def _build_deeplab(iterator, outputs_to_num_classes, ignore_label):
"""Builds a clone of DeepLab.
Args:
iterator: An iterator of type tf.data.Iterator for images and labels.
outputs_to_num_classes: A map from output type to the number of classes. For
example, for the task of semantic segmentation with 21 semantic classes,
we would have outputs_to_num_classes['semantic'] = 21.
ignore_label: Ignore label.
"""
samples = iterator.get_next()
# Add name to input and label nodes so we can add to summary.
#samples[common.IMAGE].set_shape([FLAGS.train_batch_size, FLAGS.train_crop_size[0], FLAGS.train_crop_size[1], 3])
samples[common.IMAGE] = tf.identity(samples[common.IMAGE],
name=common.IMAGE)
samples[common.LABEL] = tf.identity(samples[common.LABEL],
name=common.LABEL)
model_options = common.ModelOptions(
outputs_to_num_classes=outputs_to_num_classes,
crop_size=[int(sz) for sz in FLAGS.train_crop_size],
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride,
)
outputs_to_scales_to_logits = model.multi_scale_logits(
samples[common.IMAGE],
model_options=model_options,
image_pyramid=FLAGS.image_pyramid,
skips=FLAGS.skips,
weight_decay=FLAGS.weight_decay,
is_training=True,
fine_tune_batch_norm=FLAGS.fine_tune_batch_norm,
nas_training_hyper_parameters={
'drop_path_keep_prob': FLAGS.drop_path_keep_prob,
'total_training_steps': FLAGS.training_number_of_steps,
})
# Add name to graph node so we can add to summary.
output_type_dict = outputs_to_scales_to_logits[common.OUTPUT_TYPE]
output_type_dict[model.MERGED_LOGITS_SCOPE] = tf.identity(
output_type_dict[model.MERGED_LOGITS_SCOPE], name=common.OUTPUT_TYPE)
for output, num_classes in six.iteritems(outputs_to_num_classes):
train_utils.add_softmax_cross_entropy_loss_for_each_scale(
outputs_to_scales_to_logits[output],
samples[common.LABEL],
num_classes,
ignore_label,
loss_weight=1.0,
upsample_logits=FLAGS.upsample_logits,
hard_example_mining_step=FLAGS.hard_example_mining_step,
top_k_percent_pixels=FLAGS.top_k_percent_pixels,
scope=output)
# Log the summary
_log_summaries(samples[common.IMAGE], samples[common.LABEL],
num_classes,
output_type_dict[model.MERGED_LOGITS_SCOPE])
def _build_deeplab(inputs_queue, outputs_to_num_classes, ignore_label):
"""Builds a clone of DeepLab.
Args:
inputs_queue: A prefetch queue for images and labels.
outputs_to_num_classes: A map from output type to the number of classes.
For example, for the task of semantic segmentation with 21 semantic
classes, we would have outputs_to_num_classes['semantic'] = 21.
ignore_label: Ignore label.
Returns:
A map of maps from output_type (e.g., semantic prediction) to a
dictionary of multi-scale logits names to logits. For each output_type,
the dictionary has keys which correspond to the scales and values which
correspond to the logits. For example, if `scales` equals [1.0, 1.5],
then the keys would include 'merged_logits', 'logits_1.00' and
'logits_1.50'.
"""
samples = inputs_queue.dequeue()
print('samples:', samples)
# Add name to input and label nodes so we can add to summary.
samples[common.IMAGE] = tf.identity(samples[common.IMAGE],
name=common.IMAGE)
samples[common.LABEL] = tf.identity(samples[common.LABEL],
name=common.LABEL)
model_options = common.ModelOptions(
outputs_to_num_classes=outputs_to_num_classes,
crop_size=FLAGS.train_crop_size,
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride)
outputs_to_scales_to_logits = model.multi_scale_logits(
samples[common.IMAGE],
model_options=model_options,
image_pyramid=FLAGS.image_pyramid,
weight_decay=FLAGS.weight_decay,
is_training=True,
fine_tune_batch_norm=FLAGS.fine_tune_batch_norm)
# Add name to graph node so we can add to summary.
output_type_dict = outputs_to_scales_to_logits[common.OUTPUT_TYPE]
output_type_dict[model.MERGED_LOGITS_SCOPE] = tf.identity(
output_type_dict[model.MERGED_LOGITS_SCOPE], name=common.OUTPUT_TYPE)
for output, num_classes in six.iteritems(outputs_to_num_classes):
train_utils.add_softmax_cross_entropy_loss_for_each_scale(
outputs_to_scales_to_logits[output],
samples[common.LABEL],
num_classes,
ignore_label,
loss_weight=1.0,
upsample_logits=FLAGS.upsample_logits,
scope=output)
return outputs_to_scales_to_logits
def _build_deeplab(inputs_queue, outputs_to_num_classes, ignore_label):
"""Builds a clone of DeepLab.
Args:
inputs_queue: A prefetch queue for images and labels.
outputs_to_num_classes: A map from output type to the number of classes.
For example, for the task of semantic segmentation with 21 semantic
classes, we would have outputs_to_num_classes['semantic'] = 21.
ignore_label: Ignore label.
Returns:
A map of maps from output_type (e.g., semantic prediction) to a
dictionary of multi-scale logits names to logits. For each output_type,
the dictionary has keys which correspond to the scales and values which
correspond to the logits. For example, if `scales` equals [1.0, 1.5],
then the keys would include 'merged_logits', 'logits_1.00' and
'logits_1.50'.
"""
samples = inputs_queue.dequeue()
model_options = common.ModelOptions(
outputs_to_num_classes=outputs_to_num_classes,
crop_size=FLAGS.train_crop_size,
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride)
outputs_to_scales_to_logits = model.multi_scale_logits(
samples[common.IMAGE],
model_options=model_options,
image_pyramid=FLAGS.image_pyramid,
weight_decay=FLAGS.weight_decay,
is_training=True,
fine_tune_batch_norm=FLAGS.fine_tune_batch_norm)
for output, num_classes in outputs_to_num_classes.items():
train_utils.add_softmax_cross_entropy_loss_for_each_scale(
outputs_to_scales_to_logits[output],
samples[common.LABEL],
num_classes,
ignore_label,
loss_weight=1.0,
upsample_logits=FLAGS.upsample_logits,
scope=output)
return outputs_to_scales_to_logits
def _build_deeplab(inputs_queue, outputs_to_num_classes, ignore_label):
samples = inputs_queue.dequeue()
samples[common.IMAGE] = tf.identity(samples[common.IMAGE],
name=common.IMAGE)
samples[common.LABEL] = tf.identity(samples[common.LABEL],
name=common.LABEL)
model_options = common.ModelOptions(
outputs_to_num_classes=outputs_to_num_classes,
crop_size=FLAGS.train_crop_size,
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride)
outputs_to_scales_to_logits = model.multi_scale_logits(
samples[common.IMAGE],
model_options=model_options,
image_pyramid=FLAGS.image_pyramid,
weight_decay=FLAGS.weight_decay,
is_training=True,
fine_tune_batch_norm=FLAGS.fine_tune_batch_norm)
output_type_dict = outputs_to_scales_to_logits[common.OUTPUT_TYPE]
output_type_dict[model.get_merged_logits_scope()] = tf.identity(
output_type_dict[model.get_merged_logits_scope()],
name=common.OUTPUT_TYPE)
for output, num_classes in six.iteritems(outputs_to_num_classes):
train_utils.add_softmax_cross_entropy_loss_for_each_scale(
outputs_to_scales_to_logits[output],
samples[common.LABEL],
num_classes,
ignore_label,
loss_weight=1.0,
upsample_logits=FLAGS.upsample_logits,
scope=output)
return outputs_to_scales_to_logits
