engine.py

import math
import sys
from typing import Iterable

import torch

import pointmar.util.misc as misc
import pointmar.util.lr_sched as lr_sched
import shutil
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, 
        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, point_cloud 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)

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

        # forward
        with torch.amp.autocast(device_type=device.type):
            loss = model(point_cloud)

        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)

    # 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, batch_size=16, log_writer=None, cfg=1.0,
#              use_ema=True):
#     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{}-image{}".format(args.num_iter,
#                                                                                                      args.num_sampling_steps,
#                                                                                                      args.temperature,
#                                                                                                      args.cfg_schedule,
#                                                                                                      cfg,
#                                                                                                      args.num_images))
#     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 in range(num_steps):
#         print("Generation step {}/{}".format(i, num_steps))

#         labels_gen = class_label_gen_world[world_size * batch_size * i + local_rank * batch_size:
#                                                 world_size * batch_size * i + (local_rank + 1) * batch_size]
#         labels_gen = torch.Tensor(labels_gen).long().cuda()


#         torch.cuda.synchronize()
#         start_time = time.time()

#         # generation
#         with torch.no_grad():
#             with torch.amp.autocast():
#                 sampled_tokens = model_without_ddp.sample_tokens(bsz=batch_size, num_iter=args.num_iter, cfg=cfg,
#                                                                  cfg_schedule=args.cfg_schedule, labels=labels_gen,
#                                                                  temperature=args.temperature)
#                 sampled_images = vae.decode(sampled_tokens / 0.2325)

#         # measure speed after the first generation batch
#         if i >= 1:
#             torch.cuda.synchronize()
#             used_time += time.time() - start_time
#             gen_img_cnt += batch_size
#             print("Generating {} images takes {:.5f} seconds, {:.5f} sec per image".format(gen_img_cnt, used_time, used_time / gen_img_cnt))

#         torch.distributed.barrier()
#         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)