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train_faster_rcnn.py
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train_faster_rcnn.py
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"""Train Faster-RCNN end to end."""
import argparse
import os
# disable autotune
os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '0'
os.environ['MXNET_GPU_MEM_POOL_TYPE'] = 'Round'
os.environ['MXNET_GPU_MEM_POOL_ROUND_LINEAR_CUTOFF'] = '26'
os.environ['MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD'] = '999'
os.environ['MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_BWD'] = '25'
os.environ['MXNET_GPU_COPY_NTHREADS'] = '1'
os.environ['MXNET_OPTIMIZER_AGGREGATION_SIZE'] = '54'
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
gcv.utils.check_version('0.7.0')
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 gluoncv.data import COCODetection, VOCDetection
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='resnet50_v1b',
choices=['resnet18_v1b', 'resnet50_v1b', 'resnet101_v1d'],
help="Base network name which serves as feature extraction base.")
parser.add_argument('--dataset', type=str, default='voc',
help='Training dataset. Now support voc and coco.')
parser.add_argument('--num-workers', '-j', dest='num_workers', type=int,
default=4, 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=1, 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='100',
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, choices=[None, 'syncbn'],
help='Type of normalization layer to use. '
'If set to None, backbone normalization layer will be frozen,'
' and no normalization layer will be used in R-CNN. '
'Currently supports \'syncbn\', and None, default is None.'
'Note that if horovod is enabled, sync bn will not work correctly.')
# Loss options
parser.add_argument('--rpn-smoothl1-rho', type=float, default=1. / 9.,
help='RPN box regression transition point from L1 to L2 loss.'
'Set to 0.0 to make the loss simply L1.')
parser.add_argument('--rcnn-smoothl1-rho', type=float, default=1.,
help='RCNN box regression transition point from L1 to L2 loss.'
'Set to 0.0 to make the loss simply L1.')
# 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.')
# Advanced options. Expert Only!! Currently non-FPN model is not supported!!
# Default setting is for MS-COCO.
# The following options are only used if --custom-model is enabled
subparsers = parser.add_subparsers(dest='custom_model')
custom_model_parser = subparsers.add_parser(
'custom-model',
help='Use custom Faster R-CNN w/ FPN model. This is for expert only!'
' You can modify model internal parameters here. Once enabled, '
'custom model options become available.')
custom_model_parser.add_argument(
'--no-pretrained-base', action='store_true', help='Disable pretrained base network.')
custom_model_parser.add_argument(
'--num-fpn-filters', type=int, default=256, help='Number of filters in FPN output layers.')
custom_model_parser.add_argument(
'--num-box-head-conv', type=int, default=4,
help='Number of convolution layers to use in box head if '
'batch normalization is not frozen.')
custom_model_parser.add_argument(
'--num-box-head-conv-filters', type=int, default=256,
help='Number of filters for convolution layers in box head.'
' Only applicable if batch normalization is not frozen.')
custom_model_parser.add_argument(
'--num_box_head_dense_filters', type=int, default=1024,
help='Number of hidden units for the last fully connected layer in '
'box head.')
custom_model_parser.add_argument(
'--image-short', type=str, default='800',
help='Short side of the image. Pass a tuple to enable random scale augmentation.')
custom_model_parser.add_argument(
'--image-max-size', type=int, default=1333,
help='Max size of the longer side of the image.')
custom_model_parser.add_argument(
'--nms-thresh', type=float, default=0.5,
help='Non-maximum suppression threshold for R-CNN. '
'You can specify < 0 or > 1 to disable NMS.')
custom_model_parser.add_argument(
'--nms-topk', type=int, default=-1,
help='Apply NMS to top k detection results in R-CNN. '
'Set to -1 to disable so that every Detection result is used in NMS.')
custom_model_parser.add_argument(
'--post-nms', type=int, default=-1,
help='Only return top `post_nms` detection results, the rest is discarded.'
' Set to -1 to return all detections.')
custom_model_parser.add_argument(
'--roi-mode', type=str, default='align', choices=['align', 'pool'],
help='ROI pooling mode. Currently support \'pool\' and \'align\'.')
custom_model_parser.add_argument(
'--roi-size', type=str, default='7,7',
help='The output spatial size of ROI layer. eg. ROIAlign, ROIPooling')
custom_model_parser.add_argument(
'--strides', type=str, default='4,8,16,32,64',
help='Feature map stride with respect to original image. '
'This is usually the ratio between original image size and '
'feature map size. Since the custom model uses FPN, it is a list of ints')
custom_model_parser.add_argument(
'--clip', type=float, default=4.14,
help='Clip bounding box transformation predictions '
'to prevent exponentiation from overflowing')
custom_model_parser.add_argument(
'--rpn-channel', type=int, default=256,
help='Number of channels used in RPN convolution layers.')
custom_model_parser.add_argument(
'--anchor-base-size', type=int, default=16,
help='The width(and height) of reference anchor box.')
custom_model_parser.add_argument(
'--anchor-aspect-ratio', type=str, default='0.5,1,2',
help='The aspect ratios of anchor boxes.')
custom_model_parser.add_argument(
'--anchor-scales', type=str, default='2,4,8,16,32',
help='The scales of anchor boxes with respect to base size. '
'We use the following form to compute the shapes of anchors: '
'anchor_width = base_size * scale * sqrt(1 / ratio)'
'anchor_height = base_size * scale * sqrt(ratio)')
custom_model_parser.add_argument(
'--anchor-alloc-size', type=str, default='384,384',
help='Allocate size for the anchor boxes as (H, W). '
'We generate enough anchors for large feature map, e.g. 384x384. '
'During inference we can have variable input sizes, '
'at which time we can crop corresponding anchors from this large '
'anchor map so we can skip re-generating anchors for each input. ')
custom_model_parser.add_argument(
'--rpn-nms-thresh', type=float, default='0.7',
help='Non-maximum suppression threshold for RPN.')
custom_model_parser.add_argument(
'--rpn-train-pre-nms', type=int, default=12000,
help='Filter top proposals before NMS in RPN training.')
custom_model_parser.add_argument(
'--rpn-train-post-nms', type=int, default=2000,
help='Return top proposal results after NMS in RPN training. '
'Will be set to rpn_train_pre_nms if it is larger than '
'rpn_train_pre_nms.')
custom_model_parser.add_argument(
'--rpn-test-pre-nms', type=int, default=6000,
help='Filter top proposals before NMS in RPN testing.')
custom_model_parser.add_argument(
'--rpn-test-post-nms', type=int, default=1000,
help='Return top proposal results after NMS in RPN testing. '
'Will be set to rpn_test_pre_nms if it is larger than rpn_test_pre_nms.')
custom_model_parser.add_argument(
'--rpn-min-size', type=int, default=1,
help='Proposals whose size is smaller than ``min_size`` will be discarded.')
custom_model_parser.add_argument(
'--rcnn-num-samples', type=int, default=512, help='Number of samples for RCNN training.')
custom_model_parser.add_argument(
'--rcnn-pos-iou-thresh', type=float, default=0.5,
help='Proposal whose IOU larger than ``pos_iou_thresh`` is '
'regarded as positive samples for R-CNN.')
custom_model_parser.add_argument(
'--rcnn-pos-ratio', type=float, default=0.25,
help='``pos_ratio`` defines how many positive samples '
'(``pos_ratio * num_sample``) is to be sampled for R-CNN.')
custom_model_parser.add_argument(
'--max-num-gt', type=int, default=100,
help='Maximum ground-truth number for each example. This is only an upper bound, not'
'necessarily very precise. However, using a very big number may impact the '
'training speed.')
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 == '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 0.00125
args.lr_warmup = args.lr_warmup if args.lr_warmup else 1000
args.wd = float(args.wd) if args.wd else 1e-4
def str_args2num_args(arguments, args_name, num_type):
try:
ret = [num_type(x) for x in arguments.split(',')]
if len(ret) == 1:
return ret[0]
return ret
except ValueError:
raise ValueError('invalid value for', args_name, arguments)
if args.custom_model:
args.image_short = str_args2num_args(args.image_short, '--image-short', int)
args.roi_size = str_args2num_args(args.roi_size, '--roi-size', int)
args.strides = str_args2num_args(args.strides, '--strides', int)
args.anchor_aspect_ratio = str_args2num_args(args.anchor_aspect_ratio,
'--anchor-aspect-ratio', float)
args.anchor_scales = str_args2num_args(args.anchor_scales, '--anchor-scales', float)
args.anchor_alloc_size = str_args2num_args(args.anchor_alloc_size,
'--anchor-alloc-size', int)
if args.amp and args.norm_layer == 'syncbn':
raise NotImplementedError('SyncBatchNorm currently does not support AMP.')
return args
from gluoncv.model_zoo.vgg import vgg16
from mxnet.gluon import nn
from gluoncv.model_zoo.rcnn.faster_rcnn import get_faster_rcnn
class VOCLike(VOCDetection):
CLASSES = ['flower']
def __init__(self, root, splits, transform=None, index_map=None, preload_label=True):
super(VOCLike, self).__init__(root, splits, transform, index_map, preload_label)
def get_dataset(dataset,args):
CLASSES = ['flower']
train_dataset = VOCLike(root='C:/Users/15943/Desktop', splits=((2007, 'train'),))
val_dataset = VOCLike(root='C:/Users/15943/Desktop', splits=((2007, 'trainval'),))
val_metric = VOC07MApMetric(iou_thresh=0.5, class_names=CLASSES)
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)
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 batch in 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, _ = self.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.amp:
optimizer_params['multi_precision'] = True
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=args.rpn_smoothl1_rho) # == smoothl1
rcnn_cls_loss = mx.gluon.loss.SoftmaxCrossEntropyLoss()
rcnn_box_loss = mx.gluon.loss.HuberLoss(rho=args.rcnn_smoothl1_rho) # == 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)
if args.custom_model:
logger.info('Custom model enabled. Expert Only!! Currently non-FPN model is not supported!!'
' Default setting is for MS-COCO.')
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
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
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.
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 == 'syncbn':
kwargs['num_devices'] = len(ctx)
num_gpus = hvd.size() if args.horovod else len(ctx)
net_name = '_'.join(('faster_rcnn', *module_list, args.network, args.dataset))
if args.custom_model:
args.use_fpn = True
net_name = '_'.join(('custom_faster_rcnn_fpn', args.network, args.dataset))
if args.norm_layer == 'syncbn':
norm_layer = gluon.contrib.nn.SyncBatchNorm
norm_kwargs = {'num_devices': len(ctx)}
sym_norm_layer = mx.sym.contrib.SyncBatchNorm
sym_norm_kwargs = {'ndev': len(ctx)}
elif args.norm_layer == 'gn':
norm_layer = gluon.nn.GroupNorm
norm_kwargs = {'groups': 8}
sym_norm_layer = mx.sym.GroupNorm
sym_norm_kwargs = {'groups': 8}
else:
norm_layer = gluon.nn.BatchNorm
norm_kwargs = None
sym_norm_layer = None
sym_norm_kwargs = None
if args.dataset == 'coco':
classes = COCODetection.CLASSES
else:
# default to VOC
classes = VOCDetection.CLASSES
net = get_model('custom_faster_rcnn_fpn', classes=classes, transfer=None,
dataset=args.dataset, pretrained_base=not args.no_pretrained_base,
base_network_name=args.network, norm_layer=norm_layer,
norm_kwargs=norm_kwargs, sym_norm_kwargs=sym_norm_kwargs,
num_fpn_filters=args.num_fpn_filters,
num_box_head_conv=args.num_box_head_conv,
num_box_head_conv_filters=args.num_box_head_conv_filters,
num_box_head_dense_filters=args.num_box_head_dense_filters,
short=args.image_short, max_size=args.image_max_size, min_stage=2,
max_stage=6, nms_thresh=args.nms_thresh, nms_topk=args.nms_topk,
post_nms=args.post_nms, roi_mode=args.roi_mode, roi_size=args.roi_size,
strides=args.strides, clip=args.clip, rpn_channel=args.rpn_channel,
base_size=args.anchor_base_size, scales=args.anchor_scales,
ratios=args.anchor_aspect_ratio, alloc_size=args.anchor_alloc_size,
rpn_nms_thresh=args.rpn_nms_thresh,
rpn_train_pre_nms=args.rpn_train_pre_nms,
rpn_train_post_nms=args.rpn_train_post_nms,
rpn_test_pre_nms=args.rpn_test_pre_nms,
rpn_test_post_nms=args.rpn_test_post_nms, rpn_min_size=args.rpn_min_size,
per_device_batch_size=args.batch_size // num_gpus,
num_sample=args.rcnn_num_samples, pos_iou_thresh=args.rcnn_pos_iou_thresh,
pos_ratio=args.rcnn_pos_ratio, max_num_gt=args.max_num_gt)
else:
net = get_model(net_name, pretrained_base=True,
per_device_batch_size=args.batch_size // num_gpus, **kwargs)
args.save_prefix += net_name
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)
if args.amp:
# Cast both weights and gradients to 'float16'
net.cast('float16')
# These layers don't support type 'float16'
net.collect_params('.*batchnorm.*').setattr('dtype', 'float32')
net.collect_params('.*normalizedperclassboxcenterencoder.*').setattr('dtype', 'float32')
# training data
train_dataset, val_dataset, eval_metric = get_dataset(args.dataset, args)
batch_size = args.batch_size // num_gpus 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)