【Paddle Toolkit Development Competition No.5】 Paddle 适配 torch_harmonics

examples/paddle_harmonics/examples/minimal_example.py

@@ -0,0 +1,76 @@
+# coding=utf-8
+
+# SPDX-FileCopyrightText: Copyright (c) 2022 The torch-harmonics Authors. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+# 
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+
+# ignore this (just for development without installation)
+import sys
+sys.path.append("..")
+sys.path.append(".")
+
+import paddle
+import paddle_harmonics as harmonics
+
+try:
+    from tqdm import tqdm
+except:
+    tqdm = lambda x : x
+
+# everything is awesome on GPUs
+device = paddle.set_device("gpu")
+
+# create a batch with one sample and 21 channels
+b, c, n_theta, n_lambda = 1, 21, 360, 720
+
+# your layers to play with
+forward_transform = harmonics.RealSHT(n_theta, n_lambda).to(device)
+inverse_transform = harmonics.InverseRealSHT(n_theta, n_lambda).to(device)
+forward_transform_equi = harmonics.RealSHT(n_theta, n_lambda, grid="equiangular").to(device)
+inverse_transform_equi = harmonics.InverseRealSHT(n_theta, n_lambda, grid="equiangular").to(device)
+
+signal_leggauss = inverse_transform(paddle.randn((b, c, n_theta-1, n_theta+1), dtype=paddle.complex128))
+signal_equi = inverse_transform(paddle.randn((b, c, n_theta-1, n_theta+1), dtype=paddle.complex128))
+
+# let's check the layers
+for num_iters in [1, 8, 64, 512]:
+    base = signal_leggauss
+    for iteration in tqdm(range(num_iters)):
+        base = inverse_transform(forward_transform(base))
+    print("relative l2 error accumulation on the legendre-gauss grid: ",
+          paddle.mean(paddle.norm(base-signal_leggauss, p='fro', axis=(-1,-2)) / paddle.norm(signal_leggauss, p='fro', axis=(-1,-2)) ).item(),
+          "after", num_iters, "iterations")
+
+# let's check the equiangular layers
+for num_iters in [1, 8, 64, 512]:
+    base = signal_equi
+    for iteration in tqdm(range(num_iters)):
+        base = inverse_transform_equi(forward_transform_equi(base))
+    print("relative l2 error accumulation with interpolation onto equiangular grid: ",
+          paddle.mean(paddle.norm(base-signal_equi, p='fro', axis=(-1,-2)) / paddle.norm(signal_equi, p='fro', axis=(-1,-2)) ).item(),
+          "after", num_iters, "iterations")