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train_ransac_loftr.py
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train_ransac_loftr.py
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from tqdm import tqdm
from model_cl import *
from loftr.loftr import LoFTR
from loftr.utils.cvpr_ds_config import default_cfg
from datasets import DatasetPicture
from tensorboardX import SummaryWriter
from loss import *
from ransac import RANSAC
from loftr.loftr_loss import LoFTRLoss
CUDA_LAUNCH_BLOCKING=2, 3
import logging
logger = logging.getLogger(__name__)
def train_step(train_data, opt, loss_fn, robust_estimator, topk_flag=False, valid_flag=False, k=1):
for given_key in train_data.keys():
try:
train_data[given_key] = train_data[given_key].to(opt.device).to(torch.float32)
except:
train_data[given_key] = train_data[given_key]
# fetch the points, ground truth extrinsic and intrinsic matrices
pts1 = train_data['mkpts0_f'].to(opt.device).clone()
pts2 = train_data['mkpts1_f'].to(opt.device).clone()
K1, K2 = train_data['K1'].to(opt.device), train_data['K2'].to(opt.device)
im_size1, im_size2 = torch.as_tensor(list(train_data['hw0_i'])).to(opt.device), torch.as_tensor(
list(train_data['hw1_i'])).to(opt.device)
pts1[:, 0] -= float(im_size1[1]) / 2
pts1[:, 1] -= float(im_size1[0]) / 2
pts1 /= float(max(im_size1))
pts2[:, 0] -= float(im_size2[1]) / 2
pts2[:, 1] -= float(im_size2[0]) / 2
pts2 /= float(max(im_size2))
gt_R, gt_t = train_data['gt_R'].to(opt.device), train_data['gt_t'].to(opt.device)
gt_E = train_data['gt_E'].to(opt.device)
gt_F = train_data['gt_F'].to(opt.device)
points = torch.cat([pts1, pts2], dim=-1)
confidence = train_data['mconf'].unsqueeze(dim=0)
ground_truth = gt_F if opt.fmat else gt_E
Es, ransac_time = robust_estimator.forward(
points,
confidence,
K1,
K2,
im_size1,
im_size2,
ground_truth
)
pts1 = pts1.unsqueeze(0)
pts2 = pts2.unsqueeze(0)
im_size1 = im_size1.unsqueeze(0)
im_size2 = im_size2.unsqueeze(0)
loss = 0.
for idx, f in enumerate(loss_fn):
if opt.w[idx] != 0:
if idx == 1:
train_loss = 0
elif idx == 2:
ground_truth = ground_truth.to(torch.float32)
pts1 = pts1.to(torch.float32)
pts2 = pts2.to(torch.float32)
K1 = K1.to(torch.float32)
K2 = K2.to(torch.float32)
train_loss = f.forward(
Es.unsqueeze(dim=0),
gt_E.cpu().detach().numpy().astype(np.float64),
pts1,
pts2,
K1,
K2,
im_size1,
im_size2,
topk_flag=topk_flag,
k=k
)
else:
train_loss = f.forward(
Es.unsqueeze(0),
#gt_E,
pts1,
pts2,
gt_R,
gt_t,
K1,
K2,
im_size1,
im_size2,
topk_flag=topk_flag,
k=k,
)
loss += opt.w[idx] * train_loss
return loss, Es, ransac_time
def train_one_epoch_loftr(
model_loftr,
optimizer_loftr,
criterion_loftr,
train_loader,
valid_loader,
opt,
robust_estimator,
epoch
):
valid_loader_iter = iter(valid_loader)
for idx, train_data in enumerate(tqdm(train_loader)):
model_loftr.train()
optimizer_loftr.zero_grad()
# make sure all the data is on the same device.
for given_key in train_data.keys():
train_data[given_key] = train_data[given_key].to(opt.device)
train_data['thr'] = 0.2
try:
model_loftr(train_data)
except Exception as e:
print('error in loftr FF: ', e, flush=True)
continue
if train_data['mkpts0_f'].shape[0] < 8:
print('got too little samples in the fine!!!', flush=True)
print(train_data['mkpts0_f'].shape, flush=True)
continue
loftr_loss, Es, run_time = train_step(
train_data,
opt,
criterion_loftr,
robust_estimator,
topk_flag=opt.topk,
k = opt.k,)
if torch.isnan(loftr_loss):
print('loss is nan', flush=True)
continue
loftr_loss.backward()
torch.nn.utils.clip_grad_norm_(model_loftr.parameters(), max_norm=1.)
optimizer_loftr.step()
if torch.isnan(optimizer_loftr.param_groups[0]['params'][0]).any():
continue
else:
print('train_loss: ', loftr_loss.item(), ' num_matches: ', train_data['mkpts0_f'].shape[-2], flush=True)
return model_loftr, optimizer_loftr
if __name__ == '__main__':
# Parse the parameters
parser = create_parser(
description="train the featuers matcher LoFTR with Generalized Differentiable RANSAC.")
config = parser.parse_args()
# check if gpu device is available
config.device = torch.device('cuda:0' if torch.cuda.is_available() and config.device != 'cpu' else 'cpu')
print(f"Running on {config.device}", flush=True)
scenes = [config.datasets]
model_loftr = LoFTR(default_cfg)
model_loftr.load_state_dict(torch.load(config.model_loftr)['state_dict'])
model_loftr = model_loftr.to(config.device)
optimizer_loftr = torch.optim.AdamW(params=model_loftr.parameters(), lr=config.learning_rate)
criterion_loftr = [
PoseLoss(config.fmat),
ClassificationLoss(config.fmat),
MatchLoss(config.fmat)]
diff_ransac = RANSACLayer(config)
# normalize the weights for different losses
w0, w1, w2 = config.w0, config.w1, config.w2,
w_sum = w0 + w1 + w2
if w_sum == 0:
config.w = [1., 0., 0.]
else:
config.w = [float(w0 / w_sum), float(w1 / w_sum), float(w2 / w_sum)]
scenes = config.datasets
print(f'Working on {scenes} with scoring {config.scoring}')
folders = config.data_path + '/' + scenes + '/' #seq + '/' for seq in scenes]
train_dataset = DatasetPicture(folders, nfeatures=config.nfeatures, fmat=config.fmat)
valid_dataset = DatasetPicture(folders, nfeatures=config.nfeatures, fmat=config.fmat, valid=True)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=config.batch_size, num_workers=0,
pin_memory=True, shuffle=True)
print(f'Loading training data: {len(train_dataset)} image pairs.', flush=True)
valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=config.batch_size, num_workers=0,
pin_memory=True, shuffle=True)
print(f'Loading validation data: {len(valid_dataset)} image pairs.', flush=True)
save_folder = create_session_string(
'loftr_', config.sampler, config.epochs, config.fmat, config.nfeatures, config.snn, config.session, config.w0, config.w1, config.w2,
config.threshold
)
src_path = 'results/loftr/' + save_folder
if not os.path.isdir(src_path): os.makedirs(src_path)
for given_epoch in range(config.epochs): # train the LoFTR model
model_loftr.train()
model_loftr, optimizer_loftr = train_one_epoch_loftr(
model_loftr,
optimizer_loftr,
criterion_loftr,
train_loader,
valid_loader,
config,
diff_ransac,
given_epoch
)
print('Saving LoFTR model', flush=True)
torch.save(model_loftr, src_path + '/loftr_model_' + str(given_epoch) + '.pth')