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7 Commits
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@ -60,6 +60,7 @@ class Combined_Geo_Encoding_Volume:
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@staticmethod
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def corr(fmap1, fmap2):
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# batch, dim, ht, wd
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B, D, H, W1 = fmap1.shape
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_, _, _, W2 = fmap2.shape
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fmap1 = fmap1.view(B, D, H, W1)
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@ -167,6 +167,7 @@ class IGEVStereo(nn.Module):
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match_right = self.desc(self.conv(features_right[0]))
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gwc_volume = build_gwc_volume(match_left, match_right, self.args.max_disp//4, 8)
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gwc_volume = self.corr_stem(gwc_volume)
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# 3d unet
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gwc_volume = self.corr_feature_att(gwc_volume, features_left[0])
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geo_encoding_volume = self.cost_agg(gwc_volume, features_left)
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@ -73,7 +73,6 @@ class StereoDataset(data.Dataset):
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img2 = np.array(img2).astype(np.uint8)
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disp = np.array(disp).astype(np.float32)
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assert not (True in np.isnan(disp))
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flow = np.stack([disp, np.zeros_like(disp)], axis=-1)
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@ -294,7 +293,7 @@ class CREStereo(StereoDataset):
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class Middlebury(StereoDataset):
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def __init__(self, aug_params=None, root='/data/Middlebury', split='F'):
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def __init__(self, aug_params=None, root='/data/Middlebury', split='H'):
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super(Middlebury, self).__init__(aug_params, sparse=True, reader=frame_utils.readDispMiddlebury)
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assert os.path.exists(root)
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assert split in "FHQ"
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@ -353,7 +352,7 @@ def fetch_dataloader(args):
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train_dataset = new_dataset if train_dataset is None else train_dataset + new_dataset
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train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size,
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pin_memory=True, shuffle=True, num_workers=int(os.environ.get('SLURM_CPUS_PER_TASK', 6))-2, drop_last=True)
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pin_memory=True, shuffle=True, num_workers=12, drop_last=True)
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logging.info('Training with %d image pairs' % len(train_dataset))
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return train_loader
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@ -169,7 +169,7 @@ def validate_sceneflow(model, iters=32, mixed_prec=False, max_disp=192):
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@torch.no_grad()
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def validate_middlebury(model, iters=32, split='F', mixed_prec=False, max_disp=192):
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def validate_middlebury(model, iters=32, split='H', mixed_prec=False, max_disp=192):
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""" Peform validation using the Middlebury-V3 dataset """
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model.eval()
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aug_params = {}
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@ -22,7 +22,6 @@ from evaluate_stereo import *
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import core.stereo_datasets as datasets
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import torch.nn.functional as F
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torch.backends.cudnn.benchmark = True
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ckpt_path = './checkpoints/igev_stereo'
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log_path = './checkpoints/igev_stereo'
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