轉載自:faster rcnn源碼解讀(六)之minibatch - 野孩子的專欄 - 博客頻道 - CSDN.NET
http://blog.csdn.net/u010668907/article/details/51945917
faster rcnn用python版本的https://github.com/rbgirshick/py-faster-rcnn
minibatch源碼:https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/roi_data_layer/minibatch.py
源碼:
- # --------------------------------------------------------
- # 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 np
- import numpy.random as npr
- import cv2
- from fast_rcnn.config import cfg
- from utils.blob import prep_im_for_blob, im_list_to_blob
- def 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)#隨機索引組成的numpy,大小是roidb的長度
- 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 blobs
- def _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_weights
- def _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;用於將圖片平均後縮放。#im_scales: 每張圖片的縮放率
- # cfg.PIXEL_MEANS: 原始圖片會集體減去該值達到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)#將以list形式存放的圖片數據處理成(batch elem, channel, height, width)的im_blob形式,height,width用的是此次計算所有圖片的最大值
- return blob, im_scales#blob是一個字典,與name_to_top對應,方便把blob數據放進top
- def _project_im_rois(im_rois, im_scale_factor):
- """Project image RoIs into the rescaled training image."""
- rois = im_rois * im_scale_factor
- return rois
- def _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_weights
- def _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裏,現在進入minibatch組建真正的blob)get_minibatch
4.1 cfg.TRAIN.SCALES: 圖片被縮放的target_size列表
4.2 random_scale_inds:列表的隨機索引組成的numpy,大小是roidb的長度
4.3 cfg.PIXEL_MEANS: 原始圖片會集體減去該值達到mean
4.4 im_scales: 每張圖片的縮放率
縮放率的求法: 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;用於將圖片平均後縮放。
4.6 im_list_to_blob(ims): 將以list形式存放的圖片數據處理成(batch elem, channel, height, width)的im_blob形式,height,width用的是此次計算所有圖片的最大值
4.7 blob是一個字典:data,一個batch的處理過的所有圖片數據,即上面的im_blob;
im_info, [im_blob.shape[2], im_blob.shape[3], im_scales[0]]
gt_boxes, 是前四列是box的值,第五列是box的類別。
box=原box*im_scales
blob與name_to_top對應,方便把blob數據放進top
!!!minibatch.py中34行的代碼表明,batchsize即cfg.TRAIN.IMS_PER_BATCH只能是1