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d5ba135 | 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 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | # Copyright 2020 - 2022 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 os
from functools import partial
import nibabel as nib
import numpy as np
import torch
import torch.nn.functional as F
from torch.cuda.amp import GradScaler, autocast
import SimpleITK as sitk
from monai.inferers import sliding_window_inference
# from monai.data import decollate_batch
from monai.losses import DiceCELoss
from monai.metrics import DiceMetric
from monai.networks.nets import SwinUNETR
from monai.transforms import *
from monai.utils.enums import MetricReduction
from monai.handlers import StatsHandler, from_engine
import matplotlib.pyplot as plt
from PIL import Image
from monai import data, transforms
from monai.data import *
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (8192, rlimit[1]))
print('Setting resource limit:', str(resource.getrlimit(resource.RLIMIT_NOFILE)))
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '28890'
parser = argparse.ArgumentParser(description="process 3d to 2d")
parser.add_argument(
"--test_data_path", default="/data/imagesTr/", type=str,
help="The path to 3d image")
parser.add_argument(
"--save_path", default="/data/YOUR_DATASET_NAME/process_image/", type=str,
help="The path to save 2d image")
roi = 96
parser.add_argument("--use_normal_dataset", default=True, help="use monai Dataset class")
parser.add_argument("--feature_size", default=48, type=int, help="feature size")
parser.add_argument("--batch_size", default=1, type=int, help="number of batch size")
parser.add_argument("--sw_batch_size", default=1, type=int, help="number of sliding window batch size")
parser.add_argument("--infer_overlap", default=0.75, type=float, help="sliding window inference overlap")
parser.add_argument("--in_channels", default=1, type=int, help="number of input channels")
parser.add_argument("--out_channels", default=7, type=int, help="number of output channels")
parser.add_argument("--a_min", default=-175.0, type=float, help="a_min in ScaleIntensityRanged")
parser.add_argument("--a_max", default=250.0, type=float, help="a_max in ScaleIntensityRanged")
parser.add_argument("--b_min", default=0.0, type=float, help="b_min in ScaleIntensityRanged")
parser.add_argument("--b_max", default=1.0, type=float, help="b_max in ScaleIntensityRanged")
parser.add_argument("--space_x", default=1.5, type=float, help="spacing in x direction")
parser.add_argument("--space_y", default=1.5, type=float, help="spacing in y direction")
parser.add_argument("--space_z", default=1.5, type=float, help="spacing in z direction")
parser.add_argument("--roi_x", default=roi, type=int, help="roi size in x direction")
parser.add_argument("--roi_y", default=roi, type=int, help="roi size in y direction")
parser.add_argument("--roi_z", default=roi, type=int, help="roi size in z direction")
parser.add_argument("--dropout_rate", default=0.0, type=float, help="dropout rate")
parser.add_argument("--distributed", action="store_true", help="start distributed training")
parser.add_argument("--workers", default=4, type=int, help="number of workers")
parser.add_argument("--spatial_dims", default=3, type=int, help="spatial dimension of input data")
parser.add_argument("--use_checkpoint", default=True, help="use gradient checkpointing to save memory")
parser.add_argument("--rank", default=0, type=int, help="node rank for distributed training")
def check_dir(dir):
if not os.path.exists(dir):
os.makedirs(dir)
def get_test_loader(args):
"""
Creates training transforms, constructs a dataset, and returns a dataloader.
Args:
args: Command line arguments containing dataset paths and hyperparameters.
"""
test_transforms = transforms.Compose([
LoadImaged(keys=["image"]),
EnsureChannelFirstd(keys=["image"]),
Orientationd(keys=["image"], axcodes="RAS"),
Spacingd(keys=["image"], pixdim=(args.space_x, args.space_y, args.space_z),
mode=("bilinear")),
ScaleIntensityRanged(
keys=["image"],
a_min=args.a_min,
a_max=args.a_max,
b_min=0.0,
b_max=1.0,
clip=True,
),
CropForegroundd(keys=["image"], source_key="image"),
SpatialPadd(keys=["image"], spatial_size=(args.roi_x, args.roi_y, args.roi_z),
mode='constant'),
])
# constructing training dataset
test_img = []
test_name = []
dataset_list = os.listdir(args.test_data_path)
check_dir(args.save_path)
already_exist_list = os.listdir(args.save_path)
new_list = []
for item in dataset_list:
if item not in already_exist_list:
new_list.append(item)
for item in new_list:
name = item
print(name)
test_img_path = os.path.join(args.test_data_path, name)
test_img.append(test_img_path)
test_name.append(name)
data_dicts_test = [{'image': image, 'name': name}
for image, name in zip(test_img, test_name)]
print('test len {}'.format(len(data_dicts_test)))
test_ds = Dataset(data=data_dicts_test, transform=test_transforms)
test_loader = DataLoader(
test_ds, batch_size=1, shuffle=False, num_workers=args.workers, sampler=None, pin_memory=True
)
return test_loader, test_transforms
def main():
args = parser.parse_args()
test_loader, test_transforms = get_test_loader(args)
post_ori_transforms = Compose([EnsureTyped(keys=["image"]),
Invertd(keys=["image"],
transform=test_transforms,
orig_keys="image",
meta_keys="image_meta_dict",
orig_meta_keys="image_meta_dict",
meta_key_postfix="meta_dict",
nearest_interp=True,
to_tensor=True),
SaveImaged(keys="image", meta_keys="img_meta_dict",
output_dir=args.save_path,
separate_folder=False, folder_layout=None,
resample=False),
])
num = 0
with torch.no_grad():
for idx, batch_data in enumerate(test_loader):
img = batch_data["image"]
name = batch_data['name'][0]
with autocast(enabled=True):
for i in decollate_batch(batch_data):
post_ori_transforms(i)
os.rename(os.path.join(args.save_path, name.split('/')[-1][:-7] + '_trans.nii.gz'),
os.path.join(args.save_path, name.split('/')[-1][:-7] + '.nii.gz'))
if __name__ == "__main__":
main() |