def train_rpn(cfg,
dataset,
image_set,
root_path,
dataset_path,
frequent,
kvstore,
flip,
shuffle,
resume,
ctx,
pretrained,
epoch,
prefix,
begin_epoch,
end_epoch,
train_shared,
lr,
lr_step,
logger=None,
output_path=None):
# set up logger
if not logger:
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# set up config
cfg.TRAIN.BATCH_IMAGES = cfg.TRAIN.ALTERNATE.RPN_BATCH_IMAGES
# load symbol
sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
sym = sym_instance.get_symbol_rpn(cfg, is_train=True)
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = cfg.TRAIN.BATCH_IMAGES * batch_size
# print cfg
pprint.pprint(cfg)
logger.info('training rpn cfg:{}\n'.format(pprint.pformat(cfg)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in image_set.split('+')]
roidbs = [
load_gt_roidb(dataset,
image_set,
root_path,
dataset_path,
result_path=output_path,
flip=flip) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, cfg)
# load training data
train_data = AnchorLoader(feat_sym,
roidb,
cfg,
batch_size=input_batch_size,
shuffle=shuffle,
ctx=ctx,
feat_stride=cfg.network.RPN_FEAT_STRIDE,
anchor_scales=cfg.network.ANCHOR_SCALES,
anchor_ratios=cfg.network.ANCHOR_RATIOS,
aspect_grouping=cfg.TRAIN.ASPECT_GROUPING)
# infer max shape
max_data_shape = [('data', (cfg.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in cfg.SCALES]),
max([v[1] for v in cfg.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
print('providing maximum shape', max_data_shape, max_label_shape)
# infer shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if resume:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight_rpn(cfg, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
if train_shared:
fixed_param_prefix = cfg.network.FIXED_PARAMS_SHARED
else:
fixed_param_prefix = cfg.network.FIXED_PARAMS
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in xrange(batch_size)],
max_label_shapes=[max_label_shape for _ in xrange(batch_size)],
fixed_param_prefix=fixed_param_prefix)
# decide training params
# metric
eval_metric = metric.RPNAccMetric()
cls_metric = metric.RPNLogLossMetric()
bbox_metric = metric.RPNL1LossMetric()
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=frequent)
# epoch_end_callback = mx.callback.do_checkpoint(prefix)
epoch_end_callback = mx.callback.module_checkpoint(
mod, prefix, period=1, save_optimizer_states=True)
# decide learning rate
base_lr = lr
lr_factor = cfg.TRAIN.lr_factor
lr_epoch = [int(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_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
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
cfg.TRAIN.warmup,
cfg.TRAIN.warmup_lr,
cfg.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': cfg.TRAIN.momentum,
'wd': cfg.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def train_net(args, ctx, pretrained, pretrained_flow, epoch, prefix,
begin_epoch, end_epoch, lr, lr_step):
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_train_symbol(config)
feat_sym = sym.get_internals()['rpn_cls_score_output']
feat_conv_3x3_relu = sym.get_internals()['feat_conv_3x3_relu_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = AnchorLoader(feat_sym,
feat_conv_3x3_relu,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
normalize_target=config.network.NORMALIZE_RPN,
bbox_mean=config.network.ANCHOR_MEANS,
bbox_std=config.network.ANCHOR_STDS)
# infer max shape
#max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES]))),
# ('data_ref', (config.TRAIN.BATCH_IMAGES, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES]))),
# ('eq_flag', (1,))]
data_shape1 = {
'data_ref':
(config.TRAIN.BATCH_IMAGES, 3, max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])),
}
_, feat_shape111, _ = feat_conv_3x3_relu.infer_shape(**data_shape1)
max_data_shape = [('data_ref', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES]))),
('eq_flag', (1, )),
('motion_vector', (config.TRAIN.BATCH_IMAGES, 2,
int(feat_shape111[0][2]),
int(feat_shape111[0][3])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print 'providing maximum shape', max_data_shape, max_label_shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
#arg_params_flow, aux_params_flow = load_param(pretrained_flow, epoch, convert=True)
#arg_params.update(arg_params_flow)
#aux_params.update(aux_params_flow)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
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 = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)
]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_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
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
print('Start to train model')
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
mx.random.seed(3)
np.random.seed(3)
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_pyramid_level = np.log2(config.network.RPN_FEAT_STRIDE).astype(int)
feat_sym = [
sym.get_internals()['rpn_cls_score_p' + str(x) + '_output']
for x in feat_pyramid_level
]
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = PyramidAnchorIterator(
feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_strides=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=np.inf)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print 'providing maximum shape', max_data_shape, max_label_shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
# sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
# sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
rpn_fg_metric = metric.RPNFGFraction(config)
eval_metric = metric.RCNNAccMetric(config)
eval_fg_metric = metric.RCNNFGAccuracy(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
for child_metric in [
rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, rpn_fg_metric,
eval_fg_metric, eval_metric, cls_metric, bbox_metric
]:
eval_metrics.add(child_metric)
# callback
# batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
# epoch_end_callback = [mx.callback.module_checkpoint(mod, prefix, period=1,
# save_optimizer_states=True), callback.do_checkpoint(prefix, means, stds)]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_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
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
net = FPNNet(sym, args_pretrained=arg_params, auxes_pretrained=aux_params)
# create multi-threaded DataParallel Model.
net_parallel = DataParallelModel(net, ctx_list=ctx)
# create trainer,
# !Important: A trainer can be only created after the function `resnet_ctx` is called.
# Please Note that DataParallelModel will call reset_ctx to initialize parameters on gpus.
trainer = mx.gluon.Trainer(net.collect_params(), 'sgd', optimizer_params)
for epoch in range(begin_epoch, config.TRAIN.end_epoch):
train_data.reset()
net.hybridize(static_alloc=True, static_shape=False)
progress_bar = tqdm.tqdm(total=len(roidb))
for nbatch, data_batch in enumerate(train_data):
inputs = [[
x.astype('f').as_in_context(c) for x in d + l
] for c, d, l in zip(ctx, data_batch.data, data_batch.label)]
with ag.record():
outputs = net_parallel(*inputs)
ag.backward(sum(outputs, ()))
trainer.step(1)
eval_metrics.update(data_batch.label[0], outputs[0])
if nbatch % 100 == 0:
msg = ','.join([
'{}={:.3f}'.format(w, v)
for w, v in zip(*eval_metrics.get())
])
msg += ",lr={}".format(trainer.learning_rate)
logger.info(msg)
print(msg)
eval_metrics.reset()
progress_bar.update(len(inputs))
progress_bar.close()
net.hybridize(static_alloc=True, static_shape=False)
re = ("mAP", 0.0)
logger.info(re)
save_path = "{}-{}-{}.params".format(prefix, epoch, re[1])
net.collect_params().save(save_path)
logger.info("Saved checkpoint to {}.".format(save_path))
def train_net(args, ctx, pretrained_dir, pretrained_resnet, epoch, prefix,
begin_epoch, end_epoch, lr, lr_step):
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
git_commit_id = commands.getoutput('git rev-parse HEAD')
print("Git commit id:", git_commit_id)
logger.info('Git commit id: {}'.format(git_commit_id))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
motion_iou_path=config.dataset.motion_iou_path,
flip=config.TRAIN.FLIP,
use_philly=args.usePhilly) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = AnchorLoader(feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
normalize_target=config.network.NORMALIZE_RPN,
bbox_mean=config.network.ANCHOR_MEANS,
bbox_std=config.network.ANCHOR_STDS)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print('providing maximum shape', max_data_shape, max_label_shape)
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
# load and initialize params
params_loaded = False
if config.TRAIN.RESUME:
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
print('continue training from ', begin_epoch)
logger.info('continue training from ', begin_epoch)
params_loaded = True
elif config.TRAIN.AUTO_RESUME:
for cur_epoch in range(end_epoch - 1, begin_epoch, -1):
params_filename = '{}-{:04d}.params'.format(prefix, cur_epoch)
states_filename = '{}-{:04d}.states'.format(prefix, cur_epoch)
if os.path.exists(params_filename) and os.path.exists(
states_filename):
begin_epoch = cur_epoch
arg_params, aux_params = load_param(prefix,
cur_epoch,
convert=True)
mod._preload_opt_states = states_filename
print('auto continue training from {}, {}'.format(
params_filename, states_filename))
logger.info('auto continue training from {}, {}'.format(
params_filename, states_filename))
params_loaded = True
break
if not params_loaded:
arg_params, aux_params = load_param(os.path.join(
pretrained_dir, pretrained_resnet),
epoch,
convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# decide training params
# metric
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
if config.TRAIN.JOINT_TRAINING or (not config.TRAIN.LEARN_NMS):
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
for child_metric in [rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric]:
eval_metrics.add(child_metric)
if config.TRAIN.LEARN_NMS:
eval_metrics.add(metric.NMSLossMetric(config, 'pos'))
eval_metrics.add(metric.NMSLossMetric(config, 'neg'))
eval_metrics.add(metric.NMSAccMetric(config))
# callback
batch_end_callback = [
callback.Speedometer(train_data.batch_size, frequent=args.frequent)
]
if config.USE_PHILLY:
total_iter = (end_epoch - begin_epoch) * len(roidb) / input_batch_size
progress_frequent = min(args.frequent * 10, 100)
batch_end_callback.append(
callback.PhillyProgressCallback(total_iter, progress_frequent))
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)
]
# decide learning rate
# base_lr = lr * len(ctx) * config.TRAIN.BATCH_IMAGES
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_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
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr, lr_step):
np.random.seed(0)
mx.random.seed(0)
logger, final_output_path = create_logger(config.output_path, args.cfg, config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'), final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [load_gt_roidb(config.dataset.dataset, image_set, config.dataset.root_path, config.dataset.dataset_path,
flip=config.TRAIN.FLIP)
for image_set in image_sets]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = AnchorLoader(feat_sym, roidb, config, batch_size=input_batch_size, shuffle=config.TRAIN.SHUFFLE, ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE, anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# infer max shape
# max_dats_shape=['data', (1,3,600,1000)]
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]
# max_data_shape=[], max_lable_shape=[]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
logger.info('providing maximum shape'+str(max_data_shape)+" "+str(max_label_shape))
data_shape_dict = dict(train_data.provide_data_single + train_data.provide_label_single)
# add by chaojie
logger.info("data_sahpe_dict:\n{}".format(pprint.pformat(data_shape_dict)))
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
pprint.pprint(sym_instance.arg_shape_dict)
logger.info("sym_instance.arg_shape_dict\n")
logging.info(pprint.pformat(sym_instance.arg_shape_dict))
#dot = mx.viz.plot_network(sym, node_attrs={'shape': 'rect', 'fixedsize': 'false'})
#dot.render(os.path.join('./output/rcnn/network_vis', config.symbol + '_rcnn'))
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(sym, data_names=data_names, label_names=label_names,
logger=logger, context=ctx, max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)], fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states'%(prefix, begin_epoch)
# decide training params
# metric
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
if config.TRAIN.JOINT_TRAINING or (not config.TRAIN.LEARN_NMS):
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
for child_metric in [rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric]:
eval_metrics.add(child_metric)
for child_metric in [eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
if config.TRAIN.LEARN_NMS:
eval_metrics.add(metric.NMSLossMetric(config, 'pos'))
eval_metrics.add(metric.NMSLossMetric(config, 'neg'))
eval_metrics.add(metric.NMSAccMetric(config))
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS), 2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS), 2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [mx.callback.module_checkpoint(mod, prefix, period=1, save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [epoch - begin_epoch for epoch in lr_epoch if epoch > begin_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]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor, config.TRAIN.warmup, config.TRAIN.warmup_lr, config.TRAIN.warmup_step)
# optimizer
optimizer_params = {'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback, kvstore=config.default.kvstore,
optimizer='sgd', optimizer_params=optimizer_params,
arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
mx.random.seed(3)
np.random.seed(3)
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
config['final_output_path'] = final_output_path
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_pyramid_level = np.log2(config.network.RPN_FEAT_STRIDE).astype(int)
feat_sym = [
sym.get_internals()['rpn_cls_score_p' + str(x) + '_output']
for x in feat_pyramid_level
]
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
#leonid: adding semicolumn ";" support to allow several different datasets to be merged
datasets = config.dataset.dataset.split(';')
image_sets = config.dataset.image_set.split(';')
data_paths = config.dataset.dataset_path.split(';')
if type(config.dataset.per_category_epoch_max) is str:
per_category_epoch_max = [
float(x) for x in config.dataset.per_category_epoch_max.split(';')
]
else:
per_category_epoch_max = [float(config.dataset.per_category_epoch_max)]
roidbs = []
categ_index_offs = 0
if 'classes_list_fname' not in config.dataset:
classes_list_fname = ''
else:
classes_list_fname = config.dataset.classes_list_fname
if 'num_ex_per_class' not in config.dataset:
num_ex_per_class = ''
else:
num_ex_per_class = config.dataset.num_ex_per_class
for iD, dataset in enumerate(datasets):
# load dataset and prepare imdb for training
image_sets_cur = [iset for iset in image_sets[iD].split('+')]
for image_set in image_sets_cur:
cur_roidb, cur_num_classes = load_gt_roidb(
dataset,
image_set,
config.dataset.root_path,
data_paths[iD],
flip=config.TRAIN.FLIP,
per_category_epoch_max=per_category_epoch_max[iD],
return_num_classes=True,
categ_index_offs=categ_index_offs,
classes_list_fname=classes_list_fname,
num_ex_per_class=num_ex_per_class)
roidbs.append(cur_roidb)
categ_index_offs += cur_num_classes
# roidbs.extend([
# load_gt_roidb(
# dataset,
# image_set,
# config.dataset.root_path,
# data_paths[iD],
# flip=config.TRAIN.FLIP,
# per_category_epoch_max=per_category_epoch_max[iD])
# for image_set in image_sets])
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = PyramidAnchorIterator(
feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_strides=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=np.inf)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print 'providing maximum shape', max_data_shape, max_label_shape
if not config.network.base_net_lock:
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
else:
data_shape_dict = dict(train_data.provide_data_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
if config.TRAIN.LOAD_EMBEDDING:
import cPickle
with open(config.TRAIN.EMBEDDING_FNAME, 'rb') as fid:
model_data = cPickle.load(fid)
for fcn in ['1', '2', '3']:
layer = model_data['dense_' + fcn]
weight = ListList2ndarray(layer[0])
bias = mx.nd.array(layer[1])
arg_params['embed_dense_' + fcn + '_weight'] = weight
arg_params['embed_dense_' + fcn + '_bias'] = bias
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
alt_fixed_param_prefix = config.network.ALT_FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
if not config.network.base_net_lock:
label_names = [k[0] for k in train_data.provide_label_single]
else:
label_names = []
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix,
alt_fixed_param_prefix=alt_fixed_param_prefix)
# Leonid: Comment out the following two lines if switching to smaller number of GPUs and resuming training, then after it starts running un-comment back
# if config.TRAIN.RESUME:
# mod._preload_opt_states = '%s-%04d.states'%(prefix, begin_epoch)
#TODO: release this.
# decide training params
# metric
if not config.network.base_net_lock:
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
rpn_fg_metric = metric.RPNFGFraction(config)
eval_metric = metric.RCNNAccMetric(config)
eval_fg_metric = metric.RCNNFGAccuracy(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
if not config.network.base_net_lock:
all_child_metrics = [
rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, rpn_fg_metric,
eval_fg_metric, eval_metric, cls_metric, bbox_metric
]
else:
all_child_metrics = [
rpn_fg_metric, eval_fg_metric, eval_metric, cls_metric, bbox_metric
]
# all_child_metrics = [rpn_eval_metric, rpn_bbox_metric, rpn_fg_metric, eval_fg_metric, eval_metric, cls_metric, bbox_metric]
################################################
### added / updated by Leonid to support oneshot
################################################
if config.network.EMBEDDING_DIM != 0:
if config.network.EMBED_LOSS_ENABLED:
all_child_metrics += [
metric.RepresentativesMetric(config, final_output_path)
] # moved from above. JS.
all_child_metrics += [metric.EmbedMetric(config)]
if config.network.BG_REPS:
all_child_metrics += [metric.BGModelMetric(config)]
if config.network.REPS_CLS_LOSS:
all_child_metrics += [metric.RepsCLSMetric(config)]
if config.network.ADDITIONAL_LINEAR_CLS_LOSS:
all_child_metrics += [metric.RCNNLinLogLossMetric(config)]
if config.network.VAL_FILTER_REGRESS:
all_child_metrics += [metric.ValRegMetric(config)]
if config.network.SCORE_HIST_REGRESS:
all_child_metrics += [metric.ScoreHistMetric(config)]
################################################
for child_metric in all_child_metrics:
eval_metrics.add(child_metric)
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)
]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_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
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'clip_gradient': None
}
#
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
if args.debug == 1:
import copy
arg_params_ = copy.deepcopy(arg_params)
aux_params_ = copy.deepcopy(aux_params)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch,
config=config)
if args.debug == 1:
t = dictCompare(aux_params_, aux_params)
t = dictCompare(arg_params_, arg_params)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = AnchorLoader(feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print('providing maximum shape', max_data_shape, max_label_shape)
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
#if config.TRAIN.RESUME:
# print('continue training from ', begin_epoch)
# arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
#else:
# arg_params, aux_params = load_param(pretrained, epoch, convert=True)
# sym_instance.init_weight(config, arg_params, aux_params)
print('transfer learning...')
# Choose the initialization weights (COCO or UADETRAC or pretrained)
#arg_params, aux_params = load_param('/raid10/home_ext/Deformable-ConvNets/output/rfcn_dcn_Shuo_UADTRAC/resnet_v1_101_voc0712_rfcn_dcn_Shuo_UADETRAC/trainlist_full/rfcn_UADTRAC', 5, convert=True)
#arg_params, aux_params = load_param('/raid10/home_ext/Deformable-ConvNets/model/rfcn_dcn_coco', 0, convert=True)
arg_params, aux_params = load_param(
'/raid10/home_ext/Deformable-ConvNets/output/rfcn_dcn_Shuo_AICity/resnet_v1_101_voc0712_rfcn_dcn_Shuo_AICityVOC1080_FreezeCOCO_rpnOnly_all/1080_all/rfcn_AICityVOC1080_FreezeCOCO_rpnOnly_all',
4,
convert=True)
sym_instance.init_weight_Shuo(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
#freeze parameters using fixed_param_names:list of str
para_file = open(
'/raid10/home_ext/Deformable-ConvNets/rfcn/symbols/arg_params.txt')
para_list = [line.split('<')[0] for line in para_file.readlines()]
# para_list.remove('rfcn_cls_weight')
# para_list.remove('rfcn_cls_bias')
# para_list.remove('rfcn_cls_offset_t_weight')
# para_list.remove('rfcn_cls_offset_t_bias')
#
para_list.remove('res5a_branch2b_offset_weight')
para_list.remove('res5a_branch2b_offset_bias')
para_list.remove('res5b_branch2b_offset_weight')
para_list.remove('res5b_branch2b_offset_bias')
para_list.remove('res5c_branch2b_offset_weight')
para_list.remove('res5c_branch2b_offset_bias')
para_list.remove('conv_new_1_weight')
para_list.remove('conv_new_1_bias')
para_list.remove('rfcn_bbox_weight')
para_list.remove('rfcn_bbox_bias')
para_list.remove('rfcn_bbox_offset_t_weight')
para_list.remove('rfcn_bbox_offset_t_bias')
mod = MutableModule_Shuo(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix,
fixed_param_names=para_list)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
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 = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)
]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_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
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
# 创建logger和对应的输出路径
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
# 特征symbol，从网络sym中获取rpn_cls_score_output
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
# 使能多GPU训练，每一张卡训练一个batch
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
# 加载数据集同时准备训练的imdb，使用+分割不同的图像数据集，比如2007_trainval+2012_trainval
image_sets = [iset for iset in config.dataset.image_set.split('+')]
# load gt roidb加载gt roidb，根据数据集类型，图像集具体子类，数据集根目录和数据集路径，同时配置相关TRAIN为FLIP来增广数据
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
# 合并不同的roidb
roidb = merge_roidb(roidbs)
# 根据配置文件中对应的过滤规则来滤出roi
roidb = filter_roidb(roidb, config)
# load training data
# 加载训练数据，anchor Loader为对应分类和回归的锚点加载，通过对应的roidb，查找对应的正负样本的锚点，该生成器需要参数锚点尺度，ratios和对应的feature的stride
train_data = AnchorLoader(feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print('providing maximum shape', max_data_shape, max_label_shape)
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
# 加载并且初始化参数，如果训练中是继续上次的训练，也就是RESUME这一flag设置为True
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
# 从前缀和being_epoch中加载RESUME的arg参数和aux参数，同时需要转换为GPU NDArray
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
# 检查相关参数的shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
# 创造求解器
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
# decide training params
# metric
# 以下主要是RPN和RCNN相关的一些评价指标
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
# mxnet中合成的评估指标，可以增加以上所有的评估指标，包括rpn_eval_metrix、rpn_cls_metric、rpn_bbox_metric和rcnn_eval_metric、rcnn_cls_metric、rcnn_bbox_metric
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
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后的callback回调以及epoch后的callback回调
# batch_end_callback是在训练一定batch_size后进行的相应回调，回调频率为frequent
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
# means和stds，如果BBOX是类无关的，那么means为复制means两个，否则复制数量为NUM_CLASSES
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
# epoch为一个周期结束后的回调
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)
]
# decide learning rate
# 以下主要根据不同的学习率调整策略来决定学习率，这里如voc中默认的初始lr为0.0005
base_lr = lr
# 学习率调整因子
lr_factor = config.TRAIN.lr_factor
# 学习率调整周期，lr_step一般格式为3, 5，表示在3和5周期中进行学习率调整
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
# 如果当前周期大于begin_epoch那么lr_epoch_diff为epoch-begin_epoch
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
print('lr_epoch', lr_epoch, 'begin_epoch', begin_epoch)
# 通过当前的epoch来计算当前应该具有的lr
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
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
# learning rate调整机制，warmup multi factor scheduler预训练多因子调整器
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
# 优化器参数，包含momentum、wd、lr、lr_scheduler、rescale_grad和clip_gradient
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
print('!!!train_data is not PrefetchingIter!!!')
train_data = PrefetchingIter(train_data)
# train
# 模型训练过程，输入train_data，评估指标包括eval_metrics等一系列指标，每一个epoch结束后进入epoch_end_callback，每一个batch结束后进入batch_end_callback，优化器使用sgd，同时优化参数、输入参数和辅助参数以及begin周期和end周期
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
mx.random.seed(3)
np.random.seed(3)
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_pyramid_level = np.log2(config.network.RPN_FEAT_STRIDE).astype(int)
feat_sym = [
sym.get_internals()['rpn_cls_score_p' + str(x) + '_output']
for x in feat_pyramid_level
]
print('load symbol END')
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
print('Start load dataset and prepare imdb for training')
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb_poly(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
print('Start load training data')
# load training data
train_data = PyramidAnchorIterator_poly(
feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_strides=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=np.inf)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 300, 9)))
print 'providing maximum shape', max_data_shape, max_label_shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
# decide training params
# # metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
rpn_fg_metric = metric.RPNFGFraction(config)
eval_fg_metric = metric.RCNNFGAccuracy(config)
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
# add Rroi loss here
RCNN_proposal_fraction_metric = metric.RCNNFGFraction(config)
Rroi_fg_accuracy = metric.RRoIRCNNFGAccuracy(config)
Rroi_accuracy = metric.RRoIAccMetric(config)
Rroi_cls_metric = metric.RRoIRCNNLogLossMetric(config)
Rroi_bbox_metric = metric.RRoIRCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
for child_metric in [
rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, rpn_fg_metric,
eval_fg_metric, eval_metric, cls_metric, bbox_metric,
RCNN_proposal_fraction_metric, Rroi_fg_accuracy, Rroi_accuracy,
Rroi_cls_metric, Rroi_bbox_metric
]:
eval_metrics.add(child_metric)
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
Rroi_means = np.tile(
np.array(config.TRAIN.RRoI_BBOX_MEANS), 2
if config.network.RRoI_CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
Rroi_stds = np.tile(
np.array(config.TRAIN.RRoI_BBOX_STDS), 2
if config.network.RRoI_CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint_Rroi(prefix, means, stds, Rroi_means, Rroi_stds)
]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_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
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'clip_gradient': None
}
#
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
mx.random.seed(3)
np.random.seed(3)
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
dot = mx.viz.plot_network(sym)
dot.render('graph/nn.gv', view=False)
all_layers = sym.get_internals().list_outputs()
node_file = 'graph/nodes.txt'
if os.path.exists(node_file):
os.remove(node_file)
with open(node_file, 'a+') as f:
for layer in all_layers:
f.write(layer + '\n')
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
sdsdbs = []
for image_set in image_sets:
if image_set == 'train2014':
gt_sdsdb_file = os.path.join(config.dataset.root_path, 'cache',
'COCOMask', 'train2014',
'gt_sdsdb.pkl')
if os.path.exists(gt_sdsdb_file):
with open(gt_sdsdb_file, 'rb') as f:
sdsdbs.append(pkl.load(f))
else:
train2014_sdsdb = load_gt_sdsdb(
config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
mask_size=config.MASK_SIZE,
binary_thresh=config.BINARY_THRESH,
result_path=final_output_path,
flip=config.TRAIN.FLIP)
with open(gt_sdsdb_file, 'wb') as f:
pkl.dump(train2014_sdsdb, f, protocol=pkl.HIGHEST_PROTOCOL)
sdsdbs.append(train2014_sdsdb)
elif image_set == 'valminusminival2014':
gt_sdsdb_file = os.path.join(config.dataset.root_path, 'cache',
'COCOMask', 'val2014', 'gt_sdsdb.pkl')
if os.path.exists(gt_sdsdb_file):
with open(gt_sdsdb_file, 'rb') as f:
sdsdbs.append(pkl.load(f))
else:
val2014_sdsdb = load_gt_sdsdb(
config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
mask_size=config.MASK_SIZE,
binary_thresh=config.BINARY_THRESH,
result_path=final_output_path,
flip=config.TRAIN.FLIP)
with open(gt_sdsdb_file, 'wb') as f:
pkl.dump(val2014_sdsdb, f, protocol=pkl.HIGHEST_PROTOCOL)
sdsdbs.append(val2014_sdsdb)
sdsdb = merge_roidb(sdsdbs)
sdsdb = filter_roidb(sdsdb, config)
# load training data
train_data = AnchorLoader(feat_sym,
sdsdb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=config.TRAIN.RPN_ALLOWED_BORDER)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max(v[1] for v in config.SCALES)))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
max_data_shape.append(('gt_masks', (config.TRAIN.BATCH_IMAGES, 100,
max([v[0] for v in config.SCALES]),
max(v[1] for v in config.SCALES))))
print 'providing maximum shape', max_data_shape, max_label_shape
# infer shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
print 'data shape:'
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print 'continue training from ', begin_epoch
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in xrange(batch_size)],
max_label_shapes=[max_label_shape for _ in xrange(batch_size)],
fixed_param_prefix=fixed_param_prefix)
# decide training metric
# RPN, classification accuracy/loss, regression loss
rpn_acc = metric.RPNAccMetric()
rpn_cls_loss = metric.RPNLogLossMetric()
rpn_bbox_loss = metric.RPNL1LossMetric()
fcis_acc = metric.FCISAccMetric(config)
fcis_acc_fg = metric.FCISAccFGMetric(config)
fcis_cls_loss = metric.FCISLogLossMetric(config)
fcis_bbox_loss = metric.FCISL1LossMetric(config)
fcis_mask_loss = metric.FCISMaskLossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [
rpn_acc, rpn_cls_loss, rpn_bbox_loss, fcis_acc, fcis_acc_fg,
fcis_cls_loss, fcis_bbox_loss, fcis_mask_loss
]:
eval_metrics.add(child_metric)
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = callback.do_checkpoint(prefix, means, stds)
# print epoch, begin_epoch, end_epoch, lr_step
base_lr = lr
lr_factor = 0.1
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(sdsdb) / batch_size) for epoch in lr_epoch_diff
]
print 'lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# del sdsdb
# a = mx.viz.plot_network(sym)
# a.render('../example', view=True)
# print 'prepare sds finished'
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch,
batches_checkpoint=epoch_end_callback,
num_batches_save_ckpt=2000)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr, lr_step):
if config.dataset.dataset != 'JSONList':
logger, final_output_path = create_logger(config.output_path, args.cfg, config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
shutil.copy2(args.cfg, prefix+'.yaml')
else:
import datetime
import logging
final_output_path = config.output_path
prefix = prefix + '_' + datetime.datetime.now().strftime("%Y-%m-%d_%H_%M_%S")
prefix = os.path.join(final_output_path, prefix)
shutil.copy2(args.cfg, prefix+'.yaml')
log_file = prefix + '.log'
head = '%(asctime)-15s %(message)s'
logging.basicConfig(filename=log_file, format=head)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.info('prefix: %s' % prefix)
print('prefix: %s' % prefix)
# load symbol
#shutil.copy2(os.path.join(curr_path, '..', 'symbols', config.symbol + '.py'), final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
# setup multi-gpu
batch_size = config.TRAIN.IMAGES_PER_GPU * len(ctx)
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [load_gt_roidb(config.dataset.dataset, image_set, config.dataset.root_path, config.dataset.dataset_path,
flip=config.TRAIN.FLIP)
for image_set in image_sets]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
if config.network.MULTI_RPN:
assert Fasle, 'still developing' ###
num_layers = len(config.network.MULTI_RPN_STRIDES)
rpn_syms = [sym.get_internals()['rpn%d_cls_score_output'% l] for l in range(num_layers)]
train_data = PyramidAnchorLoader(rpn_syms, roidb, config, batch_size=batch_size, shuffle=config.TRAIN.SHUFFLE,
ctx=ctx, feat_strides=config.network.MULTI_RPN_STRIDES, anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=np.inf)
else:
feat_sym = sym.get_internals()['rpn_cls_score_output']
train_data = AnchorLoader(feat_sym, roidb, config, batch_size=batch_size, shuffle=config.TRAIN.SHUFFLE, ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE, anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# infer max shape
data_shape_dict = dict(train_data.provide_data + train_data.provide_label)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
mod = MutableModule(sym,
train_data.data_names,
train_data.label_names,
context=ctx,
logger=logger,
fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states'%(prefix, begin_epoch)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
for child_metric in [rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
if config.network.PREDICT_KEYPOINTS:
kps_cls_acc = metric.KeypointAccMetric(config)
kps_cls_loss = metric.KeypointLogLossMetric(config)
kps_pos_loss = metric.KeypointL1LossMetric(config)
eval_metrics.add(kps_cls_acc)
eval_metrics.add(kps_cls_loss)
eval_metrics.add(kps_pos_loss)
# callback
batch_end_callback = callback.Speedometer(batch_size, frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS), 2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS), 2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [mx.callback.do_checkpoint(prefix)]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [epoch - begin_epoch for epoch in lr_epoch if epoch > begin_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]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor, config.TRAIN.warmup, config.TRAIN.warmup_lr, config.TRAIN.warmup_step)
# optimizer
optimizer_params = {'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback, kvstore=config.TRAIN.kvstore,
optimizer='sgd', optimizer_params=optimizer_params,
arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
time.sleep(10)
train_data.iters[0].terminate()
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr, lr_step):
# 创建logger和对应的输出路径
logger, final_output_path = create_logger(config.output_path, args.cfg, config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'), final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
# 特征symbol，从网络sym中获取rpn_cls_score_output
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
# 使能多GPU训练，每一张卡训练一个batch
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
# 加载数据集同时准备训练的imdb，使用+分割不同的图像数据集，比如2007_trainval+2012_trainval
image_sets = [iset for iset in config.dataset.image_set.split('+')]
# load gt roidb加载gt roidb，根据数据集类型，图像集具体子类，数据集根目录和数据集路径，同时配置相关TRAIN为FLIP来增广数据
roidbs = [load_gt_roidb(config.dataset.dataset, image_set, config.dataset.root_path, config.dataset.dataset_path,
flip=config.TRAIN.FLIP)
for image_set in image_sets]
# 合并不同的roidb
roidb = merge_roidb(roidbs)
# 根据配置文件中对应的过滤规则来滤出roi
roidb = filter_roidb(roidb, config)
# load training data
# 加载训练数据，anchor Loader为对应分类和回归的锚点加载，通过对应的roidb，查找对应的正负样本的锚点，该生成器需要参数锚点尺度，ratios和对应的feature的stride
train_data = AnchorLoader(feat_sym, roidb, config, batch_size=input_batch_size, shuffle=config.TRAIN.SHUFFLE, ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE, anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print('providing maximum shape', max_data_shape, max_label_shape)
data_shape_dict = dict(train_data.provide_data_single + train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(sym, data_names=data_names, label_names=label_names,
logger=logger, context=ctx, max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)], fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states'%(prefix, begin_epoch)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
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 = callback.Speedometer(train_data.batch_size, frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS), 2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS), 2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [mx.callback.module_checkpoint(mod, prefix, period=1, save_optimizer_states=True), callback.do_checkpoint(prefix, means, stds)]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [epoch - begin_epoch for epoch in lr_epoch if epoch > begin_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]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor, config.TRAIN.warmup, config.TRAIN.warmup_lr, config.TRAIN.warmup_step)
# optimizer
optimizer_params = {'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback, kvstore=config.default.kvstore,
optimizer='sgd', optimizer_params=optimizer_params,
arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
mx.random.seed(3)
np.random.seed(3)
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = detnet.detnet()
sym = sym_instance.get_symbol(config, is_train=True)
feat_pyramid_level = np.log2(config.network.RPN_FEAT_STRIDE).astype(int)
feat_sym = [
sym.get_internals()['rpn_cls_score_p' + str(x) + '_output']
for x in feat_pyramid_level
]
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
train_data = PyramidAnchorIterator(
feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_strides=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=np.inf)
max_data_shape = [('data',
(config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([int(v[1] // 16 * 16) for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print('providing maximum shape', max_data_shape, max_label_shape)
data_shape_dict = dict(train_data.provide_data + train_data.provide_label)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = None, None
#sym_instance.init_weight(config, arg_params, aux_params)
#sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
data_names = [k[0] for k in train_data.provide_data]
label_names = [k[0] for k in train_data.provide_label]
mod = mx.mod.Module(sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx)
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
rpn_fg_metric = metric.RPNFGFraction(config)
eval_metric = metric.RCNNAccMetric(config)
eval_fg_metric = metric.RCNNFGAccuracy(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [
rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, rpn_fg_metric,
eval_fg_metric, eval_metric, cls_metric, bbox_metric
]:
eval_metrics.add(child_metric)
batch_end_callback = [
mx.callback.Speedometer(train_data.batch_size,
frequent=1,
auto_reset=False)
]
epoch_end_callback = [mx.callback.do_checkpoint(prefix, period=1)]
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_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
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(step=lr_iters,
factor=lr_factor)
optimizer_params = {
"momentum": config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'clip_gradient': None
}
if not isinstance(train_data, mx.io.PrefetchingIter):
train_data = mx.io.PrefetchingIter(train_data)
if DEBUG:
train_data.reset()
it = train_data.next()
mod.bind(data_shapes=train_data.provide_data,
label_shapes=train_data.provide_label,
for_training=True,
force_rebind=False)
mod.init_params(arg_params=arg_params,
aux_params=aux_params,
allow_missing=True)
mod.init_optimizer(optimizer_params=optimizer_params)
eval_metrics.reset()
next_data_batch = train_data.next()
for i in range(100):
print(i)
mod.forward_backward(next_data_batch)
mod.update()
mod.update_metric(eval_metrics, next_data_batch.label)
print(eval_metrics)
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
optimizer='sgd',
optimizer_params=optimizer_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def train_net(config, output_path, logger=logging):
# train_net(cfg_path, ctx, config.network.pretrained, config.network.pretrained_epoch,
# config.TRAIN.model_prefix, config.TRAIN.begin_epoch, config.TRAIN.end_epoch,
# config.TRAIN.lr, config.TRAIN.lr_step)
# train parameters
pretrained_model = config.network.pretrained
epoch = config.network.pretrained_epoch
prefix = config.TRAIN.model_prefix
begin_epoch = config.TRAIN.begin_epoch
end_epoch = config.TRAIN.end_epoch
lr = config.TRAIN.lr
lr_step = config.TRAIN.lr_step
prefix = os.path.join(output_path, prefix)
# network parameters
BATCH_IMAGES = config.TRAIN.BATCH_IMAGES
SCALES = config.SCALES
# gpu stuff
ctx = [mx.gpu(int(i)) for i in config.gpus.split(',')]
# final_output_path = output_path
# load symbol
# shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'), output_path)
# sym_instance = eval(config.symbol)()
network = resnet_v1_101_fcis()
sym = network.get_symbol(config, is_train=True)
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = BATCH_IMAGES * batch_size
# load dataset and prepare imdb for training
cfg_ds = config.dataset
ds_name = cfg_ds.dataset
image_sets = [iset for iset in cfg_ds.image_set.split('+')]
if ds_name.lower() == "labelme":
from utils.load_data import load_labelme_gt_sdsdb
sdsdbs = [
load_labelme_gt_sdsdb(image_set,
cfg_ds.dataset_path,
cfg_ds.root_path,
flip=config.TRAIN.FLIP,
mask_size=config.MASK_SIZE,
binary_thresh=config.BINARY_THRESH,
classes=cfg_ds.CLASSES)
for image_set in image_sets
]
else:
sdsdbs = [
load_gt_sdsdb(ds_name,
image_set,
cfg_ds.root_path,
cfg_ds.dataset_path,
mask_size=config.MASK_SIZE,
binary_thresh=config.BINARY_THRESH,
result_path=output_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
sdsdb = merge_roidb(sdsdbs)
sdsdb = filter_roidb(sdsdb, config)
# load training data
train_data = AnchorLoader(feat_sym,
sdsdb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=config.TRAIN.RPN_ALLOWED_BORDER)
# infer max shape
max_data_shape = [('data', (BATCH_IMAGES, 3, max([v[0] for v in SCALES]),
max(v[1] for v in SCALES)))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (BATCH_IMAGES, 100, 5)))
max_data_shape.append(
('gt_masks', (BATCH_IMAGES, 100, max([v[0] for v in SCALES]),
max(v[1] for v in SCALES))))
print 'providing maximum shape', max_data_shape, max_label_shape
# infer shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
print 'data shape:'
pprint.pprint(data_shape_dict)
network.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print 'continue training from ', begin_epoch
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained_model,
epoch,
convert=True)
network.init_weight(config, arg_params, aux_params)
# check parameter shapes
network.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in xrange(batch_size)],
max_label_shapes=[max_label_shape for _ in xrange(batch_size)],
fixed_param_prefix=fixed_param_prefix)
# decide training metric
# RPN, classification accuracy/loss, regression loss
rpn_acc = metric.RPNAccMetric()
rpn_cls_loss = metric.RPNLogLossMetric()
rpn_bbox_loss = metric.RPNL1LossMetric()
fcis_acc = metric.FCISAccMetric(config)
fcis_acc_fg = metric.FCISAccFGMetric(config)
fcis_cls_loss = metric.FCISLogLossMetric(config)
fcis_bbox_loss = metric.FCISL1LossMetric(config)
fcis_mask_loss = metric.FCISMaskLossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [
rpn_acc, rpn_cls_loss, rpn_bbox_loss, fcis_acc, fcis_acc_fg,
fcis_cls_loss, fcis_bbox_loss, fcis_mask_loss
]:
eval_metrics.add(child_metric)
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=config.default.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else cfg_ds.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else cfg_ds.NUM_CLASSES)
epoch_end_callback = callback.do_checkpoint(prefix, means, stds)
# print epoch, begin_epoch, end_epoch, lr_step
base_lr = lr
lr_factor = 0.1
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(sdsdb) / batch_size) for epoch in lr_epoch_diff
]
print 'lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# del sdsdb
# a = mx.viz.plot_network(sym)
# a.render('../example', view=True)
# print 'prepare sds finished'
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def train_net(args, ctx, pretrained_res, pretrained_vgg, epoch, prefix, begin_epoch, end_epoch, lr, lr_step):
mx.random.seed(3)
np.random.seed(3)
logger, final_output_path = create_logger(config.output_path, args.cfg, config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'), final_output_path)
sym_instance = eval(config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
sdsdbs = [load_gt_sdsdb(config.dataset.dataset, image_set, config.dataset.root_path, config.dataset.dataset_path,
mask_size=config.MASK_SIZE, binary_thresh=config.BINARY_THRESH,
result_path=final_output_path, flip=config.TRAIN.FLIP)
for image_set in image_sets]
sdsdb = merge_roidb(sdsdbs)
sdsdb = filter_roidb(sdsdb, config)
# load training data
train_data = AnchorLoader(feat_sym, sdsdb, config, batch_size=input_batch_size, shuffle=config.TRAIN.SHUFFLE,
ctx=ctx, feat_stride=config.network.RPN_FEAT_STRIDE, anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=config.TRAIN.RPN_ALLOWED_BORDER)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]), max(v[1] for v in config.SCALES)))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
max_data_shape.append(('gt_masks', (config.TRAIN.BATCH_IMAGES, 100, max([v[0] for v in config.SCALES]), max(v[1] for v in config.SCALES))))
print 'providing maximum shape', max_data_shape, max_label_shape
# infer shape
data_shape_dict = dict(train_data.provide_data_single + train_data.provide_label_single)
print 'data shape:'
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# inshape, outshape, uaxshape = sym_instance.infer_shape(data_shape_dict)
# print 'symbol inshape: %s ' % (str(inshape))
# print 'symbol outshape: %s' % (str(outshape))
'''
internals = sym.get_internals()
_, out_shapes, _ = internals.infer_shape(**data_shape_dict)
print(sym.list_outputs())
shape_dict = dict(zip(internals.list_outputs(), out_shapes))
pprint.pprint(shape_dict)
'''
# load and initialize params
if config.TRAIN.RESUME:
print 'continue training from ', begin_epoch
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
# load vgg-16 & resnet-101 parameters
# pretrained_res = pretrained
# pretrained_vgg = './model/pretrained_model/VGG_FC_ILSVRC_16'
arg_params_res, aux_params_res = load_param(pretrained_res, epoch, convert=True)
arg_params_vgg, aux_params_vgg = load_param(pretrained_vgg, epoch, convert=True)
# print 'params of resnet-101'
# print arg_params_res
# print 'params of vgg-16'
# print arg_params_vgg
arg_params = dict(arg_params_res, **arg_params_vgg)
aux_params = dict(aux_params_res, **aux_params_vgg)
# print 'arg_params: \n %s' % (str(arg_params))
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(sym, data_names=data_names, label_names=label_names,
logger=logger, context=ctx, max_data_shapes=[max_data_shape for _ in xrange(batch_size)],
max_label_shapes=[max_label_shape for _ in xrange(batch_size)], fixed_param_prefix=fixed_param_prefix)
# decide training metric
# RPN, classification accuracy/loss, regression loss
rpn_acc = metric.RPNAccMetric()
rpn_cls_loss = metric.RPNLogLossMetric()
rpn_bbox_loss = metric.RPNL1LossMetric()
fcis_acc = metric.FCISAccMetric(config)
fcis_acc_fg = metric.FCISAccFGMetric(config)
fcis_cls_loss = metric.FCISLogLossMetric(config)
fcis_bbox_loss = metric.FCISL1LossMetric(config)
fcis_mask_loss = metric.FCISMaskLossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# accumulate all loss, fcn-8s loss should be added here
for child_metric in [rpn_acc, rpn_cls_loss, rpn_bbox_loss,
# fcis_acc_fg, fcis_cls_loss, fcis_bbox_loss, fcis_mask_loss]:
fcis_acc, fcis_acc_fg, fcis_cls_loss, fcis_bbox_loss, fcis_mask_loss]:
eval_metrics.add(child_metric)
batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS), 2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS), 2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = callback.do_checkpoint(prefix, means, stds)
# print epoch, begin_epoch, end_epoch, lr_step
base_lr = lr
lr_factor = 0.1
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch]
lr = base_lr * (lr_factor ** (len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [int(epoch * len(sdsdb) / batch_size) for epoch in lr_epoch_diff]
print 'lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor, config.TRAIN.warmup, config.TRAIN.warmup_lr, config.TRAIN.warmup_step)
# optimizer
optimizer_params = {'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# del sdsdb
# a = mx.viz.plot_network(sym)
# a.render('../example', view=True)
# print 'prepare sds finished'
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback, kvstore=config.default.kvstore,
optimizer='sgd', optimizer_params=optimizer_params,
arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)