# copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

import logging
import time
from argparse import ArgumentParser

import torch
import torch.nn as nn

import datasets
from nni.nas.pytorch.callbacks import ArchitectureCheckpoint, LRSchedulerCallback
from nni.algorithms.nas.pytorch.darts import DartsTrainer
from utils import accuracy

from nni.nas.pytorch.search_space_zoo import DartsCell
from darts_stack_cells import DartsStackedCells

logger = logging.getLogger('nni')

if __name__ == "__main__":
    parser = ArgumentParser("darts")
    parser.add_argument("--layers", default=8, type=int)
    parser.add_argument("--batch-size", default=64, type=int)
    parser.add_argument("--log-frequency", default=10, type=int)
    parser.add_argument("--epochs", default=50, type=int)
    parser.add_argument("--channels", default=16, type=int)
    parser.add_argument("--unrolled", default=False, action="store_true")
    parser.add_argument("--visualization", default=False, action="store_true")
    args = parser.parse_args()

    dataset_train, dataset_valid = datasets.get_dataset("cifar10")

    model = DartsStackedCells(3, args.channels, 10, args.layers, DartsCell)
    criterion = nn.CrossEntropyLoss()

    optim = torch.optim.SGD(model.parameters(), 0.025, momentum=0.9, weight_decay=3.0E-4)
    lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optim, args.epochs, eta_min=0.001)

    trainer = DartsTrainer(model,
                           loss=criterion,
                           metrics=lambda output, target: accuracy(output, target, topk=(1,)),
                           optimizer=optim,
                           num_epochs=args.epochs,
                           dataset_train=dataset_train,
                           dataset_valid=dataset_valid,
                           batch_size=args.batch_size,
                           log_frequency=args.log_frequency,
                           unrolled=args.unrolled,
                           callbacks=[LRSchedulerCallback(lr_scheduler), ArchitectureCheckpoint("./checkpoints")])
    if args.visualization:
        trainer.enable_visualization()
    trainer.train()
