def train(sym, roidb):
'''
User function: Start training
Args:
sym (mxnet model): Mxnet model returned from set_network() function
roidb (dataloader): Dataloader returned from set_model() function
Returns:
None
'''
# print config
#logger.info('called with system_dict\n{}'.format(pprint.pformat(vars(system_dict))))
#print(system_dict)
# setup multi-gpu
if(len(system_dict["gpus"]) == 0):
ctx = [mx.cpu(0)];
else:
ctx = [mx.gpu(int(i)) for i in system_dict["gpus"]]
batch_size = system_dict["rcnn_batch_size"] * len(ctx)
# load training data
feat_sym = sym.get_internals()['rpn_cls_score_output']
ag = AnchorGenerator(feat_stride=system_dict["rpn_feat_stride"],
anchor_scales=system_dict["rpn_anchor_scales"], anchor_ratios=system_dict["rpn_anchor_ratios"])
asp = AnchorSampler(allowed_border=system_dict["rpn_allowed_border"], batch_rois=system_dict["rpn_batch_rois"],
fg_fraction=system_dict["rpn_fg_fraction"], fg_overlap=system_dict["rpn_fg_overlap"],
bg_overlap=system_dict["rpn_bg_overlap"])
train_data = AnchorLoader(roidb, batch_size, system_dict["img_short_side"], system_dict["img_long_side"],
system_dict["img_pixel_means"], system_dict["img_pixel_stds"], feat_sym, ag, asp, shuffle=True)
# produce shape max possible
_, out_shape, _ = feat_sym.infer_shape(data=(1, 3, system_dict["img_long_side"], system_dict["img_long_side"]))
feat_height, feat_width = out_shape[0][-2:]
rpn_num_anchors = len(system_dict["rpn_anchor_scales"]) * len(system_dict["rpn_anchor_ratios"])
data_names = ['data', 'im_info', 'gt_boxes']
label_names = ['label', 'bbox_target', 'bbox_weight']
data_shapes = [('data', (batch_size, 3, system_dict["img_long_side"], system_dict["img_long_side"])),
('im_info', (batch_size, 3)),
('gt_boxes', (batch_size, 100, 5))]
label_shapes = [('label', (batch_size, 1, rpn_num_anchors * feat_height, feat_width)),
('bbox_target', (batch_size, 4 * rpn_num_anchors, feat_height, feat_width)),
('bbox_weight', (batch_size, 4 * rpn_num_anchors, feat_height, feat_width))]
# print shapes
data_shape_dict, out_shape_dict = infer_data_shape(sym, data_shapes + label_shapes)
logger.info('max input shape\n%s' % pprint.pformat(data_shape_dict))
logger.info('max output shape\n%s' % pprint.pformat(out_shape_dict))
# load and initialize params
if system_dict["resume"]:
arg_params, aux_params = load_param(system_dict["resume"])
else:
arg_params, aux_params = load_param(system_dict["pretrained"])
arg_params, aux_params = initialize_frcnn(sym, data_shapes, arg_params, aux_params)
# check parameter shapes
check_shape(sym, data_shapes + label_shapes, arg_params, aux_params)
# check fixed params
fixed_param_names = get_fixed_params(sym, system_dict["net_fixed_params"])
logger.info('locking params\n%s' % pprint.pformat(fixed_param_names))
# metric
rpn_eval_metric = RPNAccMetric()
rpn_cls_metric = RPNLogLossMetric()
rpn_bbox_metric = RPNL1LossMetric()
eval_metric = RCNNAccMetric()
cls_metric = RCNNLogLossMetric()
bbox_metric = RCNNL1LossMetric()
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
# callback
batch_end_callback = mx.callback.Speedometer(batch_size, frequent=system_dict["log_interval"], auto_reset=False)
epoch_end_callback = mx.callback.do_checkpoint(system_dict["save_prefix"])
# learning schedule
base_lr = system_dict["lr"]
lr_factor = 0.1
lr_epoch = [int(epoch) for epoch in system_dict["lr_decay_epoch"].split(',')]
lr_epoch_diff = [epoch - system_dict["start_epoch"] for epoch in lr_epoch if epoch > system_dict["start_epoch"]]
lr = base_lr * (lr_factor ** (len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff]
logger.info('lr %f lr_epoch_diff %s lr_iters %s' % (lr, lr_epoch_diff, lr_iters))
lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(lr_iters, lr_factor)
# optimizer
optimizer_params = {'momentum': 0.9,
'wd': 0.0005,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': (1.0 / batch_size),
'clip_gradient': 5}
# train
mod = Module(sym, data_names=data_names, label_names=label_names,
logger=logger, context=ctx, work_load_list=None,
fixed_param_names=fixed_param_names)
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback, kvstore='device',
optimizer='sgd', optimizer_params=optimizer_params,
arg_params=arg_params, aux_params=aux_params, begin_epoch=system_dict["start_epoch"], num_epoch=system_dict["epochs"])
def train_net(sym, roidb, args):
# print config
logger.info('called with args\n{}'.format(pprint.pformat(vars(args))))
# setup multi-gpu
ctx = [mx.gpu(int(i)) for i in args.gpus.split(',')]
batch_size = args.rcnn_batch_size * len(ctx)
# load training data
feat_sym = sym.get_internals()['rpn_cls_score_output']
ag = AnchorGenerator(feat_stride=args.rpn_feat_stride,
anchor_scales=args.rpn_anchor_scales, anchor_ratios=args.rpn_anchor_ratios)
asp = AnchorSampler(allowed_border=args.rpn_allowed_border, batch_rois=args.rpn_batch_rois,
fg_fraction=args.rpn_fg_fraction, fg_overlap=args.rpn_fg_overlap,
bg_overlap=args.rpn_bg_overlap)
train_data = AnchorLoader(roidb, batch_size, args.img_short_side, args.img_long_side,
args.img_pixel_means, args.img_pixel_stds, feat_sym, ag, asp, shuffle=True)
# produce shape max possible
_, out_shape, _ = feat_sym.infer_shape(data=(1, 3, args.img_long_side, args.img_long_side))
feat_height, feat_width = out_shape[0][-2:]
rpn_num_anchors = len(args.rpn_anchor_scales) * len(args.rpn_anchor_ratios)
data_names = ['data', 'im_info', 'gt_boxes']
label_names = ['label', 'bbox_target', 'bbox_weight']
data_shapes = [('data', (batch_size, 3, args.img_long_side, args.img_long_side)),
('im_info', (batch_size, 3)),
('gt_boxes', (batch_size, 100, 5))]
label_shapes = [('label', (batch_size, 1, rpn_num_anchors * feat_height, feat_width)),
('bbox_target', (batch_size, 4 * rpn_num_anchors, feat_height, feat_width)),
('bbox_weight', (batch_size, 4 * rpn_num_anchors, feat_height, feat_width))]
# print shapes
data_shape_dict, out_shape_dict = infer_data_shape(sym, data_shapes + label_shapes)
logger.info('max input shape\n%s' % pprint.pformat(data_shape_dict))
logger.info('max output shape\n%s' % pprint.pformat(out_shape_dict))
# load and initialize params
if args.resume:
arg_params, aux_params = load_param(args.resume)
else:
arg_params, aux_params = load_param(args.pretrained)
arg_params, aux_params = initialize_frcnn(sym, data_shapes, arg_params, aux_params)
# check parameter shapes
check_shape(sym, data_shapes + label_shapes, arg_params, aux_params)
# check fixed params
fixed_param_names = get_fixed_params(sym, args.net_fixed_params)
logger.info('locking params\n%s' % pprint.pformat(fixed_param_names))
# metric
rpn_eval_metric = RPNAccMetric()
rpn_cls_metric = RPNLogLossMetric()
rpn_bbox_metric = RPNL1LossMetric()
eval_metric = RCNNAccMetric()
cls_metric = RCNNLogLossMetric()
bbox_metric = RCNNL1LossMetric()
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
# callback
batch_end_callback = mx.callback.Speedometer(batch_size, frequent=args.log_interval, auto_reset=False)
epoch_end_callback = mx.callback.do_checkpoint(args.save_prefix)
# learning schedule
base_lr = args.lr
lr_factor = 0.1
lr_epoch = [int(epoch) for epoch in args.lr_decay_epoch.split(',')]
lr_epoch_diff = [epoch - args.start_epoch for epoch in lr_epoch if epoch > args.start_epoch]
lr = base_lr * (lr_factor ** (len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff]
logger.info('lr %f lr_epoch_diff %s lr_iters %s' % (lr, lr_epoch_diff, lr_iters))
lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(lr_iters, lr_factor)
# optimizer
optimizer_params = {'momentum': 0.9,
'wd': 0.0005,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': (1.0 / batch_size),
'clip_gradient': 5}
# train
mod = Module(sym, data_names=data_names, label_names=label_names,
logger=logger, context=ctx, work_load_list=None,
fixed_param_names=fixed_param_names)
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback, kvstore='device',
optimizer='sgd', optimizer_params=optimizer_params,
arg_params=arg_params, aux_params=aux_params, begin_epoch=args.start_epoch, num_epoch=args.epochs)
def train_net(sym, roidb, args):
# print config
logger.info('called with args\n{}'.format(pprint.pformat(vars(args))))
# setup multi-gpu
ctx = [mx.cpu()] if not args.gpus else [
mx.gpu(int(i)) for i in args.gpus.split(',')
]
batch_size = args.rcnn_batch_size * len(ctx)
# load training data
feat_sym = sym.get_internals()['rpn_cls_score_output']
ag = AnchorGenerator(feat_stride=args.rpn_feat_stride,
anchor_scales=args.rpn_anchor_scales,
anchor_ratios=args.rpn_anchor_ratios)
asp = AnchorSampler(allowed_border=args.rpn_allowed_border,
batch_rois=args.rpn_batch_rois,
fg_fraction=args.rpn_fg_fraction,
fg_overlap=args.rpn_fg_overlap,
bg_overlap=args.rpn_bg_overlap)
train_data = AnchorLoader(roidb,
batch_size,
args.img_short_side,
args.img_long_side,
args.img_pixel_means,
args.img_pixel_stds,
feat_sym,
ag,
asp,
shuffle=True)
# produce shape max possible
_, out_shape, _ = feat_sym.infer_shape(data=(1, 3, args.img_long_side,
args.img_long_side))
feat_height, feat_width = out_shape[0][-2:]
rpn_num_anchors = len(args.rpn_anchor_scales) * len(args.rpn_anchor_ratios)
data_names = ['data', 'im_info', 'gt_boxes']
label_names = ['label', 'bbox_target', 'bbox_weight']
data_shapes = [('data', (batch_size, 3,
args.img_long_side, args.img_long_side)),
('im_info', (batch_size, 3)),
('gt_boxes', (batch_size, 100, 5))]
label_shapes = [('label', (batch_size, 1, rpn_num_anchors * feat_height,
feat_width)),
('bbox_target', (batch_size, 4 * rpn_num_anchors,
feat_height, feat_width)),
('bbox_weight', (batch_size, 4 * rpn_num_anchors,
feat_height, feat_width))]
# print shapes
data_shape_dict, out_shape_dict = infer_data_shape(
sym, data_shapes + label_shapes)
logger.info('max input shape\n%s' % pprint.pformat(data_shape_dict))
logger.info('max output shape\n%s' % pprint.pformat(out_shape_dict))
# load and initialize params
if args.resume:
arg_params, aux_params = load_param(args.resume)
else:
arg_params, aux_params = load_param(args.pretrained)
arg_params, aux_params = initialize_frcnn(sym, data_shapes, arg_params,
aux_params)
# check parameter shapes
check_shape(sym, data_shapes + label_shapes, arg_params, aux_params)
# check fixed params
fixed_param_names = get_fixed_params(sym, args.net_fixed_params)
logger.info('locking params\n%s' % pprint.pformat(fixed_param_names))
# metric
rpn_eval_metric = RPNAccMetric()
rpn_cls_metric = RPNLogLossMetric()
rpn_bbox_metric = RPNL1LossMetric()
eval_metric = RCNNAccMetric()
cls_metric = RCNNLogLossMetric()
bbox_metric = RCNNL1LossMetric()
eval_metrics = mx.gluon.metric.CompositeEvalMetric()
for child_metric in [
rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric,
cls_metric, bbox_metric
]:
eval_metrics.add(child_metric)
# callback
batch_end_callback = mx.callback.Speedometer(batch_size,
frequent=args.log_interval,
auto_reset=False)
epoch_end_callback = mx.callback.do_checkpoint(args.save_prefix)
# learning schedule
base_lr = args.lr
lr_factor = 0.1
lr_epoch = [int(epoch) for epoch in args.lr_decay_epoch.split(',')]
lr_epoch_diff = [
epoch - args.start_epoch for epoch in lr_epoch
if epoch > args.start_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff
]
logger.info('lr %f lr_epoch_diff %s lr_iters %s' %
(lr, lr_epoch_diff, lr_iters))
lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(lr_iters, lr_factor)
# optimizer
optimizer_params = {
'momentum': 0.9,
'wd': 0.0005,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': (1.0 / batch_size),
'clip_gradient': 5
}
# train
mod = Module(sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
work_load_list=None,
fixed_param_names=fixed_param_names)
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore='device',
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=args.start_epoch,
num_epoch=args.epochs)
print('initialize and train the new network')
if is_resume:
arg_params, aux_params = load_param(resume, ctx)
print('resume from trained-network')
# symbol data
data_names = [
'pair_im_1', 'pair_im_2', 'loca_map_1', 'loca_map_2', 'r_map',
'headsize_1', 'headsize_2'
]
label_names = ['label']
check_shape(net, train_data.provide_data + train_data.provide_label,
arg_params, aux_params)
fixed_param_names = get_fixed_params(net, net_fixed_params)
# callback
batch_end_callback = mx.callback.Speedometer(batch_size,
frequent=100,
auto_reset=False)
epoch_end_callback = mx.callback.do_checkpoint(save_prefix)
# metric
eval_metrics_1 = mx.metric.Accuracy()
eval_metrics_2 = mx.metric.CrossEntropy()
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [eval_metrics_1, eval_metrics_2]:
eval_metrics.add(child_metric)
# momentum