forked from 935963004/LaBraM
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathengine_for_vqnsp.py
200 lines (161 loc) · 8.4 KB
/
engine_for_vqnsp.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
# --------------------------------------------------------
# Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI
# By Wei-Bang Jiang
# Based on BEiT-v2, timm, DeiT, and DINO code bases
# https://github.com/microsoft/unilm/tree/master/beitv2
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit/
# https://github.com/facebookresearch/dino
# ---------------------------------------------------------
import math
import sys
from typing import Iterable
import torch
import torch.nn as nn
import utils
def train_one_epoch(model: torch.nn.Module,
data_loader_list: Iterable,
optimizer: torch.optim.Optimizer,
device: torch.device,
epoch: int,
loss_scaler,
clip_grad: float = 0,
log_writer=None,
lr_scheduler=None,
start_steps=None,
lr_schedule_values=None,
ch_names_list=None,
args=None,
):
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 hasattr(model.module, 'quantize'):
try:
model.module.quantize.reset_cluster_size(device)
print("Reset the codebook statistic info in quantizer before each epoch")
except:
pass
step_loader = 0
for data_loader, ch_names in zip(data_loader_list, ch_names_list):
input_chans = utils.get_input_chans(ch_names)
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 + step_loader # global training iteration
if lr_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.get("lr_scale", 1.0)
EEG = batch.float().to(device, non_blocking=True) / 100
with torch.cuda.amp.autocast(enabled=True):
loss, log_loss = model(EEG, input_chans=input_chans)
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value), force=True)
utils.save_nan_model(args, model)
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=clip_grad,
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)
new_log_loss = {k.split('/')[-1]:v for k, v in log_loss.items() if k not in ['total_loss']}
metric_logger.update(**new_log_loss)
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.update(**new_log_loss, head="train/loss")
log_writer.update(lr=max_lr, head="opt")
log_writer.update(min_lr=min_lr, head="opt")
log_writer.update(weight_decay=weight_decay_value, head="opt")
log_writer.update(grad_norm=grad_norm, head="opt")
log_writer.update(loss_scale=loss_scale_value, head="opt")
log_writer.set_step()
if lr_scheduler is not None:
lr_scheduler.step_update(start_steps + step + step_loader)
step_loader += step
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
# stat the codebook usage information
if hasattr(model.module, 'quantize'):
try:
codebook_cluster_size = model.module.quantize._codebook.cluster_size
except:
codebook_cluster_size = model.module.quantize.cluster_size
zero_cnt = (codebook_cluster_size == 0).sum().item()
train_stat = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
train_stat['Unused_code'] = zero_cnt
print(f"Unused code in codebook: {zero_cnt}")
return train_stat
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate(data_loader_list, model, device, log_writer=None, epoch=None, ch_names_list=None, args=None):
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Validation:'
# switch to evaluation mode
model.eval()
if hasattr(model.module, 'quantize'):
try:
model.module.quantize.reset_cluster_size(device)
print("Reset the codebook statistic info in quantizer before testing")
except:
pass
for data_loader, ch_names in zip(data_loader_list, ch_names_list):
input_chans = utils.get_input_chans(ch_names)
for step, (batch) in enumerate(metric_logger.log_every(data_loader, 10, header)):
images = batch.float().to(device, non_blocking=True) / 100
loss, log_loss = model(images, input_chans=input_chans)
metric_logger.update(loss=loss.item())
new_log_loss = {k.split('/')[-1]:v for k, v in log_loss.items() if k not in ['total_loss']}
metric_logger.update(**new_log_loss)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
# stat the codebook usage information
if hasattr(model, 'module') and hasattr(model.module, 'quantize'):
try:
codebook_cluster_size = model.module.quantize._codebook.cluster_size
except:
codebook_cluster_size = model.module.quantize.cluster_size
zero_cnt = (codebook_cluster_size == 0).sum().item()
test_stat = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
test_stat['unused_code'] = zero_cnt
print(f"Unused code in codebook: {zero_cnt}")
return test_stat
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def calculate_codebook_usage(data_loader, model, device, log_writer=None, epoch=None, args=None):
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Calculating codebook usage:'
# switch to evaluation mode
model.eval()
codebook_num = args.codebook_n_emd
codebook_cnt = torch.zeros(codebook_num, dtype=torch.float64).to(device)
for step, (images) in enumerate(metric_logger.log_every(data_loader, 10, header)):
images = images.float().to(device, non_blocking=True) / 100
outputs = utils.get_model(model).get_tokens(images)['token'].view(-1)
outputs_gather_list = [torch.zeros_like(outputs) for _ in range(utils.get_world_size())]
torch.distributed.all_gather(outputs_gather_list, outputs)
all_tokens = torch.cat(outputs_gather_list, dim=0).view(-1) # [B * N * Ngpu, ]
codebook_cnt += torch.bincount(all_tokens, minlength=codebook_num)
# statistic
zero_cnt = (codebook_cnt == 0).sum() # 0
print(f"STAT: {zero_cnt} tokens ({(zero_cnt / codebook_num) * 100}%) never are used in this codebook.")