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121 lines
4.6 KiB
Python
121 lines
4.6 KiB
Python
"""
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This file has functions about generating bounding box regression targets
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"""
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import numpy as np
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from ..logger import logger
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from .bbox_transform import bbox_overlaps, bbox_transform
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from rcnn.config import config
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def compute_bbox_regression_targets(rois, overlaps, labels):
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"""
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given rois, overlaps, gt labels, compute bounding box regression targets
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:param rois: roidb[i]['boxes'] k * 4
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:param overlaps: roidb[i]['max_overlaps'] k * 1
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:param labels: roidb[i]['max_classes'] k * 1
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:return: targets[i][class, dx, dy, dw, dh] k * 5
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"""
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# Ensure ROIs are floats
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rois = rois.astype(np.float, copy=False)
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# Sanity check
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if len(rois) != len(overlaps):
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logger.warning('bbox regression: len(rois) != len(overlaps)')
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# Indices of ground-truth ROIs
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gt_inds = np.where(overlaps == 1)[0]
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if len(gt_inds) == 0:
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logger.warning('bbox regression: len(gt_inds) == 0')
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# Indices of examples for which we try to make predictions
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ex_inds = np.where(overlaps >= config.TRAIN.BBOX_REGRESSION_THRESH)[0]
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# Get IoU overlap between each ex ROI and gt ROI
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ex_gt_overlaps = bbox_overlaps(rois[ex_inds, :], rois[gt_inds, :])
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# Find which gt ROI each ex ROI has max overlap with:
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# this will be the ex ROI's gt target
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gt_assignment = ex_gt_overlaps.argmax(axis=1)
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gt_rois = rois[gt_inds[gt_assignment], :]
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ex_rois = rois[ex_inds, :]
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targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
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targets[ex_inds, 0] = labels[ex_inds]
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targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
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return targets
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def add_bbox_regression_targets(roidb):
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"""
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given roidb, add ['bbox_targets'] and normalize bounding box regression targets
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:param roidb: roidb to be processed. must have gone through imdb.prepare_roidb
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:return: means, std variances of targets
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"""
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logger.info('bbox regression: add bounding box regression targets')
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assert len(roidb) > 0
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assert 'max_classes' in roidb[0]
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num_images = len(roidb)
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num_classes = roidb[0]['gt_overlaps'].shape[1]
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for im_i in range(num_images):
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rois = roidb[im_i]['boxes']
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max_overlaps = roidb[im_i]['max_overlaps']
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max_classes = roidb[im_i]['max_classes']
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roidb[im_i]['bbox_targets'] = compute_bbox_regression_targets(rois, max_overlaps, max_classes)
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if config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED:
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# use fixed / precomputed means and stds instead of empirical values
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means = np.tile(np.array(config.TRAIN.BBOX_MEANS), (num_classes, 1))
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stds = np.tile(np.array(config.TRAIN.BBOX_STDS), (num_classes, 1))
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else:
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# compute mean, std values
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class_counts = np.zeros((num_classes, 1)) + 1e-14
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sums = np.zeros((num_classes, 4))
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squared_sums = np.zeros((num_classes, 4))
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for im_i in range(num_images):
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targets = roidb[im_i]['bbox_targets']
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for cls in range(1, num_classes):
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cls_indexes = np.where(targets[:, 0] == cls)[0]
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if cls_indexes.size > 0:
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class_counts[cls] += cls_indexes.size
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sums[cls, :] += targets[cls_indexes, 1:].sum(axis=0)
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squared_sums[cls, :] += (targets[cls_indexes, 1:] ** 2).sum(axis=0)
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means = sums / class_counts
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# var(x) = E(x^2) - E(x)^2
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stds = np.sqrt(squared_sums / class_counts - means ** 2)
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# normalized targets
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for im_i in range(num_images):
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targets = roidb[im_i]['bbox_targets']
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for cls in range(1, num_classes):
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cls_indexes = np.where(targets[:, 0] == cls)[0]
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roidb[im_i]['bbox_targets'][cls_indexes, 1:] -= means[cls, :]
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roidb[im_i]['bbox_targets'][cls_indexes, 1:] /= stds[cls, :]
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return means.ravel(), stds.ravel()
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def expand_bbox_regression_targets(bbox_targets_data, num_classes):
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"""
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expand from 5 to 4 * num_classes; only the right class has non-zero bbox regression targets
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:param bbox_targets_data: [k * 5]
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:param num_classes: number of classes
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:return: bbox target processed [k * 4 num_classes]
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bbox_weights ! only foreground boxes have bbox regression computation!
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"""
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classes = bbox_targets_data[:, 0]
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bbox_targets = np.zeros((classes.size, 4 * num_classes), dtype=np.float32)
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bbox_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
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indexes = np.where(classes > 0)[0]
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for index in indexes:
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cls = classes[index]
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start = int(4 * cls)
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end = start + 4
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bbox_targets[index, start:end] = bbox_targets_data[index, 1:]
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bbox_weights[index, start:end] = config.TRAIN.BBOX_WEIGHTS
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return bbox_targets, bbox_weights
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