290 lines
12 KiB
Python
290 lines
12 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Mask-RCNN anchor definition."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from collections import OrderedDict
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import numpy as np
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import tensorflow as tf
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from mask_rcnn.object_detection import argmax_matcher
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from mask_rcnn.object_detection import balanced_positive_negative_sampler
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from mask_rcnn.object_detection import box_list
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from mask_rcnn.object_detection import faster_rcnn_box_coder
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from mask_rcnn.object_detection import region_similarity_calculator
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from mask_rcnn.object_detection import target_assigner
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def _generate_anchor_configs(min_level, max_level, num_scales, aspect_ratios):
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"""Generates mapping from output level to a list of anchor configurations.
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A configuration is a tuple of (num_anchors, scale, aspect_ratio).
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Args:
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min_level: integer number of minimum level of the output feature pyramid.
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max_level: integer number of maximum level of the output feature pyramid.
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num_scales: integer number representing intermediate scales added
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on each level. For instances, num_scales=2 adds two additional
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anchor scales [2^0, 2^0.5] on each level.
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aspect_ratios: list of tuples representing the aspect raito anchors added
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on each level. For instances, aspect_ratios =
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[(1, 1), (1.4, 0.7), (0.7, 1.4)] adds three anchors on each level.
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Returns:
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anchor_configs: a dictionary with keys as the levels of anchors and
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values as a list of anchor configuration.
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"""
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anchor_configs = {}
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for level in range(min_level, max_level + 1):
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anchor_configs[level] = []
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for scale_octave in range(num_scales):
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for aspect in aspect_ratios:
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anchor_configs[level].append(
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(2**level, scale_octave / float(num_scales), aspect))
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return anchor_configs
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def _generate_anchor_boxes(image_size, anchor_scale, anchor_configs):
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"""Generates multiscale anchor boxes.
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Args:
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image_size: integer number of input image size. The input image has the
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same dimension for width and height. The image_size should be divided by
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the largest feature stride 2^max_level.
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anchor_scale: float number representing the scale of size of the base
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anchor to the feature stride 2^level.
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anchor_configs: a dictionary with keys as the levels of anchors and
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values as a list of anchor configuration.
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Returns:
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anchor_boxes: a numpy array with shape [N, 4], which stacks anchors on all
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feature levels.
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Raises:
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ValueError: input size must be the multiple of largest feature stride.
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"""
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boxes_all = []
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for _, configs in anchor_configs.items():
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boxes_level = []
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for config in configs:
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stride, octave_scale, aspect = config
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if image_size[0] % stride != 0 or image_size[1] % stride != 0:
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raise ValueError('input size must be divided by the stride.')
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base_anchor_size = anchor_scale * stride * 2**octave_scale
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anchor_size_x_2 = base_anchor_size * aspect[0] / 2.0
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anchor_size_y_2 = base_anchor_size * aspect[1] / 2.0
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x = np.arange(stride / 2, image_size[1], stride)
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y = np.arange(stride / 2, image_size[0], stride)
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xv, yv = np.meshgrid(x, y)
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xv = xv.reshape(-1)
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yv = yv.reshape(-1)
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boxes = np.vstack((yv - anchor_size_y_2, xv - anchor_size_x_2,
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yv + anchor_size_y_2, xv + anchor_size_x_2))
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boxes = np.swapaxes(boxes, 0, 1)
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boxes_level.append(np.expand_dims(boxes, axis=1))
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# concat anchors on the same level to the reshape NxAx4
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boxes_level = np.concatenate(boxes_level, axis=1)
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boxes_all.append(boxes_level.reshape([-1, 4]))
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anchor_boxes = np.vstack(boxes_all)
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return anchor_boxes
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class Anchors(object):
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"""Mask-RCNN Anchors class."""
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def __init__(self, min_level, max_level, num_scales, aspect_ratios, anchor_scale, image_size):
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"""Constructs multiscale Mask-RCNN anchors.
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Args:
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min_level: integer number of minimum level of the output feature pyramid.
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max_level: integer number of maximum level of the output feature pyramid.
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num_scales: integer number representing intermediate scales added
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on each level. For instances, num_scales=2 adds two additional
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anchor scales [2^0, 2^0.5] on each level.
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aspect_ratios: list of tuples representing the aspect raito anchors added
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on each level. For instances, aspect_ratios =
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[(1, 1), (1.4, 0.7), (0.7, 1.4)] adds three anchors on each level.
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anchor_scale: float number representing the scale of size of the base
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anchor to the feature stride 2^level.
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image_size: integer number of input image size. The input image has the
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same dimension for width and height. The image_size should be divided by
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the largest feature stride 2^max_level.
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"""
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self.min_level = min_level
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self.max_level = max_level
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self.num_scales = num_scales
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self.aspect_ratios = aspect_ratios
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self.anchor_scale = anchor_scale
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self.image_size = image_size
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self.config = self._generate_configs()
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self.boxes = self._generate_boxes()
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def _generate_configs(self):
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"""Generate configurations of anchor boxes."""
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return _generate_anchor_configs(self.min_level, self.max_level,
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self.num_scales, self.aspect_ratios)
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def _generate_boxes(self):
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"""Generates multiscale anchor boxes."""
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boxes = _generate_anchor_boxes(self.image_size, self.anchor_scale,
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self.config)
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boxes = tf.convert_to_tensor(value=boxes, dtype=tf.float32)
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return boxes
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def get_anchors_per_location(self):
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return self.num_scales * len(self.aspect_ratios)
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def get_unpacked_boxes(self):
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return self.unpack_labels(self.boxes)
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def unpack_labels(self, labels):
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"""Unpacks an array of labels into multiscales labels."""
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labels_unpacked = OrderedDict()
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count = 0
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for level in range(self.min_level, self.max_level + 1):
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feat_size0 = int(self.image_size[0] / 2**level)
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feat_size1 = int(self.image_size[1] / 2**level)
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steps = feat_size0 * feat_size1 * self.get_anchors_per_location()
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indices = tf.range(count, count + steps)
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count += steps
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labels_unpacked[level] = tf.reshape(
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tf.gather(labels, indices), [feat_size0, feat_size1, -1])
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return labels_unpacked
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class AnchorLabeler(object):
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"""Labeler for multiscale anchor boxes."""
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def __init__(self, anchors, num_classes, match_threshold=0.7,
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unmatched_threshold=0.3, rpn_batch_size_per_im=256,
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rpn_fg_fraction=0.5):
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"""Constructs anchor labeler to assign labels to anchors.
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Args:
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anchors: an instance of class Anchors.
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num_classes: integer number representing number of classes in the dataset.
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match_threshold: a float number between 0 and 1 representing the
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lower-bound threshold to assign positive labels for anchors. An anchor
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with a score over the threshold is labeled positive.
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unmatched_threshold: a float number between 0 and 1 representing the
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upper-bound threshold to assign negative labels for anchors. An anchor
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with a score below the threshold is labeled negative.
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rpn_batch_size_per_im: a integer number that represents the number of
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sampled anchors per image in the first stage (region proposal network).
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rpn_fg_fraction: a float number between 0 and 1 representing the fraction
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of positive anchors (foreground) in the first stage.
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"""
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similarity_calc = region_similarity_calculator.IouSimilarity()
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matcher = argmax_matcher.ArgMaxMatcher(
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match_threshold,
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unmatched_threshold=unmatched_threshold,
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negatives_lower_than_unmatched=True,
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force_match_for_each_row=True)
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box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
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self._target_assigner = target_assigner.TargetAssigner(
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similarity_calc, matcher, box_coder)
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self._anchors = anchors
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self._match_threshold = match_threshold
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self._unmatched_threshold = unmatched_threshold
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self._rpn_batch_size_per_im = rpn_batch_size_per_im
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self._rpn_fg_fraction = rpn_fg_fraction
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self._num_classes = num_classes
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def _get_rpn_samples(self, match_results):
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"""Computes anchor labels.
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This function performs subsampling for foreground (fg) and background (bg)
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anchors.
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Args:
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match_results: A integer tensor with shape [N] representing the
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matching results of anchors. (1) match_results[i]>=0,
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meaning that column i is matched with row match_results[i].
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(2) match_results[i]=-1, meaning that column i is not matched.
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(3) match_results[i]=-2, meaning that column i is ignored.
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Returns:
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score_targets: a integer tensor with the a shape of [N].
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(1) score_targets[i]=1, the anchor is a positive sample.
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(2) score_targets[i]=0, negative. (3) score_targets[i]=-1, the anchor is
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don't care (ignore).
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"""
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sampler = (
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balanced_positive_negative_sampler.BalancedPositiveNegativeSampler(
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positive_fraction=self._rpn_fg_fraction, is_static=False))
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# indicator includes both positive and negative labels.
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# labels includes only positives labels.
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# positives = indicator & labels.
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# negatives = indicator & !labels.
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# ignore = !indicator.
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indicator = tf.greater(match_results, -2)
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labels = tf.greater(match_results, -1)
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samples = sampler.subsample(
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indicator, self._rpn_batch_size_per_im, labels)
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positive_labels = tf.where(
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tf.logical_and(samples, labels),
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tf.constant(2, dtype=tf.int32, shape=match_results.shape),
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tf.constant(0, dtype=tf.int32, shape=match_results.shape))
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negative_labels = tf.where(
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tf.logical_and(samples, tf.logical_not(labels)),
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tf.constant(1, dtype=tf.int32, shape=match_results.shape),
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tf.constant(0, dtype=tf.int32, shape=match_results.shape))
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ignore_labels = tf.fill(match_results.shape, -1)
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return (ignore_labels + positive_labels + negative_labels,
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positive_labels, negative_labels)
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def label_anchors(self, gt_boxes, gt_labels):
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"""Labels anchors with ground truth inputs.
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Args:
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gt_boxes: A float tensor with shape [N, 4] representing groundtruth boxes.
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For each row, it stores [y0, x0, y1, x1] for four corners of a box.
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gt_labels: A integer tensor with shape [N, 1] representing groundtruth
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classes.
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Returns:
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score_targets_dict: ordered dictionary with keys
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[min_level, min_level+1, ..., max_level]. The values are tensor with
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shape [height_l, width_l, num_anchors]. The height_l and width_l
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represent the dimension of class logits at l-th level.
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box_targets_dict: ordered dictionary with keys
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[min_level, min_level+1, ..., max_level]. The values are tensor with
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shape [height_l, width_l, num_anchors * 4]. The height_l and
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width_l represent the dimension of bounding box regression output at
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l-th level.
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"""
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gt_box_list = box_list.BoxList(gt_boxes)
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anchor_box_list = box_list.BoxList(self._anchors.boxes)
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# cls_targets, cls_weights, box_weights are not used
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_, _, box_targets, _, matches = self._target_assigner.assign(
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anchor_box_list, gt_box_list, gt_labels)
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# score_targets contains the subsampled positive and negative anchors.
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score_targets, _, _ = self._get_rpn_samples(matches.match_results)
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# Unpack labels.
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score_targets_dict = self._anchors.unpack_labels(score_targets)
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box_targets_dict = self._anchors.unpack_labels(box_targets)
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return score_targets_dict, box_targets_dict
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