def parse_single_example(serialized_example, params):
"""Parses a singel serialized TFExample string."""
if 'retinanet_parser' in dir(params):
parser_params = params.retinanet_parser
decoder = tf_example_decoder.TfExampleDecoder()
else:
parser_params = params.maskrcnn_parser
decoder = tf_example_decoder.TfExampleDecoder(include_mask=True)
data = decoder.decode(serialized_example)
image = data['image']
source_id = data['source_id']
source_id = dataloader_utils.process_source_id(source_id)
height = data['height']
width = data['width']
boxes = data['groundtruth_boxes']
boxes = box_utils.denormalize_boxes(boxes, tf.shape(image)[:2])
classes = data['groundtruth_classes']
is_crowds = data['groundtruth_is_crowd']
areas = data['groundtruth_area']
masks = data.get('groundtruth_instance_masks_png', None)
image = input_utils.normalize_image(image)
image, image_info = input_utils.resize_and_crop_image(
image,
parser_params.output_size,
padded_size=input_utils.compute_padded_size(
parser_params.output_size,
2 ** params.architecture.max_level),
aug_scale_min=1.0,
aug_scale_max=1.0)
labels = {
'image_info': image_info,
}
groundtruths = {
'source_id': source_id,
'height': height,
'width': width,
'num_detections': tf.shape(classes),
'boxes': boxes,
'classes': classes,
'areas': areas,
'is_crowds': tf.cast(is_crowds, tf.int32),
}
if masks is not None:
groundtruths['masks'] = masks
return image, labels, groundtruths
def _parse_single_example(self, example):
"""Parses a single serialized tf.Example proto.
Args:
example: a serialized tf.Example proto string.
Returns:
A dictionary of groundtruth with the following fields:
source_id: a scalar tensor of int64 representing the image source_id.
height: a scalar tensor of int64 representing the image height.
width: a scalar tensor of int64 representing the image width.
boxes: a float tensor of shape [K, 4], representing the groundtruth
boxes in absolute coordinates with respect to the original image size.
classes: a int64 tensor of shape [K], representing the class labels of
each instances.
is_crowds: a bool tensor of shape [K], indicating whether the instance
is crowd.
areas: a float tensor of shape [K], indicating the area of each
instance.
masks: a string tensor of shape [K], containing the bytes of the png
mask of each instance.
"""
decoder = tf_example_decoder.TfExampleDecoder(
include_mask=self._include_mask)
decoded_tensors = decoder.decode(example)
image = decoded_tensors['image']
image_size = tf.shape(image)[0:2]
boxes = box_utils.denormalize_boxes(
decoded_tensors['groundtruth_boxes'], image_size)
groundtruths = {
'source_id':
tf.string_to_number(decoded_tensors['source_id'],
out_type=tf.int64),
'height':
decoded_tensors['height'],
'width':
decoded_tensors['width'],
'num_detections':
tf.shape(decoded_tensors['groundtruth_classes'])[0],
'boxes':
boxes,
'classes':
decoded_tensors['groundtruth_classes'],
'is_crowds':
decoded_tensors['groundtruth_is_crowd'],
'areas':
decoded_tensors['groundtruth_area'],
}
if self._include_mask:
groundtruths.update({
'masks':
decoded_tensors['groundtruth_instance_masks_png'],
})
return groundtruths
def __init__(self,
output_size,
min_level,
max_level,
aug_rand_hflip=False,
aug_scale_min=1.0,
aug_scale_max=1.0,
skip_crowd_during_training=True,
include_mask=False,
mask_crop_size=112):
"""Initializes parameters for parsing annotations in the dataset.
Args:
output_size: `Tensor` or `list` for [height, width] of output image. The
output_size should be divided by the largest feature stride 2^max_level.
min_level: `int` number of minimum level of the output feature pyramid.
max_level: `int` number of maximum level of the output feature pyramid.
aug_rand_hflip: `bool`, if True, augment training with random
horizontal flip.
aug_scale_min: `float`, the minimum scale applied to `output_size` for
data augmentation during training.
aug_scale_max: `float`, the maximum scale applied to `output_size` for
data augmentation during training.
skip_crowd_during_training: `bool`, if True, skip annotations labeled with
`is_crowd` equals to 1.
include_mask: a bool to indicate whether parse mask groundtruth.
mask_crop_size: the size which groundtruth mask is cropped to.
"""
self._skip_crowd_during_training = skip_crowd_during_training
self._example_decoder = tf_example_decoder.TfExampleDecoder(
include_mask=include_mask)
# Anchor.
self._output_size = output_size
self._min_level = min_level
self._max_level = max_level
# Data augmentation.
self._aug_rand_hflip = aug_rand_hflip
self._aug_scale_min = aug_scale_min
self._aug_scale_max = aug_scale_max
# Mask.
self._include_mask = include_mask
self._mask_crop_size = mask_crop_size
self._parse_fn = self._parse_train_data
def parse_single_example(serialized_example, params):
"""Parses a singel serialized TFExample string."""
decoder = tf_example_decoder.TfExampleDecoder()
data = decoder.decode(serialized_example)
image = data['image']
source_id = data['source_id']
source_id = dataloader_utils.process_source_id(source_id)
height = data['height']
width = data['width']
boxes = data['groundtruth_boxes']
boxes = box_utils.denormalize_boxes(boxes, tf.shape(image)[:2])
classes = data['groundtruth_classes']
is_crowds = data['groundtruth_is_crowd']
areas = data['groundtruth_area']
image = input_utils.normalize_image(image)
image, image_info = input_utils.resize_and_crop_image(
image,
params.retinanet_parser.output_size,
padded_size=input_utils.compute_padded_size(
params.retinanet_parser.output_size, 2**params.anchor.max_level),
aug_scale_min=1.0,
aug_scale_max=1.0)
anchors = anchor.Anchor(params.anchor.min_level, params.anchor.max_level,
params.anchor.num_scales,
params.anchor.aspect_ratios,
params.anchor.anchor_size,
image.get_shape().as_list()[:2])
labels = {
'anchor_boxes': anchors.multilevel_boxes,
'image_info': image_info,
}
groundtruths = {
'source_id': source_id,
'height': height,
'width': width,
'num_detections': tf.shape(classes),
'boxes': boxes,
'classes': classes,
'areas': areas,
'is_crowds': tf.cast(is_crowds, tf.int32),
}
return image, labels, groundtruths
def __init__(self,
output_size,
min_level,
max_level,
num_scales,
aspect_ratios,
anchor_size,
match_threshold=0.5,
unmatched_threshold=0.5,
aug_rand_hflip=False,
aug_scale_min=1.0,
aug_scale_max=1.0,
use_autoaugment=False,
autoaugment_policy_name='v0',
skip_crowd_during_training=True,
max_num_instances=100,
use_bfloat16=True,
regenerate_source_id=False,
mode=None):
"""Initializes parameters for parsing annotations in the dataset.
Args:
output_size: `Tensor` or `list` for [height, width] of output image. The
output_size should be divided by the largest feature stride 2^max_level.
min_level: `int` number of minimum level of the output feature pyramid.
max_level: `int` number of maximum level of the output feature pyramid.
num_scales: `int` number representing intermediate scales added
on each level. For instances, num_scales=2 adds one additional
intermediate anchor scales [2^0, 2^0.5] on each level.
aspect_ratios: `list` of float numbers representing the aspect raito
anchors added on each level. The number indicates the ratio of width to
height. For instances, aspect_ratios=[1.0, 2.0, 0.5] adds three anchors
on each scale level.
anchor_size: `float` number representing the scale of size of the base
anchor to the feature stride 2^level.
match_threshold: `float` number between 0 and 1 representing the
lower-bound threshold to assign positive labels for anchors. An anchor
with a score over the threshold is labeled positive.
unmatched_threshold: `float` number between 0 and 1 representing the
upper-bound threshold to assign negative labels for anchors. An anchor
with a score below the threshold is labeled negative.
aug_rand_hflip: `bool`, if True, augment training with random
horizontal flip.
aug_scale_min: `float`, the minimum scale applied to `output_size` for
data augmentation during training.
aug_scale_max: `float`, the maximum scale applied to `output_size` for
data augmentation during training.
use_autoaugment: `bool`, if True, use the AutoAugment augmentation policy
during training.
autoaugment_policy_name: `string` that specifies the name of the
AutoAugment policy that will be used during training.
skip_crowd_during_training: `bool`, if True, skip annotations labeled with
`is_crowd` equals to 1.
max_num_instances: `int` number of maximum number of instances in an
image. The groundtruth data will be padded to `max_num_instances`.
use_bfloat16: `bool`, if True, cast output image to tf.bfloat16.
regenerate_source_id: `bool`, if True TFExampleParser will use hashed
value of `image/encoded` for `image/source_id`.
mode: a ModeKeys. Specifies if this is training, evaluation, prediction or
prediction with groundtruths in the outputs.
"""
self._mode = mode
self._max_num_instances = max_num_instances
self._skip_crowd_during_training = skip_crowd_during_training
self._is_training = (mode == ModeKeys.TRAIN)
self._example_decoder = tf_example_decoder.TfExampleDecoder(
include_mask=False, regenerate_source_id=regenerate_source_id)
# Anchor.
self._output_size = output_size
self._min_level = min_level
self._max_level = max_level
self._num_scales = num_scales
self._aspect_ratios = aspect_ratios
self._anchor_size = anchor_size
self._match_threshold = match_threshold
self._unmatched_threshold = unmatched_threshold
# Data augmentation.
self._aug_rand_hflip = aug_rand_hflip
self._aug_scale_min = aug_scale_min
self._aug_scale_max = aug_scale_max
# Data Augmentation with AutoAugment.
self._use_autoaugment = use_autoaugment
self._autoaugment_policy_name = autoaugment_policy_name
# Device.
self._use_bfloat16 = use_bfloat16
# Data is parsed depending on the model Modekey.
if mode == ModeKeys.TRAIN:
self._parse_fn = self._parse_train_data
elif mode == ModeKeys.EVAL:
self._parse_fn = self._parse_eval_data
elif mode == ModeKeys.PREDICT or mode == ModeKeys.PREDICT_WITH_GT:
self._parse_fn = self._parse_predict_data
else:
raise ValueError('mode is not defined.')
def __init__(self,
output_size,
min_level,
max_level,
num_scales,
aspect_ratios,
anchor_size,
rpn_match_threshold=0.7,
rpn_unmatched_threshold=0.3,
rpn_batch_size_per_im=256,
rpn_fg_fraction=0.5,
aug_rand_hflip=False,
aug_scale_min=1.0,
aug_scale_max=1.0,
skip_crowd_during_training=True,
max_num_instances=100,
include_mask=False,
mask_crop_size=112,
use_bfloat16=True,
mode=None):
"""Initializes parameters for parsing annotations in the dataset.
Args:
output_size: `Tensor` or `list` for [height, width] of output image. The
output_size should be divided by the largest feature stride 2^max_level.
min_level: `int` number of minimum level of the output feature pyramid.
max_level: `int` number of maximum level of the output feature pyramid.
num_scales: `int` number representing intermediate scales added
on each level. For instances, num_scales=2 adds one additional
intermediate anchor scales [2^0, 2^0.5] on each level.
aspect_ratios: `list` of float numbers representing the aspect raito
anchors added on each level. The number indicates the ratio of width to
height. For instances, aspect_ratios=[1.0, 2.0, 0.5] adds three anchors
on each scale level.
anchor_size: `float` number representing the scale of size of the base
anchor to the feature stride 2^level.
rpn_match_threshold:
rpn_unmatched_threshold:
rpn_batch_size_per_im:
rpn_fg_fraction:
aug_rand_hflip: `bool`, if True, augment training with random
horizontal flip.
aug_scale_min: `float`, the minimum scale applied to `output_size` for
data augmentation during training.
aug_scale_max: `float`, the maximum scale applied to `output_size` for
data augmentation during training.
skip_crowd_during_training: `bool`, if True, skip annotations labeled with
`is_crowd` equals to 1.
max_num_instances: `int` number of maximum number of instances in an
image. The groundtruth data will be padded to `max_num_instances`.
include_mask: a bool to indicate whether parse mask groundtruth.
mask_crop_size: the size which groundtruth mask is cropped to.
use_bfloat16: `bool`, if True, cast output image to tf.bfloat16.
mode: a ModeKeys. Specifies if this is training, evaluation, prediction
or prediction with groundtruths in the outputs.
"""
self._mode = mode
self._max_num_instances = max_num_instances
self._skip_crowd_during_training = skip_crowd_during_training
self._is_training = (mode == ModeKeys.TRAIN)
self._example_decoder = tf_example_decoder.TfExampleDecoder(
include_mask=include_mask)
# Anchor.
self._output_size = output_size
self._min_level = min_level
self._max_level = max_level
self._num_scales = num_scales
self._aspect_ratios = aspect_ratios
self._anchor_size = anchor_size
# Target assigning.
self._rpn_match_threshold = rpn_match_threshold
self._rpn_unmatched_threshold = rpn_unmatched_threshold
self._rpn_batch_size_per_im = rpn_batch_size_per_im
self._rpn_fg_fraction = rpn_fg_fraction
# Data augmentation.
self._aug_rand_hflip = aug_rand_hflip
self._aug_scale_min = aug_scale_min
self._aug_scale_max = aug_scale_max
# Mask.
self._include_mask = include_mask
self._mask_crop_size = mask_crop_size
# Device.
self._use_bfloat16 = use_bfloat16
# Data is parsed depending on the model Modekey.
if mode == ModeKeys.TRAIN:
self._parse_fn = self._parse_train_data
elif mode == ModeKeys.EVAL:
self._parse_fn = self._parse_eval_data
elif mode == ModeKeys.PREDICT or mode == ModeKeys.PREDICT_WITH_GT:
self._parse_fn = self._parse_predict_data
else:
raise ValueError('mode is not defined.')
def __init__(self,
output_size,
min_level,
max_level,
num_scales,
aspect_ratios,
anchor_size,
use_category=True,
outer_box_scale=1.0,
box_jitter_scale=0.025,
num_sampled_masks=8,
mask_crop_size=32,
mask_min_level=3,
mask_max_level=5,
upsample_factor=4,
match_threshold=0.5,
unmatched_threshold=0.5,
aug_rand_hflip=False,
aug_scale_min=1.0,
aug_scale_max=1.0,
skip_crowd_during_training=True,
max_num_instances=100,
use_bfloat16=True,
mask_train_class='all',
mode=None):
"""Initializes parameters for parsing annotations in the dataset.
Args:
output_size: `Tensor` or `list` for [height, width] of output image. The
output_size should be divided by the largest feature stride 2^max_level.
min_level: `int` number of minimum level of the output feature pyramid.
max_level: `int` number of maximum level of the output feature pyramid.
num_scales: `int` number representing intermediate scales added
on each level. For instances, num_scales=2 adds one additional
intermediate anchor scales [2^0, 2^0.5] on each level.
aspect_ratios: `list` of float numbers representing the aspect raito
anchors added on each level. The number indicates the ratio of width to
height. For instances, aspect_ratios=[1.0, 2.0, 0.5] adds three anchors
on each scale level.
anchor_size: `float` number representing the scale of size of the base
anchor to the feature stride 2^level.
use_category: if `False`, treat all object in all classes in one
foreground category.
outer_box_scale: `float` number in a range of [1.0, inf) representing
the scale from object box to outer box. The mask branch predicts
instance mask enclosed in outer box.
box_jitter_scale: `float` number representing the noise magnitude to
jitter the training groundtruth boxes for mask branch.
num_sampled_masks: `int` number of sampled masks for training.
mask_crop_size: `list` for [height, width] of output training masks.
mask_min_level: `int` number indicating the minimum feature level to
obtain instance features.
mask_max_level: `int` number indicating the maximum feature level to
obtain instance features.
upsample_factor: `int` factor of upsampling the fine mask predictions.
match_threshold: `float` number between 0 and 1 representing the
lower-bound threshold to assign positive labels for anchors. An anchor
with a score over the threshold is labeled positive.
unmatched_threshold: `float` number between 0 and 1 representing the
upper-bound threshold to assign negative labels for anchors. An anchor
with a score below the threshold is labeled negative.
aug_rand_hflip: `bool`, if True, augment training with random
horizontal flip.
aug_scale_min: `float`, the minimum scale applied to `output_size` for
data augmentation during training.
aug_scale_max: `float`, the maximum scale applied to `output_size` for
data augmentation during training.
skip_crowd_during_training: `bool`, if True, skip annotations labeled with
`is_crowd` equals to 1.
max_num_instances: `int` number of maximum number of instances in an
image. The groundtruth data will be padded to `max_num_instances`.
use_bfloat16: `bool`, if True, cast output image to tf.bfloat16.
mask_train_class: a string of experiment mode: `all`, `voc` or `nonvoc`.
mode: a ModeKeys. Specifies if this is training, evaluation, prediction
or prediction with groundtruths in the outputs.
"""
self._mode = mode
self._mask_train_class = mask_train_class
self._max_num_instances = max_num_instances
self._skip_crowd_during_training = skip_crowd_during_training
self._is_training = (mode == ModeKeys.TRAIN)
self._example_decoder = tf_example_decoder.TfExampleDecoder(
include_mask=True)
# Anchor.
self._output_size = output_size
self._min_level = min_level
self._max_level = max_level
self._num_scales = num_scales
self._aspect_ratios = aspect_ratios
self._anchor_size = anchor_size
self._match_threshold = match_threshold
self._unmatched_threshold = unmatched_threshold
# Data augmentation.
self._aug_rand_hflip = aug_rand_hflip
self._aug_scale_min = aug_scale_min
self._aug_scale_max = aug_scale_max
# Device.
self._use_bfloat16 = use_bfloat16
# ShapeMask specific.
# Control of which category to use.
self._use_category = use_category
self._num_sampled_masks = num_sampled_masks
self._mask_crop_size = mask_crop_size
self._mask_min_level = mask_min_level
self._mask_max_level = mask_max_level
self._outer_box_scale = outer_box_scale
self._box_jitter_scale = box_jitter_scale
self._up_sample_factor = upsample_factor
# Data is parsed depending on the model Modekey.
if mode == ModeKeys.TRAIN:
self._parse_fn = self.parse_train_data
elif mode == ModeKeys.EVAL:
self._parse_fn = self._parse_eval_data
elif mode == ModeKeys.PREDICT or mode == ModeKeys.PREDICT_WITH_GT:
self._parse_fn = self.parse_predict_data
else:
raise ValueError('mode is not defined.')