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run_pair_cls.py
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run_pair_cls.py
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#-------------------------------------------------------------------------------
# Name: run_pair_cls.py
# Purpose: Train a network (bonenet) to predict pair-wise connectivity cost
# RigNet Copyright 2020 University of Massachusetts
# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
#-------------------------------------------------------------------------------
import sys
sys.path.append("./")
import os
import numpy as np
import shutil
import argparse
import torch
import torch.backends.cudnn as cudnn
from torch_geometric.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from models.PairCls_GCN import PairCls
from datasets.skeleton_dataset import GraphDataset
from utils.os_utils import isdir, mkdir_p, isfile
from utils.log_utils import AverageMeter
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar', snapshot=None):
filepath = os.path.join(checkpoint, filename)
torch.save(state, filepath)
if snapshot and state['epoch'] % snapshot == 0:
shutil.copyfile(filepath, os.path.join(checkpoint, 'checkpoint_{}.pth.tar'.format(state['epoch'])))
if is_best:
shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar'))
def main(args):
global device
lowest_loss = 1e20
# create checkpoint dir and log dir
if not isdir(args.checkpoint):
print("Create new checkpoint folder " + args.checkpoint)
mkdir_p(args.checkpoint)
if not args.resume:
if isdir(args.logdir):
shutil.rmtree(args.logdir)
mkdir_p(args.logdir)
# create model
model = PairCls()
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
args.start_epoch = checkpoint['epoch']
lowest_loss = checkpoint['lowest_loss']
print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters()) / 1000000.0))
train_loader = DataLoader(GraphDataset(root=args.train_folder), batch_size=args.train_batch, shuffle=True, follow_batch=['joints', 'pairs'])
val_loader = DataLoader(GraphDataset(root=args.val_folder), batch_size=args.test_batch, shuffle=False, follow_batch=['joints', 'pairs'])
test_loader = DataLoader(GraphDataset(root=args.test_folder), batch_size=args.test_batch, shuffle=False, follow_batch=['joints', 'pairs'])
if args.evaluate:
print('\nEvaluation only')
test_loss = test(test_loader, model, args, save_result=True, best_epoch=args.start_epoch)
print('test_loss {:8f}'.format(test_loss))
return
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.schedule, gamma=args.gamma)
logger = SummaryWriter(log_dir=args.logdir)
for epoch in range(args.start_epoch, args.epochs):
lr = scheduler.get_last_lr()
print('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr[0]))
train_loss = train(train_loader, model, optimizer, args)
val_loss = test(val_loader, model, args)
test_loss = test(test_loader, model, args, best_epoch=epoch+1)
scheduler.step()
print('Epoch{:d}. train_loss: {:.6f}.'.format(epoch + 1, train_loss))
print('Epoch{:d}. val_loss: {:.6f}.'.format(epoch + 1, val_loss))
print('Epoch{:d}. test_loss: {:.6f}.'.format(epoch + 1, test_loss))
# remember best acc and save checkpoint
is_best = val_loss < lowest_loss
lowest_loss = min(val_loss, lowest_loss)
save_checkpoint({'epoch': epoch + 1, 'state_dict': model.state_dict(), 'lowest_loss': lowest_loss, 'optimizer': optimizer.state_dict()},
is_best, checkpoint=args.checkpoint)
info = {'train_loss': train_loss, 'val_loss': val_loss, 'test_loss': test_loss}
for tag, value in info.items():
logger.add_scalar(tag, value, epoch+1)
print("=> loading checkpoint '{}'".format(os.path.join(args.checkpoint, 'model_best.pth.tar')))
checkpoint = torch.load(os.path.join(args.checkpoint, 'model_best.pth.tar'))
best_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(os.path.join(args.checkpoint, 'model_best.pth.tar'), best_epoch))
test_loss = test(test_loader, model, args, save_result=True, best_epoch=best_epoch)
print('Best epoch:\n test_loss {:8f}'.format(test_loss))
def train(train_loader, model, optimizer, args):
global device
model.train() # switch to train mode
loss_meter = AverageMeter()
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
pre_label, label = model(data)
loss1 = torch.nn.functional.binary_cross_entropy_with_logits(pre_label, label, reduction='none')
topk_val, _ = torch.topk(loss1.view(-1), k=int(args.topk * len(pre_label)), dim=0, sorted=False)
loss2 = topk_val.mean()
loss = loss1.mean() + loss2
loss.backward()
optimizer.step()
loss_meter.update(loss.item())
return loss_meter.avg
def test(test_loader, model, args, save_result=False, best_epoch=None):
global device
model.eval() # switch to test mode
if save_result:
output_folder = 'results/{:s}/best_{:d}/'.format(args.checkpoint.split('/')[-1], best_epoch)
if not os.path.exists(output_folder):
mkdir_p(output_folder)
loss_meter = AverageMeter()
for data in test_loader:
data = data.to(device)
with torch.no_grad():
pre_label, label = model(data)
loss = torch.nn.functional.binary_cross_entropy_with_logits(pre_label, label.float())
if save_result:
connect_prob = torch.sigmoid(pre_label)
acc_joints = 0
for i in range(len(torch.unique(data.batch))):
pair_idx = data.pairs[data.pairs_batch==i].long()
connect_prob_i = connect_prob[data.pairs_batch==i]
num_joint = len(data.joints[data.joints_batch==i])
cost_matrix = np.zeros((num_joint, num_joint))
pair_idx = pair_idx.to("cpu").numpy()
cost_matrix[pair_idx[:, 0]-acc_joints, pair_idx[:, 1]-acc_joints] = connect_prob_i.data.cpu().numpy().squeeze(axis=1)
cost_matrix = 1 - cost_matrix
print('saving: {:s}'.format(str(data.name[i].item()) + '_cost.npy'))
np.save(os.path.join(output_folder, str(data.name[i].item()) + '_cost.npy'), cost_matrix)
acc_joints += num_joint
loss_meter.update(loss.item())
return loss_meter.avg
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='joint connectivity')
parser.add_argument('--arch', default='paircls') # paircls_fc, paircls_nogt, paircls_nogs
parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)')
parser.add_argument('--weight_decay', '--wd', default=1e-4, type=float, metavar='W',
help='weight decay (default: 1e-4)')
parser.add_argument('--gamma', type=float, default=0.2, help='LR is multiplied by gamma on schedule.')
parser.add_argument('-j', '--workers', default=1, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=300, type=int, metavar='N', help='number of total epochs to run')
parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, metavar='LR', help='initial learning rate')
parser.add_argument('--schedule', type=int, nargs='+', default=[200], help='Decrease learning rate at these epochs.')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set')
####################################################################################################################
parser.add_argument('--train_batch', default=2, type=int, metavar='N', help='train batchsize')
parser.add_argument('--test_batch', default=2, type=int, metavar='N', help='test batchsize')
parser.add_argument('-c', '--checkpoint', default='checkpoints/connect_test', type=str, metavar='PATH',
help='path to save checkpoint (default: checkpoint)')
parser.add_argument('--logdir', default='logs/connect_test', type=str, metavar='LOG', help='directory to save logs')
parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)')
parser.add_argument('--train_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/train/',
type=str, help='folder of training data')
parser.add_argument('--val_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/val/',
type=str, help='folder of validation data')
parser.add_argument('--test_folder', default='/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/test/',
type=str, help='folder of testing data')
parser.add_argument('--topk', default=0.3, type=float, help='topk ratio for ohem')
print(parser.parse_args())
main(parser.parse_args())