[pre-commit.ci] auto fixes from pre-commit.com hooks

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modules/network_contraints/unet_plusplus.ipynb

@@ -117,7 +117,7 @@
     "print(monai.networks.layers.factories.Norm.factories.keys())\n",
     "for norm_layer in monai.networks.layers.factories.Norm.factories.keys():\n",
     "    try:\n",
-    "        model = BasicUnetPlusPlus(  \n",
+    "        model = BasicUnetPlusPlus(\n",
     "            spatial_dims=3,\n",
     "            in_channels=3,\n",
     "            out_channels=3,\n",
@@ -126,8 +126,7 @@
     "            norm=norm_layer,\n",
     "        )\n",
     "    except Exception as msg:\n",
-    "        print(f\"Exception layer: {msg}\")\n",
-    "    "
+    "        print(f\"Exception layer: {msg}\")"
    ]
   },
   {
@@ -192,27 +191,24 @@
     "\n",
     "def make_model_with_layer(layer_norm):\n",
     "    return BasicUnetPlusPlus(\n",
-    "        spatial_dims=2,\n",
-    "        in_channels=3,\n",
-    "        out_channels=1,\n",
-    "        features=(32, 32, 64, 128, 256, 32),\n",
-    "        norm=layer_norm\n",
+    "        spatial_dims=2, in_channels=3, out_channels=1, features=(32, 32, 64, 128, 256, 32), norm=layer_norm\n",
     "    )\n",
     "\n",
+    "\n",
     "def test_min_dim():\n",
     "    MIN_EDGE = 16\n",
     "    batch_size, spatial_dim, H, W = 1, 3, MIN_EDGE, MIN_EDGE\n",
     "    MODEL_BY_NORM_LAYER: Dict[str, BasicUnetPlusPlus] = {}\n",
     "    print(\"Prepare model\")\n",
-    "    for norm_layer in ['instance', 'batch']:\n",
+    "    for norm_layer in [\"instance\", \"batch\"]:\n",
     "        MODEL_BY_NORM_LAYER[norm_layer] = make_model_with_layer(norm_layer)\n",
-    "        \n",
+    "\n",
     "    # print(f\"Input dimension {(batch_size, spatial_dim, H, W)} that will cause error\")\n",
-    "    for norm_layer in ['instance', 'batch']:\n",
-    "        print(\"=\"*10 + f\" USING NORM LAYER: {norm_layer.upper()} \" + \"=\"*10)\n",
+    "    for norm_layer in [\"instance\", \"batch\"]:\n",
+    "        print(\"=\" * 10 + f\" USING NORM LAYER: {norm_layer.upper()} \" + \"=\" * 10)\n",
     "        model = MODEL_BY_NORM_LAYER[norm_layer]\n",
     "        print(\"_\" * 10 + \" Changing the H dimension of 2D input \" + \"_\" * 10)\n",
-    "        for _H_temp in [H, H*2]:\n",
+    "        for _H_temp in [H, H * 2]:\n",
     "            try:\n",
     "                x = torch.ones(batch_size, spatial_dim, _H_temp, W)\n",
     "                print(f\">> Using Input.shape={x.shape}\")\n",
@@ -231,6 +227,7 @@
     "                print(f\">> Exception: {msg}\\n\")\n",
     "    pass\n",
     "\n",
+    "\n",
     "with torch.no_grad():\n",
     "    test_min_dim()"
    ]
@@ -296,13 +293,10 @@
    ],
    "source": [
     "# Example about the pooling with odd shape\n",
-    "un_pool = torch.Tensor(\n",
-    "    [[1, 2, 3, 4, 5],\n",
-    "    [1, 2, 3, 4, 5],\n",
-    "    [1, 2, 3, 4, 5],\n",
-    "    [1, 2, 3, 4, 5],\n",
-    "    [1, 2, 3, 4, 5]]\n",
-    ") * torch.Tensor([1, 2, 3, 4, 5])[..., None]\n",
+    "un_pool = (\n",
+    "    torch.Tensor([[1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [1, 2, 3, 4, 5]])\n",
+    "    * torch.Tensor([1, 2, 3, 4, 5])[..., None]\n",
+    ")\n",
     "un_pool = un_pool[None, None, ...]\n",
     "\n",
     "pooled = nn.MaxPool2d(kernel_size=2)(un_pool)\n",
@@ -342,14 +336,14 @@
     "#         [batch_size, spatial_dim, D, H, W]\n",
     "#     )\n",
     "#     # y = model.forward(x)\n",
-    "#     # print(f\"{x.shape=}\")  \n",
+    "#     # print(f\"{x.shape=}\")\n",
     "#     # print(f\"{[o.shape for o in y]=}\")\n",
-    "    \n",
+    "\n",
     "#     return model, x\n",
     "\n",
     "# # Loss edge info. if input dim is not divisible by 2 when pooling\n",
     "# # Ensure the lowest image dimension\n",
-    "# # As the lowest dim before the norm is [1x1x1] \n",
+    "# # As the lowest dim before the norm is [1x1x1]\n",
     "# # -> instance and batch norm don't allow that, so make it at least\n",
     "# # [2x1x1]\n",
     "# from monai.networks.blocks import ADN\n",
@@ -358,23 +352,23 @@
     "#     print(f\"Input {x.shape=}\")\n",
     "#     model.eval()\n",
     "#     # 2 conv\n",
-    "#     x_0_0 = model.conv_0_0(x)     \n",
+    "#     x_0_0 = model.conv_0_0(x)\n",
     "#     # down conv\n",
-    "#     x_1_0 = model.conv_1_0(x_0_0) \n",
+    "#     x_1_0 = model.conv_1_0(x_0_0)\n",
     "#     print(f\"{x_1_0.shape=}\")\n",
     "#     x_0_1 = model.upcat_0_1(x_1_0, x_0_0)\n",
     "#     print(f\"{x_0_1.shape=}\")\n",
-    "#     # x_2_0 = model.conv_2_0(x_1_0) \n",
+    "#     # x_2_0 = model.conv_2_0(x_1_0)\n",
     "#     # print(f\"{x_2_0.shape=}\")\n",
-    "#     # x_3_0 = model.conv_3_0(x_2_0) \n",
+    "#     # x_3_0 = model.conv_3_0(x_2_0)\n",
     "#     # print(f\"{x_3_0.shape=}\") # 2 x 2 -> 1 x 1\n",
     "#     # pooled = nn.MaxPool3d(kernel_size=2)(x_3_0)\n",
     "#     # print(f\"{pooled.shape=}\") # 2 x 2 -> 1 x 1\n",
     "#     # conved1 = nn.Conv3d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1)(pooled)\n",
     "#     # conved2 = nn.Conv3d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1)(conved1)\n",
     "#     # print(f\"{conved1.shape=}\") # 2 x 2 -> 1 x 1\n",
     "#     # print(f\"{conved2.shape=}\") # 2 x 2 -> 1 x 1\n",
-    "    \n",
+    "\n",
     "#     # normed = ADN(\n",
     "#     #     ordering='NDA',\n",
     "#     #     in_channels=256,\n",
@@ -385,7 +379,7 @@
     "#     # )(conved2)\n",
     "#     # print(f\"{normed.shape=}\") # 2 x 2 -> 1 x 1\n",
     "\n",
-    "#     # x_4_0 = model.conv_4_0(x_3_0) \n",
+    "#     # x_4_0 = model.conv_4_0(x_3_0)\n",
     "#     # print(f\"{x_4_0.shape=}\")\n",
     "\n",
     "#     # Up path\n",