You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
75 lines
3.2 KiB
75 lines
3.2 KiB
""" NormAct (Normalizaiton + Activation Layer) Factory
|
|
|
|
Create norm + act combo modules that attempt to be backwards compatible with separate norm + act
|
|
isntances in models. Where these are used it will be possible to swap separate BN + act layers with
|
|
combined modules like IABN or EvoNorms.
|
|
|
|
Hacked together by / Copyright 2020 Ross Wightman
|
|
"""
|
|
import types
|
|
import functools
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from .evo_norm import EvoNormBatch2d, EvoNormSample2d
|
|
from .norm_act import BatchNormAct2d, GroupNormAct
|
|
from .inplace_abn import InplaceAbn
|
|
|
|
_NORM_ACT_TYPES = {BatchNormAct2d, GroupNormAct, EvoNormBatch2d, EvoNormSample2d, InplaceAbn}
|
|
_NORM_ACT_REQUIRES_ARG = {BatchNormAct2d, GroupNormAct, InplaceAbn} # requires act_layer arg to define act type
|
|
|
|
def get_norm_act_layer(layer_class):
|
|
layer_class = layer_class.replace('_', '').lower()
|
|
if layer_class.startswith("batchnorm"):
|
|
layer = BatchNormAct2d
|
|
elif layer_class.startswith("groupnorm"):
|
|
layer = GroupNormAct
|
|
elif layer_class == "evonormbatch":
|
|
layer = EvoNormBatch2d
|
|
elif layer_class == "evonormsample":
|
|
layer = EvoNormSample2d
|
|
elif layer_class == "iabn" or layer_class == "inplaceabn":
|
|
layer = InplaceAbn
|
|
else:
|
|
assert False, "Invalid norm_act layer (%s)" % layer_class
|
|
return layer
|
|
|
|
|
|
def create_norm_act(layer_type, num_features, apply_act=True, jit=False, **kwargs):
|
|
layer_parts = layer_type.split('-') # e.g. batchnorm-leaky_relu
|
|
assert len(layer_parts) in (1, 2)
|
|
layer = get_norm_act_layer(layer_parts[0])
|
|
#activation_class = layer_parts[1].lower() if len(layer_parts) > 1 else '' # FIXME support string act selection?
|
|
layer_instance = layer(num_features, apply_act=apply_act, **kwargs)
|
|
if jit:
|
|
layer_instance = torch.jit.script(layer_instance)
|
|
return layer_instance
|
|
|
|
|
|
def convert_norm_act_type(norm_layer, act_layer, norm_kwargs=None):
|
|
assert isinstance(norm_layer, (type, str, types.FunctionType, functools.partial))
|
|
assert act_layer is None or isinstance(act_layer, (type, str, types.FunctionType, functools.partial))
|
|
norm_act_args = norm_kwargs.copy() if norm_kwargs else {}
|
|
if isinstance(norm_layer, str):
|
|
norm_act_layer = get_norm_act_layer(norm_layer)
|
|
elif norm_layer in _NORM_ACT_TYPES:
|
|
norm_act_layer = norm_layer
|
|
elif isinstance(norm_layer, (types.FunctionType, functools.partial)):
|
|
# assuming this is a lambda/fn/bound partial that creates norm_act layer
|
|
norm_act_layer = norm_layer
|
|
else:
|
|
type_name = norm_layer.__name__.lower()
|
|
if type_name.startswith('batchnorm'):
|
|
norm_act_layer = BatchNormAct2d
|
|
elif type_name.startswith('groupnorm'):
|
|
norm_act_layer = GroupNormAct
|
|
else:
|
|
assert False, f"No equivalent norm_act layer for {type_name}"
|
|
if norm_act_layer in _NORM_ACT_REQUIRES_ARG:
|
|
# Must pass `act_layer` through for backwards compat where `act_layer=None` implies no activation.
|
|
# In the future, may force use of `apply_act` with `act_layer` arg bound to relevant NormAct types
|
|
# It is intended that functions/partial does not trigger this, they should define act.
|
|
norm_act_args.update(dict(act_layer=act_layer))
|
|
return norm_act_layer, norm_act_args
|