34 lines
1.0 KiB
Python
34 lines
1.0 KiB
Python
"""Tests for the torch flow-warp (backward sampling, vanilla remap semantics)."""
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import torch
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from cadence_core import flow_warp
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def _img():
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# 1x5x5x3 ramp so horizontal shifts are visible
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x = torch.arange(5, dtype=torch.float32).repeat(5, 1) # cols vary 0..4
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return (x / 4.0)[None, :, :, None].repeat(1, 1, 1, 3)
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def test_zero_flow_is_identity():
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img = _img()
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flow = torch.zeros(1, 5, 5, 2)
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out = flow_warp(img, flow)
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assert torch.allclose(out, img, atol=1e-5)
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def test_constant_shift_moves_content_left():
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# flow_warp samples img at (x - f); f=+1 in x => out(x)=img(x-1) => content shifts RIGHT by 1
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img = _img()
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flow = torch.zeros(1, 5, 5, 2)
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flow[..., 0] = 1.0
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out = flow_warp(img, flow)
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# interior column 2 should now equal original column 1
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assert torch.allclose(out[0, 2, 2], img[0, 2, 1], atol=1e-5)
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def test_preserves_shape_and_dtype():
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img = _img()
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out = flow_warp(img, torch.zeros(1, 5, 5, 2))
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assert out.shape == img.shape and out.dtype == img.dtype
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