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| Original file line number | Diff line number | Diff line change |
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| # Copyright (c) Chris Choy (chrischoy@ai.stanford.edu). | ||
| # | ||
| # Permission is hereby granted, free of charge, to any person obtaining a copy of | ||
| # this software and associated documentation files (the "Software"), to deal in | ||
| # the Software without restriction, including without limitation the rights to | ||
| # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies | ||
| # of the Software, and to permit persons to whom the Software is furnished to do | ||
| # so, subject to the following conditions: | ||
| # | ||
| # The above copyright notice and this permission notice shall be included in all | ||
| # copies or substantial portions of the Software. | ||
| # | ||
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| # SOFTWARE. | ||
| # | ||
| # Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural | ||
| # Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part | ||
| # of the code. | ||
| import os | ||
| import argparse | ||
| import numpy as np | ||
| from urllib.request import urlretrieve | ||
|
|
||
| try: | ||
| import open3d as o3d | ||
| except ImportError: | ||
| raise ImportError("Please install open3d with `pip install open3d`.") | ||
|
|
||
| import torch | ||
| import MinkowskiEngine as ME | ||
| from MinkowskiCommon import convert_to_int_list | ||
| import examples.minkunet as UNets | ||
| from tests.python.common import data_loader, load_file, batched_coordinates | ||
| from examples.common import Timer | ||
|
|
||
| # Check if the weights and file exist and download | ||
| if not os.path.isfile("weights.pth"): | ||
| print("Downloading weights and a room ply file...") | ||
| urlretrieve( | ||
| "http://cvgl.stanford.edu/data2/minkowskiengine/weights.pth", "weights.pth" | ||
| ) | ||
| urlretrieve("http://cvgl.stanford.edu/data2/minkowskiengine/1.ply", "1.ply") | ||
|
|
||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument("--file_name", type=str, default="1.ply") | ||
| parser.add_argument("--weights", type=str, default="weights.pth") | ||
| parser.add_argument("--use_cpu", action="store_true") | ||
| parser.add_argument("--backward", action="store_true") | ||
| parser.add_argument("--max_batch", type=int, default=12) | ||
|
|
||
|
|
||
| def quantize(coordinates): | ||
| D = coordinates.size(1) - 1 | ||
| coordinate_manager = ME.CoordinateManager( | ||
| D=D, coordinate_map_type=ME.CoordinateMapType.CPU | ||
| ) | ||
| coordinate_map_key = ME.CoordinateMapKey(convert_to_int_list(1, D), "") | ||
| key, (unique_map, inverse_map) = coordinate_manager.insert_and_map( | ||
| coordinates, *coordinate_map_key.get_key() | ||
| ) | ||
| return unique_map, inverse_map | ||
|
|
||
|
|
||
| def load_file(file_name, voxel_size): | ||
| pcd = o3d.io.read_point_cloud(file_name) | ||
| coords = torch.from_numpy(np.array(pcd.points)) | ||
| feats = torch.from_numpy(np.array(pcd.colors)).float() | ||
|
|
||
| quantized_coords = torch.floor(coords / voxel_size).int() | ||
| inds, inverse_inds = quantize(quantized_coords) | ||
|
|
||
| return quantized_coords[inds], feats[inds], pcd | ||
|
|
||
|
|
||
| def forward(coords, colors, model): | ||
| # Measure time | ||
| timer = Timer() | ||
| for i in range(5): | ||
| # Feed-forward pass and get the prediction | ||
| timer.tic() | ||
| sinput = ME.SparseTensor( | ||
| features=colors, | ||
| coordinates=coords, | ||
| device=device, | ||
| allocator_type=ME.GPUMemoryAllocatorType.PYTORCH, | ||
| ) | ||
| logits = model(sinput) | ||
| timer.toc() | ||
| return timer.min_time, len(logits) | ||
|
|
||
|
|
||
| def train(coords, colors, model): | ||
| # Measure time | ||
| timer = Timer() | ||
| for i in range(5): | ||
| # Feed-forward pass and get the prediction | ||
| timer.tic() | ||
| sinput = ME.SparseTensor( | ||
| colors, | ||
| coords, | ||
| device=device, | ||
| allocator_type=ME.GPUMemoryAllocatorType.PYTORCH, | ||
| ) | ||
| logits = model(sinput) | ||
| logits.F.sum().backward() | ||
| timer.toc() | ||
| return timer.min_time, len(logits) | ||
|
|
||
|
|
||
| def test_network(coords, feats, model, batch_sizes, forward_only=True): | ||
| for batch_size in batch_sizes: | ||
| bcoords = batched_coordinates([coords for i in range(batch_size)]) | ||
| bfeats = torch.cat([feats for i in range(batch_size)], 0) | ||
| if forward_only: | ||
| with torch.no_grad(): | ||
| time, length = forward(bcoords, bfeats, model) | ||
| else: | ||
| time, length = train(bcoords, bfeats, model) | ||
|
|
||
| print(f"{net.__name__}\t{voxel_size}\t{batch_size}\t{length}\t{time}") | ||
| torch.cuda.empty_cache() | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| config = parser.parse_args() | ||
| device = torch.device( | ||
| "cuda" if (torch.cuda.is_available() and not config.use_cpu) else "cpu" | ||
| ) | ||
| print(f"Using {device}") | ||
| print(f"Using backward {config.backward}") | ||
| # Define a model and load the weights | ||
| batch_sizes = [i for i in range(2, config.max_batch + 1, 2)] | ||
| batch_sizes = [1, *batch_sizes] | ||
|
|
||
| for net in [UNets.MinkUNet14, UNets.MinkUNet18, UNets.MinkUNet34, UNets.MinkUNet50]: | ||
| model = net(3, 20).to(device) | ||
| model.eval() | ||
| for voxel_size in [0.02]: | ||
| print(voxel_size) | ||
| coords, feats, _ = load_file(config.file_name, voxel_size) | ||
| test_network(coords, feats, model, batch_sizes, not config.backward) | ||
| torch.cuda.empty_cache() | ||
| del model |
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