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0b042be956
* Model class done, pipeline class still needs more visualization stuffs * Renaming the BaseDataContainer to DataloaderOutputs and adding documentation * Pushing more documentation, removing some trivial functions * Updating dict typing * Removing config dicts for collider * Passing tests * Pushing shell new dataloader * Saving work, need to fix NGP model's get_loss_dict and Pipeline * V1 of the ngp pipeline should be done. Need train script and debugging * Updating with latest instant-ngp code * Syntax updates from latest ngp graph code * Linter, removing unneeded code in new NGP graph code * Style * Should be good now. Linter is happy and the code is verbatum current instant-ngp * Everything done except novel view testing code * Fixing some nitpicks * Debugging dataloader * merged master * WOP. integrating pipeline with configs and notebook demo * removing model folder * a lot of name changing from Graph to Model * in progress... changing trainer to use pipeline... broken right now * pipeline code runs * switched to val dataset * makes run_eval.py work * updated configs for pipeline change * fixing test_run_train script * fixing black and linting * fix docs * small updates to trainer Co-authored-by: Jake Austin <jake-austin@berkeley.edu>
123 lines
3.4 KiB
Python
123 lines
3.4 KiB
Python
"""
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Test samplers
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"""
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import torch
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from nerfactory.cameras.rays import RayBundle
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from nerfactory.models.modules.ray_sampler import (
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LinearDisparitySampler,
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LogSampler,
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PDFSampler,
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SqrtSampler,
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UniformSampler,
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)
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from nerfactory.models.modules.scene_colliders import NearFarCollider
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def test_uniform_sampler():
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"""Test uniform sampler"""
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near_plane = 2
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far_plane = 4
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num_samples = 15
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sampler = UniformSampler(num_samples=num_samples)
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origins = torch.zeros((10, 3))
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directions = torch.ones_like(origins)
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radius = torch.ones((10, 1))
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ray_bundle = RayBundle(origins=origins, directions=directions, pixel_area=radius)
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collider = NearFarCollider(near_plane, far_plane)
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ray_bundle = collider(ray_bundle)
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ray_samples = sampler(ray_bundle)
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assert ray_samples.frustums.get_positions().shape[-2] == num_samples
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# TODO Tancik: Add more precise tests
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def test_lin_disp_sampler():
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"""Test linear in disparity sampler"""
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near_plane = 2
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far_plane = 4
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num_samples = 15
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sampler = LinearDisparitySampler(num_samples=num_samples)
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origins = torch.zeros((10, 3))
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directions = torch.ones_like(origins)
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radius = torch.ones((10, 1))
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ray_bundle = RayBundle(origins=origins, directions=directions, pixel_area=radius)
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collider = NearFarCollider(near_plane, far_plane)
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ray_bundle = collider(ray_bundle)
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ray_samples = sampler(ray_bundle)
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assert ray_samples.frustums.get_positions().shape[-2] == num_samples
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def test_sqrt_sampler():
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"""Test square root sampler"""
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near_plane = 2
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far_plane = 4
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num_samples = 15
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sampler = SqrtSampler(num_samples=num_samples)
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origins = torch.zeros((10, 3))
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directions = torch.ones_like(origins)
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radius = torch.ones((10, 1))
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ray_bundle = RayBundle(origins=origins, directions=directions, pixel_area=radius)
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collider = NearFarCollider(near_plane, far_plane)
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ray_bundle = collider(ray_bundle)
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ray_samples = sampler(ray_bundle)
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assert ray_samples.frustums.get_positions().shape[-2] == num_samples
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def test_log_sampler():
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"""Test log sampler"""
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near_plane = 2
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far_plane = 4
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num_samples = 15
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sampler = LogSampler(num_samples=num_samples)
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origins = torch.zeros((10, 3))
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directions = torch.ones_like(origins)
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radius = torch.ones((10, 1))
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ray_bundle = RayBundle(origins=origins, directions=directions, pixel_area=radius)
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collider = NearFarCollider(near_plane, far_plane)
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ray_bundle = collider(ray_bundle)
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ray_samples = sampler(ray_bundle)
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assert ray_samples.frustums.get_positions().shape[-2] == num_samples
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def test_pdf_sampler():
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"""Test pdf sampler"""
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near_plane = 2
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far_plane = 4
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num_samples = 15
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origins = torch.zeros((10, 3))
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directions = torch.ones_like(origins)
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radius = torch.ones((10, 1))
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ray_bundle = RayBundle(origins=origins, directions=directions, pixel_area=radius)
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collider = NearFarCollider(near_plane, far_plane)
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ray_bundle = collider(ray_bundle)
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uniform_sampler = UniformSampler(num_samples=num_samples)
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coarse_ray_samples = uniform_sampler(ray_bundle)
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weights = torch.ones((10, num_samples, 1))
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# Just check that it doesn't crash. Need to add some actual tests.
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pdf_sampler = PDFSampler(num_samples)
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pdf_sampler(ray_bundle, coarse_ray_samples, weights, num_samples)
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# TODO Tancik: Add more precise tests
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if __name__ == "__main__":
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test_uniform_sampler()
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test_pdf_sampler()
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