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chenxuanhong
2022-03-03 21:19:52 +08:00
parent 860713323d
commit 39964bf613
12 changed files with 6121 additions and 41 deletions
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components/Generator_Invobn_config3.py
+4 -4
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@@ -5,7 +5,7 @@
# Created Date: Saturday February 26th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 27th February 2022 7:50:18 pm
# Last Modified: Thursday, 3rd March 2022 6:16:01 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
@@ -107,7 +107,7 @@ class ResnetBlock_Modulation(nn.Module):
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
res_mode = "conv"
# res_mode = "conv"
if res_mode.lower() == "conv":
conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()]
elif res_mode.lower() == "depthwise":
@@ -158,13 +158,13 @@ class Generator(nn.Module):
up_mode = kwargs["up_mode"]
aggregator = kwargs["aggregator"]
res_mode = aggregator
res_mode = kwargs["res_mode"]
padding_size= int((k_size -1)/2)
padding_type= 'reflect'
activation = nn.ReLU(True)
# from components.misc.Involution_BN import involution
if aggregator == "invo":
from components.misc.Involution_BN import involution
from components.DeConv_Invobn import DeConv
components/Generator_ori_config.py
+14 -14
View File
@@ -5,7 +5,7 @@
# Created Date: Sunday January 16th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Wednesday, 16th February 2022 1:39:02 am
# Last Modified: Thursday, 3rd March 2022 6:09:43 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
@@ -106,8 +106,8 @@ class Generator(nn.Module):
activation = nn.ReLU(True)
self.first_layer = nn.Sequential(nn.ReflectionPad2d(1),
nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False),
self.first_layer = nn.Sequential(nn.ReflectionPad2d(3),
nn.Conv2d(3, in_channel, kernel_size=7, padding=0, bias=False),
nn.BatchNorm2d(in_channel), activation)
### downsample
self.down1 = nn.Sequential(nn.Conv2d(in_channel, in_channel*2, kernel_size=3, stride=2, padding=1, bias=False),
@@ -119,8 +119,8 @@ class Generator(nn.Module):
self.down3 = nn.Sequential(nn.Conv2d(in_channel*4, in_channel*8, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(in_channel*8), activation)
self.down4 = nn.Sequential(nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(in_channel*8), activation)
# self.down4 = nn.Sequential(nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=2, padding=1, bias=False),
# nn.BatchNorm2d(in_channel*8), activation)
### resnet blocks
BN = []
@@ -130,11 +130,11 @@ class Generator(nn.Module):
padding_type=padding_type, activation=activation)]
self.BottleNeck = nn.Sequential(*BN)
self.up4 = nn.Sequential(
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(in_channel*8), activation
)
# self.up4 = nn.Sequential(
# nn.Upsample(scale_factor=2, mode='bilinear'),
# nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=1, padding=1, bias=False),
# nn.BatchNorm2d(in_channel*8), activation
# )
self.up3 = nn.Sequential(
nn.Upsample(scale_factor=2, mode='bilinear'),
@@ -153,8 +153,8 @@ class Generator(nn.Module):
nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(in_channel), activation
)
self.last_layer = nn.Sequential(nn.ReflectionPad2d(1),
nn.Conv2d(in_channel, 3, kernel_size=3, padding=0))
self.last_layer = nn.Sequential(nn.ReflectionPad2d(3),
nn.Conv2d(in_channel, 3, kernel_size=7, padding=0))
# self.__weights_init__()
@@ -174,12 +174,12 @@ class Generator(nn.Module):
res = self.down1(res)
res = self.down2(res)
res = self.down3(res)
res = self.down4(res)
# res = self.down4(res)
for i in range(len(self.BottleNeck)):
res = self.BottleNeck[i](res, id)
res = self.up4(res)
# res = self.up4(res)
res = self.up3(res)
res = self.up2(res)
res = self.up1(res)