-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy patheval_target.py
258 lines (214 loc) · 12.5 KB
/
eval_target.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
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
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
import os
import argparse
import numpy as np
import torch
from torch.utils.data import DataLoader
from pytorch_lightning import Trainer
from pytorch_lightning.loggers import WandbLogger
from utils.models.minkunet import MinkUNet34
from utils.models.minkunet_ibn import MinkUNet34IBN
from utils.models.minkunet_robustnet import MinkUNet34Robust
from utils.models.minkunet_bev import MinkUNet34BEV
from utils.datasets.initialization import get_dataset
from utils.datasets.dataset import MultiSourceDataset
from configs import get_config
from utils.collation import CollateFN
from utils.pipelines import PLTTrainer
from utils.pipelines import PLTTrainerBEV
parser = argparse.ArgumentParser()
parser.add_argument("--config_file",
default="configs/source/semantickitti.yaml",
type=str,
help="Path to config file")
parser.add_argument("--resume_checkpoint",
default=None,
help="If not provided in configs, ckpt to be evaluated")
parser.add_argument("--save_predictions",
action='store_true',
default=False,
help="Save or not predictions")
def evaluate(config):
def get_dataloader(dataset, batch_size, collate_fn, shuffle=False, pin_memory=True):
return DataLoader(dataset,
batch_size=batch_size,
collate_fn=collate_fn,
shuffle=shuffle,
num_workers=config.pipeline.dataloader.num_workers,
pin_memory=pin_memory)
def get_model(config):
if config.model.name == 'MinkUNet34':
m = MinkUNet34(in_channels=config.model.in_channels,
out_channels=config.model.out_channels,
D=config.model.D,
initial_kernel_size=config.model.conv1_kernel_size,
)
elif config.model.name == 'MinkUNet34IBN':
m = MinkUNet34IBN(in_channels=config.model.in_channels,
out_channels=config.model.out_channels,
D=config.model.D,
initial_kernel_size=config.model.conv1_kernel_size)
elif config.model.name == 'MinkUNet34Robust':
m = MinkUNet34Robust(in_channels=config.model.in_channels,
out_channels=config.model.out_channels,
D=config.model.D,
initial_kernel_size=config.model.conv1_kernel_size)
elif config.model.name == 'MinkUNet34BEV':
bottle_img_dim = dict(zip(config.model.decoder_2d_levels, config.model.bev_feats_sizes))
bottle_out_img_dim = dict(zip(config.model.decoder_2d_levels, config.model.bev_img_sizes))
try:
scaling_factors = dict(zip(config.model.decoder_2d_levels, config.model.scaling_factors))
except AttributeError:
scaling_factors = {'block8': 1.0, 'block7': 1.0, 'block6': 1.0, 'bottle': 1.0}
try:
binary_segmentation_layer = config.model.binary_segmentation_layer
except:
binary_segmentation_layer = False
m = MinkUNet34BEV(in_channels=config.model.in_channels,
out_channels=config.model.out_channels,
D=config.model.D,
initial_kernel_size=config.model.conv1_kernel_size,
decoder_2d_level=config.model.decoder_2d_levels,
bottle_img_dim=bottle_img_dim,
bottle_out_img_dim=bottle_out_img_dim,
scaling_factors=scaling_factors,
binary_seg_layer=binary_segmentation_layer)
else:
raise NotImplementedError
print(f'--> Using {config.model.name}!')
return m
def get_source_domains():
training_dataset = []
validation_dataset = []
num_source_domains = len(config.source_dataset.name)
for sd in range(len(config.source_dataset.name)):
dataset_name = config.source_dataset.name[sd]
training_dataset_tmp, validation_dataset_tmp = get_dataset(dataset_name=dataset_name,
voxel_size=config.source_dataset.voxel_size,
sub_p=config.source_dataset.sub_p,
num_classes=config.model.out_channels,
ignore_label=config.source_dataset.ignore_label,
use_cache=config.source_dataset.use_cache,
augmentation_list=config.source_dataset.augmentation_list)
training_dataset.append(training_dataset_tmp)
validation_dataset.append(validation_dataset_tmp)
if num_source_domains == 1:
training_dataset = training_dataset[0]
validation_dataset = validation_dataset[0]
else:
training_dataset = MultiSourceDataset(training_dataset)
return training_dataset, validation_dataset
def get_target_domains():
num_target_domains = len(config.target_dataset.name)
if num_target_domains == 2:
target_dataset = []
for td in range(len(config.target_dataset.name)):
dataset_name = config.target_dataset.name[td]
_, target_dataset_tmp = get_dataset(dataset_name=dataset_name,
voxel_size=config.target_dataset.voxel_size,
sub_p=config.target_dataset.sub_p,
num_classes=config.model.out_channels,
ignore_label=config.target_dataset.ignore_label,
use_cache=config.target_dataset.use_cache,
augmentation_list=config.target_dataset.augmentation_list)
target_dataset.append(target_dataset_tmp)
elif num_target_domains == 1:
dataset_name = config.target_dataset.name[0]
_, target_dataset = get_dataset(dataset_name=dataset_name,
voxel_size=config.target_dataset.voxel_size,
sub_p=config.target_dataset.sub_p,
num_classes=config.model.out_channels,
ignore_label=config.target_dataset.ignore_label,
use_cache=config.target_dataset.use_cache,
augmentation_list=config.target_dataset.augmentation_list)
else:
raise NotImplementedError
return target_dataset
model = get_model(config)
training_dataset, validation_dataset = get_source_domains()
collation_single = CollateFN()
target_dataset = get_target_domains()
if len(config.target_dataset.name) > 1:
target_dataloader = [get_dataloader(t_dataset, collate_fn=collation_single, batch_size=config.pipeline.dataloader.batch_size*2, shuffle=False) for t_dataset in target_dataset]
else:
target_dataloader = get_dataloader(target_dataset,
collate_fn=collation_single,
batch_size=config.pipeline.dataloader.batch_size*2,
shuffle=False)
if config.pipeline.lightning.resume_checkpoint is not None:
resume_from_checkpoint = config.pipeline.lightning.resume_checkpoint
elif args.resume_checkpoint is not None:
resume_from_checkpoint = args.resume_checkpoint
else:
raise AttributeError('You must provide a checkpoint for evaluation!')
ckpt_dir, _ = os.path.split(resume_from_checkpoint)
save_dir, _ = os.path.split(ckpt_dir)
_, run_name = os.path.split(save_dir)
run_name = run_name + '_EVALUATION'
save_preds_dir = os.path.join(save_dir, 'predictions')
wandb_logger = WandbLogger(project=config.pipeline.wandb.project_name,
entity=config.pipeline.wandb.entity_name,
name=run_name,
offline=True)
loggers = [wandb_logger]
strategy = None
if config.model.name in ['MinkUNet34BEV']:
pl_module = PLTTrainerBEV(training_dataset=training_dataset,
validation_dataset=validation_dataset,
model=model,
sem_criterion=config.pipeline.losses.sem_criterion,
optimizer_name=config.pipeline.optimizer.name,
batch_size=config.pipeline.dataloader.batch_size,
val_batch_size=config.pipeline.dataloader.batch_size,
lr=config.pipeline.optimizer.lr,
num_classes=config.model.out_channels,
train_num_workers=config.pipeline.dataloader.num_workers,
val_num_workers=config.pipeline.dataloader.num_workers,
clear_cache_int=config.pipeline.lightning.clear_cache_int,
scheduler_name=config.pipeline.scheduler.name,
source_domains_name=config.source_dataset.name,
target_domains_name=config.target_dataset.name,
save_dir=save_dir,
save_predictions=args.save_predictions,
save_folder=save_preds_dir)
else:
pl_module = PLTTrainer(training_dataset=training_dataset,
validation_dataset=validation_dataset,
model=model,
sem_criterion=config.pipeline.losses.sem_criterion,
optimizer_name=config.pipeline.optimizer.name,
batch_size=config.pipeline.dataloader.batch_size,
val_batch_size=config.pipeline.dataloader.batch_size,
lr=config.pipeline.optimizer.lr,
num_classes=config.model.out_channels,
train_num_workers=config.pipeline.dataloader.num_workers,
val_num_workers=config.pipeline.dataloader.num_workers,
clear_cache_int=config.pipeline.lightning.clear_cache_int,
scheduler_name=config.pipeline.scheduler.name,
source_domains_name=config.source_dataset.name,
target_domains_name=config.target_dataset.name,
save_dir=save_dir,
save_predictions=args.save_predictions,
save_folder=save_preds_dir)
trainer = Trainer(max_epochs=config.pipeline.epochs,
gpus=config.pipeline.gpus,
strategy=strategy,
default_root_dir=config.pipeline.save_dir,
precision=config.pipeline.precision,
logger=loggers,
check_val_every_n_epoch=config.pipeline.lightning.check_val_every_n_epoch,
val_check_interval=config.pipeline.lightning.val_check_interval,
num_sanity_val_steps=config.pipeline.lightning.num_sanity_val_steps,
log_every_n_steps=50)
trainer.test(pl_module,
ckpt_path=resume_from_checkpoint,
dataloaders=target_dataloader)
if __name__ == '__main__':
args = parser.parse_args()
config = get_config(args.config_file)
# fix random seed
os.environ['PYTHONHASHSEED'] = str(config.pipeline.seed)
np.random.seed(config.pipeline.seed)
torch.manual_seed(config.pipeline.seed)
torch.cuda.manual_seed(config.pipeline.seed)
torch.backends.cudnn.benchmark = True
evaluate(config)