def __init__(self, configFn, ctx, outFolder, threshold):
os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0"
config = importlib.import_module(
configFn.replace('.py', '').replace('/', '.'))
_, _, _, _, _, _, self.__pModel, _, self.__pTest, self.transform, _, _, _ = config.get_config(
is_train=False)
self.__pModel = patch_config_as_nothrow(self.__pModel)
self.__pTest = patch_config_as_nothrow(self.__pTest)
self.resizeParam = (800, 1200)
if callable(self.__pTest.nms.type):
self.__nms = self.__pTest.nms.type(self.__pTest.nms.thr)
else:
from operator_py.nms import py_nms_wrapper
self.__nms = py_nms_wrapper(self.__pTest.nms.thr)
arg_params, aux_params = load_checkpoint(self.__pTest.model.prefix,
self.__pTest.model.epoch)
sym = self.__pModel.test_symbol
from utils.graph_optimize import merge_bn
sym, arg_params, aux_params = merge_bn(sym, arg_params, aux_params)
self.__mod = DetModule(
sym,
data_names=['data', 'im_info', 'im_id', 'rec_id'],
context=ctx)
self.__mod.bind(data_shapes=[('data', (1, 3, self.resizeParam[0],
self.resizeParam[1])),
('im_info', (1, 3)), ('im_id', (1, )),
('rec_id', (1, ))],
for_training=False)
self.__mod.set_params(arg_params, aux_params, allow_extra=False)
self.__saveSymbol(sym, outFolder,
self.__pTest.model.prefix.split('/')[-1])
self.__threshold = threshold
self.outFolder = outFolder
def __init__(self, pBbox):
self.p = patch_config_as_nothrow(pBbox)
# declare weight and bias
xavier_init = mx.init.Xavier(factor_type="in",
rnd_type="uniform",
magnitude=3)
self.fc3_weight = X.var("bbox_fc3_weight", init=xavier_init)
self.fc3_bias = X.var("bbox_fc3_bias")
self._head_feat = None
def train_net(config):
General, KvstoreParam, RpnParam, RoiParam, BboxParam, DatasetParam, ModelParam, \
OptimizeParam, TestParam, transform, data_name, label_name, metric_list = config.generate_config(is_train=True)
pGen = patch_config_as_nothrow(General)
pKv = patch_config_as_nothrow(KvstoreParam)
pRpn = patch_config_as_nothrow(RpnParam)
pRoi = patch_config_as_nothrow(RoiParam)
pBbox = patch_config_as_nothrow(BboxParam)
pDataset = patch_config_as_nothrow(DatasetParam)
pModel = patch_config_as_nothrow(ModelParam)
pOpt = patch_config_as_nothrow(OptimizeParam)
pTest = patch_config_as_nothrow(TestParam)
gpus = pKv.gpus
if len(gpus) == 0:
ctx = [mx.cpu()]
else:
ctx = [mx.gpu(i) for i in gpus]
input_batch_size = pKv.batch_image * len(ctx)
pretrain_prefix = pModel.pretrain.prefix
pretrain_epoch = pModel.pretrain.epoch
save_path = os.path.join('experiments', pGen.name)
model_prefix = os.path.join(save_path, 'checkpoint')
begin_epoch = pOpt.schedule.begin_epoch
end_epoch = pOpt.schedule.end_epoch
lr_steps = pOpt.schedule.lr_steps
## load dataset
if pDataset.Dataset == 'widerface':
image_set = pDataset.image_set
roidb = load_gt_roidb(pDataset.Dataset,
image_set,
root_path='data',
dataset_path='data/widerface',
flip=True)
net = pModel.train_network
if pOpt.schedule.begin_epoch != 0:
net.load_model(model_prefix, pOpt.schedule.begin_epoch)
else:
net.load_model(pretrain_prefix)
print('hello github!')
def __init__(self, pNeck, pSEPC):
super().__init__(pNeck)
self.psepc = patch_config_as_nothrow(pSEPC)
self.neck_with_sepc = None
stride, pad_sizes = pSEPC.stride, pSEPC.pad_sizes
for i in range(len(stride)):
if pad_sizes[0] % stride[i] != 0 or pad_sizes[1] % stride[i] != 0:
print('Warning: This implementation of ibn used in SEPC expects (it\'s better) the (padded) input sizes {} dividable by the stride {}. '\
'When this is not satisfied, you should manually check that the feature_sizes at stride \'s\' statisfy the following: ' \
'\'ceil(pad_sizes[0]/s)==feature_sizes[0]\' and \'ceil(pad_sizes[1]/s)==feature_size[1]\''.format(pad_sizes, stride[i]))
self.feat_sizes = [[
math.ceil(pad_sizes[0] / stride[i]),
math.ceil(pad_sizes[1] / stride[i])
] for i in range(len(stride))]
def train_net(config):
pGen, pKv, pRpn, pRoi, pBbox, pDataset, pModel, pOpt, pTest, \
transform, data_name, label_name, metric_list = config.get_config(is_train=True)
pGen = patch_config_as_nothrow(pGen)
pKv = patch_config_as_nothrow(pKv)
pRpn = patch_config_as_nothrow(pRpn)
pRoi = patch_config_as_nothrow(pRoi)
pBbox = patch_config_as_nothrow(pBbox)
pDataset = patch_config_as_nothrow(pDataset)
pModel = patch_config_as_nothrow(pModel)
pOpt = patch_config_as_nothrow(pOpt)
pTest = patch_config_as_nothrow(pTest)
ctx = [mx.gpu(int(i)) for i in pKv.gpus]
pretrain_prefix = pModel.pretrain.prefix
pretrain_epoch = pModel.pretrain.epoch
prefix = pGen.name
save_path = os.path.join("experiments", prefix)
begin_epoch = pOpt.schedule.begin_epoch
end_epoch = pOpt.schedule.end_epoch
lr_iter = pOpt.schedule.lr_iter
# only rank==0 print all debug infos
kvstore_type = "dist_sync" if os.environ.get(
"DMLC_ROLE") == "worker" else pKv.kvstore
kv = mx.kvstore.create(kvstore_type)
rank = kv.rank
# for distributed training using shared file system
os.makedirs(save_path, exist_ok=True)
from utils.logger import config_logger
config_logger(os.path.join(save_path, "log.txt"))
model_prefix = os.path.join(save_path, "checkpoint")
# set up logger
logger = logging.getLogger()
sym = pModel.train_symbol
# setup multi-gpu
input_batch_size = pKv.batch_image * len(ctx)
# print config
# if rank == 0:
# logger.info(pprint.pformat(config))
# load dataset and prepare imdb for training
image_sets = pDataset.image_set
roidbs = [
pkl.load(open("data/cache/{}.roidb".format(i), "rb"),
encoding="latin1") for i in image_sets
]
roidb = reduce(lambda x, y: x + y, roidbs)
# filter empty image
roidb = [rec for rec in roidb if rec["gt_bbox"].shape[0] > 0]
# add flip roi record
flipped_roidb = []
for rec in roidb:
new_rec = rec.copy()
new_rec["flipped"] = True
flipped_roidb.append(new_rec)
roidb = roidb + flipped_roidb
from core.detection_input import AnchorLoader
train_data = AnchorLoader(roidb=roidb,
transform=transform,
data_name=data_name,
label_name=label_name,
batch_size=input_batch_size,
shuffle=True,
kv=kv,
num_worker=pGen.loader_worker or 12,
num_collector=pGen.loader_collector or 1,
worker_queue_depth=2,
collector_queue_depth=2)
# infer shape
worker_data_shape = dict(train_data.provide_data +
train_data.provide_label)
for key in worker_data_shape:
worker_data_shape[key] = (
pKv.batch_image, ) + worker_data_shape[key][1:]
arg_shape, _, aux_shape = sym.infer_shape(**worker_data_shape)
_, out_shape, _ = sym.get_internals().infer_shape(**worker_data_shape)
out_shape_dict = list(zip(sym.get_internals().list_outputs(), out_shape))
_, out_shape, _ = sym.infer_shape(**worker_data_shape)
terminal_out_shape_dict = zip(sym.list_outputs(), out_shape)
if rank == 0:
logger.info('parameter shape')
logger.info(
pprint.pformat(
[i for i in out_shape_dict if not i[0].endswith('output')]))
logger.info('intermediate output shape')
logger.info(
pprint.pformat(
[i for i in out_shape_dict if i[0].endswith('output')]))
logger.info('terminal output shape')
logger.info(pprint.pformat([i for i in terminal_out_shape_dict]))
# memonger
if pModel.memonger:
last_block = pModel.memonger_until or ""
if rank == 0:
logger.info("do memonger up to {}".format(last_block))
type_dict = {k: np.float32 for k in worker_data_shape}
sym = search_plan_to_layer(sym,
last_block,
1000,
type_dict=type_dict,
**worker_data_shape)
# load and initialize params
if pOpt.schedule.begin_epoch != 0:
arg_params, aux_params = load_checkpoint(model_prefix, begin_epoch)
elif pModel.from_scratch:
arg_params, aux_params = dict(), dict()
else:
arg_params, aux_params = load_checkpoint(pretrain_prefix,
pretrain_epoch)
if pModel.process_weight is not None:
pModel.process_weight(sym, arg_params, aux_params)
'''
there are some conflicts between `mergebn` and `attach_quantized_node` in graph_optimize.py
when mergebn ahead of attach_quantized_node
such as `Symbol.ComposeKeyword`
'''
if pModel.QuantizeTrainingParam is not None and pModel.QuantizeTrainingParam.quantize_flag:
pQuant = pModel.QuantizeTrainingParam
assert pGen.fp16 == False, "current quantize training only support fp32 mode."
from utils.graph_optimize import attach_quantize_node
_, out_shape, _ = sym.get_internals().infer_shape(**worker_data_shape)
out_shape_dictoinary = dict(
zip(sym.get_internals().list_outputs(), out_shape))
sym = attach_quantize_node(sym, out_shape_dictoinary,
pQuant.WeightQuantizeParam,
pQuant.ActQuantizeParam,
pQuant.quantized_op)
# merge batch normalization to save memory in fix bn training
from utils.graph_optimize import merge_bn
sym, arg_params, aux_params = merge_bn(sym, arg_params, aux_params)
if pModel.random:
import time
mx.random.seed(int(time.time()))
np.random.seed(int(time.time()))
init = mx.init.Xavier(factor_type="in", rnd_type='gaussian', magnitude=2)
init.set_verbosity(verbose=True)
# create solver
fixed_param = pModel.pretrain.fixed_param
excluded_param = pModel.pretrain.excluded_param
data_names = [k[0] for k in train_data.provide_data]
label_names = [k[0] for k in train_data.provide_label]
if pModel.teacher_param:
from models.KD.utils import create_teacher_module
from models.KD.detection_module import KDDetModule
t_mod, t_label_name, t_label_shape = create_teacher_module(
pModel.teacher_param, worker_data_shape, input_batch_size, ctx,
rank, logger)
mod = KDDetModule(sym,
teacher_module=t_mod,
teacher_label_names=t_label_name,
teacher_label_shapes=t_label_shape,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
fixed_param=fixed_param,
excluded_param=excluded_param)
else:
mod = DetModule(sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
fixed_param=fixed_param,
excluded_param=excluded_param)
eval_metrics = mx.metric.CompositeEvalMetric(metric_list)
# callback
batch_end_callback = [
callback.Speedometer(train_data.batch_size,
frequent=pGen.log_frequency)
]
batch_end_callback += pModel.batch_end_callbacks or []
epoch_end_callback = callback.do_checkpoint(model_prefix)
sym.save(model_prefix + ".json")
# decide learning rate
lr_mode = pOpt.optimizer.lr_mode or 'step'
base_lr = pOpt.optimizer.lr * kv.num_workers
lr_factor = pOpt.schedule.lr_factor or 0.1
iter_per_epoch = len(train_data) // input_batch_size
total_iter = iter_per_epoch * (end_epoch - begin_epoch)
lr_iter = [total_iter + it if it < 0 else it for it in lr_iter]
lr_iter = [it // kv.num_workers for it in lr_iter]
lr_iter = [it - iter_per_epoch * begin_epoch for it in lr_iter]
lr_iter_discount = [it for it in lr_iter if it > 0]
current_lr = base_lr * (lr_factor**(len(lr_iter) - len(lr_iter_discount)))
if rank == 0:
logging.info('total iter {}'.format(total_iter))
logging.info('lr {}, lr_iters {}'.format(current_lr, lr_iter_discount))
logging.info('lr mode: {}'.format(lr_mode))
if pOpt.warmup and pOpt.schedule.begin_epoch == 0:
if rank == 0:
logging.info('warmup lr {}, warmup step {}'.format(
pOpt.warmup.lr, pOpt.warmup.iter))
if lr_mode == 'step':
lr_scheduler = WarmupMultiFactorScheduler(
step=lr_iter_discount,
factor=lr_factor,
warmup=True,
warmup_type=pOpt.warmup.type,
warmup_lr=pOpt.warmup.lr,
warmup_step=pOpt.warmup.iter)
elif lr_mode == 'cosine':
warmup_lr_scheduler = AdvancedLRScheduler(mode='linear',
base_lr=pOpt.warmup.lr,
target_lr=base_lr,
niters=pOpt.warmup.iter)
cosine_lr_scheduler = AdvancedLRScheduler(
mode='cosine',
base_lr=base_lr,
target_lr=0,
niters=(iter_per_epoch *
(end_epoch - begin_epoch)) - pOpt.warmup.iter)
lr_scheduler = LRSequential(
[warmup_lr_scheduler, cosine_lr_scheduler])
else:
raise NotImplementedError
else:
if lr_mode == 'step':
lr_scheduler = WarmupMultiFactorScheduler(step=lr_iter_discount,
factor=lr_factor)
elif lr_mode == 'cosine':
lr_scheduler = AdvancedLRScheduler(mode='cosine',
base_lr=base_lr,
target_lr=0,
niters=iter_per_epoch *
(end_epoch - begin_epoch))
else:
lr_scheduler = None
# optimizer
optimizer_params = dict(momentum=pOpt.optimizer.momentum,
wd=pOpt.optimizer.wd,
learning_rate=current_lr,
lr_scheduler=lr_scheduler,
rescale_grad=1.0 / (len(ctx) * kv.num_workers),
clip_gradient=pOpt.optimizer.clip_gradient)
if pKv.fp16:
optimizer_params['multi_precision'] = True
optimizer_params['rescale_grad'] /= 128.0
profile = pGen.profile or False
if profile:
mx.profiler.set_config(profile_all=True,
filename=os.path.join(save_path,
"profile.json"))
# train
mod.fit(train_data=train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=kv,
optimizer=pOpt.optimizer.type,
optimizer_params=optimizer_params,
initializer=init,
allow_missing=True,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch,
profile=profile)
logging.info("Training has done")
time.sleep(10)
logging.info("Exiting")
# create dummy data batch
data = mx.nd.ones(shape=[1, 3] + shape)
im_info = mx.nd.array([x / 2.0 for x in shape] + [2.0]).reshape(1, 3)
im_id = mx.nd.array([1])
rec_id = mx.nd.array([1])
data_names = ["data", "im_info", "im_id", "rec_id"]
data_shape = [[1, 3] + shape, [1, 3], [1], [1]]
data_shape = [(name, shape) for name, shape in zip(data_names, data_shape)]
data_batch = mx.io.DataBatch(data=[data, im_info, im_id, rec_id])
'''
there are some conflicts between `mergebn` and `attach_quantized_node` in graph_optimize.py
when mergebn ahead of attach_quantized_node
such as `Symbol.ComposeKeyword`
'''
pModel = patch_config_as_nothrow(pModel)
if pModel.QuantizeTrainingParam is not None and pModel.QuantizeTrainingParam.quantize_flag:
pQuant = pModel.QuantizeTrainingParam
assert pGen.fp16 == False, "current quantize training only support fp32 mode."
from utils.graph_optimize import attach_quantize_node
worker_data_shape = dict([(name, tuple(shape)) for name, shape in data_shape])
# print(worker_data_shape)
# raise NotImplementedError
_, out_shape, _ = sym.get_internals().infer_shape(**worker_data_shape)
out_shape_dictoinary = dict(zip(sym.get_internals().list_outputs(), out_shape))
sym = attach_quantize_node(sym, out_shape_dictoinary, pQuant.WeightQuantizeParam,
pQuant.ActQuantizeParam, pQuant.quantized_op)
sym.save(pTest.model.prefix + "_infer_speed.json")
ctx = mx.gpu(gpu)
def __init__(self, pRoi):
self.p = patch_config_as_nothrow(pRoi)
def __init__(self, pNeck):
self.p = patch_config_as_nothrow(pNeck)
def __init__(self, pBackbone):
self.p = patch_config_as_nothrow(pBackbone)
def __init__(self, pBbox):
self.p = patch_config_as_nothrow(pBbox)
self._head_feat = None
def __init__(self, pTest):
self.p = patch_config_as_nothrow(pTest)
def __init__(self, pBbox, pMask, pMaskRoi):
self.pBbox = patch_config_as_nothrow(pBbox)
self.pMask = patch_config_as_nothrow(pMask)
self.pMaskRoi = patch_config_as_nothrow(pMaskRoi)
self._head_feat = None
def __init__(self, pNeck):
self.p = patch_config_as_nothrow(pNeck)
self.fpn_feat = None
def __init__(self, pHead):
self.p = patch_config_as_nothrow(pHead)
num_points = self.p.point_generate.num_points
self.dcn_kernel = int(math.sqrt(num_points))
self.dcn_pad = int((self.dcn_kernel - 1) / 2)
assert self.dcn_kernel * self.dcn_kernel == num_points, \
"The points number should be square."
assert self.dcn_kernel % 2 == 1, "The dcn kernel size should be odd."
# init moment method
dtype = "float16" if self.p.fp16 else "float32"
self.moment_transfer = X.var(name="moment_transfer",
shape=(2, ),
init=X.zero_init(),
lr_mult=0.01,
dtype=dtype)
# init bias for cls
prior_prob = 0.01
pi = -math.log((1 - prior_prob) / prior_prob)
# shared classification weight and bias
self.cls_conv1_weight = X.var("cls_conv1_weight",
init=X.gauss(std=0.01))
self.cls_conv1_bias = X.var("cls_conv1_bias", init=X.zero_init())
self.cls_conv2_weight = X.var("cls_conv2_weight",
init=X.gauss(std=0.01))
self.cls_conv2_bias = X.var("cls_conv2_bias", init=X.zero_init())
self.cls_conv3_weight = X.var("cls_conv3_weight",
init=X.gauss(std=0.01))
self.cls_conv3_bias = X.var("cls_conv3_bias", init=X.zero_init())
self.cls_conv_weight = X.var("cls_conv_weight", init=X.gauss(std=0.01))
self.cls_conv_bias = X.var("cls_conv_bias", init=X.zero_init())
self.cls_out_weight = X.var("cls_out_weight", init=X.gauss(std=0.01))
self.cls_out_bias = X.var("cls_out_bias", init=X.constant(pi))
# shared regression weight and bias
self.reg_conv1_weight = X.var("reg_conv1_weight",
init=X.gauss(std=0.01))
self.reg_conv1_bias = X.var("reg_conv1_bias", init=X.zero_init())
self.reg_conv2_weight = X.var("reg_conv2_weight",
init=X.gauss(std=0.01))
self.reg_conv2_bias = X.var("reg_conv2_bias", init=X.zero_init())
self.reg_conv3_weight = X.var("reg_conv3_weight",
init=X.gauss(std=0.01))
self.reg_conv3_bias = X.var("reg_conv3_bias", init=X.zero_init())
self.pts_init_conv_weight = X.var("pts_init_conv_weight",
init=X.gauss(std=0.01))
self.pts_init_conv_bias = X.var("pts_init_conv_bias",
init=X.zero_init())
self.pts_init_out_weight = X.var("pts_init_out_weight",
init=X.gauss(std=0.01))
self.pts_init_out_bias = X.var("pts_init_out_bias", init=X.zero_init())
self.pts_refine_conv_weight = X.var("pts_refine_conv_weight",
init=X.gauss(std=0.01))
self.pts_refine_conv_bias = X.var("pts_refine_conv_bias",
init=X.zero_init())
self.pts_refine_out_weight = X.var("pts_refine_out_weight",
init=X.gauss(std=0.01))
self.pts_refine_out_bias = X.var("pts_refine_out_bias",
init=X.zero_init())
self._pts_out_inits = None
self._pts_out_refines = None
self._cls_outs = None
default=None)
args = parser.parse_args()
config = importlib.import_module(
args.config.replace('.py', '').replace('/', '.'))
return config, args
if __name__ == "__main__":
os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0"
config, args = parse_args()
pGen, pKv, pRpn, pRoi, pBbox, pDataset, pModel, pOpt, pTest, \
transform, data_name, label_name, metric_list = config.get_config(is_train=False)
pGen = patch_config_as_nothrow(pGen)
pKv = patch_config_as_nothrow(pKv)
pRpn = patch_config_as_nothrow(pRpn)
pRoi = patch_config_as_nothrow(pRoi)
pBbox = patch_config_as_nothrow(pBbox)
pDataset = patch_config_as_nothrow(pDataset)
pModel = patch_config_as_nothrow(pModel)
pOpt = patch_config_as_nothrow(pOpt)
pTest = patch_config_as_nothrow(pTest)
sym = pModel.test_symbol
image_sets = pDataset.image_set
roidbs_all = [
pkl.load(open("data/cache/{}.roidb".format(i), "rb"),
encoding="latin1") for i in image_sets
def __init__(self, pRpn):
self.p = patch_config_as_nothrow(pRpn)
self._cls_logit = None
self._bbox_delta = None
self._proposal = None
def __init__(self, pRpn, pMask):
super().__init__(pRpn)
self.pMask = patch_config_as_nothrow(pMask)
def __init__(self, pRpn):
self.p = patch_config_as_nothrow(pRpn)
self.centerness_logit_dict = None
self.cls_logit_dict = None
self.offset_logit_dict = None
