This repository has been archived by the owner on Feb 3, 2024. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 28
/
inference.py
193 lines (175 loc) · 6.95 KB
/
inference.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
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: An Tao
@Contact: ta19@mails.tsinghua.edu.cn
@File: inference.py
@Time: 2020/1/2 10:26 AM
"""
import os
import sys
import time
import shutil
import torch
import numpy as np
import h5py
from tensorboardX import SummaryWriter
from model import ReconstructionNet, ClassificationNet
from dataset import Dataset
from utils import Logger
class Inference(object):
def __init__(self, args):
self.batch_size = args.batch_size
self.no_cuda = args.no_cuda
self.task = args.task
# create exp directory
file = [f for f in args.model_path.split('/')]
if args.exp_name != None:
self.experiment_id = args.exp_name
else:
self.experiment_id = time.strftime('%m%d%H%M%S')
cache_root = 'cache/%s' % self.experiment_id
os.makedirs(cache_root, exist_ok=True)
self.feature_dir = os.path.join(cache_root, 'features/')
sys.stdout = Logger(os.path.join(cache_root, 'log.txt'))
# check directory
if not os.path.exists(self.feature_dir):
os.makedirs(self.feature_dir)
else:
shutil.rmtree(self.feature_dir)
os.makedirs(self.feature_dir)
# print args
print(str(args))
# get gpu id
gids = ''.join(args.gpu.split())
self.gpu_ids = [int(gid) for gid in gids.split(',')]
self.first_gpu = self.gpu_ids[0]
# generate dataset
self.infer_dataset_train = Dataset(
root=args.dataset_root,
dataset_name=args.dataset,
split='train',
num_points=args.num_points,
)
self.infer_dataset_test = Dataset(
root=args.dataset_root,
dataset_name=args.dataset,
split='test',
num_points=args.num_points,
)
self.infer_loader_train = torch.utils.data.DataLoader(
self.infer_dataset_train,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers
)
self.infer_loader_test = torch.utils.data.DataLoader(
self.infer_dataset_test,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers
)
print("Inference set size (train):", self.infer_loader_train.dataset.__len__())
print("Inference set size (test):", self.infer_loader_test.dataset.__len__())
# initialize model
if args.task == "reconstruct":
self.model = ReconstructionNet(args)
elif args.task == "classify":
self.model = ClassificationNet(args)
if args.model_path != '':
self._load_pretrain(args.model_path)
# load model to gpu
if not args.no_cuda:
if len(self.gpu_ids) != 1: # multiple gpus
self.model = torch.nn.DataParallel(self.model.cuda(self.first_gpu), self.gpu_ids)
else:
self.model = self.model.cuda(self.gpu_ids[0])
def run(self):
self.model.eval()
# generate train set for SVM
loss_buf = []
feature_train = []
lbs_train = []
n = 0
for iter, (pts, lbs) in enumerate(self.infer_loader_train):
if not self.no_cuda:
pts = pts.cuda(self.first_gpu)
lbs = lbs.cuda(self.first_gpu)
if self.task == "reconstruct":
output, feature = self.model(pts)
elif self.task == "classify":
feature = self.model(pts)
feature_train.append(feature.detach().cpu().numpy().squeeze(1))
lbs_train.append(lbs.cpu().numpy().squeeze(1))
if ((iter+1) * self.batch_size % 2048) == 0 \
or (iter+1) == len(self.infer_loader_train):
feature_train = np.concatenate(feature_train, axis=0)
lbs_train = np.concatenate(lbs_train, axis=0)
f = h5py.File(os.path.join(self.feature_dir, 'train' + str(n) + '.h5'),'w')
f['data'] = feature_train
f['label'] = lbs_train
f.close()
print("Train set {} for SVM saved.".format(n))
feature_train = []
lbs_train = []
n += 1
if self.task == "reconstruct":
if len(self.gpu_ids) != 1: # multiple gpus
loss = self.model.module.get_loss(pts, output)
else:
loss = self.model.get_loss(pts, output)
loss_buf.append(loss.detach().cpu().numpy())
if self.task == "reconstruct":
print(f'Avg loss {np.mean(loss_buf)}')
print("Finish generating train set for SVM.")
# generate test set for SVM
loss_buf = []
feature_test = []
lbs_test = []
n = 0
for iter, (pts, lbs) in enumerate(self.infer_loader_test):
if not self.no_cuda:
pts = pts.cuda(self.first_gpu)
lbs = lbs.cuda(self.first_gpu)
if self.task == "reconstruct":
output, feature = self.model(pts)
elif self.task == "classify":
feature = self.model(pts)
feature_test.append(feature.detach().cpu().numpy().squeeze(1))
lbs_test.append(lbs.cpu().numpy().squeeze(1))
if ((iter+1) * self.batch_size % 2048) == 0 \
or (iter+1) == len(self.infer_loader_test):
feature_test = np.concatenate(feature_test, axis=0)
lbs_test = np.concatenate(lbs_test, axis=0)
f = h5py.File(os.path.join(self.feature_dir, 'test' + str(n) + '.h5'),'w')
f['data'] = feature_test
f['label'] = lbs_test
f.close()
print("Test set {} for SVM saved.".format(n))
feature_test = []
lbs_test = []
n += 1
if self.task == "reconstruct":
if len(self.gpu_ids) != 1: # multiple gpus
loss = self.model.module.get_loss(pts, output)
else:
loss = self.model.get_loss(pts, output)
loss_buf.append(loss.detach().cpu().numpy())
if self.task == "reconstruct":
print(f'Avg loss {np.mean(loss_buf)}')
print("Finish generating test set for SVM.")
return self.feature_dir
def _load_pretrain(self, pretrain):
state_dict = torch.load(pretrain, map_location='cpu')
from collections import OrderedDict
new_state_dict = OrderedDict()
for key, val in state_dict.items():
if key[:6] == 'module':
name = key[7:] # remove 'module.'
else:
name = key
if key[:10] == 'classifier':
continue
new_state_dict[name] = val
self.model.load_state_dict(new_state_dict)
print(f"Load model from {pretrain}")