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import json
import os
import time
from typing import Iterable
import torch
import torch.nn as nn
from timm.data.constants import (IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
import utils
from datetime import datetime
def train_one_epoch(model: torch.nn.Module,
data_loader: Iterable,
optimizer: torch.optim.Optimizer,
device: torch.device,
epoch: int,
loss_scaler,
start_steps=None,
lr_schedule_values=None,
wd_schedule_values=None,
global_rank=None,
args=None,
loss_func = nn.MSELoss(),
):
metric_logger = utils.MetricLogger(delimiter=" ")
if args.eval:
model.eval()
else:
model.train()
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = f'Epoch [{epoch}]'
patch_size = model.module.encoder.patch_size[-2:]
tubelet_size = model.module.encoder.patch_size[0]
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]
for step, batch in enumerate(metric_logger.log_every(data_loader, args.print_freq, header)):
# assign learning rate & weight decay for each iteration
it = start_steps + step # global training iteration
if (lr_schedule_values is not None or wd_schedule_values is not None) and (step % args.accum_iter == 0):
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]
# prepare input
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)
# prepare target
with torch.no_grad():
unnorm_videos = videos * std + mean # in [0, 1]
videos_patch = utils.patchify(unnorm_videos, tubelet_size, patch_size)
B, _, C = videos_patch.shape
labels = videos_patch[bool_masked_pos].reshape(B, -1, C)
# feedforward
with torch.cuda.amp.autocast(enabled=True):
outputs = model(videos, bool_masked_pos)
loss = loss_func(input=outputs, target=labels)
loss_value = loss.item()
# backward
is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
loss /= args.accum_iter
loss_scaler(loss, optimizer, clip_grad=None,
parameters=model.parameters(), create_graph=is_second_order,
update_grad=(step + 1) % args.accum_iter == 0)
torch.cuda.synchronize()
metric_logger.update(loss=loss_value)
if (step + 1) % args.accum_iter == 0:
optimizer.zero_grad()
lr = optimizer.param_groups[0]["lr"]
metric_logger.update(lr=lr)
# 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()}
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