def model_fn_with_summary(features, labels, mode, params):
del features, labels, params
loss = constant_op.constant(_EXPECTED_LOSS)
summary.scalar('loss_scalar_summary', loss)
summary.histogram('loss_histogram_summary', loss)
summary.image('loss_image_summary', loss)
return tpu_estimator.TPUEstimatorSpec(mode=mode, loss=loss)
def model_fn_with_summary(features, labels, mode, params):
del features, labels, params
loss = constant_op.constant(_EXPECTED_LOSS)
summary.scalar('loss_scalar_summary', loss)
summary.histogram('loss_histogram_summary', loss)
summary.image('loss_image_summary', loss)
return tpu_estimator.TPUEstimatorSpec(mode=mode, loss=loss)
def model_fn_with_summary(features, labels, mode, params):
del features, labels, params
loss = constant_op.constant(_EXPECTED_LOSS)
summary.scalar('loss_scalar_summary', loss)
summary.histogram('loss_histogram_summary', loss)
summary.image('loss_image_summary', loss)
return model_fn_lib.EstimatorSpec(
mode=mode, loss=loss, train_op=array_ops.identity(loss))
def model_fn_with_summary(features, labels, mode, params):
del features, labels, params
loss = constant_op.constant(_EXPECTED_LOSS)
summary.scalar('loss_scalar_summary', loss)
summary.histogram('loss_histogram_summary', loss)
summary.image('loss_image_summary', loss)
return model_fn_lib.EstimatorSpec(
mode=mode, loss=loss, train_op=array_ops.identity(loss))
def test_report_unsupported_operations_graph_mode(self):
"""Tests that unsupported operations are detected."""
context = self.create_test_xla_compile_context()
context.Enter()
dummy_tensor = constant_op.constant(1.1)
audio_summary = summary.audio('audio_summary', dummy_tensor, 0.5)
histogram_summary = summary.histogram('histogram_summary',
dummy_tensor)
image_summary = summary.image('image_summary', dummy_tensor)
scalar_summary = summary.scalar('scalar_summary', dummy_tensor)
tensor_summary = summary.tensor_summary('tensor_summary', dummy_tensor)
summary.merge([
audio_summary, histogram_summary, image_summary, scalar_summary,
tensor_summary
],
name='merge_summary')
logging_ops.Print(dummy_tensor, [dummy_tensor], name='print_op')
context.Exit()
unsupported_ops_names = [op.name for op in context._unsupported_ops]
self.assertEqual(unsupported_ops_names, [
u'audio_summary', u'histogram_summary', u'image_summary',
u'scalar_summary', u'tensor_summary',
u'merge_summary/merge_summary', u'print_op'
])
def test_report_unsupported_operations(self):
"""Tests that unsupported operations are detected."""
context = self.create_test_xla_compile_context()
context.Enter()
dummy_tensor = constant_op.constant(1.1)
audio_summary = summary.audio('audio_summary', dummy_tensor, 0.5)
histogram_summary = summary.histogram('histogram_summary', dummy_tensor)
image_summary = summary.image('image_summary', dummy_tensor)
scalar_summary = summary.scalar('scalar_summary', dummy_tensor)
tensor_summary = summary.tensor_summary('tensor_summary', dummy_tensor)
summary.merge(
[
audio_summary, histogram_summary, image_summary, scalar_summary,
tensor_summary
],
name='merge_summary')
logging_ops.Print(dummy_tensor, [dummy_tensor], name='print_op')
context.Exit()
unsupported_ops_names = [op.name for op in context._unsupported_ops]
self.assertEqual(unsupported_ops_names, [
u'audio_summary', u'histogram_summary', u'image_summary',
u'scalar_summary', u'tensor_summary', u'merge_summary/merge_summary',
u'print_op'
])
def unet_model_fn(features, labels, mode, params):
tf.local_variables_initializer()
loss, train_op, = None, None
eval_metric_ops, training_hooks, evaluation_hooks = None, None, None
predictions_dict = None
unet = Unet(params=params)
logits = unet.model(input_tensor=features['image'])
y_pred = tf.math.softmax(logits, axis=-1)
output_img = tf.expand_dims(tf.cast(tf.math.argmax(y_pred, axis=-1) * 255, dtype=tf.uint8), axis=-1)
if mode in (estimator.ModeKeys.TRAIN, estimator.ModeKeys.EVAL):
with tf.name_scope('Loss_Calculation'):
loss = Losses(logits=logits, labels=labels['label'])
loss = loss.custom_loss()
with tf.name_scope('Dice_Score_Calculation'):
dice = f1(labels=labels['label'], predictions=y_pred)
with tf.name_scope('Images_{}'.format(mode)):
with tf.name_scope('Reformat_Outputs'):
label = tf.expand_dims(tf.cast(tf.argmax(labels['label'], -1) * 255, dtype=tf.uint8), axis=-1)
image = tf.math.divide(features['image'] - tf.reduce_max(features['image'], [0, 1, 2]),
tf.reduce_max(features['image'], [0, 1, 2]) - tf.reduce_min(features['image'],
[0, 1, 2]))
summary.image('1_Medical_Image', image, max_outputs=1)
summary.image('2_Output', output_img, max_outputs=1)
summary.image('3_Output_pred', tf.expand_dims(y_pred[:, :, :, 1], -1), max_outputs=1)
summary.image('4_Output_label', label, max_outputs=1)
if mode == estimator.ModeKeys.TRAIN:
with tf.name_scope('Learning_Rate'):
global_step = tf.compat.v1.train.get_or_create_global_step()
learning_rate = tf.compat.v1.train.exponential_decay(params['lr'], global_step=global_step,
decay_steps=params['decay_steps'],
decay_rate=params['decay_rate'], staircase=False)
with tf.name_scope('Optimizer_conf'):
train_op = Adam(learning_rate=learning_rate).minimize(loss=loss, global_step=global_step)
with tf.name_scope('Metrics'):
summary.scalar('Output_DSC', dice[1])
summary.scalar('Learning_Rate', learning_rate)
if mode == estimator.ModeKeys.EVAL:
eval_metric_ops = {'Metrics/Output_DSC': dice}
eval_summary_hook = tf.estimator.SummarySaverHook(output_dir=params['eval_path'],
summary_op=summary.merge_all(),
save_steps=params['eval_steps'])
evaluation_hooks = [eval_summary_hook]
if mode == estimator.ModeKeys.PREDICT:
predictions_dict = {'image': features['image'],
'y_preds': y_pred[:, :, :, 1],
'output_img': output_img,
'path': features['path']}
return estimator.EstimatorSpec(mode,
predictions=predictions_dict,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
training_hooks=training_hooks,
evaluation_hooks=evaluation_hooks)