def vgg_a(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_a'):
"""Oxford Net VGG 11-Layers version A 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.
scope: Optional scope for the variables.
Returns:
the last op containing the log predictions and end_points dict.
"""
with variable_scope.variable_scope(scope, 'vgg_a', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d],
outputs_collections=end_points_collection):
net = layers_lib.repeat(
inputs, 1, layers.conv2d, 64, [3, 3], scope='conv1')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
net = layers_lib.repeat(net, 1, layers.conv2d, 128, [3, 3], scope='conv2')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
net = layers_lib.repeat(net, 2, layers.conv2d, 256, [3, 3], scope='conv3')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv4')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv5')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
# Use conv2d instead of fully_connected layers.
net = layers.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout7')
net = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='fc8')
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(end_points_collection)
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
def alexnet_v2(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.7,
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.
"""
with variable_scope.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope([layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d], outputs_collections=[end_points_collection]):
net = layers.conv2d(inputs, 64, [11, 11], 4, padding='VALID', scope='conv1')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool1')
net = layers.conv2d(net, 192, [5, 5], scope='conv2')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool2')
net = layers.conv2d(net, 384, [3, 3], scope='conv3')
net = layers.conv2d(net, 384, [3, 3], scope='conv4')
net = layers.conv2d(net, 256, [3, 3], scope='conv5')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool5')
# Use conv2d instead of fully_connected layers.
with arg_scope([layers.conv2d], weights_initializer=trunc_normal(0.005), biases_initializer=init_ops.constant_initializer(0.1)):
net = layers.conv2d(net, 4096, [5, 5], padding='VALID', scope='fc6')
net = slim.batch_norm(net, decay=0.9999, center=True, scale=False, epsilon=0.001, is_training=is_training, scope='bn1')
net = layers_lib.dropout(net, dropout_keep_prob, is_training=is_training, scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = slim.batch_norm(net, decay=0.9999, center=True, scale=False, epsilon=0.001, is_training=is_training, scope='bn2')
net = layers_lib.dropout(net, dropout_keep_prob, is_training=is_training, scope='dropout7')
net = layers.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, biases_initializer=init_ops.zeros_initializer(), scope='fc8')
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(end_points_collection)
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
def alexnet(inputs,
is_training=True,
dropout_keep_prob=0.5, ):
with tf.variable_scope("alexnet", reuse=tf.AUTO_REUSE):
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d],
data_format="NCHW"):
x = layers.conv2d(
inputs, 64, [3, 3], padding='VALID', scope='conv1')
x = layers_lib.max_pool2d(x, [2, 2], 2, scope='pool1')
x = layers.conv2d(x, 192, [5, 5], scope='conv2')
x = layers_lib.max_pool2d(x, [2, 2], 2, scope='pool2')
x = layers.conv2d(x, 384, [3, 3], scope='conv3')
x = layers.conv2d(x, 384, [3, 3], scope='conv4')
x = layers.conv2d(x, 256, [3, 3], scope='conv5')
x = layers_lib.max_pool2d(x, [2, 2], 2, scope='pool5')
with arg_scope(
[layers.conv2d],
weights_initializer=trunc_normal(0.005),
biases_initializer=init_ops.constant_initializer(0.1)):
x = layers.conv2d(x, 4096, [3, 3], padding='VALID', scope='fc6')
x = layers_lib.dropout(x, dropout_keep_prob, is_training=is_training, scope='dropout6')
x = layers.conv2d(x, 4096, [1, 1], scope='fc7')
x = layers_lib.dropout(x, dropout_keep_prob, is_training=is_training, scope='dropout7')
x = layers.conv2d(x, NUM_CLASSES, [1, 1], activation_fn=None, normalizer_fn=None,
biases_initializer=init_ops.zeros_initializer(), scope='fc8')
x = tf.squeeze(x, [2, 3], name='fc8/squeezed')
return x
def vgg_a(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_a'):
"""Oxford Net VGG 11-Layers version A 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.
scope: Optional scope for the variables.
Returns:
the last op containing the log predictions and end_points dict.
"""
with variable_scope.variable_scope(scope, 'vgg_a', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d],
outputs_collections=end_points_collection):
net = layers_lib.repeat(
inputs, 1, layers.conv2d, 64, [3, 3], scope='conv1')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
net = layers_lib.repeat(net, 1, layers.conv2d, 128, [3, 3], scope='conv2')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
net = layers_lib.repeat(net, 2, layers.conv2d, 256, [3, 3], scope='conv3')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv4')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv5')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
# Use conv2d instead of fully_connected layers.
net = layers.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout7')
net = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='fc8')
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(end_points_collection)
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
def _embedding_alexnet(is_training, images, params):
with tf.variable_scope('Siamese', 'CFCASiamese', [images], reuse=tf.AUTO_REUSE):
with arg_scope(
[layers.conv2d], activation_fn=tf.nn.relu):
net = layers.conv2d(
images, 96, [11, 11], 4, padding='VALID', scope='conv1')
# net = layers.batch_norm(net, decay=0.9, epsilon=1e-06, is_training=is_training)
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool1')
net = layers.conv2d(net, 256, [5, 5], scope='conv2')
# net = layers.batch_norm(net, decay=0.9, epsilon=1e-06, is_training=is_training)
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool2')
net = layers_lib.dropout(
net, keep_prob=0.7, is_training=is_training)
net = layers.conv2d(net, 384, [3, 3], scope='conv3')
net = layers.conv2d(net, 256, [3, 3], scope='conv4')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool5')
net = layers_lib.dropout(
net, keep_prob=0.7, is_training=is_training)
net = layers_lib.flatten(net, scope='flatten1')
net = layers_lib.fully_connected(net, 1024, scope='fc1',
weights_regularizer=layers.l2_regularizer(0.0005))
net = layers_lib.dropout(
net, keep_prob=0.5, is_training=is_training)
net = layers_lib.fully_connected(net, params.embedding_size, scope='fc2',
weights_regularizer=layers.l2_regularizer(0.0005))
return net
def fully_connected_networks(net, num_classes, is_training, dropout_keep_prob):
# Use conv2d instead of fully_connected layers.
net = layers.conv2d(net, 1024, net.get_shape()[1:3], padding='VALID')
net = layers_lib.dropout(net, dropout_keep_prob, is_training=is_training)
net = layers.conv2d(net, 1024, [1, 1])
net = layers_lib.dropout(net, dropout_keep_prob, is_training=is_training)
net = layers.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None)
net = array_ops.squeeze(net, [1, 2])
net = tf.nn.softmax(net, name='predicts')
return net
def vgg_16_tcorr(inputs,
corr_features,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_16_tcorr'):
with variable_scope.variable_scope(scope, 'vgg_16_tcorr', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=end_points_collection):
forder = corr_features['vgg_16/conv1/conv1_1']
net, forder = init_conv_corr(inputs, 2, 64, [3, 3], 'conv1', forder, corr_features)
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
net, forder = repeat_conv_corr(net, 2, 128, [3, 3], 'conv2', forder, corr_features)
net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
net, forder = repeat_conv_corr(net, 3, 256, [3, 3], 'conv3', forder, corr_features)
net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
net, forder = repeat_conv_corr(net, 3, 512, [3, 3], 'conv4', forder, corr_features)
net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
net, _ = repeat_conv_corr(net, 3, 512, [3, 3], 'conv5', forder, corr_features)
net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
net = layers.conv2d(net, 512, [1,1], scope = 'fuse5')
# Use conv2d instead of fully_connected layers.
net = layers.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout7')
net = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='fc8')
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(end_points_collection)
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
def slim_net_original(image, keep_prob):
with arg_scope([layers.conv2d, layers.fully_connected], biases_initializer=tf.random_normal_initializer(stddev=0.1)):
# conv2d(inputs, num_outputs, kernel_size, stride=1, padding='SAME',
# activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None,
# weights_initializer=initializers.xavier_initializer(), weights_regularizer=None,
# biases_initializer=init_ops.zeros_initializer, biases_regularizer=None, scope=None):
net = layers.conv2d(image, 32, [5, 5], scope='conv1', weights_regularizer=regularizers.l1_regularizer(0.5))
# max_pool(inputs, kernel_size, stride=2, padding='VALID', scope=None)
net = layers.max_pool2d(net, 2, scope='pool1')
net = layers.conv2d(net, 64, [5, 5], scope='conv2', weights_regularizer=regularizers.l2_regularizer(0.5))
summaries.summarize_tensor(net, tag='conv2')
net = layers.max_pool2d(net, 2, scope='pool2')
net = layers.flatten(net, scope='flatten1')
# fully_connected(inputs, num_outputs, activation_fn=nn.relu, normalizer_fn=None,
# normalizer_params=None, weights_initializer=initializers.xavier_initializer(),
# weights_regularizer=None, biases_initializer=init_ops.zeros_initializer,
# biases_regularizer=None, scope=None):
net = layers.fully_connected(net, 1024, scope='fc1')
# dropout(inputs, keep_prob=0.5, is_training=True, scope=None)
net = layers.dropout(net, keep_prob=keep_prob, scope='dropout1')
net = layers.fully_connected(net, 10, scope='fc2')
return net
def inception_v1(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
prediction_fn=layers_lib.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV1'):
"""Defines the Inception V1 architecture.
This architecture is defined in:
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
http://arxiv.org/pdf/1409.4842v1.pdf.
The default image size used to train this network is 224x224.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: the percentage of activation values that are retained.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
"""
# Final pooling and prediction
with variable_scope.variable_scope(
scope, 'InceptionV1', [inputs, num_classes], reuse=reuse) as scope:
with arg_scope(
[layers_lib.batch_norm, layers_lib.dropout], is_training=is_training):
net, end_points = inception_v1_base(inputs, scope=scope)
with variable_scope.variable_scope('Logits'):
net = layers_lib.avg_pool2d(
net, [7, 7], stride=1, scope='MaxPool_0a_7x7')
net = layers_lib.dropout(net, dropout_keep_prob, scope='Dropout_0b')
logits = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_0c_1x1')
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2], name='SpatialSqueeze')
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
def alexnet_v2(inputs,
is_training=True,
emb_size=4096,
dropout_keep_prob=0.5,
scope='alexnet_v2'):
inputs = tf.cast(inputs, tf.float32)
if new_shape is not None:
shape = new_shape
inputs = tf.image.resize_images(
inputs,
tf.constant(new_shape[:2]),
method=tf.image.ResizeMethod.BILINEAR)
else:
shape = img_shape
if is_training and augmentation_function is not None:
inputs = augmentation_function(inputs, shape)
if image_summary:
tf.summary.image('Inputs', inputs, max_outputs=3)
net = inputs
mean = tf.reduce_mean(net, [1, 2], True)
std = tf.reduce_mean(tf.square(net - mean), [1, 2], True)
net = (net - mean) / (std + 1e-5)
inputs = net
with variable_scope.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=[end_points_collection]):
net = layers.conv2d(
inputs, 64, [11, 11], 4, padding='VALID', scope='conv1')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool1')
net = layers.conv2d(net, 192, [5, 5], scope='conv2')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool2')
net = layers.conv2d(net, 384, [3, 3], scope='conv3')
net = layers.conv2d(net, 384, [3, 3], scope='conv4')
net = layers.conv2d(net, 256, [3, 3], scope='conv5')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool5')
net = slim.flatten(net, scope='flatten')
# Use conv2d instead of fully_connected layers.
with arg_scope(
[slim.fully_connected],
weights_initializer=trunc_normal(0.005),
biases_initializer=init_ops.constant_initializer(0.1)):
net = layers.fully_connected(net, 4096, scope='fc6')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout6')
net = layers.fully_connected(net, emb_size, scope='fc7')
return net
def alexnet_v2(inputs,
num_classes=2,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='alexnet_v2'):
inputs = tf.reshape(inputs, (-1, 80, 80, 1))
with variable_scope.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
debug_shape(inputs)
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=[end_points_collection]):
net = layers.conv2d(inputs,
64, [3, 3],
padding='VALID',
scope='conv1')
debug_shape(net)
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool1')
debug_shape(net)
net = layers.conv2d(net, 192, [5, 5], scope='conv2')
debug_shape(net)
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool2')
debug_shape(net)
net = layers.conv2d(net, 384, [3, 3], scope='conv3')
net = layers.conv2d(net, 384, [3, 3], scope='conv4')
net = layers.conv2d(net, 256, [3, 3], scope='conv5')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool5')
debug_shape(net)
with arg_scope(
[layers.conv2d],
weights_initializer=trunc_normal(0.005),
biases_initializer=init_ops.constant_initializer(0.1)):
net = layers.conv2d(net,
256, [7, 7],
2,
padding='VALID',
scope='fc6')
debug_shape(net)
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout6')
net = layers.conv2d(net, 256, [1, 1], scope='fc7')
debug_shape(net)
end_points = utils.convert_collection_to_dict(
end_points_collection)
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
def roi_fc(inputs, boxes, box_idx, scope='vgg_16'):
with variable_scope.variable_scope(scope, 'vgg_16', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=end_points_collection):
# Use conv2d instead of fully_connected layers.
net = roi_pooling(inputs, boxes, box_idx)
net = layers.conv2d(net,
4096, [7, 7],
padding='VALID',
scope='fc6')
net = layers_lib.dropout(net,
0.5,
is_training=True,
scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(net,
0.5,
is_training=True,
scope='dropout7')
return net
def inception_2d_fields(img,
fields,
num_classes=30,
is_training=True,
dropout_keep_prob=0.6,
prediction_fn=layers_lib.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV1_Fields'
):
with arg_scope([layers.conv2d, layers_lib.fully_connected],
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.constant_initializer(0.2),
weights_regularizer=regularizers.l2_regularizer(0.0002),
biases_regularizer=regularizers.l2_regularizer(0.0002)):
net, end_points = inception_2d.inception_v1_base(img, scope=scope, final_endpoint='Mixed_4b')
with variable_scope.variable_scope('Logits'):
net = layers_lib.avg_pool2d(net, [5, 5], stride=3, scope='AvgPool_0a_5x5')
net = layers.conv2d(inputs=net, num_outputs=128, kernel_size=1)
net = tf.reshape(net, [-1, 1, 1, 4 * 4 * 128])
net = array_ops.squeeze(net,[1,2],name='Squeeze4Fields')
net = tf.concat([net,fields],axis=1)
net = layers.fully_connected(inputs=net, num_outputs=1024)
net = layers_lib.dropout(net, dropout_keep_prob, scope='Dropout_0b')
logits = layers.fully_connected(inputs=net,
num_outputs=num_classes,
activation_fn=None,
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.constant_initializer(0.0),
weights_regularizer=regularizers.l2_regularizer(0.0002),
biases_regularizer=regularizers.l2_regularizer(0.0002),
scope='InnerProduct')
# logits = layers.conv2d(
# net,
# num_classes, [1, 1],
# activation_fn=None,
# normalizer_fn=None,
# scope='Conv2d_0c_1x1')
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2], name='SpatialSqueeze')
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
def slim_net_original(image, keep_prob):
with arg_scope(
[layers.conv2d, layers.fully_connected],
biases_initializer=tf.random_normal_initializer(stddev=0.1)):
# conv2d(inputs, num_outputs, kernel_size, stride=1, padding='SAME',
# activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None,
# weights_initializer=initializers.xavier_initializer(), weights_regularizer=None,
# biases_initializer=init_ops.zeros_initializer, biases_regularizer=None, scope=None):
net = layers.conv2d(
image,
32, [5, 5],
scope='conv1',
weights_regularizer=regularizers.l1_regularizer(0.5))
# max_pool(inputs, kernel_size, stride=2, padding='VALID', scope=None)
net = layers.max_pool2d(net, 2, scope='pool1')
net = layers.conv2d(
net,
64, [5, 5],
scope='conv2',
weights_regularizer=regularizers.l2_regularizer(0.5))
summaries.summarize_tensor(net, tag='conv2')
net = layers.max_pool2d(net, 2, scope='pool2')
net = layers.flatten(net, scope='flatten1')
# fully_connected(inputs, num_outputs, activation_fn=nn.relu, normalizer_fn=None,
# normalizer_params=None, weights_initializer=initializers.xavier_initializer(),
# weights_regularizer=None, biases_initializer=init_ops.zeros_initializer,
# biases_regularizer=None, scope=None):
net = layers.fully_connected(net, 1024, scope='fc1')
# dropout(inputs, keep_prob=0.5, is_training=True, scope=None)
net = layers.dropout(net, keep_prob=keep_prob, scope='dropout1')
net = layers.fully_connected(net, 10, scope='fc2')
return net
def alexnet_v2(inputs,
num_classes=1000,
is_training=True,
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.
"""
with variable_scope.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=[end_points_collection]):
net = layers.conv2d(
inputs, 64, [11, 11], 4, padding='VALID', scope='conv1')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool1')
net = layers.conv2d(net, 192, [5, 5], scope='conv2')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool2')
net = layers.conv2d(net, 384, [3, 3], scope='conv3')
net = layers.conv2d(net, 384, [3, 3], scope='conv4')
net = layers.conv2d(net, 256, [3, 3], scope='conv5')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool5')
# Use conv2d instead of fully_connected layers.
with arg_scope(
[layers.conv2d],
weights_initializer=trunc_normal(0.005),
biases_initializer=init_ops.constant_initializer(0.1)):
net = layers.conv2d(net, 4096, [5, 5], padding='VALID', scope='fc6')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout7')
net = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
biases_initializer=init_ops.zeros_initializer(),
scope='fc8')
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(end_points_collection)
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
def inception_v2(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
min_depth=16,
depth_multiplier=1.0,
prediction_fn=layers_lib.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV2'):
"""Inception v2 model for classification.
Constructs an Inception v2 network for classification as described in
http://arxiv.org/abs/1502.03167.
The default image size used to train this network is 224x224.
Args:
inputs: a tensor of shape [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: the percentage of activation values that are retained.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or depth_multiplier <= 0
"""
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
# Final pooling and prediction
with variable_scope.variable_scope(
scope, 'InceptionV2', [inputs, num_classes], reuse=reuse) as scope:
with arg_scope(
[layers_lib.batch_norm, layers_lib.dropout], is_training=is_training):
net, end_points = inception_v2_base(
inputs,
scope=scope,
min_depth=min_depth,
depth_multiplier=depth_multiplier)
with variable_scope.variable_scope('Logits'):
kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7])
net = layers_lib.avg_pool2d(
net,
kernel_size,
padding='VALID',
scope='AvgPool_1a_{}x{}'.format(*kernel_size))
# 1 x 1 x 1024
net = layers_lib.dropout(
net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
logits = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_1c_1x1')
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2], name='SpatialSqueeze')
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
def vgg_19_base(inputs, dropout_keep_prob=0.5, scope='None'):
"""Oxford Net VGG 19-Layers version E 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.
scope: Optional scope for the variables.
Returns:
the last op containing the log predictions and end_points dict.
"""
end_points = {}
with variable_scope.variable_scope(scope, 'vgg_19', [inputs]) as sc:
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope(
[layers.conv2d, layers_lib.fully_connected,
layers_lib.max_pool2d]):
net = layers_lib.repeat(inputs,
2,
layers.conv2d,
64, [3, 3],
scope='conv1')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
net = layers_lib.repeat(net,
2,
layers.conv2d,
128, [3, 3],
scope='conv2')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
net = layers_lib.repeat(net,
4,
layers.conv2d,
256, [3, 3],
scope='conv3')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
net = layers_lib.repeat(net,
4,
layers.conv2d,
512, [3, 3],
scope='conv4')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
net = layers_lib.repeat(net,
4,
layers.conv2d,
512, [3, 3],
scope='conv5')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
# Use conv2d instead of fully_connected layers.
net = layers.conv2d(net,
4096, [7, 7],
padding='VALID',
scope='fc6')
net = layers_lib.dropout(net, dropout_keep_prob, scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
end_point = 'VGG19_fc7'
end_points[end_point] = net
return net, end_points
def overfeat(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='overfeat'):
"""Contains the model definition for the OverFeat network.
The definition for the network was obtained from:
OverFeat: Integrated Recognition, Localization and Detection using
Convolutional Networks
Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
Yann LeCun, 2014
http://arxiv.org/abs/1312.6229
Note: All the fully_connected layers have been transformed to conv2d layers.
To use in classification mode, resize input to 231x231. To use in fully
convolutional mode, set spatial_squeeze to false.
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.
"""
with variable_scope.variable_scope(scope, 'overfeat', [inputs]) as sc:
end_points_collection = sc.name + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=end_points_collection):
net = layers.conv2d(
inputs, 64, [11, 11], 4, padding='VALID', scope='conv1')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
net = layers.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
net = layers.conv2d(net, 512, [3, 3], scope='conv3')
net = layers.conv2d(net, 1024, [3, 3], scope='conv4')
net = layers.conv2d(net, 1024, [3, 3], scope='conv5')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
with arg_scope(
[layers.conv2d],
weights_initializer=trunc_normal(0.005),
biases_initializer=init_ops.constant_initializer(0.1)):
# Use conv2d instead of fully_connected layers.
net = layers.conv2d(net, 3072, [6, 6], padding='VALID', scope='fc6')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(
net, dropout_keep_prob, is_training=is_training, scope='dropout7')
net = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
biases_initializer=init_ops.zeros_initializer(),
scope='fc8')
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(end_points_collection)
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
def overfeat(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='overfeat'):
"""Contains the model definition for the OverFeat network.
The definition for the network was obtained from:
OverFeat: Integrated Recognition, Localization and Detection using
Convolutional Networks
Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
Yann LeCun, 2014
http://arxiv.org/abs/1312.6229
Note: All the fully_connected layers have been transformed to conv2d layers.
To use in classification mode, resize input to 231x231. To use in fully
convolutional mode, set spatial_squeeze to false.
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.
"""
with variable_scope.variable_scope(scope, 'overfeat', [inputs]) as sc:
end_points_collection = sc.name + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=end_points_collection):
net = layers.conv2d(inputs,
64, [11, 11],
4,
padding='VALID',
scope='conv1')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
net = layers.conv2d(net,
256, [5, 5],
padding='VALID',
scope='conv2')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
net = layers.conv2d(net, 512, [3, 3], scope='conv3')
net = layers.conv2d(net, 1024, [3, 3], scope='conv4')
net = layers.conv2d(net, 1024, [3, 3], scope='conv5')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
with arg_scope(
[layers.conv2d],
weights_initializer=trunc_normal(0.005),
biases_initializer=init_ops.constant_initializer(0.1)):
# Use conv2d instead of fully_connected layers.
net = layers.conv2d(net,
3072, [6, 6],
padding='VALID',
scope='fc6')
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout7')
net = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
biases_initializer=init_ops.zeros_initializer(),
scope='fc8')
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(
end_points_collection)
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
def ldnet_v1(inputs,
num_classes=3,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope="ldnet",
use_deform_conv=True,
print_current_tensor=False):
"""
ldnet architecture:
input: 32*32*3
input depth kenal stride padding
conv0： net = conv(input, 32, [3, 3], 1, "same")
--> 32*32*32
conv1: net = conv(net, 32, [3, 3], 1, "same")
--> 32*32*32
conv2： net = conv(net, 64, [3, 3], 1, "same")
--> 32*32*64
maxpool1: net = pool(net, [3, 3], 1, "same")
--> 32*32*64
conv3: net = conv(net, 192, [3, 3], 1, "same")
--> 32*32*192
maxpool2: net = pool(net, [3, 3], 1, "same")
--> 32*32*192
ldnet blocks:
mixed_1: 32 x 32 x 320 Feature extraction module
mixed_2: 32 x 32 x 320 Feature extraction module
mixed_res1: 32 x 32 x 320 Feature extraction module
mixed_3: 16 x 16 x 640 Dimension reduction module
mixed_4: 16 x 16 x 640 Feature extraction module
mixed_res2: 16 x 16 x 640 Feature extraction module
mixed_5: 8 x 8 x 1280 Dimension reduction module
mixed_6: 8 x 8 x 1280 Feature extraction module
mixed_res3: 8 x 8 x 1280 Feature extraction module
Final pooling and prediction -> 3
:param inputs: the size of imputs is [batch_num, width, height, channel].
:param num_classes: num of classes predicted.
:param dropout_keep_prob: dropout probability.
:param spatial_squeeze: whether or not squeeze.
:param scope: optional scope.
:param use_deform_conv: whether to use deform conv.
:param print_current_tensor: whether or not print current tenser shape, name and type.
:return:
logits: [batch_size, num_classes]
"""
# end_points will collect relevant activations for the computation
# of shortcuts.
end_points = []
with variable_scope.variable_scope(scope, "ldnet_v1", [inputs]):
with arg_scope([layers.conv2d, layers_lib.max_pool2d],
kernel_size=[3, 3],
stride=1,
padding='SAME'):
# input: 32 * 32 * 3
net = inputs
end_point = "conv0"
# if use_deform_conv:
# net = ConvOffset2D(3, name='conv0_offset')(net) # net offset
net = layers.conv2d(net, 32, scope=end_point)
if print_current_tensor:
print(net)
# --> 32 * 32 * 32
end_point = "conv1"
if use_deform_conv:
net = ConvOffset2D(32, name='conv1_offset')(net) # net offset
net = layers.conv2d(net, 32, scope=end_point)
if print_current_tensor: print(net)
# --> 32 * 32 * 32
end_point = "conv2"
if use_deform_conv:
net = ConvOffset2D(32, name='conv2_offset')(net) # net offset
net = layers.conv2d(net, 64, scope=end_point)
if print_current_tensor: print(net)
# --> 32 * 32 * 64
end_point = "maxpool0"
net = layers_lib.max_pool2d(net,
kernel_size=[2, 2],
scope=end_point)
if print_current_tensor: print(net)
# --> 32 * 32 * 64
end_point = 'conv3'
if use_deform_conv:
net = ConvOffset2D(64, name='conv3_offset')(net) # net offset
net = layers.conv2d(net, 192, scope=end_point)
if print_current_tensor: print(net)
# end_points.append(net)
# --> 32 * 32 * 192
end_point = 'maxpool1'
net = layers_lib.max_pool2d(net,
kernel_size=[2, 2],
scope=end_point)
if print_current_tensor: print(net)
# net.alias = end_point
# end_points.append(net)
# --> 32 * 32 * 192
# ldnet blocks
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='SAME'):
# mixed_1: 32 x 32 x 320 Feature extraction module
end_point = 'mixed_1'
with variable_scope.variable_scope(end_point):
net = _feature_extraction_residual(net,
first_layer_depth=48,
second_layer_depth=64,
last_layer_depth=96,
scope='feature_extraction')
end_points.append(net)
if print_current_tensor: print(net, len(end_points))
# mixed_2: 32 x 32 x 320 Feature extraction module
end_point = 'mixed_2'
with variable_scope.variable_scope(end_point):
net = _feature_extraction_residual(
net,
first_layer_depth=48,
second_layer_depth=64,
last_layer_depth=96,
scope='feature_extraction_residual')
end_points.append(net)
if print_current_tensor: print(net, len(end_points))
# mixed_res1: 32 x 32 x 320 Feature extraction module
end_point = 'mixed_res1'
with variable_scope.variable_scope(end_point):
net = _feature_extraction_residual(
net,
first_layer_depth=48,
second_layer_depth=64,
last_layer_depth=96,
scope='feature_extraction_residual')
net_linear = layers.conv2d(net,
int(net.shape[3]), [1, 1],
activation_fn=None,
scope='net_linear_projection')
shortcuts = _shortcuts_addition(net.shape,
end_points[-1],
end_points[-2],
scope="shortcuts_addition")
net = nn_ops.relu(net_linear + shortcuts)
end_points.append(net)
if print_current_tensor: print(net, len(end_points))
# mixed_3: 16 x 16 x 640 Dimension reduction module
end_point = "mixed_3"
with variable_scope.variable_scope(end_point):
net = _dimension_reduction(net,
branch_0_depth=224,
branch_1_depth=96,
scope='dimension_reduction')
end_points.append(net)
if print_current_tensor: print(net, len(end_points))
# mixed_4: 16 x 16 x 640 Feature extraction module
end_point = "mixed_4"
with variable_scope.variable_scope(end_point):
net = _feature_extraction_residual(
net,
first_layer_depth=48 * 2,
second_layer_depth=64 * 2,
last_layer_depth=96 * 2,
scope='feature_extraction_residual')
end_points.append(net)
if print_current_tensor: print(net, len(end_points))
# mixed_res2: 16 x 16 x 640 Feature extraction module
end_point = "mixed_res2"
with variable_scope.variable_scope(end_point):
net = _feature_extraction_residual(
net,
first_layer_depth=48 * 2,
second_layer_depth=64 * 2,
last_layer_depth=96 * 2,
scope='feature_extraction_residual')
net_linear = layers.conv2d(net,
int(net.shape[3]), [1, 1],
activation_fn=None,
scope='net_linear_projection')
shortcuts = _shortcuts_addition(net.shape,
end_points[-1],
end_points[-2],
scope="shortcuts_addition")
net = nn_ops.relu(net_linear + shortcuts)
end_points.append(net)
if print_current_tensor: print(net, len(end_points))
# mixed_5: 8 x 8 x 1280 Dimension reduction module
end_point = "mixed_5"
with variable_scope.variable_scope(end_point):
net = _dimension_reduction(net,
branch_0_depth=224 * 2,
branch_1_depth=96 * 2,
scope='dimension_reduction')
end_points.append(net)
if print_current_tensor: print(net, len(end_points))
# mixed_6: 8 x 8 x 1280 Feature extraction module
end_point = "mixed_6"
with variable_scope.variable_scope(end_point):
net = _feature_extraction_residual(
net,
first_layer_depth=48 * 4,
second_layer_depth=64 * 4,
last_layer_depth=96 * 4,
scope='feature_extraction_residual')
end_points.append(net)
if print_current_tensor: print(net, len(end_points))
# mixed_res3: 8 x 8 x 1280 Feature extraction module
end_point = "mixed_res3"
with variable_scope.variable_scope(end_point):
net = _feature_extraction_residual(
net,
first_layer_depth=48 * 4,
second_layer_depth=64 * 4,
last_layer_depth=96 * 4,
scope='feature_extraction_residual')
net_linear = layers.conv2d(net,
int(net.shape[3]), [1, 1],
activation_fn=None,
scope='net_linear_projection')
shortcuts = _shortcuts_addition(net.shape,
end_points[-1],
end_points[-2],
scope="shortcuts_addition")
net = nn_ops.relu(net_linear + shortcuts)
end_points.append(net)
if print_current_tensor: print(net, len(end_points))
# Final pooling and prediction
with variable_scope.variable_scope('Logits'):
with arg_scope([layers.conv2d],
normalizer_fn=None,
normalizer_params=None):
net = layers.conv2d(net,
int(net.shape[3]), [3, 3],
stride=2,
scope='conv2d_1a_3x3')
# 4 x 4 x 1280
net = layers_lib.avg_pool2d(net, [4, 4],
padding='VALID',
scope='AvgPool_1b_4x4')
# 1 x 1 x 1280
# net = layers.conv2d(net, 640, [1, 1], scope='Conv2d_0c_1x1')
# local1
with variable_scope.variable_scope('local1') as scope:
# Move everything into depth so we can perform a single matrix multiply.
reshape = tf.reshape(net, [-1, 1280])
weights = _variable_with_weight_decay('weights',
shape=[1280, 640],
stddev=0.04,
wd=0.0001)
biases = _variable_on_cpu('biases', [640],
tf.constant_initializer(0.1))
net = tf.nn.relu(tf.matmul(reshape, weights) + biases,
name=scope.name)
# 1 x 1 x 640
net = layers_lib.dropout(net,
keep_prob=dropout_keep_prob,
scope='Dropout_0c')
# net = layers.conv2d(net, 320, [1, 1], scope='Conv2d_0d_1x1')
# local2
with variable_scope.variable_scope('local2') as scope:
weights = _variable_with_weight_decay('weights',
shape=[640, 320],
stddev=0.04,
wd=0.0001)
biases = _variable_on_cpu('biases', [320],
tf.constant_initializer(0.1))
net = tf.nn.relu(tf.matmul(net, weights) + biases,
name=scope.name)
# 1 x 1 x 320
net = layers_lib.dropout(net,
keep_prob=dropout_keep_prob,
scope='Dropout_0d')
net = tf.expand_dims(net, 1)
net = tf.expand_dims(net, 1)
logits = layers.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_0e_1x1')
# 1 x 1 x 3
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2],
name='SpatialSqueeze')
# 3
return logits
def inference(image_input):
#scope=alexnet_v2_arg_scope()
num_classes = 20
is_training = True
dropout_keep_prob = 0.5
spatial_squeeze = True
scope = 'alexnet_v2'
#with slim.arg_scope(alexnet_v2_arg_scope()):
with variable_scope.variable_scope(scope, 'alexnet_v2',
[image_input]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=[end_points_collection]) as here1:
#print(a)
net = layers.conv2d(
image_input,
64, [11, 11],
4,
weights_initializer=tf.constant_initializer(W1),
biases_initializer=tf.constant_initializer(b1),
activation_fn=nn.sigmoid,
padding='VALID',
scope='conv1')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool1')
net = layers.conv2d(
net,
192, [5, 5],
weights_initializer=tf.constant_initializer(W2),
biases_initializer=tf.constant_initializer(b2),
activation_fn=nn.sigmoid,
scope='conv2')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool2')
net = layers.conv2d(
net,
384, [3, 3],
weights_initializer=tf.constant_initializer(W3),
biases_initializer=tf.constant_initializer(b3),
activation_fn=nn.sigmoid,
scope='conv3')
net = layers.conv2d(
net,
384, [3, 3],
weights_initializer=tf.constant_initializer(W4),
biases_initializer=tf.constant_initializer(b4),
activation_fn=nn.sigmoid,
scope='conv4')
net = layers.conv2d(
net,
256, [3, 3],
weights_initializer=tf.constant_initializer(W5),
biases_initializer=tf.constant_initializer(b5),
activation_fn=nn.sigmoid,
scope='conv5')
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool5')
# print()
#print(here1)
# Use conv2d instead of fully_connected layers.
with arg_scope([layers.conv2d],
weights_initializer=trunc_normal(0.005),
biases_initializer=init_ops.constant_initializer(
0.1)) as here2:
#print(here2)
a = 3
net = layers.conv2d(net,
4096, [5, 5],
padding='VALID',
scope='fc6')
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout7')
net = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
biases_initializer=init_ops.zeros_initializer(),
scope='fc8')
#print(a)
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(
end_points_collection)
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
def inception_v3(inputs,
num_classes=12,
is_training=True,
dropout_keep_prob=0.8,
prediction_fn=layers_lib.softmax,
reuse=None,
hparam_string=None,
global_pool=True,
scope='InceptionV3'):
"""Inception model from http://arxiv.org/abs/1512.00567.
"Rethinking the Inception Architecture for Computer Vision"
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens,
Zbigniew Wojna.
With the default arguments this method constructs the exact model defined in
the paper. However, one can experiment with variations of the inception_v3
network by changing arguments dropout_keep_prob, min_depth and
depth_multiplier.
The default image size used to train this network is 299x299.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: the percentage of activation values that are retained.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: if 'depth_multiplier' is less than or equal to zero.
"""
hparams = create_hparams(hparam_string=hparam_string)
if hparams.depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * hparams.depth_multiplier), hparams.min_depth)
with variable_scope.variable_scope(scope,
'InceptionV3', [inputs, num_classes],
reuse=reuse) as scope:
with arg_scope([layers_lib.batch_norm, layers_lib.dropout],
is_training=is_training):
net, end_points = inception_v3_base(inputs,
scope=scope,
**hparams.values())
# Final pooling and prediction
with variable_scope.variable_scope('Logits'):
if global_pool:
# Global average pooling.
net_avg = math_ops.reduce_mean(net, [1, 2],
keep_dims=True,
name='global_avg_pool')
net_max = math_ops.reduce_max(net, [1, 2],
keep_dims=True,
name='global_max_pool')
net = array_ops.concat([net_avg, net_max], -1)
else:
kernel_size = _reduced_kernel_size_for_small_input(
net, [8, 8])
net = layers_lib.avg_pool2d(
net,
kernel_size,
padding='VALID',
scope='AvgPool_1a_{}x{}'.format(*kernel_size))
# 1 x 1 x 2048
net = layers_lib.dropout(net,
keep_prob=dropout_keep_prob,
scope='Dropout_1b')
end_points['PreLogits'] = net
# 2048
logits = layers.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_1c_1x1')
logits = layers_core.dense(array_ops.squeeze(
logits, [1, 2], name='SpatialSqueeze'),
num_classes,
name='logits')
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits,
scope='Predictions')
return logits, end_points
def vgg_16_small_img(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_16'):
# Collect outputs for conv2d, fully_connected and max_pool2d.
net = layers_lib.conv2d(inputs,
64, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv1")
net = tf.nn.relu(net, name="relu_conv1")
net = layers_lib.conv2d(net,
64, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv2")
net = tf.nn.relu(net, name="relu_conv2")
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
net = layers_lib.conv2d(net,
128, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv3")
net = tf.nn.relu(net, name="relu_conv3")
net = layers_lib.conv2d(net,
128, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv4")
net = tf.nn.relu(net, name="relu_conv4")
net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
net = layers_lib.conv2d(net,
256, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv5")
net = tf.nn.relu(net, name="relu_conv5")
net = layers_lib.conv2d(net,
256, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv6")
net = tf.nn.relu(net, name="relu_conv6")
net = layers_lib.conv2d(net,
256, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv7")
net = tf.nn.relu(net, name="relu_conv7")
net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
net = layers_lib.conv2d(net,
512, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv8")
net = tf.nn.relu(net, name="relu_conv8")
net = layers_lib.conv2d(net,
512, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv9")
net = tf.nn.relu(net, name="relu_conv9")
net = layers_lib.conv2d(net,
512, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv10")
net = tf.nn.relu(net, name="relu_conv10")
net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
net = layers_lib.conv2d(net,
512, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv11")
net = tf.nn.relu(net, name="relu_conv11")
net = layers_lib.conv2d(net,
512, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv12")
net = tf.nn.relu(net, name="relu_conv12")
net = layers_lib.conv2d(net,
512, [3, 3],
padding="SAME",
data_format="NHWC",
scope="conv13")
net = tf.nn.relu(net, name="relu_conv13")
net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
# Use conv2d instead of fully_connected layers.
net = layers.conv2d(net, 512, [1, 1], padding='VALID', scope='fc6')
net = tf.nn.relu(net, name="relu_fc6")
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout6')
net = layers.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='fc8')
# Convert end_points_collection into a end_point dict.
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
return net
def vgg_16(inputs,
num_classes=1000,
is_training=True,
dataset='cifar',
scope='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.
scope: Optional scope for the variables.
Returns:
the last op containing the log predictions and end_points dict.
"""
with variable_scope.variable_scope(scope, 'vgg_16', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=end_points_collection):
def ConvBatchRelu(layer_input, n_output_plane, name):
with variable_scope.variable_scope(name):
output = layers.conv2d(layer_input, n_output_plane, [3, 3], scope='conv')
output = layers.batch_norm(output, center=True, scale=True, activation_fn=tf.nn.relu,
is_training=is_training)
return output
filters = [64, 64, 128, 128, 256, 256, 256, 512, 512, 512, 512, 512, 512, 512]
if dataset == 'mnist':
filters = [_ // 4 for _ in filters]
elif dataset not in ('cifar', 'svhn'):
raise NotImplementedError("Dataset {} is not supported!".format(dataset))
net = ConvBatchRelu(inputs, filters[0], 'conv1_1')
net = ConvBatchRelu(net, filters[1], 'conv1_2')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
net = ConvBatchRelu(net, filters[2], 'conv2_1')
net = ConvBatchRelu(net, filters[3], 'conv2_2')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
net = ConvBatchRelu(net, filters[4], 'conv3_1')
net = ConvBatchRelu(net, filters[5], 'conv3_2')
net = ConvBatchRelu(net, filters[6], 'conv3_3')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
net = ConvBatchRelu(net, filters[7], 'conv4_1')
net = ConvBatchRelu(net, filters[8], 'conv4_2')
net = ConvBatchRelu(net, filters[9], 'conv4_3')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
net = ConvBatchRelu(net, filters[10], 'conv5_1')
net = ConvBatchRelu(net, filters[11], 'conv5_2')
net = ConvBatchRelu(net, filters[12], 'conv5_3')
if dataset == 'cifar':
net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
# Use conv2d instead of fully_connected layers.
net = layers.flatten(net, scope='flatten6')
net = layers_lib.dropout(net, 0.5, is_training=is_training, scope='dropout6')
net = layers.relu(net, filters[13])
net = layers_lib.dropout(net, 0.5, is_training=is_training, scope='dropout6')
net = layers.linear(net, num_classes)
# Convert end_points_collection into a end_point dict.
end_points = utils.convert_collection_to_dict(end_points_collection)
end_points[sc.name + '/fc8'] = net
return net, end_points
def ldnet(inputs, num_classes=3, dropout_keep_prob=0.5, spatial_squeeze=True, scope="ldnet",
print_current_tensor=False):
"""
ldnet architecture:
input: 32*32*3
input depth kenal stride padding
conv0： net = conv(input, 32, [3, 3], 1, "same")
--> 32*32*32
conv1: net = conv(net, 32, [3, 3], 1, "same")
--> 32*32*32
conv2： net = conv(net, 64, [3, 3], 1, "same")
--> 32*32*64
maxpool1: net = pool(net, [3, 3], 1, "same")
--> 32*32*64
conv3: net = conv(net, 192, [3, 3], 1, "same")
--> 32*32*192
maxpool2: net = pool(net, [3, 3], 1, "same")
--> 32*32*192
ldnet blocks:
mixed_1: 32 x 32 x 320 Feature extraction module
mixed_2: 16 x 16 x 640 Dimension reduction module
mixed_3: 16 x 16 x 640 Feature extraction module
mixed_4: 8 x 8 x 1280 Dimension reduction module
mixed_5: 4 x 4 x 1280 Dimension reduction module
Final pooling and prediction -> 3
:param inputs: the size of imputs is [batch_num, width, height, channel].
:param num_classes: num of classes.
:param dropout_keep_prob:
:param spatial_squeeze:
:param scope:
:param print_current_tensor: whether print current tenser shape, name and type.
:return:
logits: [batch, num_classes]
"""
with variable_scope.variable_scope(scope, "ldnet", [inputs]):
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
kernel_size=[3, 3],
stride=1,
padding='SAME'):
# input: 32 * 32 * 3
net = layers.conv2d(inputs, 32, scope="conv0")
if print_current_tensor: print(net)
# --> 32 * 32 * 32
net = layers.conv2d(net, 32, scope="conv1")
if print_current_tensor: print(net)
# --> 32 * 32 * 32
net = layers.conv2d(net, 64, scope="conv2")
if print_current_tensor: print(net)
# --> 32 * 32 * 64
net = layers_lib.max_pool2d(net, kernel_size=[2, 2], scope="maxpool0")
if print_current_tensor: print(net)
# --> 32 * 32 * 64
net = layers.conv2d(net, 192, scope="conv3")
if print_current_tensor: print(net)
# --> 32 * 32 * 192
net = layers_lib.max_pool2d(net, kernel_size=[2, 2], scope="maxpool1")
if print_current_tensor: print(net)
# --> 32 * 32 * 192
# ldnet blocks
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='SAME'):
# mixed_1: 32 x 32 x 320 Feature extraction module
with variable_scope.variable_scope("mixed_1"):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net, 48, [1, 1], scope='Conv2d_0a_1x1')
branch_0 = layers.conv2d(
branch_0, 64, [3, 3], scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, 48, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
branch_1 = layers.conv2d(
branch_1, 96, [5, 5], scope='Conv2d_0c_5x5')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net, 48, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(
branch_2, 64, [7, 7], scope='Conv2d_0b_7x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [5, 5], scope='AvgPool_0a_5x5')
branch_3 = layers.conv2d(
branch_3, 96, [1, 1], scope='Conv2d_0b_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
if print_current_tensor: print(net)
# mixed_2: 16 x 16 x 640 Dimension reduction module
with variable_scope.variable_scope("mixed_2"):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net,
224, [3, 3],
stride=2,
scope='Conv2d_1a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, 64, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
branch_1 = layers.conv2d(
branch_1,
96, [3, 3],
stride=2,
scope='Conv2d_1a_1x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(
net, [3, 3], stride=2, scope='MaxPool_1a_3x3')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
if print_current_tensor: print(net)
# mixed_3: 16 x 16 x 640 Feature extraction module
with variable_scope.variable_scope("mixed_3"):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net, 96, [1, 1], scope='Conv2d_0a_1x1')
branch_0 = layers.conv2d(
branch_0, 128, [3, 3], scope='Conv2d_0b_3x3')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, 96, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, 128, [5, 5], scope='Conv2d_0b_5x5')
branch_1 = layers.conv2d(
branch_1, 192, [5, 5], scope='Conv2d_0c_5x5')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(
net, 96, [1, 1], scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(
branch_2, 128, [7, 7], scope='Conv2d_0b_7x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [5, 5], scope='AvgPool_0a_5x5')
branch_3 = layers.conv2d(
branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
if print_current_tensor: print(net)
# mixed_4: 8 x 8 x 1280 Dimension reduction module
with variable_scope.variable_scope("mixed_4"):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net,
448, [3, 3],
stride=2,
scope='Conv2d_1a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, 64, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
branch_1 = layers.conv2d(
branch_1,
192, [3, 3],
stride=2,
scope='Conv2d_1a_1x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(
net, [3, 3], stride=2, scope='MaxPool_1a_3x3')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
if print_current_tensor: print(net)
# mixed_5: 4 x 4 x 1280 Dimension reduction module
with variable_scope.variable_scope("mixed_5"):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(
net,
448, [3, 3],
stride=2,
scope='Conv2d_1a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(
net, 64, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
branch_1 = layers.conv2d(
branch_1,
192, [3, 3],
stride=2,
scope='Conv2d_1a_1x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(
net, [3, 3], stride=2, scope='MaxPool_1a_3x3')
branch_2 = layers.conv2d(
branch_2, 640, [1, 1], scope='Conv2d_0b_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
if print_current_tensor: print(net)
# Final pooling and prediction
with variable_scope.variable_scope('Logits'):
net = layers_lib.avg_pool2d(
net,
[4, 4],
padding='VALID',
scope='AvgPool_1a_4x4')
# 1 x 1 x 1280
# net = layers.conv2d(net, 640, [1, 1], scope='Conv2d_0b_1x1')
# local1
with variable_scope.variable_scope('local1') as scope:
# Move everything into depth so we can perform a single matrix multiply.
reshape = tf.reshape(net, [-1, 1280])
weights = _variable_with_weight_decay('weights', shape=[1280, 640],
stddev=0.04, wd=0.0001)
biases = _variable_on_cpu('biases', [640], tf.constant_initializer(0.1))
net = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
# 1 x 1 x 640
net = layers_lib.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_0c')
# net = layers.conv2d(net, 320, [1, 1], scope='Conv2d_0d_1x1')
# local2
with variable_scope.variable_scope('local2') as scope:
weights = _variable_with_weight_decay('weights', shape=[640, 320],
stddev=0.04, wd=0.0001)
biases = _variable_on_cpu('biases', [320], tf.constant_initializer(0.1))
net = tf.nn.relu(tf.matmul(net, weights) + biases, name=scope.name)
# 1 x 1 x 320
net = layers_lib.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_0e')
net = tf.expand_dims(net, 1)
net = tf.expand_dims(net, 1)
logits = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_0f_1x1')
# 1 x 1 x 3
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2], name='SpatialSqueeze')
# 3
return logits
def alexnet_v2(inputs,
is_training=True,
dropout_keep_prob=0.5,
scope='alexnet_v2'):
"""Modified version of AlexNet version 2 with a deconvolutional expanding
path for semantic segmentation.
Described in: http://arxiv.org/pdf/1404.5997v2.pdf
Note: All the fully_connected layers have been transformed to conv2d layers.
Args:
inputs: a tensor of size [batch_size, 227, 227, 3].
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.
scope: Optional scope for the variables.
Returns:
The last layer containing a segmentation map of an image.
"""
net = layers.conv2d(inputs,
96, [11, 11],
4,
padding='VALID',
scope='conv1')
net = layers.conv2d(net, 192, 3, 2, padding='VALID', scope='pconv1')
net = layers.conv2d(net, 192, [5, 5], padding='VALID', scope='conv2')
net = layers.conv2d(net, 384, 3, 2, padding='VALID', scope='pconv2')
net = layers.conv2d(net, 384, [3, 3], padding='VALID', scope='conv3')
net = layers.conv2d(net, 384, [3, 3], padding='VALID', scope='conv4')
net = layers.conv2d(net, 256, [3, 3], padding='VALID', scope='conv5')
# Convolution net
with arg_scope([layers.conv2d],
weights_initializer=trunc_normal(0.005),
biases_initializer=init_ops.constant_initializer(0.1)):
net = layers.conv2d(net, 4096, [5, 5], padding='VALID', scope='fc6')
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout7')
net = layers.conv2d(
net,
2,
[1, 1], # Prediction is either 'car' or 'background' for Carvana.
padding='VALID',
activation_fn=tf.nn.sigmoid,
biases_initializer=init_ops.zeros_initializer(),
scope='fc8')
# Deconvolution net
with arg_scope([layers.conv2d_transpose],
padding='VALID',
activation_fn=nn_ops.relu):
net = layers.conv2d_transpose(net, 4096, 1, scope='convt9')
net = layers.conv2d_transpose(net, 4096, 1, scope='convt10')
net = layers.conv2d_transpose(net, 256, 5, scope='convt11')
net = layers.conv2d_transpose(net, 384, 3, scope='convt12')
net = layers.conv2d_transpose(net, 384, 3, scope='convt13')
net = layers.conv2d_transpose(net, 384, 3, scope='convt14')
net = layers.conv2d_transpose(net, 192, 3, 2, scope='convt15')
net = layers.conv2d_transpose(net, 192, 5, scope='convt16')
net = layers.conv2d_transpose(net, 96, 3, 2, scope='convt17')
net = layers.conv2d_transpose(net,
2,
11,
4,
activation_fn=tf.nn.sigmoid,
scope='convt18')
return net
def vgg_16(inputs, is_training=True, dropout_keep_prob=0.5, scope='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.
scope: Optional scope for the variables.
Returns:
the last op containing the log predictions and end_points dict.
"""
with slim.arg_scope(nets.vgg.vgg_arg_scope()):
with variable_scope.variable_scope(scope, 'vgg_16', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope([
layers.conv2d, layers_lib.fully_connected,
layers_lib.max_pool2d
],
outputs_collections=end_points_collection):
net = layers_lib.repeat(inputs,
2,
layers.conv2d,
64, [3, 3],
scope='conv1')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
net = layers_lib.repeat(net,
2,
layers.conv2d,
128, [3, 3],
scope='conv2')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
net = layers_lib.repeat(net,
3,
layers.conv2d,
256, [3, 3],
scope='conv3')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
net = layers_lib.repeat(net,
3,
layers.conv2d,
512, [3, 3],
scope='conv4')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
net = layers_lib.repeat(net,
3,
layers.conv2d,
512, [3, 3],
scope='conv5')
net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
# Use conv2d instead of fully_connected layers.
net = layers.conv2d(net,
4096, [7, 7],
padding='VALID',
scope='fc6')
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout6')
fc7 = layers.conv2d(net, 4096, [1, 1], scope='fc7')
fc7 = tf.squeeze(fc7, axis=(1, 2))
# fc8 = layers.conv2d(
# fc7,
# num_classes, [1, 1],
# activation_fn=None,
# normalizer_fn=None,
# scope='fc8')
return fc7
def inception_v3(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
min_depth=16,
depth_multiplier=1.0,
prediction_fn=layers_lib.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV3'):
"""Inception model from http://arxiv.org/abs/1512.00567.
"Rethinking the Inception Architecture for Computer Vision"
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens,
Zbigniew Wojna.
With the default arguments this method constructs the exact model defined in
the paper. However, one can experiment with variations of the inception_v3
network by changing arguments dropout_keep_prob, min_depth and
depth_multiplier.
The default image size used to train this network is 299x299.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: the percentage of activation values that are retained.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
To use this parameter, the input images must be smaller
than 300x300 pixels, in which case the output logit layer
does not contain spatial information and can be removed.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: if 'depth_multiplier' is less than or equal to zero.
"""
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with variable_scope.variable_scope(
scope, 'InceptionV3', [inputs, num_classes], reuse=reuse) as scope:
with arg_scope(
[layers_lib.batch_norm, layers_lib.dropout], is_training=is_training):
net, end_points = inception_v3_base(
inputs,
scope=scope,
min_depth=min_depth,
depth_multiplier=depth_multiplier)
# Auxiliary Head logits
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='SAME'):
aux_logits = end_points['Mixed_6e']
with variable_scope.variable_scope('AuxLogits'):
aux_logits = layers_lib.avg_pool2d(
aux_logits, [5, 5],
stride=3,
padding='VALID',
scope='AvgPool_1a_5x5')
aux_logits = layers.conv2d(
aux_logits, depth(128), [1, 1], scope='Conv2d_1b_1x1')
# Shape of feature map before the final layer.
kernel_size = _reduced_kernel_size_for_small_input(aux_logits, [5, 5])
aux_logits = layers.conv2d(
aux_logits,
depth(768),
kernel_size,
weights_initializer=trunc_normal(0.01),
padding='VALID',
scope='Conv2d_2a_{}x{}'.format(*kernel_size))
aux_logits = layers.conv2d(
aux_logits,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
weights_initializer=trunc_normal(0.001),
scope='Conv2d_2b_1x1')
if spatial_squeeze:
aux_logits = array_ops.squeeze(
aux_logits, [1, 2], name='SpatialSqueeze')
end_points['AuxLogits'] = aux_logits
# Final pooling and prediction
with variable_scope.variable_scope('Logits'):
kernel_size = _reduced_kernel_size_for_small_input(net, [8, 8])
net = layers_lib.avg_pool2d(
net,
kernel_size,
padding='VALID',
scope='AvgPool_1a_{}x{}'.format(*kernel_size))
# 1 x 1 x 2048
net = layers_lib.dropout(
net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
end_points['PreLogits'] = net
# 2048
logits = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_1c_1x1')
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2], name='SpatialSqueeze')
# 1000
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
def inception_v3_base(inputs,
final_endpoint='Mixed_7a',
min_depth=16,
depth_multiplier=1.0,
scope=None,
is_training=False):
"""Inception model from http://arxiv.org/abs/1512.00567.
Constructs an Inception v3 network from inputs to the given final endpoint.
This method can construct the network up to the final inception block
Mixed_7c.
Note that the names of the layers in the paper do not correspond to the names
of the endpoints registered by this function although they build the same
network.
Here is a mapping from the old_names to the new names:
Old name | New name
=======================================
conv0 | Conv2d_1a_3x3
conv1 | Conv2d_2a_3x3
conv2 | Conv2d_2b_3x3
pool1 | MaxPool_3a_3x3
conv3 | Conv2d_3b_1x1
conv4 | Conv2d_4a_3x3
pool2 | MaxPool_5a_3x3
mixed_35x35x256a | Mixed_5b
mixed_35x35x288a | Mixed_5c
mixed_35x35x288b | Mixed_5d
mixed_17x17x768a | Mixed_6a
mixed_17x17x768b | Mixed_6b
mixed_17x17x768c | Mixed_6c
mixed_17x17x768d | Mixed_6d
mixed_17x17x768e | Mixed_6e
mixed_8x8x1280a | Mixed_7a
mixed_8x8x2048a | Mixed_7b
mixed_8x8x2048b | Mixed_7c
Args:
inputs: a tensor of size [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c',
'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'].
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
scope: Optional variable_scope.
Returns:
tensor_out: output tensor corresponding to the final_endpoint.
end_points: a set of activations for external use, for example summaries or
losses.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or depth_multiplier <= 0
:param is_training:
"""
# end_points will collect relevant activations for external use, for example
# summaries or losses.
end_points = {}
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with variable_scope.variable_scope(scope, 'InceptionV3', [inputs]):
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='VALID'):
# 299 x 299 x 3
end_point = 'Conv2d_1a_3x3'
net = layers.conv2d(inputs,
depth(32), [3, 3],
stride=2,
scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 149 x 149 x 32
end_point = 'Conv2d_2a_3x3'
net = layers.conv2d(net, depth(32), [3, 3], scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 147 x 147 x 32
end_point = 'Conv2d_2b_3x3'
net = layers.conv2d(net,
depth(64), [3, 3],
padding='SAME',
scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 147 x 147 x 64
end_point = 'MaxPool_3a_3x3'
net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 73 x 73 x 64
end_point = 'Conv2d_3b_1x1'
net = layers.conv2d(net, depth(80), [1, 1], scope=end_point)
net = layers_lib.dropout(net,
keep_prob=0.9,
is_training=is_training)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 73 x 73 x 80.
end_point = 'Conv2d_4a_3x3'
net = layers.conv2d(net, depth(192), [3, 3], scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 71 x 71 x 192.
end_point = 'MaxPool_5a_3x3'
net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# 35 x 35 x 192.
# Inception blocks
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='SAME'):
# mixed: 35 x 35 x 256.
end_point = 'Mixed_5b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(48), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(64), [5, 5],
scope='Conv2d_0b_5x5')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(32), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_1: 35 x 35 x 288.
end_point = 'Mixed_5c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(48), [1, 1],
scope='Conv2d_0b_1x1')
branch_1 = layers.conv2d(branch_1,
depth(64), [5, 5],
scope='Conv_1_0c_5x5')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(64), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_2: 35 x 35 x 288.
end_point = 'Mixed_5d'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(48), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(64), [5, 5],
scope='Conv2d_0b_5x5')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = layers.conv2d(branch_2,
depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(64), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
net = layers_lib.dropout(net,
keep_prob=0.8,
is_training=is_training)
# mixed_3: 17 x 17 x 768.
end_point = 'Mixed_6a'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(384), [3, 3],
stride=2,
padding='VALID',
scope='Conv2d_1a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(64), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_1 = layers.conv2d(branch_1,
depth(96), [3, 3],
stride=2,
padding='VALID',
scope='Conv2d_1a_1x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(net, [3, 3],
stride=2,
padding='VALID',
scope='MaxPool_1a_3x3')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed4: 17 x 17 x 768.
end_point = 'Mixed_6b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(128), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(128), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(branch_1,
depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(128), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(128), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = layers.conv2d(branch_2,
depth(128), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = layers.conv2d(branch_2,
depth(128), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_5: 17 x 17 x 768.
end_point = 'Mixed_6c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(160), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(160), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(branch_1,
depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(160), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(160), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = layers.conv2d(branch_2,
depth(160), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = layers.conv2d(branch_2,
depth(160), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_6: 17 x 17 x 768.
end_point = 'Mixed_6d'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(160), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(160), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(branch_1,
depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(160), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(160), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = layers.conv2d(branch_2,
depth(160), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = layers.conv2d(branch_2,
depth(160), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_7: 17 x 17 x 768.
end_point = 'Mixed_6e'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(192), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(branch_1,
depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = layers.conv2d(branch_2,
depth(192), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = layers.conv2d(branch_2,
depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
net = layers_lib.dropout(net,
keep_prob=0.8,
is_training=is_training)
# mixed_8: 8 x 8 x 1280.
end_point = 'Mixed_7a'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
branch_0 = layers.conv2d(branch_0,
depth(320), [3, 3],
stride=2,
padding='VALID',
scope='Conv2d_1a_3x3')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(192), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(branch_1,
depth(192), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = layers.conv2d(branch_1,
depth(192), [7, 1],
scope='Conv2d_0c_7x1')
branch_1 = layers.conv2d(branch_1,
depth(192), [3, 3],
stride=2,
padding='VALID',
scope='Conv2d_1a_3x3')
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(net, [3, 3],
stride=2,
padding='VALID',
scope='MaxPool_1a_3x3')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_9: 8 x 8 x 2048.
end_point = 'Mixed_7b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(320), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(384), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = array_ops.concat([
layers.conv2d(branch_1,
depth(384), [1, 3],
scope='Conv2d_0b_1x3'),
layers.conv2d(branch_1,
depth(384), [3, 1],
scope='Conv2d_0b_3x1')
], 3)
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(448), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(384), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = array_ops.concat([
layers.conv2d(branch_2,
depth(384), [1, 3],
scope='Conv2d_0c_1x3'),
layers.conv2d(branch_2,
depth(384), [3, 1],
scope='Conv2d_0d_3x1')
], 3)
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
# mixed_10: 8 x 8 x 2048.
end_point = 'Mixed_7c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net,
depth(320), [1, 1],
scope='Conv2d_0a_1x1')
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net,
depth(384), [1, 1],
scope='Conv2d_0a_1x1')
branch_1 = array_ops.concat([
layers.conv2d(branch_1,
depth(384), [1, 3],
scope='Conv2d_0b_1x3'),
layers.conv2d(branch_1,
depth(384), [3, 1],
scope='Conv2d_0c_3x1')
], 3)
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net,
depth(448), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = layers.conv2d(branch_2,
depth(384), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = array_ops.concat([
layers.conv2d(branch_2,
depth(384), [1, 3],
scope='Conv2d_0c_1x3'),
layers.conv2d(branch_2,
depth(384), [3, 1],
scope='Conv2d_0d_3x1')
], 3)
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3],
scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3,
depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = array_ops.concat(
[branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
raise ValueError('Unknown final endpoint %s' % final_endpoint)
def vgg_16(inputs, boxes, box_idx, scope='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.
scope: Optional scope for the variables.
Returns:
the last op containing the log predictions and end_points dict.
"""
inputs -= tf.constant([123.68, 116.779, 103.939])
inputs /= 255
with variable_scope.variable_scope(scope, 'vgg_16', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with arg_scope(
[layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
outputs_collections=end_points_collection):
net = layers_lib.repeat(inputs,
2,
layers.conv2d,
64, [3, 3],
scope='conv1',
trainable=False)
net = layers_lib.max_pool2d(net, [2, 2],
scope='pool1',
padding="SAME")
net = layers_lib.repeat(net,
2,
layers.conv2d,
128, [3, 3],
scope='conv2',
trainable=False)
net = layers_lib.max_pool2d(net, [2, 2],
scope='pool2',
padding="SAME")
net = layers_lib.repeat(net,
3,
layers.conv2d,
256, [3, 3],
scope='conv3')
net = layers_lib.max_pool2d(net, [2, 2],
scope='pool3',
padding="SAME")
net = layers_lib.repeat(net,
3,
layers.conv2d,
512, [3, 3],
scope='conv4')
net = layers_lib.max_pool2d(net, [2, 2],
scope='pool4',
padding="SAME")
net = layers_lib.repeat(net,
3,
layers.conv2d,
512, [3, 3],
scope='conv5')
net = roi_pooling(net, boxes, box_idx)
# Use conv2d instead of fully_connected layers.
net = layers.conv2d(net,
4096, [7, 7],
padding='VALID',
scope='fc6')
net = layers_lib.dropout(net,
keep_prob=0.5,
is_training=True,
scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
net = layers_lib.dropout(net,
keep_prob=0.5,
is_training=True,
scope='dropout7')
net = tf.squeeze(net, axis=[1, 2])
return net
def stack_blocks_dense(net,
blocks,
output_stride=None,
outputs_collections=None,
dropout=False):
"""Stacks ResNet `Blocks` and controls output feature density.
First, this function creates scopes for the ResNet in the form of
'block_name/unit_1', 'block_name/unit_2', etc.
Second, this function allows the user to explicitly control the ResNet
output_stride, which is the ratio of the input to output spatial resolution.
This is useful for dense prediction tasks such as semantic segmentation or
object detection.
Most ResNets consist of 4 ResNet blocks and subsample the activations by a
factor of 2 when transitioning between consecutive ResNet blocks. This results
to a nominal ResNet output_stride equal to 8. If we set the output_stride to
half the nominal network stride (e.g., output_stride=4), then we compute
responses twice.
Control of the output feature density is implemented by atrous convolution.
Args:
net: A `Tensor` of size [batch, height, width, channels].
blocks: A list of length equal to the number of ResNet `Blocks`. Each
element is a ResNet `Block` object describing the units in the `Block`.
output_stride: If `None`, then the output will be computed at the nominal
network stride. If output_stride is not `None`, it specifies the requested
ratio of input to output spatial resolution, which needs to be equal to
the product of unit strides from the start up to some level of the ResNet.
For example, if the ResNet employs units with strides 1, 2, 1, 3, 4, 1,
then valid values for the output_stride are 1, 2, 6, 24 or None (which
is equivalent to output_stride=24).
outputs_collections: Collection to add the ResNet block outputs.
Returns:
net: Output tensor with stride equal to the specified output_stride.
Raises:
ValueError: If the target output_stride is not valid.
"""
# The current_stride variable keeps track of the effective stride of the
# activations. This allows us to invoke atrous convolution whenever applying
# the next residual unit would result in the activations having stride larger
# than the target output_stride.
current_stride = 1
# The atrous convolution rate parameter.
rate = 1
for block in blocks:
with variable_scope.variable_scope(block.scope, 'block', [net]) as sc:
for i, unit in enumerate(block.args):
if output_stride is not None and current_stride > output_stride:
raise ValueError(
'The target output_stride cannot be reached.')
with variable_scope.variable_scope('unit_%d' % (i + 1),
values=[net]):
# If we have reached the target output_stride, then we need to employ
# atrous convolution with stride=1 and multiply the atrous rate by the
# current unit's stride for use in subsequent layers.
if output_stride is not None and current_stride == output_stride:
net = block.unit_fn(net,
rate=rate,
**dict(unit, stride=1))
rate *= unit.get('stride', 1)
else:
net = block.unit_fn(net, rate=1, **unit)
current_stride *= unit.get('stride', 1)
# for dropout
if dropout and i > 0:
net = layers.dropout(net, keep_prob=0.5)
net = utils.collect_named_outputs(outputs_collections, sc.name,
net)
if output_stride is not None and current_stride != output_stride:
raise ValueError('The target output_stride cannot be reached.')
return net
def inception_g(inputs,
num_classes = 5,
dropout_keep_prob = 0.8,
prediction_fn = layers_lib.softmax,
scope='InceptionG'
):
end_points = {}
# conv2d
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='VALID'):
# 48 x 48 x 1
end_point = 'Conv2d_1a_3x3'
net = layers.conv2d(inputs, 32, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
# 23 x 23 x 32
end_point = 'Conv2d_2a_3x3'
net = layers.conv2d(net, 64, [3, 3], scope=end_point)
end_points[end_point] = net
# 21 x 21 x 64
end_point = 'MaxPool_3a_3x3'
net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope=end_point)
end_points[end_point] = net
# 10 x 10 x 64
end_point = 'Conv2d_3b_1x1'
net = layers.conv2d(net, 80, [1, 1], scope=end_point)
end_points[end_point] = net
# 8 x 8 x 80
end_point = 'Conv2d_4a_3x3'
net = layers.conv2d(net, 128, [3, 3], scope=end_point)
end_points[end_point] = net
# Inception blocks
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='SAME'):
# 8 x 8 x 144
end_point = 'Mixed_5a'
branch_0 = layers.conv2d(
net, 64, [1, 1], scope='Conv2d_0a_1x1')
branch_1 = layers.conv2d(
net, 48, [1, 1], scope='Conv2d_1a_1x1')
branch_1 = layers.conv2d(
branch_1, 64, [5, 5], scope='Conv2d_1b_5x5')
branch_2 = layers.conv2d(
net, 64, [1, 1], scope='Conv2d_2a_1x1')
branch_2 = layers.conv2d(
branch_2, 96, [3, 3], scope='Conv2d_2b_3x3')
branch_2 = layers.conv2d(
branch_2, 96, [3, 3], scope='Conv2d_2c_3x3')
branch_3 = layers_lib.avg_pool2d(net, [3, 3], scope='AvgPool_3a_3x3')
branch_3 = layers.conv2d(
branch_3, 64, [1, 1], scope='Conv2d_3c_1x1')
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
end_points[end_point] = net
# aver pooling and softmax
net = layers_lib.avg_pool2d(
net,
[8,8],
padding='VALID',
scope='AvgPool_0a_7x7')
net = layers_lib.dropout(
net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
end_points['PreLogits'] = net
logits = layers.conv2d(
net,
num_classes, # num classes
[1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_1c_1x1')
logits = array_ops.squeeze(logits, [1, 2], name='SpatialSqueeze')
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
def model_fn(features, labels, mode, params):
"""
Based on https://github.com/tensorflow/tpu/blob/master/models/experimental/inception/inception_v2_tpu_model.py
:param features:
:param labels:
:param mode:
:param params:
:return:
"""
tf.summary.image('0_input', features, max_outputs=4)
training = mode == tf.estimator.ModeKeys.TRAIN
# 224 x 224 x 3
end_point = 'Conv2d_1a_7x7'
net = layers.conv2d(features, 64, [7, 7], stride=2, weights_initializer=trunc_normal(1.0), activation_fn=None, scope=end_point)
net = tf.layers.batch_normalization(net, training=training, name='{}_bn'.format(end_point))
net = tf.nn.relu(net, name='{}_act'.format(end_point))
tf.summary.image('1_{}'.format(end_point), net[:, :, :, 0:3], max_outputs=4)
# 112 x 112 x 64
end_point = 'MaxPool_2a_3x3'
net = layers_lib.max_pool2d(net, [3, 3], scope=end_point, stride=2, padding='SAME')
tf.summary.image('2_{}'.format(end_point), net[:, :, :, 0:3], max_outputs=4)
# 56 x 56 x 64
end_point = 'Conv2d_2b_1x1'
net = layers.conv2d(net, 64, [1, 1], activation_fn=None, scope=end_point, weights_initializer=trunc_normal(0.1))
net = tf.layers.batch_normalization(net, training=training, name='{}_bn'.format(end_point))
net = tf.nn.relu(net, name='{}_act'.format(end_point))
tf.summary.image('3_{}'.format(end_point), net[:, :, :, 0:3], max_outputs=4)
# 56 x 56 x 64
end_point = 'Conv2d_2c_3x3'
net = layers.conv2d(net, 192, [3, 3], activation_fn=None, scope=end_point)
net = tf.layers.batch_normalization(net, training=training, name='{}_bn'.format(end_point))
net = tf.nn.relu(net, name='{}_act'.format(end_point))
tf.summary.image('4_{}'.format(end_point), net[:, :, :, 0:3], max_outputs=4)
# 56 x 56 x 192
end_point = 'MaxPool_3a_3x3'
net = layers_lib.max_pool2d(net, [3, 3], scope=end_point, stride=2, padding='SAME')
tf.summary.image('5_{}'.format(end_point), net[:, :, :, 0:3], max_outputs=4)
# 28 x 28 x 192
# Inception module.
end_point = 'Mixed_3b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net, 64, [1, 1], activation_fn=None, scope='Conv2d_0a_1x1')
branch_0 = tf.layers.batch_normalization(branch_0, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_0 = tf.nn.relu(branch_0, name='{}_act'.format('Conv2d_0a_1x1'))
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net, 64, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0a_1x1'))
branch_1 = layers.conv2d(branch_1, 64, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0b_3x3'))
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net, 64, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0a_1x1'))
branch_2 = layers.conv2d(branch_2, 96, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0b_3x3'))
branch_2 = layers.conv2d(branch_2, 96, [3, 3], activation_fn=None, scope='Conv2d_0c_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0c_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0c_3x3'))
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], padding='SAME', stride=1, scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3, 32, [1, 1], weights_initializer=trunc_normal(0.1), activation_fn=None, scope='Conv2d_0b_1x1')
branch_3 = tf.layers.batch_normalization(branch_3, training=training, name='{}_bn'.format('Conv2d_0b_1x1'))
branch_3 = tf.nn.relu(branch_3, name='{}_act'.format('Conv2d_0b_1x1'))
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 28 x 28 x 256
end_point = 'Mixed_3c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net, 64, [1, 1], activation_fn=None, scope='Conv2d_0a_1x1')
branch_0 = tf.layers.batch_normalization(branch_0, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_0 = tf.nn.relu(branch_0, name='{}_act'.format('Conv2d_0a_1x1'))
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net, 64, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0a_1x1'))
branch_1 = layers.conv2d(branch_1, 96, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0b_3x3'))
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net, 64, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0a_1x1'))
branch_2 = layers.conv2d(branch_2, 96, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0b_3x3'))
branch_2 = layers.conv2d(branch_2, 96, [3, 3], activation_fn=None, scope='Conv2d_0c_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0c_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0c_3x3'))
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], padding='SAME', stride=1, scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3, 64, [1, 1], weights_initializer=trunc_normal(0.1), activation_fn=None, scope='Conv2d_0b_1x1')
branch_3 = tf.layers.batch_normalization(branch_3, training=training, name='{}_bn'.format('Conv2d_0b_1x1'))
branch_3 = tf.nn.relu(branch_3, name='{}_act'.format('Conv2d_0b_1x1'))
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 28 x 28 x 320
end_point = 'Mixed_4a'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net, 128, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_0 = tf.layers.batch_normalization(branch_0, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_0 = tf.nn.relu(branch_0, name='{}_act'.format('Conv2d_0a_1x1'))
branch_0 = layers.conv2d(branch_0, 160, [3, 3], stride=2, activation_fn=None, scope='Conv2d_1a_3x3')
branch_0 = tf.layers.batch_normalization(branch_0, training=training, name='{}_bn'.format('Conv2d_1a_3x3'))
branch_0 = tf.nn.relu(branch_0, name='{}_act'.format('Conv2d_1a_3x3'))
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net, 64, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0a_1x1'))
branch_1 = layers.conv2d(branch_1, 96, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0b_3x3'))
branch_1 = layers.conv2d(branch_1, 96, [3, 3], stride=2, activation_fn=None, scope='Conv2d_1a_3x3')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_1a_3x3'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_1a_3x3'))
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(net, [3, 3], stride=2, padding='SAME', scope='MaxPool_1a_3x3')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
# 14 x 14 x 576
end_point = 'Mixed_4b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net, 224, [1, 1], activation_fn=None, scope='Conv2d_0a_1x1')
branch_0 = tf.layers.batch_normalization(branch_0, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_0 = tf.nn.relu(branch_0, name='{}_act'.format('Conv2d_0a_1x1'))
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net, 64, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0a_1x1'))
branch_1 = layers.conv2d(branch_1, 96, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0b_3x3'))
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net, 96, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0a_1x1'))
branch_2 = layers.conv2d(branch_2, 128, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0b_3x3'))
branch_2 = layers.conv2d(branch_2, 128, [3, 3], activation_fn=None, scope='Conv2d_0c_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0c_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0c_3x3'))
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], padding='SAME', stride=1, scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3, 128, [1, 1], weights_initializer=trunc_normal(0.1), activation_fn=None, scope='Conv2d_0b_1x1')
branch_3 = tf.layers.batch_normalization(branch_3, training=training, name='{}_bn'.format('Conv2d_0b_1x1'))
branch_3 = tf.nn.relu(branch_3, name='{}_act'.format('Conv2d_0b_1x1'))
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 14 x 14 x 576
end_point = 'Mixed_4c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net, 192, [1, 1], activation_fn=None, scope='Conv2d_0a_1x1')
branch_0 = tf.layers.batch_normalization(branch_0, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_0 = tf.nn.relu(branch_0, name='{}_act'.format('Conv2d_0a_1x1'))
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net, 96, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0a_1x1'))
branch_1 = layers.conv2d(branch_1, 128, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0b_3x3'))
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net, 96, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0a_1x1'))
branch_2 = layers.conv2d(branch_2, 128, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0b_3x3'))
branch_2 = layers.conv2d(branch_2, 128, [3, 3], activation_fn=None, scope='Conv2d_0c_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0c_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0c_3x3'))
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], padding='SAME', stride=1, scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3, 128, [1, 1], weights_initializer=trunc_normal(0.1), activation_fn=None, scope='Conv2d_0b_1x1')
branch_3 = tf.layers.batch_normalization(branch_3, training=training, name='{}_bn'.format('Conv2d_0b_1x1'))
branch_3 = tf.nn.relu(branch_3, name='{}_act'.format('Conv2d_0b_1x1'))
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 14 x 14 x 576
end_point = 'Mixed_4d'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net, 160, [1, 1], activation_fn=None, scope='Conv2d_0a_1x1')
branch_0 = tf.layers.batch_normalization(branch_0, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_0 = tf.nn.relu(branch_0, name='{}_act'.format('Conv2d_0a_1x1'))
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net, 128, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0a_1x1'))
branch_1 = layers.conv2d(branch_1, 160, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0b_3x3'))
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net, 128, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0a_1x1'))
branch_2 = layers.conv2d(branch_2, 160, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0b_3x3'))
branch_2 = layers.conv2d(branch_2, 160, [3, 3], activation_fn=None, scope='Conv2d_0c_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0c_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0c_3x3'))
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], padding='SAME', stride=1, scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3, 96, [1, 1], weights_initializer=trunc_normal(0.1), activation_fn=None, scope='Conv2d_0b_1x1')
branch_3 = tf.layers.batch_normalization(branch_3, training=training, name='{}_bn'.format('Conv2d_0b_1x1'))
branch_3 = tf.nn.relu(branch_3, name='{}_act'.format('Conv2d_0b_1x1'))
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 14 x 14 x 576
end_point = 'Mixed_4e'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net, 96, [1, 1], activation_fn=None, scope='Conv2d_0a_1x1')
branch_0 = tf.layers.batch_normalization(branch_0, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_0 = tf.nn.relu(branch_0, name='{}_act'.format('Conv2d_0a_1x1'))
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net, 128, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0a_1x1'))
branch_1 = layers.conv2d(branch_1, 192, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0b_3x3'))
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net, 160, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0a_1x1'))
branch_2 = layers.conv2d(branch_2, 192, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0b_3x3'))
branch_2 = layers.conv2d(branch_2, 192, [3, 3], activation_fn=None, scope='Conv2d_0c_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0c_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0c_3x3'))
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], padding='SAME', stride=1, scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3, 96, [1, 1], weights_initializer=trunc_normal(0.1), activation_fn=None, scope='Conv2d_0b_1x1')
branch_3 = tf.layers.batch_normalization(branch_3, training=training, name='{}_bn'.format('Conv2d_0b_1x1'))
branch_3 = tf.nn.relu(branch_3, name='{}_act'.format('Conv2d_0b_1x1'))
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 14 x 14 x 576
end_point = 'Mixed_5a'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net, 128, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_0 = tf.layers.batch_normalization(branch_0, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_0 = tf.nn.relu(branch_0, name='{}_act'.format('Conv2d_0a_1x1'))
branch_0 = layers.conv2d(branch_0, 192, [3, 3], stride=2, activation_fn=None, scope='Conv2d_1a_3x3')
branch_0 = tf.layers.batch_normalization(branch_0, training=training, name='{}_bn'.format('Conv2d_1a_3x3'))
branch_0 = tf.nn.relu(branch_0, name='{}_act'.format('Conv2d_1a_3x3'))
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net, 192, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0a_1x1'))
branch_1 = layers.conv2d(branch_1, 256, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0b_3x3'))
branch_1 = layers.conv2d(branch_1, 256, [3, 3], stride=2, activation_fn=None, scope='Conv2d_1a_3x3')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_1a_3x3'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_1a_3x3'))
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers_lib.max_pool2d(net, [3, 3], stride=2, padding='SAME', scope='MaxPool_1a_3x3')
net = array_ops.concat([branch_0, branch_1, branch_2], 3)
# 7 x 7 x 1024
end_point = 'Mixed_5b'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net, 352, [1, 1], activation_fn=None, scope='Conv2d_0a_1x1')
branch_0 = tf.layers.batch_normalization(branch_0, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_0 = tf.nn.relu(branch_0, name='{}_act'.format('Conv2d_0a_1x1'))
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net, 192, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0a_1x1'))
branch_1 = layers.conv2d(branch_1, 320, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0b_3x3'))
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net, 160, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0a_1x1'))
branch_2 = layers.conv2d(branch_2, 224, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0b_3x3'))
branch_2 = layers.conv2d(branch_2, 224, [3, 3], activation_fn=None, scope='Conv2d_0c_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0c_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0c_3x3'))
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.avg_pool2d(net, [3, 3], padding='SAME', stride=1, scope='AvgPool_0a_3x3')
branch_3 = layers.conv2d(branch_3, 128, [1, 1], weights_initializer=trunc_normal(0.1), activation_fn=None, scope='Conv2d_0b_1x1')
branch_3 = tf.layers.batch_normalization(branch_3, training=training, name='{}_bn'.format('Conv2d_0b_1x1'))
branch_3 = tf.nn.relu(branch_3, name='{}_act'.format('Conv2d_0b_1x1'))
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
# 7 x 7 x 1024
end_point = 'Mixed_5c'
with variable_scope.variable_scope(end_point):
with variable_scope.variable_scope('Branch_0'):
branch_0 = layers.conv2d(net, 352, [1, 1], activation_fn=None, scope='Conv2d_0a_1x1')
branch_0 = tf.layers.batch_normalization(branch_0, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_0 = tf.nn.relu(branch_0, name='{}_act'.format('Conv2d_0a_1x1'))
with variable_scope.variable_scope('Branch_1'):
branch_1 = layers.conv2d(net, 192, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0a_1x1'))
branch_1 = layers.conv2d(branch_1, 320, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_1 = tf.layers.batch_normalization(branch_1, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_1 = tf.nn.relu(branch_1, name='{}_act'.format('Conv2d_0b_3x3'))
with variable_scope.variable_scope('Branch_2'):
branch_2 = layers.conv2d(net, 192, [1, 1], weights_initializer=trunc_normal(0.09), activation_fn=None, scope='Conv2d_0a_1x1')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0a_1x1'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0a_1x1'))
branch_2 = layers.conv2d(branch_2, 224, [3, 3], activation_fn=None, scope='Conv2d_0b_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0b_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0b_3x3'))
branch_2 = layers.conv2d(branch_2, 224, [3, 3], activation_fn=None, scope='Conv2d_0c_3x3')
branch_2 = tf.layers.batch_normalization(branch_2, training=training, name='{}_bn'.format('Conv2d_0c_3x3'))
branch_2 = tf.nn.relu(branch_2, name='{}_act'.format('Conv2d_0c_3x3'))
with variable_scope.variable_scope('Branch_3'):
branch_3 = layers_lib.max_pool2d(net, [3, 3], padding='SAME', stride=1, scope='MaxPool_0a_3x3')
branch_3 = layers.conv2d(branch_3, 128, [1, 1], weights_initializer=trunc_normal(0.1), activation_fn=None, scope='Conv2d_0b_1x1')
branch_3 = tf.layers.batch_normalization(branch_3, training=training, name='{}_bn'.format('Conv2d_0b_1x1'))
branch_3 = tf.nn.relu(branch_3, name='{}_act'.format('Conv2d_0b_1x1'))
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
with variable_scope.variable_scope('Logits'):
kernel_size = util._reduced_kernel_size_for_small_input(net, [7, 7])
net = layers_lib.avg_pool2d(net, kernel_size, stride=1, padding='VALID', scope='AvgPool_1a_{}x{}'.format(*kernel_size))
# 1 x 1 x 1024
net = layers_lib.dropout(net, keep_prob=params['dropout_keep_prob'], scope='Dropout_1b')
logits = layers.conv2d(net, params['num_classes'], [1, 1], normalizer_fn=None, activation_fn=None, scope='Conv2d_1c_1x1')
if params['spatial_squeeze']:
logits = array_ops.squeeze(logits, [1, 2], name='SpatialSqueeze')
predictions = {
'argmax': tf.argmax(logits, axis=1, name='prediction_classes'),
'predictions': layers_lib.softmax(logits, scope='Predictions'),
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
loss = tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels)
tf.summary.scalar('loss', loss)
eval_metric_ops = {
'accuracy_val': tf.metrics.accuracy(labels=labels, predictions=predictions['argmax'])
}
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=params['learning_rate'])
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(extra_update_ops):
train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())
tf.summary.scalar('accuracy_train', eval_metric_ops['accuracy_val'][1])
tf.summary.histogram('labels', labels)
tf.summary.histogram('predictions', predictions['argmax'])
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
def inception_v1(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
prediction_fn=layers_lib.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV1',
use_5x5=None):
"""Defines the Inception V1 architecture.
This architecture is defined in:
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
http://arxiv.org/pdf/1409.4842v1.pdf.
The default image size used to train this network is 224x224.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: the percentage of activation values that are retained.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
use_5x5: If True, the inception filter sizes will be set to 5x5
as specified in Figure 2(b) of the paper. The default behavior is to continue
with 3x3 filter sizes.
Note: This invalidates all current checkpoints and the saved models will
be incompatible as well. The training would have to be done from the beginning.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
"""
# Final pooling and prediction
with variable_scope.variable_scope(
scope, 'InceptionV1', [inputs, num_classes], reuse=reuse) as scope:
with arg_scope(
[layers_lib.batch_norm, layers_lib.dropout], is_training=is_training):
# If we want the same filters as in the original paper. The default is 3x3.
if use_5x5:
filter_size=[5, 5]
filter_size_str=str(filter_size[0])+'x'+str(filter_size[1])
net, end_points = inception_v1_base(inputs, scope=scope)
with variable_scope.variable_scope('Logits'):
net = layers_lib.avg_pool2d(
net, [7, 7], stride=1, scope='MaxPool_0a_7x7')
net = layers_lib.dropout(net, dropout_keep_prob, scope='Dropout_0b')
logits = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_0c_1x1')
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2], name='SpatialSqueeze')
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
def inception_v2(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
min_depth=16,
depth_multiplier=1.0,
prediction_fn=layers_lib.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV2'):
"""Inception v2 model for classification.
Constructs an Inception v2 network for classification as described in
http://arxiv.org/abs/1502.03167.
The recommended image size used to train this network is 224x224. For image
sizes that differ substantially, it is recommended to use inception_v2_base()
and connect custom final layers to the output.
Args:
inputs: a tensor of shape [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: the percentage of activation values that are retained.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
Note that input image sizes other than 224x224 might lead to different
spatial dimensions, and hence cannot be squeezed. In this event,
it is best to set spatial_squeeze as False, and perform a reduce_mean
over the resulting spatial dimensions with sizes exceeding 1.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: if depth_multiplier <= 0.
"""
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
# Final pooling and prediction
with variable_scope.variable_scope(
scope, 'InceptionV2', [inputs, num_classes], reuse=reuse) as scope:
with arg_scope(
[layers_lib.batch_norm, layers_lib.dropout], is_training=is_training):
net, end_points = inception_v2_base(
inputs,
scope=scope,
min_depth=min_depth,
depth_multiplier=depth_multiplier)
with variable_scope.variable_scope('Logits'):
kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7])
net = layers_lib.avg_pool2d(
net,
kernel_size,
padding='VALID',
scope='AvgPool_1a_{}x{}'.format(*kernel_size))
# 1 x 1 x 1024
net = layers_lib.dropout(
net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
logits = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_1c_1x1')
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2], name='SpatialSqueeze')
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
def inception_v3(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
min_depth=16,
depth_multiplier=1.0,
prediction_fn=layers_lib.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV3'):
"""Inception model from http://arxiv.org/abs/1512.00567.
"Rethinking the Inception Architecture for Computer Vision"
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens,
Zbigniew Wojna.
With the default arguments this method constructs the exact model defined in
the paper. However, one can experiment with variations of the inception_v3
network by changing arguments dropout_keep_prob, min_depth and
depth_multiplier.
The default image size used to train this network is 299x299.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: the percentage of activation values that are retained.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: if 'depth_multiplier' is less than or equal to zero.
"""
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with variable_scope.variable_scope(scope,
'InceptionV3', [inputs, num_classes],
reuse=reuse) as scope:
with arg_scope([layers_lib.batch_norm, layers_lib.dropout],
is_training=is_training):
net, end_points = inception_v3_base(
inputs,
scope=scope,
min_depth=min_depth,
depth_multiplier=depth_multiplier)
# Auxiliary Head logits
with arg_scope(
[layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d],
stride=1,
padding='SAME'):
aux_logits = end_points['Mixed_6e']
with variable_scope.variable_scope('AuxLogits'):
aux_logits = layers_lib.avg_pool2d(aux_logits, [5, 5],
stride=3,
padding='VALID',
scope='AvgPool_1a_5x5')
aux_logits = layers.conv2d(aux_logits,
depth(128), [1, 1],
scope='Conv2d_1b_1x1')
# Shape of feature map before the final layer.
kernel_size = _reduced_kernel_size_for_small_input(
aux_logits, [5, 5])
aux_logits = layers.conv2d(
aux_logits,
depth(768),
kernel_size,
weights_initializer=trunc_normal(0.01),
padding='VALID',
scope='Conv2d_2a_{}x{}'.format(*kernel_size))
aux_logits = layers.conv2d(
aux_logits,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
weights_initializer=trunc_normal(0.001),
scope='Conv2d_2b_1x1')
if spatial_squeeze:
aux_logits = array_ops.squeeze(aux_logits, [1, 2],
name='SpatialSqueeze')
end_points['AuxLogits'] = aux_logits
# Final pooling and prediction
with variable_scope.variable_scope('Logits'):
kernel_size = _reduced_kernel_size_for_small_input(net, [8, 8])
net = layers_lib.avg_pool2d(
net,
kernel_size,
padding='VALID',
scope='AvgPool_1a_{}x{}'.format(*kernel_size))
# 1 x 1 x 2048
net = layers_lib.dropout(net,
keep_prob=dropout_keep_prob,
scope='Dropout_1b')
end_points['PreLogits'] = net
# 2048
logits = layers.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='Conv2d_1c_1x1')
if spatial_squeeze:
logits = array_ops.squeeze(logits, [1, 2],
name='SpatialSqueeze')
# 1000
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits,
scope='Predictions')
return logits, end_points
def create_model(input_tensor, mode, hyper_params):
"""
An alexnet network.
:param input_tensor: The input tensor dict containing a "image" rgb tensor.
:param mode: Execution mode as a tf.estimator.ModeKeys
:param hyper_params: The hyper param file.
:return: A dictionary containing all output tensors.
"""
model = {}
spatial_squeeze = False
dropout_keep_prob = 0.5
is_training = mode == tf.estimator.ModeKeys.TRAIN
num_classes = 10
with tf.variable_scope('alexnet_v2') as scope:
if mode == tf.estimator.ModeKeys.EVAL:
scope.reuse_variables()
net = layers.conv2d(input_tensor,
64, [11, 11],
4,
padding='VALID',
scope='conv1')
model["conv1"] = net
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool1')
model["pool1"] = net
net = layers.conv2d(net, 192, [5, 5], scope='conv2')
model["conv2"] = net
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool2')
model["pool2"] = net
net = layers.conv2d(net, 384, [3, 3], scope='conv3')
model["conv3"] = net
net = layers.conv2d(net, 384, [3, 3], scope='conv4')
model["conv4"] = net
net = layers.conv2d(net, 256, [3, 3], scope='conv5')
model["conv5"] = net
net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool5')
model["pool5"] = net
# Use conv2d instead of fully_connected layers.
with arg_scope([layers.conv2d],
weights_initializer=trunc_normal(0.005),
biases_initializer=init_ops.constant_initializer(0.1)):
net = layers.conv2d(net,
4096, [5, 5],
padding='VALID',
scope='fc6')
model["fc6"] = net
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout6')
net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
model["fc7"] = net
net = layers_lib.dropout(net,
dropout_keep_prob,
is_training=is_training,
scope='dropout7')
net = layers.conv2d(
net,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
biases_initializer=init_ops.zeros_initializer(),
scope='fc8')
# Convert end_points_collection into a end_point dict.
if spatial_squeeze:
net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
model['fc8'] = net
# Collect outputs for api of network.
model["logits"] = net
model["probs"] = tf.nn.softmax(net)
return model
