mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-11 18:44:21 +08:00
147 lines
3.6 KiB
Python
147 lines
3.6 KiB
Python
"""
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Encoding Tests
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"""
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import pytest
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import torch
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from pyrad.fields.modules import encoding
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def test_scaling_and_offset():
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"""Test scaling and offset encoder"""
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in_dim = 4
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in_tensor = torch.ones((2, 3, in_dim))
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scaling = 2.0
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offset = 4.5
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encoder = encoding.ScalingAndOffset(in_dim=in_dim, scaling=scaling, offset=offset)
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assert encoder.get_out_dim() == in_dim
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encoded = encoder(in_tensor)
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assert encoded.shape[-1] == in_dim
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assert in_tensor * 6.5 == pytest.approx(encoded)
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with pytest.raises(ValueError):
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encoding.ScalingAndOffset(in_dim=-1)
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def test_nerf_encoder():
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"""Test NeRF encoder"""
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in_dim = 4
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out_dim = 24
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in_tensor = torch.ones((2, 3, in_dim))
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num_frequencies = 3
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min_freq_exp = 0
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max_freq_exp = 3
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encoder = encoding.NeRFEncoding(
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in_dim=in_dim, num_frequencies=num_frequencies, min_freq_exp=min_freq_exp, max_freq_exp=max_freq_exp
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)
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assert encoder.get_out_dim() == out_dim
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in_tensor = torch.ones((2, 3, in_dim))
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encoded = encoder(in_tensor)
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assert encoded.shape[-1] == out_dim
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assert torch.max(encoded) == 1
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in_tensor = torch.zeros((2, 3, in_dim))
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encoded = encoder(in_tensor)
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assert encoded.shape[-1] == out_dim
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assert torch.min(encoded) == 0
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# Test integrated posenc
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covs = torch.ones((2, 3, in_dim, in_dim))
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encoded = encoder(in_tensor, covs=covs)
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def test_rff_encoder():
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"""Test RFF encoder"""
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in_dim = 3
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out_dim = 24
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in_tensor = torch.ones((2, 3, in_dim))
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num_frequencies = 12
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scale = 5
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encoder = encoding.RFFEncoding(in_dim=in_dim, num_frequencies=num_frequencies, scale=scale)
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assert encoder.get_out_dim() == out_dim
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in_tensor = torch.ones((2, 3, in_dim))
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encoded = encoder(in_tensor)
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assert encoded.shape[-1] == out_dim
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# Test integrated encoding
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covs = torch.ones((2, 3, in_dim, in_dim))
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encoded = encoder(in_tensor, covs=covs)
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def test_tensor_vm_encoder():
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"""Test TensorVM encoder"""
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num_components = 24
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resolution = 32
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in_dim = 3
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out_dim = 3 * num_components
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encoder = encoding.TensorVMEncoding(num_components=num_components, resolution=resolution)
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assert encoder.get_out_dim() == out_dim
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in_tensor = torch.ones((3, in_dim))
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encoded = encoder(in_tensor)
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assert encoded.shape == (3, out_dim)
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in_tensor = torch.ones((6, 3, in_dim))
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encoded = encoder(in_tensor)
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assert encoded.shape == (6, 3, out_dim)
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encoder.upsample_grid(resolution=64)
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def test_tensor_cp_encoder():
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"""Test TensorCP encoder"""
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num_components = 24
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resolution = 32
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in_dim = 3
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out_dim = num_components
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encoder = encoding.TensorCPEncoding(num_components=num_components, resolution=resolution)
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assert encoder.get_out_dim() == out_dim
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in_tensor = torch.ones((3, in_dim))
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encoded = encoder(in_tensor)
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assert encoded.shape == (3, out_dim)
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in_tensor = torch.ones((6, 3, in_dim))
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encoded = encoder(in_tensor)
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assert encoded.shape == (6, 3, out_dim)
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encoder.upsample_grid(resolution=64)
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def test_tensor_sh_encoder():
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"""Test Spherical Harmonic encoder"""
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levels = 4
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out_dim = levels**2
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with pytest.raises(ValueError):
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encoder = encoding.SHEncoding(levels=5)
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encoder = encoding.SHEncoding(levels=levels)
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assert encoder.get_out_dim() == out_dim
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in_tensor = torch.zeros((10, 3))
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in_tensor[..., 1] = 1
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encoded = encoder(in_tensor)
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assert encoded.shape == (10, out_dim)
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if __name__ == "__main__":
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test_scaling_and_offset()
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test_nerf_encoder()
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test_rff_encoder()
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test_tensor_vm_encoder()
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test_tensor_cp_encoder()
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test_tensor_sh_encoder()
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