modules/resample_benchmark.ipynb

Signed-off-by: Wenqi Li <[email protected]>

Author: wyli

modules/resample_benchmark.ipynb

@@ -215,7 +215,7 @@
     "           ),\n",
     "       monai.transforms.ScaleIntensityd(keys=[\"img\"])\n",
     "       ])\n",
-    "    monai_transformed = transform({\"img\": monai_img})[\"img\"].transpose((1, 2, 0))\n",
+    "    monai_transformed = transform({\"img\": monai_img})[\"img\"].permute((1, 2, 0))\n",
     "\n",
     "    # sitk transform\n",
     "    sitk_img = sitk.ReadImage(img_path)\n",
@@ -285,7 +285,7 @@
     "\n",
     "# Generate random samples inside the image, we will obtain the intensity/color values at these points.\n",
     "num_samples = 10\n",
-    "physical_points = np.array([np.random.randint(monai_img.shape[1:]) for _ in range(10)], dtype=np.float)  # (10, 2)\n"
+    "physical_points = np.array([np.random.randint(monai_img.shape[1:]) for _ in range(10)], dtype=float)  # (10, 2)\n"
    ]
   },
   {
@@ -322,7 +322,7 @@
     "# initialise ddf as a zero matrix\n",
     "ddf = torch.zeros(2, height, width).to(torch.float)  # (2, H, W)\n",
     "# add displacement of y coordinate to sampled locations\n",
-    "for i, pnt in enumerate(physical_points.astype(np.long)):\n",
+    "for i, pnt in enumerate(physical_points.astype(int)):\n",
     "    ddf[0, pnt[0], pnt[1]] += i\n",
     "\n",
     "# initialise warp layer\n",
@@ -360,7 +360,7 @@
     "sitk_displacement_img = sitk.Image([width, height], sitk.sitkVectorFloat64, sitk_img.GetDimension())\n",
     "# add displacement of y coordinate to sampled locations\n",
     "for i, pnt in enumerate(physical_points):\n",
-    "    sitk_displacement_img[int(pnt[1]), int(pnt[0])] = np.array([0, i], dtype=np.float)\n",
+    "    sitk_displacement_img[int(pnt[1]), int(pnt[0])] = np.array([0, i], dtype=float)\n",
     "\n",
     "# select linear interpolater to match `bilinear` mode in MONAI\n",
     "interpolator_enum = sitk.sitkLinear\n",
@@ -420,7 +420,7 @@
     }
    ],
    "source": [
-    "for _, pnt in enumerate(physical_points.astype(np.long)):\n",
+    "for _, pnt in enumerate(physical_points.astype(int)):\n",
     "    print(f\"at location {pnt}: original intensity {monai_img[:, pnt[0], pnt[1]]} \" +\n",
     "          f\"resampled to {monai_resample[:, pnt[0], pnt[1]]} by MONAI and {sitk_resample[pnt[0], pnt[1]]} by SITK\")"
    ]
@@ -449,7 +449,7 @@
    "source": [
     "img = monai.transforms.LoadImaged(keys=\"img\")({\"img\": f\"{root_dir}/mri.nii\"})[\"img\"]\n",
     "# W, H, D -> D, H, W\n",
-    "img = img.transpose((2, 1, 0))"
+    "img = img.permute((2, 1, 0))"
    ]
   },
   {
@@ -689,7 +689,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.8.0"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {