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Commit a1337e01 authored by Zhichao Lu's avatar Zhichao Lu Committed by pkulzc
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Merged commit includes the following changes: 

223075771  by lzc:

    Bring in external fixes.

--
222919755  by ronnyvotel:

    Bug fix in faster r-cnn model builder. Was previously using `inplace_batchnorm_update` for `reuse_weights`.

--
222885680  by Zhichao Lu:

    Use the result_dict_for_batched_example in models_lib
    Also fixes the visualization size on when eval is on GPU

--
222883648  by Zhichao Lu:

    Fix _unmatched_class_label for the _add_background_class == False case in ssd_meta_arch.py.

--
222836663  by Zhichao Lu:

    Adding support for visualizing grayscale images. Without this change, the images are black-red instead of grayscale.

--
222501978  by Zhichao Lu:

    Fix a bug that caused convert_to_grayscale flag not to be respected.

--
222432846  by richardmunoz:

    Fix mapping of groundtruth_confidences from shape [num_boxes] to [num_boxes, num_classes] when the input contains the groundtruth_confidences field.

--
221725755  by richardmunoz:

    Internal change.

--
221458536  by Zhichao Lu:

    Fix saver defer build bug in object detection train codepath.

--
221391590  by Zhichao Lu:

    Add support for group normalization in the object detection API. Just adding MobileNet-v1 SSD currently. This may serve as a road map for other models that wish to support group normalization as an option.

--
221367993  by Zhichao Lu:

    Bug fixes (1) Make RandomPadImage work, (2) Fix keep_checkpoint_every_n_hours.

--
221266403  by rathodv:

    Use detection boxes as proposals to compute correct mask loss in eval jobs.

--
220845934  by lzc:

    Internal change.

--
220778850  by Zhichao Lu:

    Incorporating existing metrics into Estimator framework.
    Should restore:
    -oid_challenge_detection_metrics
    -pascal_voc_detection_metrics
    -weighted_pascal_voc_detection_metrics
    -pascal_voc_instance_segmentation_metrics
    -weighted_pascal_voc_instance_segmentation_metrics
    -oid_V2_detection_metrics

--
220370391  by alirezafathi:

    Adding precision and recall to the metrics.

--
220321268  by Zhichao Lu:

    Allow the option of setting max_examples_to_draw to zero.

--
220193337  by Zhichao Lu:

    This CL fixes a bug where the Keras convolutional box predictor was applying heads in the non-deterministic dict order. The consequence of this bug was that variables were created in non-deterministic orders. This in turn led different workers in a multi-gpu training setup to have slightly different graphs which had variables assigned to mismatched parameter servers. As a result, roughly half of all workers were unable to initialize and did no work, and training time was slowed down approximately 2x.

--
220136508  by huizhongc:

    Add weight equalization loss to SSD meta arch.

--
220125875  by pengchong:

    Rename label_scores to label_weights

--
219730108  by Zhichao Lu:

    Add description of detection_keypoints in postprocessed_tensors to docstring.

--
219577519  by pengchong:

    Support parsing the class confidences and training using them.

--
219547611  by lzc:

    Stop using static shapes in GPU eval jobs.

--
219536476  by Zhichao Lu:

    Migrate TensorFlow Lite out of tensorflow/contrib

    This change moves //tensorflow/contrib/lite to //tensorflow/lite in preparation
    for TensorFlow 2.0's deprecation of contrib/. If you refer to TF Lite build
    targets or headers, you will need to update them manually. If you use TF Lite
    from the TensorFlow python package, "tf.contrib.lite" now points to "tf.lite".
    Please update your imports as soon as possible.

    For more details, see https://groups.google.com/a/tensorflow.org/forum/#!topic/tflite/iIIXOTOFvwQ

    @angersson and @aselle are conducting this migration. Please contact them if
    you have any further questions.

--
219190083  by Zhichao Lu:

    Add a second expected_loss_weights function using an alternative expectation calculation compared to previous. Integrate this op into ssd_meta_arch and losses builder. Affects files that use losses_builder.build to handle the returning of an additional element.

--
218924451  by pengchong:

    Add a new way to assign training targets using groundtruth confidences.

--
218760524  by chowdhery:

    Modify export script to add option for regular NMS in TFLite post-processing op.

--

PiperOrigin-RevId: 223075771
parent 2c680af3
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Showing with 682 additions and 428 deletions
research/object_detection/protos/losses.proto
View file @ a1337e01
......@@ -23,6 +23,44 @@ message Loss {
// If not left to default, applies random example sampling.
optional RandomExampleSampler random_example_sampler = 6;
// Equalization loss.
message EqualizationLoss {
// Weight equalization loss strength.
optional float weight = 1 [default=0.0];
// When computing equalization loss, ops that start with
// equalization_exclude_prefixes will be ignored. Only used when
// equalization_weight > 0.
repeated string exclude_prefixes = 2;
}
optional EqualizationLoss equalization_loss = 7;
enum ExpectedLossWeights {
NONE = 0;
// Use expected_classification_loss_by_expected_sampling
// from third_party/tensorflow_models/object_detection/utils/ops.py
EXPECTED_SAMPLING = 1;
// Use expected_classification_loss_by_reweighting_unmatched_anchors
// from third_party/tensorflow_models/object_detection/utils/ops.py
REWEIGHTING_UNMATCHED_ANCHORS = 2;
}
// Method to compute expected loss weights with respect to balanced
// positive/negative sampling scheme. If NONE, use explicit sampling.
// TODO(birdbrain): Move under ExpectedLossWeights.
optional ExpectedLossWeights expected_loss_weights = 18 [default = NONE];
// Minimum number of effective negative samples.
// Only applies if expected_loss_weights is not NONE.
// TODO(birdbrain): Move under ExpectedLossWeights.
optional float min_num_negative_samples = 19 [default=0];
// Desired number of effective negative samples per positive sample.
// Only applies if expected_loss_weights is not NONE.
// TODO(birdbrain): Move under ExpectedLossWeights.
optional float desired_negative_sampling_ratio = 20 [default=3];
}
// Configuration for bounding box localization loss function.
......
research/object_detection/protos/preprocessor.proto
View file @ a1337e01
......@@ -166,13 +166,13 @@ message RandomCropImage {
message RandomPadImage {
// Minimum dimensions for padded image. If unset, will use original image
// dimension as a lower bound.
optional float min_image_height = 1;
optional float min_image_width = 2;
optional int32 min_image_height = 1;
optional int32 min_image_width = 2;
// Maximum dimensions for padded image. If unset, will use double the original
// image dimension as a lower bound.
optional float max_image_height = 3;
optional float max_image_width = 4;
optional int32 max_image_height = 3;
optional int32 max_image_width = 4;
// Color of the padding. If unset, will pad using average color of the input
// image.
......
research/object_detection/protos/ssd.proto
View file @ a1337e01
......@@ -12,7 +12,7 @@ import "object_detection/protos/post_processing.proto";
import "object_detection/protos/region_similarity_calculator.proto";
// Configuration for Single Shot Detection (SSD) models.
// Next id: 22
// Next id: 26
message Ssd {
// Number of classes to predict.
......@@ -35,7 +35,7 @@ message Ssd {
// Whether background targets are to be encoded as an all
// zeros vector or a one-hot vector (where background is the 0th class).
optional bool encode_background_as_zeros = 12 [default=false];
optional bool encode_background_as_zeros = 12 [default = false];
// classification weight to be associated to negative
// anchors (default: 1.0). The weight must be in [0., 1.].
......@@ -52,11 +52,11 @@ message Ssd {
// Whether to normalize the loss by number of groundtruth boxes that match to
// the anchors.
optional bool normalize_loss_by_num_matches = 10 [default=true];
optional bool normalize_loss_by_num_matches = 10 [default = true];
// Whether to normalize the localization loss by the code size of the box
// encodings. This is applied along with other normalization factors.
optional bool normalize_loc_loss_by_codesize = 14 [default=false];
optional bool normalize_loc_loss_by_codesize = 14 [default = false];
// Loss configuration for training.
optional Loss loss = 11;
......@@ -82,29 +82,66 @@ message Ssd {
// to update the batch norm moving average parameters.
optional bool inplace_batchnorm_update = 15 [default = false];
// Whether to weight the regression loss by the score of the ground truth box
// the anchor matches to.
optional bool weight_regression_loss_by_score = 17 [default=false];
// 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 an explicit background class.
optional bool add_background_class = 21 [default = true];
// Whether to compute expected loss with respect to balanced positive/negative
// sampling scheme. If false, use explicit sampling.
optional bool use_expected_classification_loss_under_sampling = 18 [default=false];
// Whether to use an explicit background class. Set to true if using
// groundtruth labels with an explicit background class, as in multiclass
// scores.
optional bool explicit_background_class = 24 [default = false];
// Minimum number of effective negative samples.
// Only applies if use_expected_classification_loss_under_sampling is true.
optional float min_num_negative_samples = 19 [default=0];
optional bool use_confidences_as_targets = 22 [default = false];
// Desired number of effective negative samples per positive sample.
// Only applies if use_expected_classification_loss_under_sampling is true.
optional float desired_negative_sampling_ratio = 20 [default=3];
optional float implicit_example_weight = 23 [default = 1.0];
// Whether to add an implicit background class to one-hot encodings of
// groundtruth labels. Set to false if using groundtruth labels with an
// explicit background class, using multiclass scores, or if training a single
// class model.
optional bool add_background_class = 21 [default = true];
}
// Configuration proto for MaskHead.
// Next id: 11
message MaskHead {
// The height and the width of the predicted mask. Only used when
// predict_instance_masks is true.
optional int32 mask_height = 1 [default = 15];
optional int32 mask_width = 2 [default = 15];
// Whether to predict class agnostic masks. Only used when
// predict_instance_masks is true.
optional bool masks_are_class_agnostic = 3 [default = true];
// The depth for the first conv2d_transpose op applied to the
// image_features in the mask prediction branch. If set to 0, the value
// will be set automatically based on the number of channels in the image
// features and the number of classes.
optional int32 mask_prediction_conv_depth = 4 [default = 256];
// The number of convolutions applied to image_features in the mask prediction
// branch.
optional int32 mask_prediction_num_conv_layers = 5 [default = 2];
// Whether to apply convolutions on mask features before upsampling using
// nearest neighbor resizing.
// By default, mask features are resized to [`mask_height`, `mask_width`]
// before applying convolutions and predicting masks.
optional bool convolve_then_upsample_masks = 6 [default = false];
// Mask loss weight.
optional float mask_loss_weight = 7 [default=5.0];
// Number of boxes to be generated at training time for computing mask loss.
optional int32 mask_loss_sample_size = 8 [default=16];
// Hyperparameters for convolution ops used in the box predictor.
optional Hyperparams conv_hyperparams = 9;
// Output size (width and height are set to be the same) of the initial
// bilinear interpolation based cropping during ROI pooling. Only used when
// we have second stage prediction head enabled (e.g. mask head).
optional int32 initial_crop_size = 10 [default = 15];
}
// Configs for mask head.
optional MaskHead mask_head_config = 25;
}
message SsdFeatureExtractor {
reserved 6;
......@@ -113,10 +150,10 @@ message SsdFeatureExtractor {
optional string type = 1;
// The factor to alter the depth of the channels in the feature extractor.
optional float depth_multiplier = 2 [default=1.0];
optional float depth_multiplier = 2 [default = 1.0];
// Minimum number of the channels in the feature extractor.
optional int32 min_depth = 3 [default=16];
optional int32 min_depth = 3 [default = 16];
// Hyperparameters that affect the layers of feature extractor added on top
// of the base feature extractor.
......@@ -128,7 +165,8 @@ message SsdFeatureExtractor {
// layers while base feature extractor uses its own default hyperparams. If
// this value is set to true, the base feature extractor's hyperparams will be
// overridden with the `conv_hyperparams`.
optional bool override_base_feature_extractor_hyperparams = 9 [default = false];
optional bool override_base_feature_extractor_hyperparams = 9
[default = false];
// The nearest multiple to zero-pad the input height and width dimensions to.
// For example, if pad_to_multiple = 2, input dimensions are zero-padded
......@@ -138,11 +176,11 @@ message SsdFeatureExtractor {
// Whether to use explicit padding when extracting SSD multiresolution
// features. This will also apply to the base feature extractor if a MobileNet
// architecture is used.
optional bool use_explicit_padding = 7 [default=false];
optional bool use_explicit_padding = 7 [default = false];
// Whether to use depthwise separable convolutions for to extract additional
// feature maps added by SSD.
optional bool use_depthwise = 8 [default=false];
optional bool use_depthwise = 8 [default = false];
// Feature Pyramid Networks config.
optional FeaturePyramidNetworks fpn = 10;
......@@ -173,4 +211,3 @@ message FeaturePyramidNetworks {
// channel depth for additional coarse feature layers.
optional int32 additional_layer_depth = 3 [default = 256];
}
research/object_detection/protos/train.proto
View file @ a1337e01
......@@ -20,7 +20,7 @@ message TrainConfig {
optional bool sync_replicas = 3 [default=false];
// How frequently to keep checkpoints.
optional uint32 keep_checkpoint_every_n_hours = 4 [default=1000];
optional float keep_checkpoint_every_n_hours = 4 [default=10000.0];
// Optimizer used to train the DetectionModel.
optional Optimizer optimizer = 5;
......
research/object_detection/utils/object_detection_evaluation.py
View file @ a1337e01
......@@ -33,6 +33,7 @@ import collections
import logging
import unicodedata
import numpy as np
import tensorflow as tf
from object_detection.core import standard_fields
from object_detection.utils import label_map_util
......@@ -126,6 +127,7 @@ class ObjectDetectionEvaluator(DetectionEvaluator):
categories,
matching_iou_threshold=0.5,
evaluate_corlocs=False,
evaluate_precision_recall=False,
metric_prefix=None,
use_weighted_mean_ap=False,
evaluate_masks=False,
......@@ -140,6 +142,8 @@ class ObjectDetectionEvaluator(DetectionEvaluator):
boxes to detection boxes.
evaluate_corlocs: (optional) boolean which determines if corloc scores
are to be returned or not.
evaluate_precision_recall: (optional) boolean which determines if
precision and recall values are to be returned or not.
metric_prefix: (optional) string prefix for metric name; if None, no
prefix is used.
use_weighted_mean_ap: (optional) boolean which determines if the mean
......@@ -174,7 +178,50 @@ class ObjectDetectionEvaluator(DetectionEvaluator):
group_of_weight=self._group_of_weight)
self._image_ids = set([])
self._evaluate_corlocs = evaluate_corlocs
self._evaluate_precision_recall = evaluate_precision_recall
self._metric_prefix = (metric_prefix + '_') if metric_prefix else ''
self._expected_keys = set([
standard_fields.InputDataFields.key,
standard_fields.InputDataFields.groundtruth_boxes,
standard_fields.InputDataFields.groundtruth_classes,
standard_fields.InputDataFields.groundtruth_difficult,
standard_fields.InputDataFields.groundtruth_instance_masks,
standard_fields.DetectionResultFields.detection_boxes,
standard_fields.DetectionResultFields.detection_scores,
standard_fields.DetectionResultFields.detection_classes,
standard_fields.DetectionResultFields.detection_masks
])
self._build_metric_names()
def _build_metric_names(self):
"""Builds a list with metric names."""
self._metric_names = [
self._metric_prefix + 'Precision/mAP@{}IOU'.format(
self._matching_iou_threshold)
]
if self._evaluate_corlocs:
self._metric_names.append(
self._metric_prefix +
'Precision/meanCorLoc@{}IOU'.format(self._matching_iou_threshold))
category_index = label_map_util.create_category_index(self._categories)
for idx in range(self._num_classes):
if idx + self._label_id_offset in category_index:
category_name = category_index[idx + self._label_id_offset]['name']
try:
category_name = unicode(category_name, 'utf-8')
except TypeError:
pass
category_name = unicodedata.normalize('NFKD', category_name).encode(
'ascii', 'ignore')
self._metric_names.append(
self._metric_prefix + 'PerformanceByCategory/AP@{}IOU/{}'.format(
self._matching_iou_threshold, category_name))
if self._evaluate_corlocs:
self._metric_names.append(
self._metric_prefix + 'PerformanceByCategory/CorLoc@{}IOU/{}'
.format(self._matching_iou_threshold, category_name))
def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
"""Adds groundtruth for a single image to be used for evaluation.
......@@ -283,22 +330,19 @@ class ObjectDetectionEvaluator(DetectionEvaluator):
A dictionary of metrics with the following fields -
1. summary_metrics:
'Precision/mAP@<matching_iou_threshold>IOU': mean average precision at
the specified IOU threshold.
'<prefix if not empty>_Precision/mAP@<matching_iou_threshold>IOU': mean
average precision at the specified IOU threshold.
2. per_category_ap: category specific results with keys of the form
'PerformanceByCategory/mAP@<matching_iou_threshold>IOU/category'.
'<prefix if not empty>_PerformanceByCategory/
mAP@<matching_iou_threshold>IOU/category'.
"""
(per_class_ap, mean_ap, _, _, per_class_corloc, mean_corloc) = (
self._evaluation.evaluate())
pascal_metrics = {
self._metric_prefix +
'Precision/mAP@{}IOU'.format(self._matching_iou_threshold):
mean_ap
}
(per_class_ap, mean_ap, per_class_precision, per_class_recall,
per_class_corloc, mean_corloc) = (
self._evaluation.evaluate())
pascal_metrics = {self._metric_names[0]: mean_ap}
if self._evaluate_corlocs:
pascal_metrics[self._metric_prefix + 'Precision/meanCorLoc@{}IOU'.format(
self._matching_iou_threshold)] = mean_corloc
pascal_metrics[self._metric_names[1]] = mean_corloc
category_index = label_map_util.create_category_index(self._categories)
for idx in range(per_class_ap.size):
if idx + self._label_id_offset in category_index:
......@@ -314,6 +358,19 @@ class ObjectDetectionEvaluator(DetectionEvaluator):
self._matching_iou_threshold, category_name))
pascal_metrics[display_name] = per_class_ap[idx]
# Optionally add precision and recall values
if self._evaluate_precision_recall:
display_name = (
self._metric_prefix +
'PerformanceByCategory/Precision@{}IOU/{}'.format(
self._matching_iou_threshold, category_name))
pascal_metrics[display_name] = per_class_precision[idx]
display_name = (
self._metric_prefix +
'PerformanceByCategory/Recall@{}IOU/{}'.format(
self._matching_iou_threshold, category_name))
pascal_metrics[display_name] = per_class_recall[idx]
# Optionally add CorLoc metrics.classes
if self._evaluate_corlocs:
display_name = (
......@@ -332,6 +389,74 @@ class ObjectDetectionEvaluator(DetectionEvaluator):
label_id_offset=self._label_id_offset)
self._image_ids.clear()
def get_estimator_eval_metric_ops(self, eval_dict):
"""Returns dict of metrics to use with `tf.estimator.EstimatorSpec`.
Note that this must only be implemented if performing evaluation with a
`tf.estimator.Estimator`.
Args:
eval_dict: A dictionary that holds tensors for evaluating an object
detection model, returned from
eval_util.result_dict_for_single_example(). It must contain
standard_fields.InputDataFields.key.
Returns:
A dictionary of metric names to tuple of value_op and update_op that can
be used as eval metric ops in `tf.estimator.EstimatorSpec`.
"""
# remove unexpected fields
eval_dict_filtered = dict()
for key, value in eval_dict.items():
if key in self._expected_keys:
eval_dict_filtered[key] = value
eval_dict_keys = eval_dict_filtered.keys()
def update_op(image_id, *eval_dict_batched_as_list):
"""Update operation that adds batch of images to ObjectDetectionEvaluator.
Args:
image_id: image id (single id or an array)
*eval_dict_batched_as_list: the values of the dictionary of tensors.
"""
if np.isscalar(image_id):
single_example_dict = dict(
zip(eval_dict_keys, eval_dict_batched_as_list))
self.add_single_ground_truth_image_info(image_id, single_example_dict)
self.add_single_detected_image_info(image_id, single_example_dict)
else:
for unzipped_tuple in zip(*eval_dict_batched_as_list):
single_example_dict = dict(zip(eval_dict_keys, unzipped_tuple))
image_id = single_example_dict[standard_fields.InputDataFields.key]
self.add_single_ground_truth_image_info(image_id, single_example_dict)
self.add_single_detected_image_info(image_id, single_example_dict)
args = [eval_dict_filtered[standard_fields.InputDataFields.key]]
args.extend(eval_dict_filtered.values())
update_op = tf.py_func(update_op, args, [])
def first_value_func():
self._metrics = self.evaluate()
self.clear()
return np.float32(self._metrics[self._metric_names[0]])
def value_func_factory(metric_name):
def value_func():
return np.float32(self._metrics[metric_name])
return value_func
# Ensure that the metrics are only evaluated once.
first_value_op = tf.py_func(first_value_func, [], tf.float32)
eval_metric_ops = {self._metric_names[0]: (first_value_op, update_op)}
with tf.control_dependencies([first_value_op]):
for metric_name in self._metric_names[1:]:
eval_metric_ops[metric_name] = (tf.py_func(
value_func_factory(metric_name), [], np.float32), update_op)
return eval_metric_ops
class PascalDetectionEvaluator(ObjectDetectionEvaluator):
"""A class to evaluate detections using PASCAL metrics."""
......@@ -442,6 +567,15 @@ class OpenImagesDetectionEvaluator(ObjectDetectionEvaluator):
evaluate_corlocs,
metric_prefix=metric_prefix,
group_of_weight=group_of_weight)
self._expected_keys = set([
standard_fields.InputDataFields.key,
standard_fields.InputDataFields.groundtruth_boxes,
standard_fields.InputDataFields.groundtruth_classes,
standard_fields.InputDataFields.groundtruth_group_of,
standard_fields.DetectionResultFields.detection_boxes,
standard_fields.DetectionResultFields.detection_scores,
standard_fields.DetectionResultFields.detection_classes,
])
def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
"""Adds groundtruth for a single image to be used for evaluation.
......@@ -535,6 +669,16 @@ class OpenImagesDetectionChallengeEvaluator(OpenImagesDetectionEvaluator):
group_of_weight=group_of_weight)
self._evaluatable_labels = {}
self._expected_keys = set([
standard_fields.InputDataFields.key,
standard_fields.InputDataFields.groundtruth_boxes,
standard_fields.InputDataFields.groundtruth_classes,
standard_fields.InputDataFields.groundtruth_group_of,
standard_fields.InputDataFields.groundtruth_image_classes,
standard_fields.DetectionResultFields.detection_boxes,
standard_fields.DetectionResultFields.detection_scores,
standard_fields.DetectionResultFields.detection_classes,
])
def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
"""Adds groundtruth for a single image to be used for evaluation.
......@@ -890,15 +1034,14 @@ class ObjectDetectionEvaluation(object):
if self.use_weighted_mean_ap:
all_scores = np.append(all_scores, scores)
all_tp_fp_labels = np.append(all_tp_fp_labels, tp_fp_labels)
logging.info('Scores and tpfp per class label: %d', class_index)
logging.info(tp_fp_labels)
logging.info(scores)
precision, recall = metrics.compute_precision_recall(
scores, tp_fp_labels, self.num_gt_instances_per_class[class_index])
self.precisions_per_class[class_index] = precision
self.recalls_per_class[class_index] = recall
average_precision = metrics.compute_average_precision(precision, recall)
self.average_precision_per_class[class_index] = average_precision
logging.info('average_precision: %f', average_precision)
self.corloc_per_class = metrics.compute_cor_loc(
self.num_gt_imgs_per_class,
......
research/object_detection/utils/object_detection_evaluation_test.py
View file @ a1337e01
......@@ -15,9 +15,10 @@
"""Tests for object_detection.utils.object_detection_evaluation."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from object_detection import eval_util
from object_detection.core import standard_fields
from object_detection.utils import object_detection_evaluation
......@@ -683,5 +684,141 @@ class ObjectDetectionEvaluationTest(tf.test.TestCase):
self.assertAlmostEqual(expected_mean_corloc, mean_corloc)
class ObjectDetectionEvaluatorTest(tf.test.TestCase, parameterized.TestCase):
def setUp(self):
self.categories = [{
'id': 1,
'name': 'person'
}, {
'id': 2,
'name': 'dog'
}, {
'id': 3,
'name': 'cat'
}]
self.od_eval = object_detection_evaluation.ObjectDetectionEvaluator(
categories=self.categories)
def _make_evaluation_dict(self,
resized_groundtruth_masks=False,
batch_size=1,
max_gt_boxes=None,
scale_to_absolute=False):
input_data_fields = standard_fields.InputDataFields
detection_fields = standard_fields.DetectionResultFields
image = tf.zeros(shape=[batch_size, 20, 20, 3], dtype=tf.uint8)
if batch_size == 1:
key = tf.constant('image1')
else:
key = tf.constant([str(i) for i in range(batch_size)])
detection_boxes = tf.concat([
tf.tile(
tf.constant([[[0., 0., 1., 1.]]]), multiples=[batch_size - 1, 1, 1
]),
tf.constant([[[0., 0., 0.5, 0.5]]])
],
axis=0)
detection_scores = tf.concat([
tf.tile(tf.constant([[0.5]]), multiples=[batch_size - 1, 1]),
tf.constant([[0.8]])
],
axis=0)
detection_classes = tf.tile(tf.constant([[0]]), multiples=[batch_size, 1])
detection_masks = tf.tile(
tf.ones(shape=[1, 2, 20, 20], dtype=tf.float32),
multiples=[batch_size, 1, 1, 1])
groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
groundtruth_classes = tf.constant([1])
groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
num_detections = tf.ones([batch_size])
if resized_groundtruth_masks:
groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)
if batch_size > 1:
groundtruth_boxes = tf.tile(
tf.expand_dims(groundtruth_boxes, 0), multiples=[batch_size, 1, 1])
groundtruth_classes = tf.tile(
tf.expand_dims(groundtruth_classes, 0), multiples=[batch_size, 1])
groundtruth_instance_masks = tf.tile(
tf.expand_dims(groundtruth_instance_masks, 0),
multiples=[batch_size, 1, 1, 1])
detections = {
detection_fields.detection_boxes: detection_boxes,
detection_fields.detection_scores: detection_scores,
detection_fields.detection_classes: detection_classes,
detection_fields.detection_masks: detection_masks,
detection_fields.num_detections: num_detections
}
groundtruth = {
input_data_fields.groundtruth_boxes:
groundtruth_boxes,
input_data_fields.groundtruth_classes:
groundtruth_classes,
input_data_fields.groundtruth_instance_masks:
groundtruth_instance_masks,
}
if batch_size > 1:
return eval_util.result_dict_for_batched_example(
image,
key,
detections,
groundtruth,
scale_to_absolute=scale_to_absolute,
max_gt_boxes=max_gt_boxes)
else:
return eval_util.result_dict_for_single_example(
image,
key,
detections,
groundtruth,
scale_to_absolute=scale_to_absolute)
@parameterized.parameters({
'batch_size': 1,
'expected_map': 0,
'max_gt_boxes': None,
'scale_to_absolute': True
}, {
'batch_size': 8,
'expected_map': 0.765625,
'max_gt_boxes': [1],
'scale_to_absolute': True
}, {
'batch_size': 1,
'expected_map': 0,
'max_gt_boxes': None,
'scale_to_absolute': False
}, {
'batch_size': 8,
'expected_map': 0.765625,
'max_gt_boxes': [1],
'scale_to_absolute': False
})
def test_get_estimator_eval_metric_ops(self,
batch_size=1,
expected_map=1,
max_gt_boxes=None,
scale_to_absolute=False):
eval_dict = self._make_evaluation_dict(
batch_size=batch_size,
max_gt_boxes=max_gt_boxes,
scale_to_absolute=scale_to_absolute)
tf.logging.info('eval_dict: {}'.format(eval_dict))
metric_ops = self.od_eval.get_estimator_eval_metric_ops(eval_dict)
_, update_op = metric_ops['Precision/mAP@0.5IOU']
with self.test_session() as sess:
metrics = {}
for key, (value_op, _) in metric_ops.iteritems():
metrics[key] = value_op
sess.run(update_op)
metrics = sess.run(metrics)
self.assertAlmostEqual(expected_map, metrics['Precision/mAP@0.5IOU'])
if __name__ == '__main__':
tf.test.main()
research/object_detection/utils/ops.py
View file @ a1337e01
......@@ -14,6 +14,7 @@
# ==============================================================================
"""A module for helper tensorflow ops."""
import collections
import math
import numpy as np
import six
......@@ -1087,81 +1088,10 @@ def native_crop_and_resize(image, boxes, crop_size, scope=None):
return tf.reshape(cropped_regions, final_shape)
def expected_classification_loss_under_sampling(
batch_cls_targets, cls_losses, unmatched_cls_losses,
desired_negative_sampling_ratio, min_num_negative_samples):
"""Computes classification loss by background/foreground weighting.
The weighting is such that the effective background/foreground weight ratio
is the desired_negative_sampling_ratio. if p_i is the foreground probability
of anchor a_i, L(a_i) is the anchors loss, N is the number of anchors, M
is the sum of foreground probabilities across anchors, and K is the desired
ratio between the number of negative and positive samples, then the total loss
L is calculated as:
beta = K*M/(N-M)
L = sum_{i=1}^N [p_i * L_p(a_i) + beta * (1 - p_i) * L_n(a_i)]
where L_p(a_i) is the loss against target assuming the anchor was matched,
otherwise zero, and L_n(a_i) is the loss against the background target
assuming the anchor was unmatched, otherwise zero.
Args:
batch_cls_targets: A tensor with shape [batch_size, num_anchors, num_classes
+ 1], where 0'th index is the background class, containing the class
distrubution for the target assigned to a given anchor.
cls_losses: Float tensor of shape [batch_size, num_anchors] representing
anchorwise classification losses.
unmatched_cls_losses: loss for each anchor against the unmatched class
target.
desired_negative_sampling_ratio: The desired background/foreground weight
ratio.
min_num_negative_samples: Minimum number of effective negative samples.
Used only when there are no positive examples.
Returns:
The classification loss.
"""
num_anchors = tf.cast(tf.shape(batch_cls_targets)[1], tf.float32)
# find the p_i
foreground_probabilities = 1 - batch_cls_targets[:, :, 0]
foreground_sum = tf.reduce_sum(foreground_probabilities, axis=-1)
# for each anchor, expected_j is the expected number of positive anchors
# given that this anchor was sampled as negative.
tiled_foreground_sum = tf.tile(
tf.reshape(foreground_sum, [-1, 1]),
[1, tf.cast(num_anchors, tf.int32)])
expected_j = tiled_foreground_sum - foreground_probabilities
k = desired_negative_sampling_ratio
# compute beta
expected_negatives = tf.to_float(num_anchors) - expected_j
desired_negatives = k * expected_j
desired_negatives = tf.where(
tf.greater(desired_negatives, expected_negatives), expected_negatives,
desired_negatives)
# probability that an anchor is sampled for the loss computation given that it
# is negative.
beta = desired_negatives / expected_negatives
# where the foreground sum is zero, use a minimum negative weight.
min_negative_weight = 1.0 * min_num_negative_samples / num_anchors
beta = tf.where(
tf.equal(tiled_foreground_sum, 0),
min_negative_weight * tf.ones_like(beta), beta)
foreground_weights = foreground_probabilities
background_weights = (1 - foreground_weights) * beta
weighted_foreground_losses = foreground_weights * cls_losses
weighted_background_losses = background_weights * unmatched_cls_losses
EqualizationLossConfig = collections.namedtuple('EqualizationLossConfig',
['weight', 'exclude_prefixes'])
cls_losses = tf.reduce_sum(
weighted_foreground_losses, axis=-1) + tf.reduce_sum(
weighted_background_losses, axis=-1)
return cls_losses
research/object_detection/utils/ops_test.py
View file @ a1337e01
......@@ -21,6 +21,8 @@ from object_detection.core import standard_fields as fields
from object_detection.utils import ops
from object_detection.utils import test_case
slim = tf.contrib.slim
class NormalizedToImageCoordinatesTest(tf.test.TestCase):
......@@ -1466,189 +1468,9 @@ class OpsTestCropAndResize(test_case.TestCase):
self.assertAllClose(crop_output, expected_output)
class OpsTestExpectedClassificationLoss(test_case.TestCase):
def testExpectedClassificationLossUnderSamplingWithHardLabels(self):
def graph_fn(batch_cls_targets, cls_losses, unmatched_cls_losses,
negative_to_positive_ratio, min_num_negative_samples):
return ops.expected_classification_loss_under_sampling(
batch_cls_targets, cls_losses, unmatched_cls_losses,
negative_to_positive_ratio, min_num_negative_samples)
batch_cls_targets = np.array(
[[[1., 0, 0], [0, 1., 0]], [[1., 0, 0], [0, 1., 0]]], dtype=np.float32)
cls_losses = np.array([[1, 2], [3, 4]], dtype=np.float32)
unmatched_cls_losses = np.array([[10, 20], [30, 40]], dtype=np.float32)
negative_to_positive_ratio = np.array([2], dtype=np.float32)
min_num_negative_samples = np.array([1], dtype=np.float32)
classification_loss = self.execute(graph_fn, [
batch_cls_targets, cls_losses, unmatched_cls_losses,
negative_to_positive_ratio, min_num_negative_samples
])
# expected_foreground_sum = [1,1]
# expected_expected_j = [[1, 0], [1, 0]]
# expected_expected_negatives = [[1, 2], [1, 2]]
# expected_desired_negatives = [[2, 0], [2, 0]]
# expected_beta = [[1, 0], [1, 0]]
# expected_foreground_weights = [[0, 1], [0, 1]]
# expected_background_weights = [[1, 0], [1, 0]]
# expected_weighted_foreground_losses = [[0, 2], [0, 4]]
# expected_weighted_background_losses = [[10, 0], [30, 0]]
# expected_classification_loss_under_sampling = [6, 40]
expected_classification_loss_under_sampling = [2 + 10, 4 + 30]
self.assertAllClose(expected_classification_loss_under_sampling,
classification_loss)
def testExpectedClassificationLossUnderSamplingWithHardLabelsMoreNegatives(
self):
def graph_fn(batch_cls_targets, cls_losses, unmatched_cls_losses,
negative_to_positive_ratio, min_num_negative_samples):
return ops.expected_classification_loss_under_sampling(
batch_cls_targets, cls_losses, unmatched_cls_losses,
negative_to_positive_ratio, min_num_negative_samples)
batch_cls_targets = np.array(
[[[1., 0, 0], [0, 1., 0], [1., 0, 0], [1., 0, 0], [1., 0, 0]]],
dtype=np.float32)
cls_losses = np.array([[1, 2, 3, 4, 5]], dtype=np.float32)
unmatched_cls_losses = np.array([[10, 20, 30, 40, 50]], dtype=np.float32)
negative_to_positive_ratio = np.array([2], dtype=np.float32)
min_num_negative_samples = np.array([1], dtype=np.float32)
classification_loss = self.execute(graph_fn, [
batch_cls_targets, cls_losses, unmatched_cls_losses,
negative_to_positive_ratio, min_num_negative_samples
])
# expected_foreground_sum = [1]
# expected_expected_j = [[1, 0, 1, 1, 1]]
# expected_expected_negatives = [[4, 5, 4, 4, 4]]
# expected_desired_negatives = [[2, 0, 2, 2, 2]]
# expected_beta = [[.5, 0, .5, .5, .5]]
# expected_foreground_weights = [[0, 1, 0, 0, 0]]
# expected_background_weights = [[.5, 0, .5, .5, .5]]
# expected_weighted_foreground_losses = [[0, 2, 0, 0, 0]]
# expected_weighted_background_losses = [[10*.5, 0, 30*.5, 40*.5, 50*.5]]
# expected_classification_loss_under_sampling = [5+2+15+20+25]
expected_classification_loss_under_sampling = [5 + 2 + 15 + 20 + 25]
self.assertAllClose(expected_classification_loss_under_sampling,
classification_loss)
def testExpectedClassificationLossUnderSamplingWithAllNegative(self):
def graph_fn(batch_cls_targets, cls_losses, unmatched_cls_losses):
return ops.expected_classification_loss_under_sampling(
batch_cls_targets, cls_losses, unmatched_cls_losses,
negative_to_positive_ratio, min_num_negative_samples)
batch_cls_targets = np.array(
[[[1, 0, 0], [1, 0, 0]], [[1, 0, 0], [1, 0, 0]]], dtype=np.float32)
cls_losses = np.array([[1, 2], [3, 4]], dtype=np.float32)
unmatched_cls_losses = np.array([[10, 20], [30, 40]], dtype=np.float32)
negative_to_positive_ratio = np.array([2], dtype=np.float32)
min_num_negative_samples = np.array([1], dtype=np.float32)
classification_loss = self.execute(
graph_fn, [batch_cls_targets, cls_losses, unmatched_cls_losses])
# expected_foreground_sum = [0,0]
# expected_expected_j = [[0, 0], [0, 0]]
# expected_expected_negatives = [[2, 2], [2, 2]]
# expected_desired_negatives = [[0, 0], [0, 0]]
# expected_beta = [[0, 0],[0, 0]]
# expected_foreground_weights = [[0, 0], [0, 0]]
# expected_background_weights = [[.5, .5], [.5, .5]]
# expected_weighted_foreground_losses = [[0, 0], [0, 0]]
# expected_weighted_background_losses = [[5, 10], [15, 20]]
# expected_classification_loss_under_sampling = [15, 35]
expected_classification_loss_under_sampling = [
10 * .5 + 20 * .5, 30 * .5 + 40 * .5
]
self.assertAllClose(expected_classification_loss_under_sampling,
classification_loss)
def testExpectedClassificationLossUnderSamplingWithAllPositive(self):
def graph_fn(batch_cls_targets, cls_losses, unmatched_cls_losses):
return ops.expected_classification_loss_under_sampling(
batch_cls_targets, cls_losses, unmatched_cls_losses,
negative_to_positive_ratio, min_num_negative_samples)
batch_cls_targets = np.array(
[[[0, 1., 0], [0, 1., 0]], [[0, 1, 0], [0, 0, 1]]], dtype=np.float32)
cls_losses = np.array([[1, 2], [3, 4]], dtype=np.float32)
unmatched_cls_losses = np.array([[10, 20], [30, 40]], dtype=np.float32)
negative_to_positive_ratio = np.array([2], dtype=np.float32)
min_num_negative_samples = np.array([1], dtype=np.float32)
classification_loss = self.execute(
graph_fn, [batch_cls_targets, cls_losses, unmatched_cls_losses])
# expected_foreground_sum = [2,2]
# expected_expected_j = [[1, 1], [1, 1]]
# expected_expected_negatives = [[1, 1], [1, 1]]
# expected_desired_negatives = [[1, 1], [1, 1]]
# expected_beta = [[1, 1],[1, 1]]
# expected_foreground_weights = [[1, 1], [1, 1]]
# expected_background_weights = [[0, 0], [0, 0]]
# expected_weighted_foreground_losses = [[1, 2], [3, 4]]
# expected_weighted_background_losses = [[0, 0], [0, 0]]
# expected_classification_loss_under_sampling = [15, 35]
expected_classification_loss_under_sampling = [1 + 2, 3 + 4]
self.assertAllClose(expected_classification_loss_under_sampling,
classification_loss)
def testExpectedClassificationLossUnderSamplingWithSoftLabels(self):
def graph_fn(batch_cls_targets, cls_losses, unmatched_cls_losses,
negative_to_positive_ratio, min_num_negative_samples):
return ops.expected_classification_loss_under_sampling(
batch_cls_targets, cls_losses, unmatched_cls_losses,
negative_to_positive_ratio, min_num_negative_samples)
batch_cls_targets = np.array([[[.75, .25, 0], [0.25, .75, 0], [.75, .25, 0],
[0.25, .75, 0], [1., 0, 0]]],
dtype=np.float32)
cls_losses = np.array([[1, 2, 3, 4, 5]], dtype=np.float32)
unmatched_cls_losses = np.array([[10, 20, 30, 40, 50]], dtype=np.float32)
negative_to_positive_ratio = np.array([2], dtype=np.float32)
min_num_negative_samples = np.array([1], dtype=np.float32)
classification_loss = self.execute(graph_fn, [
batch_cls_targets, cls_losses, unmatched_cls_losses,
negative_to_positive_ratio, min_num_negative_samples
])
# expected_foreground_sum = [2]
# expected_expected_j = [[1.75, 1.25, 1.75, 1.25, 2]]
# expected_expected_negatives = [[3.25, 3.75, 3.25, 3.75, 3]]
# expected_desired_negatives = [[3.25, 2.5, 3.25, 2.5, 3]]
# expected_beta = [[1, 2/3, 1, 2/3, 1]]
# expected_foreground_weights = [[0.25, .75, .25, .75, 0]]
# expected_background_weights = [[[.75, 1/6., .75, 1/6., 1]]]
# expected_weighted_foreground_losses = [[.25*1, .75*2, .25*3, .75*4, 0*5]]
# expected_weighted_background_losses = [[
# .75*10, 1/6.*20, .75*30, 1/6.*40, 1*50]]
# expected_classification_loss_under_sampling = sum([
# .25*1, .75*2, .25*3, .75*4, 0, .75*10, 1/6.*20, .75*30,
# 1/6.*40, 1*50])
expected_classification_loss_under_sampling = [
sum([
.25 * 1, .75 * 2, .25 * 3, .75 * 4, 0, .75 * 10, 1 / 6. * 20,
.75 * 30, 1 / 6. * 40, 1 * 50
])
]
self.assertAllClose(expected_classification_loss_under_sampling,
classification_loss)
if __name__ == '__main__':
......
research/object_detection/utils/test_utils.py
View file @ a1337e01
......@@ -42,14 +42,25 @@ class MockBoxCoder(box_coder.BoxCoder):
return box_list.BoxList(rel_codes + anchors.get())
class MockMaskHead(object):
"""Simple maskhead that returns all zeros as mask predictions."""
def __init__(self, num_classes):
self._num_classes = num_classes
def predict(self, features):
batch_size = tf.shape(features)[0]
return tf.zeros((batch_size, 1, self._num_classes, DEFAULT_MASK_SIZE,
DEFAULT_MASK_SIZE),
dtype=tf.float32)
class MockBoxPredictor(box_predictor.BoxPredictor):
"""Simple box predictor that ignores inputs and outputs all zeros."""
def __init__(self, is_training, num_classes, add_background_class=True,
predict_mask=False):
def __init__(self, is_training, num_classes, add_background_class=True):
super(MockBoxPredictor, self).__init__(is_training, num_classes)
self._add_background_class = add_background_class
self._predict_mask = predict_mask
def _predict(self, image_features, num_predictions_per_location):
image_feature = image_features[0]
......@@ -66,31 +77,22 @@ class MockBoxPredictor(box_predictor.BoxPredictor):
(batch_size, num_anchors, 1, code_size), dtype=tf.float32)
class_predictions_with_background = zero + tf.zeros(
(batch_size, num_anchors, num_class_slots), dtype=tf.float32)
masks = zero + tf.zeros(
(batch_size, num_anchors, self.num_classes, DEFAULT_MASK_SIZE,
DEFAULT_MASK_SIZE),
dtype=tf.float32)
predictions_dict = {
box_predictor.BOX_ENCODINGS:
box_encodings,
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
class_predictions_with_background
}
if self._predict_mask:
predictions_dict[box_predictor.MASK_PREDICTIONS] = masks
return predictions_dict
class MockKerasBoxPredictor(box_predictor.KerasBoxPredictor):
"""Simple box predictor that ignores inputs and outputs all zeros."""
def __init__(self, is_training, num_classes, add_background_class=True,
predict_mask=False):
def __init__(self, is_training, num_classes, add_background_class=True):
super(MockKerasBoxPredictor, self).__init__(
is_training, num_classes, False, False)
self._add_background_class = add_background_class
self._predict_mask = predict_mask
def _predict(self, image_features, **kwargs):
image_feature = image_features[0]
......@@ -107,18 +109,12 @@ class MockKerasBoxPredictor(box_predictor.KerasBoxPredictor):
(batch_size, num_anchors, 1, code_size), dtype=tf.float32)
class_predictions_with_background = zero + tf.zeros(
(batch_size, num_anchors, num_class_slots), dtype=tf.float32)
masks = zero + tf.zeros(
(batch_size, num_anchors, self.num_classes, DEFAULT_MASK_SIZE,
DEFAULT_MASK_SIZE),
dtype=tf.float32)
predictions_dict = {
box_predictor.BOX_ENCODINGS:
box_encodings,
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
class_predictions_with_background
}
if self._predict_mask:
predictions_dict[box_predictor.MASK_PREDICTIONS] = masks
return predictions_dict
......
research/object_detection/utils/visualization_utils.py
View file @ a1337e01
......@@ -19,8 +19,7 @@ These functions often receive an image, perform some visualization on the image.
The functions do not return a value, instead they modify the image itself.
"""
from abc import ABCMeta
from abc import abstractmethod
import abc
import collections
import functools
# Set headless-friendly backend.
......@@ -35,7 +34,7 @@ import six
import tensorflow as tf
from object_detection.core import standard_fields as fields
from object_detection.utils import shape_utils
_TITLE_LEFT_MARGIN = 10
_TITLE_TOP_MARGIN = 10
......@@ -309,11 +308,23 @@ def _visualize_boxes_and_masks_and_keypoints(
**kwargs)
def _resize_original_image(image, image_shape):
image = tf.expand_dims(image, 0)
image = tf.image.resize_images(
image,
image_shape,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
align_corners=True)
return tf.cast(tf.squeeze(image, 0), tf.uint8)
def draw_bounding_boxes_on_image_tensors(images,
boxes,
classes,
scores,
category_index,
original_image_spatial_shape=None,
true_image_shape=None,
instance_masks=None,
keypoints=None,
max_boxes_to_draw=20,
......@@ -323,13 +334,18 @@ def draw_bounding_boxes_on_image_tensors(images,
Args:
images: A 4D uint8 image tensor of shape [N, H, W, C]. If C > 3, additional
channels will be ignored.
channels will be ignored. If C = 1, then we convert the images to RGB
images.
boxes: [N, max_detections, 4] float32 tensor of detection boxes.
classes: [N, max_detections] int tensor of detection classes. Note that
classes are 1-indexed.
scores: [N, max_detections] float32 tensor of detection scores.
category_index: a dict that maps integer ids to category dicts. e.g.
{1: {1: 'dog'}, 2: {2: 'cat'}, ...}
original_image_spatial_shape: [N, 2] tensor containing the spatial size of
the original image.
true_image_shape: [N, 3] tensor containing the spatial size of unpadded
original_image.
instance_masks: A 4D uint8 tensor of shape [N, max_detection, H, W] with
instance masks.
keypoints: A 4D float32 tensor of shape [N, max_detection, num_keypoints, 2]
......@@ -344,7 +360,10 @@ def draw_bounding_boxes_on_image_tensors(images,
4D image tensor of type uint8, with boxes drawn on top.
"""
# Additional channels are being ignored.
images = images[:, :, :, 0:3]
if images.shape[3] > 3:
images = images[:, :, :, 0:3]
elif images.shape[3] == 1:
images = tf.image.grayscale_to_rgb(images)
visualization_keyword_args = {
'use_normalized_coordinates': use_normalized_coordinates,
'max_boxes_to_draw': max_boxes_to_draw,
......@@ -352,35 +371,61 @@ def draw_bounding_boxes_on_image_tensors(images,
'agnostic_mode': False,
'line_thickness': 4
}
if true_image_shape is None:
true_shapes = tf.constant(-1, shape=[images.shape.as_list()[0], 3])
else:
true_shapes = true_image_shape
if original_image_spatial_shape is None:
original_shapes = tf.constant(-1, shape=[images.shape.as_list()[0], 2])
else:
original_shapes = original_image_spatial_shape
if instance_masks is not None and keypoints is None:
visualize_boxes_fn = functools.partial(
_visualize_boxes_and_masks,
category_index=category_index,
**visualization_keyword_args)
elems = [images, boxes, classes, scores, instance_masks]
elems = [
true_shapes, original_shapes, images, boxes, classes, scores,
instance_masks
]
elif instance_masks is None and keypoints is not None:
visualize_boxes_fn = functools.partial(
_visualize_boxes_and_keypoints,
category_index=category_index,
**visualization_keyword_args)
elems = [images, boxes, classes, scores, keypoints]
elems = [
true_shapes, original_shapes, images, boxes, classes, scores, keypoints
]
elif instance_masks is not None and keypoints is not None:
visualize_boxes_fn = functools.partial(
_visualize_boxes_and_masks_and_keypoints,
category_index=category_index,
**visualization_keyword_args)
elems = [images, boxes, classes, scores, instance_masks, keypoints]
elems = [
true_shapes, original_shapes, images, boxes, classes, scores,
instance_masks, keypoints
]
else:
visualize_boxes_fn = functools.partial(
_visualize_boxes,
category_index=category_index,
**visualization_keyword_args)
elems = [images, boxes, classes, scores]
elems = [
true_shapes, original_shapes, images, boxes, classes, scores
]
def draw_boxes(image_and_detections):
"""Draws boxes on image."""
image_with_boxes = tf.py_func(visualize_boxes_fn, image_and_detections,
true_shape = image_and_detections[0]
original_shape = image_and_detections[1]
if true_image_shape is not None:
image = shape_utils.pad_or_clip_nd(image_and_detections[2],
[true_shape[0], true_shape[1], 3])
if original_image_spatial_shape is not None:
image_and_detections[2] = _resize_original_image(image, original_shape)
image_with_boxes = tf.py_func(visualize_boxes_fn, image_and_detections[2:],
tf.uint8)
return image_with_boxes
......@@ -400,6 +445,7 @@ def draw_side_by_side_evaluation_image(eval_dict,
Args:
eval_dict: The evaluation dictionary returned by
eval_util.result_dict_for_batched_example() or
eval_util.result_dict_for_single_example().
category_index: A category index (dictionary) produced from a labelmap.
max_boxes_to_draw: The maximum number of boxes to draw for detections.
......@@ -409,53 +455,85 @@ def draw_side_by_side_evaluation_image(eval_dict,
Default is True.
Returns:
A [1, H, 2 * W, C] uint8 tensor. The subimage on the left corresponds to
detections, while the subimage on the right corresponds to groundtruth.
A list of [1, H, 2 * W, C] uint8 tensor. The subimage on the left
corresponds to detections, while the subimage on the right corresponds to
groundtruth.
"""
detection_fields = fields.DetectionResultFields()
input_data_fields = fields.InputDataFields()
instance_masks = None
if detection_fields.detection_masks in eval_dict:
instance_masks = tf.cast(
tf.expand_dims(eval_dict[detection_fields.detection_masks], axis=0),
tf.uint8)
keypoints = None
if detection_fields.detection_keypoints in eval_dict:
keypoints = tf.expand_dims(
eval_dict[detection_fields.detection_keypoints], axis=0)
groundtruth_instance_masks = None
if input_data_fields.groundtruth_instance_masks in eval_dict:
groundtruth_instance_masks = tf.cast(
images_with_detections_list = []
# Add the batch dimension if the eval_dict is for single example.
if len(eval_dict[detection_fields.detection_classes].shape) == 1:
for key in eval_dict:
if key != input_data_fields.original_image:
eval_dict[key] = tf.expand_dims(eval_dict[key], 0)
for indx in range(eval_dict[input_data_fields.original_image].shape[0]):
instance_masks = None
if detection_fields.detection_masks in eval_dict:
instance_masks = tf.cast(
tf.expand_dims(
eval_dict[detection_fields.detection_masks][indx], axis=0),
tf.uint8)
keypoints = None
if detection_fields.detection_keypoints in eval_dict:
keypoints = tf.expand_dims(
eval_dict[detection_fields.detection_keypoints][indx], axis=0)
groundtruth_instance_masks = None
if input_data_fields.groundtruth_instance_masks in eval_dict:
groundtruth_instance_masks = tf.cast(
tf.expand_dims(
eval_dict[input_data_fields.groundtruth_instance_masks][indx],
axis=0), tf.uint8)
images_with_detections = draw_bounding_boxes_on_image_tensors(
tf.expand_dims(
eval_dict[input_data_fields.groundtruth_instance_masks], axis=0),
tf.uint8)
images_with_detections = draw_bounding_boxes_on_image_tensors(
eval_dict[input_data_fields.original_image],
tf.expand_dims(eval_dict[detection_fields.detection_boxes], axis=0),
tf.expand_dims(eval_dict[detection_fields.detection_classes], axis=0),
tf.expand_dims(eval_dict[detection_fields.detection_scores], axis=0),
category_index,
instance_masks=instance_masks,
keypoints=keypoints,
max_boxes_to_draw=max_boxes_to_draw,
min_score_thresh=min_score_thresh,
use_normalized_coordinates=use_normalized_coordinates)
images_with_groundtruth = draw_bounding_boxes_on_image_tensors(
eval_dict[input_data_fields.original_image],
tf.expand_dims(eval_dict[input_data_fields.groundtruth_boxes], axis=0),
tf.expand_dims(eval_dict[input_data_fields.groundtruth_classes], axis=0),
tf.expand_dims(
tf.ones_like(
eval_dict[input_data_fields.groundtruth_classes],
dtype=tf.float32),
axis=0),
category_index,
instance_masks=groundtruth_instance_masks,
keypoints=None,
max_boxes_to_draw=None,
min_score_thresh=0.0,
use_normalized_coordinates=use_normalized_coordinates)
return tf.concat([images_with_detections, images_with_groundtruth], axis=2)
eval_dict[input_data_fields.original_image][indx], axis=0),
tf.expand_dims(
eval_dict[detection_fields.detection_boxes][indx], axis=0),
tf.expand_dims(
eval_dict[detection_fields.detection_classes][indx], axis=0),
tf.expand_dims(
eval_dict[detection_fields.detection_scores][indx], axis=0),
category_index,
original_image_spatial_shape=tf.expand_dims(
eval_dict[input_data_fields.original_image_spatial_shape][indx],
axis=0),
true_image_shape=tf.expand_dims(
eval_dict[input_data_fields.true_image_shape][indx], axis=0),
instance_masks=instance_masks,
keypoints=keypoints,
max_boxes_to_draw=max_boxes_to_draw,
min_score_thresh=min_score_thresh,
use_normalized_coordinates=use_normalized_coordinates)
images_with_groundtruth = draw_bounding_boxes_on_image_tensors(
tf.expand_dims(
eval_dict[input_data_fields.original_image][indx], axis=0),
tf.expand_dims(
eval_dict[input_data_fields.groundtruth_boxes][indx], axis=0),
tf.expand_dims(
eval_dict[input_data_fields.groundtruth_classes][indx], axis=0),
tf.expand_dims(
tf.ones_like(
eval_dict[input_data_fields.groundtruth_classes][indx],
dtype=tf.float32),
axis=0),
category_index,
original_image_spatial_shape=tf.expand_dims(
eval_dict[input_data_fields.original_image_spatial_shape][indx],
axis=0),
true_image_shape=tf.expand_dims(
eval_dict[input_data_fields.true_image_shape][indx], axis=0),
instance_masks=groundtruth_instance_masks,
keypoints=None,
max_boxes_to_draw=None,
min_score_thresh=0.0,
use_normalized_coordinates=use_normalized_coordinates)
images_with_detections_list.append(
tf.concat([images_with_detections, images_with_groundtruth], axis=2))
return images_with_detections_list
def draw_keypoints_on_image_array(image,
......@@ -744,7 +822,7 @@ class EvalMetricOpsVisualization(object):
responsible for accruing images (with overlaid detections and groundtruth)
and returning a dictionary that can be passed to `eval_metric_ops`.
"""
__metaclass__ = ABCMeta
__metaclass__ = abc.ABCMeta
def __init__(self,
category_index,
......@@ -792,26 +870,33 @@ class EvalMetricOpsVisualization(object):
Args:
eval_dict: A dictionary that holds an image, groundtruth, and detections
for a single example. See eval_util.result_dict_for_single_example() for
a convenient method for constructing such a dictionary. The dictionary
for a batched example. Note that, we use only the first example for
visualization. See eval_util.result_dict_for_batched_example() for a
convenient method for constructing such a dictionary. The dictionary
contains
fields.InputDataFields.original_image: [1, H, W, 3] image.
fields.InputDataFields.groundtruth_boxes - [num_boxes, 4] float32
tensor with groundtruth boxes in range [0.0, 1.0].
fields.InputDataFields.groundtruth_classes - [num_boxes] int64
tensor with 1-indexed groundtruth classes.
fields.InputDataFields.original_image: [batch_size, H, W, 3] image.
fields.InputDataFields.original_image_spatial_shape: [batch_size, 2]
tensor containing the size of the original image.
fields.InputDataFields.true_image_shape: [batch_size, 3]
tensor containing the spatial size of the upadded original image.
fields.InputDataFields.groundtruth_boxes - [batch_size, num_boxes, 4]
float32 tensor with groundtruth boxes in range [0.0, 1.0].
fields.InputDataFields.groundtruth_classes - [batch_size, num_boxes]
int64 tensor with 1-indexed groundtruth classes.
fields.InputDataFields.groundtruth_instance_masks - (optional)
[num_boxes, H, W] int64 tensor with instance masks.
fields.DetectionResultFields.detection_boxes - [max_num_boxes, 4]
float32 tensor with detection boxes in range [0.0, 1.0].
fields.DetectionResultFields.detection_classes - [max_num_boxes]
int64 tensor with 1-indexed detection classes.
fields.DetectionResultFields.detection_scores - [max_num_boxes]
float32 tensor with detection scores.
fields.DetectionResultFields.detection_masks - (optional)
[max_num_boxes, H, W] float32 tensor of binarized masks.
[batch_size, num_boxes, H, W] int64 tensor with instance masks.
fields.DetectionResultFields.detection_boxes - [batch_size,
max_num_boxes, 4] float32 tensor with detection boxes in range [0.0,
1.0].
fields.DetectionResultFields.detection_classes - [batch_size,
max_num_boxes] int64 tensor with 1-indexed detection classes.
fields.DetectionResultFields.detection_scores - [batch_size,
max_num_boxes] float32 tensor with detection scores.
fields.DetectionResultFields.detection_masks - (optional) [batch_size,
max_num_boxes, H, W] float32 tensor of binarized masks.
fields.DetectionResultFields.detection_keypoints - (optional)
[max_num_boxes, num_keypoints, 2] float32 tensor with keypooints.
[batch_size, max_num_boxes, num_keypoints, 2] float32 tensor with
keypoints.
Returns:
A dictionary of image summary names to tuple of (value_op, update_op). The
......@@ -820,6 +905,8 @@ class EvalMetricOpsVisualization(object):
groundtruth. Each `value_op` holds the tf.summary.image string for a given
image.
"""
if self._max_examples_to_draw == 0:
return {}
images = self.images_from_evaluation_dict(eval_dict)
def get_images():
......@@ -837,7 +924,7 @@ class EvalMetricOpsVisualization(object):
lambda: tf.summary.image(summary_name, image),
lambda: tf.constant(''))
update_op = tf.py_func(self.add_images, [images], [])
update_op = tf.py_func(self.add_images, [[images[0]]], [])
image_tensors = tf.py_func(
get_images, [], [tf.uint8] * self._max_examples_to_draw)
eval_metric_ops = {}
......@@ -847,7 +934,7 @@ class EvalMetricOpsVisualization(object):
eval_metric_ops[summary_name] = (value_op, update_op)
return eval_metric_ops
@abstractmethod
@abc.abstractmethod
def images_from_evaluation_dict(self, eval_dict):
"""Converts evaluation dictionary into a list of image tensors.
......@@ -882,9 +969,6 @@ class VisualizeSingleFrameDetections(EvalMetricOpsVisualization):
summary_name_prefix=summary_name_prefix)
def images_from_evaluation_dict(self, eval_dict):
return [draw_side_by_side_evaluation_image(
eval_dict,
self._category_index,
self._max_boxes_to_draw,
self._min_score_thresh,
self._use_normalized_coordinates)]
return draw_side_by_side_evaluation_image(
eval_dict, self._category_index, self._max_boxes_to_draw,
self._min_score_thresh, self._use_normalized_coordinates)
research/object_detection/utils/visualization_utils_test.py
View file @ a1337e01
......@@ -52,6 +52,9 @@ class VisualizationUtilsTest(tf.test.TestCase):
def create_test_image_with_five_channels(self):
return np.full([100, 200, 5], 255, dtype=np.uint8)
def create_test_grayscale_image(self):
return np.full([100, 200, 1], 255, dtype=np.uint8)
def test_draw_bounding_box_on_image(self):
test_image = self.create_colorful_test_image()
test_image = Image.fromarray(test_image)
......@@ -119,9 +122,11 @@ class VisualizationUtilsTest(tf.test.TestCase):
fname = os.path.join(_TESTDATA_PATH, 'image1.jpg')
image_np = np.array(Image.open(fname))
images_np = np.stack((image_np, image_np), axis=0)
original_image_shape = [[636, 512], [636, 512]]
with tf.Graph().as_default():
images_tensor = tf.constant(value=images_np, dtype=tf.uint8)
image_shape = tf.constant(original_image_shape, dtype=tf.int32)
boxes = tf.constant([[[0.4, 0.25, 0.75, 0.75], [0.5, 0.3, 0.6, 0.9]],
[[0.25, 0.25, 0.75, 0.75], [0.1, 0.3, 0.6, 1.0]]])
classes = tf.constant([[1, 1], [1, 2]], dtype=tf.int64)
......@@ -133,6 +138,8 @@ class VisualizationUtilsTest(tf.test.TestCase):
classes,
scores,
category_index,
original_image_spatial_shape=image_shape,
true_image_shape=image_shape,
min_score_thresh=0.2))
with self.test_session() as sess:
......@@ -140,7 +147,10 @@ class VisualizationUtilsTest(tf.test.TestCase):
# Write output images for visualization.
images_with_boxes_np = sess.run(images_with_boxes)
self.assertEqual(images_np.shape, images_with_boxes_np.shape)
self.assertEqual(images_np.shape[0], images_with_boxes_np.shape[0])
self.assertEqual(images_np.shape[3], images_with_boxes_np.shape[3])
self.assertEqual(
tuple(original_image_shape[0]), images_with_boxes_np.shape[1:3])
for i in range(images_with_boxes_np.shape[0]):
img_name = 'image_' + str(i) + '.png'
output_file = os.path.join(self.get_temp_dir(), img_name)
......@@ -174,6 +184,35 @@ class VisualizationUtilsTest(tf.test.TestCase):
final_images_np = sess.run(images_with_boxes)
self.assertEqual((2, 100, 200, 3), final_images_np.shape)
def test_draw_bounding_boxes_on_image_tensors_grayscale(self):
"""Tests the case where input image tensor has one channel."""
category_index = {1: {'id': 1, 'name': 'dog'}}
image_np = self.create_test_grayscale_image()
images_np = np.stack((image_np, image_np), axis=0)
with tf.Graph().as_default():
images_tensor = tf.constant(value=images_np, dtype=tf.uint8)
image_shape = tf.constant([[100, 200], [100, 200]], dtype=tf.int32)
boxes = tf.constant(0, dtype=tf.float32, shape=[2, 0, 4])
classes = tf.constant(0, dtype=tf.int64, shape=[2, 0])
scores = tf.constant(0, dtype=tf.float32, shape=[2, 0])
images_with_boxes = (
visualization_utils.draw_bounding_boxes_on_image_tensors(
images_tensor,
boxes,
classes,
scores,
category_index,
original_image_spatial_shape=image_shape,
true_image_shape=image_shape,
min_score_thresh=0.2))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
final_images_np = sess.run(images_with_boxes)
self.assertEqual((2, 100, 200, 3), final_images_np.shape)
def test_draw_keypoints_on_image(self):
test_image = self.create_colorful_test_image()
test_image = Image.fromarray(test_image)
......@@ -234,34 +273,46 @@ class VisualizationUtilsTest(tf.test.TestCase):
category_index,
max_examples_to_draw=max_examples_to_draw,
summary_name_prefix=metric_op_base)
original_image = tf.placeholder(tf.uint8, [1, None, None, 3])
detection_boxes = tf.random_uniform([20, 4],
original_image = tf.placeholder(tf.uint8, [4, None, None, 3])
original_image_spatial_shape = tf.placeholder(tf.int32, [4, 2])
true_image_shape = tf.placeholder(tf.int32, [4, 3])
detection_boxes = tf.random_uniform([4, 20, 4],
minval=0.0,
maxval=1.0,
dtype=tf.float32)
detection_classes = tf.random_uniform([20],
detection_classes = tf.random_uniform([4, 20],
minval=1,
maxval=3,
dtype=tf.int64)
detection_scores = tf.random_uniform([20],
detection_scores = tf.random_uniform([4, 20],
minval=0.,
maxval=1.,
dtype=tf.float32)
groundtruth_boxes = tf.random_uniform([8, 4],
groundtruth_boxes = tf.random_uniform([4, 8, 4],
minval=0.0,
maxval=1.0,
dtype=tf.float32)
groundtruth_classes = tf.random_uniform([8],
groundtruth_classes = tf.random_uniform([4, 8],
minval=1,
maxval=3,
dtype=tf.int64)
eval_dict = {
fields.DetectionResultFields.detection_boxes: detection_boxes,
fields.DetectionResultFields.detection_classes: detection_classes,
fields.DetectionResultFields.detection_scores: detection_scores,
fields.InputDataFields.original_image: original_image,
fields.InputDataFields.groundtruth_boxes: groundtruth_boxes,
fields.InputDataFields.groundtruth_classes: groundtruth_classes}
fields.DetectionResultFields.detection_boxes:
detection_boxes,
fields.DetectionResultFields.detection_classes:
detection_classes,
fields.DetectionResultFields.detection_scores:
detection_scores,
fields.InputDataFields.original_image:
original_image,
fields.InputDataFields.original_image_spatial_shape: (
original_image_spatial_shape),
fields.InputDataFields.true_image_shape: (true_image_shape),
fields.InputDataFields.groundtruth_boxes:
groundtruth_boxes,
fields.InputDataFields.groundtruth_classes:
groundtruth_classes
}
metric_ops = eval_metric_ops.get_estimator_eval_metric_ops(eval_dict)
_, update_op = metric_ops[metric_ops.keys()[0]]
......@@ -274,12 +325,20 @@ class VisualizationUtilsTest(tf.test.TestCase):
# First run enough update steps to surpass `max_examples_to_draw`.
for i in range(max_examples_to_draw):
# Use a unique image shape on each eval image.
sess.run(update_op, feed_dict={
original_image: np.random.randint(low=0,
high=256,
size=(1, 6 + i, 7 + i, 3),
dtype=np.uint8)
})
sess.run(
update_op,
feed_dict={
original_image:
np.random.randint(
low=0,
high=256,
size=(4, 6 + i, 7 + i, 3),
dtype=np.uint8),
original_image_spatial_shape: [[6 + i, 7 + i], [6 + i, 7 + i],
[6 + i, 7 + i], [6 + i, 7 + i]],
true_image_shape: [[6 + i, 7 + i, 3], [6 + i, 7 + i, 3],
[6 + i, 7 + i, 3], [6 + i, 7 + i, 3]]
})
value_ops_out = sess.run(value_ops)
for key, value_op in value_ops_out.iteritems():
self.assertNotEqual('', value_op)
......@@ -289,12 +348,20 @@ class VisualizationUtilsTest(tf.test.TestCase):
# produced.
for i in range(max_examples_to_draw - 1):
# Use a unique image shape on each eval image.
sess.run(update_op, feed_dict={
original_image: np.random.randint(low=0,
high=256,
size=(1, 6 + i, 7 + i, 3),
dtype=np.uint8)
})
sess.run(
update_op,
feed_dict={
original_image:
np.random.randint(
low=0,
high=256,
size=(4, 6 + i, 7 + i, 3),
dtype=np.uint8),
original_image_spatial_shape: [[6 + i, 7 + i], [6 + i, 7 + i],
[6 + i, 7 + i], [6 + i, 7 + i]],
true_image_shape: [[6 + i, 7 + i, 3], [6 + i, 7 + i, 3],
[6 + i, 7 + i, 3], [6 + i, 7 + i, 3]]
})
value_ops_out = sess.run(value_ops)
self.assertEqual(
'',
......
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