update tutorials/modules for metatensor

acceleration/fast_training_tutorial.ipynb

@@ -614,7 +614,7 @@
     "                with torch.no_grad():\n",
     "                    val_loader_iterator = iter(val_loader)\n",
     "\n",
-    "                    for val_step in range(len(val_loader)):\n",
+    "                    for _ in range(len(val_loader)):\n",
     "                        # profiling: val dataload\n",
     "                        with nvtx.annotate(\"dataload\", color=\"red\") if profiling else no_profiling:\n",
     "                            val_data = next(val_loader_iterator)\n",

modules/2d_slices_from_3d_sampling.ipynb

@@ -519,7 +519,7 @@
     "            device=torch.device(\"cpu\"),\n",
     "            padding_mode=\"replicate\",\n",
     "        )\n",
-    "        val_output = slice_inferer(val_images, model)\n",
+    "        val_output = slice_inferer(val_images, model).cpu()\n",
     "        dice_metric(y_pred=val_output > 0.5, y=val_data[\"seg\"])\n",
     "        print(\"Dice: \", dice_metric.get_buffer()[-1][0])\n",
     "        fig = plt.figure(figsize=(10, 4))\n",

modules/2d_slices_from_3d_training.ipynb

@@ -501,7 +501,7 @@
     "            device=torch.device(\"cpu\"),\n",
     "            padding_mode=\"replicate\",\n",
     "        )\n",
-    "        val_output = slice_inferer(val_images, model)\n",
+    "        val_output = slice_inferer(val_images, model).to(\"cpu\")\n",
     "        dice_metric(y_pred=val_output > 0.5, y=val_data[\"seg\"])\n",
     "        print(\"Dice: \", dice_metric.get_buffer()[-1][0])\n",
     "        fig = plt.figure(figsize=(10, 4))\n",
@@ -549,7 +549,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.8.0"
   }
  },
  "nbformat": 4,

modules/benchmark_global_mutual_information.ipynb

@@ -232,8 +232,8 @@
     "        a2: numpy array of shape HWD\n",
     "    \"\"\"\n",
     "    antspyx_result = ants.image_mutual_information(\n",
-    "        ants.from_numpy(a1),\n",
-    "        ants.from_numpy(a2)\n",
+    "        ants.from_numpy(a1.detach().cpu().numpy()),\n",
+    "        ants.from_numpy(a2.detach().cpu().numpy())\n",
     "    )\n",
     "    monai_result = GlobalMutualInformationLoss(\n",
     "        kernel_type=\"b-spline\",\n",
@@ -471,7 +471,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.8.0"
   }
  },
  "nbformat": 4,

modules/image_dataset.ipynb

@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-01-14T12:34:04.909819Z",
@@ -59,26 +59,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MONAI version: 0.6.0rc1+23.gc6793fd0\n",
-      "Numpy version: 1.20.3\n",
-      "Pytorch version: 1.9.0a0+c3d40fd\n",
-      "MONAI flags: HAS_EXT = True, USE_COMPILED = False\n",
-      "MONAI rev id: c6793fd0f316a448778d0047664aaf8c1895fe1c\n",
+      "MONAI version: 0.9.1rc1+6.gcbe16eb3\n",
+      "Numpy version: 1.23.0\n",
+      "Pytorch version: 1.12.0+cu102\n",
+      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
+      "MONAI rev id: cbe16eb326830ef31479fd34ae1e44d27fc2d64d\n",
+      "MONAI __file__: /home/wenqil/Documents/MONAI/monai/__init__.py\n",
       "\n",
       "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.4.5\n",
-      "Nibabel version: 3.2.1\n",
-      "scikit-image version: 0.15.0\n",
-      "Pillow version: 8.2.0\n",
-      "Tensorboard version: 2.5.0\n",
-      "gdown version: 3.13.0\n",
-      "TorchVision version: 0.10.0a0\n",
-      "ITK version: 5.1.2\n",
-      "tqdm version: 4.53.0\n",
-      "lmdb version: 1.2.1\n",
-      "psutil version: 5.8.0\n",
-      "pandas version: 1.1.4\n",
-      "einops version: 0.3.0\n",
+      "Pytorch Ignite version: 0.4.9\n",
+      "Nibabel version: 4.0.1\n",
+      "scikit-image version: 0.19.3\n",
+      "Pillow version: 9.2.0\n",
+      "Tensorboard version: 2.9.1\n",
+      "gdown version: 4.5.1\n",
+      "TorchVision version: 0.13.0+cu102\n",
+      "tqdm version: 4.64.0\n",
+      "lmdb version: 1.3.0\n",
+      "psutil version: 5.9.1\n",
+      "pandas version: 1.4.3\n",
+      "einops version: 0.4.1\n",
+      "transformers version: 4.20.1\n",
+      "mlflow version: 1.27.0\n",
+      "pynrrd version: 0.4.3\n",
       "\n",
       "For details about installing the optional dependencies, please visit:\n",
       "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
@@ -124,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2021-01-14T12:34:07.530773Z",
@@ -173,8 +176,9 @@
    "source": [
     "class TestCompose(Compose):\n",
     "    def __call__(self, data, meta):\n",
-    "        data = self.transforms[0](data, meta)  # ensure channel first\n",
-    "        data, _, meta[\"affine\"] = self.transforms[1](data, meta[\"affine\"])  # spacing\n",
+    "        data = self.transforms[0](data)  # ensure channel first\n",
+    "        data = self.transforms[1](data)  # spacing\n",
+    "        meta = data.meta\n",
     "        if len(self.transforms) == 3:\n",
     "            return self.transforms[2](data), meta  # image contrast\n",
     "        return data, meta\n",
@@ -213,7 +217,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -227,7 +231,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.0"
   }
  },
  "nbformat": 4,

modules/integrate_3rd_party_transforms.ipynb

@@ -272,6 +272,7 @@
     "    ScaleIntensityRanged(keys=[\"image\"], a_min=-57,\n",
     "                         a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
     "    CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
+    "    EnsureTyped(keys=[\"image\", \"label\"], data_type=\"numpy\"),\n",
     "]"
    ]
   },
@@ -472,7 +473,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -486,7 +487,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.8.0"
   }
  },
  "nbformat": 4,

modules/interpretability/cats_and_dogs.ipynb

@@ -200,7 +200,7 @@
     "    axes = np.asarray(axes) if nims == 1 else axes\n",
     "    for d, ax in zip(data, axes.ravel()):\n",
     "        # channel last for matplotlib\n",
-    "        im = np.moveaxis(d[\"image\"], 0, -1)\n",
+    "        im = np.moveaxis(d[\"image\"].detach().cpu().numpy(), 0, -1)\n",
     "        ax.imshow(im, cmap='gray')\n",
     "        ax.set_title(Animals(d['label']).name, fontsize=25)\n",
     "        ax.axis(\"off\")\n",
@@ -608,7 +608,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.0"
   }
  },
  "nbformat": 4,

modules/nifti_read_example.ipynb

@@ -47,27 +47,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MONAI version: 0.8.1\n",
-      "Numpy version: 1.21.5\n",
-      "Pytorch version: 1.10.2\n",
-      "MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
-      "MONAI rev id: 71ff399a3ea07aef667b23653620a290364095b1\n",
+      "MONAI version: 0.9.1rc2+2.gc0268266.dirty\n",
+      "Numpy version: 1.23.0\n",
+      "Pytorch version: 1.12.0+cu102\n",
+      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
+      "MONAI rev id: c0268266f688c3bacd9c92ef7458f54b6afdf609\n",
+      "MONAI __file__: /home/wenqil/Documents/MONAI/monai/__init__.py\n",
       "\n",
       "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.4.8\n",
-      "Nibabel version: 3.2.1\n",
-      "scikit-image version: 0.19.1\n",
-      "Pillow version: 9.0.0\n",
-      "Tensorboard version: 2.8.0\n",
-      "gdown version: 4.2.0\n",
-      "TorchVision version: 0.11.3\n",
-      "tqdm version: 4.62.3\n",
+      "Pytorch Ignite version: 0.4.9\n",
+      "Nibabel version: 4.0.1\n",
+      "scikit-image version: 0.19.3\n",
+      "Pillow version: 9.2.0\n",
+      "Tensorboard version: 2.9.1\n",
+      "gdown version: 4.5.1\n",
+      "TorchVision version: 0.13.0+cu102\n",
+      "tqdm version: 4.64.0\n",
       "lmdb version: 1.3.0\n",
-      "psutil version: 5.9.0\n",
-      "pandas version: 1.1.5\n",
-      "einops version: 0.4.0\n",
-      "transformers version: 4.16.1\n",
-      "mlflow version: 1.23.1\n",
+      "psutil version: 5.9.1\n",
+      "pandas version: 1.4.3\n",
+      "einops version: 0.4.1\n",
+      "transformers version: 4.20.1\n",
+      "mlflow version: 1.27.0\n",
+      "pynrrd version: 0.4.3\n",
       "\n",
       "For details about installing the optional dependencies, please visit:\n",
       "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
@@ -100,7 +102,6 @@
     "from monai.data import (\n",
     "    ArrayDataset, GridPatchDataset, create_test_image_3d, PatchIter)\n",
     "from monai.transforms import (\n",
-    "    AddChannel,\n",
     "    Compose,\n",
     "    LoadImage,\n",
     "    RandSpatialCrop,\n",
@@ -125,11 +126,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/tmp/tmpkzde8m5b\n"
+     ]
+    }
+   ],
    "source": [
     "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
     "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
@@ -177,7 +186,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([5, 1, 64, 64, 64]) torch.Size([5, 1, 64, 64, 64])\n"
+      "(5, 1, 64, 64, 64) (5, 1, 64, 64, 64)\n"
      ]
     }
    ],
@@ -187,20 +196,16 @@
     "\n",
     "imtrans = Compose(\n",
     "    [\n",
-    "        LoadImage(image_only=True),\n",
+    "        LoadImage(image_only=True, ensure_channel_first=True),\n",
     "        ScaleIntensity(),\n",
-    "        AddChannel(),\n",
     "        RandSpatialCrop((64, 64, 64), random_size=False),\n",
-    "        EnsureType(),\n",
     "    ]\n",
     ")\n",
     "\n",
     "segtrans = Compose(\n",
     "    [\n",
-    "        LoadImage(image_only=True),\n",
-    "        AddChannel(),\n",
+    "        LoadImage(image_only=True, ensure_channel_first=True),\n",
     "        RandSpatialCrop((64, 64, 64), random_size=False),\n",
-    "        EnsureType(),\n",
     "    ]\n",
     ")\n",
     "\n",
@@ -236,10 +241,10 @@
     }
    ],
    "source": [
-    "imtrans = Compose([LoadImage(image_only=True),\n",
-    "                   ScaleIntensity(), AddChannel(), EnsureType()])\n",
+    "imtrans = Compose([LoadImage(image_only=True, ensure_channel_first=True),\n",
+    "                   ScaleIntensity(), EnsureType()])\n",
     "\n",
-    "segtrans = Compose([LoadImage(image_only=True), AddChannel(), EnsureType()])\n",
+    "segtrans = Compose([LoadImage(image_only=True, ensure_channel_first=True), EnsureType()])\n",
     "\n",
     "ds = ArrayDataset(images, imtrans, segs, segtrans)\n",
     "patch_iter = PatchIter(patch_size=(64, 64, 64), start_pos=(0, 0, 0))\n",
@@ -283,7 +288,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -297,7 +302,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.8.0"
   }
  },
  "nbformat": 4,