def __init__(self,
*args,
upsample=dict(type='nearest', scale_factor=2),
**kwargs):
super().__init__(*args, **kwargs)
self.with_upsample = upsample is not None
if self.with_upsample:
if upsample.get('type') == 'fused_nn':
assert isinstance(self.conv, nn.ConvTranspose2d)
self.conv.register_forward_pre_hook(
EqualizedLRConvUpModule.fused_nn_hook)
else:
self.upsample_layer = build_upsample_layer(upsample)
def test_upsample_layer():
with pytest.raises(TypeError):
# cfg must be a dict
cfg = 'bilinear'
build_upsample_layer(cfg)
with pytest.raises(KeyError):
# `type` must be in cfg
cfg = dict()
build_upsample_layer(cfg)
with pytest.raises(KeyError):
# unsupported activation type
cfg = dict(type='FancyUpsample')
build_upsample_layer(cfg)
for type_name in ['nearest', 'bilinear']:
cfg['type'] = type_name
layer = build_upsample_layer(cfg)
assert isinstance(layer, nn.Upsample)
assert layer.mode == type_name
cfg = dict(type='deconv',
in_channels=3,
out_channels=3,
kernel_size=3,
stride=2)
layer = build_upsample_layer(cfg)
assert isinstance(layer, nn.ConvTranspose2d)
cfg = dict(type='pixel_shuffle',
in_channels=3,
out_channels=3,
scale_factor=2,
upsample_kernel=3)
layer = build_upsample_layer(cfg)
assert isinstance(layer, PixelShufflePack)
assert layer.scale_factor == 2
assert layer.upsample_kernel == 3
def __init__(self,
in_channels,
out_channels,
dim_after_concat,
act_cfg=dict(type='ReLU'),
upsample_cfg=dict(type='nearest', scale_factor=2),
sn_eps=1e-6,
with_spectral_norm=True,
input_is_label=False,
auto_sync_bn=True):
super().__init__()
self.activation = build_activation_layer(act_cfg)
self.upsample_cfg = deepcopy(upsample_cfg)
self.with_upsample = upsample_cfg is not None
if self.with_upsample:
self.upsample_layer = build_upsample_layer(self.upsample_cfg)
self.learnable_sc = in_channels != out_channels or self.with_upsample
if self.learnable_sc:
self.shortcut = SNConvModule(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=1,
padding=0,
act_cfg=None,
with_spectral_norm=with_spectral_norm,
spectral_norm_cfg=dict(eps=sn_eps))
# Here in_channels of BigGANGenResBlock equal to num_features of
# BigGANConditionBN
self.bn1 = BigGANConditionBN(
in_channels,
dim_after_concat,
sn_eps=sn_eps,
input_is_label=input_is_label,
with_spectral_norm=with_spectral_norm,
auto_sync_bn=auto_sync_bn)
# Here out_channels of BigGANGenResBlock equal to num_features of
# BigGANConditionBN
self.bn2 = BigGANConditionBN(
out_channels,
dim_after_concat,
sn_eps=sn_eps,
input_is_label=input_is_label,
with_spectral_norm=with_spectral_norm,
auto_sync_bn=auto_sync_bn)
self.conv1 = SNConvModule(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=1,
padding=1,
act_cfg=None,
with_spectral_norm=with_spectral_norm,
spectral_norm_cfg=dict(eps=sn_eps))
self.conv2 = SNConvModule(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=1,
padding=1,
act_cfg=None,
with_spectral_norm=with_spectral_norm,
spectral_norm_cfg=dict(eps=sn_eps))
def __init__(self,
in_channels,
out_channels,
dim_after_concat,
act_cfg=dict(type='ReLU'),
upsample_cfg=dict(type='nearest', scale_factor=2),
sn_eps=1e-6,
sn_style='ajbrock',
bn_eps=1e-5,
with_spectral_norm=True,
input_is_label=False,
auto_sync_bn=True,
channel_ratio=4):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.hidden_channels = self.in_channels // channel_ratio
self.activation = build_activation_layer(act_cfg)
self.upsample_cfg = deepcopy(upsample_cfg)
self.with_upsample = upsample_cfg is not None
if self.with_upsample:
self.upsample_layer = build_upsample_layer(self.upsample_cfg)
# Here in_channels of BigGANGenResBlock equal to num_features of
# BigGANConditionBN
self.bn1 = BigGANConditionBN(in_channels,
dim_after_concat,
sn_eps=sn_eps,
sn_style=sn_style,
bn_eps=bn_eps,
input_is_label=input_is_label,
with_spectral_norm=with_spectral_norm,
auto_sync_bn=auto_sync_bn)
# Here out_channels of BigGANGenResBlock equal to num_features of
# BigGANConditionBN
self.bn2 = BigGANConditionBN(self.hidden_channels,
dim_after_concat,
sn_eps=sn_eps,
sn_style=sn_style,
bn_eps=bn_eps,
input_is_label=input_is_label,
with_spectral_norm=with_spectral_norm,
auto_sync_bn=auto_sync_bn)
self.bn3 = BigGANConditionBN(self.hidden_channels,
dim_after_concat,
sn_eps=sn_eps,
sn_style=sn_style,
bn_eps=bn_eps,
input_is_label=input_is_label,
with_spectral_norm=with_spectral_norm,
auto_sync_bn=auto_sync_bn)
self.bn4 = BigGANConditionBN(self.hidden_channels,
dim_after_concat,
sn_eps=sn_eps,
sn_style=sn_style,
bn_eps=bn_eps,
input_is_label=input_is_label,
with_spectral_norm=with_spectral_norm,
auto_sync_bn=auto_sync_bn)
self.conv1 = SNConvModule(in_channels=in_channels,
out_channels=self.hidden_channels,
kernel_size=1,
stride=1,
padding=0,
act_cfg=None,
with_spectral_norm=with_spectral_norm,
spectral_norm_cfg=dict(eps=sn_eps,
sn_style=sn_style))
self.conv2 = SNConvModule(in_channels=self.hidden_channels,
out_channels=self.hidden_channels,
kernel_size=3,
stride=1,
padding=1,
act_cfg=None,
with_spectral_norm=with_spectral_norm,
spectral_norm_cfg=dict(eps=sn_eps,
sn_style=sn_style))
self.conv3 = SNConvModule(in_channels=self.hidden_channels,
out_channels=self.hidden_channels,
kernel_size=3,
stride=1,
padding=1,
act_cfg=None,
with_spectral_norm=with_spectral_norm,
spectral_norm_cfg=dict(eps=sn_eps,
sn_style=sn_style))
self.conv4 = SNConvModule(in_channels=self.hidden_channels,
out_channels=out_channels,
kernel_size=1,
stride=1,
padding=0,
act_cfg=None,
with_spectral_norm=with_spectral_norm,
spectral_norm_cfg=dict(eps=sn_eps,
sn_style=sn_style))
