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engine_for_pretraining_amd.py
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engine_for_pretraining_amd.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
import torch.nn as nn
import utils
from einops import rearrange
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
def train_one_epoch(args,model_teacher: torch.nn.Module,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_teacher.eval()
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
loss_func = nn.MSELoss()
if args.align_loss=='l2':
loss_func_distil = nn.MSELoss()
if args.align_loss=='l1':
loss_func_distil = nn.L1Loss()
if args.align_loss=='sml1':
loss_func_distil = nn.SmoothL1Loss()
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]
images, bool_masked_pos = batch
images = images.to(device, non_blocking=True)
bool_masked_pos_student, bool_masked_pos_teacher, bool_masked_pos_diff = bool_masked_pos
bool_masked_pos_student = bool_masked_pos_student.to(device, non_blocking=True).flatten(1).to(torch.bool)
bool_masked_pos_teacher = bool_masked_pos_teacher.to(device, non_blocking=True).flatten(1).to(torch.bool)
bool_masked_pos_diff = bool_masked_pos_diff.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)
# we find that the mean is about 0.48 and standard deviation is about 0.08.
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, _, C = images_patch.shape
labels = images_patch[bool_masked_pos_student].reshape(B, -1, C)
with torch.cuda.amp.autocast():
with torch.no_grad():
dir_teacher_feat, gen_teacher_feat = model_teacher(images, (bool_masked_pos_student ,bool_masked_pos_diff))
outputs, dir_student_feat, gen_student_feat = model(images, (bool_masked_pos_student, bool_masked_pos_diff, bool_masked_pos_teacher))
loss_mae = loss_func(input=outputs, target=labels)
loss_distil_sum_dir = 0.0
loss_distil_sum_gen = 0.0
for idx in range(len(dir_student_feat)):
loss_distil_sum_dir += loss_func_distil(dir_student_feat[idx], dir_teacher_feat[idx])
loss_distil_dir = loss_distil_sum_dir / len(dir_student_feat)
for idx in range(len(gen_student_feat)):
loss_distil_sum_gen += loss_func_distil(gen_student_feat[idx], gen_teacher_feat[idx])
loss_distil_gen = loss_distil_sum_gen / len(gen_student_feat)
loss = args.recfac * loss_mae + args.dirfac * loss_distil_dir + args.genfac * loss_distil_gen
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
sys.exit(2)
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_mae=loss_mae.item())
metric_logger.update(loss_distil_dir=loss_distil_dir.item())
metric_logger.update(loss_distil_gen=loss_distil_gen.item())
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_mae=loss_mae.item(), head="loss")
log_writer.update(loss_distil_dir=loss_distil_dir.item(), head="loss")
log_writer.update(loss_distil_gen=loss_distil_gen.item(), 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()}