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Update compute_metric for MetaTensor #780

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Merged
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Jul 12, 2022
Merged

Update compute_metric for MetaTensor #780

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18 changes: 14 additions & 4 deletions modules/compute_metric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,7 @@

import nibabel as nib
import numpy as np
import torch
import torch.distributed as dist

from monai.data import create_test_image_3d, partition_dataset
Expand All @@ -57,7 +58,7 @@
KeepLargestConnectedComponentd,
LoadImaged,
ScaleIntensityd,
EnsureTyped,
ToDeviced,
)
from monai.utils import string_list_all_gather

Expand All @@ -77,8 +78,8 @@ def compute(args):
n = nib.Nifti1Image(label, np.eye(4))
nib.save(n, os.path.join(args.dir, f"label{i:d}.nii.gz"))

# initialize the distributed evaluation process, change to NCCL backend if computing on GPU
dist.init_process_group(backend="gloo", init_method="env://")
# initialize the distributed evaluation process, change to gloo backend if computing on CPU
dist.init_process_group(backend="nccl", init_method="env://")

preds = sorted(glob(os.path.join(args.dir, "pred*.nii.gz")))
labels = sorted(glob(os.path.join(args.dir, "label*.nii.gz")))
Expand All @@ -92,13 +93,15 @@ def compute(args):
even_divisible=False,
)[dist.get_rank()]

device = torch.device(f"cuda:{args.local_rank}")
torch.cuda.set_device(device)
# define transforms for predictions and labels
transforms = Compose(
[
LoadImaged(keys=["pred", "label"]),
ToDeviced(keys=["pred", "label"], device=device),
EnsureChannelFirstd(keys=["pred", "label"]),
ScaleIntensityd(keys="pred"),
EnsureTyped(keys=["pred", "label"]),
AsDiscreted(keys="pred", threshold=0.5),
KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
]
Expand Down Expand Up @@ -129,6 +132,13 @@ def compute(args):
dist.destroy_process_group()


# usage example(refer to https://github.com/pytorch/pytorch/blob/master/torch/distributed/launch.py):

# python -m torch.distributed.launch --nproc_per_node=NUM_GPUS_PER_NODE
# --nnodes=NUM_NODES --node_rank=INDEX_CURRENT_NODE
# --master_addr="192.168.1.1" --master_port=1234
# compute_metric.py -d DIR_OF_OUTPUT

def main():
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--dir", default="./output", type=str, help="root directory of labels and predictions.")
Expand Down