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train_source_31.py
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train_source_31.py
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import argparse
import os, sys
import os.path as osp
import torchvision
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
import network_new
from network_new import *
from torch.utils.data import DataLoader
from data_list import ImageList, ImageList_idx
import random, pdb, math, copy
from loss import *
import torch.nn.functional as F
from utils import *
def op_copy(optimizer):
for param_group in optimizer.param_groups:
param_group['lr0'] = param_group['lr']
return optimizer
def lr_scheduler(optimizer, iter_num, max_iter, gamma=10, power=0.75):
decay = (1 + gamma * iter_num / max_iter) ** (-power)
for param_group in optimizer.param_groups:
param_group['lr'] = param_group['lr0'] * decay
param_group['weight_decay'] = 5e-4
param_group['momentum'] = 0.9
param_group['nesterov'] = True
return optimizer
def data_load(args):
train_transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
test_transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.CenterCrop(224),
transforms.ToTensor(),
torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
## prepare data
dsets = {}
dset_loaders = {}
train_bs = args.batch_size
txt_src = open(args.s_dset_path).readlines()
txt_tar = open(args.t_dset_path).readlines()
txt_test = open(args.test_dset_path).readlines()
if args.trte == "val":
dsize = len(txt_src)
tr_size = int(args.split * dsize)
test_size = dsize - tr_size
print(dsize, tr_size, test_size)
tr_txt, te_txt = torch.utils.data.random_split(
txt_src, [tr_size, test_size]
)
else:
tr_txt = txt_src
te_txt = txt_src
dsets["source_tr"] = ImageList(tr_txt, transform=train_transform)
dset_loaders["source_tr"] = DataLoader(
dsets["source_tr"],
batch_size=train_bs,
shuffle=True,
num_workers=args.worker,
drop_last=False,
)
dsets["source_te"] = ImageList(te_txt, transform=test_transform)
dset_loaders["source_te"] = DataLoader(
dsets["source_te"],
batch_size=train_bs,
shuffle=True,
num_workers=args.worker,
drop_last=False,
)
dsets["test"] = ImageList(txt_test, transform=test_transform)
dset_loaders["test"] = DataLoader(
dsets["test"],
batch_size=train_bs * 3,
shuffle=False,
num_workers=args.worker,
drop_last=False,
)
return dset_loaders
def train_source(args):
dset_loaders = data_load(args)
## set base network
netG = network_new.ResBase(res_name=args.net).cuda()
netF = network_new.bottleneck(type=args.classifier, feature_dim=netG.in_features, bottleneck_dim=args.bottleneck).cuda()
netC = network_new.classifier_C(type=args.layer, class_num=args.class_num, bottleneck_dim=args.bottleneck).cuda()
netD = network_new.classifier_D(type=args.layer, feature_dim=netG.in_features, class_num=args.class_num).cuda()
param_group_g = []
param_group_c = []
param_group_d = []
learning_rate = args.lr
for k, v in netG.named_parameters(): # k是网络层的名字 v是其中的参数
param_group_g += [{"params": v, "lr": learning_rate * 0.1}] # 1
for k, v in netF.named_parameters(): # k是网络层的名字 v是其中的参数
param_group_g += [{"params": v, "lr": learning_rate * 1.0}] # 1
for k, v in netC.named_parameters():
param_group_c += [{"params": v, "lr": learning_rate * 1.0}]# 10
for k, v in netD.named_parameters():
param_group_d += [{"params": v, "lr": learning_rate * 1.0}]
# optimizer_g = optim.SGD(param_group_g, momentum=0.9, weight_decay=1e-3, nesterov=True)
# optimizer_c = optim.SGD(param_group_c, momentum=0.9, weight_decay=1e-3, nesterov=True)
# optimizer_d = optim.SGD(param_group_d, momentum=0.9, weight_decay=1e-3, nesterov=True)
optimizer_g = optim.SGD(param_group_g)
optimizer_c = optim.SGD(param_group_c)
optimizer_d = optim.SGD(param_group_d)
optimizer_g = op_copy(optimizer_g)
optimizer_c = op_copy(optimizer_c)
optimizer_d = op_copy(optimizer_d)
netG.train()
netF.train()
netC.train()
netD.train()
acc_init = 0
iter_num = 0
iter_source = iter(dset_loaders["source_tr"])
interval_iter = int(args.interval * len(dset_loaders["source_tr"]))
max_iter = args.max_epoch * len(dset_loaders["source_tr"])
while iter_num < max_iter:
try:
inputs_source, labels_source = iter_source.next()
except:
iter_source = iter(dset_loaders["source_tr"])
inputs_source, labels_source = iter_source.next()
if inputs_source.size(0) == 1:
continue
iter_num += 1
lr_scheduler(optimizer_g, iter_num=iter_num, max_iter=max_iter)
lr_scheduler(optimizer_c, iter_num=iter_num, max_iter=max_iter)
lr_scheduler(optimizer_d, iter_num=iter_num, max_iter=max_iter)
inputs_source, labels_source = inputs_source.cuda(), labels_source.cuda()
features_d = netG(inputs_source)
features = netF(features_d)
outputs_source1 = netC(features)
outputs_source2 = netD(features_d)
classifier_loss1 = CrossEntropyLabelSmooth(num_classes=args.class_num, epsilon=args.smooth)(outputs_source1, labels_source)
classifier_loss2 = CrossEntropyLabelSmooth(num_classes=args.class_num, epsilon=args.smooth)(outputs_source2, labels_source)
classifier_loss = classifier_loss1 + classifier_loss2
all_loss = classifier_loss
optimizer_g.zero_grad()
optimizer_c.zero_grad()
optimizer_d.zero_grad()
all_loss.backward()
optimizer_g.step()
optimizer_c.step()
optimizer_d.step()
if iter_num % interval_iter == 0 or iter_num == max_iter:
netG.eval()
netF.eval()
netC.eval()
netD.eval()
_, acc_list1, accuracy1 = cal_acc(dset_loaders["source_te"], netG, netF, netC)
_, acc_list2, accuracy2 = cal_acc_easy(dset_loaders["source_te"], netG, netD)
acc_best = accuracy1
log_str = (
"Task: {}, Iter:{}; Accuracy_c = {:.2f}%, Accuracy_d = {:.2f}%".format(
args.name_src, iter_num, accuracy1, accuracy2
)
+ "\n"
+ str(acc_list1)
)
args.out_file.write(log_str + "\n")
args.out_file.flush()
print(log_str + "\n")
if acc_best >= acc_init:
acc_init = acc_best
best_netG = netG.state_dict()
best_netF = netF.state_dict()
best_netC = netC.state_dict()
best_netD = netD.state_dict()
torch.save(best_netG, osp.join(args.output_dir_src, "source_G.pt"))
torch.save(best_netF, osp.join(args.output_dir_src, "source_F.pt"))
torch.save(best_netC, osp.join(args.output_dir_src, "source_C.pt"))
torch.save(best_netD, osp.join(args.output_dir_src, "source_D.pt"))
netG.train()
netF.train()
netC.train()
netD.train()
print('Best Model Saved!!')
return netG, netF, netC, netD
def test_target(args):
dset_loaders = data_load(args)
## set base network
netG = network_new.ResBase(res_name=args.net).cuda()
netF = network_new.bottleneck(type=args.classifier, feature_dim=netG.in_features, bottleneck_dim=args.bottleneck).cuda()
netC = network_new.classifier_C(type=args.layer, class_num=args.class_num, bottleneck_dim=args.bottleneck).cuda()
netD = network_new.classifier_D(type=args.layer, feature_dim=netG.in_features, class_num=args.class_num).cuda()
args.modelpath = args.output_dir_src + "/source_G" + ".pt"
netG.load_state_dict(torch.load(args.modelpath))
args.modelpath = args.output_dir_src + "/source_F" + ".pt"
netF.load_state_dict(torch.load(args.modelpath))
args.modelpath = args.output_dir_src + "/source_C" + ".pt"
netC.load_state_dict(torch.load(args.modelpath))
args.modelpath = args.output_dir_src + "/source_D" + ".pt"
netD.load_state_dict(torch.load(args.modelpath))
netG.eval()
netF.eval()
netC.eval()
netD.eval()
acc1, acc_list1, accuracy1 = cal_acc(dset_loaders["test"], netG, netF, netC)
acc2, acc_list2, accuracy2 = cal_acc_easy(dset_loaders["test"], netG, netD)
log_str = (
"\nDateset: {}, Task: {}, Accuracy_c = {:.2f}%, Accuracy_d = {:.2f}%".format(args.dset, args.name, accuracy1, accuracy2)
+ "\n"
+ str(acc_list1)
)
args.out_file.write(log_str + "\n")
args.out_file.flush()
print(log_str + "\n")
def print_args(args):
s = "==========================================\n"
for arg, content in args.__dict__.items():
s += "{}:{}\n".format(arg, content)
return s
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="DCA on office-31")
parser.add_argument(
"--gpu_id", type=str, nargs="?", default="0", help="device id to run"
)
parser.add_argument("--s", type=int, default=0, help="source")
parser.add_argument("--t", type=int, default=0, help="target")
parser.add_argument("--batch_size", type=int, default=64, help="batch_size") # 128
parser.add_argument("--worker", type=int, default=4, help="number of workers")
parser.add_argument("--dset", type=str, default="office", choices=["office", "officehome", "visda"])
parser.add_argument("--lr", type=float, default=0.01, help="learning rate")
parser.add_argument("--net", type=str, default="resnet50", help="resnet50, resnet101")
parser.add_argument("--seed", type=int, default=2042, help="random seed")
parser.add_argument("--max_epoch", type=int, default=20, help="max iterations")
parser.add_argument("--interval", type=float, default=0.2, help="max iterations")
parser.add_argument("--bottleneck", type=int, default=256)
parser.add_argument("--epsilon", type=float, default=1e-5)
parser.add_argument("--layer", type=str, default="mme", choices=["linear", "wn", "mme"])
parser.add_argument("--classifier", type=str, default="bn", choices=["ori", "bn"])
parser.add_argument("--smooth", type=float, default=0.1)
parser.add_argument("--output", type=str, default="double")
parser.add_argument("--da", type=str, default="SFDA")
parser.add_argument("--trte", type=str, default="val", choices=["full", "val"])
parser.add_argument("--split", type=float, default=0.9, help="split parameter")
args = parser.parse_args()
args.interval = args.max_epoch / 10
if args.dset == "office":
names = ["amazon", "dslr", "webcam"]
args.class_num = 31
elif args.dset == "officehome":
names = ["Art", "Clipart", "Product", "RealWorld"]
args.class_num = 65
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
SEED = args.seed
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
np.random.seed(SEED)
random.seed(SEED)
# torch.backends.cudnn.deterministic = True
folder = "./data/"
if args.dset == "office":
args.s_dset_path = folder + args.dset + "/" + names[args.s] + "_31.txt"
args.t_dset_path = folder + args.dset + "/" + names[args.t] + "_31.txt"
elif args.dset == "officehome":
args.s_dset_path = folder + args.dset + "/" + names[args.s] + "_65.txt"
args.t_dset_path = folder + args.dset + "/" + names[args.t] + "_65.txt"
args.test_dset_path = args.t_dset_path
current_folder = "./ckps/"
args.output_dir_src = osp.join(
current_folder, args.da, args.output, args.dset, names[args.s][0].upper()
) # ./ckps/uda/bait/office/A
args.name_src = names[args.s][0].upper()
if not osp.exists(args.output_dir_src):
os.system("mkdir -p " + args.output_dir_src)
if not osp.exists(args.output_dir_src):
os.mkdir(args.output_dir_src)
args.output_dir = osp.join(
current_folder,
args.da,
args.output,
args.dset,
names[args.s][0].upper() + names[args.t][0].upper(),
)
args.name = names[args.s][0].upper() + names[args.t][0].upper()
if not osp.exists(args.output_dir):
os.system("mkdir -p " + args.output_dir)
if not osp.exists(args.output_dir):
os.mkdir(args.output_dir)
args.out_file = open(osp.join(args.output_dir_src, "log.txt"), "w")
args.out_file.write(print_args(args) + "\n")
args.out_file.flush()
train_source(args)
test_target(args)
args.out_file = open(osp.join(args.output_dir_src, 'model_test.txt'), 'w')
for i in range(len(names)):
if i == args.s:
continue
args.t = i
args.name = names[args.s][0].upper() + names[args.t][0].upper()
folder = "./data/"
args.s_dset_path = folder + args.dset + "/" + names[args.s] + "_31.txt"
args.t_dset_path = folder + args.dset + "/" + names[args.t] + "_31.txt"
args.test_dset_path = args.t_dset_path
test_target(args)