PlantNetLibre-300K/main.py

# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (c) 2021, Pl@ntNet
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#   this list of conditions and the following disclaimer in the documentation
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import os
from tqdm import tqdm
import pickle
import argparse
import time
import torch
from torch.optim import SGD
from torch.nn import CrossEntropyLoss

from utils import set_seed, load_model, save, get_model, update_optimizer, get_data
from epoch import train_epoch, val_epoch, test_epoch
from cli import add_all_parsers


def train(args):
    set_seed(args, use_gpu=torch.cuda.is_available())
    train_loader, val_loader, test_loader, dataset_attributes = get_data(
        args.root,
        args.image_size,
        args.crop_size,
        args.batch_size,
        args.num_workers,
        args.pretrained,
    )

    model = get_model(args, n_classes=dataset_attributes["n_classes"])
    criteria = CrossEntropyLoss()

    if args.use_gpu:
        print("USING GPU")
        torch.cuda.set_device(0)
        model.cuda()
        criteria.cuda()

    optimizer = SGD(
        model.parameters(),
        lr=args.lr,
        momentum=0.9,
        weight_decay=args.mu,
        nesterov=True,
    )

    # Containers for storing metrics over epochs
    loss_train, acc_train, topk_acc_train = [], [], []
    loss_val, acc_val, topk_acc_val, avgk_acc_val, class_acc_val = [], [], [], [], []

    save_name = args.save_name_xp.strip()
    save_dir = os.path.join(os.getcwd(), "results", save_name)
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)

    print("args.k : ", args.k)

    lmbda_best_acc = None
    best_val_acc = float("-inf")

    for epoch in tqdm(range(args.n_epochs), desc="epoch", position=0):
        t = time.time()
        optimizer = update_optimizer(
            optimizer, lr_schedule=args.epoch_decay, epoch=epoch
        )

        loss_epoch_train, acc_epoch_train, topk_acc_epoch_train = train_epoch(
            model,
            optimizer,
            train_loader,
            criteria,
            loss_train,
            acc_train,
            topk_acc_train,
            args.k,
            dataset_attributes["n_train"],
            args.use_gpu,
        )

        (
            loss_epoch_val,
            acc_epoch_val,
            topk_acc_epoch_val,
            avgk_acc_epoch_val,
            lmbda_val,
        ) = val_epoch(
            model,
            val_loader,
            criteria,
            loss_val,
            acc_val,
            topk_acc_val,
            avgk_acc_val,
            class_acc_val,
            args.k,
            dataset_attributes,
            args.use_gpu,
        )

        # save model at every epoch
        save(
            model, optimizer, epoch, os.path.join(save_dir, save_name + "_weights.tar")
        )

        # save model with best val accuracy
        if acc_epoch_val > best_val_acc:
            best_val_acc = acc_epoch_val
            lmbda_best_acc = lmbda_val
            save(
                model,
                optimizer,
                epoch,
                os.path.join(save_dir, save_name + "_weights_best_acc.tar"),
            )

        print()
        print(f"epoch {epoch} took {time.time()-t:.2f}")
        print(f"loss_train : {loss_epoch_train}")
        print(f"loss_val : {loss_epoch_val}")
        print(
            f"acc_train : {acc_epoch_train} / topk_acc_train : {topk_acc_epoch_train}"
        )
        print(
            f"acc_val : {acc_epoch_val} / topk_acc_val : {topk_acc_epoch_val} / "
            f"avgk_acc_val : {avgk_acc_epoch_val}"
        )

    # load weights corresponding to best val accuracy and evaluate on test
    load_model(
        model, os.path.join(save_dir, save_name + "_weights_best_acc.tar"), args.use_gpu
    )
    (
        loss_test_ba,
        acc_test_ba,
        topk_acc_test_ba,
        avgk_acc_test_ba,
        class_acc_test,
    ) = test_epoch(
        model,
        test_loader,
        criteria,
        args.k,
        lmbda_best_acc,
        args.use_gpu,
        dataset_attributes,
    )

    # Save the results as a dictionary and save it as a pickle file in desired location

    results = {
        "loss_train": loss_train,
        "acc_train": acc_train,
        "topk_acc_train": topk_acc_train,
        "loss_val": loss_val,
        "acc_val": acc_val,
        "topk_acc_val": topk_acc_val,
        "class_acc_val": class_acc_val,
        "avgk_acc_val": avgk_acc_val,
        "test_results": {
            "loss": loss_test_ba,
            "accuracy": acc_test_ba,
            "topk_accuracy": topk_acc_test_ba,
            "avgk_accuracy": avgk_acc_test_ba,
            "class_acc_dict": class_acc_test,
        },
        "params": args.__dict__,
    }

    with open(os.path.join(save_dir, save_name + ".pkl"), "wb") as f:
        pickle.dump(results, f)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    add_all_parsers(parser)
    args = parser.parse_args()
    train(args)