Merge branch 'main' into main

Project-MONAI · Aug 28, 2024 · a5d4bbb · a5d4bbb
2 parents 578de7f + 1e09634
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diff --git a/3d_classification/densenet_training_array.ipynb b/3d_classification/densenet_training_array.ipynb
@@ -200,7 +200,7 @@
    ],
    "source": [
     "if not os.path.isfile(images[0]):\n",
-    "    resource = \"http://biomedic.doc.ic.ac.uk/brain-development/downloads/IXI/IXI-T1.tar\"\n",
+    "    resource = \"https://drive.google.com/file/d/1f5odq9smadgeJmDeyEy_UOjEtE_pkKc0/view?usp=sharing\"\n",
     "    md5 = \"34901a0593b41dd19c1a1f746eac2d58\"\n",
     "\n",
     "    dataset_dir = os.path.join(root_dir, \"ixi\")\n",

diff --git a/3d_regression/densenet_training_array.ipynb b/3d_regression/densenet_training_array.ipynb
@@ -103,12 +103,12 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## Setup data directory"
-   ],
    "metadata": {
     "id": "QVfv5JUVR892"
-   }
+   },
+   "source": [
+    "## Setup data directory"
+   ]
   },
   {
    "cell_type": "code",
@@ -196,16 +196,16 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "is_executing": true,
     "ExecuteTime": {
      "start_time": "2024-06-23T00:20:35.525723Z"
     },
-    "id": "1zcWs3THeVPn"
+    "id": "1zcWs3THeVPn",
+    "is_executing": true
    },
    "outputs": [],
    "source": [
     "if not os.path.isfile(images[0]):\n",
-    "    resource = \"http://biomedic.doc.ic.ac.uk/brain-development/downloads/IXI/IXI-T1.tar\"\n",
+    "    resource = \"https://drive.google.com/file/d/1f5odq9smadgeJmDeyEy_UOjEtE_pkKc0/view?usp=sharing\"\n",
     "    md5 = \"34901a0593b41dd19c1a1f746eac2d58\"\n",
     "\n",
     "    dataset_dir = os.path.join(root_dir, \"ixi\")\n",
@@ -216,19 +216,19 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## Create data loaders"
-   ],
    "metadata": {
     "id": "P3-MIx_lSNKF"
-   }
+   },
+   "source": [
+    "## Create data loaders"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "is_executing": true,
-    "id": "yVvt3XtHeVPn"
+    "id": "yVvt3XtHeVPn",
+    "is_executing": true
    },
    "outputs": [],
    "source": [
@@ -255,19 +255,19 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## Create model and train"
-   ],
    "metadata": {
     "id": "YgH8asdqSaiT"
-   }
+   },
+   "source": [
+    "## Create model and train"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "is_executing": true,
-    "id": "peVze9d7eVPo"
+    "id": "peVze9d7eVPo",
+    "is_executing": true
    },
    "outputs": [],
    "source": [
@@ -357,8 +357,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "is_executing": true,
-    "id": "66kVvM1eeVPp"
+    "id": "66kVvM1eeVPp",
+    "is_executing": true
    },
    "outputs": [],
    "source": [
@@ -368,6 +368,11 @@
   }
  ],
  "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "name": "python3"
@@ -383,12 +388,7 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.6"
-  },
-  "colab": {
-   "provenance": [],
-   "gpuType": "T4"
-  },
-  "accelerator": "GPU"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 0

diff --git a/generation/maisi/README.md b/generation/maisi/README.md
@@ -66,6 +66,8 @@ The information for the inference input, like body region and anatomy to generat
 - `"autoencoder_sliding_window_infer_size"`: in order to save GPU memory, we use sliding window inference when decoding latents to image when `"output_size"` is large. This is the patch size of the sliding window. Small value will reduce GPU memory but increase time cost. They need to be divisible by 16.
 - `"autoencoder_sliding_window_infer_overlap"`: float between 0 and 1. Large value will reduce the stitching artifacts when stitching patches during sliding window inference, but increase time cost. If you do not observe seam lines in the generated image result, you can use a smaller value to save inference time.
 
+To generate images with substantial dimensions, such as 512 &times; 512 &times; 512 or larger, using GPUs with 80GB of memory, it is advisable to configure the `"num_splits"` parameter in [the auto-encoder configuration](./configs/config_maisi.json#L11-L37) to 16. This adjustment is crucial to avoid out-of-memory issues during inference.
+
 #### Execute Inference:
 To run the inference script, please run:
 ```bash

diff --git a/modules/public_datasets.ipynb b/modules/public_datasets.ipynb
@@ -595,7 +595,7 @@
    "outputs": [],
    "source": [
     "class IXIDataset(Randomizable, CacheDataset):\n",
-    "    resource = \"http://biomedic.doc.ic.ac.uk/\" + \"brain-development/downloads/IXI/IXI-T1.tar\"\n",
+    "    resource = \"https://drive.google.com/file/d/1f5odq9smadgeJmDeyEy_UOjEtE_pkKc0/view?usp=sharing\"\n",
     "    md5 = \"34901a0593b41dd19c1a1f746eac2d58\"\n",
     "\n",
     "    def __init__(\n",