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train_dg_single_gpu.py
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train_dg_single_gpu.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from easydict import EasyDict
from model.model_pointnet import Pointnet_cls as Pointnet_cls
import model.Model as mM
import time
import os
import glob
import pdb
import model.mmd as mmd
# from utils import *
import math
import warnings
import datetime
import copy
from utils.eval_utils import eval_worker
from utils.train_utils import save_checkpoint, checkpoint_state, adjust_learning_rate, discrepancy, Sampler
from model.model_utils import focal_loss
from utils.common_utils import create_logger, exp_log_folder_creator, set_random_seed
from utils.config import parser_config, log_config_to_file
from data.dataloader import create_splitted_dataset, create_single_dataset
from model.KPConv_model import KPFCls, p2p_fitting_regularizer
from tensorboardX import SummaryWriter
warnings.filterwarnings("ignore")
def main():
args, cfg = parser_config()
if args.fix_random_seed:
set_random_seed(666 + cfg.LOCAL_RANK)
device = 'cuda'
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
BATCH_SIZE = args.batch_size * len(args.gpu.split(','))
output_dir, ckpt_dir = exp_log_folder_creator(cfg, extra_tag=args.source)
log_name = 'log_train_dg%s.txt' % datetime.datetime.now().strftime('%Y%m%d-%H%M%S')
log_file = os.path.join(output_dir, log_name)
logger = create_logger(log_file=log_file)
logger.info('**********************Start logging**********************')
if not os.path.exists(os.path.join(output_dir, 'tensorboard')):
os.makedirs(os.path.join(output_dir,'tensorboard'))
writer = SummaryWriter(log_dir=str(os.path.join(output_dir,'tensorboard')))
for key, val in vars(args).items():
logger.info('{:16} {}'.format(key, val))
log_config_to_file(cfg, logger=logger)
logger.info('Start Training\nInitiliazing\n')
logger.info(f'The source domain is set to: {args.source}')
dataset_list = ["scannet", "shapenet", "modelnet"]
test_datasets = list(set(dataset_list) - {args.source})
logger.info(f'The datasets used for testing: {test_datasets}')
set_random_seed(666 + cfg.LOCAL_RANK)
# Data loading
multi_spliter = False
# when split_config is a dict which means only one split method is used
split_config = cfg["DATASET_SPLITTER"]
if type(split_config) is EasyDict:
source_train_subsets = create_splitted_dataset(dataset_type=args.source, status="train", logger=logger, config=split_config)
source_train_dataset = source_train_subsets[split_config["TRAIN_BASE"]]
target_train_dataset1 = source_train_subsets[1-split_config["TRAIN_BASE"]]
elif type(split_config) is list:
logger.info(f"{len(split_config)} types split methods are used.")
multi_spliter = True
source_train_datasets = []
target_train_datasets = []
for config_ in split_config:
source_train_subsets = create_splitted_dataset(dataset_type=args.source, status="train", logger=logger, config=config_, model=cfg.get("Model", "Pointnet"))
source_train_datasets.append(source_train_subsets[config_["TRAIN_BASE"]])
target_train_datasets.append(source_train_subsets[1-config_["TRAIN_BASE"]])
else:
raise RuntimeError(f"Unsupported Splitter Config {type(split_config)}")
# split 2 is fullsize
source_test_dataset = create_single_dataset(args.source, status="test", aug=False, model=cfg.get("Model", "Pointnet"))
target_test_dataset1 = create_single_dataset(test_datasets[0], status="test", aug=False, model=cfg.get("Model", "Pointnet"))
target_test_dataset2 = create_single_dataset(test_datasets[-1], status="test", aug=False, model=cfg.get("Model", "Pointnet"))
if not multi_spliter:
num_source_train = len(source_train_dataset)
num_target_train1 = len(target_train_dataset1)
logger.info(f"Num of source train: {num_source_train}, Num of target train: {num_target_train1}")
if cfg.get("CLASS_BALANCE", False):
sampler_1 = Sampler(source_train_dataset.classes(), class_per_batch=10, batch_size=BATCH_SIZE)
sampler_2 = Sampler(target_train_dataset1.classes(), class_per_batch=10, batch_size=BATCH_SIZE)
source_train_dataloader = DataLoader(source_train_dataset, batch_sampler=sampler_1, num_workers=2)
target_train_dataloader = DataLoader(target_train_dataset1, batch_sampler=sampler_2, num_workers=2)
else:
source_train_dataloader = DataLoader(source_train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,
drop_last=True)
target_train_dataloader = DataLoader(target_train_dataset1, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,
drop_last=True)
else:
source_train_dataloaders = []
target_train_dataloaders = []
for st,tt in zip(source_train_datasets, target_train_datasets):
num_source_train = len(st)
num_target_train1 = len(tt)
logger.info(f"For Current Split Method: Num of source train: {num_source_train}, Num of target train: {num_target_train1}")
if cfg.get("CLASS_BALANCE", False):
sampler_1 = Sampler(st.classes(), class_per_batch=10, batch_size=BATCH_SIZE)
sampler_2 = Sampler(tt.classes(), class_per_batch=10, batch_size=BATCH_SIZE)
source_train_dataloader = DataLoader(st, batch_sampler=sampler_1, num_workers=2)
target_train_dataloader = DataLoader(tt, batch_sampler=sampler_2, num_workers=2)
else:
source_train_dataloader = DataLoader(st, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,
drop_last=True)
target_train_dataloader = DataLoader(tt, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,
drop_last=True)
source_train_dataloaders.append(source_train_dataloader)
target_train_dataloaders.append(target_train_dataloader)
num_source_test = len(source_test_dataset)
num_target_test1 = len(target_test_dataset1)
num_target_test2 = len(target_test_dataset2)
logger.info(f"Num of source test: {num_source_test}, Num of test on {test_datasets[0]} {num_target_test1}, on {test_datasets[-1]} {num_target_test2}")
source_test_dataloader = DataLoader(source_test_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,
drop_last=True)
target_test_dataloader1 = DataLoader(target_test_dataset1, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,
drop_last=True)
target_test_dataloader2 = DataLoader(target_test_dataset2, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,
drop_last=True)
performance_test_sets = {"source": source_test_dataloader, "test1": target_test_dataloader1,
"test2": target_test_dataloader2}
logger.info(f'batch_size: {BATCH_SIZE}')
best_test_acc = {"source": [0, 0], "test1":[0, 0], "test2":[0, 0]}
# best_target_acc_epoch + best_target_acc
dataset_remapping = {"source":args.source, "test1": test_datasets[0],
"test2": test_datasets[1]}
# pool_eval = Pool(processes=len(dataset_list))
# AssertionError: daemonic processes are not allowed to have children
# Model
model = mM.Net_MDA(model_name=cfg.get("Model", "Pointnet"))
logger.info(model)
model = model.to(device=device)
KPC_Flag = cfg["Model"] == "KPConv"
opt_cfg = cfg["OPTIMIZATION"]
if opt_cfg.get("CLS_LOSS", "CrossEntropyLoss") == "FocalLoss":
cls_weights=None
if opt_cfg.get("CLS_WEIGHT", None):
cls_weights = source_train_dataset.cls_wights(weighting=opt_cfg["CLS_WEIGHT"])
criterion = focal_loss(num_classes=cfg["DATASET"]["NUM_CLASS"], gamma=opt_cfg["FOCAL_GAMMA"], alpha=cls_weights)
logger.info(f"FocalLoss: alpha {cls_weights}")
logger.info(f"FocalLoss: gamma {opt_cfg['FOCAL_GAMMA']}")
elif opt_cfg.get("CLS_LOSS", "CrossEntropyLoss") == "ClassWeighting":
gamma = 0.0
# when gamma is zero, FL degrades to class re-weighting
if not opt_cfg.get("CLS_WEIGHT", None):
raise RuntimeError("When setting ClassWeighting, CLS_WEIGHT should be provided")
cls_weights = source_train_dataset.cls_wights(weighting=opt_cfg["CLS_WEIGHT"], q_=opt_cfg.get("DLSA_Q", None))
criterion = focal_loss(num_classes=cfg["DATASET"]["NUM_CLASS"], gamma=gamma, alpha=cls_weights)
logger.info(f"ClassWeighting: Weights: {cls_weights}")
else:
criterion = nn.CrossEntropyLoss()
criterion = criterion.to(device=device)
logger.info("Use the plain CrossEntropy")
# Optimizer Setting
remain_epoch = 0
max_epoch_num = opt_cfg["NUM_EPOCHES"]
LR = opt_cfg["LR"]
weight_decay = opt_cfg["WEIGHT_DECAY"]
scaler = opt_cfg["LR_SCALER"]
pure_cls_epoch = cfg["METHODS"]["PURE_CLS_EPOCH"]
params = [{'params': v} for k, v in model.g.named_parameters() if 'pred_offset' not in k]
optimizer_g = optim.Adam(params, lr=LR, weight_decay=weight_decay)
lr_schedule_g = optim.lr_scheduler.CosineAnnealingLR(optimizer_g, T_max=max_epoch_num)
optimizer_c = optim.Adam([{'params': model.c1.parameters()}, {'params': model.c2.parameters()}], lr=LR,
weight_decay=weight_decay)
lr_schedule_c = optim.lr_scheduler.CosineAnnealingLR(optimizer_c, T_max=max_epoch_num)
optimizer_dis = optim.Adam([{'params': model.g.parameters()}, {'params': model.attention_s.parameters()},
{'params': model.attention_t.parameters()}],
lr=LR * scaler, weight_decay=weight_decay)
lr_schedule_dis = optim.lr_scheduler.CosineAnnealingLR(optimizer_dis, T_max=max_epoch_num)
cls_eval = opt_cfg.get("CLS_EVAL", True)
for epoch in range(max_epoch_num):
since_e = time.time()
lr_schedule_g.step(epoch=epoch)
lr_schedule_c.step(epoch=epoch)
adjust_learning_rate(optimizer_dis, epoch, LR, scaler, writer)
writer.add_scalar('lr_g', lr_schedule_g.get_lr()[0], epoch)
writer.add_scalar('lr_c', lr_schedule_c.get_lr()[0], epoch)
model.train()
loss_cls_total = 0
loss_adv_total = 0
loss_geo_total = 0
loss_sem_total = 0
correct_total = 0
data_total = 0
data_t_total = 0
cons = math.sin((epoch + 1) / max_epoch_num * math.pi / 2)
# choose for current epoch, which split method will be used
if multi_spliter:
idx = epoch % len(source_train_dataloaders)
source_train_dataloader = source_train_dataloaders[idx]
target_train_dataloader = target_train_dataloaders[idx]
geo_mmd_cfg = cfg["METHODS"]["GEO_MMD"][0]
sem_mmd_cfg = cfg["METHODS"]["SEM_MMD"][0]
geo_CL_criterion = None
sem_CL_criterion = None
if geo_mmd_cfg["NAME"] == "CL":
geo_CL_criterion = nn.CosineEmbeddingLoss(margin=0.2, reduction="none")
if sem_mmd_cfg["NAME"] == "CL":
sem_CL_criterion = nn.CosineEmbeddingLoss(margin=0.2, reduction="none")
# Training
for batch_idx, (batch_s, batch_t) in enumerate(zip(source_train_dataloader, target_train_dataloader)):
data, label = batch_s
data_t, label_t = batch_t
# 64 * 3 * 1024
data = data.to(device=device)
# label: 64
label = label.to(device=device).long()
data_t = data_t.to(device=device)
label_t = label_t.to(device=device).long()
# Senmantic MMD loss
# data: 64 * 3 * 1024 * 1
pred_s1, pred_s2, sem_fea_s1, sem_fea_s2 = model(data, semantic_adaption=True)
if cfg["METHODS"].get("GRL", None):
pred_t1, pred_t2, sem_fea_t1, sem_fea_t2 = model(data_t, semantic_adaption=True, constant=cons, adaptation=True)
else:
pred_t1, pred_t2, sem_fea_t1, sem_fea_t2 = model(data_t, semantic_adaption=True)
# no need for GRL now
# sem_fea_s1:64 * 256
# Classification loss
loss_s1 = criterion(pred_s1, label)
loss_s2 = criterion(pred_s2, label)
loss_adv = None
# Adversarial loss -> let two heads of the model output similar
if cfg["METHODS"]["ADV_WEIGHT"] > 0:
loss_adv = - cfg["METHODS"]["ADV_WEIGHT"] * discrepancy(pred_t1, pred_t2)
loss_s = loss_s + loss_adv
# TODO Ablation to check wether need add loss_adv
loss_s = 0.5 * loss_s1 + 0.5 * loss_s2
if KPC_Flag:
reg_loss = p2p_fitting_regularizer(model.g.encoder.encoder_blocks, deform_fitting_power=model.g.deform_fitting_power)
loss_s += reg_loss
if cfg["METHODS"]["TARGET_LOSS"] > 0:
loss_t1 = criterion(pred_t1, label)
loss_t2 = criterion(pred_t2, label)
loss_t = 0.5 * loss_t1 + 0.5 * loss_t2
loss = 0.5 * loss_s + 0.5 * loss_t
else:
loss = cfg["METHODS"]["SRC_LOSS_WEIGHT"] * loss_s
loss_cls = cfg["METHODS"]["CLS_WEIGHT"] * loss
if epoch < pure_cls_epoch or cfg["METHODS"]["MMD_WEIGHT"] <= 0:
loss = loss_cls
loss.backward()
optimizer_dis.step()
optimizer_g.step()
optimizer_c.step()
optimizer_g.zero_grad()
optimizer_c.zero_grad()
optimizer_dis.zero_grad()
loss_cls_total += loss_cls.item() * data.size(0)
else:
# should only activate the MMD loss after some epoch, to let the classifier gain the basic ability for classifying
# ******************************** MMD alignment part *****************************************
# geometric info
# Local Alignment -> self-adaptive node: contains geometry info
feat_node_s = model(data, node_adaptation_s=True) # shape: batch_size * 4096 -> 64 * 64
feat_node_t = model(data_t, node_adaptation_t=True)
# Add geometric weights
geo_mmd_cfg = cfg["METHODS"]["GEO_MMD"][0]
loss_geo_mmd = cfg["METHODS"]["MMD_WEIGHT"] * geo_mmd_cfg["GEO_SCALE"] * mmd.mmd_cal(label, feat_node_s, label_t, feat_node_t, geo_mmd_cfg, data_s=data, data_t=data_t, KPC=KPC_Flag)
sem_mmd_cfg = cfg["METHODS"]["SEM_MMD"][0]
loss_sem_mmd = None
if sem_mmd_cfg["SEM_SCALE"] > 0:
loss_sem_mmd_1 = sem_mmd_cfg["SEM_SCALE"] * mmd.mmd_cal(label, sem_fea_s1, label_t, sem_fea_t1, sem_mmd_cfg, data_s=pred_s1, data_t=pred_t1, KPC=KPC_Flag)
loss_sem_mmd_2 = sem_mmd_cfg["SEM_SCALE"] * mmd.mmd_cal(label, sem_fea_s2, label_t, sem_fea_t2, sem_mmd_cfg, data_s=pred_s2, data_t=pred_t2, KPC=KPC_Flag)
loss_sem_mmd = cfg["METHODS"]["MMD_WEIGHT"] * (0.5 * loss_sem_mmd_1 + 0.5 * loss_sem_mmd_2)
if loss_sem_mmd is not None:
loss = loss_cls + loss_geo_mmd + loss_sem_mmd
else:
loss = loss_cls + loss_geo_mmd
loss.backward()
optimizer_dis.step()
optimizer_g.step()
optimizer_c.step()
optimizer_g.zero_grad()
optimizer_c.zero_grad()
optimizer_dis.zero_grad()
loss_geo_total += loss_geo_mmd.item() * data.size(0)
if loss_sem_mmd is not None:
loss_sem_total += loss_sem_mmd.item() * data.size(0)
data_total += data.size(0)
data_t_total += data_t.size(0)
loss_cls_total += loss_cls.item() * data.size(0)
if loss_adv:
loss_adv_total += loss_adv.item() * data.size(0)
if (batch_idx + 1) % 10 == 0:
logger.info(f"Train Epoch {epoch} [{data_total} {data_t_total}/{num_source_train}:] loss_cls {loss_cls_total / data_total} ")
if epoch >= pure_cls_epoch:
logger.info(f"loss_adv: {loss_adv_total / data_total} loss_geo_mmd {loss_geo_total / data_total} loss_sem_mmd {loss_sem_total / data_total}")
writer.add_scalar("loss/cls", loss_cls_total / data_total, epoch)
writer.add_scalar("loss/adv", loss_adv_total / data_total, epoch)
writer.add_scalar("loss/mmd_geo", loss_geo_total / data_total, epoch)
writer.add_scalar("loss/mmd_sem", loss_sem_total / data_total, epoch)
# Testing
with torch.no_grad():
model.eval()
for eval_dataset in performance_test_sets.keys():
eval_dict = {
"model": copy.deepcopy(model),
"dataloader": performance_test_sets[eval_dataset],
"dataset": eval_dataset,
"best_target_acc": best_test_acc[eval_dataset][1],
"device": device,
"criterion": criterion,
"epoch": epoch,
"best_target_acc_epoch": best_test_acc[eval_dataset][0],
"dataset_name": dataset_remapping[eval_dataset],
"num_class": cfg["DATASET"]["NUM_CLASS"],
"cls_eval": cls_eval
}
eval_result = eval_worker(eval_dict, logger)
best_test_acc[eval_dataset][1] = eval_result["best_target_acc"]
best_test_acc[eval_dataset][0] = eval_result["best_target_acc_epoch"]
writer_item = 'acc/' + eval_result["dataset"] + "_" + dataset_remapping[eval_result["dataset"]] + "_best_acc"
writer.add_scalar(writer_item, eval_result["best_target_acc"], epoch)
writer_item = 'acc/' + eval_result["dataset"] + "_" + dataset_remapping[eval_result["dataset"]] + "_cur_acc"
writer.add_scalar(writer_item, eval_result["cur_target_acc"], epoch)
trained_epoch = epoch + 1
if trained_epoch % args.ckpt_save_interval == 0:
ckpt_list = [os.path.join(ckpt_dir, cpkt) for cpkt in os.listdir(ckpt_dir) if ".pth" in cpkt]
ckpt_list.sort(key=os.path.getmtime)
if ckpt_list.__len__() >= args.max_ckpt_save_num:
for cur_file_idx in range(0, len(ckpt_list) - args.max_ckpt_save_num + 1):
os.remove(ckpt_list[cur_file_idx])
ckpt_name = os.path.join(ckpt_dir, args.source + ('_checkpoint_epoch_%d' % trained_epoch))
logger.info(f"Save current ckpt to {ckpt_name}")
save_checkpoint(checkpoint_state(model, epoch=trained_epoch), filename=ckpt_name)
time_pass_e = time.time() - since_e
logger.info('The {} epoch takes {:.0f}m {:.0f}s'.format(epoch, time_pass_e // 60, time_pass_e % 60))
logger.info('****************Finished One Epoch****************')
if __name__ == '__main__':
since = time.time()
main()
time_pass = since - time.time()
print('Training complete in {:.0f}m {:.0f}s'.format(time_pass // 60, time_pass % 60))