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train_dance.py
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train_dance.py
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from __future__ import print_function
import yaml
import easydict
import os
import numpy as np
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.transforms as transforms
from apex import amp, optimizers
from data_loader.get_loader import get_loader
from utils.utils import *
from utils.lr_schedule import inv_lr_scheduler
from utils.loss import *
from models.LinearAverage import LinearAverage
from eval import test
# Training settings
import argparse
parser = argparse.ArgumentParser(description='Pytorch DA',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--config', type=str, default='config.yaml', help='/path/to/config/file')
parser.add_argument('--source_path', type=str, default='./utils/source_list.txt', metavar='B',
help='path to source list')
parser.add_argument('--target_path', type=str, default='./utils/target_list.txt', metavar='B',
help='path to target list')
parser.add_argument('--log-interval', type=int, default=100, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--exp_name', type=str, default='office_close', help='/path/to/config/file')
parser.add_argument("--gpu_devices", type=int, nargs='+', default=None, help="")
# args = parser.parse_args()
args = parser.parse_args()
config_file = args.config
conf = yaml.load(open(config_file))
save_config = yaml.load(open(config_file))
conf = easydict.EasyDict(conf)
gpu_devices = ','.join([str(id) for id in args.gpu_devices])
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_devices
args.cuda = torch.cuda.is_available()
source_data = args.source_path
target_data = args.target_path
evaluation_data = args.target_path
batch_size = conf.data.dataloader.batch_size
filename = source_data.split("_")[1] + "2" + target_data.split("_")[1]
filename = os.path.join("record", args.exp_name,
config_file.replace(".yaml", ""), filename)
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
print("record in %s " % filename)
data_transforms = {
source_data: transforms.Compose([
transforms.Scale((256, 256)),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
target_data: transforms.Compose([
transforms.Scale((256, 256)),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
evaluation_data: transforms.Compose([
transforms.Scale((256, 256)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
use_gpu = torch.cuda.is_available()
source_loader, target_loader, \
test_loader, target_folder = get_loader(source_data, target_data,
evaluation_data, data_transforms,
batch_size=batch_size, return_id=True,
balanced=conf.data.dataloader.class_balance)
dataset_test = test_loader
n_share = conf.data.dataset.n_share
n_source_private = conf.data.dataset.n_source_private
num_class = n_share + n_source_private
G, C1 = get_model_mme(conf.model.base_model, num_class=num_class,
temp=conf.model.temp)
device = torch.device("cuda")
if args.cuda:
G.cuda()
C1.cuda()
G.to(device)
C1.to(device)
ndata = target_folder.__len__()
## Memory
lemniscate = LinearAverage(2048, ndata, conf.model.temp, conf.train.momentum).cuda()
params = []
for key, value in dict(G.named_parameters()).items():
if value.requires_grad and "features" in key:
if 'bias' in key:
params += [{'params': [value], 'lr': conf.train.multi,
'weight_decay': conf.train.weight_decay}]
else:
params += [{'params': [value], 'lr': conf.train.multi,
'weight_decay': conf.train.weight_decay}]
else:
if 'bias' in key:
params += [{'params': [value], 'lr': 1.0,
'weight_decay': conf.train.weight_decay}]
else:
params += [{'params': [value], 'lr': 1.0,
'weight_decay': conf.train.weight_decay}]
criterion = torch.nn.CrossEntropyLoss().cuda()
opt_g = optim.SGD(params, momentum=conf.train.sgd_momentum,
weight_decay=0.0005, nesterov=True)
opt_c1 = optim.SGD(list(C1.parameters()), lr=1.0,
momentum=conf.train.sgd_momentum, weight_decay=0.0005,
nesterov=True)
[G, C1], [opt_g, opt_c1] = amp.initialize([G, C1],
[opt_g, opt_c1],
opt_level="O1")
G = nn.DataParallel(G)
C1 = nn.DataParallel(C1)
param_lr_g = []
for param_group in opt_g.param_groups:
param_lr_g.append(param_group["lr"])
param_lr_f = []
for param_group in opt_c1.param_groups:
param_lr_f.append(param_group["lr"])
def train():
criterion = nn.CrossEntropyLoss().cuda()
print('train start!')
data_iter_s = iter(source_loader)
data_iter_t = iter(target_loader)
len_train_source = len(source_loader)
len_train_target = len(target_loader)
for step in range(conf.train.min_step + 1):
G.train()
C1.train()
if step % len_train_target == 0:
data_iter_t = iter(target_loader)
if step % len_train_source == 0:
data_iter_s = iter(source_loader)
data_t = next(data_iter_t)
data_s = next(data_iter_s)
inv_lr_scheduler(param_lr_g, opt_g, step,
init_lr=conf.train.lr,
max_iter=conf.train.min_step)
inv_lr_scheduler(param_lr_f, opt_c1, step,
init_lr=conf.train.lr,
max_iter=conf.train.min_step)
img_s = data_s[0]
label_s = data_s[1]
img_t = data_t[0]
index_t = data_t[2]
img_s, label_s = Variable(img_s.cuda()), \
Variable(label_s.cuda())
img_t = Variable(img_t.cuda())
index_t = Variable(index_t.cuda())
if len(img_t) < batch_size:
break
if len(img_s) < batch_size:
break
opt_g.zero_grad()
opt_c1.zero_grad()
## Weight normalizztion
C1.module.weight_norm()
## Source loss calculation
feat = G(img_s)
out_s = C1(feat)
loss_s = criterion(out_s, label_s)
feat_t = G(img_t)
out_t = C1(feat_t)
feat_t = F.normalize(feat_t)
### Calculate mini-batch x memory similarity
feat_mat = lemniscate(feat_t, index_t)
### We do not use memory features present in mini-batch
feat_mat[:, index_t] = -1 / conf.model.temp
### Calculate mini-batch x mini-batch similarity
feat_mat2 = torch.matmul(feat_t,
feat_t.t()) / conf.model.temp
mask = torch.eye(feat_mat2.size(0),
feat_mat2.size(0)).bool().cuda()
feat_mat2.masked_fill_(mask, -1 / conf.model.temp)
loss_nc = conf.train.eta * entropy(torch.cat([out_t, feat_mat,
feat_mat2], 1))
loss_ent = conf.train.eta * entropy_margin(out_t, conf.train.thr,
conf.train.margin)
all = loss_nc + loss_s + loss_ent
with amp.scale_loss(all, [opt_g, opt_c1]) as scaled_loss:
scaled_loss.backward()
opt_g.step()
opt_c1.step()
opt_g.zero_grad()
opt_c1.zero_grad()
lemniscate.update_weight(feat_t, index_t)
if step % conf.train.log_interval == 0:
print('Train [{}/{} ({:.2f}%)]\tLoss Source: {:.6f} '
'Loss NC: {:.6f} Loss ENS: {:.6f}\t'.format(
step, conf.train.min_step,
100 * float(step / conf.train.min_step),
loss_s.item(), loss_nc.item(), loss_ent.item()))
if step > 0 and step % conf.test.test_interval == 0:
test(step, dataset_test, filename, n_share, num_class, G, C1,
conf.train.thr)
G.train()
C1.train()
train()