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main.py
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main.py
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import argparse
import os
import shutil
import time
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
from torchvision.utils import make_grid
import flow_transforms
import models
import datasets
from loss import compute_semantic_pos_loss
import datetime
from tensorboardX import SummaryWriter
from train_util import *
'''
Main code for training
author: Fengting
Last modification: March 8th, 2019
'''
model_names = sorted(name for name in models.__dict__
if not name.startswith("__"))
dataset_names = sorted(name for name in datasets.__all__)
parser = argparse.ArgumentParser(description='PyTorch SpixelFCN Training on BSDS500',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# ================ training setting ====================
parser.add_argument('--dataset', metavar='DATASET', default='BSD500', choices=dataset_names,
help='dataset type : ' + ' | '.join(dataset_names))
parser.add_argument('--arch', '-a', metavar='ARCH', default='SpixelNet1l_bn', help='model architecture')
parser.add_argument('--data', metavar='DIR',default='', help='path to input dataset')
parser.add_argument('--savepath',default='', help='path to save ckpt')
parser.add_argument('--train_img_height', '-t_imgH', default=208, type=int, help='img height')
parser.add_argument('--train_img_width', '-t_imgW', default=208, type=int, help='img width')
parser.add_argument('--input_img_height', '-v_imgH', default=320, type=int, help='img height_must be 16*n') #
parser.add_argument('--input_img_width', '-v_imgW', default=320, type=int, help='img width must be 16*n')
# ======== learning schedule ================
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',help='number of data loading workers')
parser.add_argument('--epochs', default=3000000, type=int, metavar='N', help='number of total epoches, make it big enough to follow the iteration maxmium')
parser.add_argument('--start_epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)')
parser.add_argument('--epoch_size', default= 6000, help='choose any value > 408 to use all the train and val data')
parser.add_argument('-b', '--batch-size', default=4, type=int, metavar='N', help='mini-batch size')
parser.add_argument('--solver', default='adam',choices=['adam','sgd'], help='solver algorithms, we use adam')
parser.add_argument('--lr', '--learning-rate', default=0.00005, type=float,metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum for sgd, alpha parameter for adam')
parser.add_argument('--beta', default=0.999, type=float, metavar='M', help='beta parameter for adam')
parser.add_argument('--weight_decay', '--wd', default=4e-4, type=float, metavar='W', help='weight decay')
parser.add_argument('--bias_decay', default=0, type=float, metavar='B', help='bias decay, we never use it')
parser.add_argument('--milestones', default=[200000], metavar='N', nargs='*', help='epochs at which learning rate is divided by 2')
parser.add_argument('--additional_step', default= 100000, help='the additional iteration, after lr decay')
# ============== hyper-param ====================
parser.add_argument('--pos_weight', '-p_w', default=0.003, type=float, help='weight of the pos term')
parser.add_argument('--downsize', default=16, type=float,help='grid cell size for superpixel training ')
# ================= other setting ===================
parser.add_argument('--gpu', default= '0', type=str, help='gpu id')
parser.add_argument('--print_freq', '-p', default=10, type=int, help='print frequency (step)')
parser.add_argument('--record_freq', '-rf', default=5, type=int, help='record frequency (epoch)')
parser.add_argument('--label_factor', default=5, type=int, help='constant multiplied to label index for viz.')
parser.add_argument('--pretrained', dest='pretrained', default=None, help='path to pre-trained model')
parser.add_argument('--no-date', action='store_true', help='don\'t append date timestamp to folder' )
best_EPE = -1
n_iter = 0
args = parser.parse_args()
# !----- NOTE the current code does not support cpu training -----!
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if torch.cuda.is_available():
device = torch.device("cuda")
else:
print('Current code does not support CPU training! Sorry about that.')
exit(1)
def main():
global args, best_EPE, save_path, intrinsic
# ============= savor setting ===================
save_path = '{}_{}_{}epochs{}_b{}_lr{}_posW{}'.format(
args.arch,
args.solver,
args.epochs,
'_epochSize'+str(args.epoch_size) if args.epoch_size > 0 else '',
args.batch_size,
args.lr,
args.pos_weight,
)
if not args.no_date:
timestamp = datetime.datetime.now().strftime("%y_%m_%d_%H_%M")
else:
timestamp = ''
save_path = os.path.abspath(args.savepath) + '/' + os.path.join(args.dataset, save_path + '_' + timestamp )
# ========== Data loading code ==============
input_transform = transforms.Compose([
flow_transforms.ArrayToTensor(),
transforms.Normalize(mean=[0,0,0], std=[255,255,255]),
transforms.Normalize(mean=[0.411,0.432,0.45], std=[1,1,1])
])
val_input_transform = transforms.Compose([
flow_transforms.ArrayToTensor(),
transforms.Normalize(mean=[0, 0, 0], std=[255, 255, 255]),
transforms.Normalize(mean=[0.411, 0.432, 0.45], std=[1, 1, 1])
])
target_transform = transforms.Compose([
flow_transforms.ArrayToTensor(),
])
co_transform = flow_transforms.Compose([
flow_transforms.RandomCrop((args.train_img_height ,args.train_img_width)),
flow_transforms.RandomVerticalFlip(),
flow_transforms.RandomHorizontalFlip()
])
print("=> loading img pairs from '{}'".format(args.data))
train_set, val_set = datasets.__dict__[args.dataset](
args.data,
transform=input_transform,
val_transform = val_input_transform,
target_transform=target_transform,
co_transform=co_transform
)
print('{} samples found, {} train samples and {} val samples '.format(len(val_set)+len(train_set),
len(train_set),
len(val_set)))
train_loader = torch.utils.data.DataLoader(
train_set, batch_size=args.batch_size,
num_workers=args.workers, pin_memory=True, shuffle=True, drop_last=True)
val_loader = torch.utils.data.DataLoader(
val_set, batch_size=args.batch_size,
num_workers=args.workers, pin_memory=True, shuffle=False, drop_last=True)
# ============== create model ====================
if args.pretrained:
network_data = torch.load(args.pretrained)
args.arch = network_data['arch']
print("=> using pre-trained model '{}'".format(args.arch))
else:
network_data = None
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]( data = network_data).cuda()
model = torch.nn.DataParallel(model).cuda()
cudnn.benchmark = True
#=========== creat optimizer, we use adam by default ==================
assert(args.solver in ['adam', 'sgd'])
print('=> setting {} solver'.format(args.solver))
param_groups = [{'params': model.module.bias_parameters(), 'weight_decay': args.bias_decay},
{'params': model.module.weight_parameters(), 'weight_decay': args.weight_decay}]
if args.solver == 'adam':
optimizer = torch.optim.Adam(param_groups, args.lr,
betas=(args.momentum, args.beta))
elif args.solver == 'sgd':
optimizer = torch.optim.SGD(param_groups, args.lr,
momentum=args.momentum)
# for continues training
if args.pretrained and ('dataset' in network_data):
if args.pretrained and args.dataset == network_data['dataset'] :
optimizer.load_state_dict(network_data['optimizer'])
best_EPE = network_data['best_EPE']
args.start_epoch = network_data['epoch']
save_path = os.path.dirname(args.pretrained)
print('=> will save everything to {}'.format(save_path))
if not os.path.exists(save_path):
os.makedirs(save_path)
train_writer = SummaryWriter(os.path.join(save_path, 'train'))
val_writer = SummaryWriter(os.path.join(save_path, 'val'))
# spixelID: superpixel ID for visualization,
# XY_feat: the coordinate feature for position loss term
spixelID, XY_feat_stack = init_spixel_grid(args)
val_spixelID, val_XY_feat_stack = init_spixel_grid(args, b_train=False)
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train_avg_slic, train_avg_sem, iteration = train(train_loader, model, optimizer, epoch,
train_writer, spixelID, XY_feat_stack )
if epoch % args.record_freq == 0:
train_writer.add_scalar('Mean avg_slic', train_avg_slic, epoch)
# evaluate on validation set and save the module( and choose the best)
with torch.no_grad():
avg_slic, avg_sem = validate(val_loader, model, epoch, val_writer, val_spixelID, val_XY_feat_stack)
if epoch % args.record_freq == 0:
val_writer.add_scalar('Mean avg_slic', avg_slic, epoch)
rec_dict = {
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.module.state_dict(),
'best_EPE': best_EPE,
'optimizer': optimizer.state_dict(),
'dataset': args.dataset
}
if (iteration) >= (args.milestones[-1] + args.additional_step):
save_checkpoint(rec_dict, is_best =False, filename='%d_step.tar' % iteration)
print("Train finished!")
break
if best_EPE < 0:
best_EPE = avg_sem
is_best = avg_sem < best_EPE
best_EPE = min(avg_sem, best_EPE)
save_checkpoint(rec_dict, is_best)
def train(train_loader, model, optimizer, epoch, train_writer, init_spixl_map_idx, xy_feat):
global n_iter, args, intrinsic
batch_time = AverageMeter()
data_time = AverageMeter()
total_loss = AverageMeter()
losses_sem = AverageMeter()
losses_pos = AverageMeter()
epoch_size = len(train_loader) if args.epoch_size == 0 else min(len(train_loader), args.epoch_size)
# switch to train mode
model.train()
end = time.time()
iteration = 0
for i, (input, label) in enumerate(train_loader):
iteration = i + epoch * epoch_size
# ========== adjust lr if necessary ===============
if (iteration + 1) in args.milestones:
state_dict = optimizer.state_dict()
for param_group in state_dict['param_groups']:
param_group['lr'] = args.lr * ((0.5) ** (args.milestones.index(iteration + 1) + 1))
optimizer.load_state_dict(state_dict)
# ========== complete data loading ================
label_1hot = label2one_hot_torch(label.to(device), C=50) # set C=50 as SSN does
input_gpu = input.to(device)
LABXY_feat_tensor = build_LABXY_feat(label_1hot, xy_feat) # B* (50+2 )* H * W
torch.cuda.synchronize()
data_time.update(time.time() - end)
# ========== predict association map ============
output = model(input_gpu)
slic_loss, loss_sem, loss_pos = compute_semantic_pos_loss( output, LABXY_feat_tensor,
pos_weight= args.pos_weight, kernel_size=args.downsize)
# ========= back propagate ===============
optimizer.zero_grad()
slic_loss.backward()
optimizer.step()
# ======== measure batch time ===========
torch.cuda.synchronize()
batch_time.update(time.time() - end)
end = time.time()
# =========== record and display the loss ===========
# record loss and EPE
total_loss.update(slic_loss.item(), input_gpu.size(0))
losses_sem.update(loss_sem.item(), input_gpu.size(0))
losses_pos.update(loss_pos.item(), input_gpu.size(0))
if i % args.print_freq == 0:
print('train Epoch: [{0}][{1}/{2}]\t Time {3}\t Data {4}\t Total_loss {5}\t Loss_sem {6}\t Loss_pos {7}\t'
.format(epoch, i, epoch_size, batch_time, data_time, total_loss, losses_sem, losses_pos))
train_writer.add_scalar('Train_loss', slic_loss.item(), i + epoch*epoch_size)
train_writer.add_scalar('learning rate',optimizer.param_groups[0]['lr'], i + epoch * epoch_size)
n_iter += 1
if i >= epoch_size:
break
if (iteration) >= (args.milestones[-1] + args.additional_step):
break
# =========== write information to tensorboard ===========
if epoch % args.record_freq == 0:
train_writer.add_scalar('Train_loss_epoch', slic_loss.item(), epoch )
train_writer.add_scalar('loss_sem', loss_sem.item(), epoch )
train_writer.add_scalar('loss_pos', loss_pos.item(), epoch)
#save image
mean_values = torch.tensor([0.411, 0.432, 0.45], dtype=input_gpu.dtype).view(3, 1, 1)
input_l_save = (make_grid((input + mean_values).clamp(0, 1), nrow=args.batch_size))
label_save = make_grid(args.label_factor * label)
train_writer.add_image('Input', input_l_save, epoch)
train_writer.add_image('label', label_save, epoch)
curr_spixl_map = update_spixl_map(init_spixl_map_idx,output)
spixel_lab_save = make_grid(curr_spixl_map, nrow=args.batch_size)[0, :, :]
spixel_viz, _ = get_spixel_image(input_l_save, spixel_lab_save)
train_writer.add_image('Spixel viz', spixel_viz, epoch)
#save associ map, --- for debug only
# _, prob_idx = torch.max(output, dim=1, keepdim=True)
# prob_map_save = make_grid(assign2uint8(prob_idx))
# train_writer.add_image('assigment idx', prob_map_save, epoch)
print('==> write train step %dth to tensorboard' % i)
return total_loss.avg, losses_sem.avg, iteration
def validate(val_loader, model, epoch, val_writer, init_spixl_map_idx, xy_feat):
global n_iter, args, intrinsic
batch_time = AverageMeter()
data_time = AverageMeter()
total_loss = AverageMeter()
losses_sem = AverageMeter()
losses_pos = AverageMeter()
# set the validation epoch-size, we only randomly val. 400 batches during training to save time
epoch_size = min(len(val_loader), 400)
# switch to train mode
model.eval()
end = time.time()
for i, (input, label) in enumerate(val_loader):
# measure data loading time
label_1hot = label2one_hot_torch(label.to(device), C=50)
input_gpu = input.to(device)
LABXY_feat_tensor = build_LABXY_feat(label_1hot, xy_feat) # B* 50+2 * H * W
torch.cuda.synchronize()
data_time.update(time.time() - end)
# compute output
with torch.no_grad():
output = model(input_gpu)
slic_loss, loss_sem, loss_pos = compute_semantic_pos_loss(output, LABXY_feat_tensor,
pos_weight=args.pos_weight, kernel_size=args.downsize)
# measure elapsed time
torch.cuda.synchronize()
batch_time.update(time.time() - end)
end = time.time()
# record loss and EPE
total_loss.update(slic_loss.item(), input_gpu.size(0))
losses_sem.update(loss_sem.item(), input_gpu.size(0))
losses_pos.update(loss_pos.item(), input_gpu.size(0))
if i % args.print_freq == 0:
print('val Epoch: [{0}][{1}/{2}]\t Time {3}\t Data {4}\t Total_loss {5}\t Loss_sem {6}\t Loss_pos {7}\t'
.format(epoch, i, epoch_size, batch_time, data_time, total_loss, losses_sem, losses_pos))
if i >= epoch_size:
break
# ============= write result to tensorboard ======================
if epoch % args.record_freq == 0:
val_writer.add_scalar('Train_loss_epoch', slic_loss.item(), epoch)
val_writer.add_scalar('loss_sem', loss_sem.item(), epoch)
val_writer.add_scalar('loss_pos', loss_pos.item(), epoch)
mean_values = torch.tensor([0.411, 0.432, 0.45], dtype=input_gpu.dtype).view(3, 1, 1)
input_l_save = (make_grid((input + mean_values).clamp(0, 1), nrow=args.batch_size))
curr_spixl_map = update_spixl_map(init_spixl_map_idx, output)
spixel_lab_save = make_grid(curr_spixl_map, nrow=args.batch_size)[0, :, :]
spixel_viz, _ = get_spixel_image(input_l_save, spixel_lab_save)
label_save = make_grid(args.label_factor * label)
val_writer.add_image('Input', input_l_save, epoch)
val_writer.add_image('label', label_save, epoch)
val_writer.add_image('Spixel viz', spixel_viz, epoch)
# --- for debug
# _, prob_idx = torch.max(assign, dim=1, keepdim=True)
# prob_map_save = make_grid(assign2uint8(prob_idx))
# val_writer.add_image('assigment idx level %d' % j, prob_map_save, epoch)
print('==> write val step %dth to tensorboard' % i)
return total_loss.avg, losses_sem.avg
def save_checkpoint(state, is_best, filename='checkpoint.tar'):
torch.save(state, os.path.join(save_path,filename))
if is_best:
shutil.copyfile(os.path.join(save_path,filename), os.path.join(save_path,'model_best.tar'))
if __name__ == '__main__':
main()