mirror of https://github.com/koide3/small_gicp.git
128 lines
4.7 KiB
Python
128 lines
4.7 KiB
Python
#!/usr/bin/python3
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import re
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import os
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import numpy
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from collections import namedtuple
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from matplotlib import pyplot
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Result = namedtuple('Result', ['time_mean', 'time_std', 'points_mean', 'points_std'])
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def parse_result(filename):
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leaf_sizes = []
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results = {}
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with open(filename, 'r') as f:
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for line in f.readlines():
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if '(warmup)' in line:
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continue
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found = re.findall(r'leaf_size=(\S+)', line)
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if found:
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leaf_sizes.append(float(found[0]))
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if len(leaf_sizes) == 0:
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continue
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stat = r'\s*(\S+)\s*\+\-\s*(\S+)\s*'
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found = re.findall(stat, line)
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if len(found) != 2:
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continue
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name = line.split(':')[0].strip()
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if name not in results:
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results[name] = []
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results[name].append(Result(float(found[0][0]), float(found[0][1]), float(found[1][0]), float(found[1][1])))
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return leaf_sizes, results
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def main():
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results_path = os.path.dirname(__file__) + '/results'
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leaf_sizes = None
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raw_results = []
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for filename in os.listdir(results_path):
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found = re.findall(r'downsampling_benchmark_(\d+).txt', filename)
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if not found:
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continue
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leaf_sizes, rets = parse_result(results_path + '/' + filename)
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raw_results.append((int(found[0]), rets))
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raw_results = sorted(raw_results, key=lambda x: x[0])
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def summarize(rets):
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time_mean = numpy.array([x.time_mean for x in rets])
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time_std = numpy.array([x.time_std for x in rets])
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points_mean = numpy.array([x.points_mean for x in rets])
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points_std = numpy.array([x.points_std for x in rets])
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return Result(time_mean, time_std, points_mean, points_std)
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results = {}
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results['pcl_voxelgrid'] = summarize(raw_results[0][1]['pcl_voxelgrid'])
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results['pcl_approx_voxelgrid'] = summarize(raw_results[0][1]['pcl_approx_voxelgrid'])
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results['small_voxelgrid'] = summarize(raw_results[0][1]['small_voxelgrid'])
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for num_threads, rets in raw_results:
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results['small_voxelgrid_tbb ({} threads)'.format(num_threads)] = summarize(rets['small_voxelgrid_tbb'])
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results['small_voxelgrid_omp ({} threads)'.format(num_threads)] = summarize(rets['small_voxelgrid_omp'])
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fig, axes = pyplot.subplots(1, 5)
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fig.set_size_inches(18, 3)
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leaf_size_indices = [0, 1, 4, 9, -1]
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for i, leaf_size_index in enumerate(leaf_size_indices):
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leaf_size = leaf_sizes[leaf_size_index]
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num_threads = [x[0] for x in raw_results]
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time_tbb = []
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time_omp = []
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for N in [x[0] for x in raw_results]:
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time_tbb.append(results['small_voxelgrid_tbb ({} threads)'.format(N)].time_mean[leaf_size_index])
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time_omp.append(results['small_voxelgrid_omp ({} threads)'.format(N)].time_mean[leaf_size_index])
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baseline = results['pcl_voxelgrid'].time_mean[leaf_size_index]
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axes[i].plot([num_threads[0], num_threads[-1]], [baseline, baseline], label='pcl_voxelgrid', linestyle='--')
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axes[i].scatter(num_threads, time_tbb, label='small_voxelgrid_tbb', marker='^')
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axes[i].scatter(num_threads, time_omp, label='small_voxelgrid_omp', marker='^')
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axes[i].grid()
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axes[i].set_xscale('log')
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axes[i].set_xlabel('Num threads')
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axes[i].set_title('Leaf size = {} m'.format(leaf_size))
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axes[0].set_ylabel('Processing time [msec/scan]')
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axes[2].legend(loc='upper center', bbox_to_anchor=(0.5, -0.11), ncol=3)
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fig.savefig('downsampling_threads.svg')
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fig, axes = pyplot.subplots(1, 2)
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fig.set_size_inches(18, 3)
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methods = ['small_voxelgrid', 'small_voxelgrid_tbb (2 threads)', 'small_voxelgrid_tbb (3 threads)', 'small_voxelgrid_tbb (4 threads)', 'small_voxelgrid_tbb (6 threads)', 'pcl_voxelgrid', 'pcl_approx_voxelgrid']
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labels = ['small_voxelgrid', 'small_voxelgrid_tbb (2 threads)', 'small_voxelgrid_tbb (3 threads)', 'small_voxelgrid_tbb (4 threads)', 'small_voxelgrid_tbb (6 threads)', 'pcl_voxelgrid', 'pcl_approx_voxelgrid']
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markers = ['^', '^', '^', '^', '^', 'o', 'o']
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for method, label, marker in zip(methods, labels, markers):
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axes[0].plot(leaf_sizes, results[method].time_mean, label=label, linestyle='--' if 'pcl' in method else '-', marker=marker)
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if 'threads' not in method or '2 threads' in method:
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axes[1].plot(leaf_sizes, results[method].points_mean / results['pcl_voxelgrid'].points_mean, label=label, linestyle='--' if 'pcl' in method else '-', marker=marker)
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axes[0].set_xlabel('Leaf size [m]')
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axes[0].set_ylabel('Processing time [msec/scan]')
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axes[1].set_xlabel('Leaf size [m]')
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axes[1].set_ylabel('Num points ratio to pcl_voxelgrid')
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axes[0].legend()
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axes[1].legend()
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axes[0].grid()
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axes[1].grid()
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fig.savefig('downsampling_comp.svg')
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pyplot.show()
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if __name__ == '__main__':
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main()
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