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###
# Pretrain on all data with all losses
###
# Experiment general info
name: "FinalOVPretrain"
rng_seed: 42
num_gpu: 1
mode: "warmup"
note: ""
num_views: 32
# Choose keywords to feature your saving directory
naming_keywords: ["dataloader.batchsize", "task", "note", "time"]
base_dir: "results"
exp_dir: ""
save_frequency: 10
resume: False
stage: ['warmup', 'pretrain']
is_pretrain: True
debug:
flag: False
debug_size: 20
hard_debug: False
logger:
name: "wandb"
entity: "yem"
project: "3DReason"
# dataset details
data:
note: "all"
# train: ['ScanNetSpatialRefer','ARKitSceneSpatialRefer','MultiScanSpatialRefer','HMSpatialRefer','RScanSpatialRefer']
warmup: ['LSUNSpatialRefer']
train: ['ScanNetSpatialRefer']
val: ['ScanNetSpatialRefer']
test: ['ScanNetSpatialRefer']
# train: ['ScanNetSpatialRefer']
# val: ['ScanNetSpatialRefer']
# test: ['ScanNetSpatialRefer']
args:
max_obj_len: 80
max_seq_len: 50
num_points: 1024
pc_type: 'pred'
sem_type: '607'
filter_lang: False
txt_mask_ratio: 0.15
pc_mask_ratio: 0.1
rot_aug: True
mask_strategy: random
use_scene_cap: False
max_scene_cap_len: 600
ScanNetSpatialRefer:
train:
# sources: [ 'scanrefer', 'referit3d', 'sgrefer', 'sgcaption' ]
sources: ['scannet_view_cap']
referit3d:
anno_type: ['sr3d', 'nr3d']
sr3d_plus_aug: True
sgrefer:
anno_type: [ 'rel2_gpt', 'rel2_template', 'relm_gpt', 'relm_template', 'star_gpt', 'star_template'] #
sgcaption:
anno_type: ['gpt', 'template']
val:
sources: ['scannet_view_cap']
referit3d:
anno_type: ['sr3d'] # 'nr3d', 'sr3d'
sr3d_plus_aug: False
sgrefer:
anno_type: ['template'] # 'template', 'gpt_chain'
sgcaption:
anno_type: ['gpt']
test:
sources: ['scannet_view_cap']
referit3d:
anno_type: ['sr3d'] # 'nr3d', 'sr3d'
sr3d_plus_aug: False
sgrefer:
anno_type: ['template'] # 'template', 'gpt', 'gpt_chain'
sgcaption:
anno_type: ['gpt']
LSUNSpatialRefer:
warmup:
sources: ['lsun_view_cap']
RScanSpatialRefer:
train:
sources: ['3rscan_view_cap']
MultiScanSpatialRefer:
train:
sources: ['anno','rel2_template','rel2_gpt','relm_template','relm_gpt','star_template','star_gpt']
val:
sources: [ 'anno', 'rel2_template', 'relm_gpt', 'relm_template', 'star_template', 'star_gpt' ]
test:
sources: [ 'anno', 'rel2_template', 'relm_gpt', 'relm_template', 'star_template', 'star_gpt' ]
ARKitSceneSpatialRefer:
train:
sources: ['arkitscenes_view_cap']
HMSpatialRefer:
train:
sources: ['anno','rel2_template','rel2_gpt','relm_template','relm_gpt','star_template','star_gpt']
val:
sources: [ 'anno', 'rel2_template', 'relm_gpt', 'relm_template', 'star_template' ]
test:
sources: [ 'anno', 'rel2_template', 'relm_gpt', 'relm_template', 'star_template' ]
use_voxel: False
scan_family_base: "../PointMapVerse/existing_datasets/ScanNet"
lsun_base: "../PointMapVerse/existing_datasets/lsun"
rscan_base: "../PointMapVerse/existing_datasets/3RScan"
arkitscene_base: '../PointMapVerse/existing_datasets/Arkitscenes'
multiscan_base: '../PointMapVerse/existing_datasets/MultiScan'
hm_base: '../PointMapVerse/existing_datasets/HM3D'
procthor_base: '../PointMapVerse/existing_datasets/ProcThor'
s3d_base: '../PointMapVerse/existing_datasets/Structured3D'
data_aug:
aug_list: ['scene_aug']
scene_aug:
translation:
enabled: False
value: [1.0, 1.0, 1.0]
p: 1.0
scaling:
enabled: False
p: 1.0
value: [0.9, 1.1]
flip:
enabled: False
p: 0.5
rotation:
enabled: True
p: 1.0
axis_align: True
value: [0.0, 0.0, 1.0]
shuffle: True
color_jitter: False
order_shuffle: False
obj_aug:
translation:
enabled: False
value: [0.1, 0.1, 0.1]
p: 1.0
rotation:
enabled: False
p: 1.0
axis_align: False
value: [0.0, 0.0, 0.1]
shuffle: True
random_jitter:
enabled: False
value: 0.01
accord_to_size: False
p: 1.0
pts_shuffle: True
# task details: 'Pretrain', 'scanqa', 'spatialrefer'
task: 'Pretrain'
# 'MaskDatasetWrapper', 'ScanFamilyDatasetWrapper', 'MaskMVDatasetWrapper'
data_wrapper:
warmup: 'SceneDatasetWrapper'
train: 'SceneDatasetWrapper'
val: 'SceneDatasetWrapper'
test: 'SceneDatasetWrapper'
# Training details
trainer: "OpenVocabTrainer"
ckpt_path: ""
pretrain_ckpt_path: ""
# dataloader details
dataloader:
batchsize: 256
num_workers: 2
balance_dataset: False
filter_empty_annotations: False
solver:
gradient_accumulation_steps: 1
epochs_per_save: 20
epochs_per_eval: 1
lr: 1e-4
grad_norm: 5.0
warmup_epochs: 100
epochs: 100
optim:
name: 'AdamW'
args:
betas: [0.9, 0.98]
weight_decay: 0.05
sched:
name: 'warmup_cosine'
args:
warmup_steps: 500
minimum_ratio: 0.1
eval:
train:
name: 'PretrainEval'
val:
name: 'ScanReferEval'
save: False
# Model details
model:
name: OpenVocab
language:
# This part could be further optimized to be using
# huggingface yaml config files
name: 'SigLIPLanguageEncoder'
args:
weights: 'fg-clip-base'
# hidden_size: 768
# num_hidden_layers: 4
# num_attention_heads: 12
# type_vocab_size: 2
lr: 1e-4
vision:
name: 'fg-clip-base'
args:
backbone: 'pointnet++'
hidden_size: 768
freeze: True
path: 'pretrained_weights/pointnetpp-open-bert'
num_attention_heads: 12
spatial_dim: 5
num_layers: 4
dim_loc: 6
dim_feedforward: 2048
attn_type: spatial
pairwise_rel_type: 'center'
use_matmul_label: False
lang_type: 'bert'
lang_path: 'pretrained_weights/607_text_embeddings'
lr: 1e-4
grounding:
name: 'UnifiedSpatialCrossEncoderV2'
args:
hidden_size: 768
num_attention_heads: 12
num_layers: 4
dim_feedforward: 2048
dim_loc: 6
lr: 1e-4
inter: before
heads:
head_list: []
pretrain_head:
name: 'OVPretrainHead'
args:
hidden_size: 768
vocab_size: 30522
loss_type: 'ListLoss'
loss_list: [
'WarmUpPM_loss'
]
vis_loss_list: [
'WarmUpPM_loss'
] |