def metric():
rpn_eval_metric = AccuracyMetric(use_ignore=True, ignore=-1, ex_rpn=True)
rpn_cls_metric = LogLossMetric(use_ignore=True, ignore=-1, ex_rpn=True)
rpn_bbox_metric = SmoothL1LossMetric(ex_rpn=True)
eval_metric = AccuracyMetric()
cls_metric = LogLossMetric()
bbox_metric = SmoothL1LossMetric()
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)
return eval_metrics
def end2end_train(image_set, test_image_set, year, root_path, devkit_path, pretrained, epoch, prefix,
ctx, begin_epoch, num_epoch, frequent, kv_store, mom, wd, lr, num_classes, monitor,
work_load_list=None, resume=False, use_flip=True, factor_step=50000):
# set up logger
logger = logging.getLogger()
logger.setLevel(logging.INFO)
mon = None
config.TRAIN.BG_THRESH_HI = 0.5 # TODO(verify)
config.TRAIN.BG_THRESH_LO = 0.0 # TODO(verify)
config.TRAIN.RPN_MIN_SIZE = 16
logging.info('########## TRAIN FASTER-RCNN WITH APPROXIMATE JOINT END2END #############')
config.TRAIN.HAS_RPN = True
config.END2END = 1
config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True
sym = get_faster_rcnn(num_classes=num_classes)
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
config.TRAIN.IMS_PER_BATCH *= len(ctx)
config.TRAIN.BATCH_SIZE *= len(ctx) # no used here
# infer max shape
max_data_shape = [('data', (config.TRAIN.IMS_PER_BATCH, 3, 1000, 1000))]
max_data_shape_dict = {k: v for k, v in max_data_shape}
_, feat_shape, _ = feat_sym.infer_shape(**max_data_shape_dict)
from rcnn.minibatch import assign_anchor
import numpy as np
label = assign_anchor(feat_shape[0], np.zeros((0, 5)), [[1000, 1000, 1.0]])
max_label_shape = [('label', label['label'].shape),
('bbox_target', label['bbox_target'].shape),
('bbox_inside_weight', label['bbox_inside_weight'].shape),
('bbox_outside_weight', label['bbox_outside_weight'].shape),
('gt_boxes', (config.TRAIN.IMS_PER_BATCH, 5*100))] # assume at most 100 object in image
print 'providing maximum shape', max_data_shape, max_label_shape
# load training data
voc, roidb = load_gt_roidb(image_set, year, root_path, devkit_path, flip=use_flip)
train_data = AnchorLoader(feat_sym, roidb, batch_size=config.TRAIN.IMS_PER_BATCH, shuffle=True, mode='train',
ctx=ctx, work_load_list=work_load_list)
# load pretrained
args, auxs, _ = load_param(pretrained, epoch, convert=True)
# initialize params
if not resume:
del args['fc8_weight']
del args['fc8_bias']
input_shapes = {k: (1,)+ v[1::] for k, v in train_data.provide_data + train_data.provide_label}
arg_shape, _, _ = sym.infer_shape(**input_shapes)
arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape))
args['rpn_conv_3x3_weight'] = mx.random.normal(0, 0.01, shape=arg_shape_dict['rpn_conv_3x3_weight'])
args['rpn_conv_3x3_bias'] = mx.nd.zeros(shape=arg_shape_dict['rpn_conv_3x3_bias'])
args['rpn_cls_score_weight'] = mx.random.normal(0, 0.01, shape=arg_shape_dict['rpn_cls_score_weight'])
args['rpn_cls_score_bias'] = mx.nd.zeros(shape=arg_shape_dict['rpn_cls_score_bias'])
args['rpn_bbox_pred_weight'] = mx.random.normal(0, 0.001, shape=arg_shape_dict['rpn_bbox_pred_weight']) # guarantee not likely explode with bbox_delta
args['rpn_bbox_pred_bias'] = mx.nd.zeros(shape=arg_shape_dict['rpn_bbox_pred_bias'])
args['cls_score_weight'] = mx.random.normal(0, 0.01, shape=arg_shape_dict['cls_score_weight'])
args['cls_score_bias'] = mx.nd.zeros(shape=arg_shape_dict['cls_score_bias'])
args['bbox_pred_weight'] = mx.random.normal(0, 0.01, shape=arg_shape_dict['bbox_pred_weight'])
args['bbox_pred_bias'] = mx.nd.zeros(shape=arg_shape_dict['bbox_pred_bias'])
# prepare training
if config.TRAIN.FINETUNE:
fixed_param_prefix = ['conv1', 'conv2', 'conv3', 'conv4', 'conv5']
else:
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]
batch_end_callback = Speedometer(train_data.batch_size, frequent=frequent)
epoch_end_callback = do_checkpoint(prefix)
rpn_eval_metric = AccuracyMetric(use_ignore=True, ignore=-1, ex_rpn=True)
rpn_cls_metric = LogLossMetric(use_ignore=True, ignore=-1, ex_rpn=True)
rpn_bbox_metric = SmoothL1LossMetric(ex_rpn=True)
eval_metric = AccuracyMetric()
cls_metric = LogLossMetric()
bbox_metric = SmoothL1LossMetric()
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)
optimizer_params = {'momentum': mom,
'wd': wd,
'learning_rate': lr,
'lr_scheduler': mx.lr_scheduler.FactorScheduler(factor_step, 0.1),
'clip_gradient': 1.0,
'rescale_grad': 1.0 }
# 'rescale_grad': (1.0 / config.TRAIN.RPN_BATCH_SIZE)}
# train
mod = MutableModule(sym, data_names=data_names, label_names=label_names,
logger=logger, context=ctx, work_load_list=work_load_list,
max_data_shapes=max_data_shape, max_label_shapes=max_label_shape,
fixed_param_prefix=fixed_param_prefix)
if monitor:
def norm_stat(d):
return mx.nd.norm(d)/np.sqrt(d.size)
mon = mx.mon.Monitor(100, norm_stat)
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback, kvstore=kv_store,
optimizer='sgd', optimizer_params=optimizer_params, monitor=mon,
arg_params=args, aux_params=auxs, begin_epoch=begin_epoch, num_epoch=num_epoch)
def train_rcnn(image_set,
year,
root_path,
devkit_path,
pretrained,
epoch,
prefix,
ctx,
begin_epoch,
end_epoch,
frequent,
kv_store,
work_load_list=None,
resume=False,
proposal='rpn'):
# set up logger
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# load symbol
sym = get_vgg_rcnn()
# setup multi-gpu
config.TRAIN.BATCH_IMAGES *= len(ctx)
config.TRAIN.BATCH_SIZE *= len(ctx)
# load training data
voc, roidb, means, stds = eval('load_' + proposal + '_roidb')(image_set,
year,
root_path,
devkit_path,
flip=True)
train_data = ROIIter(roidb,
batch_size=config.TRAIN.BATCH_IMAGES,
shuffle=True,
mode='train',
ctx=ctx,
work_load_list=work_load_list)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3, 1000, 1000))]
# load pretrained
args, auxs, _ = load_param(pretrained, epoch, convert=True)
# initialize params
if not resume:
input_shapes = {
k: v
for k, v in train_data.provide_data + train_data.provide_label
}
arg_shape, _, _ = sym.infer_shape(**input_shapes)
arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape))
args['cls_score_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['cls_score_weight'])
args['cls_score_bias'] = mx.nd.zeros(
shape=arg_shape_dict['cls_score_bias'])
args['bbox_pred_weight'] = mx.random.normal(
0, 0.001, shape=arg_shape_dict['bbox_pred_weight'])
args['bbox_pred_bias'] = mx.nd.zeros(
shape=arg_shape_dict['bbox_pred_bias'])
# prepare training
if config.TRAIN.FINETUNE:
fixed_param_prefix = ['conv1', 'conv2', 'conv3', 'conv4', 'conv5']
else:
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]
batch_end_callback = Speedometer(train_data.batch_size, frequent=frequent)
epoch_end_callback = mx.callback.do_checkpoint(prefix)
if config.TRAIN.HAS_RPN is True:
eval_metric = AccuracyMetric(use_ignore=True, ignore=-1)
cls_metric = LogLossMetric(use_ignore=True, ignore=-1)
else:
eval_metric = AccuracyMetric()
cls_metric = LogLossMetric()
bbox_metric = SmoothL1LossMetric()
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
optimizer_params = {
'momentum': 0.9,
'wd': 0.0005,
'learning_rate': 0.001,
'lr_scheduler': mx.lr_scheduler.FactorScheduler(30000, 0.1),
'rescale_grad': (1.0 / config.TRAIN.BATCH_SIZE)
}
# train
mod = MutableModule(sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
work_load_list=work_load_list,
max_data_shapes=max_data_shape,
fixed_param_prefix=fixed_param_prefix)
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=kv_store,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=args,
aux_params=auxs,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
# edit params and save
for epoch in range(begin_epoch + 1, end_epoch + 1):
arg_params, aux_params = load_checkpoint(prefix, epoch)
arg_params['bbox_pred_weight'] = (arg_params['bbox_pred_weight'].T *
mx.nd.array(stds)).T
arg_params['bbox_pred_bias'] = arg_params['bbox_pred_bias'] * mx.nd.array(stds) + \
mx.nd.array(means)
save_checkpoint(prefix, epoch, arg_params, aux_params)
def train_rcnn(image_set, year, root_path, devkit_path, pretrained, epoch,
prefix, ctx, begin_epoch, end_epoch, frequent, kv_store,
work_load_list=None, resume=False, proposal='rpn'):
# set up logger
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# load symbol
sym = get_vgg_rcnn()
# setup multi-gpu
config.TRAIN.BATCH_IMAGES *= len(ctx)
config.TRAIN.BATCH_SIZE *= len(ctx)
# load training data
voc, roidb, means, stds = eval('load_' + proposal + '_roidb')(image_set, year, root_path, devkit_path, flip=True)
train_data = ROIIter(roidb, batch_size=config.TRAIN.BATCH_IMAGES, shuffle=True, mode='train',
ctx=ctx, work_load_list=work_load_list)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3, 1000, 1000))]
# load pretrained
args, auxs = load_param(pretrained, epoch, convert=True)
# initialize params
if not resume:
input_shapes = {k: v for k, v in train_data.provide_data + train_data.provide_label}
arg_shape, _, _ = sym.infer_shape(**input_shapes)
arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape))
args['cls_score_weight'] = mx.random.normal(0, 0.01, shape=arg_shape_dict['cls_score_weight'])
args['cls_score_bias'] = mx.nd.zeros(shape=arg_shape_dict['cls_score_bias'])
args['bbox_pred_weight'] = mx.random.normal(0, 0.001, shape=arg_shape_dict['bbox_pred_weight'])
args['bbox_pred_bias'] = mx.nd.zeros(shape=arg_shape_dict['bbox_pred_bias'])
# prepare training
if config.TRAIN.FINETUNE:
fixed_param_prefix = ['conv1', 'conv2', 'conv3', 'conv4', 'conv5']
else:
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]
#batch_end_callback = Speedometer(train_data.batch_size, frequent=frequent)
epoch_end_callback = mx.callback.do_checkpoint(prefix)
if config.TRAIN.HAS_RPN is True:
eval_metric = AccuracyMetric(use_ignore=True, ignore=-1)
cls_metric = LogLossMetric(use_ignore=True, ignore=-1)
else:
eval_metric = AccuracyMetric()
cls_metric = LogLossMetric()
bbox_metric = SmoothL1LossMetric()
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
optimizer_params = {'momentum': 0.9,
'wd': 0.0005,
'learning_rate': 0.001,
'lr_scheduler': mx.lr_scheduler.FactorScheduler(30000, 0.1),
'rescale_grad': (1.0 / config.TRAIN.BATCH_SIZE)}
# train
# mod = MutableModule(sym, data_names=data_names, label_names=label_names,
# logger=logger, context=ctx, work_load_list=work_load_list,
# max_data_shapes=max_data_shape, fixed_param_prefix=fixed_param_prefix)
# mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
# batch_end_callback=batch_end_callback, kvstore=kv_store,
# optimizer='sgd', optimizer_params=optimizer_params,
# arg_params=args, aux_params=auxs, begin_epoch=begin_epoch, num_epoch=end_epoch)
from mxnet.executor import Executor
def batch_end_callback(batch_size, frequent):
call_back = mx.callback.Speedometer(batch_size, frequent)
def AverageL2Norm(d):
return (mx.nd.norm(d) / np.sqrt(d.size)).asnumpy()[0]
def decorator(parameter):
call_back(parameter)
if parameter.locals['nbatch'] % frequent == 0:
executor_manager = parameter.locals['executor_manager']
d = Executor._get_dict(executor_manager.aux_names, executor_manager.aux_arrays)
for key, value in sorted(list(zip(d.keys(), d.values()))):
print(key, 'AverageL2Norm(aux):', AverageL2Norm(value[0]))
for (index, value) in enumerate(executor_manager.param_names):
print (value, 'AverageL2Norm(param,grad):', (AverageL2Norm(executor_manager.param_arrays[index][0]),
AverageL2Norm(executor_manager.grad_arrays[index][0])))
return decorator
mod = mx.model.FeedForward(sym, data_names=data_names, label_names=label_names,
logger=logger, ctx=ctx, work_load_list=work_load_list,
fixed_param_prefix=fixed_param_prefix,
optimizer=mx.optimizer.SGD(**optimizer_params),
arg_params=args, aux_params=auxs, begin_epoch=begin_epoch, num_epoch=end_epoch
)
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback(train_data.batch_size, frequent=frequent), kvstore=kv_store)
# edit params and save
for epoch in range(begin_epoch + 1, end_epoch + 1):
arg_params, aux_params = load_checkpoint(prefix, epoch)
arg_params['bbox_pred_weight'] = (arg_params['bbox_pred_weight'].T * mx.nd.array(stds)).T
arg_params['bbox_pred_bias'] = arg_params['bbox_pred_bias'] * mx.nd.array(stds) + \
mx.nd.array(means)
save_checkpoint(prefix, epoch, arg_params, aux_params)
def train_rpn(image_set,
year,
root_path,
devkit_path,
pretrained,
epoch,
prefix,
ctx,
begin_epoch,
end_epoch,
frequent,
kv_store,
work_load_list=None,
resume=False):
# set up logger
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# load symbol
sym = get_vgg_rpn()
feat_sym = get_vgg_rpn().get_internals()['rpn_cls_score_output']
# setup multi-gpu
config.TRAIN.BATCH_IMAGES *= len(ctx)
config.TRAIN.BATCH_SIZE *= len(ctx)
# load training data
voc, roidb = load_gt_roidb(image_set,
year,
root_path,
devkit_path,
flip=True)
train_data = AnchorLoader(feat_sym,
roidb,
batch_size=config.TRAIN.BATCH_SIZE,
shuffle=True,
mode='train',
ctx=ctx,
work_load_list=work_load_list)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_SIZE, 3, 1000, 1000))]
max_data_shape_dict = {k: v for k, v in max_data_shape}
_, feat_shape, _ = feat_sym.infer_shape(**max_data_shape_dict)
from rcnn.minibatch import assign_anchor
import numpy as np
label = assign_anchor(feat_shape[0], np.zeros((0, 5)), [[1000, 1000, 1.0]])
max_label_shape = [
('label', label['label'].shape),
('bbox_target', label['bbox_target'].shape),
('bbox_inside_weight', label['bbox_inside_weight'].shape),
('bbox_outside_weight', label['bbox_outside_weight'].shape)
]
print 'providing maximum shape', max_data_shape, max_label_shape
# load pretrained
args, auxs = load_param(pretrained, epoch, convert=True)
# initialize params
if not resume:
input_shapes = {
k: v
for k, v in train_data.provide_data + train_data.provide_label
}
arg_shape, _, _ = sym.infer_shape(**input_shapes)
arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape))
args['rpn_conv_3x3_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['rpn_conv_3x3_weight'])
args['rpn_conv_3x3_bias'] = mx.nd.zeros(
shape=arg_shape_dict['rpn_conv_3x3_bias'])
args['rpn_cls_score_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['rpn_cls_score_weight'])
args['rpn_cls_score_bias'] = mx.nd.zeros(
shape=arg_shape_dict['rpn_cls_score_bias'])
args['rpn_bbox_pred_weight'] = mx.random.normal(
0, 0.01, shape=arg_shape_dict['rpn_bbox_pred_weight'])
args['rpn_bbox_pred_bias'] = mx.nd.zeros(
shape=arg_shape_dict['rpn_bbox_pred_bias'])
# prepare training
if config.TRAIN.FINETUNE:
fixed_param_prefix = ['conv1', 'conv2', 'conv3', 'conv4', 'conv5']
else:
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]
batch_end_callback = Speedometer(train_data.batch_size, frequent=frequent)
epoch_end_callback = mx.callback.do_checkpoint(prefix)
if config.TRAIN.HAS_RPN is True:
eval_metric = AccuracyMetric(use_ignore=True, ignore=-1)
cls_metric = LogLossMetric(use_ignore=True, ignore=-1)
else:
eval_metric = AccuracyMetric()
cls_metric = LogLossMetric()
bbox_metric = SmoothL1LossMetric()
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
optimizer_params = {
'momentum': 0.9,
'wd': 0.0005,
'learning_rate': 0.001,
'lr_scheduler': mx.lr_scheduler.FactorScheduler(60000, 0.1),
'rescale_grad': (1.0 / config.TRAIN.BATCH_SIZE)
}
# train
mod = MutableModule(sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
work_load_list=work_load_list,
max_data_shapes=max_data_shape,
max_label_shapes=max_label_shape,
fixed_param_prefix=fixed_param_prefix)
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=kv_store,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=args,
aux_params=auxs,
begin_epoch=begin_epoch,
num_epoch=end_epoch)