def __init__(self,
in_channels,
out_channels,
expansion=0.5,
pooling_kernel_size=(5, 9, 13),
csp_act_cfg=dict(type='Mish'),
**kwargs):
super(SPPCSP, self).__init__()
hidden_channels = int(2 * out_channels * expansion) # hidden channels
self.conv1 = Conv(in_channels,
hidden_channels,
kernel_size=1,
**kwargs)
self.conv2 = nn.Conv2d(in_channels, hidden_channels, 1, 1, bias=False)
self.conv3 = Conv(hidden_channels,
hidden_channels,
kernel_size=3,
**kwargs)
self.conv4 = Conv(hidden_channels,
hidden_channels,
kernel_size=1,
**kwargs)
self.maxpools = nn.ModuleList([
nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2)
for x in pooling_kernel_size
])
self.conv5 = Conv(4 * hidden_channels,
hidden_channels,
kernel_size=1,
**kwargs)
self.conv6 = Conv(hidden_channels,
hidden_channels,
kernel_size=3,
**kwargs)
csp_norm_cfg = kwargs.get('norm_cfg', dict(type='BN')).copy()
self.bn = build_norm_layer(csp_norm_cfg, 2 * hidden_channels)[-1]
csp_act_cfg_ = csp_act_cfg.copy()
if csp_act_cfg_['type'] not in [
'Tanh', 'PReLU', 'Sigmoid', 'HSigmoid', 'Swish'
]:
csp_act_cfg_.setdefault('inplace', True)
self.csp_act = build_activation_layer(csp_act_cfg_)
self.conv7 = Conv(2 * hidden_channels,
out_channels,
kernel_size=1,
**kwargs)
def __init__(self,
in_channels,
out_channels,
repetition=1,
shortcut=True,
groups=1,
expansion=0.5,
csp_act_cfg=dict(type='Mish'),
**kwargs):
super(BottleneckCSP, self).__init__()
hidden_channels = int(out_channels * expansion) # hidden channels
self.conv1 = Conv(in_channels,
hidden_channels,
kernel_size=1,
**kwargs)
self.conv2 = nn.Conv2d(in_channels, hidden_channels, 1, 1, bias=False)
self.conv3 = nn.Conv2d(hidden_channels,
hidden_channels,
1,
1,
bias=False)
self.conv4 = Conv(2 * hidden_channels,
out_channels,
kernel_size=1,
**kwargs)
csp_norm_cfg = kwargs.get('norm_cfg', dict(type='BN')).copy()
self.bn = build_norm_layer(csp_norm_cfg, 2 * hidden_channels)[-1]
csp_act_cfg_ = csp_act_cfg.copy()
if csp_act_cfg_['type'] not in [
'Tanh', 'PReLU', 'Sigmoid', 'HSigmoid', 'Swish'
]:
csp_act_cfg_.setdefault('inplace', True)
self.csp_act = build_activation_layer(csp_act_cfg_)
self.bottlenecks = nn.Sequential(*[
Bottleneck(hidden_channels,
hidden_channels,
shortcut,
groups,
expansion=1.0,
**kwargs) for _ in range(repetition)
])
def __init__(self,
*args,
equalized_lr_cfg=dict(gain=1., lr_mul=1.),
bias=True,
bias_init=0.,
act_cfg=None,
**kwargs):
super().__init__()
self.with_activation = act_cfg is not None
# w/o bias in linear layer
self.linear = EqualizedLRLinearModule(
*args, bias=False, equalized_lr_cfg=equalized_lr_cfg, **kwargs)
if equalized_lr_cfg is not None:
self.lr_mul = equalized_lr_cfg.get('lr_mul', 1.)
else:
self.lr_mul = 1.
# define bias outside linear layer
if bias:
self.bias = nn.Parameter(
torch.zeros(self.linear.out_features).fill_(bias_init))
else:
self.bias = None
if self.with_activation:
act_cfg = deepcopy(act_cfg)
if act_cfg['type'] == 'fused_bias':
self.act_type = act_cfg.pop('type')
assert self.bias is not None
self.activate = partial(fused_bias_leakyrelu, **act_cfg)
else:
self.act_type = 'normal'
self.activate = build_activation_layer(act_cfg)
else:
self.act_type = None