def alternate_train(
image_set,
year,
root_path,
devkit_path,
pretrained,
epoch,
ctx,
begin_epoch,
rpn_epoch,
rcnn_epoch,
frequent,
kv_store,
work_load_list=None,
):
# set up logger
logger = logging.getLogger()
logger.setLevel(logging.INFO)
config.TRAIN.BG_THRESH_LO = 0.0
logging.info("########## TRAIN RPN WITH IMAGENET INIT")
config.TRAIN.HAS_RPN = True
config.TRAIN.BATCH_SIZE = 1
train_rpn(
image_set,
year,
root_path,
devkit_path,
pretrained,
epoch,
"model/rpn1",
ctx,
begin_epoch,
rpn_epoch,
frequent,
kv_store,
work_load_list,
)
logging.info("########## GENERATE RPN DETECTION")
config.TEST.HAS_RPN = True
config.TEST.RPN_PRE_NMS_TOP_N = -1
config.TEST.RPN_POST_NMS_TOP_N = 2000
test_rpn(image_set, year, root_path, devkit_path, "model/rpn1", rpn_epoch, ctx)
logging.info("########## TRAIN RCNN WITH IMAGENET INIT AND RPN DETECTION")
config.TRAIN.HAS_RPN = False
config.TRAIN.BATCH_SIZE = 128
train_rcnn(
image_set,
year,
root_path,
devkit_path,
pretrained,
epoch,
"model/rcnn1",
ctx,
begin_epoch,
rcnn_epoch,
frequent,
kv_store,
work_load_list,
)
logging.info("########## TRAIN RPN WITH RCNN INIT")
config.TRAIN.HAS_RPN = True
config.TRAIN.BATCH_SIZE = 1
config.TRAIN.FINETUNE = True
train_rpn(
image_set,
year,
root_path,
devkit_path,
"model/rcnn1",
rcnn_epoch,
"model/rpn2",
ctx,
begin_epoch,
rpn_epoch,
frequent,
kv_store,
work_load_list,
)
logging.info("########## GENERATE RPN DETECTION")
config.TEST.HAS_RPN = True
config.TEST.RPN_PRE_NMS_TOP_N = -1
config.TEST.RPN_POST_NMS_TOP_N = 2000
test_rpn(image_set, year, root_path, devkit_path, "model/rpn2", rpn_epoch, ctx)
logger.info("########## COMBINE RPN2 WITH RCNN1")
combine_model("model/rpn2", rpn_epoch, "model/rcnn1", rcnn_epoch, "model/rcnn2", 0)
logger.info("########## TRAIN RCNN WITH RPN INIT AND DETECTION")
config.TRAIN.HAS_RPN = False
config.TRAIN.BATCH_SIZE = 128
train_rcnn(
image_set,
year,
root_path,
devkit_path,
"model/rcnn2",
0,
"model/rcnn2",
ctx,
begin_epoch,
rcnn_epoch,
frequent,
kv_store,
work_load_list,
)
logger.info("########## COMBINE RPN2 WITH RCNN2")
combine_model("model/rpn2", rpn_epoch, "model/rcnn2", rcnn_epoch, "model/final", 0)
def alternate_train(image_set,
test_image_set,
year,
root_path,
devkit_path,
pretrained,
epoch,
ctx,
begin_epoch,
rpn_epoch,
rcnn_epoch,
frequent,
kv_store,
work_load_list=None):
# set up logger
logger = logging.getLogger()
logger.setLevel(logging.INFO)
config.TRAIN.BG_THRESH_LO = 0.0
logging.info('########## TRAIN RPN WITH IMAGENET INIT')
config.TRAIN.HAS_RPN = True
config.TRAIN.BATCH_SIZE = 1
train_rpn(image_set, year, root_path, devkit_path, pretrained, epoch,
'model/rpn1', ctx, begin_epoch, rpn_epoch, frequent, kv_store,
work_load_list)
logging.info('########## GENERATE RPN DETECTION')
config.TEST.HAS_RPN = True
config.TEST.RPN_PRE_NMS_TOP_N = -1
config.TEST.RPN_POST_NMS_TOP_N = 2000
test_rpn(image_set, year, root_path, devkit_path, 'model/rpn1', rpn_epoch,
ctx[0])
logging.info('########## TRAIN RCNN WITH IMAGENET INIT AND RPN DETECTION')
config.TRAIN.HAS_RPN = False
config.TRAIN.BATCH_SIZE = 128
train_rcnn(image_set, year, root_path, devkit_path, pretrained, epoch,
'model/rcnn1', ctx, begin_epoch, rcnn_epoch, frequent, kv_store,
work_load_list)
logging.info('########## TRAIN RPN WITH RCNN INIT')
config.TRAIN.HAS_RPN = True
config.TRAIN.BATCH_SIZE = 1
config.TRAIN.FINETUNE = True
train_rpn(image_set, year, root_path, devkit_path, 'model/rcnn1',
rcnn_epoch, 'model/rpn2', ctx, begin_epoch, rpn_epoch, frequent,
kv_store, work_load_list)
logging.info('########## GENERATE RPN DETECTION')
config.TEST.HAS_RPN = True
config.TEST.RPN_PRE_NMS_TOP_N = -1
config.TEST.RPN_POST_NMS_TOP_N = 2000
test_rpn(image_set, year, root_path, devkit_path, 'model/rpn2', rpn_epoch,
ctx[0])
logger.info('########## COMBINE RPN2 WITH RCNN1')
combine_model('model/rpn2', rpn_epoch, 'model/rcnn1', rcnn_epoch,
'model/rcnn2', 0)
logger.info('########## TRAIN RCNN WITH RPN INIT AND DETECTION')
config.TRAIN.HAS_RPN = False
config.TRAIN.BATCH_SIZE = 128
train_rcnn(image_set, year, root_path, devkit_path, 'model/rcnn2', 0,
'model/rcnn2', ctx, begin_epoch, rcnn_epoch, frequent, kv_store,
work_load_list)
logger.info('########## COMBINE RPN2 WITH RCNN2')
combine_model('model/rpn2', rpn_epoch, 'model/rcnn2', rcnn_epoch,
'model/final', 0)
def alternate_train(args, ctx, pretrained, epoch):
# set up logger
logger, output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
# basic config
begin_epoch = 0
# logging.info('########## TRAIN RPN WITH IMAGENET INIT')
rpn1_prefix = os.path.join(output_path, 'rpn1')
if not os.path.exists(rpn1_prefix):
os.makedirs(rpn1_prefix)
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RPN_BATCH_IMAGES
train_rpn(config,
config.dataset.dataset,
config.dataset.image_set,
config.dataset.root_path,
config.dataset.dataset_path,
args.frequent,
config.default.kvstore,
config.TRAIN.FLIP,
config.TRAIN.SHUFFLE,
config.TRAIN.RESUME,
ctx,
pretrained,
epoch,
rpn1_prefix,
begin_epoch,
config.TRAIN.ALTERNATE.rpn1_epoch,
train_shared=False,
lr=config.TRAIN.ALTERNATE.rpn1_lr,
lr_step=config.TRAIN.ALTERNATE.rpn1_lr_step,
logger=logger,
output_path=output_path)
logging.info('########## GENERATE RPN DETECTION')
image_sets = [iset for iset in config.dataset.image_set.split('+')]
image_sets.extend(
[iset for iset in config.dataset.test_image_set.split('+')])
for image_set in image_sets:
test_rpn(config,
config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
ctx,
rpn1_prefix,
config.TRAIN.ALTERNATE.rpn1_epoch,
vis=False,
shuffle=False,
thresh=0,
logger=logger,
output_path=rpn1_prefix)
logging.info('########## TRAIN rfcn WITH IMAGENET INIT AND RPN DETECTION')
rfcn1_prefix = os.path.join(output_path, 'rfcn1')
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RCNN_BATCH_IMAGES
train_rcnn(config,
config.dataset.dataset,
config.dataset.image_set,
config.dataset.root_path,
config.dataset.dataset_path,
args.frequent,
config.default.kvstore,
config.TRAIN.FLIP,
config.TRAIN.SHUFFLE,
config.TRAIN.RESUME,
ctx,
pretrained,
epoch,
rfcn1_prefix,
begin_epoch,
config.TRAIN.ALTERNATE.rfcn1_epoch,
train_shared=False,
lr=config.TRAIN.ALTERNATE.rfcn1_lr,
lr_step=config.TRAIN.ALTERNATE.rfcn1_lr_step,
proposal='rpn',
logger=logger,
output_path=rpn1_prefix)
logging.info('########## TRAIN RPN WITH rfcn INIT')
rpn2_prefix = os.path.join(output_path, 'rpn2')
if not os.path.exists(rpn2_prefix):
os.makedirs(rpn2_prefix)
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RPN_BATCH_IMAGES
train_rpn(config,
config.dataset.dataset,
config.dataset.image_set,
config.dataset.root_path,
config.dataset.dataset_path,
args.frequent,
config.default.kvstore,
config.TRAIN.FLIP,
config.TRAIN.SHUFFLE,
config.TRAIN.RESUME,
ctx,
rfcn1_prefix,
config.TRAIN.ALTERNATE.rpn2_epoch,
rpn2_prefix,
begin_epoch,
config.TRAIN.ALTERNATE.rpn2_epoch,
train_shared=True,
lr=config.TRAIN.ALTERNATE.rpn2_lr,
lr_step=config.TRAIN.ALTERNATE.rpn2_lr_step,
logger=logger,
output_path=output_path)
logging.info('########## GENERATE RPN FIXED_PARAMS_SHAREDDETECTION')
image_sets = [iset for iset in config.dataset.image_set.split('+')]
for image_set in image_sets:
test_rpn(config,
config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
ctx,
rpn2_prefix,
config.TRAIN.ALTERNATE.rpn2_epoch,
vis=False,
shuffle=False,
thresh=0,
logger=logger,
output_path=rpn2_prefix)
logger.info('########## COMBINE RPN2 WITH rfcn1')
rfcn2_prefix = os.path.join(output_path, 'rfcn2')
combine_model(rpn2_prefix, config.TRAIN.ALTERNATE.rpn2_epoch, rfcn1_prefix,
config.TRAIN.ALTERNATE.rfcn1_epoch, rfcn2_prefix, 0)
logger.info('########## TRAIN rfcn WITH RPN INIT AND DETECTION')
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RCNN_BATCH_IMAGES
train_rcnn(config,
config.dataset.dataset,
config.dataset.image_set,
config.dataset.root_path,
config.dataset.dataset_path,
args.frequent,
config.default.kvstore,
config.TRAIN.FLIP,
config.TRAIN.SHUFFLE,
config.TRAIN.RESUME,
ctx,
rfcn2_prefix,
0,
rfcn2_prefix,
begin_epoch,
config.TRAIN.ALTERNATE.rfcn2_epoch,
train_shared=True,
lr=config.TRAIN.ALTERNATE.rfcn2_lr,
lr_step=config.TRAIN.ALTERNATE.rfcn2_lr_step,
proposal='rpn',
logger=logger,
output_path=rpn2_prefix)
logger.info('########## COMBINE RPN2 WITH rfcn2')
final_prefix = os.path.join(output_path, 'final')
combine_model(rpn2_prefix, config.TRAIN.ALTERNATE.rpn2_epoch, rfcn2_prefix,
config.TRAIN.ALTERNATE.rfcn2_epoch, final_prefix, 0)
def alternate_train(args, ctx, pretrained, epoch):
# set up logger
logger, output_path = create_logger(config.output_path, args.cfg, config.dataset.image_set)
# basic config
begin_epoch = 0
# logging.info('########## TRAIN RPN WITH IMAGENET INIT')
rpn1_prefix = os.path.join(output_path, 'rpn1')
if not os.path.exists(rpn1_prefix):
os.makedirs(rpn1_prefix)
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RPN_BATCH_IMAGES
train_rpn(config, config.dataset.dataset, config.dataset.image_set, config.dataset.root_path, config.dataset.dataset_path,
args.frequent, config.default.kvstore, config.TRAIN.FLIP, config.TRAIN.SHUFFLE, config.TRAIN.RESUME,
ctx, pretrained, epoch, rpn1_prefix, begin_epoch, config.TRAIN.ALTERNATE.rpn1_epoch, train_shared=False,
lr=config.TRAIN.ALTERNATE.rpn1_lr, lr_step=config.TRAIN.ALTERNATE.rpn1_lr_step, logger=logger, output_path=output_path)
logging.info('########## GENERATE RPN DETECTION')
image_sets = [iset for iset in config.dataset.image_set.split('+')]
image_sets.extend([iset for iset in config.dataset.test_image_set.split('+')])
for image_set in image_sets:
test_rpn(config, config.dataset.dataset, image_set, config.dataset.root_path, config.dataset.dataset_path,
ctx, rpn1_prefix, config.TRAIN.ALTERNATE.rpn1_epoch, vis=False, shuffle=False, thresh=0, logger=logger,
output_path=rpn1_prefix)
logging.info('########## TRAIN rfcn WITH IMAGENET INIT AND RPN DETECTION')
rfcn1_prefix = os.path.join(output_path, 'rfcn1')
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RCNN_BATCH_IMAGES
train_rcnn(config, config.dataset.dataset, config.dataset.image_set, config.dataset.root_path, config.dataset.dataset_path,
args.frequent, config.default.kvstore, config.TRAIN.FLIP, config.TRAIN.SHUFFLE, config.TRAIN.RESUME,
ctx, pretrained, epoch, rfcn1_prefix, begin_epoch, config.TRAIN.ALTERNATE.rfcn1_epoch, train_shared=False,
lr=config.TRAIN.ALTERNATE.rfcn1_lr, lr_step=config.TRAIN.ALTERNATE.rfcn1_lr_step, proposal='rpn', logger=logger,
output_path=rpn1_prefix)
logging.info('########## TRAIN RPN WITH rfcn INIT')
rpn2_prefix = os.path.join(output_path, 'rpn2')
if not os.path.exists(rpn2_prefix):
os.makedirs(rpn2_prefix)
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RPN_BATCH_IMAGES
train_rpn(config, config.dataset.dataset, config.dataset.image_set, config.dataset.root_path, config.dataset.dataset_path,
args.frequent, config.default.kvstore, config.TRAIN.FLIP, config.TRAIN.SHUFFLE, config.TRAIN.RESUME,
ctx, rfcn1_prefix, config.TRAIN.ALTERNATE.rpn2_epoch, rpn2_prefix, begin_epoch, config.TRAIN.ALTERNATE.rpn2_epoch,
train_shared=True, lr=config.TRAIN.ALTERNATE.rpn2_lr, lr_step=config.TRAIN.ALTERNATE.rpn2_lr_step, logger=logger,
output_path=output_path)
logging.info('########## GENERATE RPN DETECTION')
image_sets = [iset for iset in config.dataset.image_set.split('+')]
for image_set in image_sets:
test_rpn(config, config.dataset.dataset, image_set, config.dataset.root_path, config.dataset.dataset_path,
ctx, rpn2_prefix, config.TRAIN.ALTERNATE.rpn2_epoch, vis=False, shuffle=False, thresh=0, logger=logger,
output_path=rpn2_prefix)
logger.info('########## COMBINE RPN2 WITH rfcn1')
rfcn2_prefix = os.path.join(output_path, 'rfcn2')
combine_model(rpn2_prefix, config.TRAIN.ALTERNATE.rpn2_epoch, rfcn1_prefix, config.TRAIN.ALTERNATE.rfcn1_epoch, rfcn2_prefix, 0)
logger.info('########## TRAIN rfcn WITH RPN INIT AND DETECTION')
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RCNN_BATCH_IMAGES
train_rcnn(config, config.dataset.dataset, config.dataset.image_set, config.dataset.root_path, config.dataset.dataset_path,
args.frequent, config.default.kvstore, config.TRAIN.FLIP, config.TRAIN.SHUFFLE, config.TRAIN.RESUME,
ctx, rfcn2_prefix, 0, rfcn2_prefix, begin_epoch, config.TRAIN.ALTERNATE.rfcn2_epoch, train_shared=True,
lr=config.TRAIN.ALTERNATE.rfcn2_lr, lr_step=config.TRAIN.ALTERNATE.rfcn2_lr_step, proposal='rpn', logger=logger,
output_path=rpn2_prefix)
logger.info('########## COMBINE RPN2 WITH rfcn2')
final_prefix = os.path.join(output_path, 'final')
combine_model(rpn2_prefix, config.TRAIN.ALTERNATE.rpn2_epoch, rfcn2_prefix, config.TRAIN.ALTERNATE.rfcn2_epoch, final_prefix, 0)