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# --------------------------------------------------------
# References:
# MAE: https://github.com/facebookresearch/mae
# DeiT: https://github.com/facebookresearch/deit
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
# --------------------------------------------------------
import math
import sys
from typing import Iterable
import torch
import torch.nn.functional as F
import util.misc as misc
import util.lr_sched as lr_sched
import numpy as np
import os
import pickle as p
import torch.distributed as dist
import time
from models.modules.encoder import DiagonalGaussianDistribution
def train_one_epoch(model: torch.nn.Module, ae: torch.nn.Module, criterion: torch.nn.Module,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
log_writer=None,log_dir=None, args=None):
model.train(True)
metric_logger = misc.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 20
accum_iter = args.accum_iter
use_cls_free= args.use_cls_free
optimizer.zero_grad()
if log_writer is not None:
print('log_dir: {}'.format(log_writer.log_dir))
for data_iter_step, data_batch in enumerate(
metric_logger.log_every(data_loader, print_freq, header)):
# we use a per iteration (instead of per epoch) lr scheduler
if not args.constant_lr:
if data_iter_step % accum_iter == 0:
lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)
input_dict=model.module.prepare_data(data_batch)
with torch.cuda.amp.autocast(enabled=False):
loss_all = criterion(model,input_dict,classifier_free=use_cls_free)
loss=loss_all.mean()
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
sys.exit(1)
loss /= accum_iter
loss_scaler(loss, optimizer, clip_grad=max_norm,
parameters=model.parameters(), create_graph=False,
update_grad=(data_iter_step + 1) % accum_iter == 0)
if (data_iter_step + 1) % accum_iter == 0:
optimizer.zero_grad()
torch.cuda.synchronize()
metric_logger.update(loss=loss_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)
loss_value_reduce = misc.all_reduce_mean(loss_value)
if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
""" We use epoch_1000x as the x-axis in tensorboard.
This calibrates different curves when batch size changes.
"""
epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
log_writer.add_scalar('loss', loss_value_reduce, epoch_1000x)
log_writer.add_scalar('lr', max_lr, epoch_1000x)
# 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()}
@torch.no_grad()
def evaluate_reconstruction(data_loader, model, ae, criterion, device):
metric_logger = misc.MetricLogger(delimiter=" ")
header = 'Test:'
# switch to evaluation mode
model.eval()
for data_batch in metric_logger.log_every(data_loader, 50, header):
with torch.no_grad():
input_dict=model.module.prepare_data(data_batch)
loss_all = criterion(model, input_dict,classifier_free=False)
loss = loss_all.mean()
sample_input=model.module.prepare_sample_data(data_batch)
sampled_array = model.module.sample(sample_input).float()
sampled_array = torch.nn.functional.interpolate(sampled_array, scale_factor=2, mode="bilinear")
eval_input=model.module.prepare_eval_data(data_batch)
samples=eval_input["samples"]
labels=eval_input["labels"]
for j in range(sampled_array.shape[0]):
output = ae.decode(sampled_array[j:j + 1], samples[j:j+1]).squeeze(-1)
pred = torch.zeros_like(output)
pred[output >= 0.0] = 1
label=labels[j:j+1]
accuracy = (pred == label).float().sum(dim=1) / label.shape[1]
accuracy = accuracy.mean()
intersection = (pred * label).sum(dim=1)
union = (pred + label).gt(0).sum(dim=1)
iou = intersection * 1.0 / union + 1e-5
iou = iou.mean()
metric_logger.update(iou=iou.item())
metric_logger.update(accuracy=accuracy.item())
metric_logger.update(loss=loss.item())
metric_logger.synchronize_between_processes()
print('* iou {ious.global_avg:.3f}'
.format(ious=metric_logger.iou))
print('* accuracy {accuracies.global_avg:.3f}'
.format(accuracies=metric_logger.accuracy))
print('* loss {losses.global_avg:.3f}'
.format(losses=metric_logger.loss))
return {k: meter.global_avg for k, meter in metric_logger.meters.items()} |