def load_ckp(ckp_path, model):
state = torch.load(ckp_path)
model.load_state_dict(state['state_dict'])
print("model load from %s" % ckp_path)
if __name__ == "__main__":
torch.manual_seed(SEED)
device = torch.device(f'cuda:{gpus[0]}' if torch.cuda.is_available() else 'cpu')
print("Loading test dataset...")
test_data = PointNetDataset("./dataset/modelnet40_normal_resampled", train=1)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)
model = PointNet().to(device=device)
if ckp_path:
load_ckp(ckp_path, model)
model = model.to(device)
model.eval()
with torch.no_grad():
accs = []
gt_ys = []
pred_ys = []
for x, y in test_loader:
x = x.to(device)
y = y.to(device)
# TODO: put x into network and get out
out =
# TODO: get pred_y from out
def train(args, io):
train_loader = DataLoader(ModelNet40(partition='train',
num_points=args.num_points),
num_workers=8,
batch_size=args.batch_size,
shuffle=True,
drop_last=True)
test_loader = DataLoader(ModelNet40(partition='test',
num_points=args.num_points),
num_workers=8,
batch_size=args.test_batch_size,
shuffle=True,
drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
#Try to load models
if args.model == 'pointnet':
model = PointNet(args).to(device)
elif args.model == 'dgcnn':
model = DGCNN(args).to(device)
elif args.model == 'ssg':
model = PointNet2SSG(output_classes=40, dropout_prob=args.dropout)
model.to(device)
elif args.model == 'msg':
model = PointNet2MSG(output_classes=40, dropout_prob=args.dropout)
model.to(device)
elif args.model == 'ognet':
# [64,128,256,512]
model = Model_dense(20,
args.feature_dims, [512],
output_classes=40,
init_points=768,
input_dims=3,
dropout_prob=args.dropout,
id_skip=args.id_skip,
drop_connect_rate=args.drop_connect_rate,
cluster='xyzrgb',
pre_act=args.pre_act,
norm=args.norm_layer)
if args.efficient:
model = ModelE_dense(20,
args.feature_dims, [512],
output_classes=40,
init_points=768,
input_dims=3,
dropout_prob=args.dropout,
id_skip=args.id_skip,
drop_connect_rate=args.drop_connect_rate,
cluster='xyzrgb',
pre_act=args.pre_act,
norm=args.norm_layer,
gem=args.gem,
ASPP=args.ASPP)
model.to(device)
elif args.model == 'ognet-small':
# [48,96,192,384]
model = Model_dense(20,
args.feature_dims, [512],
output_classes=40,
init_points=768,
input_dims=3,
dropout_prob=args.dropout,
id_skip=args.id_skip,
drop_connect_rate=args.drop_connect_rate,
cluster='xyzrgb',
pre_act=args.pre_act,
norm=args.norm_layer)
model.to(device)
else:
raise Exception("Not implemented")
print(str(model))
model = nn.DataParallel(model)
print("Let's use", torch.cuda.device_count(), "GPUs!")
if args.use_sgd:
print("Use SGD")
opt = optim.SGD(model.parameters(),
lr=args.lr * 100,
momentum=args.momentum,
weight_decay=1e-4)
scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=args.lr)
else:
print("Use Adam")
opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4)
scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=0.01 * args.lr)
criterion = cal_loss
best_test_acc = 0
best_avg_per_class_acc = 0
warm_up = 0.1 # We start from the 0.1*lrRate
warm_iteration = round(
len(ModelNet40(partition='train', num_points=args.num_points)) /
args.batch_size) * args.warm_epoch # first 5 epoch
for epoch in range(args.epochs):
scheduler.step()
####################
# Train
####################
train_loss = 0.0
count = 0.0
model.train()
train_pred = []
train_true = []
for data, label in train_loader:
data, label = data.to(device), label.to(device).squeeze()
batch_size = data.size()[0]
opt.zero_grad()
if args.model == 'ognet' or args.model == 'ognet-small' or args.model == 'ssg' or args.model == 'msg':
logits = model(data, data)
else:
data = data.permute(0, 2, 1)
logits = model(data)
loss = criterion(logits, label)
if epoch < args.warm_epoch:
warm_up = min(1.0, warm_up + 0.9 / warm_iteration)
loss *= warm_up
loss.backward()
opt.step()
preds = logits.max(dim=1)[1]
count += batch_size
train_loss += loss.item() * batch_size
train_true.append(label.cpu().numpy())
train_pred.append(preds.detach().cpu().numpy())
train_true = np.concatenate(train_true)
train_pred = np.concatenate(train_pred)
outstr = 'Train %d, loss: %.6f, train acc: %.6f, train avg acc: %.6f' % (
epoch, train_loss * 1.0 / count,
metrics.accuracy_score(train_true, train_pred),
metrics.balanced_accuracy_score(train_true, train_pred))
io.cprint(outstr)
####################
# Test
####################
test_loss = 0.0
count = 0.0
model.eval()
test_pred = []
test_true = []
for data, label in test_loader:
data, label = data.to(device), label.to(device).squeeze()
batch_size = data.size()[0]
if args.model == 'ognet' or args.model == 'ognet-small' or args.model == 'ssg' or args.model == 'msg':
logits = model(data, data)
else:
data = data.permute(0, 2, 1)
logits = model(data)
loss = criterion(logits, label)
preds = logits.max(dim=1)[1]
count += batch_size
test_loss += loss.item() * batch_size
test_true.append(label.cpu().numpy())
test_pred.append(preds.detach().cpu().numpy())
test_true = np.concatenate(test_true)
test_pred = np.concatenate(test_pred)
test_acc = metrics.accuracy_score(test_true, test_pred)
avg_per_class_acc = metrics.balanced_accuracy_score(
test_true, test_pred)
outstr = 'Test %d, loss: %.6f, test acc: %.6f, test avg acc: %.6f' % (
epoch, test_loss * 1.0 / count, test_acc, avg_per_class_acc)
io.cprint(outstr)
if test_acc + avg_per_class_acc >= best_test_acc + best_avg_per_class_acc:
best_test_acc = test_acc
best_avg_per_class_acc = avg_per_class_acc
print('This is the current best.')
torch.save(model.state_dict(),
'checkpoints/%s/models/model.t7' % args.exp_name)
def train(modelin=args.model, modelout=args.out,device=args.device,opt=args.opt):
# define model, dataloader, 3dmm eigenvectors, optimization method
calib_net = PointNet(n=1)
sfm_net = PointNet(n=199)
if modelin != "":
calib_path = os.path.join('model','calib_' + modelin)
sfm_path = os.path.join('model','sfm_' + modelin)
pretrained1 = torch.load(calib_path)
pretrained2 = torch.load(sfm_path)
calib_dict = calib_net.state_dict()
sfm_dict = sfm_net.state_dict()
pretrained1 = {k: v for k,v in pretrained1.items() if k in calib_dict}
pretrained2 = {k: v for k,v in pretrained2.items() if k in sfm_dict}
calib_dict.update(pretrained1)
sfm_dict.update(pretrained2)
calib_net.load_state_dict(pretrained1)
sfm_net.load_state_dict(pretrained2)
calib_net.to(device=device)
sfm_net.to(device=device)
opt1 = torch.optim.Adam(calib_net.parameters(),lr=1e-3)
opt2 = torch.optim.Adam(sfm_net.parameters(),lr=1e-3)
# dataloader
data = dataloader.Data()
loader = data.batchloader
batch_size = data.batchsize
# mean shape and eigenvectors for 3dmm
mu_lm = torch.from_numpy(data.mu_lm).float()#.to(device=device)
mu_lm[:,2] = mu_lm[:,2] * -1
mu_lm = torch.stack(batch_size * [mu_lm.to(device=device)])
shape = mu_lm
lm_eigenvec = torch.from_numpy(data.lm_eigenvec).float().to(device=device)
lm_eigenvec = torch.stack(batch_size * [lm_eigenvec])
M = data.M
N = data.N
# main training loop
for epoch in itertools.count():
for j,batch in enumerate(loader):
# get the input and gt values
x_cam_gt = batch['x_cam_gt'].to(device=device)
shape_gt = batch['x_w_gt'].to(device=device)
fgt = batch['f_gt'].to(device=device)
x_img = batch['x_img'].to(device=device)
#beta_gt = batch['beta_gt'].to(device=device)
#x_img_norm = batch['x_img_norm']
x_img_gt = batch['x_img_gt'].to(device=device).permute(0,2,1,3)
batch_size = fgt.shape[0]
one = torch.ones(batch_size,M*N,1).to(device=device)
x_img_one = torch.cat([x_img,one],dim=2)
x_cam_pt = x_cam_gt.permute(0,1,3,2).reshape(batch_size,6800,3)
x = x_img.permute(0,2,1)
#x = x_img.permute(0,2,1).reshape(batch_size,2,M,N)
ptsI = x_img_one.reshape(batch_size,M,N,3).permute(0,1,3,2)[:,:,:2,:]
# if just optimizing
if not opt:
# calibration
f = calib_net(x) + 300
K = torch.zeros((batch_size,3,3)).float().to(device=device)
K[:,0,0] = f.squeeze()
K[:,1,1] = f.squeeze()
K[:,2,2] = 1
# sfm
betas = sfm_net(x)
betas = betas.unsqueeze(-1)
shape = mu_lm + torch.bmm(lm_eigenvec,betas).squeeze().view(batch_size,N,3)
opt1.zero_grad()
opt2.zero_grad()
f_error = torch.mean(torch.abs(f - fgt))
#error2d = torch.mean(torch.abs(pred - x_img_gt))
error3d = torch.mean(torch.abs(shape - shape_gt))
error = f_error + error3d
error.backward()
opt1.step()
opt2.step()
print(f"f_error: {f_error.item():.3f} | error3d: {error3d.item():.3f} | f/fgt: {f[0].item():.1f}/{fgt[0].item():.1f} | f/fgt: {f[1].item():.1f}/{fgt[1].item():.1f} | f/fgt: {f[2].item():.1f}/{fgt[2].item():.1f} | f/fgt: {f[3].item():.1f}/{fgt[3].item():.1f} ")
continue
# get shape error from image projection
print(f"f/fgt: {f[0].item():.3f}/{fgt[0].item():.3f} | rmse: {rmse:.3f} | f_rel: {f_error.item():.4f} | loss1: {loss1.item():.3f} | loss2: {loss2.item():.3f}")
# save model and increment weight decay
print("saving!")
torch.save(sfm_net.state_dict(), os.path.join('model','sfm_'+modelout))
torch.save(calib_net.state_dict(), os.path.join('model','calib_'+modelout))
test(modelin=args.out,outfile=args.out,optimize=False)
def test(args, io):
test_loader = DataLoader(ModelNet40(partition='test',
num_points=args.num_points),
batch_size=args.test_batch_size,
shuffle=True,
drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
#Try to load models
if args.model == 'pointnet':
model = PointNet(args).to(device)
elif args.model == 'dgcnn':
model = DGCNN(args).to(device)
elif args.model == 'ssg':
model = PointNet2SSG(output_classes=40, dropout_prob=0)
model.to(device)
elif args.model == 'msg':
model = PointNet2MSG(output_classes=40, dropout_prob=0)
model.to(device)
elif args.model == 'ognet':
# [64,128,256,512]
model = Model_dense(20,
args.feature_dims, [512],
output_classes=40,
init_points=768,
input_dims=3,
dropout_prob=args.dropout,
id_skip=args.id_skip,
drop_connect_rate=args.drop_connect_rate,
cluster='xyzrgb',
pre_act=args.pre_act,
norm=args.norm_layer)
if args.efficient:
model = ModelE_dense(20,
args.feature_dims, [512],
output_classes=40,
init_points=768,
input_dims=3,
dropout_prob=args.dropout,
id_skip=args.id_skip,
drop_connect_rate=args.drop_connect_rate,
cluster='xyzrgb',
pre_act=args.pre_act,
norm=args.norm_layer,
gem=args.gem,
ASPP=args.ASPP)
model.to(device)
elif args.model == 'ognet-small':
# [48,96,192,384]
model = Model_dense(20,
args.feature_dims, [512],
output_classes=40,
init_points=768,
input_dims=3,
dropout_prob=args.dropout,
id_skip=args.id_skip,
drop_connect_rate=args.drop_connect_rate,
cluster='xyzrgb',
pre_act=args.pre_act,
norm=args.norm_layer)
model.to(device)
else:
raise Exception("Not implemented")
try:
model.load_state_dict(torch.load(args.model_path))
except:
model = nn.DataParallel(model)
model.load_state_dict(torch.load(args.model_path))
model = model.eval()
model = model.module
batch0, label0 = next(iter(test_loader))
batch0 = batch0[0].unsqueeze(0)
print(batch0.shape)
print(model)
macs, params = get_model_complexity_info(model,
batch0, ((1024, 3)),
as_strings=True,
print_per_layer_stat=False,
verbose=True)
print('{:<30} {:<8}'.format('Computational complexity: ', macs))
print('{:<30} {:<8}'.format('Number of parameters: ', params))
test_acc = 0.0
count = 0.0
test_true = []
test_pred = []
for data, label in test_loader:
data, label = data.to(device), label.to(device).squeeze()
batch_size = data.size()[0]
if args.model == 'ognet' or args.model == 'ognet-small' or args.model == 'ssg' or args.model == 'msg':
logits = model(data, data)
#logits = model(1.1*data, 1.1*data)
else:
data = data.permute(0, 2, 1)
logits = model(data)
preds = logits.max(dim=1)[1]
test_true.append(label.cpu().numpy())
test_pred.append(preds.detach().cpu().numpy())
test_true = np.concatenate(test_true)
test_pred = np.concatenate(test_pred)
test_acc = metrics.accuracy_score(test_true, test_pred)
avg_per_class_acc = metrics.balanced_accuracy_score(test_true, test_pred)
outstr = 'Test :: test acc: %.6f, test avg acc: %.6f' % (test_acc,
avg_per_class_acc)
io.cprint(outstr)
