def layer(input_tensor):
# extracting params and names for layers
conv_params = get_conv_params()
bn_params = get_bn_params()
conv_name, bn_name, relu_name, sc_name = handle_block_names(
stage, block)
x = Conv2D(filters, (1, 1), name=conv_name + '1',
**conv_params)(input_tensor)
x = BatchNormalization(name=bn_name + '1', **bn_params)(x)
x = Activation('relu', name=relu_name + '1')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = GroupConv2D(filters, (3, 3),
conv_params,
conv_name + '2',
strides=strides)(x)
x = BatchNormalization(name=bn_name + '2', **bn_params)(x)
x = Activation('relu', name=relu_name + '2')(x)
x = Conv2D(filters * 2, (1, 1), name=conv_name + '3', **conv_params)(x)
x = BatchNormalization(name=bn_name + '3', **bn_params)(x)
shortcut = Conv2D(filters * 2, (1, 1),
name=sc_name,
strides=strides,
**conv_params)(input_tensor)
shortcut = BatchNormalization(name=sc_name + '_bn',
**bn_params)(shortcut)
x = Add()([x, shortcut])
x = Activation('relu', name=relu_name)(x)
return x
def layer(input_tensor):
# extracting params and names for layers
conv_params = get_conv_params(padding='same')
bn_params = get_bn_params()
conv_name, bn_name, relu_name, sc_name, add_name, attention_name = handle_block_names(
stage, block)
x = Conv2D(int(filters) // 2, (1, 1),
name=conv_name + '1',
**conv_params)(input_tensor)
x = BatchNormalization(name=bn_name + '1x1_1', **bn_params)(x)
x = Activation('relu', name=relu_name + '1x1_1')(x)
x = SK_block_deepwise(filters, stage, block, r=r, L=L)(x)
x = Conv2D(filters, (1, 1), name=conv_name + '2', **conv_params)(x)
x = BatchNormalization(name=bn_name + '1x1_2', **bn_params)(x)
shortcut = Conv2D(filters, (1, 1), name=sc_name,
**conv_params)(input_tensor)
shortcut = BatchNormalization(name=sc_name + '_bn',
**bn_params)(shortcut)
x = Add()([x, shortcut])
x = Activation('relu', name=relu_name + 'addout')(x)
return x
def layer(input_tensor):
# extracting params and names for layers
conv_params = get_conv_params(padding='same', use_bias=False)
conv_params_dilation = get_conv_params(padding='same',
use_bias=False,
dilation_rate=2)
bn_params = get_bn_params()
fc_bn_params = get_bn_params(axis=-1)
conv_name, bn_name, relu_name, sc_name, add_name, attention_name = handle_block_names(
stage, block)
x_3 = DepthwiseConv2D((3, 3), padding='same',
name=conv_name + '3_3')(input_tensor)
x_3 = BatchNormalization(name=bn_name + '3_3', **bn_params)(x_3)
x_3 = Activation('relu', name=relu_name + '3_3')(x_3)
x_5 = DepthwiseConv2D((5, 5), padding='same',
name=conv_name + '5_5')(input_tensor)
x_5 = BatchNormalization(name=bn_name + '5_5', **bn_params)(x_5)
x_5 = Activation('relu', name=relu_name + '5_5')(x_5)
u = Add(name=add_name)([x_3, x_5])
s = GlobalAveragePooling2D(name=attention_name + 'gap')(u)
d = max(int(s.shape[-1]) // r, L)
z = Dense(d, name=attention_name + 'fc1')(s)
z = BatchNormalization(name=bn_name + 'vector', **fc_bn_params)(z)
z = Activation('relu', name=relu_name + 'vector')(z)
c = s.get_shape().as_list()[-1]
z1 = Dense(c * 2, name=attention_name + 'fc2')(z)
z1 = Reshape([2, -1], name=attention_name + 'reshape1')(z1)
soft = Softmax(axis=1, name=attention_name + 'softmax')(z1)
A = Lambda(lambda vector: vector[:, 0, :])(soft)
A = Reshape([1, 1, -1], name=attention_name + 'reshape_A')(A)
B = Lambda(lambda vector: vector[:, 1, :])(soft)
B = Reshape([1, 1, -1], name=attention_name + 'reshape_B')(B)
x_3 = Lambda(scale_feature)([A, x_3])
x_5 = Lambda(scale_feature)([B, x_5])
v = Add()([x_3, x_5])
return v
def layer(x):
# extracting params and names for layers
conv_params = get_conv_params(padding='same')
bn_params = get_bn_params()
conv_name, bn_name, relu_name, sc_name, add_name, attention_name = handle_block_names(
stage, block)
x = Conv2D(int(filters) // 2, (1, 1),
name=conv_name + '1',
**conv_params)(x)
x = BatchNormalization(name=bn_name + '1x1_1', **bn_params)(x)
x = Activation('relu', name=relu_name + '1x1_1')(x)
x = SK_block(filters, stage, block, r=r, L=L)(x)
x = Conv2D(filters, (1, 1), name=conv_name + '2', **conv_params)(x)
x = BatchNormalization(name=bn_name + '1x1_2', **bn_params)(x)
x = Activation('relu', name=relu_name + '1x1_2')(x)
return x
def layer(input_tensor):
conv_params = get_conv_params()
bn_params = get_bn_params()
conv_name, bn_name, relu_name, sc_name = handle_block_names(
stage, block)
x = Conv2D(filters, (1, 1), name=conv_name + '1',
**conv_params)(input_tensor)
x = BatchNormalization(name=bn_name + '1', **bn_params)(x)
x = Activation('relu', name=relu_name + '1')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = GroupConv2D(filters, (3, 3), conv_params, conv_name + '2')(x)
x = BatchNormalization(name=bn_name + '2', **bn_params)(x)
x = Activation('relu', name=relu_name + '2')(x)
x = Conv2D(filters * 2, (1, 1), name=conv_name + '3', **conv_params)(x)
x = BatchNormalization(name=bn_name + '3', **bn_params)(x)
x = Add()([x, input_tensor])
x = Activation('relu', name=relu_name)(x)
return x
def layer(x):
# extracting params and names for layers
conv_params = get_conv_params(padding='same')
bn_params = get_bn_params()
conv_name, bn_name, relu_name, sc_name, add_name, attention_name = handle_block_names(
stage, block)
if x.shape[-1] < filters:
behind_sknet = True
else:
behind_sknet = False
if not behind_sknet:
x = Conv2D(filters, (1, 1), name=conv_name + '1', **conv_params)(x)
x = BatchNormalization(name=bn_name + '1x1_1', **bn_params)(x)
x = Activation('relu', name=relu_name + '1x1_1')(x)
x = SK_block(filters, stage, block, r=r, L=L)(x)
if behind_sknet:
x = Conv2D(filters, (1, 1), name=conv_name + '1', **conv_params)(x)
x = BatchNormalization(name=bn_name + '1x1_1', **bn_params)(x)
x = Activation('relu', name=relu_name + '1x1_1')(x)
return x
def build_mycov(repetitions=(2, 2, 2, 2),
input_tensor=None,
input_shape=None,
classes=1,
first_filters=64):
"""
TODO
"""
if input_tensor is None:
img_input = Input(shape=input_shape, name='data')
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# get parameters for model layers
no_scale_bn_params = get_bn_params(scale=False)
conv_params = get_conv_params(padding='same')
init_filters = first_filters
# resnext bottom
x = BatchNormalization(name='bn_data', **no_scale_bn_params)(img_input)
# down
dw_h_convs = OrderedDict()
for stage, rep in enumerate(repetitions):
for block in range(rep):
filters = init_filters * (2**stage)
x = SK_change_block(filters, stage, block, behind_sknet=True)(x)
if block == 1:
dw_h_convs[stage] = x
x = MaxPooling2D((2, 2), name='pooling' + str(stage + 1))(x)
filters = init_filters * (2**4)
x = SK_change_block(filters, 4, 0, behind_sknet=True)(x)
x = SK_change_block(filters, 4, 1, behind_sknet=True)(x)
# up
for stage, rep in enumerate(repetitions):
stage += 5
filters = init_filters * (2**(4 - stage + 5 - 1))
x = Conv2DTranspose(filters, (2, 2),
strides=(2, 2),
name='up' + str(stage + 1),
padding='same')(x)
x = Concatenate(axis=-1,
name='merge' + str(stage + 1) +
str(9 - stage))([x, dw_h_convs[8 - stage]])
for block in range(rep):
x = SK_change_block(filters, stage, block, behind_sknet=False)(x)
x = Conv2D(classes, (1, 1),
activation='sigmoid',
name='output',
**conv_params)(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
model = Model(inputs, x)
return model