""" # -*- coding: utf-8 -*- ----------------------------------------------------------------------------------- # Author: Nguyen Mau Dung # DoC: 2020.08.17 # email: nguyenmaudung93.kstn@gmail.com ----------------------------------------------------------------------------------- # Description: The configurations of the project will be defined here """ import os import argparse import torch from easydict import EasyDict as edict def parse_train_configs(): parser = argparse.ArgumentParser(description='The Implementation using PyTorch') parser.add_argument('--seed', type=int, default=2020, help='re-produce the results with seed random') parser.add_argument('--saved_fn', type=str, default='fpn_resnet_18', metavar='FN', help='The name using for saving logs, models,...') parser.add_argument('--root-dir', type=str, default='../', metavar='PATH', help='The ROOT working directory') #################################################################### ############## Model configs ######################## #################################################################### parser.add_argument('--arch', type=str, default='fpn_resnet_18', metavar='ARCH', help='The name of the model architecture') parser.add_argument('--pretrained_path', type=str, default=None, metavar='PATH', help='the path of the pretrained checkpoint') #################################################################### ############## Dataloader and Running configs ####### #################################################################### parser.add_argument('--hflip_prob', type=float, default=0.5, help='The probability of horizontal flip') parser.add_argument('--no-val', action='store_true', help='If true, dont evaluate the model on the val set') parser.add_argument('--num_samples', type=int, default=None, help='Take a subset of the dataset to run and debug') parser.add_argument('--num_workers', type=int, default=4, help='Number of threads for loading data') parser.add_argument('--batch_size', type=int, default=16, help='mini-batch size (default: 16), this is the total' 'batch size of all GPUs on the current node when using' 'Data Parallel or Distributed Data Parallel') parser.add_argument('--print_freq', type=int, default=50, metavar='N', help='print frequency (default: 50)') parser.add_argument('--tensorboard_freq', type=int, default=50, metavar='N', help='frequency of saving tensorboard (default: 50)') parser.add_argument('--checkpoint_freq', type=int, default=2, metavar='N', help='frequency of saving checkpoints (default: 5)') #################################################################### ############## Training strategy #################### #################################################################### parser.add_argument('--start_epoch', type=int, default=1, metavar='N', help='the starting epoch') parser.add_argument('--num_epochs', type=int, default=300, metavar='N', help='number of total epochs to run') parser.add_argument('--lr_type', type=str, default='cosin', help='the type of learning rate scheduler (cosin or multi_step or one_cycle)') parser.add_argument('--lr', type=float, default=0.001, metavar='LR', help='initial learning rate') parser.add_argument('--minimum_lr', type=float, default=1e-7, metavar='MIN_LR', help='minimum learning rate during training') parser.add_argument('--momentum', type=float, default=0.949, metavar='M', help='momentum') parser.add_argument('-wd', '--weight_decay', type=float, default=0., metavar='WD', help='weight decay (default: 0.)') parser.add_argument('--optimizer_type', type=str, default='adam', metavar='OPTIMIZER', help='the type of optimizer, it can be sgd or adam') parser.add_argument('--steps', nargs='*', default=[150, 180], help='number of burn in step') #################################################################### ############## Loss weight ########################## #################################################################### #################################################################### ############## Distributed Data Parallel ############ #################################################################### parser.add_argument('--world-size', default=-1, type=int, metavar='N', help='number of nodes for distributed training') parser.add_argument('--rank', default=-1, type=int, metavar='N', help='node rank for distributed training') parser.add_argument('--dist-url', default='tcp://127.0.0.1:29500', type=str, help='url used to set up distributed training') parser.add_argument('--dist-backend', default='nccl', type=str, help='distributed backend') parser.add_argument('--gpu_idx', default=None, type=int, help='GPU index to use.') parser.add_argument('--no_cuda', action='store_true', help='If true, cuda is not used.') parser.add_argument('--multiprocessing-distributed', action='store_true', help='Use multi-processing distributed training to launch ' 'N processes per node, which has N GPUs. This is the ' 'fastest way to use PyTorch for either single node or ' 'multi node data parallel training') #################################################################### ############## Evaluation configurations ################### #################################################################### parser.add_argument('--evaluate', action='store_true', help='only evaluate the model, not training') parser.add_argument('--resume_path', type=str, default=None, metavar='PATH', help='the path of the resumed checkpoint') parser.add_argument('--K', type=int, default=50, help='the number of top K') configs = edict(vars(parser.parse_args())) #################################################################### ############## Hardware configurations ############################# #################################################################### configs.device = torch.device('cpu' if configs.no_cuda else 'cuda') configs.ngpus_per_node = torch.cuda.device_count() configs.pin_memory = True configs.input_size = (608, 608) configs.hm_size = (152, 152) configs.down_ratio = 4 configs.max_objects = 50 configs.imagenet_pretrained = True configs.head_conv = 64 configs.num_classes = 3 configs.num_center_offset = 2 configs.num_z = 1 configs.num_dim = 3 configs.num_direction = 2 # sin, cos configs.heads = { 'hm_cen': configs.num_classes, 'cen_offset': configs.num_center_offset, 'direction': configs.num_direction, 'z_coor': configs.num_z, 'dim': configs.num_dim } configs.num_input_features = 4 #################################################################### ############## Dataset, logs, Checkpoints dir ###################### #################################################################### configs.dataset_dir = os.path.join(configs.root_dir, 'dataset', 'kitti') configs.checkpoints_dir = os.path.join(configs.root_dir, 'checkpoints', configs.saved_fn) configs.logs_dir = os.path.join(configs.root_dir, 'logs', configs.saved_fn) if not os.path.isdir(configs.checkpoints_dir): os.makedirs(configs.checkpoints_dir) if not os.path.isdir(configs.logs_dir): os.makedirs(configs.logs_dir) return configs