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

import argparse
import logging
import random
import time
from itertools import cycle

import nni
import numpy as np
import torch
import torch.nn as nn
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from nni.algorithms.nas.pytorch.classic_nas import get_and_apply_next_architecture
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from nni.nas.pytorch.utils import AverageMeterGroup

from dataloader import get_imagenet_iter_dali
from network import ShuffleNetV2OneShot, load_and_parse_state_dict
from utils import CrossEntropyLabelSmooth, accuracy

logger = logging.getLogger("nni.spos.tester")


def retrain_bn(model, criterion, max_iters, log_freq, loader):
    with torch.no_grad():
        logger.info("Clear BN statistics...")
        for m in model.modules():
            if isinstance(m, nn.BatchNorm2d):
                m.running_mean = torch.zeros_like(m.running_mean)
                m.running_var = torch.ones_like(m.running_var)

        logger.info("Train BN with training set (BN sanitize)...")
        model.train()
        meters = AverageMeterGroup()
        for step in range(max_iters):
            inputs, targets = next(loader)
            logits = model(inputs)
            loss = criterion(logits, targets)
            metrics = accuracy(logits, targets)
            metrics["loss"] = loss.item()
            meters.update(metrics)
            if step % log_freq == 0 or step + 1 == max_iters:
                logger.info("Train Step [%d/%d] %s", step + 1, max_iters, meters)


def test_acc(model, criterion, log_freq, loader):
    logger.info("Start testing...")
    model.eval()
    meters = AverageMeterGroup()
    start_time = time.time()
    with torch.no_grad():
        for step, (inputs, targets) in enumerate(loader):
            logits = model(inputs)
            loss = criterion(logits, targets)
            metrics = accuracy(logits, targets)
            metrics["loss"] = loss.item()
            meters.update(metrics)
            if step % log_freq == 0 or step + 1 == len(loader):
                logger.info("Valid Step [%d/%d] time %.3fs acc1 %.4f acc5 %.4f loss %.4f",
                            step + 1, len(loader), time.time() - start_time,
                            meters.acc1.avg, meters.acc5.avg, meters.loss.avg)
    return meters.acc1.avg


def evaluate_acc(model, criterion, args, loader_train, loader_test):
    acc_before = test_acc(model, criterion, args.log_frequency, loader_test)
    nni.report_intermediate_result(acc_before)

    retrain_bn(model, criterion, args.train_iters, args.log_frequency, loader_train)
    acc = test_acc(model, criterion, args.log_frequency, loader_test)
    assert isinstance(acc, float)
    nni.report_intermediate_result(acc)
    nni.report_final_result(acc)


if __name__ == "__main__":
    parser = argparse.ArgumentParser("SPOS Candidate Tester")
    parser.add_argument("--imagenet-dir", type=str, default="./data/imagenet")
    parser.add_argument("--checkpoint", type=str, default="./data/checkpoint-150000.pth.tar")
    parser.add_argument("--spos-preprocessing", action="store_true", default=False,
                        help="When true, image values will range from 0 to 255 and use BGR "
                             "(as in original repo).")
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--workers", type=int, default=6)
    parser.add_argument("--train-batch-size", type=int, default=128)
    parser.add_argument("--train-iters", type=int, default=200)
    parser.add_argument("--test-batch-size", type=int, default=512)
    parser.add_argument("--log-frequency", type=int, default=10)

    args = parser.parse_args()

    # use a fixed set of image will improve the performance
    torch.manual_seed(args.seed)
    torch.cuda.manual_seed_all(args.seed)
    np.random.seed(args.seed)
    random.seed(args.seed)
    torch.backends.cudnn.deterministic = True

    assert torch.cuda.is_available()

    model = ShuffleNetV2OneShot()
    criterion = CrossEntropyLabelSmooth(1000, 0.1)
    get_and_apply_next_architecture(model)
    model.load_state_dict(load_and_parse_state_dict(filepath=args.checkpoint))
    model.cuda()

    train_loader = get_imagenet_iter_dali("train", args.imagenet_dir, args.train_batch_size, args.workers,
                                          spos_preprocessing=args.spos_preprocessing,
                                          seed=args.seed, device_id=0)
    val_loader = get_imagenet_iter_dali("val", args.imagenet_dir, args.test_batch_size, args.workers,
                                        spos_preprocessing=args.spos_preprocessing, shuffle=True,
                                        seed=args.seed, device_id=0)
    train_loader = cycle(train_loader)

    evaluate_acc(model, criterion, args, train_loader, val_loader)