-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_dist_mod.py
353 lines (311 loc) · 13.7 KB
/
train_dist_mod.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
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
# ------------------------------------------------------------------------
# BEAUTY DETR
# Copyright (c) 2022 Ayush Jain & Nikolaos Gkanatsios
# Licensed under CC-BY-NC [see LICENSE for details]
# All Rights Reserved
# ------------------------------------------------------------------------
# Parts adapted from Group-Free
# Copyright (c) 2021 Ze Liu. All Rights Reserved.
# Licensed under the MIT License.
# ---------------------------------------------------------------BeaUTyDETR_dks---------
"""Main script for language modulation."""
import os
import numpy as np
import torch
import torch.distributed as dist
from main_utils import parse_option, BaseTrainTester
from data.model_util_scannet import ScannetDatasetConfig
from models import MCLN
from src.joint_det_dataset import Joint3DDataset
from src.grounding_evaluator import GroundingEvaluator
from models import APCalculator, parse_predictions, parse_groundtruths
from tqdm import tqdm
import datetime
import ipdb
st = ipdb.set_trace
import numpy as np
class TrainTester(BaseTrainTester):
"""Train/test a language grounder."""
# logger.
def __init__(self, args):
"""Initialize."""
super().__init__(args)
# BRIEF Initialize dataset.
@staticmethod
def get_datasets(args):
"""Initialize datasets."""
dataset_dict = {} # dict to use multiple datasets
for dset in args.dataset:
dataset_dict[dset] = 1
if args.joint_det:
dataset_dict['scannet'] = 10
print('Loading datasets:', sorted(list(dataset_dict.keys())))
if args.eval:
train_dataset = None
else:
train_dataset = Joint3DDataset(
dataset_dict=dataset_dict,
test_dataset=args.test_dataset,
split='train' if not args.debug else 'val',
use_color=args.use_color, use_height=args.use_height,
overfit=args.debug,
data_path=args.data_root,
detect_intermediate=args.detect_intermediate,
use_multiview=args.use_multiview,
butd=args.butd,
butd_gt=args.butd_gt,
butd_cls=args.butd_cls,
augment_det=args.augment_det
)
test_dataset = Joint3DDataset(
dataset_dict=dataset_dict,
test_dataset=args.test_dataset,
split='val' if not args.eval_train else 'train',
use_color=args.use_color, use_height=args.use_height,
overfit=args.debug,
data_path=args.data_root,
detect_intermediate=args.detect_intermediate,
use_multiview=args.use_multiview,
butd=args.butd,
butd_gt=args.butd_gt,
butd_cls=args.butd_cls,
wo_obj_name=args.wo_obj_name
)
return train_dataset, test_dataset
# BRIEF Initialize the model.
@staticmethod
def get_model(args):
"""Initialize the model."""
num_input_channel = int(args.use_color) * 3
if args.use_height:
num_input_channel += 1
if args.use_multiview:
num_input_channel += 128
if args.use_soft_token_loss:
num_class = 256
else:
num_class = 19
ModelClass = eval(args.model)
model = ModelClass(
num_class=num_class,
num_obj_class=485,
input_feature_dim=num_input_channel,
num_queries=args.num_target,
num_decoder_layers=args.num_decoder_layers,
self_position_embedding=args.self_position_embedding,
contrastive_align_loss=args.use_contrastive_align,
butd=args.butd or args.butd_gt or args.butd_cls,
pointnet_ckpt=args.pp_checkpoint,
data_path = args.data_root,
self_attend=args.self_attend
)
# params = sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6
# print(f'Total parameters: {params:.2f}M')
return model
# BRIEF input data.
@staticmethod
def _get_inputs(batch_data):
return {
'point_clouds': batch_data['point_clouds'].float(), # ([B, 50000, 6]) xyz + colour
'text': batch_data['utterances'], # list[B] text
"det_boxes": batch_data['all_detected_boxes'], # ([B, 132, 6]) groupfree detection boxes
"det_bbox_label_mask": batch_data['all_detected_bbox_label_mask'], # ([B, 132]) mask
"det_class_ids": batch_data['all_detected_class_ids'], # ([B, 132]) box id
"superpoint": batch_data['superpoint'], # ([B, 50000]) superpoint map
}
# BRIEF only eval one epoch.
@torch.no_grad()
def evaluate_one_epoch(self, epoch, test_loader,
model, criterion, set_criterion, args):
"""
Eval grounding after a single epoch.
Some of the args:
model: a nn.Module that returns end_points (dict)
criterion: a function that returns (loss, end_points)
"""
# [Option] Object detection evaluation on ScanNet dataset.
if args.test_dataset == 'scannet':
return self.evaluate_one_epoch_det(
epoch, test_loader, model,
criterion, set_criterion, args
)
stat_dict = {}
model.eval() # set model to eval mode (for bn and dp)
# 7 layers: proposal, last, 0-4
if args.num_decoder_layers > 0:
prefixes = ['last_', 'proposal_']
prefixes = ['last_']
prefixes.append('proposal_')
else:
prefixes = ['proposal_'] # only proposal
prefixes += [f'{i}head_' for i in range(args.num_decoder_layers - 1)]
evaluator = GroundingEvaluator(
only_root=True, thresholds=[0.25, 0.5], # TODO only_root=True
topks=[1, 5, 10], prefixes=prefixes,
filter_non_gt_boxes=args.butd_cls,
logger=self.logger,
model=args.model
)
# NOTE Main eval branch
test_loader = tqdm(test_loader, ascii=True)
for batch_idx, batch_data in enumerate(test_loader):
# note forward and compute loss
stat_dict, end_points = self._main_eval_branch(
batch_idx, batch_data, test_loader, model, stat_dict,
criterion, set_criterion, args
)
if evaluator is not None:
for prefix in prefixes:
# note only consider the last layer
if prefix != 'last_':
continue
# evaluation
evaluator.evaluate(end_points, prefix)
evaluator.synchronize_between_processes()
if dist.get_rank() == 0:
if evaluator is not None:
# tensorboard eval socre
s_25 = evaluator.dets[("last_", 0.25, 1, "bbs")] / max(evaluator.gts[("last_", 0.25, 1, "bbs")], 1)
s_50 = evaluator.dets[("last_", 0.5, 1, "bbs")] / max(evaluator.gts[("last_", 0.5, 1, "bbs")], 1)
c_25 = evaluator.dets[("last_", 0.25, 1, "bbf")] / max(evaluator.gts[("last_", 0.25, 1, "bbf")], 1)
c_50 = evaluator.dets[("last_", 0.5, 1, "bbf")] / max(evaluator.gts[("last_", 0.5, 1, "bbf")], 1)
self.tensorboard.item["val_score"]["soft_token_0.25"] = s_25
self.tensorboard.item["val_score"]["soft_token_0.5"] = s_50
self.tensorboard.item["val_score"]["contrastive_0.25"] = c_25
self.tensorboard.item["val_score"]["contrastive_0.5"] = c_50
self.tensorboard.dump_tensorboard("val_score", epoch)
# tensorboard eval loss
for key in self.tensorboard.item["val_loss"]:
self.tensorboard.item["val_loss"][key] = stat_dict[key] / len(test_loader)
self.tensorboard.dump_tensorboard("val_loss", epoch)
evaluator.print_stats()
return None
# BRIEF Scannet detection evalution
@torch.no_grad()
def evaluate_one_epoch_det(self, epoch, test_loader,
model, criterion, set_criterion, args):
"""
Eval grounding after a single epoch.
Some of the args:
model: a nn.Module that returns end_points (dict)
criterion: a function that returns (loss, end_points)
"""
dataset_config = ScannetDatasetConfig(18)
# Used for AP calculation
CONFIG_DICT = {
'remove_empty_box': False, 'use_3d_nms': True,
'nms_iou': 0.25, 'use_old_type_nms': False, 'cls_nms': True,
'per_class_proposal': True, 'conf_thresh': 0.0,
'dataset_config': dataset_config,
'hungarian_loss': True
}
stat_dict = {}
model.eval() # set model to eval mode (for bn and dp)
if set_criterion is not None:
set_criterion.eval()
if args.num_decoder_layers > 0:
prefixes = ['last_', 'proposal_']
prefixes += [
f'{i}head_' for i in range(args.num_decoder_layers - 1)
]
else:
prefixes = ['proposal_'] # only proposal
prefixes = ['last_']
ap_calculator_list = [
APCalculator(iou_thresh, dataset_config.class2type)
for iou_thresh in args.ap_iou_thresholds
]
mAPs = [
[iou_thresh, {k: 0 for k in prefixes}]
for iou_thresh in args.ap_iou_thresholds
]
batch_pred_map_cls_dict = {k: [] for k in prefixes}
batch_gt_map_cls_dict = {k: [] for k in prefixes}
# Main eval branch
# NOTE char span and token span.
wordidx = np.array([
0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 7, 7, 8, 9, 10, 11,
12, 13, 13, 14, 15, 16, 16, 17, 17, 18, 18
]) # 18+1（not mentioned）
tokenidx = np.array([
1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 18, 19, 21, 23,
25, 27, 29, 31, 32, 34, 36, 38, 39, 41, 42, 44, 45
]) # 18 token span
test_loader = tqdm(test_loader, ascii=True)
for batch_idx, batch_data in enumerate(test_loader):
# note eval
stat_dict, end_points = self._main_eval_branch(
batch_idx, batch_data, test_loader, model, stat_dict,
criterion, set_criterion, args
)
# step score contrast
proj_tokens = end_points['proj_tokens'] # (B, tokens, 64)
proj_queries = end_points['last_proj_queries'] # (B, Q, 64)
sem_scores = torch.matmul(proj_queries, proj_tokens.transpose(-1, -2))
sem_scores_ = sem_scores / 0.07 # (B, Q, tokens)
sem_scores = torch.zeros(sem_scores_.size(0), sem_scores_.size(1), 256)
sem_scores = sem_scores.to(sem_scores_.device)
sem_scores[:, :sem_scores_.size(1), :sem_scores_.size(2)] = sem_scores_
end_points['last_sem_cls_scores'] = sem_scores # ([B, 256, 256])
# step
sem_cls = torch.zeros_like(end_points['last_sem_cls_scores'])[..., :19] # ([B, 256, 19])
for w, t in zip(wordidx, tokenidx):
sem_cls[..., w] += end_points['last_sem_cls_scores'][..., t]
end_points['last_sem_cls_scores'] = sem_cls # ([B, 256, 19])
# step Parse predictions
# for prefix in prefixes:
prefix = 'last_'
# pred
batch_pred_map_cls = parse_predictions(
end_points, CONFIG_DICT, prefix,
size_cls_agnostic=True)
batch_gt_map_cls = parse_groundtruths(
end_points, CONFIG_DICT,
size_cls_agnostic=True)
batch_pred_map_cls_dict[prefix].append(batch_pred_map_cls)
batch_gt_map_cls_dict[prefix].append(batch_gt_map_cls)
mAP = 0.0
# for prefix in prefixes:
prefix = 'last_'
for (batch_pred_map_cls, batch_gt_map_cls) in zip(
batch_pred_map_cls_dict[prefix],
batch_gt_map_cls_dict[prefix]):
for ap_calculator in ap_calculator_list:
ap_calculator.step(batch_pred_map_cls, batch_gt_map_cls)
# Evaluate average precision
for i, ap_calculator in enumerate(ap_calculator_list):
metrics_dict = ap_calculator.compute_metrics()
self.logger.info(
'=====================>'
f'{prefix} IOU THRESH: {args.ap_iou_thresholds[i]}'
'<====================='
)
for key in metrics_dict:
self.logger.info(f'{key} {metrics_dict[key]}')
if prefix == 'last_' and ap_calculator.ap_iou_thresh > 0.3:
mAP = metrics_dict['mAP']
mAPs[i][1][prefix] = metrics_dict['mAP']
ap_calculator.reset()
for mAP in mAPs:
self.logger.info(
f'IoU[{mAP[0]}]:\t'
+ ''.join([
f'{key}: {mAP[1][key]:.4f} \t'
for key in sorted(mAP[1].keys())
])
)
return None
if __name__ == '__main__':
# huggingface
os.environ["TOKENIZERS_PARALLELISM"] = "false"
opt = parse_option()
# distributed
torch.cuda.set_device(opt.local_rank)
# https://github.com/open-mmlab/mmcv/issues/1969#issuecomment-1304721237
torch.distributed.init_process_group(backend='nccl', init_method='env://', timeout=datetime.timedelta(seconds=5400))
# cudnn
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
train_tester = TrainTester(opt)
ckpt_path = train_tester.main(opt)