def _conv_block(ip, nb_filter, bottleneck=False, dropout_rate=None, weight_decay=1e-4):
''' Apply BatchNorm, Relu, 3x3 Conv2D, optional bottleneck block and dropout
Args:
ip: Input keras tensor
nb_filter: number of filters
bottleneck: add bottleneck block
dropout_rate: dropout rate
weight_decay: weight decay factor
Returns: keras tensor with batch_norm, relu and convolution2d added (optional bottleneck)
'''
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
with K.name_scope('conv_block'):
x = BatchNormalization(axis=concat_axis, momentum=0.1, epsilon=1e-5)(ip)
x = Activation('relu')(x)
if bottleneck:
inter_channel = nb_filter * 4 # Obtained from https://github.com/liuzhuang13/DenseNet/blob/master/densenet.lua
x = Conv2D(inter_channel, (1, 1), kernel_initializer='he_normal', padding='same', use_bias=False,
kernel_regularizer=l2(weight_decay))(x)
x = BatchNormalization(axis=concat_axis, epsilon=1e-5, momentum=0.1)(x)
x = Activation('relu')(x)
x = Conv2D(nb_filter, (3, 3), kernel_initializer='he_normal', padding='same', use_bias=False)(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
return x
def __init__(self, lr=0.01, momentum=0., decay=0.,
nesterov=False, lr_mult=None, **kwargs):
super(MultiSGD, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.iterations = K.variable(0, dtype='int64', name='iterations')
self.lr = K.variable(lr, name='lr')
self.momentum = K.variable(momentum, name='momentum')
self.decay = K.variable(decay, name='decay')
self.initial_decay = decay
self.nesterov = nesterov
self.lr_mult = lr_mult
def __conv_block(ip, nb_filter, bottleneck=False, dropout_rate=None, weight_decay=1e-4, block_prefix=None):
'''
Adds a convolution layer (with batch normalization and relu),
and optionally a bottleneck layer.
# Arguments
ip: Input tensor
nb_filter: integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution)
bottleneck: if True, adds a bottleneck convolution block
dropout_rate: dropout rate
weight_decay: weight decay factor
block_prefix: str, for unique layer naming
# Input shape
4D tensor with shape:
`(samples, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(samples, filters, new_rows, new_cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, new_rows, new_cols, filters)` if data_format='channels_last'.
`rows` and `cols` values might have changed due to stride.
# Returns
output tensor of block
'''
with K.name_scope('ConvBlock'):
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5, name=name_or_none(block_prefix, '_bn'))(ip)
x = Activation('relu')(x)
if bottleneck:
inter_channel = nb_filter * 4
x = Conv2D(inter_channel, (1, 1), kernel_initializer='he_normal', padding='same', use_bias=False,
kernel_regularizer=l2(weight_decay), name=name_or_none(block_prefix, '_bottleneck_conv2D'))(x)
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5,
name=name_or_none(block_prefix, '_bottleneck_bn'))(x)
x = Activation('relu')(x)
x = Conv2D(nb_filter, (3, 3), kernel_initializer='he_normal', padding='same', use_bias=False,
name=name_or_none(block_prefix, '_conv2D'))(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
return x
def __dense_block(x, nb_layers, nb_filter, growth_rate, bottleneck=False, dropout_rate=None,
weight_decay=1e-4, grow_nb_filters=True, return_concat_list=False, block_prefix=None):
'''
Build a dense_block where the output of each conv_block is fed
to subsequent ones
# Arguments
x: input keras tensor
nb_layers: the number of conv_blocks to append to the model
nb_filter: integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution)
growth_rate: growth rate of the dense block
bottleneck: if True, adds a bottleneck convolution block to
each conv_block
dropout_rate: dropout rate
weight_decay: weight decay factor
grow_nb_filters: if True, allows number of filters to grow
return_concat_list: set to True to return the list of
feature maps along with the actual output
block_prefix: str, for block unique naming
# Return
If return_concat_list is True, returns a list of the output
keras tensor, the number of filters and a list of all the
dense blocks added to the keras tensor
If return_concat_list is False, returns a list of the output
keras tensor and the number of filters
'''
with K.name_scope('DenseBlock'):
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
x_list = [x]
for i in range(nb_layers):
cb = __conv_block(x, growth_rate, bottleneck, dropout_rate, weight_decay,
block_prefix=name_or_none(block_prefix, '_%i' % i))
x_list.append(cb)
x = concatenate([x, cb], axis=concat_axis)
if grow_nb_filters:
nb_filter += growth_rate
if return_concat_list:
return x, nb_filter, x_list
else:
return x, nb_filter
def __transition_block(ip, nb_filter, compression=1.0, weight_decay=1e-4, block_prefix=None, transition_pooling='max'):
'''
Adds a pointwise convolution layer (with batch normalization and relu),
and an average pooling layer. The number of output convolution filters
can be reduced by appropriately reducing the compression parameter.
# Arguments
ip: input keras tensor
nb_filter: integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution)
compression: calculated as 1 - reduction. Reduces the number
of feature maps in the transition block.
weight_decay: weight decay factor
block_prefix: str, for block unique naming
# Input shape
4D tensor with shape:
`(samples, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(samples, nb_filter * compression, rows / 2, cols / 2)`
if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows / 2, cols / 2, nb_filter * compression)`
if data_format='channels_last'.
# Returns
a keras tensor
'''
with K.name_scope('Transition'):
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5, name=name_or_none(block_prefix, '_bn'))(ip)
x = Activation('relu')(x)
x = Conv2D(int(nb_filter * compression), (1, 1), kernel_initializer='he_normal', padding='same',
use_bias=False, kernel_regularizer=l2(weight_decay), name=name_or_none(block_prefix, '_conv2D'))(x)
if transition_pooling == 'avg':
x = AveragePooling2D((2, 2), strides=(2, 2))(x)
elif transition_pooling == 'max':
x = MaxPooling2D((2, 2), strides=(2, 2))(x)
return x
def __transition_up_block(ip, nb_filters, type='deconv', weight_decay=1E-4, block_prefix=None):
'''Adds an upsampling block. Upsampling operation relies on the the type parameter.
# Arguments
ip: input keras tensor
nb_filters: integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution)
type: can be 'upsampling', 'subpixel', 'deconv'. Determines
type of upsampling performed
weight_decay: weight decay factor
block_prefix: str, for block unique naming
# Input shape
4D tensor with shape:
`(samples, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(samples, nb_filter, rows * 2, cols * 2)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows * 2, cols * 2, nb_filter)` if data_format='channels_last'.
# Returns
a keras tensor
'''
with K.name_scope('TransitionUp'):
if type == 'upsampling':
x = UpSampling2D(name=name_or_none(block_prefix, '_upsampling'))(ip)
elif type == 'subpixel':
x = Conv2D(nb_filters, (3, 3), activation='relu', padding='same', kernel_regularizer=l2(weight_decay),
use_bias=False, kernel_initializer='he_normal', name=name_or_none(block_prefix, '_conv2D'))(ip)
x = SubPixelUpscaling(scale_factor=2, name=name_or_none(block_prefix, '_subpixel'))(x)
x = Conv2D(nb_filters, (3, 3), activation='relu', padding='same', kernel_regularizer=l2(weight_decay),
use_bias=False, kernel_initializer='he_normal', name=name_or_none(block_prefix, '_conv2D'))(x)
else:
x = Conv2DTranspose(nb_filters, (3, 3), activation='relu', padding='same', strides=(2, 2),
kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay),
name=name_or_none(block_prefix, '_conv2DT'))(ip)
return x
def _transition_block(ip, nb_filter, compression=1.0, weight_decay=1e-4):
''' Apply BatchNorm, Relu 1x1, Conv2D, optional compression, dropout and Maxpooling2D
Args:
ip: keras tensor
nb_filter: number of filters
compression: calculated as 1 - reduction. Reduces the number of feature maps
in the transition block.
dropout_rate: dropout rate
weight_decay: weight decay factor
Returns: keras tensor, after applying batch_norm, relu-conv, dropout, maxpool
'''
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
with K.name_scope('transition_block'):
x = BatchNormalization(axis=concat_axis, epsilon=1e-5, momentum=0.1)(ip)
x = Activation('relu')(x)
x = Conv2D(int(nb_filter * compression), (1, 1), kernel_initializer='he_normal', padding='same', use_bias=False,
kernel_regularizer=l2(weight_decay))(x)
x = AveragePooling2D((2, 2), strides=(2, 2))(x)
return x
def _add_auxiliary_head(x, classes, weight_decay, pooling, include_top):
'''Adds an auxiliary head for training the model
From section A.7 "Training of ImageNet models" of the paper, all NASNet models are
trained using an auxiliary classifier around 2/3 of the depth of the network, with
a loss weight of 0.4
# Arguments
x: input tensor
classes: number of output classes
weight_decay: l2 regularization weight
# Returns
a keras Tensor
'''
img_height = 1 if K.image_data_format() == 'channels_last' else 2
img_width = 2 if K.image_data_format() == 'channels_last' else 3
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
with K.name_scope('auxiliary_branch'):
auxiliary_x = Activation('relu')(x)
auxiliary_x = AveragePooling2D((5, 5),
strides=(3, 3),
padding='valid',
name='aux_pool')(auxiliary_x)
auxiliary_x = Conv2D(128, (1, 1),
padding='same',
use_bias=False,
name='aux_conv_projection',
kernel_initializer='he_normal',
kernel_regularizer=l2(weight_decay))(auxiliary_x)
auxiliary_x = BatchNormalization(axis=channel_axis,
momentum=_BN_DECAY,
epsilon=_BN_EPSILON,
name='aux_bn_projection')(auxiliary_x)
auxiliary_x = Activation('relu')(auxiliary_x)
auxiliary_x = Conv2D(768, (auxiliary_x._keras_shape[img_height],
auxiliary_x._keras_shape[img_width]),
padding='valid',
use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=l2(weight_decay),
name='aux_conv_reduction')(auxiliary_x)
auxiliary_x = BatchNormalization(axis=channel_axis,
momentum=_BN_DECAY,
epsilon=_BN_EPSILON,
name='aux_bn_reduction')(auxiliary_x)
auxiliary_x = Activation('relu')(auxiliary_x)
if include_top:
auxiliary_x = Flatten()(auxiliary_x)
auxiliary_x = Dense(classes,
activation='softmax',
kernel_regularizer=l2(weight_decay),
name='aux_predictions')(auxiliary_x)
else:
if pooling == 'avg':
auxiliary_x = GlobalAveragePooling2D()(auxiliary_x)
elif pooling == 'max':
auxiliary_x = GlobalMaxPooling2D()(auxiliary_x)
return auxiliary_x
def __init__(self, weight_decay, **kwargs):
with K.name_scope(self.__class__.__name__):
self.weight_decay = K.variable(weight_decay, name='weight_decay')
super(DecoupleWeightDecay, self).__init__(**kwargs)
def get_gradient_dynamic_norm(model):
with K.name_scope('gradient_dyn_norm'):
grads_dyn = K.gradients(model.total_loss, model.trainable_weights[22:])
norm = K.sqrt(sum([K.sum(K.square(g)) for g in grads_dyn]))
return norm
def _adjust_block(p, ip, filters, weight_decay=5e-5, id=None):
'''
Adjusts the input `p` to match the shape of the `input`
or situations where the output number of filters needs to
be changed
# Arguments:
p: input tensor which needs to be modified
ip: input tensor whose shape needs to be matched
filters: number of output filters to be matched
weight_decay: l2 regularization weight
id: string id
# Returns:
an adjusted Keras tensor
'''
channel_dim = 1 if K.image_data_format() == 'channels_first' else -1
img_dim = 2 if K.image_data_format() == 'channels_first' else -2
with K.name_scope('adjust_block'):
if p is None:
p = ip
elif p._keras_shape[img_dim] != ip._keras_shape[img_dim]:
with K.name_scope('adjust_reduction_block_%s' % id):
p = Activation('relu', name='adjust_relu_1_%s' % id)(p)
p1 = AveragePooling2D((1, 1),
strides=(2, 2),
padding='valid',
name='adjust_avg_pool_1_%s' % id)(p)
p1 = Conv2D(filters // 2, (1, 1),
padding='same',
use_bias=False,
kernel_regularizer=l2(weight_decay),
name='adjust_conv_1_%s' % id,
kernel_initializer='he_normal')(p1)
p2 = ZeroPadding2D(padding=((0, 1), (0, 1)))(p)
p2 = Cropping2D(cropping=((1, 0), (1, 0)))(p2)
p2 = AveragePooling2D((1, 1),
strides=(2, 2),
padding='valid',
name='adjust_avg_pool_2_%s' % id)(p2)
p2 = Conv2D(filters // 2, (1, 1),
padding='same',
use_bias=False,
kernel_regularizer=l2(weight_decay),
name='adjust_conv_2_%s' % id,
kernel_initializer='he_normal')(p2)
p = concatenate([p1, p2], axis=channel_dim)
p = BatchNormalization(axis=channel_dim,
momentum=_BN_DECAY,
epsilon=_BN_EPSILON,
name='adjust_bn_%s' % id)(p)
elif p._keras_shape[channel_dim] != filters:
with K.name_scope('adjust_projection_block_%s' % id):
p = Activation('relu')(p)
p = Conv2D(filters, (1, 1),
strides=(1, 1),
padding='same',
name='adjust_conv_projection_%s' % id,
use_bias=False,
kernel_regularizer=l2(weight_decay),
kernel_initializer='he_normal')(p)
p = BatchNormalization(axis=channel_dim,
momentum=_BN_DECAY,
epsilon=_BN_EPSILON,
name='adjust_bn_%s' % id)(p)
return p
def remove_squeezable_dimensions(
labels, predictions, expected_rank_diff=0, name=None):
"""Squeeze last dim if ranks differ from expected by exactly 1.
In the common case where we expect shapes to match, `expected_rank_diff`
defaults to 0, and we squeeze the last dimension of the larger rank if they
differ by 1.
But, for example, if `labels` contains class IDs and `predictions` contains 1
probability per class, we expect `predictions` to have 1 more dimension than
`labels`, so `expected_rank_diff` would be 1. In this case, we'd squeeze
`labels` if `rank(predictions) - rank(labels) == 0`, and
`predictions` if `rank(predictions) - rank(labels) == 2`.
This will use static shape if available. Otherwise, it will add graph
operations, which could result in a performance hit.
Args:
labels: Label values, a `Tensor` whose dimensions match `predictions`.
predictions: Predicted values, a `Tensor` of arbitrary dimensions.
expected_rank_diff: Expected result of `rank(predictions) - rank(labels)`.
name: Name of the op.
Returns:
Tuple of `labels` and `predictions`, possibly with last dim squeezed.
"""
with K.name_scope(name or 'remove_squeezable_dimensions'):
if not isinstance(predictions, tf.RaggedTensor):
predictions = tf.convert_to_tensor(predictions)
if not isinstance(labels, tf.RaggedTensor):
labels = tf.convert_to_tensor(labels)
predictions_shape = predictions.shape
predictions_rank = predictions_shape.ndims
labels_shape = labels.shape
labels_rank = labels_shape.ndims
if (labels_rank is not None) and (predictions_rank is not None):
# Use static rank.
rank_diff = predictions_rank - labels_rank
if (rank_diff == expected_rank_diff + 1 and
predictions_shape.dims[-1].is_compatible_with(1)):
predictions = tf.compat.v1.squeeze(predictions, [-1])
elif (rank_diff == expected_rank_diff - 1 and
labels_shape.dims[-1].is_compatible_with(1)):
labels = tf.compat.v1.squeeze(labels, [-1])
return labels, predictions
# Use dynamic rank.
rank_diff = tf.rank(predictions) - tf.rank(labels)
if (predictions_rank is None) or (
predictions_shape.dims[-1].is_compatible_with(1)):
predictions = tf.compat.v1.cond(
tf.equal(expected_rank_diff + 1, rank_diff),
lambda: tf.compat.v1.squeeze(predictions, [-1]),
lambda: predictions)
if (labels_rank is None) or (
labels_shape.dims[-1].is_compatible_with(1)):
labels = tf.compat.v1.cond(
tf.equal(expected_rank_diff - 1, rank_diff),
lambda: tf.compat.v1.squeeze(labels, [-1]),
lambda: labels)
return labels, predictions
def vgg16(config, fake, real, layers):
features = []
parameters = []
with K.name_scope("VGG16"):
# Preprocess
mean = tf.constant([123.68, 116.779, 103.939],
dtype=tf.float32,
shape=[1, 1, 1, 3],
name="img_mean")
fake = tf.image.resize_images(fake, size=[224, 224]) * 255.0 - mean
real = tf.image.resize_images(real, size=[224, 224]) * 255.0 - mean
# First Convolution
w_conv11 = tf.Variable(tf.truncated_normal([3, 3, 3, 64],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv11 = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv11, b_conv11]
# Output
conv11 = tf.nn.conv2d(
fake, w_conv11, strides=[1, 1, 1, 1], padding='SAME') + b_conv11
conv11 = tf.nn.relu(conv11)
# Ground-Truth
conv11_gt = tf.nn.conv2d(
real, w_conv11, strides=[1, 1, 1, 1], padding='SAME') + b_conv11
conv11_gt = tf.nn.relu(conv11_gt)
# Loss
if "relu11" in layers:
features += [conv11, conv11_gt]
# Second Convolution
w_conv12 = tf.Variable(tf.truncated_normal([3, 3, 64, 64],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv12 = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv12, b_conv12]
# Output
conv12 = tf.nn.conv2d(
conv11, w_conv12, strides=[1, 1, 1, 1], padding='SAME') + b_conv12
conv12 = tf.nn.relu(conv12)
# Ground-Truth
conv12_gt = tf.nn.conv2d(
conv11_gt, w_conv12, strides=[1, 1, 1, 1
], padding='SAME') + b_conv12
conv12_gt = tf.nn.relu(conv12_gt)
# Loss
if "relu12" in layers:
features += [conv12, conv12_gt]
# First Maxpool
pool1 = tf.nn.max_pool(conv12,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name="pool1")
pool1_gt = tf.nn.max_pool(conv12_gt,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name="pool1_gt")
# Third Convolution
w_conv21 = tf.Variable(tf.truncated_normal([3, 3, 64, 128],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv21 = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv21, b_conv21]
# Output
conv21 = tf.nn.conv2d(
pool1, w_conv21, strides=[1, 1, 1, 1], padding='SAME') + b_conv21
conv21 = tf.nn.relu(conv21)
# Ground-Truth
conv21_gt = tf.nn.conv2d(
pool1_gt, w_conv21, strides=[1, 1, 1, 1
], padding='SAME') + b_conv21
conv21_gt = tf.nn.relu(conv21_gt)
# Loss
if "relu21" in layers:
features += [conv21, conv21_gt]
# Fourth Convolution
w_conv22 = tf.Variable(tf.truncated_normal([3, 3, 128, 128],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv22 = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv22, b_conv22]
# Output
conv22 = tf.nn.conv2d(
conv21, w_conv22, strides=[1, 1, 1, 1], padding='SAME') + b_conv22
conv22 = tf.nn.relu(conv22)
# Ground-Truth
conv22_gt = tf.nn.conv2d(
conv21_gt, w_conv22, strides=[1, 1, 1, 1
], padding='SAME') + b_conv22
conv22_gt = tf.nn.relu(conv22_gt)
# Loss
if "relu22" in layers:
features += [conv22, conv22_gt]
# Second Maxpool
pool2 = tf.nn.max_pool(conv22,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name="pool2")
pool2_gt = tf.nn.max_pool(conv22_gt,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name="pool2_gt")
# Fifth Convolution
w_conv31 = tf.Variable(tf.truncated_normal([3, 3, 128, 256],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv31 = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv31, b_conv31]
# Output
conv31 = tf.nn.conv2d(
pool2, w_conv31, strides=[1, 1, 1, 1], padding='SAME') + b_conv31
conv31 = tf.nn.relu(conv31)
# Ground-Truth
conv31_gt = tf.nn.conv2d(
pool2_gt, w_conv31, strides=[1, 1, 1, 1
], padding='SAME') + b_conv31
conv31_gt = tf.nn.relu(conv31_gt)
# Loss
if "relu31" in layers:
features += [conv31, conv31_gt]
# Sixth Convolution
w_conv32 = tf.Variable(tf.truncated_normal([3, 3, 256, 256],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv32 = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv32, b_conv32]
# Output
conv32 = tf.nn.conv2d(
conv31, w_conv32, strides=[1, 1, 1, 1], padding='SAME') + b_conv32
conv32 = tf.nn.relu(conv32)
# Ground-Truth
conv32_gt = tf.nn.conv2d(
conv31_gt, w_conv32, strides=[1, 1, 1, 1
], padding='SAME') + b_conv32
conv32_gt = tf.nn.relu(conv32_gt)
# Loss
if "relu32" in layers:
features += [conv32, conv32_gt]
# Seventh Convolution
w_conv33 = tf.Variable(tf.truncated_normal([3, 3, 256, 256],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv33 = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv33, b_conv33]
# Output
conv33 = tf.nn.conv2d(
conv32, w_conv33, strides=[1, 1, 1, 1], padding='SAME') + b_conv33
conv33 = tf.nn.relu(conv33)
# Ground-Truth
conv33_gt = tf.nn.conv2d(
conv32_gt, w_conv33, strides=[1, 1, 1, 1
], padding='SAME') + b_conv33
conv33_gt = tf.nn.relu(conv33_gt)
# Loss
if "relu33" in layers:
features += [conv33, conv33_gt]
# Third Maxpool
pool3 = tf.nn.max_pool(conv33,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name="pool3")
pool3_gt = tf.nn.max_pool(conv33_gt,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name="pool3_gt")
# Eighth Convolution
w_conv41 = tf.Variable(tf.truncated_normal([3, 3, 256, 512],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv41 = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv41, b_conv41]
# Output
conv41 = tf.nn.conv2d(
pool3, w_conv41, strides=[1, 1, 1, 1], padding='SAME') + b_conv41
conv41 = tf.nn.relu(conv41)
# Ground-Truth
conv41_gt = tf.nn.conv2d(
pool3_gt, w_conv41, strides=[1, 1, 1, 1
], padding='SAME') + b_conv41
conv41_gt = tf.nn.relu(conv41_gt)
# Loss
if "relu41" in layers:
features += [conv41, conv41_gt]
# Nineth Convolution
w_conv42 = tf.Variable(tf.truncated_normal([3, 3, 512, 512],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv42 = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv42, b_conv42]
# Output
conv42 = tf.nn.conv2d(
conv41, w_conv42, strides=[1, 1, 1, 1], padding='SAME') + b_conv42
conv42 = tf.nn.relu(conv42)
# Ground-Truth
conv42_gt = tf.nn.conv2d(
conv41_gt, w_conv42, strides=[1, 1, 1, 1
], padding='SAME') + b_conv42
conv42_gt = tf.nn.relu(conv42_gt)
# Loss
if "relu42" in layers:
features += [conv42, conv42_gt]
# Tenth Convolution
w_conv43 = tf.Variable(tf.truncated_normal([3, 3, 512, 512],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv43 = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv43, b_conv43]
# Output
conv43 = tf.nn.conv2d(
conv42, w_conv43, strides=[1, 1, 1, 1], padding='SAME') + b_conv43
conv43 = tf.nn.relu(conv43)
# Ground-Truth
conv43_gt = tf.nn.conv2d(
conv42_gt, w_conv43, strides=[1, 1, 1, 1
], padding='SAME') + b_conv43
conv43_gt = tf.nn.relu(conv43_gt)
# Loss
if "relu43" in layers:
features += [conv43, conv43_gt]
# Fourth Maxpool
pool4 = tf.nn.max_pool(conv43,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name="pool4")
pool4_gt = tf.nn.max_pool(conv43_gt,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name="pool4_gt")
# Eleventh Convolution
w_conv51 = tf.Variable(tf.truncated_normal([3, 3, 512, 512],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv51 = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv51, b_conv51]
# Output
conv51 = tf.nn.conv2d(
pool4, w_conv51, strides=[1, 1, 1, 1], padding='SAME') + b_conv51
conv51 = tf.nn.relu(conv51)
# Ground-Truth
conv51_gt = tf.nn.conv2d(
pool4_gt, w_conv51, strides=[1, 1, 1, 1
], padding='SAME') + b_conv51
conv51_gt = tf.nn.relu(conv51_gt)
# Loss
if "relu51" in layers:
features += [conv51, conv51_gt]
# Twelfth Convolution
w_conv52 = tf.Variable(tf.truncated_normal([3, 3, 512, 512],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv52 = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv52, b_conv52]
# Output
conv52 = tf.nn.conv2d(
conv51, w_conv52, strides=[1, 1, 1, 1], padding='SAME') + b_conv52
conv52 = tf.nn.relu(conv52)
# Ground-Truth
conv52_gt = tf.nn.conv2d(
conv51_gt, w_conv52, strides=[1, 1, 1, 1
], padding='SAME') + b_conv52
conv52_gt = tf.nn.relu(conv52_gt)
# Loss
if "relu52" in layers:
features += [conv52, conv52_gt]
# Thirteenth Convolution
w_conv53 = tf.Variable(tf.truncated_normal([3, 3, 512, 512],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_conv53 = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_conv53, b_conv53]
# Output
conv53 = tf.nn.conv2d(
conv52, w_conv53, strides=[1, 1, 1, 1], padding='SAME') + b_conv53
conv53 = tf.nn.relu(conv53)
# Ground-Truth
conv53_gt = tf.nn.conv2d(
conv52_gt, w_conv53, strides=[1, 1, 1, 1
], padding='SAME') + b_conv53
conv53_gt = tf.nn.relu(conv53_gt)
# Loss
if "relu53" in layers:
features += [conv53, conv53_gt]
# Fifth Maxpool
pool5 = tf.nn.max_pool(conv53,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name="pool5")
pool5_gt = tf.nn.max_pool(conv53_gt,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name="pool5_gt")
# FC Parameters
shape = int(np.prod(pool5.get_shape()[1:]))
pool5_flat = tf.reshape(pool5, [-1, shape])
pool5_gt_flat = tf.reshape(pool5_gt, [-1, shape])
# First FC
w_fc1 = tf.Variable(tf.truncated_normal([shape, 4096],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_fc1 = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_fc1, b_fc1]
# Output
fc1 = tf.matmul(pool5_flat, w_fc1) + b_fc1
fc1 = tf.nn.relu(fc1)
# Ground-Truth
fc1_gt = tf.matmul(pool5_gt_flat, w_fc1) + b_fc1
fc1_gt = tf.nn.relu(fc1_gt)
# Loss
if "fc1" in layers:
features += [fc1, fc1_gt]
# Second FC
w_fc2 = tf.Variable(tf.truncated_normal([4096, 4096],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_fc2 = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_fc2, b_fc2]
# Output
fc2 = tf.matmul(fc1, w_fc2) + b_fc2
fc2 = tf.nn.relu(fc2)
# Ground-Truth
fc2_gt = tf.matmul(fc1_gt, w_fc2) + b_fc2
fc2_gt = tf.nn.relu(fc2_gt)
# Loss
if "fc2" in layers:
features += [fc2, fc2_gt]
# Third FC
w_fc3 = tf.Variable(tf.truncated_normal([4096, 1000],
dtype=tf.float32,
stddev=1e-1),
trainable=False,
name="weights")
b_fc3 = tf.Variable(tf.constant(1.0, shape=[1000], dtype=tf.float32),
trainable=False,
name="biases")
parameters += [w_fc3, b_fc3]
# Output
fc3 = tf.matmul(fc2, w_fc3) + b_fc3
# Ground-Truth
fc3_gt = tf.matmul(fc2_gt, w_fc3) + b_fc3
# Loss
if "fc3" in layers:
features += [fc3, fc3_gt]
# Load Weights
load_weights(config, parameters)
return features
def _call(self, inputs, **kwargs):
if self.proto_number == self.capsule_number:
return inputs
else:
signals = inputs[0]
diss = inputs[1]
signal_shape = mixed_shape(signals)
if self.use_for_loop:
diss_stack = []
signals_stack = []
sub_idx = None
with K.name_scope('for_loop'):
for p in self._proto_distrib:
with K.name_scope('compute_slices'):
diss_ = diss[:, p[0]:(p[-1]+1)]
signals_ = K.reshape(signals[:, p[0]:(p[-1]+1), :],
[signal_shape[0] * len(p)] + list(signal_shape[2:]))
with K.name_scope('competition'):
if len(p) > 1:
with K.name_scope('competition_indices'):
argmin_idx = K.argmin(diss_, axis=-1)
if sub_idx is None:
sub_idx = K.arange(0, signal_shape[0], dtype=argmin_idx.dtype)
argmin_idx = argmin_idx + len(p) * sub_idx
with K.name_scope('dissimilarity_competition'):
diss_stack.append(K.expand_dims(K.gather(K.flatten(diss_), argmin_idx), -1))
with K.name_scope('signal_competition'):
signals_stack.append(K.gather(signals_, argmin_idx))
else:
diss_stack.append(diss_)
signals_stack.append(signals_)
diss = K.concatenate(diss_stack, 1)
with K.name_scope('signal_concatenation'):
signals = K.concatenate(signals_stack, 1)
signals = K.reshape(signals, [signal_shape[0], self.capsule_number] + list(signal_shape[2:]))
else:
with K.name_scope('dissimilarity_preprocessing'):
# extend if it is not equally distributed
if not self._equally_distributed:
# permute to first dimension is prototype (protos x batch)
diss = K.permute_dimensions(diss, [1, 0])
# gather regarding extension (preparing for reshape to block)
diss = K.gather(diss, self._proto_extension)
# permute back (max_proto_number x (max_proto_number * batch))
diss = K.permute_dimensions(diss, [1, 0])
# reshape to block form
diss = K.reshape(diss, [signal_shape[0] * self.capsule_number, self._max_proto_number_in_capsule])
with K.name_scope('competition_indices'):
# get minimal idx in each class and batch for element selection in diss and signals
argmin_idx = K.argmin(diss, axis=-1)
argmin_idx = argmin_idx + self._max_proto_number_in_capsule * \
K.arange(0, signal_shape[0] * self.capsule_number, dtype=argmin_idx.dtype)
with K.name_scope('dissimilarity_competition'):
# get minimal values in the form (batch x capsule)
diss = K.gather(K.flatten(diss), argmin_idx)
diss = K.reshape(diss, [signal_shape[0], self.capsule_number])
with K.name_scope('signal_preprocessing'):
# apply the same steps as above for signals
# get signals in: (batch x protos x dim1 x ... x dimN) --> out: (batch x capsule x dim1 x ... x dimN)
# extend if is not equally distributed
if not self._equally_distributed:
signals = K.permute_dimensions(signals, [1, 0] + list(range(2, len(signal_shape))))
signals = K.gather(signals, self._proto_extension)
signals = K.permute_dimensions(signals, [1, 0] + list(range(2, len(signal_shape))))
signals = K.reshape(signals,
[signal_shape[0] * self.capsule_number * self._max_proto_number_in_capsule]
+ list(signal_shape[2:]))
with K.name_scope('signal_competition'):
signals = K.gather(signals, argmin_idx)
signals = K.reshape(signals, [signal_shape[0], self.capsule_number] + list(signal_shape[2:]))
return {0: signals, 1: diss}
def _normal_A(ip, p, filters, weight_decay=5e-5, id=None):
'''Adds a Normal cell for NASNet-A (Fig. 4 in the paper)
# Arguments:
ip: input tensor `x`
p: input tensor `p`
filters: number of output filters
weight_decay: l2 regularization weight
id: string id
# Returns:
a Keras tensor
'''
global NORMAL_IDX
channel_dim = 1 if K.image_data_format() == 'channels_first' else -1
weights = load_normal_call(NORMAL_IDX)
NORMAL_IDX += 1
with K.name_scope('normal_A_block_%s' % id):
p = _adjust_block(p, ip, filters, weight_decay, id, weights)
h = Activation('relu')(ip)
h = Conv2D(filters, (1, 1),
strides=(1, 1),
padding='same',
name='normal_conv_1_%s' % id,
use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=l2(weight_decay),
weights=[weights['begin_W']])(h)
h = BatchNormalization(axis=channel_dim,
momentum=_BN_DECAY,
epsilon=_BN_EPSILON,
name='normal_bn_1_%s' % id,
weights=weights['begin_bn'])(h)
with K.name_scope('block_1'):
x1_1 = _separable_conv_block(h,
filters,
kernel_size=(5, 5),
weight_decay=weight_decay,
id='normal_left1_%s' % id,
weights=weights['left_0'])
x1_2 = _separable_conv_block(p,
filters,
weight_decay=weight_decay,
id='normal_right1_%s' % id,
weights=weights['right_0'])
x1 = add([x1_1, x1_2], name='normal_add_1_%s' % id)
with K.name_scope('block_2'):
x2_1 = _separable_conv_block(p,
filters, (5, 5),
weight_decay=weight_decay,
id='normal_left2_%s' % id,
weights=weights['left_1'])
x2_2 = _separable_conv_block(p,
filters, (3, 3),
weight_decay=weight_decay,
id='normal_right2_%s' % id,
weights=weights['right_1'])
x2 = add([x2_1, x2_2], name='normal_add_2_%s' % id)
with K.name_scope('block_3'):
x3 = AveragePooling2D((3, 3),
strides=(1, 1),
padding='same',
name='normal_left3_%s' % (id))(h)
x3 = add([x3, p], name='normal_add_3_%s' % id)
with K.name_scope('block_4'):
x4_1 = AveragePooling2D((3, 3),
strides=(1, 1),
padding='same',
name='normal_left4_%s' % (id))(p)
x4_2 = AveragePooling2D((3, 3),
strides=(1, 1),
padding='same',
name='normal_right4_%s' % (id))(p)
x4 = add([x4_1, x4_2], name='normal_add_4_%s' % id)
with K.name_scope('block_5'):
x5 = _separable_conv_block(h,
filters,
weight_decay=weight_decay,
id='normal_left5_%s' % id,
weights=weights['left_4'])
x5 = add([x5, h], name='normal_add_5_%s' % id)
x = concatenate([p, x1, x2, x3, x4, x5],
axis=channel_dim,
name='normal_concat_%s' % id)
return x, ip
def _define_generator_loss(self, logits):
with K.name_scope('G_loss'):
return losses.l1_distance(self.fake_images, logits)
def _call(self, inputs, **kwargs):
if self.proto_number == self.capsule_number:
return inputs
else:
signals = inputs[0]
diss = inputs[1]
signal_shape = None
# signal.shape: (batch, proto_num, caps_dim1, ..., caps_dimN)
if self.input_spec[0].ndim > 3:
signal_shape = mixed_shape(signals)
signals = K.reshape(signals, signal_shape[0:2] + (-1,))
if not self._equally_distributed:
if self.use_for_loop:
signals_stack = []
diss_stack = []
with K.name_scope('for_loop'):
for i, p in enumerate(self._proto_distrib):
with K.name_scope('compute_slices'):
diss_ = diss[:, p[0]:(p[-1]+1)]
signals_ = signals[:, p[0]:(p[-1] + 1), :]
if len(p) > 1:
with K.name_scope('competition_probabilities'):
coefficients = prob_trans.NegSoftmax(axis=-1, max_stabilization=True)(
diss_ * self.beta[i])
with K.name_scope('signal_competition'):
signals_stack.append(K.expand_dims(K.batch_dot(coefficients, signals_, [1, 1]), 1))
with K.name_scope('dissimilarity_competition'):
diss_stack.append(K.batch_dot(coefficients, diss_, [1, 1]))
else:
signals_stack.append(signals_)
diss_stack.append(diss_)
signals = K.concatenate(signals_stack, axis=1)
diss = K.concatenate(diss_stack, axis=-1)
else:
extension_idx = []
for i in self._proto_extension:
if i not in extension_idx:
extension_idx.append(i)
else:
extension_idx.append(max(self._proto_extension)+1)
batch_size = K.shape(signals)[0] if signal_shape is None else signal_shape[0]
# reshape to block
with K.name_scope('competition_probabilities'):
with K.name_scope('neg_softmax'):
with K.name_scope('coefficients'):
beta = K.gather(self.beta, self._capsule_extension)
coefficients = -diss * beta
# max stabilization
coefficients = coefficients - K.max(coefficients, axis=-1, keepdims=True)
coefficients = K.exp(coefficients)
coefficients = K.concatenate([coefficients,
K.zeros_like(coefficients[:, 0:1])], axis=-1)
coefficients = K.transpose(coefficients)
coefficients = K.gather(coefficients, extension_idx)
coefficients = K.transpose(coefficients)
coefficients = K.reshape(coefficients,
[batch_size, self.capsule_number,
self._max_proto_number_in_capsule])
# could never be a zero division
with K.name_scope('normalization_constant'):
constant = K.sum(coefficients, axis=-1, keepdims=True)
probs = coefficients / constant
with K.name_scope('dissimilarity_preprocessing'):
diss = K.transpose(diss)
diss = K.gather(diss, self._proto_extension)
diss = K.transpose(diss)
diss = K.reshape(diss,
[batch_size, self.capsule_number, self._max_proto_number_in_capsule])
with K.name_scope('dissimilarity_competition'):
diss = K.squeeze(K.batch_dot(probs, K.expand_dims(diss), [2, 2]), -1)
with K.name_scope('signal_preprocessing'):
signals = K.permute_dimensions(signals, [1, 0, 2])
signals = K.gather(signals, self._proto_extension)
signals = K.permute_dimensions(signals, [1, 0, 2])
signals = K.reshape(signals,
[batch_size, self.capsule_number, self._max_proto_number_in_capsule, -1])
with K.name_scope('signal_competition'):
signals = K.batch_dot(probs, signals, [2, 2])
else:
batch_size = K.shape(signals)[0] if signal_shape is None else signal_shape[0]
diss = K.reshape(diss, [batch_size, self.capsule_number, self._max_proto_number_in_capsule])
with K.name_scope('competition_probabilities'):
coefficients = prob_trans.NegSoftmax(axis=-1, max_stabilization=True)(
diss * K.expand_dims(self.beta, -1))
with K.name_scope('signal_competition'):
signals = K.reshape(signals,
[batch_size, self.capsule_number, self._max_proto_number_in_capsule, -1])
signals = K.batch_dot(coefficients, signals, [2, 2])
with K.name_scope('dissimilarity_competition'):
diss = K.squeeze(K.batch_dot(coefficients, K.expand_dims(diss), [2, 2]), -1)
if self.input_spec[0].ndim > 3:
signals = K.reshape(signals, [signal_shape[0], self.capsule_number] + list(signal_shape[2:]))
return {0: signals, 1: diss}
def multi_gpu_model(model, gpus, cpu_merge=True, cpu_relocation=False):
"""Replicates a model on different GPUs.
Specifically, this function implements single-machine
multi-GPU data parallelism. It works in the following way:
- Divide the model's input(s) into multiple sub-batches.
- Apply a model copy on each sub-batch. Every model copy
is executed on a dedicated GPU.
- Concatenate the results (on CPU) into one big batch.
E.g. if your `batch_size` is 64 and you use `gpus=2`,
then we will divide the input into 2 sub-batches of 32 samples,
process each sub-batch on one GPU, then return the full
batch of 64 processed samples.
This induces quasi-linear speedup on up to 8 GPUs.
This function is only available with the TensorFlow backend
for the time being.
Args:
model: A Keras model instance. To avoid OOM errors,
this model could have been built on CPU, for instance
(see usage example below).
gpus: Integer >= 2, number of on GPUs on which to create
model replicas.
cpu_merge: A boolean value to identify whether to force
merging model weights under the scope of the CPU or not.
cpu_relocation: A boolean value to identify whether to
create the model's weights under the scope of the CPU.
If the model is not defined under any preceding device
scope, you can still rescue it by activating this option.
Returns:
A Keras `Model` instance which can be used just like the initial
`model` argument, but which distributes its workload on multiple GPUs.
Example 1: Training models with weights merge on CPU
```python
import tensorflow as tf
from keras.applications import Xception
from keras.utils import multi_gpu_model
import numpy as np
num_samples = 1000
height = 224
width = 224
num_classes = 1000
# Instantiate the base model (or "template" model).
# We recommend doing this with under a CPU device scope,
# so that the model's weights are hosted on CPU memory.
# Otherwise they may end up hosted on a GPU, which would
# complicate weight sharing.
with tf.device('/cpu:0'):
model = Xception(weights=None,
input_shape=(height, width, 3),
classes=num_classes)
# Replicates the model on 8 GPUs.
# This assumes that your machine has 8 available GPUs.
parallel_model = multi_gpu_model(model, gpus=8)
parallel_model.compile(loss='categorical_crossentropy',
optimizer='rmsprop')
# Generate dummy data.
x = np.random.random((num_samples, height, width, 3))
y = np.random.random((num_samples, num_classes))
# This `fit` call will be distributed on 8 GPUs.
# Since the batch size is 256, each GPU will process 32 samples.
parallel_model.fit(x, y, epochs=20, batch_size=256)
# Save model via the template model (which shares the same weights):
model.save('my_model.h5')
```
Example 2: Training models with weights merge on CPU using cpu_relocation
```python
..
# Not needed to change the device scope for model definition:
model = Xception(weights=None, ..)
try:
model = multi_gpu_model(model, cpu_relocation=True)
print("Training using multiple GPUs..")
except:
print("Training using single GPU or CPU..")
model.compile(..)
..
```
Example 3: Training models with weights merge on GPU (recommended for NV-link)
```python
..
# Not needed to change the device scope for model definition:
model = Xception(weights=None, ..)
try:
model = multi_gpu_model(model, cpu_merge=False)
print("Training using multiple GPUs..")
except:
print("Training using single GPU or CPU..")
model.compile(..)
..
```
Raises:
ValueError: if the `gpus` argument does not match available devices.
"""
if isinstance(gpus, (list, tuple)):
if len(gpus) <= 1:
raise ValueError('For multi-gpu usage to be effective, '
'call `multi_gpu_model` with `len(gpus) >= 2`. '
'Received: `gpus=%s`' % gpus)
num_gpus = len(gpus)
target_gpu_ids = gpus
else:
if gpus <= 1:
raise ValueError('For multi-gpu usage to be effective, '
'call `multi_gpu_model` with `gpus >= 2`. '
'Received: `gpus=%s`' % gpus)
num_gpus = gpus
target_gpu_ids = range(num_gpus)
target_devices = ['/cpu:0'] + ['/gpu:%d' % i for i in target_gpu_ids]
available_devices = _get_available_devices()
available_devices = [
_normalize_device_name(name) for name in available_devices
]
for device in target_devices:
if device not in available_devices:
raise ValueError(
'To call `multi_gpu_model` with `gpus=%s`, '
'we expect the following devices to be available: %s. '
'However this machine only has: %s. '
'Try reducing `gpus`.' %
(gpus, target_devices, available_devices))
def get_slice(data, i, parts):
"""Slice an array into `parts` slices and return slice `i`.
Args:
data: array to slice.
i: index of slice to return.
parts: number of slices to make.
Returns:
Slice `i` of `data`.
"""
shape = tf.compat.v1.shape(data)
batch_size = shape[:1]
input_shape = shape[1:]
step = batch_size // parts
if i == parts - 1:
size = batch_size - step * i
else:
size = step
size = tf.concat([size, input_shape], axis=0)
stride = tf.concat([step, input_shape * 0], axis=0)
start = stride * i
return tf.slice(data, start, size)
# Relocate the model definition under CPU device scope if needed
if cpu_relocation:
from keras.models import clone_model # pylint: disable=g-import-not-at-top
with tf.compat.v1.device('/cpu:0'):
model = clone_model(model)
all_outputs = [[] for _ in range(len(model.outputs))]
# Place a copy of the model on each GPU,
# each getting a slice of the inputs.
for i, gpu_id in enumerate(target_gpu_ids):
with tf.compat.v1.device('/gpu:%d' % gpu_id):
with backend.name_scope('replica_%d' % gpu_id):
inputs = []
# Retrieve a slice of the input.
for x in model.inputs:
input_shape = tuple(x.shape.as_list())[1:]
slice_i = Lambda(get_slice,
output_shape=input_shape,
arguments={
'i': i,
'parts': num_gpus
})(x)
inputs.append(slice_i)
# Apply model on slice
# (creating a model replica on the target device).
outputs = model(inputs)
if not isinstance(outputs, list):
outputs = [outputs]
# Save the outputs for merging back together later.
for o, output in enumerate(outputs):
all_outputs[o].append(output)
# Deduplicate output names to handle Siamese networks.
occurrences = {}
for n in model.output_names:
if n not in occurrences:
occurrences[n] = 1
else:
occurrences[n] += 1
conflict_counter = {n: 0 for n, count in occurrences.items() if count > 1}
output_names = []
for n in model.output_names:
if n in conflict_counter:
conflict_counter[n] += 1
n += '_%d' % conflict_counter[n]
output_names.append(n)
# Merge outputs under expected scope.
with tf.compat.v1.device('/cpu:0' if cpu_merge else '/gpu:%d' %
target_gpu_ids[0]):
merged = []
for name, outputs in zip(output_names, all_outputs):
merged.append(concatenate(outputs, axis=0, name=name))
return Model(model.inputs, merged)
def gradient_norm(model):
with K.name_scope('gradient_norm'):
grads = K.gradients(model.total_loss, model.trainable_weights)
norm = K.sqrt(sum([K.sum(K.square(g)) for g in grads]))
return norm
def build(self, input_shape):
with backend.name_scope(self.forward_layer.name):
self.forward_layer.build(input_shape)
with backend.name_scope(self.backward_layer.name):
self.backward_layer.build(input_shape)
self.built = True
def __create_dense_net(nb_classes, img_input, include_top, depth=40, nb_dense_block=3, growth_rate=12, nb_filter=-1,
nb_layers_per_block=-1, bottleneck=False, reduction=0.0, dropout_rate=None, weight_decay=1e-4,
subsample_initial_block=False, pooling=None, activation='sigmoid', transition_pooling='avg'):
''' Build the DenseNet model
# Arguments
nb_classes: number of classes
img_input: tuple of shape (channels, rows, columns) or (rows, columns, channels)
include_top: flag to include the final Dense layer
depth: number or layers
nb_dense_block: number of dense blocks to add to end (generally = 3)
growth_rate: number of filters to add per dense block
nb_filter: initial number of filters. Default -1 indicates initial number of filters is 2 * growth_rate
nb_layers_per_block: number of layers in each dense block.
Can be a -1, positive integer or a list.
If -1, calculates nb_layer_per_block from the depth of the network.
If positive integer, a set number of layers per dense block.
If list, nb_layer is used as provided. Note that list size must
be (nb_dense_block + 1)
bottleneck: add bottleneck blocks
reduction: reduction factor of transition blocks. Note : reduction value is inverted to compute compression
dropout_rate: dropout rate
weight_decay: weight decay rate
subsample_initial_block: Changes model type to suit different datasets.
Should be set to True for ImageNet, and False for CIFAR datasets.
When set to True, the initial convolution will be strided and
adds a MaxPooling2D before the initial dense block.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model
will be the 4D tensor output of the
last convolutional layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a
2D tensor.
- `max` means that global max pooling will
be applied.
activation: Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'.
Note that if sigmoid is used, classes must be 1.
transition_pooling: `avg` for avg pooling (default), `max` for max pooling,
None for no pooling during scale transition blocks. Please note that this
default differs from the DenseNetFCN paper in accordance with the DenseNet
paper.
# Returns
a keras tensor
# Raises
ValueError: in case of invalid argument for `reduction`
or `nb_dense_block`
'''
with K.name_scope('DenseNet'):
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
if reduction != 0.0:
if not (reduction <= 1.0 and reduction > 0.0):
raise ValueError('`reduction` value must lie between 0.0 and 1.0')
# layers in each dense block
if type(nb_layers_per_block) is list or type(nb_layers_per_block) is tuple:
nb_layers = list(nb_layers_per_block) # Convert tuple to list
if len(nb_layers) != (nb_dense_block):
raise ValueError('If `nb_dense_block` is a list, its length must match '
'the number of layers provided by `nb_layers`.')
final_nb_layer = nb_layers[-1]
nb_layers = nb_layers[:-1]
else:
if nb_layers_per_block == -1:
assert (depth - 4) % 3 == 0, 'Depth must be 3 N + 4 if nb_layers_per_block == -1'
count = int((depth - 4) / 3)
if bottleneck:
count = count // 2
nb_layers = [count for _ in range(nb_dense_block)]
final_nb_layer = count
else:
final_nb_layer = nb_layers_per_block
nb_layers = [nb_layers_per_block] * nb_dense_block
# compute initial nb_filter if -1, else accept users initial nb_filter
if nb_filter <= 0:
nb_filter = 2 * growth_rate
# compute compression factor
compression = 1.0 - reduction
# Initial convolution
if subsample_initial_block:
initial_kernel = (7, 7)
initial_strides = (2, 2)
else:
initial_kernel = (3, 3)
initial_strides = (1, 1)
x = Conv2D(nb_filter, initial_kernel, kernel_initializer='he_normal', padding='same', name='initial_conv2D',
strides=initial_strides, use_bias=False, kernel_regularizer=l2(weight_decay))(img_input)
if subsample_initial_block:
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5, name='initial_bn')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
# Add dense blocks
for block_idx in range(nb_dense_block - 1):
x, nb_filter = __dense_block(x, nb_layers[block_idx], nb_filter, growth_rate, bottleneck=bottleneck,
dropout_rate=dropout_rate, weight_decay=weight_decay,
block_prefix='dense_%i' % block_idx)
# add transition_block
x = __transition_block(x, nb_filter, compression=compression, weight_decay=weight_decay,
block_prefix='tr_%i' % block_idx, transition_pooling=transition_pooling)
nb_filter = int(nb_filter * compression)
# The last dense_block does not have a transition_block
x, nb_filter = __dense_block(x, final_nb_layer, nb_filter, growth_rate, bottleneck=bottleneck,
dropout_rate=dropout_rate, weight_decay=weight_decay,
block_prefix='dense_%i' % (nb_dense_block - 1))
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5, name='final_bn')(x)
x = Activation('relu')(x)
if include_top:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
x = Dense(nb_classes, activation=activation)(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
return x
def _call(self, inputs, **kwargs):
# inverse permutation
def inv_perm(perm):
inverse = [0] * len(perm)
for i, p in enumerate(perm):
inverse[p] = i
return inverse
# signal is dict: extract signal from diss
signals = inputs[0]
signal_shape = mixed_shape(signals)
ndim = self.input_spec[0].ndim
atom_axes = list(range(3, ndim))
atom_axes.remove(self.axis)
if self.scope == 'local':
with K.name_scope('signal_preprocessing'):
perm = [1, 2, self.axis, 0] + atom_axes
signals = K.permute_dimensions(signals, perm)
if ndim > 4:
signals = K.reshape(signals, [
signal_shape[1], signal_shape[2],
signal_shape[self.axis], -1
])
with K.name_scope('linear_mapping'):
# multiply over all batches by using the Theano behavior
signals = K.batch_dot(self.linear_maps, signals, axes=[2, 2])
with K.name_scope('signal_postprocessing'):
if ndim > 4:
signals = K.reshape(signals, [
signal_shape[1], signal_shape[2], self.output_dim,
signal_shape[0]
] + [signal_shape[i] for i in atom_axes])
signals = K.permute_dimensions(signals, inv_perm(perm))
elif self.scope == 'global':
with K.name_scope('signal_preprocessing'):
dims = list(range(ndim))
dims.remove(self.axis)
perm = dims + [self.axis]
signals = K.permute_dimensions(signals, perm)
with K.name_scope('linear_mapping'):
signals = K.dot(signals, self.linear_maps)
with K.name_scope('signal_postprocessing'):
signals = K.permute_dimensions(signals, inv_perm(perm))
elif self.scope == 'channel_wise':
with K.name_scope('signal_preprocessing'):
perm = [2, self.axis, 0, 1] + atom_axes
signals = K.permute_dimensions(signals, perm)
signals = K.reshape(
signals, [signal_shape[2], signal_shape[self.axis], -1])
with K.name_scope('linear_mapping'):
# multiply over all batches by using the Theano behavior
signals = K.batch_dot(self.linear_maps, signals, axes=[1, 1])
with K.name_scope('signal_postprocessing'):
signals = K.reshape(signals, [
signal_shape[2], self.output_dim, signal_shape[0],
signal_shape[1]
] + [signal_shape[i] for i in atom_axes])
signals = K.permute_dimensions(signals, inv_perm(perm))
else: # capsule_wise
with K.name_scope('signal_preprocessing'):
perm = [1, self.axis, 0, 2] + atom_axes
signals = K.permute_dimensions(signals, perm)
signals = K.reshape(
signals, [signal_shape[1], signal_shape[self.axis], -1])
with K.name_scope('linear_mapping'):
# multiply over all batches by using the Theano behavior
signals = K.batch_dot(self.linear_maps, signals, axes=[1, 1])
with K.name_scope('signal_postprocessing'):
signals = K.reshape(signals, [
signal_shape[1], self.output_dim, signal_shape[0],
signal_shape[2]
] + [signal_shape[i] for i in atom_axes])
signals = K.permute_dimensions(signals, inv_perm(perm))
inputs[0] = signals
return inputs
def _reduction_a_cell(ip, p, filters, block_id=None):
'''Adds a Reduction cell for NASNet-A (Fig. 4 in the paper).
# Arguments
ip: Input tensor `x`
p: Input tensor `p`
filters: Number of output filters
block_id: String block_id
# Returns
A Keras tensor
'''
channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1
with backend.name_scope('reduction_A_block_%s' % block_id):
p = _adjust_block(p, ip, filters, block_id)
h = layers.Activation('relu')(ip)
h = layers.Conv2D(filters, (1, 1),
strides=(1, 1),
padding='same',
kernel_regularizer=l2(weight_decay),
name='reduction_conv_1_%s' % block_id,
use_bias=False,
kernel_initializer='he_normal')(h)
if use_bn:
h = layers.BatchNormalization(axis=channel_dim,
momentum=bn_momentum,
epsilon=1e-3,
name='reduction_bn_1_%s' %
block_id)(h)
h = layers.SpatialDropout2D(drop_p)(h)
h3 = layers.ZeroPadding2D(padding=correct_pad(backend, h, 3),
name='reduction_pad_1_%s' % block_id)(h)
with backend.name_scope('block_1'):
x1_1 = _separable_conv_block(h,
filters, (5, 5),
strides=(2, 2),
block_id='reduction_left1_%s' %
block_id)
x1_2 = _separable_conv_block(p,
filters, (7, 7),
strides=(2, 2),
block_id='reduction_right1_%s' %
block_id)
x1 = layers.add([x1_1, x1_2], name='reduction_add_1_%s' % block_id)
with backend.name_scope('block_2'):
x2_1 = layers.MaxPooling2D(
(3, 3),
strides=(2, 2),
padding='valid',
name='reduction_left2_%s' % block_id)(h3)
x2_2 = _separable_conv_block(p,
filters, (7, 7),
strides=(2, 2),
block_id='reduction_right2_%s' %
block_id)
x2 = layers.add([x2_1, x2_2], name='reduction_add_2_%s' % block_id)
with backend.name_scope('block_3'):
x3_1 = layers.AveragePooling2D(
(3, 3),
strides=(2, 2),
padding='valid',
name='reduction_left3_%s' % block_id)(h3)
x3_2 = _separable_conv_block(p,
filters, (5, 5),
strides=(2, 2),
block_id='reduction_right3_%s' %
block_id)
x3 = layers.add([x3_1, x3_2], name='reduction_add3_%s' % block_id)
with backend.name_scope('block_4'):
x4 = layers.AveragePooling2D(
(3, 3),
strides=(1, 1),
padding='same',
name='reduction_left4_%s' % block_id)(x1)
x4 = layers.add([x2, x4])
with backend.name_scope('block_5'):
x5_1 = _separable_conv_block(x1,
filters, (3, 3),
block_id='reduction_left4_%s' %
block_id)
x5_2 = layers.MaxPooling2D(
(3, 3),
strides=(2, 2),
padding='valid',
name='reduction_right5_%s' % block_id)(h3)
x5 = layers.add([x5_1, x5_2], name='reduction_add4_%s' % block_id)
x = layers.concatenate([x2, x3, x4, x5],
axis=channel_dim,
name='reduction_concat_%s' % block_id)
return x, ip
def __init__(self, standard_deviation=0.3, **kwargs):
super(NoisyOptimizer, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.standard_deviation = K.variable(standard_deviation,
name='standard_deviation')
def _num_elements(losses):
"""Computes the number of elements in `losses` tensor."""
with K.name_scope('num_elements') as scope:
return tf.cast(tf.compat.v1.size(losses, name=scope), dtype=losses.dtype)
def NASNet(input_shape=None,
penultimate_filters=4032,
nb_blocks=6,
stem_filters=96,
skip_reduction=True,
use_auxilary_branch=False,
filters_multiplier=2,
dropout=0.5,
include_top=True,
weights=None,
input_tensor=None,
pooling=None,
classes=1000,
default_size=None):
"""Instantiates a NASNet architecture.
Note that only TensorFlow is supported for now,
therefore it only works with the data format
`image_data_format='channels_last'` in your Keras config
at `~/.keras/keras.json`.
# Arguments
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(331, 331, 3)` for NASNetLarge or
`(224, 224, 3)` for NASNetMobile
It should have exactly 3 inputs channels,
and width and height should be no smaller than 32.
E.g. `(224, 224, 3)` would be one valid value.
penultimate_filters: number of filters in the penultimate layer.
NASNet models use the notation `NASNet (N @ P)`, where:
- N is the number of blocks
- P is the number of penultimate filters
nb_blocks: number of repeated blocks of the NASNet model.
NASNet models use the notation `NASNet (N @ P)`, where:
- N is the number of blocks
- P is the number of penultimate filters
stem_filters: number of filters in the initial stem block
skip_reduction: Whether to skip the reduction step at the tail
end of the network. Set to `False` for CIFAR models.
use_auxilary_branch: Whether to use the auxilary branch during
training or evaluation.
filters_multiplier: controls the width of the network.
- If `filters_multiplier` < 1.0, proportionally decreases the number
of filters in each layer.
- If `filters_multiplier` > 1.0, proportionally increases the number
of filters in each layer.
- If `filters_multiplier` = 1, default number of filters from the paper
are used at each layer.
dropout: dropout rate
include_top: whether to include the fully-connected
layer at the top of the network.
weights: `None` (random initialization) or
`imagenet` (ImageNet weights)
input_tensor: optional Keras tensor (i.e. output of
`layers.Input()`)
to use as image input for the model.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model
will be the 4D tensor output of the
last convolutional layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a
2D tensor.
- `max` means that global max pooling will
be applied.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
default_size: specifies the default image size of the model
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
RuntimeError: If attempting to run this model with a
backend that does not support separable convolutions.
"""
if K.backend() != 'tensorflow':
raise RuntimeError('Only Tensorflow backend is currently supported, '
'as other backends do not support '
'separable convolution.')
if weights not in {'imagenet', None}:
raise ValueError('The `weights` argument should be either '
'`None` (random initialization) or `imagenet` '
'(pre-training on ImageNet).')
if weights == 'imagenet' and include_top and classes != 1000:
raise ValueError('If using `weights` as ImageNet with `include_top` '
'as true, `classes` should be 1000')
if default_size is None:
default_size = 331
# Determine proper input shape and default size.
input_shape = _obtain_input_shape(input_shape,
default_size=default_size,
min_size=32,
data_format=K.image_data_format(),
require_flatten=include_top or weights)
if K.image_data_format() != 'channels_last':
warnings.warn('The MobileNet family of models is only available '
'for the input data format "channels_last" '
'(width, height, channels). '
'However your settings specify the default '
'data format "channels_first" (channels, width, height).'
' You should set `image_data_format="channels_last"` '
'in your Keras config located at ~/.keras/keras.json. '
'The model being returned right now will expect inputs '
'to follow the "channels_last" data format.')
K.set_image_data_format('channels_last')
old_data_format = 'channels_first'
else:
old_data_format = None
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
assert penultimate_filters % 24 == 0, "`penultimate_filters` needs to be divisible " \
"by 6 * (2^N)."
channel_dim = 1 if K.image_data_format() == 'channels_first' else -1
filters = penultimate_filters // 24
x = Conv2D(stem_filters, (3, 3),
strides=(2, 2),
padding='valid',
use_bias=False,
name='stem_conv1',
kernel_initializer='he_normal')(img_input)
x = BatchNormalization(axis=channel_dim,
momentum=_BN_DECAY,
epsilon=_BN_EPSILON,
name='stem_bn1')(x)
x, p = _reduction_A(x,
None,
filters // (filters_multiplier**2),
id='stem_1')
x, p = _reduction_A(x, p, filters // filters_multiplier, id='stem_2')
for i in range(nb_blocks):
x, p = _normal_A(x, p, filters, id='%d' % (i))
x, p0 = _reduction_A(x,
p,
filters * filters_multiplier,
id='reduce_%d' % (nb_blocks))
p = p0 if not skip_reduction else p
for i in range(nb_blocks):
x, p = _normal_A(x,
p,
filters * filters_multiplier,
id='%d' % (nb_blocks + i + 1))
auxilary_x = None
if use_auxilary_branch:
img_height = 1 if K.image_data_format() == 'channels_first' else 2
img_width = 2 if K.image_data_format() == 'channels_first' else 3
with K.name_scope('auxilary_branch'):
auxilary_x = Activation('relu')(x)
auxilary_x = AveragePooling2D((5, 5),
strides=(3, 3),
padding='valid',
name='aux_pool')(auxilary_x)
auxilary_x = Conv2D(128, (1, 1),
padding='same',
use_bias=False,
name='aux_conv_projection',
kernel_initializer='he_normal')(auxilary_x)
auxilary_x = BatchNormalization(
axis=channel_dim,
momentum=_BN_DECAY,
epsilon=_BN_EPSILON,
name='aux_bn_projection')(auxilary_x)
auxilary_x = Activation('relu')(auxilary_x)
auxilary_x = Conv2D(768, (auxilary_x._keras_shape[img_height],
auxilary_x._keras_shape[img_width]),
padding='valid',
use_bias=False,
kernel_initializer='he_normal',
name='aux_conv_reduction')(auxilary_x)
auxilary_x = BatchNormalization(
axis=channel_dim,
momentum=_BN_DECAY,
epsilon=_BN_EPSILON,
name='aux_bn_reduction')(auxilary_x)
auxilary_x = Activation('relu')(auxilary_x)
auxilary_x = GlobalAveragePooling2D()(auxilary_x)
auxilary_x = Dense(classes,
activation='softmax',
name='aux_predictions')(auxilary_x)
x, p0 = _reduction_A(x,
p,
filters * filters_multiplier**2,
id='reduce_%d' % (2 * nb_blocks))
p = p0 if not skip_reduction else p
for i in range(nb_blocks):
x, p = _normal_A(x,
p,
filters * filters_multiplier**2,
id='%d' % (2 * nb_blocks + i + 1))
x = Activation('relu')(x)
if include_top:
x = GlobalAveragePooling2D()(x)
x = Dropout(dropout)(x)
x = Dense(classes, activation='softmax')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
if use_auxilary_branch:
model = Model(inputs, [x, auxilary_x], name='NASNet_with_auxilary')
else:
model = Model(inputs, x, name='NASNet')
# load weights (when available)
warnings.warn(
'Weights of NASNet models have not been ported yet for Keras.')
if old_data_format:
K.set_image_data_format(old_data_format)
return model
def __call__(self, inputs, **kwargs):
if isinstance(inputs, list):
inputs = inputs[:]
with K.name_scope(self.name):
# Raise exceptions in case the input is not compatible
# with the input_spec specified in the layer constructor.
self.assert_input_compatibility(inputs)
# Handle laying building (weight creating, input spec locking).
if not self.built:
self.build(inputs)
self.built = True
# Handle mask propagation.
previous_mask = _collect_previous_mask(inputs)
user_kwargs = copy.copy(kwargs)
if not _is_all_none(previous_mask):
# The previous layer generated a mask.
if has_arg(self.call, 'mask'):
if 'mask' not in kwargs:
# If mask is explicitly passed to __call__,
# we should override the default mask.
kwargs['mask'] = previous_mask
# Handle automatic shape inference (only useful for Theano).
input_shape = _collect_input_shape(inputs)
# Actually call the layer, collecting output(s), mask(s), and shape(s).
output = self.call(inputs, **kwargs)
output_mask = self.compute_mask(inputs, previous_mask)
# If the layer returns tensors from its inputs, unmodified,
# we copy them to avoid loss of tensor metadata.
output_ls = _to_list(output)
inputs_ls = _to_list(inputs)
output_ls_copy = []
for x in output_ls:
if x in inputs_ls:
x = K.identity(x)
output_ls_copy.append(x)
if len(output_ls_copy) == 1:
output = output_ls_copy[0]
else:
output = output_ls_copy
# Inferring the output shape is only relevant for Theano.
if all([s is not None for s in _to_list(input_shape)]):
output_shape = self.compute_output_shape(input_shape)
else:
if isinstance(input_shape, list):
output_shape = [None for _ in input_shape]
else:
output_shape = None
if not isinstance(output_mask,
(list, tuple)) and len(output_ls) > 1:
# Augment the mask to match the length of the output.
output_mask = [output_mask] * len(output_ls)
# Add an inbound node to the layer, so that it keeps track
# of the call and of all new variables created during the call.
# This also updates the layer history of the output tensor(s).
# If the input tensor(s) had not previous Keras history,
# this does nothing.
self._add_inbound_node(input_tensors=inputs,
output_tensors=output,
input_masks=previous_mask,
output_masks=output_mask,
input_shapes=input_shape,
output_shapes=output_shape,
arguments=user_kwargs)
# Apply activity regularizer if any:
if hasattr(self, 'activity_regularizer'
) and self.activity_regularizer is not None:
regularization_losses = [
self.activity_regularizer(x) for x in _to_list(output)
]
self.add_loss(regularization_losses, _to_list(inputs))
return output
def transform(images,
transforms,
fill_mode='reflect',
fill_value=0.0,
interpolation='bilinear',
output_shape=None,
name=None):
"""Applies the given transform(s) to the image(s).
Args:
images: A tensor of shape (num_images, num_rows, num_columns, num_channels)
(NHWC), (num_rows, num_columns, num_channels) (HWC), or (num_rows,
num_columns) (HW). The rank must be statically known (the shape is not
`TensorShape(None)`.
transforms: Projective transform matrix/matrices. A vector of length 8 or
tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0, b1, b2,
c0, c1], then it maps the *output* point `(x, y)` to a transformed *input*
point `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`, where
`k = c0 x + c1 y + 1`. The transforms are *inverted* compared to the
transform mapping input points to output points. Note that gradients are
not backpropagated into transformation parameters.
fill_mode: Points outside the boundaries of the input are filled according
to the given mode (one of `{'constant', 'reflect', 'wrap', 'nearest'}`).
fill_value: a float represents the value to be filled outside the boundaries
when `fill_mode` is "constant".
interpolation: Interpolation mode. Supported values: "nearest", "bilinear".
output_shape: Output dimesion after the transform, [height, width]. If None,
output is the same size as input image.
name: The name of the op. ## Fill mode.
Behavior for each valid value is as follows: reflect (d c b a | a b c d | d c
b a) The input is extended by reflecting about the edge of the last pixel.
constant (k k k k | a b c d | k k k k) The input is extended by filling all
values beyond the edge with the same constant value k = 0. wrap (a b c d |
a b c d | a b c d) The input is extended by wrapping around to the opposite
edge. nearest (a a a a | a b c d | d d d d) The input is extended by the
nearest pixel.
Input shape:
4D tensor with shape: `(samples, height, width, channels)`,
data_format='channels_last'.
Output shape:
4D tensor with shape: `(samples, height, width, channels)`,
data_format='channels_last'.
Returns:
Image(s) with the same type and shape as `images`, with the given
transform(s) applied. Transformed coordinates outside of the input image
will be filled with zeros.
Raises:
TypeError: If `image` is an invalid type.
ValueError: If output shape is not 1-D int32 Tensor.
"""
with backend.name_scope(name or 'transform'):
if output_shape is None:
output_shape = tf.compat.v1.shape(images)[1:3]
if not tf.executing_eagerly():
output_shape_value = tf.get_static_value(output_shape)
if output_shape_value is not None:
output_shape = output_shape_value
output_shape = tf.convert_to_tensor(
output_shape, tf.int32, name='output_shape')
if not output_shape.get_shape().is_compatible_with([2]):
raise ValueError('output_shape must be a 1-D Tensor of 2 elements: '
'new_height, new_width, instead got '
'{}'.format(output_shape))
fill_value = tf.convert_to_tensor(
fill_value, tf.float32, name='fill_value')
return tf.raw_ops.ImageProjectiveTransformV3(
images=images,
output_shape=output_shape,
fill_value=fill_value,
transforms=transforms,
fill_mode=fill_mode.upper(),
interpolation=interpolation.upper())
def get_weight_static_norm(model):
with K.name_scope('w_static_norm'):
weights = model.trainable_weights[0:22]
w_norm = K.sqrt(sum([K.sum(K.square(w)) for w in weights]))
return w_norm
def _ReductionCell(self, filters, prefix, prev, cur):
with K.name_scope('reduce'):
prev = self._Fit(filters=filters,
target_layer=cur,
prefix=prefix,
net=prev)
cur = self._SqueezeChannels(filters=filters, prefix=prefix, x=cur)
# Full in
with K.name_scope('comb_iter_0'):
prefix = '{}/comb_iter_0'.format(prefix)
add_0 = Add()([
self._Separable(filters=filters,
kernel_size=5,
strides=2,
prefix='{}/left'.format(prefix),
net=cur),
self._Separable(filters=filters,
kernel_size=7,
strides=2,
prefix='{}/right'.format(prefix),
net=prev)
])
with K.name_scope('comb_iter_1'):
prefix = '{}/comb_iter_1'.format(prefix)
add_1 = Add()([
MaxPooling2D(3, strides=2, padding='same')(cur),
self._Separable(filters=filters,
kernel_size=7,
strides=2,
prefix='{}/right'.format(prefix),
net=prev)
])
with K.name_scope('comb_iter_2'):
prefix = '{}/comb_iter_2'.format(prefix)
add_2 = Add()([
AveragePooling2D(3, strides=2, padding='same')(cur),
self._Separable(filters=filters,
kernel_size=5,
strides=2,
prefix='{}/right'.format(prefix),
net=prev)
])
# Reduced after stride
with K.name_scope('comb_iter_3'):
add_3 = Add()([
AveragePooling2D(3, strides=1, padding='same')(add_0),
add_1
])
with K.name_scope('comb_iter_4'):
prefix = '{}/comb_iter_4'.format(prefix)
add_4 = Add()([
self._Separable(filters=filters,
kernel_size=3,
strides=1,
prefix='{}/left'.format(prefix),
net=add_0),
MaxPooling2D(3, strides=2, padding='same')(cur)
])
return Concatenate(axis=-1)([add_1, add_2, add_3, add_4])
def get_gradient_static_norm(model):
with K.name_scope('gradient_static_norm'):
grads_static = K.gradients(model.total_loss,
model.trainable_weights[0:22])
norm = K.sqrt(sum([K.sum(K.square(g)) for g in grads_static]))
return norm
def __init__(self, optimizer, gdev_list=None):
self.optimizer = optimizer
self._gdev_list = gdev_list
with K.name_scope(self.__class__.__name__):
self.iterations = K.variable(0, dtype='int64', name='iterations')
def _reduction_A(ip, p, filters, weight_decay=5e-5, id=None):
'''Adds a Reduction cell for NASNet-A (Fig. 4 in the paper)
# Arguments:
ip: input tensor `x`
p: input tensor `p`
filters: number of output filters
weight_decay: l2 regularization weight
id: string id
# Returns:
a Keras tensor
'''
""""""
channel_dim = 1 if K.image_data_format() == 'channels_first' else -1
with K.name_scope('reduction_A_block_%s' % id):
p = _adjust_block(p, ip, filters, weight_decay, id)
h = Activation('relu')(ip)
h = Conv2D(filters, (1, 1),
strides=(1, 1),
padding='same',
name='reduction_conv_1_%s' % id,
use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=l2(weight_decay))(h)
h = BatchNormalization(axis=channel_dim,
momentum=_BN_DECAY,
epsilon=_BN_EPSILON,
name='reduction_bn_1_%s' % id)(h)
with K.name_scope('block_1'):
x1_1 = _separable_conv_block(h,
filters, (5, 5),
strides=(2, 2),
weight_decay=weight_decay,
id='reduction_left1_%s' % id)
x1_2 = _separable_conv_block(p,
filters, (7, 7),
strides=(2, 2),
weight_decay=weight_decay,
id='reduction_1_%s' % id)
x1 = add([x1_1, x1_2], name='reduction_add_1_%s' % id)
with K.name_scope('block_2'):
x2_1 = MaxPooling2D((3, 3),
strides=(2, 2),
padding='same',
name='reduction_left2_%s' % id)(h)
x2_2 = _separable_conv_block(p,
filters, (7, 7),
strides=(2, 2),
weight_decay=weight_decay,
id='reduction_right2_%s' % id)
x2 = add([x2_1, x2_2], name='reduction_add_2_%s' % id)
with K.name_scope('block_3'):
x3_1 = AveragePooling2D((3, 3),
strides=(2, 2),
padding='same',
name='reduction_left3_%s' % id)(h)
x3_2 = _separable_conv_block(p,
filters, (5, 5),
strides=(2, 2),
weight_decay=weight_decay,
id='reduction_right3_%s' % id)
x3 = add([x3_1, x3_2], name='reduction_add3_%s' % id)
with K.name_scope('block_4'):
x4 = AveragePooling2D((3, 3),
strides=(1, 1),
padding='same',
name='reduction_left4_%s' % id)(x1)
x4 = add([x2, x4])
with K.name_scope('block_5'):
x5_1 = _separable_conv_block(x1,
filters, (3, 3),
weight_decay=weight_decay,
id='reduction_left4_%s' % id)
x5_2 = MaxPooling2D((3, 3),
strides=(2, 2),
padding='same',
name='reduction_right5_%s' % id)(h)
x5 = add([x5_1, x5_2], name='reduction_add4_%s' % id)
x = concatenate([x2, x3, x4, x5],
axis=channel_dim,
name='reduction_concat_%s' % id)
return x, ip
def __create_dense_net(nb_classes, img_input, include_top, depth=40, nb_dense_block=3, growth_rate=12, nb_filter=-1,
nb_layers_per_block=-1, bottleneck=False, reduction=0.0, dropout_rate=None, weight_decay=1e-4,
subsample_initial_block=False, pooling=None, activation='softmax', transition_pooling='avg'):
''' Build the DenseNet model
# Arguments
nb_classes: number of classes
img_input: tuple of shape (channels, rows, columns) or (rows, columns, channels)
include_top: flag to include the final Dense layer
depth: number or layers
nb_dense_block: number of dense blocks to add to end (generally = 3)
growth_rate: number of filters to add per dense block
nb_filter: initial number of filters. Default -1 indicates initial number of filters is 2 * growth_rate
nb_layers_per_block: number of layers in each dense block.
Can be a -1, positive integer or a list.
If -1, calculates nb_layer_per_block from the depth of the network.
If positive integer, a set number of layers per dense block.
If list, nb_layer is used as provided. Note that list size must
be (nb_dense_block + 1)
bottleneck: add bottleneck blocks
reduction: reduction factor of transition blocks. Note : reduction value is inverted to compute compression
dropout_rate: dropout rate
weight_decay: weight decay rate
subsample_initial_block: Changes model type to suit different datasets.
Should be set to True for ImageNet, and False for CIFAR datasets.
When set to True, the initial convolution will be strided and
adds a MaxPooling2D before the initial dense block.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model
will be the 4D tensor output of the
last convolutional layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a
2D tensor.
- `max` means that global max pooling will
be applied.
activation: Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'.
Note that if sigmoid is used, classes must be 1.
transition_pooling: `avg` for avg pooling (default), `max` for max pooling,
None for no pooling during scale transition blocks. Please note that this
default differs from the DenseNetFCN paper in accordance with the DenseNet
paper.
# Returns
a keras tensor
# Raises
ValueError: in case of invalid argument for `reduction`
or `nb_dense_block`
'''
with K.name_scope('DenseNet'):
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
if reduction != 0.0:
if not (reduction <= 1.0 and reduction > 0.0):
raise ValueError('`reduction` value must lie between 0.0 and 1.0')
# layers in each dense block
if type(nb_layers_per_block) is list or type(nb_layers_per_block) is tuple:
nb_layers = list(nb_layers_per_block) # Convert tuple to list
if len(nb_layers) != (nb_dense_block):
raise ValueError('If `nb_dense_block` is a list, its length must match '
'the number of layers provided by `nb_layers`.')
final_nb_layer = nb_layers[-1]
nb_layers = nb_layers[:-1]
else:
if nb_layers_per_block == -1:
assert (depth - 4) % 3 == 0, 'Depth must be 3 N + 4 if nb_layers_per_block == -1'
count = int((depth - 4) / 3)
if bottleneck:
count = count // 2
nb_layers = [count for _ in range(nb_dense_block)]
final_nb_layer = count
else:
final_nb_layer = nb_layers_per_block
nb_layers = [nb_layers_per_block] * nb_dense_block
# compute initial nb_filter if -1, else accept users initial nb_filter
if nb_filter <= 0:
nb_filter = 2 * growth_rate
# compute compression factor
compression = 1.0 - reduction
# Initial convolution
if subsample_initial_block:
initial_kernel = (7, 7)
initial_strides = (2, 2)
else:
initial_kernel = (3, 3)
initial_strides = (1, 1)
x = Conv2D(nb_filter, initial_kernel, kernel_initializer='he_normal', padding='same', name='initial_conv2D',
strides=initial_strides, use_bias=False, kernel_regularizer=l2(weight_decay))(img_input)
if subsample_initial_block:
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5, name='initial_bn')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
# Add dense blocks
for block_idx in range(nb_dense_block - 1):
x, nb_filter = __dense_block(x, nb_layers[block_idx], nb_filter, growth_rate, bottleneck=bottleneck,
dropout_rate=dropout_rate, weight_decay=weight_decay,
block_prefix='dense_%i' % block_idx)
# add transition_block
x = __transition_block(x, nb_filter, compression=compression, weight_decay=weight_decay,
block_prefix='tr_%i' % block_idx, transition_pooling=transition_pooling)
nb_filter = int(nb_filter * compression)
# The last dense_block does not have a transition_block
x, nb_filter = __dense_block(x, final_nb_layer, nb_filter, growth_rate, bottleneck=bottleneck,
dropout_rate=dropout_rate, weight_decay=weight_decay,
block_prefix='dense_%i' % (nb_dense_block - 1))
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5, name='final_bn')(x)
x = Activation('relu')(x)
if include_top:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
x = Dense(nb_classes, activation=activation)(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
return x
def dense_bn(x, units, use_bias=True, scope=None, activation=None):
with K.name_scope(scope):
x = Dense(units=units, use_bias=use_bias)(x)
x = BatchNormalization(momentum=0.9)(x)
x = Activation(activation)(x)
return x
def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5, growth_rate=12,
reduction=0.0, dropout_rate=None, weight_decay=1e-4,
nb_layers_per_block=4, nb_upsampling_conv=128, upsampling_type='deconv',
init_conv_filters=48, input_shape=None, activation='softmax',
early_transition=False, transition_pooling='max', initial_kernel_size=(3, 3)):
''' Build the DenseNet-FCN model
# Arguments
nb_classes: number of classes
img_input: tuple of shape (channels, rows, columns) or (rows, columns, channels)
include_top: flag to include the final Dense layer
nb_dense_block: number of dense blocks to add to end (generally = 3)
growth_rate: number of filters to add per dense block
reduction: reduction factor of transition blocks. Note : reduction value is inverted to compute compression
dropout_rate: dropout rate
weight_decay: weight decay
nb_layers_per_block: number of layers in each dense block.
Can be a positive integer or a list.
If positive integer, a set number of layers per dense block.
If list, nb_layer is used as provided. Note that list size must
be (nb_dense_block + 1)
nb_upsampling_conv: number of convolutional layers in upsampling via subpixel convolution
upsampling_type: Can be one of 'upsampling', 'deconv' and 'subpixel'. Defines
type of upsampling algorithm used.
input_shape: Only used for shape inference in fully convolutional networks.
activation: Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'.
Note that if sigmoid is used, classes must be 1.
early_transition: Start with an extra initial transition down and end with an extra
transition up to reduce the network size.
transition_pooling: 'max' for max pooling (default), 'avg' for average pooling,
None for no pooling. Please note that this default differs from the DenseNet
paper in accordance with the DenseNetFCN paper.
initial_kernel_size: The first Conv2D kernel might vary in size based on the
application, this parameter makes it configurable.
# Returns
a keras tensor
# Raises
ValueError: in case of invalid argument for `reduction`,
`nb_dense_block` or `nb_upsampling_conv`.
'''
with K.name_scope('DenseNetFCN'):
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
if concat_axis == 1: # channels_first dim ordering
_, rows, cols = input_shape
else:
rows, cols, _ = input_shape
if reduction != 0.0:
if not (reduction <= 1.0 and reduction > 0.0):
raise ValueError('`reduction` value must lie between 0.0 and 1.0')
# check if upsampling_conv has minimum number of filters
# minimum is set to 12, as at least 3 color channels are needed for correct upsampling
if not (nb_upsampling_conv > 12 and nb_upsampling_conv % 4 == 0):
raise ValueError('Parameter `nb_upsampling_conv` number of channels must '
'be a positive number divisible by 4 and greater than 12')
# layers in each dense block
if type(nb_layers_per_block) is list or type(nb_layers_per_block) is tuple:
nb_layers = list(nb_layers_per_block) # Convert tuple to list
if len(nb_layers) != (nb_dense_block + 1):
raise ValueError('If `nb_dense_block` is a list, its length must be '
'(`nb_dense_block` + 1)')
bottleneck_nb_layers = nb_layers[-1]
rev_layers = nb_layers[::-1]
nb_layers.extend(rev_layers[1:])
else:
bottleneck_nb_layers = nb_layers_per_block
nb_layers = [nb_layers_per_block] * (2 * nb_dense_block + 1)
# compute compression factor
compression = 1.0 - reduction
# Initial convolution
x = Conv2D(init_conv_filters, initial_kernel_size, kernel_initializer='he_normal', padding='same', name='initial_conv2D',
use_bias=False, kernel_regularizer=l2(weight_decay))(img_input)
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5, name='initial_bn')(x)
x = Activation('relu')(x)
nb_filter = init_conv_filters
skip_list = []
if early_transition:
x = __transition_block(x, nb_filter, compression=compression, weight_decay=weight_decay,
block_prefix='tr_early', transition_pooling=transition_pooling)
# Add dense blocks and transition down block
for block_idx in range(nb_dense_block):
x, nb_filter = __dense_block(x, nb_layers[block_idx], nb_filter, growth_rate, dropout_rate=dropout_rate,
weight_decay=weight_decay, block_prefix='dense_%i' % block_idx)
# Skip connection
skip_list.append(x)
# add transition_block
x = __transition_block(x, nb_filter, compression=compression, weight_decay=weight_decay,
block_prefix='tr_%i' % block_idx, transition_pooling=transition_pooling)
nb_filter = int(nb_filter * compression) # this is calculated inside transition_down_block
# The last dense_block does not have a transition_down_block
# return the concatenated feature maps without the concatenation of the input
_, nb_filter, concat_list = __dense_block(x, bottleneck_nb_layers, nb_filter, growth_rate,
dropout_rate=dropout_rate, weight_decay=weight_decay,
return_concat_list=True,
block_prefix='dense_%i' % nb_dense_block)
skip_list = skip_list[::-1] # reverse the skip list
# Add dense blocks and transition up block
for block_idx in range(nb_dense_block):
n_filters_keep = growth_rate * nb_layers[nb_dense_block + block_idx]
# upsampling block must upsample only the feature maps (concat_list[1:]),
# not the concatenation of the input with the feature maps (concat_list[0].
l = concatenate(concat_list[1:], axis=concat_axis)
t = __transition_up_block(l, nb_filters=n_filters_keep, type=upsampling_type, weight_decay=weight_decay,
block_prefix='tr_up_%i' % block_idx)
# concatenate the skip connection with the transition block
x = concatenate([t, skip_list[block_idx]], axis=concat_axis)
# Dont allow the feature map size to grow in upsampling dense blocks
x_up, nb_filter, concat_list = __dense_block(x, nb_layers[nb_dense_block + block_idx + 1],
nb_filter=growth_rate, growth_rate=growth_rate,
dropout_rate=dropout_rate, weight_decay=weight_decay,
return_concat_list=True, grow_nb_filters=False,
block_prefix='dense_%i' % (nb_dense_block + 1 + block_idx))
if early_transition:
x_up = __transition_up_block(x_up, nb_filters=nb_filter, type=upsampling_type, weight_decay=weight_decay,
block_prefix='tr_up_early')
if include_top:
x = Conv2D(nb_classes, (1, 1), activation='linear', padding='same', use_bias=False)(x_up)
if K.image_data_format() == 'channels_first':
channel, row, col = input_shape
else:
row, col, channel = input_shape
x = Reshape((row * col, nb_classes))(x)
x = Activation(activation)(x)
x = Reshape((row, col, nb_classes))(x)
else:
x = x_up
return x
def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5, growth_rate=12,
reduction=0.0, dropout_rate=None, weight_decay=1e-4,
nb_layers_per_block=4, nb_upsampling_conv=128, upsampling_type='deconv',
init_conv_filters=48, input_shape=None, activation='sigmoid',
early_transition=False, transition_pooling='max', initial_kernel_size=(3, 3)):
''' Build the DenseNet-FCN model
# Arguments
nb_classes: number of classes
img_input: tuple of shape (channels, rows, columns) or (rows, columns, channels)
include_top: flag to include the final Dense layer
nb_dense_block: number of dense blocks to add to end (generally = 3)
growth_rate: number of filters to add per dense block
reduction: reduction factor of transition blocks. Note : reduction value is inverted to compute compression
dropout_rate: dropout rate
weight_decay: weight decay
nb_layers_per_block: number of layers in each dense block.
Can be a positive integer or a list.
If positive integer, a set number of layers per dense block.
If list, nb_layer is used as provided. Note that list size must
be (nb_dense_block + 1)
nb_upsampling_conv: number of convolutional layers in upsampling via subpixel convolution
upsampling_type: Can be one of 'upsampling', 'deconv' and 'subpixel'. Defines
type of upsampling algorithm used.
input_shape: Only used for shape inference in fully convolutional networks.
activation: Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'.
Note that if sigmoid is used, classes must be 1.
early_transition: Start with an extra initial transition down and end with an extra
transition up to reduce the network size.
transition_pooling: 'max' for max pooling (default), 'avg' for average pooling,
None for no pooling. Please note that this default differs from the DenseNet
paper in accordance with the DenseNetFCN paper.
initial_kernel_size: The first Conv2D kernel might vary in size based on the
application, this parameter makes it configurable.
# Returns
a keras tensor
# Raises
ValueError: in case of invalid argument for `reduction`,
`nb_dense_block` or `nb_upsampling_conv`.
'''
with K.name_scope('DenseNetFCN'):
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
if concat_axis == 1: # channels_first dim ordering
_, rows, cols = input_shape
else:
rows, cols, _ = input_shape
if reduction != 0.0:
if not (reduction <= 1.0 and reduction > 0.0):
raise ValueError('`reduction` value must lie between 0.0 and 1.0')
# check if upsampling_conv has minimum number of filters
# minimum is set to 12, as at least 3 color channels are needed for correct upsampling
if not (nb_upsampling_conv > 12 and nb_upsampling_conv % 4 == 0):
raise ValueError('Parameter `nb_upsampling_conv` number of channels must '
'be a positive number divisible by 4 and greater than 12')
# layers in each dense block
if type(nb_layers_per_block) is list or type(nb_layers_per_block) is tuple:
nb_layers = list(nb_layers_per_block) # Convert tuple to list
if len(nb_layers) != (nb_dense_block + 1):
raise ValueError('If `nb_dense_block` is a list, its length must be '
'(`nb_dense_block` + 1)')
bottleneck_nb_layers = nb_layers[-1]
rev_layers = nb_layers[::-1]
nb_layers.extend(rev_layers[1:])
else:
bottleneck_nb_layers = nb_layers_per_block
nb_layers = [nb_layers_per_block] * (2 * nb_dense_block + 1)
# compute compression factor
compression = 1.0 - reduction
# Initial convolution
x = Conv2D(init_conv_filters, initial_kernel_size, kernel_initializer='he_normal', padding='same', name='initial_conv2D',
use_bias=False, kernel_regularizer=l2(weight_decay))(img_input)
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5, name='initial_bn')(x)
x = Activation('relu')(x)
nb_filter = init_conv_filters
skip_list = []
if early_transition:
x = __transition_block(x, nb_filter, compression=compression, weight_decay=weight_decay,
block_prefix='tr_early', transition_pooling=transition_pooling)
# Add dense blocks and transition down block
for block_idx in range(nb_dense_block):
x, nb_filter = __dense_block(x, nb_layers[block_idx], nb_filter, growth_rate, dropout_rate=dropout_rate,
weight_decay=weight_decay, block_prefix='dense_%i' % block_idx)
# Skip connection
skip_list.append(x)
# add transition_block
x = __transition_block(x, nb_filter, compression=compression, weight_decay=weight_decay,
block_prefix='tr_%i' % block_idx, transition_pooling=transition_pooling)
nb_filter = int(nb_filter * compression) # this is calculated inside transition_down_block
# The last dense_block does not have a transition_down_block
# return the concatenated feature maps without the concatenation of the input
_, nb_filter, concat_list = __dense_block(x, bottleneck_nb_layers, nb_filter, growth_rate,
dropout_rate=dropout_rate, weight_decay=weight_decay,
return_concat_list=True,
block_prefix='dense_%i' % nb_dense_block)
skip_list = skip_list[::-1] # reverse the skip list
# Add dense blocks and transition up block
for block_idx in range(nb_dense_block):
n_filters_keep = growth_rate * nb_layers[nb_dense_block + block_idx]
# upsampling block must upsample only the feature maps (concat_list[1:]),
# not the concatenation of the input with the feature maps (concat_list[0].
l = concatenate(concat_list[1:], axis=concat_axis)
t = __transition_up_block(l, nb_filters=n_filters_keep, type=upsampling_type, weight_decay=weight_decay,
block_prefix='tr_up_%i' % block_idx)
# concatenate the skip connection with the transition block
x = concatenate([t, skip_list[block_idx]], axis=concat_axis)
# Dont allow the feature map size to grow in upsampling dense blocks
x_up, nb_filter, concat_list = __dense_block(x, nb_layers[nb_dense_block + block_idx + 1],
nb_filter=growth_rate, growth_rate=growth_rate,
dropout_rate=dropout_rate, weight_decay=weight_decay,
return_concat_list=True, grow_nb_filters=False,
block_prefix='dense_%i' % (nb_dense_block + 1 + block_idx))
if early_transition:
x_up = __transition_up_block(x_up, nb_filters=nb_filter, type=upsampling_type, weight_decay=weight_decay,
block_prefix='tr_up_early')
if include_top:
x = Conv2D(nb_classes, (1, 1), activation='linear', padding='same', use_bias=False)(x_up)
if K.image_data_format() == 'channels_first':
channel, row, col = input_shape
else:
row, col, channel = input_shape
x = Reshape((row * col, nb_classes))(x)
x = Activation(activation)(x)
x = Reshape((row, col, nb_classes))(x)
else:
x = x_up
return x
def build(self, input_shapes):
vdim = input_shapes[0][2]
edim = input_shapes[1][2]
udim = input_shapes[2][2]
with kb.name_scope(self.name):
with kb.name_scope('phi_v'):
v_shapes = [self.units_e[-1] + vdim + udim] + self.units_v
v_shapes = list(zip(v_shapes[:-1], v_shapes[1:]))
self.phi_v_weights = [
self.add_weight(shape=i,
initializer=self.kernel_initializer,
name='weight_v_%d' % j,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
for j, i in enumerate(v_shapes)
]
if self.use_bias:
self.phi_v_biases = [
self.add_weight(shape=(i[-1], ),
initializer=self.bias_initializer,
name='bias_v_%d' % j,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
for j, i in enumerate(v_shapes)
]
else:
self.phi_v_biases = None
with kb.name_scope('phi_e'):
e_shapes = [2 * vdim + edim + udim] + self.units_e
e_shapes = list(zip(e_shapes[:-1], e_shapes[1:]))
self.phi_e_weights = [
self.add_weight(shape=i,
initializer=self.kernel_initializer,
name='weight_e_%d' % j,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
for j, i in enumerate(e_shapes)
]
if self.use_bias:
self.phi_e_biases = [
self.add_weight(shape=(i[-1], ),
initializer=self.bias_initializer,
name='bias_e_%d' % j,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
for j, i in enumerate(e_shapes)
]
else:
self.phi_e_biases = None
with kb.name_scope('phi_u'):
u_shapes = [self.units_e[-1] + self.units_v[-1] + udim
] + self.units_u
u_shapes = list(zip(u_shapes[:-1], u_shapes[1:]))
self.phi_u_weights = [
self.add_weight(shape=i,
initializer=self.kernel_initializer,
name='weight_u_%d' % j,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
for j, i in enumerate(u_shapes)
]
if self.use_bias:
self.phi_u_biases = [
self.add_weight(shape=(i[-1], ),
initializer=self.bias_initializer,
name='bias_u_%d' % j,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
for j, i in enumerate(u_shapes)
]
else:
self.phi_u_biases = None
self.built = True
def build(self):
inputs = Input(batch_shape=self.input_tensor)
prev, cur = self.stem(filters=self.filters,
stem_filters=self.stem_filters,
net=inputs)
for repeat in range(self.num_reduction_cells + 1):
if repeat == self.num_reduction_cells and self.add_aux_output:
prefix = 'aux_{}'.format(repeat * self.num_cell_repeats - 1)
aux_outputs = self._AuxiliaryTop(classes=self.num_classes,
prefix=prefix,
net=cur)
if repeat > 0:
self.filters *= 2
prev, cur = cur, prev
cur = self._ReductionCell(
filters=self.filters,
prefix='reduction_cell_{}'.format(repeat - 1),
cur=prev,
prev=cur)
for cell_index in range(self.num_cell_repeats):
prev, cur = cur, prev
cur = self._NormalCell(
filters=self.filters,
prefix='cell_{}'.format(cell_index +
repeat * self.num_cell_repeats),
cur=prev,
prev=cur)
with K.name_scope('final_layer'):
x = Activation('relu', name='last_relu')(cur)
if self.include_top:
x = GlobalAveragePooling2D(name='avg_pool')(x)
x = Dropout(rate=self.dropout_rate)(x)
outputs = Dense(self.num_classes,
activation='softmax',
name='final_layer/FC')(x)
model_suffix = 'with_top'
else:
if self.pooling == 'avg':
outputs = GlobalAveragePooling2D(name='avg_pool')(x)
elif self.pooling == 'max':
outputs = GlobalMaxPooling2D(name='max_pool')(x)
else:
outputs = None
raise Exception(
'Supported options for pooling: `avg` or `max` given pooling: {}'
.format(self.pooling))
model_suffix = 'no_top'
model_name = 'NASNet-A_{}@{}_{}_{}'.format(self.num_cell_repeats,
self.penultimate_filters,
self.num_classes,
model_suffix)
if self.add_aux_output:
model = Model(inputs, [outputs, aux_outputs],
name='{}_with_auxiliary_output'.format(model_name))
model.summary()
return model
else:
model = Model(inputs, outputs, name=model_name)
model.summary()
return model
def _adjust_block(p, ip, filters, block_id=None):
'''Adjusts the input `previous path` to match the shape of the `input`.
Used in situations where the output number of filters needs to be changed.
# Arguments
p: Input tensor which needs to be modified
ip: Input tensor whose shape needs to be matched
filters: Number of output filters to be matched
block_id: String block_id
# Returns
Adjusted Keras tensor
'''
channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1
img_dim = 2 if backend.image_data_format() == 'channels_first' else -2
ip_shape = backend.int_shape(ip)
if p is not None:
p_shape = backend.int_shape(p)
with backend.name_scope('adjust_block'):
if p is None:
p = ip
elif p_shape[img_dim] != ip_shape[img_dim]:
with backend.name_scope('adjust_reduction_block_%s' % block_id):
p = layers.Activation('relu',
name='adjust_relu_1_%s' % block_id)(p)
p1 = layers.AveragePooling2D(
(1, 1),
strides=(2, 2),
padding='valid',
name='adjust_avg_pool_1_%s' % block_id)(p)
p1 = layers.Conv2D(filters // 2, (1, 1),
padding='same',
kernel_regularizer=l2(weight_decay),
use_bias=False,
name='adjust_conv_1_%s' % block_id,
kernel_initializer='he_normal')(p1)
p2 = layers.ZeroPadding2D(padding=((0, 1), (0, 1)))(p)
p2 = layers.Cropping2D(cropping=((1, 0), (1, 0)))(p2)
p2 = layers.AveragePooling2D(
(1, 1),
strides=(2, 2),
padding='valid',
name='adjust_avg_pool_2_%s' % block_id)(p2)
p2 = layers.Conv2D(filters // 2, (1, 1),
padding='same',
kernel_regularizer=l2(weight_decay),
use_bias=False,
name='adjust_conv_2_%s' % block_id,
kernel_initializer='he_normal')(p2)
p = layers.concatenate([p1, p2], axis=channel_dim)
if use_bn:
p = layers.BatchNormalization(axis=channel_dim,
momentum=bn_momentum,
epsilon=1e-3,
name='adjust_bn_%s' %
block_id)(p)
p = layers.SpatialDropout2D(drop_p)(p)
elif p_shape[channel_dim] != filters:
with backend.name_scope('adjust_projection_block_%s' % block_id):
p = layers.Activation('relu')(p)
p = layers.Conv2D(filters, (1, 1),
strides=(1, 1),
kernel_regularizer=l2(weight_decay),
padding='same',
name='adjust_conv_projection_%s' % block_id,
use_bias=False,
kernel_initializer='he_normal')(p)
if use_bn:
p = layers.BatchNormalization(axis=channel_dim,
momentum=bn_momentum,
epsilon=1e-3,
name='adjust_bn_%s' %
block_id)(p)
p = layers.SpatialDropout2D(drop_p)(p)
return p