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
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
|
import os
from typing import Union, Optional, Tuple, List, Dict
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.data.dataloader import default_collate
from torch.utils.tensorboard import SummaryWriter
from models.rgb_part_net import RGBPartNet
from utils.configuration import DataloaderConfiguration, \
HyperparameterConfiguration, DatasetConfiguration, ModelConfiguration, \
SystemConfiguration
from utils.dataset import CASIAB
from utils.sampler import TripletSampler
class Model:
def __init__(
self,
system_config: SystemConfiguration,
model_config: ModelConfiguration,
hyperparameter_config: HyperparameterConfiguration
):
self.disable_acc = system_config['disable_acc']
if self.disable_acc:
self.device = torch.device('cpu')
else: # Enable accelerator
if torch.cuda.is_available():
self.device = torch.device('cuda')
else:
print('No accelerator available, fallback to CPU.')
self.device = torch.device('cpu')
self.save_dir = system_config['save_dir']
self.checkpoint_dir = os.path.join(self.save_dir, 'checkpoint')
self.log_dir = os.path.join(self.save_dir, 'logs')
for dir_ in (self.save_dir, self.log_dir, self.checkpoint_dir):
if not os.path.exists(dir_):
os.mkdir(dir_)
self.meta = model_config
self.hp = hyperparameter_config
self.curr_iter = self.meta['restore_iter']
self.total_iter = self.meta['total_iter']
self.is_train: bool = True
self.train_size: int = 74
self.in_channels: int = 3
self.pr: Optional[int] = None
self.k: Optional[int] = None
self._model_sig: str = self._make_signature(self.meta, ['restore_iter'])
self._hp_sig: str = self._make_signature(self.hp)
self._dataset_sig: str = 'undefined'
self._log_sig: str = '_'.join((self._model_sig, self._hp_sig))
self.log_name: str = os.path.join(self.log_dir, self._log_sig)
self.rgb_pn: Optional[RGBPartNet] = None
self.optimizer: Optional[optim.Adam] = None
self.scheduler: Optional[optim.lr_scheduler.StepLR] = None
self.writer: Optional[SummaryWriter] = None
@property
def signature(self) -> str:
return '_'.join((self._model_sig, str(self.curr_iter), self._hp_sig,
self._dataset_sig, str(self.pr), str(self.k)))
@property
def checkpoint_name(self) -> str:
return os.path.join(self.checkpoint_dir, self.signature)
def fit(
self,
dataset_config: DatasetConfiguration,
dataloader_config: DataloaderConfiguration,
):
self.is_train = True
dataset = self._parse_dataset_config(dataset_config)
dataloader = self._parse_dataloader_config(dataset, dataloader_config)
# Prepare for model, optimizer and scheduler
hp = self.hp.copy()
lr, betas = hp.pop('lr', 1e-4), hp.pop('betas', (0.9, 0.999))
self.rgb_pn = RGBPartNet(self.train_size, self.in_channels, **hp)
self.optimizer = optim.Adam(self.rgb_pn.parameters(), lr, betas)
self.scheduler = optim.lr_scheduler.StepLR(self.optimizer, 500, 0.9)
self.writer = SummaryWriter(self.log_name)
if not self.disable_acc:
if torch.cuda.device_count() > 1:
self.rgb_pn = nn.DataParallel(self.rgb_pn)
self.rgb_pn = self.rgb_pn.to(self.device)
self.rgb_pn.train()
# Init weights at first iter
if self.curr_iter == 0:
self.rgb_pn.apply(self.init_weights)
else: # Load saved state dicts
checkpoint = torch.load(self.checkpoint_name)
iter_, loss = checkpoint['iter'], checkpoint['loss']
print('{0:5d} loss: {1:.3f}'.format(iter_, loss))
self.rgb_pn.load_state_dict(checkpoint['model_state_dict'])
self.optimizer.load_state_dict(checkpoint['optim_state_dict'])
for (batch_c1, batch_c2) in dataloader:
self.curr_iter += 1
# Zero the parameter gradients
self.optimizer.zero_grad()
# forward + backward + optimize
x_c1 = batch_c1['clip'].to(self.device)
x_c2 = batch_c2['clip'].to(self.device)
y = batch_c1['label'].to(self.device)
loss, metrics = self.rgb_pn(x_c1, x_c2, y)
loss.backward()
self.optimizer.step()
# Step scheduler
self.scheduler.step()
# Write losses to TensorBoard
self.writer.add_scalar('Loss/all', loss.item(), self.curr_iter)
self.writer.add_scalars('Loss/details', dict(zip([
'Cross reconstruction loss', 'Pose similarity loss',
'Canonical consistency loss', 'Batch All triplet loss'
], metrics)), self.curr_iter)
if self.curr_iter % 100 == 0:
print('{0:5d} loss: {1:.3f}'.format(self.curr_iter, loss),
'(xrecon = {:f}, pose_sim = {:f},'
' cano_cons = {:f}, ba_trip = {:f})'.format(*metrics),
'lr:', self.scheduler.get_last_lr()[0])
if self.curr_iter % 1000 == 0:
torch.save({
'iter': self.curr_iter,
'model_state_dict': self.rgb_pn.state_dict(),
'optim_state_dict': self.optimizer.state_dict(),
'loss': loss,
}, self.checkpoint_name)
if self.curr_iter == self.total_iter:
self.writer.close()
break
@staticmethod
def init_weights(m):
if isinstance(m, nn.modules.conv._ConvNd):
nn.init.normal_(m.weight, 0.0, 0.01)
elif isinstance(m, nn.modules.batchnorm._NormBase):
nn.init.normal_(m.weight, 1.0, 0.01)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
elif isinstance(m, RGBPartNet):
nn.init.xavier_uniform_(m.fc_mat)
def _parse_dataset_config(
self,
dataset_config: DatasetConfiguration
) -> Union[CASIAB]:
self.train_size = dataset_config.get('train_size', 74)
self.in_channels = dataset_config.get('num_input_channels', 3)
self._dataset_sig = self._make_signature(
dataset_config,
popped_keys=['root_dir', 'cache_on']
)
self.log_name = '_'.join((self.log_name, self._dataset_sig))
config: Dict = dataset_config.copy()
name = config.pop('name')
if name == 'CASIA-B':
return CASIAB(**config, is_train=self.is_train)
elif name == 'FVG':
# TODO
pass
raise ValueError('Invalid dataset: {0}'.format(name))
def _parse_dataloader_config(
self,
dataset: Union[CASIAB],
dataloader_config: DataloaderConfiguration
) -> DataLoader:
config: Dict = dataloader_config.copy()
if self.is_train:
(self.pr, self.k) = config.pop('batch_size')
self.log_name = '_'.join((self.log_name, str(self.pr), str(self.k)))
triplet_sampler = TripletSampler(dataset, (self.pr, self.k))
return DataLoader(dataset,
batch_sampler=triplet_sampler,
collate_fn=self._batch_splitter,
**config)
else: # is_test
config.pop('batch_size')
return DataLoader(dataset, **config)
def _batch_splitter(
self,
batch: List[Dict[str, Union[np.int64, str, torch.Tensor]]]
) -> Tuple[Dict[str, Union[List[str], torch.Tensor]],
Dict[str, Union[List[str], torch.Tensor]]]:
"""
Disentanglement need two random conditions, this function will
split pr * k * 2 samples to 2 dicts each containing pr * k
samples. labels and clip data are tensor, and others are list.
"""
_batch = [[], []]
for i in range(0, self.pr * self.k * 2, self.k * 2):
_batch[0] += batch[i:i + self.k]
_batch[1] += batch[i + self.k:i + self.k * 2]
return default_collate(_batch[0]), default_collate(_batch[1])
def _make_signature(self,
config: Dict,
popped_keys: Optional[List] = None) -> str:
_config = config.copy()
if popped_keys:
for key in popped_keys:
_config.pop(key)
return self._gen_sig(list(_config.values()))
def _gen_sig(self, values: Union[Tuple, List, str, int, float]) -> str:
strings = []
for v in values:
if isinstance(v, str):
strings.append(v)
elif isinstance(v, (Tuple, List)):
strings.append(self._gen_sig(v))
else:
strings.append(str(v))
return '_'.join(strings)
|
