optimize

IGEV-Stereo/core/geometry.py

@@ -37,8 +37,8 @@ class Combined_Geo_Encoding_Volume:
         out_pyramid = []
         for i in range(self.num_levels):
             geo_volume = self.geo_volume_pyramid[i]
-            dx = torch.linspace(-r, r, 2*r+1)
-            dx = dx.view(1, 1, 2*r+1, 1).to(disp.device)
+            dx = torch.linspace(-r, r, 2*r+1, device=disp.device)
+            dx = dx.view(1, 1, 2*r+1, 1)
             x0 = dx + disp.reshape(b*h*w, 1, 1, 1) / 2**i
             y0 = torch.zeros_like(x0)
 

IGEV-Stereo/core/igev_stereo.py

@@ -191,7 +191,7 @@ class IGEVStereo(nn.Module):
         geo_block = Combined_Geo_Encoding_Volume
         geo_fn = geo_block(match_left.float(), match_right.float(), geo_encoding_volume.float(), radius=self.args.corr_radius, num_levels=self.args.corr_levels)
         b, c, h, w = match_left.shape
-        coords = torch.arange(w).float().to(match_left.device).reshape(1,1,w,1).repeat(b, h, 1, 1)
+        coords = torch.arange(w, device=match_left.device).float().reshape(1,1,w,1).repeat(b, h, 1, 1)
         disp = init_disp
         disp_preds = []
 

IGEV-Stereo/core/stereo_datasets.py

@@ -353,7 +353,7 @@ def fetch_dataloader(args):
         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=False, shuffle=True, num_workers=int(os.environ.get('SLURM_CPUS_PER_TASK', 6))-2, drop_last=True)
+        pin_memory=True, 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

IGEV-Stereo/train_stereo.py

@@ -22,6 +22,7 @@ from evaluate_stereo import *
 import core.stereo_datasets as datasets
 import torch.nn.functional as F
 
+torch.backends.cudnn.benchmark = True
 
 ckpt_path = './checkpoints/igev_stereo'
 log_path = './checkpoints/igev_stereo'