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import torch.nn as nn
from isegm.model import ops
class ConvHead(nn.Module):
def __init__(self, out_channels, in_channels=32, num_layers=1,
kernel_size=3, padding=1,
norm_layer=nn.BatchNorm2d):
super(ConvHead, self).__init__()
convhead = []
for i in range(num_layers):
convhead.extend([
nn.Conv2d(in_channels, in_channels, kernel_size, padding=padding),
nn.ReLU(),
norm_layer(in_channels) if norm_layer is not None else nn.Identity()
])
convhead.append(nn.Conv2d(in_channels, out_channels, 1, padding=0))
self.convhead = nn.Sequential(*convhead)
def forward(self, *inputs):
return self.convhead(inputs[0])
class SepConvHead(nn.Module):
def __init__(self, num_outputs, in_channels, mid_channels, num_layers=1,
kernel_size=3, padding=1, dropout_ratio=0.0, dropout_indx=0,
norm_layer=nn.BatchNorm2d):
super(SepConvHead, self).__init__()
sepconvhead = []
for i in range(num_layers):
sepconvhead.append(
SeparableConv2d(in_channels=in_channels if i == 0 else mid_channels,
out_channels=mid_channels,
dw_kernel=kernel_size, dw_padding=padding,
norm_layer=norm_layer, activation='relu')
)
if dropout_ratio > 0 and dropout_indx == i:
sepconvhead.append(nn.Dropout(dropout_ratio))
sepconvhead.append(
nn.Conv2d(in_channels=mid_channels, out_channels=num_outputs, kernel_size=1, padding=0)
)
self.layers = nn.Sequential(*sepconvhead)
def forward(self, *inputs):
x = inputs[0]
return self.layers(x)
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, dw_kernel, dw_padding, dw_stride=1,
activation=None, use_bias=False, norm_layer=None):
super(SeparableConv2d, self).__init__()
_activation = ops.select_activation_function(activation)
self.body = nn.Sequential(
nn.Conv2d(in_channels, in_channels, kernel_size=dw_kernel, stride=dw_stride,
padding=dw_padding, bias=use_bias, groups=in_channels),
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=use_bias),
norm_layer(out_channels) if norm_layer is not None else nn.Identity(),
_activation()
)
def forward(self, x):
return self.body(x)
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