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()}