elif opt.setting == 'InterDataset':
annotation_file = 'Pascal3D.txt'
test_cls = [
'aeroplane', 'bicycle', 'boat', 'bottle', 'bus', 'car', 'chair',
'diningtable', 'motorbike', 'sofa', 'train', 'tvmonitor'
]
else:
sys.exit('Wrong setting!')
mean_class_data = pickle.load(open(opt.class_data, 'rb'))
Err_All = []
for cls in test_cls:
class_data = mean_class_data[cls]
dataset_test = Pascal3D(train=False,
root_dir=opt.root_dir,
annotation_file=annotation_file,
cls_choice=[cls],
input_dim=opt.input_dim,
point_num=opt.point_num)
Acc, Med, Errs = test_category(dataset_test, model, opt.bin_size, cls,
opt.output, logname, class_data)
Err_All.extend(Errs)
print('Acc30 is {:.2f} and MerErr is {:.2f} for {} images in class {}\n'.
format(Acc, Med, len(dataset_test), cls))
print(
'Performance across all classes: Acc_pi/6 is {:.2f} and Med_Err is {:.2f}'.
format(np.mean(np.array(Err_All) <= 30), np.median(np.array(Err_All))))
# =================CREATE DATASET=========================== #
root_dir = os.path.join('data', opt.dataset)
annotation_file = '{}.txt'.format(opt.dataset)
if opt.dataset == 'ObjectNet3D':
test_cats = [
'bed', 'bookshelf', 'calculator', 'cellphone', 'computer', 'door',
'filing_cabinet', 'guitar', 'iron', 'knife', 'microwave', 'pen', 'pot',
'rifle', 'shoe', 'slipper', 'stove', 'toilet', 'tub', 'wheelchair'
]
dataset_train = Pascal3D(train=True,
root_dir=root_dir,
annotation_file=annotation_file,
cat_choice=test_cats,
keypoint=opt.keypoint,
novel=opt.novel,
shape=opt.shape,
shape_dir=opt.shape_dir,
view_num=opt.view_num,
tour=opt.tour,
random_range=opt.random_range,
random=opt.random)
dataset_eval = Pascal3D(train=False,
root_dir=root_dir,
annotation_file=annotation_file,
cat_choice=test_cats,
keypoint=opt.keypoint,
random=False,
novel=opt.novel,
shape=opt.shape,
shape_dir=opt.shape_dir,
view_num=opt.view_num,
print(opt)
# ========================================================== #
# =================CREATE DATASET=========================== #
if opt.novel:
print('Excluding novel classes in training')
else:
print('Including novel classes in training')
# 20 novel classes on ObjectNet3D
if opt.setting == 'IntraDataset':
test_cats = ['bed', 'bookshelf', 'calculator', 'cellphone', 'computer', 'door', 'filing_cabinet', 'guitar', 'iron',
'knife', 'microwave', 'pen', 'pot', 'rifle', 'shoe', 'slipper', 'stove', 'toilet', 'tub', 'wheelchair']
dataset_train = Pascal3D(train=True, root_dir=opt.root_dir_train, annotation_file=opt.annot_train,
cls_choice=test_cats, input_dim=opt.input_dim, point_num=opt.point_num,
keypoint=opt.keypoint, novel=opt.novel, shot=opt.shot)
# 12 novel classes on Pascal3D
elif opt.setting == 'InterDataset':
test_cats = ['aeroplane', 'bicycle', 'boat', 'bottle', 'bus', 'car',
'chair', 'diningtable', 'motorbike', 'sofa', 'train', 'tvmonitor']
dataset_train = Pascal3D(train=True, root_dir=opt.root_dir_train, annotation_file=opt.annot_train,
cls_choice=test_cats, input_dim=opt.input_dim, point_num=opt.point_num,
keypoint=opt.keypoint, novel=opt.novel, shot=opt.shot)
else:
sys.exit('Wrong setting!')
train_loader = DataLoader(dataset_train, batch_size=opt.batch_size, shuffle=True, num_workers=opt.workers, drop_last=True)
print('train data consist of {} samples'.format(len(dataset_train)))
f.close()
# ========================================================== #
# ================TESTING LOOP============================== #
Accs = {}
Meds = {}
root_dir = os.path.join('data', opt.dataset)
annotation_file = '{}.txt'.format(opt.dataset)
if opt.dataset == 'Pascal3D':
test_cats = ['aeroplane', 'bicycle', 'boat', 'bottle', 'bus', 'car', 'chair', 'diningtable', 'motorbike', 'sofa',
'train', 'tvmonitor']
for cat in tqdm(test_cats):
dataset_test = Pascal3D(root_dir=root_dir, annotation_file=annotation_file,
cat_choice=[cat], train=False, random=False, shape=opt.shape, shape_dir=opt.shape_dir,
view_num=opt.view_num, tour=opt.tour, random_model=opt.random_model)
Accs[cat], Meds[cat] = test_category(opt.shape, opt.batch_size, opt.dataset, dataset_test, model, opt.bin_size,
cat, predictions_path, logname)
elif opt.dataset == 'ObjectNet3D':
test_cats = ['bed', 'bookshelf', 'calculator', 'cellphone', 'computer', 'door', 'filing_cabinet', 'guitar', 'iron',
'knife', 'microwave', 'pen', 'pot', 'rifle', 'shoe', 'slipper', 'stove', 'toilet', 'tub', 'wheelchair']
for cat in tqdm(test_cats):
dataset_test = Pascal3D(root_dir=root_dir, annotation_file=annotation_file, shape_dir=opt.shape_dir,
cat_choice=[cat], train=False, random=False, keypoint=True, shape=opt.shape,
view_num=opt.view_num, tour=opt.tour, random_model=opt.random_model)
Accs[cat], Meds[cat] = test_category(opt.shape, opt.batch_size, opt.dataset, dataset_test, model, opt.bin_size,
cat, predictions_path, logname)
f.write('\n')
f.close()
# ========================================================== #
# ================TESTING LOOP============================== #
Accs = {}
Meds = {}
if opt.dataset == 'Pascal3D':
test_cats = ['aeroplane', 'bicycle', 'boat', 'bottle', 'bus', 'car', 'chair', 'diningtable', 'motorbike', 'sofa',
'train', 'tvmonitor']
for cat in test_cats:
dataset_test = Pascal3D(root_dir='data/Pascal3D', annotation_file='pascal3d_annotation.txt',
cat_choice=[cat], train=False, mutated=False, shape=opt.shape, mode=opt.mode,
render_number=opt.num_render, tour=opt.tour, random_model=opt.random_model)
Accs[cat], Meds[cat] = test_category(opt.shape, opt.batch_size, opt.dataset, dataset_test, model, bin_size, cat,
predictions_path, logname)
elif opt.dataset == 'Object3D':
test_cats = ['bed', 'bookshelf', 'calculator', 'cellphone', 'computer', 'door', 'filing_cabinet', 'guitar', 'iron',
'knife', 'microwave', 'pen', 'pot', 'rifle', 'shoe', 'slipper', 'stove', 'toilet', 'tub', 'wheelchair']
for cat in test_cats:
dataset_test = Pascal3D(root_dir='data/Object3D', annotation_file='object3d_annotation.txt',
cat_choice=[cat], train=False, mutated=False, keypoint=True, shape=opt.shape,
mode=opt.mode, render_number=opt.num_render, tour=opt.tour, random_model=opt.random_model)
Accs[cat], Meds[cat] = test_category(opt.shape, opt.batch_size, opt.dataset, dataset_test, model, bin_size, cat,
predictions_path, logname)
f.close()
# ========================================================== #
# ================TESTING LOOP============================== #
Accs = {}
Meds = {}
root_dir = os.path.join('data', opt.dataset)
annotation_file = '{}.txt'.format(opt.dataset)
if opt.dataset == 'Pascal3D':
test_cats = ['aeroplane', 'bicycle', 'boat', 'bottle', 'bus', 'car', 'chair', 'diningtable', 'motorbike', 'sofa',
'train', 'tvmonitor']
for cat in test_cats:
dataset_test = Pascal3D(root_dir=root_dir, annotation_file=annotation_file,
cat_choice=[cat], train=False, mutated=False, shape=opt.shape,
render_number=opt.num_render, tour=opt.tour, random_model=opt.random_model)
Accs[cat], Meds[cat] = test_category(opt.shape, opt.batch_size, opt.dataset, dataset_test, model, bin_size, cat,
predictions_path, logname)
elif opt.dataset == 'ObjectNet3D':
test_cats = ['bed', 'bookshelf', 'calculator', 'cellphone', 'computer', 'door', 'filing_cabinet', 'guitar', 'iron',
'knife', 'microwave', 'pen', 'pot', 'rifle', 'shoe', 'slipper', 'stove', 'toilet', 'tub', 'wheelchair']
for cat in test_cats:
dataset_test = Pascal3D(root_dir=root_dir, annotation_file=annotation_file,
cat_choice=[cat], train=False, mutated=False, keypoint=True, shape=opt.shape,
render_number=opt.num_render, tour=opt.tour, random_model=opt.random_model)
Accs[cat], Meds[cat] = test_category(opt.shape, opt.batch_size, opt.dataset, dataset_test, model, bin_size, cat,
predictions_path, logname)