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import monai
import torch
import pandas as pd
import nibabel as nib
import numpy as np
from monai.data import DataLoader
from monai.utils.enums import CommonKeys
from scipy import ndimage
from monai.data import Dataset
from monai.inferers import sliding_window_inference
from monai.metrics import DiceMetric
from monai.transforms import (
Activationsd,
AsDiscreted,
Compose,
ConcatItemsd,
KeepLargestConnectedComponentd,
LoadImaged,
EnsureChannelFirstd,
EnsureTyped,
SaveImaged,
ScaleIntensityd,
NormalizeIntensityd,
Spacingd,
Orientationd,
)
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# print("Using device:", device)
# model = monai.networks.nets.UNet(
# in_channels=1,
# out_channels=3,
# spatial_dims=3,
# channels=[16, 32, 64, 128, 256, 512],
# strides=[2, 2, 2, 2, 2],
# num_res_units=4,
# act="PRELU",
# norm="BATCH",
# dropout=0.15,
# )
# model.load_state_dict(torch.load("anatomy.pt", map_location=device))
# keys = ("t2", "t2_anatomy_reader1")
# transforms = Compose(
# [
# LoadImaged(keys=keys, image_only=False),
# EnsureChannelFirstd(keys=keys),
# Spacingd(keys=keys, pixdim=[0.5, 0.5, 0.5], mode=("bilinear", "nearest")),
# Orientationd(keys=keys, axcodes="RAS"),
# ScaleIntensityd(keys=keys, minv=0, maxv=1),
# NormalizeIntensityd(keys=keys),
# EnsureTyped(keys=keys),
# ConcatItemsd(keys=("t2"), name=CommonKeys.IMAGE, dim=0),
# ConcatItemsd(keys=("t2_anatomy_reader1"), name=CommonKeys.LABEL, dim=0),
# ],
# )
# postprocessing = Compose(
# [
# EnsureTyped(keys=[CommonKeys.PRED, CommonKeys.LABEL]),
# KeepLargestConnectedComponentd(
# keys=CommonKeys.PRED,
# applied_labels=list(range(1, 3))
# ),
# ],
# )
keys = ("t2")
transforms = Compose(
[
LoadImaged(keys=keys, image_only=False),
EnsureChannelFirstd(keys=keys),
Spacingd(keys=keys, pixdim=[0.5, 0.5, 0.5], mode=("bilinear")),
Orientationd(keys=keys, axcodes="RAS"),
ScaleIntensityd(keys=keys, minv=0, maxv=1),
NormalizeIntensityd(keys=keys),
EnsureTyped(keys=keys),
ConcatItemsd(keys=("t2"), name=CommonKeys.IMAGE, dim=0),
],
)
postprocessing = Compose(
[
EnsureTyped(keys=[CommonKeys.PRED]),
KeepLargestConnectedComponentd(
keys=CommonKeys.PRED,
applied_labels=list(range(1, 3))
),
],
)
inferer = monai.inferers.SlidingWindowInferer(
roi_size=(96, 96, 96),
sw_batch_size=4,
overlap=0.5,
)
def resize_image(image: np.array, target_shape: tuple):
depth_factor = target_shape[0] / image.shape[0]
width_factor = target_shape[1] / image.shape[1]
height_factor = target_shape[2] / image.shape[2]
return ndimage.zoom(image, (depth_factor, width_factor, height_factor), order=1)
# model.eval()
# with torch.no_grad():
# for i in range(len(test_ds)):
# example = test_ds[i]
# label = example["t2_anatomy_reader1"]
# input_tensor = example["t2"].unsqueeze(0)
# input_tensor = input_tensor.to(device)
# output_tensor = inferer(input_tensor, model)
# output_tensor = output_tensor.argmax(dim=1, keepdim=False)
# output_tensor = output_tensor.squeeze(0).to(torch.device("cpu"))
# output_tensor = postprocessing({"pred": output_tensor, "label": label})["pred"]
# output_tensor = output_tensor.numpy().astype(np.uint8)
# target_shape = example["t2_meta_dict"]["spatial_shape"]
# output_tensor = resize_image(output_tensor, target_shape)
# # flip first two dimensions
# output_tensor = np.flip(output_tensor, axis=0)
# output_tensor = np.flip(output_tensor, axis=1)
# new_image = nib.Nifti1Image(output_tensor, affine=example["t2_meta_dict"]["affine"])
# nib.save(new_image, f"test/{i+1:03}/predicted.nii.gz")
# print("Saved", i+1)
def make_inference(data_dict:list) -> str:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using device:", device)
model = monai.networks.nets.UNet(
in_channels=1,
out_channels=3,
spatial_dims=3,
channels=[16, 32, 64, 128, 256, 512],
strides=[2, 2, 2, 2, 2],
num_res_units=4,
act="PRELU",
norm="BATCH",
dropout=0.15,
)
model.load_state_dict(torch.load("anatomy.pt", map_location=device))
test_ds = Dataset(
data=data_dict,
transform=transforms,
)
model.eval()
with torch.no_grad():
example = test_ds[0]
# label = example["t2_anatomy_reader1"]
input_tensor = example["t2"].unsqueeze(0)
input_tensor = input_tensor.to(device)
output_tensor = inferer(input_tensor, model)
output_tensor = output_tensor.argmax(dim=1, keepdim=False)
output_tensor = output_tensor.squeeze(0).to(torch.device("cpu"))
# output_tensor = postprocessing({"pred": output_tensor, "label": label})["pred"]
output_tensor = postprocessing({"pred": output_tensor})["pred"]
output_tensor = output_tensor.numpy().astype(np.uint8)
target_shape = example["t2_meta_dict"]["spatial_shape"]
output_tensor = resize_image(output_tensor, target_shape)
# flip first two dimensions
output_tensor = np.flip(output_tensor, axis=0)
output_tensor = np.flip(output_tensor, axis=1)
new_image = nib.Nifti1Image(output_tensor, affine=example["t2_meta_dict"]["affine"])
nib.save(new_image, "predicted.nii.gz")
return "predicted.nii.gz"
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