added crestereo datasets & fixed some bugs

IGEV-Stereo/core/igev_stereo.py

@@ -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)

IGEV-Stereo/core/stereo_datasets.py

@@ -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

IGEV-Stereo/core/utils/frame_utils.py

@@ -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'

IGEV-Stereo/demo_imgs.py

@@ -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()
 

IGEV-Stereo/train_stereo.py

@@ -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.")