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DFAD_nyu_deeplab.py
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DFAD_nyu_deeplab.py
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from __future__ import print_function
import argparse
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
import torchvision
import network
from utils import soft_cross_entropy, kldiv
from utils.visualizer import VisdomPlotter
from utils.misc import pack_images, denormalize
from dataloader import get_dataloader
from utils.stream_metrics import StreamSegMetrics
import random, os
import numpy as np
from PIL import Image
vp = VisdomPlotter('15550', env='DFAD-nyuv2')
def train(args, teacher, student, generator, device, optimizer, epoch):
teacher.eval()
student.train()
generator.train()
optimizer_S, optimizer_G = optimizer
for i in range( args.epoch_itrs ):
for k in range(5):
z = torch.randn( (args.batch_size, args.nz, 1, 1) ).to(device)
optimizer_S.zero_grad()
fake = generator(z).detach()
t_logit = teacher(fake)
s_logit = student(fake)
loss_S = F.l1_loss(s_logit, t_logit.detach()) #(s_logit - t_logit.detach()).abs().mean() #+ kldiv(s_logit, t_logit.detach()) #kldiv(s_logit, t_logit.detach())
loss_S.backward()
optimizer_S.step()
z = torch.randn( (args.batch_size, args.nz, 1, 1) ).to(device)
optimizer_G.zero_grad()
generator.train()
fake = generator(z)
t_logit = teacher(fake)
s_logit = student(fake)
loss_G = -torch.log( F.l1_loss( s_logit, t_logit )+1 )
#loss_G = -F.l1_loss( s_logit, t_logit )
loss_G.backward()
optimizer_G.step()
if i % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tG_Loss: {:.6f} S_loss: {:.6f}'.format(
epoch, i, args.epoch_itrs, 100*float(i)/float(args.epoch_itrs), loss_G.item(), loss_S.item()))
vp.add_scalar('Loss_S', (epoch-1)*args.epoch_itrs+i, loss_S.item())
vp.add_scalar('Loss_G', (epoch-1)*args.epoch_itrs+i, loss_G.item())
def test(args, student, teacher, generator, device, test_loader):
student.eval()
generator.eval()
teacher.eval()
if args.save_img:
os.makedirs('results/nyu-DFAD', exist_ok=True)
seg_metrics = StreamSegMetrics(13)
img_idx = 0
with torch.no_grad():
for i, (data, target) in enumerate(test_loader):
data, target = data.to(device), target.to(device)
z = torch.randn( (data.shape[0], args.nz, 1, 1), device=data.device, dtype=data.dtype )
fake = generator(z)
output = student(data)
if args.save_img:
t_out = teacher(data)
input_imgs = (((data+1)/2)*255).clamp(0,255).detach().cpu().numpy().transpose(0,2,3,1).astype('uint8')
colored_preds = test_loader.dataset.decode_target( output.max(1)[1].detach().cpu().numpy() ).astype('uint8')
colored_teacher_preds = test_loader.dataset.decode_target( t_out.max(1)[1].detach().cpu().numpy() ).astype('uint8')
colored_targets = test_loader.dataset.decode_target( target.detach().cpu().numpy() ).astype('uint8')
for _pred, _img, _target, _tpred in zip( colored_preds, input_imgs, colored_targets, colored_teacher_preds ):
Image.fromarray( _pred ).save('results/nyu-DFAD/%d_pred.png'%img_idx)
Image.fromarray( _img ).save('results/nyu-DFAD/%d_img.png'%img_idx)
Image.fromarray( _target ).save('results/nyu-DFAD/%d_target.png'%img_idx)
Image.fromarray( _tpred ).save('results/nyu-DFAD/%d_teacher.png'%img_idx)
img_idx+=1
if i==0:
t_out = teacher(data)
t_out_onfake = teacher(fake)
s_out_onfake = student(fake)
vp.add_image( 'input', pack_images( ((data+1)/2).clamp(0,1).detach().cpu().numpy() ) )
vp.add_image( 'generated', pack_images( ((fake+1)/2).clamp(0,1).detach().cpu().numpy() ) )
vp.add_image( 'target', pack_images( test_loader.dataset.decode_target(target.cpu().numpy()), channel_last=True ).astype('uint8') )
vp.add_image( 'pred', pack_images( test_loader.dataset.decode_target(output.max(1)[1].detach().cpu().numpy().astype('uint8')), channel_last=True ).astype('uint8') )
vp.add_image( 'teacher', pack_images( test_loader.dataset.decode_target(t_out.max(1)[1].detach().cpu().numpy().astype('uint8')), channel_last=True ).astype('uint8') )
vp.add_image( 'teacher-onfake', pack_images( test_loader.dataset.decode_target(t_out_onfake.max(1)[1].detach().cpu().numpy().astype('uint8')), channel_last=True ).astype('uint8') )
vp.add_image( 'student-onfake', pack_images( test_loader.dataset.decode_target(s_out_onfake.max(1)[1].detach().cpu().numpy().astype('uint8')), channel_last=True ).astype('uint8') )
seg_metrics.update(output.max(1)[1].detach().cpu().numpy().astype('uint8'), target.detach().cpu().numpy().astype('uint8'))
results = seg_metrics.get_results()
print('\nTest set: Acc= %.6f, mIoU: %.6f\n'%(results['Overall Acc'],results['Mean IoU']))
return results
def main():
# Training settings
parser = argparse.ArgumentParser(description='DFAD NYUv2')
parser.add_argument('--batch_size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test_batch_size', type=int, default=9, metavar='N',
help='input batch size for testing (default: 9)')
parser.add_argument('--epochs', type=int, default=300, metavar='N',
help='number of epochs to train (default: 300)')
parser.add_argument('--epoch_itrs', type=int, default=50)
parser.add_argument('--lr_S', type=float, default=0.1, metavar='LR',
help='learning rate (default: 0.1)')
parser.add_argument('--lr_G', type=float, default=1e-3,
help='learning rate (default: 0.1)')
parser.add_argument('--data_root', type=str, default='data')
parser.add_argument('--dataset', type=str, default='nyuv2', choices=['nyuv2'],
help='dataset name (default: nyuv2)')
parser.add_argument('--model', type=str, default='deeplabv3_resnet50', choices=['deeplabv3_resnet50'],
help='model name (default: deeplabv3_resnet50)')
parser.add_argument('--weight_decay', type=float, default=5e-5)
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--no_cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--ckpt', type=str, default='checkpoint/teacher/nyuv2-deeplabv3_resnet50-256.pt')
parser.add_argument('--stu_ckpt', type=str, default=None)
parser.add_argument('--log_interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--nz', type=int, default=256)
parser.add_argument('--test_only', action='store_true', default=False)
parser.add_argument('--download', action='store_true', default=False)
parser.add_argument('--step_size', type=int, default=100)
parser.add_argument('--scheduler', action='store_true', default=False)
parser.add_argument('--save_img', action='store_true', default=False)
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
print(args)
_, test_loader = get_dataloader(args)
teacher = network.segmentation.deeplabv3.deeplabv3_resnet50(num_classes=13)
student = network.segmentation.deeplabv3.deeplabv3_mobilenet(num_classes=13, dropout_p=0.5, pretrained_backbone=False)
generator = network.gan.GeneratorB(nz=args.nz, nc=3, img_size=128)
teacher.load_state_dict( torch.load( args.ckpt ) )
print("Teacher restored from %s"%(args.ckpt))
if args.stu_ckpt is not None:
student.load_state_dict( torch.load( args.stu_ckpt ) )
generator.load_state_dict( torch.load( args.stu_ckpt[:-3]+'-generator.pt' ) )
print('student loaded from %s'%args.stu_ckpt)
teacher = teacher.to(device)
student = student.to(device)
generator = generator.to(device)
teacher.eval()
optimizer_S = optim.SGD( student.parameters(), lr=args.lr_S, weight_decay=args.weight_decay, momentum=0.9)
optimizer_G = optim.Adam( generator.parameters(), lr=args.lr_G)
if args.scheduler:
scheduler_S = optim.lr_scheduler.StepLR(optimizer_S, args.step_size, gamma=0.3)
scheduler_G = optim.lr_scheduler.StepLR(optimizer_G, args.step_size, gamma=0.3)
best_result = 0
if args.test_only:
results = test(args, student, teacher, generator, device, test_loader)
return
for epoch in range(1, args.epochs + 1):
# Train
train(args, teacher=teacher, student=student, generator=generator, device=device, optimizer=[optimizer_S, optimizer_G], epoch=epoch)
# Test
results = test(args, student, teacher, generator, device, test_loader)
if results['Mean IoU']>best_result:
best_result = results['Mean IoU']
torch.save(student.state_dict(),"checkpoint/student/%s-%s.pt"%('nyuv2', 'deeplabv3_mobilenet'))
torch.save(generator.state_dict(),"checkpoint/student/%s-%s-generator.pt"%('nyuv2', 'deeplabv3_mobilenet'))
vp.add_scalar('mIoU', epoch, results['Mean IoU'])
if args.scheduler:
scheduler_S.step()
scheduler_G.step()
print("Best mIoU=%.6f"%best_result)
import csv
os.makedirs('log', exist_ok=True)
with open('log/DFAD-nyuv2.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(miou_list)
if __name__ == '__main__':
main()