engine_mar_vton.py

import math
import sys
from typing import Iterable
from torchvision.utils import save_image
import torch

import util.misc as misc
import util.lr_sched as lr_sched
from models.vae import DiagonalGaussianDistribution
import torch_fidelity
import shutil
import cv2
import numpy as np
import os
import copy
import time


def update_ema(target_params, source_params, rate=0.99):
    """
    Update target parameters to be closer to those of source parameters using
    an exponential moving average.

    :param target_params: the target parameter sequence.
    :param source_params: the source parameter sequence.
    :param rate: the EMA rate (closer to 1 means slower).
    """
    for targ, src in zip(target_params, source_params):
        targ.detach().mul_(rate).add_(src, alpha=1 - rate)


def train_one_epoch(model, vae,
                    model_params, ema_params,
                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
                    device: torch.device, epoch: int, loss_scaler,
                    log_writer=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

    optimizer.zero_grad()

    if log_writer is not None:
        print('log_dir: {}'.format(log_writer.log_dir))

    for data_iter_step, (batch, labels) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
        # we use a per iteration (instead of per epoch) lr scheduler
        lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)

        for k, v in batch.items():
            batch[k] = v.to(device, non_blocking=True)

        with torch.no_grad():
            # if args.use_cached:
            #     moments = samples
            #     posterior = DiagonalGaussianDistribution(moments)
            # else:
            batch_images ={"gt": batch['gt'], "inpaint": batch['inpaint'], "garment": batch['garment']}
            posterior = vae.encode(batch_images)

            # normalize the std of latent to be 1. Change it if you use a different tokenizer
            for key, image in posterior.items():
                batch[key] = image.sample().mul_(0.2325) # [bs, 16, 16, 16]

        # forward
        with torch.cuda.amp.autocast():
            loss = model(batch, labels)

        loss_value = loss.item()

        if not math.isfinite(loss_value):
            print("Loss is {}, stopping training".format(loss_value))
            sys.exit(1)

        loss_scaler(loss, optimizer, clip_grad=args.grad_clip, parameters=model.parameters(), update_grad=True)
        optimizer.zero_grad()

        torch.cuda.synchronize()

        update_ema(ema_params, model_params, rate=args.ema_rate)

        metric_logger.update(loss=loss_value)

        lr = optimizer.param_groups[0]["lr"]
        metric_logger.update(lr=lr)

        loss_value_reduce = misc.all_reduce_mean(loss_value)
        if log_writer is not None:
            """ 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('train_loss', loss_value_reduce, epoch_1000x)
            log_writer.add_scalar('lr', lr, epoch_1000x)
        break

    # 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()}


def evaluate(model_without_ddp, vae, ema_params, args, epoch, data_loader_test, batch_size=16, log_writer=None, cfg=1.0,
             use_ema=True, device='cuda'):
    model_without_ddp.eval()
    # num_steps = args.num_images // (batch_size * misc.get_world_size()) + 1
    save_folder = os.path.join(args.output_dir, "ariter{}-diffsteps{}-temp{}-{}cfg{}".format(args.num_iter,
                                                                                                     args.num_sampling_steps,
                                                                                                     args.temperature,
                                                                                                     args.cfg_schedule,
                                                                                                     cfg))
    if use_ema:
        save_folder = save_folder + "_ema"
    if args.evaluate:
        save_folder = save_folder + "_evaluate"
    print("Save to:", save_folder)
    if misc.get_rank() == 0:
        if not os.path.exists(save_folder):
            os.makedirs(save_folder)

    # switch to ema params
    if use_ema:
        model_state_dict = copy.deepcopy(model_without_ddp.state_dict())
        ema_state_dict = copy.deepcopy(model_without_ddp.state_dict())
        for i, (name, _value) in enumerate(model_without_ddp.named_parameters()):
            assert name in ema_state_dict
            ema_state_dict[name] = ema_params[i]
        print("Switch to ema")
        model_without_ddp.load_state_dict(ema_state_dict)

    class_num = args.class_num
    # assert args.num_images % class_num == 0  # number of images per class must be the same
    # class_label_gen_world = np.arange(0, class_num).repeat(args.num_images // class_num)
    # class_label_gen_world = np.hstack([class_label_gen_world, np.zeros(50000)])
    world_size = misc.get_world_size()
    local_rank = misc.get_rank()
    used_time = 0
    gen_img_cnt = 0

    for i, (batch, labels) in enumerate(data_loader_test):
        for k, v in batch.items():
            batch[k] = v.to(device, non_blocking=True)
        
        with torch.no_grad():
            batch_images ={"gt": batch['gt'], "inpaint": batch['inpaint'], "garment": batch['garment']}
            posterior = vae.encode(batch_images)

            # normalize the std of latent to be 1. Change it if you use a different tokenizer
            for key, image in posterior.items():
                batch[key] = image.sample().mul_(0.2325) # [bs, 16, 16, 16]

        # generation
        with torch.no_grad():
            with torch.cuda.amp.autocast():
                sampled_tokens = model_without_ddp.sample_tokens(bsz=1, batch=batch, num_iter=args.num_iter, cfg=cfg,
                                                                 cfg_schedule=args.cfg_schedule, labels=None,
                                                                 temperature=args.temperature)
                sampled_images = vae.decode(sampled_tokens / 0.2325) # bs, 16, 16, 16  -10184  -> 12728
        output_path = os.path.join(save_folder, f"{i}.png")
        save_image(sampled_images, output_path, nrow=int(1), normalize=True, value_range=(-1, 1))
        print(f"Saved image{output_path}")

    #     sampled_images = sampled_images.detach().cpu()
    #     sampled_images = (sampled_images + 1) / 2

    #     # distributed save
    #     for b_id in range(sampled_images.size(0)):
    #         img_id = i * sampled_images.size(0) * world_size + local_rank * sampled_images.size(0) + b_id
    #         # if img_id >= args.num_images:
    #         #     break
    #         gen_img = np.round(np.clip(sampled_images[b_id].numpy().transpose([1, 2, 0]) * 255, 0, 255))
    #         gen_img = gen_img.astype(np.uint8)[:, :, ::-1]
    #         cv2.imwrite(os.path.join(save_folder, '{}.png'.format(str(img_id).zfill(5))), gen_img)

    # torch.distributed.barrier()
    # time.sleep(10)

    # back to no ema
    if use_ema:
        print("Switch back from ema")
        model_without_ddp.load_state_dict(model_state_dict)

    # compute FID and IS
    if log_writer is not None:
        if args.img_size == 256:
            input2 = None
            fid_statistics_file = 'fid_stats/adm_in256_stats.npz'
        else:
            raise NotImplementedError
        metrics_dict = torch_fidelity.calculate_metrics(
            input1=save_folder,
            input2=input2,
            fid_statistics_file=fid_statistics_file,
            cuda=True,
            isc=True,
            fid=True,
            kid=False,
            prc=False,
            verbose=False,
        )
        fid = metrics_dict['frechet_inception_distance']
        inception_score = metrics_dict['inception_score_mean']
        postfix = ""
        if use_ema:
           postfix = postfix + "_ema"
        if not cfg == 1.0:
           postfix = postfix + "_cfg{}".format(cfg)
        log_writer.add_scalar('fid{}'.format(postfix), fid, epoch)
        log_writer.add_scalar('is{}'.format(postfix), inception_score, epoch)
        print("FID: {:.4f}, Inception Score: {:.4f}".format(fid, inception_score))
        # remove temporal saving folder
        shutil.rmtree(save_folder)

    torch.distributed.barrier()
    time.sleep(10)


def cache_latents(vae,
                  data_loader: Iterable,
                  device: torch.device,
                  args=None):
    metric_logger = misc.MetricLogger(delimiter="  ")
    header = 'Caching: '
    print_freq = 20

    for data_iter_step, (samples, _, paths) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):

        samples = samples.to(device, non_blocking=True)

        with torch.no_grad():
            posterior = vae.encode(samples)
            moments = posterior.parameters
            posterior_flip = vae.encode(samples.flip(dims=[3]))
            moments_flip = posterior_flip.parameters

        for i, path in enumerate(paths):
            save_path = os.path.join(args.cached_path, path + '.npz')
            os.makedirs(os.path.dirname(save_path), exist_ok=True)
            np.savez(save_path, moments=moments[i].cpu().numpy(), moments_flip=moments_flip[i].cpu().numpy())

        if misc.is_dist_avail_and_initialized():
            torch.cuda.synchronize()

    return