from config.easy_dict import EasyDict
config = EasyDict()
config.cols_to_use = [0,1,2,3]
# may want to change: feat_size, layer_dims, etc.
config.model_kwargs = {"num_features":1, "num_classes": 3}
config.data_path = "/fast_scratch/WatChMaL/data/pointnet/pointnet_trainval.h5"
config.indices_file = "/fast_scratch/WatChMaL/data/pointnet/pointnet_trainval_idxs.npz"
#make sure to change this
config.dump_path = "/home/dgreen/training_outputs/pointnet2/no_time/adam/"
config.num_data_workers = 0 # Sometime crashes if we do multiprocessing
config.device = 'cuda:6'
config.optimizer = "Adam"
config.optimizer_kwargs = {"lr":1e-3, "betas": (0.9, 0.999)}
config.use_scheduler = False
config.scheduler_kwargs = {"mode":"min", "min_lr":1e-6, "patience":1, "verbose":True}
config.scheduler_step = 190
config.batch_size = 32
config.epochs = 20
config.report_interval = 200
config.num_val_batches = 256
config.valid_interval = 1000
config.validate_batch_size = 32
from config.easy_dict import EasyDict
config = EasyDict()
config.model_name = "Pointnet"
config.cols_to_use = [0,1,2,3,4,5,6,7]
# may want to change: feat_size, layer_dims, etc.
config.model_kwargs = {"in_channels": len(config.cols_to_use),
"num_classes": 3}
config.data_path = "/fast_scratch/WatChMaL/data/pointnet/pointnet_trainval.h5"
config.indices_file = "/fast_scratch/WatChMaL/data/pointnet/pointnet_trainval_idxs.npz"
#make sure to change this
config.dump_path = "/home/dgreen/training_outputs/pointnet/all_features/adam/"
config.num_data_workers = 0 # Sometime crashes if we do multiprocessing
config.device = 'cuda:7'
config.optimizer = "Adam"
config.optimizer_kwargs = {"lr":1e-3, "betas": (0.9, 0.999)}
config.use_scheduler = False
config.scheduler_kwargs = {"mode":"min", "min_lr":1e-6, "patience":1, "verbose":True}
config.scheduler_step = 190
config.batch_size = 32
config.epochs = 20
config.report_interval = 200