-
Notifications
You must be signed in to change notification settings - Fork 0
/
engine_for_pretraining.py
executable file
·160 lines (135 loc) · 7.45 KB
/
engine_for_pretraining.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
import math
import sys
from typing import Iterable
import torch
import torch.nn as nn
import utils
from einops import rearrange
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
import torch.nn.functional as F
from visualize_masks import denormalize_video, overlay_video_cmap
from PIL import Image
import wandb
from info_nce import InfoNCE, info_nce
from timm.models.vision_transformer import vit_small_patch16_224
from utils import chromatic_correction_fct
def train_one_epoch(model: torch.nn.Module, feature_extraction_model:torch.nn.Module, data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler, max_norm: float = 0, patch_size: int = 16, mask_type='tube',
target_type='pixel', normlize_target: bool = True, log_writer=None, lr_scheduler=None, start_steps=None,
lr_schedule_values=None, wd_schedule_values=None, output_dir='./', loss_func='L2'):
model.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter('min_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
if loss_func == 'L2':
loss_cpt = nn.MSELoss()
elif loss_func == 'SWAV':
loss_cpt = nn.CrossEntropyLoss()
for step, batch in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
# assign learning rate & weight decay for each step
it = start_steps + step # global training iteration
if lr_schedule_values is not None or wd_schedule_values is not None:
for i, param_group in enumerate(optimizer.param_groups):
if lr_schedule_values is not None:
param_group["lr"] = lr_schedule_values[it] * param_group["lr_scale"]
if wd_schedule_values is not None and param_group["weight_decay"] > 0:
param_group["weight_decay"] = wd_schedule_values[it]
videos, bool_masked_pos = batch
videos = videos.to(device, non_blocking=True)
bool_masked_pos = bool_masked_pos.to(device, non_blocking=True).flatten(1).to(torch.bool)
bs, _, nf, h, w = videos.shape
if ('pixel' in target_type) or ('mlp' in target_type):
with torch.no_grad():
# calculate the predict label
mean = torch.as_tensor(IMAGENET_DEFAULT_MEAN).to(device)[None, :, None, None, None]
std = torch.as_tensor(IMAGENET_DEFAULT_STD).to(device)[None, :, None, None, None]
unnorm_videos = videos * std + mean # in [0, 1]
if normlize_target:
videos_squeeze = rearrange(unnorm_videos, 'b c (t p0) (h p1) (w p2) -> b (t h w) (p0 p1 p2) c', p0=2, p1=patch_size, p2=patch_size)
videos_norm = (videos_squeeze - videos_squeeze.mean(dim=-2, keepdim=True)
) / (videos_squeeze.var(dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6)
# we find that the mean is about 0.48 and standard deviation is about 0.08.
videos_patch = rearrange(videos_norm, 'b n p c -> b n (p c)')
else:
videos_patch = rearrange(unnorm_videos, 'b c (t p0) (h p1) (w p2) -> b (t h w) (p0 p1 p2 c)', p0=2, p1=patch_size, p2=patch_size)
B, _, C = videos_patch.shape
labels = videos_patch[bool_masked_pos].reshape(B, -1, C)
print("Target type is {}, ".format(target_type))
elif 'dino' in target_type:
with torch.no_grad():
permuted_video = videos.permute(0, 2, 1, 3, 4)
bs, nf, _, h, w = permuted_video.shape
permuted_video = permuted_video[:, ::2].flatten(0, 1)
permuted_video = permuted_video.to(device, non_blocking=True)
# feature_extraction_model = feature_extraction_model.to(device)
features = feature_extraction_model(permuted_video)
_, np, dim = features.shape
features = features.reshape(bs, nf//2, np, dim)
features.requires_grad = False
features = features.to(device, non_blocking=True)
with torch.no_grad():
features_squeeze = rearrange(features, 'b n o c -> b (n o) c')
if normlize_target:
labels = (features_squeeze - features_squeeze.mean(dim=-2, keepdim=True)
) / (features_squeeze.var(dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6)
else:
labels = features_squeeze
B, _, C = labels.shape
labels = labels[bool_masked_pos].reshape(B, -1, C)
print("Target type is {}, ".format(target_type))
with torch.cuda.amp.autocast():
outputs, (scores1, q1), (scores2, q2) = model(videos, bool_masked_pos, labels)
if loss_func == 'SWAV': #features dino means without projection head
q1 = q1.argmax(dim=-1)
scores1 = scores1 / 0.1
q2 = q2.argmax(dim=-1)
scores2 = scores2 / 0.1
loss1 = loss_cpt(scores1.permute(0, 2, 1), q2.long())
loss2 = loss_cpt(scores2.permute(0, 2, 1), q1.long())
loss = loss1 + loss2
#print("Loss SAWV is {}, ".format(loss_func))
else:
loss = loss_cpt(outputs, labels).mean()
#print("Loss L2 is {}, ".format(loss_func))
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
sys.exit(1)
optimizer.zero_grad()
# this attribute is added by timm on one optimizer (adahessian)
is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm,
parameters=model.parameters(), create_graph=is_second_order)
loss_scale_value = loss_scaler.state_dict()["scale"]
torch.cuda.synchronize()
metric_logger.update(loss=loss_value)
metric_logger.update(loss_scale=loss_scale_value)
min_lr = 10.
max_lr = 0.
for group in optimizer.param_groups:
min_lr = min(min_lr, group["lr"])
max_lr = max(max_lr, group["lr"])
metric_logger.update(lr=max_lr)
metric_logger.update(min_lr=min_lr)
weight_decay_value = None
for group in optimizer.param_groups:
if group["weight_decay"] > 0:
weight_decay_value = group["weight_decay"]
metric_logger.update(weight_decay=weight_decay_value)
metric_logger.update(grad_norm=grad_norm)
if log_writer is not None:
log_writer.log({
"mae_Loss": loss_value,
"Lr_max": max_lr,
"Lr_min": min_lr,
"Weight_decay": weight_decay_value,
"Grad_norm": grad_norm,
})
if lr_scheduler is not None:
lr_scheduler.step_update(start_steps + step)
# 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()}