############################################################## # Copyright (c) 2018-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. ############################################################## # -------------------------------------------------------- # 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.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import numpy as np from core.config import cfg import utils.blob as blob_utils import roi_data.rpn import roi_data.fast_rcnn import utils.image as image_utils import logging logger = logging.getLogger(__name__) def get_minibatch_blob_names(): """Returns blob names in the order in which they are read by the data loader. """ # data blob: holds a batch of N images, each with 3 channels blob_names = ['data'] if cfg.RPN.ON: # RPN-only or end-to-end Faster R-CNN blob_names += roi_data.rpn.get_rpn_blob_names() else: # Fast R-CNN like models trained on precomputed proposals blob_names += roi_data.fast_rcnn.get_fast_rcnn_blob_names() return blob_names def get_minibatch(roidb): """Given a roidb, construct a minibatch sampled from it.""" # We collect blobs from each image onto a list and then concat them into a # single tensor, hence we initialize each blob to an empty list blobs = {k: [] for k in get_minibatch_blob_names()} # Get the input image blob, formatted for caffe2 im_blob, im_scales = _get_image_blob(roidb) blobs['data'] = im_blob if cfg.RPN.ON: # RPN-only or end-to-end Faster R-CNN valid = roi_data.rpn.add_rpn_blobs(blobs, im_scales, roidb) else: # Fast R-CNN like models trained on precomputed proposals valid = roi_data.fast_rcnn.add_fast_rcnn_blobs(blobs, im_scales, roidb) return blobs, valid def _get_image_blob(roidb): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) # Sample random scales to use for each image in this batch scale_inds = np.random.randint( 0, high=len(cfg.TRAIN.SCALES), size=num_images) processed_ims = [] im_scales = [] for i in range(num_images): ims = image_utils.read_image_video(roidb[i]) for im_id, im in enumerate(ims): if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale = blob_utils.prep_im_for_blob( im, cfg.PIXEL_MEANS, [target_size], cfg.TRAIN.MAX_SIZE) ims[im_id] = im[0] # Just taking the im_scale for the last im in ims is fine (all are same) im_scales.append(im_scale[0]) processed_ims += ims # Create a blob to hold the input images blob = blob_utils.im_list_to_blob(processed_ims) return blob, im_scales