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fix multi-batch inference

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This commit is contained in:
Nikita Karaev 2024-01-04 16:53:22 +00:00
parent 3716e36249
commit f084a93f28
1 changed files with 5 additions and 4 deletions
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9
cotracker/predictor.py
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@ -23,11 +23,11 @@ class CoTrackerPredictor(torch.nn.Module):
@torch.no_grad() @torch.no_grad()
def forward( def forward(
self, self,
video, # (1, T, 3, H, W) video, # (B, T, 3, H, W)
# input prompt types: # input prompt types:
# - None. Dense tracks are computed in this case. You can adjust *query_frame* to compute tracks starting from a specific frame. # - None. Dense tracks are computed in this case. You can adjust *query_frame* to compute tracks starting from a specific frame.
# *backward_tracking=True* will compute tracks in both directions. # *backward_tracking=True* will compute tracks in both directions.
# - queries. Queried points of shape (1, N, 3) in format (t, x, y) for frame index and pixel coordinates. # - queries. Queried points of shape (B, N, 3) in format (t, x, y) for frame index and pixel coordinates.
# - grid_size. Grid of N*N points from the first frame. if segm_mask is provided, then computed only for the mask. # - grid_size. Grid of N*N points from the first frame. if segm_mask is provided, then computed only for the mask.
# You can adjust *query_frame* and *backward_tracking* for the regular grid in the same way as for dense tracks. # You can adjust *query_frame* and *backward_tracking* for the regular grid in the same way as for dense tracks.
queries: torch.Tensor = None, queries: torch.Tensor = None,
@ -120,13 +120,14 @@ class CoTrackerPredictor(torch.nn.Module):
queries = torch.cat( queries = torch.cat(
[torch.ones_like(grid_pts[:, :, :1]) * grid_query_frame, grid_pts], [torch.ones_like(grid_pts[:, :, :1]) * grid_query_frame, grid_pts],
dim=2, dim=2,
) ).repeat(B, 1, 1)
if add_support_grid: if add_support_grid:
grid_pts = get_points_on_a_grid( grid_pts = get_points_on_a_grid(
self.support_grid_size, self.interp_shape, device=video.device self.support_grid_size, self.interp_shape, device=video.device
) )
grid_pts = torch.cat([torch.zeros_like(grid_pts[:, :, :1]), grid_pts], dim=2) grid_pts = torch.cat([torch.zeros_like(grid_pts[:, :, :1]), grid_pts], dim=2)
grid_pts = grid_pts.repeat(B, 1, 1)
queries = torch.cat([queries, grid_pts], dim=1) queries = torch.cat([queries, grid_pts], dim=1)
tracks, visibilities, __ = self.model.forward(video=video, queries=queries, iters=6) tracks, visibilities, __ = self.model.forward(video=video, queries=queries, iters=6)
@ -173,7 +174,7 @@ class CoTrackerPredictor(torch.nn.Module):
inv_visibilities = inv_visibilities.flip(1) inv_visibilities = inv_visibilities.flip(1)
arange = torch.arange(video.shape[1], device=queries.device)[None, :, None] arange = torch.arange(video.shape[1], device=queries.device)[None, :, None]
mask = (arange < queries[None, :, :, 0]).unsqueeze(-1).repeat(1, 1, 1, 2) mask = (arange < queries[:, None, :, 0]).unsqueeze(-1).repeat(1, 1, 1, 2)
tracks[mask] = inv_tracks[mask] tracks[mask] = inv_tracks[mask]
visibilities[mask[:, :, :, 0]] = inv_visibilities[mask[:, :, :, 0]] visibilities[mask[:, :, :, 0]] = inv_visibilities[mask[:, :, :, 0]]