CharlesGaydon
commited on
Commit
•
8afb234
1
Parent(s):
0333411
Upload Myria3D V3.3 inference configuration
Browse files
FRACTAL-LidarHD_7cl_randlanet-inference-Myria3DV3.3.yaml
ADDED
@@ -0,0 +1,169 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
seed: 12345
|
2 |
+
work_dir: ${hydra:runtime.cwd}
|
3 |
+
debug: false
|
4 |
+
print_config: true
|
5 |
+
ignore_warnings: true
|
6 |
+
datamodule:
|
7 |
+
transforms:
|
8 |
+
preparations:
|
9 |
+
eval:
|
10 |
+
TargetTransform:
|
11 |
+
_target_: myria3d.pctl.transforms.transforms.TargetTransform
|
12 |
+
_args_:
|
13 |
+
- ${dataset_description.classification_preprocessing_dict}
|
14 |
+
- ${dataset_description.classification_dict}
|
15 |
+
DropPointsByClass:
|
16 |
+
_target_: myria3d.pctl.transforms.transforms.DropPointsByClass
|
17 |
+
CopyFullPos:
|
18 |
+
_target_: myria3d.pctl.transforms.transforms.CopyFullPos
|
19 |
+
CopyFullPreparedTargets:
|
20 |
+
_target_: myria3d.pctl.transforms.transforms.CopyFullPreparedTargets
|
21 |
+
GridSampling:
|
22 |
+
_target_: torch_geometric.transforms.GridSampling
|
23 |
+
_args_:
|
24 |
+
- 0.25
|
25 |
+
MinimumNumNodes:
|
26 |
+
_target_: myria3d.pctl.transforms.transforms.MinimumNumNodes
|
27 |
+
_args_:
|
28 |
+
- 300
|
29 |
+
MaximumNumNodes:
|
30 |
+
_target_: myria3d.pctl.transforms.transforms.MaximumNumNodes
|
31 |
+
_args_:
|
32 |
+
- 40000
|
33 |
+
CopySampledPos:
|
34 |
+
_target_: myria3d.pctl.transforms.transforms.CopySampledPos
|
35 |
+
Center:
|
36 |
+
_target_: torch_geometric.transforms.Center
|
37 |
+
predict:
|
38 |
+
DropPointsByClass:
|
39 |
+
_target_: myria3d.pctl.transforms.transforms.DropPointsByClass
|
40 |
+
CopyFullPos:
|
41 |
+
_target_: myria3d.pctl.transforms.transforms.CopyFullPos
|
42 |
+
GridSampling:
|
43 |
+
_target_: torch_geometric.transforms.GridSampling
|
44 |
+
_args_:
|
45 |
+
- 0.25
|
46 |
+
MinimumNumNodes:
|
47 |
+
_target_: myria3d.pctl.transforms.transforms.MinimumNumNodes
|
48 |
+
_args_:
|
49 |
+
- 300
|
50 |
+
MaximumNumNodes:
|
51 |
+
_target_: myria3d.pctl.transforms.transforms.MaximumNumNodes
|
52 |
+
_args_:
|
53 |
+
- 40000
|
54 |
+
CopySampledPos:
|
55 |
+
_target_: myria3d.pctl.transforms.transforms.CopySampledPos
|
56 |
+
Center:
|
57 |
+
_target_: torch_geometric.transforms.Center
|
58 |
+
normalizations:
|
59 |
+
NullifyLowestZ:
|
60 |
+
_target_: myria3d.pctl.transforms.transforms.NullifyLowestZ
|
61 |
+
NormalizePos:
|
62 |
+
_target_: myria3d.pctl.transforms.transforms.NormalizePos
|
63 |
+
subtile_width: ${datamodule.subtile_width}
|
64 |
+
StandardizeRGBAndIntensity:
|
65 |
+
_target_: myria3d.pctl.transforms.transforms.StandardizeRGBAndIntensity
|
66 |
+
preparations_eval_list: '${oc.dict.values: datamodule.transforms.preparations.eval}'
|
67 |
+
preparations_predict_list: '${oc.dict.values: datamodule.transforms.preparations.predict}'
|
68 |
+
normalizations_list: '${oc.dict.values: datamodule.transforms.normalizations}'
|
69 |
+
_target_: myria3d.pctl.datamodule.hdf5.HDF5LidarDataModule
|
70 |
+
epsg: 2154
|
71 |
+
data_dir: null
|
72 |
+
split_csv_path: null
|
73 |
+
hdf5_file_path: null
|
74 |
+
points_pre_transform:
|
75 |
+
_target_: functools.partial
|
76 |
+
_args_:
|
77 |
+
- ${get_method:myria3d.pctl.points_pre_transform.lidar_hd.lidar_hd_pre_transform}
|
78 |
+
pre_filter:
|
79 |
+
_target_: functools.partial
|
80 |
+
_args_:
|
81 |
+
- ${get_method:myria3d.pctl.dataset.utils.pre_filter_below_n_points}
|
82 |
+
min_num_nodes: 1
|
83 |
+
tile_width: 1000
|
84 |
+
subtile_width: 50
|
85 |
+
subtile_overlap_predict: ${predict.subtile_overlap}
|
86 |
+
batch_size: 10
|
87 |
+
num_workers: 3
|
88 |
+
prefetch_factor: 3
|
89 |
+
dataset_description:
|
90 |
+
_convert_: all
|
91 |
+
classification_preprocessing_dict:
|
92 |
+
3: 5
|
93 |
+
4: 5
|
94 |
+
66: 65
|
95 |
+
classification_dict:
|
96 |
+
1: unclassified
|
97 |
+
2: ground
|
98 |
+
5: vegetation
|
99 |
+
6: building
|
100 |
+
9: water
|
101 |
+
17: bridge
|
102 |
+
64: lasting_above
|
103 |
+
d_in: 9
|
104 |
+
num_classes: 7
|
105 |
+
callbacks:
|
106 |
+
log_code:
|
107 |
+
_target_: myria3d.callbacks.comet_callbacks.LogCode
|
108 |
+
code_dir: ${work_dir}/myria3d
|
109 |
+
log_logs_dir:
|
110 |
+
_target_: myria3d.callbacks.comet_callbacks.LogLogsPath
|
111 |
+
lr_monitor:
|
112 |
+
_target_: pytorch_lightning.callbacks.LearningRateMonitor
|
113 |
+
logging_interval: step
|
114 |
+
log_momentum: true
|
115 |
+
model_checkpoint:
|
116 |
+
_target_: pytorch_lightning.callbacks.ModelCheckpoint
|
117 |
+
monitor: val/loss_epoch
|
118 |
+
mode: min
|
119 |
+
save_top_k: 1
|
120 |
+
save_last: true
|
121 |
+
verbose: true
|
122 |
+
dirpath: checkpoints/
|
123 |
+
filename: epoch_{epoch:03d}
|
124 |
+
auto_insert_metric_name: false
|
125 |
+
early_stopping:
|
126 |
+
_target_: pytorch_lightning.callbacks.EarlyStopping
|
127 |
+
monitor: val/loss_epoch
|
128 |
+
mode: min
|
129 |
+
patience: 6
|
130 |
+
min_delta: 0
|
131 |
+
model:
|
132 |
+
_target_: myria3d.models.model.Model
|
133 |
+
d_in: ${dataset_description.d_in}
|
134 |
+
num_classes: ${dataset_description.num_classes}
|
135 |
+
classification_dict: ${dataset_description.classification_dict}
|
136 |
+
ckpt_path: FRACTAL-LidarHD_7cl_randlanet.ckpt
|
137 |
+
neural_net_class_name: PyGRandLANet
|
138 |
+
neural_net_hparams:
|
139 |
+
num_features: ${model.d_in}
|
140 |
+
num_classes: ${model.num_classes}
|
141 |
+
num_neighbors: 16
|
142 |
+
decimation: 4
|
143 |
+
return_logits: true
|
144 |
+
interpolation_k: ${predict.interpolator.interpolation_k}
|
145 |
+
num_workers: 4
|
146 |
+
logger:
|
147 |
+
comet:
|
148 |
+
_target_: pytorch_lightning.loggers.comet.CometLogger
|
149 |
+
api_key: ${oc.env:COMET_API_TOKEN}
|
150 |
+
workspace: ${oc.env:COMET_WORKSPACE}
|
151 |
+
project_name: ${oc.env:COMET_PROJECT_NAME}
|
152 |
+
experiment_name: DATAPAPER-LidarHD-20240416_100k_fractal-6GPUs
|
153 |
+
auto_log_co2: false
|
154 |
+
disabled: false
|
155 |
+
task:
|
156 |
+
task_name: predict
|
157 |
+
predict:
|
158 |
+
src_las: /path/to/input.las
|
159 |
+
output_dir: /path/to/output_dir/
|
160 |
+
ckpt_path: FRACTAL-LidarHD_7cl_randlanet.ckpt
|
161 |
+
gpus: 0
|
162 |
+
subtile_overlap: 0
|
163 |
+
interpolator:
|
164 |
+
_target_: myria3d.models.interpolation.Interpolator
|
165 |
+
interpolation_k: 10
|
166 |
+
classification_dict: ${dataset_description.classification_dict}
|
167 |
+
probas_to_save: [building,ground]
|
168 |
+
predicted_classification_channel: PredictedClassification
|
169 |
+
entropy_channel: entropy
|