def __init__(self, db):
super(NetworkFactory, self).__init__()
module_file = "models.{}".format(system_configs.snapshot_name)
print("module_file: {}".format(module_file))
nnet_module = importlib.import_module(module_file)
self.model = DummyModule(nnet_module.model(db))
self.loss = nnet_module.loss
self.network = Network(self.model, self.loss)
self.network = DataParallel(self.network,
chunk_sizes=system_configs.chunk_sizes)
total_params = 0
for params in self.model.parameters():
num_params = 1
for x in params.size():
num_params *= x
total_params += num_params
print("total parameters: {}".format(total_params))
if system_configs.opt_algo == "adam":
self.optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, self.model.parameters()))
elif system_configs.opt_algo == "sgd":
self.optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad,
self.model.parameters()),
lr=system_configs.learning_rate,
momentum=0.9,
weight_decay=0.0001)
else:
raise ValueError("unknown optimizer")
def __init__(self, flag=False):
super(NetworkFactory, self).__init__()
nnet_module = importlib.import_module('models.LSTR')
self.model = DummyModule(nnet_module.model(flag=flag))
self.loss = nnet_module.loss()
self.network = Network(self.model, self.loss)
self.network = DataParallel(self.network, chunk_sizes=[16])
self.flag = flag
def __init__(self, db):
# db is a MSCOCO instance.
# and images, tl, br, center heatmaps and tl,br,center regression are all
# prepared into torch.Tensor
super(NetworkFactory, self).__init__()
module_file = "models.{}".format(system_configs.snapshot_name)
# snapshot_name check CenterNet-104.json
# snapshot = 5000 but snapshot_name wasn't in CenterNet-104.json
# snapshot_name in config.py is None
# snapshot_name is set to "CenterNet-104" in train.py
# so here module_file = models.CenterNet-104
# it is to say we should go to models/CenterNet-104.py to see the classes
print("module_file: {}".format(module_file))
nnet_module = importlib.import_module(module_file)
# this is importing the models/CenterNet-104.py
# include five functions and 6 classes
self.model = DummyModule(nnet_module.model(db))
# nnet_module.model(db) means CenterNet-104.py class model()
# model inherit kp
#
self.loss = nnet_module.loss
# CenterNet-104.py loss
# loss = AELoss(pull_weight=1e-1, push_weight=1e-1, focal_loss=_neg_loss)
# AELoss in kp.py
self.network = Network(self.model, self.loss)
self.network = DataParallel(self.network, chunk_sizes=system_configs.chunk_sizes).cuda()
total_params = 0
for params in self.model.parameters():
num_params = 1
for x in params.size():
num_params *= x
total_params += num_params
print("total parameters: {}".format(total_params))
if system_configs.opt_algo == "adam":
self.optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, self.model.parameters())
)
elif system_configs.opt_algo == "sgd":
self.optimizer = torch.optim.SGD(
filter(lambda p: p.requires_grad, self.model.parameters()),
lr=system_configs.learning_rate,
momentum=0.9, weight_decay=0.0001
)
else:
raise ValueError("unknown optimizer")
def __init__(self, flag=False):
super(NetworkFactory, self).__init__()
module_file = "models.{}".format(system_configs.snapshot_name)
# print("module_file: {}".format(module_file)) # models.CornerNet
nnet_module = importlib.import_module(module_file)
self.model = DummyModule(nnet_module.model(flag=flag))
self.loss = nnet_module.loss()
self.network = Network(self.model, self.loss)
self.network = DataParallel(self.network,
chunk_sizes=system_configs.chunk_sizes)
self.flag = flag
# Count total parameters
total_params = 0
for params in self.model.parameters():
num_params = 1
for x in params.size():
num_params *= x
total_params += num_params
print("Total parameters: {}".format(total_params))
# Count MACs when input is 360 x 640 x 3
input_test = torch.randn(1, 3, 360, 640).cuda()
input_mask = torch.randn(1, 3, 360, 640).cuda()
macs, params, = profile(self.model,
inputs=(input_test, input_mask),
verbose=False)
macs, _ = clever_format([macs, params], "%.3f")
print('MACs: {}'.format(macs))
if system_configs.opt_algo == "adam":
self.optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, self.model.parameters()))
elif system_configs.opt_algo == "sgd":
self.optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad,
self.model.parameters()),
lr=system_configs.learning_rate,
momentum=0.9,
weight_decay=0.0001)
elif system_configs.opt_algo == 'adamW':
self.optimizer = torch.optim.AdamW(filter(
lambda p: p.requires_grad, self.model.parameters()),
lr=system_configs.learning_rate,
weight_decay=1e-4)
else:
raise ValueError("unknown optimizer")
def __init__(self, db):
super(NetworkFactory, self).__init__()
module_file = "models.{}".format(system_configs.snapshot_name)
print("module_file: {}".format(
module_file)) # module_file: models.CenterNet-52
nnet_module = importlib.import_module(
module_file
) # 导入models.CenterNet-52; NetworkFactory又通过importlib.import_module(module_file)导入了self.model和self.loss
# module_file使用的system_configs.snapshot_name来自train.py中的configs["system"]["snapshot_name"] = args.cfg_file
# NetworkFactory中的self.model和self.loss, 这二者来自CenterNet-52.py中的class model(kp), 这个model继承自kp.py中的class kp(nn.Module), 这个loss也是来自kp.py中的class AELoss(nn.Module)
# 所以model主要框架都在这个class kp(nn.Module)里.
# 只传入1张图片的list时, 模型执行_test函数. 所以在测试的时候(看test/coco.py中的def kp_decode函数), 输入被封装为[images](只有images这个元素)
self.model = DummyModule(nnet_module.model(db))
self.loss = nnet_module.loss
self.network = Network(self.model, self.loss)
self.network = DataParallel(
self.network, chunk_sizes=system_configs.chunk_sizes).cuda()
# 此处的DataParallel是作者自己写的一个在模块级别实现数据并行的类，该容器通过 将batch size个输入按照chunk_size分配给指定GPU 来并行化数据。
total_params = 0
for params in self.model.parameters(): # 计算参数量
num_params = 1
for x in params.size():
num_params *= x
total_params += num_params
print("total parameters: {}".format(total_params)) # 打印总的参数量
if system_configs.opt_algo == "adam": # 参数更新策略:adam
self.optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, self.model.parameters())
) # # 只有requires_grad=True的参数需要optimize
elif system_configs.opt_algo == "sgd":
self.optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad,
self.model.parameters()),
lr=system_configs.learning_rate,
momentum=0.9,
weight_decay=0.0001)
else:
raise ValueError("unknown optimizer")
def __init__(self, db, cuda_flag):
super(NetworkFactory, self).__init__()
# print("[NetworkFactory __init__] db", db )
module_file = "models.{}".format(system_configs.snapshot_name)
# print("[NetworkFactory __init__] module_file: {}".format(module_file))
# [NetworkFactory __init__] module_file: models.medical_ExtremeNet
nnet_module = importlib.import_module(module_file)
# print("[NetworkFactory __init__] nnet_module", nnet_module)
self.model = DummyModule(nnet_module.model(db))
self.loss = nnet_module.loss # yezheng: this is last line in models/ExtremeNet.py
self.network = Network(self.model, self.loss)
self.cuda_flag = cuda_flag
if self.cuda_flag:
self.network = DataParallel(self.network, chunk_sizes=system_configs.chunk_sizes)
total_params = 0
for params in self.model.parameters():
num_params = 1
for x in params.size():
num_params *= x
total_params += num_params
print("total parameters: {}".format(total_params))
if system_configs.opt_algo == "adam":
self.optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, self.model.parameters())
)
elif system_configs.opt_algo == "sgd":
self.optimizer = torch.optim.SGD(
filter(lambda p: p.requires_grad, self.model.parameters()),
lr=system_configs.learning_rate,
momentum=0.9, weight_decay=0.0001
)
else:
raise ValueError("unknown optimizer")