fix copy right error

Signed-off-by: Yiheng Wang <[email protected]>

Author: yiheng-wang-nv

generation/image_to_image_translation/tutorial_segmentation_with_ddpm.ipynb

@@ -1,21 +1,19 @@
 {
     "cells": [
         {
-            "cell_type": "code",
-            "execution_count": null,
+            "cell_type": "markdown",
             "metadata": {},
-            "outputs": [],
             "source": [
-                "# Copyright (c) MONAI Consortium\n",
-                "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-                "# you may not use this file except in compliance with the License.\n",
-                "# You may obtain a copy of the License at\n",
-                "#     http://www.apache.org/licenses/LICENSE-2.0\n",
-                "# Unless required by applicable law or agreed to in writing, software\n",
-                "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-                "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-                "# See the License for the specific language governing permissions and\n",
-                "# limitations under the License."
+                "Copyright (c) MONAI Consortium  \n",
+                "Licensed under the Apache License, Version 2.0 (the \"License\");  \n",
+                "you may not use this file except in compliance with the License.  \n",
+                "You may obtain a copy of the License at  \n",
+                "    http://www.apache.org/licenses/LICENSE-2.0  \n",
+                "Unless required by applicable law or agreed to in writing, software  \n",
+                "distributed under the License is distributed on an \"AS IS\" BASIS,  \n",
+                "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  \n",
+                "See the License for the specific language governing permissions and  \n",
+                "limitations under the License."
             ]
         },
         {
@@ -210,7 +208,7 @@
                 "    section=\"training\",  # validation\n",
                 "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
                 "    num_workers=4,\n",
-                "    download=False,  # Set download to True if the dataset hasnt been downloaded yet\n",
+                "    download=True,  # Set download to True if the dataset hasnt been downloaded yet\n",
                 "    seed=0,\n",
                 "    transform=train_transforms,\n",
                 ")\n",
@@ -612,7 +610,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "n_epochs = 4000\n",
+                "max_epochs = 4000\n",
                 "val_interval = 50\n",
                 "epoch_loss_list = []\n",
                 "val_epoch_loss_list = []"
@@ -725,7 +723,7 @@
                 "scaler = GradScaler()\n",
                 "total_start = time.time()\n",
                 "\n",
-                "for epoch in range(n_epochs):\n",
+                "for epoch in range(max_epochs):\n",
                 "    model.train()\n",
                 "    epoch_loss = 0\n",
                 "\n",
@@ -776,9 +774,9 @@
                 "print(f\"train diffusion completed, total time: {total_time}.\")\n",
                 "\n",
                 "plt.title(\"Learning Curves Diffusion Model\", fontsize=20)\n",
-                "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
+                "plt.plot(np.linspace(1, max_epochs, max_epochs), epoch_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
                 "plt.plot(\n",
-                "    np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n",
+                "    np.linspace(val_interval, max_epochs, int(max_epochs / val_interval)),\n",
                 "    val_epoch_loss_list,\n",
                 "    color=\"C1\",\n",
                 "    linewidth=2.0,\n",