Key Error Index during Testing for Custom Dataset in Scannet format

[Adam Valin]

I run into this error :

Traceback (most recent call last): File "tools/test.py", line 38, in <module> main() File "tools/test.py", line 27, in main launch( File "/gpfs/home4/avalin/CityThesis/Pointcept/pointcept/engines/launch.py", line 89, in launch main_func(*cfg) File "tools/test.py", line 20, in main_worker tester.test() File "/gpfs/home4/avalin/CityThesis/Pointcept/pointcept/engines/test.py", line 185, in test idx_part = input_dict["index"] KeyError: 'index'

Here is my config file

`base = ["../base/default_runtime.py"]

misc custom setting

batch_size = 4 # bs: total bs in all gpus
num_worker = 4
mix_prob = 0.8
empty_cache = False
enable_amp = True

model settings

model = dict(
type="DefaultSegmentorV2",
num_classes=2,
backbone_out_channels=64,
backbone=dict(
type="PT-v3m1",
in_channels=9,
order=("z", "z-trans", "hilbert", "hilbert-trans"),
stride=(2, 2, 2, 2),
enc_depths=(2, 2, 2, 6, 2),
enc_channels=(32, 64, 128, 256, 512),
enc_num_head=(2, 4, 8, 16, 32),
enc_patch_size=(1024, 1024, 1024, 1024, 1024),
dec_depths=(2, 2, 2, 2),
dec_channels=(64, 64, 128, 256),
dec_num_head=(4, 4, 8, 16),
dec_patch_size=(1024, 1024, 1024, 1024),
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
attn_drop=0.0,
proj_drop=0.0,
drop_path=0.3,
shuffle_orders=True,
pre_norm=True,
enable_rpe=False,
enable_flash=True,
upcast_attention=False,
upcast_softmax=False,
cls_mode=False,
pdnorm_bn=False,
pdnorm_ln=False,
pdnorm_decouple=True,
pdnorm_adaptive=False,
pdnorm_affine=True,
pdnorm_conditions=("ScanNet", "S3DIS", "Structured3D"),
),
criteria=[
dict(type="CrossEntropyLoss", loss_weight=1.0, ignore_index=-1),
dict(type="LovaszLoss", mode="multiclass", loss_weight=1.0, ignore_index=-1),
],
)

scheduler settings

epoch = 800
optimizer = dict(type="AdamW", lr=0.006, weight_decay=0.05)
scheduler = dict(
type="OneCycleLR",
max_lr=[0.006, 0.0006],
pct_start=0.05,
anneal_strategy="cos",
div_factor=10.0,
final_div_factor=1000.0,
)
param_dicts = [dict(keyword="block", lr=0.0006)]

dataset settings

dataset_type = "CustomDataset"
data_root = "data/debug_padded"

data = dict(
num_classes=2,
ignore_index=-1,
names=[
"unlabeled",
"light source"
],
train=dict(
type=dataset_type,
split="train",
data_root=data_root,
transform=[
dict(type="CenterShift", apply_z=True),
dict(
type="RandomDropout", dropout_ratio=0.2, dropout_application_ratio=0.2
),
# dict(type="RandomRotateTargetAngle", angle=(1/2, 1, 3/2), center=[0, 0, 0], axis="z", p=0.75),
dict(type="RandomRotate", angle=[-1, 1], axis="z", center=[0, 0, 0], p=0.5),
dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="x", p=0.5),
dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="y", p=0.5),
dict(type="RandomScale", scale=[0.9, 1.1]),
# dict(type="RandomShift", shift=[0.2, 0.2, 0.2]),
dict(type="RandomFlip", p=0.5),
dict(type="RandomJitter", sigma=0.005, clip=0.02),
# dict(type="ElasticDistortion", distortion_params=[[0.2, 0.4], [0.8, 1.6]]),
dict(type="ChromaticAutoContrast", p=0.2, blend_factor=None),
dict(type="ChromaticTranslation", p=0.95, ratio=0.05),
dict(type="ChromaticJitter", p=0.95, std=0.05),
# dict(type="HueSaturationTranslation", hue_max=0.2, saturation_max=0.2),
# dict(type="RandomColorDrop", p=0.2, color_augment=0.0),
dict(
type="GridSample",
grid_size=0.3,
hash_type="fnv",
mode="train",
return_grid_coord=True,
),
# dict(type="Copy", keys_dict={"grid_size": 0.01}),
dict(type="SphereCrop", point_max=102400, mode="random"),
dict(type="CenterShift", apply_z=False),
dict(type="NormalizeColor"),
# dict(type="ShufflePoint"),
dict(type="ToTensor"),
dict(
type="Collect",
keys=("coord","grid_coord", "segment"),
feat_keys=("coord","color","normal"),
),
],
test_mode=False,
),
val=dict(
type=dataset_type,
split="val",
data_root=data_root,
transform=[
dict(type="CenterShift", apply_z=True),
dict(
type="GridSample",
grid_size=0.3,
hash_type="fnv",
mode="train",
return_grid_coord=True,
),
dict(type="CenterShift", apply_z=False),
dict(type="NormalizeColor"),
dict(type="ToTensor"),
dict(
type="Collect",
keys=("coord","grid_coord", "segment"),
feat_keys=("coord","color","normal"),
),
],
test_mode=False,
),
test=dict(
type=dataset_type,
split="val",
data_root=data_root,
transform=[
dict(type="CenterShift", apply_z=True),
dict(type="NormalizeColor"),
],
test_mode=True,
test_cfg=dict(
voxelize=dict(
type="GridSample",
grid_size=0.3,
hash_type="fnv",
mode="train",
keys = ("coord", "color", "intensity", "normal"),
return_grid_coord=True,
),
crop=None,
post_transform=[
dict(type="CenterShift", apply_z=False),
dict(type="ToTensor"),
dict(
type="Collect",
keys=("coord","grid_coord"),
feat_keys=("coord","color","normal"),
),
],
aug_transform=[
[
dict(
type="RandomRotateTargetAngle",
angle=[0],
axis="z",
center=[0, 0, 0],
p=1,
)
],
[
dict(
type="RandomRotateTargetAngle",
angle=[1 / 2],
axis="z",
center=[0, 0, 0],
p=1,
)
],
[
dict(
type="RandomRotateTargetAngle",
angle=[1],
axis="z",
center=[0, 0, 0],
p=1,
)
],
[
dict(
type="RandomRotateTargetAngle",
angle=[3 / 2],
axis="z",
center=[0, 0, 0],
p=1,
)
],
[
dict(
type="RandomRotateTargetAngle",
angle=[0],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[0.95, 0.95]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[1 / 2],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[0.95, 0.95]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[1],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[0.95, 0.95]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[3 / 2],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[0.95, 0.95]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[0],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[1.05, 1.05]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[1 / 2],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[1.05, 1.05]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[1],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[1.05, 1.05]),
],
[
dict(
type="RandomRotateTargetAngle",
angle=[3 / 2],
axis="z",
center=[0, 0, 0],
p=1,
),
dict(type="RandomScale", scale=[1.05, 1.05]),
],
[dict(type="RandomFlip", p=1)],
],
),
),
)`

I know that my Collect transform does not pick up the "index" index because my dataset does not include this key. However, this "index" index is present in the semseg-pt-v3m1-0-base.py file on which I based my config. Got any idea to help me ? thank you very much