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Avoid plain division involving integers
Summary: To avoid pytorch warnings and future behaviour changes, stop using torch.div and / with tensors of integers. Reviewed By: gkioxari, mruberry Differential Revision: D21857955 fbshipit-source-id: fb9f3000f3d953352cdc721d2a5f73d3a4bbf4b7
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@ -18,9 +18,9 @@ def unravel_index(idx, dims) -> torch.Tensor:
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if len(dims) != 4:
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raise ValueError("Expects a 4-element list.")
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N, H, W, D = dims
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n = torch.div(idx, H * W * D)
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h = torch.div(idx - n * H * W * D, W * D)
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w = torch.div(idx - n * H * W * D - h * W * D, D)
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n = idx // (H * W * D)
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h = (idx - n * H * W * D) // (W * D)
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w = (idx - n * H * W * D - h * W * D) // D
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d = idx - n * H * W * D - h * W * D - w * D
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return torch.stack((n, h, w, d), dim=1)
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@ -1031,7 +1031,7 @@ class Meshes(object):
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unique_mask[1:] = sorted_hash[1:] != sorted_hash[:-1]
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unique_idx = sort_idx[unique_mask]
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self._edges_packed = torch.stack([u / V, u % V], dim=1)
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self._edges_packed = torch.stack([u // V, u % V], dim=1)
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self._edges_packed_to_mesh_idx = edge_to_mesh[unique_idx]
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face_to_edge = torch.arange(3 * F).view(3, F).t()
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@ -24,11 +24,8 @@ DEBUG = False # Set DEBUG to true to save outputs from the tests.
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def convert_image_to_binary_mask(filename):
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with Image.open(filename) as raw_image:
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image = torch.from_numpy(np.array(raw_image))
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min = image.min()
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max = image.max()
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image_norm = (image - min) / (max - min)
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image_norm[image_norm > 0] == 1.0
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image_norm = image_norm.to(torch.int64)
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mx = image.max()
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image_norm = (image == mx).to(torch.int64)
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return image_norm
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