def build_model(self, inputs, training=True, reuse=False):
with var_storage.model_variable_scope(
self.model_hparams.model_name,
reuse=reuse,
dtype=self.model_hparams.dtype):
with tf.variable_scope("input_reshape"):
if self.model_hparams.input_format == 'NHWC' and self.model_hparams.compute_format == 'NCHW':
# Reshape inputs: NHWC => NCHW
inputs = tf.transpose(inputs, [0, 3, 1, 2])
elif self.model_hparams.input_format == 'NCHW' and self.model_hparams.compute_format == 'NHWC':
# Reshape inputs: NCHW => NHWC
inputs = tf.transpose(inputs, [0, 2, 3, 1])
if self.model_hparams.dtype != inputs.dtype:
inputs = tf.cast(inputs, self.model_hparams.dtype)
net = blocks.conv2d_block(
inputs,
n_channels=64,
kernel_size=(7, 7),
strides=(2, 2),
mode='SAME',
use_batch_norm=True,
activation='relu',
is_training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
name='conv2d'
)
net = layers.max_pooling2d(
net,
pool_size=(3, 3),
strides=(2, 2),
padding='SAME',
data_format=self.model_hparams.compute_format,
name="max_pooling2d",
)
model_bottlenecks = self.model_hparams.layers_depth
for block_id, block_bottleneck in enumerate(model_bottlenecks):
for layer_id in range(self.model_hparams.layers_count[block_id]):
stride = 2 if (layer_id == 0 and block_id != 0) else 1
net = blocks.bottleneck_block(
inputs=net,
depth=block_bottleneck * self.model_hparams.expansions,
depth_bottleneck=block_bottleneck,
cardinality=self.model_hparams.cardinality,
stride=stride,
training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
block_name="btlnck_block_%d_%d" % (block_id, layer_id),
use_se=self.model_hparams.use_se,
ratio=self.model_hparams.se_ratio)
with tf.variable_scope("output"):
net = layers.reduce_mean(
net, keepdims=False, data_format=self.model_hparams.compute_format, name='spatial_mean')
logits = layers.dense(
inputs=net,
units=self.model_hparams.n_classes,
use_bias=True,
trainable=training,
kernel_initializer=self.dense_hparams.kernel_initializer,
bias_initializer=self.dense_hparams.bias_initializer)
if logits.dtype != tf.float32:
logits = tf.cast(logits, tf.float32)
probs = layers.softmax(logits, name="softmax", axis=1)
return probs, logits
def build_model(self, inputs, training=True, reuse=False):
with var_storage.model_variable_scope(self.model_hparams.model_name,
reuse=reuse,
dtype=self.model_hparams.dtype):
with tf.variable_scope("input_reshape"):
if self.model_hparams.input_format == 'NHWC' and self.model_hparams.compute_format == 'NCHW':
# Reshape inputs: NHWC => NCHW
inputs = tf.transpose(inputs, [0, 3, 1, 2])
elif self.model_hparams.input_format == 'NCHW' and self.model_hparams.compute_format == 'NHWC':
# Reshape inputs: NCHW => NHWC
inputs = tf.transpose(inputs, [0, 2, 3, 1])
if self.model_hparams.dtype != inputs.dtype:
inputs = tf.cast(inputs, self.model_hparams.dtype)
net = blocks.conv2d_block(
inputs,
n_channels=64,
# n_channels=16,
kernel_size=(7, 7),
strides=(2, 2),
mode='SAME_RESNET',
use_batch_norm=True,
activation='relu',
is_training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
name='conv2d')
net = layers.max_pooling2d(
net,
pool_size=(3, 3),
strides=(2, 2),
padding='SAME',
data_format=self.model_hparams.compute_format,
name="max_pooling2d",
)
for block_id, _ in enumerate(
range(self.model_hparams.layer_counts[0])):
net = blocks.bottleneck_block(
inputs=net,
depth=256,
depth_bottleneck=64,
stride=1,
training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
block_name="btlnck_block_1_%d" % (block_id + 1))
for block_id, i in enumerate(
range(self.model_hparams.layer_counts[1])):
stride = 2 if i == 0 else 1
net = blocks.bottleneck_block(
inputs=net,
depth=512,
depth_bottleneck=128,
stride=stride,
training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
block_name="btlnck_block_2_%d" % (block_id + 1))
for block_id, i in enumerate(
range(self.model_hparams.layer_counts[2])):
block_id += 1
stride = 2 if i == 0 else 1
net = blocks.bottleneck_block(
inputs=net,
depth=1024,
depth_bottleneck=256,
stride=stride,
training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
block_name="btlnck_block_3_%d" % (block_id + 1))
for block_id, i in enumerate(
range(self.model_hparams.layer_counts[3])):
stride = 2 if i == 0 else 1
net = blocks.bottleneck_block(
inputs=net,
depth=2048,
depth_bottleneck=512,
stride=stride,
training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
block_name="btlnck_block_4_%d" % (block_id + 1))
with tf.variable_scope("output"):
net = layers.reduce_mean(
net,
keepdims=False,
data_format=self.model_hparams.compute_format,
name='spatial_mean')
logits = layers.dense(
inputs=net,
units=self.model_hparams.n_classes,
use_bias=True,
trainable=training,
kernel_initializer=self.dense_hparams.kernel_initializer,
bias_initializer=self.dense_hparams.bias_initializer)
if logits.dtype != tf.float32:
logits = tf.cast(logits, tf.float32, name="logits")
probs = layers.softmax(logits, name="softmax", axis=1)
return probs, logits
def _model_fn(features, labels, mode, params):
""" Model function for tf.Estimator
Controls how the training is performed by specifying how the
total_loss is computed and applied in the backward pass.
Args:
features (tf.Tensor): Tensor samples
labels (tf.Tensor): Tensor labels
mode (tf.estimator.ModeKeys): Indicates if we train, evaluate or predict
params (dict): Additional parameters supplied to the estimator
Returns:
Appropriate tf.estimator.EstimatorSpec for the current mode
"""
dtype = params['dtype']
max_steps = params['max_steps']
lr_init = params['learning_rate']
momentum = params['momentum']
device = '/gpu:0'
global_step = tf.train.get_global_step()
learning_rate = tf.train.exponential_decay(lr_init, global_step,
decay_steps=max_steps,
decay_rate=0.96)
with tf.device(device):
features = tf.cast(features, dtype)
with model_variable_scope(
'UNet',
reuse=tf.AUTO_REUSE,
dtype=tf.float16,
debug_mode=False
):
output_map = unet_v1(features, mode)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {'logits': tf.nn.softmax(output_map, axis=-1)}
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
n_classes = output_map.shape[-1].value
flat_logits = tf.reshape(tf.cast(output_map, tf.float32),
[tf.shape(output_map)[0], -1, n_classes])
flat_labels = tf.reshape(labels,
[tf.shape(output_map)[0], -1, n_classes])
crossentropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=flat_logits,
labels=flat_labels),
name='cross_loss_ref')
dice_loss = tf.reduce_mean(1 - dice_coef(flat_logits, flat_labels), name='dice_loss_ref')
total_loss = tf.add(crossentropy_loss, dice_loss, name="total_loss_ref")
opt = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum)
if is_using_hvd():
opt = hvd.DistributedOptimizer(opt, device_dense='/gpu:0')
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
deterministic = True
gate_gradients = (
tf.train.Optimizer.GATE_OP
if deterministic
else tf.train.Optimizer.GATE_NONE)
train_op = opt.minimize(total_loss, gate_gradients=gate_gradients, global_step=global_step)
return tf.estimator.EstimatorSpec(mode, loss=total_loss, train_op=train_op,
eval_metric_ops={})
def vnet_v2(features, labels, mode, params):
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
is_eval = (mode == tf.estimator.ModeKeys.EVAL)
is_predict = (mode == tf.estimator.ModeKeys.PREDICT)
num_classes = len(params.labels)
channel_axis = -1
with model_variable_scope('vnet',
reuse=tf.AUTO_REUSE,
dtype=tf.float16,
debug_mode=False):
features = tf.reshape(features,
[params.batch_size] + params.input_shape + [1])
if labels is not None:
labels = tf.reshape(labels,
[params.batch_size] + params.input_shape + [1])
logits = Builder(kernel_size=params.convolution_size,
n_classes=num_classes,
downscale_blocks=params.downscale_blocks,
upscale_blocks=params.upscale_blocks,
upsampling=params.upsampling,
pooling=params.pooling,
normalization=params.normalization_layer,
activation=params.activation,
mode=mode)(features)
softmax = tf.nn.softmax(logits=logits, axis=channel_axis)
if is_predict:
prediction = tf.argmax(input=softmax, axis=channel_axis)
predictions = {'prediction': prediction}
return tf.estimator.EstimatorSpec(mode=mode,
predictions=predictions)
# Flattened logits and softmax - in FP32
flattened_softmax = tf.reshape(softmax,
[tf.shape(logits)[0], -1, num_classes])
flattened_softmax = tf.cast(flattened_softmax, tf.float32)
# One hot encoding
flattened_labels = tf.layers.flatten(labels)
one_hot_labels = tf.one_hot(indices=flattened_labels,
depth=num_classes,
dtype=tf.float32)
with tf.name_scope("loss"):
if params.loss == 'dice':
loss = dice_coef(predict=tf.cast(flattened_softmax,
tf.float32),
target=one_hot_labels,
dice_type='sorensen')
total_loss = tf.identity(tf.reduce_sum(1. - loss),
name='total_loss_ref')
else:
raise NotImplementedError
train_op = None
if is_training:
global_step = tf.train.get_or_create_global_step()
with tf.name_scope("optimizer"):
if params.optimizer == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(
learning_rate=params.base_lr,
momentum=params.momentum,
centered=True)
else:
raise NotImplementedError
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
gradients, variables = zip(
*optimizer.compute_gradients(total_loss))
if params.gradient_clipping == 'global_norm':
gradients, _ = tf.clip_by_global_norm(gradients, 1.0)
tf.logging.info('clipping: global_norm')
else:
return NotImplementedError
optimizer = hvd.DistributedOptimizer(optimizer)
try:
amp_envar_enabled = (int(
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION']) == 1)
except KeyError:
amp_envar_enabled = False
if params.use_amp and not amp_envar_enabled:
optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(
optimizer, loss_scale='dynamic')
train_op = optimizer.minimize(total_loss,
global_step=global_step)
eval_metric_ops = None
if is_eval:
dice_loss = dice_coef(predict=tf.cast(flattened_softmax,
tf.float32),
target=one_hot_labels,
dice_type='sorensen')
eval_loss = tf.identity(dice_loss, name='eval_loss_ref')
eval_metric_ops = {}
for i in range(num_classes):
eval_metric_ops['%s dice' %
params.labels[str(i)]] = tf.metrics.mean(
eval_loss[i])
return tf.estimator.EstimatorSpec(mode=mode,
loss=total_loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops)
