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MyCity/traffic_analyzer/object_detection/meta_architectures/ssd_meta_arch.py

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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""SSD Meta-architecture definition.
General tensorflow implementation of convolutional Multibox/SSD detection
models.
"""
import abc
import tensorflow as tf
from object_detection.core import box_list
from object_detection.core import box_list_ops
from object_detection.core import model
from object_detection.core import standard_fields as fields
from object_detection.core import target_assigner
from object_detection.utils import ops
from object_detection.utils import shape_utils
from object_detection.utils import visualization_utils
slim = tf.contrib.slim
class SSDFeatureExtractor(object):
"""SSD Slim Feature Extractor definition."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams_fn,
reuse_weights=None,
use_explicit_padding=False,
use_depthwise=False,
override_base_feature_extractor_hyperparams=False):
"""Constructor.
Args:
is_training: whether the network is in training mode.
depth_multiplier: float depth multiplier for feature extractor.
min_depth: minimum feature extractor depth.
pad_to_multiple: the nearest multiple to zero pad the input height and
width dimensions to.
conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d
and separable_conv2d ops in the layers that are added on top of the
base feature extractor.
reuse_weights: whether to reuse variables. Default is None.
use_explicit_padding: Whether to use explicit padding when extracting
features. Default is False.
use_depthwise: Whether to use depthwise convolutions. Default is False.
override_base_feature_extractor_hyperparams: Whether to override
hyperparameters of the base feature extractor with the one from
`conv_hyperparams_fn`.
"""
self._is_training = is_training
self._depth_multiplier = depth_multiplier
self._min_depth = min_depth
self._pad_to_multiple = pad_to_multiple
self._conv_hyperparams_fn = conv_hyperparams_fn
self._reuse_weights = reuse_weights
self._use_explicit_padding = use_explicit_padding
self._use_depthwise = use_depthwise
self._override_base_feature_extractor_hyperparams = (
override_base_feature_extractor_hyperparams)
@property
def is_keras_model(self):
return False
@abc.abstractmethod
def preprocess(self, resized_inputs):
"""Preprocesses images for feature extraction (minus image resizing).
Args:
resized_inputs: a [batch, height, width, channels] float tensor
representing a batch of images.
Returns:
preprocessed_inputs: a [batch, height, width, channels] float tensor
representing a batch of images.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
"""
pass
@abc.abstractmethod
def extract_features(self, preprocessed_inputs):
"""Extracts features from preprocessed inputs.
This function is responsible for extracting feature maps from preprocessed
images.
Args:
preprocessed_inputs: a [batch, height, width, channels] float tensor
representing a batch of images.
Returns:
feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i]
"""
raise NotImplementedError
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
"""Returns a map of variables to load from a foreign checkpoint.
Args:
feature_extractor_scope: A scope name for the feature extractor.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph.
"""
variables_to_restore = {}
for variable in tf.global_variables():
var_name = variable.op.name
if var_name.startswith(feature_extractor_scope + '/'):
var_name = var_name.replace(feature_extractor_scope + '/', '')
variables_to_restore[var_name] = variable
return variables_to_restore
class SSDKerasFeatureExtractor(tf.keras.Model):
"""SSD Feature Extractor definition."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams,
freeze_batchnorm,
inplace_batchnorm_update,
use_explicit_padding=False,
use_depthwise=False,
override_base_feature_extractor_hyperparams=False,
name=None):
"""Constructor.
Args:
is_training: whether the network is in training mode.
depth_multiplier: float depth multiplier for feature extractor.
min_depth: minimum feature extractor depth.
pad_to_multiple: the nearest multiple to zero pad the input height and
width dimensions to.
conv_hyperparams: `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
freeze_batchnorm: Whether to freeze batch norm parameters during
training or not. When training with a small batch size (e.g. 1), it is
desirable to freeze batch norm update and use pretrained batch norm
params.
inplace_batchnorm_update: Whether to update batch norm moving average
values inplace. When this is false train op must add a control
dependency on tf.graphkeys.UPDATE_OPS collection in order to update
batch norm statistics.
use_explicit_padding: Whether to use explicit padding when extracting
features. Default is False.
use_depthwise: Whether to use depthwise convolutions. Default is False.
override_base_feature_extractor_hyperparams: Whether to override
hyperparameters of the base feature extractor with the one from
`conv_hyperparams_config`.
name: A string name scope to assign to the model. If 'None', Keras
will auto-generate one from the class name.
"""
super(SSDKerasFeatureExtractor, self).__init__(name=name)
self._is_training = is_training
self._depth_multiplier = depth_multiplier
self._min_depth = min_depth
self._pad_to_multiple = pad_to_multiple
self._conv_hyperparams = conv_hyperparams
self._freeze_batchnorm = freeze_batchnorm
self._inplace_batchnorm_update = inplace_batchnorm_update
self._use_explicit_padding = use_explicit_padding
self._use_depthwise = use_depthwise
self._override_base_feature_extractor_hyperparams = (
override_base_feature_extractor_hyperparams)
@property
def is_keras_model(self):
return True
@abc.abstractmethod
def preprocess(self, resized_inputs):
"""Preprocesses images for feature extraction (minus image resizing).
Args:
resized_inputs: a [batch, height, width, channels] float tensor
representing a batch of images.
Returns:
preprocessed_inputs: a [batch, height, width, channels] float tensor
representing a batch of images.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
"""
raise NotImplementedError
@abc.abstractmethod
def _extract_features(self, preprocessed_inputs):
"""Extracts features from preprocessed inputs.
This function is responsible for extracting feature maps from preprocessed
images.
Args:
preprocessed_inputs: a [batch, height, width, channels] float tensor
representing a batch of images.
Returns:
feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i]
"""
raise NotImplementedError
# This overrides the keras.Model `call` method with the _extract_features
# method.
def call(self, inputs, **kwargs):
return self._extract_features(inputs)
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
"""Returns a map of variables to load from a foreign checkpoint.
Args:
feature_extractor_scope: A scope name for the feature extractor.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph.
"""
variables_to_restore = {}
for variable in tf.global_variables():
var_name = variable.op.name
if var_name.startswith(feature_extractor_scope + '/'):
var_name = var_name.replace(feature_extractor_scope + '/', '')
variables_to_restore[var_name] = variable
return variables_to_restore
class SSDMetaArch(model.DetectionModel):
"""SSD Meta-architecture definition."""
def __init__(self,
is_training,
anchor_generator,
box_predictor,
box_coder,
feature_extractor,
encode_background_as_zeros,
image_resizer_fn,
non_max_suppression_fn,
score_conversion_fn,
classification_loss,
localization_loss,
classification_loss_weight,
localization_loss_weight,
normalize_loss_by_num_matches,
hard_example_miner,
target_assigner_instance,
add_summaries=True,
normalize_loc_loss_by_codesize=False,
freeze_batchnorm=False,
inplace_batchnorm_update=False,
add_background_class=True,
explicit_background_class=False,
random_example_sampler=None,
expected_loss_weights_fn=None,
use_confidences_as_targets=False,
implicit_example_weight=0.5,
equalization_loss_config=None):
"""SSDMetaArch Constructor.
TODO(rathodv,jonathanhuang): group NMS parameters + score converter into
a class and loss parameters into a class and write config protos for
postprocessing and losses.
Args:
is_training: A boolean indicating whether the training version of the
computation graph should be constructed.
anchor_generator: an anchor_generator.AnchorGenerator object.
box_predictor: a box_predictor.BoxPredictor object.
box_coder: a box_coder.BoxCoder object.
feature_extractor: a SSDFeatureExtractor object.
encode_background_as_zeros: boolean determining whether background
targets are to be encoded as an all zeros vector or a one-hot
vector (where background is the 0th class).
image_resizer_fn: a callable for image resizing. This callable always
takes a rank-3 image tensor (corresponding to a single image) and
returns a rank-3 image tensor, possibly with new spatial dimensions and
a 1-D tensor of shape [3] indicating shape of true image within
the resized image tensor as the resized image tensor could be padded.
See builders/image_resizer_builder.py.
non_max_suppression_fn: batch_multiclass_non_max_suppression
callable that takes `boxes`, `scores` and optional `clip_window`
inputs (with all other inputs already set) and returns a dictionary
hold tensors with keys: `detection_boxes`, `detection_scores`,
`detection_classes` and `num_detections`. See `post_processing.
batch_multiclass_non_max_suppression` for the type and shape of these
tensors.
score_conversion_fn: callable elementwise nonlinearity (that takes tensors
as inputs and returns tensors). This is usually used to convert logits
to probabilities.
classification_loss: an object_detection.core.losses.Loss object.
localization_loss: a object_detection.core.losses.Loss object.
classification_loss_weight: float
localization_loss_weight: float
normalize_loss_by_num_matches: boolean
hard_example_miner: a losses.HardExampleMiner object (can be None)
target_assigner_instance: target_assigner.TargetAssigner instance to use.
add_summaries: boolean (default: True) controlling whether summary ops
should be added to tensorflow graph.
normalize_loc_loss_by_codesize: whether to normalize localization loss
by code size of the box encoder.
freeze_batchnorm: Whether to freeze batch norm parameters during
training or not. When training with a small batch size (e.g. 1), it is
desirable to freeze batch norm update and use pretrained batch norm
params.
inplace_batchnorm_update: Whether to update batch norm moving average
values inplace. When this is false train op must add a control
dependency on tf.graphkeys.UPDATE_OPS collection in order to update
batch norm statistics.
add_background_class: Whether to add an implicit background class to
one-hot encodings of groundtruth labels. Set to false if training a
single class model or using groundtruth labels with an explicit
background class.
explicit_background_class: Set to true if using groundtruth labels with an
explicit background class, as in multiclass scores.
random_example_sampler: a BalancedPositiveNegativeSampler object that can
perform random example sampling when computing loss. If None, random
sampling process is skipped. Note that random example sampler and hard
example miner can both be applied to the model. In that case, random
sampler will take effect first and hard example miner can only process
the random sampled examples.
expected_loss_weights_fn: If not None, use to calculate
loss by background/foreground weighting. Should take batch_cls_targets
as inputs and return foreground_weights, background_weights. See
expected_classification_loss_by_expected_sampling and
expected_classification_loss_by_reweighting_unmatched_anchors in
third_party/tensorflow_models/object_detection/utils/ops.py as examples.
use_confidences_as_targets: Whether to use groundtruth_condifences field
to assign the targets.
implicit_example_weight: a float number that specifies the weight used
for the implicit negative examples.
equalization_loss_config: a namedtuple that specifies configs for
computing equalization loss.
"""
super(SSDMetaArch, self).__init__(num_classes=box_predictor.num_classes)
self._is_training = is_training
self._freeze_batchnorm = freeze_batchnorm
self._inplace_batchnorm_update = inplace_batchnorm_update
self._anchor_generator = anchor_generator
self._box_predictor = box_predictor
self._box_coder = box_coder
self._feature_extractor = feature_extractor
self._add_background_class = add_background_class
self._explicit_background_class = explicit_background_class
if add_background_class and explicit_background_class:
raise ValueError("Cannot have both 'add_background_class' and"
" 'explicit_background_class' true.")
# Needed for fine-tuning from classification checkpoints whose
# variables do not have the feature extractor scope.
if self._feature_extractor.is_keras_model:
# Keras feature extractors will have a name they implicitly use to scope.
# So, all contained variables are prefixed by this name.
# To load from classification checkpoints, need to filter out this name.
self._extract_features_scope = feature_extractor.name
else:
# Slim feature extractors get an explicit naming scope
self._extract_features_scope = 'FeatureExtractor'
if encode_background_as_zeros:
background_class = [0]
else:
background_class = [1]
if self._add_background_class:
num_foreground_classes = self.num_classes
else:
num_foreground_classes = self.num_classes - 1
self._unmatched_class_label = tf.constant(
background_class + num_foreground_classes * [0], tf.float32)
self._target_assigner = target_assigner_instance
self._classification_loss = classification_loss
self._localization_loss = localization_loss
self._classification_loss_weight = classification_loss_weight
self._localization_loss_weight = localization_loss_weight
self._normalize_loss_by_num_matches = normalize_loss_by_num_matches
self._normalize_loc_loss_by_codesize = normalize_loc_loss_by_codesize
self._hard_example_miner = hard_example_miner
self._random_example_sampler = random_example_sampler
self._parallel_iterations = 16
self._image_resizer_fn = image_resizer_fn
self._non_max_suppression_fn = non_max_suppression_fn
self._score_conversion_fn = score_conversion_fn
self._anchors = None
self._add_summaries = add_summaries
self._batched_prediction_tensor_names = []
self._expected_loss_weights_fn = expected_loss_weights_fn
self._use_confidences_as_targets = use_confidences_as_targets
self._implicit_example_weight = implicit_example_weight
self._equalization_loss_config = equalization_loss_config
@property
def anchors(self):
if not self._anchors:
raise RuntimeError('anchors have not been constructed yet!')
if not isinstance(self._anchors, box_list.BoxList):
raise RuntimeError('anchors should be a BoxList object, but is not.')
return self._anchors
@property
def batched_prediction_tensor_names(self):
if not self._batched_prediction_tensor_names:
raise RuntimeError('Must call predict() method to get batched prediction '
'tensor names.')
return self._batched_prediction_tensor_names
def preprocess(self, inputs):
"""Feature-extractor specific preprocessing.
SSD meta architecture uses a default clip_window of [0, 0, 1, 1] during
post-processing. On calling `preprocess` method, clip_window gets updated
based on `true_image_shapes` returned by `image_resizer_fn`.
Args:
inputs: a [batch, height_in, width_in, channels] float tensor representing
a batch of images with values between 0 and 255.0.
Returns:
preprocessed_inputs: a [batch, height_out, width_out, channels] float
tensor representing a batch of images.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
Raises:
ValueError: if inputs tensor does not have type tf.float32
"""
if inputs.dtype is not tf.float32:
raise ValueError('`preprocess` expects a tf.float32 tensor')
with tf.name_scope('Preprocessor'):
# TODO(jonathanhuang): revisit whether to always use batch size as
# the number of parallel iterations vs allow for dynamic batching.
outputs = shape_utils.static_or_dynamic_map_fn(
self._image_resizer_fn,
elems=inputs,
dtype=[tf.float32, tf.int32])
resized_inputs = outputs[0]
true_image_shapes = outputs[1]
return (self._feature_extractor.preprocess(resized_inputs),
true_image_shapes)
def _compute_clip_window(self, preprocessed_images, true_image_shapes):
"""Computes clip window to use during post_processing.
Computes a new clip window to use during post-processing based on
`resized_image_shapes` and `true_image_shapes` only if `preprocess` method
has been called. Otherwise returns a default clip window of [0, 0, 1, 1].
Args:
preprocessed_images: the [batch, height, width, channels] image
tensor.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros. Or None if the clip window should cover the full image.
Returns:
a 2-D float32 tensor of the form [batch_size, 4] containing the clip
window for each image in the batch in normalized coordinates (relative to
the resized dimensions) where each clip window is of the form [ymin, xmin,
ymax, xmax] or a default clip window of [0, 0, 1, 1].
"""
if true_image_shapes is None:
return tf.constant([0, 0, 1, 1], dtype=tf.float32)
resized_inputs_shape = shape_utils.combined_static_and_dynamic_shape(
preprocessed_images)
true_heights, true_widths, _ = tf.unstack(
tf.to_float(true_image_shapes), axis=1)
padded_height = tf.to_float(resized_inputs_shape[1])
padded_width = tf.to_float(resized_inputs_shape[2])
return tf.stack(
[
tf.zeros_like(true_heights),
tf.zeros_like(true_widths), true_heights / padded_height,
true_widths / padded_width
],
axis=1)
def predict(self, preprocessed_inputs, true_image_shapes):
"""Predicts unpostprocessed tensors from input tensor.
This function takes an input batch of images and runs it through the forward
pass of the network to yield unpostprocessesed predictions.
A side effect of calling the predict method is that self._anchors is
populated with a box_list.BoxList of anchors. These anchors must be
constructed before the postprocess or loss functions can be called.
Args:
preprocessed_inputs: a [batch, height, width, channels] image tensor.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
Returns:
prediction_dict: a dictionary holding "raw" prediction tensors:
1) preprocessed_inputs: the [batch, height, width, channels] image
tensor.
2) box_encodings: 4-D float tensor of shape [batch_size, num_anchors,
box_code_dimension] containing predicted boxes.
3) class_predictions_with_background: 3-D float tensor of shape
[batch_size, num_anchors, num_classes+1] containing class predictions
(logits) for each of the anchors. Note that this tensor *includes*
background class predictions (at class index 0).
4) feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i].
5) anchors: 2-D float tensor of shape [num_anchors, 4] containing
the generated anchors in normalized coordinates.
"""
if self._inplace_batchnorm_update:
batchnorm_updates_collections = None
else:
batchnorm_updates_collections = tf.GraphKeys.UPDATE_OPS
if self._feature_extractor.is_keras_model:
feature_maps = self._feature_extractor(preprocessed_inputs)
else:
with slim.arg_scope([slim.batch_norm],
is_training=(self._is_training and
not self._freeze_batchnorm),
updates_collections=batchnorm_updates_collections):
with tf.variable_scope(None, self._extract_features_scope,
[preprocessed_inputs]):
feature_maps = self._feature_extractor.extract_features(
preprocessed_inputs)
feature_map_spatial_dims = self._get_feature_map_spatial_dims(
feature_maps)
image_shape = shape_utils.combined_static_and_dynamic_shape(
preprocessed_inputs)
self._anchors = box_list_ops.concatenate(
self._anchor_generator.generate(
feature_map_spatial_dims,
im_height=image_shape[1],
im_width=image_shape[2]))
if self._box_predictor.is_keras_model:
predictor_results_dict = self._box_predictor(feature_maps)
else:
with slim.arg_scope([slim.batch_norm],
is_training=(self._is_training and
not self._freeze_batchnorm),
updates_collections=batchnorm_updates_collections):
predictor_results_dict = self._box_predictor.predict(
feature_maps, self._anchor_generator.num_anchors_per_location())
predictions_dict = {
'preprocessed_inputs': preprocessed_inputs,
'feature_maps': feature_maps,
'anchors': self._anchors.get()
}
for prediction_key, prediction_list in iter(predictor_results_dict.items()):
prediction = tf.concat(prediction_list, axis=1)
if (prediction_key == 'box_encodings' and prediction.shape.ndims == 4 and
prediction.shape[2] == 1):
prediction = tf.squeeze(prediction, axis=2)
predictions_dict[prediction_key] = prediction
self._batched_prediction_tensor_names = [x for x in predictions_dict
if x != 'anchors']
return predictions_dict
def _get_feature_map_spatial_dims(self, feature_maps):
"""Return list of spatial dimensions for each feature map in a list.
Args:
feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i].
Returns:
a list of pairs (height, width) for each feature map in feature_maps
"""
feature_map_shapes = [
shape_utils.combined_static_and_dynamic_shape(
feature_map) for feature_map in feature_maps
]
return [(shape[1], shape[2]) for shape in feature_map_shapes]
def postprocess(self, prediction_dict, true_image_shapes):
"""Converts prediction tensors to final detections.
This function converts raw predictions tensors to final detection results by
slicing off the background class, decoding box predictions and applying
non max suppression and clipping to the image window.
See base class for output format conventions. Note also that by default,
scores are to be interpreted as logits, but if a score_conversion_fn is
used, then scores are remapped (and may thus have a different
interpretation).
Args:
prediction_dict: a dictionary holding prediction tensors with
1) preprocessed_inputs: a [batch, height, width, channels] image
tensor.
2) box_encodings: 3-D float tensor of shape [batch_size, num_anchors,
box_code_dimension] containing predicted boxes.
3) class_predictions_with_background: 3-D float tensor of shape
[batch_size, num_anchors, num_classes+1] containing class predictions
(logits) for each of the anchors. Note that this tensor *includes*
background class predictions.
4) mask_predictions: (optional) a 5-D float tensor of shape
[batch_size, num_anchors, q, mask_height, mask_width]. `q` can be
either number of classes or 1 depending on whether a separate mask is
predicted per class.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros. Or None, if the clip window should cover the full image.
Returns:
detections: a dictionary containing the following fields
detection_boxes: [batch, max_detections, 4] tensor with post-processed
detection boxes.
detection_scores: [batch, max_detections] tensor with scalar scores for
post-processed detection boxes.
detection_classes: [batch, max_detections] tensor with classes for
post-processed detection classes.
detection_keypoints: [batch, max_detections, num_keypoints, 2] (if
encoded in the prediction_dict 'box_encodings')
detection_masks: [batch_size, max_detections, mask_height, mask_width]
(optional)
num_detections: [batch]
raw_detection_boxes: [batch, total_detections, 4] tensor with decoded
detection boxes before Non-Max Suppression.
raw_detection_score: [batch, total_detections,
num_classes_with_background] tensor of multi-class score logits for
raw detection boxes.
Raises:
ValueError: if prediction_dict does not contain `box_encodings` or
`class_predictions_with_background` fields.
"""
if ('box_encodings' not in prediction_dict or
'class_predictions_with_background' not in prediction_dict):
raise ValueError('prediction_dict does not contain expected entries.')
with tf.name_scope('Postprocessor'):
preprocessed_images = prediction_dict['preprocessed_inputs']
box_encodings = prediction_dict['box_encodings']
box_encodings = tf.identity(box_encodings, 'raw_box_encodings')
class_predictions = prediction_dict['class_predictions_with_background']
detection_boxes, detection_keypoints = self._batch_decode(box_encodings)
detection_boxes = tf.identity(detection_boxes, 'raw_box_locations')
detection_boxes = tf.expand_dims(detection_boxes, axis=2)
detection_scores = self._score_conversion_fn(class_predictions)
detection_scores = tf.identity(detection_scores, 'raw_box_scores')
if self._add_background_class or self._explicit_background_class:
detection_scores = tf.slice(detection_scores, [0, 0, 1], [-1, -1, -1])
additional_fields = None
batch_size = (
shape_utils.combined_static_and_dynamic_shape(preprocessed_images)[0])
if 'feature_maps' in prediction_dict:
feature_map_list = []
for feature_map in prediction_dict['feature_maps']:
feature_map_list.append(tf.reshape(feature_map, [batch_size, -1]))
box_features = tf.concat(feature_map_list, 1)
box_features = tf.identity(box_features, 'raw_box_features')
if detection_keypoints is not None:
additional_fields = {
fields.BoxListFields.keypoints: detection_keypoints}
(nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
nmsed_additional_fields, num_detections) = self._non_max_suppression_fn(
detection_boxes,
detection_scores,
clip_window=self._compute_clip_window(preprocessed_images,
true_image_shapes),
additional_fields=additional_fields,
masks=prediction_dict.get('mask_predictions'))
detection_dict = {
fields.DetectionResultFields.detection_boxes:
nmsed_boxes,
fields.DetectionResultFields.detection_scores:
nmsed_scores,
fields.DetectionResultFields.detection_classes:
nmsed_classes,
fields.DetectionResultFields.num_detections:
tf.to_float(num_detections),
fields.DetectionResultFields.raw_detection_boxes:
tf.squeeze(detection_boxes, axis=2),
fields.DetectionResultFields.raw_detection_scores:
class_predictions
}
if (nmsed_additional_fields is not None and
fields.BoxListFields.keypoints in nmsed_additional_fields):
detection_dict[fields.DetectionResultFields.detection_keypoints] = (
nmsed_additional_fields[fields.BoxListFields.keypoints])
if nmsed_masks is not None:
detection_dict[
fields.DetectionResultFields.detection_masks] = nmsed_masks
return detection_dict
def loss(self, prediction_dict, true_image_shapes, scope=None):
"""Compute scalar loss tensors with respect to provided groundtruth.
Calling this function requires that groundtruth tensors have been
provided via the provide_groundtruth function.
Args:
prediction_dict: a dictionary holding prediction tensors with
1) box_encodings: 3-D float tensor of shape [batch_size, num_anchors,
box_code_dimension] containing predicted boxes.
2) class_predictions_with_background: 3-D float tensor of shape
[batch_size, num_anchors, num_classes+1] containing class predictions
(logits) for each of the anchors. Note that this tensor *includes*
background class predictions.
true_image_shapes: int32 tensor of shape [batch, 3] where each row is
of the form [height, width, channels] indicating the shapes
of true images in the resized images, as resized images can be padded
with zeros.
scope: Optional scope name.
Returns:
a dictionary mapping loss keys (`localization_loss` and
`classification_loss`) to scalar tensors representing corresponding loss
values.
"""
with tf.name_scope(scope, 'Loss', prediction_dict.values()):
keypoints = None
if self.groundtruth_has_field(fields.BoxListFields.keypoints):
keypoints = self.groundtruth_lists(fields.BoxListFields.keypoints)
weights = None
if self.groundtruth_has_field(fields.BoxListFields.weights):
weights = self.groundtruth_lists(fields.BoxListFields.weights)
confidences = None
if self.groundtruth_has_field(fields.BoxListFields.confidences):
confidences = self.groundtruth_lists(fields.BoxListFields.confidences)
(batch_cls_targets, batch_cls_weights, batch_reg_targets,
batch_reg_weights, match_list) = self._assign_targets(
self.groundtruth_lists(fields.BoxListFields.boxes),
self.groundtruth_lists(fields.BoxListFields.classes),
keypoints, weights, confidences)
if self._add_summaries:
self._summarize_target_assignment(
self.groundtruth_lists(fields.BoxListFields.boxes), match_list)
if self._random_example_sampler:
batch_cls_per_anchor_weights = tf.reduce_mean(
batch_cls_weights, axis=-1)
batch_sampled_indicator = tf.to_float(
shape_utils.static_or_dynamic_map_fn(
self._minibatch_subsample_fn,
[batch_cls_targets, batch_cls_per_anchor_weights],
dtype=tf.bool,
parallel_iterations=self._parallel_iterations,
back_prop=True))
batch_reg_weights = tf.multiply(batch_sampled_indicator,
batch_reg_weights)
batch_cls_weights = tf.multiply(
tf.expand_dims(batch_sampled_indicator, -1),
batch_cls_weights)
losses_mask = None
if self.groundtruth_has_field(fields.InputDataFields.is_annotated):
losses_mask = tf.stack(self.groundtruth_lists(
fields.InputDataFields.is_annotated))
location_losses = self._localization_loss(
prediction_dict['box_encodings'],
batch_reg_targets,
ignore_nan_targets=True,
weights=batch_reg_weights,
losses_mask=losses_mask)
cls_losses = self._classification_loss(
prediction_dict['class_predictions_with_background'],
batch_cls_targets,
weights=batch_cls_weights,
losses_mask=losses_mask)
if self._expected_loss_weights_fn:
# Need to compute losses for assigned targets against the
# unmatched_class_label as well as their assigned targets.
# simplest thing (but wasteful) is just to calculate all losses
# twice
batch_size, num_anchors, num_classes = batch_cls_targets.get_shape()
unmatched_targets = tf.ones([batch_size, num_anchors, 1
]) * self._unmatched_class_label
unmatched_cls_losses = self._classification_loss(
prediction_dict['class_predictions_with_background'],
unmatched_targets,
weights=batch_cls_weights,
losses_mask=losses_mask)
if cls_losses.get_shape().ndims == 3:
batch_size, num_anchors, num_classes = cls_losses.get_shape()
cls_losses = tf.reshape(cls_losses, [batch_size, -1])
unmatched_cls_losses = tf.reshape(unmatched_cls_losses,
[batch_size, -1])
batch_cls_targets = tf.reshape(
batch_cls_targets, [batch_size, num_anchors * num_classes, -1])
batch_cls_targets = tf.concat(
[1 - batch_cls_targets, batch_cls_targets], axis=-1)
location_losses = tf.tile(location_losses, [1, num_classes])
foreground_weights, background_weights = (
self._expected_loss_weights_fn(batch_cls_targets))
cls_losses = (
foreground_weights * cls_losses +
background_weights * unmatched_cls_losses)
location_losses *= foreground_weights
classification_loss = tf.reduce_sum(cls_losses)
localization_loss = tf.reduce_sum(location_losses)
elif self._hard_example_miner:
cls_losses = ops.reduce_sum_trailing_dimensions(cls_losses, ndims=2)
(localization_loss, classification_loss) = self._apply_hard_mining(
location_losses, cls_losses, prediction_dict, match_list)
if self._add_summaries:
self._hard_example_miner.summarize()
else:
cls_losses = ops.reduce_sum_trailing_dimensions(cls_losses, ndims=2)
localization_loss = tf.reduce_sum(location_losses)
classification_loss = tf.reduce_sum(cls_losses)
# Optionally normalize by number of positive matches
normalizer = tf.constant(1.0, dtype=tf.float32)
if self._normalize_loss_by_num_matches:
normalizer = tf.maximum(tf.to_float(tf.reduce_sum(batch_reg_weights)),
1.0)
localization_loss_normalizer = normalizer
if self._normalize_loc_loss_by_codesize:
localization_loss_normalizer *= self._box_coder.code_size
localization_loss = tf.multiply((self._localization_loss_weight /
localization_loss_normalizer),
localization_loss,
name='localization_loss')
classification_loss = tf.multiply((self._classification_loss_weight /
normalizer), classification_loss,
name='classification_loss')
loss_dict = {
str(localization_loss.op.name): localization_loss,
str(classification_loss.op.name): classification_loss
}
return loss_dict
def _minibatch_subsample_fn(self, inputs):
"""Randomly samples anchors for one image.
Args:
inputs: a list of 2 inputs. First one is a tensor of shape [num_anchors,
num_classes] indicating targets assigned to each anchor. Second one
is a tensor of shape [num_anchors] indicating the class weight of each
anchor.
Returns:
batch_sampled_indicator: bool tensor of shape [num_anchors] indicating
whether the anchor should be selected for loss computation.
"""
cls_targets, cls_weights = inputs
if self._add_background_class:
# Set background_class bits to 0 so that the positives_indicator
# computation would not consider background class.
background_class = tf.zeros_like(tf.slice(cls_targets, [0, 0], [-1, 1]))
regular_class = tf.slice(cls_targets, [0, 1], [-1, -1])
cls_targets = tf.concat([background_class, regular_class], 1)
positives_indicator = tf.reduce_sum(cls_targets, axis=1)
return self._random_example_sampler.subsample(
tf.cast(cls_weights, tf.bool),
batch_size=None,
labels=tf.cast(positives_indicator, tf.bool))
def _summarize_anchor_classification_loss(self, class_ids, cls_losses):
positive_indices = tf.where(tf.greater(class_ids, 0))
positive_anchor_cls_loss = tf.squeeze(
tf.gather(cls_losses, positive_indices), axis=1)
visualization_utils.add_cdf_image_summary(positive_anchor_cls_loss,
'PositiveAnchorLossCDF')
negative_indices = tf.where(tf.equal(class_ids, 0))
negative_anchor_cls_loss = tf.squeeze(
tf.gather(cls_losses, negative_indices), axis=1)
visualization_utils.add_cdf_image_summary(negative_anchor_cls_loss,
'NegativeAnchorLossCDF')
def _assign_targets(self,
groundtruth_boxes_list,
groundtruth_classes_list,
groundtruth_keypoints_list=None,
groundtruth_weights_list=None,
groundtruth_confidences_list=None):
"""Assign groundtruth targets.
Adds a background class to each one-hot encoding of groundtruth classes
and uses target assigner to obtain regression and classification targets.
Args:
groundtruth_boxes_list: a list of 2-D tensors of shape [num_boxes, 4]
containing coordinates of the groundtruth boxes.
Groundtruth boxes are provided in [y_min, x_min, y_max, x_max]
format and assumed to be normalized and clipped
relative to the image window with y_min <= y_max and x_min <= x_max.
groundtruth_classes_list: a list of 2-D one-hot (or k-hot) tensors of
shape [num_boxes, num_classes] containing the class targets with the 0th
index assumed to map to the first non-background class.
groundtruth_keypoints_list: (optional) a list of 3-D tensors of shape
[num_boxes, num_keypoints, 2]
groundtruth_weights_list: A list of 1-D tf.float32 tensors of shape
[num_boxes] containing weights for groundtruth boxes.
groundtruth_confidences_list: A list of 2-D tf.float32 tensors of shape
[num_boxes, num_classes] containing class confidences for
groundtruth boxes.
Returns:
batch_cls_targets: a tensor with shape [batch_size, num_anchors,
num_classes],
batch_cls_weights: a tensor with shape [batch_size, num_anchors],
batch_reg_targets: a tensor with shape [batch_size, num_anchors,
box_code_dimension]
batch_reg_weights: a tensor with shape [batch_size, num_anchors],
match_list: a list of matcher.Match objects encoding the match between
anchors and groundtruth boxes for each image of the batch,
with rows of the Match objects corresponding to groundtruth boxes
and columns corresponding to anchors.
"""
groundtruth_boxlists = [
box_list.BoxList(boxes) for boxes in groundtruth_boxes_list
]
train_using_confidences = (self._is_training and
self._use_confidences_as_targets)
if self._add_background_class:
groundtruth_classes_with_background_list = [
tf.pad(one_hot_encoding, [[0, 0], [1, 0]], mode='CONSTANT')
for one_hot_encoding in groundtruth_classes_list
]
if train_using_confidences:
groundtruth_confidences_with_background_list = [
tf.pad(groundtruth_confidences, [[0, 0], [1, 0]], mode='CONSTANT')
for groundtruth_confidences in groundtruth_confidences_list
]
else:
groundtruth_classes_with_background_list = groundtruth_classes_list
if groundtruth_keypoints_list is not None:
for boxlist, keypoints in zip(
groundtruth_boxlists, groundtruth_keypoints_list):
boxlist.add_field(fields.BoxListFields.keypoints, keypoints)
if train_using_confidences:
return target_assigner.batch_assign_confidences(
self._target_assigner,
self.anchors,
groundtruth_boxlists,
groundtruth_confidences_with_background_list,
groundtruth_weights_list,
self._unmatched_class_label,
self._add_background_class,
self._implicit_example_weight)
else:
return target_assigner.batch_assign_targets(
self._target_assigner,
self.anchors,
groundtruth_boxlists,
groundtruth_classes_with_background_list,
self._unmatched_class_label,
groundtruth_weights_list)
def _summarize_target_assignment(self, groundtruth_boxes_list, match_list):
"""Creates tensorflow summaries for the input boxes and anchors.
This function creates four summaries corresponding to the average
number (over images in a batch) of (1) groundtruth boxes, (2) anchors
marked as positive, (3) anchors marked as negative, and (4) anchors marked
as ignored.
Args:
groundtruth_boxes_list: a list of 2-D tensors of shape [num_boxes, 4]
containing corners of the groundtruth boxes.
match_list: a list of matcher.Match objects encoding the match between
anchors and groundtruth boxes for each image of the batch,
with rows of the Match objects corresponding to groundtruth boxes
and columns corresponding to anchors.
"""
num_boxes_per_image = tf.stack(
[tf.shape(x)[0] for x in groundtruth_boxes_list])
pos_anchors_per_image = tf.stack(
[match.num_matched_columns() for match in match_list])
neg_anchors_per_image = tf.stack(
[match.num_unmatched_columns() for match in match_list])
ignored_anchors_per_image = tf.stack(
[match.num_ignored_columns() for match in match_list])
tf.summary.scalar('AvgNumGroundtruthBoxesPerImage',
tf.reduce_mean(tf.to_float(num_boxes_per_image)),
family='TargetAssignment')
tf.summary.scalar('AvgNumPositiveAnchorsPerImage',
tf.reduce_mean(tf.to_float(pos_anchors_per_image)),
family='TargetAssignment')
tf.summary.scalar('AvgNumNegativeAnchorsPerImage',
tf.reduce_mean(tf.to_float(neg_anchors_per_image)),
family='TargetAssignment')
tf.summary.scalar('AvgNumIgnoredAnchorsPerImage',
tf.reduce_mean(tf.to_float(ignored_anchors_per_image)),
family='TargetAssignment')
def _apply_hard_mining(self, location_losses, cls_losses, prediction_dict,
match_list):
"""Applies hard mining to anchorwise losses.
Args:
location_losses: Float tensor of shape [batch_size, num_anchors]
representing anchorwise location losses.
cls_losses: Float tensor of shape [batch_size, num_anchors]
representing anchorwise classification losses.
prediction_dict: p a dictionary holding prediction tensors with
1) box_encodings: 3-D float tensor of shape [batch_size, num_anchors,
box_code_dimension] containing predicted boxes.
2) class_predictions_with_background: 3-D float tensor of shape
[batch_size, num_anchors, num_classes+1] containing class predictions
(logits) for each of the anchors. Note that this tensor *includes*
background class predictions.
match_list: a list of matcher.Match objects encoding the match between
anchors and groundtruth boxes for each image of the batch,
with rows of the Match objects corresponding to groundtruth boxes
and columns corresponding to anchors.
Returns:
mined_location_loss: a float scalar with sum of localization losses from
selected hard examples.
mined_cls_loss: a float scalar with sum of classification losses from
selected hard examples.
"""
class_predictions = prediction_dict['class_predictions_with_background']
if self._add_background_class:
class_predictions = tf.slice(class_predictions, [0, 0, 1], [-1, -1, -1])
decoded_boxes, _ = self._batch_decode(prediction_dict['box_encodings'])
decoded_box_tensors_list = tf.unstack(decoded_boxes)
class_prediction_list = tf.unstack(class_predictions)
decoded_boxlist_list = []
for box_location, box_score in zip(decoded_box_tensors_list,
class_prediction_list):
decoded_boxlist = box_list.BoxList(box_location)
decoded_boxlist.add_field('scores', box_score)
decoded_boxlist_list.append(decoded_boxlist)
return self._hard_example_miner(
location_losses=location_losses,
cls_losses=cls_losses,
decoded_boxlist_list=decoded_boxlist_list,
match_list=match_list)
def _batch_decode(self, box_encodings):
"""Decodes a batch of box encodings with respect to the anchors.
Args:
box_encodings: A float32 tensor of shape
[batch_size, num_anchors, box_code_size] containing box encodings.
Returns:
decoded_boxes: A float32 tensor of shape
[batch_size, num_anchors, 4] containing the decoded boxes.
decoded_keypoints: A float32 tensor of shape
[batch_size, num_anchors, num_keypoints, 2] containing the decoded
keypoints if present in the input `box_encodings`, None otherwise.
"""
combined_shape = shape_utils.combined_static_and_dynamic_shape(
box_encodings)
batch_size = combined_shape[0]
tiled_anchor_boxes = tf.tile(
tf.expand_dims(self.anchors.get(), 0), [batch_size, 1, 1])
tiled_anchors_boxlist = box_list.BoxList(
tf.reshape(tiled_anchor_boxes, [-1, 4]))
decoded_boxes = self._box_coder.decode(
tf.reshape(box_encodings, [-1, self._box_coder.code_size]),
tiled_anchors_boxlist)
decoded_keypoints = None
if decoded_boxes.has_field(fields.BoxListFields.keypoints):
decoded_keypoints = decoded_boxes.get_field(
fields.BoxListFields.keypoints)
num_keypoints = decoded_keypoints.get_shape()[1]
decoded_keypoints = tf.reshape(
decoded_keypoints,
tf.stack([combined_shape[0], combined_shape[1], num_keypoints, 2]))
decoded_boxes = tf.reshape(decoded_boxes.get(), tf.stack(
[combined_shape[0], combined_shape[1], 4]))
return decoded_boxes, decoded_keypoints
def regularization_losses(self):
"""Returns a list of regularization losses for this model.
Returns a list of regularization losses for this model that the estimator
needs to use during training/optimization.
Returns:
A list of regularization loss tensors.
"""
losses = []
slim_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
# Copy the slim losses to avoid modifying the collection
if slim_losses:
losses.extend(slim_losses)
if self._box_predictor.is_keras_model:
losses.extend(self._box_predictor.losses)
if self._feature_extractor.is_keras_model:
losses.extend(self._feature_extractor.losses)
return losses
def restore_map(self,
fine_tune_checkpoint_type='detection',
load_all_detection_checkpoint_vars=False):
"""Returns a map of variables to load from a foreign checkpoint.
See parent class for details.
Args:
fine_tune_checkpoint_type: whether to restore from a full detection
checkpoint (with compatible variable names) or to restore from a
classification checkpoint for initialization prior to training.
Valid values: `detection`, `classification`. Default 'detection'.
load_all_detection_checkpoint_vars: whether to load all variables (when
`fine_tune_checkpoint_type='detection'`). If False, only variables
within the appropriate scopes are included. Default False.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph.
Raises:
ValueError: if fine_tune_checkpoint_type is neither `classification`
nor `detection`.
"""
if fine_tune_checkpoint_type not in ['detection', 'classification']:
raise ValueError('Not supported fine_tune_checkpoint_type: {}'.format(
fine_tune_checkpoint_type))
if fine_tune_checkpoint_type == 'classification':
return self._feature_extractor.restore_from_classification_checkpoint_fn(
self._extract_features_scope)
if fine_tune_checkpoint_type == 'detection':
variables_to_restore = {}
for variable in tf.global_variables():
var_name = variable.op.name
if load_all_detection_checkpoint_vars:
variables_to_restore[var_name] = variable
else:
if var_name.startswith(self._extract_features_scope):
variables_to_restore[var_name] = variable
return variables_to_restore
def updates(self):
"""Returns a list of update operators for this model.
Returns a list of update operators for this model that must be executed at
each training step. The estimator's train op needs to have a control
dependency on these updates.
Returns:
A list of update operators.
"""
update_ops = []
slim_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# Copy the slim ops to avoid modifying the collection
if slim_update_ops:
update_ops.extend(slim_update_ops)
if self._box_predictor.is_keras_model:
update_ops.extend(self._box_predictor.get_updates_for(None))
update_ops.extend(self._box_predictor.get_updates_for(
self._box_predictor.inputs))
if self._feature_extractor.is_keras_model:
update_ops.extend(self._feature_extractor.get_updates_for(None))
update_ops.extend(self._feature_extractor.get_updates_for(
self._feature_extractor.inputs))
return update_ops