DeepLearningExamples/PyTorch/Classification/ConvNets/quant_main.py

# Copyright (c) 2018-2019, NVIDIA CORPORATION
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import argparse
import random
from copy import deepcopy

import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed

from image_classification.training import *
from image_classification.utils import *
from image_classification.quantization import *
from image_classification.models import efficientnet_quant_b0, efficientnet_quant_b4

from main import prepare_for_training, add_parser_arguments as parse_training

import dllogger


def available_models():
    models = {
        m.name: m
        for m in [
            efficientnet_quant_b0,
            efficientnet_quant_b4,
        ]
    }
    return models


def parse_quantization(parser):
    model_names = available_models().keys()
    parser.add_argument(
        "--arch",
        "-a",
        metavar="ARCH",
        default="efficientnet-quant-b0",
        choices=model_names,
        help="model architecture: "
        + " | ".join(model_names)
        + " (default: efficientnet-quant-b0)",
    )

    parser.add_argument(
        "--skip-calibration",
        action="store_true",
        help="skip calibration before training, (default: false)",
    )


def parse_training_args(parser):
    from main import add_parser_arguments

    return add_parser_arguments(parser)


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

    skip_calibration = args.skip_calibration or args.evaluate or args.resume is not None

    select_default_calib_method()

    (
        trainer,
        lr_policy,
        train_loader,
        train_loader_len,
        val_loader,
        logger,
        start_epoch,
    ) = prepare_for_training(args, model_args, model_arch)

    print(f"RUNNING QUANTIZATION")

    if not skip_calibration:
        calibrate(trainer.model_and_loss.model, train_loader, logger, calib_iter=10)

    train_loop(
        trainer,
        lr_policy,
        train_loader,
        train_loader_len,
        val_loader,
        logger,
        should_backup_checkpoint(args),
        start_epoch=start_epoch,
        end_epoch=min((start_epoch + args.run_epochs), args.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,
        checkpoint_dir=args.workspace,
        checkpoint_filename="quantized_" + args.checkpoint_filename,
    )

    if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
        logger.end()
    print("Experiment ended")


if __name__ == "__main__":
    epilog = [
        "Based on the architecture picked by --arch flag, you may use the following options:\n"
    ]
    for model, ep in available_models().items():
        model_help = "\n".join(ep.parser().format_help().split("\n")[2:])
        epilog.append(model_help)
    parser = argparse.ArgumentParser(
        description="PyTorch ImageNet Training",
        epilog="\n".join(epilog),
        formatter_class=argparse.RawDescriptionHelpFormatter,
    )

    parse_quantization(parser)
    parse_training(parser, skip_arch=True)

    args, rest = parser.parse_known_args()

    model_arch = available_models()[args.arch]
    model_args, rest = model_arch.parser().parse_known_args(rest)
    print(model_args)

    assert len(rest) == 0, f"Unknown args passed: {rest}"

    cudnn.benchmark = True

    main(args, model_args, model_arch)