torchgeo/benchmark.py

#!/usr/bin/env python3

"""dataset and sampler benchmarking script."""

import argparse
import csv
import os
import time

import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.models import resnet34

from torchgeo.datasets import CDL, Landsat8, stack_samples
from torchgeo.samplers import GridGeoSampler, RandomBatchGeoSampler, RandomGeoSampler


def set_up_parser() -> argparse.ArgumentParser:
    """Set up the argument parser.

    Returns:
        the argument parser
    """
    parser = argparse.ArgumentParser(
        description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    parser.add_argument(
        "--landsat-root",
        default=os.path.join("data", "landsat"),
        help="directory containing Landsat data",
        metavar="ROOT",
    )
    parser.add_argument(
        "--cdl-root",
        default=os.path.join("data", "cdl"),
        help="directory containing CDL data",
        metavar="ROOT",
    )
    parser.add_argument(
        "-d", "--device", default=0, type=int, help="CPU/GPU ID to use", metavar="ID"
    )
    parser.add_argument(
        "-c",
        "--cache",
        action="store_true",
        help="cache file handles during data loading",
    )
    parser.add_argument(
        "-b",
        "--batch-size",
        default=2**4,
        type=int,
        help="number of samples in each mini-batch",
        metavar="SIZE",
    )
    group = parser.add_mutually_exclusive_group(required=True)
    group.add_argument(
        "-n",
        "--num-batches",
        type=int,
        help="number of batches to load",
        metavar="SIZE",
    )
    group.add_argument(
        "-e",
        "--epoch-size",
        type=int,
        help="number of samples to load, should be evenly divisible by batch size",
        metavar="SIZE",
    )
    parser.add_argument(
        "-p",
        "--patch-size",
        default=224,
        type=int,
        help="height/width of each patch in pixels",
        metavar="PIXELS",
    )
    parser.add_argument(
        "-s",
        "--stride",
        default=112,
        type=int,
        help="sampling stride for GridGeoSampler in pixels",
        metavar="PIXELS",
    )
    parser.add_argument(
        "-w",
        "--num-workers",
        default=0,
        type=int,
        help="number of workers for parallel data loading",
        metavar="NUM",
    )
    parser.add_argument(
        "--seed", default=0, type=int, help="random seed for reproducibility"
    )
    parser.add_argument(
        "--output-fn",
        default="benchmark-results.csv",
        type=str,
        help="path to the CSV file to write results",
        metavar="FILE",
    )
    parser.add_argument(
        "-v", "--verbose", action="store_true", help="print results to stdout"
    )

    return parser


def main(args: argparse.Namespace) -> None:
    """High-level pipeline.

    Benchmarks performance of various samplers with and without caching.

    Args:
        args: command-line arguments
    """
    bands = ["B1", "B2", "B3", "B4", "B5", "B6", "B7"]

    # Benchmark samplers

    # Initialize datasets
    cdl = CDL(args.cdl_root, cache=args.cache)
    landsat = Landsat8(
        args.landsat_root, crs=cdl.crs, res=cdl.res, cache=args.cache, bands=bands
    )
    dataset = landsat & cdl

    # Initialize samplers
    if args.epoch_size:
        length = args.epoch_size
        num_batches = args.epoch_size // args.batch_size
    elif args.num_batches:
        length = args.num_batches * args.batch_size
        num_batches = args.num_batches

    samplers = [
        RandomGeoSampler(landsat, size=args.patch_size, length=length),
        GridGeoSampler(landsat, size=args.patch_size, stride=args.stride),
        RandomBatchGeoSampler(
            landsat, size=args.patch_size, batch_size=args.batch_size, length=length
        ),
    ]

    results_rows = []
    for sampler in samplers:
        if args.verbose:
            print(f"\n{sampler.__class__.__name__}:")

        if isinstance(sampler, RandomBatchGeoSampler):
            dataloader = DataLoader(
                dataset,
                batch_sampler=sampler,
                num_workers=args.num_workers,
                collate_fn=stack_samples,
            )
        else:
            dataloader = DataLoader(
                dataset,
                batch_size=args.batch_size,
                sampler=sampler,
                num_workers=args.num_workers,
                collate_fn=stack_samples,
            )

        tic = time.time()
        num_total_patches = 0
        for i, batch in enumerate(dataloader):
            num_total_patches += args.batch_size
            # This is to stop the GridGeoSampler from enumerating everything
            if i == num_batches - 1:
                break
        toc = time.time()
        duration = toc - tic

        if args.verbose:
            print(f"  duration: {duration:.3f} sec")
            print(f"  count: {num_total_patches} patches")
            print(f"  rate: {num_total_patches / duration:.3f} patches/sec")

        if args.cache:
            if args.verbose:
                print(landsat._cached_load_warp_file.cache_info())

            # Clear cache for fair comparison between samplers
            # Both `landsat` and `cdl` share the same cache
            landsat._cached_load_warp_file.cache_clear()

        results_rows.append(
            {
                "cached": args.cache,
                "seed": args.seed,
                "duration": duration,
                "count": num_total_patches,
                "rate": num_total_patches / duration,
                "sampler": sampler.__class__.__name__,
                "batch_size": args.batch_size,
                "num_workers": args.num_workers,
            }
        )

    # Benchmark model
    model = resnet34()
    # Change number of input channels to match Landsat
    model.conv1 = nn.Conv2d(
        len(bands), 64, kernel_size=7, stride=2, padding=3, bias=False
    )

    criterion = nn.CrossEntropyLoss()
    params = model.parameters()
    optimizer = optim.SGD(params, lr=0.0001)

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu", args.device)
    model = model.to(device)

    tic = time.time()
    num_total_patches = 0
    for _ in range(num_batches):
        num_total_patches += args.batch_size
        x = torch.rand(args.batch_size, len(bands), args.patch_size, args.patch_size)
        # y = torch.randint(0, 256, (args.batch_size, args.patch_size, args.patch_size))
        y = torch.randint(0, 256, (args.batch_size,))
        x = x.to(device)
        y = y.to(device)

        optimizer.zero_grad()
        prediction = model(x)
        loss = criterion(prediction, y)
        loss.backward()
        optimizer.step()

    toc = time.time()
    duration = toc - tic

    if args.verbose:
        print("\nResNet-34:")
        print(f"  duration: {duration:.3f} sec")
        print(f"  count: {num_total_patches} patches")
        print(f"  rate: {num_total_patches / duration:.3f} patches/sec")

    results_rows.append(
        {
            "cached": args.cache,
            "seed": args.seed,
            "duration": duration,
            "count": num_total_patches,
            "rate": num_total_patches / duration,
            "sampler": "ResNet-34",
            "batch_size": args.batch_size,
            "num_workers": args.num_workers,
        }
    )

    fieldnames = [
        "cached",
        "seed",
        "duration",
        "count",
        "rate",
        "sampler",
        "batch_size",
        "num_workers",
    ]
    if not os.path.exists(args.output_fn):
        with open(args.output_fn, "w") as f:
            writer = csv.DictWriter(f, fieldnames=fieldnames)
            writer.writeheader()
    with open(args.output_fn, "a") as f:
        writer = csv.DictWriter(f, fieldnames=fieldnames)
        writer.writerows(results_rows)


if __name__ == "__main__":
    parser = set_up_parser()
    args = parser.parse_args()

    if args.epoch_size:
        assert args.epoch_size % args.batch_size == 0

    pl.seed_everything(args.seed)

    main(args)