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train.py
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train.py
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"""
author:guopei
"""
import argparse
import glob
import os
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from tqdm import tqdm
import transforms
from tensorboardX import SummaryWriter
from conf import settings
from utils import *
from lr_scheduler import WarmUpLR
from criterion import LSR
from models import resnest50
import warnings
warnings.filterwarnings('ignore')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--net', type=str, default="resnest50", help='net type')
parser.add_argument('-w', type=int, default=4, help='number of workers for dataloader')
parser.add_argument('-b', type=int, default=32, help='batch size for dataloader')
parser.add_argument('-lr', type=float, default=0.001, help='initial learning rate')
parser.add_argument('-e', type=int, default=50, help='training epoches')
parser.add_argument('-warm', type=int, default=1, help='warm up phase')
parser.add_argument('-gpus', nargs='+', type=int, default=0, help='gpu device')
args = parser.parse_args()
#checkpoint directory
checkpoint_path = os.path.join(settings.CHECKPOINT_PATH, args.net, settings.TIME_NOW)
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
checkpoint_path = os.path.join(checkpoint_path, '{net}-{epoch}-{type}.pth')
#tensorboard log directory
log_path = os.path.join(settings.LOG_DIR, args.net, settings.TIME_NOW)
if not os.path.exists(log_path):
os.makedirs(log_path)
writer = SummaryWriter(log_dir=log_path)
#get dataloader
train_transforms = transforms.Compose([
transforms.ToCVImage(),
#transforms.RandomResizedCrop(settings.IMAGE_SIZE),
transforms.Resize(settings.IMAGE_SIZE),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=0.4, saturation=0.4, hue=0.4),
#transforms.RandomErasing(),
#transforms.CutOut(56),
transforms.ToTensor(),
transforms.Normalize(settings.TRAIN_MEAN, settings.TRAIN_STD)
])
test_transforms = transforms.Compose([
transforms.ToCVImage(),
#transforms.CenterCrop(settings.IMAGE_SIZE),
transforms.Resize(settings.IMAGE_SIZE),
transforms.ToTensor(),
transforms.Normalize(settings.TRAIN_MEAN, settings.TRAIN_STD)
])
train_dataloader = get_train_dataloader(
settings.DATA_PATH,
train_transforms,
args.b,
args.w
)
test_dataloader = get_test_dataloader(
settings.DATA_PATH,
test_transforms,
int(args.b/8),
args.w
)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#net = get_network(args)
net = resnest50(pretrained=False)
net = init_weights(net)
# load pretraines model
weights = torch.load("pretrain_models/resnest50-528c19ca.pth")
net_dict = net.state_dict()
for k,v in weights.items():
if k in weights.items():
net_dict[k] = v
net.load_state_dict(net_dict)
print("=>load model form pretrain_models/resnest50-528c19ca.pth")
if isinstance(args.gpus, int):
args.gpus = [args.gpus]
net = nn.DataParallel(net, device_ids=args.gpus)
net = net.cuda()
#visualize the network
#visualize_network(writer, net.module)
cross_entropy = nn.CrossEntropyLoss(ignore_index=-1)
#lsr_loss = LSR()
#apply no weight decay on bias
params = split_weights(net)
optimizer = optim.SGD(params, lr=args.lr, momentum=0.9, weight_decay=1e-4, nesterov=True)
#set up warmup phase learning rate scheduler
iter_per_epoch = len(train_dataloader)
warmup_scheduler = WarmUpLR(optimizer, iter_per_epoch * args.warm)
#set up training phase learning rate scheduler
train_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=settings.MILESTONES)
#train_scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, args.e - args.warm)
best_acc = 0.0
for epoch in range(1, args.e + 1):
if epoch > args.warm:
train_scheduler.step(epoch)
#training procedure
net.train()
for batch_index, (images, labels) in enumerate(train_dataloader):
if epoch <= args.warm:
warmup_scheduler.step()
images = images.cuda()
labels = labels.cuda()
optimizer.zero_grad()
predicts = net(images)
#loss = lsr_loss(predicts, labels)
# print(" predicts[0]", predicts[0])
# print(" labels[:, 0]", labels[:, 0])
# print(" predicts[1]", predicts[1])
# print(" labels[:, 1]", labels[:, 1])
loss_gender = cross_entropy(predicts[0], labels[:, 0].long())
loss_age = cross_entropy(predicts[1], labels[:, 1].long())
loss_orientation = cross_entropy(predicts[2], labels[:, 2].long())
loss_hat = cross_entropy(predicts[3], labels[:, 3].long())
loss_glasses = cross_entropy(predicts[4], labels[:, 4].long())
loss_handBag = cross_entropy(predicts[5], labels[:, 5].long())
loss_shoulderBag = cross_entropy(predicts[6], labels[:, 6].long())
loss_backBag = cross_entropy(predicts[7], labels[:, 7].long())
loss_upClothing = cross_entropy(predicts[8], labels[:, 8].long())
loss_downClothing= cross_entropy(predicts[9], labels[:, 9].long())
loss = loss_gender + loss_age + loss_orientation + loss_hat + loss_glasses + loss_handBag + loss_shoulderBag + loss_backBag + loss_upClothing + loss_downClothing
loss.backward()
optimizer.step()
n_iter = (epoch - 1) * len(train_dataloader) + batch_index + 1
if batch_index % 10 == 0:
print('Training Epoch: {epoch} [{trained_samples}/{total_samples}]\tLoss: {:0.4f}\tLoss_gender: {:0.4f}\tLoss_age: {:0.4f}\tLoss_ori: {:0.4f}\tLoss_hat: {:0.4f}\tLoss_glasses: {:0.4f}\tLoss_handBag: {:0.4f}\t'.format(
loss.item(),
loss_gender.item(),
loss_age.item(),
loss_orientation.item(),
loss_hat.item(),
loss_glasses.item(),
loss_handBag.item(),
epoch=epoch,
trained_samples=batch_index * args.b + len(images),
total_samples=len(train_dataloader.dataset),
))
#visualization
visualize_lastlayer(writer, net, n_iter)
visualize_train_loss(writer, loss.item(), n_iter)
visualize_learning_rate(writer, optimizer.param_groups[0]['lr'], epoch)
visualize_param_hist(writer, net, epoch)
net.eval()
total_loss = 0
correct = np.zeros(10)
ignore = np.zeros(10)
print("=>test model")
for images, labels in tqdm(test_dataloader):
images = images.cuda()
labels = labels.cuda()
predicts = net(images)
for index in range(10):
_, preds = predicts[index].max(1)
ignore[index] += int((labels[:, index]==-1).sum())
correct[index] += preds.eq(labels[:, index]).sum().float()
loss = cross_entropy(predicts[index], labels[:, index].long())
total_loss += loss.item()
test_loss = total_loss / len(test_dataloader)
all_list = np.array([len(test_dataloader.dataset) for i in range(10)])-ignore
acc_list = correct / all_list
print(acc_list.tolist())
print("gender_acc:%.4f, age_acc:%.4f, orientation_acc:%.4f, hat_acc:%.4f, glasses_acc:%.4f, handBag_acc:%.4f, shoulderBag_acc:%.4f, backBag_acc:%.4f, upClothing_acc:%.4f, downClothing_acc:%.4f" % tuple(acc_list))
acc = float(acc_list.mean())
print('Test set: loss: {:.4f}, Accuracy: {:.4f}'.format(test_loss, acc))
print()
visualize_test_loss(writer, test_loss, epoch)
visualize_test_acc(writer, acc, epoch)
# save weights file
if best_acc < acc:
torch.save(net.state_dict(), checkpoint_path.format(net=args.net, epoch=epoch, type='best'))
best_acc = acc
continue
if not epoch % settings.SAVE_EPOCH:
torch.save(net.state_dict(), checkpoint_path.format(net=args.net, epoch=epoch, type='regular'))
writer.close()
#python3 ./.conda/envs/pytorch-tensorrt/lib/python3.7/site-packages/tensorboard/main.py --logdir=/home/py/code/mana/attribute/runs