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main.py
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main.py
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# coding=utf-8
import argparse
import os
from thop import profile
import numpy as np
import csv
from models.model2stage import Cross_PCC,Refine
from torch.utils.data import DataLoader
from utils.dataloader_epn import *
from utils.data_util import remove_store
from utils.io_util import save_mat
import torch
import torch.nn as nn
from utils import meter
import time
from tensorboardX import SummaryWriter
from tqdm import tqdm
from utils.cdloss import chamfer_distance, chamfer_distance_sqrt, chamfer_distance_sqrtp
parser = argparse.ArgumentParser()
parser.add_argument('--lr',default=1e-4, type=float, help='learning rate')
parser.add_argument('--gpu',default='0')
parser.add_argument('--bz',default=32, type=int,help='batch size')
parser.add_argument('--epoch',default=201, type=int, help='training epoch of stage 1')
parser.add_argument('--refine_epoch', type=int, default=51, help='training epoch of stage 2')
parser.add_argument('--eval_epoch',default=10, type=int, help='evaluation epoch') # default=3
parser.add_argument('--num_workers',default=3, type=int, help='num_workers of dataloader')
parser.add_argument('--resume',default=False, type=bool, help='restore from saved model')
parser.add_argument('--lamda_part', default=1.0) # weight of partial cd loss
parser.add_argument('--data_list', default='./data/3depn/3depn_list')
parser.add_argument('--data_path', default='./data/3depn')
parser.add_argument('--root_path', default='.', help='log path')
parser.add_argument('--input_num', default=2048, type=int)
parser.add_argument('--gt_num', default=4096, type=int, help='point num of 2d gt')
parser.add_argument('--category', default='cabinet')
parser.add_argument('--view_num', type=int, default=8)
parser.add_argument('--step_decay', type=int, default=10, help='lr decay step of stage 2')
parser.add_argument('--gamma', type=float, default=0.1, help='step decay rate')
args = parser.parse_args()
cat_map = {
'plane':'02691156',
'cabinet':'02933112',
'car':'02958343',
'chair':'03001627',
'lamp':'03636649',
'couch':'04256520',
'table':'04379243',
'watercraft':'04530566'
}
MODEL = 'cross-pcc'
DEVICE = 'cuda:0'
TIME_FLAG = time.asctime(time.localtime(time.time()))
SAVE_PC_DIR = f'{args.root_path}/log/{args.category}/saved_pc'
SAVE_BESTPC_DIR = f'{args.root_path}/log/{args.category}/best_pc'
SAVE_VIEWCD_DIR = f'{args.root_path}/log/{args.category}/saved_viewcd'
CKPT_RECORD_FOLDER = f'{args.root_path}/log/{args.category}/record'
CKPT_FILE = f'{args.root_path}/log/{args.category}/'
os.makedirs(CKPT_FILE, exist_ok=True)
# os.makedirs(SAVE_BESTPC_DIR, exist_ok=True)
# os.makedirs(SAVE_VIEWCD_DIR, exist_ok=True)
# os.makedirs(CKPT_RECORD_FOLDER, exist_ok=True)
# os.makedirs(SAVE_PC_DIR, exist_ok=True)
losses_all = meter.AverageValueMeter()
losses_cd = meter.AverageValueMeter()
def save_record(epoch, prec1, type, net:nn.Module):
state_dict = net.state_dict()
torch.save(state_dict,
os.path.join(CKPT_RECORD_FOLDER, f'epoch{epoch}_{type}_{prec1:.4f}.pth'))
def save_ckpt(epoch, net, type, optimizer_all):
ckpt = dict(
epoch=epoch,
model=net.state_dict(),
optimizer_all=optimizer_all.state_dict(),
)
torch.save(ckpt,CKPT_FILE+type +str(epoch)+'_ckpt.pth')
def train(epoch,model,g_optimizer,train_loader,board_writer):
loss = None
pbar = tqdm(total=len(train_loader))
for iteration, (views, pc_parts, params, point2d, mask, inv_param, bounds, key) in enumerate(train_loader):
model.train()
views = views.to(device=DEVICE)
view_id = np.random.randint(low=0, high=args.view_num)
pc_parts = pc_parts.to(device=DEVICE)
params = params.to(device=DEVICE)
point2d = point2d.to(device=DEVICE)
mask = mask.to(device=DEVICE)
inv_param = inv_param.to(device=DEVICE)
bounds = bounds.to(device=DEVICE)
batch_size = views.size(0)
g_optimizer.zero_grad()
pcd_list, proj_list, calres = model(views[:,view_id, :3, :, :],pc_parts, mask, params, bounds, view_id, inv_param) #
if args.lamda_part !=0:
loss_part1,_ = chamfer_distance_sqrtp(pc_parts, pcd_list[0], point_reduction='mean')
loss_part2,_ = chamfer_distance_sqrtp(pc_parts, pcd_list[2], point_reduction='mean')
loss_part3,_ = chamfer_distance_sqrtp(pc_parts, pcd_list[3], point_reduction='mean')
proj_coarse1_re = proj_list[0].reshape(batch_size*args.view_num, 256, 2)
proj_coarse2_re = proj_list[1].reshape(batch_size*args.view_num, 512, 2)
proj_fine_re = proj_list[2].reshape(batch_size*args.view_num, 2048, 2)
point2d_re = point2d.reshape(batch_size*args.view_num, args.gt_num, 2)
loss2d1,_ = chamfer_distance(proj_coarse1_re/224.0, point2d_re, point_reduction='mean')
loss2d2,_ = chamfer_distance(proj_coarse2_re/224.0, point2d_re, point_reduction='mean')
loss2d3,_ = chamfer_distance(proj_fine_re/224.0, point2d_re, point_reduction='mean')
loss = loss2d1+loss2d2+loss2d3
if args.lamda_part!=0:
loss = loss + args.lamda_part*loss_part3+args.lamda_part*loss_part2 +args.lamda_part*loss_part1
assert torch.isnan(loss).sum() == 0 or torch.isinf(loss).sum() == 0
loss.backward()
g_optimizer.step()
losses_all.add(float(loss)) # batchsize
board_writer.add_scalar('loss/loss_all', losses_all.value()[0], global_step=iteration+epoch*len(train_loader))
board_writer.add_scalar('lr',g_optimizer.state_dict()['param_groups'][0]['lr'],global_step=iteration+epoch*len(train_loader))
pbar.set_postfix(loss=losses_all.value()[0])
pbar.update(1)
if epoch % args.eval_epoch ==0:
save_ckpt(epoch, model, '', g_optimizer)
save_record(epoch, losses_all.value()[0], '', model)
pbar.close()
return loss
def refine(epoch,refiner,g_optimizer,train_loader,board_writer):
loss = None
pbar = tqdm(total=len(train_loader))
for iteration, (views, pc_parts, params, point2d, mask, inv_param, bounds, key) in enumerate(train_loader):
refiner.train()
views = views.to(device=DEVICE)
view_id = np.random.randint(low=0, high=args.view_num)
pc_parts = pc_parts.to(device=DEVICE)
params = params.to(device=DEVICE)
point2d = point2d.to(device=DEVICE)
mask = mask.to(device=DEVICE)
inv_param = inv_param.to(device=DEVICE)
bounds = bounds.to(device=DEVICE)
batch_size = views.size(0)
g_optimizer.zero_grad()
refine_pc, refine_proj, calib_pc, pc0 = refiner(views[:,view_id, :3, :, :],pc_parts, mask, params, bounds, view_id, inv_param)
loss_part, _ = chamfer_distance_sqrtp(pc_parts, refine_pc, point_reduction='mean')
refine_proj_re = refine_proj.reshape(batch_size*args.view_num, 2048, 2)
point2d_re = point2d.reshape(batch_size*args.view_num, args.gt_num, 2)
loss2d, _ = chamfer_distance_sqrt(refine_proj_re/224.0, point2d_re, point_reduction='mean')
loss = loss_part+loss2d
assert torch.isnan(loss).sum() == 0 and torch.isinf(loss).sum() == 0
loss.backward()
g_optimizer.step()
losses_all.add(float(loss))
board_writer.add_scalar('loss/loss_refine', losses_all.value()[0], global_step=iteration+epoch*len(train_loader))
board_writer.add_scalar('lr',g_optimizer.state_dict()['param_groups'][0]['lr'],global_step=iteration+epoch*len(train_loader))
pbar.set_postfix(loss=losses_all.value()[0])
pbar.update(1)
if epoch % args.eval_epoch ==0:
save_ckpt(epoch, refiner, 'stage2_', g_optimizer)
save_record(epoch, losses_all.value()[0], 'stage2_', refiner)
pbar.close()
return loss
def model_eval(epoch,model,test_loader,last_loss):
print('---------Evaluation-----------')
losses_eval_cd = meter.AverageValueMeter()
pbar = tqdm(total=len(test_loader))
model.eval()
with torch.no_grad():
if args.category == 'all':
min_list = {
'02691156': [], '02933112': [], '02958343': [], '03001627': [], '03636649': [], '04256520': [], '04379243': [], '04530566': []
}
avg_list = {
'02691156': [], '02933112': [], '02958343': [], '03001627': [], '03636649': [], '04256520': [], '04379243': [], '04530566': []
}
else:
min_list = {cat_map[args.category]: []}
avg_list = {cat_map[args.category]: []}
for model_idx, (views, pc_parts, pcs, params, inv_params, mask, bounds, key) in enumerate(test_loader):
fname = key[0].split('/')[0]
views = views.to(device=DEVICE)
pc_parts = pc_parts.to(device=DEVICE)
pcs = pcs.to(device=DEVICE)
params = params.to(device=DEVICE)
inv_params = inv_params.to(device=DEVICE)
mask = mask.to(device=DEVICE)
bounds = bounds.to(device=DEVICE)
views_minloss = []
assert views.shape[1] == args.view_num
save_viewcd_path = os.path.join(SAVE_VIEWCD_DIR, key[0].split('/')[0])
os.makedirs(save_viewcd_path,exist_ok=True)
csv_file = open(os.path.join(save_viewcd_path, key[0].split('/')[1]+'_'+key[0].split('/')[-1]+'.csv'), 'w')
writer = csv.writer(csv_file)
writer.writerow(['view_id', 'cd'])
for i in range(args.view_num):
pcd_list, proj_list, _ = model(views[:,i, :3, :, :],pc_parts, mask, params, bounds, i, inv_params) #
loss_cd,_ = chamfer_distance(pcd_list[-1], pcs, point_reduction='mean')
loss_cd = loss_cd*10000
writer.writerow([i,float(loss_cd)])
views_minloss.append(float(loss_cd))
avg_list[fname].append(float(loss_cd))
losses_eval_cd.add(float(loss_cd))
if epoch%args.eval_epoch==0:
fine_2d = torch.squeeze(proj_list[-1], 0).detach().cpu().numpy()
fine_2d = fine_2d.astype(np.int32)
pc_result = torch.squeeze(pcd_list[3], 0).detach().cpu().numpy()
pc_coarse1 = torch.squeeze(pcd_list[2], 0).detach().cpu().numpy()
pc_cat = torch.squeeze(pcd_list[1], 0).detach().cpu().numpy()
pc_coarse0 = torch.squeeze(pcd_list[0], 0).detach().cpu().numpy()
os.makedirs(os.path.join(SAVE_PC_DIR,key[0].split('/')[0],key[0].split('/')[1],str(model_idx)),exist_ok=True)
save_mat(os.path.join(SAVE_PC_DIR,key[0].split('/')[0],key[0].split('/')[1], str(model_idx),'fine_id_'+'_view_'+str(i)+'.mat'), pc_result)
save_mat(os.path.join(SAVE_PC_DIR,key[0].split('/')[0],key[0].split('/')[1], str(model_idx),'coarse1_id_'+'_view_'+str(i)+'.mat'), pc_coarse1)
save_mat(os.path.join(SAVE_PC_DIR,key[0].split('/')[0],key[0].split('/')[1], str(model_idx),'cat_id_'+'_view_'+str(i)+'.mat'), pc_cat)
save_mat(os.path.join(SAVE_PC_DIR,key[0].split('/')[0],key[0].split('/')[1], str(model_idx),'coarse0_id_'+'_view_'+str(i)+'.mat'), pc_coarse0)
min_list[fname].append(min(views_minloss))
csv_file.close()
pbar.set_postfix(loss=losses_eval_cd.value()[0])
pbar.update(1)
for synset_id in min_list.keys():
min_list[synset_id] = np.mean(min_list[synset_id])
avg_list[synset_id] = np.mean(avg_list[synset_id])
if np.mean(list(avg_list.values())) < last_loss:
os.makedirs(SAVE_BESTPC_DIR, exist_ok=True)
remove_store(SAVE_PC_DIR, SAVE_BESTPC_DIR+os.sep+'pc')
pbar.close()
return min_list, avg_list
def refine_eval(epoch,refiner,test_loader,last_loss):
print('---------Evaluation-----------')
losses_eval_cd = meter.AverageValueMeter()
pbar = tqdm(total=len(test_loader))
refiner.eval()
with torch.no_grad():
if args.category == 'all':
min_list = {
'02691156': [], '02933112': [], '02958343': [], '03001627': [], '03636649': [], '04256520': [], '04379243': [], '04530566': []
}
avg_list = {
'02691156': [], '02933112': [], '02958343': [], '03001627': [], '03636649': [], '04256520': [], '04379243': [], '04530566': []
}
else:
min_list = {cat_map[args.category]: []}
avg_list = {cat_map[args.category]: []}
for model_idx, (views, pc_part, pcs, params, inv_params, mask, bounds, key) in enumerate(test_loader):
fname = key[0].split('/')[0]
views = views.to(device=DEVICE)
pc_part = pc_part.to(device=DEVICE)
pcs = pcs.to(device=DEVICE)
params = params.to(device=DEVICE)
inv_params = inv_params.to(device=DEVICE)
mask = mask.to(device=DEVICE)
bounds = bounds.to(device=DEVICE)
views_minloss = []
assert views.shape[1] == args.view_num
save_viewcd_path = os.path.join(SAVE_VIEWCD_DIR+'_refine', key[0].split('/')[0])
os.makedirs(save_viewcd_path,exist_ok=True)
csv_file2 = open(os.path.join(save_viewcd_path, key[0].split('/')[1]+'_stage2.csv'), 'w')
writer2 = csv.writer(csv_file2)
writer2.writerow(['view_id', 'cd'])
for i in range(args.view_num):
refine_pc, refine_proj, calib_pc, pc0 = refiner(views[:,i, :3, :, :],pc_part, mask, params, bounds, i, inv_params)
loss_cd,_ = chamfer_distance(calib_pc, pcs, point_reduction='mean')
loss_cd = loss_cd*10000
writer2.writerow([i,float(loss_cd)])
views_minloss.append(float(loss_cd))
avg_list[fname].append(float(loss_cd))
losses_eval_cd.add(float(loss_cd))
if epoch%args.eval_epoch==0:
fine_2d = torch.squeeze(refine_proj, 0).detach().cpu().numpy()
fine_2d = fine_2d.astype(np.int32)
pc_result = torch.squeeze(calib_pc, 0).detach().cpu().numpy()
pc_refine = torch.squeeze(refine_pc, 0).detach().cpu().numpy()
pc_0 = torch.squeeze(pc0, 0).detach().cpu().numpy()
os.makedirs((os.path.join(SAVE_PC_DIR+'_refine',key[0].split('/')[0],key[0].split('/')[1])),exist_ok=True)
save_mat(os.path.join(SAVE_PC_DIR+'_refine',key[0].split('/')[0],key[0].split('/')[1],'calib_id_'+str(model_idx)+'_view_'+str(i)+'.mat'), pc_result)
save_mat(os.path.join(SAVE_PC_DIR+'_refine',key[0].split('/')[0],key[0].split('/')[1],'refine_id_'+str(model_idx)+'_view_'+str(i)+'.mat'), pc_refine)
save_mat(os.path.join(SAVE_PC_DIR+'_refine',key[0].split('/')[0],key[0].split('/')[1],'calib0_id_'+str(model_idx)+'_view_'+str(i)+'.mat'), pc_0)
min_list[fname].append(min(views_minloss))
csv_file2.close()
pbar.set_postfix(loss=losses_eval_cd.value()[0])
pbar.update(1)
for synset_id in min_list.keys():
min_list[synset_id] = np.mean(min_list[synset_id])
avg_list[synset_id] = np.mean(avg_list[synset_id])
if np.mean(list(avg_list.values())) < last_loss:
os.makedirs(SAVE_BESTPC_DIR+'_refine',exist_ok=True)
remove_store(SAVE_PC_DIR+'_refine', SAVE_BESTPC_DIR+'_refine'+os.sep+'pc')
pbar.close()
return min_list, avg_list
def stage1():
torch.backends.cudnn.benchmark = True
model = Cross_PCC(encoder_dim=512, global_dim=512,cal_flag=False, branch2d=True)
model.to(device=DEVICE)
g_optimizer = torch.optim.Adam(model.parameters(), lr=args.lr,weight_decay=0.0001)
mstone = list(range(10, 140, 10))
scheduler = torch.optim.lr_scheduler.MultiStepLR(g_optimizer, milestones = mstone, gamma = 0.7, last_epoch=-1)
TRAIN_DATA = TrainDataLoader(filepath=args.data_list, data_path=args.data_path, pc_input_num=args.input_num,category=args.category)
TEST_DATA = TestDataLoader(filepath=args.data_list,data_path=args.data_path,status='test', pc_input_num=args.input_num,category=args.category)
train_loader = DataLoader(TRAIN_DATA,
batch_size=args.bz ,
num_workers=args.num_workers,
shuffle=True,
drop_last=True,
pin_memory=True
)
test_loader = DataLoader(TEST_DATA,
batch_size=1 ,
num_workers=args.num_workers,
shuffle=False,
drop_last=False,
pin_memory=True
)
resume_epoch = 0 # default 0
board_writer = SummaryWriter(comment=f'{MODEL}_bs{args.bz}_lr{args.lr}_{args.category}_{TIME_FLAG}')
if args.resume:
ckpt_path = CKPT_FILE
ckpt_dict = torch.load(ckpt_path+os.sep+'.pth')
model.load_state_dict(ckpt_dict['model'])
g_optimizer.load_state_dict(ckpt_dict['optimizer_all'])
resume_epoch = ckpt_dict['epoch']
os.makedirs(os.path.join(CKPT_RECORD_FOLDER),exist_ok=True)
best_loss = 9999
best_epoch = 0
for epoch in range(resume_epoch, args.epoch):
losses=train(epoch,model,g_optimizer,train_loader,board_writer)
if epoch % args.eval_epoch ==0:# and epoch!=0:
loss_min, loss_avg = model_eval(epoch,model,test_loader, best_loss)
current_mean = np.mean(list(loss_avg.values()))
with open(os.path.join(CKPT_FILE, 'result.txt'), 'a') as rf:
for cid in loss_min.keys():
rf.write(f'epoch: {epoch:0>4} | {cid}: min_CD--{loss_min[cid]:.6f} | avg_CD--{loss_avg[cid]:.6f}\n')
rf.write(f'epoch: {epoch:0>4} | min: {np.mean(list(loss_min.values())):.6f} | avg: {current_mean:.6f}\n')
rf.write('\n')
if current_mean < best_loss:
best_loss = current_mean
best_epoch = epoch
scheduler.step()
print('best epoch:',best_epoch,' | ', 'best avg loss',best_loss)
print('****************************')
print('Train Finished!')
return best_epoch
def stage2(b_epoch):
torch.backends.cudnn.benchmark = True
args.pthfile = os.path.join(CKPT_FILE, str(b_epoch)+'_ckpt.pth')
refiner = Refine(args=args)
refiner.to(device=DEVICE)
g_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, refiner.parameters()), lr=args.lr,weight_decay=0.0001)
scheduler = torch.optim.lr_scheduler.StepLR(g_optimizer, step_size = args.step_decay, gamma = args.gamma, last_epoch=-1)
TRAIN_DATA = TrainDataLoader(filepath=args.data_list, data_path=args.data_path, pc_input_num=args.input_num,category=args.category)
TEST_DATA = TestDataLoader(filepath=args.data_list,data_path=args.data_path,status='test', pc_input_num=args.input_num,category=args.category)
train_loader = DataLoader(TRAIN_DATA,
batch_size=args.bz ,
num_workers=args.num_workers,
shuffle=True,
drop_last=True,
pin_memory=True
)
test_loader = DataLoader(TEST_DATA,
batch_size=1 ,
num_workers=args.num_workers,
shuffle=False,
drop_last=False,
pin_memory=True
)
resume_epoch = 0 # default 0
board_writer = SummaryWriter(comment=f'{MODEL}_bs{args.bz}_lr{args.lr}_{args.category}_{TIME_FLAG}')
if args.resume:
ckpt_path = os.path.join(CKPT_FILE, '.pth')
ckpt_dict = torch.load(ckpt_path)
refiner.load_state_dict(ckpt_dict['model'])
g_optimizer.load_state_dict(ckpt_dict['optimizer_all'])
# g_optimizer.param_groups[0]['lr']= 5e-6
resume_epoch = ckpt_dict['epoch']
os.makedirs(os.path.join(CKPT_RECORD_FOLDER),exist_ok=True)
best_loss = 9999
best_epoch = 0
for epoch in range(args.refine_epoch):
losses=refine(epoch, refiner,g_optimizer,train_loader,board_writer)
if epoch % args.eval_epoch ==0 and epoch!=0:
loss_min, loss_avg = refine_eval(epoch,refiner,test_loader, best_loss)
current_mean = np.mean(list(loss_avg.values()))
with open(os.path.join(CKPT_FILE, 'result_refine.txt'), 'a') as rf:
for cid in loss_min.keys():
rf.write(f'epoch: {epoch:0>4} | {cid}: min_CD--{loss_min[cid]:.6f} | avg_CD--{loss_avg[cid]:.6f}\n')
rf.write(f'epoch: {epoch:0>4} | min: {np.mean(list(loss_min.values())):.6f} | avg: {current_mean:.6f}\n')
rf.write('\n')
if current_mean < best_loss:
best_loss = current_mean
best_epoch = epoch
scheduler.step()
print('best epoch:',best_epoch,' | ', 'best avg loss',best_loss)
print('****************************')
print('Train Finished!')
if __name__ == '__main__':
best_epoch = stage1()
best_epoch = 40
stage2(best_epoch)