args = common_layer_args(is_training, reuse)
conv_args = make_args(batch_norm=True, activation=prelu, w_init=initz.he_normal(
scale=1), untie_biases=False, **common_args)
logit_args = make_args(
activation=None, w_init=initz.he_normal(scale=1), **common_args)
pred_args = make_args(
activation=prelu, w_init=initz.he_normal(scale=1), **common_args)
pool_args = make_args(padding='SAME', **common_args)
inputs = input((None, crop_size[1], crop_size[0], 3), **common_args)
with tf.variable_scope(name, 'vgg_16', [inputs]):
net = repeat(inputs, 2, conv2d,
64, filter_size=(3, 3), name='conv1', **conv_args)
net = max_pool(net, name='pool1', **pool_args)
net = repeat(net, 2, conv2d, 128, filter_size=(
3, 3), name='conv2', **conv_args)
net = max_pool(net, name='pool2', **pool_args)
net = repeat(net, 3, conv2d, 256, filter_size=(
3, 3), name='conv3', **conv_args)
net = max_pool(net, name='pool3', **pool_args)
net = repeat(net, 3, conv2d, 512, filter_size=(
3, 3), name='conv4', **conv_args)
net = max_pool(net, name='pool4', **pool_args)
net = repeat(net, 3, conv2d, 512, filter_size=(
3, 3), name='conv5', **conv_args)
net = max_pool(net, name='pool5', **pool_args)
# Use conv2d instead of fully_connected layers.
net = conv2d(net, 4096, filter_size=(7, 7), name='fc6', **conv_args)
net = dropout(net, drop_p=1 - dropout_keep_prob, is_training=is_training,
name='dropout6', **common_args)
net = conv2d(net, 4096, filter_size=(1, 1), name='fc7', **conv_args)
net = dropout(net, drop_p=1 - dropout_keep_prob, is_training=is_training,
name='dropout7', **common_args)
logits = conv2d(net, num_classes, filter_size=(1, 1),
activation=None,
name='logits', **logit_args)
# Convert end_points_collection into a end_point dict.
if spatial_squeeze:
logits = tf.squeeze(logits, [1, 2], name='logits/squeezed')
predictions = softmax(logits, name='predictions', **pred_args)
return end_points(is_training)
def model(is_training, reuse, inputs=None):
common_trainable_args = common_layer_args(is_training,
reuse,
trainable=True)
common_frozen_args = common_layer_args(is_training, reuse, trainable=False)
conv_args = make_conv_args(activation=relu, **common_frozen_args)
logit_args = make_args(activation=None, **common_trainable_args)
common_args = common_frozen_args
# move this down to train only a few layers
common_args = common_trainable_args
if inputs is None:
net = input((None, crop_size[1], crop_size[0], 3), **common_args)
else:
net = inputs
with tf.variable_scope('resnet_v1_50', reuse=reuse):
mean_rgb = tf.get_variable(name='mean_rgb',
initializer=tf.truncated_normal(shape=[3]),
trainable=False)
net = net - mean_rgb
net = conv2d_same(net,
64,
filter_size=(7, 7),
stride=(2, 2),
name='conv1',
**conv_args)
net = max_pool(net,
filter_size=(3, 3),
stride=(2, 2),
padding='SAME',
name='pool1')
with tf.variable_scope('block1') as sc:
with tf.variable_scope('unit_1'):
net = bottleneck(net, 256, 64, 1, **common_args)
with tf.variable_scope('unit_2'):
net = bottleneck(net, 256, 64, 1, **common_args)
with tf.variable_scope('unit_3'):
net = bottleneck(net, 256, 64, 2, **common_args)
net = collect_named_outputs(common_args['outputs_collections'],
sc.name, net)
with tf.variable_scope('block2') as sc:
with tf.variable_scope('unit_1'):
net = bottleneck(net, 512, 128, 1, **common_args)
with tf.variable_scope('unit_2'):
net = bottleneck(net, 512, 128, 1, **common_args)
with tf.variable_scope('unit_3'):
net = bottleneck(net, 512, 128, 1, **common_args)
with tf.variable_scope('unit_4'):
net = bottleneck(net, 512, 128, 2, **common_args)
net = collect_named_outputs(common_args['outputs_collections'],
sc.name, net)
with tf.variable_scope('block3') as sc:
with tf.variable_scope('unit_1'):
net = bottleneck(net, 1024, 256, 1, **common_args)
with tf.variable_scope('unit_2'):
net = bottleneck(net, 1024, 256, 1, **common_args)
with tf.variable_scope('unit_3'):
net = bottleneck(net, 1024, 256, 1, **common_args)
with tf.variable_scope('unit_4'):
net = bottleneck(net, 1024, 256, 1, **common_args)
with tf.variable_scope('unit_5'):
net = bottleneck(net, 1024, 256, 1, **common_args)
with tf.variable_scope('unit_6'):
net = bottleneck(net, 1024, 256, 2, **common_args)
net = collect_named_outputs(common_args['outputs_collections'],
sc.name, net)
with tf.variable_scope('block4') as sc:
with tf.variable_scope('unit_1'):
net = bottleneck(net, 2048, 512, 1, **common_args)
with tf.variable_scope('unit_2'):
net = bottleneck(net, 2048, 512, 1, **common_args)
with tf.variable_scope('unit_3'):
net = bottleneck(net, 2048, 512, 1, **common_args)
net = collect_named_outputs(common_args['outputs_collections'],
sc.name, net)
net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)
net = conv2d(net,
1000,
filter_size=(1, 1),
name='logits',
**logit_args)
logits = squeeze(net, axis=[1, 2], name='logits', **common_args)
predictions = softmax(logits, name='predictions', **common_args)
return end_points(common_args['is_training'])
def alexnet_v2(is_training,
reuse,
num_classes=1000,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='alexnet_v2'):
"""AlexNet version 2.
Described in: http://arxiv.org/pdf/1404.5997v2.pdf
Parameters from:
github.com/akrizhevsky/cuda-convnet2/blob/master/layers/
layers-imagenet-1gpu.cfg
Note: All the fully_connected layers have been transformed to conv2d layers.
To use in classification mode, resize input to 224x224. To use in fully
convolutional mode, set spatial_squeeze to false.
The LRN layers have been removed and change the initializers from
random_normal_initializer to xavier_initializer.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether or not the model is being trained.
dropout_keep_prob: the probability that activations are kept in the dropout
layers during training.
spatial_squeeze: whether or not should squeeze the spatial dimensions of the
outputs. Useful to remove unnecessary dimensions for classification.
scope: Optional scope for the variables.
Returns:
the last op containing the log predictions and end_points dict.
"""
common_args = common_layer_args(is_training, reuse)
conv_args = make_args(batch_norm=True,
activation=prelu,
w_init=initz.he_normal(scale=1),
untie_biases=False,
**common_args)
logit_args = make_args(activation=None,
w_init=initz.he_normal(scale=1),
**common_args)
pred_args = make_args(activation=prelu,
w_init=initz.he_normal(scale=1),
**common_args)
pool_args = make_args(padding='SAME', **common_args)
inputs = input((None, crop_size[1], crop_size[0], 3), **common_args)
with tf.variable_scope(scope, 'alexnet_v2', [inputs]):
net = conv2d(inputs,
64,
filter_size=(11, 11),
stride=(4, 4),
scope='conv1',
**conv_args)
net = max_pool(net, stride=(2, 2), scope='pool1', **pool_args)
net = conv2d(net, 192, filter_size=(5, 5), scope='conv2', **conv_args)
net = max_pool(net, stride=(2, 2), scope='pool2', **pool_args)
net = conv2d(net, 384, scope='conv3', **conv_args)
net = conv2d(net, 384, scope='conv4', **conv_args)
net = conv2d(net, 256, scope='conv5', **conv_args)
net = max_pool(net, stride=(2, 2), scope='pool5', **pool_args)
# Use conv2d instead of fully_connected layers.
net = conv2d(net, 4096, filter_size=(5, 5), scope='fc6', **conv_args)
net = dropout(net,
drop_p=1 - dropout_keep_prob,
scope='dropout6',
**common_args)
net = conv2d(net, 4096, filter_size=(1, 1), scope='fc7', **conv_args)
net = dropout(net,
drop_p=1 - dropout_keep_prob,
scope='dropout7',
**common_args)
logits = conv2d(net,
num_classes,
filter_size=(1, 1),
activation=None,
scope='logits',
**logit_args)
if spatial_squeeze:
logits = tf.squeeze(logits, [1, 2], name='fc8/squeezed')
predictions = softmax(logits, name='predictions', **pred_args)
return end_points(is_training)
