File size: 17,202 Bytes
bdab1da
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
import argparse


def get_default_params(model_name):
    # Params from paper (https://arxiv.org/pdf/2103.00020.pdf)
    model_name = model_name.lower()
    if "vit" in model_name:
        return {"lr": 5.0e-4, "beta1": 0.9, "beta2": 0.98, "eps": 1.0e-6}
    else:
        return {"lr": 5.0e-4, "beta1": 0.9, "beta2": 0.999, "eps": 1.0e-8}


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--train-data",
        type=str,
        default=None,
        help="Path to h5 filewith training data",
    )
    parser.add_argument(
        "--val-data",
        type=str,
        default=None,
        help="Path to h5 file with validation data",
    )
    parser.add_argument(
        "--freeze-text",
        default=False,
        action="store_true",
        help="if you need to freeze the text encoder, make this True",
    )
    parser.add_argument(
        "--freeze-text-after",
        type=int,
        default=-1,
        help="if you need to freeze the text encoder after (include) epoch x, set this param to x. Set -1 to disable it",
    )
    parser.add_argument(
        "--train-ipc",
        type=str,
        default=None,
        help="Path to npy file of the number of instance per class in training data",
    )
    parser.add_argument(
        "--val-ipc",
        type=str,
        default=None,
        help="Path to npy file of the number of instance per class in validation data",
    )
    parser.add_argument(
        "--train-num-samples",
        type=int,
        default=None,
        help="Number of samples in dataset. Required for webdataset if not available in info file.",
    )
    parser.add_argument(
        "--val-num-samples",
        type=int,
        default=None,
        help="Number of samples in dataset. Useful for webdataset if not available in info file.",
    )
    parser.add_argument(
        "--dataset-type",
        choices=["webdataset", "csv", "auto", "toy"],
        default="auto",
        help="Which type of dataset to process.",
    )
    parser.add_argument(
        "--csv-separator",
        type=str,
        default="\t",
        help="For csv-like datasets, which separator to use.",
    )
    parser.add_argument(
        "--csv-img-key",
        type=str,
        default="filepath",
        help="For csv-like datasets, the name of the key for the image paths.",
    )
    parser.add_argument(
        "--csv-caption-key",
        type=str,
        default="title",
        help="For csv-like datasets, the name of the key for the captions.",
    )
    parser.add_argument(
        "--imagenet-val",
        type=str,
        default=None,
        help="Path to imagenet val set for conducting zero shot evaluation.",
    )
    parser.add_argument(
        "--imagenet-v2",
        type=str,
        default=None,
        help="Path to imagenet v2 for conducting zero shot evaluation.",
    )
    parser.add_argument(
        "--datasetnames",
        nargs="+",
        default=None,
        help="If loading webdataset, spedify the dataset names to load. Can be some of these: Clotho, audioset, audiocaps, BBCSoundEffects",
    )
    parser.add_argument(
        "--full-train-dataset",
        nargs="+",
        default=None,
        help="Which dataset will be trained with all the subsets. (train+test)",
    )
    parser.add_argument(
        "--exclude-eval-dataset",
        nargs="+",
        default=None,
        help="Which dataset will be excluded with evaluation",
    )
    parser.add_argument(
        "--datasetinfos",
        nargs="+",
        default=None,
        help="If loading webdataset, spedify the dataset types to load. Can be some of these: train, test, valid, unbalanced_train, balanced_train, eval",
    )
    parser.add_argument(
        "--dataset-proportion",
        type=float,
        default=1.0,
        help="How much proportion of dataset we want to train.",
    )
    parser.add_argument(
        "--remotedata",
        default=False,
        action="store_true",
        help="if the dataset is remote, set this flag",
    )
    parser.add_argument(
        "--class-label-path",
        type=str,
        default=None,
        help="The path of the class label pickle or csv.",
    )
    parser.add_argument(
        "--datasetpath",
        type=str,
        default="/mnt/audio_clip/webdataset_tar",
        help="The path to the dataset",
    )
    parser.add_argument(
        "--logs",
        type=str,
        default="./logs/",
        help="Where to store tensorboard logs. Use None to avoid storing logs.",
    )
    parser.add_argument(
        "--log-local",
        action="store_true",
        default=False,
        help="log files on local master, otherwise global master only.",
    )
    parser.add_argument(
        "--name",
        type=str,
        default=None,
        help="Optional identifier for the experiment when storing logs. Otherwise use current time.",
    )
    parser.add_argument(
        "--workers", type=int, default=1, help="Number of workers per GPU."
    )
    parser.add_argument(
        "--batch-size", type=int, default=64, help="Batch size per GPU."
    )
    parser.add_argument(
        "--epochs", type=int, default=32, help="Number of epochs to train for."
    )
    parser.add_argument("--lr", type=float, default=None, help="Learning rate.")
    parser.add_argument("--beta1", type=float, default=None, help="Adam beta 1.")
    parser.add_argument("--beta2", type=float, default=None, help="Adam beta 2.")
    parser.add_argument("--eps", type=float, default=None, help="Adam epsilon.")
    parser.add_argument("--momentum", type=float, default=None, help="SGD epsilon.")
    parser.add_argument("--wd", type=float, default=0.2, help="Weight decay.")

    parser.add_argument(
        "--split-opt",
        action="store_true",
        default=False,
        help="Use this flag to skip the learning rate decay.",
    )
    parser.add_argument(
        "--lr-pretrained", type=float, default=None, help="Learning rate for text."
    )
    parser.add_argument(
        "--beta1-pretrained", type=float, default=None, help="Adam beta 1 for text."
    )
    parser.add_argument(
        "--beta2-pretrained", type=float, default=None, help="Adam beta 2 for text."
    )
    parser.add_argument(
        "--eps-pretrained", type=float, default=None, help="Adam epsilon for text."
    )
    parser.add_argument(
        "--wd-pretrained", type=float, default=0.2, help="Weight decay for text."
    )
    parser.add_argument(
        "--momentum-pretrained", type=float, default=0.9, help="Momentum for text."
    )
    parser.add_argument(
        "--lr-new", type=float, default=None, help="Learning rate for audio."
    )
    parser.add_argument(
        "--beta1-new", type=float, default=None, help="Adam beta 1 for audio."
    )
    parser.add_argument(
        "--beta2-new", type=float, default=None, help="Adam beta 2 for audio."
    )
    parser.add_argument(
        "--eps-new", type=float, default=None, help="Adam epsilon for audio."
    )
    parser.add_argument(
        "--wd-new", type=float, default=0.2, help="Weight decay for audio."
    )
    parser.add_argument(
        "--momentum-new", type=float, default=0.9, help="Momentum for audio."
    )
    parser.add_argument(
        "--warmup", type=int, default=10000, help="Number of steps to warmup for."
    )
    parser.add_argument(
        "--use-bn-sync",
        default=False,
        action="store_true",
        help="Whether to use batch norm sync.",
    )
    parser.add_argument(
        "--skip-scheduler",
        action="store_true",
        default=False,
        help="Use this flag to skip the learning rate decay.",
    )
    parser.add_argument(
        "--save-frequency", type=int, default=1, help="How often to save checkpoints."
    )
    parser.add_argument(
        "--save-top-performance",
        type=int,
        default=0,
        help="Save the top x performance weights if the value >0",
    )
    parser.add_argument(
        "--save-most-recent",
        action="store_true",
        default=False,
        help="Always save the most recent model trained to epoch_latest.pt.",
    )
    parser.add_argument(
        "--zeroshot-frequency", type=int, default=2, help="How often to run zero shot."
    )
    parser.add_argument(
        "--val-frequency",
        type=int,
        default=1,
        help="How often to run evaluation with val data.",
    )
    parser.add_argument(
        "--resume",
        default=None,
        type=str,
        help="path to latest checkpoint (default: none)",
    )
    parser.add_argument(
        "--precision",
        choices=["amp", "fp16", "fp32"],
        default="amp",
        help="Floating point precision.",
    )
    parser.add_argument(
        "--amodel",
        type=str,
        default="RN50",
        help="Name of the audio backbone to use.",
    )
    parser.add_argument(
        "--tmodel",
        type=str,
        default="transformer",
        help="Name of the text backbone to use. Can be [transformer, bert, roberta, bart]",
    )
    parser.add_argument(
        "--pretrained-audio",
        default="",
        type=str,
        help="Use a pretrained audio model weights for the audio encoder of CLAP",
    )
    parser.add_argument(
        "--pretrained-text",
        default="",
        type=str,
        help="Use a pretrained text model weights for the text encoder of CLAP",
    )
    parser.add_argument(
        "--pretrained",
        default="",
        type=str,
        help="Use a pretrained CLIP model weights with the specified tag or file path.",
    )
    parser.add_argument(
        "--pretrained-image",
        default=False,
        action="store_true",
        help="Load imagenet pretrained weights for image tower backbone if available.",
    )
    parser.add_argument(
        "--lock-image",
        default=False,
        action="store_true",
        help="Lock full image tower by disabling gradients.",
    )
    parser.add_argument(
        "--lock-image-unlocked-groups",
        type=int,
        default=0,
        help="Leave last n image tower layer groups unlocked.",
    )
    parser.add_argument(
        "--lock-image-freeze-bn-stats",
        default=False,
        action="store_true",
        help="Freeze BatchNorm running stats in image tower for any locked layers.",
    )
    parser.add_argument(
        "--local-loss",
        default=False,
        action="store_true",
        help="calculate loss w/ local features @ global (instead of realizing full global @ global matrix)",
    )
    parser.add_argument(
        "--gather-with-grad",
        default=False,
        action="store_true",
        help="enable full distributed gradient for feature gather",
    )
    parser.add_argument(
        "--force-quick-gelu",
        default=False,
        action="store_true",
        help="Force use of QuickGELU activation for non-OpenAI transformer models.",
    )
    parser.add_argument(
        "--torchscript",
        default=False,
        action="store_true",
        help="torch.jit.script the model, also uses jit version of OpenAI models if pretrained=='openai'",
    )
    parser.add_argument(
        "--trace",
        default=False,
        action="store_true",
        help="torch.jit.trace the model for inference / eval only",
    )
    # arguments for distributed training
    parser.add_argument(
        "--dist-url",
        default="env://",
        type=str,
        help="url used to set up distributed training",
    )
    parser.add_argument(
        "--dist-backend", default="nccl", type=str, help="distributed backend"
    )
    parser.add_argument(
        "--report-to",
        default="",
        type=str,
        help="Options are ['wandb', 'tensorboard', 'wandb,tensorboard']",
    )
    parser.add_argument(
        "--wandb-notes", default="", type=str, help="Notes if logging with wandb"
    )
    parser.add_argument(
        "--C", type=float, default=3.16, help="inverse regularizer for logistic reg."
    )
    parser.add_argument(
        "--debug",
        default=False,
        action="store_true",
        help="If true, more information is logged.",
    )
    parser.add_argument(
        "--copy-codebase",
        default=False,
        action="store_true",
        help="If true, we copy the entire base on the log diretory, and execute from there.",
    )
    parser.add_argument(
        "--horovod",
        default=False,
        action="store_true",
        help="Use horovod for distributed training.",
    )
    parser.add_argument(
        "--ddp-static-graph",
        default=False,
        action="store_true",
        help="Enable static graph optimization for DDP in PyTorch >= 1.11.",
    )
    parser.add_argument(
        "--no-set-device-rank",
        default=False,
        action="store_true",
        help="Don't set device index from local rank (when CUDA_VISIBLE_DEVICES restricted to one per proc).",
    )
    parser.add_argument("--seed", type=int, default=4242, help="Default random seed.")

    parser.add_argument(
        "--top-k-checkpoint-select-dataset",
        type=str,
        default="all",
        help="The dataset of selecting top-k checkpoint.",
    )

    # @R10, @R@5, @R1, mAP@10
    parser.add_argument(
        "--top-k-checkpoint-select-metric",
        type=str,
        default="_R@10",
        help="The metric for selecting top-k checkpoint.",
    )
    parser.add_argument(
        "--openai-model-cache-dir",
        type=str,
        default="~/.cache/clip",
        help="Directory to download OpenAI models.",
    )
    parser.add_argument(
        "--optimizer",
        type=str,
        default="adamw",
        help="can be AdamW or SGD",
    )
    parser.add_argument(
        "--parallel-eval",
        default=False,
        action="store_true",
        help="Eval in parallel (multi-GPU, multi-node).",
    )

    parser.add_argument(
        "--no-eval",
        default=False,
        action="store_true",
        help="Training without evaluation.",
    )

    parser.add_argument(
        "--lp-mlp",
        default=False,
        action="store_true",
        help="Linear Probe using MLP layer or not.",
    )

    parser.add_argument(
        "--lp-freeze",
        default=False,
        action="store_true",
        help="Linear Probe using Freeze CLAP or not",
    )

    parser.add_argument(
        "--lp-act",
        default="None",
        type=str,
        help="Options are ['relu','elu','prelu','softmax','sigmoid']",
    )

    parser.add_argument(
        "--lp-loss", type=str, default="bce", help="Loss func of Linear Probe."
    )

    parser.add_argument(
        "--lp-metrics",
        type=str,
        default="map,mauc,acc",
        help="Metrics of Linear Probe.",
    )

    parser.add_argument(
        "--lp-lr", type=float, default=1e-4, help="learning rate of linear probe"
    )
    parser.add_argument(
        "--kappa",
        type=float,
        default=0,
        help="the kappa in the weighted contrastive loss, default is to turn off the weighted contrastive loss",
    )

    parser.add_argument(
        "--data-filling",
        type=str,
        default="pad",
        help="type of data filling when the audio length is shorter than the max length."
        "Can be one of the following: repeat, repeatpad, pad",
    )
    parser.add_argument(
        "--data-truncating",
        type=str,
        default="rand_trunc",
        help="type of data truncation when the audio length is longer than the max length."
        "Can be one of the following: rand_trunc, fusion",
    )

    parser.add_argument(
        "--clap-mlploss",
        default=False,
        action="store_true",
        help="Using MLP loss for CLAP model or not",
    )

    parser.add_argument(
        "--wandb-id",
        type=str,
        default=None,
        help="the id of wandb experiment to restore.",
    )

    parser.add_argument(
        "--sleep", type=float, default=0, help="sleep n seconds before start training"
    )

    # variable length processing
    parser.add_argument(
        "--enable-fusion",
        default=False,
        action="store_true",
        help="Enable feature funsion for variable-length data",
    )

    parser.add_argument(
        "--fusion-type",
        type=str,
        default="None",
        help="Type is among ['channel_map', 'daf_1d','aff_1d','iaff_1d','daf_2d','aff_2d','iaff_2d']",
    )

    parser.add_argument(
        "--mixup",
        default=False,
        action="store_true",
        help="Enable mixup in finetuning training.",
    )
    parser.add_argument(
        "--text-augment-selection",
        type=str,
        default=None,
        help="For selecting levels of augmented text. Type is among ['all', 'augment_only', 'none']",
    )

    args = parser.parse_args()

    # If some params are not passed, we use the default values based on model name.
    default_params = get_default_params(args.amodel)
    for name, val in default_params.items():
        if getattr(args, name) is None:
            setattr(args, name, val)

    return args