Update download_models.sh with more robust URL. (#168)

The old models.zip URL didn't work with certain .wgetrc settings. This new one seems to be more robust.

fixes #167

README.md

@@ -12,11 +12,7 @@ The code has been tested with PyTorch 1.6 and Cuda 10.1.
 ```Shell
 conda create --name raft
 conda activate raft
-conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 -c pytorch
+conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 matplotlib tensorboard scipy opencv -c pytorch
-conda install matplotlib
-conda install tensorboard
-conda install scipy
-conda install opencv
 ```
 
 ## Demos
@@ -24,21 +20,13 @@ Pretrained models can be downloaded by running
 ```Shell
 ./download_models.sh
 ```
-or downloaded from [google drive](https://drive.google.com/file/d/10-BYgHqRNPGvmNUWr8razjb1xHu55pyA/view?usp=sharing)
+or downloaded from [google drive](https://drive.google.com/drive/folders/1sWDsfuZ3Up38EUQt7-JDTT1HcGHuJgvT?usp=sharing)
 
 You can demo a trained model on a sequence of frames
 ```Shell
 python demo.py --model=models/raft-things.pth --path=demo-frames
 ```
 
-## (Optional) Efficent Implementation
-You can optionally use our alternate (efficent) implementation by compiling the provided cuda extension
-```Shell
-cd alt_cuda_corr && python setup.py install && cd ..
-```
-and running `demo.py` and `evaluate.py` with the `--alternate_corr` flag.Note, this implementation is somewhat slower than all-pairs, but uses significantly less GPU memory during the forward pass.
-
-
 ## Required Data
 To evaluate/train RAFT, you will need to download the required datasets. 
 * [FlyingChairs](https://lmb.informatik.uni-freiburg.de/resources/datasets/FlyingChairs.en.html#flyingchairs)
@@ -83,3 +71,10 @@ If you have a RTX GPU, training can be accelerated using mixed precision. You ca
 ```Shell
 ./train_mixed.sh
 ```
+
+## (Optional) Efficent Implementation
+You can optionally use our alternate (efficent) implementation by compiling the provided cuda extension
+```Shell
+cd alt_cuda_corr && python setup.py install && cd ..
+```
+and running `demo.py` and `evaluate.py` with the `--alternate_corr` flag Note, this implementation is somewhat slower than all-pairs, but uses significantly less GPU memory during the forward pass.

core/corr.py

@@ -34,9 +34,9 @@ class CorrBlock:
         out_pyramid = []
         for i in range(self.num_levels):
             corr = self.corr_pyramid[i]
-            dx = torch.linspace(-r, r, 2*r+1)
+            dx = torch.linspace(-r, r, 2*r+1, device=coords.device)
-            dy = torch.linspace(-r, r, 2*r+1)
+            dy = torch.linspace(-r, r, 2*r+1, device=coords.device)
-            delta = torch.stack(torch.meshgrid(dy, dx), axis=-1).to(coords.device)
+            delta = torch.stack(torch.meshgrid(dy, dx), axis=-1)
 
             centroid_lvl = coords.reshape(batch*h1*w1, 1, 1, 2) / 2**i
             delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2)
@@ -60,26 +60,6 @@ class CorrBlock:
         return corr  / torch.sqrt(torch.tensor(dim).float())
 
 
-class CorrLayer(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, fmap1, fmap2, coords, r):
-        fmap1 = fmap1.contiguous()
-        fmap2 = fmap2.contiguous()
-        coords = coords.contiguous()
-        ctx.save_for_backward(fmap1, fmap2, coords)
-        ctx.r = r
-        corr, = correlation_cudaz.forward(fmap1, fmap2, coords, ctx.r)
-        return corr
-
-    @staticmethod
-    def backward(ctx, grad_corr):
-        fmap1, fmap2, coords = ctx.saved_tensors
-        grad_corr = grad_corr.contiguous()
-        fmap1_grad, fmap2_grad, coords_grad = \
-            correlation_cudaz.backward(fmap1, fmap2, coords, grad_corr, ctx.r)
-        return fmap1_grad, fmap2_grad, coords_grad, None
-
-
 class AlternateCorrBlock:
     def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
         self.num_levels = num_levels
@@ -92,20 +72,20 @@ class AlternateCorrBlock:
             self.pyramid.append((fmap1, fmap2))
 
     def __call__(self, coords):
-
         coords = coords.permute(0, 2, 3, 1)
         B, H, W, _ = coords.shape
+        dim = self.pyramid[0][0].shape[1]
 
         corr_list = []
         for i in range(self.num_levels):
             r = self.radius
-            fmap1_i = self.pyramid[0][0].permute(0, 2, 3, 1)
+            fmap1_i = self.pyramid[0][0].permute(0, 2, 3, 1).contiguous()
-            fmap2_i = self.pyramid[i][1].permute(0, 2, 3, 1)
+            fmap2_i = self.pyramid[i][1].permute(0, 2, 3, 1).contiguous()
 
             coords_i = (coords / 2**i).reshape(B, 1, H, W, 2).contiguous()
-            corr = alt_cuda_corr(fmap1_i, fmap2_i, coords_i, r)
+            corr, = alt_cuda_corr.forward(fmap1_i, fmap2_i, coords_i, r)
             corr_list.append(corr.squeeze(1))
 
         corr = torch.stack(corr_list, dim=1)
         corr = corr.reshape(B, -1, H, W)
-        return corr / 16.0
+        return corr / torch.sqrt(torch.tensor(dim).float())

core/raft.py

@@ -38,11 +38,11 @@ class RAFT(nn.Module):
             args.corr_levels = 4
             args.corr_radius = 4
 
-        if 'dropout' not in args._get_kwargs():
+        if 'dropout' not in self.args:
-            args.dropout = 0
+            self.args.dropout = 0
 
-        if 'alternate_corr' not in args._get_kwargs():
+        if 'alternate_corr' not in self.args:
-            args.alternate_corr = False
+            self.args.alternate_corr = False
 
         # feature network, context network, and update block
         if args.small:
@@ -55,7 +55,6 @@ class RAFT(nn.Module):
             self.cnet = BasicEncoder(output_dim=hdim+cdim, norm_fn='batch', dropout=args.dropout)
             self.update_block = BasicUpdateBlock(self.args, hidden_dim=hdim)
 
-
     def freeze_bn(self):
         for m in self.modules():
             if isinstance(m, nn.BatchNorm2d):
@@ -64,8 +63,8 @@ class RAFT(nn.Module):
     def initialize_flow(self, img):
         """ Flow is represented as difference between two coordinate grids flow = coords1 - coords0"""
         N, C, H, W = img.shape
-        coords0 = coords_grid(N, H//8, W//8).to(img.device)
+        coords0 = coords_grid(N, H//8, W//8, device=img.device)
-        coords1 = coords_grid(N, H//8, W//8).to(img.device)
+        coords1 = coords_grid(N, H//8, W//8, device=img.device)
 
         # optical flow computed as difference: flow = coords1 - coords0
         return coords0, coords1
@@ -103,7 +102,7 @@ class RAFT(nn.Module):
         fmap1 = fmap1.float()
         fmap2 = fmap2.float()
         if self.args.alternate_corr:
-            corr_fn = CorrBlockAlternate(fmap1, fmap2, radius=self.args.corr_radius)
+            corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
         else:
             corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
 

core/utils/utils.py

@@ -71,8 +71,8 @@ def bilinear_sampler(img, coords, mode='bilinear', mask=False):
     return img
 
 
-def coords_grid(batch, ht, wd):
+def coords_grid(batch, ht, wd, device):
-    coords = torch.meshgrid(torch.arange(ht), torch.arange(wd))
+    coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device))
     coords = torch.stack(coords[::-1], dim=0).float()
     return coords[None].repeat(batch, 1, 1, 1)
 

demo.py

@@ -20,19 +20,7 @@ DEVICE = 'cuda'
 def load_image(imfile):
     img = np.array(Image.open(imfile)).astype(np.uint8)
     img = torch.from_numpy(img).permute(2, 0, 1).float()
-    return img
+    return img[None].to(DEVICE)
-
-
-def load_image_list(image_files):
-    images = []
-    for imfile in sorted(image_files):
-        images.append(load_image(imfile))
- 
-    images = torch.stack(images, dim=0)
-    images = images.to(DEVICE)
-
-    padder = InputPadder(images.shape)
-    return padder.pad(images)[0]
 
 
 def viz(img, flo):
@@ -43,6 +31,10 @@ def viz(img, flo):
     flo = flow_viz.flow_to_image(flo)
     img_flo = np.concatenate([img, flo], axis=0)
 
+    # import matplotlib.pyplot as plt
+    # plt.imshow(img_flo / 255.0)
+    # plt.show()
+
     cv2.imshow('image', img_flo[:, :, [2,1,0]]/255.0)
     cv2.waitKey()
 
@@ -59,10 +51,13 @@ def demo(args):
         images = glob.glob(os.path.join(args.path, '*.png')) + \
                  glob.glob(os.path.join(args.path, '*.jpg'))
         
-        images = load_image_list(images)
+        images = sorted(images)
-        for i in range(images.shape[0]-1):
+        for imfile1, imfile2 in zip(images[:-1], images[1:]):
-            image1 = images[i,None]
+            image1 = load_image(imfile1)
-            image2 = images[i+1,None]
+            image2 = load_image(imfile2)
+
+            padder = InputPadder(image1.shape)
+            image1, image2 = padder.pad(image1, image2)
 
             flow_low, flow_up = model(image1, image2, iters=20, test_mode=True)
             viz(image1, flow_up)

download_models.sh

@@ -1,3 +1,3 @@
 #!/bin/bash
-wget https://www.dropbox.com/s/npt24nvhoojdr0n/models.zip
+wget https://dl.dropboxusercontent.com/s/4j4z58wuv8o0mfz/models.zip
 unzip models.zip

train.py

@@ -44,7 +44,7 @@ SUM_FREQ = 100
 VAL_FREQ = 5000
 
 
-def sequence_loss(flow_preds, flow_gt, valid, max_flow=MAX_FLOW):
+def sequence_loss(flow_preds, flow_gt, valid, gamma=0.8, max_flow=MAX_FLOW):
     """ Loss function defined over sequence of flow predictions """
 
     n_predictions = len(flow_preds)    
@@ -55,7 +55,7 @@ def sequence_loss(flow_preds, flow_gt, valid, max_flow=MAX_FLOW):
     valid = (valid >= 0.5) & (mag < max_flow)
 
     for i in range(n_predictions):
-        i_weight = 0.8**(n_predictions - i - 1)
+        i_weight = gamma**(n_predictions - i - 1)
         i_loss = (flow_preds[i] - flow_gt).abs()
         flow_loss += i_weight * (valid[:, None] * i_loss).mean()
 
@@ -71,16 +71,11 @@ def sequence_loss(flow_preds, flow_gt, valid, max_flow=MAX_FLOW):
 
     return flow_loss, metrics
 
-def show_image(img):
-    img = img.permute(1,2,0).cpu().numpy()
-    plt.imshow(img/255.0)
-    plt.show()
-    # cv2.imshow('image', img/255.0)
-    # cv2.waitKey()
 
 def count_parameters(model):
     return sum(p.numel() for p in model.parameters() if p.requires_grad)
 
+
 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=args.epsilon)
@@ -169,9 +164,6 @@ def train(args):
             optimizer.zero_grad()
             image1, image2, flow, valid = [x.cuda() for x in data_blob]
 
-            # show_image(image1[0])
-            # show_image(image2[0])
-
             if args.add_noise:
                 stdv = np.random.uniform(0.0, 5.0)
                 image1 = (image1 + stdv * torch.randn(*image1.shape).cuda()).clamp(0.0, 255.0)
@@ -179,7 +171,7 @@ def train(args):
 
             flow_predictions = model(image1, image2, iters=args.iters)            
 
-            loss, metrics = sequence_loss(flow_predictions, flow, valid)
+            loss, metrics = sequence_loss(flow_predictions, flow, valid, args.gamma)
             scaler.scale(loss).backward()
             scaler.unscale_(optimizer)                
             torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
@@ -188,7 +180,6 @@ def train(args):
             scheduler.step()
             scaler.update()
 
-
             logger.push(metrics)
 
             if total_steps % VAL_FREQ == VAL_FREQ - 1:
@@ -243,6 +234,7 @@ if __name__ == '__main__':
     parser.add_argument('--epsilon', type=float, default=1e-8)
     parser.add_argument('--clip', type=float, default=1.0)
     parser.add_argument('--dropout', type=float, default=0.0)
+    parser.add_argument('--gamma', type=float, default=0.8, help='exponential weighting')
     parser.add_argument('--add_noise', action='store_true')
     args = parser.parse_args()
 

train_mixed.sh

@@ -2,5 +2,5 @@
 mkdir -p checkpoints
 python -u train.py --name raft-chairs --stage chairs --validation chairs --gpus 0 --num_steps 120000 --batch_size 8 --lr 0.00025 --image_size 368 496 --wdecay 0.0001 --mixed_precision 
 python -u train.py --name raft-things --stage things --validation sintel --restore_ckpt checkpoints/raft-chairs.pth --gpus 0 --num_steps 120000 --batch_size 5 --lr 0.0001 --image_size 400 720 --wdecay 0.0001 --mixed_precision
-python -u train.py --name raft-sintel --stage sintel --validation sintel --restore_ckpt checkpoints/raft-things.pth --gpus 0 --num_steps 120000 --batch_size 5 --lr 0.0001 --image_size 368 768 --wdecay 0.00001 --mixed_precision
+python -u train.py --name raft-sintel --stage sintel --validation sintel --restore_ckpt checkpoints/raft-things.pth --gpus 0 --num_steps 120000 --batch_size 5 --lr 0.0001 --image_size 368 768 --wdecay 0.00001 --gamma=0.85 --mixed_precision
-python -u train.py --name raft-kitti  --stage kitti --validation kitti --restore_ckpt checkpoints/raft-sintel.pth --gpus 0 --num_steps 50000 --batch_size 5 --lr 0.0001 --image_size 288 960 --wdecay 0.00001 --mixed_precision
+python -u train.py --name raft-kitti  --stage kitti --validation kitti --restore_ckpt checkpoints/raft-sintel.pth --gpus 0 --num_steps 50000 --batch_size 5 --lr 0.0001 --image_size 288 960 --wdecay 0.00001 --gamma=0.85 --mixed_precision

train_standard.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 mkdir -p checkpoints
-python -u train.py --name raft-chairs --stage chairs --validation chairs --gpus 0 1 --num_steps 100000 --batch_size 12 --lr 0.0004 --image_size 368 496 --wdecay 0.0001
+python -u train.py --name raft-chairs --stage chairs --validation chairs --gpus 0 1 --num_steps 100000 --batch_size 10 --lr 0.0004 --image_size 368 496 --wdecay 0.0001
 python -u train.py --name raft-things --stage things --validation sintel --restore_ckpt checkpoints/raft-chairs.pth --gpus 0 1 --num_steps 100000 --batch_size 6 --lr 0.000125 --image_size 400 720 --wdecay 0.0001
-python -u train.py --name raft-sintel --stage sintel --validation sintel --restore_ckpt checkpoints/raft-things.pth --gpus 0 1 --num_steps 100000 --batch_size 6 --lr 0.000125 --image_size 368 768 --wdecay 0.00001
+python -u train.py --name raft-sintel --stage sintel --validation sintel --restore_ckpt checkpoints/raft-things.pth --gpus 0 1 --num_steps 100000 --batch_size 6 --lr 0.000125 --image_size 368 768 --wdecay 0.00001 --gamma=0.85
-python -u train.py --name raft-kitti  --stage kitti --validation kitti --restore_ckpt checkpoints/raft-sintel.pth --gpus 0 1 --num_steps 50000 --batch_size 6 --lr 0.0001 --image_size 288 960 --wdecay 0.00001
+python -u train.py --name raft-kitti  --stage kitti --validation kitti --restore_ckpt checkpoints/raft-sintel.pth --gpus 0 1 --num_steps 50000 --batch_size 6 --lr 0.0001 --image_size 288 960 --wdecay 0.00001 --gamma=0.85