DeepLearningExamples/PyTorch/Classification/ConvNets/main.py

# Copyright (c) 2018-2019, NVIDIA CORPORATION
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import argparse
import os
import shutil
import time
import random

import numpy as np
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets

try:
    from apex.parallel import DistributedDataParallel as DDP
    from apex.fp16_utils import *
    from apex import amp
except ImportError:
    raise ImportError(
        "Please install apex from https://www.github.com/nvidia/apex to run this example."
    )

import image_classification.resnet as models
import image_classification.logger as log

from image_classification.smoothing import LabelSmoothing
from image_classification.mixup import NLLMultiLabelSmooth, MixUpWrapper
from image_classification.dataloaders import *
from image_classification.training import *
from image_classification.utils import *

import dllogger


def add_parser_arguments(parser):
    model_names = models.resnet_versions.keys()
    model_configs = models.resnet_configs.keys()

    parser.add_argument("data", metavar="DIR", help="path to dataset")
    parser.add_argument(
        "--data-backend",
        metavar="BACKEND",
        default="dali-cpu",
        choices=DATA_BACKEND_CHOICES,
        help="data backend: "
        + " | ".join(DATA_BACKEND_CHOICES)
        + " (default: dali-cpu)",
    )

    parser.add_argument(
        "--arch",
        "-a",
        metavar="ARCH",
        default="resnet50",
        choices=model_names,
        help="model architecture: " + " | ".join(model_names) + " (default: resnet50)",
    )

    parser.add_argument(
        "--model-config",
        "-c",
        metavar="CONF",
        default="classic",
        choices=model_configs,
        help="model configs: " + " | ".join(model_configs) + "(default: classic)",
    )

    parser.add_argument(
        "--num-classes",
        metavar="N",
        default=1000,
        type=int,
        help="number of classes in the dataset",
    )

    parser.add_argument(
        "-j",
        "--workers",
        default=5,
        type=int,
        metavar="N",
        help="number of data loading workers (default: 5)",
    )
    parser.add_argument(
        "--epochs",
        default=90,
        type=int,
        metavar="N",
        help="number of total epochs to run",
    )
    parser.add_argument(
        "--run-epochs",
        default=-1,
        type=int,
        metavar="N",
        help="run only N epochs, used for checkpointing runs",
    )
    parser.add_argument(
        "-b",
        "--batch-size",
        default=256,
        type=int,
        metavar="N",
        help="mini-batch size (default: 256) per gpu",
    )

    parser.add_argument(
        "--optimizer-batch-size",
        default=-1,
        type=int,
        metavar="N",
        help="size of a total batch size, for simulating bigger batches using gradient accumulation",
    )

    parser.add_argument(
        "--lr",
        "--learning-rate",
        default=0.1,
        type=float,
        metavar="LR",
        help="initial learning rate",
    )
    parser.add_argument(
        "--lr-schedule",
        default="step",
        type=str,
        metavar="SCHEDULE",
        choices=["step", "linear", "cosine"],
        help="Type of LR schedule: {}, {}, {}".format("step", "linear", "cosine"),
    )

    parser.add_argument(
        "--warmup", default=0, type=int, metavar="E", help="number of warmup epochs"
    )

    parser.add_argument(
        "--label-smoothing",
        default=0.0,
        type=float,
        metavar="S",
        help="label smoothing",
    )
    parser.add_argument(
        "--mixup", default=0.0, type=float, metavar="ALPHA", help="mixup alpha"
    )

    parser.add_argument(
        "--momentum", default=0.9, type=float, metavar="M", help="momentum"
    )
    parser.add_argument(
        "--weight-decay",
        "--wd",
        default=1e-4,
        type=float,
        metavar="W",
        help="weight decay (default: 1e-4)",
    )
    parser.add_argument(
        "--bn-weight-decay",
        action="store_true",
        help="use weight_decay on batch normalization learnable parameters, (default: false)",
    )
    parser.add_argument(
        "--nesterov",
        action="store_true",
        help="use nesterov momentum, (default: false)",
    )

    parser.add_argument(
        "--print-freq",
        "-p",
        default=10,
        type=int,
        metavar="N",
        help="print frequency (default: 10)",
    )
    parser.add_argument(
        "--resume",
        default=None,
        type=str,
        metavar="PATH",
        help="path to latest checkpoint (default: none)",
    )
    parser.add_argument(
        "--pretrained-weights",
        default="",
        type=str,
        metavar="PATH",
        help="load weights from here",
    )

    parser.add_argument("--fp16", action="store_true", help="Run model fp16 mode.")
    parser.add_argument(
        "--static-loss-scale",
        type=float,
        default=1,
        help="Static loss scale, positive power of 2 values can improve fp16 convergence.",
    )
    parser.add_argument(
        "--dynamic-loss-scale",
        action="store_true",
        help="Use dynamic loss scaling.  If supplied, this argument supersedes "
        + "--static-loss-scale.",
    )
    parser.add_argument(
        "--prof", type=int, default=-1, metavar="N", help="Run only N iterations"
    )
    parser.add_argument(
        "--amp",
        action="store_true",
        help="Run model AMP (automatic mixed precision) mode.",
    )

    parser.add_argument(
        "--seed", default=None, type=int, help="random seed used for numpy and pytorch"
    )

    parser.add_argument(
        "--gather-checkpoints",
        action="store_true",
        help="Gather checkpoints throughout the training, without this flag only best and last checkpoints will be stored",
    )

    parser.add_argument(
        "--raport-file",
        default="experiment_raport.json",
        type=str,
        help="file in which to store JSON experiment raport",
    )

    parser.add_argument(
        "--evaluate", action="store_true", help="evaluate checkpoint/model"
    )
    parser.add_argument("--training-only", action="store_true", help="do not evaluate")

    parser.add_argument(
        "--no-checkpoints",
        action="store_false",
        dest="save_checkpoints",
        help="do not store any checkpoints, useful for benchmarking",
    )

    parser.add_argument("--checkpoint-filename", default="checkpoint.pth.tar", type=str)
    
    parser.add_argument(
        "--workspace",
        type=str,
        default="./",
        metavar="DIR",
        help="path to directory where checkpoints will be stored",
    )
    parser.add_argument(
        "--memory-format",
        type=str,
        default="nchw",
        choices=["nchw", "nhwc"],
        help="memory layout, nchw or nhwc",
    )


def main(args):
    exp_start_time = time.time()
    global best_prec1
    best_prec1 = 0

    args.distributed = False
    if "WORLD_SIZE" in os.environ:
        args.distributed = int(os.environ["WORLD_SIZE"]) > 1
        args.local_rank = int(os.environ["LOCAL_RANK"])

    args.gpu = 0
    args.world_size = 1

    if args.distributed:
        args.gpu = args.local_rank % torch.cuda.device_count()
        torch.cuda.set_device(args.gpu)
        dist.init_process_group(backend="nccl", init_method="env://")
        args.world_size = torch.distributed.get_world_size()

    if args.amp and args.fp16:
        print("Please use only one of the --fp16/--amp flags")
        exit(1)

    if args.seed is not None:
        print("Using seed = {}".format(args.seed))
        torch.manual_seed(args.seed + args.local_rank)
        torch.cuda.manual_seed(args.seed + args.local_rank)
        np.random.seed(seed=args.seed + args.local_rank)
        random.seed(args.seed + args.local_rank)

        def _worker_init_fn(id):
            np.random.seed(seed=args.seed + args.local_rank + id)
            random.seed(args.seed + args.local_rank + id)

    else:

        def _worker_init_fn(id):
            pass

    if args.fp16:
        assert (
            torch.backends.cudnn.enabled
        ), "fp16 mode requires cudnn backend to be enabled."

    if args.static_loss_scale != 1.0:
        if not args.fp16:
            print("Warning:  if --fp16 is not used, static_loss_scale will be ignored.")

    if args.optimizer_batch_size < 0:
        batch_size_multiplier = 1
    else:
        tbs = args.world_size * args.batch_size
        if args.optimizer_batch_size % tbs != 0:
            print(
                "Warning: simulated batch size {} is not divisible by actual batch size {}".format(
                    args.optimizer_batch_size, tbs
                )
            )
        batch_size_multiplier = int(args.optimizer_batch_size / tbs)
        print("BSM: {}".format(batch_size_multiplier))

    pretrained_weights = None
    if args.pretrained_weights:
        if os.path.isfile(args.pretrained_weights):
            print(
                "=> loading pretrained weights from '{}'".format(
                    args.pretrained_weights
                )
            )
            pretrained_weights = torch.load(args.pretrained_weights)
        else:
            print("=> no pretrained weights found at '{}'".format(args.resume))

    start_epoch = 0
    # optionally resume from a checkpoint
    if args.resume is not None:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(
                args.resume, map_location=lambda storage, loc: storage.cuda(args.gpu)
            )
            start_epoch = checkpoint["epoch"]
            best_prec1 = checkpoint["best_prec1"]
            model_state = checkpoint["state_dict"]
            optimizer_state = checkpoint["optimizer"]
            print(
                "=> loaded checkpoint '{}' (epoch {})".format(
                    args.resume, checkpoint["epoch"]
                )
            )
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))
            model_state = None
            optimizer_state = None
    else:
        model_state = None
        optimizer_state = None

    loss = nn.CrossEntropyLoss
    if args.mixup > 0.0:
        loss = lambda: NLLMultiLabelSmooth(args.label_smoothing)
    elif args.label_smoothing > 0.0:
        loss = lambda: LabelSmoothing(args.label_smoothing)

    memory_format = (
        torch.channels_last if args.memory_format == "nhwc" else torch.contiguous_format
    )

    model_and_loss = ModelAndLoss(
        (args.arch, args.model_config, args.num_classes),
        loss,
        pretrained_weights=pretrained_weights,
        cuda=True,
        fp16=args.fp16,
        memory_format=memory_format,
    )

    # Create data loaders and optimizers as needed
    if args.data_backend == "pytorch":
        get_train_loader = get_pytorch_train_loader
        get_val_loader = get_pytorch_val_loader
    elif args.data_backend == "dali-gpu":
        get_train_loader = get_dali_train_loader(dali_cpu=False)
        get_val_loader = get_dali_val_loader()
    elif args.data_backend == "dali-cpu":
        get_train_loader = get_dali_train_loader(dali_cpu=True)
        get_val_loader = get_dali_val_loader()
    elif args.data_backend == "syntetic":
        get_val_loader = get_syntetic_loader
        get_train_loader = get_syntetic_loader

    train_loader, train_loader_len = get_train_loader(
        args.data,
        args.batch_size,
        args.num_classes,
        args.mixup > 0.0,
        start_epoch=start_epoch,
        workers=args.workers,
        fp16=args.fp16,
        memory_format=memory_format,
    )
    if args.mixup != 0.0:
        train_loader = MixUpWrapper(args.mixup, train_loader)

    val_loader, val_loader_len = get_val_loader(
        args.data,
        args.batch_size,
        args.num_classes,
        False,
        workers=args.workers,
        fp16=args.fp16,
        memory_format=memory_format,
    )

    if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
        logger = log.Logger(
            args.print_freq,
            [
                dllogger.StdOutBackend(
                    dllogger.Verbosity.DEFAULT, step_format=log.format_step
                ),
                dllogger.JSONStreamBackend(
                    dllogger.Verbosity.VERBOSE,
                    os.path.join(args.workspace, args.raport_file),
                ),
            ],
            start_epoch=start_epoch - 1,
        )

    else:
        logger = log.Logger(args.print_freq, [], start_epoch=start_epoch - 1)

    logger.log_parameter(args.__dict__, verbosity=dllogger.Verbosity.DEFAULT)

    optimizer = get_optimizer(
        list(model_and_loss.model.named_parameters()),
        args.fp16,
        args.lr,
        args.momentum,
        args.weight_decay,
        nesterov=args.nesterov,
        bn_weight_decay=args.bn_weight_decay,
        state=optimizer_state,
        static_loss_scale=args.static_loss_scale,
        dynamic_loss_scale=args.dynamic_loss_scale,
    )

    if args.lr_schedule == "step":
        lr_policy = lr_step_policy(
            args.lr, [30, 60, 80], 0.1, args.warmup, logger=logger
        )
    elif args.lr_schedule == "cosine":
        lr_policy = lr_cosine_policy(args.lr, args.warmup, args.epochs, logger=logger)
    elif args.lr_schedule == "linear":
        lr_policy = lr_linear_policy(args.lr, args.warmup, args.epochs, logger=logger)

    if args.amp:
        model_and_loss, optimizer = amp.initialize(
            model_and_loss,
            optimizer,
            opt_level="O1",
            loss_scale="dynamic" if args.dynamic_loss_scale else args.static_loss_scale,
        )

    if args.distributed:
        model_and_loss.distributed()

    model_and_loss.load_model_state(model_state)

    train_loop(
        model_and_loss,
        optimizer,
        lr_policy,
        train_loader,
        val_loader,
        args.fp16,
        logger,
        should_backup_checkpoint(args),
        use_amp=args.amp,
        batch_size_multiplier=batch_size_multiplier,
        start_epoch=start_epoch,
        end_epoch=(start_epoch + args.run_epochs)
        if args.run_epochs != -1
        else args.epochs,
        best_prec1=best_prec1,
        prof=args.prof,
        skip_training=args.evaluate,
        skip_validation=args.training_only,
        save_checkpoints=args.save_checkpoints and not args.evaluate,
        checkpoint_dir=args.workspace,
        checkpoint_filename=args.checkpoint_filename,
    )
    exp_duration = time.time() - exp_start_time
    if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
        logger.end()
    print("Experiment ended")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="PyTorch ImageNet Training")

    add_parser_arguments(parser)
    args = parser.parse_args()
    cudnn.benchmark = True

    main(args)