main_pixel_attention.py

# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import math
import os
import shutil
import time
from logging import getLogger

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import apex
from apex.parallel.LARC import LARC

from src.utils import (
    initialize_exp,
    restart_from_checkpoint,
    fix_random_seeds,
    AverageMeter,
    init_distributed_mode,
    distributed_sinkhorn
)
from src.multicropdataset import MultiCropDataset
import src.resnet as resnet_models
from options import getOption

logger = getLogger()
parser = getOption()


def main():
    global args
    args = parser.parse_args()
    init_distributed_mode(args)
    fix_random_seeds(args.seed)
    logger, training_stats = initialize_exp(args, "epoch", "loss")

    # build data
    train_dataset = MultiCropDataset(
        args.data_path,
        args.size_crops,
        args.nmb_crops,
        args.min_scale_crops,
        args.max_scale_crops,
    )
    sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        sampler=sampler,
        batch_size=args.batch_size,
        num_workers=args.workers,
        pin_memory=True,
        drop_last=True
    )
    logger.info("Building data done with {} images loaded.".format(len(train_dataset)))

    # build model
    model = resnet_models.__dict__[args.arch](
        normalize=True,
        hidden_mlp=args.hidden_mlp,
        output_dim=args.feat_dim,
        nmb_prototypes=args.nmb_prototypes,
        train_mode='pixelattn'
    )
    # for pixel attention, only finetuning the attention head and prototypes
    for name, param in model.named_parameters():
        if "fbg" not in name and "prototypes" not in name:
            param.requires_grad = False
    
    # loading pretrained weights
    checkpoint = torch.load(args.pretrained, map_location="cpu")["state_dict"]
    for k in list(checkpoint.keys()):
        if k.startswith("module."):
            checkpoint[k[len("module.") :]] = checkpoint[k]
        del checkpoint[k]
    msg = model.load_state_dict(checkpoint, strict=False)
    logger.info("Loaded pretrained weights '{0}' with missing {1}".format(args.pretrained, msg.missing_keys))

    # synchronize batch norm layers
    if args.sync_bn == "pytorch":
        model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
    elif args.sync_bn == "apex":
        # with apex syncbn we sync bn per group because it speeds up computation
        # compared to global syncbn
        process_group = apex.parallel.create_syncbn_process_group(args.syncbn_process_group_size)
        model = apex.parallel.convert_syncbn_model(model, process_group=process_group)
    # copy model to GPU
    model = model.cuda()
    if args.rank == 0:
        logger.info(model)
    logger.info("Building model done.")

    # build optimizer
    optimizer = torch.optim.SGD(
        model.parameters(),
        lr=args.base_lr,
        momentum=0.9,
        weight_decay=args.wd,
    )
    optimizer = LARC(optimizer=optimizer, trust_coefficient=0.001, clip=False)
    warmup_lr_schedule = np.linspace(args.start_warmup, args.base_lr, len(train_loader) * args.warmup_epochs)
    iters = np.arange(len(train_loader) * (args.epochs - args.warmup_epochs))
    cosine_lr_schedule = np.array([args.final_lr + 0.5 * (args.base_lr - args.final_lr) * (1 + \
                         math.cos(math.pi * t / (len(train_loader) * (args.epochs - args.warmup_epochs)))) for t in iters])
    lr_schedule = np.concatenate((warmup_lr_schedule, cosine_lr_schedule))
    logger.info("Building optimizer done.")

    # init mixed precision
    if args.use_fp16:
        model, optimizer = apex.amp.initialize(model, optimizer, opt_level="O1")
        logger.info("Initializing mixed precision done.")

    # wrap model
    model = nn.parallel.DistributedDataParallel(
        model,
        device_ids=[args.gpu_to_work_on]
    )

    # optionally resume from a checkpoint
    to_restore = {"epoch": 0}
    restart_from_checkpoint(
        os.path.join(args.dump_path, "checkpoint.pth.tar"),
        run_variables=to_restore,
        state_dict=model,
        optimizer=optimizer,
        amp=apex.amp,
    )
    start_epoch = to_restore["epoch"]

    # build the queue
    queue = None
    queue_path = os.path.join(args.dump_path, "queue" + str(args.rank) + ".pth")
    if os.path.isfile(queue_path):
        queue = torch.load(queue_path)["queue"]
    # the queue needs to be divisible by the batch size
    args.queue_length -= args.queue_length % (args.batch_size * args.world_size)

    cudnn.benchmark = True

    for epoch in range(start_epoch, args.epochs):

        # train the network for one epoch
        logger.info("============ Starting epoch %i ... ============" % epoch)

        # set sampler
        train_loader.sampler.set_epoch(epoch)

        # optionally starts a queue
        if args.queue_length > 0 and epoch >= args.epoch_queue_starts and queue is None:
            queue = torch.zeros(
                len(args.crops_for_assign),
                args.queue_length // args.world_size,
                args.feat_dim,
            ).cuda()

        # train the network
        scores, queue = train(train_loader, model, optimizer, epoch, lr_schedule, queue)
        training_stats.update(scores)

        # save checkpoints
        if args.rank == 0:
            save_dict = {
                "epoch": epoch + 1,
                "state_dict": model.state_dict(),
                "optimizer": optimizer.state_dict(),
            }
            if args.use_fp16:
                save_dict["amp"] = apex.amp.state_dict()
            torch.save(
                save_dict,
                os.path.join(args.dump_path, "checkpoint.pth.tar"),
            )
            if epoch % args.checkpoint_freq == 0 or epoch == args.epochs - 1:
                shutil.copyfile(
                    os.path.join(args.dump_path, "checkpoint.pth.tar"),
                    os.path.join(args.dump_checkpoints, "ckp-" + str(epoch) + ".pth"),
                )
        if queue is not None:
            torch.save({"queue": queue}, queue_path)


def train(train_loader, model, optimizer, epoch, lr_schedule, queue):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()

    model.train()
    use_the_queue = False

    end = time.time()
    for it, inputs in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        # update learning rate
        iteration = epoch * len(train_loader) + it
        for param_group in optimizer.param_groups:
            param_group["lr"] = lr_schedule[iteration]

        # normalize the prototypes
        with torch.no_grad():
            w = model.module.prototypes.weight.data.clone()
            w = nn.functional.normalize(w, dim=1, p=2)
            model.module.prototypes.weight.copy_(w)

        # ============ multi-res forward passes ... ============
        embedding, output = model(inputs)
        embedding = embedding.detach()
        bs = inputs[0].size(0)

        # ============ swav loss ... ============
        loss = 0
        for i, crop_id in enumerate(args.crops_for_assign):
            with torch.no_grad():
                out = output[bs * crop_id: bs * (crop_id + 1)].detach()

                # time to use the queue
                if queue is not None:
                    if use_the_queue or not torch.all(queue[i, -1, :] == 0):
                        use_the_queue = True
                        out = torch.cat((torch.mm(
                            queue[i],
                            model.module.prototypes.weight.t()
                        ), out))
                    # fill the queue
                    queue[i, bs:] = queue[i, :-bs].clone()
                    queue[i, :bs] = embedding[crop_id * bs: (crop_id + 1) * bs]

                # get assignments
                q = distributed_sinkhorn(args, out)[-bs:]

            # cluster assignment prediction
            subloss = 0
            for v in np.delete(np.arange(np.sum(args.nmb_crops)), crop_id):
                x = output[bs * v: bs * (v + 1)] / args.temperature
                subloss -= torch.mean(torch.sum(q * F.log_softmax(x, dim=1), dim=1))
            loss += subloss / (np.sum(args.nmb_crops) - 1)
        loss /= len(args.crops_for_assign)

        # ============ backward and optim step ... ============
        optimizer.zero_grad()
        if args.use_fp16:
            with apex.amp.scale_loss(loss, optimizer) as scaled_loss:
                scaled_loss.backward()
        else:
            loss.backward()
        # cancel gradients for the prototypes
        if iteration < args.freeze_prototypes_niters:
            for name, p in model.named_parameters():
                if "prototypes" in name:
                    p.grad = None
        optimizer.step()

        # ============ misc ... ============
        losses.update(loss.item(), inputs[0].size(0))
        batch_time.update(time.time() - end)
        end = time.time()
        if args.rank ==0 and it % 50 == 0:
            logger.info(
                "Epoch: [{0}][{1}]\t"
                "Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t"
                "Data {data_time.val:.3f} ({data_time.avg:.3f})\t"
                "Loss {loss.val:.4f} ({loss.avg:.4f})\t"
                "Lr: {lr:.4f}".format(
                    epoch,
                    it,
                    batch_time=batch_time,
                    data_time=data_time,
                    loss=losses,
                    lr=optimizer.optim.param_groups[0]["lr"],
                )
            )
    return (epoch, losses.avg), queue


if __name__ == "__main__":
    main()