def __init__(self, option, model_type, dataset, modules):
UnwrappedUnetBasedModel.__init__(self, option, model_type, dataset, modules)
self.mode = option.loss_mode
self.normalize_feature = option.normalize_feature
self.loss_names = ["loss_reg", "loss"]
self.metric_loss_module, self.miner_module = BaseModel.get_metric_loss_and_miner(
getattr(option, "metric_loss", None), getattr(option, "miner", None)
)
# Last Layer
if option.mlp_cls is not None:
last_mlp_opt = option.mlp_cls
in_feat = last_mlp_opt.nn[0]
self.FC_layer = Seq()
for i in range(1, len(last_mlp_opt.nn)):
self.FC_layer.append(
str(i),
Sequential(
*[
Linear(in_feat, last_mlp_opt.nn[i], bias=False),
FastBatchNorm1d(last_mlp_opt.nn[i], momentum=last_mlp_opt.bn_momentum),
LeakyReLU(0.2),
]
),
)
in_feat = last_mlp_opt.nn[i]
if last_mlp_opt.dropout:
self.FC_layer.append(Dropout(p=last_mlp_opt.dropout))
self.FC_layer.append(Linear(in_feat, in_feat, bias=False))
else:
self.FC_layer = torch.nn.Identity()
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()
self.save_sampling_id = opt.down_conv.get('save_sampling_id')
# 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 if opt.get(
'up_conv') is not None else None
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
if up_conv_cls_name:
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, "metric_loss", None), getattr(opt, "miner", None))
def _init_from_compact_format(self, opt, model_type, dataset, modules_lib):
"""Create a backbonebasedmodel 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)
self.down_modules = nn.ModuleList()
factory_module_cls = self._get_factory(model_type, modules_lib)
down_conv_cls_name = opt.down_conv.module_name
self._factory_module = factory_module_cls(down_conv_cls_name, None,
modules_lib)
# Down modules
for i in range(num_convs):
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)
self.metric_loss_module, self.miner_module = BaseModel.get_metric_loss_and_miner(
getattr(opt, "metric_loss", None), getattr(opt, "miner", None))