run_net.py

# Copyright (c) MONAI Consortium
# 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 glob
import logging
import os
import shutil
import sys

import torch
import torch.nn as nn
from ignite.contrib.handlers import ProgressBar

import monai
from monai.handlers import CheckpointSaver, MeanDice, StatsHandler, ValidationHandler, from_engine
from monai.transforms import (
    AsDiscreted,
    CastToTyped,
    LoadImaged,
    Orientationd,
    RandAffined,
    RandCropByPosNegLabeld,
    RandFlipd,
    RandGaussianNoised,
    ScaleIntensityRanged,
    Spacingd,
    SpatialPadd,
)


def get_xforms(mode="train", keys=("image", "label")):
    """returns a composed transform for train/val/infer."""

    xforms = [
        LoadImaged(keys, ensure_channel_first=True, image_only=True),
        Orientationd(keys, axcodes="LPS"),
        Spacingd(keys, pixdim=(1.25, 1.25, 5.0), mode=("bilinear", "nearest")[: len(keys)]),
        ScaleIntensityRanged(keys[0], a_min=-1000.0, a_max=500.0, b_min=0.0, b_max=1.0, clip=True),
    ]
    if mode == "train":
        xforms.extend(
            [
                SpatialPadd(keys, spatial_size=(192, 192, -1), mode="reflect"),  # ensure at least 192x192
                RandAffined(
                    keys,
                    prob=0.15,
                    rotate_range=(0.05, 0.05, None),  # 3 parameters control the transform on 3 dimensions
                    scale_range=(0.1, 0.1, None),
                    mode=("bilinear", "nearest"),
                ),
                RandCropByPosNegLabeld(keys, label_key=keys[1], spatial_size=(192, 192, 16), num_samples=3),
                RandGaussianNoised(keys[0], prob=0.15, std=0.01),
                RandFlipd(keys, spatial_axis=0, prob=0.5),
                RandFlipd(keys, spatial_axis=1, prob=0.5),
                RandFlipd(keys, spatial_axis=2, prob=0.5),
            ]
        )
        dtype = (torch.float32, torch.uint8)
    if mode == "val":
        dtype = (torch.float32, torch.uint8)
    if mode == "infer":
        dtype = (torch.float32,)
    xforms.extend([CastToTyped(keys, dtype=dtype)])
    return monai.transforms.Compose(xforms)


def get_net():
    """returns a unet model instance."""

    num_classes = 2
    net = monai.networks.nets.BasicUNet(
        spatial_dims=3,
        in_channels=1,
        out_channels=num_classes,
        features=(32, 32, 64, 128, 256, 32),
        dropout=0.1,
    )
    return net


def get_inferer(_mode=None):
    """returns a sliding window inference instance."""

    patch_size = (192, 192, 16)
    sw_batch_size, overlap = 2, 0.5
    inferer = monai.inferers.SlidingWindowInferer(
        roi_size=patch_size,
        sw_batch_size=sw_batch_size,
        overlap=overlap,
        mode="gaussian",
        padding_mode="replicate",
    )
    return inferer


class DiceCELoss(nn.Module):
    """Dice and Xentropy loss"""

    def __init__(self):
        super().__init__()
        self.dice = monai.losses.DiceLoss(to_onehot_y=True, softmax=True)
        self.cross_entropy = nn.CrossEntropyLoss()

    def forward(self, y_pred, y_true):
        dice = self.dice(y_pred, y_true)
        # CrossEntropyLoss target needs to have shape (B, D, H, W)
        # Target from pipeline has shape (B, 1, D, H, W)
        cross_entropy = self.cross_entropy(y_pred, torch.squeeze(y_true, dim=1).long())
        return dice + cross_entropy


def train(data_folder=".", model_folder="runs"):
    """run a training pipeline."""

    images = sorted(glob.glob(os.path.join(data_folder, "*_ct.nii.gz")))
    labels = sorted(glob.glob(os.path.join(data_folder, "*_seg.nii.gz")))
    logging.info(f"training: image/label ({len(images)}) folder: {data_folder}")

    amp = True  # auto. mixed precision
    keys = ("image", "label")
    train_frac, val_frac = 0.8, 0.2
    n_train = int(train_frac * len(images)) + 1
    n_val = min(len(images) - n_train, int(val_frac * len(images)))
    logging.info(f"training: train {n_train} val {n_val}, folder: {data_folder}")

    train_files = [{keys[0]: img, keys[1]: seg} for img, seg in zip(images[:n_train], labels[:n_train])]
    val_files = [{keys[0]: img, keys[1]: seg} for img, seg in zip(images[-n_val:], labels[-n_val:])]

    # create a training data loader
    batch_size = 2
    logging.info(f"batch size {batch_size}")
    train_transforms = get_xforms("train", keys)
    train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms)
    train_loader = monai.data.DataLoader(
        train_ds,
        batch_size=batch_size,
        shuffle=True,
        num_workers=2,
        pin_memory=torch.cuda.is_available(),
    )

    # create a validation data loader
    val_transforms = get_xforms("val", keys)
    val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms)
    val_loader = monai.data.DataLoader(
        val_ds,
        batch_size=1,  # image-level batch to the sliding window method, not the window-level batch
        num_workers=2,
        pin_memory=torch.cuda.is_available(),
    )

    # create BasicUNet, DiceLoss and Adam optimizer
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    net = get_net().to(device)
    max_epochs, lr, momentum = 500, 1e-4, 0.95
    logging.info(f"epochs {max_epochs}, lr {lr}, momentum {momentum}")
    opt = torch.optim.Adam(net.parameters(), lr=lr)

    # create evaluator (to be used to measure model quality during training
    val_post_transform = monai.transforms.Compose(
        [AsDiscreted(keys=("pred", "label"), argmax=(True, False), to_onehot=2)]
    )
    val_handlers = [
        ProgressBar(),
        CheckpointSaver(save_dir=model_folder, save_dict={"net": net}, save_key_metric=True, key_metric_n_saved=3),
    ]
    evaluator = monai.engines.SupervisedEvaluator(
        device=device,
        val_data_loader=val_loader,
        network=net,
        inferer=get_inferer(),
        postprocessing=val_post_transform,
        key_val_metric={
            "val_mean_dice": MeanDice(include_background=False, output_transform=from_engine(["pred", "label"]))
        },
        val_handlers=val_handlers,
        amp=amp,
    )

    # evaluator as an event handler of the trainer
    train_handlers = [
        ValidationHandler(validator=evaluator, interval=1, epoch_level=True),
        StatsHandler(tag_name="train_loss", output_transform=from_engine(["loss"], first=True)),
    ]
    trainer = monai.engines.SupervisedTrainer(
        device=device,
        max_epochs=max_epochs,
        train_data_loader=train_loader,
        network=net,
        optimizer=opt,
        loss_function=DiceCELoss(),
        inferer=get_inferer(),
        key_train_metric=None,
        train_handlers=train_handlers,
        amp=amp,
    )
    trainer.run()


def infer(data_folder=".", model_folder="runs", prediction_folder="output"):
    """
    run inference, the output folder will be "./output"
    """
    ckpts = sorted(glob.glob(os.path.join(model_folder, "*.pt")))
    ckpt = ckpts[-1]
    for x in ckpts:
        logging.info(f"available model file: {x}.")
    logging.info("----")
    logging.info(f"using {ckpt}.")

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    net = get_net().to(device)
    net.load_state_dict(torch.load(ckpt, map_location=device))
    net.eval()

    image_folder = os.path.abspath(data_folder)
    images = sorted(glob.glob(os.path.join(image_folder, "*_ct.nii.gz")))
    logging.info(f"infer: image ({len(images)}) folder: {data_folder}")
    infer_files = [{"image": img} for img in images]

    keys = ("image",)
    infer_transforms = get_xforms("infer", keys)
    infer_ds = monai.data.Dataset(data=infer_files, transform=infer_transforms)
    infer_loader = monai.data.DataLoader(
        infer_ds,
        batch_size=1,  # image-level batch to the sliding window method, not the window-level batch
        num_workers=2,
        pin_memory=torch.cuda.is_available(),
    )

    inferer = get_inferer()
    saver = monai.transforms.SaveImage(output_dir=prediction_folder, mode="nearest", resample=True)
    with torch.no_grad():
        for infer_data in infer_loader:
            logging.info(f"segmenting {infer_data['image'].meta['filename_or_obj']}")
            preds = inferer(infer_data[keys[0]].to(device), net)
            n = 1.0
            for _ in range(4):
                # test time augmentations
                _img = RandGaussianNoised(keys[0], prob=1.0, std=0.01)(infer_data)[keys[0]]
                pred = inferer(_img.to(device), net)
                preds = preds + pred
                n = n + 1.0
                for dims in [[2], [3]]:
                    flip_pred = inferer(torch.flip(_img.to(device), dims=dims), net)
                    pred = torch.flip(flip_pred, dims=dims)
                    preds = preds + pred
                    n = n + 1.0
            preds = preds / n
            preds = (preds.argmax(dim=1, keepdims=True)).float()
            for p in preds:  # save each image+metadata in the batch respectively
                saver(p)

    # copy the saved segmentations into the required folder structure for submission
    submission_dir = os.path.join(prediction_folder, "to_submit")
    if not os.path.exists(submission_dir):
        os.makedirs(submission_dir)
    files = glob.glob(os.path.join(prediction_folder, "volume*", "*.nii.gz"))
    for f in files:
        new_name = os.path.basename(f)
        new_name = new_name[len("volume-covid19-A-0") :]
        new_name = new_name[: -len("_ct_seg.nii.gz")] + ".nii.gz"
        to_name = os.path.join(submission_dir, new_name)
        shutil.copy(f, to_name)
    logging.info(f"predictions copied to {submission_dir}.")


if __name__ == "__main__":
    """
    Usage:
        python run_net.py train --data_folder "COVID-19-20_v2/Train" # run the training pipeline
        python run_net.py infer --data_folder "COVID-19-20_v2/Validation" # run the inference pipeline
    """
    parser = argparse.ArgumentParser(description="Run a basic UNet segmentation baseline.")
    parser.add_argument(
        "mode", metavar="mode", default="train", choices=("train", "infer"), type=str, help="mode of workflow"
    )
    parser.add_argument("--data_folder", default="", type=str, help="training data folder")
    parser.add_argument("--model_folder", default="runs", type=str, help="model folder")
    args = parser.parse_args()

    monai.config.print_config()
    monai.utils.set_determinism(seed=0)
    logging.basicConfig(stream=sys.stdout, level=logging.INFO)

    if args.mode == "train":
        data_folder = args.data_folder or os.path.join("COVID-19-20_v2", "Train")
        train(data_folder=data_folder, model_folder=args.model_folder)
    elif args.mode == "infer":
        data_folder = args.data_folder or os.path.join("COVID-19-20_v2", "Validation")
        infer(data_folder=data_folder, model_folder=args.model_folder)
    else:
        raise ValueError("Unknown mode.")