def bn_feature_net_2D(receptive_field=61,
input_shape=(256, 256, 1),
inputs=None,
n_features=3,
n_channels=1,
reg=1e-5,
n_conv_filters=64,
n_dense_filters=200,
VGG_mode=False,
init='he_normal',
norm_method='std',
location=False,
dilated=False,
padding=False,
padding_mode='reflect',
multires=False,
include_top=True):
"""Creates a 2D featurenet.
Args:
receptive_field (int): the receptive field of the neural network.
input_shape (tuple): If no input tensor, create one with this shape.
inputs (tensor): optional input tensor
n_features (int): Number of output features
n_channels (int): number of input channels
reg (int): regularization value
n_conv_filters (int): number of convolutional filters
n_dense_filters (int): number of dense filters
VGG_mode (bool): If multires, uses VGG_mode for multiresolution
init (str): Method for initalizing weights.
norm_method (str): ImageNormalization mode to use
location (bool): Whether to include location data
dilated (bool): Whether to use dilated pooling.
padding (bool): Whether to use padding.
padding_mode (str): Type of padding, one of 'reflect' or 'zero'
multires (bool): Enables multi-resolution mode
include_top (bool): Whether to include the final layer of the model
Returns:
tensorflow.keras.Model: 2D FeatureNet
"""
# Create layers list (x) to store all of the layers.
# We need to use the functional API to enable the multiresolution mode
x = []
win = (receptive_field - 1) // 2
if dilated:
padding = True
if K.image_data_format() == 'channels_first':
channel_axis = 1
row_axis = 2
col_axis = 3
if not dilated:
input_shape = (n_channels, receptive_field, receptive_field)
else:
row_axis = 1
col_axis = 2
channel_axis = -1
if not dilated:
input_shape = (receptive_field, receptive_field, n_channels)
if inputs is not None:
if not K.is_keras_tensor(inputs):
img_input = Input(tensor=inputs, shape=input_shape)
else:
img_input = inputs
x.append(img_input)
else:
x.append(Input(shape=input_shape))
x.append(
ImageNormalization2D(norm_method=norm_method,
filter_size=receptive_field)(x[-1]))
if padding:
if padding_mode == 'reflect':
x.append(ReflectionPadding2D(padding=(win, win))(x[-1]))
elif padding_mode == 'zero':
x.append(ZeroPadding2D(padding=(win, win))(x[-1]))
if location:
x.append(Location2D(in_shape=tuple(x[-1].shape.as_list()[1:]))(x[-1]))
x.append(Concatenate(axis=channel_axis)([x[-2], x[-1]]))
layers_to_concat = []
rf_counter = receptive_field
block_counter = 0
d = 1
while rf_counter > 4:
filter_size = 3 if rf_counter % 2 == 0 else 4
x.append(
Conv2D(n_conv_filters,
filter_size,
dilation_rate=d,
kernel_initializer=init,
padding='valid',
kernel_regularizer=l2(reg))(x[-1]))
x.append(BatchNormalization(axis=channel_axis)(x[-1]))
x.append(Activation('relu')(x[-1]))
block_counter += 1
rf_counter -= filter_size - 1
if block_counter % 2 == 0:
if dilated:
x.append(
DilatedMaxPool2D(dilation_rate=d, pool_size=(2, 2))(x[-1]))
d *= 2
else:
x.append(MaxPool2D(pool_size=(2, 2))(x[-1]))
if VGG_mode:
n_conv_filters *= 2
rf_counter = rf_counter // 2
if multires:
layers_to_concat.append(len(x) - 1)
if multires:
c = []
for l in layers_to_concat:
output_shape = x[l].get_shape().as_list()
target_shape = x[-1].get_shape().as_list()
row_crop = int(output_shape[row_axis] - target_shape[row_axis])
if row_crop % 2 == 0:
row_crop = (row_crop // 2, row_crop // 2)
else:
row_crop = (row_crop // 2, row_crop // 2 + 1)
col_crop = int(output_shape[col_axis] - target_shape[col_axis])
if col_crop % 2 == 0:
col_crop = (col_crop // 2, col_crop // 2)
else:
col_crop = (col_crop // 2, col_crop // 2 + 1)
cropping = (row_crop, col_crop)
c.append(Cropping2D(cropping=cropping)(x[l]))
if multires:
x.append(Concatenate(axis=channel_axis)(c))
x.append(
Conv2D(n_dense_filters, (rf_counter, rf_counter),
dilation_rate=d,
kernel_initializer=init,
padding='valid',
kernel_regularizer=l2(reg))(x[-1]))
x.append(BatchNormalization(axis=channel_axis)(x[-1]))
x.append(Activation('relu')(x[-1]))
if include_top:
x.append(
TensorProduct(n_dense_filters,
kernel_initializer=init,
kernel_regularizer=l2(reg))(x[-1]))
x.append(BatchNormalization(axis=channel_axis)(x[-1]))
x.append(Activation('relu')(x[-1]))
x.append(
TensorProduct(n_features,
kernel_initializer=init,
kernel_regularizer=l2(reg))(x[-1]))
if not dilated:
x.append(Flatten()(x[-1]))
x.append(Softmax(axis=channel_axis)(x[-1]))
if inputs is not None:
real_inputs = keras_utils.get_source_inputs(x[0])
else:
real_inputs = x[0]
model = Model(inputs=real_inputs, outputs=x[-1])
return model
def bn_feature_net_2D(receptive_field=61,
input_shape=(256, 256, 1),
n_features=3,
n_channels=1,
reg=1e-5,
n_conv_filters=64,
n_dense_filters=200,
VGG_mode=False,
init='he_normal',
norm_method='std',
location=False,
dilated=False,
padding=False,
padding_mode='reflect',
multires=False,
include_top=True):
# Create layers list (x) to store all of the layers.
# We need to use the functional API to enable the multiresolution mode
x = []
win = (receptive_field - 1) // 2
if dilated:
padding = True
if K.image_data_format() == 'channels_first':
channel_axis = 1
row_axis = 2
col_axis = 3
if not dilated:
input_shape = (n_channels, receptive_field, receptive_field)
else:
row_axis = 1
col_axis = 2
channel_axis = -1
if not dilated:
input_shape = (receptive_field, receptive_field, n_channels)
x.append(Input(shape=input_shape))
x.append(ImageNormalization2D(norm_method=norm_method, filter_size=receptive_field)(x[-1]))
if padding:
if padding_mode == 'reflect':
x.append(ReflectionPadding2D(padding=(win, win))(x[-1]))
elif padding_mode == 'zero':
x.append(ZeroPadding2D(padding=(win, win))(x[-1]))
if location:
x.append(Location(in_shape=tuple(x[-1].shape.as_list()[1:]))(x[-1]))
x.append(Concatenate(axis=channel_axis)([x[-2], x[-1]]))
if multires:
layers_to_concat = []
rf_counter = receptive_field
block_counter = 0
d = 1
while rf_counter > 4:
filter_size = 3 if rf_counter % 2 == 0 else 4
x.append(Conv2D(n_conv_filters, (filter_size, filter_size), dilation_rate=d, kernel_initializer=init, padding='valid', kernel_regularizer=l2(reg))(x[-1]))
x.append(BatchNormalization(axis=channel_axis)(x[-1]))
x.append(Activation('relu')(x[-1]))
block_counter += 1
rf_counter -= filter_size - 1
if block_counter % 2 == 0:
if dilated:
x.append(DilatedMaxPool2D(dilation_rate=d, pool_size=(2, 2))(x[-1]))
d *= 2
else:
x.append(MaxPool2D(pool_size=(2, 2))(x[-1]))
if VGG_mode:
n_conv_filters *= 2
rf_counter = rf_counter // 2
if multires:
layers_to_concat.append(len(x) - 1)
if multires:
c = []
for l in layers_to_concat:
output_shape = x[l].get_shape().as_list()
target_shape = x[-1].get_shape().as_list()
row_crop = int(output_shape[row_axis] - target_shape[row_axis])
if row_crop % 2 == 0:
row_crop = (row_crop // 2, row_crop // 2)
else:
row_crop = (row_crop // 2, row_crop // 2 + 1)
col_crop = int(output_shape[col_axis] - target_shape[col_axis])
if col_crop % 2 == 0:
col_crop = (col_crop // 2, col_crop // 2)
else:
col_crop = (col_crop // 2, col_crop // 2 + 1)
cropping = (row_crop, col_crop)
c.append(Cropping2D(cropping=cropping)(x[l]))
x.append(Concatenate(axis=channel_axis)(c))
x.append(Conv2D(n_dense_filters, (rf_counter, rf_counter), dilation_rate=d, kernel_initializer=init, padding='valid', kernel_regularizer=l2(reg))(x[-1]))
x.append(BatchNormalization(axis=channel_axis)(x[-1]))
x.append(Activation('relu')(x[-1]))
x.append(TensorProd2D(n_dense_filters, n_dense_filters, kernel_initializer=init, kernel_regularizer=l2(reg))(x[-1]))
x.append(BatchNormalization(axis=channel_axis)(x[-1]))
x.append(Activation('relu')(x[-1]))
x.append(TensorProd2D(n_dense_filters, n_features, kernel_initializer=init, kernel_regularizer=l2(reg))(x[-1]))
if not dilated:
x.append(Flatten()(x[-1]))
if include_top:
x.append(Softmax(axis=channel_axis)(x[-1]))
model = Model(inputs=x[0], outputs=x[-1])
return model