-
Notifications
You must be signed in to change notification settings - Fork 0
/
final_all_mix_360_b2_dur64_noreg_nomask.py
executable file
·425 lines (351 loc) · 13.6 KB
/
final_all_mix_360_b2_dur64_noreg_nomask.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
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
from copy import deepcopy
import argparse
import gc
import os
import numpy as np
import pandas as pd
import torch
from sklearn.metrics import average_precision_score
from torch.cuda.amp import GradScaler, autocast
from tqdm import tqdm
from timm.scheduler import CosineLRScheduler
try:
# import training only modules
import wandb
except:
print('wandb is not installed.')
from configs.base import cfg
from models.cnn_3d import ImageUNetLNMixup as Net
from utils.debugger import set_debugger
from utils.common import set_seed, create_checkpoint, resume_checkpoint, batch_to_device, nms, log_results
from utils.ema import ModelEmaV2
from datasets.e2e import get_trainval_dataloader
from datasets import video_transforms
EVENT_CLASSES = [
'challenge',
'play',
'throwin'
]
FPS = 25.0
HEIGHT, WIDTH = 360, 640
DURATION = 64
cfg = deepcopy(cfg)
cfg.project = 'kaggle-dfl-pt'
cfg.exp_name = 'final_all_mix_360_b2_dur64_noreg_nomask'
cfg.output_dir = f'output/{cfg.exp_name}'
cfg.debug = False
cfg.train.df_path = '../input/folds.csv'
cfg.train.video_feature_dir = '../input/train_frames'
cfg.train.label_dir = '../input/event_labels'
cfg.train.duration = DURATION
cfg.train.offset = 5
cfg.train.batch_size = 4
cfg.train.num_workers = 8 if not cfg.debug else 0
cfg.train.image_size = (HEIGHT, WIDTH)
cfg.train.bg_sampling_rate = 0.5
cfg.train.transforms = video_transforms.Compose([
video_transforms.RandomHorizontalFlip(),
video_transforms.RandomRotation(10),
video_transforms.ColorJitter(brightness=0.2, contrast=0.1),
# video_transforms.RandomCrop((int(HEIGHT*0.8), int(WIDTH*0.8))),
video_transforms.Resize((HEIGHT, WIDTH)),
])
cfg.valid.df_path = '../input/folds.csv'
cfg.valid.all_df_path = '../input/folds_all.csv'
cfg.valid.video_feature_dir = '../input/train_frames'
cfg.valid.label_dir = '../input/event_labels'
cfg.valid.duration = DURATION
cfg.valid.offset = 5
cfg.valid.batch_size = 4
cfg.valid.num_workers = 4 if not cfg.debug else 0
cfg.valid.image_size = (HEIGHT, WIDTH)
# cfg.test.video_paths = sorted(glob.glob('../input/dfl-bundesliga-data-shootout/test/*'))
# if len(cfg.test.video_paths) == 32: # public test
# cfg.test.video_paths = cfg.test.video_paths[:1]
cfg.test.video_paths = [
'../input/dfl-bundesliga-data-shootout/train/9a97dae4_1.mp4',
'../input/dfl-bundesliga-data-shootout/train/ecf251d4_0.mp4']
cfg.test.duration = DURATION
cfg.test.offset = 5
cfg.test.batch_size = DURATION
cfg.test.num_workers = 0
cfg.test.score_th = 0.01
cfg.test.nms_thresholds = [12, 6, 6]
cfg.test.weight_paths = [
'../input/stage0-021-fold0/best_fold0.pth',
'../input/stage0-032-fold0/best_fold0.pth']
cfg.test.image_size = (HEIGHT, WIDTH)
cfg.model.model_name = 'tf_efficientnet_b2_ns'
cfg.model.in_channels = 1408
cfg.model.num_classes = 3
cfg.model.cls_weight = 1.0
cfg.model.reg_weight = 0.0
cfg.model.cls_loss_type = 'no_mask_cb_focal'
cfg.model.norm_type = 'ln'
cfg.model.duration = DURATION
cfg.model.pretrained_path = './output/pretrain_b2/best_fold0.pth'
# cfg.model.resume_exp = 'pretrain_b2'
cfg.model.alpha = 0.25
cfg.model.beta = 0.9999
cfg.model.temporal_shift = True
cfg.model.drop = 0.3
cfg.model.drop_path = 0.2
cfg.model.drop_block = 0.0
cfg.model.grad_checkpointing = True
cfg.model.mix_beta = 0.5
cfg.model.manifold_mixup = True
# others
cfg.seed = 42
cfg.device = 'cuda'
cfg.lr = 1.0e-3
cfg.wd = 1.0e-3
cfg.min_lr = 5.0e-5
cfg.warmup_lr = 1.0e-5
cfg.warmup_epochs = 3
cfg.warmup = 1
cfg.epochs = 50
cfg.eval_intervals = 5
cfg.mixed_precision = True
cfg.ema_start_epoch = 1
def rescale_layer_norm(model, state_dict):
model_state_dict = model.state_dict()
for k, v in state_dict.items():
new_shape = model_state_dict[k].shape
old_shape = v.shape
if new_shape != old_shape:
print(f'rescale {k} from {old_shape} -> {new_shape}')
state_dict[k] = torch.nn.functional.interpolate(
v[None, None], new_shape, mode='bilinear').squeeze()
return state_dict
def get_model(cfg, weight_path=None):
model = Net(cfg.model)
if cfg.model.resume_exp is not None:
weight_path = os.path.join(
cfg.root, 'output', cfg.model.resume_exp, f'best_fold{cfg.fold}.pth')
if weight_path is not None:
state_dict = torch.load(weight_path, map_location='cpu')
epoch = state_dict['epoch']
model_key = 'model_ema'
if model_key not in state_dict.keys():
model_key = 'model'
print(f'load epoch {epoch} model from {weight_path}')
else:
print(f'load epoch {epoch} ema model from {weight_path}')
if cfg.model.rescale_layer_norm:
state_dict[model_key] = rescale_layer_norm(
model, state_dict[model_key])
model.load_state_dict(state_dict[model_key])
return model.to(cfg.device)
def save_val_results(targets, preds, save_path):
num_classes = targets.shape[1]
df = pd.DataFrame()
for c in range(num_classes):
df[f'target_{c}'] = targets[:, c]
df[f'pred_{c}'] = preds[:, c]
df.to_csv(save_path, index=False)
def post_process(val_keys, val_cls_preds, val_reg_preds, val_masks=None, score_threshold=0.01, nms_thresholds=(12, 6, 6)):
FPS = 25.0
event_classes = [
'challenge',
'play',
'throwin'
]
has_mask = val_masks is not None
val_keys = pd.Series(val_keys)
val_videos = val_keys.map(lambda x: "_".join(x.split('_')[:2]))
unique_val_videos = sorted(val_videos.unique())
records = []
for video in unique_val_videos:
video_index = (val_videos == video)
video_cls_preds = val_cls_preds[video_index]
video_reg_preds = val_reg_preds[video_index]
video_cls_preds = np.transpose(
video_cls_preds, [0, 2, 1]).reshape(-1, 3)
video_reg_preds = np.transpose(
video_reg_preds, [0, 2, 1]).reshape(-1, 3)
if has_mask:
video_masks = val_masks[video_index]
video_masks = np.transpose(video_masks, [0, 2, 1]).reshape(-1, 3)
for c, (class_name, nms_th) in enumerate(zip(event_classes, nms_thresholds)):
this_video_cls_preds = video_cls_preds[:, c]
this_video_reg_preds = video_reg_preds[:, c]
if has_mask:
this_video_masks = video_masks[:, c]
predictions = np.where(
(this_video_cls_preds > score_threshold) & (this_video_masks == 1))[0]
else:
predictions = np.where(
(this_video_cls_preds > score_threshold))[0]
offsets = this_video_reg_preds[predictions]
offsets = offsets * FPS # convert to frame scale
scores = this_video_cls_preds[predictions]
# predictions = predictions + offsets
keep = nms(predictions, scores, nms_th)
predictions = predictions[keep]
scores = scores[keep]
predictions = predictions / FPS
for prediction, score in zip(predictions, scores):
records.append((video, prediction, class_name, score))
result_df = pd.DataFrame(data=records, columns=[
'video_id', 'time', 'event', 'score'])
return result_df
def get_optimizer(model, cfg):
def exclude(
n, p): return p.ndim < 2 or "bn" in n or "ln" in n or "bias" in n or 'logit_scale' in n
def include(n, p): return not exclude(n, p)
named_parameters = list(model.named_parameters())
gain_or_bias_params = [
p for n, p in named_parameters if exclude(n, p) and p.requires_grad]
rest_params = [p for n, p in named_parameters if include(
n, p) and p.requires_grad]
optimizer = torch.optim.AdamW(
[
{"params": gain_or_bias_params, "weight_decay": 0.},
{"params": rest_params, "weight_decay": cfg.wd},
],
lr=cfg.lr,
betas=(0.9, 0.999),
eps=1.0e-8,
)
return optimizer
def train(cfg, fold):
os.makedirs(str(cfg.output_dir + "/"), exist_ok=True)
cfg.fold = fold
mode = 'disabled' if cfg.debug else None
wandb.init(project=cfg.project,
name=f'{cfg.exp_name}_fold{fold}', config=cfg, reinit=True, mode=mode)
set_seed(cfg.seed)
train_dataloader = get_trainval_dataloader(cfg.train, fold)
cfg.model.samples_per_class = train_dataloader.dataset.samples_per_class
model = get_model(cfg)
if cfg.model.grad_checkpointing:
model.set_grad_checkpointing(enable=True)
# setup exponential moving average of model weights, SWA could be used here too
model_ema = ModelEmaV2(model, decay=0.999)
optimizer = get_optimizer(model, cfg)
steps_per_epoch = len(train_dataloader)
scheduler = CosineLRScheduler(
optimizer,
t_initial=cfg.epochs*steps_per_epoch,
lr_min=cfg.min_lr,
warmup_lr_init=cfg.warmup_lr,
warmup_t=cfg.warmup_epochs*steps_per_epoch,
k_decay=1.0,
)
scaler = GradScaler(enabled=cfg.mixed_precision)
init_epoch = 0
best_val_score = 0
ckpt_path = f"{cfg.output_dir}/last_fold{fold}.pth"
if cfg.resume and os.path.exists(ckpt_path):
model, optimizer, init_epoch, best_val_score, scheduler, scaler, model_ema = resume_checkpoint(
f"{cfg.output_dir}/last_fold{fold}.pth",
model,
optimizer,
scheduler,
scaler,
model_ema
)
cfg.curr_step = 0
i = init_epoch * steps_per_epoch
optimizer.zero_grad()
for epoch in range(init_epoch, cfg.epochs):
set_seed(cfg.seed + epoch)
cfg.curr_epoch = epoch
progress_bar = tqdm(range(len(train_dataloader)),
leave=False, dynamic_ncols=True)
tr_it = iter(train_dataloader)
cls_losses = []
reg_losses = []
targets = []
cls_preds = []
reg_preds = []
masks = []
gc.collect()
# ==== TRAIN LOOP
for itr in progress_bar:
i += 1
cfg.curr_step += cfg.train.batch_size
model.train()
torch.set_grad_enabled(True)
inputs = next(tr_it)
inputs = batch_to_device(inputs, cfg.device, cfg.mixed_precision)
optimizer.zero_grad()
with autocast(enabled=cfg.mixed_precision):
outputs = model(inputs)
loss_dict = model.get_loss(outputs, inputs)
loss = loss_dict['loss']
cls_losses.append(loss_dict['cls'].item())
reg_losses.append(loss_dict['reg'].item())
targets.append(inputs['labels'].cpu().numpy())
cls_preds.append(outputs['cls'].sigmoid().detach().cpu().numpy())
reg_preds.append(outputs['reg'].detach().cpu().numpy())
masks.append(inputs['masks'].cpu().numpy())
if torch.isfinite(loss):
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
if model_ema is not None:
model_ema.update(model)
if scheduler is not None:
scheduler.step(i)
avg_cls_loss = np.mean(cls_losses[-10:])
avg_reg_loss = np.mean(reg_losses[-10:])
lr = optimizer.param_groups[0]['lr']
progress_bar.set_description(
f"step:{i} cls_loss: {avg_cls_loss:.4f} reg_loss: {avg_reg_loss:.4f} lr:{lr:.6}")
targets = np.concatenate(targets, axis=0).reshape(-1)
cls_preds = np.concatenate(cls_preds, axis=0).reshape(-1)
masks = np.concatenate(masks, axis=0).reshape(-1)
score = average_precision_score(
targets == 1, cls_preds, sample_weight=masks)
lr = optimizer.param_groups[0]['lr']
all_results = {
'epoch': epoch,
'lr': lr,
}
train_results = {
'cls_loss': avg_cls_loss,
'reg_loss': avg_reg_loss,
'score': score,
}
val_results = {}
log_results(all_results, train_results, val_results)
if epoch % 5 == 0:
checkpoint = create_checkpoint(
model, optimizer, epoch, scheduler=scheduler, scaler=scaler, score=best_val_score,
model_ema=model_ema
)
torch.save(checkpoint, f"{cfg.output_dir}/epoch{epoch:02}.pth")
checkpoint = create_checkpoint(
model, optimizer, epoch, scheduler=scheduler, scaler=scaler, model_ema=model_ema)
torch.save(checkpoint, f"{cfg.output_dir}/last_fold{fold}.pth")
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--root", default="./", type=str)
parser.add_argument("--device_id", "-d", default="0", type=str)
parser.add_argument("--start_fold", "-s", default=0, type=int)
parser.add_argument("--end_fold", "-e", default=5, type=int)
parser.add_argument("--validate", "-v", action="store_true")
parser.add_argument("--infer", "-i", action="store_true")
parser.add_argument("--debug", "-db", action="store_true")
parser.add_argument("--resume", "-r", action="store_true")
return parser.parse_args()
def update_cfg(cfg, args, fold):
if args.debug:
cfg.debug = True
set_debugger()
cfg.fold = fold
if args.resume:
cfg.resume = True
cfg.root = args.root
cfg.output_dir = os.path.join(args.root, cfg.output_dir)
if cfg.model.resume_exp is not None:
cfg.model.pretrained_path = os.path.join(
cfg.root, 'output', cfg.model.resume_exp, f'best_fold{cfg.fold}.pth')
return cfg
if __name__ == "__main__":
args = parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.device_id)
fold = -1
cfg = update_cfg(cfg, args, fold)
train(cfg, fold)