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import torch
import torch.nn as nn
class Inception_Block_V1(nn.Module):
def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True):
super(Inception_Block_V1, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.num_kernels = num_kernels
kernels = []
for i in range(self.num_kernels):
kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=2 * i + 1, padding=i))
self.kernels = nn.ModuleList(kernels)
if init_weight:
self._initialize_weights()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, x):
res_list = []
for i in range(self.num_kernels):
res_list.append(self.kernels[i](x))
res = torch.stack(res_list, dim=-1).mean(-1)
return res
class Inception_Block_V2(nn.Module):
def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True):
super(Inception_Block_V2, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.num_kernels = num_kernels
kernels = []
for i in range(self.num_kernels // 2):
kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=[1, 2 * i + 3], padding=[0, i + 1]))
kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=[2 * i + 3, 1], padding=[i + 1, 0]))
kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=1))
self.kernels = nn.ModuleList(kernels)
if init_weight:
self._initialize_weights()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, x):
res_list = []
for i in range(self.num_kernels // 2 * 2 + 1):
res_list.append(self.kernels[i](x))
res = torch.stack(res_list, dim=-1).mean(-1)
return res
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