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test_samplenet_cls.py
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test_samplenet_cls.py
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"""
Author: Benny
Date: Nov 2019
"""
from data_utils.ModelNetDataLoader import ModelNetDataLoader
import argparse
import numpy as np
import os
import torch
import logging
from tqdm import tqdm
import sys
import importlib
from models import samplenet
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = BASE_DIR
sys.path.append(os.path.join(ROOT_DIR, 'models'))
def parse_args():
'''PARAMETERS'''
parser = argparse.ArgumentParser('Testing')
parser.add_argument('--use_cpu', action='store_true', default=False, help='use cpu mode')
parser.add_argument('--gpu', type=str, default='0', help='specify gpu device')
parser.add_argument('--batch_size', type=int, default=24, help='batch size in training')
parser.add_argument('--num_category', default=40, type=int, choices=[10, 40], help='training on ModelNet10/40')
parser.add_argument('--num_point', type=int, default=1024, help='Point Number')
parser.add_argument('--log_dir', type=str, default='ptnet_cls_4', help='Experiment root')
parser.add_argument('--use_normals', action='store_true', default=False, help='use normals')
parser.add_argument('--use_uniform_sample', action='store_true', default=False, help='use uniform sampiling')
parser.add_argument('--num_votes', type=int, default=3, help='Aggregate classification scores with voting')
return parser.parse_args()
def test(model, sampler, loader, num_class=40, vote_num=1):
mean_correct = []
classifier = model.eval()
class_acc = np.zeros((num_class, 3))
for j, (points, target) in tqdm(enumerate(loader), total=len(loader)):
if not args.use_cpu:
points, target = points.cuda(), target.cuda()
# points = points.transpose(2, 1)
vote_pool = torch.zeros(target.size()[0], num_class).cuda()
simp_pc, proj_pc = sampler(points)
proj_pc = proj_pc.transpose(2, 1)
for _ in range(vote_num):
pred, _ = classifier(proj_pc)
vote_pool += pred
pred = vote_pool / vote_num
pred_choice = pred.data.max(1)[1]
for cat in np.unique(target.cpu()):
classacc = pred_choice[target == cat].eq(target[target == cat].long().data).cpu().sum()
class_acc[cat, 0] += classacc.item() / float(points[target == cat].size()[0])
class_acc[cat, 1] += 1
correct = pred_choice.eq(target.long().data).cpu().sum()
mean_correct.append(correct.item() / float(points.size()[0]))
class_acc[:, 2] = class_acc[:, 0] / class_acc[:, 1]
class_acc = np.mean(class_acc[:, 2])
instance_acc = np.mean(mean_correct)
return instance_acc, class_acc
def main(args):
def log_string(str):
logger.info(str)
print(str)
'''HYPER PARAMETER'''
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
'''CREATE DIR'''
experiment_dir = 'log/classification/' + args.log_dir
'''LOG'''
args = parse_args()
logger = logging.getLogger("Model")
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler = logging.FileHandler('%s/eval.txt' % experiment_dir)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
log_string('PARAMETER ...')
log_string(args)
'''DATA LOADING'''
log_string('Load dataset ...')
data_path = 'data/modelnet40_normal_resampled/'
test_dataset = ModelNetDataLoader(root=data_path, args=args, split='test', process_data=False)
testDataLoader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=10)
'''MODEL LOADING'''
num_class = args.num_category
model_name = os.listdir(experiment_dir + '/logs')[0].split('.')[0]
model = importlib.import_module(model_name)
classifier = model.get_model(num_class, normal_channel=args.use_normals)
if not args.use_cpu:
classifier = classifier.cuda()
checkpoint = torch.load(str(experiment_dir) + '/checkpoints/best_model.pth')
classifier.load_state_dict(checkpoint['model_state_dict'])
'''Samplenet Loading'''
out_points = 16
bottleneck_size = 128
group_size = 10
sampler = samplenet.SampleNet(
num_out_points=out_points,
bottleneck_size=bottleneck_size,
group_size=group_size,
initial_temperature=1.0,
input_shape="bnc",
output_shape="bnc",
)
# checkpoint = torch.load('log/samplenet/64withbce2021-11-19_09-26-07/best.pth')
checkpoint = torch.load('log/samplenet_meta/16_3ensemble2021-12-14_04-37-42/best.pth')
"""
16_3ensemble2021-12-14_04-35-38 16_3tasks_1282021-12-14_00-33-10 16_3tasks_SGD_1282021-12-13_18-38-20
16_3ensemble2021-12-14_04-37-42 16_3tasks_1282021-12-14_00-49-23 16_3tasks_sgd_2021-12-13_07-07-14
16_3tasks2021-12-12_21-45-08 16_3tasks_SGD2021-12-13_17-36-39 16no_task2021-12-13_04-50-15
16_3tasks2021-12-12_22-46-05 16_3tasks_SGD2021-12-13_17-41-15 16single_task2021-12-12_22-41-22
16_3tasks2021-12-13_08-14-26 16_3tasks_SGD2021-12-13_17-53-11
"""
#64badtask2021-11-24_19-06-23
# print('bad task 64')
sampler.load_state_dict(checkpoint['model_state_dict'])
print(checkpoint['best_acc'])
sampler = sampler.cuda()
sampler.training=False
with torch.no_grad():
instance_acc, class_acc = test(classifier.eval(), sampler.eval(), testDataLoader, vote_num=args.num_votes, num_class=num_class)
log_string('Test Instance Accuracy: %f, Class Accuracy: %f' % (instance_acc, class_acc))
if __name__ == '__main__':
args = parse_args()
main(args)