Fix validation data in tutorials

tutorials/generative/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb

@@ -316,7 +316,7 @@
    ],
    "source": [
     "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)\n",
-    "val_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"Hand\"]\n",
+    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"Hand\"]\n",
     "val_transforms = transforms.Compose(\n",
     "    [\n",
     "        transforms.LoadImaged(keys=[\"image\"]),\n",

tutorials/generative/2d_autoencoderkl/2d_autoencoderkl_tutorial.py

@@ -101,7 +101,7 @@
 # ### Download the validation set
 
 val_data = MedNISTDataset(root_dir=root_dir, section="validation", download=True, seed=0)
-val_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "Hand"]
+val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "Hand"]
 val_transforms = transforms.Compose(
     [
         transforms.LoadImaged(keys=["image"]),

tutorials/generative/2d_ddpm/2d_ddpm_compare_schedulers.ipynb

@@ -301,7 +301,7 @@
    ],
    "source": [
     "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)\n",
-    "val_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"Hand\"]\n",
+    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"Hand\"]\n",
     "val_transforms = transforms.Compose(\n",
     "    [\n",
     "        transforms.LoadImaged(keys=[\"image\"]),\n",
@@ -1096,7 +1096,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

tutorials/generative/2d_ddpm/2d_ddpm_compare_schedulers.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.1
+#       jupytext_version: 1.14.4
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -131,7 +131,7 @@
 
 # %%
 val_data = MedNISTDataset(root_dir=root_dir, section="validation", download=True, seed=0)
-val_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "Hand"]
+val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "Hand"]
 val_transforms = transforms.Compose(
     [
         transforms.LoadImaged(keys=["image"]),

tutorials/generative/2d_ldm/2d_ldm_tutorial.ipynb

@@ -309,7 +309,7 @@
    ],
    "source": [
     "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)\n",
-    "val_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"Hand\"]\n",
+    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"Hand\"]\n",
     "val_transforms = transforms.Compose(\n",
     "    [\n",
     "        transforms.LoadImaged(keys=[\"image\"]),\n",
@@ -425,20 +425,10 @@
    ],
    "source": [
     "unet = DiffusionModelUNet(\n",
-    "    spatial_dims=2,\n",
-    "    in_channels=3,\n",
-    "    out_channels=3,\n",
-    "    num_res_blocks=1,\n",
-    "    num_channels=(128, 256, 256),\n",
-    "    num_head_channels=256,\n",
+    "    spatial_dims=2, in_channels=3, out_channels=3, num_res_blocks=1, num_channels=(128, 256, 256), num_head_channels=256\n",
     ")\n",
     "\n",
-    "scheduler = DDPMScheduler(\n",
-    "    num_train_timesteps=1000,\n",
-    "    beta_schedule=\"linear\",\n",
-    "    beta_start=0.0015,\n",
-    "    beta_end=0.0195,\n",
-    ")\n",
+    "scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule=\"linear\", beta_start=0.0015, beta_end=0.0195)\n",
     "\n",
     "discriminator = PatchDiscriminator(\n",
     "    spatial_dims=2,\n",
@@ -1433,11 +1423,7 @@
     "\n",
     "        epoch_loss += loss.item()\n",
     "\n",
-    "        progress_bar.set_postfix(\n",
-    "            {\n",
-    "                \"loss\": epoch_loss / (step + 1),\n",
-    "            }\n",
-    "        )\n",
+    "        progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
     "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
     "\n",
     "    if (epoch + 1) % val_interval == 0:\n",

tutorials/generative/2d_ldm/2d_ldm_tutorial.py

@@ -104,7 +104,7 @@
 # ## Download the validation set
 
 val_data = MedNISTDataset(root_dir=root_dir, section="validation", download=True, seed=0)
-val_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "Hand"]
+val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "Hand"]
 val_transforms = transforms.Compose(
     [
         transforms.LoadImaged(keys=["image"]),

tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.ipynb

@@ -368,7 +368,7 @@
    ],
    "source": [
     "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)\n",
-    "val_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
+    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
     "val_transforms = transforms.Compose(\n",
     "    [\n",
     "        transforms.LoadImaged(keys=[\"image\"]),\n",

tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.py

@@ -139,7 +139,7 @@
 
 # %%
 val_data = MedNISTDataset(root_dir=root_dir, section="validation", download=True, seed=0)
-val_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"]
+val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "HeadCT"]
 val_transforms = transforms.Compose(
     [
         transforms.LoadImaged(keys=["image"]),

tutorials/generative/2d_vqgan/2d_vqgan_tutorial.ipynb

@@ -266,7 +266,7 @@
    ],
    "source": [
     "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, progress=False, seed=0)\n",
-    "val_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
+    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
     "val_transforms = transforms.Compose(\n",
     "    [\n",
     "        transforms.LoadImaged(keys=[\"image\"]),\n",

tutorials/generative/2d_vqgan/2d_vqgan_tutorial.py

@@ -124,7 +124,7 @@
 
 # %%
 val_data = MedNISTDataset(root_dir=root_dir, section="validation", download=True, progress=False, seed=0)
-val_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"]
+val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "HeadCT"]
 val_transforms = transforms.Compose(
     [
         transforms.LoadImaged(keys=["image"]),

tutorials/generative/2d_vqvae/2d_vqvae_tutorial.ipynb

@@ -315,7 +315,7 @@
    ],
    "source": [
     "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)\n",
-    "val_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
+    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
     "val_transforms = transforms.Compose(\n",
     "    [\n",
     "        transforms.LoadImaged(keys=[\"image\"]),\n",

tutorials/generative/2d_vqvae/2d_vqvae_tutorial.py

@@ -99,7 +99,7 @@
 # ### Download the validation set
 
 val_data = MedNISTDataset(root_dir=root_dir, section="validation", download=True, seed=0)
-val_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"]
+val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "HeadCT"]
 val_transforms = transforms.Compose(
     [
         transforms.LoadImaged(keys=["image"]),

tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb

@@ -294,7 +294,7 @@
    ],
    "source": [
     "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)\n",
-    "val_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
+    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
     "val_transforms = transforms.Compose(\n",
     "    [\n",
     "        transforms.LoadImaged(keys=[\"image\"]),\n",
@@ -490,7 +490,6 @@
     "    in_channels=1,\n",
     "    out_channels=1,\n",
     "    num_res_layers=2,\n",
-    "    num_levels=2,\n",
     "    downsample_parameters=((2, 4, 1, 1), (2, 4, 1, 1)),\n",
     "    upsample_parameters=((2, 4, 1, 1, 0), (2, 4, 1, 1, 0)),\n",
     "    num_channels=(256, 256),\n",

tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.py

@@ -123,7 +123,7 @@
 
 # %%
 val_data = MedNISTDataset(root_dir=root_dir, section="validation", download=True, seed=0)
-val_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"]
+val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "HeadCT"]
 val_transforms = transforms.Compose(
     [
         transforms.LoadImaged(keys=["image"]),
@@ -149,7 +149,6 @@
     in_channels=1,
     out_channels=1,
     num_res_layers=2,
-    num_levels=2,
     downsample_parameters=((2, 4, 1, 1), (2, 4, 1, 1)),
     upsample_parameters=((2, 4, 1, 1, 0), (2, 4, 1, 1, 0)),
     num_channels=(256, 256),