https://github.com/yanx27/Pointnet_Pointnet2_pytorch 中的源代码
class S3DISDataset(Dataset):
def __init__(self, split='train', data_root='trainval_fullarea',
num_point=4096, test_area=5, block_size=1.0, sample_rate=1.0, transform=None):
super().__init__()
self.num_point = num_point # 采样点数
self.block_size = block_size # 将采样房间变为的大小
self.transform = transform
rooms = sorted(os.listdir(data_root))
rooms = [room for room in rooms if 'Area_' in room]
if split == 'train':
rooms_split = [room for room in rooms if not 'Area_{}'.format(test_area) in room] # ['Area_1_WC_1.txt','Area_1_conferenceRoom_1.txt',...]
else:
rooms_split = [room for room in rooms if 'Area_{}'.format(test_area) in room] # ['Area_5_WC_1.txt', 'Area_5_WC_2.txt',...]
self.room_points, self.room_labels = [], [] # 点云数据，点云目标值
self.room_coord_min, self.room_coord_max = [], [] # 最小坐标，最大坐标
num_point_all = []
labelweights = np.zeros(13) # array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
for room_name in tqdm(rooms_split, total=len(rooms_split)):
room_path = os.path.join(data_root, room_name)
room_data = np.load(room_path) # xyzrgbl, N*7
points, labels = room_data[:, 0:6], room_data[:, 6] # xyzrgb, N*6; l, N
tmp, _ = np.histogram(labels, range(14))
labelweights += tmp
coord_min, coord_max = np.amin(points, axis=0)[:3], np.amax(points, axis=0)[:3]
self.room_points.append(points), self.room_labels.append(labels)
self.room_coord_min.append(coord_min), self.room_coord_max.append(coord_max)
num_point_all.append(labels.size)
labelweights = labelweights.astype(np.float32)
labelweights = labelweights / np.sum(labelweights)
self.labelweights = np.power(np.amax(labelweights) / labelweights, 1 / 3.0)
print(self.labelweights)
sample_prob = num_point_all / np.sum(num_point_all)
num_iter = int(np.sum(num_point_all) * sample_rate / num_point)
room_idxs = []
for index in range(len(rooms_split)):
room_idxs.extend([index] * int(round(sample_prob[index] * num_iter)))
self.room_idxs = np.array(room_idxs)
print("Totally {} samples in {} set.".format(len(self.room_idxs), split))
def __getitem__(self, idx):
room_idx = self.room_idxs[idx]
points = self.room_points[room_idx] # N * 6
labels = self.room_labels[room_idx] # N
N_points = points.shape[0]
while (True):
center = points[np.random.choice(N_points)][:3]
block_min = center - [self.block_size / 2.0, self.block_size / 2.0, 0]
block_max = center + [self.block_size / 2.0, self.block_size / 2.0, 0]
point_idxs = np.where((points[:, 0] >= block_min[0]) & (points[:, 0] <= block_max[0]) & (points[:, 1] >= block_min[1]) & (points[:, 1] <= block_max[1]))[0]
if point_idxs.size > 1024:
break
if point_idxs.size >= self.num_point:
selected_point_idxs = np.random.choice(point_idxs, self.num_point, replace=False)
else:
selected_point_idxs = np.random.choice(point_idxs, self.num_point, replace=True)
# normalize
selected_points = points[selected_point_idxs, :] # num_point * 6
current_points = np.zeros((self.num_point, 9)) # num_point * 9
current_points[:, 6] = selected_points[:, 0] / self.room_coord_max[room_idx][0]
current_points[:, 7] = selected_points[:, 1] / self.room_coord_max[room_idx][1]
current_points[:, 8] = selected_points[:, 2] / self.room_coord_max[room_idx][2]
selected_points[:, 0] = selected_points[:, 0] - center[0]
selected_points[:, 1] = selected_points[:, 1] - center[1]
selected_points[:, 3:6] /= 255.0
current_points[:, 0:6] = selected_points
current_labels = labels[selected_point_idxs]
if self.transform is not None:
current_points, current_labels = self.transform(current_points, current_labels)
return current_points, current_labels
def __len__(self):
return len(self.room_idxs)
上面是获取S3DIS数据集代码，但我无法理解某些代码。
以下代码是我不明白的部分。我想知道它的作用。
我看不懂labelweights的意思，也不懂代码的最后一行。
labelweights = labelweights.astype(np.float32)
labelweights = labelweights / np.sum(labelweights)
self.labelweights = np.power(np.amax(labelweights) / labelweights, 1 / 3.0)
我不明白下面的代码。我想知道每行代码的含义。
sample_prob = num_point_all / np.sum(num_point_all)
num_iter = int(np.sum(num_point_all) * sample_rate / num_point)
room_idxs = []
for index in range(len(rooms_split)):
room_idxs.extend([index] * int(round(sample_prob[index] * num_iter)))
self.room_idxs = np.array(room_idxs)
你能向我解释代码吗？ 谢谢！！！
我还没有读过这篇文章，我建议你深入研究它，希望能获得更多细节。鉴于此，这就是我从代码中理解的内容。
self.labelweights = np.power(np.amax(labelweights) / labelweights, 1 / 3.0)
在赋值标签权重包含概率分布之前。然后labelweights数组按最大概率归一化，然后反转(np.amax(labelweights) / labelweights)。最后，将立方根应用于这些倒置比率。
总之，如果p是概率分布，则线计算cubic_root((p / max(p)) ^ {-1})。
给定名称，此变量可能用于根据观测值的类频率对观测值进行采样，方法是为代表性较少的类分配更多权重，并且立方根以某种方式展平权重以使其可变性较小。
sample_prob = num_point_all / np.sum(num_point_all)
num_iter = int(np.sum(num_point_all) * sample_rate / num_point)
room_idxs = []
for index in range(len(rooms_split)):
room_idxs.extend([index] * int(round(sample_prob[index] * num_iter)))
self.room_idxs = np.array(room_idxs)
sample_prob是包含每个房间中点的频率的概率分布。
num_iter以所有房间(np.sum(num_point_all))总积分sample_rate的百分比获得。该百分比除以num_point，以获得批次总数或类似的东西。
room_idxs是通过将几个list连接在一起来创建的([el] * n生成一个包含el元素n倍的list)。
这些list中的每一个都包含复制sample_prob[index] * num_iter次的相同房间索引(因此len(room_idxs)num_iter)。
总之，此代码旨在根据房间索引包含的点数对其进行采样。