File size: 16,898 Bytes
4d4dd90
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# training code for DUSt3R
# --------------------------------------------------------
# References:
# MAE: https://github.com/facebookresearch/mae
# DeiT: https://github.com/facebookresearch/deit
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
# --------------------------------------------------------
import argparse
import datetime
import json
import numpy as np
import os
import sys
import time
import math
from collections import defaultdict
from pathlib import Path
from typing import Sized

import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
torch.backends.cuda.matmul.allow_tf32 = True  # for gpu >= Ampere and pytorch >= 1.12

from dust3r.model import AsymmetricCroCo3DStereo, inf  # noqa: F401, needed when loading the model
from dust3r.datasets import get_data_loader  # noqa
from dust3r.losses import *  # noqa: F401, needed when loading the model
from dust3r.inference import loss_of_one_batch  # noqa

import dust3r.utils.path_to_croco  # noqa: F401
import croco.utils.misc as misc  # noqa
from croco.utils.misc import NativeScalerWithGradNormCount as NativeScaler  # noqa


def get_args_parser():
    parser = argparse.ArgumentParser('DUST3R training', add_help=False)
    # model and criterion
    parser.add_argument('--model', default="AsymmetricCroCo3DStereo(patch_embed_cls='ManyAR_PatchEmbed')",
                        type=str, help="string containing the model to build")
    parser.add_argument('--pretrained', default=None, help='path of a starting checkpoint')
    parser.add_argument('--train_criterion', default="ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)",
                        type=str, help="train criterion")
    parser.add_argument('--test_criterion', default=None, type=str, help="test criterion")

    # dataset
    parser.add_argument('--train_dataset', required=True, type=str, help="training set")
    parser.add_argument('--test_dataset', default='[None]', type=str, help="testing set")

    # training
    parser.add_argument('--seed', default=0, type=int, help="Random seed")
    parser.add_argument('--batch_size', default=64, type=int,
                        help="Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus")
    parser.add_argument('--accum_iter', default=1, type=int,
                        help="Accumulate gradient iterations (for increasing the effective batch size under memory constraints)")
    parser.add_argument('--epochs', default=800, type=int, help="Maximum number of epochs for the scheduler")

    parser.add_argument('--weight_decay', type=float, default=0.05, help="weight decay (default: 0.05)")
    parser.add_argument('--lr', type=float, default=None, metavar='LR', help='learning rate (absolute lr)')
    parser.add_argument('--blr', type=float, default=1.5e-4, metavar='LR',
                        help='base learning rate: absolute_lr = base_lr * total_batch_size / 256')
    parser.add_argument('--min_lr', type=float, default=0., metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0')
    parser.add_argument('--warmup_epochs', type=int, default=40, metavar='N', help='epochs to warmup LR')

    parser.add_argument('--amp', type=int, default=0,
                        choices=[0, 1], help="Use Automatic Mixed Precision for pretraining")

    # others
    parser.add_argument('--num_workers', default=8, type=int)
    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
    parser.add_argument('--local_rank', default=-1, type=int)
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')

    parser.add_argument('--eval_freq', type=int, default=1, help='Test loss evaluation frequency')
    parser.add_argument('--save_freq', default=1, type=int,
                        help='frequence (number of epochs) to save checkpoint in checkpoint-last.pth')
    parser.add_argument('--keep_freq', default=20, type=int,
                        help='frequence (number of epochs) to save checkpoint in checkpoint-%d.pth')
    parser.add_argument('--print_freq', default=20, type=int,
                        help='frequence (number of iterations) to print infos while training')

    # output dir
    parser.add_argument('--output_dir', default='./output/', type=str, help="path where to save the output")
    return parser


def main(args):
    misc.init_distributed_mode(args)
    global_rank = misc.get_rank()
    world_size = misc.get_world_size()

    print("output_dir: "+args.output_dir)
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)

    # auto resume
    last_ckpt_fname = os.path.join(args.output_dir, f'checkpoint-last.pth')
    args.resume = last_ckpt_fname if os.path.isfile(last_ckpt_fname) else None

    print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
    print("{}".format(args).replace(', ', ',\n'))

    device = "cuda" if torch.cuda.is_available() else "cpu"
    device = torch.device(device)

    # fix the seed
    seed = args.seed + misc.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)

    cudnn.benchmark = True

    # training dataset and loader
    print('Building train dataset {:s}'.format(args.train_dataset))
    #  dataset and loader
    data_loader_train = build_dataset(args.train_dataset, args.batch_size, args.num_workers, test=False)
    print('Building test dataset {:s}'.format(args.train_dataset))
    data_loader_test = {dataset.split('(')[0]: build_dataset(dataset, args.batch_size, args.num_workers, test=True)
                        for dataset in args.test_dataset.split('+')}

    # model
    print('Loading model: {:s}'.format(args.model))
    model = eval(args.model)
    print(f'>> Creating train criterion = {args.train_criterion}')
    train_criterion = eval(args.train_criterion).to(device)
    print(f'>> Creating test criterion = {args.test_criterion or args.train_criterion}')
    test_criterion = eval(args.test_criterion or args.criterion).to(device)

    model.to(device)
    model_without_ddp = model
    print("Model = %s" % str(model_without_ddp))

    if args.pretrained and not args.resume:
        print('Loading pretrained: ', args.pretrained)
        ckpt = torch.load(args.pretrained, map_location=device)
        print(model.load_state_dict(ckpt['model'], strict=False))
        del ckpt  # in case it occupies memory

    eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size()
    if args.lr is None:  # only base_lr is specified
        args.lr = args.blr * eff_batch_size / 256
    print("base lr: %.2e" % (args.lr * 256 / eff_batch_size))
    print("actual lr: %.2e" % args.lr)
    print("accumulate grad iterations: %d" % args.accum_iter)
    print("effective batch size: %d" % eff_batch_size)

    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(
            model, device_ids=[args.gpu], find_unused_parameters=True, static_graph=True)
        model_without_ddp = model.module

    # following timm: set wd as 0 for bias and norm layers
    param_groups = misc.get_parameter_groups(model_without_ddp, args.weight_decay)
    optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95))
    print(optimizer)
    loss_scaler = NativeScaler()

    def write_log_stats(epoch, train_stats, test_stats):
        if misc.is_main_process():
            if log_writer is not None:
                log_writer.flush()

            log_stats = dict(epoch=epoch, **{f'train_{k}': v for k, v in train_stats.items()})
            for test_name in data_loader_test:
                if test_name not in test_stats:
                    continue
                log_stats.update({test_name+'_'+k: v for k, v in test_stats[test_name].items()})

            with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
                f.write(json.dumps(log_stats) + "\n")

    def save_model(epoch, fname, best_so_far):
        misc.save_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer,
                        loss_scaler=loss_scaler, epoch=epoch, fname=fname, best_so_far=best_so_far)

    best_so_far = misc.load_model(args=args, model_without_ddp=model_without_ddp,
                                  optimizer=optimizer, loss_scaler=loss_scaler)
    if best_so_far is None:
        best_so_far = float('inf')
    if global_rank == 0 and args.output_dir is not None:
        log_writer = SummaryWriter(log_dir=args.output_dir)
    else:
        log_writer = None

    print(f"Start training for {args.epochs} epochs")
    start_time = time.time()
    train_stats = test_stats = {}
    for epoch in range(args.start_epoch, args.epochs+1):

        # Save immediately the last checkpoint
        if epoch > args.start_epoch:
            if args.save_freq and epoch % args.save_freq == 0 or epoch == args.epochs:
                save_model(epoch-1, 'last', best_so_far)

        # Test on multiple datasets
        new_best = False
        if (epoch > 0 and args.eval_freq > 0 and epoch % args.eval_freq == 0):
            test_stats = {}
            for test_name, testset in data_loader_test.items():
                stats = test_one_epoch(model, test_criterion, testset,
                                       device, epoch, log_writer=log_writer, args=args, prefix=test_name)
                test_stats[test_name] = stats

                # Save best of all
                if stats['loss_med'] < best_so_far:
                    best_so_far = stats['loss_med']
                    new_best = True

        # Save more stuff
        write_log_stats(epoch, train_stats, test_stats)

        if epoch > args.start_epoch:
            if args.keep_freq and epoch % args.keep_freq == 0:
                save_model(epoch-1, str(epoch), best_so_far)
            if new_best:
                save_model(epoch-1, 'best', best_so_far)
        if epoch >= args.epochs:
            break  # exit after writing last test to disk

        # Train
        train_stats = train_one_epoch(
            model, train_criterion, data_loader_train,
            optimizer, device, epoch, loss_scaler,
            log_writer=log_writer,
            args=args)

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Training time {}'.format(total_time_str))

    save_final_model(args, args.epochs, model_without_ddp, best_so_far=best_so_far)


def save_final_model(args, epoch, model_without_ddp, best_so_far=None):
    output_dir = Path(args.output_dir)
    checkpoint_path = output_dir / 'checkpoint-final.pth'
    to_save = {
        'args': args,
        'model': model_without_ddp if isinstance(model_without_ddp, dict) else model_without_ddp.cpu().state_dict(),
        'epoch': epoch
    }
    if best_so_far is not None:
        to_save['best_so_far'] = best_so_far
    print(f'>> Saving model to {checkpoint_path} ...')
    misc.save_on_master(to_save, checkpoint_path)


def build_dataset(dataset, batch_size, num_workers, test=False):
    split = ['Train', 'Test'][test]
    print(f'Building {split} Data loader for dataset: ', dataset)
    loader = get_data_loader(dataset,
                             batch_size=batch_size,
                             num_workers=num_workers,
                             pin_mem=True,
                             shuffle=not (test),
                             drop_last=not (test))

    print(f"{split} dataset length: ", len(loader))
    return loader


def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
                    data_loader: Sized, optimizer: torch.optim.Optimizer,
                    device: torch.device, epoch: int, loss_scaler,
                    args,
                    log_writer=None):
    assert torch.backends.cuda.matmul.allow_tf32 == True

    model.train(True)
    metric_logger = misc.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    header = 'Epoch: [{}]'.format(epoch)
    accum_iter = args.accum_iter

    if log_writer is not None:
        print('log_dir: {}'.format(log_writer.log_dir))

    if hasattr(data_loader, 'dataset') and hasattr(data_loader.dataset, 'set_epoch'):
        data_loader.dataset.set_epoch(epoch)
    if hasattr(data_loader, 'sampler') and hasattr(data_loader.sampler, 'set_epoch'):
        data_loader.sampler.set_epoch(epoch)

    optimizer.zero_grad()

    for data_iter_step, batch in enumerate(metric_logger.log_every(data_loader, args.print_freq, header)):
        epoch_f = epoch + data_iter_step / len(data_loader)

        # we use a per iteration (instead of per epoch) lr scheduler
        if data_iter_step % accum_iter == 0:
            misc.adjust_learning_rate(optimizer, epoch_f, args)

        loss_tuple = loss_of_one_batch(batch, model, criterion, device,
                                       symmetrize_batch=True,
                                       use_amp=bool(args.amp), ret='loss')
        loss, loss_details = loss_tuple  # criterion returns two values
        loss_value = float(loss)

        if not math.isfinite(loss_value):
            print("Loss is {}, stopping training".format(loss_value), force=True)
            sys.exit(1)

        loss /= accum_iter
        loss_scaler(loss, optimizer, parameters=model.parameters(),
                    update_grad=(data_iter_step + 1) % accum_iter == 0)
        if (data_iter_step + 1) % accum_iter == 0:
            optimizer.zero_grad()

        del loss
        del batch

        lr = optimizer.param_groups[0]["lr"]
        metric_logger.update(epoch=epoch_f)
        metric_logger.update(lr=lr)
        metric_logger.update(loss=loss_value, **loss_details)

        if (data_iter_step + 1) % accum_iter == 0 and ((data_iter_step + 1) % (accum_iter * args.print_freq)) == 0:
            loss_value_reduce = misc.all_reduce_mean(loss_value)  # MUST BE EXECUTED BY ALL NODES
            if log_writer is None:
                continue
            """ We use epoch_1000x as the x-axis in tensorboard.
            This calibrates different curves when batch size changes.
            """
            epoch_1000x = int(epoch_f * 1000)
            log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x)
            log_writer.add_scalar('train_lr', lr, epoch_1000x)
            log_writer.add_scalar('train_iter', epoch_1000x, epoch_1000x)
            for name, val in loss_details.items():
                log_writer.add_scalar('train_'+name, val, epoch_1000x)

    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger)
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}


@torch.no_grad()
def test_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
                   data_loader: Sized, device: torch.device, epoch: int,
                   args, log_writer=None, prefix='test'):

    model.eval()
    metric_logger = misc.MetricLogger(delimiter="  ")
    metric_logger.meters = defaultdict(lambda: misc.SmoothedValue(window_size=9**9))
    header = 'Test Epoch: [{}]'.format(epoch)

    if log_writer is not None:
        print('log_dir: {}'.format(log_writer.log_dir))

    if hasattr(data_loader, 'dataset') and hasattr(data_loader.dataset, 'set_epoch'):
        data_loader.dataset.set_epoch(epoch)
    if hasattr(data_loader, 'sampler') and hasattr(data_loader.sampler, 'set_epoch'):
        data_loader.sampler.set_epoch(epoch)

    for _, batch in enumerate(metric_logger.log_every(data_loader, args.print_freq, header)):
        loss_tuple = loss_of_one_batch(batch, model, criterion, device,
                                       symmetrize_batch=True,
                                       use_amp=bool(args.amp), ret='loss')
        loss_value, loss_details = loss_tuple  # criterion returns two values
        metric_logger.update(loss=float(loss_value), **loss_details)

    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger)

    aggs = [('avg', 'global_avg'), ('med', 'median')]
    results = {f'{k}_{tag}': getattr(meter, attr) for k, meter in metric_logger.meters.items() for tag, attr in aggs}

    if log_writer is not None:
        for name, val in results.items():
            log_writer.add_scalar(prefix+'_'+name, val, 1000*epoch)

    return results


if __name__ == '__main__':
    args = get_args_parser()
    args = args.parse_args()
    main(args)