def _conv_block(x, filters, bottleneck):
bn_scale = PARAMS['bn_scale']
activation = PARAMS['activation']
kernel_initializer = PARAMS['kernel_initializer']
weight_decay = PARAMS['weight_decay']
dropout_rate = PARAMS['dropout_rate']
x = BatchNormalization(scale=bn_scale, axis=-1)(x)
x = activation()(x)
x = Conv3D(filters * bottleneck,
kernel_size=(1, 1, 1),
padding='same',
use_bias=False,
kernel_initializer=kernel_initializer,
kernel_regularizer=l2_penalty(weight_decay))(x)
if dropout_rate is not None:
x = SpatialDropout3D(dropout_rate)(x)
x = BatchNormalization(scale=bn_scale, axis=-1)(x)
x = activation()(x)
x = Conv3D(filters,
kernel_size=(3, 3, 3),
padding='same',
use_bias=True,
kernel_initializer=kernel_initializer,
kernel_regularizer=l2_penalty(weight_decay))(x)
return x
def get_model(weights=None, **kwargs):
for k, v in kwargs.items():
assert k in PARAMS
PARAMS[k] = v
print("Model hyper-parameters:", PARAMS)
dhw = PARAMS['dhw']
first_scale = PARAMS['first_scale']
first_layer = PARAMS['first_layer']
kernel_initializer = PARAMS['kernel_initializer']
weight_decay = PARAMS['weight_decay']
down_structure = PARAMS['down_structure']
output_size = PARAMS['output_size']
bottleneck = PARAMS['bottleneck']
shape = dhw + [1]
inputs = Input(shape=shape)
if first_scale is not None:
scaled = Lambda(first_scale)(inputs)
else:
scaled = inputs
conv = Conv3D(first_layer,
kernel_size=(3, 3, 3),
padding='same',
use_bias=True,
kernel_initializer=kernel_initializer,
kernel_regularizer=l2_penalty(weight_decay))(scaled)
downsample_times = len(down_structure)
for l, n in enumerate(down_structure):
db = _dense_block(conv, n, bottleneck[l])
conv = _transmit_block(db, l == downsample_times - 1)
if output_size == 1:
last_activation = 'sigmoid'
else:
last_activation = 'softmax'
outputs = Dense(output_size,
activation=last_activation,
kernel_regularizer=l2_penalty(weight_decay),
kernel_initializer=kernel_initializer)(conv)
model = Model(inputs, outputs)
model.summary()
if weights is not None:
model.load_weights(weights)
print('load weights:', weights)
return model
def _transmit_block(x, is_last):
bn_scale = PARAMS['bn_scale']
activation = PARAMS['activation']
kernel_initializer = PARAMS['kernel_initializer']
weight_decay = PARAMS['weight_decay']
compression = PARAMS['compression']
x = BatchNormalization(scale=bn_scale, axis=-1)(x)
x = activation()(x)
if is_last:
x = GlobalAvgPool3D()(x)
else:
*_, f = x.get_shape().as_list()
x = Conv3D(f // compression, kernel_size=(1, 1, 1), padding='same', use_bias=True,
kernel_initializer=kernel_initializer,
kernel_regularizer=l2_penalty(weight_decay))(x)
x = AveragePooling3D((2, 2, 2), padding='valid')(x)
return x
def get_model(weights=None, verbose=True, **kwargs):
for k, v in kwargs.items():
assert k in PARAMS
PARAMS[k] = v
if verbose:
print("Model hyper-parameters:", PARAMS)
dhw = PARAMS['dhw']
first_scale = PARAMS['first_scale']
first_layer = PARAMS['first_layer']
kernel_initializer = PARAMS['kernel_initializer']
weight_decay = PARAMS['weight_decay']
down_structure = PARAMS['down_structure']
output_size = PARAMS['output_size']
shape = dhw + [1]
inputs = Input(shape=shape)
if first_scale is not None:
scaled = Lambda(first_scale)(inputs)
else:
scaled = inputs
conv = Conv3D(first_layer, kernel_size=(3, 3, 3), padding='same', use_bias=True,
kernel_initializer=kernel_initializer,
kernel_regularizer=l2_penalty(weight_decay))(scaled)
downsample_times = len(down_structure)
top_down = []
for l, n in enumerate(down_structure):
db = _dense_block(conv, n)
top_down.append(db)
conv = _transmit_block(db, l == downsample_times - 1)
feat = top_down[-1]
for top_feat in reversed(top_down[:-1]):
*_, f = top_feat.get_shape().as_list()
deconv = Conv3DTranspose(filters=f, kernel_size=2, strides=2, use_bias=True,
kernel_initializer=kernel_initializer,
kernel_regularizer=l2_penalty(weight_decay))(feat)
feat = add([top_feat, deconv])
seg_head = Conv3D(1, kernel_size=(1, 1, 1), padding='same',
activation='sigmoid', use_bias=True,
kernel_initializer=kernel_initializer,
kernel_regularizer=l2_penalty(weight_decay),
name='seg')(feat)
if output_size == 1:
last_activation = 'sigmoid'
else:
last_activation = 'softmax'
clf_head = Dense(output_size, activation=last_activation,
kernel_regularizer=l2_penalty(weight_decay),
kernel_initializer=kernel_initializer,
name='clf')(conv)
model = Model(inputs, [clf_head, seg_head])
if verbose:
model.summary()
if weights is not None:
model.load_weights(weights)
return model