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Add full weight support (NASNet Large and NASNet Mobile)

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Somshubra Majumdar
2017-12-06 14:35:48 -06:00
parent 0fa1db5e5c
commit aeb39bd426
1 changed files with 113 additions and 64 deletions
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keras_contrib/applications/nasnet.py
+113 -64
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@@ -44,13 +44,22 @@ from keras import backend as K
_BN_DECAY = 0.9997
_BN_EPSILON = 1e-3
NASNET_MOBILE_WEIGHT_PATH = "https://github.com/titu1994/Keras-NASNet/releases/download/v1.0/NASNet-mobile.h5"
NASNET_MOBILE_WEIGHT_PATH_NO_TOP = "https://github.com/titu1994/Keras-NASNet/releases/download/v1.0/NASNet-mobile-no-top.h5"
NASNET_MOBILE_WEIGHT_PATH_WITH_AUXULARY = "https://github.com/titu1994/Keras-NASNet/releases/download/v1.0/NASNet-auxiliary-mobile.h5"
NASNET_MOBILE_WEIGHT_PATH_WITH_AUXULARY_NO_TOP = "https://github.com/titu1994/Keras-NASNet/releases/download/v1.0/NASNet-auxiliary-mobile-no-top.h5"
NASNET_LARGE_WEIGHT_PATH = "https://github.com/titu1994/Keras-NASNet/releases/download/v1.1/NASNet-large.h5"
NASNET_LARGE_WEIGHT_PATH_NO_TOP = "https://github.com/titu1994/Keras-NASNet/releases/download/v1.1/NASNet-large-no-top.h5"
NASNET_LARGE_WEIGHT_PATH_WITH_auxiliary = "https://github.com/titu1994/Keras-NASNet/releases/download/v1.1/NASNet-auxiliary-large.h5"
NASNET_LARGE_WEIGHT_PATH_WITH_auxiliary_NO_TOP = "https://github.com/titu1994/Keras-NASNet/releases/download/v1.1/NASNet-auxiliary-large-no-top.h5"
def NASNet(input_shape=None,
penultimate_filters=4032,
nb_blocks=6,
stem_filters=96,
skip_reduction=True,
use_auxilary_branch=False,
use_auxiliary_branch=False,
filters_multiplier=2,
dropout=0.5,
weight_decay=5e-5,
@@ -85,7 +94,7 @@ def NASNet(input_shape=None,
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
use_auxiliary_branch: Whether to use the auxiliary branch during
training or evaluation.
filters_multiplier: controls the width of the network.
- If `filters_multiplier` < 1.0, proportionally decreases the number
@@ -205,16 +214,16 @@ def NASNet(input_shape=None,
for i in range(nb_blocks):
x, p = _normal_A(x, p, filters * filters_multiplier, weight_decay, id='%d' % (nb_blocks + i + 1))
auxilary_x = None
auxiliary_x = None
if not skip_reduction: # imagenet / mobile mode
if use_auxilary_branch:
auxilary_x = _add_auxilary_head(x, classes, weight_decay)
if use_auxiliary_branch:
auxiliary_x = _add_auxiliary_head(x, classes, weight_decay)
x, p0 = _reduction_A(x, p, filters * filters_multiplier ** 2, weight_decay, id='reduce_%d' % (2 * nb_blocks))
if skip_reduction: # CIFAR mode
if use_auxilary_branch:
auxilary_x = _add_auxilary_head(x, classes, weight_decay)
if use_auxiliary_branch:
auxiliary_x = _add_auxiliary_head(x, classes, weight_decay)
p = p0 if not skip_reduction else p
@@ -241,14 +250,53 @@ def NASNet(input_shape=None,
inputs = img_input
# Create model.
if use_auxilary_branch:
model = Model(inputs, [x, auxilary_x], name='NASNet_with_auxilary')
if use_auxiliary_branch:
model = Model(inputs, [x, auxiliary_x], name='NASNet_with_auxiliary')
else:
model = Model(inputs, x, name='NASNet')
# load weights (when available)
if weights is not None:
warnings.warn('Weights of NASNet models have not yet been ported to Keras')
# load weights
if weights == 'imagenet':
if default_size == 224: # mobile version
if include_top:
if use_auxiliary_branch:
weight_path = NASNET_MOBILE_WEIGHT_PATH_WITH_AUXULARY
model_name = 'nasnet_mobile_with_aux.h5'
else:
weight_path = NASNET_MOBILE_WEIGHT_PATH
model_name = 'nasnet_mobile.h5'
else:
if use_auxiliary_branch:
weight_path = NASNET_MOBILE_WEIGHT_PATH_WITH_AUXULARY_NO_TOP
model_name = 'nasnet_mobile_with_aux_no_top.h5'
else:
weight_path = NASNET_MOBILE_WEIGHT_PATH_NO_TOP
model_name = 'nasnet_mobile_no_top.h5'
weights_file = get_file(model_name, weight_path, cache_subdir='models')
model.load_weights(weights_file, by_name=True)
elif default_size == 331: # large version
if include_top:
if use_auxiliary_branch:
weight_path = NASNET_LARGE_WEIGHT_PATH_WITH_auxiliary
model_name = 'nasnet_large_with_aux.h5'
else:
weight_path = NASNET_LARGE_WEIGHT_PATH
model_name = 'nasnet_large.h5'
else:
if use_auxiliary_branch:
weight_path = NASNET_LARGE_WEIGHT_PATH_WITH_auxiliary_NO_TOP
model_name = 'nasnet_large_with_aux_no_top.h5'
else:
weight_path = NASNET_LARGE_WEIGHT_PATH_NO_TOP
model_name = 'nasnet_large_no_top.h5'
weights_file = get_file(model_name, weight_path, cache_subdir='models')
model.load_weights(weights_file, by_name=True)
else:
raise ValueError('ImageNet weights can only be loaded on NASNetLarge or NASNetMobile')
if old_data_format:
K.set_image_data_format(old_data_format)
@@ -259,9 +307,9 @@ def NASNet(input_shape=None,
def NASNetLarge(input_shape=(331, 331, 3),
dropout=0.5,
weight_decay=5e-5,
use_auxilary_branch=False,
use_auxiliary_branch=False,
include_top=True,
weights=None,
weights='imagenet',
input_tensor=None,
pooling=None,
classes=1000):
@@ -278,7 +326,7 @@ def NASNetLarge(input_shape=(331, 331, 3),
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.
use_auxilary_branch: Whether to use the auxilary branch during
use_auxiliary_branch: Whether to use the auxiliary branch during
training or evaluation.
dropout: dropout rate
weight_decay: l2 regularization weight
@@ -321,8 +369,8 @@ def NASNetLarge(input_shape=(331, 331, 3),
penultimate_filters=4032,
nb_blocks=6,
stem_filters=96,
skip_reduction=True,
use_auxilary_branch=use_auxilary_branch,
skip_reduction=False,
use_auxiliary_branch=use_auxiliary_branch,
filters_multiplier=2,
dropout=dropout,
weight_decay=weight_decay,
@@ -337,9 +385,9 @@ def NASNetLarge(input_shape=(331, 331, 3),
def NASNetMobile(input_shape=(224, 224, 3),
dropout=0.5,
weight_decay=4e-5,
use_auxilary_branch=False,
use_auxiliary_branch=False,
include_top=True,
weights=None,
weights='imagenet',
input_tensor=None,
pooling=None,
classes=1000):
@@ -356,7 +404,7 @@ def NASNetMobile(input_shape=(224, 224, 3),
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.
use_auxilary_branch: Whether to use the auxilary branch during
use_auxiliary_branch: Whether to use the auxiliary branch during
training or evaluation.
dropout: dropout rate
weight_decay: l2 regularization weight
@@ -400,7 +448,7 @@ def NASNetMobile(input_shape=(224, 224, 3),
nb_blocks=4,
stem_filters=32,
skip_reduction=False,
use_auxilary_branch=use_auxilary_branch,
use_auxiliary_branch=use_auxiliary_branch,
filters_multiplier=2,
dropout=dropout,
weight_decay=weight_decay,
@@ -415,7 +463,7 @@ def NASNetMobile(input_shape=(224, 224, 3),
def NASNetCIFAR(input_shape=(32, 32, 3),
dropout=0.0,
weight_decay=5e-4,
use_auxilary_branch=False,
use_auxiliary_branch=False,
include_top=True,
weights=None,
input_tensor=None,
@@ -434,7 +482,7 @@ def NASNetCIFAR(input_shape=(32, 32, 3),
It should have exactly 3 inputs channels,
and width and height should be no smaller than 32.
E.g. `(32, 32, 3)` would be one valid value.
use_auxilary_branch: Whether to use the auxilary branch during
use_auxiliary_branch: Whether to use the auxiliary branch during
training or evaluation.
dropout: dropout rate
weight_decay: l2 regularization weight
@@ -476,9 +524,9 @@ def NASNetCIFAR(input_shape=(32, 32, 3),
return NASNet(input_shape,
penultimate_filters=768,
nb_blocks=6,
stem_filters=96,
stem_filters=32,
skip_reduction=True,
use_auxilary_branch=use_auxilary_branch,
use_auxiliary_branch=use_auxiliary_branch,
filters_multiplier=2,
dropout=dropout,
weight_decay=weight_decay,
@@ -545,7 +593,7 @@ def _adjust_block(p, ip, filters, weight_decay=5e-5, id=None):
if p is None:
p = ip
if p._keras_shape[img_dim] != ip._keras_shape[img_dim]:
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)
@@ -598,13 +646,14 @@ def _normal_A(ip, p, filters, weight_decay=5e-5, id=None):
name='normal_bn_1_%s' % id)(h)
with K.name_scope('block_1'):
x1 = _separable_conv_block(h, filters, weight_decay=weight_decay, id='normal_left1_%s' % id)
x1 = add([x1, h], name='normal_add_1_%s' % id)
x1_1 = _separable_conv_block(h, filters, kernel_size=(5, 5), weight_decay=weight_decay,
id='normal_left1_%s' % id)
x1_2 = _separable_conv_block(p, filters, weight_decay=weight_decay, id='normal_right1_%s' % id)
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, weight_decay=weight_decay, id='normal_left2_%s' % id)
x2_2 = _separable_conv_block(h, filters, kernel_size=(5, 5), weight_decay=weight_decay,
id='normal_right2_%s' % id)
x2_1 = _separable_conv_block(p, filters, (5, 5), weight_decay=weight_decay, id='normal_left2_%s' % id)
x2_2 = _separable_conv_block(p, filters, (3, 3), weight_decay=weight_decay, id='normal_right2_%s' % id)
x2 = add([x2_1, x2_2], name='normal_add_2_%s' % id)
with K.name_scope('block_3'):
@@ -617,11 +666,10 @@ def _normal_A(ip, p, filters, weight_decay=5e-5, id=None):
x4 = add([x4_1, x4_2], name='normal_add_4_%s' % id)
with K.name_scope('block_5'):
x5_1 = _separable_conv_block(p, filters, (5, 5), weight_decay=weight_decay, id='normal_left5_%s' % id)
x5_2 = _separable_conv_block(p, filters, (3, 3), weight_decay=weight_decay, id='normal_right5_%s' % id)
x5 = add([x5_1, x5_2], name='normal_add_5_%s' % id)
x5 = _separable_conv_block(h, filters, weight_decay=weight_decay, id='normal_left5_%s' % id)
x5 = add([x5, h], name='normal_add_5_%s' % id)
x = concatenate([p, x2, x5, x3, x4, x1], axis=channel_dim, name='normal_concat_%s' % id)
x = concatenate([p, x1, x2, x3, x4, x5], axis=channel_dim, name='normal_concat_%s' % id)
return x, ip
@@ -651,10 +699,10 @@ def _reduction_A(ip, p, filters, weight_decay=5e-5, id=None):
name='reduction_bn_1_%s' % id)(h)
with K.name_scope('block_1'):
x1_1 = _separable_conv_block(p, filters, (7, 7), strides=(2, 2), weight_decay=weight_decay,
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(h, filters, (5, 5), strides=(2, 2), weight_decay=weight_decay,
id='reduction_right1_%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'):
@@ -670,22 +718,23 @@ def _reduction_A(ip, p, filters, weight_decay=5e-5, id=None):
x3 = add([x3_1, x3_2], name='reduction_add3_%s' % id)
with K.name_scope('block_4'):
x4_1 = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='reduction_left4_%s' % id)(h)
x4_2 = _separable_conv_block(x1, filters, (3, 3), weight_decay=weight_decay, id='reduction_right4_%s' % id)
x4 = add([x4_1, x4_2], name='reduction_add4_%s' % id)
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 = AveragePooling2D((3, 3), strides=(1, 1), padding='same', name='reduction_left5_%s' % id)(x1)
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, x5, x4], axis=channel_dim, name='reduction_concat_%s' % id)
x = concatenate([x2, x3, x4, x5], axis=channel_dim, name='reduction_concat_%s' % id)
return x, ip
def _add_auxilary_head(x, classes, weight_decay):
'''Adds an auxilary head for training the model
def _add_auxiliary_head(x, classes, weight_decay):
'''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 auxilary classifier around 2/3 of the depth of the network, with
trained using an auxiliary classifier around 2/3 of the depth of the network, with
a loss weight of 0.4
# Arguments
@@ -700,23 +749,23 @@ def _add_auxilary_head(x, classes, weight_decay):
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('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', kernel_regularizer=l2(weight_decay))(auxilary_x)
auxilary_x = BatchNormalization(axis=channel_axis, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
name='aux_bn_projection')(auxilary_x)
auxilary_x = Activation('relu')(auxilary_x)
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)
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',
kernel_regularizer=l2(weight_decay), name='aux_conv_reduction')(auxilary_x)
auxilary_x = BatchNormalization(axis=channel_axis, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
name='aux_bn_reduction')(auxilary_x)
auxilary_x = Activation('relu')(auxilary_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)
auxilary_x = GlobalAveragePooling2D()(auxilary_x)
auxilary_x = Dense(classes, activation='softmax', kernel_regularizer=l2(weight_decay),
name='aux_predictions')(auxilary_x)
return auxilary_x
auxiliary_x = GlobalAveragePooling2D()(auxiliary_x)
auxiliary_x = Dense(classes, activation='softmax', kernel_regularizer=l2(weight_decay),
name='aux_predictions')(auxiliary_x)
return auxiliary_x