-
Notifications
You must be signed in to change notification settings - Fork 4
/
shapenet_model_evaluation_OMSN.py
160 lines (114 loc) · 5.75 KB
/
shapenet_model_evaluation_OMSN.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
from dataset.ShapeNetDataset import *
from torch.utils.data import DataLoader
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from losses.emd import emd_module as emd
from losses.chamfer import champfer_loss as chamfer
from models.msn_model import MSN_Original
from utils.utils import weights_init, visdom_show_pc, save_paths, save_model, vis_curve
from utils.metrics import AverageValueMeter
from utils.pcutils import mean_min_square_distance, save_point_cloud
from losses.MDS import MDS_module
import sys
#from extensions.chamfer_dist import ChamferDistance
class DevNull:
def write(self, msg):
pass
#Only for testing
parser = argparse.ArgumentParser()
parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
parser.add_argument('--model', type=str, default = 'model', help='optional reload model path')
parser.add_argument('--workers', type=int, help='number of data loading workers', default=12)
parser.add_argument('--num_points', type=int, default = 2048, help='number of points')
parser.add_argument('--n_primitives', type=int, default = 16, help='number of primitives')
parser.add_argument('--holeSize', type=int, default=35, help='hole size')
parser.add_argument('--outputFolder', type=str, default='', help='Folder output')
parser.add_argument('--inputTestFolder', type=str, default='', help='Input folder with test data')
opt = parser.parse_args()
# -------------------------------- Load network----------------------------------------
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
print("Using cuda device")
torch.cuda.set_device(device)
network = MSN_Original(opt.num_points, device).to(device)
network.apply(weights_init)
network.cuda()
#load model
print(opt.model, os.path.isfile(opt.model + "/model.pth"))
if opt.model != '' and os.path.isfile(opt.model + "/model.pth"):
model_checkpoint = torch.load(opt.model + "/model.pth",map_location='cuda:0')
#residual_checkpoint = torch.load("log/" + opt.model + "/residual.pth",map_location='cuda:0')
print("Model network weights loaded ")
network.model.load_state_dict(model_checkpoint['state_dict'])
print(f'************************** MSN - {opt.holeSize/100} ***********************************')
# Shapenet
n_models = 13
class_choice = {'Airplane': 0, 'Bag': 1, 'Cap': 2, 'Car': 3, 'Chair': 4, 'Guitar': 6, 'Lamp': 8, 'Laptop': 9, 'Motorbike': 10, 'Mug': 11, 'Pistol': 12, 'Skateboard': 14, 'Table': 15}
categories = class_choice.keys()
R = []
#chamfer_dist = ChamferDistance()
L = []
#Reproducibility
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
np.random.seed(1)
random.seed(1)
for categorie in categories:
pred_error = AverageValueMeter()
gt_error = AverageValueMeter()
chamfer_error = AverageValueMeter()
dataset_dir = './data/shapenet_part'
dataset_test = ShapeNetDataset(root_dir=dataset_dir, class_choice={categorie}, npoints=2048, split='test', hole_size=opt.holeSize/100)
dataloader_test = DataLoader(dataset_test, batch_size=opt.batchSize, shuffle=False, num_workers=0)
network.model.eval()
with torch.no_grad():
for i, data in enumerate(dataloader_test, 0):
name, in_partial, in_hole, in_complete = data
pathPartial = os.path.join(opt.inputTestFolder, categorie, name[0] + '_partial.xyz')
pathComplete = os.path.join(opt.inputTestFolder, categorie, name[0] + '_gt.xyz')
in_partial = np.loadtxt(pathPartial).astype(np.float32)
in_complete = np.loadtxt(pathComplete).astype(np.float32)
in_partial = torch.unsqueeze(torch.from_numpy(in_partial), 0)
in_complete= torch.unsqueeze(torch.from_numpy(in_complete), 0)
in_partial = in_partial.contiguous().float().to(device)
in_complete = in_complete.contiguous().float().to(device)
output, output2, rec_loss1, rec_loss2, exp_loss = network(in_partial, in_complete, 0.005, 50)
rec_g_loss = rec_loss1 + rec_loss2 + exp_loss
#dist = chamfer_dist(output2, in_complete)
#chamfer_error.update(dist.item()*10000)
pred = output2.cpu().numpy()[0]
gt = in_complete.cpu().numpy()[0]
partial = in_partial.cpu().numpy()[0]
pred_error.update(mean_min_square_distance(pred, gt)*10000)
gt_error.update(mean_min_square_distance(gt, pred)*10000)
if opt.outputFolder != "":
#Save models and metric
log_table = {"name":name, "chamfer": dist.item()*10000}
L.append(log_table)
#print(name)
save_point_cloud(os.path.join(opt.outputFolder, categorie, name[0]+'_gt.xyz'), gt)
save_point_cloud(os.path.join(opt.outputFolder, categorie, name[0]+'_partial.xyz'), partial)
save_point_cloud(os.path.join(opt.outputFolder, categorie, name[0]+'_pred.xyz'), pred)
gt_error.end_epoch()
pred_error.end_epoch()
#chamfer_error.end_epoch()
if opt.outputFolder != "":
with open(os.path.join(opt.outputFolder, categorie+".txt"), 'w') as fi:
fi.write(json.dumps(L))
R.append({'cat': categorie, 'chamfer': chamfer_error.avg, 'pred': pred_error.avg, 'gt':gt_error.avg})
print('Categorie:', end='\t')
print('Chamfer:', end='\t')
print('Pred->GT:', end='\t')
print('GT->Pred:', end='\t')
print()
for dc in R:
print(dc['cat'], end='\t')
print(dc['chamfer'], end='\t')
print(dc['pred'], end='\t')
print(dc['gt'], end='\t')
print()