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eval_voting.py
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eval_voting.py
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from __future__ import print_function
import os
import argparse
import torch
from util.data_util import PartNormalDataset
from model.DGCNN_PAConv_vote import PAConv
import numpy as np
from torch.utils.data import DataLoader
from util.util import to_categorical, compute_overall_iou, load_cfg_from_cfg_file, merge_cfg_from_list, IOStream
from tqdm import tqdm
from collections import defaultdict
from torch.autograd import Variable
import torch.nn.functional as F
classes_str =['aero','bag','cap','car','chair','ear','guitar','knife','lamp','lapt','moto','mug','Pistol','rock','stake','table']
class PointcloudScale(object):
def __init__(self, scale_low=2. / 3., scale_high=3. / 2.):
self.scale_low = scale_low
self.scale_high = scale_high
def __call__(self, pc):
bsize = pc.size()[0]
for i in range(bsize):
xyz = np.random.uniform(low=self.scale_low, high=self.scale_high, size=[3])
pc[i, :, 0:3] = torch.mul(pc[i, :, 0:3], torch.from_numpy(xyz).float().cuda())
return pc
def get_parser():
parser = argparse.ArgumentParser(description='3D Shape Part Segmentation')
parser.add_argument('--config', type=str, default='dgcnn_paconv_test.yaml', help='config file')
parser.add_argument('opts', help='see config/dgcnn_paconv_test.yaml for all options', default=None, nargs=argparse.REMAINDER)
args = parser.parse_args()
assert args.config is not None
cfg = load_cfg_from_cfg_file(args.config)
if args.opts is not None:
cfg = merge_cfg_from_list(cfg, args.opts)
cfg['manual_seed'] = cfg.get('manual_seed', 0)
cfg['workers'] = cfg.get('workers', 6)
return cfg
def _init_():
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
if not os.path.exists('checkpoints/' + args.exp_name):
os.makedirs('checkpoints/' + args.exp_name)
# backup the running files:
os.system('cp eval_voting.py checkpoints' + '/' + args.exp_name + '/' + 'eval_voting.py.backup')
def test(args, io):
# Try to load models
num_part = 50
device = torch.device("cuda" if args.cuda else "cpu")
model = PAConv(args, num_part).to(device)
io.cprint(str(model))
from collections import OrderedDict
state_dict = torch.load("checkpoints/%s/best_insiou_model.pth" % args.exp_name,
map_location=torch.device('cpu'))['model']
new_state_dict = OrderedDict()
for layer in state_dict:
new_state_dict[layer.replace('module.', '')] = state_dict[layer]
model.load_state_dict(new_state_dict)
# Dataloader
test_data = PartNormalDataset(npoints=2048, split='test', normalize=False)
print("The number of test data is:%d", len(test_data))
test_loader = DataLoader(test_data, batch_size=args.test_batch_size, shuffle=False, num_workers=args.workers,
drop_last=False)
NUM_PEPEAT = 100
NUM_VOTE = 10
global_Class_mIoU, global_Inst_mIoU = 0, 0
global_total_per_cat_iou = np.zeros((16)).astype(np.float32)
num_part = 50
num_classes = 16
pointscale = PointcloudScale(scale_low=0.87, scale_high=1.15)
model.eval()
for i in range(NUM_PEPEAT):
metrics = defaultdict(lambda: list())
shape_ious = []
total_per_cat_iou = np.zeros((16)).astype(np.float32)
total_per_cat_seen = np.zeros((16)).astype(np.int32)
for batch_id, (points, label, target, norm_plt) in tqdm(enumerate(test_loader), total=len(test_loader),
smoothing=0.9):
batch_size, num_point, _ = points.size()
points, label, target, norm_plt = Variable(points.float()), Variable(label.long()), Variable(
target.long()), Variable(norm_plt.float())
# points = points.transpose(2, 1)
norm_plt = norm_plt.transpose(2, 1)
points, label, target, norm_plt = points.cuda(non_blocking=True), label.squeeze().cuda(
non_blocking=True), target.cuda(non_blocking=True), norm_plt.cuda(non_blocking=True)
seg_pred = 0
new_points = Variable(torch.zeros(points.size()[0], points.size()[1], points.size()[2]).cuda(),
volatile=True)
for v in range(NUM_VOTE):
if v > 0:
new_points.data = pointscale(points.data)
with torch.no_grad():
seg_pred += F.softmax(
model(points.contiguous().transpose(2, 1), new_points.contiguous().transpose(2, 1),
norm_plt, to_categorical(label, num_classes)), dim=2) # xyz,x: only scale feature input
seg_pred /= NUM_VOTE
# instance iou without considering the class average at each batch_size:
batch_shapeious = compute_overall_iou(seg_pred, target, num_part) # [b]
shape_ious += batch_shapeious # iou +=, equals to .append
# per category iou at each batch_size:
for shape_idx in range(seg_pred.size(0)): # sample_idx
cur_gt_label = label[shape_idx] # label[sample_idx]
total_per_cat_iou[cur_gt_label] += batch_shapeious[shape_idx]
total_per_cat_seen[cur_gt_label] += 1
# accuracy:
seg_pred = seg_pred.contiguous().view(-1, num_part)
target = target.view(-1, 1)[:, 0]
pred_choice = seg_pred.data.max(1)[1]
correct = pred_choice.eq(target.data).cpu().sum()
metrics['accuracy'].append(correct.item() / (batch_size * num_point))
metrics['shape_avg_iou'] = np.mean(shape_ious)
for cat_idx in range(16):
if total_per_cat_seen[cat_idx] > 0:
total_per_cat_iou[cat_idx] = total_per_cat_iou[cat_idx] / total_per_cat_seen[cat_idx]
print('\n------ Repeat %3d ------' % (i + 1))
# First we need to calculate the iou of each class and the avg class iou:
class_iou = 0
for cat_idx in range(16):
class_iou += total_per_cat_iou[cat_idx]
io.cprint(classes_str[cat_idx] + ' iou: ' + str(total_per_cat_iou[cat_idx])) # print the iou of each class
avg_class_iou = class_iou / 16
outstr = 'Test :: test class mIOU: %f, test instance mIOU: %f' % (avg_class_iou, metrics['shape_avg_iou'])
io.cprint(outstr)
if avg_class_iou > global_Class_mIoU:
global_Class_mIoU = avg_class_iou
global_total_per_cat_iou = total_per_cat_iou
if metrics['shape_avg_iou'] > global_Inst_mIoU:
global_Inst_mIoU = metrics['shape_avg_iou']
# final avg print:
final_out_str = 'Best voting result :: test class mIOU: %f, test instance mIOU: %f' % (global_Class_mIoU, global_Inst_mIoU)
io.cprint(final_out_str)
# final per cat print:
for cat_idx in range(16):
io.cprint(classes_str[cat_idx] + ' iou: ' + str(global_total_per_cat_iou[cat_idx])) # print iou of each class
if __name__ == "__main__":
args = get_parser()
_init_()
io = IOStream('checkpoints/' + args.exp_name + '/%s_voting.log' % (args.exp_name))
io.cprint(str(args))
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.manual_seed)
if args.cuda:
io.cprint(
'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices')
torch.cuda.manual_seed(args.manual_seed)
else:
io.cprint('Using CPU')
test(args, io)