def test(config):
log_dir = os.path.join(config.log_dir, config.name + '_stage_2')
val_path = os.path.join(config.data, "*/test")
val_dataset = MultiviewImgDataset(val_path,
scale_aug=False,
rot_aug=False,
num_views=config.num_views)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=config.stage2_batch_size,
shuffle=False,
num_workers=0)
pretraining = not config.no_pretraining
cnet = SVCNN(config.name,
nclasses=config.num_classes,
cnn_name=config.cnn_name,
pretraining=pretraining)
cnet_2 = MVCNN(config.name,
cnet,
nclasses=config.num_classes,
cnn_name=config.cnn_name,
num_views=config.num_views)
cnet_2.load(
os.path.join(log_dir, config.snapshot_prefix + str(config.weights)))
optimizer = optim.Adam(cnet_2.parameters(),
lr=config.learning_rate,
weight_decay=config.weight_decay,
betas=(0.9, 0.999))
trainer = ModelNetTrainer(cnet_2,
None,
val_loader,
optimizer,
nn.CrossEntropyLoss(),
config,
log_dir,
num_views=config.num_views)
labels, predictions = trainer.update_validation_accuracy(config.weights,
test=True)
import Evaluation_tools as et
eval_file = os.path.join(config.log_dir, '{}.txt'.format(config.name))
et.write_eval_file(config.data, eval_file, predictions, labels,
config.name)
et.make_matrix(config.data, eval_file, config.log_dir)
train_dataset = SingleImgDataset(args.train_path, scale_aug=False, rot_aug=False, num_models=n_models_train, num_views=args.num_views)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=0)
val_dataset = SingleImgDataset(args.val_path, scale_aug=False, rot_aug=False, test_mode=True)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=64, shuffle=False, num_workers=0)
print('num_train_files: '+str(len(train_dataset.filepaths)))
print('num_val_files: '+str(len(val_dataset.filepaths)))
trainer = ModelNetTrainer(cnet, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'svcnn', log_dir, num_views=1)
trainer.train(30)
# STAGE 2
log_dir = args.name+'_stage_2'
create_folder(log_dir)
cnet_2 = MVCNN(args.name, cnet, nclasses=40, cnn_name=args.cnn_name, num_views=args.num_views)
del cnet
optimizer = optim.Adam(cnet_2.parameters(), lr=args.lr, weight_decay=args.weight_decay, betas=(0.9, 0.999))
train_dataset = MultiviewImgDataset(args.train_path, scale_aug=False, rot_aug=False, num_models=n_models_train, num_views=args.num_views)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0)# shuffle needs to be false! it's done within the trainer
val_dataset = MultiviewImgDataset(args.val_path, scale_aug=False, rot_aug=False, num_views=args.num_views,test_mode=True)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0)
print('num_train_files: '+str(len(train_dataset.filepaths)))
print('num_val_files: '+str(len(val_dataset.filepaths)))
trainer = ModelNetTrainer(cnet_2, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'mvcnn', log_dir, num_views=args.num_views)
trainer.train(30)
def train(config):
log(config.log_file, 'Starting...')
pretraining = not config.no_pretraining
log_dir = config.name
create_folder(config.name)
log(config.log_file, '--------------stage 1--------------')
# STAGE 1
log_dir = os.path.join(config.log_dir, config.name + '_stage_1')
create_folder(log_dir)
cnet = SVCNN(config, pretraining=pretraining)
optimizer = optim.Adam(cnet.parameters(),
lr=config.learning_rate,
weight_decay=config.weight_decay)
train_path = os.path.join(config.data, "*/train")
train_dataset = SingleImgDataset(train_path,
config,
scale_aug=False,
rot_aug=False)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.stage1_batch_size,
shuffle=True,
num_workers=0)
val_path = os.path.join(config.data, "*/test")
val_dataset = SingleImgDataset(val_path,
config,
scale_aug=False,
rot_aug=False,
test_mode=True)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=config.stage1_batch_size,
shuffle=False,
num_workers=0)
log(config.log_file,
'num_train_files: ' + str(len(train_dataset.filepaths)))
log(config.log_file, 'num_val_files: ' + str(len(val_dataset.filepaths)))
trainer = ModelNetTrainer(cnet,
train_loader,
val_loader,
optimizer,
nn.CrossEntropyLoss(),
config,
log_dir,
num_views=1)
trainer.train(config, config.stage1_batch_size)
#cnet.load(os.path.join(log_dir, config.snapshot_prefix + str(30)))
# STAGE 2
log(config.log_file, '--------------stage 2--------------')
log_dir = os.path.join(config.log_dir, config.name + '_stage_2')
create_folder(log_dir)
cnet_2 = MVCNN(cnet, config)
del cnet
optimizer = optim.Adam(cnet_2.parameters(),
lr=config.learning_rate,
weight_decay=config.weight_decay,
betas=(0.9, 0.999))
train_dataset = MultiviewImgDataset(train_path,
config,
scale_aug=False,
rot_aug=False)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.stage2_batch_size,
shuffle=False,
num_workers=0
) # shuffle needs to be false! it's done within the trainer
val_dataset = MultiviewImgDataset(val_path,
config,
scale_aug=False,
rot_aug=False)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=config.stage2_batch_size,
shuffle=False,
num_workers=0)
log(config.log_file,
'num_train_files: ' + str(len(train_dataset.filepaths)))
log(config.log_file, 'num_val_files: ' + str(len(val_dataset.filepaths)))
trainer = ModelNetTrainer(cnet_2,
train_loader,
val_loader,
optimizer,
nn.CrossEntropyLoss(),
config,
log_dir,
num_views=config.num_views)
trainer.train(config, config.stage2_batch_size)
print('OA: ', val_overall_acc)
print('AA: ', val_mean_class_acc)
return features_list, labels_list
args = parser.parse_args()
print('###################Retrieval####################')
# 创建MVCNN_stage_1文件夹
log_dir = args.name + '_retrieval'
create_folder(log_dir)
gallery_dataset = MultiviewImgDataset(args.train_path,
scale_aug=False,
rot_aug=False,
test_mode=True,
shuffle=False,
num_views=args.num_views)
gallery_loader = torch.utils.data.DataLoader(gallery_dataset,
batch_size=args.batchSize,
shuffle=False,
num_workers=0)
query_dataset = MultiviewImgDataset(args.val_path,
scale_aug=False,
rot_aug=False,
test_mode=True,
shuffle=False,
num_views=args.num_views)
query_loader = torch.utils.data.DataLoader(query_dataset,
batch_size=args.batchSize,
shuffle=False,
pretraining = not args.no_pretraining
cnet = SVCNN(args.name,
nclasses=args.num_class,
pretraining=pretraining,
cnn_name=args.cnn_name)
cnet_2 = MVCNN(args.name,
cnet,
nclasses=args.num_class,
cnn_name=args.cnn_name,
num_views=args.num_views)
del cnet
val_dataset = MultiviewImgDataset(args.val_path,
scale_aug=False,
rot_aug=False,
num_views=args.num_views)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batchSize,
shuffle=False,
num_workers=0)
def update_validation_accuracy(model, val_loader, log_dir, num_class=3):
all_correct_points = 0
all_points = 0
DUMP_DIR = os.path.join(log_dir, 'dump')
if not os.path.exists(DUMP_DIR): os.mkdir(DUMP_DIR)
# in_data = None
print('OA: ', val_overall_acc)
print('AA: ', val_mean_class_acc)
return features_list, labels_list
args = parser.parse_args()
print('###################Retrieval####################')
# 创建MVCNN_stage_1文件夹
log_dir = args.name + '_retrieval'
create_folder(log_dir)
sketch_dataset = MultiviewImgDataset(args.sketch_path,
scale_aug=False,
rot_aug=False,
test_mode=True,
shuffle=False,
num_views=args.num_views)
sketch_loader = torch.utils.data.DataLoader(sketch_dataset,
batch_size=args.batchSize,
shuffle=False,
num_workers=0)
print('num_gallery_files: ' + str(len(sketch_dataset.filepaths)))
cnet = SVCNN(args.name, nclasses=40, pretraining=False, cnn_name=args.cnn_name)
cnet_2 = MVCNN(args.name,
cnet,
nclasses=40,
cnn_name=args.cnn_name,
num_views=args.num_views)
del cnet
### -----------------------stage 2의 30epoch 이상 학습시 필요한 코드!---------------------------------------------
# if use_dataparallel == True:
# cnet_2.module.load_state_dict(torch.load("Vgg11_Seg_white_stage_2/Vgg11_Seg_white/model-00027.pth"))
# cnet_2.module.eval()
# else:
# cnet_2.load_state_dict(torch.load("Vgg11_Seg_white_stage_2/Vgg11_Seg_white/model-00027.pth"))
# cnet_2.eval()
### -------------------------------------------------------------------------------------------------------------
if use_dataparallel:
cnet_2 = nn.DataParallel(cnet_2)
cnet_2.to(device)
optimizer = optim.Adam(cnet_2.parameters(), lr=args.lr, weight_decay=args.weight_decay, betas=(0.9, 0.999))
train_dataset = MultiviewImgDataset(args.train_path, scale_aug=False, rot_aug=False, num_models=n_models_train, num_views=args.num_views,KNU_data=args.KNU_Data,pixel_augmentation=args.pixel_augmentation)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0)# shuffle needs to be false! it's done within the trainer
val_dataset = MultiviewImgDataset(args.val_path, scale_aug=False, rot_aug=False, num_views=args.num_views,KNU_data=args.KNU_Data,pixel_augmentation=args.pixel_augmentation)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0)
print('num_train_files: '+str(len(train_dataset.filepaths)))
print('num_val_files: '+str(len(val_dataset.filepaths)))
if(args.loss_type == 'focal_loss'):
focal_loss = FocalLoss(gamma=2, alpha=0.25)
trainer = ModelNetTrainer(cnet_2, train_loader, val_loader, optimizer, focal_loss, 'mvcnn', log_dir, num_views=args.num_views, nClasses=nclasses)
else:
trainer = ModelNetTrainer(cnet_2, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'mvcnn', log_dir, num_views=args.num_views, nClasses=nclasses)
trainer.train(30, use_dataparallel)
print('num_train_files: '+str(len(train_dataset.filepaths)))
print('num_val_files: '+str(len(val_dataset.filepaths)))
trainer = ModelNetTrainer(cnet, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'svcnn', log_dir, num_views=1)
trainer.train(args.epoch)
# STAGE 2
log_dir = args.name+'_stage_2'
create_folder(log_dir)
cnet_2 = MVCNN(args.name, cnet, nclasses=len(classnames), cnn_name=args.cnn_name, num_views=args.num_views)
#if torch.cuda.device_count()>1:
# print('Use',torch.cuda.device_count(),'GPUs')
# cnet_2 = nn.DataParallel(cnet_2)
del cnet
optimizer = optim.Adam(cnet_2.parameters(), lr=args.lr, weight_decay=args.weight_decay, betas=(0.9, 0.999))
train_dataset = MultiviewImgDataset(args.train_path, classnames,objs,scale_aug=False, rot_aug=False, num_views=args.num_views)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0)# shuffle needs to be false! it's done within the trainer
val_dataset = MultiviewImgDataset(args.val_path, classnames,objs,scale_aug=False, rot_aug=False, num_views=args.num_views)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0)
print('num_train_files: '+str(len(train_dataset.filepaths)))
print('num_val_files: '+str(len(val_dataset.filepaths)))
trainer = ModelNetTrainer(cnet_2, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'mvcnn', log_dir, num_views=args.num_views)
trainer.train(args.epoch)
if __name__ == '__main__':
classnames=['airplane','bathtub','bed','bench','bookshelf','bottle','bowl','car','chair',
'cone','cup','curtain','desk','door','dresser','flower_pot','glass_box',
'guitar','keyboard','lamp','laptop','mantel','monitor','night_stand',
'person','piano','plant','radio','range_hood','sink','sofa','stairs',
'stool','table','tent','toilet','tv_stand','vase','wardrobe','xbox']
cnet = SVCNN('mvcnn', nclasses=40, pretraining=False, cnn_name='resnet18')
vgcnn = view_GCN('mvcnn', cnet, nclasses=40, cnn_name='resnet18', num_views=20)
vgcnn.eval()
vgcnn.load("./view-gcn", "trained_view_gcn.pth")
val_dataset = MultiviewImgDataset('data/modelnet40v2png_ori4/*/test', scale_aug=False, rot_aug=False, num_views=20,test_mode=False)
Counter = 0
index = 0
for _, data in enumerate(val_dataset, 0):
V, C, H, W = data[1].size()
in_data = Variable(data[1]).view(-1, C, H, W).cpu()
target = data[0]
out_data,F1,F2=vgcnn(in_data)
pred = torch.max(out_data, 1)[1]
pred = pred[0].cpu().detach().numpy().tolist()
if pred == target:
Counter += 1
index += 1
print(Counter, index, Counter/len(val_dataset))
print("total acc:", Counter/len(val_dataset))