#!/usr/bin/env python3

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# 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.


# Copyright 2019-2022 Ross Wightman
#
# 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 logging
import os
import glob
import re
from pathlib import Path
import time
from collections import OrderedDict
from contextlib import suppress
from datetime import datetime
import dllogger

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.utils
import yaml
from timm.data import (
    AugMixDataset,
    FastCollateMixup,
    Mixup,
    create_dataset,
    create_loader,
    resolve_data_config,
)
from timm.loss import (
    JsdCrossEntropy,
    LabelSmoothingCrossEntropy,
    SoftTargetCrossEntropy,
)
from timm.models import (
    convert_splitbn_model,
    create_model,
    load_checkpoint,
    model_parameters,
    resume_checkpoint,
    safe_model_name,
)
from timm.optim import create_optimizer_v2, optimizer_kwargs
from timm.scheduler import create_scheduler
from timm.utils import *
from timm.utils import ApexScaler, NativeScaler
from torch.nn.parallel import DistributedDataParallel as NativeDDP


def cross_entropy_loss_with_soft_target(pred, soft_target):
    logsoftmax = nn.LogSoftmax()
    return torch.mean(torch.sum(-soft_target * logsoftmax(pred), 1))


try:
    from apex import amp
    from apex.parallel import DistributedDataParallel as ApexDDP
    from apex.parallel import convert_syncbn_model

    has_apex = True
except ImportError:
    has_apex = False

has_native_amp = False
try:
    if getattr(torch.cuda.amp, "autocast") is not None:
        has_native_amp = True
except AttributeError:
    pass

try:
    import wandb

    has_wandb = True
except ImportError:
    has_wandb = False

torch.backends.cudnn.benchmark = True
_logger = logging.getLogger("train")


# to enable Boolean in add_argument
def str2bool(v):
    if isinstance(v, bool):
        return v
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise argparse.ArgumentTypeError("Boolean value expected.")


# The first arg parser parses out only the --config argument, this argument is used to
# load a yaml file containing key-values that override the defaults for the main parser below
config_parser = parser = argparse.ArgumentParser(
    description="Training Config", add_help=False
)
parser.add_argument(
    "-c",
    "--config",
    default="",
    type=str,
    metavar="FILE",
    help="YAML config file specifying default arguments",
)


parser = argparse.ArgumentParser(description="PyTorch ImageNet Training")

# DLlogger
parser.add_argument(
    "--dllogger-name", default="/logs/log.json", type=str, help="name of dllogger file"
)

# Dataset / Model parameters
parser.add_argument("data_dir", metavar="DIR", help="path to dataset")
parser.add_argument(
    "--dataset",
    "-d",
    metavar="NAME",
    default="",
    help="dataset type (default: ImageFolder/ImageTar if empty)",
)
parser.add_argument(
    "--train-split",
    metavar="NAME",
    default="train",
    help="dataset train split (default: train)",
)
parser.add_argument(
    "--val-split",
    metavar="NAME",
    default="validation",
    help="dataset validation split (default: validation)",
)
parser.add_argument(
    "--model",
    default="resnet101",
    type=str,
    metavar="MODEL",
    help='Name of model to train (default: "countception"',
)
parser.add_argument(
    "--pretrained",
    action="store_true",
    default=False,
    help="Start with pretrained version of specified network (if avail)",
)
parser.add_argument(
    "--initial-checkpoint",
    default="",
    type=str,
    metavar="PATH",
    help="Initialize model from this checkpoint (default: none)",
)
parser.add_argument(
    "--resume",
    default="",
    type=str,
    metavar="PATH",
    help="Resume full model and optimizer state from checkpoint (default: none)",
)
parser.add_argument(
    "--no-resume-opt",
    action="store_true",
    default=False,
    help="prevent resume of optimizer state when resuming model",
)
parser.add_argument(
    "--num-classes",
    type=int,
    default=None,
    metavar="N",
    help="number of label classes (Model default if None)",
)
parser.add_argument(
    "--gp",
    default=None,
    type=str,
    metavar="POOL",
    help="Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.",
)
parser.add_argument(
    "--img-size",
    type=int,
    default=None,
    metavar="N",
    help="Image patch size (default: None => model default)",
)
parser.add_argument(
    "--input-size",
    default=None,
    nargs=3,
    type=int,
    metavar="N N N",
    help="Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty",
)
parser.add_argument(
    "--crop-pct",
    default=None,
    type=float,
    metavar="N",
    help="Input image center crop percent (for validation only)",
)
parser.add_argument(
    "--mean",
    type=float,
    nargs="+",
    default=None,
    metavar="MEAN",
    help="Override mean pixel value of dataset",
)
parser.add_argument(
    "--std",
    type=float,
    nargs="+",
    default=None,
    metavar="STD",
    help="Override std deviation of of dataset",
)
parser.add_argument(
    "--interpolation",
    default="",
    type=str,
    metavar="NAME",
    help="Image resize interpolation type (overrides model)",
)
parser.add_argument(
    "-b",
    "--batch-size",
    type=int,
    default=32,
    metavar="N",
    help="input batch size for training (default: 32)",
)
parser.add_argument(
    "-vb",
    "--validation-batch-size-multiplier",
    type=int,
    default=1,
    metavar="N",
    help="ratio of validation batch size to training batch size (default: 1)",
)

# Optimizer parameters
parser.add_argument(
    "--opt",
    default="sgd",
    type=str,
    metavar="OPTIMIZER",
    help='Optimizer (default: "sgd"',
)
parser.add_argument(
    "--opt-eps",
    default=None,
    type=float,
    metavar="EPSILON",
    help="Optimizer Epsilon (default: None, use opt default)",
)
parser.add_argument(
    "--opt-betas",
    default=None,
    type=float,
    nargs="+",
    metavar="BETA",
    help="Optimizer Betas (default: None, use opt default)",
)
parser.add_argument(
    "--momentum",
    type=float,
    default=0.9,
    metavar="M",
    help="Optimizer momentum (default: 0.9)",
)
parser.add_argument(
    "--weight-decay", type=float, default=0.0001, help="weight decay (default: 0.0001)"
)
parser.add_argument(
    "--clip-grad",
    type=float,
    default=None,
    metavar="NORM",
    help="Clip gradient norm (default: None, no clipping)",
)
parser.add_argument(
    "--clip-mode",
    type=str,
    default="norm",
    help='Gradient clipping mode. One of ("norm", "value", "agc")',
)


# Learning rate schedule parameters
parser.add_argument(
    "--sched",
    default="step",
    type=str,
    metavar="SCHEDULER",
    help='LR scheduler (default: "step"',
)
parser.add_argument(
    "--lr", type=float, default=0.01, metavar="LR", help="learning rate (default: 0.01)"
)
parser.add_argument(
    "--lr-noise",
    type=float,
    nargs="+",
    default=None,
    metavar="pct, pct",
    help="learning rate noise on/off epoch percentages",
)
parser.add_argument(
    "--lr-noise-pct",
    type=float,
    default=0.67,
    metavar="PERCENT",
    help="learning rate noise limit percent (default: 0.67)",
)
parser.add_argument(
    "--lr-noise-std",
    type=float,
    default=1.0,
    metavar="STDDEV",
    help="learning rate noise std-dev (default: 1.0)",
)
parser.add_argument(
    "--lr-cycle-mul",
    type=float,
    default=1.0,
    metavar="MULT",
    help="learning rate cycle len multiplier (default: 1.0)",
)
parser.add_argument(
    "--lr-cycle-limit",
    type=int,
    default=1,
    metavar="N",
    help="learning rate cycle limit",
)
parser.add_argument(
    "--warmup-lr",
    type=float,
    default=0.0001,
    metavar="LR",
    help="warmup learning rate (default: 0.0001)",
)
parser.add_argument(
    "--min-lr",
    type=float,
    default=1e-5,
    metavar="LR",
    help="lower lr bound for cyclic schedulers that hit 0 (1e-5)",
)
parser.add_argument(
    "--epochs",
    type=int,
    default=200,
    metavar="N",
    help="number of epochs to train (default: 2)",
)
parser.add_argument(
    "--epoch-repeats",
    type=float,
    default=0.0,
    metavar="N",
    help="epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).",
)
parser.add_argument(
    "--start-epoch",
    default=None,
    type=int,
    metavar="N",
    help="manual epoch number (useful on restarts)",
)
parser.add_argument(
    "--benchmark-steps",
    default=None,
    type=int,
    metavar="N",
    help="For benchmarking, run this number of steps per epoch instead of all.",
)
parser.add_argument(
    "--decay-epochs",
    type=float,
    default=30,
    metavar="N",
    help="epoch interval to decay LR",
)
parser.add_argument(
    "--warmup-epochs",
    type=int,
    default=3,
    metavar="N",
    help="epochs to warmup LR, if scheduler supports",
)
parser.add_argument(
    "--cooldown-epochs",
    type=int,
    default=10,
    metavar="N",
    help="epochs to cooldown LR at min_lr, after cyclic schedule ends",
)
parser.add_argument(
    "--patience-epochs",
    type=int,
    default=10,
    metavar="N",
    help="patience epochs for Plateau LR scheduler (default: 10",
)
parser.add_argument(
    "--decay-rate",
    "--dr",
    type=float,
    default=0.1,
    metavar="RATE",
    help="LR decay rate (default: 0.1)",
)

# Augmentation & regularization parameters
parser.add_argument(
    "--no-aug",
    action="store_true",
    default=False,
    help="Disable all training augmentation, override other train aug args",
)
parser.add_argument(
    "--scale",
    type=float,
    nargs="+",
    default=[0.08, 1.0],
    metavar="PCT",
    help="Random resize scale (default: 0.08 1.0)",
)
parser.add_argument(
    "--ratio",
    type=float,
    nargs="+",
    default=[3.0 / 4.0, 4.0 / 3.0],
    metavar="RATIO",
    help="Random resize aspect ratio (default: 0.75 1.33)",
)
parser.add_argument(
    "--hflip", type=float, default=0.5, help="Horizontal flip training aug probability"
)
parser.add_argument(
    "--vflip", type=float, default=0.0, help="Vertical flip training aug probability"
)
parser.add_argument(
    "--color-jitter",
    type=float,
    default=0.4,
    metavar="PCT",
    help="Color jitter factor (default: 0.4)",
)
parser.add_argument(
    "--aa",
    type=str,
    default=None,
    metavar="NAME",
    help='Use AutoAugment policy. "v0" or "original". (default: None)',
),
parser.add_argument(
    "--aug-splits",
    type=int,
    default=0,
    help="Number of augmentation splits (default: 0, valid: 0 or >=2)",
)
parser.add_argument(
    "--jsd",
    action="store_true",
    default=False,
    help="Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.",
)
parser.add_argument(
    "--reprob",
    type=float,
    default=0.0,
    metavar="PCT",
    help="Random erase prob (default: 0.)",
)
parser.add_argument(
    "--remode", type=str, default="const", help='Random erase mode (default: "const")'
)
parser.add_argument(
    "--recount", type=int, default=1, help="Random erase count (default: 1)"
)
parser.add_argument(
    "--resplit",
    action="store_true",
    default=False,
    help="Do not random erase first (clean) augmentation split",
)
parser.add_argument(
    "--mixup",
    type=float,
    default=0.0,
    help="mixup alpha, mixup enabled if > 0. (default: 0.)",
)
parser.add_argument(
    "--cutmix",
    type=float,
    default=0.0,
    help="cutmix alpha, cutmix enabled if > 0. (default: 0.)",
)
parser.add_argument(
    "--cutmix-minmax",
    type=float,
    nargs="+",
    default=None,
    help="cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)",
)
parser.add_argument(
    "--mixup-prob",
    type=float,
    default=1.0,
    help="Probability of performing mixup or cutmix when either/both is enabled",
)
parser.add_argument(
    "--mixup-switch-prob",
    type=float,
    default=0.5,
    help="Probability of switching to cutmix when both mixup and cutmix enabled",
)
parser.add_argument(
    "--mixup-mode",
    type=str,
    default="batch",
    help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"',
)
parser.add_argument(
    "--mixup-off-epoch",
    default=0,
    type=int,
    metavar="N",
    help="Turn off mixup after this epoch, disabled if 0 (default: 0)",
)
parser.add_argument(
    "--smoothing", type=float, default=0.1, help="Label smoothing (default: 0.1)"
)
parser.add_argument(
    "--train-interpolation",
    type=str,
    default="random",
    help='Training interpolation (random, bilinear, bicubic default: "random")',
)
parser.add_argument(
    "--drop", type=float, default=0.0, metavar="PCT", help="Dropout rate (default: 0.)"
)
parser.add_argument(
    "--drop-connect",
    type=float,
    default=None,
    metavar="PCT",
    help="Drop connect rate, DEPRECATED, use drop-path (default: None)",
)
parser.add_argument(
    "--drop-path",
    type=float,
    default=None,
    metavar="PCT",
    help="Drop path rate (default: None)",
)
parser.add_argument(
    "--drop-block",
    type=float,
    default=None,
    metavar="PCT",
    help="Drop block rate (default: None)",
)

# Batch norm parameters (only works with gen_efficientnet based models currently)
parser.add_argument(
    "--bn-tf",
    action="store_true",
    default=False,
    help="Use Tensorflow BatchNorm defaults for models that support it (default: False)",
)
parser.add_argument(
    "--bn-momentum",
    type=float,
    default=None,
    help="BatchNorm momentum override (if not None)",
)
parser.add_argument(
    "--bn-eps",
    type=float,
    default=None,
    help="BatchNorm epsilon override (if not None)",
)
parser.add_argument(
    "--sync-bn",
    action="store_true",
    help="Enable NVIDIA Apex or Torch synchronized BatchNorm.",
)
parser.add_argument(
    "--dist-bn",
    type=str,
    default="",
    help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")',
)
parser.add_argument(
    "--split-bn",
    action="store_true",
    help="Enable separate BN layers per augmentation split.",
)

# Model Exponential Moving Average
parser.add_argument(
    "--model-ema",
    action="store_true",
    default=False,
    help="Enable tracking moving average of model weights",
)
parser.add_argument(
    "--model-ema-force-cpu",
    action="store_true",
    default=False,
    help="Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.",
)
parser.add_argument(
    "--model-ema-decay",
    type=float,
    default=0.9998,
    help="decay factor for model weights moving average (default: 0.9998)",
)

# Misc
parser.add_argument(
    "--seed", type=int, default=42, metavar="S", help="random seed (default: 42)"
)
parser.add_argument(
    "--log-interval",
    type=int,
    default=50,
    metavar="N",
    help="how many batches to wait before logging training status",
)
parser.add_argument(
    "--recovery-interval",
    type=int,
    default=0,
    metavar="N",
    help="how many batches to wait before writing recovery checkpoint",
)
parser.add_argument(
    "--checkpoint-hist",
    type=int,
    default=10,
    metavar="N",
    help="number of checkpoints to keep (default: 10)",
)
parser.add_argument(
    "-j",
    "--workers",
    type=int,
    default=2,
    metavar="N",
    help="how many training processes to use (default: 1)",
)
parser.add_argument(
    "--save-images",
    action="store_true",
    default=False,
    help="save images of input bathes every log interval for debugging",
)
parser.add_argument(
    "--amp",
    action="store_true",
    default=False,
    help="use NVIDIA Apex AMP or Native AMP for mixed precision training",
)
parser.add_argument(
    "--apex-amp",
    action="store_true",
    default=False,
    help="Use NVIDIA Apex AMP mixed precision",
)
parser.add_argument(
    "--native-amp",
    action="store_true",
    default=False,
    help="Use Native Torch AMP mixed precision",
)
parser.add_argument(
    "--channels-last",
    action="store_true",
    default=False,
    help="Use channels_last memory layout",
)
parser.add_argument(
    "--pin-mem",
    action="store_true",
    default=False,
    help="Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.",
)
parser.add_argument(
    "--no-prefetcher",
    action="store_true",
    default=False,
    help="disable fast prefetcher",
)
parser.add_argument(
    "--output",
    default="",
    type=str,
    metavar="PATH",
    help="path to output folder (default: none, current dir)",
)
parser.add_argument(
    "--experiment",
    default="",
    type=str,
    metavar="NAME",
    help="name of train experiment, name of sub-folder for output",
)
parser.add_argument(
    "--eval-metric",
    default="top1",
    type=str,
    metavar="EVAL_METRIC",
    help='Best metric (default: "top1"',
)
parser.add_argument(
    "--tta",
    type=int,
    default=0,
    metavar="N",
    help="Test/inference time augmentation (oversampling) factor. 0=None (default: 0)",
)
parser.add_argument("--local_rank", default=0, type=int)
parser.add_argument(
    "--use-multi-epochs-loader",
    action="store_true",
    default=False,
    help="use the multi-epochs-loader to save time at the beginning of every epoch",
)
parser.add_argument(
    "--torchscript",
    dest="torchscript",
    action="store_true",
    help="convert model torchscript for inference",
)
parser.add_argument(
    "--log-wandb",
    action="store_true",
    default=False,
    help="log training and validation metrics to wandb",
)

# Distillation
parser.add_argument(
    "--enable-distill",
    type=str2bool,
    nargs="?",
    const=True,
    default=False,
    metavar="Boolean",
    help="to use distillation",
)
parser.add_argument(
    "--test-teacher",
    type=str2bool,
    nargs="?",
    const=True,
    default=False,
    metavar="Boolean",
    help="to test the teacher before training",
)

parser.add_argument(
    "--teacher", default="", type=str, metavar="MODEL", help="Name of teacher model"
)
parser.add_argument(
    "--teacher-checkpoint",
    default="",
    type=str,
    metavar="CHECKPOINT PATH",
    help="The checkpoint to the teacher model",
)
parser.add_argument(
    "--teacher-img-size",
    default=224,
    type=int,
    metavar="INT",
    help="image resolution for teacher",
)


from timm.models.registry import register_model

from configs.model_hub import get_configs
from models.gpunet_builder import GPUNet_Builder


@register_model
def gpunet_2(pretrained=False, **kwargs):
    """Constructs GPUNet-2."""
    modelJSON, checkpoint_path = get_configs(
        batch=1, latency="1.75ms", gpuType="GV100", download=False
    )
    builder = GPUNet_Builder()
    model = builder.get_model(modelJSON)
    model.default_cfg = {
        "architecture": "gpunet_2",
        "crop_pct": 1.0,
        "interpolation": "bicubic",
        "input_size": (3, model.imgRes, model.imgRes),
        "num_classes": 1000,
        "mean": (0.485, 0.456, 0.406),
        "std": (0.229, 0.224, 0.225),
    }
    for key in model.default_cfg:
        setattr(model, key, model.default_cfg[key])
    if pretrained:
        load_checkpoint(model, checkpoint_path, use_ema=True)
    return model


@register_model
def gpunet_1(pretrained=False, **kwargs):
    """Constructs GPUNet-1."""
    modelJSON, checkpoint_path = get_configs(
        batch=1, latency="0.85ms", gpuType="GV100", download=False
    )
    builder = GPUNet_Builder()
    model = builder.get_model(modelJSON)
    model.default_cfg = {
        "architecture": "gpunet_1",
        "crop_pct": 1.0,
        "interpolation": "bicubic",
        "input_size": (3, model.imgRes, model.imgRes),
        "num_classes": 1000,
        "mean": (0.485, 0.456, 0.406),
        "std": (0.229, 0.224, 0.225),
    }
    print("model CFG:", model.default_cfg)
    for key in model.default_cfg:
        setattr(model, key, model.default_cfg[key])
    if pretrained:
        load_checkpoint(model, checkpoint_path, use_ema=True)
    return model


@register_model
def gpunet_0(pretrained=False, **kwargs):
    """Constructs GPUNet-0."""
    modelJSON, checkpoint_path = get_configs(
        batch=1, latency="0.65ms", gpuType="GV100", download=False
    )
    builder = GPUNet_Builder()
    model = builder.get_model(modelJSON)
    model.default_cfg = {
        "architecture": "gpunet_0",
        "crop_pct": 1.0,
        "interpolation": "bicubic",
        "input_size": (3, model.imgRes, model.imgRes),
        "num_classes": 1000,
        "mean": (0.485, 0.456, 0.406),
        "std": (0.229, 0.224, 0.225),
    }
    print("model CFG:", model.default_cfg)
    for key in model.default_cfg:
        setattr(model, key, model.default_cfg[key])
    if pretrained:
        load_checkpoint(model, checkpoint_path, use_ema=True)
    return model


@register_model
def gpunet_d1(pretrained=False, **kwargs):
    """Constructs GPUNet-D1."""
    modelJSON, checkpoint_path = get_configs(
        batch=1, latency="1.25ms-D", gpuType="GV100", download=False
    )
    builder = GPUNet_Builder()
    model = builder.get_model(modelJSON)
    model.default_cfg = {
        "architecture": "gpunet_d1",
        "crop_pct": 1.0,
        "interpolation": "bicubic",
        "input_size": (3, model.imgRes, model.imgRes),
        "num_classes": 1000,
        "mean": (0.485, 0.456, 0.406),
        "std": (0.229, 0.224, 0.225),
    }
    print("model CFG:", model.default_cfg)
    for key in model.default_cfg:
        setattr(model, key, model.default_cfg[key])
    if pretrained:
        load_checkpoint(model, checkpoint_path, use_ema=True)
    return model


@register_model
def gpunet_d2(pretrained=False, **kwargs):
    """Constructs GPUNet-D2."""
    modelJSON, checkpoint_path = get_configs(
        batch=1, latency="2.25ms-D", gpuType="GV100", download=False
    )
    builder = GPUNet_Builder()
    model = builder.get_model(modelJSON)
    model.default_cfg = {
        "architecture": "gpunet_d2",
        "crop_pct": 1.0,
        "interpolation": "bicubic",
        "input_size": (3, model.imgRes, model.imgRes),
        "num_classes": 1000,
        "mean": (0.485, 0.456, 0.406),
        "std": (0.229, 0.224, 0.225),
    }
    print("model CFG:", model.default_cfg)
    for key in model.default_cfg:
        setattr(model, key, model.default_cfg[key])
    if pretrained:
        load_checkpoint(model, checkpoint_path, use_ema=True)
    return model


@register_model
def gpunet_p0(pretrained=False, **kwargs):
    """Constructs GPUNet-P0."""
    modelJSON, checkpoint_path = get_configs(
        batch=1, latency="0.5ms-D", gpuType="GV100", download=False
    )
    builder = GPUNet_Builder()
    model = builder.get_model(modelJSON)
    model.default_cfg = {
        "architecture": "gpunet_p0",
        "crop_pct": 0.875,
        "interpolation": "bicubic",
        "input_size": (3, model.imgRes, model.imgRes),
        "num_classes": 1000,
        "mean": (0.485, 0.456, 0.406),
        "std": (0.229, 0.224, 0.225),
    }
    print("model CFG:", model.default_cfg)
    for key in model.default_cfg:
        setattr(model, key, model.default_cfg[key])
    if pretrained:
        model.load_state_dict(torch.load(checkpoint_path))
    return model


@register_model
def gpunet_p1(pretrained=False, **kwargs):
    """Constructs GPUNet-P1."""
    modelJSON, checkpoint_path = get_configs(
        batch=1, latency="0.8ms-D", gpuType="GV100", download=False
    )
    builder = GPUNet_Builder()
    model = builder.get_model(modelJSON)
    model.default_cfg = {
        "architecture": "gpunet_p1",
        "crop_pct": 0.875,
        "interpolation": "bicubic",
        "input_size": (3, model.imgRes, model.imgRes),
        "num_classes": 1000,
        "mean": (0.485, 0.456, 0.406),
        "std": (0.229, 0.224, 0.225),
    }
    print("model CFG:", model.default_cfg)
    for key in model.default_cfg:
        setattr(model, key, model.default_cfg[key])
    if pretrained:
        model.load_state_dict(torch.load(checkpoint_path))
    return model


def _parse_args():
    # Do we have a config file to parse?
    args_config, remaining = config_parser.parse_known_args()
    if args_config.config:
        with open(args_config.config, "r") as f:
            cfg = yaml.safe_load(f)
            parser.set_defaults(**cfg)

    # The main arg parser parses the rest of the args, the usual
    # defaults will have been overridden if config file specified.
    args = parser.parse_args(remaining)

    # Cache the args as a text string to save them in the output dir later
    args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
    return args, args_text

def unique_log_fpath(fpath):
    """Have a unique log filename for every separate run"""
    log_num = max([0] + [int(re.search("\.(\d+)", Path(f).suffix).group(1))
                         for f in glob.glob(f"{fpath}.*")])
    return f"{fpath}.{log_num + 1}"

def main():

    setup_default_logging()
    args, args_text = _parse_args()

    if args.log_wandb:
        if has_wandb:
            wandb.init(project=args.experiment, config=args)
        else:
            _logger.warning(
                "You've requested to log metrics to wandb but package not found. "
                "Metrics not being logged to wandb, try `pip install wandb`"
            )

    args.prefetcher = not args.no_prefetcher
    args.distributed = False
    if "WORLD_SIZE" in os.environ:
        args.distributed = int(os.environ["WORLD_SIZE"]) > 1
    args.device = "cuda:0"
    args.world_size = 1
    args.rank = 0  # global rank
    if args.distributed:
        args.local_rank = int(os.environ.get("LOCAL_RANK", args.local_rank))
        args.device = "cuda:%d" % args.local_rank
        torch.cuda.set_device(args.local_rank)
        print("->setting device:", args.local_rank)
        torch.distributed.init_process_group(backend="nccl", init_method="env://")
        args.world_size = torch.distributed.get_world_size()
        args.rank = torch.distributed.get_rank()
        _logger.info(
            "Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d."
            % (args.rank, args.world_size)
        )
    else:
        _logger.info("Training with a single process on 1 GPUs.")
    assert args.rank >= 0

    # resolve AMP arguments based on PyTorch / Apex availability
    use_amp = None
    if args.amp:
        # `--amp` chooses native amp before apex (APEX ver not actively maintained)
        if has_native_amp:
            args.native_amp = True
        elif has_apex:
            args.apex_amp = True
    if args.apex_amp and has_apex:
        use_amp = "apex"
    elif args.native_amp and has_native_amp:
        use_amp = "native"
    elif args.apex_amp or args.native_amp:
        _logger.warning(
            "Neither APEX or native Torch AMP is available, using float32. "
            "Install NVIDA apex or upgrade to PyTorch 1.6"
        )

    random_seed(args.seed, args.rank)

    model = create_model(
        args.model,
        pretrained=args.pretrained,
        num_classes=args.num_classes,
        drop_rate=args.drop,
        drop_connect_rate=args.drop_connect,  # DEPRECATED, use drop_path
        drop_path_rate=args.drop_path,
        drop_block_rate=args.drop_block,
        global_pool=args.gp,
        bn_tf=args.bn_tf,
        bn_momentum=args.bn_momentum,
        bn_eps=args.bn_eps,
        scriptable=args.torchscript,
        checkpoint_path=args.initial_checkpoint,
    )
    if args.num_classes is None:
        assert hasattr(
            model, "num_classes"
        ), "Model must have `num_classes` attr if not set on cmd line/config."
        args.num_classes = (
            model.num_classes
        )

    if args.distributed:
        torch.distributed.barrier()

    if args.local_rank == 0:
        _logger.info(
            f"Model {safe_model_name(args.model)} created, param count:{sum([m.numel() for m in model.parameters()])}"
        )
        print(model)
        dllogger_dir = os.path.dirname(args.dllogger_name)
        if dllogger_dir and not os.path.exists(dllogger_dir):
            os.makedirs(dllogger_dir, exist_ok=True)
        log_path = args.dllogger_name
        dllogger.init(
            backends=[
                dllogger.JSONStreamBackend(verbosity=1, filename=log_path, append=True),
                dllogger.JSONStreamBackend(verbosity=1, filename=unique_log_fpath(log_path)),
                dllogger.StdOutBackend(verbosity=0),
            ]
        )
    else:
        dllogger.init(backends=[])
        
    dllogger.metadata("train_loss", {"unit": None})
    dllogger.metadata("items_sec", {"unit": "images/s"})
    dllogger.metadata("val_loss", {"unit": None})
    dllogger.metadata("val_top1", {"unit": None})
    dllogger.metadata("val_top5", {"unit": None})
    dllogger.metadata("top1", {"unit": None})
    dllogger.metadata("top5", {"unit": None})
    dllogger.metadata("average_ips", {"unit": "images/s"})

    data_config = resolve_data_config(
        vars(args), model=model, verbose=args.local_rank == 0
    )

    # setup augmentation batch splits for contrastive loss or split bn
    num_aug_splits = 0
    if args.aug_splits > 0:
        assert args.aug_splits > 1, "A split of 1 makes no sense"
        num_aug_splits = args.aug_splits

    # enable split bn (separate bn stats per batch-portion)
    if args.split_bn:
        assert num_aug_splits > 1 or args.resplit
        model = convert_splitbn_model(model, max(num_aug_splits, 2))

    # move model to GPU, enable channels last layout if set
    model.cuda()
    if args.channels_last:
        model = model.to(memory_format=torch.channels_last)

    # setup synchronized BatchNorm for distributed training
    if args.distributed and args.sync_bn:
        assert not args.split_bn
        if has_apex and use_amp != "native":
            # Apex SyncBN preferred unless native amp is activated
            model = convert_syncbn_model(model)
        else:
            model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
        if args.local_rank == 0:
            _logger.info(
                "Converted model to use Synchronized BatchNorm. WARNING: You may have issues if using "
                "zero initialized BN layers (enabled by default for ResNets) while sync-bn enabled."
            )

    if args.torchscript:
        assert not use_amp == "apex", "Cannot use APEX AMP with torchscripted model"
        assert not args.sync_bn, "Cannot use SyncBatchNorm with torchscripted model"
        model = torch.jit.script(model)

    optimizer = create_optimizer_v2(model, **optimizer_kwargs(cfg=args))

    # setup automatic mixed-precision (AMP) loss scaling and op casting
    amp_autocast = suppress  # do nothing
    loss_scaler = None
    if use_amp == "apex":
        model, optimizer = amp.initialize(model, optimizer, opt_level="O1")
        loss_scaler = ApexScaler()
        if args.local_rank == 0:
            _logger.info("Using NVIDIA APEX AMP. Training in mixed precision.")
    elif use_amp == "native":
        amp_autocast = torch.cuda.amp.autocast
        loss_scaler = NativeScaler()
        if args.local_rank == 0:
            _logger.info("Using native Torch AMP. Training in mixed precision.")
    else:
        if args.local_rank == 0:
            _logger.info("AMP not enabled. Training in float32.")

    # optionally resume from a checkpoint
    resume_epoch = None
    if args.resume and os.path.isfile(args.resume):
        resume_epoch = resume_checkpoint(
            model,
            args.resume,
            optimizer=None if args.no_resume_opt else optimizer,
            loss_scaler=None if args.no_resume_opt else loss_scaler,
            log_info=args.local_rank == 0,
        )
    elif args.resume and not os.path.isfile(args.resume):
        print("Warning, resume indicated, but file not found, starting training over")

    # setup exponential moving average of model weights, SWA could be used here too
    model_ema = None
    if args.model_ema:
        # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
        model_ema = ModelEmaV2(
            model,
            decay=args.model_ema_decay,
            device="cpu" if args.model_ema_force_cpu else None,
        )
        if args.resume and os.path.isfile(args.resume):
            load_checkpoint(model_ema.module, args.resume, use_ema=True)

    # setup distributed training
    if args.distributed:
        if has_apex and use_amp != "native":
            # Apex DDP preferred unless native amp is activated
            if args.local_rank == 0:
                _logger.info("Using NVIDIA APEX DistributedDataParallel.")
            model = ApexDDP(model, delay_allreduce=True)
        else:
            if args.local_rank == 0:
                _logger.info("Using native Torch DistributedDataParallel.")
            model = NativeDDP(
                model, device_ids=[args.local_rank]
            )  # can use device str in Torch >= 1.1

    # NOTE: EMA model does not need to be wrapped by DDP
    # setup learning rate schedule and starting epoch
    lr_scheduler, num_epochs = create_scheduler(args, optimizer)
    start_epoch = 0
    if args.start_epoch is not None:
        # a specified start_epoch will always override the resume epoch
        start_epoch = args.start_epoch
    elif resume_epoch is not None:
        start_epoch = resume_epoch
    if lr_scheduler is not None and start_epoch > 0:
        lr_scheduler.step(start_epoch)

    if args.local_rank == 0:
        _logger.info("Scheduled epochs: {}".format(num_epochs))

    # create the train and eval datasets
    dataset_train = create_dataset(
        args.dataset,
        root=args.data_dir,
        split=args.train_split,
        is_training=True,
        batch_size=args.batch_size,
        repeats=args.epoch_repeats,
    )
    dataset_eval = create_dataset(
        args.dataset,
        root=args.data_dir,
        split=args.val_split,
        is_training=False,
        batch_size=args.batch_size,
    )

    # setup mixup / cutmix
    collate_fn = None
    mixup_fn = None
    mixup_active = args.mixup > 0 or args.cutmix > 0.0 or args.cutmix_minmax is not None
    if mixup_active:
        mixup_args = dict(
            mixup_alpha=args.mixup,
            cutmix_alpha=args.cutmix,
            cutmix_minmax=args.cutmix_minmax,
            prob=args.mixup_prob,
            switch_prob=args.mixup_switch_prob,
            mode=args.mixup_mode,
            label_smoothing=args.smoothing,
            num_classes=args.num_classes,
        )
        if args.prefetcher:
            assert (
                not num_aug_splits
            )  # collate conflict (need to support deinterleaving in collate mixup)
            collate_fn = FastCollateMixup(**mixup_args)
        else:
            mixup_fn = Mixup(**mixup_args)

    # wrap dataset in AugMix helper
    if num_aug_splits > 1:
        dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits)

    # create data loaders w/ augmentation pipeiine
    train_interpolation = args.train_interpolation
    if args.no_aug or not train_interpolation:
        train_interpolation = data_config["interpolation"]
    _logger.info("Before creating loader from GPU: %s", args.local_rank)

    student_res = data_config["input_size"]
    useTwoRes = False
    if student_res != data_config["input_size"]:
        useTwoRes = True

    loader_train = create_loader(
        dataset_train,
        input_size=data_config["input_size"],
        batch_size=args.batch_size,
        is_training=True,
        use_prefetcher=args.prefetcher,
        no_aug=args.no_aug,
        re_prob=args.reprob,
        re_mode=args.remode,
        re_count=args.recount,
        re_split=args.resplit,
        scale=args.scale,
        ratio=args.ratio,
        hflip=args.hflip,
        vflip=args.vflip,
        color_jitter=args.color_jitter,
        auto_augment=args.aa,
        num_aug_splits=num_aug_splits,
        interpolation=train_interpolation,
        mean=data_config["mean"],
        std=data_config["std"],
        num_workers=args.workers,
        distributed=args.distributed,
        collate_fn=collate_fn,
        pin_memory=args.pin_mem,
        use_multi_epochs_loader=args.use_multi_epochs_loader,
    )
    teacher_res = (3, args.teacher_img_size, args.teacher_img_size)
    student_res = (3, args.img_size, args.img_size)

    print(
        "teacher eval resolution: ",
        teacher_res,
        " student resolution:",
        student_res,
        " train resolution:",
        data_config["input_size"],
    )

    # setup loss function
    if args.jsd:
        assert num_aug_splits > 1  # JSD only valid with aug splits set
        train_loss_fn = JsdCrossEntropy(
            num_splits=num_aug_splits, smoothing=args.smoothing
        ).cuda()
    elif mixup_active:
        # smoothing is handled with mixup target transform
        train_loss_fn = SoftTargetCrossEntropy().cuda()
    elif args.smoothing:
        train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing).cuda()
    else:
        train_loss_fn = nn.CrossEntropyLoss().cuda()
    validate_loss_fn = nn.CrossEntropyLoss().cuda()

    # setup the distillation
    teacher_model = None
    if args.enable_distill:
        loader_teacher_eval = create_loader(
            dataset_eval,
            input_size=teacher_res,
            batch_size=args.validation_batch_size_multiplier * args.batch_size,
            is_training=False,
            use_prefetcher=args.prefetcher,
            interpolation=data_config["interpolation"],
            mean=data_config["mean"],
            std=data_config["std"],
            num_workers=args.workers,
            distributed=args.distributed,
            crop_pct=data_config["crop_pct"],
            pin_memory=args.pin_mem,
        )
        if args.local_rank == 0:
            _logger.info("#" * 10)
            _logger.info("create distillation")
            _logger.info("model: %s", args.teacher)
            _logger.info("checkpoint: %s", args.teacher_checkpoint)
            _logger.info("teacher image size: %s", args.teacher_img_size)
            _logger.info("#" * 10)
        assert args.teacher != ""
        _logger.info("#####GPU: %s, reached the barrier", args.local_rank)
        if args.distributed:
            torch.distributed.barrier()

        teacher_model = create_model(
            args.teacher, pretrained=True, num_classes=args.num_classes, in_chans=3
        )

        teacher_model.cuda()
        teacher_model.eval()
        if args.test_teacher:
            print("==start testing the teacher==")
        if args.local_rank == 0 and args.test_teacher:
            eval_metrics = validate(
                teacher_model, loader_teacher_eval, validate_loss_fn, args
            )
            print(
                "teacher evaluation results:",
                " loss:",
                eval_metrics["loss"],
                " top1:",
                eval_metrics["top1"],
                " top5:",
                eval_metrics["top5"],
            )

        if args.distributed:
            torch.distributed.barrier()

    loader_eval = create_loader(
        dataset_eval,
        input_size=student_res,
        batch_size=args.validation_batch_size_multiplier * args.batch_size,
        is_training=False,
        use_prefetcher=args.prefetcher,
        interpolation=data_config["interpolation"],
        mean=data_config["mean"],
        std=data_config["std"],
        num_workers=args.workers,
        distributed=args.distributed,
        crop_pct=data_config["crop_pct"],
        pin_memory=args.pin_mem,
    )
    # setup checkpoint saver and eval metric tracking
    eval_metric = args.eval_metric
    best_metric = None
    best_epoch = None
    saver = None
    output_dir = None
    epoch_throughput = []
    if args.local_rank == 0:
        if args.experiment:
            exp_name = args.experiment
        else:
            exp_name = "-".join(
                [
                    datetime.now().strftime("%Y%m%d-%H%M%S"),
                    safe_model_name(args.model),
                    str(data_config["input_size"][-1]),
                ]
            )
        exp_name = "checkpoints"
        output_dir = get_outdir(
            args.output if args.output else "./output/train", exp_name
        )
        decreasing = True if eval_metric == "loss" else False
        saver = CheckpointSaver(
            model=model,
            optimizer=optimizer,
            args=args,
            model_ema=model_ema,
            amp_scaler=loss_scaler,
            checkpoint_dir=output_dir,
            recovery_dir=output_dir,
            decreasing=decreasing,
            max_history=args.checkpoint_hist,
        )
        with open(os.path.join(output_dir, "args.yaml"), "w") as f:
            f.write(args_text)

    try:
        for epoch in range(start_epoch, num_epochs):
            if args.distributed and hasattr(loader_train.sampler, "set_epoch"):
                loader_train.sampler.set_epoch(epoch)

            train_metrics = train_one_epoch(
                epoch,
                model,
                loader_train,
                optimizer,
                train_loss_fn,
                args,
                lr_scheduler=lr_scheduler,
                saver=saver,
                output_dir=output_dir,
                amp_autocast=amp_autocast,
                loss_scaler=loss_scaler,
                model_ema=model_ema,
                mixup_fn=mixup_fn,
                teacher_model=teacher_model,
                student_res=student_res,
                useTwoRes=useTwoRes,
                benchmark_steps=args.benchmark_steps,
            )
            epoch_throughput.append(train_metrics["items_sec"])
            dllogger.log(step=epoch, data={"train_loss": train_metrics["loss"], "items_sec": train_metrics["items_sec"]}, verbosity=1)
            dllogger.log(step=(), data={"train_loss": train_metrics["loss"], "items_sec": train_metrics["items_sec"]}, verbosity=1)

            if args.distributed and args.dist_bn in ("broadcast", "reduce"):
                if args.local_rank == 0:
                    _logger.info("Distributing BatchNorm running means and vars")
                distribute_bn(model, args.world_size, args.dist_bn == "reduce")

            eval_metrics = validate(
                model, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast
            )

            if model_ema is not None and not args.model_ema_force_cpu:
                if args.distributed and args.dist_bn in ("broadcast", "reduce"):
                    distribute_bn(model_ema, args.world_size, args.dist_bn == "reduce")
                ema_eval_metrics = validate(
                    model_ema.module,
                    loader_eval,
                    validate_loss_fn,
                    args,
                    amp_autocast=amp_autocast,
                    log_suffix=" (EMA)",
                )
                eval_metrics = ema_eval_metrics

            dllogger.log(step=epoch, data={"val_loss": eval_metrics["loss"], "val_top1": eval_metrics["top1"], "val_top5": eval_metrics["top5"]}, verbosity=1)
            dllogger.log(step=(), data={"val_loss": eval_metrics["loss"], "val_top1": eval_metrics["top1"], "val_top5": eval_metrics["top5"]}, verbosity=1)
            dllogger.flush()
            if lr_scheduler is not None:
                # step LR for next epoch
                lr_scheduler.step(epoch + 1, eval_metrics[eval_metric])

            if output_dir is not None:
                update_summary(
                    epoch,
                    train_metrics,
                    eval_metrics,
                    os.path.join(output_dir, "summary.csv"),
                    write_header=best_metric is None,
                    log_wandb=args.log_wandb and has_wandb,
                )

            if saver is not None:
                # save proper checkpoint with eval metric
                save_metric = eval_metrics[eval_metric]
                best_metric, best_epoch = saver.save_checkpoint(
                    epoch, metric=save_metric
                )
        if len(epoch_throughput) > 0:
            mean_train_throughput = sum(epoch_throughput) / len(epoch_throughput)
        else:
            mean_train_throughput = 0
        log_metrics = dict(eval_metrics)
        log_metrics["average_ips"] = mean_train_throughput
        dllogger.log(step=tuple(), data=log_metrics, verbosity=0)
        dllogger.flush()
    except KeyboardInterrupt:
        pass
    if best_metric is not None:
        _logger.info("*** Best metric: {0} (epoch {1})".format(best_metric, best_epoch))


def train_one_epoch(
    epoch,
    model,
    loader,
    optimizer,
    loss_fn,
    args,
    lr_scheduler=None,
    saver=None,
    output_dir=None,
    amp_autocast=suppress,
    loss_scaler=None,
    model_ema=None,
    mixup_fn=None,
    teacher_model=None,
    student_res=None,
    useTwoRes=False,
    benchmark_steps=None,
):
    if teacher_model is not None:
        assert student_res is not None

    if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
        if args.prefetcher and loader.mixup_enabled:
            loader.mixup_enabled = False
        elif mixup_fn is not None:
            mixup_fn.mixup_enabled = False

    second_order = hasattr(optimizer, "is_second_order") and optimizer.is_second_order
    batch_time_m = AverageMeter()
    data_time_m = AverageMeter()
    losses_m = AverageMeter()

    model.train()
    if teacher_model is not None:
        teacher_model.eval()

    end = time.time()
    last_idx = len(loader) - 1
    num_updates = epoch * len(loader)
    rate_avg = 0
    for batch_idx, (input, target) in enumerate(loader):
        last_batch = (batch_idx == last_idx) or (batch_idx == benchmark_steps)
        data_time_m.update(time.time() - end)
        if not args.prefetcher:
            input, target = input.cuda(), target.cuda()
            if mixup_fn is not None:
                input, target = mixup_fn(input, target)
        if args.channels_last:
            input = input.contiguous(memory_format=torch.channels_last)

        if teacher_model is not None and useTwoRes:
            student_input = F.interpolate(
                input, size=(student_res[1], student_res[2]), mode="bicubic"
            )

        with amp_autocast():
            if teacher_model is not None and useTwoRes:
                output = model(student_input)
            else:
                output = model(input)
            loss = loss_fn(output, target)

        if teacher_model is not None:
            with torch.no_grad():
                soft_logits = teacher_model(input).detach()
                soft_label = F.softmax(soft_logits, dim=1)
            kd_loss = cross_entropy_loss_with_soft_target(output, soft_label)
            loss = kd_loss + loss

        if not args.distributed:
            losses_m.update(loss.item(), input.size(0))

        optimizer.zero_grad()
        if loss_scaler is not None:
            loss_scaler(
                loss,
                optimizer,
                clip_grad=args.clip_grad,
                clip_mode=args.clip_mode,
                parameters=model_parameters(
                    model, exclude_head="agc" in args.clip_mode
                ),
                create_graph=second_order,
            )
        else:
            loss.backward(create_graph=second_order)
            if args.clip_grad is not None:
                dispatch_clip_grad(
                    model_parameters(model, exclude_head="agc" in args.clip_mode),
                    value=args.clip_grad,
                    mode=args.clip_mode,
                )
            optimizer.step()

        if model_ema is not None:
            model_ema.update(model)

        torch.cuda.synchronize()
        num_updates += 1
        batch_time_m.update(time.time() - end)
        if last_batch or batch_idx % args.log_interval == 0:
            lrl = [param_group["lr"] for param_group in optimizer.param_groups]
            lr = sum(lrl) / len(lrl)

            if args.distributed:
                reduced_loss = reduce_tensor(loss.data, args.world_size)
                losses_m.update(reduced_loss.item(), input.size(0))

            rate_avg = input.size(0) * args.world_size / batch_time_m.avg
            if args.local_rank == 0:
                _logger.info(
                    "{} Train: {} [{:>4d}/{} ({:>3.0f}%)]  "
                    "Loss: {loss.val:>9.6f} ({loss.avg:>6.4f})  "
                    "Time: {batch_time.val:.3f}s, {rate:>7.2f}/s  "
                    "({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s)  "
                    "LR: {lr:.3e}  "
                    "Data: {data_time.val:.3f} ({data_time.avg:.3f})".format(
                        datetime.now().strftime("%d.%b %Y %H:%M:%S"),
                        epoch,
                        batch_idx,
                        len(loader),
                        100.0 * batch_idx / last_idx,
                        loss=losses_m,
                        batch_time=batch_time_m,
                        rate=input.size(0) * args.world_size / batch_time_m.val,
                        rate_avg=input.size(0) * args.world_size / batch_time_m.avg,
                        lr=lr,
                        data_time=data_time_m,
                    )
                )

                if args.save_images and output_dir:
                    torchvision.utils.save_image(
                        input,
                        os.path.join(output_dir, "train-batch-%d.jpg" % batch_idx),
                        padding=0,
                        normalize=True,
                    )

        if (
            saver is not None
            and args.recovery_interval
            and (last_batch or (batch_idx + 1) % args.recovery_interval == 0)
        ):
            saver.save_recovery(epoch, batch_idx=batch_idx)

        if lr_scheduler is not None:
            lr_scheduler.step_update(num_updates=num_updates, metric=losses_m.avg)

        end = time.time()
        # end for
        if (batch_idx == benchmark_steps):
            break

    if hasattr(optimizer, "sync_lookahead"):
        optimizer.sync_lookahead()

    return OrderedDict([("loss", losses_m.avg), ("items_sec", rate_avg)])


def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_suffix=""):
    batch_time_m = AverageMeter()
    losses_m = AverageMeter()
    top1_m = AverageMeter()
    top5_m = AverageMeter()

    model.eval()

    end = time.time()
    last_idx = len(loader) - 1
    with torch.no_grad():
        for batch_idx, (input, target) in enumerate(loader):
            last_batch = batch_idx == last_idx
            if not args.prefetcher:
                input = input.cuda()
                target = target.cuda()
            if args.channels_last:
                input = input.contiguous(memory_format=torch.channels_last)

            with amp_autocast():
                output = model(input)
            if isinstance(output, (tuple, list)):
                output = output[0]

            # augmentation reduction
            reduce_factor = args.tta
            if reduce_factor > 1:
                output = output.unfold(0, reduce_factor, reduce_factor).mean(dim=2)
                target = target[0 : target.size(0) : reduce_factor]

            loss = loss_fn(output, target)
            acc1, acc5 = accuracy(output, target, topk=(1, 5))

            if args.distributed:
                reduced_loss = reduce_tensor(loss.data, args.world_size)
                acc1 = reduce_tensor(acc1, args.world_size)
                acc5 = reduce_tensor(acc5, args.world_size)
            else:
                reduced_loss = loss.data

            torch.cuda.synchronize()

            losses_m.update(reduced_loss.item(), input.size(0))
            top1_m.update(acc1.item(), output.size(0))
            top5_m.update(acc5.item(), output.size(0))

            batch_time_m.update(time.time() - end)
            end = time.time()
            if args.local_rank == 0 and (
                last_batch or batch_idx % args.log_interval == 0
            ):
                log_name = "Test" + log_suffix
                _logger.info(
                    "{0}: [{1:>4d}/{2}]  "
                    "Time: {batch_time.val:.3f} ({batch_time.avg:.3f})  "
                    "Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  "
                    "Acc@1: {top1.val:>7.4f} ({top1.avg:>7.4f})  "
                    "Acc@5: {top5.val:>7.4f} ({top5.avg:>7.4f})".format(
                        log_name,
                        batch_idx,
                        last_idx,
                        batch_time=batch_time_m,
                        loss=losses_m,
                        top1=top1_m,
                        top5=top5_m,
                    )
                )

    metrics = OrderedDict(
        [("loss", losses_m.avg), ("top1", top1_m.avg), ("top5", top5_m.avg)]
    )

    return metrics


if __name__ == "__main__":
    main()
