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import numpy as np |
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import unittest |
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import time |
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import torch |
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import MinkowskiEngineTest._C as _C |
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from utils import load_file, batched_coordinates |
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from gradcheck import gradcheck |
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class ConvolutionTestCase(unittest.TestCase): |
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def test(self): |
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D, IC, OC = 2, 3, 5 |
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coordinates = torch.IntTensor([[0, 1], [0, 2]]).to(0) |
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in_features = torch.rand(len(coordinates), IC).to(0) |
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manager = _C.CoordinateMapManager() |
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in_key, unique_inverse_map = manager.insert_and_map(coordinates, [1], "") |
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kernel_size = [3] |
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kernel_stride = [2] |
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kernel_dilation = [1] |
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out_key = _C.CoordinateMapKey(D) |
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kernel = torch.rand(3, IC, OC).to(0) |
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out_features = _C.ConvolutionForwardGPU( |
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in_features, |
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kernel, |
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kernel_size, |
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kernel_stride, |
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kernel_dilation, |
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_C.RegionType.HYPER_CUBE, |
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torch.IntTensor().to(0), |
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in_key, |
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out_key, |
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manager, |
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) |
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print(in_features, out_features) |
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def test_backward(self): |
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IC, OC = 3, 5 |
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coordinates = torch.IntTensor([[0, 1, -1], [0, 2, 1]]).to(0) |
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in_features = torch.rand(len(coordinates), IC).to(0) |
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manager = _C.CoordinateMapManager() |
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in_key, unique_inverse_map = manager.insert_and_map(coordinates, [1, 1], "") |
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kernel_size = [3, 3] |
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kernel_stride = [2, 2] |
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kernel_dilation = [1, 1] |
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out_key = _C.CoordinateMapKey(3) |
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kernel = torch.rand(9, IC, OC).to(0) |
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out_features = _C.ConvolutionForwardGPU( |
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in_features, |
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kernel, |
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kernel_size, |
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kernel_stride, |
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kernel_dilation, |
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_C.RegionType.HYPER_CUBE, |
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torch.IntTensor().to(0), |
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in_key, |
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out_key, |
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manager, |
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) |
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out_feat_grad = torch.rand_like(out_features) |
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in_feat_grad, kernel_grad = _C.ConvolutionBackwardGPU( |
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in_features, |
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out_feat_grad, |
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kernel, |
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kernel_size, |
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kernel_stride, |
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kernel_dilation, |
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_C.RegionType.HYPER_CUBE, |
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torch.IntTensor().to(0), |
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in_key, |
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out_key, |
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manager, |
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) |
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print(in_feat_grad, kernel_grad) |
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def test_pcd(self): |
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IC, OC = 3, 16 |
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coords, colors, pcd = load_file("1.ply") |
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kernel_size = [3, 3, 3] |
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kernel_stride = [2, 2, 2] |
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kernel_dilation = [1, 1, 1] |
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kernel = torch.rand(np.prod(kernel_size), IC, OC).to(0) |
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for batch_size in [1, 5, 10, 20, 40]: |
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for voxel_size in [0.05, 0.035, 0.02]: |
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min_time = 100000 |
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dcoords = torch.from_numpy(np.floor(coords / voxel_size)).int() |
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bcoords = batched_coordinates([dcoords for i in range(batch_size)]) |
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for i in range(10): |
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manager = _C.CoordinateMapManager() |
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in_key, (unique_map, inverse_map) = manager.insert_and_map( |
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bcoords.to(0), [1, 1, 1], "" |
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) |
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in_feats = torch.rand(manager.size(in_key), IC).to(0) |
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out_key = _C.CoordinateMapKey(4) |
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stime = time.time() |
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out_features = _C.ConvolutionForwardGPU( |
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in_feats, |
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kernel, |
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kernel_size, |
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kernel_stride, |
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kernel_dilation, |
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_C.RegionType.HYPER_CUBE, |
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torch.IntTensor(), |
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in_key, |
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out_key, |
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manager, |
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) |
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min_time = min(time.time() - stime, min_time) |
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print( |
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f"{batch_size}\t{manager.size(in_key)}\t{manager.size(out_key)}\t{min_time}" |
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) |
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def test_pcd2(self): |
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IC, OC = 128, 128 |
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coords, colors, pcd = load_file("1.ply") |
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kernel_size = [3, 3, 3] |
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kernel_stride = [2, 2, 2] |
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kernel_dilation = [1, 1, 1] |
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for IC in [3, 8, 16, 32, 64, 128]: |
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for OC in [16, 32, 64, 128, 256]: |
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kernel = torch.rand(np.prod(kernel_size), IC, OC).to(0) |
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for batch_size in [1]: |
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for voxel_size in [0.02]: |
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min_time = 100000 |
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dcoords = torch.from_numpy(np.floor(coords / voxel_size)).int() |
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bcoords = batched_coordinates( |
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[dcoords for i in range(batch_size)] |
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) |
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for i in range(10): |
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manager = _C.CoordinateMapManager() |
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in_key, (unique_map, inverse_map) = manager.insert_and_map( |
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bcoords.to(0), [1, 1, 1], "" |
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) |
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in_feats = torch.rand(manager.size(in_key), IC).to(0) |
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out_key = _C.CoordinateMapKey(4) |
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stime = time.time() |
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out_features = _C.ConvolutionForwardGPU( |
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in_feats, |
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kernel, |
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kernel_size, |
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kernel_stride, |
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kernel_dilation, |
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_C.RegionType.HYPER_CUBE, |
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torch.IntTensor(), |
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in_key, |
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out_key, |
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manager, |
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) |
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min_time = min(time.time() - stime, min_time) |
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print( |
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f"{batch_size}\t{manager.size(in_key)}\t{manager.size(out_key)}\t{IC}\t{OC}\t{min_time}" |
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) |
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