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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()} | |