import os from dataclasses import dataclass, field from typing import Any, Dict, List, Optional, Iterable import os.path as osp from hydra.core.config_store import ConfigStore from hydra.conf import RunDir @dataclass class CustomHydraRunDir(RunDir): dir: str = './outputs/${run.name}/single' @dataclass class RunConfig: name: str = 'debug' job: str = 'train' mixed_precision: str = 'fp16' # 'no' cpu: bool = False seed: int = 42 val_before_training: bool = True vis_before_training: bool = True limit_train_batches: Optional[int] = None limit_val_batches: Optional[int] = None max_steps: int = 100_000 checkpoint_freq: int = 1_000 val_freq: int = 5_000 vis_freq: int = 5_000 # vis_freq: int = 10_000 log_step_freq: int = 20 print_step_freq: int = 100 # config to run demo stage1_name: str = 'stage1' # experiment name to the stage 1 model stage2_name: str = 'stage2' # experiment name to the stage 2 model image_path: str = '' # the path to the images for running demo, can be a single file or a glob pattern share: bool = False # whether to run gradio with a temporal public url or not # abs path to working dir code_dir_abs: str = osp.dirname(osp.dirname(osp.abspath(__file__))) # Inference configs num_inference_steps: int = 1000 diffusion_scheduler: Optional[str] = 'ddpm' num_samples: int = 1 # num_sample_batches: Optional[int] = None num_sample_batches: Optional[int] = 2000 # XH: change to 2 sample_from_ema: bool = False sample_save_evolutions: bool = False # temporarily set by default save_name: str = 'sample' # XH: additional save name redo: bool = False # for parallel sampling in slurm batch_start: int = 0 batch_end: Optional[int] = None # Training configs freeze_feature_model: bool = True # Coloring training configs coloring_training_noise_std: float = 0.0 coloring_sample_dir: Optional[str] = None sample_mode: str = 'sample' # whether from noise or from some intermediate steps sample_noise_step: int = 500 # add noise to GT up to some steps, and then denoise sample_save_gt: bool = True @dataclass class LoggingConfig: wandb: bool = True wandb_project: str = 'pc2' @dataclass class PointCloudProjectionModelConfig: # Feature extraction arguments image_size: int = '${dataset.image_size}' image_feature_model: str = 'vit_base_patch16_224_mae' # or 'vit_small_patch16_224_msn' or 'identity' use_local_colors: bool = True use_local_features: bool = True use_global_features: bool = False use_mask: bool = True use_distance_transform: bool = True # Point cloud data arguments. Note these are here because the processing happens # inside the model, rather than inside the dataset. scale_factor: float = "${dataset.scale_factor}" colors_mean: float = 0.5 colors_std: float = 0.5 color_channels: int = 3 predict_shape: bool = True predict_color: bool = False # added by XH load_sample_init: bool = False # load init samples from file sample_init_scale: float = 1.0 # scale the initial pc samples test_init_with_gtpc: bool = False # test time init samples with GT samples consistent_center: bool = True # use consistent center prediction by CCD-3DR voxel_resolution_multiplier: float = 1 # increase network voxel resolution # predict binary segmentation predict_binary: bool = False # True for stage 1 model, False for others lw_binary: float = 3.0 # to have roughly the same magnitude of the binary segmentation loss # for separate model binary_training_noise_std: float = 0.1 # from github doc for predicting color self_conditioning: bool = False @dataclass class PVCNNAEModelConfig(PointCloudProjectionModelConfig): "my own model config, must inherit parent class" model_name: str = 'pvcnn-ae' latent_dim: int = 1024 num_dec_blocks: int = 6 block_dims: List[int] = field(default_factory=lambda: [512, 256]) num_points: int = 1500 bottleneck_dim: int = -1 # the input dim to the last MLP layer @dataclass class PointCloudDiffusionModelConfig(PointCloudProjectionModelConfig): model_name: str = 'pc2-diff-ho' # default as behave # Diffusion arguments beta_start: float = 1e-5 # 0.00085 beta_end: float = 8e-3 # 0.012 beta_schedule: str = 'linear' # 'custom' dm_pred_type: str = 'epsilon' # diffusion model prediction type, sample (x0) or noise # Point cloud model arguments point_cloud_model: str = 'pvcnn' point_cloud_model_embed_dim: int = 64 dataset_type: str = '${dataset.type}' @dataclass class CrossAttnHOModelConfig(PointCloudDiffusionModelConfig): model_name: str = 'diff-ho-attn' attn_type: str = 'coord3d+posenc-learnable' attn_weight: float = 1.0 point_visible_test: str = 'combine' # To compute point visibility: use all points or only human/object points @dataclass class DirectTransModelConfig(PointCloudProjectionModelConfig): model_name: str = 'direct-transl-ho' pooling: str = "avg" act: str = 'gelu' out_act: str = 'relu' # feat_dims_transl: Iterable[Any] = (384, 256, 128, 6) # cannot use List[int] https://github.com/facebookresearch/hydra/issues/1752#issuecomment-893174197 # feat_dims_scale: Iterable[Any] = (384, 128, 64, 2) feat_dims_transl: List[int] = field(default_factory=lambda: [384, 256, 128, 6]) feat_dims_scale: List[int] = field(default_factory=lambda: [384, 128, 64, 2]) lw_transl: float = 10000.0 lw_scale: float = 10000.0 @dataclass class PointCloudColoringModelConfig(PointCloudProjectionModelConfig): # Projection arguments predict_shape: bool = False predict_color: bool = True # Point cloud model arguments point_cloud_model: str = 'pvcnn' point_cloud_model_layers: int = 1 point_cloud_model_embed_dim: int = 64 @dataclass class DatasetConfig: type: str @dataclass class PointCloudDatasetConfig(DatasetConfig): eval_split: str = 'val' max_points: int = 16_384 image_size: int = 224 scale_factor: float = 1.0 restrict_model_ids: Optional[List] = None # for only running on a subset of data points @dataclass class CO3DConfig(PointCloudDatasetConfig): type: str = 'co3dv2' # root: str = os.getenv('CO3DV2_DATASET_ROOT') root: str = "/BS/xxie-2/work/co3d/hydrant" category: str = 'hydrant' subset_name: str = 'fewview_dev' mask_images: bool = '${model.use_mask}' @dataclass class ShapeNetR2N2Config(PointCloudDatasetConfig): # added by XH fix_sample: bool = True category: str = 'chair' type: str = 'shapenet_r2n2' root: str = "/BS/chiban2/work/data_shapenet/ShapeNetCore.v1" r2n2_dir: str = "/BS/databases20/3d-r2n2" shapenet_dir: str = "/BS/chiban2/work/data_shapenet/ShapeNetCore.v1" preprocessed_r2n2_dir: str = "${dataset.root}/r2n2_preprocessed_renders" splits_file: str = "${dataset.root}/r2n2_standard_splits_from_ShapeNet_taxonomy.json" # splits_file: str = "${dataset.root}/pix2mesh_splits_val05.json" # <-- incorrect scale_factor: float = 7.0 point_cloud_filename: str = 'pointcloud_r2n2.npz' # should use 'pointcloud_mesh.npz' @dataclass class BehaveDatasetConfig(PointCloudDatasetConfig): # added by XH type: str = 'behave' fix_sample: bool = True behave_dir: str = "/BS/xxie-5/static00/behave_release/sequences/" split_file: str = "" # specify you dataset split file here scale_factor: float = 7.0 # use the same as shapenet sample_ratio_hum: float = 0.5 image_size: int = 224 normalize_type: str = 'comb' smpl_type: str = 'gt' # use which SMPL mesh to obtain normalization parameters test_transl_type: str = 'norm' load_corr_points: bool = False # load autoencoder points for object and SMPL uniform_obj_sample: bool = False # configs for direct translation prediction bkg_type: str = 'none' bbox_params: str = 'none' ho_segm_pred_path: Optional[str] = None use_gt_transl: bool = False cam_noise_std: float = 0. # add noise to the camera pose sep_same_crop: bool = False # use same input image crop to separate models aug_blur: float = 0. # blur augmentation std_coverage: float=3.5 # a heuristic value to estimate translation v2v_path: str = '' # object v2v corr path @dataclass class ShapeDatasetConfig(BehaveDatasetConfig): "the dataset to train AE for aligned shapes" type: str = 'shape' fix_sample: bool = False split_file: str = "/BS/xxie-2/work/pc2-diff/experiments/splits/shapes-chair.pkl" # TODO @dataclass class ShapeNetNMRConfig(PointCloudDatasetConfig): type: str = 'shapenet_nmr' shapenet_nmr_dir: str = "/work/lukemk/machine-learning-datasets/3d-reconstruction/ShapeNet_NMR/NMR_Dataset" synset_names: str = 'chair' # comma-separated or 'all' augmentation: str = 'all' scale_factor: float = 7.0 @dataclass class AugmentationConfig: # need to specify the variable type in order to define it properly max_radius: int = 0 # generate a random square to mask object, this is the radius for the square in pixel size, zero means no occlusion @dataclass class DataloaderConfig: # batch_size: int = 8 # 2 for debug batch_size: int = 16 num_workers: int = 14 # 0 for debug # suggested by accelerator for gpu20 @dataclass class LossConfig: diffusion_weight: float = 1.0 rgb_weight: float = 1.0 consistency_weight: float = 1.0 @dataclass class CheckpointConfig: resume: Optional[str] = "test" resume_training: bool = True resume_training_optimizer: bool = True resume_training_scheduler: bool = True resume_training_state: bool = True @dataclass class ExponentialMovingAverageConfig: use_ema: bool = False # # From Diffusers EMA (should probably switch) # ema_inv_gamma: float = 1.0 # ema_power: float = 0.75 # ema_max_decay: float = 0.9999 decay: float = 0.999 update_every: int = 20 @dataclass class OptimizerConfig: type: str name: str lr: float = 3e-4 weight_decay: float = 0.0 scale_learning_rate_with_batch_size: bool = False gradient_accumulation_steps: int = 1 clip_grad_norm: Optional[float] = 50.0 # 5.0 kwargs: Dict = field(default_factory=lambda: dict()) @dataclass class AdadeltaOptimizerConfig(OptimizerConfig): type: str = 'torch' name: str = 'Adadelta' kwargs: Dict = field(default_factory=lambda: dict( weight_decay=1e-6, )) @dataclass class AdamOptimizerConfig(OptimizerConfig): type: str = 'torch' name: str = 'AdamW' weight_decay: float = 1e-6 kwargs: Dict = field(default_factory=lambda: dict(betas=(0.95, 0.999))) @dataclass class SchedulerConfig: type: str kwargs: Dict = field(default_factory=lambda: dict()) @dataclass class LinearSchedulerConfig(SchedulerConfig): type: str = 'transformers' kwargs: Dict = field(default_factory=lambda: dict( name='linear', num_warmup_steps=0, num_training_steps="${run.max_steps}", )) @dataclass class CosineSchedulerConfig(SchedulerConfig): type: str = 'transformers' kwargs: Dict = field(default_factory=lambda: dict( name='cosine', num_warmup_steps=2000, # 0 num_training_steps="${run.max_steps}", )) @dataclass class ProjectConfig: run: RunConfig logging: LoggingConfig dataset: PointCloudDatasetConfig augmentations: AugmentationConfig dataloader: DataloaderConfig loss: LossConfig model: PointCloudProjectionModelConfig ema: ExponentialMovingAverageConfig checkpoint: CheckpointConfig optimizer: OptimizerConfig scheduler: SchedulerConfig defaults: List[Any] = field(default_factory=lambda: [ 'custom_hydra_run_dir', {'run': 'default'}, {'logging': 'default'}, {'model': 'ho-attn'}, # {'dataset': 'co3d'}, {'dataset': 'behave'}, {'augmentations': 'default'}, {'dataloader': 'default'}, {'ema': 'default'}, {'loss': 'default'}, {'checkpoint': 'default'}, {'optimizer': 'adam'}, # default adamw {'scheduler': 'linear'}, # {'scheduler': 'cosine'}, ]) cs = ConfigStore.instance() cs.store(name='custom_hydra_run_dir', node=CustomHydraRunDir, package="hydra.run") cs.store(group='run', name='default', node=RunConfig) cs.store(group='logging', name='default', node=LoggingConfig) cs.store(group='model', name='diffrec', node=PointCloudDiffusionModelConfig) cs.store(group='model', name='coloring_model', node=PointCloudColoringModelConfig) cs.store(group='model', name='direct-transl', node=DirectTransModelConfig) cs.store(group='model', name='ho-attn', node=CrossAttnHOModelConfig) cs.store(group='model', name='pvcnn-ae', node=PVCNNAEModelConfig) cs.store(group='dataset', name='co3d', node=CO3DConfig) # TODO cs.store(group='dataset', name='shapenet_r2n2', node=ShapeNetR2N2Config) cs.store(group='dataset', name='behave', node=BehaveDatasetConfig) cs.store(group='dataset', name='shape', node=ShapeDatasetConfig) # cs.store(group='dataset', name='shapenet_nmr', node=ShapeNetNMRConfig) cs.store(group='augmentations', name='default', node=AugmentationConfig) cs.store(group='dataloader', name='default', node=DataloaderConfig) cs.store(group='loss', name='default', node=LossConfig) cs.store(group='ema', name='default', node=ExponentialMovingAverageConfig) cs.store(group='checkpoint', name='default', node=CheckpointConfig) cs.store(group='optimizer', name='adadelta', node=AdadeltaOptimizerConfig) cs.store(group='optimizer', name='adam', node=AdamOptimizerConfig) cs.store(group='scheduler', name='linear', node=LinearSchedulerConfig) cs.store(group='scheduler', name='cosine', node=CosineSchedulerConfig) cs.store(name='configs', node=ProjectConfig)