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chenxuanhong
2022-02-17 16:30:33 +08:00
parent d82e46f0e7
commit 913e4916d4
17 changed files with 45302 additions and 71 deletions
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.gitignore
+5 -1
View File
@@ -129,4 +129,8 @@ train_logs/
test_logs/
arcface_ckpt/
insightface_func/
parsing_model/
parsing_model/
*.rar
*.zip
*.tar
GUI/file_sync/filestate_machine0.json
+21 -9
View File
@@ -1,6 +1,6 @@
{
"GUI.py": 1644423287.9844918,
"test.py": 1643529962.5602193,
"test.py": 1645015463.4072468,
"train.py": 1643397924.974299,
"components\\Generator.py": 1644689001.9005148,
"components\\projected_discriminator.py": 1642348101.4661522,
@@ -40,7 +40,7 @@
"train_yamls\\train_512FM.yaml": 1643021615.8106658,
"train_scripts\\trainer_2layer_FM.py": 1642826548.2530458,
"train_yamls\\train_2layer_FM.yaml": 1642411635.5534878,
"components\\Generator_reduce.py": 1643021243.6658802,
"components\\Generator_reduce.py": 1645020911.0651233,
"insightface_func\\face_detect_crop_multi.py": 1643796928.6362474,
"insightface_func\\face_detect_crop_single.py": 1638370471.7967434,
"insightface_func\\__init__.py": 1624197300.011183,
@@ -60,13 +60,13 @@
"face_crop.py": 1643789609.1834445,
"face_crop_video.py": 1643815024.5516832,
"similarity.py": 1643269705.1073737,
"train_multigpu.py": 1644689820.5085416,
"train_multigpu.py": 1645035569.415791,
"components\\arcface_decoder.py": 1643396144.2575414,
"components\\Generator_nobias.py": 1643179001.810856,
"data_tools\\data_loader_VGGFace2HQ_multigpu.py": 1644330414.9587426,
"data_tools\\data_loader_VGGFace2HQ_multigpu.py": 1644861019.9044807,
"data_tools\\data_loader_VGGFace2HQ_Rec.py": 1643398754.86898,
"test_scripts\\tester_arcface_Rec.py": 1643431261.9333818,
"test_scripts\\tester_image.py": 1643428951.5532105,
"test_scripts\\tester_image.py": 1644934851.442447,
"torch_utils\\custom_ops.py": 1640773190.0,
"torch_utils\\misc.py": 1640773190.0,
"torch_utils\\persistence.py": 1640773190.0,
@@ -83,7 +83,7 @@
"torch_utils\\ops\\__init__.py": 1640773190.0,
"train_scripts\\trainer_arcface_rec.py": 1643399647.0182135,
"train_scripts\\trainer_multigpu_base.py": 1644131205.772292,
"train_scripts\\trainer_multi_gpu.py": 1644549528.075511,
"train_scripts\\trainer_multi_gpu.py": 1644854424.6483445,
"train_yamls\\train_arcface_rec.yaml": 1643398807.3434353,
"train_yamls\\train_multigpu.yaml": 1644549590.0652373,
"wandb\\run-20220129_032741-340btp9k\\files\\conda-environment.yaml": 1643398065.409959,
@@ -104,12 +104,24 @@
"dnnlib\\__init__.py": 1640773190.0,
"components\\Generator_ori.py": 1644689174.414655,
"losses\\cos.py": 1644229583.4023254,
"data_tools\\data_loader_VGGFace2HQ_multigpu1.py": 1644297868.397411,
"speed_test.py": 1644476745.605093,
"data_tools\\data_loader_VGGFace2HQ_multigpu1.py": 1644860106.943826,
"speed_test.py": 1645034614.282678,
"components\\DeConv_Invo.py": 1644426607.1588645,
"components\\Generator_reduce_up.py": 1644688655.2096283,
"components\\Generator_upsample.py": 1644689723.8293872,
"components\\misc\\Involution.py": 1644509321.5267963,
"train_yamls\\train_Invoup.yaml": 1644689981.9794765,
"flops.py": 1644689428.618648
"flops.py": 1645034657.122085,
"detection_test.py": 1644935512.6830947,
"components\\DeConv_Depthwise.py": 1645027608.8040042,
"components\\DeConv_Depthwise1.py": 1644946969.5054545,
"components\\Generator_modulation_depthwise.py": 1644861291.4467516,
"components\\Generator_modulation_depthwise_config.py": 1645034769.4103642,
"components\\Generator_modulation_up.py": 1644946498.7005584,
"components\\Generator_oriae_modulation.py": 1644897798.1987727,
"components\\Generator_ori_config.py": 1644946742.3635018,
"train_scripts\\trainer_multi_gpu1.py": 1644859528.8428593,
"train_yamls\\train_Depthwise.yaml": 1644860961.099242,
"train_yamls\\train_depthwise_modulation.yaml": 1645035964.9551077,
"train_yamls\\train_oriae_modulation.yaml": 1644897891.2576747
}
components/DeConv_Depthwise.py
+6 -3
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@@ -5,7 +5,7 @@
# Created Date: Tuesday July 20th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Monday, 14th February 2022 4:54:28 pm
# Last Modified: Thursday, 17th February 2022 10:20:46 am
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
@@ -13,9 +13,12 @@ from tokenize import group
from torch import nn
class DeConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero"):
def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero", up_mode = "bilinear"):
super().__init__()
self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale)
if up_mode.lower() == "bilinear":
self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale)
elif up_mode.lower() == "nearest":
self.upsampling = nn.UpsamplingNearest2d(scale_factor=upsampl_scale)
padding_size = int((kernel_size -1)/2)
self.conv1x1 = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= 1)
self.conv = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=padding_size, bias=False, groups=out_channels)
components/DeConv_Depthwise1.py
+33
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@@ -0,0 +1,33 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: DeConv copy.py
# Created Date: Tuesday July 20th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Wednesday, 16th February 2022 1:42:49 am
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
from audioop import bias
from tokenize import group
from torch import nn
class DeConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="zero"):
super().__init__()
self.upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampl_scale)
padding_size = int((kernel_size -1)/2)
self.conv1x1 = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= 1, bias = False)
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=padding_size, groups=in_channels)
# nn.init.xavier_uniform_(self.conv.weight)
# self.__weights_init__()
# def __weights_init__(self):
# nn.init.xavier_uniform_(self.conv.weight)
def forward(self, input):
h = self.upsampling(input)
h = self.conv(h)
h = self.conv1x1(h)
return h
components/Generator_modulation_depthwise_config.py
+48 -22
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@@ -5,11 +5,12 @@
# Created Date: Sunday January 16th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 15th February 2022 12:52:22 pm
# Last Modified: Thursday, 17th February 2022 2:06:09 am
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
from audioop import bias
import torch
from torch import nn
from components.DeConv_Depthwise import DeConv
@@ -59,7 +60,7 @@ class Modulation(nn.Module):
return x
class ResnetBlock_Modulation(nn.Module):
def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True),res_mode="depthwise"):
super(ResnetBlock_Modulation, self).__init__()
p = 0
@@ -72,7 +73,12 @@ class ResnetBlock_Modulation(nn.Module):
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()]
if res_mode.lower() == "conv":
conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()]
elif res_mode.lower() == "depthwise":
conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False),
nn.Conv2d(dim, dim, kernel_size=1),
Demodule()]
self.conv1 = nn.Sequential(*conv1)
self.style1 = Modulation(latent_size, dim)
self.act1 = activation
@@ -87,17 +93,25 @@ class ResnetBlock_Modulation(nn.Module):
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()]
if res_mode.lower() == "conv":
conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()]
elif res_mode.lower() == "depthwise":
conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False),
nn.Conv2d(dim, dim, kernel_size=1),
Demodule()]
self.conv2 = nn.Sequential(*conv2)
self.style2 = Modulation(latent_size, dim)
def forward(self, x, dlatents_in_slice):
y = self.conv1(x)
y = self.style1(y, dlatents_in_slice)
y = self.style1(x, dlatents_in_slice)
y = self.conv1(y)
y = self.act1(y)
y = self.conv2(y)
y = self.style2(y, dlatents_in_slice)
y = self.conv2(y)
out = x + y
return out
@@ -112,6 +126,8 @@ class Generator(nn.Module):
k_size = kwargs["g_kernel_size"]
res_num = kwargs["res_num"]
in_channel = kwargs["in_channel"]
up_mode = kwargs["up_mode"]
res_mode = kwargs["res_mode"]
padding_size= int((k_size -1)/2)
padding_type= 'reflect'
@@ -121,52 +137,62 @@ class Generator(nn.Module):
# self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False),
# nn.BatchNorm2d(64), activation)
self.first_layer = nn.Sequential(nn.ReflectionPad2d(1),
nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False),
nn.BatchNorm2d(in_channel), activation)
nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False),
nn.BatchNorm2d(in_channel),
activation)
# self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
# nn.BatchNorm2d(64), activation)
### downsample
self.down1 = nn.Sequential(nn.Conv2d(in_channel, in_channel, kernel_size=3, groups=in_channel, padding=1, stride=2),
nn.Conv2d(in_channel, in_channel*2, kernel_size=1, bias=False),
nn.BatchNorm2d(in_channel*2), activation)
nn.BatchNorm2d(in_channel*2),
activation)
self.down2 = nn.Sequential(nn.Conv2d(in_channel*2, in_channel*2, kernel_size=3, groups=in_channel*2, padding=1, stride=2),
nn.Conv2d(in_channel*2, in_channel*4, kernel_size=1, bias=False),
nn.BatchNorm2d(in_channel*4), activation)
nn.BatchNorm2d(in_channel*4),
activation)
self.down3 = nn.Sequential(nn.Conv2d(in_channel*4, in_channel*4, kernel_size=3, groups=in_channel*4, padding=1, stride=2),
nn.Conv2d(in_channel*4, in_channel*8, kernel_size=1, bias=False),
nn.BatchNorm2d(in_channel*8), activation)
nn.BatchNorm2d(in_channel*8),
activation)
self.down4 = nn.Sequential(nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, groups=in_channel*8, padding=1, stride=2),
nn.Conv2d(in_channel*8, in_channel*8, kernel_size=1, bias=False),
nn.BatchNorm2d(in_channel*8), activation)
nn.BatchNorm2d(in_channel*8),
activation)
### resnet blocks
BN = []
for i in range(res_num):
BN += [
ResnetBlock_Modulation(in_channel*8, latent_size=id_dim, padding_type=padding_type, activation=activation)]
ResnetBlock_Modulation(in_channel*8, latent_size=id_dim,
padding_type=padding_type, activation=activation, res_mode=res_mode)]
self.BottleNeck = nn.Sequential(*BN)
self.up4 = nn.Sequential(
DeConv(in_channel*8,in_channel*8,3),
nn.BatchNorm2d(in_channel*8), activation
DeConv(in_channel*8,in_channel*8,3,up_mode=up_mode),
nn.BatchNorm2d(in_channel*8),
activation
)
self.up3 = nn.Sequential(
DeConv(in_channel*8,in_channel*4,3),
nn.BatchNorm2d(in_channel*4), activation
DeConv(in_channel*8,in_channel*4,3,up_mode=up_mode),
nn.BatchNorm2d(in_channel*4),
activation
)
self.up2 = nn.Sequential(
DeConv(in_channel*4,in_channel*2,3),
nn.BatchNorm2d(in_channel*2), activation
DeConv(in_channel*4,in_channel*2,3,up_mode=up_mode),
nn.BatchNorm2d(in_channel*2),
activation
)
self.up1 = nn.Sequential(
DeConv(in_channel*2,in_channel,3),
nn.BatchNorm2d(in_channel), activation
DeConv(in_channel*2,in_channel,3,up_mode=up_mode),
nn.BatchNorm2d(in_channel),
activation
)
# self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1))
self.last_layer = nn.Sequential(nn.ReflectionPad2d(1),
components/Generator_modulation_up.py
+6 -6
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@@ -5,7 +5,7 @@
# Created Date: Sunday January 16th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 13th February 2022 1:35:21 pm
# Last Modified: Wednesday, 16th February 2022 1:34:58 am
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
@@ -107,7 +107,7 @@ class Generator(nn.Module):
):
super().__init__()
chn = kwargs["g_conv_dim"]
chn = kwargs["id_dim"]
k_size = kwargs["g_kernel_size"]
res_num = kwargs["res_num"]
@@ -121,16 +121,16 @@ class Generator(nn.Module):
self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(64), activation)
### downsample
self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128), activation)
self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(256), activation)
self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(512), activation)
self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(512), activation)
### resnet blocks
components/Generator_ori_config.py
+9 -5
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@@ -5,7 +5,7 @@
# Created Date: Sunday January 16th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 15th February 2022 12:40:14 pm
# Last Modified: Wednesday, 16th February 2022 1:39:02 am
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
@@ -131,22 +131,26 @@ class Generator(nn.Module):
self.BottleNeck = nn.Sequential(*BN)
self.up4 = nn.Sequential(
DeConv(in_channel*8,in_channel*8,3),
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(
DeConv(in_channel*8,in_channel*4,3),
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.Conv2d(in_channel*8, in_channel*4, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(in_channel*4), activation
)
self.up2 = nn.Sequential(
DeConv(in_channel*4,in_channel*2,3),
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.Conv2d(in_channel*4, in_channel*2, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(in_channel*2), activation
)
self.up1 = nn.Sequential(
DeConv(in_channel*2,in_channel,3),
nn.Upsample(scale_factor=2, mode='bilinear'),
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),
components/Generator_reduce.py
+2 -1
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@@ -5,7 +5,7 @@
# Created Date: Sunday January 16th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 13th February 2022 3:47:59 am
# Last Modified: Wednesday, 16th February 2022 10:15:11 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
@@ -119,6 +119,7 @@ class Generator(nn.Module):
self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(64), activation)
### downsample
self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128), activation)
detection_test.py
+20
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@@ -0,0 +1,20 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: detection_test.py
# Created Date: Tuesday February 15th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 15th February 2022 10:31:52 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
from insightface.app import FaceAnalysis
import insightface
if __name__ == "__main__":
app = FaceAnalysis(allowed_modules=['detection']) # enable detection model only
app.prepare(ctx_id=0, det_size=(640, 640))
flops.py
+7 -2
View File
@@ -5,7 +5,7 @@
# Created Date: Sunday February 13th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 15th February 2022 12:52:53 pm
# Last Modified: Thursday, 17th February 2022 2:32:48 am
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
@@ -21,14 +21,19 @@ from thop import clever_format
if __name__ == '__main__':
# script = "Generator_modulation_up"
script = "Generator_modulation_depthwise_config"
# script = "Generator_ori_config"
class_name = "Generator"
arcface_ckpt= "arcface_ckpt/arcface_checkpoint.tar"
model_config={
"id_dim": 512,
"g_kernel_size": 3,
"in_channel":16,
"res_num": 9
"res_num": 9,
# "up_mode": "nearest",
"up_mode": "bilinear",
"res_mode": "depthwise"
}
speed_test.py
+10 -3
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@@ -5,7 +5,7 @@
# Created Date: Thursday February 10th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 15th February 2022 12:54:56 pm
# Last Modified: Thursday, 17th February 2022 2:33:30 am
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
@@ -13,19 +13,26 @@ import os
import time
import torch
from torch.backends import cudnn
if __name__ == '__main__':
# cudnn.benchmark = True
# cudnn.enabled = True
# script = "Generator_modulation_up"
script = "Generator_modulation_depthwise_config"
# script = "Generator_ori_config"
class_name = "Generator"
arcface_ckpt= "arcface_ckpt/arcface_checkpoint.tar"
model_config={
"id_dim": 512,
"g_kernel_size": 3,
"in_channel":16,
"res_num": 9
"res_num": 9,
# "up_mode": "nearest",
"up_mode": "bilinear",
"res_mode": "depthwise"
}
os.environ['CUDA_VISIBLE_DEVICES'] = str(0)
start_train.sh
+1 -1
View File
@@ -1,3 +1,3 @@
nohup python train.py > GramFM.log 2>&1 &
nohup python train_multigpu.py > depthwise_config0.log 2>&1 &
test.py
+6 -5
View File
@@ -5,7 +5,7 @@
# Created Date: Saturday July 3rd 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 30th January 2022 4:05:17 pm
# Last Modified: Thursday, 17th February 2022 10:23:54 am
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
@@ -30,11 +30,11 @@ def getParameters():
parser = argparse.ArgumentParser()
# general settings
parser.add_argument('-v', '--version', type=str, default='GramFM',
parser.add_argument('-v', '--version', type=str, default='depthwise',
help="version name for train, test, finetune")
parser.add_argument('-c', '--cuda', type=int, default=0) # >0 if it is set as -1, program will use CPU
parser.add_argument('-s', '--checkpoint_step', type=int, default=480000,
parser.add_argument('-s', '--checkpoint_step', type=int, default=300000,
help="checkpoint epoch for test phase or finetune phase")
# test
@@ -44,8 +44,9 @@ def getParameters():
choices=['localhost', '4card','8card','new4card'])
parser.add_argument('-i', '--id_imgs', type=str, default='G:\\swap_data\\ID\\dlrb2.jpeg')
parser.add_argument('-a', '--attr_files', type=str, default='G:\\swap_data\\ID',
parser.add_argument('-i', '--id_imgs', type=str, default='G:\\swap_data\\ID\\gxt3.jpeg')
# parser.add_argument('-i', '--id_imgs', type=str, default='G:\\VGGFace2-HQ\\VGGface2_ffhq_align_256_9_28_512_bygfpgan\\n000002\\0027_01.jpg')
parser.add_argument('-a', '--attr_files', type=str, default='G:\\swap_data\\8',
help="file path for attribute images or video")
parser.add_argument('--use_specified_data', action='store_true')
test_scripts/tester_image.py
+8 -6
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@@ -5,7 +5,7 @@
# Created Date: Saturday July 3rd 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Saturday, 29th January 2022 12:02:31 pm
# Last Modified: Tuesday, 15th February 2022 10:20:50 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
@@ -67,17 +67,19 @@ class Tester(object):
self.arcface = arcface1['model'].module
self.arcface.eval()
self.arcface.requires_grad_(False)
model_path = os.path.join(self.config["project_checkpoints"],
"step%d_%s.pth"%(self.config["checkpoint_step"],
self.config["checkpoint_names"]["generator_name"]))
self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"]))
# train in GPU
if self.config["cuda"] >=0:
self.network = self.network.cuda()
self.arcface = self.arcface.cuda()
model_path = os.path.join(self.config["project_checkpoints"],
"step%d_%s.pth"%(self.config["checkpoint_step"],
self.config["checkpoint_names"]["generator_name"]))
self.network.load_state_dict(torch.load(model_path))
print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"]))
def test(self):
train_multigpu.py
+7 -7
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@@ -5,7 +5,7 @@
# Created Date: Tuesday April 28th 2020
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 15th February 2022 12:06:30 pm
# Last Modified: Thursday, 17th February 2022 2:19:29 am
# Modified By: Chen Xuanhong
# Copyright (c) 2020 Shanghai Jiao Tong University
#############################################################
@@ -31,28 +31,28 @@ def getParameters():
parser = argparse.ArgumentParser()
# general settings
parser.add_argument('-v', '--version', type=str, default='oriae_modulation',
parser.add_argument('-v', '--version', type=str, default='depthwise_config0',
help="version name for train, test, finetune")
parser.add_argument('-t', '--tag', type=str, default='oriae_modulation',
parser.add_argument('-t', '--tag', type=str, default='tiny',
help="tag for current experiment")
parser.add_argument('-p', '--phase', type=str, default="train",
choices=['train', 'finetune','debug'],
help="The phase of current project")
parser.add_argument('-c', '--gpus', type=int, nargs='+', default=[0,1,2,3]) # <0 if it is set as -1, program will use CPU
parser.add_argument('-c', '--gpus', type=int, nargs='+', default=[0,1]) # <0 if it is set as -1, program will use CPU
parser.add_argument('-e', '--ckpt', type=int, default=74,
help="checkpoint epoch for test phase or finetune phase")
# training
parser.add_argument('--experiment_description', type=str,
default="验证是否是Decoder导致的发紫")
default="尝试直接训练最小规模的网络")
parser.add_argument('--train_yaml', type=str, default="train_oriae_modulation.yaml")
parser.add_argument('--train_yaml', type=str, default="train_depthwise_modulation.yaml")
# system logger
parser.add_argument('--logger', type=str,
default="tensorboard", choices=['tensorboard', 'wandb','none'], help='system logger')
default="wandb", choices=['tensorboard', 'wandb','none'], help='system logger')
# # logs (does not to be changed in most time)
# parser.add_argument('--dataloader_workers', type=int, default=6)
train_yamls/train_depthwise_modulation.yaml
+66
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@@ -0,0 +1,66 @@
# Related scripts
train_script_name: multi_gpu
# models' scripts
model_configs:
g_model:
script: Generator_modulation_depthwise_config
class_name: Generator
module_params:
id_dim: 512
g_kernel_size: 3
in_channel: 16
res_num: 9
up_mode: bilinear
res_mode: depthwise
d_model:
script: projected_discriminator
class_name: ProjectedDiscriminator
module_params:
diffaug: False
interp224: False
backbone_kwargs: {}
arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar
# Training information
batch_size: 64
# Dataset
dataloader: VGGFace2HQ_multigpu
dataset_name: vggface2_hq
dataset_params:
random_seed: 1234
dataloader_workers: 6
eval_dataloader: DIV2K_hdf5
eval_dataset_name: DF2K_H5_Eval
eval_batch_size: 2
# Dataset
# Optimizer
optim_type: Adam
g_optim_config:
lr: 0.0004
betas: [ 0, 0.99]
eps: !!float 1e-8
d_optim_config:
lr: 0.0004
betas: [ 0, 0.99]
eps: !!float 1e-8
id_weight: 20.0
reconstruct_weight: 10.0
feature_match_weight: 10.0
# Log
log_step: 300
model_save_step: 10000
sample_step: 1000
total_step: 1000000
checkpoint_names:
generator_name: Generator
discriminator_name: Discriminator
vggface2hq_failed.txt
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