Error in loading a stored checkpoint

Hello,

When I load a stored checkpoint, I get the following error:

RuntimeError: output with shape [128, 3, 1] doesn’t match the broadcast shape [128, 3, 3]

If I am reading a state_dict correctly, then I think there is probably a bug in you load_state_dict. For your convenience I slightly modified your CompressAI/examples/train.py example to accept also a checkpoint as input to continue from a previously stored checkpoint. For that, you just need to run the following code twice (I used bmshj2018-hyperprior model):

1. Once without --checkpoint-file for 1-2 epochs just to save a checkpoint
2. Then run the script with --checkpoint-file [/address/of/stored/checkpoint].

# Copyright 2020 InterDigital Communications, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import math
import random
import shutil
import sys

import torch
import torch.nn as nn
import torch.optim as optim

from torch.utils.data import DataLoader
from torchvision import transforms

from compressai.datasets import ImageFolder
from compressai.zoo import models


class RateDistortionLoss(nn.Module):
    """Custom rate distortion loss with a Lagrangian parameter."""

    def __init__(self, lmbda=1e-2):
        super().__init__()
        self.mse = nn.MSELoss()
        self.lmbda = lmbda

    def forward(self, output, target):
        N, _, H, W = target.size()
        out = {}
        num_pixels = N * H * W

        out["bpp_loss"] = sum(
            (torch.log(likelihoods).sum() / (-math.log(2) * num_pixels))
            for likelihoods in output["likelihoods"].values()
        )
        out["mse_loss"] = self.mse(output["x_hat"], target)
        out["loss"] = self.lmbda * 255 ** 2 * out["mse_loss"] + out["bpp_loss"]

        return out


class AverageMeter:
    """Compute running average."""

    def __init__(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


class CustomDataParallel(nn.DataParallel):
    """Custom DataParallel to access the module methods."""

    def __getattr__(self, key):
        try:
            return super().__getattr__(key)
        except AttributeError:
            return getattr(self.module, key)


def configure_optimizers(net, args):
    """Separate parameters for the main optimizer and the auxiliary optimizer.
    Return two optimizers"""

    parameters = set(
        p for n, p in net.named_parameters() if not n.endswith(".quantiles")
    )
    aux_parameters = set(
        p for n, p in net.named_parameters() if n.endswith(".quantiles")
    )

    # Make sure we don't have an intersection of parameters
    params_dict = dict(net.named_parameters())
    inter_params = parameters & aux_parameters
    union_params = parameters | aux_parameters

    assert len(inter_params) == 0
    assert len(union_params) - len(params_dict.keys()) == 0

    optimizer = optim.Adam(
        (p for p in parameters if p.requires_grad),
        lr=args.learning_rate,
    )
    aux_optimizer = optim.Adam(
        (p for p in aux_parameters if p.requires_grad),
        lr=args.aux_learning_rate,
    )
    return optimizer, aux_optimizer


def train_one_epoch(
    model, criterion, train_dataloader, optimizer, aux_optimizer, epoch, clip_max_norm
):
    model.train()
    device = next(model.parameters()).device

    for i, d in enumerate(train_dataloader):
        d = d.to(device)

        optimizer.zero_grad()
        aux_optimizer.zero_grad()

        out_net = model(d)

        out_criterion = criterion(out_net, d)
        out_criterion["loss"].backward()
        if clip_max_norm > 0:
            torch.nn.utils.clip_grad_norm_(model.parameters(), clip_max_norm)
        optimizer.step()

        aux_loss = model.aux_loss()
        aux_loss.backward()
        aux_optimizer.step()

        if i % 10 == 0:
            print(
                f"Train epoch {epoch}: ["
                f"{i*len(d)}/{len(train_dataloader.dataset)}"
                f" ({100. * i / len(train_dataloader):.0f}%)]"
                f'\tLoss: {out_criterion["loss"].item():.3f} |'
                f'\tMSE loss: {out_criterion["mse_loss"].item():.3f} |'
                f'\tBpp loss: {out_criterion["bpp_loss"].item():.2f} |'
                f"\tAux loss: {aux_loss.item():.2f}"
            )


def test_epoch(epoch, test_dataloader, model, criterion):
    model.eval()
    device = next(model.parameters()).device

    loss = AverageMeter()
    bpp_loss = AverageMeter()
    mse_loss = AverageMeter()
    aux_loss = AverageMeter()

    with torch.no_grad():
        for d in test_dataloader:
            d = d.to(device)
            out_net = model(d)
            out_criterion = criterion(out_net, d)

            aux_loss.update(model.aux_loss())
            bpp_loss.update(out_criterion["bpp_loss"])
            loss.update(out_criterion["loss"])
            mse_loss.update(out_criterion["mse_loss"])

    print(
        f"Test epoch {epoch}: Average losses:"
        f"\tLoss: {loss.avg:.3f} |"
        f"\tMSE loss: {mse_loss.avg:.3f} |"
        f"\tBpp loss: {bpp_loss.avg:.2f} |"
        f"\tAux loss: {aux_loss.avg:.2f}\n"
    )

    return loss.avg


def save_checkpoint(state, is_best, filename="checkpoint.pth.tar"):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, "checkpoint_best_loss.pth.tar")


def parse_args(argv):
    parser = argparse.ArgumentParser(description="Example training script.")
    parser.add_argument(
        "-m",
        "--model",
        default="bmshj2018-factorized",
        choices=models.keys(),
        help="Model architecture (default: %(default)s)",
    )
    parser.add_argument(
        "-d", "--dataset", type=str, required=True, help="Training dataset"
    )
    parser.add_argument(
        "-e",
        "--epochs",
        default=100,
        type=int,
        help="Number of epochs (default: %(default)s)",
    )
    parser.add_argument(
        "-lr",
        "--learning-rate",
        default=1e-4,
        type=float,
        help="Learning rate (default: %(default)s)",
    )
    parser.add_argument(
        "-n",
        "--num-workers",
        type=int,
        default=30,
        help="Dataloaders threads (default: %(default)s)",
    )
    parser.add_argument(
        "--lambda",
        dest="lmbda",
        type=float,
        default=1e-2,
        help="Bit-rate distortion parameter (default: %(default)s)",
    )
    parser.add_argument(
        "--batch-size", type=int, default=16, help="Batch size (default: %(default)s)"
    )
    parser.add_argument(
        "--test-batch-size",
        type=int,
        default=64,
        help="Test batch size (default: %(default)s)",
    )
    parser.add_argument(
        "--aux-learning-rate",
        default=1e-3,
        help="Auxiliary loss learning rate (default: %(default)s)",
    )
    parser.add_argument(
        "--patch-size",
        type=int,
        nargs=2,
        default=(256, 256),
        help="Size of the patches to be cropped (default: %(default)s)",
    )
    parser.add_argument("--cuda", action="store_true", help="Use cuda")
    parser.add_argument("--save", action="store_true", help="Save model to disk")
    parser.add_argument(
        "--seed", type=float, help="Set random seed for reproducibility"
    )
    parser.add_argument(
        "--clip_max_norm",
        default=1.0,
        type=float,
        help="gradient clipping max norm (default: %(default)s",
    )
    parser.add_argument('--checkpoint-file', type=str, help='File address to resume training from the previous saved checkpoint')
    args = parser.parse_args(argv)
    return args


def main(argv):
    args = parse_args(argv)

    if args.seed is not None:
        torch.manual_seed(args.seed)
        random.seed(args.seed)

    train_transforms = transforms.Compose(
        [transforms.RandomCrop(args.patch_size), transforms.ToTensor()]
    )

    test_transforms = transforms.Compose(
        [transforms.CenterCrop(args.patch_size), transforms.ToTensor()]
    )

    train_dataset = ImageFolder(args.dataset, split="train", transform=train_transforms)
    test_dataset = ImageFolder(args.dataset, split="test", transform=test_transforms)

    train_dataloader = DataLoader(
        train_dataset,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        shuffle=True,
        pin_memory=True,
    )

    test_dataloader = DataLoader(
        test_dataset,
        batch_size=args.test_batch_size,
        num_workers=args.num_workers,
        shuffle=False,
        pin_memory=True,
    )

    device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu"

    net = models[args.model](quality=3)
    net = net.to(device)

    if args.cuda and torch.cuda.device_count() > 1:
        net = CustomDataParallel(net)

    optimizer, aux_optimizer = configure_optimizers(net, args)
    lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, "min")
    criterion = RateDistortionLoss(lmbda=args.lmbda)

    last_epoch = -1
    if args.checkpoint_file:  # load from previous checkpoint
        print("Loading", args.checkpoint_file)
        checkpoint = torch.load(args.checkpoint_file, map_location=device)
        last_epoch = checkpoint["epoch"]
        net.load_state_dict((checkpoint["state_dict"]))
        net.update(force=True)  # update the model CDFs parameters.
        optimizer.load_state_dict((checkpoint["optimizer"]))
        aux_optimizer.load_state_dict((checkpoint["aux_optimizer"]))
        lr_scheduler.load_state_dict((checkpoint["lr_scheduler"]))

    best_loss = 1e10
    for epoch in range(last_epoch + 1, args.epochs):
        print(f"Learning rate: {optimizer.param_groups[0]['lr']}")
        train_one_epoch(
            net,
            criterion,
            train_dataloader,
            optimizer,
            aux_optimizer,
            epoch,
            args.clip_max_norm,
        )

        loss = test_epoch(epoch, test_dataloader, net, criterion)
        lr_scheduler.step(loss)

        is_best = loss < best_loss
        best_loss = min(loss, best_loss)
        if args.save:
            save_checkpoint(
                {
                    "epoch": epoch,
                    "state_dict": net.state_dict(),
                    "loss": loss,
                    "optimizer": optimizer.state_dict(),
                    "aux_optimizer": aux_optimizer.state_dict(),
                    "lr_scheduler": lr_scheduler.state_dict(),
                },
                is_best,
            )





if __name__ == "__main__":
    main(sys.argv[1:])

[jbegaint]

So, this line optimizer.load_state_dict((checkpoint["optimizer"])) results in the RuntimeError. It has to do with the use of set in the optimizer definitions. Since sets are not sorted, the indexing of the parameter info in the optimizer state dict is different between initializations. Took me a while to figure out the issue since internally I coded it correctly 😉. I’ll push a fix soon.

Thanks again for reporting!

[jbegaint]

Hi Navid, i’ll keep our current implementation as it’s easier to maintain w.r.t to our internal codebase. Also our intention was just to provide an example training for people to get started and take it from here.