File size: 2,898 Bytes
e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | # Copyright 2020 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 os
from glob import glob
import numpy as np
import torch
from coplenet import CopleNet
import monai
from monai.data import NiftiSaver
from monai.inferers import sliding_window_inference
from monai.transforms import AddChanneld, Compose, LoadNiftid, Orientationd, ToTensord
IMAGE_FOLDER = os.path.join(".", "images")
MODEL_FILE = os.path.join(".", "model", "coplenet_pretrained_monai_dict.pt")
OUTPUT_FOLDER = os.path.join(".", "output") # writer will create this folder if it doesn't exist.
def main():
images = sorted(glob(os.path.join(IMAGE_FOLDER, "case*.nii.gz")))
val_files = [{"img": img} for img in images]
# define transforms for image and segmentation
infer_transforms = Compose(
[
LoadNiftid("img"),
AddChanneld("img"),
Orientationd("img", "SPL"), # coplenet works on the plane defined by the last two axes
ToTensord("img"),
]
)
test_ds = monai.data.Dataset(data=val_files, transform=infer_transforms)
# sliding window inference need to input 1 image in every iteration
data_loader = torch.utils.data.DataLoader(
test_ds, batch_size=1, num_workers=0, pin_memory=torch.cuda.is_available()
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = CopleNet().to(device)
model.load_state_dict(torch.load(MODEL_FILE)["model_state_dict"])
model.eval()
with torch.no_grad():
saver = NiftiSaver(output_dir=OUTPUT_FOLDER)
for idx, val_data in enumerate(data_loader):
print(f"Inference on {idx+1} of {len(data_loader)}")
val_images = val_data["img"].to(device)
# define sliding window size and batch size for windows inference
slice_shape = np.ceil(np.asarray(val_images.shape[3:]) / 32) * 32
roi_size = (20, int(slice_shape[0]), int(slice_shape[1]))
sw_batch_size = 2
val_outputs = sliding_window_inference(
val_images, roi_size, sw_batch_size, model, 0.0, padding_mode="circular"
)
# val_outputs = (val_outputs.sigmoid() >= 0.5).float()
val_outputs = val_outputs.argmax(dim=1, keepdim=True)
saver.save_batch(val_outputs, val_data["img_meta_dict"])
if __name__ == "__main__":
main()
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