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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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GUI/file_sync/filestate_machine0.json
+5 -3
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@@ -60,7 +60,7 @@
"face_crop.py": 1643789609.1834445,
"face_crop_video.py": 1643815024.5516832,
"similarity.py": 1643269705.1073737,
"train_multigpu.py": 1645935139.5672748,
"train_multigpu.py": 1646101637.160833,
"components\\arcface_decoder.py": 1643396144.2575414,
"components\\Generator_nobias.py": 1643179001.810856,
"data_tools\\data_loader_VGGFace2HQ_multigpu.py": 1644861019.9044807,
@@ -111,7 +111,7 @@
"components\\Generator_upsample.py": 1644689723.8293872,
"components\\misc\\Involution.py": 1644509321.5267963,
"train_yamls\\train_Invoup.yaml": 1644689981.9794765,
"flops.py": 1645883189.3803008,
"flops.py": 1646101039.8459642,
"detection_test.py": 1644935512.6830947,
"components\\DeConv_Depthwise.py": 1645064447.4379447,
"components\\DeConv_Depthwise1.py": 1644946969.5054545,
@@ -140,5 +140,7 @@
"components\\Generator_Invobn_config1.py": 1645862695.8743145,
"components\\misc\\Involution_BN.py": 1645867197.3984175,
"components\\misc\\Involution_ECA.py": 1645869012.4927464,
"train_yamls\\train_Invobn_config.yaml": 1645934993.5420852
"train_yamls\\train_Invobn_config.yaml": 1646101598.499709,
"components\\Generator_Invobn_config2.py": 1645962618.7056074,
"components\\Generator_Invobn_config3.py": 1646100847.8995547
}
breakpoint.json
+2 -2
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@@ -1,6 +1,6 @@
{
"breakpoint": [
1054,
0
1877,
29
]
}
check_list.txt
+5850
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File diff suppressed because it is too large Load Diff
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
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@@ -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)
flops.py
+3 -2
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@@ -5,7 +5,7 @@
# Created Date: Sunday February 13th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 27th February 2022 8:15:11 pm
# Last Modified: Thursday, 3rd March 2022 6:15:37 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
@@ -34,7 +34,8 @@ if __name__ == '__main__':
"res_num": 9,
# "up_mode": "nearest",
"up_mode": "bilinear",
"aggregator": "eca_invo"
"aggregator": "eca_invo",
"res_mode": "eca_invo"
}
speed_test.py
+9 -7
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@@ -5,7 +5,7 @@
# Created Date: Thursday February 10th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Saturday, 26th February 2022 4:13:24 pm
# Last Modified: Thursday, 3rd March 2022 6:44:57 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
@@ -21,20 +21,22 @@ if __name__ == '__main__':
# cudnn.benchmark = True
# cudnn.enabled = True
# script = "Generator_modulation_up"
script = "Generator_Invobn_config1"
# script = "Generator_modulation_up"
# script = "Generator_Invobn_config3"
script = "Generator_ori_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": 4,
"in_channel":64,
"res_num": 9,
# "up_mode": "nearest",
"up_mode": "bilinear",
"res_mode": "depthwise"
"aggregator": "eca_invo",
"res_mode": "eca_invo"
}
os.environ['CUDA_VISIBLE_DEVICES'] = str(0)
print("GPU used : ", os.environ['CUDA_VISIBLE_DEVICES'])
@@ -55,7 +57,7 @@ if __name__ == '__main__':
id_latent = torch.rand((4,512)).cuda()
# cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0]
attr = torch.rand((4,3,512,512)).cuda()
attr = torch.rand((4,3,224,224)).cuda()
import datetime
start_time = time.time()
test.py
+7 -4
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@@ -5,7 +5,7 @@
# Created Date: Saturday July 3rd 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 20th February 2022 4:13:22 pm
# Last Modified: Thursday, 3rd March 2022 9:04:25 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
@@ -30,15 +30,17 @@ def getParameters():
parser = argparse.ArgumentParser()
# general settings
parser.add_argument('-v', '--version', type=str, default='depthwise_config0', # depthwise depthwise_config0
parser.add_argument('-v', '--version', type=str, default='Invobn_resinvo1', # depthwise depthwise_config0 Invobn_resinvo1
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=250000,
parser.add_argument('-s', '--checkpoint_step', type=int, default=150000,
help="checkpoint epoch for test phase or finetune phase")
parser.add_argument('--start_checkpoint_step', type=int, default=10000,
help="checkpoint epoch for test phase or finetune phase")
# test
parser.add_argument('-t', '--test_script_name', type=str, default='image')
parser.add_argument('-t', '--test_script_name', type=str, default='image_allstep')
parser.add_argument('-b', '--batch_size', type=int, default=1)
parser.add_argument('-n', '--node_ip', type=str, default='101.33.242.26') # 101.33.242.26 2001:da8:8000:6880:f284:d61c:3c76:f9cb
parser.add_argument('--crop_mode', type=str, default="vggface", choices=['ffhq','vggface'], help='crop mode for face detector')
@@ -193,6 +195,7 @@ def main():
break
if not nodeinf:
raise Exception(print("Configuration of node %s is unavaliable"%sys_state["node_ip"]))
sys_state["remote_machine"] = nodeinf
print("ready to fetch related files from server: %s ......"%nodeinf["ip"])
uploader = fileUploaderClass(nodeinf["ip"],nodeinf["user"],nodeinf["passwd"])
test_scripts/tester_image_allstep.py
+215
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@@ -0,0 +1,215 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: tester_commonn.py
# Created Date: Saturday July 3rd 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Thursday, 3rd March 2022 9:03:57 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
import os
import cv2
import time
import glob
import torch
import torch.nn.functional as F
from torchvision import transforms
import numpy as np
from PIL import Image
from insightface_func.face_detect_crop_single import Face_detect_crop
class Tester(object):
def __init__(self, config, reporter):
self.config = config
# logger
self.reporter = reporter
self.transformer_Arcface = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1)
self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1)
def __init_framework__(self):
'''
This function is designed to define the framework,
and print the framework information into the log file
'''
#===============build models================#
print("build models...")
# TODO [import models here]
model_config = self.config["model_configs"]
gscript_name = self.config["com_base"] + model_config["g_model"]["script"]
class_name = model_config["g_model"]["class_name"]
package = __import__(gscript_name, fromlist=True)
gen_class = getattr(package, class_name)
self.network = gen_class(**model_config["g_model"]["module_params"])
# TODO replace below lines to define the model framework
self.network = gen_class(**model_config["g_model"]["module_params"])
self.network = self.network.eval()
# for name in self.network.state_dict():
# print(name)
# print and recorde model structure
self.reporter.writeInfo("Model structure:")
self.reporter.writeModel(self.network.__str__())
arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu"))
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()
def test(self):
save_dir = self.config["test_samples_path"]
ckp_step = self.config["checkpoint_step"]
version = self.config["version"]
id_imgs = self.config["id_imgs"]
crop_mode = self.config["crop_mode"]
attr_files = self.config["attr_files"]
specified_save_path = self.config["specified_save_path"]
self.arcface_ckpt= self.config["arcface_ckpt"]
imgs_list = []
self.reporter.writeInfo("Version %s"%version)
if os.path.isdir(specified_save_path):
print("Input a legal specified save path!")
save_dir = specified_save_path
if os.path.isdir(attr_files):
print("Input a dir....")
imgs = glob.glob(os.path.join(attr_files,"**"), recursive=True)
for item in imgs:
imgs_list.append(item)
print(imgs_list)
else:
print("Input an image....")
imgs_list.append(attr_files)
id_basename = os.path.basename(id_imgs)
id_basename = os.path.splitext(os.path.basename(id_imgs))[0]
# models
self.__init_framework__()
mode = crop_mode.lower()
if mode == "vggface":
mode = "none"
self.detect = Face_detect_crop(name='antelope', root='./insightface_func/models')
self.detect.prepare(ctx_id = 0, det_thresh=0.6, det_size=(640,640),mode = mode)
id_img = cv2.imread(id_imgs)
id_img_align_crop, _ = self.detect.get(id_img,512)
id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img_align_crop[0],cv2.COLOR_BGR2RGB))
id_img = self.transformer_Arcface(id_img_align_crop_pil)
id_img = id_img.unsqueeze(0).cuda()
#create latent id
id_img = F.interpolate(id_img,size=(112,112), mode='bicubic')
latend_id = self.arcface(id_img)
latend_id = F.normalize(latend_id, p=2, dim=1)
cos_loss = torch.nn.CosineSimilarity()
font = cv2.FONT_HERSHEY_SIMPLEX
# Start time
import datetime
print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
print('Start =================================== test...')
start_time = time.time()
self.network.eval()
total_dict = {}
from utilities.sshupload import fileUploaderClass
nodeinf = self.config["remote_machine"]
uploader = fileUploaderClass(nodeinf["ip"],nodeinf["user"],nodeinf["passwd"])
remotebase = os.path.join(nodeinf['path'],"train_logs",self.config["version"]).replace('\\','/')
for istep in range(self.config["start_checkpoint_step"],self.config["checkpoint_step"]+1,10000):
ckpt_name = "step%d_%s.pth"%(istep,
self.config["checkpoint_names"]["generator_name"])
localFile = os.path.join(self.config["project_checkpoints"],ckpt_name)
if self.config["node_ip"]!="localhost":
if not os.path.exists(localFile):
remoteFile = os.path.join(remotebase, "checkpoints", ckpt_name).replace('\\','/')
ssh_state = uploader.sshScpGet(remoteFile, localFile, True)
if not ssh_state:
raise Exception(print("Get file %s failed! Checkpoint file does not exist!"%remoteFile))
print("Get the checkpoint %s successfully!"%(ckpt_name))
else:
print("%s exists!"%(ckpt_name))
self.network.load_state_dict(torch.load(localFile, map_location=torch.device("cpu")))
print('loaded trained backbone model step {}...!'.format(istep))
cos_dict = {}
# train in GPU
if self.config["cuda"] >=0:
self.network = self.network.cuda()
average_cos = 0
with torch.no_grad():
for img in imgs_list:
print(img)
attr_img_ori= cv2.imread(img)
try:
attr_img_align_crop, _ = self.detect.get(attr_img_ori,512)
except:
continue
attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_align_crop[0],cv2.COLOR_BGR2RGB))
attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda()
attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic')
# cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0])
attr_id = self.arcface(attr_img_arc)
attr_id = F.normalize(attr_id, p=2, dim=1)
results = self.network(attr_img, latend_id)
results_arc = F.interpolate(results,size=(112,112), mode='bicubic')
results_arc = self.arcface(results_arc)
results_arc = F.normalize(results_arc, p=2, dim=1)
results_cos_dis = 1 - cos_loss(latend_id, results_arc)
cos_dict[img] = results_cos_dis.item()
average_cos += results_cos_dis
average_cos /= len(imgs_list)
total_dict[str(istep)] = {
"step":istep,
"Average_cosin": average_cos.item(),
"images": cos_dict
}
print("Step: [{}], average cosin similarity between ID and results [{}]".format(istep, average_cos.item()))
self.reporter.writeInfo("Step: [{}], average cosin similarity between ID and results [{}]".format(istep, average_cos.item()))
self.reporter.writeJson(total_dict)
elapsed = time.time() - start_time
elapsed = str(datetime.timedelta(seconds=elapsed))
print("Elapsed [{}]".format(elapsed))
train_multigpu.py
+3 -3
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@@ -5,7 +5,7 @@
# Created Date: Tuesday April 28th 2020
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 27th February 2022 12:12:19 pm
# Last Modified: Tuesday, 1st March 2022 10:27:16 am
# Modified By: Chen Xuanhong
# Copyright (c) 2020 Shanghai Jiao Tong University
#############################################################
@@ -31,7 +31,7 @@ def getParameters():
parser = argparse.ArgumentParser()
# general settings
parser.add_argument('-v', '--version', type=str, default='Invobn_eca1',
parser.add_argument('-v', '--version', type=str, default='Invobn_resinvo1',
help="version name for train, test, finetune")
parser.add_argument('-t', '--tag', type=str, default='tiny',
help="tag for current experiment")
@@ -46,7 +46,7 @@ def getParameters():
# training
parser.add_argument('--experiment_description', type=str,
default="尝试直接训练最小规模的网络,正往由ECA Invo构成")
default="尝试直接训练最小规模的网络,正往由Invo构成，Resblock用Invo+conv, 对齐batchsize 64")
parser.add_argument('--train_yaml', type=str, default="train_Invobn_config.yaml")
train_yamls/train_Invobn_config.yaml
+2 -1
View File
@@ -13,6 +13,7 @@ model_configs:
res_num: 9
up_mode: bilinear
aggregator: "invo"
res_mode: "invo"
d_model:
script: projected_discriminator
@@ -25,7 +26,7 @@ model_configs:
arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar
# Training information
batch_size: 24
batch_size: 64
# Dataset
dataloader: VGGFace2HQ_multigpu
utilities/reporter.py
+7 -1
View File
@@ -5,13 +5,14 @@
# Created Date: Tuesday September 24th 2019
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 4th July 2021 11:50:12 pm
# Last Modified: Thursday, 3rd March 2022 8:42:13 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2019 Shanghai Jiao Tong University
#############################################################
import datetime
import os
import json
class Reporter:
def __init__(self,reportPath):
@@ -54,3 +55,8 @@ class Reporter:
timeStr = datetime.datetime.strftime(datetime.datetime.now(),self.timeStrFormat)
logf.writelines("[%d]-[%s]-[logInfo]-epoch[%d]-step[%d] %s\n"%(self.index,timeStr,epoch,step,logText))
self.index += 1
def writeJson(self, info):
with open(self.path, 'a+') as cf:
configjson = json.dumps(info, indent=4)
cf.writelines(configjson)