faster rcnn源码解读（六）之minibatch

来源：互联网发布：centos net snmp 安装编辑：程序博客网时间：2024/05/21 14:40

faster rcnn用python版本的https://github.com/rbgirshick/py-faster-rcnn

minibatch源码：https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/roi_data_layer/minibatch.py

源码：

# --------------------------------------------------------# Fast R-CNN# Copyright (c) 2015 Microsoft# Licensed under The MIT License [see LICENSE for details]# Written by Ross Girshick# --------------------------------------------------------"""Compute minibatch blobs for training a Fast R-CNN network."""import numpy as npimport numpy.random as nprimport cv2from fast_rcnn.config import cfgfrom utils.blob import prep_im_for_blob, im_list_to_blobdef get_minibatch(roidb, num_classes):    """Given a roidb, construct a minibatch sampled from it."""    num_images = len(roidb)    # Sample random scales to use for each image in this batch    random_scale_inds = npr.randint(0, high=len(cfg.TRAIN.SCALES),                                    size=num_images)#随机索引组成的numpy，大小是roidb的长度    assert(cfg.TRAIN.BATCH_SIZE % num_images == 0), \        'num_images ({}) must divide BATCH_SIZE ({})'. \        format(num_images, cfg.TRAIN.BATCH_SIZE)    rois_per_image = cfg.TRAIN.BATCH_SIZE / num_images    fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image)    # Get the input image blob, formatted for caffe    im_blob, im_scales = _get_image_blob(roidb, random_scale_inds)    blobs = {'data': im_blob}    if cfg.TRAIN.HAS_RPN:        assert len(im_scales) == 1, "Single batch only"        assert len(roidb) == 1, "Single batch only"        # gt boxes: (x1, y1, x2, y2, cls)        gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]        gt_boxes = np.empty((len(gt_inds), 5), dtype=np.float32)        gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :] * im_scales[0]        gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]        blobs['gt_boxes'] = gt_boxes        blobs['im_info'] = np.array(            [[im_blob.shape[2], im_blob.shape[3], im_scales[0]]],            dtype=np.float32)    else: # not using RPN        # Now, build the region of interest and label blobs        rois_blob = np.zeros((0, 5), dtype=np.float32)        labels_blob = np.zeros((0), dtype=np.float32)        bbox_targets_blob = np.zeros((0, 4 * num_classes), dtype=np.float32)        bbox_inside_blob = np.zeros(bbox_targets_blob.shape, dtype=np.float32)        # all_overlaps = []        for im_i in xrange(num_images):            labels, overlaps, im_rois, bbox_targets, bbox_inside_weights \                = _sample_rois(roidb[im_i], fg_rois_per_image, rois_per_image,                               num_classes)            # Add to RoIs blob            rois = _project_im_rois(im_rois, im_scales[im_i])            batch_ind = im_i * np.ones((rois.shape[0], 1))            rois_blob_this_image = np.hstack((batch_ind, rois))            rois_blob = np.vstack((rois_blob, rois_blob_this_image))            # Add to labels, bbox targets, and bbox loss blobs            labels_blob = np.hstack((labels_blob, labels))            bbox_targets_blob = np.vstack((bbox_targets_blob, bbox_targets))            bbox_inside_blob = np.vstack((bbox_inside_blob, bbox_inside_weights))            # all_overlaps = np.hstack((all_overlaps, overlaps))        # For debug visualizations        # _vis_minibatch(im_blob, rois_blob, labels_blob, all_overlaps)        blobs['rois'] = rois_blob        blobs['labels'] = labels_blob        if cfg.TRAIN.BBOX_REG:            blobs['bbox_targets'] = bbox_targets_blob            blobs['bbox_inside_weights'] = bbox_inside_blob            blobs['bbox_outside_weights'] = \                np.array(bbox_inside_blob > 0).astype(np.float32)    return blobsdef _sample_rois(roidb, fg_rois_per_image, rois_per_image, num_classes):    """Generate a random sample of RoIs comprising foreground and background    examples.    """    # label = class RoI has max overlap with    labels = roidb['max_classes']    overlaps = roidb['max_overlaps']    rois = roidb['boxes']    # Select foreground RoIs as those with >= FG_THRESH overlap    fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]    # Guard against the case when an image has fewer than fg_rois_per_image    # foreground RoIs    fg_rois_per_this_image = np.minimum(fg_rois_per_image, fg_inds.size)    # Sample foreground regions without replacement    if fg_inds.size > 0:        fg_inds = npr.choice(                fg_inds, size=fg_rois_per_this_image, replace=False)    # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)    bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &                       (overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]    # Compute number of background RoIs to take from this image (guarding    # against there being fewer than desired)    bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image    bg_rois_per_this_image = np.minimum(bg_rois_per_this_image,                                        bg_inds.size)    # Sample foreground regions without replacement    if bg_inds.size > 0:        bg_inds = npr.choice(                bg_inds, size=bg_rois_per_this_image, replace=False)    # The indices that we're selecting (both fg and bg)    keep_inds = np.append(fg_inds, bg_inds)    # Select sampled values from various arrays:    labels = labels[keep_inds]    # Clamp labels for the background RoIs to 0    labels[fg_rois_per_this_image:] = 0    overlaps = overlaps[keep_inds]    rois = rois[keep_inds]    bbox_targets, bbox_inside_weights = _get_bbox_regression_labels(            roidb['bbox_targets'][keep_inds, :], num_classes)    return labels, overlaps, rois, bbox_targets, bbox_inside_weightsdef _get_image_blob(roidb, scale_inds):    """Builds an input blob from the images in the roidb at the specified    scales.    """    num_images = len(roidb)    processed_ims = []    im_scales = []    for i in xrange(num_images):        im = cv2.imread(roidb[i]['image'])        if roidb[i]['flipped']:            im = im[:, ::-1, :]        target_size = cfg.TRAIN.SCALES[scale_inds[i]]        im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,                                        cfg.TRAIN.MAX_SIZE)prep_im_for_blob： util的blob.py中；用于将图片平均后缩放。#im_scales： 每张图片的缩放率

        #  cfg.PIXEL_MEANS： 原始图片会集体减去该值达到mean        im_scales.append(im_scale)        processed_ims.append(im)    # Create a blob to hold the input images    blob = im_list_to_blob(processed_ims)#将以list形式存放的图片数据处理成(batch elem, channel, height, width)的im_blob形式，height，width用的是此次计算所有图片的最大值    return blob, im_scales#blob是一个字典，与name_to_top对应，方便把blob数据放进topdef _project_im_rois(im_rois, im_scale_factor):    """Project image RoIs into the rescaled training image."""    rois = im_rois * im_scale_factor    return roisdef _get_bbox_regression_labels(bbox_target_data, num_classes):    """Bounding-box regression targets are stored in a compact form in the    roidb.    This function expands those targets into the 4-of-4*K representation used    by the network (i.e. only one class has non-zero targets). The loss weights    are similarly expanded.    Returns:        bbox_target_data (ndarray): N x 4K blob of regression targets        bbox_inside_weights (ndarray): N x 4K blob of loss weights    """    clss = bbox_target_data[:, 0]    bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32)    bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32)    inds = np.where(clss > 0)[0]    for ind in inds:        cls = clss[ind]        start = 4 * cls        end = start + 4        bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]        bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS    return bbox_targets, bbox_inside_weightsdef _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps):    """Visualize a mini-batch for debugging."""    import matplotlib.pyplot as plt    for i in xrange(rois_blob.shape[0]):        rois = rois_blob[i, :]        im_ind = rois[0]        roi = rois[1:]        im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()        im += cfg.PIXEL_MEANS        im = im[:, :, (2, 1, 0)]        im = im.astype(np.uint8)        cls = labels_blob[i]        plt.imshow(im)        print 'class: ', cls, ' overlap: ', overlaps[i]        plt.gca().add_patch(            plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],                          roi[3] - roi[1], fill=False,                          edgecolor='r', linewidth=3)            )        plt.show()

solver.step(1)-》reshape-》forward-》_get_next_minbatch-》_get_next_minbatch_inds-》(前面在layers里,现在进入minibatch组建真正的blob)get_minibatch

4.1 cfg.TRAIN.SCALES：图片被缩放的target_size列表

4.2 random_scale_inds：列表的随机索引组成的numpy，大小是roidb的长度

4.3 cfg.PIXEL_MEANS：原始图片会集体减去该值达到mean

4.4 im_scales：每张图片的缩放率

缩放率的求法： im_scales = target_size/min(width, height);

if im_scales*max(width, height) > cfg.TRAIN.MAX_SIZE

im_scales = cfg.TRAIN.MAX_SIZE * max(width, height)

4.5 prep_im_for_blob： util的blob.py中；用于将图片平均后缩放。

4.6 im_list_to_blob(ims): 将以list形式存放的图片数据处理成(batch elem, channel, height, width)的im_blob形式，height，width用的是此次计算所有图片的最大值

4.7 blob是一个字典：data，一个batch的处理过的所有图片数据，即上面的im_blob；

im_info， [im_blob.shape[2], im_blob.shape[3], im_scales[0]]