"""Train Faster-RCNN end to end.""" import argparse import os # disable autotune os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '0' import logging import time import numpy as np import mxnet as mx from mxnet import gluon from mxnet import autograd from mxnet.contrib import amp import gluoncv as gcv from gluoncv import data as gdata from gluoncv import utils as gutils from gluoncv.model_zoo import get_model from gluoncv.data.batchify import FasterRCNNTrainBatchify, Tuple, Append from gluoncv.data.transforms.presets.rcnn import FasterRCNNDefaultTrainTransform, \ FasterRCNNDefaultValTransform from gluoncv.utils.metrics.voc_detection import VOC07MApMetric from gluoncv.utils.metrics.coco_detection import COCODetectionMetric from gluoncv.utils.parallel import Parallelizable, Parallel from gluoncv.utils.metrics.rcnn import RPNAccMetric, RPNL1LossMetric, RCNNAccMetric, \ RCNNL1LossMetric from data import * from model import faster_rcnn_resnet101_v1d_custom, faster_rcnn_resnet50_v1b_custom try: import horovod.mxnet as hvd except ImportError: hvd = None def parse_args(): parser = argparse.ArgumentParser(description='Train Faster-RCNN networks e2e.') parser.add_argument('--network', type=str, default='resnet101_v1d', help="Base network name which serves as feature extraction base.") parser.add_argument('--dataset', type=str, default='visualgenome', help='Training dataset. Now support voc and coco.') parser.add_argument('--num-workers', '-j', dest='num_workers', type=int, default=8, help='Number of data workers, you can use larger ' 'number to accelerate data loading, ' 'if your CPU and GPUs are powerful.') parser.add_argument('--batch-size', type=int, default=8, help='Training mini-batch size.') parser.add_argument('--gpus', type=str, default='0', help='Training with GPUs, you can specify 1,3 for example.') parser.add_argument('--epochs', type=str, default='', help='Training epochs.') parser.add_argument('--resume', type=str, default='', help='Resume from previously saved parameters if not None. ' 'For example, you can resume from ./faster_rcnn_xxx_0123.params') parser.add_argument('--start-epoch', type=int, default=0, help='Starting epoch for resuming, default is 0 for new training.' 'You can specify it to 100 for example to start from 100 epoch.') parser.add_argument('--lr', type=str, default='', help='Learning rate, default is 0.001 for voc single gpu training.') parser.add_argument('--lr-decay', type=float, default=0.1, help='decay rate of learning rate. default is 0.1.') parser.add_argument('--lr-decay-epoch', type=str, default='', help='epochs at which learning rate decays. default is 14,20 for voc.') parser.add_argument('--lr-warmup', type=str, default='', help='warmup iterations to adjust learning rate, default is 0 for voc.') parser.add_argument('--lr-warmup-factor', type=float, default=1. / 3., help='warmup factor of base lr.') parser.add_argument('--momentum', type=float, default=0.9, help='SGD momentum, default is 0.9') parser.add_argument('--wd', type=str, default='', help='Weight decay, default is 5e-4 for voc') parser.add_argument('--log-interval', type=int, default=100, help='Logging mini-batch interval. Default is 100.') parser.add_argument('--save-prefix', type=str, default='', help='Saving parameter prefix') parser.add_argument('--save-interval', type=int, default=1, help='Saving parameters epoch interval, best model will always be saved.') parser.add_argument('--val-interval', type=int, default=1, help='Epoch interval for validation, increase the number will reduce the ' 'training time if validation is slow.') parser.add_argument('--seed', type=int, default=233, help='Random seed to be fixed.') parser.add_argument('--verbose', dest='verbose', action='store_true', help='Print helpful debugging info once set.') parser.add_argument('--mixup', action='store_true', help='Use mixup training.') parser.add_argument('--no-mixup-epochs', type=int, default=20, help='Disable mixup training if enabled in the last N epochs.') # Norm layer options parser.add_argument('--norm-layer', type=str, default=None, help='Type of normalization layer to use. ' 'If set to None, backbone normalization layer will be fixed,' ' and no normalization layer will be used. ' 'Currently supports \'bn\', and None, default is None.' 'Note that if horovod is enabled, sync bn will not work correctly.') # FPN options parser.add_argument('--use-fpn', action='store_true', help='Whether to use feature pyramid network.') # Performance options parser.add_argument('--disable-hybridization', action='store_true', help='Whether to disable hybridize the model. ' 'Memory usage and speed will decrese.') parser.add_argument('--static-alloc', action='store_true', help='Whether to use static memory allocation. Memory usage will increase.') parser.add_argument('--amp', action='store_true', help='Use MXNet AMP for mixed precision training.') parser.add_argument('--horovod', action='store_true', help='Use MXNet Horovod for distributed training. Must be run with OpenMPI. ' '--gpus is ignored when using --horovod.') parser.add_argument('--executor-threads', type=int, default=1, help='Number of threads for executor for scheduling ops. ' 'More threads may incur higher GPU memory footprint, ' 'but may speed up throughput. Note that when horovod is used, ' 'it is set to 1.') parser.add_argument('--kv-store', type=str, default='nccl', help='KV store options. local, device, nccl, dist_sync, dist_device_sync, ' 'dist_async are available.') args = parser.parse_args() if args.horovod: if hvd is None: raise SystemExit("Horovod not found, please check if you installed it correctly.") hvd.init() if args.dataset == 'voc': args.epochs = int(args.epochs) if args.epochs else 20 args.lr_decay_epoch = args.lr_decay_epoch if args.lr_decay_epoch else '14,20' args.lr = float(args.lr) if args.lr else 0.001 args.lr_warmup = args.lr_warmup if args.lr_warmup else -1 args.wd = float(args.wd) if args.wd else 5e-4 elif args.dataset == 'visualgenome': args.epochs = int(args.epochs) if args.epochs else 20 args.lr_decay_epoch = args.lr_decay_epoch if args.lr_decay_epoch else '14,20' args.lr = float(args.lr) if args.lr else 0.001 args.lr_warmup = args.lr_warmup if args.lr_warmup else -1 args.wd = float(args.wd) if args.wd else 5e-4 elif args.dataset == 'coco': args.epochs = int(args.epochs) if args.epochs else 26 args.lr_decay_epoch = args.lr_decay_epoch if args.lr_decay_epoch else '17,23' args.lr = float(args.lr) if args.lr else 0.01 args.lr_warmup = args.lr_warmup if args.lr_warmup else 1000 args.wd = float(args.wd) if args.wd else 1e-4 return args def get_dataset(dataset, args): if dataset.lower() == 'voc': train_dataset = gdata.VOCDetection( splits=[(2007, 'trainval'), (2012, 'trainval')]) val_dataset = gdata.VOCDetection( splits=[(2007, 'test')]) val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=val_dataset.classes) elif dataset.lower() == 'coco': train_dataset = gdata.COCODetection(splits='instances_train2017', use_crowd=False) val_dataset = gdata.COCODetection(splits='instances_val2017', skip_empty=False) val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True) elif dataset.lower() == 'visualgenome': train_dataset = VGObject(root=os.path.join('~', '.mxnet', 'datasets', 'visualgenome'), splits='detections_train', use_crowd=False) val_dataset = VGObject(root=os.path.join('~', '.mxnet', 'datasets', 'visualgenome'), splits='detections_val', skip_empty=False) val_metric = COCODetectionMetric(val_dataset, args.save_prefix + '_eval', cleanup=True) else: raise NotImplementedError('Dataset: {} not implemented.'.format(dataset)) if args.mixup: from gluoncv.data.mixup import detection train_dataset = detection.MixupDetection(train_dataset) return train_dataset, val_dataset, val_metric def get_dataloader(net, train_dataset, val_dataset, train_transform, val_transform, batch_size, num_shards, args): """Get dataloader.""" train_bfn = FasterRCNNTrainBatchify(net, num_shards) if hasattr(train_dataset, 'get_im_aspect_ratio'): im_aspect_ratio = train_dataset.get_im_aspect_ratio() else: im_aspect_ratio = [1.] * len(train_dataset) train_sampler = \ gcv.nn.sampler.SplitSortedBucketSampler(im_aspect_ratio, batch_size, num_parts=hvd.size() if args.horovod else 1, part_index=hvd.rank() if args.horovod else 0, shuffle=True) train_loader = mx.gluon.data.DataLoader(train_dataset.transform( train_transform(net.short, net.max_size, net, ashape=net.ashape, multi_stage=args.use_fpn)), batch_sampler=train_sampler, batchify_fn=train_bfn, num_workers=args.num_workers) if val_dataset is None: val_loader = None else: val_bfn = Tuple(*[Append() for _ in range(3)]) short = net.short[-1] if isinstance(net.short, (tuple, list)) else net.short # validation use 1 sample per device val_loader = mx.gluon.data.DataLoader( val_dataset.transform(val_transform(short, net.max_size)), num_shards, False, batchify_fn=val_bfn, last_batch='keep', num_workers=args.num_workers) return train_loader, val_loader def save_params(net, logger, best_map, current_map, epoch, save_interval, prefix): current_map = float(current_map) if current_map > best_map[0]: logger.info('[Epoch {}] mAP {} higher than current best {} saving to {}'.format( epoch, current_map, best_map, '{:s}_best.params'.format(prefix))) best_map[0] = current_map net.save_parameters('{:s}_best.params'.format(prefix)) with open(prefix + '_best_map.log', 'a') as f: f.write('{:04d}:\t{:.4f}\n'.format(epoch, current_map)) if save_interval and (epoch + 1) % save_interval == 0: logger.info('[Epoch {}] Saving parameters to {}'.format( epoch, '{:s}_{:04d}_{:.4f}.params'.format(prefix, epoch, current_map))) net.save_parameters('{:s}_{:04d}_{:.4f}.params'.format(prefix, epoch, current_map)) def split_and_load(batch, ctx_list): """Split data to 1 batch each device.""" new_batch = [] for i, data in enumerate(batch): if isinstance(data, (list, tuple)): new_data = [x.as_in_context(ctx) for x, ctx in zip(data, ctx_list)] else: new_data = [data.as_in_context(ctx_list[0])] new_batch.append(new_data) return new_batch def validate(net, val_data, ctx, eval_metric, args): """Test on validation dataset.""" clipper = gcv.nn.bbox.BBoxClipToImage() eval_metric.reset() if not args.disable_hybridization: # input format is differnet than training, thus rehybridization is needed. net.hybridize(static_alloc=args.static_alloc) for i, batch in enumerate(val_data): batch = split_and_load(batch, ctx_list=ctx) det_bboxes = [] det_ids = [] det_scores = [] gt_bboxes = [] gt_ids = [] gt_difficults = [] for x, y, im_scale in zip(*batch): # get prediction results ids, scores, bboxes = net(x) det_ids.append(ids) det_scores.append(scores) # clip to image size det_bboxes.append(clipper(bboxes, x)) # rescale to original resolution im_scale = im_scale.reshape((-1)).asscalar() det_bboxes[-1] *= im_scale # split ground truths gt_ids.append(y.slice_axis(axis=-1, begin=4, end=5)) gt_bboxes.append(y.slice_axis(axis=-1, begin=0, end=4)) gt_bboxes[-1] *= im_scale gt_difficults.append(y.slice_axis(axis=-1, begin=5, end=6) if y.shape[-1] > 5 else None) # update metric for det_bbox, det_id, det_score, gt_bbox, gt_id, gt_diff in zip(det_bboxes, det_ids, det_scores, gt_bboxes, gt_ids, gt_difficults): eval_metric.update(det_bbox, det_id, det_score, gt_bbox, gt_id, gt_diff) return eval_metric.get() def get_lr_at_iter(alpha, lr_warmup_factor=1. / 3.): return lr_warmup_factor * (1 - alpha) + alpha class ForwardBackwardTask(Parallelizable): def __init__(self, net, optimizer, rpn_cls_loss, rpn_box_loss, rcnn_cls_loss, rcnn_box_loss, mix_ratio): super(ForwardBackwardTask, self).__init__() self.net = net self._optimizer = optimizer self.rpn_cls_loss = rpn_cls_loss self.rpn_box_loss = rpn_box_loss self.rcnn_cls_loss = rcnn_cls_loss self.rcnn_box_loss = rcnn_box_loss self.mix_ratio = mix_ratio def forward_backward(self, x): data, label, rpn_cls_targets, rpn_box_targets, rpn_box_masks = x with autograd.record(): gt_label = label[:, :, 4:5] gt_box = label[:, :, :4] cls_pred, box_pred, roi, samples, matches, rpn_score, rpn_box, anchors, cls_targets, \ box_targets, box_masks, _ = net(data, gt_box, gt_label) # losses of rpn rpn_score = rpn_score.squeeze(axis=-1) num_rpn_pos = (rpn_cls_targets >= 0).sum() rpn_loss1 = self.rpn_cls_loss(rpn_score, rpn_cls_targets, rpn_cls_targets >= 0) * rpn_cls_targets.size / num_rpn_pos rpn_loss2 = self.rpn_box_loss(rpn_box, rpn_box_targets, rpn_box_masks) * rpn_box.size / num_rpn_pos # rpn overall loss, use sum rather than average rpn_loss = rpn_loss1 + rpn_loss2 # losses of rcnn num_rcnn_pos = (cls_targets >= 0).sum() rcnn_loss1 = self.rcnn_cls_loss(cls_pred, cls_targets, cls_targets.expand_dims(-1) >= 0) * cls_targets.size / \ num_rcnn_pos rcnn_loss2 = self.rcnn_box_loss(box_pred, box_targets, box_masks) * box_pred.size / \ num_rcnn_pos rcnn_loss = rcnn_loss1 + rcnn_loss2 # overall losses total_loss = rpn_loss.sum() * self.mix_ratio + rcnn_loss.sum() * self.mix_ratio rpn_loss1_metric = rpn_loss1.mean() * self.mix_ratio rpn_loss2_metric = rpn_loss2.mean() * self.mix_ratio rcnn_loss1_metric = rcnn_loss1.mean() * self.mix_ratio rcnn_loss2_metric = rcnn_loss2.mean() * self.mix_ratio rpn_acc_metric = [[rpn_cls_targets, rpn_cls_targets >= 0], [rpn_score]] rpn_l1_loss_metric = [[rpn_box_targets, rpn_box_masks], [rpn_box]] rcnn_acc_metric = [[cls_targets], [cls_pred]] rcnn_l1_loss_metric = [[box_targets, box_masks], [box_pred]] if args.amp: with amp.scale_loss(total_loss, self._optimizer) as scaled_losses: autograd.backward(scaled_losses) else: total_loss.backward() return rpn_loss1_metric, rpn_loss2_metric, rcnn_loss1_metric, rcnn_loss2_metric, \ rpn_acc_metric, rpn_l1_loss_metric, rcnn_acc_metric, rcnn_l1_loss_metric def train(net, train_data, val_data, eval_metric, batch_size, ctx, args): """Training pipeline""" args.kv_store = 'device' if (args.amp and 'nccl' in args.kv_store) else args.kv_store kv = mx.kvstore.create(args.kv_store) net.collect_params().setattr('grad_req', 'null') net.collect_train_params().setattr('grad_req', 'write') optimizer_params = {'learning_rate': args.lr, 'wd': args.wd, 'momentum': args.momentum} if args.horovod: hvd.broadcast_parameters(net.collect_params(), root_rank=0) trainer = hvd.DistributedTrainer( net.collect_train_params(), # fix batchnorm, fix first stage, etc... 'sgd', optimizer_params) else: trainer = gluon.Trainer( net.collect_train_params(), # fix batchnorm, fix first stage, etc... 'sgd', optimizer_params, update_on_kvstore=(False if args.amp else None), kvstore=kv) if args.amp: amp.init_trainer(trainer) # lr decay policy lr_decay = float(args.lr_decay) lr_steps = sorted([float(ls) for ls in args.lr_decay_epoch.split(',') if ls.strip()]) lr_warmup = float(args.lr_warmup) # avoid int division # TODO(zhreshold) losses? rpn_cls_loss = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss(from_sigmoid=False) rpn_box_loss = mx.gluon.loss.HuberLoss(rho=1 / 9.) # == smoothl1 rcnn_cls_loss = mx.gluon.loss.SoftmaxCrossEntropyLoss() rcnn_box_loss = mx.gluon.loss.HuberLoss() # == smoothl1 metrics = [mx.metric.Loss('RPN_Conf'), mx.metric.Loss('RPN_SmoothL1'), mx.metric.Loss('RCNN_CrossEntropy'), mx.metric.Loss('RCNN_SmoothL1'), ] rpn_acc_metric = RPNAccMetric() rpn_bbox_metric = RPNL1LossMetric() rcnn_acc_metric = RCNNAccMetric() rcnn_bbox_metric = RCNNL1LossMetric() metrics2 = [rpn_acc_metric, rpn_bbox_metric, rcnn_acc_metric, rcnn_bbox_metric] # set up logger logging.basicConfig() logger = logging.getLogger() logger.setLevel(logging.INFO) log_file_path = args.save_prefix + '_train.log' log_dir = os.path.dirname(log_file_path) if log_dir and not os.path.exists(log_dir): os.makedirs(log_dir) fh = logging.FileHandler(log_file_path) logger.addHandler(fh) logger.info(args) if args.verbose: logger.info('Trainable parameters:') logger.info(net.collect_train_params().keys()) logger.info('Start training from [Epoch {}]'.format(args.start_epoch)) best_map = [0] for epoch in range(args.start_epoch, args.epochs): mix_ratio = 1.0 if not args.disable_hybridization: net.hybridize(static_alloc=args.static_alloc) rcnn_task = ForwardBackwardTask(net, trainer, rpn_cls_loss, rpn_box_loss, rcnn_cls_loss, rcnn_box_loss, mix_ratio=1.0) executor = Parallel(args.executor_threads, rcnn_task) if not args.horovod else None if args.mixup: # TODO(zhreshold) only support evenly mixup now, target generator needs to be modified otherwise train_data._dataset._data.set_mixup(np.random.uniform, 0.5, 0.5) mix_ratio = 0.5 if epoch >= args.epochs - args.no_mixup_epochs: train_data._dataset._data.set_mixup(None) mix_ratio = 1.0 while lr_steps and epoch >= lr_steps[0]: new_lr = trainer.learning_rate * lr_decay lr_steps.pop(0) trainer.set_learning_rate(new_lr) logger.info("[Epoch {}] Set learning rate to {}".format(epoch, new_lr)) for metric in metrics: metric.reset() tic = time.time() btic = time.time() base_lr = trainer.learning_rate rcnn_task.mix_ratio = mix_ratio logger.info('Total Num of Batches: %d'%(len(train_data))) for i, batch in enumerate(train_data): if epoch == 0 and i <= lr_warmup: # adjust based on real percentage new_lr = base_lr * get_lr_at_iter(i / lr_warmup, args.lr_warmup_factor) if new_lr != trainer.learning_rate: if i % args.log_interval == 0: logger.info( '[Epoch 0 Iteration {}] Set learning rate to {}'.format(i, new_lr)) trainer.set_learning_rate(new_lr) batch = split_and_load(batch, ctx_list=ctx) metric_losses = [[] for _ in metrics] add_losses = [[] for _ in metrics2] if executor is not None: for data in zip(*batch): executor.put(data) for j in range(len(ctx)): if executor is not None: result = executor.get() else: result = rcnn_task.forward_backward(list(zip(*batch))[0]) if (not args.horovod) or hvd.rank() == 0: for k in range(len(metric_losses)): metric_losses[k].append(result[k]) for k in range(len(add_losses)): add_losses[k].append(result[len(metric_losses) + k]) for metric, record in zip(metrics, metric_losses): metric.update(0, record) for metric, records in zip(metrics2, add_losses): for pred in records: metric.update(pred[0], pred[1]) trainer.step(batch_size) # update metrics if (not args.horovod or hvd.rank() == 0) and args.log_interval \ and not (i + 1) % args.log_interval: msg = ','.join( ['{}={:.3f}'.format(*metric.get()) for metric in metrics + metrics2]) logger.info('[Epoch {}][Batch {}], Speed: {:.3f} samples/sec, {}'.format( epoch, i, args.log_interval * args.batch_size / (time.time() - btic), msg)) btic = time.time() if (not args.horovod) or hvd.rank() == 0: msg = ','.join(['{}={:.3f}'.format(*metric.get()) for metric in metrics]) logger.info('[Epoch {}] Training cost: {:.3f}, {}'.format( epoch, (time.time() - tic), msg)) if not (epoch + 1) % args.val_interval: # consider reduce the frequency of validation to save time if val_data is not None: map_name, mean_ap = validate(net, val_data, ctx, eval_metric, args) val_msg = '\n'.join(['{}={}'.format(k, v) for k, v in zip(map_name, mean_ap)]) logger.info('[Epoch {}] Validation: \n{}'.format(epoch, val_msg)) current_map = float(mean_ap[-1]) else: current_map = 0 else: current_map = 0. save_params(net, logger, best_map, current_map, epoch, args.save_interval, args.save_prefix) if __name__ == '__main__': import sys sys.setrecursionlimit(1100) args = parse_args() # fix seed for mxnet, numpy and python builtin random generator. gutils.random.seed(args.seed) if args.amp: amp.init() # training contexts if args.horovod: ctx = [mx.gpu(hvd.local_rank())] else: ctx = [mx.gpu(int(i)) for i in args.gpus.split(',') if i.strip()] ctx = ctx if ctx else [mx.cpu()] # network kwargs = {} module_list = [] if args.use_fpn: module_list.append('fpn') if args.norm_layer is not None: module_list.append(args.norm_layer) if args.norm_layer == 'bn': kwargs['num_devices'] = len(args.gpus.split(',')) net_name = '_'.join(('faster_rcnn', *module_list, args.network, 'custom')) args.save_prefix += net_name gutils.makedirs(args.save_prefix) train_dataset, val_dataset, eval_metric = get_dataset(args.dataset, args) net = faster_rcnn_resnet101_v1d_custom(classes=train_dataset.classes, transfer='coco', pretrained_base=False, additional_output=False, per_device_batch_size=args.batch_size // len(ctx), **kwargs) if args.resume.strip(): net.load_parameters(args.resume.strip()) else: for param in net.collect_params().values(): if param._data is not None: continue param.initialize() net.collect_params().reset_ctx(ctx) # training data batch_size = args.batch_size // len(ctx) if args.horovod else args.batch_size train_data, val_data = get_dataloader( net, train_dataset, val_dataset, FasterRCNNDefaultTrainTransform, FasterRCNNDefaultValTransform, batch_size, len(ctx), args) # training train(net, train_data, val_data, eval_metric, batch_size, ctx, args)