def test_check_optimizer_for_training():
with pytest.raises(ValueError):
check_optimizer_for_training(optimizer=None,
mode=tf.estimator.ModeKeys.TRAIN)
check_optimizer_for_training(optimizer='Adam',
mode=tf.estimator.ModeKeys.TRAIN)
check_optimizer_for_training(optimizer=None,
mode=tf.estimator.ModeKeys.EVAL)
check_optimizer_for_training(optimizer=None,
mode=tf.estimator.ModeKeys.PREDICT)
def model_fn(features,
labels,
mode,
params,
config=None):
"""MeshNet model function.
Args:
features: 5D float `Tensor`, input tensor. This is the first item
returned from the `input_fn` passed to `train`, `evaluate`, and
`predict`. Use `NDHWC` format.
labels: 4D float `Tensor`, labels tensor. This is the second item
returned from the `input_fn` passed to `train`, `evaluate`, and
`predict`. Labels should not be one-hot encoded.
mode: Optional. Specifies if this training, evaluation or prediction.
params: `dict` of parameters.
- n_classes: (required) number of classes to classify.
- optimizer: instance of TensorFlow optimizer. Required if
training.
- n_filters: number of filters to use in each convolution. The
original implementation used 21 filters to classify brainmask
and 71 filters for the multi-class problem.
- dropout_rate: rate of dropout. For example, 0.1 would drop 10% of
input units.
config: configuration object.
Returns:
`tf.estimator.EstimatorSpec`
Raises:
`ValueError` if required parameters are not in `params`.
"""
volume = features
if isinstance(volume, dict):
volume = features['volume']
required_keys = {'n_classes'}
default_params = {'optimizer': None, 'n_filters': 96, 'keep_prob': 0.5}
check_required_params(params=params, required_keys=required_keys)
set_default_params(params=params, defaults=default_params)
check_optimizer_for_training(optimizer=params['optimizer'], mode=mode)
tf.logging.debug("Parameters for model:")
tf.logging.debug(params)
# Dilation rate by layer.
dilation_rates = (
(1, 1, 1),
(1, 1, 1),
(1, 1, 1),
(2, 2, 2),
(4, 4, 4),
(8, 8, 8),
(1, 1, 1))
is_mc_v = tf.constant(False,dtype=tf.bool)
is_mc_b = tf.constant(True,dtype=tf.bool)
outputs = volume
for ii, dilation_rate in enumerate(dilation_rates):
outputs = _layer(
outputs, mode=mode, layer_num=ii + 1, filters=params['n_filters'],
kernel_size=3, dilation_rate=dilation_rate,keep_prob=params['keep_prob'], is_mc_v=is_mc_v, is_mc_b=is_mc_b)
with tf.variable_scope('logits'):
logits = vwn_conv.conv3d(
inputs=outputs, filters=params['n_classes'], kernel_size=(1, 1, 1),
padding='SAME', activation=None, is_mc=is_mc_v)
predicted_classes = tf.argmax(logits, axis=-1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class_ids': predicted_classes,
'probabilities': tf.nn.softmax(logits),
'logits': logits,
}
export_outputs = {
'outputs': tf.estimator.export.PredictOutput(predictions)}
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
export_outputs=export_outputs)
if params['prior_path'] != None:
prior_np = np.load(params['prior_path'])
with tf.variable_scope("prior"):
i=-1
for v in tf.get_collection('ms'):
i += 1
if params['prior_path'] == None:
tf.add_to_collection('ms_prior',tf.Variable(tf.constant(0, dtype = v.dtype, shape = v.shape),trainable = False))
else:
tf.add_to_collection('ms_prior',tf.Variable(tf.convert_to_tensor(prior_np[0][i], dtype = tf.float32),trainable = False))
ms = tf.get_collection('ms')
ms_prior = tf.get_collection('ms_prior')
i=-1
for v in tf.get_collection('ms'):
i += 1
if params['prior_path'] == None:
tf.add_to_collection('sigmas_prior',tf.Variable(tf.constant(1, dtype = v.dtype, shape = v.shape),trainable = False))
else:
tf.add_to_collection('sigmas_prior',tf.Variable(tf.convert_to_tensor(prior_np[1][i], dtype = tf.float32),trainable = False))
sigmas = tf.get_collection('sigmas')
sigmas_prior = tf.get_collection('sigmas_prior')
nll_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits))
tf.summary.scalar('nll_loss', nll_loss)
n_examples = tf.constant(params['n_examples'],dtype=ms[0].dtype)
tf.summary.scalar('n_examples', n_examples)
l2_loss = tf.add_n([tf.reduce_sum((tf.square(ms[i] - ms_prior[i])) / ((tf.square(sigmas_prior[i]) + 1e-8) * 2.0)) for i in range(len(ms))], name = 'l2_loss') / n_examples
tf.summary.scalar('l2_loss', l2_loss)
loss = nll_loss + l2_loss
assert mode == tf.estimator.ModeKeys.TRAIN
global_step = tf.train.get_global_step()
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = params['optimizer'].minimize(loss, global_step=global_step)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
def model_fn(features,
labels,
mode,
params,
config=None):
"""HighRes3DNet model function.
Args:
features: 5D float `Tensor`, input tensor. This is the first item
returned from the `input_fn` passed to `train`, `evaluate`, and
`predict`. Use `NDHWC` format.
labels: 4D float `Tensor`, labels tensor. This is the second item
returned from the `input_fn` passed to `train`, `evaluate`, and
`predict`. Labels should not be one-hot encoded.
mode: Optional. Specifies if this training, evaluation or prediction.
params: `dict` of parameters. All parameters below are required.
- n_classes: (required) number of classes to classify.
- optimizer: instance of TensorFlow optimizer. Required if
training.
config: configuration object.
Returns:
`tf.estimator.EstimatorSpec`
Raises:
`ValueError` if required parameters are not in `params`.
"""
required_keys = {'n_classes'}
default_params = {'optimizer': None}
check_required_params(params=params, required_keys=required_keys)
set_default_params(params=params, defaults=default_params)
check_optimizer_for_training(optimizer=params['optimizer'], mode=mode)
tf.logging.debug("Parameters for model:")
tf.logging.debug(params)
training = mode == tf.estimator.ModeKeys.TRAIN
with tf.variable_scope('conv_0'):
conv = tf.layers.conv3d(
features, filters=16, kernel_size=3, padding='SAME')
with tf.variable_scope('batchnorm_0'):
conv = tf.layers.batch_normalization(
conv, training=training, fused=FUSED_BATCH_NORM)
with tf.variable_scope('relu_0'):
outputs = tf.nn.relu(conv)
for ii in range(3):
offset = 1
layer_num = ii + offset
outputs = _resblock(
outputs, mode=mode, layer_num=layer_num, filters=16, kernel_size=3,
dilation_rate=1)
for ii in range(3):
offset = 4
layer_num = ii + offset
outputs = _resblock(
outputs, mode=mode, layer_num=layer_num, filters=16, kernel_size=3,
dilation_rate=2)
for ii in range(3):
offset = 7
layer_num = ii + offset
outputs = _resblock(
outputs, mode=mode, layer_num=layer_num, filters=16, kernel_size=3,
dilation_rate=4)
with tf.variable_scope('logits'):
logits = tf.layers.conv3d(
outputs, filters=params['n_classes'], kernel_size=1,
padding='SAME')
predicted_classes = tf.argmax(logits, axis=-1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class_ids': predicted_classes,
'probabilities': tf.nn.softmax(logits),
'logits': logits,
}
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels, logits=logits)
loss = tf.reduce_mean(cross_entropy)
# Add evaluation metrics for class 1.
labels = tf.cast(labels, predicted_classes.dtype)
labels_onehot = tf.one_hot(labels, params['n_classes'])
predictions_onehot = tf.one_hot(predicted_classes, params['n_classes'])
eval_metric_ops = {
'accuracy': tf.metrics.accuracy(labels, predicted_classes),
'dice': streaming_dice(
labels_onehot[..., 1], predictions_onehot[..., 1]),
'hamming': streaming_hamming(
labels_onehot[..., 1], predictions_onehot[..., 1]),
}
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
assert mode == tf.estimator.ModeKeys.TRAIN
global_step = tf.train.get_global_step()
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = params['optimizer'].minimize(loss, global_step=global_step)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
def model_fn(features,
labels,
mode,
params,
config=None):
"""MeshNet model function.
Args:
features: 5D float `Tensor`, input tensor. This is the first item
returned from the `input_fn` passed to `train`, `evaluate`, and
`predict`. Use `NDHWC` format.
labels: 4D float `Tensor`, labels tensor. This is the second item
returned from the `input_fn` passed to `train`, `evaluate`, and
`predict`. Labels should not be one-hot encoded.
mode: Optional. Specifies if this training, evaluation or prediction.
params: `dict` of parameters.
- n_classes: (required) number of classes to classify.
- optimizer: instance of TensorFlow optimizer. Required if
training.
- n_filters: number of filters to use in each convolution. The
original implementation used 21 filters to classify brainmask
and 71 filters for the multi-class problem.
- dropout_rate: rate of dropout. For example, 0.1 would drop 10% of
input units.
config: configuration object.
Returns:
`tf.estimator.EstimatorSpec`
Raises:
`ValueError` if required parameters are not in `params`.
"""
required_keys = {'n_classes'}
default_params = {'optimizer': None, 'n_filters': 21, 'dropout_rate': 0.25}
check_required_params(params=params, required_keys=required_keys)
set_default_params(params=params, defaults=default_params)
check_optimizer_for_training(optimizer=params['optimizer'], mode=mode)
tf.logging.debug("Parameters for model:")
tf.logging.debug(params)
# Dilation rate by layer.
dilation_rates = (
(1, 1, 1),
(1, 1, 1),
(1, 1, 1),
(2, 2, 2),
(4, 4, 4),
(8, 8, 8),
(1, 1, 1),
)
outputs = features
for ii, dilation_rate in enumerate(dilation_rates):
outputs = _layer(
outputs, mode=mode, layer_num=ii + 1, filters=params['n_filters'],
kernel_size=3, dilation_rate=dilation_rate,
dropout_rate=params['dropout_rate'],
)
with tf.variable_scope('logits'):
logits = tf.layers.conv3d(
inputs=outputs, filters=params['n_classes'], kernel_size=(1, 1, 1),
padding='SAME', activation=None,
)
predicted_classes = tf.argmax(logits, axis=-1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class_ids': predicted_classes,
'probabilities': tf.nn.softmax(logits),
'logits': logits,
}
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels, logits=logits)
loss = tf.reduce_mean(cross_entropy)
# Add evaluation metrics for class 1.
labels = tf.cast(labels, predicted_classes.dtype)
labels_onehot = tf.one_hot(labels, params['n_classes'])
predictions_onehot = tf.one_hot(predicted_classes, params['n_classes'])
eval_metric_ops = {
'accuracy': tf.metrics.accuracy(labels, predicted_classes),
'dice': streaming_dice(
labels_onehot[..., 1], predictions_onehot[..., 1]),
'hamming': streaming_hamming(
labels_onehot[..., 1], predictions_onehot[..., 1]),
}
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
assert mode == tf.estimator.ModeKeys.TRAIN
global_step = tf.train.get_global_step()
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = params['optimizer'].minimize(loss, global_step=global_step)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
def model_fn(features, labels, mode, params, config=None):
"""HighRes3DNet model function.
Args:
features: 5D float `Tensor`, input tensor. This is the first item
returned from the `input_fn` passed to `train`, `evaluate`, and
`predict`. Use `NDHWC` format.
labels: 4D float `Tensor`, labels tensor. This is the second item
returned from the `input_fn` passed to `train`, `evaluate`, and
`predict`. Labels should not be one-hot encoded.
mode: Optional. Specifies if this training, evaluation or prediction.
params: `dict` of parameters. All parameters below are required.
- n_classes: (required) number of classes to classify.
- optimizer: instance of TensorFlow optimizer. Required if
training.
- one_batchnorm_per_resblock: (default false) if true, only apply
first batch normalization layer in each residually connected
block. Empirically, only using first batch normalization layer
allowed the model to model to be trained on 128**3 float32
inputs.
- dropout_rate: (default 0), value between 0 and 1, dropout rate
to be applied immediately before last convolution layer. If 0
or false, dropout is not applied.
config: configuration object.
Returns:
`tf.estimator.EstimatorSpec`
Raises:
`ValueError` if required parameters are not in `params`.
"""
volume = features
if isinstance(volume, dict):
volume = features['volume']
required_keys = {'n_classes'}
default_params = {
'optimizer': None,
'one_batchnorm_per_resblock': False,
'dropout_rate': 0,
}
check_required_params(params=params, required_keys=required_keys)
set_default_params(params=params, defaults=default_params)
check_optimizer_for_training(optimizer=params['optimizer'], mode=mode)
tf.logging.debug("Parameters for model:")
tf.logging.debug(params)
training = mode == tf.estimator.ModeKeys.TRAIN
with tf.variable_scope('conv_0'):
x = tf.layers.conv3d(volume, filters=16, kernel_size=3, padding='SAME')
with tf.variable_scope('batchnorm_0'):
x = tf.layers.batch_normalization(x,
training=training,
fused=FUSED_BATCH_NORM)
with tf.variable_scope('relu_0'):
x = tf.nn.relu(x)
layer_num = 0
one_batchnorm = params['one_batchnorm_per_resblock']
# 16-filter residually connected blocks.
for ii in range(3):
layer_num += 1
x = _resblock(x,
mode=mode,
layer_num=layer_num,
filters=16,
kernel_size=3,
dilation_rate=1,
one_batchnorm=one_batchnorm)
# 32-filter residually connected blocks. Pad inputs immediately before
# first elementwise sum to match shape of last dimension.
layer_num += 1
paddings = [[0, 0], [0, 0], [0, 0], [0, 0], [8, 8]]
x = _resblock(x,
mode=mode,
layer_num=layer_num,
filters=32,
kernel_size=3,
dilation_rate=2,
paddings=paddings,
one_batchnorm=one_batchnorm)
for ii in range(2):
layer_num += 1
x = _resblock(x,
mode=mode,
layer_num=layer_num,
filters=32,
kernel_size=3,
dilation_rate=2,
one_batchnorm=one_batchnorm)
# 64-filter residually connected blocks. Pad inputs immediately before
# first elementwise sum to match shape of last dimension.
layer_num += 1
paddings = [[0, 0], [0, 0], [0, 0], [0, 0], [16, 16]]
x = _resblock(x,
mode=mode,
layer_num=layer_num,
filters=64,
kernel_size=3,
dilation_rate=4,
paddings=paddings,
one_batchnorm=one_batchnorm)
for ii in range(2):
layer_num += 1
x = _resblock(x,
mode=mode,
layer_num=layer_num,
filters=64,
kernel_size=3,
dilation_rate=4,
one_batchnorm=one_batchnorm)
with tf.variable_scope('conv_1'):
x = tf.layers.conv3d(x, filters=80, kernel_size=1, padding='SAME')
if params['dropout_rate']:
x = tf.layers.dropout(x,
rate=params['dropout_rate'],
training=training)
with tf.variable_scope('logits'):
logits = tf.layers.conv3d(x,
filters=params['n_classes'],
kernel_size=1,
padding='SAME')
predictions = tf.nn.softmax(logits=logits)
predicted_classes = tf.argmax(logits, axis=-1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class_ids': predicted_classes,
'probabilities': predictions,
'logits': logits
}
# Outputs for SavedModel.
export_outputs = {
'outputs': tf.estimator.export.PredictOutput(predictions)
}
return tf.estimator.EstimatorSpec(mode=mode,
predictions=predictions,
export_outputs=export_outputs)
onehot_labels = tf.one_hot(labels, params['n_classes'])
# loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=onehot_labels, logits=logits)
# loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels, logits=logits)
# loss = losses.dice(labels=labels, predictions=predictions[..., 1], axis=(1, 2, 3))
loss = losses.tversky(labels=onehot_labels,
predictions=predictions,
axis=(1, 2, 3))
# loss = losses.generalized_dice(labels=onehot_labels, predictions=predictions, axis=(1, 2, 3))
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
eval_metric_ops={
'dice':
metrics.streaming_dice(
labels,
predicted_classes,
axis=(1, 2, 3)),
})
assert mode == tf.estimator.ModeKeys.TRAIN, "unknown mode key {}".format(
"mode")
global_step = tf.train.get_global_step()
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = params['optimizer'].minimize(loss, global_step=global_step)
# Get Dice score of each class.
dice_coefficients = tf.reduce_mean(metrics.dice(onehot_labels,
tf.one_hot(
tf.argmax(predictions,
axis=-1),
params['n_classes']),
axis=(1, 2, 3)),
axis=0)
logging_hook = tf.train.LoggingTensorHook(
{
"loss": loss,
"dice": dice_coefficients
}, every_n_iter=100)
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
training_hooks=[logging_hook])
def model_fn(features,
labels,
mode,
params,
config=None):
"""HighRes3DNet model function.
Args:
features: 5D float `Tensor`, input tensor. This is the first item
returned from the `input_fn` passed to `train`, `evaluate`, and
`predict`. Use `NDHWC` format.
labels: 4D float `Tensor`, labels tensor. This is the second item
returned from the `input_fn` passed to `train`, `evaluate`, and
`predict`. Labels should not be one-hot encoded.
mode: Optional. Specifies if this training, evaluation or prediction.
params: `dict` of parameters. All parameters below are required.
- n_classes: (required) number of classes to classify.
- optimizer: instance of TensorFlow optimizer. Required if
training.
- one_batchnorm_per_resblock: (default false) if true, only apply
first batch normalization layer in each residually connected
block. Empirically, only using first batch normalization layer
allowed the model to model to be trained on 128**3 float32
inputs.
- dropout_rate: (default 0), value between 0 and 1, dropout rate
to be applied immediately before last convolution layer. If 0
or false, dropout is not applied.
config: configuration object.
Returns:
`tf.estimator.EstimatorSpec`
Raises:
`ValueError` if required parameters are not in `params`.
"""
volume = features
if isinstance(volume, dict):
volume = features['volume']
required_keys = {'n_classes'}
default_params = {
'optimizer': None,
'one_batchnorm_per_resblock': False,
'dropout_rate': 0,
}
check_required_params(params=params, required_keys=required_keys)
set_default_params(params=params, defaults=default_params)
check_optimizer_for_training(optimizer=params['optimizer'], mode=mode)
tf.logging.debug("Parameters for model:")
tf.logging.debug(params)
training = mode == tf.estimator.ModeKeys.TRAIN
with tf.variable_scope('conv_0'):
conv = tf.layers.conv3d(
volume, filters=16, kernel_size=3, padding='SAME')
with tf.variable_scope('batchnorm_0'):
conv = tf.layers.batch_normalization(
conv, training=training, fused=FUSED_BATCH_NORM)
with tf.variable_scope('relu_0'):
outputs = tf.nn.relu(conv)
layer_num = 0
one_batchnorm = params['one_batchnorm_per_resblock']
for ii in range(3):
layer_num += 1
outputs = _resblock(
outputs, mode=mode, layer_num=layer_num, filters=16, kernel_size=3,
dilation_rate=1, one_batchnorm=one_batchnorm)
for ii in range(3):
layer_num += 1
outputs = _resblock(
outputs, mode=mode, layer_num=layer_num, filters=16, kernel_size=3,
dilation_rate=2, one_batchnorm=one_batchnorm)
for ii in range(3):
layer_num += 1
outputs = _resblock(
outputs, mode=mode, layer_num=layer_num, filters=16, kernel_size=3,
dilation_rate=4, one_batchnorm=one_batchnorm)
if params['dropout_rate']:
outputs = tf.layers.dropout(
outputs, rate=params['dropout_rate'], training=training)
with tf.variable_scope('logits'):
logits = tf.layers.conv3d(
outputs, filters=params['n_classes'], kernel_size=1,
padding='SAME')
predicted_classes = tf.argmax(logits, axis=-1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class_ids': predicted_classes,
'probabilities': tf.nn.softmax(logits),
'logits': logits}
# Outputs for SavedModel.
export_outputs = {
'outputs': tf.estimator.export.PredictOutput(predictions)}
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
export_outputs=export_outputs)
loss = tf.losses.sparse_softmax_cross_entropy(
labels=labels,
logits=logits)
# Add evaluation metrics for class 1.
labels = tf.cast(labels, predicted_classes.dtype)
labels_onehot = tf.one_hot(labels, params['n_classes'])
predictions_onehot = tf.one_hot(predicted_classes, params['n_classes'])
eval_metric_ops = {
'accuracy': tf.metrics.accuracy(labels, predicted_classes),
'dice': streaming_dice(
labels_onehot[..., 1], predictions_onehot[..., 1]),
'hamming': streaming_hamming(
labels_onehot[..., 1], predictions_onehot[..., 1]),
}
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode=mode,
loss=loss,
eval_metric_ops=eval_metric_ops)
assert mode == tf.estimator.ModeKeys.TRAIN
global_step = tf.train.get_global_step()
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = params['optimizer'].minimize(loss, global_step=global_step)
return tf.estimator.EstimatorSpec(
mode=mode,
loss=loss,
train_op=train_op)
def model_fn(features, labels, mode, params, config=None):
"""3D U-Net model function.
Args:
Returns:
Raises:
"""
volume = features
if isinstance(volume, dict):
volume = features['volume']
required_keys = {'n_classes'}
default_params = {
'optimizer': None,
'batchnorm': True,
}
check_required_params(params=params, required_keys=required_keys)
set_default_params(params=params, defaults=default_params)
check_optimizer_for_training(optimizer=params['optimizer'], mode=mode)
bn = params['batchnorm']
# start encoding
shortcut_1 = _conv_block(volume,
filters1=32,
filters2=64,
mode=mode,
layer_num=0,
batchnorm=bn)
with tf.variable_scope('maxpool_1'):
x = tf.layers.max_pooling3d(inputs=shortcut_1,
pool_size=(2, 2, 2),
strides=(2, 2, 2),
padding='same')
shortcut_2 = _conv_block(x,
filters1=64,
filters2=128,
mode=mode,
layer_num=1,
batchnorm=bn)
with tf.variable_scope('maxpool_2'):
x = tf.layers.max_pooling3d(inputs=shortcut_2,
pool_size=(2, 2, 2),
strides=(2, 2, 2),
padding='same')
shortcut_3 = _conv_block(x,
filters1=128,
filters2=256,
mode=mode,
layer_num=2,
batchnorm=bn)
with tf.variable_scope('maxpool_3'):
x = tf.layers.max_pooling3d(inputs=shortcut_3,
pool_size=(2, 2, 2),
strides=(2, 2, 2),
padding='same')
x = _conv_block(x,
filters1=256,
filters2=512,
mode=mode,
layer_num=3,
batchnorm=bn)
# start decoding
with tf.variable_scope("upconv_0"):
x = tf.layers.conv3d_transpose(inputs=x,
filters=512,
kernel_size=(2, 2, 2),
strides=(2, 2, 2),
kernel_regularizer=_regularizer)
with tf.variable_scope('concat_1'):
x = tf.concat((shortcut_3, x), axis=-1)
x = _conv_block(x,
filters1=256,
filters2=256,
mode=mode,
layer_num=4,
batchnorm=bn)
with tf.variable_scope("upconv_1"):
x = tf.layers.conv3d_transpose(inputs=x,
filters=256,
kernel_size=(2, 2, 2),
strides=(2, 2, 2),
kernel_regularizer=_regularizer)
with tf.variable_scope('concat_2'):
x = tf.concat((shortcut_2, x), axis=-1)
x = _conv_block(x,
filters1=128,
filters2=128,
mode=mode,
layer_num=5,
batchnorm=bn)
with tf.variable_scope("upconv_2"):
x = tf.layers.conv3d_transpose(inputs=x,
filters=128,
kernel_size=(2, 2, 2),
strides=(2, 2, 2),
kernel_regularizer=_regularizer)
with tf.variable_scope('concat_3'):
x = tf.concat((shortcut_1, x), axis=-1)
x = _conv_block(x,
filters1=64,
filters2=64,
mode=mode,
layer_num=6,
batchnorm=bn)
with tf.variable_scope('logits'):
logits = tf.layers.conv3d(inputs=x,
filters=params['n_classes'],
kernel_size=(1, 1, 1),
padding='same',
activation=None,
kernel_regularizer=_regularizer)
# end decoding
with tf.variable_scope('predictions'):
predictions = tf.nn.softmax(logits=logits)
class_ids = tf.argmax(logits, axis=-1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class_ids': class_ids,
'probabilities': predictions,
'logits': logits
}
# Outputs for SavedModel.
export_outputs = {
'outputs': tf.estimator.export.PredictOutput(predictions)
}
return tf.estimator.EstimatorSpec(mode=mode,
predictions=predictions,
export_outputs=export_outputs)
onehot_labels = tf.one_hot(labels, params['n_classes'])
loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=onehot_labels,
logits=logits)
# loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels, logits=logits)
l2_loss = tf.losses.get_regularization_loss()
loss += l2_loss
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
eval_metric_ops={
'dice':
metrics.streaming_dice(labels,
class_ids,
axis=(1,
2,
3)),
})
assert mode == tf.estimator.ModeKeys.TRAIN
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = params['optimizer'].minimize(
loss, global_step=tf.train.get_global_step())
dice_coefficients = tf.reduce_mean(
metrics.dice(onehot_labels,
tf.one_hot(class_ids, axis=(1, 2, 3)),
axis=0))
logging_hook = tf.train.LoggingTensorHook(
{
"loss": loss,
"dice": dice_coefficients
}, every_n_iter=100)
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
training_hooks=[logging_hook])