| import torch |
| import torch.nn as nn |
| from collections import OrderedDict |
|
|
|
|
| class SeparableConv2d(nn.Module): |
| def __init__(self, inplanes, planes, kernel_size=3, stride=1, dilation=1, relu_first=True, |
| bias=False, norm_layer=nn.BatchNorm2d): |
| super().__init__() |
| depthwise = nn.Conv2d(inplanes, inplanes, kernel_size, |
| stride=stride, padding=dilation, |
| dilation=dilation, groups=inplanes, bias=bias) |
| bn_depth = norm_layer(inplanes) |
| pointwise = nn.Conv2d(inplanes, planes, 1, bias=bias) |
| bn_point = norm_layer(planes) |
|
|
| if relu_first: |
| self.block = nn.Sequential(OrderedDict([('relu', nn.ReLU()), |
| ('depthwise', depthwise), |
| ('bn_depth', bn_depth), |
| ('pointwise', pointwise), |
| ('bn_point', bn_point) |
| ])) |
| else: |
| self.block = nn.Sequential(OrderedDict([('depthwise', depthwise), |
| ('bn_depth', bn_depth), |
| ('relu1', nn.ReLU(inplace=True)), |
| ('pointwise', pointwise), |
| ('bn_point', bn_point), |
| ('relu2', nn.ReLU(inplace=True)) |
| ])) |
|
|
| def forward(self, x): |
| return self.block(x) |
