Dynamic-VFM/train.py at main · zhuyu-cs/Dynamic-VFM · GitHub

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
import logging
import os
from argparse import ArgumentParser
from collections import OrderedDict
import torch
from torch.nn.parallel import DataParallel
from models import make_video_model_adaptive, make_video_model_mean
from utils import mouse_video_loader, compute_loss, get_correlations
import random
import numpy as np
from neuralpredictors.training import LongCycler
from mmcv import Config
from mmcv.runner import Runner
import warnings
warnings.filterwarnings("ignore")

def get_logger(log_level):
    logging.basicConfig(
        format='%(asctime)s - %(levelname)s - %(message)s', level=log_level)
    logger = logging.getLogger()
    return logger

def parse_args():
    parser = ArgumentParser(description='Train CIFAR-10 classification')
    parser.add_argument('config', help='train config file path')
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch'],
        default='none',
        help='job launcher')
    return parser.parse_args()

def set_random_seed(seed, deterministic=True):
    """Set random seed.

    Args:
        seed (int): Seed to be used.
        deterministic (bool): Whether to set the deterministic option for
            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
            to True and `torch.backends.cudnn.benchmark` to False.
            Default: False.
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    if deterministic:
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False


def main():
    args = parse_args()
    for mode in ['0','2_4_8_16', '2_4_8','2_4_16','2_8_16','4_8_16','2_4','2_8','2_16','4_8','4_16','8_16','2', '4','8','16']:
        for readout_type in ['mean', 'adaptive']:
            cfg = Config.fromfile(args.config)

            set_random_seed(cfg.seed)

            logger = get_logger(cfg.log_level)

            # build datasets and dataloaders
            num_workers = cfg.data_workers * len(cfg.gpus)
            batch_size = cfg.batch_size


            mice = ['dynamic29156-11-10-Video-8744edeac3b4d1ce16b680916b5267ce',
                'dynamic29228-2-10-Video-8744edeac3b4d1ce16b680916b5267ce',
                'dynamic29234-6-9-Video-8744edeac3b4d1ce16b680916b5267ce',
                'dynamic29513-3-5-Video-8744edeac3b4d1ce16b680916b5267ce',
                'dynamic29514-2-9-Video-8744edeac3b4d1ce16b680916b5267ce',
                'dynamic29515-10-12-Video-9b4f6a1a067fe51e15306b9628efea20',
                'dynamic29623-4-9-Video-9b4f6a1a067fe51e15306b9628efea20',
                'dynamic29647-19-8-Video-9b4f6a1a067fe51e15306b9628efea20',
                'dynamic29712-5-9-Video-9b4f6a1a067fe51e15306b9628efea20',
                'dynamic29755-2-8-Video-9b4f6a1a067fe51e15306b9628efea20',
            ]
            data_path = [os.path.join(cfg.data_root, m) for m in mice]
            all_loaders = mouse_video_loader(paths = data_path,
                                            frames = cfg.frames,
                                            num_workers = num_workers,
                                            batch_size = batch_size,
                                            include_include_behavior_as_channels=cfg.include_include_behavior_as_channels,
                                            include_pupil_centers_as_channels=cfg.include_pupil_centers_as_channels,
                                            video_enhance=cfg.video_enhance)
            logger.info('Load dataset Done!')
            # build model
            if readout_type == 'mean':
                model = make_video_model_mean(dataloaders=all_loaders,
                            core_dict=cfg.model_dict['core_dict'],
                            core_type=cfg.model_dict['core_type'],
                            readout_dict=cfg.model_dict['readout_dict'],
                            readout_type=cfg.model_dict['readout_type'],
                            use_shifter=cfg.model_dict['use_shifter'],
                            shifter_dict=cfg.model_dict['shifter_dict'],
                            shifter_type=cfg.model_dict['shifter_type'])
            else:
                model = make_video_model_adaptive(dataloaders=all_loaders,
                            core_dict=cfg.model_dict['core_dict'],
                            core_type=cfg.model_dict['core_type'],
                            readout_dict=cfg.model_dict['readout_dict'],
                            readout_type=cfg.model_dict['readout_type'],
                            use_shifter=cfg.model_dict['use_shifter'],
                            shifter_dict=cfg.model_dict['shifter_dict'],
                            shifter_type=cfg.model_dict['shifter_type'])
            logger.info('Build model Done!')
            model = DataParallel(model, device_ids=cfg.gpus).cuda()

            def batch_processor(model, data, train_mode):
                data_key, real_data = data
                videos=real_data['videos']
                responses=real_data['responses']
                behavior=real_data['behavior']
                pupil_center=real_data['pupil_center']
                videos = videos.cuda(non_blocking=True)
                responses = responses.cuda(non_blocking=True)
                behavior = behavior.cuda(non_blocking=True)
                pupil_center = pupil_center.cuda(non_blocking=True)

                model_output = model(inputs=videos,
                                data_key=data_key,
                                behavior=behavior,
                                pupil_center=pupil_center,
                                infer_mode=mode,
                                detach_core=False)

                if model.training == True:
                    loss = compute_loss(
                        loss_type=cfg.loss_type,
                        model=model,
                        model_output=model_output,
                        responses=responses,
                        dataloader=all_loaders['train'],
                        data_key=data_key,
                        detach_core=cfg.detach_core,
                        scale_loss=cfg.scale_loss
                    )
                else:
                    loss = compute_loss(
                        loss_type=cfg.loss_type,
                        model=model,
                        model_output=model_output,
                        responses=responses,
                        dataloader=all_loaders['oracle'],
                        data_key=data_key,
                        detach_core=cfg.detach_core,
                        scale_loss=cfg.scale_loss
                    )
                total_loss = loss
                log_vars = OrderedDict()
                log_vars['whole_loss'] = total_loss.item()
                log_vars['scaled_reconstruct_loss'] = loss.item()
                if model.training == False:
                    skip = responses.shape[2] - model_output.shape[1]
                    validation_correlation = get_correlations(
                                                model_output=model_output,
                                                responses=responses,
                                                as_dict=False,
                                                per_neuron=False,
                                                data_key=data_key,
                                                skip = skip,
                                            )
                    log_vars['correlation'] = validation_correlation.item()
                outputs = dict(loss=loss, log_vars=log_vars, num_samples=videos.shape[0])
                return outputs

            # build runner and register hooks
            runner = Runner(
                model,
                batch_processor,
                cfg.optimizer,
                cfg.work_dir+f'_{readout_type}_{mode}',
                log_level=cfg.log_level)
            runner.register_training_hooks(
                lr_config=cfg.lr_config,
                optimizer_config=cfg.optimizer_config,
                checkpoint_config=cfg.checkpoint_config,
                log_config=cfg.log_config)

            if cfg.get('resume_from') is not None:
                runner.resume(cfg.resume_from)
            elif cfg.get('load_from') is not None:
                runner.load_checkpoint(cfg.load_from)

            runner.run([LongCycler(all_loaders['train']), LongCycler(all_loaders['oracle'])], cfg.workflow, cfg.total_epochs)


if __name__ == '__main__':
    main()