research/object_detection/core/model.py
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""Abstract detection model.
This file defines a generic base class for detection models. Programs that are
designed to work with arbitrary detection models should only depend on this
class. We intend for the functions in this class to follow tensor-in/tensor-out
design, thus all functions have tensors or lists/dictionaries holding tensors as
inputs and outputs.
Abstractly, detection models predict output tensors given input images
which can be passed to a loss function at training time or passed to a
postprocessing function at eval time. The computation graphs at a high level
consequently look as follows:
Training time:
inputs (images tensor) -> preprocess -> predict -> loss -> outputs (loss tensor)
Evaluation time:
inputs (images tensor) -> preprocess -> predict -> postprocess
-> outputs (boxes tensor, scores tensor, classes tensor, num_detections tensor)
DetectionModels must thus implement four functions (1) preprocess, (2) predict,
(3) postprocess and (4) loss. DetectionModels should make no assumptions about
the input size or aspect ratio --- they are responsible for doing any
resize/reshaping necessary (see docstring for the preprocess function).
Output classes are always integers in the range [0, num_classes). Any mapping
of these integers to semantic labels is to be handled outside of this class.
Images are resized in the `preprocess` method. All of `preprocess`, `predict`,
and `postprocess` should be reentrant.
The `preprocess` method runs `image_resizer_fn` that returns resized_images and
`true_image_shapes`. Since `image_resizer_fn` can pad the images with zeros,
true_image_shapes indicate the slices that contain the image without padding.
This is useful for padding images to be a fixed size for batching.
The `postprocess` method uses the true image shapes to clip predictions that lie
outside of images.
By default, DetectionModels produce bounding box detections; However, we support
a handful of auxiliary annotations associated with each bounding box, namely,
instance masks and keypoints.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import six
import tensorflow.compat.v1 as tf
from object_detection.core import standard_fields as fields
# If using a new enough version of TensorFlow, detection models should be a
# tf module or keras model for tracking.
try:
_BaseClass = tf.keras.layers.Layer
except AttributeError:
_BaseClass = object
class DetectionModel(six.with_metaclass(abc.ABCMeta, _BaseClass)):
"""Abstract base class for detection models.
Extends tf.Module to guarantee variable tracking.
"""
def __init__(self, num_classes):
"""Constructor.
Args:
num_classes: number of classes. Note that num_classes *does not* include
background categories that might be implicitly predicted in various
implementations.
"""
self._num_classes = num_classes
self._groundtruth_lists = {}
self._training_step = None
super(DetectionModel, self).__init__()
@property
def num_classes(self):
return self._num_classes
def groundtruth_lists(self, field):
"""Access list of groundtruth tensors.
Args:
field: a string key, options are
fields.BoxListFields.{boxes,classes,masks,mask_weights,keypoints,
keypoint_visibilities, densepose_*, track_ids,
temporal_offsets, track_match_flags}
fields.InputDataFields.is_annotated.
Returns:
a list of tensors holding groundtruth information (see also
provide_groundtruth function below), with one entry for each image in the
batch.
Raises:
RuntimeError: if the field has not been provided via provide_groundtruth.
"""
if field not in self._groundtruth_lists:
raise RuntimeError('Groundtruth tensor {} has not been provided'.format(
field))
return self._groundtruth_lists[field]
def groundtruth_has_field(self, field):
"""Determines whether the groundtruth includes the given field.
Args:
field: a string key, options are
fields.BoxListFields.{boxes,classes,masks,mask_weights,keypoints,
keypoint_visibilities, densepose_*, track_ids} or
fields.InputDataFields.is_annotated.
Returns:
True if the groundtruth includes the given field, False otherwise.
"""
return field in self._groundtruth_lists
@property
def training_step(self):
if self._training_step is None:
raise ValueError('Training step was not provided to the model.')
return self._training_step
@staticmethod
def get_side_inputs(features):
"""Get side inputs from input features.
This placeholder method provides a way for a meta-architecture to specify
how to grab additional side inputs from input features (in addition to the
image itself) and allows models to depend on contextual information. By
default, detection models do not use side information (and thus this method
returns an empty dictionary by default. However it can be overridden if
side inputs are necessary."
Args:
features: A dictionary of tensors.
Returns:
An empty dictionary by default.
"""
return {}
@abc.abstractmethod
def preprocess(self, inputs):
"""Input preprocessing.
To be overridden by implementations.
This function is responsible for any scaling/shifting of input values that
is necessary prior to running the detector on an input image.
It is also responsible for any resizing, padding that might be necessary
as images are assumed to arrive in arbitrary sizes. While this function
could conceivably be part of the predict method (below), it is often
convenient to keep these separate --- for example, we may want to preprocess
on one device, place onto a queue, and let another device (e.g., the GPU)
handle prediction.
A few important notes about the preprocess function:
+ We assume that this operation does not have any trainable variables nor
does it affect the groundtruth annotations in any way (thus data
augmentation operations such as random cropping should be performed
externally).
+ There is no assumption that the batchsize in this function is the same as
the batch size in the predict function. In fact, we recommend calling the
preprocess function prior to calling any batching operations (which should
happen outside of the model) and thus assuming that batch sizes are equal
to 1 in the preprocess function.
+ There is also no explicit assumption that the output resolutions
must be fixed across inputs --- this is to support "fully convolutional"
settings in which input images can have different shapes/resolutions.
Args:
inputs: a [batch, height_in, width_in, channels] float32 tensor
representing a batch of images with values between 0 and 255.0.
Returns:
preprocessed_inputs: a [batch, height_out, width_out, channels] float32
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 predict(self, preprocessed_inputs, true_image_shapes, **side_inputs):
"""Predict prediction tensors from inputs tensor.
Outputs of this function can be passed to loss or postprocess functions.
Args:
preprocessed_inputs: a [batch, height, width, channels] float32 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.
**side_inputs: additional tensors that are required by the network.
Returns:
prediction_dict: a dictionary holding prediction tensors to be
passed to the Loss or Postprocess functions.
"""
pass
@abc.abstractmethod
def postprocess(self, prediction_dict, true_image_shapes, **params):
"""Convert predicted output tensors to final detections.
This stage typically performs a few things such as
* Non-Max Suppression to remove overlapping detection boxes.
* Score conversion and background class removal.
Outputs adhere to the following conventions:
* Classes are integers in [0, num_classes); background classes are removed
and the first non-background class is mapped to 0. If the model produces
class-agnostic detections, then no output is produced for classes.
* Boxes are to be interpreted as being in [y_min, x_min, y_max, x_max]
format and normalized relative to the image window.
* `num_detections` is provided for settings where detections are padded to a
fixed number of boxes.
* We do not specifically assume any kind of probabilistic interpretation
of the scores --- the only important thing is their relative ordering.
Thus implementations of the postprocess function are free to output
logits, probabilities, calibrated probabilities, or anything else.
Args:
prediction_dict: a dictionary holding prediction tensors.
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.
**params: Additional keyword arguments for specific implementations of
DetectionModel.
Returns:
detections: a dictionary containing the following fields
detection_boxes: [batch, max_detections, 4]
detection_scores: [batch, max_detections]
detection_classes: [batch, max_detections]
(If a model is producing class-agnostic detections, this field may be
missing)
detection_masks: [batch, max_detections, mask_height, mask_width]
(optional)
detection_keypoints: [batch, max_detections, num_keypoints, 2]
(optional)
detection_keypoint_scores: [batch, max_detections, num_keypoints]
(optional)
detection_surface_coords: [batch, max_detections, mask_height,
mask_width, 2] (optional)
num_detections: [batch]
In addition to the above fields this stage also outputs the following
raw tensors:
raw_detection_boxes: [batch, total_detections, 4] tensor containing
all detection boxes from `prediction_dict` in the format
[ymin, xmin, ymax, xmax] and normalized co-ordinates.
raw_detection_scores: [batch, total_detections,
num_classes_with_background] tensor of class score logits for
raw detection boxes.
"""
pass
@abc.abstractmethod
def loss(self, prediction_dict, true_image_shapes):
"""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 predicted tensors
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:
a dictionary mapping strings (loss names) to scalar tensors representing
loss values.
"""
pass
def provide_groundtruth(
self,
groundtruth_boxes_list,
groundtruth_classes_list,
groundtruth_masks_list=None,
groundtruth_mask_weights_list=None,
groundtruth_keypoints_list=None,
groundtruth_keypoint_visibilities_list=None,
groundtruth_dp_num_points_list=None,
groundtruth_dp_part_ids_list=None,
groundtruth_dp_surface_coords_list=None,
groundtruth_track_ids_list=None,
groundtruth_temporal_offsets_list=None,
groundtruth_track_match_flags_list=None,
groundtruth_weights_list=None,
groundtruth_confidences_list=None,
groundtruth_is_crowd_list=None,
groundtruth_group_of_list=None,
groundtruth_area_list=None,
is_annotated_list=None,
groundtruth_labeled_classes=None,
groundtruth_verified_neg_classes=None,
groundtruth_not_exhaustive_classes=None,
groundtruth_keypoint_depths_list=None,
groundtruth_keypoint_depth_weights_list=None,
groundtruth_image_classes=None,
training_step=None):
"""Provide groundtruth tensors.
Args:
groundtruth_boxes_list: a list of 2-D tf.float32 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 tf.float32 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_masks_list: a list of 3-D tf.float32 tensors of
shape [num_boxes, height_in, width_in] containing instance
masks with values in {0, 1}. If None, no masks are provided.
Mask resolution `height_in`x`width_in` must agree with the resolution
of the input image tensor provided to the `preprocess` function.
groundtruth_mask_weights_list: a list of 1-D tf.float32 tensors of shape
[num_boxes] with weights for each instance mask.
groundtruth_keypoints_list: a list of 3-D tf.float32 tensors of
shape [num_boxes, num_keypoints, 2] containing keypoints.
Keypoints are assumed to be provided in normalized coordinates and
missing keypoints should be encoded as NaN (but it is recommended to use
`groundtruth_keypoint_visibilities_list`).
groundtruth_keypoint_visibilities_list: a list of 3-D tf.bool tensors
of shape [num_boxes, num_keypoints] containing keypoint visibilities.
groundtruth_dp_num_points_list: a list of 1-D tf.int32 tensors of shape
[num_boxes] containing the number of DensePose sampled points.
groundtruth_dp_part_ids_list: a list of 2-D tf.int32 tensors of shape
[num_boxes, max_sampled_points] containing the DensePose part ids
(0-indexed) for each sampled point. Note that there may be padding.
groundtruth_dp_surface_coords_list: a list of 3-D tf.float32 tensors of
shape [num_boxes, max_sampled_points, 4] containing the DensePose
surface coordinates for each sampled point. Note that there may be
padding.
groundtruth_track_ids_list: a list of 1-D tf.int32 tensors of shape
[num_boxes] containing the track IDs of groundtruth objects.
groundtruth_temporal_offsets_list: a list of 2-D tf.float32 tensors
of shape [num_boxes, 2] containing the spatial offsets of objects'
centers compared with the previous frame.
groundtruth_track_match_flags_list: a list of 1-D tf.float32 tensors
of shape [num_boxes] containing 0-1 flags that indicate if an object
has existed in the previous frame.
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.
groundtruth_is_crowd_list: A list of 1-D tf.bool tensors of shape
[num_boxes] containing is_crowd annotations.
groundtruth_group_of_list: A list of 1-D tf.bool tensors of shape
[num_boxes] containing group_of annotations.
groundtruth_area_list: A list of 1-D tf.float32 tensors of shape
[num_boxes] containing the area (in the original absolute coordinates)
of the annotations.
is_annotated_list: A list of scalar tf.bool tensors indicating whether
images have been labeled or not.
groundtruth_labeled_classes: A list of 1-D tf.float32 tensors of shape
[num_classes], containing label indices encoded as k-hot of the classes
that are exhaustively annotated.
groundtruth_verified_neg_classes: A list of 1-D tf.float32 tensors of
shape [num_classes], containing a K-hot representation of classes
which were verified as not present in the image.
groundtruth_not_exhaustive_classes: A list of 1-D tf.float32 tensors of
shape [num_classes], containing a K-hot representation of classes
which don't have all of their instances marked exhaustively.
groundtruth_keypoint_depths_list: a list of 2-D tf.float32 tensors
of shape [num_boxes, num_keypoints] containing keypoint relative depths.
groundtruth_keypoint_depth_weights_list: a list of 2-D tf.float32 tensors
of shape [num_boxes, num_keypoints] containing the weights of the
relative depths.
groundtruth_image_classes: A list of 1-D tf.float32 tensors of shape
[num_classes], containing label indices encoded as k-hot of the classes
that are present or not present in the image.
training_step: An integer denoting the current training step. This is
useful when models want to anneal loss terms.
"""
self._groundtruth_lists[fields.BoxListFields.boxes] = groundtruth_boxes_list
self._groundtruth_lists[
fields.BoxListFields.classes] = groundtruth_classes_list
if groundtruth_weights_list:
self._groundtruth_lists[fields.BoxListFields.
weights] = groundtruth_weights_list
if groundtruth_confidences_list:
self._groundtruth_lists[fields.BoxListFields.
confidences] = groundtruth_confidences_list
if groundtruth_masks_list:
self._groundtruth_lists[
fields.BoxListFields.masks] = groundtruth_masks_list
if groundtruth_mask_weights_list:
self._groundtruth_lists[
fields.BoxListFields.mask_weights] = groundtruth_mask_weights_list
if groundtruth_keypoints_list:
self._groundtruth_lists[
fields.BoxListFields.keypoints] = groundtruth_keypoints_list
if groundtruth_keypoint_visibilities_list:
self._groundtruth_lists[
fields.BoxListFields.keypoint_visibilities] = (
groundtruth_keypoint_visibilities_list)
if groundtruth_keypoint_depths_list:
self._groundtruth_lists[
fields.BoxListFields.keypoint_depths] = (
groundtruth_keypoint_depths_list)
if groundtruth_keypoint_depth_weights_list:
self._groundtruth_lists[
fields.BoxListFields.keypoint_depth_weights] = (
groundtruth_keypoint_depth_weights_list)
if groundtruth_dp_num_points_list:
self._groundtruth_lists[
fields.BoxListFields.densepose_num_points] = (
groundtruth_dp_num_points_list)
if groundtruth_dp_part_ids_list:
self._groundtruth_lists[
fields.BoxListFields.densepose_part_ids] = (
groundtruth_dp_part_ids_list)
if groundtruth_dp_surface_coords_list:
self._groundtruth_lists[
fields.BoxListFields.densepose_surface_coords] = (
groundtruth_dp_surface_coords_list)
if groundtruth_track_ids_list:
self._groundtruth_lists[
fields.BoxListFields.track_ids] = groundtruth_track_ids_list
if groundtruth_temporal_offsets_list:
self._groundtruth_lists[
fields.BoxListFields.temporal_offsets] = (
groundtruth_temporal_offsets_list)
if groundtruth_track_match_flags_list:
self._groundtruth_lists[
fields.BoxListFields.track_match_flags] = (
groundtruth_track_match_flags_list)
if groundtruth_is_crowd_list:
self._groundtruth_lists[
fields.BoxListFields.is_crowd] = groundtruth_is_crowd_list
if groundtruth_group_of_list:
self._groundtruth_lists[
fields.BoxListFields.group_of] = groundtruth_group_of_list
if groundtruth_area_list:
self._groundtruth_lists[
fields.InputDataFields.groundtruth_area] = groundtruth_area_list
if is_annotated_list:
self._groundtruth_lists[
fields.InputDataFields.is_annotated] = is_annotated_list
if groundtruth_labeled_classes:
self._groundtruth_lists[
fields.InputDataFields
.groundtruth_labeled_classes] = groundtruth_labeled_classes
if groundtruth_verified_neg_classes:
self._groundtruth_lists[
fields.InputDataFields
.groundtruth_verified_neg_classes] = groundtruth_verified_neg_classes
if groundtruth_image_classes:
self._groundtruth_lists[
fields.InputDataFields
.groundtruth_image_classes] = groundtruth_image_classes
if groundtruth_not_exhaustive_classes:
self._groundtruth_lists[
fields.InputDataFields
.groundtruth_not_exhaustive_classes] = (
groundtruth_not_exhaustive_classes)
if training_step is not None:
self._training_step = training_step
@abc.abstractmethod
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.
"""
pass
@abc.abstractmethod
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.
Returns a map of variable names to load from a checkpoint to variables in
the model graph. This enables the model to initialize based on weights from
another task. For example, the feature extractor variables from a
classification model can be used to bootstrap training of an object
detector. When loading from an object detection model, the checkpoint model
should have the same parameters as this detection model with exception of
the num_classes parameter.
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` is `detection`). If False, only variables
within the feature extractor scope are included. Default False.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph.
"""
pass
@abc.abstractmethod
def restore_from_objects(self, fine_tune_checkpoint_type='detection'):
"""Returns a map of variables to load from a foreign checkpoint.
Returns a dictionary of Tensorflow 2 Trackable objects (e.g. tf.Module
or Checkpoint). This enables the model to initialize based on weights from
another task. For example, the feature extractor variables from a
classification model can be used to bootstrap training of an object
detector. When loading from an object detection model, the checkpoint model
should have the same parameters as this detection model with exception of
the num_classes parameter.
Note that this function is intended to be used to restore Keras-based
models when running Tensorflow 2, whereas restore_map (above) is intended
to be used to restore Slim-based models when running Tensorflow 1.x.
TODO(jonathanhuang,rathodv): Check tf_version and raise unimplemented
error for both restore_map and restore_from_objects depending on version.
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'.
Returns:
A dict mapping keys to Trackable objects (tf.Module or Checkpoint).
"""
pass
@abc.abstractmethod
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.
"""
pass
def call(self, images):
"""Returns detections from a batch of images.
This method calls the preprocess, predict and postprocess function
sequentially and returns the output.
Args:
images: a [batch_size, height, width, channels] float tensor.
Returns:
detetcions: The dict of tensors returned by the postprocess function.
"""
preprocessed_images, shapes = self.preprocess(images)
prediction_dict = self.predict(preprocessed_images, shapes)
return self.postprocess(prediction_dict, shapes)