def __init__(
self,
hiddens,
layer_fn=nn.Linear,
bias=True,
norm_fn=None,
activation_fn=nn.ReLU,
dropout=None,
layer_order=None,
residual=False
):
super().__init__()
# hack to prevent cycle imports
from catalyst.contrib.registry import Registry
layer_fn = Registry.name2nn(layer_fn)
activation_fn = Registry.name2nn(activation_fn)
norm_fn = Registry.name2nn(norm_fn)
dropout = Registry.name2nn(dropout)
layer_order = layer_order or ["layer", "norm", "drop", "act"]
if isinstance(dropout, float):
dropout_fn = lambda: nn.Dropout(dropout)
else:
dropout_fn = dropout
def _layer_fn(f_in, f_out, bias):
return layer_fn(f_in, f_out, bias=bias)
def _normalize_fn(f_in, f_out, bias):
return norm_fn(f_out) if norm_fn is not None else None
def _dropout_fn(f_in, f_out, bias):
return dropout_fn() if dropout_fn is not None else None
def _activation_fn(f_in, f_out, bias):
return activation_fn() if activation_fn is not None else None
name2fn = {
"layer": _layer_fn,
"norm": _normalize_fn,
"drop": _dropout_fn,
"act": _activation_fn,
}
net = []
for i, (f_in, f_out) in enumerate(pairwise(hiddens)):
block = []
for key in layer_order:
fn = name2fn[key](f_in, f_out, bias)
if fn is not None:
block.append((f"{key}", fn))
block = torch.nn.Sequential(OrderedDict(block))
if residual:
block = ResidualWrapper(net=block)
net.append((f"block_{i}", block))
self.net = torch.nn.Sequential(OrderedDict(net))
def __init__(self,
hiddens,
layer_fn=nn.Linear,
bias=True,
norm_fn=None,
activation_fn=nn.ReLU,
dropout=None,
layer_order=None,
residual=False):
super().__init__()
assert len(hiddens) > 1, "No sequence found"
layer_fn = MODULES.get_if_str(layer_fn)
activation_fn = MODULES.get_if_str(activation_fn)
norm_fn = MODULES.get_if_str(norm_fn)
dropout = MODULES.get_if_str(dropout)
inner_init = create_optimal_inner_init(nonlinearity=activation_fn)
layer_order = layer_order or ["layer", "norm", "drop", "act"]
if isinstance(dropout, float):
dropout_fn = lambda: nn.Dropout(dropout)
else:
dropout_fn = dropout
def _layer_fn(f_in, f_out, bias):
return layer_fn(f_in, f_out, bias=bias)
def _normalize_fn(f_in, f_out, bias):
return norm_fn(f_out) if norm_fn is not None else None
def _dropout_fn(f_in, f_out, bias):
return dropout_fn() if dropout_fn is not None else None
def _activation_fn(f_in, f_out, bias):
return activation_fn() if activation_fn is not None else None
name2fn = {
"layer": _layer_fn,
"norm": _normalize_fn,
"drop": _dropout_fn,
"act": _activation_fn,
}
net = []
for i, (f_in, f_out) in enumerate(pairwise(hiddens)):
block = []
for key in layer_order:
fn = name2fn[key](f_in, f_out, bias)
if fn is not None:
block.append((f"{key}", fn))
block = torch.nn.Sequential(OrderedDict(block))
if residual:
block = ResidualWrapper(net=block)
net.append((f"block_{i}", block))
self.net = torch.nn.Sequential(OrderedDict(net))
self.net.apply(inner_init)
def __init__(
self,
hiddens,
layer_fn: Union[str, Dict, List],
norm_fn: Union[str, Dict, List] = None,
dropout_fn: Union[str, Dict, List] = None,
activation_fn: Union[str, Dict, List] = None,
residual: Union[bool, str] = False,
layer_order: List = None,
):
super().__init__()
assert len(hiddens) > 1, "No sequence found"
# layer params
layer_fn = _process_additional_params(layer_fn, hiddens[1:])
# normalization params
norm_fn = _process_additional_params(norm_fn, hiddens[1:])
# dropout params
dropout_fn = _process_additional_params(dropout_fn, hiddens[1:])
# activation params
activation_fn = _process_additional_params(activation_fn, hiddens[1:])
if isinstance(residual, bool) and residual:
residual = "hard"
residual = _process_additional_params(residual, hiddens[1:])
layer_order = layer_order or ["layer", "norm", "drop", "act"]
def _layer_fn(layer_fn, f_in, f_out, **kwargs):
layer_fn = MODULES.get_if_str(layer_fn)
layer_fn = layer_fn(f_in, f_out, **kwargs)
return layer_fn
def _normalization_fn(normalization_fn, f_in, f_out, **kwargs):
normalization_fn = MODULES.get_if_str(normalization_fn)
normalization_fn = \
normalization_fn(f_out, **kwargs) \
if normalization_fn is not None \
else None
return normalization_fn
def _dropout_fn(dropout_fn, f_in, f_out, **kwargs):
dropout_fn = MODULES.get_if_str(dropout_fn)
dropout_fn = dropout_fn(**kwargs) \
if dropout_fn is not None \
else None
return dropout_fn
def _activation_fn(activation_fn, f_in, f_out, **kwargs):
activation_fn = MODULES.get_if_str(activation_fn)
activation_fn = activation_fn(**kwargs) \
if activation_fn is not None \
else None
return activation_fn
name2fn = {
"layer": _layer_fn,
"norm": _normalization_fn,
"drop": _dropout_fn,
"act": _activation_fn,
}
name2params = {
"layer": layer_fn,
"norm": norm_fn,
"drop": dropout_fn,
"act": activation_fn,
}
net = []
for i, (f_in, f_out) in enumerate(pairwise(hiddens)):
block = []
for key in layer_order:
sub_fn = name2fn[key]
sub_params = deepcopy(name2params[key][i])
if isinstance(sub_params, Dict):
sub_module = sub_params.pop("module")
else:
sub_module = sub_params
sub_params = {}
sub_block = sub_fn(sub_module, f_in, f_out, **sub_params)
if sub_block is not None:
block.append((f"{key}", sub_block))
block_ = OrderedDict(block)
block = torch.nn.Sequential(block_)
if block_.get("act", None) is not None:
activation = block_["act"]
activation_init = \
create_optimal_inner_init(nonlinearity=activation)
block.apply(activation_init)
if residual == "hard" or (residual == "soft" and f_in == f_out):
block = ResidualWrapper(net=block)
net.append((f"block_{i}", block))
self.net = torch.nn.Sequential(OrderedDict(net))