engine_for_pretraining.py

import math
import sys
from typing import Iterable
import torch
import torch.nn as nn
import utils_mae as utils
from einops import rearrange
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD

def train_one_epoch(model: torch.nn.Module, data_loader: Iterable, optimizer: torch.optim.Optimizer,
                    device: torch.device, epoch: int, loss_scaler, max_norm: float = 0, patch_size: int = 16, 
                    normlize_target: bool = True, log_writer=None, lr_scheduler=None, start_steps=None,
                    lr_schedule_values=None, wd_schedule_values=None,teacher_model=None,target_type='pixel', multiple_sampling=False):

    model.train()
    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    metric_logger.add_meter('min_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    header = 'Epoch: [{}]'.format(epoch)
    print_freq = 10

    loss_func = nn.MSELoss()

    for step, batch in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
        # assign learning rate & weight decay for each step
        it = start_steps + step  # global training iteration
        if lr_schedule_values is not None or wd_schedule_values is not None:
            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]

        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).to(torch.bool)
        #print("input_1",videos.size(),bool_masked_pos.size())
        bs, _, nf, h, w = videos.shape

        idx = torch.randperm(bool_masked_pos.size(0))
        shuffled_bool_masked_pos = bool_masked_pos[idx,:]

        if 'pixel' in target_type:

            with torch.no_grad():
                # calculate the predict label
                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]
                unnorm_videos = videos * std + mean  # in [0, 1]

                if normlize_target:
                    videos_squeeze = rearrange(unnorm_videos, 'b c (t p0) (h p1) (w p2) -> b (t h w) (p0 p1 p2) c', p0=2, p1=patch_size, p2=patch_size)
                    videos_norm = (videos_squeeze - videos_squeeze.mean(dim=-2, keepdim=True)
                        ) / (videos_squeeze.var(dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6)
                    # we find that the mean is about 0.48 and standard deviation is about 0.08.
                    videos_patch = rearrange(videos_norm, 'b n p c -> b n (p c)')
                else:
                    videos_patch = rearrange(unnorm_videos, 'b c (t p0) (h p1) (w p2) -> b (t h w) (p0 p1 p2 c)', p0=2, p1=patch_size, p2=patch_size)

                B, _, C = videos_patch.shape
                if not multiple_sampling:
                    labels = videos_patch[bool_masked_pos].reshape(B, -1, C)
                else:
                    labels_1 = videos_patch[bool_masked_pos].reshape(B, -1, C)
                    labels_2 = videos_patch[shuffled_bool_masked_pos].reshape(B, -1, C)

        elif 'dino' in target_type or 'clip' in target_type:

            with torch.no_grad():
                permuted_video = videos.permute(0, 2, 1, 3, 4)
                bs, nf, _, h, w = permuted_video.shape
                permuted_video = permuted_video[:, ::2].flatten(0, 1)
                permuted_video = permuted_video.to(device, non_blocking=True)
                features = teacher_model(permuted_video)
                _, np, dim = features.shape
                features = features.reshape(bs, nf//2, np, dim)
                features.requires_grad = False
                
            features = features.to(device, non_blocking=True)
            with torch.no_grad():
                features_squeeze = rearrange(features, 'b n o c -> b (n o) c')
                if normlize_target:
                    labels = (features_squeeze - features_squeeze.mean(dim=-2, keepdim=True)
                        ) / (features_squeeze.var(dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6)
                else:
                    labels = features_squeeze
                B, _, C = labels.shape
                if not multiple_sampling:
                    labels = labels[bool_masked_pos].reshape(B, -1, C)
                else:
                    labels_1 = labels[bool_masked_pos].reshape(B, -1, C)
                    labels_2 = labels[shuffled_bool_masked_pos].reshape(B, -1, C)


        with torch.cuda.amp.autocast():
            if not multiple_sampling:
                outputs = model(videos, bool_masked_pos)
            else:
                outputs_1 = model(videos, bool_masked_pos)
                outputs_2 = model(videos,shuffled_bool_masked_pos)

                labels = torch.cat((labels_1,labels_2),dim=0)
                outputs = torch.cat((outputs_1,outputs_2),dim=0)
            
            loss = loss_func(input=outputs, target=labels)

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

        optimizer.zero_grad()
        # this attribute is added by timm on one optimizer (adahessian)
        is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
        grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm,
                                parameters=model.parameters(), create_graph=is_second_order)
        loss_scale_value = loss_scaler.state_dict()["scale"]

        torch.cuda.synchronize()

        metric_logger.update(loss=loss_value)
        metric_logger.update(loss_scale=loss_scale_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)
        metric_logger.update(min_lr=min_lr)
        weight_decay_value = None
        for group in optimizer.param_groups:
            if group["weight_decay"] > 0:
                weight_decay_value = group["weight_decay"]
        metric_logger.update(weight_decay=weight_decay_value)
        metric_logger.update(grad_norm=grad_norm)

        if log_writer is not None:
            log_writer.update(loss=loss_value, head="loss")
            log_writer.update(loss_scale=loss_scale_value, head="opt")
            log_writer.update(lr=max_lr, head="opt")
            log_writer.update(min_lr=min_lr, head="opt")
            log_writer.update(weight_decay=weight_decay_value, head="opt")
            log_writer.update(grad_norm=grad_norm, head="opt")
            log_writer.set_step()

        if lr_scheduler is not None:
            lr_scheduler.step_update(start_steps + step)
    # 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()}