engine.py

# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
# ------------------------------------------
# Modification:
# Added code for adjusting keep rate and visualization -- Youwei Liang
"""
Train and eval functions used in main.py
"""
import math
import sys
from typing import Iterable, Optional

import torch

from timm.data import Mixup
from timm.utils import accuracy, ModelEma

from losses import DistillationLoss
import utils

from helpers import adjust_keep_rate
from visualize_mask import get_real_idx, mask, save_img_batch
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


def train_one_epoch(model: torch.nn.Module, criterion: DistillationLoss,
                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
                    device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
                    model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None,
                    writer=None,
                    set_training_mode=True,
                    args=None):
    model.train(set_training_mode)
    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    header = 'Epoch: [{}]'.format(epoch)
    print_freq = 200
    log_interval = 100
    it = epoch * len(data_loader)
    ITERS_PER_EPOCH = len(data_loader)

    base_rate = args.base_keep_rate

    for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
        samples = samples.to(device, non_blocking=True)
        targets = targets.to(device, non_blocking=True)

        keep_rate = adjust_keep_rate(it, epoch, warmup_epochs=args.shrink_start_epoch,
                                         total_epochs=args.shrink_start_epoch + args.shrink_epochs,
                                         ITERS_PER_EPOCH=ITERS_PER_EPOCH, base_keep_rate=base_rate)

        if mixup_fn is not None:
            samples, targets = mixup_fn(samples, targets)

        with torch.cuda.amp.autocast():
            outputs = model(samples, keep_rate)
            loss = criterion(samples, outputs, targets)

        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
        loss_scaler(loss, optimizer, clip_grad=max_norm,
                    parameters=model.parameters(), create_graph=is_second_order)

        torch.cuda.synchronize()
        if model_ema is not None:
            model_ema.update(model)

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

        if torch.distributed.get_rank() == 0 and it % log_interval == 0:
            writer.add_scalar('loss', loss_value, it)
            writer.add_scalar('lr', optimizer.param_groups[0]["lr"], it)
            writer.add_scalar('keep_rate', keep_rate, it)
        it += 1

    # 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()}, keep_rate


@torch.no_grad()
def evaluate(data_loader, model, device, keep_rate=None):
    criterion = torch.nn.CrossEntropyLoss()

    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Test:'

    # switch to evaluation mode
    model.eval()

    for images, target in metric_logger.log_every(data_loader, 10, header):
        images = images.to(device, non_blocking=True)
        target = target.to(device, non_blocking=True)

        # compute output
        with torch.cuda.amp.autocast():
            output = model(images, keep_rate)
            loss = criterion(output, target)

        acc1, acc5 = accuracy(output, target, topk=(1, 5))

        batch_size = images.shape[0]
        metric_logger.update(loss=loss.item())
        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
        metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
          .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))

    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}


@torch.no_grad()
def get_acc(data_loader, model, device, keep_rate=None, tokens=None):
    criterion = torch.nn.CrossEntropyLoss()

    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Test:'

    # switch to evaluation mode
    model.eval()

    for images, target in metric_logger.log_every(data_loader, 10, header):
        images = images.to(device, non_blocking=True)
        target = target.to(device, non_blocking=True)

        # compute output
        with torch.cuda.amp.autocast():
            output = model(images, keep_rate, tokens)
            loss = criterion(output, target)

        acc1, acc5 = accuracy(output, target, topk=(1, 5))

        batch_size = images.shape[0]
        metric_logger.update(loss=loss.item())
        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
        metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
          .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))

    return metric_logger.acc1.global_avg


@torch.no_grad()
def visualize_mask(data_loader, model, device, output_dir, n_visualization, fuse_token, keep_rate=None):
    criterion = torch.nn.CrossEntropyLoss()

    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Visualize:'
    rank = torch.distributed.get_rank()
    world_size = torch.distributed.get_world_size()
    mean = torch.tensor(IMAGENET_DEFAULT_MEAN, device=device).reshape(3, 1, 1)
    std = torch.tensor(IMAGENET_DEFAULT_STD, device=device).reshape(3, 1, 1)

    # switch to evaluation mode
    model.eval()

    ii = 0
    for images, target in metric_logger.log_every(data_loader, 10, header):
        images = images.to(device, non_blocking=True)
        target = target.to(device, non_blocking=True)
        B = images.size(0)

        with torch.cuda.amp.autocast():
            output, idx = model(images, keep_rate, get_idx=True)
            loss = criterion(output, target)

        acc1, acc5 = accuracy(output, target, topk=(1, 5))

        # denormalize
        images = images * std + mean

        idxs = get_real_idx(idx, fuse_token)
        for jj, idx in enumerate(idxs):
            masked_img = mask(images, patch_size=16, idx=idx)
            save_img_batch(masked_img, output_dir, file_name='img_{}' + f'_l{jj}.jpg', start_idx=world_size * B * ii + rank * B)

        save_img_batch(images, output_dir, file_name='img_{}_a.jpg', start_idx=world_size * B * ii + rank * B)

        batch_size = images.shape[0]
        metric_logger.update(loss=loss.item())
        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
        metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
        metric_logger.synchronize_between_processes()
        ii += 1
        if world_size * B * ii >= n_visualization:
            break

    metric_logger.synchronize_between_processes()
    print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
          .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))

    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}