328 lines
10 KiB
Python
328 lines
10 KiB
Python
#
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#
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# 0=================================0
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# | Kernel Point Convolutions |
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# 0=================================0
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#
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Callable script to start a training on SemanticKitti dataset
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Hugues THOMAS - 06/03/2020
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Imports and global variables
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# \**********************************/
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#
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# Common libs
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import signal
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import os
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import numpy as np
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import sys
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import torch
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# Dataset
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from datasetss.SemanticKitti import *
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from torch.utils.data import DataLoader
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from utils.config import Config
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from utils.trainer import ModelTrainer
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from models.architectures import KPFCNN
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Config Class
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# \******************/
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#
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class SemanticKittiConfig(Config):
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"""
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Override the parameters you want to modify for this dataset
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"""
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####################
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# Dataset parameters
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####################
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# Dataset name
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dataset = 'SemanticKitti'
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# Number of classes in the dataset (This value is overwritten by dataset class when Initializating dataset).
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num_classes = None
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# Type of task performed on this dataset (also overwritten)
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dataset_task = ''
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# Number of CPU threads for the input pipeline
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input_threads = 10
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#########################
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# Architecture definition
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#########################
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# Define layers
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architecture = ['simple',
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'resnetb',
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'resnetb_strided',
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'resnetb',
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'resnetb',
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'resnetb_strided',
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'resnetb',
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'resnetb',
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'resnetb_strided',
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'resnetb',
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'resnetb',
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'resnetb_strided',
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'resnetb',
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'nearest_upsample',
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'unary',
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'nearest_upsample',
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'unary',
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'nearest_upsample',
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'unary',
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'nearest_upsample',
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'unary']
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###################
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# KPConv parameters
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###################
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# Radius of the input sphere
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in_radius = 4.0
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val_radius = 4.0
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n_frames = 1
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max_in_points = 100000
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max_val_points = 100000
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# Number of batch
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batch_num = 8
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val_batch_num = 8
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# Number of kernel points
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num_kernel_points = 15
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# Size of the first subsampling grid in meter
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first_subsampling_dl = 0.06
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# Radius of convolution in "number grid cell". (2.5 is the standard value)
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conv_radius = 2.5
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# Radius of deformable convolution in "number grid cell". Larger so that deformed kernel can spread out
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deform_radius = 6.0
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# Radius of the area of influence of each kernel point in "number grid cell". (1.0 is the standard value)
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KP_extent = 1.2
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# Behavior of convolutions in ('constant', 'linear', 'gaussian')
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KP_influence = 'linear'
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# Aggregation function of KPConv in ('closest', 'sum')
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aggregation_mode = 'sum'
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# Choice of input features
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first_features_dim = 128
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in_features_dim = 2
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# Can the network learn modulations
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modulated = False
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# Batch normalization parameters
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use_batch_norm = True
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batch_norm_momentum = 0.02
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# Deformable offset loss
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# 'point2point' fitting geometry by penalizing distance from deform point to input points
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# 'point2plane' fitting geometry by penalizing distance from deform point to input point triplet (not implemented)
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deform_fitting_mode = 'point2point'
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deform_fitting_power = 1.0 # Multiplier for the fitting/repulsive loss
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deform_lr_factor = 0.1 # Multiplier for learning rate applied to the deformations
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repulse_extent = 1.2 # Distance of repulsion for deformed kernel points
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#####################
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# Training parameters
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#####################
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# Maximal number of epochs
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max_epoch = 800
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# Learning rate management
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learning_rate = 1e-2
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momentum = 0.98
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lr_decays = {i: 0.1 ** (1 / 150) for i in range(1, max_epoch)}
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grad_clip_norm = 100.0
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# Number of steps per epochs
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epoch_steps = 500
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# Number of validation examples per epoch
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validation_size = 200
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# Number of epoch between each checkpoint
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checkpoint_gap = 50
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# Augmentations
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augment_scale_anisotropic = True
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augment_symmetries = [True, False, False]
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augment_rotation = 'vertical'
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augment_scale_min = 0.8
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augment_scale_max = 1.2
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augment_noise = 0.001
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augment_color = 0.8
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# Choose weights for class (used in segmentation loss). Empty list for no weights
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# class proportion for R=10.0 and dl=0.08 (first is unlabeled)
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# 19.1 48.9 0.5 1.1 5.6 3.6 0.7 0.6 0.9 193.2 17.7 127.4 6.7 132.3 68.4 283.8 7.0 78.5 3.3 0.8
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#
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#
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# sqrt(Inverse of proportion * 100)
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# class_w = [1.430, 14.142, 9.535, 4.226, 5.270, 11.952, 12.910, 10.541, 0.719,
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# 2.377, 0.886, 3.863, 0.869, 1.209, 0.594, 3.780, 1.129, 5.505, 11.180]
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# sqrt(Inverse of proportion * 100) capped (0.5 < X < 5)
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# class_w = [1.430, 5.000, 5.000, 4.226, 5.000, 5.000, 5.000, 5.000, 0.719, 2.377,
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# 0.886, 3.863, 0.869, 1.209, 0.594, 3.780, 1.129, 5.000, 5.000]
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# Do we nee to save convergence
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saving = True
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saving_path = None
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Main Call
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# \***************/
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#
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if __name__ == '__main__':
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############################
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# Initialize the environment
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############################
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# Set which gpu is going to be used
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GPU_ID = '0'
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# Set GPU visible device
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os.environ['CUDA_VISIBLE_DEVICES'] = GPU_ID
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###############
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# Previous chkp
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###############
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# Choose here if you want to start training from a previous snapshot (None for new training)
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# previous_training_path = 'Log_2020-03-19_19-53-27'
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previous_training_path = ''
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# Choose index of checkpoint to start from. If None, uses the latest chkp
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chkp_idx = None
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if previous_training_path:
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# Find all snapshot in the chosen training folder
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chkp_path = os.path.join('results', previous_training_path, 'checkpoints')
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chkps = [f for f in os.listdir(chkp_path) if f[:4] == 'chkp']
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# Find which snapshot to restore
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if chkp_idx is None:
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chosen_chkp = 'current_chkp.tar'
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else:
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chosen_chkp = np.sort(chkps)[chkp_idx]
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chosen_chkp = os.path.join('results', previous_training_path, 'checkpoints', chosen_chkp)
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else:
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chosen_chkp = None
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##############
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# Prepare Data
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##############
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print()
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print('Data Preparation')
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print('****************')
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# Initialize configuration class
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config = SemanticKittiConfig()
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if previous_training_path:
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config.load(os.path.join('results', previous_training_path))
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config.saving_path = None
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# Get path from argument if given
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if len(sys.argv) > 1:
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config.saving_path = sys.argv[1]
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# Initialize datasets
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training_dataset = SemanticKittiDataset(config, set='training',
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balance_classes=True)
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test_dataset = SemanticKittiDataset(config, set='validation',
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balance_classes=False)
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# Initialize samplers
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training_sampler = SemanticKittiSampler(training_dataset)
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test_sampler = SemanticKittiSampler(test_dataset)
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# Initialize the dataloader
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training_loader = DataLoader(training_dataset,
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batch_size=1,
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sampler=training_sampler,
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collate_fn=SemanticKittiCollate,
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num_workers=config.input_threads,
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pin_memory=True)
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test_loader = DataLoader(test_dataset,
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batch_size=1,
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sampler=test_sampler,
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collate_fn=SemanticKittiCollate,
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num_workers=config.input_threads,
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pin_memory=True)
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# Calibrate max_in_point value
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training_sampler.calib_max_in(config, training_loader, verbose=True)
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test_sampler.calib_max_in(config, test_loader, verbose=True)
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# Calibrate samplers
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training_sampler.calibration(training_loader, verbose=True)
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test_sampler.calibration(test_loader, verbose=True)
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# debug_timing(training_dataset, training_loader)
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# debug_timing(test_dataset, test_loader)
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# debug_class_w(training_dataset, training_loader)
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print('\nModel Preparation')
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print('*****************')
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# Define network model
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t1 = time.time()
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net = KPFCNN(config, training_dataset.label_values, training_dataset.ignored_labels)
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debug = False
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if debug:
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print('\n*************************************\n')
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print(net)
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print('\n*************************************\n')
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for param in net.parameters():
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if param.requires_grad:
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print(param.shape)
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print('\n*************************************\n')
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print("Model size %i" % sum(param.numel() for param in net.parameters() if param.requires_grad))
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print('\n*************************************\n')
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# Define a trainer class
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trainer = ModelTrainer(net, config, chkp_path=chosen_chkp)
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print('Done in {:.1f}s\n'.format(time.time() - t1))
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print('\nStart training')
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print('**************')
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# Training
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trainer.train(net, training_loader, test_loader, config)
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print('Forcing exit now')
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os.kill(os.getpid(), signal.SIGINT)
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