mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
34bbb3ad32
Summary: Applies new import merging and sorting from µsort v1.0. When merging imports, µsort will make a best-effort to move associated comments to match merged elements, but there are known limitations due to the diynamic nature of Python and developer tooling. These changes should not produce any dangerous runtime changes, but may require touch-ups to satisfy linters and other tooling. Note that µsort uses case-insensitive, lexicographical sorting, which results in a different ordering compared to isort. This provides a more consistent sorting order, matching the case-insensitive order used when sorting import statements by module name, and ensures that "frog", "FROG", and "Frog" always sort next to each other. For details on µsort's sorting and merging semantics, see the user guide: https://usort.readthedocs.io/en/stable/guide.html#sorting Reviewed By: bottler Differential Revision: D35553814 fbshipit-source-id: be49bdb6a4c25264ff8d4db3a601f18736d17be1
428 lines
14 KiB
Python
428 lines
14 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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import torch
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from common_testing import get_random_cuda_device, TestCaseMixin
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from pytorch3d.renderer.compositing import (
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alpha_composite,
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norm_weighted_sum,
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weighted_sum,
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)
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class TestAccumulatePoints(TestCaseMixin, unittest.TestCase):
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# NAIVE PYTHON IMPLEMENTATIONS (USED FOR TESTING)
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@staticmethod
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def accumulate_alphacomposite_python(points_idx, alphas, features):
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"""
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Naive pure PyTorch implementation of alpha_composite.
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Inputs / Outputs: Same as function
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"""
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B, K, H, W = points_idx.size()
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C = features.size(0)
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output = torch.zeros(B, C, H, W, dtype=alphas.dtype)
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for b in range(0, B):
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for c in range(0, C):
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for i in range(0, W):
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for j in range(0, H):
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t_alpha = 1
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for k in range(0, K):
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n_idx = points_idx[b, k, j, i]
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if n_idx < 0:
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continue
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alpha = alphas[b, k, j, i]
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output[b, c, j, i] += features[c, n_idx] * alpha * t_alpha
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t_alpha = (1 - alpha) * t_alpha
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return output
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@staticmethod
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def accumulate_weightedsum_python(points_idx, alphas, features):
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"""
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Naive pure PyTorch implementation of weighted_sum rasterization.
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Inputs / Outputs: Same as function
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"""
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B, K, H, W = points_idx.size()
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C = features.size(0)
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output = torch.zeros(B, C, H, W, dtype=alphas.dtype)
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for b in range(0, B):
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for c in range(0, C):
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for i in range(0, W):
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for j in range(0, H):
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for k in range(0, K):
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n_idx = points_idx[b, k, j, i]
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if n_idx < 0:
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continue
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alpha = alphas[b, k, j, i]
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output[b, c, j, i] += features[c, n_idx] * alpha
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return output
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@staticmethod
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def accumulate_weightedsumnorm_python(points_idx, alphas, features):
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"""
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Naive pure PyTorch implementation of norm_weighted_sum.
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Inputs / Outputs: Same as function
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"""
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B, K, H, W = points_idx.size()
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C = features.size(0)
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output = torch.zeros(B, C, H, W, dtype=alphas.dtype)
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for b in range(0, B):
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for c in range(0, C):
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for i in range(0, W):
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for j in range(0, H):
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t_alpha = 0
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for k in range(0, K):
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n_idx = points_idx[b, k, j, i]
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if n_idx < 0:
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continue
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t_alpha += alphas[b, k, j, i]
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t_alpha = max(t_alpha, 1e-4)
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for k in range(0, K):
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n_idx = points_idx[b, k, j, i]
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if n_idx < 0:
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continue
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alpha = alphas[b, k, j, i]
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output[b, c, j, i] += features[c, n_idx] * alpha / t_alpha
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return output
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def test_python(self):
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device = torch.device("cpu")
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self._simple_alphacomposite(self.accumulate_alphacomposite_python, device)
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self._simple_wsum(self.accumulate_weightedsum_python, device)
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self._simple_wsumnorm(self.accumulate_weightedsumnorm_python, device)
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def test_cpu(self):
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device = torch.device("cpu")
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self._simple_alphacomposite(alpha_composite, device)
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self._simple_wsum(weighted_sum, device)
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self._simple_wsumnorm(norm_weighted_sum, device)
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def test_cuda(self):
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device = get_random_cuda_device()
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self._simple_alphacomposite(alpha_composite, device)
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self._simple_wsum(weighted_sum, device)
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self._simple_wsumnorm(norm_weighted_sum, device)
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def test_python_vs_cpu_vs_cuda(self):
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self._python_vs_cpu_vs_cuda(
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self.accumulate_alphacomposite_python, alpha_composite
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)
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self._python_vs_cpu_vs_cuda(
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self.accumulate_weightedsumnorm_python, norm_weighted_sum
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)
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self._python_vs_cpu_vs_cuda(self.accumulate_weightedsum_python, weighted_sum)
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def _python_vs_cpu_vs_cuda(self, accumulate_func_python, accumulate_func):
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torch.manual_seed(231)
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device = torch.device("cpu")
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W = 8
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C = 3
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P = 32
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for d in ["cpu", get_random_cuda_device()]:
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# TODO(gkioxari) add torch.float64 to types after double precision
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# support is added to atomicAdd
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for t in [torch.float32]:
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device = torch.device(d)
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# Create values
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alphas = torch.rand(2, 4, W, W, dtype=t).to(device)
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alphas.requires_grad = True
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alphas_cpu = alphas.detach().cpu()
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alphas_cpu.requires_grad = True
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features = torch.randn(C, P, dtype=t).to(device)
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features.requires_grad = True
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features_cpu = features.detach().cpu()
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features_cpu.requires_grad = True
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inds = torch.randint(P + 1, size=(2, 4, W, W)).to(device) - 1
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inds_cpu = inds.detach().cpu()
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args_cuda = (inds, alphas, features)
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args_cpu = (inds_cpu, alphas_cpu, features_cpu)
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self._compare_impls(
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accumulate_func_python,
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accumulate_func,
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args_cpu,
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args_cuda,
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(alphas_cpu, features_cpu),
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(alphas, features),
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compare_grads=True,
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)
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def _compare_impls(
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self, fn1, fn2, args1, args2, grads1, grads2, compare_grads=False
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):
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res1 = fn1(*args1)
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res2 = fn2(*args2)
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self.assertClose(res1.cpu(), res2.cpu(), atol=1e-6)
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if not compare_grads:
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return
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# Compare gradients
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torch.manual_seed(231)
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grad_res = torch.randn_like(res1)
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loss1 = (res1 * grad_res).sum()
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loss1.backward()
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grads1 = [gradsi.grad.data.clone().cpu() for gradsi in grads1]
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grad_res = grad_res.to(res2)
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loss2 = (res2 * grad_res).sum()
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loss2.backward()
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grads2 = [gradsi.grad.data.clone().cpu() for gradsi in grads2]
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for i in range(0, len(grads1)):
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self.assertClose(grads1[i].cpu(), grads2[i].cpu(), atol=1e-6)
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def _simple_wsum(self, accum_func, device):
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# Initialise variables
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features = torch.Tensor([[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]).to(device)
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alphas = torch.Tensor(
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[
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[
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[
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[0.5, 0.5, 0.5, 0.5],
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[0.5, 1.0, 1.0, 0.5],
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[0.5, 1.0, 1.0, 0.5],
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[0.5, 0.5, 0.5, 0.5],
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],
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[
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[0.5, 0.5, 0.5, 0.5],
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[0.5, 1.0, 1.0, 0.5],
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[0.5, 1.0, 1.0, 0.5],
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[0.5, 0.5, 0.5, 0.5],
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],
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]
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]
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).to(device)
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points_idx = (
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torch.Tensor(
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[
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[
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# fmt: off
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[
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[0, 0, 0, 0], # noqa: E241, E201
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[0, -1, -1, -1], # noqa: E241, E201
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[0, 1, 1, 0], # noqa: E241, E201
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[0, 0, 0, 0], # noqa: E241, E201
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],
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[
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[2, 2, 2, 2], # noqa: E241, E201
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[2, 3, 3, 2], # noqa: E241, E201
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[2, 3, 3, 2], # noqa: E241, E201
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[2, 2, -1, 2], # noqa: E241, E201
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],
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# fmt: on
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]
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]
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)
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.long()
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.to(device)
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)
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result = accum_func(points_idx, alphas, features)
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self.assertTrue(result.shape == (1, 2, 4, 4))
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true_result = torch.Tensor(
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[
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[
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[
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[0.35, 0.35, 0.35, 0.35],
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[0.35, 0.90, 0.90, 0.30],
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[0.35, 1.30, 1.30, 0.35],
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[0.35, 0.35, 0.05, 0.35],
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],
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[
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[0.35, 0.35, 0.35, 0.35],
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[0.35, 0.90, 0.90, 0.30],
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[0.35, 1.30, 1.30, 0.35],
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[0.35, 0.35, 0.05, 0.35],
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],
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]
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]
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).to(device)
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self.assertClose(result.cpu(), true_result.cpu(), rtol=1e-3)
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def _simple_wsumnorm(self, accum_func, device):
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# Initialise variables
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features = torch.Tensor([[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]).to(device)
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alphas = torch.Tensor(
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[
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[
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[
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[0.5, 0.5, 0.5, 0.5],
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[0.5, 1.0, 1.0, 0.5],
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[0.5, 1.0, 1.0, 0.5],
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[0.5, 0.5, 0.5, 0.5],
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],
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[
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[0.5, 0.5, 0.5, 0.5],
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[0.5, 1.0, 1.0, 0.5],
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[0.5, 1.0, 1.0, 0.5],
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[0.5, 0.5, 0.5, 0.5],
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],
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]
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]
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).to(device)
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# fmt: off
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points_idx = (
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torch.Tensor(
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[
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[
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[
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[0, 0, 0, 0], # noqa: E241, E201
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[0, -1, -1, -1], # noqa: E241, E201
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[0, 1, 1, 0], # noqa: E241, E201
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[0, 0, 0, 0], # noqa: E241, E201
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],
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[
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[2, 2, 2, 2], # noqa: E241, E201
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[2, 3, 3, 2], # noqa: E241, E201
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[2, 3, 3, 2], # noqa: E241, E201
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[2, 2, -1, 2], # noqa: E241, E201
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],
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]
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]
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)
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.long()
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.to(device)
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)
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# fmt: on
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result = accum_func(points_idx, alphas, features)
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self.assertTrue(result.shape == (1, 2, 4, 4))
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true_result = torch.Tensor(
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[
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[
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[
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[0.35, 0.35, 0.35, 0.35],
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[0.35, 0.90, 0.90, 0.60],
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[0.35, 0.65, 0.65, 0.35],
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[0.35, 0.35, 0.10, 0.35],
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],
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[
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[0.35, 0.35, 0.35, 0.35],
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[0.35, 0.90, 0.90, 0.60],
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[0.35, 0.65, 0.65, 0.35],
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[0.35, 0.35, 0.10, 0.35],
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],
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]
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]
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).to(device)
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self.assertClose(result.cpu(), true_result.cpu(), rtol=1e-3)
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def _simple_alphacomposite(self, accum_func, device):
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# Initialise variables
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features = torch.Tensor([[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]).to(device)
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alphas = torch.Tensor(
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[
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[
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[
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[0.5, 0.5, 0.5, 0.5],
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[0.5, 1.0, 1.0, 0.5],
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[0.5, 1.0, 1.0, 0.5],
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[0.5, 0.5, 0.5, 0.5],
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],
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[
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[0.5, 0.5, 0.5, 0.5],
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[0.5, 1.0, 1.0, 0.5],
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[0.5, 1.0, 1.0, 0.5],
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[0.5, 0.5, 0.5, 0.5],
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],
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]
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]
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).to(device)
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# fmt: off
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points_idx = (
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torch.Tensor(
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[
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[
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[
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[0, 0, 0, 0], # noqa: E241, E201
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[0, -1, -1, -1], # noqa: E241, E201
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[0, 1, 1, 0], # noqa: E241, E201
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[0, 0, 0, 0], # noqa: E241, E201
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],
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[
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[2, 2, 2, 2], # noqa: E241, E201
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[2, 3, 3, 2], # noqa: E241, E201
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[2, 3, 3, 2], # noqa: E241, E201
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[2, 2, -1, 2], # noqa: E241, E201
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],
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]
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]
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)
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.long()
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.to(device)
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)
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# fmt: on
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result = accum_func(points_idx, alphas, features)
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self.assertTrue(result.shape == (1, 2, 4, 4))
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true_result = torch.Tensor(
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[
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[
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[
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[0.20, 0.20, 0.20, 0.20],
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[0.20, 0.90, 0.90, 0.30],
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[0.20, 0.40, 0.40, 0.20],
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[0.20, 0.20, 0.05, 0.20],
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],
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[
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[0.20, 0.20, 0.20, 0.20],
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[0.20, 0.90, 0.90, 0.30],
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[0.20, 0.40, 0.40, 0.20],
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[0.20, 0.20, 0.05, 0.20],
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],
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]
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]
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).to(device)
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self.assertTrue((result == true_result).all().item())
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