Fix issue with the plots for autoencoder tutorials. Add anomaly detec…

…tion nbs to the README.

Signed-off-by: Virginia Fernandez <virginia.fernandez@kcl.ac.uk>

generation/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb

@@ -92,6 +92,7 @@
     "from torch.nn import L1Loss\n",
     "from monai.losses import PatchAdversarialLoss, PerceptualLoss\n",
     "from monai.networks.nets import AutoencoderKL, PatchDiscriminator\n",
+    "from monai.utils.misc import ensure_tuple\n",
     "\n",
     "print_config()"
    ]
@@ -694,6 +695,7 @@
     "# Plot every evaluation as a new line and example as columns\n",
     "val_samples = np.linspace(val_interval, max_epochs, int(max_epochs / val_interval))\n",
     "fig, ax = plt.subplots(nrows=len(val_samples), ncols=1, sharey=True)\n",
+    "ax = ensure_tuple(ax) \n",
     "for image_n in range(len(val_samples)):\n",
     "    reconstructions = torch.reshape(intermediary_images[image_n], (64 * n_example_images, 64)).T\n",
     "    ax[image_n].imshow(reconstructions.cpu(), cmap=\"gray\")\n",

generation/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb

@@ -88,6 +88,7 @@
     "from torch.amp import autocast\n",
     "from monai.networks.nets import AutoencoderKL, PatchDiscriminator\n",
     "from monai.losses import PatchAdversarialLoss, PerceptualLoss\n",
+    "from monai.utils.misc import ensure_tuple\n",
     "\n",
     "print_config()"
    ]
@@ -731,6 +732,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
+    "ax = ensure_tuple(ax)\n",
     "ax[0].imshow(images[0, channel, ..., images.shape[2] // 2].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
     "ax[0].axis(\"off\")\n",
     "ax[0].title.set_text(\"Inputted Image\")\n",

generation/README.md

@@ -63,3 +63,7 @@ Example shows how to use a DDPM to inpaint of 2D images from the MedNIST dataset
 
 ## [Guiding the 2D diffusion synthesis using ControlNet](./controlnet/2d_controlnet.ipynb)
 Example shows how to use ControlNet to condition a diffusion model trained on 2D brain MRI images on binary brain masks.
+
+## Performing anomaly detection with diffusion models: [implicit guidance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb)
+Examples show how to perform anomaly detection in 2D, using implicit guidance [2D-classifier free guiance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), transformers [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomalydetection_tutorial_classifier_guidance).
+