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added crestereo datasets & fixed some bugs

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This commit is contained in:
HTensor 2023-04-22 11:32:10 +08:00
parent 93371a3c4d
commit a301ebb020
5 changed files with 45 additions and 8 deletions
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2
IGEV-Stereo/core/igev_stereo.py
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@ -165,7 +165,7 @@ class IGEVStereo(nn.Module):
match_left = self.desc(self.conv(features_left[0]))
match_right = self.desc(self.conv(features_right[0]))
gwc_volume = build_gwc_volume(match_left, match_right, 192//4, 8)
gwc_volume = build_gwc_volume(match_left, match_right, self.args.max_disp//4, 8)
gwc_volume = self.corr_stem(gwc_volume)
gwc_volume = self.corr_feature_att(gwc_volume, features_left[0])
geo_encoding_volume = self.cost_agg(gwc_volume, features_left)

31
IGEV-Stereo/core/stereo_datasets.py
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@ -73,6 +73,7 @@ class StereoDataset(data.Dataset):
img2 = np.array(img2).astype(np.uint8)
disp = np.array(disp).astype(np.float32)
assert not (True in np.isnan(disp))
flow = np.stack([disp, np.zeros_like(disp)], axis=-1)
@ -266,6 +267,31 @@ class KITTI(StereoDataset):
self.image_list += [ [img1, img2] ]
self.disparity_list += [ disp ]
class CREStereo(StereoDataset):
def __init__(self, aug_params=None, root='/data/CREStereo'):
super(CREStereo, self).__init__(aug_params, sparse=True, reader=frame_utils.readDispCREStereo)
self.root = root
assert os.path.exists(root)
disp_list = self.selector('_left.disp.png')
image1_list = self.selector('_left.jpg')
image2_list = self.selector('_right.jpg')
assert len(image1_list) == len(image2_list) == len(disp_list) > 0
for img1, img2, disp in zip(image1_list, image2_list, disp_list):
# if random.randint(1, 20000) != 1:
# continue
self.image_list += [[img1, img2]]
self.disparity_list += [disp]
def selector(self, suffix):
files = list(glob(os.path.join(self.root, f"hole/*{suffix}")))
files += list(glob(os.path.join(self.root, f"shapenet/*{suffix}")))
files += list(glob(os.path.join(self.root, f"tree/*{suffix}")))
files += list(glob(os.path.join(self.root, f"reflective/*{suffix}")))
return sorted(files)
class Middlebury(StereoDataset):
def __init__(self, aug_params=None, root='/data/Middlebury', split='F'):
@ -321,10 +347,13 @@ def fetch_dataloader(args):
elif dataset_name.startswith('tartan_air'):
new_dataset = TartanAir(aug_params, keywords=dataset_name.split('_')[2:])
logging.info(f"Adding {len(new_dataset)} samples from Tartain Air")
elif dataset_name.startswith('crestereo'):
new_dataset = CREStereo(aug_params)
logging.info(f"Adding {len(new_dataset)} samples from CREStereo")
train_dataset = new_dataset if train_dataset is None else train_dataset + new_dataset
train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size,
pin_memory=True, shuffle=True, num_workers=int(os.environ.get('SLURM_CPUS_PER_TASK', 6))-2, drop_last=True)
pin_memory=False, shuffle=True, num_workers=int(os.environ.get('SLURM_CPUS_PER_TASK', 6))-2, drop_last=True)
logging.info('Training with %d image pairs' % len(train_dataset))
return train_loader

5
IGEV-Stereo/core/utils/frame_utils.py
View File

@ -152,6 +152,11 @@ def readDispTartanAir(file_name):
valid = disp > 0
return disp, valid
def readDispCREStereo(file_name):
disp = cv2.imread(file_name, cv2.IMREAD_UNCHANGED)
disp = disp.astype(np.float32) / 32.0
valid = disp > 0.0
return disp, valid
def readDispMiddlebury(file_name):
assert basename(file_name) == 'disp0GT.pfm'

1
IGEV-Stereo/demo_imgs.py
View File

@ -26,6 +26,7 @@ def demo(args):
model.load_state_dict(torch.load(args.restore_ckpt))
model = model.module
# model = torch.compile(model)
model.to(DEVICE)
model.eval()

12
IGEV-Stereo/train_stereo.py
View File

@ -1,6 +1,7 @@
from __future__ import print_function, division
import math
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1'
@ -53,7 +54,7 @@ def sequence_loss(disp_preds, disp_init_pred, disp_gt, valid, loss_gamma=0.9, ma
assert not torch.isinf(disp_gt[valid.bool()]).any()
disp_loss += 1.0 * F.smooth_l1_loss(disp_init_pred[valid.bool()], disp_gt[valid.bool()], size_average=True)
disp_loss += 1.0 * F.smooth_l1_loss(disp_init_pred[valid.bool()], disp_gt[valid.bool()], reduction='mean')
for i in range(n_predictions):
adjusted_loss_gamma = loss_gamma**(15/(n_predictions - 1))
i_weight = adjusted_loss_gamma**(n_predictions - i - 1)
@ -78,6 +79,7 @@ def fetch_optimizer(args, model):
""" Create the optimizer and learning rate scheduler """
optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=1e-8)
# todo: cosine scheduler, warm-up
scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps+100,
pct_start=0.01, cycle_momentum=False, anneal_strategy='linear')
return optimizer, scheduler
@ -151,14 +153,13 @@ def train(args):
model.train()
model.module.freeze_bn() # We keep BatchNorm frozen
validation_frequency = 10000
validation_frequency = 1000
scaler = GradScaler(enabled=args.mixed_precision)
should_keep_training = True
global_batch_num = 0
while should_keep_training:
for i_batch, (_, *data_blob) in enumerate(tqdm(train_loader)):
optimizer.zero_grad()
image1, image2, disp_gt, valid = [x.cuda() for x in data_blob]
@ -175,6 +176,7 @@ def train(args):
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
#warning
scaler.step(optimizer)
scheduler.step()
scaler.update()
@ -184,7 +186,7 @@ def train(args):
save_path = Path(ckpt_path + '/%d_%s.pth' % (total_steps + 1, args.name))
logging.info(f"Saving file {save_path.absolute()}")
torch.save(model.state_dict(), save_path)
results = validate_sceneflow(model.module, iters=args.valid_iters)
results = validate_middlebury(model.module, iters=args.valid_iters)
logger.write_dict(results)
model.train()
model.module.freeze_bn()
@ -212,7 +214,7 @@ if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--name', default='igev-stereo', help="name your experiment")
parser.add_argument('--restore_ckpt', default=None, help="")
parser.add_argument('--mixed_precision', default=True, action='store_true', help='use mixed precision')
parser.add_argument('--mixed_precision', default=False, action='store_true', help='use mixed precision')
# Training parameters
parser.add_argument('--batch_size', type=int, default=8, help="batch size used during training.")