-

app.py

@@ -1,143 +1,7 @@
 import gradio as gr
-from monai.utils import first, set_determinism
-from monai.transforms import(
- Compose,
- AddChanneld,
- LoadImaged,
- Resized,
- ToTensord,
- Spacingd,
- Orientationd,
- ScaleIntensityRanged,
- CropForegroundd,
- Activations,
-)
 
-from monai.networks.nets import UNet
-from monai.networks.layers import Norm
-from monai.data import CacheDataset, DataLoader, Dataset
-
-import torch
-import matplotlib.pyplot as plt
-
-import os
-from glob import glob
-import numpy as np
-
-from monai.inferers import sliding_window_inference
-
-in_dir = 'Data_Train_Test'
-model_dir = 'results/results'
-
-train_loss = np.load(os.path.join(model_dir, 'loss_train.npy'))
-train_metric = np.load(os.path.join(model_dir, 'metric_train.npy'))
-test_loss = np.load(os.path.join(model_dir, 'loss_test.npy'))
-test_metric = np.load(os.path.join(model_dir, 'metric_test.npy'))
-
-def plot_result():
- plt.figure("Results 25 june", (12, 6))
- plt.subplot(2, 2, 1)
- plt.title("Train dice loss")
- x = [i + 1 for i in range(len(train_loss))]
- y = train_loss
- plt.xlabel("epoch")
- plt.plot(x, y)
- 
- plt.subplot(2, 2, 2)
- plt.title("Train metric DICE")
- x = [i + 1 for i in range(len(train_metric))]
- y = train_metric
- plt.xlabel("epoch")
- plt.plot(x, y)
- 
- plt.subplot(2, 2, 3)
- plt.title("Test dice loss")
- x = [i + 1 for i in range(len(test_loss))]
- y = test_loss
- plt.xlabel("epoch")
- plt.plot(x, y)
- 
- plt.subplot(2, 2, 4)
- plt.title("Test metric DICE")
- x = [i + 1 for i in range(len(test_metric))]
- y = test_metric
- plt.xlabel("epoch")
- plt.plot(x, y)
- 
- plt.show()
-
-
-
-path_train_volumes = sorted(glob(os.path.join(in_dir, "TrainVolumes", "*.nii.gz")))
-path_train_segmentation = sorted(glob(os.path.join(in_dir, "TrainSegmentation", "*.nii.gz")))
-
-path_test_volumes = sorted(glob(os.path.join(in_dir, "TestVolumes", "*.nii.gz")))
-path_test_segmentation = sorted(glob(os.path.join(in_dir, "TestSegmentation", "*.nii.gz")))
-
-train_files = [{"vol": image_name, "seg": label_name} for image_name, label_name in zip(path_train_volumes, path_train_segmentation)]
-test_files = [{"vol": image_name, "seg": label_name} for image_name, label_name in zip(path_test_volumes, path_test_segmentation)]
-test_files = test_files[0:9]
-
-
-test_transforms = Compose(
- [
- LoadImaged(keys=["vol", "seg"]),
- AddChanneld(keys=["vol", "seg"]),
- Spacingd(keys=["vol", "seg"], pixdim=(1.5,1.5,1.0), mode=("bilinear", "nearest")),
- Orientationd(keys=["vol", "seg"], axcodes="RAS"),
- ScaleIntensityRanged(keys=["vol"], a_min=-200, a_max=200,b_min=0.0, b_max=1.0, clip=True), 
- CropForegroundd(keys=['vol', 'seg'], source_key='vol'),
- Resized(keys=["vol", "seg"], spatial_size=[128,128,64]), 
- ToTensord(keys=["vol", "seg"]),
- ]
-)
-
-test_ds = Dataset(data=test_files, transform=test_transforms)
-test_loader = DataLoader(test_ds, batch_size=1)
-
-device = torch.device("cuda:0")
-model = UNet(
- dimensions=3,
- in_channels=1,
- out_channels=2,
- channels=(16, 32, 64, 128, 256), 
- strides=(2, 2, 2, 2),
- num_res_units=2,
- norm=Norm.BATCH,
-).to(device)
-
-model.load_state_dict(torch.load(
- os.path.join(model_dir, "best_metric_model.pth")))
-model.eval()
-
-
-sw_batch_size = 4
-roi_size = (128, 128, 64)
-with torch.no_grad():
- test_patient = first(test_loader)
- t_volume = test_patient['vol']
- #t_segmentation = test_patient['seg']
- 
- test_outputs = sliding_window_inference(t_volume.to(device), roi_size, sw_batch_size, model)
- sigmoid_activation = Activations(sigmoid=True)
- test_outputs = sigmoid_activation(test_outputs)
- test_outputs = test_outputs > 0.53
- 
-# plot the slice [:, :, 80]
 def predict():
- i =55
- plt.figure("check", (18, 6))
- plt.subplot(1, 3, 1)
- plt.title(f"image {i}")
- plt.imshow(test_patient["vol"][0, 0, :, :, i], cmap="gray")
- plt.subplot(1, 3, 2)
- plt.title(f"label {i}")
- plt.imshow(test_patient["seg"][0, 0, :, :, i] != 0)
- plt.subplot(1, 3, 3)
- plt.title(f"output {i}")
- plt.imshow(test_outputs.detach().cpu()[0, 1, :, :, i])
- plt.show()
-
+ break
 
 
 gr.Interface(fn=predict,