add chamfer loss

test.py

@@ -18,6 +18,52 @@ from models.depth_estim import DepthEstimation
 from models.convnext import ConvNeXt
 
 
+
+
+def plot3d(depth, mask):
+    import kornia
+    import plotly.graph_objects as go
+    from data.render import intrinsic
+    K = torch.tensor(intrinsic()[None, :, :], device='cuda')
+    pc = kornia.geometry.depth.depth_to_3d(depth, K, normalize_points=True)
+    pc = pc[0,:,mask[0]].detach().cpu().numpy()
+
+    go.Figure(data = [go.Scatter3d(x=pc[0,:], y=pc[1,:], z=pc[2,:], marker=dict(size=3, line=dict(width=0)), mode='markers'),
+                        ]).show()
+
+
+def chamfer_loss(depth_image, vertices, depth_mask, mesh_mask):
+    import kornia
+    import pytorch3d
+    from data.render import intrinsic
+    from pytorch3d.structures import Pointclouds
+    from utils.pytorch3d_chamfer import chamfer_distance
+    from pytorch3d.ops.points_normals import estimate_pointcloud_normals
+    from utils.data_utils import get_normals_from_depth
+    K = torch.tensor(intrinsic()[None, :, :], device='cuda')
+    pc = kornia.geometry.depth.depth_to_3d(depth_image, K, normalize_points=True)
+    pc = [torch.permute(pc[i,:,depth_mask[i]], [1,0]) for i in range(depth_image.shape[0])]
+    pc_pc = Pointclouds(pc)
+    mesh_gt = [vertices[i, mesh_mask[i], :] for i in range(vertices.shape[0])]
+    mesh_gt_pc = Pointclouds(mesh_gt)
+    pc_norm = get_normals_from_depth(depth_image, depth_mask, K)
+    mesh_norm = estimate_pointcloud_normals(mesh_gt_pc, neighborhood_size=50)
+
+    chamfer = chamfer_distance(pc_pc, mesh_gt_pc, x_normals=pc_norm, y_normals=mesh_norm)
+
+    return chamfer
+
+
+def normal_loss(depth_image, depth_mask, normal_image_gt):
+    import kornia
+    import pytorch3d
+    from data.render import intrinsic
+    K = torch.tensor(intrinsic()[None, :, :], device='cuda')
+    pc = kornia.geometry.depth.depth_to_3d(depth_image, K, normalize_points=True)
+    pc = torch.stack([pc[i,:,depth_mask[i]] for i in range(depth_image.shape[0])])
+    normal_image = get_normals_from_depth(depth_image[None,:,:], depth_mask[None,:,:], K)
+    pass
+
 def train_step(batch):
     ### Training
     model.train() # train mode is on by default after construction
@@ -26,7 +72,9 @@ def train_step(batch):
     y_pred = model([batch['rgb_image'], batch['view']])
 
     # 2. Calculate loss
-    loss = loss_fn(y_pred * batch['depth_mask'][:,None,:,:], batch['depth_image'][:,None,:,:] * batch['depth_mask'][:,None,:,:])
+    mse = loss_fn(y_pred * batch['depth_mask'][:,None,:,:], batch['depth_image'][:,None,:,:] * batch['depth_mask'][:,None,:,:])
+    chamfer, _ = chamfer_loss(y_pred, batch['mesh_posed'], batch['depth_mask'], batch['mesh_mask'])
+    loss = mse + 1e-1 * chamfer
 
     # 3. Zero grad optimizer
     optimizer.zero_grad()
@@ -46,9 +94,11 @@ def valid_step(batch):
     # 1. Forward pass
     with torch.inference_mode():
         pred = model([batch['rgb_image'], batch['view']])
-    
+
         # 2. Calculate the loss
-        loss = loss_fn(pred * batch['depth_mask'][:,None,:,:], batch['depth_image'][:,None,:,:] * batch['depth_mask'][:,None,:,:])
+        mse = loss_fn(pred * batch['depth_mask'][:,None,:,:], batch['depth_image'][:,None,:,:] * batch['depth_mask'][:,None,:,:])
+        chamfer, _ = chamfer_loss(pred, batch['mesh_posed'], batch['depth_mask'], batch['mesh_mask'])
+        loss = mse + 1e-1 * chamfer
 
     return { 'valid/loss': loss.detach().cpu().numpy() }
 
@@ -63,14 +113,18 @@ def batch_to_device(batch, device):
 if __name__ == '__main__':
     device = "cuda" if torch.cuda.is_available() else "cpu"
 
-    train_dataset = Dataset('dataset/CLOTH3D/train', 'dataset/CLOTH3D2RGB/train', 'frames_small.txt')
-    valid_dataset = Dataset('dataset/CLOTH3D/train', 'dataset/CLOTH3D2RGB/train', 'frames_small.txt')
-    train_dataloader = DataLoader(train_dataset, batch_size=2, drop_last=True, num_workers=0)
-    valid_dataloader = DataLoader(valid_dataset, batch_size=2, drop_last=True, num_workers=0)
+    random_view = False
+    use_skip = False
+    batch_size = 2
+
+    train_dataset = Dataset('dataset/CLOTH3D/train', 'dataset/CLOTH3D2RGB/train', 'frames_small.txt', random_view=random_view, device=device)
+    valid_dataset = Dataset('dataset/CLOTH3D/train', 'dataset/CLOTH3D2RGB/train', 'frames_small.txt', random_view=random_view, device=device)
+    train_dataloader = DataLoader(train_dataset, batch_size=batch_size, drop_last=True, num_workers=0)
+    valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, drop_last=True, num_workers=0)
     train_accumulator = Accumulator()
     valid_accumulator = Accumulator()
 
-    model = DepthEstimation(device=device)
+    model = DepthEstimation(device=device, use_skip=use_skip)
 
     loss_fn = nn.MSELoss()
     optimizer = optim.Adam(model.parameters(), lr=0.001)