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train_shapenet55.py
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train_shapenet55.py
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import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1, 2, 3'
import argparse
import os
import numpy as np
import torch
import json
import time
import utils.data_loaders
from easydict import EasyDict as edict
from importlib import import_module
from pprint import pprint
from manager import Manager, Manager_shapenet55
from models.crapcn import CRAPCN_sn55, CRAPCN_sn55_d
TRAIN_NAME = os.path.splitext(os.path.basename(__file__))[0]
# Arguments
parser = argparse.ArgumentParser()
parser.add_argument('--desc', type=str, default='Training/Testing CRA-PCN', help='description')
parser.add_argument('--net_model', type=str, default='model', help='Import module.')
parser.add_argument('--arch_model', type=str, default='seedformer_dim128', help='Model to use.')
parser.add_argument('--test', dest='test', help='Test neural networks', action='store_true')
parser.add_argument('--inference', dest='inference', help='Inference for benchmark', action='store_true')
parser.add_argument('--output', type=int, default=False, help='Output testing results.')
parser.add_argument('--pretrained', type=str, default='', help='Pretrained path for testing.')
parser.add_argument('--mode', type=str, default='median', help='Testing mode [easy, median, hard].')
args = parser.parse_args()
# Configuration for ShapeNet55
def ShapeNet55Config():
__C = edict()
cfg = __C
#
# Dataset Config
#
__C.DATASETS = edict()
__C.DATASETS.SHAPENET55 = edict()
__C.DATASETS.SHAPENET55.CATEGORY_FILE_PATH = './data/ShapeNet55-34/ShapeNet-55/'
__C.DATASETS.SHAPENET55.N_POINTS = 2048
__C.DATASETS.SHAPENET55.COMPLETE_POINTS_PATH = './data//ShapeNet55-34/shapenet_pc/%s'
#
# Dataset
#
__C.DATASET = edict()
# Dataset Options: Completion3D, ShapeNet, ShapeNetCars, Completion3DPCCT
__C.DATASET.TRAIN_DATASET = 'ShapeNet55'
__C.DATASET.TEST_DATASET = 'ShapeNet55'
#
# Constants
#
__C.CONST = edict()
__C.CONST.NUM_WORKERS = 8
__C.CONST.N_INPUT_POINTS = 2048
#
# Directories
#
__C.DIR = edict()
__C.DIR.OUT_PATH = 'results/shapenet55'
__C.DIR.TEST_PATH = 'test/'
__C.CONST.DEVICE = '0, 1, 2, 3'
# __C.CONST.WEIGHTS = None # 'ckpt-best.pth' # specify a path to run test and inference
#
# Network
#
__C.NETWORK = edict()
__C.NETWORK.UPSAMPLE_FACTORS = [1, 4, 4]
#
# Train
#
__C.TRAIN = edict()
__C.TRAIN.BATCH_SIZE = 40
__C.TRAIN.N_EPOCHS = 400
__C.TRAIN.LEARNING_RATE = 0.001
__C.TRAIN.LR_DECAY = 100
__C.TRAIN.WARMUP_EPOCHS = 20
__C.TRAIN.GAMMA = .5
__C.TRAIN.BETAS = (.9, .999)
__C.TRAIN.WEIGHT_DECAY = 0
#
# Test
#
__C.TEST = edict()
__C.TEST.METRIC_NAME = 'ChamferDistance'
return cfg
def train_net(cfg):
# Enable the inbuilt cudnn auto-tuner to find the best algorithm to use
torch.backends.cudnn.benchmark = True
########################
# Load Train/Val Dataset
########################
train_dataset_loader = utils.data_loaders.DATASET_LOADER_MAPPING[cfg.DATASET.TRAIN_DATASET](cfg)
val_dataset_loader = utils.data_loaders.DATASET_LOADER_MAPPING[cfg.DATASET.TEST_DATASET](cfg)
train_data_loader = torch.utils.data.DataLoader(dataset=train_dataset_loader.get_dataset(
utils.data_loaders.DatasetSubset.TRAIN),
batch_size=cfg.TRAIN.BATCH_SIZE,
num_workers=cfg.CONST.NUM_WORKERS,
collate_fn=utils.data_loaders.collate_fn,
pin_memory=True,
shuffle=True,
drop_last=False)
val_data_loader = torch.utils.data.DataLoader(dataset=val_dataset_loader.get_dataset(
utils.data_loaders.DatasetSubset.TEST),
batch_size=cfg.TRAIN.BATCH_SIZE,
num_workers=cfg.CONST.NUM_WORKERS//2,
collate_fn=utils.data_loaders.collate_fn,
pin_memory=True,
shuffle=False)
timestr = time.strftime('_Log_%Y_%m_%d_%H_%M_%S', time.gmtime())
cfg.DIR.OUT_PATH = os.path.join(cfg.DIR.OUT_PATH, TRAIN_NAME+timestr)
cfg.DIR.CHECKPOINTS = os.path.join(cfg.DIR.OUT_PATH, 'checkpoints')
cfg.DIR.LOGS = cfg.DIR.OUT_PATH
print('Saving outdir: {}'.format(cfg.DIR.OUT_PATH))
if not os.path.exists(cfg.DIR.CHECKPOINTS):
os.makedirs(cfg.DIR.CHECKPOINTS)
# save config file
# pprint(cfg)
config_filename = os.path.join(cfg.DIR.LOGS, 'config.json')
with open(config_filename, 'w') as file:
json.dump(cfg, file, indent=4, sort_keys=True)
# Save Arguments
torch.save(args, os.path.join(cfg.DIR.LOGS, 'args_training.pth'))
#######################
# Prepare Network Model
#######################
model = CRAPCN_sn55() # or 'CRAPCN_sn55_d'
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
##################
# Training Manager
##################
manager = Manager_shapenet55(model, cfg)
# Start training
manager.train(model, train_data_loader, val_data_loader, cfg)
def set_seed(seed):
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if __name__ == '__main__':
seed = 1128
set_seed(seed)
print('cuda available ', torch.cuda.is_available())
cfg = ShapeNet55Config()
train_net(cfg)