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scannet200.txt
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2024-08-25 05:02:05,423 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0]
CUDA available: True
GPU 0,1,2: NVIDIA GeForce RTX 3090
CUDA_HOME: /usr/local/cuda-11.7
NVCC: Build cuda_11.7.r11.7/compiler.31294372_0
GCC: gcc (Ubuntu 10.5.0-1ubuntu1~22.04) 10.5.0
PyTorch: 1.7.1+cu110
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.0
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80
- CuDNN 8.0.5
- Magma 2.5.2
- Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,
TorchVision: 0.8.2+cu110
OpenCV: 4.10.0
MMCV: 1.2.7
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.0
MMDetection: 2.10.0
MMDetection3D: 0.8.0+a5dfdf3
------------------------------------------------------------
2024-08-25 05:02:05,424 - mmdet - INFO - Distributed training: False
2024-08-25 05:02:06,881 - mmdet - INFO - Config:
model = dict(
type='ImGeoNet',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=128,
num_outs=4),
neck_3d=dict(
type='FastIndoorImVoxelNeck',
in_channels=128,
out_channels=128,
n_blocks=[1, 1, 1]),
bbox_head=dict(
type='ScanNetImVoxelHeadV2',
loss_bbox=dict(type='AxisAlignedIoULoss', loss_weight=1.0),
n_classes=189,
n_channels=128,
n_reg_outs=6,
n_scales=3,
limit=27,
centerness_topk=18),
occ_head=dict(
type='OccupancyHead',
in_channels=256,
hidden_channels=128,
n_blocks=[1, 1, 1],
loss=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=10.0)),
voxel_size=(0.08, 0.08, 0.08),
n_voxels=(80, 80, 32),
depth_cast_margin=4)
train_cfg = dict()
test_cfg = dict(nms_pre=1000, iou_thr=0.25, score_thr=0.01)
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
dataset_type = 'ScanNetMultiViewDataset'
data_root = '../data/scannet/'
class_names = [
'wall', 'chair', 'floor', 'table', 'door', 'couch', 'cabinet', 'shelf',
'desk', 'office chair', 'bed', 'pillow', 'sink', 'picture', 'window',
'toilet', 'bookshelf', 'monitor', 'curtain', 'book', 'armchair',
'coffee table', 'box', 'refrigerator', 'lamp', 'kitchen cabinet', 'towel',
'clothes', 'tv', 'nightstand', 'counter', 'dresser', 'stool', 'cushion',
'plant', 'ceiling', 'bathtub', 'end table', 'dining table', 'keyboard',
'bag', 'backpack', 'toilet paper', 'printer', 'tv stand', 'whiteboard',
'blanket', 'shower curtain', 'trash can', 'closet', 'stairs', 'microwave',
'stove', 'shoe', 'computer tower', 'bottle', 'bin', 'ottoman', 'bench',
'board', 'washing machine', 'mirror', 'copier', 'basket', 'sofa chair',
'file cabinet', 'fan', 'laptop', 'shower', 'paper', 'person',
'paper towel dispenser', 'oven', 'blinds', 'rack', 'plate', 'blackboard',
'piano', 'suitcase', 'rail', 'radiator', 'recycling bin', 'container',
'wardrobe', 'soap dispenser', 'telephone', 'bucket', 'clock', 'stand',
'light', 'laundry basket', 'pipe', 'clothes dryer', 'guitar',
'toilet paper holder', 'seat', 'speaker', 'column', 'ladder',
'bathroom stall', 'shower wall', 'cup', 'jacket', 'storage bin',
'coffee maker', 'dishwasher', 'paper towel roll', 'machine', 'mat',
'windowsill', 'bar', 'toaster', 'bulletin board', 'ironing board',
'fireplace', 'soap dish', 'kitchen counter', 'doorframe',
'toilet paper dispenser', 'mini fridge', 'fire extinguisher', 'ball',
'hat', 'shower curtain rod', 'water cooler', 'paper cutter', 'tray',
'shower door', 'pillar', 'ledge', 'toaster oven', 'mouse',
'toilet seat cover dispenser', 'furniture', 'cart', 'scale', 'tissue box',
'light switch', 'crate', 'power outlet', 'decoration', 'sign', 'projector',
'closet door', 'vacuum cleaner', 'plunger', 'stuffed animal', 'headphones',
'dish rack', 'broom', 'range hood', 'dustpan', 'hair dryer',
'water bottle', 'handicap bar', 'vent', 'shower floor', 'water pitcher',
'mailbox', 'bowl', 'paper bag', 'projector screen', 'divider',
'laundry detergent', 'bathroom counter', 'object', 'bathroom vanity',
'closet wall', 'laundry hamper', 'bathroom stall door', 'ceiling light',
'trash bin', 'dumbbell', 'stair rail', 'tube', 'bathroom cabinet',
'closet rod', 'coffee kettle', 'shower head', 'keyboard piano',
'case of water bottles', 'coat rack', 'folded chair', 'fire alarm',
'power strip', 'calendar', 'poster', 'potted plant', 'mattress'
]
train_pipeline = [
dict(type='LoadAnnotations3D'),
dict(
type='MultiViewPipeline',
n_images=20,
transforms=[
dict(type='LoadImageFromFile'),
dict(type='Resize', img_scale=(640, 480), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(480, 640))
]),
dict(type='LoadDepthMap', depth_shift=1000.0),
dict(type='RandomShiftOrigin', std=(0.7, 0.7, 0.0)),
dict(
type='DefaultFormatBundle3D',
class_names=[
'wall', 'chair', 'floor', 'table', 'door', 'couch', 'cabinet',
'shelf', 'desk', 'office chair', 'bed', 'pillow', 'sink',
'picture', 'window', 'toilet', 'bookshelf', 'monitor', 'curtain',
'book', 'armchair', 'coffee table', 'box', 'refrigerator', 'lamp',
'kitchen cabinet', 'towel', 'clothes', 'tv', 'nightstand',
'counter', 'dresser', 'stool', 'cushion', 'plant', 'ceiling',
'bathtub', 'end table', 'dining table', 'keyboard', 'bag',
'backpack', 'toilet paper', 'printer', 'tv stand', 'whiteboard',
'blanket', 'shower curtain', 'trash can', 'closet', 'stairs',
'microwave', 'stove', 'shoe', 'computer tower', 'bottle', 'bin',
'ottoman', 'bench', 'board', 'washing machine', 'mirror', 'copier',
'basket', 'sofa chair', 'file cabinet', 'fan', 'laptop', 'shower',
'paper', 'person', 'paper towel dispenser', 'oven', 'blinds',
'rack', 'plate', 'blackboard', 'piano', 'suitcase', 'rail',
'radiator', 'recycling bin', 'container', 'wardrobe',
'soap dispenser', 'telephone', 'bucket', 'clock', 'stand', 'light',
'laundry basket', 'pipe', 'clothes dryer', 'guitar',
'toilet paper holder', 'seat', 'speaker', 'column', 'ladder',
'bathroom stall', 'shower wall', 'cup', 'jacket', 'storage bin',
'coffee maker', 'dishwasher', 'paper towel roll', 'machine', 'mat',
'windowsill', 'bar', 'toaster', 'bulletin board', 'ironing board',
'fireplace', 'soap dish', 'kitchen counter', 'doorframe',
'toilet paper dispenser', 'mini fridge', 'fire extinguisher',
'ball', 'hat', 'shower curtain rod', 'water cooler',
'paper cutter', 'tray', 'shower door', 'pillar', 'ledge',
'toaster oven', 'mouse', 'toilet seat cover dispenser',
'furniture', 'cart', 'scale', 'tissue box', 'light switch',
'crate', 'power outlet', 'decoration', 'sign', 'projector',
'closet door', 'vacuum cleaner', 'plunger', 'stuffed animal',
'headphones', 'dish rack', 'broom', 'range hood', 'dustpan',
'hair dryer', 'water bottle', 'handicap bar', 'vent',
'shower floor', 'water pitcher', 'mailbox', 'bowl', 'paper bag',
'projector screen', 'divider', 'laundry detergent',
'bathroom counter', 'object', 'bathroom vanity', 'closet wall',
'laundry hamper', 'bathroom stall door', 'ceiling light',
'trash bin', 'dumbbell', 'stair rail', 'tube', 'bathroom cabinet',
'closet rod', 'coffee kettle', 'shower head', 'keyboard piano',
'case of water bottles', 'coat rack', 'folded chair', 'fire alarm',
'power strip', 'calendar', 'poster', 'potted plant', 'mattress'
]),
dict(
type='Collect3D',
keys=[
'img', 'depth_maps', 'depth_masks', 'gt_bboxes_3d', 'gt_labels_3d'
])
]
test_pipeline = [
dict(
type='MultiViewPipeline',
n_images=50,
sample_method='linear',
transforms=[
dict(type='LoadImageFromFile'),
dict(type='Resize', img_scale=(640, 480), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(480, 640))
]),
dict(type='LoadDepthMap', depth_shift=1000.0),
dict(
type='DefaultFormatBundle3D',
class_names=[
'wall', 'chair', 'floor', 'table', 'door', 'couch', 'cabinet',
'shelf', 'desk', 'office chair', 'bed', 'pillow', 'sink',
'picture', 'window', 'toilet', 'bookshelf', 'monitor', 'curtain',
'book', 'armchair', 'coffee table', 'box', 'refrigerator', 'lamp',
'kitchen cabinet', 'towel', 'clothes', 'tv', 'nightstand',
'counter', 'dresser', 'stool', 'cushion', 'plant', 'ceiling',
'bathtub', 'end table', 'dining table', 'keyboard', 'bag',
'backpack', 'toilet paper', 'printer', 'tv stand', 'whiteboard',
'blanket', 'shower curtain', 'trash can', 'closet', 'stairs',
'microwave', 'stove', 'shoe', 'computer tower', 'bottle', 'bin',
'ottoman', 'bench', 'board', 'washing machine', 'mirror', 'copier',
'basket', 'sofa chair', 'file cabinet', 'fan', 'laptop', 'shower',
'paper', 'person', 'paper towel dispenser', 'oven', 'blinds',
'rack', 'plate', 'blackboard', 'piano', 'suitcase', 'rail',
'radiator', 'recycling bin', 'container', 'wardrobe',
'soap dispenser', 'telephone', 'bucket', 'clock', 'stand', 'light',
'laundry basket', 'pipe', 'clothes dryer', 'guitar',
'toilet paper holder', 'seat', 'speaker', 'column', 'ladder',
'bathroom stall', 'shower wall', 'cup', 'jacket', 'storage bin',
'coffee maker', 'dishwasher', 'paper towel roll', 'machine', 'mat',
'windowsill', 'bar', 'toaster', 'bulletin board', 'ironing board',
'fireplace', 'soap dish', 'kitchen counter', 'doorframe',
'toilet paper dispenser', 'mini fridge', 'fire extinguisher',
'ball', 'hat', 'shower curtain rod', 'water cooler',
'paper cutter', 'tray', 'shower door', 'pillar', 'ledge',
'toaster oven', 'mouse', 'toilet seat cover dispenser',
'furniture', 'cart', 'scale', 'tissue box', 'light switch',
'crate', 'power outlet', 'decoration', 'sign', 'projector',
'closet door', 'vacuum cleaner', 'plunger', 'stuffed animal',
'headphones', 'dish rack', 'broom', 'range hood', 'dustpan',
'hair dryer', 'water bottle', 'handicap bar', 'vent',
'shower floor', 'water pitcher', 'mailbox', 'bowl', 'paper bag',
'projector screen', 'divider', 'laundry detergent',
'bathroom counter', 'object', 'bathroom vanity', 'closet wall',
'laundry hamper', 'bathroom stall door', 'ceiling light',
'trash bin', 'dumbbell', 'stair rail', 'tube', 'bathroom cabinet',
'closet rod', 'coffee kettle', 'shower head', 'keyboard piano',
'case of water bottles', 'coat rack', 'folded chair', 'fire alarm',
'power strip', 'calendar', 'poster', 'potted plant', 'mattress'
],
with_label=False),
dict(type='Collect3D', keys=['img'])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=1,
train=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type='ScanNetMultiViewDataset',
data_root='../data/scannet/',
ann_file='../data/scannet/scannet200_infos_train.pkl',
pipeline=[
dict(type='LoadAnnotations3D'),
dict(
type='MultiViewPipeline',
n_images=20,
transforms=[
dict(type='LoadImageFromFile'),
dict(
type='Resize',
img_scale=(640, 480),
keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(480, 640))
]),
dict(type='LoadDepthMap', depth_shift=1000.0),
dict(type='RandomShiftOrigin', std=(0.7, 0.7, 0.0)),
dict(
type='DefaultFormatBundle3D',
class_names=[
'wall', 'chair', 'floor', 'table', 'door', 'couch',
'cabinet', 'shelf', 'desk', 'office chair', 'bed',
'pillow', 'sink', 'picture', 'window', 'toilet',
'bookshelf', 'monitor', 'curtain', 'book', 'armchair',
'coffee table', 'box', 'refrigerator', 'lamp',
'kitchen cabinet', 'towel', 'clothes', 'tv',
'nightstand', 'counter', 'dresser', 'stool', 'cushion',
'plant', 'ceiling', 'bathtub', 'end table',
'dining table', 'keyboard', 'bag', 'backpack',
'toilet paper', 'printer', 'tv stand', 'whiteboard',
'blanket', 'shower curtain', 'trash can', 'closet',
'stairs', 'microwave', 'stove', 'shoe',
'computer tower', 'bottle', 'bin', 'ottoman', 'bench',
'board', 'washing machine', 'mirror', 'copier',
'basket', 'sofa chair', 'file cabinet', 'fan',
'laptop', 'shower', 'paper', 'person',
'paper towel dispenser', 'oven', 'blinds', 'rack',
'plate', 'blackboard', 'piano', 'suitcase', 'rail',
'radiator', 'recycling bin', 'container', 'wardrobe',
'soap dispenser', 'telephone', 'bucket', 'clock',
'stand', 'light', 'laundry basket', 'pipe',
'clothes dryer', 'guitar', 'toilet paper holder',
'seat', 'speaker', 'column', 'ladder',
'bathroom stall', 'shower wall', 'cup', 'jacket',
'storage bin', 'coffee maker', 'dishwasher',
'paper towel roll', 'machine', 'mat', 'windowsill',
'bar', 'toaster', 'bulletin board', 'ironing board',
'fireplace', 'soap dish', 'kitchen counter',
'doorframe', 'toilet paper dispenser', 'mini fridge',
'fire extinguisher', 'ball', 'hat',
'shower curtain rod', 'water cooler', 'paper cutter',
'tray', 'shower door', 'pillar', 'ledge',
'toaster oven', 'mouse', 'toilet seat cover dispenser',
'furniture', 'cart', 'scale', 'tissue box',
'light switch', 'crate', 'power outlet', 'decoration',
'sign', 'projector', 'closet door', 'vacuum cleaner',
'plunger', 'stuffed animal', 'headphones', 'dish rack',
'broom', 'range hood', 'dustpan', 'hair dryer',
'water bottle', 'handicap bar', 'vent', 'shower floor',
'water pitcher', 'mailbox', 'bowl', 'paper bag',
'projector screen', 'divider', 'laundry detergent',
'bathroom counter', 'object', 'bathroom vanity',
'closet wall', 'laundry hamper', 'bathroom stall door',
'ceiling light', 'trash bin', 'dumbbell', 'stair rail',
'tube', 'bathroom cabinet', 'closet rod',
'coffee kettle', 'shower head', 'keyboard piano',
'case of water bottles', 'coat rack', 'folded chair',
'fire alarm', 'power strip', 'calendar', 'poster',
'potted plant', 'mattress'
]),
dict(
type='Collect3D',
keys=[
'img', 'depth_maps', 'depth_masks', 'gt_bboxes_3d',
'gt_labels_3d'
])
],
classes=[
'wall', 'chair', 'floor', 'table', 'door', 'couch', 'cabinet',
'shelf', 'desk', 'office chair', 'bed', 'pillow', 'sink',
'picture', 'window', 'toilet', 'bookshelf', 'monitor',
'curtain', 'book', 'armchair', 'coffee table', 'box',
'refrigerator', 'lamp', 'kitchen cabinet', 'towel', 'clothes',
'tv', 'nightstand', 'counter', 'dresser', 'stool', 'cushion',
'plant', 'ceiling', 'bathtub', 'end table', 'dining table',
'keyboard', 'bag', 'backpack', 'toilet paper', 'printer',
'tv stand', 'whiteboard', 'blanket', 'shower curtain',
'trash can', 'closet', 'stairs', 'microwave', 'stove', 'shoe',
'computer tower', 'bottle', 'bin', 'ottoman', 'bench', 'board',
'washing machine', 'mirror', 'copier', 'basket', 'sofa chair',
'file cabinet', 'fan', 'laptop', 'shower', 'paper', 'person',
'paper towel dispenser', 'oven', 'blinds', 'rack', 'plate',
'blackboard', 'piano', 'suitcase', 'rail', 'radiator',
'recycling bin', 'container', 'wardrobe', 'soap dispenser',
'telephone', 'bucket', 'clock', 'stand', 'light',
'laundry basket', 'pipe', 'clothes dryer', 'guitar',
'toilet paper holder', 'seat', 'speaker', 'column', 'ladder',
'bathroom stall', 'shower wall', 'cup', 'jacket',
'storage bin', 'coffee maker', 'dishwasher',
'paper towel roll', 'machine', 'mat', 'windowsill', 'bar',
'toaster', 'bulletin board', 'ironing board', 'fireplace',
'soap dish', 'kitchen counter', 'doorframe',
'toilet paper dispenser', 'mini fridge', 'fire extinguisher',
'ball', 'hat', 'shower curtain rod', 'water cooler',
'paper cutter', 'tray', 'shower door', 'pillar', 'ledge',
'toaster oven', 'mouse', 'toilet seat cover dispenser',
'furniture', 'cart', 'scale', 'tissue box', 'light switch',
'crate', 'power outlet', 'decoration', 'sign', 'projector',
'closet door', 'vacuum cleaner', 'plunger', 'stuffed animal',
'headphones', 'dish rack', 'broom', 'range hood', 'dustpan',
'hair dryer', 'water bottle', 'handicap bar', 'vent',
'shower floor', 'water pitcher', 'mailbox', 'bowl',
'paper bag', 'projector screen', 'divider',
'laundry detergent', 'bathroom counter', 'object',
'bathroom vanity', 'closet wall', 'laundry hamper',
'bathroom stall door', 'ceiling light', 'trash bin',
'dumbbell', 'stair rail', 'tube', 'bathroom cabinet',
'closet rod', 'coffee kettle', 'shower head', 'keyboard piano',
'case of water bottles', 'coat rack', 'folded chair',
'fire alarm', 'power strip', 'calendar', 'poster',
'potted plant', 'mattress'
],
filter_empty_gt=True,
box_type_3d='Depth')),
val=dict(
type='ScanNetMultiViewDataset',
data_root='../data/scannet/',
ann_file='../data/scannet/scannet200_infos_val.pkl',
pipeline=[
dict(
type='MultiViewPipeline',
n_images=50,
sample_method='linear',
transforms=[
dict(type='LoadImageFromFile'),
dict(type='Resize', img_scale=(640, 480), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(480, 640))
]),
dict(type='LoadDepthMap', depth_shift=1000.0),
dict(
type='DefaultFormatBundle3D',
class_names=[
'wall', 'chair', 'floor', 'table', 'door', 'couch',
'cabinet', 'shelf', 'desk', 'office chair', 'bed',
'pillow', 'sink', 'picture', 'window', 'toilet',
'bookshelf', 'monitor', 'curtain', 'book', 'armchair',
'coffee table', 'box', 'refrigerator', 'lamp',
'kitchen cabinet', 'towel', 'clothes', 'tv', 'nightstand',
'counter', 'dresser', 'stool', 'cushion', 'plant',
'ceiling', 'bathtub', 'end table', 'dining table',
'keyboard', 'bag', 'backpack', 'toilet paper', 'printer',
'tv stand', 'whiteboard', 'blanket', 'shower curtain',
'trash can', 'closet', 'stairs', 'microwave', 'stove',
'shoe', 'computer tower', 'bottle', 'bin', 'ottoman',
'bench', 'board', 'washing machine', 'mirror', 'copier',
'basket', 'sofa chair', 'file cabinet', 'fan', 'laptop',
'shower', 'paper', 'person', 'paper towel dispenser',
'oven', 'blinds', 'rack', 'plate', 'blackboard', 'piano',
'suitcase', 'rail', 'radiator', 'recycling bin',
'container', 'wardrobe', 'soap dispenser', 'telephone',
'bucket', 'clock', 'stand', 'light', 'laundry basket',
'pipe', 'clothes dryer', 'guitar', 'toilet paper holder',
'seat', 'speaker', 'column', 'ladder', 'bathroom stall',
'shower wall', 'cup', 'jacket', 'storage bin',
'coffee maker', 'dishwasher', 'paper towel roll',
'machine', 'mat', 'windowsill', 'bar', 'toaster',
'bulletin board', 'ironing board', 'fireplace',
'soap dish', 'kitchen counter', 'doorframe',
'toilet paper dispenser', 'mini fridge',
'fire extinguisher', 'ball', 'hat', 'shower curtain rod',
'water cooler', 'paper cutter', 'tray', 'shower door',
'pillar', 'ledge', 'toaster oven', 'mouse',
'toilet seat cover dispenser', 'furniture', 'cart',
'scale', 'tissue box', 'light switch', 'crate',
'power outlet', 'decoration', 'sign', 'projector',
'closet door', 'vacuum cleaner', 'plunger',
'stuffed animal', 'headphones', 'dish rack', 'broom',
'range hood', 'dustpan', 'hair dryer', 'water bottle',
'handicap bar', 'vent', 'shower floor', 'water pitcher',
'mailbox', 'bowl', 'paper bag', 'projector screen',
'divider', 'laundry detergent', 'bathroom counter',
'object', 'bathroom vanity', 'closet wall',
'laundry hamper', 'bathroom stall door', 'ceiling light',
'trash bin', 'dumbbell', 'stair rail', 'tube',
'bathroom cabinet', 'closet rod', 'coffee kettle',
'shower head', 'keyboard piano', 'case of water bottles',
'coat rack', 'folded chair', 'fire alarm', 'power strip',
'calendar', 'poster', 'potted plant', 'mattress'
],
with_label=False),
dict(type='Collect3D', keys=['img'])
],
classes=[
'wall', 'chair', 'floor', 'table', 'door', 'couch', 'cabinet',
'shelf', 'desk', 'office chair', 'bed', 'pillow', 'sink',
'picture', 'window', 'toilet', 'bookshelf', 'monitor', 'curtain',
'book', 'armchair', 'coffee table', 'box', 'refrigerator', 'lamp',
'kitchen cabinet', 'towel', 'clothes', 'tv', 'nightstand',
'counter', 'dresser', 'stool', 'cushion', 'plant', 'ceiling',
'bathtub', 'end table', 'dining table', 'keyboard', 'bag',
'backpack', 'toilet paper', 'printer', 'tv stand', 'whiteboard',
'blanket', 'shower curtain', 'trash can', 'closet', 'stairs',
'microwave', 'stove', 'shoe', 'computer tower', 'bottle', 'bin',
'ottoman', 'bench', 'board', 'washing machine', 'mirror', 'copier',
'basket', 'sofa chair', 'file cabinet', 'fan', 'laptop', 'shower',
'paper', 'person', 'paper towel dispenser', 'oven', 'blinds',
'rack', 'plate', 'blackboard', 'piano', 'suitcase', 'rail',
'radiator', 'recycling bin', 'container', 'wardrobe',
'soap dispenser', 'telephone', 'bucket', 'clock', 'stand', 'light',
'laundry basket', 'pipe', 'clothes dryer', 'guitar',
'toilet paper holder', 'seat', 'speaker', 'column', 'ladder',
'bathroom stall', 'shower wall', 'cup', 'jacket', 'storage bin',
'coffee maker', 'dishwasher', 'paper towel roll', 'machine', 'mat',
'windowsill', 'bar', 'toaster', 'bulletin board', 'ironing board',
'fireplace', 'soap dish', 'kitchen counter', 'doorframe',
'toilet paper dispenser', 'mini fridge', 'fire extinguisher',
'ball', 'hat', 'shower curtain rod', 'water cooler',
'paper cutter', 'tray', 'shower door', 'pillar', 'ledge',
'toaster oven', 'mouse', 'toilet seat cover dispenser',
'furniture', 'cart', 'scale', 'tissue box', 'light switch',
'crate', 'power outlet', 'decoration', 'sign', 'projector',
'closet door', 'vacuum cleaner', 'plunger', 'stuffed animal',
'headphones', 'dish rack', 'broom', 'range hood', 'dustpan',
'hair dryer', 'water bottle', 'handicap bar', 'vent',
'shower floor', 'water pitcher', 'mailbox', 'bowl', 'paper bag',
'projector screen', 'divider', 'laundry detergent',
'bathroom counter', 'object', 'bathroom vanity', 'closet wall',
'laundry hamper', 'bathroom stall door', 'ceiling light',
'trash bin', 'dumbbell', 'stair rail', 'tube', 'bathroom cabinet',
'closet rod', 'coffee kettle', 'shower head', 'keyboard piano',
'case of water bottles', 'coat rack', 'folded chair', 'fire alarm',
'power strip', 'calendar', 'poster', 'potted plant', 'mattress'
],
test_mode=True,
box_type_3d='Depth'),
test=dict(
type='ScanNetMultiViewDataset',
data_root='../data/scannet/',
ann_file='../data/scannet/scannet200_infos_val.pkl',
pipeline=[
dict(
type='MultiViewPipeline',
n_images=50,
sample_method='linear',
transforms=[
dict(type='LoadImageFromFile'),
dict(type='Resize', img_scale=(640, 480), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(480, 640))
]),
dict(type='LoadDepthMap', depth_shift=1000.0),
dict(
type='DefaultFormatBundle3D',
class_names=[
'wall', 'chair', 'floor', 'table', 'door', 'couch',
'cabinet', 'shelf', 'desk', 'office chair', 'bed',
'pillow', 'sink', 'picture', 'window', 'toilet',
'bookshelf', 'monitor', 'curtain', 'book', 'armchair',
'coffee table', 'box', 'refrigerator', 'lamp',
'kitchen cabinet', 'towel', 'clothes', 'tv', 'nightstand',
'counter', 'dresser', 'stool', 'cushion', 'plant',
'ceiling', 'bathtub', 'end table', 'dining table',
'keyboard', 'bag', 'backpack', 'toilet paper', 'printer',
'tv stand', 'whiteboard', 'blanket', 'shower curtain',
'trash can', 'closet', 'stairs', 'microwave', 'stove',
'shoe', 'computer tower', 'bottle', 'bin', 'ottoman',
'bench', 'board', 'washing machine', 'mirror', 'copier',
'basket', 'sofa chair', 'file cabinet', 'fan', 'laptop',
'shower', 'paper', 'person', 'paper towel dispenser',
'oven', 'blinds', 'rack', 'plate', 'blackboard', 'piano',
'suitcase', 'rail', 'radiator', 'recycling bin',
'container', 'wardrobe', 'soap dispenser', 'telephone',
'bucket', 'clock', 'stand', 'light', 'laundry basket',
'pipe', 'clothes dryer', 'guitar', 'toilet paper holder',
'seat', 'speaker', 'column', 'ladder', 'bathroom stall',
'shower wall', 'cup', 'jacket', 'storage bin',
'coffee maker', 'dishwasher', 'paper towel roll',
'machine', 'mat', 'windowsill', 'bar', 'toaster',
'bulletin board', 'ironing board', 'fireplace',
'soap dish', 'kitchen counter', 'doorframe',
'toilet paper dispenser', 'mini fridge',
'fire extinguisher', 'ball', 'hat', 'shower curtain rod',
'water cooler', 'paper cutter', 'tray', 'shower door',
'pillar', 'ledge', 'toaster oven', 'mouse',
'toilet seat cover dispenser', 'furniture', 'cart',
'scale', 'tissue box', 'light switch', 'crate',
'power outlet', 'decoration', 'sign', 'projector',
'closet door', 'vacuum cleaner', 'plunger',
'stuffed animal', 'headphones', 'dish rack', 'broom',
'range hood', 'dustpan', 'hair dryer', 'water bottle',
'handicap bar', 'vent', 'shower floor', 'water pitcher',
'mailbox', 'bowl', 'paper bag', 'projector screen',
'divider', 'laundry detergent', 'bathroom counter',
'object', 'bathroom vanity', 'closet wall',
'laundry hamper', 'bathroom stall door', 'ceiling light',
'trash bin', 'dumbbell', 'stair rail', 'tube',
'bathroom cabinet', 'closet rod', 'coffee kettle',
'shower head', 'keyboard piano', 'case of water bottles',
'coat rack', 'folded chair', 'fire alarm', 'power strip',
'calendar', 'poster', 'potted plant', 'mattress'
],
with_label=False),
dict(type='Collect3D', keys=['img'])
],
classes=[
'wall', 'chair', 'floor', 'table', 'door', 'couch', 'cabinet',
'shelf', 'desk', 'office chair', 'bed', 'pillow', 'sink',
'picture', 'window', 'toilet', 'bookshelf', 'monitor', 'curtain',
'book', 'armchair', 'coffee table', 'box', 'refrigerator', 'lamp',
'kitchen cabinet', 'towel', 'clothes', 'tv', 'nightstand',
'counter', 'dresser', 'stool', 'cushion', 'plant', 'ceiling',
'bathtub', 'end table', 'dining table', 'keyboard', 'bag',
'backpack', 'toilet paper', 'printer', 'tv stand', 'whiteboard',
'blanket', 'shower curtain', 'trash can', 'closet', 'stairs',
'microwave', 'stove', 'shoe', 'computer tower', 'bottle', 'bin',
'ottoman', 'bench', 'board', 'washing machine', 'mirror', 'copier',
'basket', 'sofa chair', 'file cabinet', 'fan', 'laptop', 'shower',
'paper', 'person', 'paper towel dispenser', 'oven', 'blinds',
'rack', 'plate', 'blackboard', 'piano', 'suitcase', 'rail',
'radiator', 'recycling bin', 'container', 'wardrobe',
'soap dispenser', 'telephone', 'bucket', 'clock', 'stand', 'light',
'laundry basket', 'pipe', 'clothes dryer', 'guitar',
'toilet paper holder', 'seat', 'speaker', 'column', 'ladder',
'bathroom stall', 'shower wall', 'cup', 'jacket', 'storage bin',
'coffee maker', 'dishwasher', 'paper towel roll', 'machine', 'mat',
'windowsill', 'bar', 'toaster', 'bulletin board', 'ironing board',
'fireplace', 'soap dish', 'kitchen counter', 'doorframe',
'toilet paper dispenser', 'mini fridge', 'fire extinguisher',
'ball', 'hat', 'shower curtain rod', 'water cooler',
'paper cutter', 'tray', 'shower door', 'pillar', 'ledge',
'toaster oven', 'mouse', 'toilet seat cover dispenser',
'furniture', 'cart', 'scale', 'tissue box', 'light switch',
'crate', 'power outlet', 'decoration', 'sign', 'projector',
'closet door', 'vacuum cleaner', 'plunger', 'stuffed animal',
'headphones', 'dish rack', 'broom', 'range hood', 'dustpan',
'hair dryer', 'water bottle', 'handicap bar', 'vent',
'shower floor', 'water pitcher', 'mailbox', 'bowl', 'paper bag',
'projector screen', 'divider', 'laundry detergent',
'bathroom counter', 'object', 'bathroom vanity', 'closet wall',
'laundry hamper', 'bathroom stall door', 'ceiling light',
'trash bin', 'dumbbell', 'stair rail', 'tube', 'bathroom cabinet',
'closet rod', 'coffee kettle', 'shower head', 'keyboard piano',
'case of water bottles', 'coat rack', 'folded chair', 'fire alarm',
'power strip', 'calendar', 'poster', 'potted plant', 'mattress'
],
test_mode=True,
box_type_3d='Depth'))
optimizer = dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0001,
paramwise_cfg=dict(
custom_keys=dict(backbone=dict(lr_mult=0.1, decay_mult=1.0))))
optimizer_config = dict(grad_clip=dict(max_norm=35.0, norm_type=2))
total_epochs = 30
lr_config = dict(policy='step', step=[8, 29])
checkpoint_config = dict(interval=1, max_keep_ckpts=30)
log_config = dict(
interval=50,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])
evaluation = dict(interval=1)
dist_params = dict(backend='nccl')
find_unused_parameters = True
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
work_dir = 'work_dir/imgeonet_scannet200_vx808032'
gpu_ids = range(0, 1)
2024-08-25 05:02:06,882 - mmdet - INFO - Set random seed to 0, deterministic: False
2024-08-25 05:02:07,130 - mmdet - INFO - load model from: torchvision://resnet50
2024-08-25 05:02:07,130 - mmdet - INFO - Use load_from_torchvision loader
2024-08-25 05:02:07,258 - mmdet - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: fc.weight, fc.bias
2024-08-25 05:02:07,270 - mmdet - INFO - Model:
ImGeoNet(
(backbone): ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): ResLayer(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer2): ResLayer(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer3): ResLayer(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer4): ResLayer(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
)
(neck): FPN(
(lateral_convs): ModuleList(
(0): ConvModule(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
)
(1): ConvModule(
(conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
)
(2): ConvModule(
(conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1))
)
(3): ConvModule(
(conv): Conv2d(2048, 128, kernel_size=(1, 1), stride=(1, 1))
)
)
(fpn_convs): ModuleList(
(0): ConvModule(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): ConvModule(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): ConvModule(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): ConvModule(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
(neck_3d): FastIndoorImVoxelNeck(
(down_layer_0): Sequential(
(0): BasicBlock3dV2(
(conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(norm1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(norm2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(out_block_0): Sequential(
(0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(down_layer_1): Sequential(
(0): BasicBlock3dV2(
(conv1): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
(norm1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(norm2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False)
(1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(up_block_1): Sequential(
(0): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)
(1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(4): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
(out_block_1): Sequential(
(0): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(down_layer_2): Sequential(
(0): BasicBlock3dV2(
(conv1): Conv3d(256, 512, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
(norm1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(norm2): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False)
(1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(up_block_2): Sequential(
(0): ConvTranspose3d(512, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)
(1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(4): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
(out_block_2): Sequential(
(0): Conv3d(512, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(bbox_head): ScanNetImVoxelHeadV2(
(loss_centerness): CrossEntropyLoss()
(loss_bbox): AxisAlignedIoULoss()
(loss_cls): FocalLoss()
(centerness_conv): Conv3d(128, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(reg_conv): Conv3d(128, 6, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(cls_conv): Conv3d(128, 189, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
(scales): ModuleList(
(0): Scale()
(1): Scale()
(2): Scale()
)
)
(occ_head): OccupancyHead(
(pre_proj): Sequential(
(0): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
(1): LeakyReLU(negative_slope=0.01)
)
(net): OccupancyNet(
(down_layer_0): Sequential(
(0): BasicBlock3dV2(
(conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(norm1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(norm2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(out_block_0): Sequential(
(0): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(down_layer_1): Sequential(
(0): BasicBlock3dV2(
(conv1): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
(norm1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(norm2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False)
(1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(up_block_1): Sequential(
(0): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)
(1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(4): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
(out_block_1): Sequential(
(0): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(down_layer_2): Sequential(
(0): BasicBlock3dV2(
(conv1): Conv3d(256, 512, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
(norm1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
(norm2): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False)
(1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(up_block_2): Sequential(
(0): ConvTranspose3d(512, 256, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)
(1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)