mirror of
https://github.com/wassname/keras-contrib.git
synced 2026-06-27 16:10:11 +08:00
densenet.py add name scope
This commit is contained in:
Andrew Hundt
@@ -535,22 +535,23 @@ def __conv_block(ip, nb_filter, bottleneck=False, dropout_rate=None, weight_deca
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# Returns
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output tensor of block
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'''
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concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
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with K.name_scope('ConvBlock'):
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concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
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x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(ip)
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x = Activation('relu')(x)
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if bottleneck:
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inter_channel = nb_filter * 4
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x = Conv2D(inter_channel, (1, 1), kernel_initializer='he_normal', padding='same', use_bias=False,
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kernel_regularizer=l2(weight_decay))(x)
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x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(x)
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x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(ip)
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x = Activation('relu')(x)
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x = Conv2D(nb_filter, (3, 3), kernel_initializer='he_normal', padding='same', use_bias=False)(x)
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if dropout_rate:
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x = Dropout(dropout_rate)(x)
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if bottleneck:
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inter_channel = nb_filter * 4
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x = Conv2D(inter_channel, (1, 1), kernel_initializer='he_normal', padding='same', use_bias=False,
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kernel_regularizer=l2(weight_decay))(x)
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x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(x)
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x = Activation('relu')(x)
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x = Conv2D(nb_filter, (3, 3), kernel_initializer='he_normal', padding='same', use_bias=False)(x)
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if dropout_rate:
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x = Dropout(dropout_rate)(x)
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return x
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@@ -583,23 +584,24 @@ def __dense_block(x, nb_layers, nb_filter, growth_rate, bottleneck=False, dropou
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If return_concat_list is False, returns a list of the output
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keras tensor and the number of filters
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'''
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concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
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with K.name_scope('DenseBlock'):
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concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
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x_list = [x]
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x_list = [x]
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for i in range(nb_layers):
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cb = __conv_block(x, growth_rate, bottleneck, dropout_rate, weight_decay)
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x_list.append(cb)
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for i in range(nb_layers):
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cb = __conv_block(x, growth_rate, bottleneck, dropout_rate, weight_decay)
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x_list.append(cb)
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x = concatenate([x, cb], axis=concat_axis)
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x = concatenate([x, cb], axis=concat_axis)
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if grow_nb_filters:
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nb_filter += growth_rate
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if grow_nb_filters:
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nb_filter += growth_rate
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if return_concat_list:
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return x, nb_filter, x_list
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else:
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return x, nb_filter
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if return_concat_list:
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return x, nb_filter, x_list
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else:
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return x, nb_filter
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def __transition_block(ip, nb_filter, compression=1.0, weight_decay=1e-4):
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@@ -633,15 +635,16 @@ def __transition_block(ip, nb_filter, compression=1.0, weight_decay=1e-4):
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# Returns
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a keras tensor
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'''
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concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
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with K.name_scope('Transition'):
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concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
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x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(ip)
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x = Activation('relu')(x)
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x = Conv2D(int(nb_filter * compression), (1, 1), kernel_initializer='he_normal', padding='same',
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use_bias=False, kernel_regularizer=l2(weight_decay))(x)
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x = AveragePooling2D((2, 2), strides=(2, 2))(x)
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x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(ip)
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x = Activation('relu')(x)
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x = Conv2D(int(nb_filter * compression), (1, 1), kernel_initializer='he_normal', padding='same',
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use_bias=False, kernel_regularizer=l2(weight_decay))(x)
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x = AveragePooling2D((2, 2), strides=(2, 2))(x)
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return x
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return x
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def __transition_up_block(ip, nb_filters, type='deconv', weight_decay=1E-4):
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@@ -670,20 +673,21 @@ def __transition_up_block(ip, nb_filters, type='deconv', weight_decay=1E-4):
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# Returns
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a keras tensor
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'''
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with K.name_scope('TransitionUp'):
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if type == 'upsampling':
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x = UpSampling2D()(ip)
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elif type == 'subpixel':
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x = Conv2D(nb_filters, (3, 3), activation='relu', padding='same', kernel_regularizer=l2(weight_decay),
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use_bias=False, kernel_initializer='he_normal')(ip)
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x = SubPixelUpscaling(scale_factor=2)(x)
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x = Conv2D(nb_filters, (3, 3), activation='relu', padding='same', kernel_regularizer=l2(weight_decay),
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use_bias=False, kernel_initializer='he_normal')(x)
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else:
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x = Conv2DTranspose(nb_filters, (3, 3), activation='relu', padding='same', strides=(2, 2),
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kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay))(ip)
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if type == 'upsampling':
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x = UpSampling2D()(ip)
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elif type == 'subpixel':
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x = Conv2D(nb_filters, (3, 3), activation='relu', padding='same', kernel_regularizer=l2(weight_decay),
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use_bias=False, kernel_initializer='he_normal')(ip)
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x = SubPixelUpscaling(scale_factor=2)(x)
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x = Conv2D(nb_filters, (3, 3), activation='relu', padding='same', kernel_regularizer=l2(weight_decay),
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use_bias=False, kernel_initializer='he_normal')(x)
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else:
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x = Conv2DTranspose(nb_filters, (3, 3), activation='relu', padding='same', strides=(2, 2),
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kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay))(ip)
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return x
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return x
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def __create_dense_net(nb_classes, img_input, include_top, depth=40, nb_dense_block=3, growth_rate=12, nb_filter=-1,
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@@ -735,81 +739,81 @@ def __create_dense_net(nb_classes, img_input, include_top, depth=40, nb_dense_bl
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ValueError: in case of invalid argument for `reduction`
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or `nb_dense_block`
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'''
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with K.name_scope('DenseNet'):
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concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
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concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
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if reduction != 0.0:
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if not (reduction <= 1.0 and reduction > 0.0):
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raise ValueError('`reduction` value must lie between 0.0 and 1.0')
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if reduction != 0.0:
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if not (reduction <= 1.0 and reduction > 0.0):
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raise ValueError('`reduction` value must lie between 0.0 and 1.0')
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# layers in each dense block
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if type(nb_layers_per_block) is list or type(nb_layers_per_block) is tuple:
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nb_layers = list(nb_layers_per_block) # Convert tuple to list
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# layers in each dense block
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if type(nb_layers_per_block) is list or type(nb_layers_per_block) is tuple:
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nb_layers = list(nb_layers_per_block) # Convert tuple to list
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if len(nb_layers) != (nb_dense_block):
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raise ValueError('If `nb_dense_block` is a list, its length must match '
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'the number of layers provided by `nb_layers`.')
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if len(nb_layers) != (nb_dense_block):
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raise ValueError('If `nb_dense_block` is a list, its length must match '
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'the number of layers provided by `nb_layers`.')
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final_nb_layer = nb_layers[-1]
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nb_layers = nb_layers[:-1]
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else:
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if nb_layers_per_block == -1:
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assert (depth - 4) % 3 == 0, 'Depth must be 3 N + 4 if nb_layers_per_block == -1'
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count = int((depth - 4) / 3)
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nb_layers = [count for _ in range(nb_dense_block)]
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final_nb_layer = count
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final_nb_layer = nb_layers[-1]
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nb_layers = nb_layers[:-1]
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else:
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final_nb_layer = nb_layers_per_block
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nb_layers = [nb_layers_per_block] * nb_dense_block
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if nb_layers_per_block == -1:
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assert (depth - 4) % 3 == 0, 'Depth must be 3 N + 4 if nb_layers_per_block == -1'
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count = int((depth - 4) / 3)
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nb_layers = [count for _ in range(nb_dense_block)]
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final_nb_layer = count
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else:
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final_nb_layer = nb_layers_per_block
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nb_layers = [nb_layers_per_block] * nb_dense_block
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# compute initial nb_filter if -1, else accept users initial nb_filter
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if nb_filter <= 0:
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nb_filter = 2 * growth_rate
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# compute initial nb_filter if -1, else accept users initial nb_filter
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if nb_filter <= 0:
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nb_filter = 2 * growth_rate
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# compute compression factor
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compression = 1.0 - reduction
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# compute compression factor
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compression = 1.0 - reduction
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# Initial convolution
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if subsample_initial_block:
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initial_kernel = (7, 7)
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initial_strides = (2, 2)
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else:
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initial_kernel = (3, 3)
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initial_strides = (1, 1)
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# Initial convolution
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if subsample_initial_block:
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initial_kernel = (7, 7)
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initial_strides = (2, 2)
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else:
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initial_kernel = (3, 3)
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initial_strides = (1, 1)
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x = Conv2D(nb_filter, initial_kernel, kernel_initializer='he_normal', padding='same',
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strides=initial_strides, use_bias=False, kernel_regularizer=l2(weight_decay))(img_input)
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x = Conv2D(nb_filter, initial_kernel, kernel_initializer='he_normal', padding='same',
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strides=initial_strides, use_bias=False, kernel_regularizer=l2(weight_decay))(img_input)
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if subsample_initial_block:
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x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(x)
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x = Activation('relu')(x)
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x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
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# Add dense blocks
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for block_idx in range(nb_dense_block - 1):
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x, nb_filter = __dense_block(x, nb_layers[block_idx], nb_filter, growth_rate, bottleneck=bottleneck,
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dropout_rate=dropout_rate, weight_decay=weight_decay)
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# add transition_block
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x = __transition_block(x, nb_filter, compression=compression, weight_decay=weight_decay)
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nb_filter = int(nb_filter * compression)
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# The last dense_block does not have a transition_block
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x, nb_filter = __dense_block(x, final_nb_layer, nb_filter, growth_rate, bottleneck=bottleneck,
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dropout_rate=dropout_rate, weight_decay=weight_decay)
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if subsample_initial_block:
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x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(x)
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x = Activation('relu')(x)
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x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
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# Add dense blocks
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for block_idx in range(nb_dense_block - 1):
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x, nb_filter = __dense_block(x, nb_layers[block_idx], nb_filter, growth_rate, bottleneck=bottleneck,
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dropout_rate=dropout_rate, weight_decay=weight_decay)
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# add transition_block
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x = __transition_block(x, nb_filter, compression=compression, weight_decay=weight_decay)
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nb_filter = int(nb_filter * compression)
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# The last dense_block does not have a transition_block
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x, nb_filter = __dense_block(x, final_nb_layer, nb_filter, growth_rate, bottleneck=bottleneck,
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dropout_rate=dropout_rate, weight_decay=weight_decay)
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x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(x)
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x = Activation('relu')(x)
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if include_top:
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x = GlobalAveragePooling2D()(x)
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x = Dense(nb_classes, activation=activation)(x)
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else:
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if pooling == 'avg':
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if include_top:
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x = GlobalAveragePooling2D()(x)
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if pooling == 'max':
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x = GlobalMaxPooling2D()(x)
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x = Dense(nb_classes, activation=activation)(x)
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else:
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if pooling == 'avg':
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x = GlobalAveragePooling2D()(x)
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if pooling == 'max':
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x = GlobalMaxPooling2D()(x)
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return x
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return x
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def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5, growth_rate=12,
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@@ -846,104 +850,104 @@ def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5,
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ValueError: in case of invalid argument for `reduction`,
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`nb_dense_block` or `nb_upsampling_conv`.
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'''
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with K.name_scope('DenseNetFCN'):
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concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
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concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
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if concat_axis == 1: # channels_first dim ordering
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_, rows, cols = input_shape
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else:
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rows, cols, _ = input_shape
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if reduction != 0.0:
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if not (reduction <= 1.0 and reduction > 0.0):
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raise ValueError('`reduction` value must lie between 0.0 and 1.0')
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# check if upsampling_conv has minimum number of filters
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# minimum is set to 12, as at least 3 color channels are needed for correct upsampling
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if not (nb_upsampling_conv > 12 and nb_upsampling_conv % 4 == 0):
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raise ValueError('Parameter `nb_upsampling_conv` number of channels must '
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'be a positive number divisible by 4 and greater than 12')
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# layers in each dense block
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if type(nb_layers_per_block) is list or type(nb_layers_per_block) is tuple:
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nb_layers = list(nb_layers_per_block) # Convert tuple to list
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if len(nb_layers) != (nb_dense_block + 1):
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raise ValueError('If `nb_dense_block` is a list, its length must be '
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'(`nb_dense_block` + 1)')
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bottleneck_nb_layers = nb_layers[-1]
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rev_layers = nb_layers[::-1]
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nb_layers.extend(rev_layers[1:])
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else:
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bottleneck_nb_layers = nb_layers_per_block
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nb_layers = [nb_layers_per_block] * (2 * nb_dense_block + 1)
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# compute compression factor
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compression = 1.0 - reduction
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# Initial convolution
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x = Conv2D(init_conv_filters, (7, 7), kernel_initializer='he_normal', padding='same', name='initial_conv2D',
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use_bias=False, kernel_regularizer=l2(weight_decay))(img_input)
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x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(x)
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x = Activation('relu')(x)
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nb_filter = init_conv_filters
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skip_list = []
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# Add dense blocks and transition down block
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for block_idx in range(nb_dense_block):
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x, nb_filter = __dense_block(x, nb_layers[block_idx], nb_filter, growth_rate, dropout_rate=dropout_rate,
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weight_decay=weight_decay)
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# Skip connection
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skip_list.append(x)
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# add transition_block
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x = __transition_block(x, nb_filter, compression=compression, weight_decay=weight_decay)
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nb_filter = int(nb_filter * compression) # this is calculated inside transition_down_block
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# The last dense_block does not have a transition_down_block
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# return the concatenated feature maps without the concatenation of the input
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_, nb_filter, concat_list = __dense_block(x, bottleneck_nb_layers, nb_filter, growth_rate,
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dropout_rate=dropout_rate, weight_decay=weight_decay,
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return_concat_list=True)
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skip_list = skip_list[::-1] # reverse the skip list
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# Add dense blocks and transition up block
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for block_idx in range(nb_dense_block):
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n_filters_keep = growth_rate * nb_layers[nb_dense_block + block_idx]
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# upsampling block must upsample only the feature maps (concat_list[1:]),
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# not the concatenation of the input with the feature maps (concat_list[0].
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l = concatenate(concat_list[1:], axis=concat_axis)
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t = __transition_up_block(l, nb_filters=n_filters_keep, type=upsampling_type, weight_decay=weight_decay)
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# concatenate the skip connection with the transition block
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x = concatenate([t, skip_list[block_idx]], axis=concat_axis)
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# Dont allow the feature map size to grow in upsampling dense blocks
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x_up, nb_filter, concat_list = __dense_block(x, nb_layers[nb_dense_block + block_idx + 1], nb_filter=growth_rate,
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growth_rate=growth_rate, dropout_rate=dropout_rate,
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weight_decay=weight_decay, return_concat_list=True,
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grow_nb_filters=False)
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if include_top:
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x = Conv2D(nb_classes, (1, 1), activation='linear', padding='same', use_bias=False)(x_up)
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if K.image_data_format() == 'channels_first':
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channel, row, col = input_shape
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if concat_axis == 1: # channels_first dim ordering
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_, rows, cols = input_shape
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else:
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row, col, channel = input_shape
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rows, cols, _ = input_shape
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x = Reshape((row * col, nb_classes))(x)
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x = Activation(activation)(x)
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x = Reshape((row, col, nb_classes))(x)
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else:
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x = x_up
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if reduction != 0.0:
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if not (reduction <= 1.0 and reduction > 0.0):
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raise ValueError('`reduction` value must lie between 0.0 and 1.0')
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return x
|
||||
# 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, (7, 7), 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)(x)
|
||||
x = Activation('relu')(x)
|
||||
|
||||
nb_filter = init_conv_filters
|
||||
|
||||
skip_list = []
|
||||
|
||||
# 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)
|
||||
|
||||
# Skip connection
|
||||
skip_list.append(x)
|
||||
|
||||
# add transition_block
|
||||
x = __transition_block(x, nb_filter, compression=compression, weight_decay=weight_decay)
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
|
||||
# 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)
|
||||
|
||||
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
|
||||
|
||||
Reference in New Issue
Block a user