testGeneralizedDice.py

import logging
import os
from monai.metrics import DiceMetric, GeneralizedDiceScore
from monai.losses import GeneralizedDiceFocalLoss
import json
import sys
from monai.visualize import plot_2d_or_3d_image
import tempfile
from glob import glob
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
import torch
from monai.data import list_data_collate, decollate_batch, DataLoader
from monai.inferers import SimpleInferer
from monai.transforms import (
  AsDiscrete, Activations, Transpose, Resized, EnsureChannelFirstd,
  Compose, CropForegroundd, LoadImaged, Orientationd, RandFlipd, RandCropByPosNegLabeld,
  RandShiftIntensityd, ScaleIntensityRanged, Spacingd, RandRotate90d, AddChanneld )
import monai
 
model = monai.networks.nets.SegResNet(
		spatial_dims =3, 
		blocks_down = [1, 2, 2, 4],
		blocks_up = [1, 1, 1],
		init_filters = 16, 
		in_channels = 1, 
		out_channels=1, 
		dropout_prob=0.2)

validation_transforms = Compose([ LoadImaged(keys=["image", "mask"]),
	EnsureChannelFirstd(keys=["image", "mask"]),
	Resized(keys=["image", "mask"], spatial_size=(256, 256, 24)),
	ScaleIntensityRanged(keys="image",a_min=20, a_max=1200, b_min=0, b_max=1, clip=True),
	RandFlipd(keys=["image", "mask"], spatial_axis=0),
	RandFlipd(keys=["image", "mask"], spatial_axis=1),
	RandFlipd(keys=["image", "mask"], spatial_axis=2),
	RandShiftIntensityd(keys="image", offsets=0.1, prob=0.5), 
	])


dice_metric = GeneralizedDiceScore(include_background=False, reduction="mean_batch")

_file={'image': './image.nii.gz', 'mask': './segmentation.nii.gz'}
val_list = [_file]*10  # Just generating a data list

batch_size=2
val_ds = monai.data.Dataset(data=val_list, transform=validation_transforms)
val_loader = DataLoader(val_ds, batch_size=batch_size, num_workers=4)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])

model.to(device)
model.eval()

for _val in val_loader:
	val_image = _val['image'].to(device)
	val_mask = _val['mask'].to(device)
	_val_output = model(val_image)
	val_out = post_trans(_val_output)
	print(val_out.shape)
	dice_metric(y_pred=val_out, y=val_mask)