C.network = 'vgg' from keras_frcnn import vgg as nn elif options.network == 'resnet50': from keras_frcnn import resnet as nn C.network = 'resnet50' else: print('Not a valid model') raise ValueError # check if weight path was passed via command line if options.input_weight_path: C.base_net_weights = options.input_weight_path else: # set the path to weights based on backend and model C.base_net_weights = nn.get_weight_path() all_imgs, classes_count, class_mapping = get_data(options.train_path) if 'bg' not in classes_count: classes_count['bg'] = 0 class_mapping['bg'] = len(class_mapping) C.class_mapping = class_mapping inv_map = {v: k for k, v in class_mapping.items()} print('Training images per class:') pprint.pprint(classes_count) print('Num classes (including bg) = {}'.format(len(classes_count)))
C.network = 'vgg19' elif options.network == 'mobilenetv1': from keras_frcnn import mobilenetv1 as nn C.network = 'mobilenetv1' elif options.network == 'mobilenetv2': from keras_frcnn import mobilenetv2 as nn C.network = 'mobilenetv2' else: print('Not a valid model') raise ValueError if options.input_weight_path: C.base_net_weights = options.input_weight_path else: # set the path to weights based on backend and model C.base_net_weights = nn.get_weight_path() all_imgs, classes_count, class_mapping = get_data(options.train_path) if 'bg' not in classes_count: classes_count['bg'] = 0 class_mapping['bg'] = len(class_mapping) C.class_mapping = class_mapping inv_map = {v: k for k, v in class_mapping.items()} print('Training images per class:') pprint.pprint(classes_count) print('Num classes (including bg) = {}'.format(len(classes_count)))
def work(num_ep, textEdit, traval_path, weights, dataAug_hf, dataAug_vf, dataAug_rot, base_network, curvelist): from keras_frcnn import losses as losses ##python /run train_frcnn.py -p ./ --input_weight_path ./model_frcnn.hdf5 sys.setrecursionlimit(40000) num_roi = 128 num_epoch = num_ep model_save = "./model_frcnn.hdf5" parser = OptionParser() parser.add_option("-p", "--path", dest="train_path", help="Path to training data.", default=traval_path) ### parser.add_option("-o", "--parser", dest="parser", help="Parser to use. One of simple or pascal_voc", default="pascal_voc") parser.add_option("-n", "--num_rois", type="int", dest="num_rois", help="Number of RoIs to process at once.", default=num_roi) ### parser.add_option("--network", dest="network", help="Base network to use. Supports vgg or resnet50.", default=base_network) ###resnet50 parser.add_option( "--hf", dest="horizontal_flips", help="Augment with horizontal flips in training. (Default=false).", action="store_true", default=dataAug_hf) ## parser.add_option( "--vf", dest="vertical_flips", help="Augment with vertical flips in training. (Default=false).", action="store_true", default=dataAug_vf) ## parser.add_option( "--rot", "--rot_90", dest="rot_90", help="Augment with 90 degree rotations in training. (Default=false).", action="store_true", default=dataAug_rot) ## parser.add_option("--num_epochs", type="int", dest="num_epochs", help="Number of epochs.", default=num_epoch) ###2000 parser.add_option( "--config_filename", dest="config_filename", help= "Location to store all the metadata related to the training (to be used when testing).", default="config.pickle") parser.add_option("--output_weight_path", dest="output_weight_path", help="Output path for weights.", default=model_save) ### parser.add_option( "--input_weight_path", dest="input_weight_path", help= "Input path for weights. If not specified, will try to load default weights provided by keras.", default=traval_path + weights) (options, args) = parser.parse_args() if not options.train_path: # if filename is not given parser.error( 'Error: path to training data must be specified. Pass --path to command line' ) if options.parser == 'pascal_voc': from keras_frcnn.pascal_voc_parser import get_data elif options.parser == 'simple': from keras_frcnn.simple_parser import get_data else: raise ValueError( "Command line option parser must be one of 'pascal_voc' or 'simple'" ) # pass the settings from the command line, and persist them in the config object C = config.Config() C.use_horizontal_flips = bool(options.horizontal_flips) C.use_vertical_flips = bool(options.vertical_flips) C.rot_90 = bool(options.rot_90) C.model_path = options.output_weight_path C.num_rois = int(options.num_rois) if options.network == 'vgg': C.network = 'vgg' from keras_frcnn import vgg as nn elif options.network == 'resnet50': from keras_frcnn import resnet as nn C.network = 'resnet50' else: print('Not a valid model') raise ValueError # check if weight path was passed via command line if options.input_weight_path: C.base_net_weights = options.input_weight_path else: # set the path to weights based on backend and model C.base_net_weights = nn.get_weight_path() all_imgs, classes_count, class_mapping = get_data( options.train_path) ####### #all_imgs, classes_count, class_mapping = get_data(train_path)####### if 'bg' not in classes_count: classes_count['bg'] = 0 class_mapping['bg'] = len(class_mapping) C.class_mapping = class_mapping inv_map = {v: k for k, v in class_mapping.items()} print('Training images per class:') textEdit.append('Training images per class:') pprint.pprint(classes_count) print('Num classes (including bg) = {}'.format(len(classes_count))) textEdit.append('Num classes (including bg) = {}'.format( len(classes_count))) config_output_filename = options.config_filename with open(config_output_filename, 'wb') as config_f: pickle.dump(C, config_f) print( 'Config has been written to {}, and can be loaded when testing to ensure correct results' .format(config_output_filename)) textEdit.append( 'Config has been written to {}, and can be loaded when testing to ensure correct results' .format(config_output_filename)) random.shuffle(all_imgs) ##随机打乱 num_imgs = len(all_imgs) train_imgs = [s for s in all_imgs if s['imageset'] == 'train'] val_imgs = [s for s in all_imgs if s['imageset'] == 'val'] print('Num train samples {}'.format(len(train_imgs))) textEdit.append('Num train samples {}'.format(len(train_imgs))) print('Num val samples {}'.format(len(val_imgs))) textEdit.append('Num val samples {}'.format(len(val_imgs))) #####RPN计算 data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, C, nn.get_img_output_length, K.image_dim_ordering(), mode='train') data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, C, nn.get_img_output_length, K.image_dim_ordering(), mode='val') if K.image_dim_ordering() == 'th': input_shape_img = (3, None, None) else: input_shape_img = (None, None, 3) img_input = Input(shape=input_shape_img) roi_input = Input(shape=(None, 4)) # define the base network (resnet here, can be VGG, Inception, etc) shared_layers = nn.nn_base(img_input, trainable=True) # define the RPN, built on the base layers num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios) rpn = nn.rpn(shared_layers, num_anchors) classifier = nn.classifier(shared_layers, roi_input, C.num_rois, nb_classes=len(classes_count), trainable=True) model_rpn = Model(img_input, rpn[:2]) model_classifier = Model([img_input, roi_input], classifier) # this is a model that holds both the RPN and the classifier, used to load/save weights for the models model_all = Model([img_input, roi_input], rpn[:2] + classifier) try: print('loading weights from {}'.format(C.base_net_weights)) textEdit.append('loading weights from {}'.format(C.base_net_weights)) model_rpn.load_weights(C.base_net_weights, by_name=True) model_classifier.load_weights(C.base_net_weights, by_name=True) except: print( 'Could not load pretrained model weights. Weights can be found in the keras application folder \ https://github.com/fchollet/keras/tree/master/keras/applications') optimizer = Adam(lr=1e-5) optimizer_classifier = Adam(lr=1e-5) model_rpn.compile(optimizer=optimizer, loss=[ losses.rpn_loss_cls(num_anchors), losses.rpn_loss_regr(num_anchors) ]) model_classifier.compile( optimizer=optimizer_classifier, loss=[ losses.class_loss_cls, losses.class_loss_regr(len(classes_count) - 1) ], metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'}) model_all.compile(optimizer='sgd', loss='mae') print(' training') epoch_length = 1 num_epochs = int(options.num_epochs) iter_num = 0 losses = np.zeros((epoch_length, 5)) rpn_accuracy_rpn_monitor = [] rpn_accuracy_for_epoch = [] start_time = time.time() best_loss = np.Inf class_mapping_inv = {v: k for k, v in class_mapping.items()} print('Starting training') textEdit.append('Starting training') vis = True save_num = 1 loss_rpn_cls_list = [] loss_rpn_regr_list = [] loss_class_cls_list = [] loss_class_regr_list = [] curr_loss_list = [] for epoch_num in range(num_epochs): progbar = generic_utils.Progbar(epoch_length) print('Epoch {}/{}'.format(epoch_num + 1, num_epochs)) textEdit.append('Epoch {}/{}'.format(epoch_num + 1, num_epochs)) while True: try: if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose: mean_overlapping_bboxes = float( sum(rpn_accuracy_rpn_monitor)) / len( rpn_accuracy_rpn_monitor) rpn_accuracy_rpn_monitor = [] print( 'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations' .format(mean_overlapping_bboxes, epoch_length)) if mean_overlapping_bboxes == 0: print( 'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.' ) X, Y, img_data = next(data_gen_train) loss_rpn = model_rpn.train_on_batch(X, Y) P_rpn = model_rpn.predict_on_batch(X) R = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], C, K.image_dim_ordering(), use_regr=True, overlap_thresh=0.7, max_boxes=300) # note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format X2, Y1, Y2, IouS = roi_helpers.calc_iou( R, img_data, C, class_mapping) if X2 is None: rpn_accuracy_rpn_monitor.append(0) rpn_accuracy_for_epoch.append(0) continue neg_samples = np.where(Y1[0, :, -1] == 1) pos_samples = np.where(Y1[0, :, -1] == 0) if len(neg_samples) > 0: neg_samples = neg_samples[0] else: neg_samples = [] if len(pos_samples) > 0: pos_samples = pos_samples[0] else: pos_samples = [] rpn_accuracy_rpn_monitor.append(len(pos_samples)) rpn_accuracy_for_epoch.append((len(pos_samples))) if C.num_rois > 1: if len(pos_samples) < C.num_rois // 2: selected_pos_samples = pos_samples.tolist() else: selected_pos_samples = np.random.choice( pos_samples, C.num_rois // 2, replace=False).tolist() try: selected_neg_samples = np.random.choice( neg_samples, C.num_rois - len(selected_pos_samples), replace=False).tolist() except: selected_neg_samples = np.random.choice( neg_samples, C.num_rois - len(selected_pos_samples), replace=True).tolist() sel_samples = selected_pos_samples + selected_neg_samples else: # in the extreme case where num_rois = 1, we pick a random pos or neg sample selected_pos_samples = pos_samples.tolist() selected_neg_samples = neg_samples.tolist() if np.random.randint(0, 2): sel_samples = random.choice(neg_samples) else: sel_samples = random.choice(pos_samples) loss_class = model_classifier.train_on_batch( [X, X2[:, sel_samples, :]], [Y1[:, sel_samples, :], Y2[:, sel_samples, :]]) losses[iter_num, 0] = loss_rpn[1] losses[iter_num, 1] = loss_rpn[2] losses[iter_num, 2] = loss_class[1] losses[iter_num, 3] = loss_class[2] losses[iter_num, 4] = loss_class[3] iter_num += 1 ####数据更新在这里 progbar.update( iter_num, [('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])), ('detector_cls', np.mean(losses[:iter_num, 2])), ('detector_regr', np.mean(losses[:iter_num, 3]))]) if iter_num == epoch_length: loss_rpn_cls = np.mean(losses[:, 0]) loss_rpn_cls_list.append(loss_rpn_cls) curvelist[1].setData(loss_rpn_cls_list) loss_rpn_regr = np.mean(losses[:, 1]) loss_rpn_regr_list.append(loss_rpn_regr) curvelist[2].setData(loss_rpn_regr_list) loss_class_cls = np.mean(losses[:, 2]) loss_class_cls_list.append(loss_class_cls) curvelist[3].setData(loss_class_cls_list) loss_class_regr = np.mean(losses[:, 3]) loss_class_regr_list.append(loss_class_regr) curvelist[4].setData(loss_class_regr_list) class_acc = np.mean(losses[:, 4]) mean_overlapping_bboxes = float(sum( rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch) rpn_accuracy_for_epoch = [] if C.verbose: print( 'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}' .format(mean_overlapping_bboxes)) textEdit.append( 'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}' .format(mean_overlapping_bboxes)) print( 'Classifier accuracy for bounding boxes from RPN: {}' .format(class_acc)) textEdit.append( 'Classifier accuracy for bounding boxes from RPN: {}' .format(class_acc)) print('Loss RPN classifier: {}'.format(loss_rpn_cls)) textEdit.append( 'Loss RPN classifier: {}'.format(loss_rpn_cls)) print('Loss RPN regression: {}'.format(loss_rpn_regr)) textEdit.append( 'Loss RPN regression: {}'.format(loss_rpn_regr)) print('Loss Detector classifier: {}'.format( loss_class_cls)) textEdit.append('Loss Detector classifier: {}'.format( loss_class_cls)) print('Loss Detector regression: {}'.format( loss_class_regr)) textEdit.append('Loss Detector regression: {}'.format( loss_class_regr)) print('Elapsed time: {}'.format(time.time() - start_time)) textEdit.append('Elapsed time: {}'.format(time.time() - start_time)) curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr curr_loss_list.append(curr_loss) curvelist[0].setData(curr_loss_list) iter_num = 0 start_time = time.time() if curr_loss < best_loss: if C.verbose: print( 'Total loss decreased from {} to {}, saving weights' .format(best_loss, curr_loss)) textEdit.append( 'Total loss decreased from {} to {}, saving weights' .format(best_loss, curr_loss)) best_loss = curr_loss model_all.save_weights(C.model_path) break except Exception as e: print('Exception: {}'.format(e)) textEdit.append('Exception: {}'.format(e)) continue if (epoch_num == (save_num * 30)): model_all.save_weights(C.model_path + str(save_num * 30) + ".hdf5") save_num = save_num + 1 print('Training complete, exiting.') textEdit.append('Training complete, exiting.')
def train_kitti(): # config for data argument cfg = config.Config() cfg.use_horizontal_flips = True cfg.use_vertical_flips = True cfg.rot_90 = True cfg.num_rois = 32 cfg.base_net_weights = os.path.join('./model/', nn.get_weight_path()) # cfg.base_net_weights=r'' # TODO: the only file should to be change for other data to train cfg.model_path = '/media/private/Ci/log/plane/frcnn/vgg-adam' now = datetime.datetime.now() day = now.strftime('%y-%m-%d') for i in range(10000): if not os.path.exists('%s-%s-%d' % (cfg.model_path, day, i)): cfg.model_path = '%s-%s-%d' % (cfg.model_path, day, i) break make_dir(cfg.model_path) make_dir(cfg.model_path + '/loss') make_dir(cfg.model_path + '/loss_rpn_cls') make_dir(cfg.model_path + '/loss_rpn_regr') make_dir(cfg.model_path + '/loss_class_cls') make_dir(cfg.model_path + '/loss_class_regr') cfg.simple_label_file = '/media/public/GEOWAY/plane/plane0817.csv' all_images, classes_count, class_mapping = get_data(cfg.simple_label_file) if 'bg' not in classes_count: classes_count['bg'] = 0 class_mapping['bg'] = len(class_mapping) cfg.class_mapping = class_mapping cfg.config_save_file = os.path.join(cfg.model_path, 'config.pickle') with open(cfg.config_save_file, 'wb') as config_f: pickle.dump(cfg, config_f) print( 'Config has been written to {}, and can be loaded when testing to ensure correct results' .format(cfg.config_save_file)) inv_map = {v: k for k, v in class_mapping.items()} print('Training images per class:') pprint.pprint(classes_count) print('Num classes (including bg) = {}'.format(len(classes_count))) random.shuffle(all_images) num_imgs = len(all_images) train_imgs = [s for s in all_images if s['imageset'] == 'trainval'] val_imgs = [s for s in all_images if s['imageset'] == 'test'] print('Num train samples {}'.format(len(train_imgs))) print('Num val samples {}'.format(len(val_imgs))) data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, cfg, nn.get_img_output_length, K.image_dim_ordering(), mode='train') data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, cfg, nn.get_img_output_length, K.image_dim_ordering(), mode='val') Q = multiprocessing.Manager().Queue(maxsize=30) def fill_Q(n): while True: if not Q.full(): Q.put(next(data_gen_train)) #print(Q.qsize(),'put',n) else: time.sleep(0.00001) threads = [] for i in range(4): thread = multiprocessing.Process(target=fill_Q, args=(i, )) threads.append(thread) thread.start() if K.image_dim_ordering() == 'th': input_shape_img = (3, None, None) else: input_shape_img = (None, None, 3) img_input = Input(shape=input_shape_img) roi_input = Input(shape=(None, 4)) # define the base network (resnet here, can be VGG, Inception, etc) shared_layers = nn.nn_base(img_input, trainable=True) # define the RPN, built on the base layers num_anchors = len(cfg.anchor_box_scales) * len(cfg.anchor_box_ratios) rpn = nn.rpn(shared_layers, num_anchors) classifier = nn.classifier(shared_layers, roi_input, cfg.num_rois, nb_classes=len(classes_count), trainable=True) model_rpn = Model(img_input, rpn[:2]) model_classifier = Model([img_input, roi_input], classifier) # this is a model that holds both the RPN and the classifier, used to load/save weights for the models model_all = Model([img_input, roi_input], rpn[:2] + classifier) # model_all.summary() from keras.utils import plot_model # os.environ['PATH'] = os.environ['PATH'] + r';C:\Program Files (x86)\Graphviz2.38\bin;' plot_model(model_all, 'model_all.png', show_layer_names=True, show_shapes=True) plot_model(model_classifier, 'model_classifier.png', show_layer_names=True, show_shapes=True) plot_model(model_rpn, 'model_rpn.png', show_layer_names=True, show_shapes=True) ''' try: print('loading weights from {}'.format(cfg.base_net_weights)) model_rpn.load_weights(cfg.model_path, by_name=True) model_classifier.load_weights(cfg.model_path, by_name=True) except Exception as e: print(e) print('Could not load pretrained model weights. Weights can be found in the keras application folder ' 'https://github.com/fchollet/keras/tree/master/keras/applications') ''' optimizer = adadelta() optimizer_classifier = adadelta() model_rpn.compile(optimizer=optimizer, loss=[ losses_fn.rpn_loss_cls(num_anchors), losses_fn.rpn_loss_regr(num_anchors) ]) model_classifier.compile( optimizer=optimizer_classifier, loss=[ losses_fn.class_loss_cls, losses_fn.class_loss_regr(len(classes_count) - 1) ], metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'}) model_all.compile(optimizer='sgd', loss='mae') epoch_length = 10 num_epochs = int(cfg.num_epochs) iter_num = 0 losses = np.zeros((epoch_length, 5)) rpn_accuracy_rpn_monitor = [] rpn_accuracy_for_epoch = [] start_time = time.time() best_loss = np.Inf best_rpn_cls = np.Inf best_rpn_regr = np.Inf best_class_cls = np.Inf best_class_regr = np.Inf class_mapping_inv = {v: k for k, v in class_mapping.items()} print('Starting training') vis = True for epoch_num in range(num_epochs): progbar = generic_utils.Progbar(epoch_length) print('Epoch {}/{}'.format(epoch_num + 1, num_epochs)) while True: try: if len(rpn_accuracy_rpn_monitor ) == epoch_length and cfg.verbose: mean_overlapping_bboxes = float( sum(rpn_accuracy_rpn_monitor)) / len( rpn_accuracy_rpn_monitor) rpn_accuracy_rpn_monitor = [] print( 'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations' .format(mean_overlapping_bboxes, epoch_length)) if mean_overlapping_bboxes == 0: print( 'RPN is not producing bounding boxes that overlap' ' the ground truth boxes. Check RPN settings or keep training.' ) # X, Y, img_data = next(data_gen_train) while True: if Q.empty(): time.sleep(0.00001) continue X, Y, img_data = Q.get() # print(Q.qsize(),'get') break # print(X.shape,Y.shape) loss_rpn = model_rpn.train_on_batch(X, Y) P_rpn = model_rpn.predict_on_batch(X) result = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], cfg, K.image_dim_ordering(), use_regr=True, overlap_thresh=0.7, max_boxes=300) # note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format X2, Y1, Y2, IouS = roi_helpers.calc_iou( result, img_data, cfg, class_mapping) if X2 is None: rpn_accuracy_rpn_monitor.append(0) rpn_accuracy_for_epoch.append(0) continue neg_samples = np.where(Y1[0, :, -1] == 1) pos_samples = np.where(Y1[0, :, -1] == 0) if len(neg_samples) > 0: neg_samples = neg_samples[0] else: neg_samples = [] if len(pos_samples) > 0: pos_samples = pos_samples[0] else: pos_samples = [] rpn_accuracy_rpn_monitor.append(len(pos_samples)) rpn_accuracy_for_epoch.append((len(pos_samples))) if cfg.num_rois > 1: if len(pos_samples) < cfg.num_rois // 2: selected_pos_samples = pos_samples.tolist() else: selected_pos_samples = np.random.choice( pos_samples, cfg.num_rois // 2, replace=False).tolist() try: selected_neg_samples = np.random.choice( neg_samples, cfg.num_rois - len(selected_pos_samples), replace=False).tolist() except: selected_neg_samples = np.random.choice( neg_samples, cfg.num_rois - len(selected_pos_samples), replace=True).tolist() sel_samples = selected_pos_samples + selected_neg_samples else: # in the extreme case where num_rois = 1, we pick a random pos or neg sample selected_pos_samples = pos_samples.tolist() selected_neg_samples = neg_samples.tolist() if np.random.randint(0, 2): sel_samples = random.choice(neg_samples) else: sel_samples = random.choice(pos_samples) loss_class = model_classifier.train_on_batch( [X, X2[:, sel_samples, :]], [Y1[:, sel_samples, :], Y2[:, sel_samples, :]]) losses[iter_num, 0] = loss_rpn[1] losses[iter_num, 1] = loss_rpn[2] losses[iter_num, 2] = loss_class[1] losses[iter_num, 3] = loss_class[2] losses[iter_num, 4] = loss_class[3] iter_num += 1 progbar.update( iter_num, [('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])), ('detector_cls', np.mean(losses[:iter_num, 2])), ('detector_regr', np.mean(losses[:iter_num, 3]))]) if iter_num == epoch_length: loss_rpn_cls = np.mean(losses[:, 0]) loss_rpn_regr = np.mean(losses[:, 1]) loss_class_cls = np.mean(losses[:, 2]) loss_class_regr = np.mean(losses[:, 3]) class_acc = np.mean(losses[:, 4]) mean_overlapping_bboxes = float(sum( rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch) rpn_accuracy_for_epoch = [] if cfg.verbose: print( 'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}' .format(mean_overlapping_bboxes)) print( 'Classifier accuracy for bounding boxes from RPN: {}' .format(class_acc)) print('Loss RPN classifier: {}'.format(loss_rpn_cls)) print('Loss RPN regression: {}'.format(loss_rpn_regr)) print('Loss Detector classifier: {}'.format( loss_class_cls)) print('Loss Detector regression: {}'.format( loss_class_regr)) print('Elapsed time: {}'.format(time.time() - start_time)) curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr iter_num = 0 start_time = time.time() if curr_loss < best_loss: if cfg.verbose: print( 'Total loss decreased from {} to {}, saving weights' .format(best_loss, curr_loss)) best_loss = curr_loss model_all.save_weights( '%s/%s/E-%d-loss-%.4f-rpnc-%.4f-rpnr-%.4f-cls-%.4f-cr-%.4f.hdf5' % (cfg.model_path, 'loss', epoch_num, curr_loss, loss_rpn_cls, loss_rpn_regr, loss_class_cls, loss_class_regr)) if loss_rpn_cls < best_rpn_cls: if cfg.verbose: print( 'loss_rpn_cls decreased from {} to {}, saving weights' .format(best_rpn_cls, loss_rpn_cls)) best_rpn_cls = loss_rpn_cls model_all.save_weights( '%s/%s/E-%d-loss-%.4f-rpnc-%.4f-rpnr-%.4f-cls-%.4f-cr-%.4f.hdf5' % (cfg.model_path, 'loss_rpn_cls', epoch_num, curr_loss, loss_rpn_cls, loss_rpn_regr, loss_class_cls, loss_class_regr)) if loss_rpn_regr < best_rpn_regr: if cfg.verbose: print( 'loss_rpn_regr decreased from {} to {}, saving weights' .format(best_rpn_regr, loss_rpn_regr)) best_rpn_regr = loss_rpn_regr model_all.save_weights( '%s/%s/E-%d-loss-%.4f-rpnc-%.4f-rpnr-%.4f-cls-%.4f-cr-%.4f.hdf5' % (cfg.model_path, 'loss_rpn_regr', epoch_num, curr_loss, loss_rpn_cls, loss_rpn_regr, loss_class_cls, loss_class_regr)) if loss_class_cls < best_class_cls: if cfg.verbose: print( 'loss_class_cls decreased from {} to {}, saving weights' .format(best_loss, loss_class_cls)) best_class_cls = loss_class_cls model_all.save_weights( '%s/%s/E-%d-loss-%.4f-rpnc-%.4f-rpnr-%.4f-cls-%.4f-cr-%.4f.hdf5' % (cfg.model_path, 'loss_class_cls', epoch_num, curr_loss, loss_rpn_cls, loss_rpn_regr, loss_class_cls, loss_class_regr)) if loss_class_regr < best_class_regr: if cfg.verbose: print( 'loss_class_regr decreased from {} to {}, saving weights' .format(best_loss, loss_class_regr)) best_class_regr = loss_class_regr model_all.save_weights( '%s/%s/E-%d-loss-%.4f-rpnc-%.4f-rpnr-%.4f-cls-%.4f-cr-%.4f.hdf5' % (cfg.model_path, 'loss_class_regr', epoch_num, curr_loss, loss_rpn_cls, loss_rpn_regr, loss_class_cls, loss_class_regr)) break except Exception as e: # print('Exception: {}'.format(e)) # save model # model_all.save_weights(cfg.model_path) continue print('Training complete, exiting.')
def run_train(train_path, output_weight_path, config_filename, parser='simple', input_weight_path=None, network='resnet50', num_rois=32, lr=1e-5, iters=100, num_epochs=100, overlap_th=0.7): C = config.Config() C.model_path = output_weight_path C.num_rois = int(num_rois) if network == 'vgg': C.network = 'vgg' from keras_frcnn import vgg as nn elif network == 'resnet50': from keras_frcnn import resnet as nn C.network = 'resnet50' else: print('Not a valid model') raise ValueError if parser == 'pascal_voc': from keras_frcnn.pascal_voc_parser import get_data elif parser == 'simple': from keras_frcnn.simple_parser import get_data else: print('Wrong parser method') raise ValueError if input_weight_path is not None: C.base_net_weights = input_weight_path else: C.base_net_weights = nn.get_weight_path() all_imgs, classes_count, class_mapping = get_data(train_path) if 'bg' not in classes_count: classes_count['bg'] = 0 class_mapping['bg'] = len(class_mapping) C.class_mapping = class_mapping inv_map = {v: k for k, v in class_mapping.items()} print('Training images per class:') pprint.pprint(classes_count) print('Num classes (including bg) = {}'.format(len(classes_count))) config_output_filename = config_filename with open(config_output_filename, 'wb') as config_f: pickle.dump(C, config_f) print( 'Config has been written to {}, and can be loaded when testing to ensure correct results' .format(config_output_filename)) random.shuffle(all_imgs) num_imgs = len(all_imgs) train_imgs = [s for s in all_imgs if s['imageset'] == 'trainval'] val_imgs = [s for s in all_imgs if s['imageset'] == 'test'] print('Num train samples {}'.format(len(train_imgs))) print('Num val samples {}'.format(len(val_imgs))) data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, C, nn.get_img_output_length, K.image_dim_ordering(), mode='train') data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, C, nn.get_img_output_length, K.image_dim_ordering(), mode='val') if K.image_dim_ordering() == 'th': input_shape_img = (3, None, None) else: input_shape_img = (None, None, 3) img_input = Input(shape=input_shape_img) roi_input = Input(shape=(None, 4)) # define the base network (resnet here, can be VGG, Inception, etc) shared_layers = nn.nn_base(img_input, trainable=True) # define the RPN, built on the base layers num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios) rpn = nn.rpn(shared_layers, num_anchors) classifier = nn.classifier(shared_layers, roi_input, C.num_rois, nb_classes=len(classes_count), trainable=True) model_rpn = Model(img_input, rpn[:2]) model_classifier = Model([img_input, roi_input], classifier) # this is a model that holds both the RPN and the classifier, used to load/save weights for the models model_all = Model([img_input, roi_input], rpn[:2] + classifier) try: print('loading weights from {}'.format(C.base_net_weights)) model_rpn.load_weights(C.base_net_weights, by_name=True) model_classifier.load_weights(C.base_net_weights, by_name=True) except: print('Could not load pretrained model weights...') optimizer = Adam(lr=lr) optimizer_classifier = Adam(lr=lr) from keras_frcnn import losses as losses model_rpn.compile(optimizer=optimizer, loss=[ losses.rpn_loss_cls(num_anchors), losses.rpn_loss_regr(num_anchors) ]) model_classifier.compile( optimizer=optimizer_classifier, loss=[ losses.class_loss_cls, losses.class_loss_regr(len(classes_count) - 1) ], metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'}) model_all.compile(optimizer='sgd', loss='mae') epoch_length = int(iters) num_epochs = int(num_epochs) iter_num = 0 losses = np.zeros((epoch_length, 5)) rpn_accuracy_rpn_monitor = [] rpn_accuracy_for_epoch = [] start_time = time.time() best_loss = np.Inf class_mapping_inv = {v: k for k, v in class_mapping.items()} print('Starting training') vis = True for epoch_num in range(num_epochs): progbar = generic_utils.Progbar(epoch_length) print('Epoch {}/{}'.format(epoch_num + 1, num_epochs)) while True: try: if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose: mean_overlapping_bboxes = float( sum(rpn_accuracy_rpn_monitor)) / len( rpn_accuracy_rpn_monitor) rpn_accuracy_rpn_monitor = [] print( 'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations' .format(mean_overlapping_bboxes, epoch_length)) if mean_overlapping_bboxes == 0: print( 'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.' ) X, Y, img_data = next(data_gen_train) loss_rpn = model_rpn.train_on_batch(X, Y) P_rpn = model_rpn.predict_on_batch(X) R = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], C, K.image_dim_ordering(), use_regr=True, overlap_thresh=overlap_th, max_boxes=300) # note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format X2, Y1, Y2, IouS = roi_helpers.calc_iou( R, img_data, C, class_mapping) if X2 is None: rpn_accuracy_rpn_monitor.append(0) rpn_accuracy_for_epoch.append(0) continue neg_samples = np.where(Y1[0, :, -1] == 1) pos_samples = np.where(Y1[0, :, -1] == 0) if len(neg_samples) > 0: neg_samples = neg_samples[0] else: neg_samples = [] if len(pos_samples) > 0: pos_samples = pos_samples[0] else: pos_samples = [] rpn_accuracy_rpn_monitor.append(len(pos_samples)) rpn_accuracy_for_epoch.append((len(pos_samples))) if C.num_rois > 1: if len(pos_samples) < C.num_rois // 2: selected_pos_samples = pos_samples.tolist() else: selected_pos_samples = np.random.choice( pos_samples, C.num_rois // 2, replace=False).tolist() try: selected_neg_samples = np.random.choice( neg_samples, C.num_rois - len(selected_pos_samples), replace=False).tolist() except: selected_neg_samples = np.random.choice( neg_samples, C.num_rois - len(selected_pos_samples), replace=True).tolist() sel_samples = selected_pos_samples + selected_neg_samples else: # in the extreme case where num_rois = 1, we pick a random pos or neg sample selected_pos_samples = pos_samples.tolist() selected_neg_samples = neg_samples.tolist() if np.random.randint(0, 2): sel_samples = random.choice(neg_samples) else: sel_samples = random.choice(pos_samples) loss_class = model_classifier.train_on_batch( [X, X2[:, sel_samples, :]], [Y1[:, sel_samples, :], Y2[:, sel_samples, :]]) losses[iter_num, 0] = loss_rpn[1] losses[iter_num, 1] = loss_rpn[2] losses[iter_num, 2] = loss_class[1] losses[iter_num, 3] = loss_class[2] losses[iter_num, 4] = loss_class[3] iter_num += 1 progbar.update( iter_num, [('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])), ('detector_cls', np.mean(losses[:iter_num, 2])), ('detector_regr', np.mean(losses[:iter_num, 3]))]) if iter_num == epoch_length: loss_rpn_cls = np.mean(losses[:, 0]) loss_rpn_regr = np.mean(losses[:, 1]) loss_class_cls = np.mean(losses[:, 2]) loss_class_regr = np.mean(losses[:, 3]) class_acc = np.mean(losses[:, 4]) mean_overlapping_bboxes = float(sum( rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch) rpn_accuracy_for_epoch = [] if C.verbose: print( 'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}' .format(mean_overlapping_bboxes)) print( 'Classifier accuracy for bounding boxes from RPN: {}' .format(class_acc)) print('Loss RPN classifier: {}'.format(loss_rpn_cls)) print('Loss RPN regression: {}'.format(loss_rpn_regr)) print('Loss Detector classifier: {}'.format( loss_class_cls)) print('Loss Detector regression: {}'.format( loss_class_regr)) print('Elapsed time: {}'.format(time.time() - start_time)) curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr iter_num = 0 start_time = time.time() if curr_loss < best_loss: if C.verbose: print( 'Total loss decreased from {} to {}, saving weights' .format(best_loss, curr_loss)) best_loss = curr_loss model_all.save_weights(C.model_path) break except Exception as e: print('Exception: {}'.format(e)) continue print('Training complete, exiting.')
if options.network == 'vgg': C.network = 'vgg' from keras_frcnn import vgg as nn elif options.network == 'resnet50': from keras_frcnn import resnet as nn C.network = 'resnet50' else: print('Not a valid model') raise ValueError # check if weight path was passed via command line if options.input_weight_path: C.base_net_weights = options.input_weight_path else: # set the path to weights based on backend and model C.base_net_weights = nn.get_weight_path( ) #'resnet50_weights_th_dim_ordering_th_kernels_notop.h5' all_imgs, classes_count, class_mapping = get_data(options.train_path) if 'bg' not in classes_count: classes_count['bg'] = 0 class_mapping['bg'] = len(class_mapping) C.class_mapping = class_mapping inv_map = {v: k for k, v in class_mapping.items()} print('Training images per class:') pprint.pprint(classes_count) print('Num classes (including bg) = {}'.format(len(classes_count)))
def train_kitti(): # config for data argument cfg = config.Config() cfg.use_horizontal_flips = True cfg.use_vertical_flips = True cfg.rot_90 = True cfg.num_rois = 32 cfg.base_net_weights = os.path.join('./model/', nn.get_weight_path()) # TODO: the only file should to be change for other data to train cfg.model_path = './model/kitti_frcnn_last.hdf5' cfg.simple_label_file = 'kitti_simple_label.txt' all_images, classes_count, class_mapping = get_data(cfg.simple_label_file) # all_images,一个dict 将 文件地址,图像尺寸,以及所有bounding box 的所有位置标出来,以及这个图像是用来trainval还是test: # {'filepath': './Datasets/training/image_2/000001.png', 'width': 1242, 'height': 375, # 'bboxes': [{'class': 'Truck', 'x1': 599, 'x2': 629, 'y1': 156, 'y2': 189}, # {'class': 'Car', 'x1': 387, 'x2': 423, 'y1': 181, 'y2': 203}, # {'class': 'Cyclist', 'x1': 676, 'x2': 688, 'y1': 163, 'y2': 193}, # {'class': 'DontCare', 'x1': 503, 'x2': 590, 'y1': 169, 'y2': 190}, # {'class': 'DontCare', 'x1': 511, 'x2': 527, 'y1': 174, 'y2': 187}, # {'class': 'DontCare', 'x1': 532, 'x2': 542, 'y1': 176, 'y2': 185}, # {'class': 'DontCare', 'x1': 559, 'x2': 575, 'y1': 175, 'y2': 183}], 'imageset': 'trainval'} # classes_count 一个dict: # {'Pedestrian': 3, 'Truck': 1, 'Car': 29, 'Cyclist': 2, 'DontCare': 29, 'Misc': 1} # class_mapping 一个 dict: # {'Pedestrian': 0, 'Truck': 1, 'Car': 2, 'Cyclist': 3, 'DontCare': 4, 'Misc': 5} # bg 类为 background 类,有时候增加 hard negative mining 会增加一些什么都没有的background 的背景类 if 'bg' not in classes_count: classes_count['bg'] = 0 class_mapping['bg'] = len(class_mapping) cfg.class_mapping = class_mapping with open(cfg.config_save_file, 'wb') as config_f: pickle.dump(cfg, config_f) print( 'Config has been written to {}, and can be loaded when testing to ensure correct results' .format(cfg.config_save_file)) inv_map = {v: k for k, v in class_mapping.items()} print('Training images per class:') pprint.pprint(classes_count) print('Num classes (including bg) = {}'.format(len(classes_count))) random.shuffle(all_images) num_imgs = len(all_images) train_imgs = [s for s in all_images if s['imageset'] == 'trainval'] val_imgs = [s for s in all_images if s['imageset'] == 'test'] print('Num train samples {}'.format(len(train_imgs))) print('Num val samples {}'.format(len(val_imgs))) data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, cfg, nn.get_img_output_length, K.image_dim_ordering(), mode='train') data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, cfg, nn.get_img_output_length, K.image_dim_ordering(), mode='val') if K.image_dim_ordering() == 'th': input_shape_img = (3, None, None) else: input_shape_img = (None, None, 3) img_input = Input(shape=input_shape_img) roi_input = Input(shape=(None, 4)) # define the base network (resnet here, can be VGG, Inception, etc) shared_layers = nn.nn_base(img_input, trainable=True) # define the RPN, built on the base layers num_anchors = len(cfg.anchor_box_scales) * len(cfg.anchor_box_ratios) rpn = nn.rpn(shared_layers, num_anchors) classifier = nn.classifier(shared_layers, roi_input, cfg.num_rois, nb_classes=len(classes_count), trainable=True) model_rpn = Model(img_input, rpn[:2]) model_classifier = Model([img_input, roi_input], classifier) # this is a model that holds both the RPN and the classifier, used to load/save weights for the models model_all = Model([img_input, roi_input], rpn[:2] + classifier) try: print('loading weights from {}'.format(cfg.base_net_weights)) model_rpn.load_weights(cfg.model_path, by_name=True) model_classifier.load_weights(cfg.model_path, by_name=True) except Exception as e: print(e) print( 'Could not load pretrained model weights. Weights can be found in the keras application folder ' 'https://github.com/fchollet/keras/tree/master/keras/applications') optimizer = Adam(lr=1e-5) optimizer_classifier = Adam(lr=1e-5) model_rpn.compile(optimizer=optimizer, loss=[ losses_fn.rpn_loss_cls(num_anchors), losses_fn.rpn_loss_regr(num_anchors) ]) model_classifier.compile( optimizer=optimizer_classifier, loss=[ losses_fn.class_loss_cls, losses_fn.class_loss_regr(len(classes_count) - 1) ], metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'}) model_all.compile(optimizer='sgd', loss='mae') epoch_length = 1000 num_epochs = int(cfg.num_epochs) iter_num = 0 losses = np.zeros((epoch_length, 5)) rpn_accuracy_rpn_monitor = [] rpn_accuracy_for_epoch = [] start_time = time.time() best_loss = np.Inf class_mapping_inv = {v: k for k, v in class_mapping.items()} print('Starting training') vis = True rpncallback = TensorBoard(log_dir='./logs/rpn', histogram_freq=1, batch_size=1, write_graph=True, write_grads=False, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None, embeddings_data=None, update_freq='epoch') classifiercallback = TensorBoard(log_dir='./logs/classifier', histogram_freq=1, batch_size=1, write_graph=True, write_grads=False, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None, embeddings_data=None, update_freq='epoch') allcallback = TensorBoard(log_dir='./logs/all', histogram_freq=1, batch_size=1, write_graph=True, write_grads=False, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None, embeddings_data=None, update_freq='epoch') for epoch_num in range(num_epochs): progbar = generic_utils.Progbar(epoch_length) print('Epoch {}/{}'.format(epoch_num + 1, num_epochs)) while True: try: if len(rpn_accuracy_rpn_monitor ) == epoch_length and cfg.verbose: mean_overlapping_bboxes = float( sum(rpn_accuracy_rpn_monitor)) / len( rpn_accuracy_rpn_monitor) rpn_accuracy_rpn_monitor = [] print( 'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations' .format(mean_overlapping_bboxes, epoch_length)) if mean_overlapping_bboxes == 0: print( 'RPN is not producing bounding boxes that overlap' ' the ground truth boxes. Check RPN settings or keep training.' ) X, Y, img_data = next(data_gen_train) loss_rpn = model_rpn.train_on_batch(X, Y) P_rpn = model_rpn.predict_on_batch(X) result = roi_helpers.rpn_to_roi(P_rpn[0], P_rpn[1], cfg, K.image_dim_ordering(), use_regr=True, overlap_thresh=0.7, max_boxes=300) # note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format X2, Y1, Y2, IouS = roi_helpers.calc_iou( result, img_data, cfg, class_mapping) if X2 is None: rpn_accuracy_rpn_monitor.append(0) rpn_accuracy_for_epoch.append(0) continue neg_samples = np.where(Y1[0, :, -1] == 1) pos_samples = np.where(Y1[0, :, -1] == 0) if len(neg_samples) > 0: neg_samples = neg_samples[0] else: neg_samples = [] if len(pos_samples) > 0: pos_samples = pos_samples[0] else: pos_samples = [] rpn_accuracy_rpn_monitor.append(len(pos_samples)) rpn_accuracy_for_epoch.append((len(pos_samples))) if cfg.num_rois > 1: if len(pos_samples) < cfg.num_rois // 2: selected_pos_samples = pos_samples.tolist() else: selected_pos_samples = np.random.choice( pos_samples, cfg.num_rois // 2, replace=False).tolist() try: selected_neg_samples = np.random.choice( neg_samples, cfg.num_rois - len(selected_pos_samples), replace=False).tolist() except: selected_neg_samples = np.random.choice( neg_samples, cfg.num_rois - len(selected_pos_samples), replace=True).tolist() sel_samples = selected_pos_samples + selected_neg_samples else: # in the extreme case where num_rois = 1, we pick a random pos or neg sample selected_pos_samples = pos_samples.tolist() selected_neg_samples = neg_samples.tolist() if np.random.randint(0, 2): sel_samples = random.choice(neg_samples) else: sel_samples = random.choice(pos_samples) loss_class = model_classifier.train_on_batch( [X, X2[:, sel_samples, :]], [Y1[:, sel_samples, :], Y2[:, sel_samples, :]]) losses[iter_num, 0] = loss_rpn[1] losses[iter_num, 1] = loss_rpn[2] losses[iter_num, 2] = loss_class[1] losses[iter_num, 3] = loss_class[2] losses[iter_num, 4] = loss_class[3] iter_num += 1 progbar.update( iter_num, [('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])), ('detector_cls', np.mean(losses[:iter_num, 2])), ('detector_regr', np.mean(losses[:iter_num, 3]))]) if iter_num == epoch_length: loss_rpn_cls = np.mean(losses[:, 0]) loss_rpn_regr = np.mean(losses[:, 1]) loss_class_cls = np.mean(losses[:, 2]) loss_class_regr = np.mean(losses[:, 3]) class_acc = np.mean(losses[:, 4]) mean_overlapping_bboxes = float(sum( rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch) rpn_accuracy_for_epoch = [] if cfg.verbose: print( 'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}' .format(mean_overlapping_bboxes)) print( 'Classifier accuracy for bounding boxes from RPN: {}' .format(class_acc)) print('Loss RPN classifier: {}'.format(loss_rpn_cls)) print('Loss RPN regression: {}'.format(loss_rpn_regr)) print('Loss Detector classifier: {}'.format( loss_class_cls)) print('Loss Detector regression: {}'.format( loss_class_regr)) print('Elapsed time: {}'.format(time.time() - start_time)) curr_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr iter_num = 0 start_time = time.time() if curr_loss < best_loss: if cfg.verbose: print( 'Total loss decreased from {} to {}, saving weights' .format(best_loss, curr_loss)) best_loss = curr_loss model_all.save_weights(cfg.model_path) break except Exception as e: print('Exception: {}'.format(e)) # save model model_all.save_weights(cfg.model_path) continue print('Training complete, exiting.')