# ==============================================================================
# Copyright (C) 2023 Haresh Rengaraj Rajamohan, Tianyu Wang, Kevin Leung,
# Gregory Chang, Kyunghyun Cho, Richard Kijowski & Cem M. Deniz
#
# This file is part of OAI-MRI-TKR
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
# ==============================================================================
import six
from keras.models import Model
from keras.layers import (
Input,
Activation,
Dense,
Flatten,
GlobalMaxPooling3D,
GlobalAveragePooling3D
)
from keras.layers import Dropout, Dense, Conv3D, MaxPooling3D,GlobalMaxPooling3D, GlobalAveragePooling3D, Activation, BatchNormalization,Flatten
from keras.layers.convolutional import (
Conv3D,
AveragePooling3D,
MaxPooling3D
)
from keras.layers.merge import add
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
from keras import backend as K
from math import ceil
def _bn_relu(input):
"""Helper to build a BN -> relu block (copy-paster from
raghakot/keras-resnet)
"""
norm = BatchNormalization(axis=CHANNEL_AXIS)(input)
return Activation("relu")(norm)
def _conv_bn_relu3D(**conv_params):
filters = conv_params["filters"]
kernel_size = conv_params["kernel_size"]
strides = conv_params.setdefault("strides", (1, 1, 1))
kernel_initializer = conv_params.setdefault(
"kernel_initializer", "he_normal")
padding = conv_params.setdefault("padding", "same")
kernel_regularizer = conv_params.setdefault("kernel_regularizer",
l2(5e-3))
def f(input):
conv = Conv3D(filters=filters, kernel_size=kernel_size,
strides=strides, kernel_initializer=kernel_initializer,
padding=padding,
kernel_regularizer=kernel_regularizer)(input)
return _bn_relu(conv)
return f
def _bn_relu_conv3d(**conv_params):
"""Helper to build a BN -> relu -> conv3d block.
"""
filters = conv_params["filters"]
kernel_size = conv_params["kernel_size"]
strides = conv_params.setdefault("strides", (1, 1, 1))
kernel_initializer = conv_params.setdefault("kernel_initializer",
"he_normal")
padding = conv_params.setdefault("padding", "same")
kernel_regularizer = conv_params.setdefault("kernel_regularizer",
l2(5e-3))
def f(input):
activation = _bn_relu(input)
return Conv3D(filters=filters, kernel_size=kernel_size,
strides=strides, kernel_initializer=kernel_initializer,
padding=padding,
kernel_regularizer=kernel_regularizer)(activation)
return f
def _shortcut3d(input, residual):
"""3D shortcut to match input and residual and merges them with "sum" at
the same time
"""
stride_dim1 = ceil(input._keras_shape[DIM1_AXIS]*1.0 \
/ residual._keras_shape[DIM1_AXIS])
stride_dim2 = ceil(input._keras_shape[DIM2_AXIS]*1.0 \
/ residual._keras_shape[DIM2_AXIS])
stride_dim3 = ceil(input._keras_shape[DIM3_AXIS]*1.0 \
/ residual._keras_shape[DIM3_AXIS])
equal_channels = residual._keras_shape[CHANNEL_AXIS] \
== input._keras_shape[CHANNEL_AXIS]
shortcut = input
if stride_dim1 > 1 or stride_dim2 > 1 or stride_dim3 > 1 \
or not equal_channels:
shortcut = Conv3D(
filters=residual._keras_shape[CHANNEL_AXIS],
kernel_size=(1, 1, 1),
strides=(stride_dim1, stride_dim2, stride_dim3),
kernel_initializer="he_normal", padding="valid",
kernel_regularizer=l2(5e-3)
)(input)
return add([shortcut, residual])
def _residual_block3d(block_function, filters, repetitions,
is_first_layer=False):
def f(input):
for i in range(repetitions):
strides = (1, 1, 1)
if i == 0 and not is_first_layer:
strides = (2, 2, 2)
input = block_function(filters=filters,
strides=strides,
is_first_block_of_first_layer=(
is_first_layer and i == 0)
)(input)
return input
return f
def basic_block(filters, strides=(1, 1, 1),
is_first_block_of_first_layer=False):
"""Basic 3 X 3 X 3 convolution blocks. Extended from raghakot's 2D impl.
"""
def f(input):
if is_first_block_of_first_layer:
# don't repeat bn->relu since we just did bn->relu->maxpool
conv1 = Conv3D(filters=filters, kernel_size=(3, 3, 3),
strides=strides, kernel_initializer="he_normal",
padding="same", kernel_regularizer=l2(5e-3)
)(input)
else:
conv1 = _bn_relu_conv3d(filters=filters,
kernel_size=(3, 3, 3),
strides=strides
)(input)
residual = _bn_relu_conv3d(filters=filters, kernel_size=(3, 3, 3),
)(conv1)
return _shortcut3d(input, residual)
return f
def bottleneck(filters, strides=(1, 1, 1),
is_first_block_of_first_layer=False):
"""Basic 3 X 3 X 3 convolution blocks. Extended from raghakot's 2D impl.
"""
def f(input):
if is_first_block_of_first_layer:
# don't repeat bn->relu since we just did bn->relu->maxpool
conv_1_1 = Conv3D(filters=filters, kernel_size=(1, 1, 1),
strides=strides, padding="same",
kernel_initializer="he_normal",
kernel_regularizer=l2(5e-3))(input)
else:
conv_1_1 = _bn_relu_conv3d(filters=filters, kernel_size=(1, 1, 1),
strides=strides)(input)
conv_3_3 = _bn_relu_conv3d(filters=filters,
kernel_size=(3, 3, 3))(conv_1_1)
residual = _bn_relu_conv3d(filters=filters * 4,
kernel_size=(1, 1, 1))(conv_3_3)
return _shortcut3d(input, residual)
return f
def _handle_data_format():
global DIM1_AXIS
global DIM2_AXIS
global DIM3_AXIS
global CHANNEL_AXIS
if K.image_data_format() == 'channels_last':
DIM1_AXIS = 1
DIM2_AXIS = 2
DIM3_AXIS = 3
CHANNEL_AXIS = 4
else:
CHANNEL_AXIS = 1
DIM1_AXIS = 2
DIM2_AXIS = 3
DIM3_AXIS = 4
def _get_block(identifier):
if isinstance(identifier, six.string_types):
res = globals().get(identifier)
if not res:
raise ValueError('Invalid {}'.format(identifier))
return res
return identifier
class Resnet3DBuilder(object):
@staticmethod
def build(input_shape, num_outputs, block_fn, repetitions):
"""Instantiate a vanilla ResNet3D keras model
# Arguments
input_shape: Tuple of input shape in the format
(conv_dim1, conv_dim2, conv_dim3, channels) if dim_ordering='tf'
(filter, conv_dim1, conv_dim2, conv_dim3) if dim_ordering='th'
num_outputs: The number of outputs at the final softmax layer
block_fn: Unit block to use {'basic_block', 'bottlenack_block'}
repetitions: Repetitions of unit blocks
# Returns
model: a 3D ResNet model that takes a 5D tensor (volumetric images
in batch) as input and returns a 1D vector (prediction) as output.
"""
_handle_data_format()
if len(input_shape) != 4:
raise Exception("Input shape should be a tuple "
"(conv_dim1, conv_dim2, conv_dim3, channels) "
"for tensorflow as backend or "
"(channels, conv_dim1, conv_dim2, conv_dim3) "
"for theano as backend")
block_fn = _get_block(block_fn)
input = Input(shape=input_shape)
filters = 32
# first conv
conv1 = _conv_bn_relu3D(filters=filters, kernel_size=(7, 7, 7),
strides=(2, 2, 2))(input)
pool1 = MaxPooling3D(pool_size=(3, 3, 3), strides=(2, 2, 2),
padding="same")(conv1)
conv2 = _conv_bn_relu3D(filters=filters, kernel_size=(3, 3, 3),
strides=(2, 2, 1))(pool1)
#conv3 = _conv_bn_relu3D(filters=32, kernel_size=(3, 3, 3),
# strides=(2, 2, 2))(conv2)
#pool2 = MaxPooling3D(pool_size=(3, 3, 3), strides=(2, 2, 2),
# padding="same")(conv2)
# repeat blocks
block = conv2
#filters = 32
for i, r in enumerate(repetitions):
block = _residual_block3d(block_fn, filters=filters,
repetitions=r, is_first_layer=(i == 0)
)(block)
filters *= 2
# last activation
block_output = _bn_relu(block)
# average poll and classification
#pool3 = AveragePooling3D(pool_size=(block._keras_shape[DIM1_AXIS],
# block._keras_shape[DIM2_AXIS],
# block._keras_shape[DIM3_AXIS]),
# strides=(1, 1, 1))(block_output)
#flatten1 = Flatten()(pool3)
flatten1 = GlobalAveragePooling3D()(block_output)
if num_outputs > 1:
dense = Dense(units=num_outputs,
kernel_initializer="he_normal",
activation="softmax",
kernel_regularizer=l2(5e-3))(flatten1)
else:
dense = Dense(units=num_outputs,
kernel_initializer="he_normal",
activation="sigmoid",
kernel_regularizer=l2(5e-3))(flatten1)
model = Model(inputs=input, outputs=dense)
return model
@staticmethod
def build_resnet_18(input_shape, num_outputs):
return Resnet3DBuilder.build(input_shape, num_outputs, basic_block,
[2, 2, 2, 2])
@staticmethod
def build_resnet_34(input_shape, num_outputs):
return Resnet3DBuilder.build(input_shape, num_outputs, basic_block,
[3, 4, 6, 3])
@staticmethod
def build_resnet_50(input_shape, num_outputs):
return Resnet3DBuilder.build(input_shape, num_outputs, bottleneck,
[3, 4, 6, 3])
@staticmethod
def build_resnet_101(input_shape, num_outputs):
return Resnet3DBuilder.build(input_shape, num_outputs, bottleneck,
[3, 4, 23, 3])
@staticmethod
def build_resnet_152(input_shape, num_outputs):
return Resnet3DBuilder.build(input_shape, num_outputs, bottleneck,
[3, 8, 36, 3])
Datasets
Models
