Updates

Signed-off-by: Virginia <[email protected]>

Author: Virginia

generation/2d_diffusion_autoencoder/2d_diffusion_autoencoder_tutorial.ipynb

@@ -104,7 +104,6 @@
     "import torch\n",
     "import torch.nn.functional as F\n",
     "import torchvision\n",
-    "import sys\n",
     "from monai import transforms\n",
     "from monai.apps import DecathlonDataset\n",
     "from monai.config import print_config\n",
@@ -191,8 +190,9 @@
     "2. `EnsureChannelFirstd` ensures the original data to construct \"channel first\" shape.\n",
     "3.  The first `Lambdad` transform chooses the first channel of the image, which is the Flair image.\n",
     "4. `Spacingd` resamples the image to the specified voxel spacing, we use 3,3,2 mm.\n",
-    "5. `ScaleIntensityRangePercentilesd` Apply range scaling to a numpy array based on the intensity distribution of the input. Transform is very common with MRI images.\n",
-    "6. `RandSpatialCropd` randomly crop out a 2D patch from the 3D image.\n",
+    "5. `CenterSpatialCropd`: we crop the 3D images to a specific size\n",
+    "6. `ScaleIntensityRangePercentilesd` Apply range scaling to a numpy array based on the intensity distribution of the input. Transform is very common with MRI images.\n",
+    "7. `RandSpatialCropd` randomly crop out a 2D patch from the 3D image.\n",
     "6. The last `Lambdad` transform obtains `slice_label` by summing up the label to have a single scalar value (healthy `=1` or not `=2` )."
    ]
   },
@@ -388,7 +388,7 @@
    },
    "outputs": [],
    "source": [
-    "class Diffusion_AE(torch.nn.Module):\n",
+    "class DiffusionAE(torch.nn.Module):\n",
     "    def __init__(self, embedding_dimension=64):\n",
     "        super().__init__()\n",
     "        self.unet = DiffusionModelUNet(\n",
@@ -413,7 +413,7 @@
     "\n",
     "\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model = Diffusion_AE(embedding_dimension=512).to(device)\n",
+    "model = DiffusionAE(embedding_dimension=512).to(device)\n",
     "scheduler = DDIMScheduler(num_train_timesteps=1000)\n",
     "optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-5)\n",
     "inferer = DiffusionInferer(scheduler)"
@@ -492,7 +492,8 @@
     "        # Create timesteps\n",
     "        timesteps = torch.randint(0, inferer.scheduler.num_train_timesteps, (batch_size,)).to(device).long()\n",
     "        # Get model prediction\n",
-    "        # cross attention expects shape [batch size, sequence length, channels], we are use channels = latent dimension and sequence length = 1\n",
+    "        # cross attention expects shape [batch size, sequence length, channels], \n",
+    "        #we are use channels = latent dimension and sequence length = 1\n",
     "        latent = model.semantic_encoder(images)\n",
     "        noise_pred = inferer(\n",
     "            inputs=images, diffusion_model=model.unet, noise=noise, timesteps=timesteps, condition=latent.unsqueeze(2)\n",
@@ -509,7 +510,7 @@
     "    if epoch % val_interval == 0:\n",
     "        model.eval()\n",
     "        val_iter_loss = 0\n",
-    "        for val_step, val_batch in enumerate(val_loader):\n",
+    "        for _, val_batch in enumerate(val_loader):\n",
     "            with torch.no_grad():\n",
     "                images = val_batch[\"image\"].to(device)\n",
     "                timesteps = torch.randint(0, inferer.scheduler.num_train_timesteps, (batch_size,)).to(device).long()\n",
@@ -526,10 +527,11 @@
     "\n",
     "            val_iter_loss += val_loss.item()\n",
     "        iter_loss_list.append(iter_loss / val_interval)\n",
-    "        val_iter_loss_list.append(val_iter_loss / (val_step + 1))\n",
+    "        val_iter_loss_list.append(val_iter_loss / len(val_loader))\n",
     "        iter_loss = 0\n",
     "        print(\n",
-    "            f\"Iteration {epoch} - Interval Loss {iter_loss_list[-1]:.4f}, Interval Loss Val {val_iter_loss_list[-1]:.4f}\"\n",
+    "            f\"Iteration {epoch} - Interval Loss {iter_loss_list[-1]:.4f}, \n",
+    "            Interval Loss Val {val_iter_loss_list[-1]:.4f}\"\n",
     "        )\n",
     "\n",
     "total_time = time.time() - total_start\n",
@@ -566,8 +568,10 @@
     "plt.title(\"Learning Curves Diffusion Model\", fontsize=20)\n",
     "plt.plot(list(range(len(iter_loss_list))), iter_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
     "plt.plot(list(range(len(iter_loss_list))), val_iter_loss_list, color=\"C4\", linewidth=2.0, label=\"Validation\")\n",
-    "plt.yticks(fontsize=12), plt.xticks(fontsize=12)\n",
-    "plt.xlabel(\"Iterations\", fontsize=16), plt.ylabel(\"Loss\", fontsize=16)\n",
+    "plt.yticks(fontsize=12)\n",
+    "plt.xticks(fontsize=12)\n",
+    "plt.xlabel(\"Iterations\", fontsize=16)\n",
+    "plt.ylabel(\"Loss\", fontsize=16)\n",
     "plt.legend(prop={\"size\": 14})\n",
     "plt.show()"
    ]
@@ -713,7 +717,8 @@
     }
    ],
    "source": [
-    "latents_train.shape, classes_train.shape"
+    "print(latents_train.shape)\n",
+    "print(classes_train.shape)"
    ]
   },
   {
@@ -735,17 +740,8 @@
    ],
    "source": [
     "clf = LogisticRegression(solver=\"newton-cg\", random_state=0).fit(latents_train, classes_train)\n",
-    "clf.score(latents_train, classes_train), clf.score(latents_val, classes_val)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "73df71e0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w = torch.Tensor(clf.coef_).float().to(device)"
+    "print(clf.score(latents_train, classes_train))\n",
+    "print(clf.score(latents_val, classes_val))"
    ]
   },
   {
@@ -777,6 +773,7 @@
    "source": [
     "s = -1.5\n",
     "\n",
+    "w = torch.Tensor(clf.coef_).float().to(device)\n",
     "scheduler.set_timesteps(num_inference_steps=100)\n",
     "batch = next(iter(val_loader))\n",
     "images = batch[\"image\"].to(device)\n",
@@ -802,6 +799,14 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "525702b5",
+   "metadata": {},
+   "source": [
+    "Although not perfectly, the manipulated slices do not present a tumour (unlike the middle - \"reconstructed\" - ones), because we tweaked the latents to move away from the abnormality cluster: "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 28,
@@ -831,15 +836,18 @@
     "plt.figure(figsize=(15, 5))\n",
     "plt.imshow(grid.detach().cpu().numpy()[0], cmap=\"gray\")\n",
     "plt.axis(\"off\")\n",
-    "plt.title(f\"Original (top), Reconstruction (middle), Manipulated (bottom) s = {s}\");"
+    "plt.title(f\"Original (top), Reconstruction (middle), Manipulated (bottom) s = {s}\")"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "b5ac0b8c-0f9d-43ba-9959-488ab62e892e",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cf8fbf9",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Although not perfectly, the manipulated slices do not present a tumour (unlike the middle - \"reconstructed\" - ones), because we tweaked the latents to move away from the abnormality cluster."
+    "if directory is None:\n",
+    "    shutil.rmtree(root_dir)"
    ]
   }
  ],