FER/main.py

import shutil
import warnings
from sklearn import metrics
from sklearn.metrics import confusion_matrix
from PIL import Image

warnings.filterwarnings("ignore")
import torch.utils.data as data
import os
import argparse
from sklearn.metrics import f1_score, confusion_matrix
from data_preprocessing.sam import SAM
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import matplotlib.pyplot as plt
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import numpy as np
import datetime
from torchsampler import ImbalancedDatasetSampler
from models.PosterV2_7cls import pyramid_trans_expr2


warnings.filterwarnings("ignore", category=UserWarning)

now = datetime.datetime.now()
time_str = now.strftime("[%m-%d]-[%H-%M]-")
if torch.backends.mps.is_available():
    device = "mps"
elif torch.cuda.is_available():
    device = "cuda"
else:
    device = "cpu"

print(f"Using device: {device}")

parser = argparse.ArgumentParser()
parser.add_argument("--data", type=str, default=r"raf-db/DATASET")
parser.add_argument(
    "--data_type",
    default="RAF-DB",
    choices=["RAF-DB", "AffectNet-7", "CAER-S"],
    type=str,
    help="dataset option",
)
parser.add_argument(
    "--checkpoint_path", type=str, default="./checkpoint/" + time_str + "model.pth"
)
parser.add_argument(
    "--best_checkpoint_path",
    type=str,
    default="./checkpoint/" + time_str + "model_best.pth",
)
parser.add_argument(
    "-j",
    "--workers",
    default=4,
    type=int,
    metavar="N",
    help="number of data loading workers",
)
parser.add_argument(
    "--epochs", default=200, type=int, metavar="N", help="number of total epochs to run"
)
parser.add_argument(
    "--start-epoch",
    default=0,
    type=int,
    metavar="N",
    help="manual epoch number (useful on restarts)",
)
parser.add_argument("-b", "--batch-size", default=2, type=int, metavar="N")
parser.add_argument(
    "--optimizer", type=str, default="adam", help="Optimizer, adam or sgd."
)

parser.add_argument(
    "--lr", "--learning-rate", default=0.000035, type=float, metavar="LR", dest="lr"
)
parser.add_argument("--momentum", default=0.9, type=float, metavar="M")
parser.add_argument(
    "--wd", "--weight-decay", default=1e-4, type=float, metavar="W", dest="weight_decay"
)
parser.add_argument(
    "-p", "--print-freq", default=30, type=int, metavar="N", help="print frequency"
)
parser.add_argument(
    "--resume", default=None, type=str, metavar="PATH", help="path to checkpoint"
)
parser.add_argument(
    "-e", "--evaluate", default=None, type=str, help="evaluate model on test set"
)
parser.add_argument("--beta", type=float, default=0.6)
parser.add_argument("--gpu", type=str, default="0")

parser.add_argument(
    "-i", "--image", type=str, help="upload a single image to test the prediction"
)
parser.add_argument("-t", "--test", type=str, help="test model on single image")
args = parser.parse_args()


def main():
    # os.environ["CUDA_VISIBLE_DEVICES"] = device
    best_acc = 0
    # print("Training time: " + now.strftime("%m-%d %H:%M"))

    # create model
    model = pyramid_trans_expr2(img_size=224, num_classes=7)

    model = torch.nn.DataParallel(model)
    model = model.to(device)

    criterion = torch.nn.CrossEntropyLoss()

    if args.optimizer == "adamw":
        base_optimizer = torch.optim.AdamW
    elif args.optimizer == "adam":
        base_optimizer = torch.optim.Adam
    elif args.optimizer == "sgd":
        base_optimizer = torch.optim.SGD
    else:
        raise ValueError("Optimizer not supported.")

    optimizer = SAM(
        model.parameters(),
        base_optimizer,
        lr=args.lr,
        rho=0.05,
        adaptive=False,
    )
    scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.98)
    recorder = RecorderMeter(args.epochs)
    recorder1 = RecorderMeter1(args.epochs)

    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint["epoch"]
            best_acc = checkpoint["best_acc"]
            recorder = checkpoint["recorder"]
            recorder1 = checkpoint["recorder1"]
            best_acc = best_acc.to()
            model.load_state_dict(checkpoint["state_dict"])
            optimizer.load_state_dict(checkpoint["optimizer"])
            print(
                "=> loaded checkpoint '{}' (epoch {})".format(
                    args.resume, checkpoint["epoch"]
                )
            )
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))
    cudnn.benchmark = True

    # Data loading code
    traindir = os.path.join(args.data, "train")

    valdir = os.path.join(args.data, "test")

    if args.evaluate is None:
        if args.data_type == "RAF-DB":
            train_dataset = datasets.ImageFolder(
                traindir,
                transforms.Compose(
                    [
                        transforms.Resize((224, 224)),
                        transforms.RandomHorizontalFlip(),
                        transforms.ToTensor(),
                        transforms.Normalize(
                            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
                        ),
                        transforms.RandomErasing(scale=(0.02, 0.1)),
                    ]
                ),
            )
        else:
            train_dataset = datasets.ImageFolder(
                traindir,
                transforms.Compose(
                    [
                        transforms.Resize((224, 224)),
                        transforms.RandomHorizontalFlip(),
                        transforms.ToTensor(),
                        transforms.Normalize(
                            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
                        ),
                        transforms.RandomErasing(p=1, scale=(0.05, 0.05)),
                    ]
                ),
            )

        if args.data_type == "AffectNet-7":
            train_loader = torch.utils.data.DataLoader(
                train_dataset,
                sampler=ImbalancedDatasetSampler(train_dataset),
                batch_size=args.batch_size,
                shuffle=False,
                num_workers=args.workers,
                pin_memory=True,
            )

        else:
            train_loader = torch.utils.data.DataLoader(
                train_dataset,
                batch_size=args.batch_size,
                shuffle=True,
                num_workers=args.workers,
                pin_memory=True,
            )

    test_dataset = datasets.ImageFolder(
        valdir,
        transforms.Compose(
            [
                transforms.Resize((224, 224)),
                transforms.ToTensor(),
                transforms.Normalize(
                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
                ),
            ]
        ),
    )

    val_loader = torch.utils.data.DataLoader(
        test_dataset,
        batch_size=args.batch_size,
        shuffle=False,
        num_workers=args.workers,
        pin_memory=True,
    )

    if args.evaluate is not None:
        from validation import validate

        if os.path.isfile(args.evaluate):
            print("=> loading checkpoint '{}'".format(args.evaluate))
            checkpoint = torch.load(args.evaluate, map_location=device)
            best_acc = checkpoint["best_acc"]
            best_acc = best_acc.to()
            print(f"best_acc:{best_acc}")
            model.load_state_dict(checkpoint["state_dict"])
            print(
                "=> loaded checkpoint '{}' (epoch {})".format(
                    args.evaluate, checkpoint["epoch"]
                )
            )
        else:
            print("=> no checkpoint found at '{}'".format(args.evaluate))
        validate(val_loader, model, criterion, args)
        return

    if args.test is not None:
        from prediction import predict

        if os.path.isfile(args.test):
            print("=> loading checkpoint '{}'".format(args.test))
            checkpoint = torch.load(args.test, map_location=device)
            best_acc = checkpoint["best_acc"]
            best_acc = best_acc.to()
            print(f"best_acc:{best_acc}")
            model.load_state_dict(checkpoint["state_dict"])
            print(
                "=> loaded checkpoint '{}' (epoch {})".format(
                    args.test, checkpoint["epoch"]
                )
            )
        else:
            print("=> no checkpoint found at '{}'".format(args.test))
        predict(model, image_path=args.image)

        return
    matrix = None

    for epoch in range(args.start_epoch, args.epochs):
        current_learning_rate = optimizer.state_dict()["param_groups"][0]["lr"]
        print("Current learning rate: ", current_learning_rate)
        txt_name = "./log/" + time_str + "log.txt"
        with open(txt_name, "a") as f:
            f.write("Current learning rate: " + str(current_learning_rate) + "\n")

        # train for one epoch
        train_acc, train_los = train(
            train_loader, model, criterion, optimizer, epoch, args
        )

        # evaluate on validation set
        val_acc, val_los, output, target, D = validate(
            val_loader, model, criterion, args
        )

        scheduler.step()

        recorder.update(epoch, train_los, train_acc, val_los, val_acc)
        recorder1.update(output, target)

        curve_name = time_str + "cnn.png"
        recorder.plot_curve(os.path.join("./log/", curve_name))

        # remember best acc and save checkpoint
        is_best = val_acc > best_acc
        best_acc = max(val_acc, best_acc)

        print("Current best accuracy: ", best_acc.item())

        if is_best:
            matrix = D

        print("Current best matrix: ", matrix)

        txt_name = "./log/" + time_str + "log.txt"
        with open(txt_name, "a") as f:
            f.write("Current best accuracy: " + str(best_acc.item()) + "\n")

        save_checkpoint(
            {
                "epoch": epoch + 1,
                "state_dict": model.state_dict(),
                "best_acc": best_acc,
                "optimizer": optimizer.state_dict(),
                "recorder1": recorder1,
                "recorder": recorder,
            },
            is_best,
            args,
        )


def train(train_loader, model, criterion, optimizer, epoch, args):
    losses = AverageMeter("Loss", ":.4f")
    top1 = AverageMeter("Accuracy", ":6.3f")
    progress = ProgressMeter(
        len(train_loader), [losses, top1], prefix="Epoch: [{}]".format(epoch)
    )

    # switch to train mode
    model.train()

    for i, (images, target) in enumerate(train_loader):
        images = images.to(device)
        target = target.to(device)

        # compute output
        output = model(images)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, _ = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), images.size(0))
        top1.update(acc1[0], images.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        # optimizer.step()
        optimizer.first_step(zero_grad=True)
        images = images.to(device)
        target = target.to(device)

        # compute output
        output = model(images)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, _ = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), images.size(0))
        top1.update(acc1[0], images.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.second_step(zero_grad=True)

        # print loss and accuracy
        if i % args.print_freq == 0:
            progress.display(i)

    return top1.avg, losses.avg


def save_checkpoint(state, is_best, args):
    torch.save(state, args.checkpoint_path)
    if is_best:
        best_state = state.pop("optimizer")
        torch.save(best_state, args.best_checkpoint_path)


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self, name, fmt=":f"):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(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

    def __str__(self):
        fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
        return fmtstr.format(**self.__dict__)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print_txt = "\t".join(entries)
        print(print_txt)
        txt_name = "./log/" + time_str + "log.txt"
        with open(txt_name, "a") as f:
            f.write(print_txt + "\n")

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = "{:" + str(num_digits) + "d}"
        return "[" + fmt + "/" + fmt.format(num_batches) + "]"


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)
        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))
        res = []
        for k in topk:
            correct_k = correct[:k].contiguous().view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


labels = ["A", "B", "C", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O"]


class RecorderMeter1(object):
    """Computes and stores the minimum loss value and its epoch index"""

    def __init__(self, total_epoch):
        self.reset(total_epoch)

    def reset(self, total_epoch):
        self.total_epoch = total_epoch
        self.current_epoch = 0
        self.epoch_losses = np.zeros(
            (self.total_epoch, 2), dtype=np.float32
        )  # [epoch, train/val]
        self.epoch_accuracy = np.zeros(
            (self.total_epoch, 2), dtype=np.float32
        )  # [epoch, train/val]

    def update(self, output, target):
        self.y_pred = output
        self.y_true = target

    def plot_confusion_matrix(self, cm, title="Confusion Matrix", cmap=plt.cm.binary):
        plt.imshow(cm, interpolation="nearest", cmap=cmap)
        y_true = self.y_true
        y_pred = self.y_pred

        plt.title(title)
        plt.colorbar()
        xlocations = np.array(range(len(labels)))
        plt.xticks(xlocations, labels, rotation=90)
        plt.yticks(xlocations, labels)
        plt.ylabel("True label")
        plt.xlabel("Predicted label")

        cm = confusion_matrix(y_true, y_pred)
        np.set_printoptions(precision=2)
        cm_normalized = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis]
        plt.figure(figsize=(12, 8), dpi=120)

        ind_array = np.arange(len(labels))
        x, y = np.meshgrid(ind_array, ind_array)
        for x_val, y_val in zip(x.flatten(), y.flatten()):
            c = cm_normalized[y_val][x_val]
            if c > 0.01:
                plt.text(
                    x_val,
                    y_val,
                    "%0.2f" % (c,),
                    color="red",
                    fontsize=7,
                    va="center",
                    ha="center",
                )
        # offset the tick
        tick_marks = np.arange(len(7))
        plt.gca().set_xticks(tick_marks, minor=True)
        plt.gca().set_yticks(tick_marks, minor=True)
        plt.gca().xaxis.set_ticks_position("none")
        plt.gca().yaxis.set_ticks_position("none")
        plt.grid(True, which="minor", linestyle="-")
        plt.gcf().subplots_adjust(bottom=0.15)

        plot_confusion_matrix(cm_normalized, title="Normalized confusion matrix")
        # show confusion matrix
        plt.savefig("./log/confusion_matrix.png", format="png")
        # fig.savefig(save_path, dpi=dpi, bbox_inches='tight')
        print("Saved figure")
        plt.show()

    def matrix(self):
        target = self.y_true
        output = self.y_pred
        im_re_label = np.array(target)
        im_pre_label = np.array(output)
        y_ture = im_re_label.flatten()
        # im_re_label.transpose()
        y_pred = im_pre_label.flatten()
        im_pre_label.transpose()


class RecorderMeter(object):
    """Computes and stores the minimum loss value and its epoch index"""

    def __init__(self, total_epoch):
        self.reset(total_epoch)

    def reset(self, total_epoch):
        self.total_epoch = total_epoch
        self.current_epoch = 0
        self.epoch_losses = np.zeros(
            (self.total_epoch, 2), dtype=np.float32
        )  # [epoch, train/val]
        self.epoch_accuracy = np.zeros(
            (self.total_epoch, 2), dtype=np.float32
        )  # [epoch, train/val]

    def update(self, idx, train_loss, train_acc, val_loss, val_acc):
        self.epoch_losses[idx, 0] = train_loss * 30
        self.epoch_losses[idx, 1] = val_loss * 30
        self.epoch_accuracy[idx, 0] = train_acc
        self.epoch_accuracy[idx, 1] = val_acc
        self.current_epoch = idx + 1

    def plot_curve(self, save_path):
        title = "the accuracy/loss curve of train/val"
        dpi = 80
        width, height = 1800, 800
        legend_fontsize = 10
        figsize = width / float(dpi), height / float(dpi)

        fig = plt.figure(figsize=figsize)
        x_axis = np.array([i for i in range(self.total_epoch)])  # epochs
        y_axis = np.zeros(self.total_epoch)

        plt.xlim(0, self.total_epoch)
        plt.ylim(0, 100)
        interval_y = 5
        interval_x = 5
        plt.xticks(np.arange(0, self.total_epoch + interval_x, interval_x))
        plt.yticks(np.arange(0, 100 + interval_y, interval_y))
        plt.grid()
        plt.title(title, fontsize=20)
        plt.xlabel("the training epoch", fontsize=16)
        plt.ylabel("accuracy", fontsize=16)

        y_axis[:] = self.epoch_accuracy[:, 0]
        plt.plot(x_axis, y_axis, color="g", linestyle="-", label="train-accuracy", lw=2)
        plt.legend(loc=4, fontsize=legend_fontsize)

        y_axis[:] = self.epoch_accuracy[:, 1]
        plt.plot(x_axis, y_axis, color="y", linestyle="-", label="valid-accuracy", lw=2)
        plt.legend(loc=4, fontsize=legend_fontsize)

        y_axis[:] = self.epoch_losses[:, 0]
        plt.plot(x_axis, y_axis, color="g", linestyle=":", label="train-loss-x30", lw=2)
        plt.legend(loc=4, fontsize=legend_fontsize)

        y_axis[:] = self.epoch_losses[:, 1]
        plt.plot(x_axis, y_axis, color="y", linestyle=":", label="valid-loss-x30", lw=2)
        plt.legend(loc=4, fontsize=legend_fontsize)

        if save_path is not None:
            fig.savefig(save_path, dpi=dpi, bbox_inches="tight")
            print("Saved figure")
        plt.close(fig)


if __name__ == "__main__":
    main()