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同步操作将从 王志伟/PFLD-pytorch 强制同步,此操作会覆盖自 Fork 仓库以来所做的任何修改,且无法恢复!!!
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#!/usr/bin/env python3
#-*- coding:utf-8 -*-
import argparse
import logging
from pathlib import Path
import time
import os
import numpy as np
import torch
from torch.utils import data
from torch.utils.data import DataLoader
import torchvision
from torchvision import datasets, transforms
import torchvision.utils as vutils
from tensorboardX import SummaryWriter
from dataset.datasets import WLFWDatasets
from models.pfld import PFLDInference, AuxiliaryNet
from pfld.loss import PFLDLoss
from pfld.utils import AverageMeter
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def print_args(args):
for arg in vars(args):
s = arg + ': ' + str(getattr(args, arg))
logging.info(s)
def save_checkpoint(state, filename='checkpoint.pth.tar'):
torch.save(state, filename)
logging.info('Save checkpoint to {0:}'.format(filename))
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected')
def train(train_loader, pfld_backbone, auxiliarynet, criterion, optimizer,
epoch):
losses = AverageMeter()
weighted_loss, loss = None, None
for img, landmark_gt, attribute_gt, euler_angle_gt in train_loader:
img = img.to(device)
attribute_gt = attribute_gt.to(device)
landmark_gt = landmark_gt.to(device)
euler_angle_gt = euler_angle_gt.to(device)
pfld_backbone = pfld_backbone.to(device)
auxiliarynet = auxiliarynet.to(device)
features, landmarks = pfld_backbone(img)
angle = auxiliarynet(features)
weighted_loss, loss = criterion(attribute_gt, landmark_gt,
euler_angle_gt, angle, landmarks,
args.train_batchsize)
optimizer.zero_grad()
weighted_loss.backward()
optimizer.step()
losses.update(loss.item())
return weighted_loss, loss
def validate(wlfw_val_dataloader, pfld_backbone, auxiliarynet, criterion):
pfld_backbone.eval()
auxiliarynet.eval()
losses = []
with torch.no_grad():
for img, landmark_gt, attribute_gt, euler_angle_gt in wlfw_val_dataloader:
img = img.to(device)
attribute_gt = attribute_gt.to(device)
landmark_gt = landmark_gt.to(device)
euler_angle_gt = euler_angle_gt.to(device)
pfld_backbone = pfld_backbone.to(device)
auxiliarynet = auxiliarynet.to(device)
_, landmark = pfld_backbone(img)
loss = torch.mean(torch.sum((landmark_gt - landmark)**2, axis=1))
losses.append(loss.cpu().numpy())
print("===> Evaluate:")
print('Eval set: Average loss: {:.4f} '.format(np.mean(losses)))
return np.mean(losses)
def main(args):
# Step 1: parse args config
logging.basicConfig(
format=
'[%(asctime)s] [p%(process)s] [%(pathname)s:%(lineno)d] [%(levelname)s] %(message)s',
level=logging.INFO,
handlers=[
logging.FileHandler(args.log_file, mode='w'),
logging.StreamHandler()
])
print_args(args)
# Step 2: model, criterion, optimizer, scheduler
pfld_backbone = PFLDInference().to(device)
auxiliarynet = AuxiliaryNet().to(device)
criterion = PFLDLoss()
optimizer = torch.optim.Adam([{
'params': pfld_backbone.parameters()
}, {
'params': auxiliarynet.parameters()
}],
lr=args.base_lr,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='min', patience=args.lr_patience, verbose=True)
if args.resume:
checkpoint = torch.load(args.resume)
auxiliarynet.load_state_dict(checkpoint["auxiliarynet"])
pfld_backbone.load_state_dict(checkpoint["pfld_backbone"])
args.start_epoch = checkpoint["epoch"]
# step 3: data
# argumetion
transform = transforms.Compose([transforms.ToTensor()])
wlfwdataset = WLFWDatasets(args.dataroot, transform)
dataloader = DataLoader(wlfwdataset,
batch_size=args.train_batchsize,
shuffle=True,
num_workers=args.workers,
drop_last=False)
wlfw_val_dataset = WLFWDatasets(args.val_dataroot, transform)
wlfw_val_dataloader = DataLoader(wlfw_val_dataset,
batch_size=args.val_batchsize,
shuffle=False,
num_workers=args.workers)
# step 4: run
writer = SummaryWriter(args.tensorboard)
for epoch in range(args.start_epoch, args.end_epoch + 1):
weighted_train_loss, train_loss = train(dataloader, pfld_backbone,
auxiliarynet, criterion,
optimizer, epoch)
filename = os.path.join(str(args.snapshot),
"checkpoint_epoch_" + str(epoch) + '.pth.tar')
save_checkpoint(
{
'epoch': epoch,
'pfld_backbone': pfld_backbone.state_dict(),
'auxiliarynet': auxiliarynet.state_dict()
}, filename)
val_loss = validate(wlfw_val_dataloader, pfld_backbone, auxiliarynet,
criterion)
scheduler.step(val_loss)
writer.add_scalar('data/weighted_loss', weighted_train_loss, epoch)
writer.add_scalars('data/loss', {
'val loss': val_loss,
'train loss': train_loss
}, epoch)
writer.close()
def parse_args():
parser = argparse.ArgumentParser(description='pfld')
# general
parser.add_argument('-j', '--workers', default=0, type=int)
parser.add_argument('--devices_id', default='0', type=str) #TBD
parser.add_argument('--test_initial', default='false', type=str2bool) #TBD
# training
## -- optimizer
parser.add_argument('--base_lr', default=0.0001, type=int)
parser.add_argument('--weight-decay', '--wd', default=1e-6, type=float)
# -- lr
parser.add_argument("--lr_patience", default=40, type=int)
# -- epoch
parser.add_argument('--start_epoch', default=1, type=int)
parser.add_argument('--end_epoch', default=500, type=int)
# -- snapshot、tensorboard log and checkpoint
parser.add_argument('--snapshot',
default='./checkpoint/snapshot/',
type=str,
metavar='PATH')
parser.add_argument('--log_file',
default="./checkpoint/train.logs",
type=str)
parser.add_argument('--tensorboard',
default="./checkpoint/tensorboard",
type=str)
parser.add_argument(
'--resume',
default='',
type=str,
metavar='PATH')
# --dataset
parser.add_argument('--dataroot',
default='./data/train_data/list.txt',
type=str,
metavar='PATH')
parser.add_argument('--val_dataroot',
default='./data/test_data/list.txt',
type=str,
metavar='PATH')
parser.add_argument('--train_batchsize', default=256, type=int)
parser.add_argument('--val_batchsize', default=256, type=int)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
main(args)
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