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test_classification.py
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"""
Author: Benny
Date: Nov 2019
"""
from data_utils.KinectDataLoader import KinectDataLoader
from sklearn.metrics import classification_report, confusion_matrix
import argparse
import numpy as np
import os
import torch
import logging
from tqdm import tqdm
import sys
import importlib
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_points', type=int, default=1024, help='Point Number')
parser.add_argument('--num_category', default=2, type=int)
parser.add_argument('--log_dir', type=str, required=True, 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')
parser.add_argument('--data_path', type=str, required=True, help='Data root')
parser.add_argument('--tex_out', action='store_true', default=False, help='generate tex tables')
parser.add_argument('--class_names', type=str, default='', help='class names separated by commas, no spaces')
return parser.parse_args()
def test(model, loader, num_class=40, vote_num=1):
mean_correct = []
classifier = model.eval()
class_acc = np.zeros((num_class, 3))
predictions = torch.tensor([]).cpu()
targets = torch.tensor([]).cpu()
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()
for _ in range(vote_num):
pred, _ = classifier(points)
vote_pool += pred
pred = vote_pool / vote_num
pred_choice = pred.data.max(1)[1]
predictions = torch.cat((predictions, pred_choice.cpu()), 0)
targets = torch.cat((targets, target.cpu()), 0)
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)
labels = list(range(args.num_category))
report = classification_report(targets, predictions, labels=labels)
conf_matrix = confusion_matrix(targets, predictions, labels=labels)
cm = confusion_matrix(targets, predictions, labels=labels, normalize='true')
print(report)
print(conf_matrix)
if args.tex_out:
class_names = args.class_names.split(',')
report_dict = classification_report(targets, predictions, labels=labels, output_dict=True)
acc_str = '''\\begin{table}[]
\\caption{}
\\label{tab:}
\\centering
\\begin{tabular}{l|r|r|r|r}
& Precision & Recall & F1-score & Support <rows> \\\\\\hline\\hline
Accuracy & & & <acc> & <supp> \\\\ \\hline
Average & <avg_precision> & <avg_recall> & <avg_fscore> & <supp> \\\\ \\hline
Weighted Average & <wavg_precision> & <wavg_recall> & <wavg_fscore> & <supp> \\\\ \\hline
\\end{tabular}
\\end{table}'''
rows = ''
for i, class_name in enumerate(class_names):
rows += f'\\\\\\hline\n{class_name} & {report_dict[str(i)]["precision"]:.3f} & {report_dict[str(i)]["recall"]:.3f} & {report_dict[str(i)]["f1-score"]:.3f} & {report_dict[str(i)]["support"]}'
acc_str = acc_str.replace('<rows>', rows)
acc_str = acc_str.replace('<acc>', str(round(report_dict['accuracy'], 3)))
acc_str = acc_str.replace('<supp>', str(round(report_dict['macro avg']['support'], 3)))
acc_str = acc_str.replace('<avg_precision>', str(round(report_dict['macro avg']['precision'], 3)))
acc_str = acc_str.replace('<avg_recall>', str(round(report_dict['macro avg']['recall'], 3)))
acc_str = acc_str.replace('<avg_fscore>', str(round(report_dict['macro avg']['f1-score'], 3)))
acc_str = acc_str.replace('<wavg_precision>', str(round(report_dict['weighted avg']['precision'], 3)))
acc_str = acc_str.replace('<wavg_recall>', str(round(report_dict['weighted avg']['recall'], 3)))
acc_str = acc_str.replace('<wavg_fscore>', str(round(report_dict['weighted avg']['f1-score'], 3)))
conf_mat_str = '''
\\begin{table}[t!]
\\caption{}
\\label{tab:}
\\renewcommand{\\arraystretch}{1.5}
\\setlength\\tabcolsep{0.08cm}
\\centering
\\begin{tabular}{|c|''' + "c|" * len(class_names) + '''}
\\cline{2-''' + str(len(class_names) + 1) + '''}
\\multicolumn{1}{c|}{} & \\rule{0pt}{17mm} <classes>\\\\\\hline
<class_scores>
\\end{tabular}
\\end{table}'''
classes_str = " & ".join(["\\rot{" + name + "}" for name in class_names])
class_scores = '\n'.join([name + " & " + " & ".join(['\cellcolor{blue!' + str(int(cm[i][j] * 100)) + '}{' + str(round(cm[i][j],3)) + '}' for j in range(len(class_names))]) + '\\\\\\hline' for i, name in enumerate(class_names)])
conf_mat_str = conf_mat_str.replace('<classes>', classes_str)
conf_mat_str = conf_mat_str.replace('<class_scores>', class_scores)
with open(args.log_dir + '_eval.tex', 'w') as f:
f.write(acc_str + '\n\n\n' + conf_mat_str)
with open(args.log_dir + '_eval.txt', 'w') as f:
f.writelines([str(report), ' ', str(conf_matrix), ''])
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 = args.data_path #'/homeL/5fiedler/data/t3_t4_t5_prep/'
test_dataset = KinectDataLoader(root=data_path, split='test', include_normals=args.use_normals, num_points=args.num_points, center_pointclouds=True, random_scaling=True)
testDataLoader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=10)
'''MODEL LOADING'''
class_names = args.class_names.split(',')
num_class = test_dataset.get_num_classes()
if args.tex_out:
if len(class_names) != num_class:
print('class names and class count mismatch!')
print(num_class)
print(class_names)
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'])
with torch.no_grad():
instance_acc, class_acc = test(classifier.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)