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engine_for_pretraining.py
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engine_for_pretraining.py
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# --------------------------------------------------------
# Based on BEiT, timm, DINO and DeiT code bases
# https://github.com/microsoft/unilm/tree/master/beit
# 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
from einops import rearrange
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
import utils
def train_one_epoch(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,
normlize_target: bool = True,
log_writer=None,
lr_scheduler=None,
start_steps=None,
lr_schedule_values=None,
wd_schedule_values=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 = 20
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]
# NOTE: When the decoder mask ratio is 0,
# in other words, when decoder masking is not used,
# decode_masked_pos = ~bool_masked_pos
images, bool_masked_pos, decode_masked_pos = batch
images = images.to(device, non_blocking=True)
bool_masked_pos = bool_masked_pos.to(
device, non_blocking=True).flatten(1).to(torch.bool)
decode_masked_pos = decode_masked_pos.to(
device, non_blocking=True).flatten(1).to(torch.bool)
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_images = images * std + mean # in [0, 1]
if normlize_target:
images_squeeze = rearrange(
unnorm_images,
'b c (t p0) (h p1) (w p2) -> b (t h w) (p0 p1 p2) c',
p0=2,
p1=patch_size,
p2=patch_size)
images_norm = (images_squeeze - images_squeeze.mean(
dim=-2, keepdim=True)) / (
images_squeeze.var(
dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6)
images_patch = rearrange(images_norm, 'b n p c -> b n (p c)')
else:
images_patch = rearrange(
unnorm_images,
'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, N, C = images_patch.shape
labels = images_patch[~decode_masked_pos].reshape(B, -1, C)
if loss_scaler is None:
outputs = model(images, bool_masked_pos, decode_masked_pos)
loss = (outputs - labels)**2
loss = loss.mean(dim=-1)
cal_loss_mask = bool_masked_pos[~decode_masked_pos].reshape(B, -1)
loss = (loss * cal_loss_mask).sum() / cal_loss_mask.sum()
else:
with torch.cuda.amp.autocast():
outputs = model(images, bool_masked_pos, decode_masked_pos)
loss = (outputs - labels)**2
loss = loss.mean(dim=-1)
cal_loss_mask = bool_masked_pos[~decode_masked_pos].reshape(
B, -1)
loss = (loss * cal_loss_mask).sum() / cal_loss_mask.sum()
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
sys.exit(2)
optimizer.zero_grad()
if loss_scaler is None:
loss.backward()
if max_norm is None:
grad_norm = utils.get_grad_norm_(model.parameters())
else:
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), max_norm)
optimizer.step()
loss_scale_value = 0
else:
# 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.update(loss=loss_value, head="loss")
log_writer.update(loss_scale=loss_scale_value, head="opt")
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.set_step()
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()}