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https://github.com/wassname/keras-contrib.git
synced 2026-06-27 16:10:11 +08:00
Add name scopes and correct a flaw with assertion
This commit is contained in:
Somshubra Majumdar
@@ -1,14 +1,14 @@
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"""NASNet (Neural Architecture Search Networks) for Keras
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# References
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"""Collection of NASNet models
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The reference paper:
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- [Learning Transferable Architectures for Scalable Image Recognition]
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(https://arxiv.org/abs/1707.07012)
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Reference material for extended functionality:
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The reference implementation:
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1. TF Slim
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- https://github.com/tensorflow/models/blob/master/research/slim/nets/
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nasnet/nasnet.py
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2. TensorNets
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- https://github.com/taehoonlee/tensornets/blob/master/tensornets/nasnets.py
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"""
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from __future__ import print_function
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@@ -170,13 +170,15 @@ def NASNet(input_shape=None,
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else:
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img_input = input_tensor
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assert penultimate_filters % ((2 ** nb_blocks) * 6), "`penultimate_filters` needs to be divisible " \
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"by 6 * (2^N)."
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assert penultimate_filters % 24 == 0, "`penultimate_filters` needs to be divisible by 24"
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filters = penultimate_filters // ((2 ** nb_blocks) * 6)
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channel_dim = 1 if K.image_data_format() == 'channels_first' else -1
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filters = penultimate_filters // 24
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x = Conv2D(stem_filters, (3, 3), strides=(2, 2), padding='valid', use_bias=False, name='stem_conv1',
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kernel_initializer='he_normal')(img_input)
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x = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
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name='stem_bn1')(x)
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x, p = _reduction_A(x, None, filters // (filters_multiplier ** 2), id='stem_1')
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x, p = _reduction_A(x, p, filters // filters_multiplier, id='stem_2')
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@@ -193,7 +195,6 @@ def NASNet(input_shape=None,
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auxilary_x = None
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if use_auxilary_branch:
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channel_dim = 1 if K.image_data_format() == 'channels_first' else -1
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img_dim = 2 if K.image_data_format() == 'channels_first' else -2
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auxilary_x = Activation('relu')(x)
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@@ -411,16 +412,17 @@ def _separable_conv_block(ip, filters, kernel_size=(3, 3), strides=(1, 1), id=No
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'''
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channel_dim = 1 if K.image_data_format() == 'channels_first' else -1
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x = Activation('relu')(ip)
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x = SeparableConv2D(filters, kernel_size, strides=strides, name='separable_conv_1_%s' % id,
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padding='same', use_bias=False, kernel_initializer='he_normal')(x)
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x = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
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name="separable_conv_1_bn_%s" % (id))(x)
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x = Activation('relu')(x)
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x = SeparableConv2D(filters, kernel_size, name='separable_conv_2_%s' % id,
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padding='same', use_bias=False, kernel_initializer='he_normal')(x)
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x = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
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name="separable_conv_2_bn_%s" % (id))(x)
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with K.name_scope('separable_conv_block_%s' % id):
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x = Activation('relu')(ip)
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x = SeparableConv2D(filters, kernel_size, strides=strides, name='separable_conv_1_%s' % id,
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padding='same', use_bias=False, kernel_initializer='he_normal')(x)
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x = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
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name="separable_conv_1_bn_%s" % (id))(x)
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x = Activation('relu')(x)
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x = SeparableConv2D(filters, kernel_size, name='separable_conv_2_%s' % id,
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padding='same', use_bias=False, kernel_initializer='he_normal')(x)
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x = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
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name="separable_conv_2_bn_%s" % (id))(x)
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return x
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@@ -446,26 +448,28 @@ def _adjust_block(p, ip, filters, id=None):
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p = ip
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elif p._keras_shape[img_dim] != ip._keras_shape[img_dim]:
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p = Activation('relu', name='adjust_relu_1_%s' % id)(p)
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with K.name_scope('adjust_reduction_block_%s' % id):
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p = Activation('relu', name='adjust_relu_1_%s' % id)(p)
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p1 = AveragePooling2D((1, 1), strides=(2, 2), padding='valid', name='adjust_avg_pool_1_%s' % id)(p)
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p1 = Conv2D(filters // 2, (1, 1), padding='same', use_bias=False,
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name='adjust_conv_1_%s' % id, kernel_initializer='he_normal')(p1)
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p1 = AveragePooling2D((1, 1), strides=(2, 2), padding='valid', name='adjust_avg_pool_1_%s' % id)(p)
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p1 = Conv2D(filters // 2, (1, 1), padding='same', use_bias=False,
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name='adjust_conv_1_%s' % id, kernel_initializer='he_normal')(p1)
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p2 = AveragePooling2D((1, 1), strides=(2, 2), padding='valid', name='adjust_avg_pool_2_%s' % id)(p)
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p2 = Conv2D(filters // 2, (1, 1), padding='same', use_bias=False,
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name='adjust_conv_2_%s' % id, kernel_initializer='he_normal')(p2)
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p2 = AveragePooling2D((1, 1), strides=(2, 2), padding='valid', name='adjust_avg_pool_2_%s' % id)(p)
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p2 = Conv2D(filters // 2, (1, 1), padding='same', use_bias=False,
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name='adjust_conv_2_%s' % id, kernel_initializer='he_normal')(p2)
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p = concatenate([p1, p2], axis=channel_dim)
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p = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
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name='adjust_bn_%s' % id)(p)
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p = concatenate([p1, p2], axis=channel_dim)
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p = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
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name='adjust_bn_%s' % id)(p)
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elif p._keras_shape[channel_dim] != filters:
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p = Activation('relu')(p)
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p = Conv2D(filters, (1, 1), strides=(1, 1), padding='same', name='adjust_conv_projection_%s' % id,
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use_bias=False, kernel_initializer='he_normal')(p)
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p = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
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name='adjust_bn_%s' % id)(p)
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with K.name_scope('adjust_projection_block_%s' % id):
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p = Activation('relu')(p)
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p = Conv2D(filters, (1, 1), strides=(1, 1), padding='same', name='adjust_conv_projection_%s' % id,
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use_bias=False, kernel_initializer='he_normal')(p)
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p = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
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name='adjust_bn_%s' % id)(p)
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return p
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@@ -491,23 +495,28 @@ def _normal_A(ip, p, filters, id=None):
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h = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
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name='normal_bn_1_%s' % id)(h)
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x1 = _separable_conv_block(h, filters, id='normal_left1_%s' % id)
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x1 = add([x1, h], name='normal_add_1_%s' % id)
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with K.name_scope('normal_A_block_1'):
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x1 = _separable_conv_block(h, filters, id='normal_left1_%s' % id)
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x1 = add([x1, h], name='normal_add_1_%s' % id)
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x2_1 = _separable_conv_block(p, filters, id='normal_left2_%s' % id)
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x2_2 = _separable_conv_block(h, filters, kernel_size=(5, 5), id='normal_right2_%s' % id)
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x2 = add([x2_1, x2_2], name='normal_add_2_%s' % id)
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with K.name_scope('normal_A_block_2'):
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x2_1 = _separable_conv_block(p, filters, id='normal_left2_%s' % id)
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x2_2 = _separable_conv_block(h, filters, kernel_size=(5, 5), id='normal_right2_%s' % id)
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x2 = add([x2_1, x2_2], name='normal_add_2_%s' % id)
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x3 = AveragePooling2D((3, 3), strides=(1, 1), padding='same', name='normal_left3_%s' % (id))(h)
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x3 = add([x3, p], name='normal_add_3_%s' % id)
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with K.name_scope('normal_A_block_3'):
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x3 = AveragePooling2D((3, 3), strides=(1, 1), padding='same', name='normal_left3_%s' % (id))(h)
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x3 = add([x3, p], name='normal_add_3_%s' % id)
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x4_1 = AveragePooling2D((3, 3), strides=(1, 1), padding='same', name='normal_left4_%s' % (id))(p)
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x4_2 = AveragePooling2D((3, 3), strides=(1, 1), padding='same', name='normal_right4_%s' % (id))(p)
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x4 = add([x4_1, x4_2], name='normal_add_4_%s' % id)
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with K.name_scope('normal_A_block_4'):
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x4_1 = AveragePooling2D((3, 3), strides=(1, 1), padding='same', name='normal_left4_%s' % (id))(p)
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x4_2 = AveragePooling2D((3, 3), strides=(1, 1), padding='same', name='normal_right4_%s' % (id))(p)
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x4 = add([x4_1, x4_2], name='normal_add_4_%s' % id)
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x5_1 = _separable_conv_block(p, filters, (5, 5), id='normal_left5_%s' % id)
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x5_2 = _separable_conv_block(p, filters, (3, 3), id='normal_right5_%s' % id)
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x5 = add([x5_1, x5_2], name='normal_add_5_%s' % id)
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with K.name_scope('normal_A_block_5'):
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x5_1 = _separable_conv_block(p, filters, (5, 5), id='normal_left5_%s' % id)
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x5_2 = _separable_conv_block(p, filters, (3, 3), id='normal_right5_%s' % id)
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x5 = add([x5_1, x5_2], name='normal_add_5_%s' % id)
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x = concatenate([p, x2, x5, x3, x4, x1], axis=channel_dim, name='normal_concat_%s' % id)
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return x, ip
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@@ -536,23 +545,28 @@ def _reduction_A(ip, p, filters, id=None):
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h = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
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name='reduction_bn_1_%s' % id)(h)
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x1_1 = _separable_conv_block(p, filters, (7, 7), strides=(2, 2), id='reduction_left1_%s' % id)
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x1_2 = _separable_conv_block(h, filters, (5, 5), strides=(2, 2), id='reduction_right1_%s' % id)
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x1 = add([x1_1, x1_2], name='reduction_add_1_%s' % id)
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with K.name_scope('reduction_A_block_1'):
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x1_1 = _separable_conv_block(p, filters, (7, 7), strides=(2, 2), id='reduction_left1_%s' % id)
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x1_2 = _separable_conv_block(h, filters, (5, 5), strides=(2, 2), id='reduction_right1_%s' % id)
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x1 = add([x1_1, x1_2], name='reduction_add_1_%s' % id)
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x2_1 = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='reduction_left2_%s' % id)(h)
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x2_2 = _separable_conv_block(p, filters, (7, 7), strides=(2, 2), id='reduction_right2_%s' % id)
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x2 = add([x2_1, x2_2], name='reduction_add_2_%s' % id)
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with K.name_scope('reduction_A_block_2'):
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x2_1 = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='reduction_left2_%s' % id)(h)
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x2_2 = _separable_conv_block(p, filters, (7, 7), strides=(2, 2), id='reduction_right2_%s' % id)
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x2 = add([x2_1, x2_2], name='reduction_add_2_%s' % id)
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x3_1 = AveragePooling2D((3, 3), strides=(2, 2), padding='same', name='reduction_left3_%s' % id)(h)
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x3_2 = _separable_conv_block(p, filters, (5, 5), strides=(2, 2), id='reduction_right3_%s' % id)
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x3 = add([x3_1, x3_2], name='reduction_add3_%s' % id)
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with K.name_scope('reduction_A_block_3'):
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x3_1 = AveragePooling2D((3, 3), strides=(2, 2), padding='same', name='reduction_left3_%s' % id)(h)
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x3_2 = _separable_conv_block(p, filters, (5, 5), strides=(2, 2), id='reduction_right3_%s' % id)
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x3 = add([x3_1, x3_2], name='reduction_add3_%s' % id)
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x4_1 = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='reduction_left4_%s' % id)(h)
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x4_2 = _separable_conv_block(x1, filters, (3, 3), id='reduction_right4_%s' % id)
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x4 = add([x4_1, x4_2], name='reduction_add4_%s' % id)
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with K.name_scope('reduction_A_block_4'):
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x4_1 = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='reduction_left4_%s' % id)(h)
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x4_2 = _separable_conv_block(x1, filters, (3, 3), id='reduction_right4_%s' % id)
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x4 = add([x4_1, x4_2], name='reduction_add4_%s' % id)
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x5 = AveragePooling2D((3, 3), strides=(1, 1), padding='same', name='reduction_left5_%s' % id)(x1)
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with K.name_scope('reduction_A_block_5'):
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x5 = AveragePooling2D((3, 3), strides=(1, 1), padding='same', name='reduction_left5_%s' % id)(x1)
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x = concatenate([x2, x3, x5, x4], axis=channel_dim, name='reduction_concat_%s' % id)
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return x, ip
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