yaml.scanner.ScannerError: mapping values are not allowed here
yaml.scanner.ScannerError: mapping values are not allowed here
我正在尝试在 Google Colab 中使用 SemanticKitti 数据集训练 KPCONV。代码来自这里：https://github.com/HuguesTHOMAS/KPConv-PyTorch.
但是，这是我的输出。
Traceback (most recent call last):
File "/content/KPConv-PyTorch/train_SemanticKitti.py", line 263, in <module>
balance_classes=True)
File "/content/KPConv-PyTorch/datasets/SemanticKitti.py", line 103, in __init__
doc = yaml.safe_load(stream)
File "/usr/local/lib/python3.7/dist-packages/yaml/__init__.py", line 94, in safe_load
return load(stream, SafeLoader)
File "/usr/local/lib/python3.7/dist-packages/yaml/__init__.py", line 72, in load
return loader.get_single_data()
File "/usr/local/lib/python3.7/dist-packages/yaml/constructor.py", line 35, in get_single_data
node = self.get_single_node()
File "/usr/local/lib/python3.7/dist-packages/yaml/composer.py", line 36, in get_single_node
document = self.compose_document()
File "/usr/local/lib/python3.7/dist-packages/yaml/composer.py", line 58, in compose_document
self.get_event()
File "/usr/local/lib/python3.7/dist-packages/yaml/parser.py", line 118, in get_event
self.current_event = self.state()
File "/usr/local/lib/python3.7/dist-packages/yaml/parser.py", line 193, in parse_document_end
token = self.peek_token()
File "/usr/local/lib/python3.7/dist-packages/yaml/scanner.py", line 128, in peek_token
self.fetch_more_tokens()
File "/usr/local/lib/python3.7/dist-packages/yaml/scanner.py", line 220, in fetch_more_tokens
return self.fetch_value()
File "/usr/local/lib/python3.7/dist-packages/yaml/scanner.py", line 576, in fetch_value
self.get_mark())
yaml.scanner.ScannerError: mapping values are not allowed here
in "/content/Data/SemanticKitti/semantic-kitti.yaml", line 26, column 66
我已经使用 pip 安装了 PyYAML。
这是我的 .yaml 文件：
labels:
0 : "unlabeled"
1 : "outlier"
10: "car"
11: "bicycle"
13: "bus"
15: "motorcycle"
16: "on-rails"
18: "truck"
20: "other-vehicle"
30: "person"
31: "bicyclist"
32: "motorcyclist"
40: "road"
44: "parking"
48: "sidewalk"
49: "other-ground"
50: "building"
51: "fence"
52: "other-structure"
60: "lane-marking"
70: "vegetation"
71: "trunk"
72: "terrain"
80: "pole"
81: "traffic-sign"
99: "other-object"
252: "moving-car"
253: "moving-bicyclist"
254: "moving-person"
255: "moving-motorcyclist"
256: "moving-on-rails"
257: "moving-bus"
258: "moving-truck"
259: "moving-other-vehicle"
color_map: # bgr
0 : [0, 0, 0]
1 : [0, 0, 255]
10: [245, 150, 100]
11: [245, 230, 100]
13: [250, 80, 100]
15: [150, 60, 30]
16: [255, 0, 0]
18: [180, 30, 80]
20: [255, 0, 0]
30: [30, 30, 255]
31: [200, 40, 255]
32: [90, 30, 150]
40: [255, 0, 255]
44: [255, 150, 255]
48: [75, 0, 75]
49: [75, 0, 175]
50: [0, 200, 255]
51: [50, 120, 255]
52: [0, 150, 255]
60: [170, 255, 150]
70: [0, 175, 0]
71: [0, 60, 135]
72: [80, 240, 150]
80: [150, 240, 255]
81: [0, 0, 255]
99: [255, 255, 50]
252: [245, 150, 100]
256: [255, 0, 0]
253: [200, 40, 255]
254: [30, 30, 255]
255: [90, 30, 150]
257: [250, 80, 100]
258: [180, 30, 80]
259: [255, 0, 0]
content: # as a ratio with the total number of points
0: 0.018889854628292943
1: 0.0002937197336781505
10: 0.040818519255974316
11: 0.00016609538710764618
13: 2.7879693665067774e-05
15: 0.00039838616015114444
16: 0.0
18: 0.0020633612104619787
20: 0.0016218197275284021
30: 0.00017698551338515307
31: 1.1065903904919655e-08
32: 5.532951952459828e-09
40: 0.1987493871255525
44: 0.014717169549888214
48: 0.14392298360372
49: 0.0039048553037472045
50: 0.1326861944777486
51: 0.0723592229456223
52: 0.002395131480328884
60: 4.7084144280367186e-05
70: 0.26681502148037506
71: 0.006035012012626033
72: 0.07814222006271769
80: 0.002855498193863172
81: 0.0006155958086189918
99: 0.009923127583046915
252: 0.001789309418528068
253: 0.00012709999297008662
254: 0.00016059776092534436
255: 3.745553104802113e-05
256: 0.0
257: 0.00011351574470342043
258: 0.00010157861367183268
259: 4.3840131989471124e-05
# classes that are indistinguishable from single scan or inconsistent in
# ground truth are mapped to their closest equivalent
learning_map:
0 : 0 # "unlabeled"
1 : 0 # "outlier" mapped to "unlabeled" --------------------------mapped
10: 1 # "car"
11: 2 # "bicycle"
13: 5 # "bus" mapped to "other-vehicle" --------------------------mapped
15: 3 # "motorcycle"
16: 5 # "on-rails" mapped to "other-vehicle" ---------------------mapped
18: 4 # "truck"
20: 5 # "other-vehicle"
30: 6 # "person"
31: 7 # "bicyclist"
32: 8 # "motorcyclist"
40: 9 # "road"
44: 10 # "parking"
48: 11 # "sidewalk"
49: 12 # "other-ground"
50: 13 # "building"
51: 14 # "fence"
52: 0 # "other-structure" mapped to "unlabeled" ------------------mapped
60: 9 # "lane-marking" to "road" ---------------------------------mapped
70: 15 # "vegetation"
71: 16 # "trunk"
72: 17 # "terrain"
80: 18 # "pole"
81: 19 # "traffic-sign"
99: 0 # "other-object" to "unlabeled" ----------------------------mapped
252: 1 # "moving-car" to "car" ------------------------------------mapped
253: 7 # "moving-bicyclist" to "bicyclist" ------------------------mapped
254: 6 # "moving-person" to "person" ------------------------------mapped
255: 8 # "moving-motorcyclist" to "motorcyclist" ------------------mapped
256: 5 # "moving-on-rails" mapped to "other-vehicle" --------------mapped
257: 5 # "moving-bus" mapped to "other-vehicle" -------------------mapped
258: 4 # "moving-truck" to "truck" --------------------------------mapped
259: 5 # "moving-other"-vehicle to "other-vehicle" ----------------mapped
learning_map_inv: # inverse of previous map
0: 0 # "unlabeled", and others ignored
1: 10 # "car"
2: 11 # "bicycle"
3: 15 # "motorcycle"
4: 18 # "truck"
5: 20 # "other-vehicle"
6: 30 # "person"
7: 31 # "bicyclist"
8: 32 # "motorcyclist"
9: 40 # "road"
10: 44 # "parking"
11: 48 # "sidewalk"
12: 49 # "other-ground"
13: 50 # "building"
14: 51 # "fence"
15: 70 # "vegetation"
16: 71 # "trunk"
17: 72 # "terrain"
18: 80 # "pole"
19: 81 # "traffic-sign"
learning_ignore: # Ignore classes
0: True # "unlabeled", and others ignored
1: False # "car"
2: False # "bicycle"
3: False # "motorcycle"
4: False # "truck"
5: False # "other-vehicle"
6: False # "person"
7: False # "bicyclist"
8: False # "motorcyclist"
9: False # "road"
10: False # "parking"
11: False # "sidewalk"
12: False # "other-ground"
13: False # "building"
14: False # "fence"
15: False # "vegetation"
16: False # "trunk"
17: False # "terrain"
18: False # "pole"
19: False # "traffic-sign"
split: # sequence numbers
train:
- 0
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 9
- 10
valid:
- 8
test:
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
这是读取.yaml 文件的代码片段
# Read labels
if config.n_frames == 1:
config_file = join(self.path, 'semantic-kitti.yaml')
elif config.n_frames > 1:
config_file = join(self.path, 'semantic-kitti-all.yaml')
else:
raise ValueError('number of frames has to be >= 1')
with open(config_file, 'r') as stream:
doc = yaml.safe_load(stream)
all_labels = doc['labels']
learning_map_inv = doc['learning_map_inv']
learning_map = doc['learning_map']
self.learning_map = np.zeros((np.max([k for k in learning_map.keys()]) + 1), dtype=np.int32)
for k, v in learning_map.items():
self.learning_map[k] = v
self.learning_map_inv = np.zeros((np.max([k for k in learning_map_inv.keys()]) + 1), dtype=np.int32)
for k, v in learning_map_inv.items():
self.learning_map_inv[k] = v
我还没有发现类似的错误喜欢我的。请帮我解决这个错误 Ü.
您确实使用 wget 获取文件：
wget https://github.com/PRBonn/semantic-kitti-api/blob/master/config/semantic-kitti.yaml
在生成的文件中，第 26 行第 66 列出现错误，该行如下所示：
<meta name="optimizely-datafile" content="{"groups": [], "environmentKey&
^ this colon causes the error
上面看起来不像 YAML，那是因为在从 github 复制和粘贴 URL 之前，您应该首先 select 框 [Raw] 然后复制 URL:
wget https://raw.githubusercontent.com/PRBonn/semantic-kitti-api/master/config/semantic-kitti.yaml
因为 raw. 部分 a 开始，你只能得到文件的 contents，而不是 HTML 页面那
显示文件内容 与第一次下载时一样。
您可以使用 YAML 解析器加载该文件：
import sys
import ruamel.yaml
from pathlib import Path
file_in = Path('semantic-kitti.yaml')
yaml = ruamel.yaml.YAML(typ='rt')
data = yaml.load(file_in.open())
lm = data['learning_map']
print('value: ', lm[0])
print('comment:', lm.ca.items[0])
给出：
value: 0
comment: [None, None, CommentToken('# "unlabeled"\n', line: 109, col: 12), None]
如果您使用 'safe' 而不是 'rt' 您将无法访问评论，但您将加载大约一个量级
更快（与 PyYAML 的 safe_load() 相比）。
我正在尝试在 Google Colab 中使用 SemanticKitti 数据集训练 KPCONV。代码来自这里：https://github.com/HuguesTHOMAS/KPConv-PyTorch.
但是，这是我的输出。
Traceback (most recent call last):
File "/content/KPConv-PyTorch/train_SemanticKitti.py", line 263, in <module>
balance_classes=True)
File "/content/KPConv-PyTorch/datasets/SemanticKitti.py", line 103, in __init__
doc = yaml.safe_load(stream)
File "/usr/local/lib/python3.7/dist-packages/yaml/__init__.py", line 94, in safe_load
return load(stream, SafeLoader)
File "/usr/local/lib/python3.7/dist-packages/yaml/__init__.py", line 72, in load
return loader.get_single_data()
File "/usr/local/lib/python3.7/dist-packages/yaml/constructor.py", line 35, in get_single_data
node = self.get_single_node()
File "/usr/local/lib/python3.7/dist-packages/yaml/composer.py", line 36, in get_single_node
document = self.compose_document()
File "/usr/local/lib/python3.7/dist-packages/yaml/composer.py", line 58, in compose_document
self.get_event()
File "/usr/local/lib/python3.7/dist-packages/yaml/parser.py", line 118, in get_event
self.current_event = self.state()
File "/usr/local/lib/python3.7/dist-packages/yaml/parser.py", line 193, in parse_document_end
token = self.peek_token()
File "/usr/local/lib/python3.7/dist-packages/yaml/scanner.py", line 128, in peek_token
self.fetch_more_tokens()
File "/usr/local/lib/python3.7/dist-packages/yaml/scanner.py", line 220, in fetch_more_tokens
return self.fetch_value()
File "/usr/local/lib/python3.7/dist-packages/yaml/scanner.py", line 576, in fetch_value
self.get_mark())
yaml.scanner.ScannerError: mapping values are not allowed here
in "/content/Data/SemanticKitti/semantic-kitti.yaml", line 26, column 66
我已经使用 pip 安装了 PyYAML。 这是我的 .yaml 文件：
labels:
0 : "unlabeled"
1 : "outlier"
10: "car"
11: "bicycle"
13: "bus"
15: "motorcycle"
16: "on-rails"
18: "truck"
20: "other-vehicle"
30: "person"
31: "bicyclist"
32: "motorcyclist"
40: "road"
44: "parking"
48: "sidewalk"
49: "other-ground"
50: "building"
51: "fence"
52: "other-structure"
60: "lane-marking"
70: "vegetation"
71: "trunk"
72: "terrain"
80: "pole"
81: "traffic-sign"
99: "other-object"
252: "moving-car"
253: "moving-bicyclist"
254: "moving-person"
255: "moving-motorcyclist"
256: "moving-on-rails"
257: "moving-bus"
258: "moving-truck"
259: "moving-other-vehicle"
color_map: # bgr
0 : [0, 0, 0]
1 : [0, 0, 255]
10: [245, 150, 100]
11: [245, 230, 100]
13: [250, 80, 100]
15: [150, 60, 30]
16: [255, 0, 0]
18: [180, 30, 80]
20: [255, 0, 0]
30: [30, 30, 255]
31: [200, 40, 255]
32: [90, 30, 150]
40: [255, 0, 255]
44: [255, 150, 255]
48: [75, 0, 75]
49: [75, 0, 175]
50: [0, 200, 255]
51: [50, 120, 255]
52: [0, 150, 255]
60: [170, 255, 150]
70: [0, 175, 0]
71: [0, 60, 135]
72: [80, 240, 150]
80: [150, 240, 255]
81: [0, 0, 255]
99: [255, 255, 50]
252: [245, 150, 100]
256: [255, 0, 0]
253: [200, 40, 255]
254: [30, 30, 255]
255: [90, 30, 150]
257: [250, 80, 100]
258: [180, 30, 80]
259: [255, 0, 0]
content: # as a ratio with the total number of points
0: 0.018889854628292943
1: 0.0002937197336781505
10: 0.040818519255974316
11: 0.00016609538710764618
13: 2.7879693665067774e-05
15: 0.00039838616015114444
16: 0.0
18: 0.0020633612104619787
20: 0.0016218197275284021
30: 0.00017698551338515307
31: 1.1065903904919655e-08
32: 5.532951952459828e-09
40: 0.1987493871255525
44: 0.014717169549888214
48: 0.14392298360372
49: 0.0039048553037472045
50: 0.1326861944777486
51: 0.0723592229456223
52: 0.002395131480328884
60: 4.7084144280367186e-05
70: 0.26681502148037506
71: 0.006035012012626033
72: 0.07814222006271769
80: 0.002855498193863172
81: 0.0006155958086189918
99: 0.009923127583046915
252: 0.001789309418528068
253: 0.00012709999297008662
254: 0.00016059776092534436
255: 3.745553104802113e-05
256: 0.0
257: 0.00011351574470342043
258: 0.00010157861367183268
259: 4.3840131989471124e-05
# classes that are indistinguishable from single scan or inconsistent in
# ground truth are mapped to their closest equivalent
learning_map:
0 : 0 # "unlabeled"
1 : 0 # "outlier" mapped to "unlabeled" --------------------------mapped
10: 1 # "car"
11: 2 # "bicycle"
13: 5 # "bus" mapped to "other-vehicle" --------------------------mapped
15: 3 # "motorcycle"
16: 5 # "on-rails" mapped to "other-vehicle" ---------------------mapped
18: 4 # "truck"
20: 5 # "other-vehicle"
30: 6 # "person"
31: 7 # "bicyclist"
32: 8 # "motorcyclist"
40: 9 # "road"
44: 10 # "parking"
48: 11 # "sidewalk"
49: 12 # "other-ground"
50: 13 # "building"
51: 14 # "fence"
52: 0 # "other-structure" mapped to "unlabeled" ------------------mapped
60: 9 # "lane-marking" to "road" ---------------------------------mapped
70: 15 # "vegetation"
71: 16 # "trunk"
72: 17 # "terrain"
80: 18 # "pole"
81: 19 # "traffic-sign"
99: 0 # "other-object" to "unlabeled" ----------------------------mapped
252: 1 # "moving-car" to "car" ------------------------------------mapped
253: 7 # "moving-bicyclist" to "bicyclist" ------------------------mapped
254: 6 # "moving-person" to "person" ------------------------------mapped
255: 8 # "moving-motorcyclist" to "motorcyclist" ------------------mapped
256: 5 # "moving-on-rails" mapped to "other-vehicle" --------------mapped
257: 5 # "moving-bus" mapped to "other-vehicle" -------------------mapped
258: 4 # "moving-truck" to "truck" --------------------------------mapped
259: 5 # "moving-other"-vehicle to "other-vehicle" ----------------mapped
learning_map_inv: # inverse of previous map
0: 0 # "unlabeled", and others ignored
1: 10 # "car"
2: 11 # "bicycle"
3: 15 # "motorcycle"
4: 18 # "truck"
5: 20 # "other-vehicle"
6: 30 # "person"
7: 31 # "bicyclist"
8: 32 # "motorcyclist"
9: 40 # "road"
10: 44 # "parking"
11: 48 # "sidewalk"
12: 49 # "other-ground"
13: 50 # "building"
14: 51 # "fence"
15: 70 # "vegetation"
16: 71 # "trunk"
17: 72 # "terrain"
18: 80 # "pole"
19: 81 # "traffic-sign"
learning_ignore: # Ignore classes
0: True # "unlabeled", and others ignored
1: False # "car"
2: False # "bicycle"
3: False # "motorcycle"
4: False # "truck"
5: False # "other-vehicle"
6: False # "person"
7: False # "bicyclist"
8: False # "motorcyclist"
9: False # "road"
10: False # "parking"
11: False # "sidewalk"
12: False # "other-ground"
13: False # "building"
14: False # "fence"
15: False # "vegetation"
16: False # "trunk"
17: False # "terrain"
18: False # "pole"
19: False # "traffic-sign"
split: # sequence numbers
train:
- 0
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 9
- 10
valid:
- 8
test:
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
这是读取.yaml 文件的代码片段
# Read labels
if config.n_frames == 1:
config_file = join(self.path, 'semantic-kitti.yaml')
elif config.n_frames > 1:
config_file = join(self.path, 'semantic-kitti-all.yaml')
else:
raise ValueError('number of frames has to be >= 1')
with open(config_file, 'r') as stream:
doc = yaml.safe_load(stream)
all_labels = doc['labels']
learning_map_inv = doc['learning_map_inv']
learning_map = doc['learning_map']
self.learning_map = np.zeros((np.max([k for k in learning_map.keys()]) + 1), dtype=np.int32)
for k, v in learning_map.items():
self.learning_map[k] = v
self.learning_map_inv = np.zeros((np.max([k for k in learning_map_inv.keys()]) + 1), dtype=np.int32)
for k, v in learning_map_inv.items():
self.learning_map_inv[k] = v
我还没有发现类似的错误喜欢我的。请帮我解决这个错误 Ü.
您确实使用 wget 获取文件：
wget https://github.com/PRBonn/semantic-kitti-api/blob/master/config/semantic-kitti.yaml
在生成的文件中，第 26 行第 66 列出现错误，该行如下所示：
<meta name="optimizely-datafile" content="{"groups": [], "environmentKey&
^ this colon causes the error
上面看起来不像 YAML，那是因为在从 github 复制和粘贴 URL 之前，您应该首先 select 框 [Raw] 然后复制 URL:
wget https://raw.githubusercontent.com/PRBonn/semantic-kitti-api/master/config/semantic-kitti.yaml
因为 raw. 部分 a 开始，你只能得到文件的 contents，而不是 HTML 页面那
显示文件内容 与第一次下载时一样。
您可以使用 YAML 解析器加载该文件：
import sys
import ruamel.yaml
from pathlib import Path
file_in = Path('semantic-kitti.yaml')
yaml = ruamel.yaml.YAML(typ='rt')
data = yaml.load(file_in.open())
lm = data['learning_map']
print('value: ', lm[0])
print('comment:', lm.ca.items[0])
给出：
value: 0
comment: [None, None, CommentToken('# "unlabeled"\n', line: 109, col: 12), None]
如果您使用 'safe' 而不是 'rt' 您将无法访问评论，但您将加载大约一个量级
更快（与 PyYAML 的 safe_load() 相比）。