def train_net(args,
ctx,
pretrained,
epoch,
prefix,
begin_epoch,
end_epoch,
lr=0.001,
lr_step='5'):
# setup config
config.TRAIN.BATCH_IMAGES = 1
config.TRAIN.BATCH_ROIS = 128
config.TRAIN.END2END = True
config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True
# load symbol
sym = eval('get_' + args.network + '_train')(
num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS)
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
logger.info(pprint.pformat(config))
# load dataset and prepare imdb for training
image_sets = [iset for iset in args.image_set.split('+')]
roidbs = [
load_gt_roidb(args.dataset,
image_set,
args.root_path,
args.dataset_path,
flip=not args.no_flip) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb)
# load training data
train_data = AnchorLoader(feat_sym,
roidb,
batch_size=input_batch_size,
shuffle=not args.no_shuffle,
ctx=ctx,
work_load_list=args.work_load_list,
feat_stride=config.RPN_FEAT_STRIDE,
anchor_scales=config.ANCHOR_SCALES,
anchor_ratios=config.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# infer max shape
max_data_shape = [('data', (input_batch_size, 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', (input_batch_size, 100, 5)))
logger.info('providing maximum shape %s %s' %
(max_data_shape, max_label_shape))
# infer shape
data_shape_dict = dict(train_data.provide_data + train_data.provide_label)
arg_shape, out_shape, aux_shape = sym.infer_shape(**data_shape_dict)
arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape))
out_shape_dict = dict(zip(sym.list_outputs(), out_shape))
aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape))
logger.info('output shape %s' % pprint.pformat(out_shape_dict))
# load and initialize params
if args.resume:
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
arg_params['rpn_conv_3x3_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['rpn_conv_3x3_weight'])
arg_params['rpn_conv_3x3_bias'] = mx.nd.zeros(
shape=arg_shape_dict['rpn_conv_3x3_bias'])
arg_params['rpn_cls_score_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['rpn_cls_score_weight'])
arg_params['rpn_cls_score_bias'] = mx.nd.zeros(
shape=arg_shape_dict['rpn_cls_score_bias'])
arg_params['rpn_bbox_pred_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['rpn_bbox_pred_weight'])
arg_params['rpn_bbox_pred_bias'] = mx.nd.zeros(
shape=arg_shape_dict['rpn_bbox_pred_bias'])
arg_params['cls_score_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['cls_score_weight'])
arg_params['cls_score_bias'] = mx.nd.zeros(
shape=arg_shape_dict['cls_score_bias'])
arg_params['bbox_pred_weight'] = mx.random.normal(
0, 0.001, shape=arg_shape_dict['bbox_pred_weight'])
arg_params['bbox_pred_bias'] = mx.nd.zeros(
shape=arg_shape_dict['bbox_pred_bias'])
# check parameter shapes
for k in sym.list_arguments():
if k in data_shape_dict:
continue
assert k in arg_params, k + ' not initialized'
assert arg_params[k].shape == arg_shape_dict[k], \
'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape)
for k in sym.list_auxiliary_states():
assert k in aux_params, k + ' not initialized'
assert aux_params[k].shape == aux_shape_dict[k], \
'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape)
# create solver
fixed_param_prefix = config.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data]
label_names = [k[0] for k in train_data.provide_label]
mod = MutableModule(sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
work_load_list=args.work_load_list,
max_data_shapes=max_data_shape,
max_label_shapes=max_label_shape,
fixed_param_prefix=fixed_param_prefix)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric()
cls_metric = metric.RCNNLogLossMetric()
bbox_metric = 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(train_data.batch_size,
frequent=args.frequent,
auto_reset=False)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS), config.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS), config.NUM_CLASSES)
epoch_end_callback = callback.do_checkpoint(prefix, means, stds)
# decide learning rate
base_lr = lr
lr_factor = 0.1
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
]
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.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=args.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=0.001,
lr_step='5'):
# setup config
#init_config()
#print(config)
# setup multi-gpu
input_batch_size = config.TRAIN.BATCH_IMAGES * len(ctx)
# print config
logger.info(pprint.pformat(config))
# load dataset and prepare imdb for training
image_sets = [iset for iset in args.image_set.split('+')]
roidbs = [
load_gt_roidb(args.dataset,
image_set,
args.root_path,
args.dataset_path,
flip=not args.no_flip) for image_set in image_sets
]
#roidb = merge_roidb(roidbs)
#roidb = filter_roidb(roidb)
roidb = roidbs[0]
# load symbol
#sym = eval('get_' + args.network + '_train')(num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS)
#feat_sym = sym.get_internals()['rpn_cls_score_output']
#train_data = AnchorLoader(feat_sym, roidb, batch_size=input_batch_size, shuffle=not args.no_shuffle,
# ctx=ctx, work_load_list=args.work_load_list,
# feat_stride=config.RPN_FEAT_STRIDE, anchor_scales=config.ANCHOR_SCALES,
# anchor_ratios=config.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# load and initialize params
sym = None
if len(pretrained) == 0:
arg_params = {}
aux_params = {}
else:
logger.info('loading %s,%d' % (pretrained, epoch))
sym, arg_params, aux_params = mx.model.load_checkpoint(
pretrained, epoch)
#arg_params, aux_params = load_param(pretrained, epoch, convert=True)
#for k in ['rpn_conv_3x3', 'rpn_cls_score', 'rpn_bbox_pred', 'cls_score', 'bbox_pred']:
# _k = k+"_weight"
# if _k in arg_shape_dict:
# v = 0.001 if _k.startswith('bbox_') else 0.01
# arg_params[_k] = mx.random.normal(0, v, shape=arg_shape_dict[_k])
# print('init %s with normal %.5f'%(_k,v))
# _k = k+"_bias"
# if _k in arg_shape_dict:
# arg_params[_k] = mx.nd.zeros(shape=arg_shape_dict[_k])
# print('init %s with zero'%(_k))
sym = eval('get_' + args.network + '_train')(sym)
feat_sym = []
for stride in config.RPN_FEAT_STRIDE:
feat_sym.append(
sym.get_internals()['face_rpn_cls_score_stride%s_output' % stride])
train_data = CropLoader(feat_sym,
roidb,
batch_size=input_batch_size,
shuffle=not args.no_shuffle,
ctx=ctx,
work_load_list=args.work_load_list)
# infer max shape
max_data_shape = [('data', (1, 3, max([v[1] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
#max_data_shape = [('data', (1, 3, max([v[1] 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', (1, roidb[0]['max_num_boxes'], 5)))
logger.info('providing maximum shape %s %s' %
(max_data_shape, max_label_shape))
# infer shape
data_shape_dict = dict(train_data.provide_data + train_data.provide_label)
arg_shape, out_shape, aux_shape = sym.infer_shape(**data_shape_dict)
arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape))
out_shape_dict = dict(zip(sym.list_outputs(), out_shape))
aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape))
logger.info('output shape %s' % pprint.pformat(out_shape_dict))
for k, v in arg_shape_dict.items():
if k.find('upsampling') >= 0:
print('initializing upsampling_weight', k)
arg_params[k] = mx.nd.zeros(shape=v)
init = mx.init.Initializer()
init._init_bilinear(k, arg_params[k])
#print(args[k])
# check parameter shapes
#for k in sym.list_arguments():
# if k in data_shape_dict:
# continue
# assert k in arg_params, k + ' not initialized'
# assert arg_params[k].shape == arg_shape_dict[k], \
# 'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape)
#for k in sym.list_auxiliary_states():
# assert k in aux_params, k + ' not initialized'
# assert aux_params[k].shape == aux_shape_dict[k], \
# 'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape)
fixed_param_prefix = config.FIXED_PARAMS
# create solver
data_names = [k[0] for k in train_data.provide_data]
label_names = [k[0] for k in train_data.provide_label]
fixed_param_names = get_fixed_params(sym, fixed_param_prefix)
print('fixed', fixed_param_names, file=sys.stderr)
mod = Module(sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
work_load_list=args.work_load_list,
fixed_param_names=fixed_param_names)
# metric
eval_metrics = mx.metric.CompositeEvalMetric()
mid = 0
for m in range(len(config.RPN_FEAT_STRIDE)):
stride = config.RPN_FEAT_STRIDE[m]
#mid = m*MSTEP
_metric = metric.RPNAccMetric(pred_idx=mid,
label_idx=mid + 1,
name='RPNAcc_s%s' % stride)
eval_metrics.add(_metric)
mid += 2
#_metric = metric.RPNLogLossMetric(pred_idx=mid, label_idx=mid+1)
#eval_metrics.add(_metric)
_metric = metric.RPNL1LossMetric(loss_idx=mid,
weight_idx=mid + 1,
name='RPNL1Loss_s%s' % stride)
eval_metrics.add(_metric)
mid += 2
if config.FACE_LANDMARK:
_metric = metric.RPNL1LossMetric(loss_idx=mid,
weight_idx=mid + 1,
name='RPNLandMarkL1Loss_s%s' %
stride)
eval_metrics.add(_metric)
mid += 2
if config.HEAD_BOX:
_metric = metric.RPNAccMetric(pred_idx=mid,
label_idx=mid + 1,
name='RPNAcc_head_s%s' % stride)
eval_metrics.add(_metric)
mid += 2
#_metric = metric.RPNLogLossMetric(pred_idx=mid, label_idx=mid+1)
#eval_metrics.add(_metric)
_metric = metric.RPNL1LossMetric(loss_idx=mid,
weight_idx=mid + 1,
name='RPNL1Loss_head_s%s' %
stride)
eval_metrics.add(_metric)
mid += 2
# callback
#means = np.tile(np.array(config.TRAIN.BBOX_MEANS), config.NUM_CLASSES)
#stds = np.tile(np.array(config.TRAIN.BBOX_STDS), config.NUM_CLASSES)
#epoch_end_callback = callback.do_checkpoint(prefix)
epoch_end_callback = None
# decide learning rate
#base_lr = lr
#lr_factor = 0.1
#lr = base_lr * (lr_factor ** (len(lr_epoch) - len(lr_epoch_diff)))
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_iters = [
int(epoch * len(roidb) / input_batch_size) for epoch in lr_epoch_diff
]
lr_steps = []
if len(lr_iters) == 5:
factors = [0.5, 0.5, 0.4, 0.1, 0.1]
for i in range(5):
lr_steps.append((lr_iters[i], factors[i]))
elif len(lr_iters) == 8: #warmup
for li in lr_iters[0:5]:
lr_steps.append((li, 1.5849))
for li in lr_iters[5:]:
lr_steps.append((li, 0.1))
else:
for li in lr_iters:
lr_steps.append((li, 0.1))
#lr_steps = [ (20,0.1), (40, 0.1) ] #XXX
end_epoch = 10000
logger.info('lr %f lr_epoch_diff %s lr_steps %s' %
(lr, lr_epoch_diff, lr_steps))
# optimizer
opt = optimizer.SGD(learning_rate=lr,
momentum=0.9,
wd=0.0005,
rescale_grad=1.0 / len(ctx),
clip_gradient=None)
initializer = mx.init.Xavier()
#initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="out", magnitude=2) #resnet style
train_data = mx.io.PrefetchingIter(train_data)
_cb = mx.callback.Speedometer(train_data.batch_size,
frequent=args.frequent,
auto_reset=False)
global_step = [0]
def save_model(epoch):
arg, aux = mod.get_params()
all_layers = mod.symbol.get_internals()
outs = []
for stride in config.RPN_FEAT_STRIDE:
num_anchors = config.RPN_ANCHOR_CFG[str(stride)]['NUM_ANCHORS']
_name = 'face_rpn_cls_score_stride%d_output' % stride
rpn_cls_score = all_layers[_name]
# prepare rpn data
rpn_cls_score_reshape = mx.symbol.Reshape(
data=rpn_cls_score,
shape=(0, 2, -1, 0),
name="face_rpn_cls_score_reshape_stride%d" % stride)
rpn_cls_prob = mx.symbol.SoftmaxActivation(
data=rpn_cls_score_reshape,
mode="channel",
name="face_rpn_cls_prob_stride%d" % stride)
rpn_cls_prob_reshape = mx.symbol.Reshape(
data=rpn_cls_prob,
shape=(0, 2 * num_anchors, -1, 0),
name='face_rpn_cls_prob_reshape_stride%d' % stride)
_name = 'face_rpn_bbox_pred_stride%d_output' % stride
rpn_bbox_pred = all_layers[_name]
outs.append(rpn_cls_prob_reshape)
outs.append(rpn_bbox_pred)
if config.FACE_LANDMARK:
_name = 'face_rpn_landmark_pred_stride%d_output' % stride
rpn_landmark_pred = all_layers[_name]
outs.append(rpn_landmark_pred)
_sym = mx.sym.Group(outs)
mx.model.save_checkpoint(prefix, epoch, _sym, arg, aux)
def _batch_callback(param):
#global global_step
_cb(param)
global_step[0] += 1
mbatch = global_step[0]
for step in lr_steps:
if mbatch == step[0]:
opt.lr *= step[1]
print('lr change to',
opt.lr,
' in batch',
mbatch,
file=sys.stderr)
break
if mbatch == lr_steps[-1][0]:
print('saving final checkpoint', mbatch, file=sys.stderr)
save_model(0)
#arg, aux = mod.get_params()
#mx.model.save_checkpoint(prefix, 99, mod.symbol, arg, aux)
sys.exit(0)
if args.checkpoint is not None:
_, arg_params, aux_params = mx.model.load_checkpoint(
args.checkpoint, 0)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=checkpoint_callback('model/testR50'),
batch_end_callback=_batch_callback,
kvstore=args.kvstore,
optimizer=opt,
initializer=initializer,
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=0.001,
lr_step='5'):
# set up logger
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# setup config
config.TRAIN.BATCH_IMAGES = 1
config.TRAIN.BATCH_ROIS = 128
config.TRAIN.END2END = True
config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True
# load symbol
if args.use_global_context or args.use_roi_align:
sym = eval('get_' + args.network + '_train')(
num_classes=config.NUM_CLASSES,
num_anchors=config.NUM_ANCHORS,
use_global_context=args.use_global_context,
use_roi_align=args.use_roi_align)
else:
sym = eval('get_' + args.network + '_train')(
num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS)
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)
if not args.use_ava_recordio:
# load dataset and prepare imdb for training
image_sets = [iset for iset in args.image_set.split('+')]
roidbs = [
load_gt_roidb(args.dataset,
image_set,
args.root_path,
args.dataset_path,
flip=not args.no_flip) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb)
# load training data
train_data = AnchorLoader(
feat_sym,
roidb,
batch_size=input_batch_size,
shuffle=not args.no_shuffle,
ctx=ctx,
work_load_list=args.work_load_list,
feat_stride=config.RPN_FEAT_STRIDE,
anchor_scales=config.ANCHOR_SCALES,
anchor_ratios=config.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
use_data_augmentation=args.use_data_augmentation)
else:
f = open(args.classes_names)
classes = ['__background__']
for line in f.readlines():
classes.append(line.strip().split(' ')[0])
path_imgidx = args.ava_recordio_name + '.idx'
path_imgrec = args.ava_recordio_name + '.rec'
record = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') # pylint: disable=redefined-variable-type
train_data = AnchorLoaderAvaRecordIO(
feat_sym,
record,
classes,
batch_size=input_batch_size,
shuffle=not args.no_shuffle,
ctx=ctx,
work_load_list=args.work_load_list,
feat_stride=config.RPN_FEAT_STRIDE,
anchor_scales=config.ANCHOR_SCALES,
anchor_ratios=config.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
use_data_augmentation=args.use_data_augmentation)
# infer max shape
max_data_shape = [('data', (input_batch_size, 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', (input_batch_size, 100, 5)))
print('providing maximum shape', max_data_shape, max_label_shape)
# infer shape
data_shape_dict = dict(train_data.provide_data + train_data.provide_label)
arg_shape, out_shape, aux_shape = sym.infer_shape(**data_shape_dict)
arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape))
out_shape_dict = dict(zip(sym.list_outputs(), out_shape))
aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape))
print('output shape')
pprint.pprint(out_shape_dict)
print('arg shape')
# pprint.pprint(arg_shape_dict)
# load and initialize params
if args.resume:
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
arg_params['rpn_conv_3x3_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['rpn_conv_3x3_weight'])
arg_params['rpn_conv_3x3_bias'] = mx.nd.zeros(
shape=arg_shape_dict['rpn_conv_3x3_bias'])
arg_params['rpn_cls_score_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['rpn_cls_score_weight'])
arg_params['rpn_cls_score_bias'] = mx.nd.zeros(
shape=arg_shape_dict['rpn_cls_score_bias'])
arg_params['rpn_bbox_pred_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['rpn_bbox_pred_weight'])
arg_params['rpn_bbox_pred_bias'] = mx.nd.zeros(
shape=arg_shape_dict['rpn_bbox_pred_bias'])
arg_params['cls_score_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['cls_score_weight'])
arg_params['cls_score_bias'] = mx.nd.zeros(
shape=arg_shape_dict['cls_score_bias'])
arg_params['bbox_pred_weight'] = mx.random.normal(
0, 0.001, shape=arg_shape_dict['bbox_pred_weight'])
arg_params['bbox_pred_bias'] = mx.nd.zeros(
shape=arg_shape_dict['bbox_pred_bias'])
if args.use_global_context:
# additional params for using global context
"""
for arg_param_name in sym.list_arguments():
if 'stage5' in arg_param_name:
# print(arg_param_name, arg_param_name.replace('stage5', 'stage4'))
arg_params[arg_param_name] = arg_params[arg_param_name.replace('stage5', 'stage4')].copy() # params of stage5 is initialized from stage4
arg_params['bn2_gamma'] = arg_params['bn1_gamma'].copy()
arg_params['bn2_beta'] = arg_params['bn1_beta'].copy()
"""
for aux_param_name in sym.list_auxiliary_states():
if 'stage5' in aux_param_name:
# print(aux_param_name, aux_param_name.replace('stage5', 'stage4'))
aux_params[aux_param_name] = aux_params[aux_param_name.replace(
'stage5', 'stage4')].copy(
) # params of stage5 is initialized from stage4
aux_params['bn2_moving_mean'] = aux_params['bn1_moving_mean'].copy()
aux_params['bn2_moving_var'] = aux_params['bn1_moving_var'].copy()
# check parameter shapes
for k in sym.list_arguments():
if k in data_shape_dict:
continue
assert k in arg_params, k + ' not initialized'
assert arg_params[k].shape == arg_shape_dict[k], \
'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape)
for k in sym.list_auxiliary_states():
assert k in aux_params, k + ' not initialized'
assert aux_params[k].shape == aux_shape_dict[k], \
'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape)
# create solver
fixed_param_prefix = config.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data]
label_names = [k[0] for k in train_data.provide_label]
mod = MutableModule(sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
work_load_list=args.work_load_list,
max_data_shapes=max_data_shape,
max_label_shapes=max_label_shape,
fixed_param_prefix=fixed_param_prefix)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric()
cls_metric = metric.RCNNLogLossMetric()
bbox_metric = 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 = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS), config.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS), config.NUM_CLASSES)
epoch_end_callback = callback.do_checkpoint(prefix, means, stds)
# decide learning rate
base_lr = lr
lr_factor = 0.1
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)))
if not args.use_ava_recordio:
lr_iters = [
int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff
]
else:
lr_iters = [
int(epoch * train_data.provide_size() / 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(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.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=args.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=0.001,
lr_step='5'):
# setup config
#init_config()
#print(config)
# setup multi-gpu
input_batch_size = config.TRAIN.BATCH_IMAGES * len(ctx)
# print config
logger.info(pprint.pformat(config))
# load dataset and prepare imdb for training
image_sets = [iset for iset in args.image_set.split('+')]
roidbs = [
load_gt_roidb(args.dataset,
image_set,
args.root_path,
args.dataset_path,
flip=not args.no_flip) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb)
# load symbol
#sym = eval('get_' + args.network + '_train')(num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS)
#feat_sym = sym.get_internals()['rpn_cls_score_output']
#train_data = AnchorLoader(feat_sym, roidb, batch_size=input_batch_size, shuffle=not args.no_shuffle,
# ctx=ctx, work_load_list=args.work_load_list,
# feat_stride=config.RPN_FEAT_STRIDE, anchor_scales=config.ANCHOR_SCALES,
# anchor_ratios=config.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING)
sym = eval('get_' + args.network + '_train')()
#print(sym.get_internals())
feat_sym = []
for stride in config.RPN_FEAT_STRIDE:
feat_sym.append(sym.get_internals()['rpn_cls_score_stride%s_output' %
stride])
#train_data = AnchorLoaderFPN(feat_sym, roidb, batch_size=input_batch_size, shuffle=not args.no_shuffle,
# ctx=ctx, work_load_list=args.work_load_list)
train_data = CropLoader(feat_sym,
roidb,
batch_size=input_batch_size,
shuffle=not args.no_shuffle,
ctx=ctx,
work_load_list=args.work_load_list)
# infer max shape
max_data_shape = [('data', (1, 3, max([v[1] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
#max_data_shape = [('data', (1, 3, max([v[1] 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', (1, roidb[0]['max_num_boxes'], 5)))
logger.info('providing maximum shape %s %s' %
(max_data_shape, max_label_shape))
# infer shape
data_shape_dict = dict(train_data.provide_data + train_data.provide_label)
arg_shape, out_shape, aux_shape = sym.infer_shape(**data_shape_dict)
arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape))
out_shape_dict = dict(zip(sym.list_outputs(), out_shape))
aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape))
logger.info('output shape %s' % pprint.pformat(out_shape_dict))
# load and initialize params
if args.resume:
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
#for k in ['rpn_conv_3x3', 'rpn_cls_score', 'rpn_bbox_pred', 'cls_score', 'bbox_pred']:
# _k = k+"_weight"
# if _k in arg_shape_dict:
# v = 0.001 if _k.startswith('bbox_') else 0.01
# arg_params[_k] = mx.random.normal(0, v, shape=arg_shape_dict[_k])
# print('init %s with normal %.5f'%(_k,v))
# _k = k+"_bias"
# if _k in arg_shape_dict:
# arg_params[_k] = mx.nd.zeros(shape=arg_shape_dict[_k])
# print('init %s with zero'%(_k))
for k, v in arg_shape_dict.iteritems():
if k.find('upsampling') >= 0:
print('initializing upsampling_weight', k)
arg_params[k] = mx.nd.zeros(shape=v)
init = mx.init.Initializer()
init._init_bilinear(k, arg_params[k])
#print(args[k])
# check parameter shapes
#for k in sym.list_arguments():
# if k in data_shape_dict:
# continue
# assert k in arg_params, k + ' not initialized'
# assert arg_params[k].shape == arg_shape_dict[k], \
# 'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape)
#for k in sym.list_auxiliary_states():
# assert k in aux_params, k + ' not initialized'
# assert aux_params[k].shape == aux_shape_dict[k], \
# 'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape)
# create solver
fixed_param_prefix = config.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data]
label_names = [k[0] for k in train_data.provide_label]
#mod = MutableModule(sym, data_names=data_names, label_names=label_names,
# logger=logger, context=ctx, work_load_list=args.work_load_list,
# max_data_shapes=max_data_shape, max_label_shapes=max_label_shape,
# fixed_param_prefix=fixed_param_prefix)
fixed_param_names = get_fixed_params(sym, fixed_param_prefix)
print('fixed', fixed_param_names, file=sys.stderr)
mod = Module(sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
work_load_list=args.work_load_list,
fixed_param_names=fixed_param_names)
# decide training params
# metric
eval_metrics = mx.metric.CompositeEvalMetric()
#if len(sym.list_outputs())>4:
# metric_names = ['RPNAccMetric', 'RPNLogLossMetric', 'RPNL1LossMetric', 'RCNNAccMetric', 'RCNNLogLossMetric', 'RCNNL1LossMetric']
#else:#train rpn only
#print('sym', sym.list_outputs())
#metric_names = ['RPNAccMetric', 'RPNLogLossMetric', 'RPNL1LossMetric']
mids = [0, 4, 8]
for mid in mids:
_metric = metric.RPNAccMetric(pred_idx=mid, label_idx=mid + 1)
eval_metrics.add(_metric)
#_metric = metric.RPNLogLossMetric(pred_idx=mid, label_idx=mid+1)
#eval_metrics.add(_metric)
_metric = metric.RPNL1LossMetric(loss_idx=mid + 2, weight_idx=mid + 3)
eval_metrics.add(_metric)
#rpn_eval_metric = metric.RPNAccMetric()
#rpn_cls_metric = metric.RPNLogLossMetric()
#rpn_bbox_metric = metric.RPNL1LossMetric()
#eval_metric = metric.RCNNAccMetric()
#cls_metric = metric.RCNNLogLossMetric()
#bbox_metric = metric.RCNNL1LossMetric()
#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
means = np.tile(np.array(config.TRAIN.BBOX_MEANS), config.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS), config.NUM_CLASSES)
#epoch_end_callback = callback.do_checkpoint(prefix, means, stds)
epoch_end_callback = None
# decide learning rate
base_lr = lr
lr_factor = 0.1
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) / input_batch_size) for epoch in lr_epoch_diff
]
#lr_iters = [36000,42000] #TODO
#lr_iters = [40000,50000,60000] #TODO
#lr_iters = [40,50,60] #TODO
end_epoch = 10000
#lr_iters = [4,8] #TODO
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
opt = optimizer.SGD(learning_rate=lr,
momentum=0.9,
wd=0.0005,
rescale_grad=1.0 / len(ctx),
clip_gradient=None)
initializer = mx.init.Xavier()
#initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="out", magnitude=2) #resnet style
if len(ctx) > 1:
train_data = mx.io.PrefetchingIter(train_data)
_cb = mx.callback.Speedometer(train_data.batch_size,
frequent=args.frequent,
auto_reset=False)
global_step = [0]
def save_model(epoch):
arg, aux = mod.get_params()
all_layers = mod.symbol.get_internals()
outs = []
for stride in config.RPN_FEAT_STRIDE:
num_anchors = config.RPN_ANCHOR_CFG[str(stride)]['NUM_ANCHORS']
_name = 'rpn_cls_score_stride%d_output' % stride
rpn_cls_score = all_layers[_name]
# prepare rpn data
rpn_cls_score_reshape = mx.symbol.Reshape(
data=rpn_cls_score,
shape=(0, 2, -1, 0),
name="rpn_cls_score_reshape_stride%d" % stride)
rpn_cls_prob = mx.symbol.SoftmaxActivation(
data=rpn_cls_score_reshape,
mode="channel",
name="rpn_cls_prob_stride%d" % stride)
rpn_cls_prob_reshape = mx.symbol.Reshape(
data=rpn_cls_prob,
shape=(0, 2 * num_anchors, -1, 0),
name='rpn_cls_prob_reshape_stride%d' % stride)
_name = 'rpn_bbox_pred_stride%d_output' % stride
rpn_bbox_pred = all_layers[_name]
outs.append(rpn_cls_prob_reshape)
outs.append(rpn_bbox_pred)
_sym = mx.sym.Group(outs)
mx.model.save_checkpoint(prefix, epoch, _sym, arg, aux)
def _batch_callback(param):
#global global_step
_cb(param)
global_step[0] += 1
mbatch = global_step[0]
for _iter in lr_iters:
if mbatch == _iter:
opt.lr *= 0.1
print('lr change to',
opt.lr,
' in batch',
mbatch,
file=sys.stderr)
break
if mbatch % 1000 == 0:
print('saving final checkpoint', mbatch, file=sys.stderr)
save_model(mbatch)
if mbatch == lr_iters[-1]:
print('saving final checkpoint', mbatch, file=sys.stderr)
save_model(0)
#arg, aux = mod.get_params()
#mx.model.save_checkpoint(prefix, 99, mod.symbol, arg, aux)
sys.exit(0)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=_batch_callback,
kvstore=args.kvstore,
optimizer=opt,
initializer=initializer,
allow_missing=True,
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=0.001, lr_step='5'):
# set up logger
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# setup config
config.TRAIN.BATCH_IMAGES = 1
config.TRAIN.BATCH_ROIS = 128
config.TRAIN.END2END = True
config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True
# load symbol
sym_instance = eval('symbol_' + args.network)()
sym_gen = sym_instance.get_symbol
sym = sym_gen(46,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)
# load dataset and prepare imdb for training
dataset = Dataset(args.root_path,args.dataset,args.subset,split = args.split)
roidb = dataset.gt_roidb()
W = dataset.W
# load training data
train_data = AnchorLoader(feat_sym, roidb, batch_size=input_batch_size, shuffle=not args.no_shuffle,
ctx=ctx, work_load_list=args.work_load_list,
feat_stride=config.RPN_FEAT_STRIDE, anchor_scales=config.ANCHOR_SCALES,
anchor_ratios=config.ANCHOR_RATIOS)
# infer max shape
max_data_shape = [('data', (input_batch_size, 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', (input_batch_size, 100, 5)))
print('providing maximum shape', max_data_shape, max_label_shape)
# infer shape
#get a new symbol
bucket_key = train_data.bucket_key
print(train_data.provide_data)
data_shape_dict = dict(train_data.provide_data + train_data.provide_label)
sym_instance.infer_shape(data_shape_dict)
#arg_shape, out_shape, aux_shape = curr_sym.infer_shape(**data_shape_dict)
#arg_shape_dict = dict(zip(curr_sym.list_arguments(), arg_shape))
#out_shape_dict = dict(zip(curr_sym.list_outputs(), out_shape))
#aux_shape_dict = dict(zip(curr_sym.list_auxiliary_states(), aux_shape))
#del arg_shape_dict['lstm_parameters']
#print(curr_sym.list_arguments())
#print(aux_shape_dict)
# load and initialize params
if args.resume:
print("continue training from epoch {}".format(begin_epoch))
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
if config.RNN.USE_W2V:
arg_params['embed_weight'] = mx.nd.array(W)
else:
arg_params['embed_weight'] = mx.random.uniform(0,0.01,shape=arg_shape_dict['embed_weight'])
sym_instance.init_weight(config,arg_params,aux_params)
#no checking
#for k in arg_shape_dict.iterkeys():
# if k in data_shape_dict:
# continue
# assert k in arg_params, k + ' not initialized'
#assert arg_params[k].shape == arg_shape_dict[k], \
# 'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape)
#for k in sym.list_auxiliary_states():
# assert k in aux_params, k + ' not initialized'
# assert aux_params[k].shape == aux_shape_dict[k], \
# 'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape)
# create solver
fixed_param_prefix = config.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data]
label_names = [k[0] for k in train_data.provide_label]
mod = MutableModule(sym_gen,config, data_names=data_names, label_names=label_names,
logger=logger, context=ctx, work_load_list=args.work_load_list,
max_data_shapes=max_data_shape, max_label_shapes=max_label_shape,
fixed_param_prefix=fixed_param_prefix)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric()
cls_metric = metric.RCNNLogLossMetric()
bbox_metric = 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 = callback.Speedometer(train_data.batch_size, frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS), config.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS), config.NUM_CLASSES)
epoch_end_callback = callback.do_checkpoint(config.ENCODER_CELL,prefix, means, stds)
# decide learning rate
base_lr = lr
lr_factor = 0.1
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 = 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}
#initializer for fused RNN
#TODO:not successfully added,try ask it on github issues.
initializer = mx.initializer.FusedRNN(init=mx.init.Xavier(factor_type='in', magnitude=2.34),
num_hidden = 1024,num_layers=2,mode='lstm')
# train
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback, kvstore=args.kvstore,
optimizer='sgd', optimizer_params=optimizer_params,allow_missing=True,initializer=mx.init.Xavier(factor_type='in', magnitude=2.34),
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=0.001,
lr_step=50000):
# set up logger
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# setup config
config.TRAIN.HAS_RPN = True
config.TRAIN.BATCH_SIZE = 1
config.TRAIN.BATCH_IMAGES = 1
config.TRAIN.BATCH_ROIS = 128
config.TRAIN.END2END = True
config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True
config.TRAIN.BG_THRESH_LO = 0.0
# load symbol
sym = eval('get_' + args.network + '_train')()
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
config.TRAIN.BATCH_IMAGES *= len(ctx)
config.TRAIN.BATCH_SIZE *= len(ctx)
# print config
pprint.pprint(config)
# load dataset and prepare imdb for training
imdb = eval(args.dataset)(args.image_set, args.root_path,
args.dataset_path)
roidb = imdb.gt_roidb()
if args.flip:
roidb = imdb.append_flipped_images(roidb)
# load training data
train_data = AnchorLoader(feat_sym,
roidb,
batch_size=config.TRAIN.BATCH_SIZE,
shuffle=True,
ctx=ctx,
work_load_list=args.work_load_list)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_SIZE, 3, 1000, 1000))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_SIZE, 100, 5)))
print 'providing maximum shape', max_data_shape, max_label_shape
# load pretrained
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
# infer shape
data_shape_dict = dict(train_data.provide_data + train_data.provide_label)
arg_shape, out_shape, aux_shape = sym.infer_shape(**data_shape_dict)
arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape))
out_shape_dict = dict(zip(sym.list_outputs(), out_shape))
aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape))
print 'output shape'
pprint.pprint(out_shape_dict)
# initialize params
if not args.resume:
arg_params['rpn_conv_3x3_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['rpn_conv_3x3_weight'])
arg_params['rpn_conv_3x3_bias'] = mx.nd.zeros(
shape=arg_shape_dict['rpn_conv_3x3_bias'])
arg_params['rpn_cls_score_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['rpn_cls_score_weight'])
arg_params['rpn_cls_score_bias'] = mx.nd.zeros(
shape=arg_shape_dict['rpn_cls_score_bias'])
arg_params['rpn_bbox_pred_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['rpn_bbox_pred_weight'])
arg_params['rpn_bbox_pred_bias'] = mx.nd.zeros(
shape=arg_shape_dict['rpn_bbox_pred_bias'])
arg_params['cls_score_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['cls_score_weight'])
arg_params['cls_score_bias'] = mx.nd.zeros(
shape=arg_shape_dict['cls_score_bias'])
arg_params['bbox_pred_weight'] = mx.random.normal(
0, 0.001, shape=arg_shape_dict['bbox_pred_weight'])
arg_params['bbox_pred_bias'] = mx.nd.zeros(
shape=arg_shape_dict['bbox_pred_bias'])
# check parameter shapes
for k in sym.list_arguments():
if k in data_shape_dict:
continue
assert arg_params[k].shape == arg_shape_dict[k], \
'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape)
for k in sym.list_auxiliary_states():
assert aux_params[k].shape == aux_shape_dict[k], \
'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape)
# create solver
fixed_param_prefix = ['conv1', 'conv2']
data_names = [k[0] for k in train_data.provide_data]
label_names = [k[0] for k in train_data.provide_label]
mod = MutableModule(sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
work_load_list=args.work_load_list,
max_data_shapes=max_data_shape,
max_label_shapes=max_label_shape,
fixed_param_prefix=fixed_param_prefix)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric()
cls_metric = metric.RCNNLogLossMetric()
bbox_metric = 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 = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS), imdb.num_classes)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS), imdb.num_classes)
epoch_end_callback = callback.do_checkpoint(prefix, means, stds)
# optimizer
optimizer_params = {
'momentum': 0.9,
'wd': 0.0005,
'learning_rate': lr,
'lr_scheduler': mx.lr_scheduler.FactorScheduler(lr_step, 0.1),
'rescale_grad': (1.0 / config.TRAIN.BATCH_SIZE)
}
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=args.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)