def _init_from_compact_format(self, opt, model_type, dataset, modules_lib):
"""Create a unetbasedmodel from the compact options format - where the
same convolution is given for each layer, and arguments are given
in lists
"""
num_convs = len(opt.down_conv.down_conv_nn)
# Factory for creating up and down modules
factory_module_cls = self._get_factory(model_type, modules_lib)
down_conv_cls_name = opt.down_conv.module_name
up_conv_cls_name = opt.up_conv.module_name
self._factory_module = factory_module_cls(
down_conv_cls_name, up_conv_cls_name,
modules_lib) # Create the factory object
# construct unet structure
contains_global = hasattr(opt,
"innermost") and opt.innermost is not None
if contains_global:
assert len(opt.down_conv.down_conv_nn) + 1 == len(
opt.up_conv.up_conv_nn)
args_up = self._fetch_arguments_from_list(opt.up_conv, 0)
args_up["up_conv_cls"] = self._factory_module.get_module("UP")
unet_block = UnetSkipConnectionBlock(
args_up=args_up,
args_innermost=opt.innermost,
modules_lib=modules_lib,
submodule=None,
innermost=True,
) # add the innermost layer
else:
unet_block = Identity()
if num_convs > 1:
for index in range(num_convs - 1, 0, -1):
args_up, args_down = self._fetch_arguments_up_and_down(
opt, index)
unet_block = UnetSkipConnectionBlock(args_up=args_up,
args_down=args_down,
submodule=unet_block)
self._save_sampling_and_search(unet_block)
else:
index = num_convs
index -= 1
args_up, args_down = self._fetch_arguments_up_and_down(opt, index)
args_down["nb_feature"] = dataset.feature_dimension
args_up["nb_feature"] = dataset.feature_dimension
self.model = UnetSkipConnectionBlock(
args_up=args_up,
args_down=args_down,
submodule=unet_block,
outermost=True) # add the outermost layer
self._save_sampling_and_search(self.model)
def _init_from_compact_format(self, opt, model_type, dataset, modules_lib):
"""Create a unetbasedmodel from the compact options format - where the
same convolution is given for each layer, and arguments are given
in lists
"""
self.down_modules = nn.ModuleList()
self.inner_modules = nn.ModuleList()
self.up_modules = nn.ModuleList()
# Factory for creating up and down modules
factory_module_cls = self._get_factory(model_type, modules_lib)
down_conv_cls_name = opt.down_conv.module_name
up_conv_cls_name = opt.up_conv.module_name
self._factory_module = factory_module_cls(
down_conv_cls_name, up_conv_cls_name,
modules_lib) # Create the factory object
# Loal module
contains_global = hasattr(opt,
"innermost") and opt.innermost is not None
if contains_global:
inners = self._create_inner_modules(opt.innermost, modules_lib)
for inner in inners:
self.inner_modules.append(inner)
else:
self.inner_modules.append(Identity())
# Down modules
for i in range(len(opt.down_conv.down_conv_nn)):
args = self._fetch_arguments(opt.down_conv, i, "DOWN")
conv_cls = self._get_from_kwargs(args, "conv_cls")
down_module = conv_cls(**args)
self._save_sampling_and_search(down_module)
self.down_modules.append(down_module)
# Up modules
for i in range(len(opt.up_conv.up_conv_nn)):
args = self._fetch_arguments(opt.up_conv, i, "UP")
conv_cls = self._get_from_kwargs(args, "conv_cls")
up_module = conv_cls(**args)
self._save_upsample(up_module)
self.up_modules.append(up_module)
self.metric_loss_module, self.miner_module = BaseModel.get_metric_loss_and_miner(
getattr(opt, "loss", None), getattr(opt, "miner", None))
def _init_from_compact_format(self, opt, model_type, dataset, modules_lib):
"""Create a unetbasedmodel from the compact options format - where the
same convolution is given for each layer, and arguments are given
in lists
"""
self.down_modules = nn.ModuleList()
self.inner_modules = nn.ModuleList()
self.up_modules = nn.ModuleList()
num_convs = len(opt.down_conv.down_conv_nn)
# Factory for creating up and down modules
factory_module_cls = self._get_factory(model_type, modules_lib)
down_conv_cls_name = opt.down_conv.module_name
up_conv_cls_name = opt.up_conv.module_name
self._factory_module = factory_module_cls(
down_conv_cls_name, up_conv_cls_name,
modules_lib) # Create the factory object
# construct unet structure
contains_global = hasattr(opt,
"innermost") and opt.innermost is not None
if contains_global:
# assert len(opt.down_conv.down_conv_nn) + 1 == len(opt.up_conv.up_conv_nn)
args_up = self._fetch_arguments_from_list(opt.up_conv, 0)
args_up["up_conv_cls"] = self._factory_module.get_module(0, "UP")
inners, up = self._create_inner_modules(opt.innermost, args_up,
modules_lib)
for inner in inners:
self.inner_modules.append(inner)
self.up_modules.append(up)
else:
self.inner_modules.append(Identity())
if num_convs > 1:
for index in range(num_convs - 1, 0, -1):
args_up, args_down = self._fetch_arguments_up_and_down(
opt, index)
down_module, up_module = self._create_down_and_up_modules(
args_down, args_up, modules_lib)
self.down_modules.append(down_module)
self.up_modules.append(up_module)
self._save_sampling_and_search(down_module, index)
else:
index = num_convs
index -= 1
args_up, args_down = self._fetch_arguments_up_and_down(opt, index)
args_down["nb_feature"] = dataset.feature_dimension
args_up["nb_feature"] = dataset.feature_dimension
down_module, up_module = self._create_down_and_up_modules(
args_down, args_up, modules_lib)
self.down_modules.append(down_module)
self.up_modules.append(up_module)
self._save_sampling_and_search(down_module, index)
self.down_modules = self.down_modules[::-1]