Remove vq and unet notebooks

3 files changed, 0 insertions, 968 deletions

diff --git a/notebooks/04-vq-transformer.ipynb b/notebooks/04-vq-transformer.ipynb
deleted file mode 100644
index 69d2688..0000000
--- a/notebooks/04-vq-transformer.ipynb
+++ /dev/null
@@ -1,253 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7c02ae76-b540-4b16-9492-e9210b3b9249",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ['CUDA_VISIBLE_DEVICE'] = ''\n",
-    "import random\n",
-    "\n",
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import numpy as np\n",
-    "from omegaconf import OmegaConf\n",
-    "\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "from importlib.util import find_spec\n",
-    "if find_spec(\"text_recognizer\") is None:\n",
-    "    import sys\n",
-    "    sys.path.append('..')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ccdb6dde-47e5-429a-88f2-0764fb7e259a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from hydra import compose, initialize\n",
-    "from omegaconf import OmegaConf\n",
-    "from hydra.utils import instantiate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3cf50475-39f2-4642-a7d1-5bcbc0a036f7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"../training/conf/experiment/vqgan_htr_char_iam_lines.yaml\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e52ecb01-c975-4e55-925d-1182c7aea473",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path, \"rb\") as f:\n",
-    "    cfg = OmegaConf.load(f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f939aa37-7b1d-45cc-885c-323c4540bda1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cfg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aaeab329-aeb0-4a1b-aa35-5a2aab81b1d0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "net = instantiate(cfg.network)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a564ac7a-b67f-4bc1-af36-0fe0a58c1bc9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aeddcc5c-e48d-4d90-8efa-963011ef40bc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = torch.randn((16, 1, 16, 64))\n",
-    "y = torch.randint(0, 56, (16, 89))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0f0d78bc-7e0a-4d06-8e38-49b29ad25933",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e9f4ee2a-c93f-4461-8d75-40c8c12d9d48",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "yy = net(x, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7a7493a9-0e1d-46ef-8180-27605e18d082",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "yy[0].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "75bc9695-2afd-455c-a4fb-2e182456ccbd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z = torch.randn((16, 8, 32))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3df6f9a0-6e66-4f46-a5b7-c0bb71b16b9b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z, _ = net.encode(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6d6e9dd1-c56e-4169-8216-bcc84ea980e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8f1539cb-b9b2-40b7-a843-d7479ddbddd7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "yy = net.decode(z, y[:, :2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5cdba0a9-da7d-4e33-b209-7f360d1a38e5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "yy.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6da8065f-f93f-4aec-a60e-408712a28c3b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.argmax(yy,dim=-2).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "beabbda7-6a1f-4294-8f01-f9d866ffe088",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "yy[0].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchinfo import summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "25759b7b-8deb-4163-b75d-a1357c9fe88f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summary(net, (1, 1, 576, 640), device=\"cpu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62ca0d97-625c-474b-8d6c-d0caba79e198",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/04-vqvae.ipynb b/notebooks/04-vqvae.ipynb
deleted file mode 100644
index 1b31671..0000000
--- a/notebooks/04-vqvae.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "136a80f5-10e1-40c4-973a-a7eb7939bb1f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "os.environ['CUDA_VISIBLE_DEVICE'] = ''\n",
-    "import random\n",
-    "\n",
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import numpy as np\n",
-    "from omegaconf import OmegaConf\n",
-    "from hydra import compose, initialize\n",
-    "from omegaconf import OmegaConf\n",
-    "from hydra.utils import instantiate\n",
-    "from torchinfo import summary\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "from importlib.util import find_spec\n",
-    "if find_spec(\"text_recognizer\") is None:\n",
-    "    import sys\n",
-    "    sys.path.append('..')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "1a0fb9ca-1886-4fd4-839f-dc111a450cfd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"../training/conf/network/vqvae.yaml\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0182a614-5781-44a6-b659-008e7c584fa7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "encoder:\n",
-      "  _target_: text_recognizer.networks.vqvae.encoder.Encoder\n",
-      "  in_channels: 1\n",
-      "  hidden_dim: 32\n",
-      "  channels_multipliers:\n",
-      "  - 1\n",
-      "  - 2\n",
-      "  - 4\n",
-      "  dropout_rate: 0.0\n",
-      "  activation: mish\n",
-      "  use_norm: true\n",
-      "  num_residuals: 4\n",
-      "  residual_channels: 32\n",
-      "decoder:\n",
-      "  _target_: text_recognizer.networks.vqvae.decoder.Decoder\n",
-      "  out_channels: 1\n",
-      "  hidden_dim: 32\n",
-      "  channels_multipliers:\n",
-      "  - 4\n",
-      "  - 2\n",
-      "  - 1\n",
-      "  dropout_rate: 0.0\n",
-      "  activation: mish\n",
-      "  use_norm: true\n",
-      "  num_residuals: 4\n",
-      "  residual_channels: 32\n",
-      "_target_: text_recognizer.networks.vqvae.vqvae.VQVAE\n",
-      "hidden_dim: 128\n",
-      "embedding_dim: 32\n",
-      "num_embeddings: 8192\n",
-      "decay: 0.99\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'vqvae': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information\n",
-      "  warnings.warn(msg, UserWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "with initialize(config_path=\"../training/conf/network/\", job_name=\"test_app\"):\n",
-    "    cfg = compose(config_name=\"vqvae\")\n",
-    "    print(OmegaConf.to_yaml(cfg))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "a500f94c-7dae-477e-a3fb-2a2d62ee7b72",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "net = instantiate(cfg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "7f3b3559-5e23-485e-bf57-9405568a1fbf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "====================================================================================================\n",
-       "Layer (type:depth-idx)                             Output Shape              Param #\n",
-       "====================================================================================================\n",
-       "VQVAE                                              --                        --\n",
-       "├─Encoder: 1-1                                     [1, 128, 72, 80]          --\n",
-       "│    └─Sequential: 2-1                             [1, 128, 72, 80]          --\n",
-       "│    │    └─Conv2d: 3-1                            [1, 32, 576, 640]         320\n",
-       "│    │    └─Normalize: 3-2                         [1, 32, 576, 640]         64\n",
-       "│    │    └─Mish: 3-3                              [1, 32, 576, 640]         --\n",
-       "│    │    └─Mish: 3-4                              [1, 32, 576, 640]         --\n",
-       "│    │    └─Mish: 3-5                              [1, 32, 576, 640]         --\n",
-       "│    │    └─Conv2d: 3-6                            [1, 32, 288, 320]         16,416\n",
-       "│    │    └─Normalize: 3-7                         [1, 32, 288, 320]         64\n",
-       "│    │    └─Mish: 3-8                              [1, 32, 288, 320]         --\n",
-       "│    │    └─Mish: 3-9                              [1, 32, 288, 320]         --\n",
-       "│    │    └─Mish: 3-10                             [1, 32, 288, 320]         --\n",
-       "│    │    └─Conv2d: 3-11                           [1, 64, 144, 160]         32,832\n",
-       "│    │    └─Normalize: 3-12                        [1, 64, 144, 160]         128\n",
-       "│    │    └─Mish: 3-13                             [1, 64, 144, 160]         --\n",
-       "│    │    └─Mish: 3-14                             [1, 64, 144, 160]         --\n",
-       "│    │    └─Mish: 3-15                             [1, 64, 144, 160]         --\n",
-       "│    │    └─Conv2d: 3-16                           [1, 128, 72, 80]          131,200\n",
-       "│    │    └─Residual: 3-17                         [1, 128, 72, 80]          41,280\n",
-       "│    │    └─Residual: 3-18                         [1, 128, 72, 80]          41,280\n",
-       "│    │    └─Residual: 3-19                         [1, 128, 72, 80]          41,280\n",
-       "│    │    └─Residual: 3-20                         [1, 128, 72, 80]          41,280\n",
-       "├─Conv2d: 1-2                                      [1, 32, 72, 80]           4,128\n",
-       "├─VectorQuantizer: 1-3                             [1, 32, 72, 80]           --\n",
-       "├─Conv2d: 1-4                                      [1, 128, 72, 80]          4,224\n",
-       "├─Decoder: 1-5                                     [1, 1, 576, 640]          --\n",
-       "│    └─Sequential: 2-2                             [1, 1, 576, 640]          --\n",
-       "│    │    └─Residual: 3-21                         [1, 128, 72, 80]          41,280\n",
-       "│    │    └─Residual: 3-22                         [1, 128, 72, 80]          41,280\n",
-       "│    │    └─Residual: 3-23                         [1, 128, 72, 80]          41,280\n",
-       "│    │    └─Residual: 3-24                         [1, 128, 72, 80]          41,280\n",
-       "│    │    └─Normalize: 3-25                        [1, 128, 72, 80]          256\n",
-       "│    │    └─Mish: 3-26                             [1, 128, 72, 80]          --\n",
-       "│    │    └─Mish: 3-27                             [1, 128, 72, 80]          --\n",
-       "│    │    └─Mish: 3-28                             [1, 128, 72, 80]          --\n",
-       "│    │    └─ConvTranspose2d: 3-29                  [1, 64, 144, 160]         131,136\n",
-       "│    │    └─Normalize: 3-30                        [1, 64, 144, 160]         128\n",
-       "│    │    └─Mish: 3-31                             [1, 64, 144, 160]         --\n",
-       "│    │    └─Mish: 3-32                             [1, 64, 144, 160]         --\n",
-       "│    │    └─Mish: 3-33                             [1, 64, 144, 160]         --\n",
-       "│    │    └─ConvTranspose2d: 3-34                  [1, 32, 288, 320]         32,800\n",
-       "│    │    └─Normalize: 3-35                        [1, 32, 288, 320]         64\n",
-       "│    │    └─Mish: 3-36                             [1, 32, 288, 320]         --\n",
-       "│    │    └─Mish: 3-37                             [1, 32, 288, 320]         --\n",
-       "│    │    └─Mish: 3-38                             [1, 32, 288, 320]         --\n",
-       "│    │    └─ConvTranspose2d: 3-39                  [1, 32, 576, 640]         16,416\n",
-       "│    │    └─Normalize: 3-40                        [1, 32, 576, 640]         64\n",
-       "│    │    └─Conv2d: 3-41                           [1, 1, 576, 640]          289\n",
-       "====================================================================================================\n",
-       "Total params: 700,769\n",
-       "Trainable params: 700,769\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (G): 17.28\n",
-       "====================================================================================================\n",
-       "Input size (MB): 1.47\n",
-       "Forward/backward pass size (MB): 659.13\n",
-       "Params size (MB): 2.80\n",
-       "Estimated Total Size (MB): 663.41\n",
-       "===================================================================================================="
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary(net, (1, 1, 576, 640), device=\"cpu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f880b03-d641-4640-acd3-aa5666ca5184",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/05a-UNet.ipynb b/notebooks/05a-UNet.ipynb
deleted file mode 100644
index 3070e2d..0000000
--- a/notebooks/05a-UNet.ipynb
+++ /dev/null
@@ -1,482 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from PIL import Image\n",
-    "import torch\n",
-    "from torch import nn\n",
-    "from importlib.util import find_spec\n",
-    "if find_spec(\"text_recognizer\") is None:\n",
-    "    import sys\n",
-    "    sys.path.append('..')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.networks.unet import UNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "net = UNet()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = torch.rand(1, 1, 256, 256)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ModuleList(\n",
-       "  (0): _DilationBlock(\n",
-       "    (activation): ELU(alpha=1.0, inplace=True)\n",
-       "    (conv): Sequential(\n",
-       "      (0): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n",
-       "      (1): ELU(alpha=1.0, inplace=True)\n",
-       "    )\n",
-       "    (conv1): Sequential(\n",
-       "      (0): Conv2d(1, 32, kernel_size=(1, 1), stride=(1, 1))\n",
-       "      (1): ELU(alpha=1.0, inplace=True)\n",
-       "    )\n",
-       "    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "    (down_sampling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-       "  )\n",
-       "  (1): _DilationBlock(\n",
-       "    (activation): ELU(alpha=1.0, inplace=True)\n",
-       "    (conv): Sequential(\n",
-       "      (0): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n",
-       "      (1): ELU(alpha=1.0, inplace=True)\n",
-       "    )\n",
-       "    (conv1): Sequential(\n",
-       "      (0): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "      (1): ELU(alpha=1.0, inplace=True)\n",
-       "    )\n",
-       "    (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "    (down_sampling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-       "  )\n",
-       "  (2): _DilationBlock(\n",
-       "    (activation): ELU(alpha=1.0, inplace=True)\n",
-       "    (conv): Sequential(\n",
-       "      (0): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n",
-       "      (1): ELU(alpha=1.0, inplace=True)\n",
-       "    )\n",
-       "    (conv1): Sequential(\n",
-       "      (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
-       "      (1): ELU(alpha=1.0, inplace=True)\n",
-       "    )\n",
-       "    (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "    (down_sampling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-       "  )\n",
-       "  (3): _DilationBlock(\n",
-       "    (activation): ELU(alpha=1.0, inplace=True)\n",
-       "    (conv): Sequential(\n",
-       "      (0): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n",
-       "      (1): ELU(alpha=1.0, inplace=True)\n",
-       "    )\n",
-       "    (conv1): Sequential(\n",
-       "      (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
-       "      (1): ELU(alpha=1.0, inplace=True)\n",
-       "    )\n",
-       "    (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "net.encoder_blocks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ModuleList(\n",
-       "  (0): _UpSamplingBlock(\n",
-       "    (conv_block): _ConvBlock(\n",
-       "      (activation): ReLU(inplace=True)\n",
-       "      (block): Sequential(\n",
-       "        (0): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "        (2): ReLU(inplace=True)\n",
-       "        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "        (5): ReLU(inplace=True)\n",
-       "      )\n",
-       "    )\n",
-       "    (up_sampling): Upsample(scale_factor=2.0, mode=bilinear)\n",
-       "  )\n",
-       "  (1): _UpSamplingBlock(\n",
-       "    (conv_block): _ConvBlock(\n",
-       "      (activation): ReLU(inplace=True)\n",
-       "      (block): Sequential(\n",
-       "        (0): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "        (2): ReLU(inplace=True)\n",
-       "        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "        (5): ReLU(inplace=True)\n",
-       "      )\n",
-       "    )\n",
-       "    (up_sampling): Upsample(scale_factor=2.0, mode=bilinear)\n",
-       "  )\n",
-       "  (2): _UpSamplingBlock(\n",
-       "    (conv_block): _ConvBlock(\n",
-       "      (activation): ReLU(inplace=True)\n",
-       "      (block): Sequential(\n",
-       "        (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "        (2): ReLU(inplace=True)\n",
-       "        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "        (5): ReLU(inplace=True)\n",
-       "      )\n",
-       "    )\n",
-       "    (up_sampling): Upsample(scale_factor=2.0, mode=bilinear)\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "net.decoder_blocks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Conv2d(64, 3, kernel_size=(1, 1), stride=(1, 1))"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "net.head"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "yy = net(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y = (torch.randn(1, 256, 256) > 0).long()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 3, 256, 256])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "yy.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[[1, 0, 1,  ..., 0, 1, 0],\n",
-       "         [1, 0, 1,  ..., 0, 1, 0],\n",
-       "         [1, 1, 0,  ..., 1, 1, 0],\n",
-       "         ...,\n",
-       "         [1, 0, 0,  ..., 0, 1, 1],\n",
-       "         [0, 0, 1,  ..., 1, 1, 0],\n",
-       "         [0, 0, 1,  ..., 0, 0, 0]]])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "loss = nn.CrossEntropyLoss()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(1.2502, grad_fn=<NllLoss2DBackward>)"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loss(yy, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[[[-0.1692,  0.1223,  0.1750,  ..., -0.1869, -0.0585,  0.0462],\n",
-       "          [-0.1302, -0.0230,  0.3185,  ..., -0.3760,  0.0204, -0.0686],\n",
-       "          [-0.1062, -0.0216,  0.4592,  ...,  0.0990,  0.0808, -0.1419],\n",
-       "          ...,\n",
-       "          [ 0.1386, -0.2856,  0.3074,  ..., -0.3874, -0.0322,  0.0503],\n",
-       "          [ 0.3562, -0.0960,  0.0815,  ...,  0.1893,  0.1438,  0.2804],\n",
-       "          [-0.2106, -0.1988,  0.0016,  ..., -0.0031, -0.2820,  0.0113]],\n",
-       "\n",
-       "         [[-0.1542, -0.1322, -0.3917,  ..., -0.2297, -0.2328,  0.0103],\n",
-       "          [ 0.1040,  0.2189, -0.3661,  ...,  0.4818, -0.3737,  0.1117],\n",
-       "          [ 0.0735, -0.6487, -0.1899,  ...,  0.2213, -0.1529, -0.1020],\n",
-       "          ...,\n",
-       "          [-0.2046, -0.1477,  0.2941,  ...,  0.0652, -0.7276,  0.1676],\n",
-       "          [ 0.0413, -0.2013, -0.3192,  ..., -0.4947, -0.1179, -0.1000],\n",
-       "          [-0.4108,  0.0199,  0.2238,  ..., -0.4482, -0.2370,  0.0119]],\n",
-       "\n",
-       "         [[ 0.0834,  0.1303,  0.0629,  ...,  0.4766, -0.0481,  0.2538],\n",
-       "          [ 0.1218,  0.1324,  0.2464,  ...,  0.0081,  0.4444,  0.4583],\n",
-       "          [ 0.1155,  0.1417,  0.2248,  ...,  0.6365, -0.0040,  0.3144],\n",
-       "          ...,\n",
-       "          [ 0.0744, -0.0751, -0.5654,  ..., -0.2890, -0.0437,  0.2719],\n",
-       "          [ 0.1057, -0.1093, -0.3803,  ...,  0.0229,  0.1403,  0.0944],\n",
-       "          [-0.0958, -0.3931, -0.0186,  ...,  0.2102, -0.0842,  0.1909]]]],\n",
-       "       grad_fn=<MkldnnConvolutionBackward>)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "yy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchsummary import summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==========================================================================================\n",
-      "Layer (type:depth-idx)                   Output Shape              Param #\n",
-      "==========================================================================================\n",
-      "├─ModuleList: 1                          []                        --\n",
-      "|    └─DownSamplingBlock: 2-1            [-1, 64, 128, 128]        --\n",
-      "|    |    └─ConvBlock: 3-1               [-1, 64, 256, 256]        37,824\n",
-      "|    |    └─MaxPool2d: 3-2               [-1, 64, 128, 128]        --\n",
-      "|    └─DownSamplingBlock: 2-2            [-1, 128, 64, 64]         --\n",
-      "|    |    └─ConvBlock: 3-3               [-1, 128, 128, 128]       221,952\n",
-      "|    |    └─MaxPool2d: 3-4               [-1, 128, 64, 64]         --\n",
-      "|    └─DownSamplingBlock: 2-3            [-1, 256, 32, 32]         --\n",
-      "|    |    └─ConvBlock: 3-5               [-1, 256, 64, 64]         886,272\n",
-      "|    |    └─MaxPool2d: 3-6               [-1, 256, 32, 32]         --\n",
-      "|    └─DownSamplingBlock: 2-4            [-1, 512, 32, 32]         --\n",
-      "|    |    └─ConvBlock: 3-7               [-1, 512, 32, 32]         3,542,016\n",
-      "├─ModuleList: 1                          []                        --\n",
-      "|    └─UpSamplingBlock: 2-5              [-1, 256, 64, 64]         --\n",
-      "|    |    └─Upsample: 3-8                [-1, 512, 64, 64]         --\n",
-      "|    |    └─ConvBlock: 3-9               [-1, 256, 64, 64]         2,360,832\n",
-      "|    └─UpSamplingBlock: 2-6              [-1, 128, 128, 128]       --\n",
-      "|    |    └─Upsample: 3-10               [-1, 256, 128, 128]       --\n",
-      "|    |    └─ConvBlock: 3-11              [-1, 128, 128, 128]       590,592\n",
-      "|    └─UpSamplingBlock: 2-7              [-1, 64, 256, 256]        --\n",
-      "|    |    └─Upsample: 3-12               [-1, 128, 256, 256]       --\n",
-      "|    |    └─ConvBlock: 3-13              [-1, 64, 256, 256]        147,840\n",
-      "├─Conv2d: 1-1                            [-1, 3, 256, 256]         195\n",
-      "==========================================================================================\n",
-      "Total params: 7,787,523\n",
-      "Trainable params: 7,787,523\n",
-      "Non-trainable params: 0\n",
-      "Total mult-adds (M): 35.93\n",
-      "==========================================================================================\n",
-      "Input size (MB): 0.25\n",
-      "Forward/backward pass size (MB): 1.50\n",
-      "Params size (MB): 29.71\n",
-      "Estimated Total Size (MB): 31.46\n",
-      "==========================================================================================\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "==========================================================================================\n",
-       "Layer (type:depth-idx)                   Output Shape              Param #\n",
-       "==========================================================================================\n",
-       "├─ModuleList: 1                          []                        --\n",
-       "|    └─DownSamplingBlock: 2-1            [-1, 64, 128, 128]        --\n",
-       "|    |    └─ConvBlock: 3-1               [-1, 64, 256, 256]        37,824\n",
-       "|    |    └─MaxPool2d: 3-2               [-1, 64, 128, 128]        --\n",
-       "|    └─DownSamplingBlock: 2-2            [-1, 128, 64, 64]         --\n",
-       "|    |    └─ConvBlock: 3-3               [-1, 128, 128, 128]       221,952\n",
-       "|    |    └─MaxPool2d: 3-4               [-1, 128, 64, 64]         --\n",
-       "|    └─DownSamplingBlock: 2-3            [-1, 256, 32, 32]         --\n",
-       "|    |    └─ConvBlock: 3-5               [-1, 256, 64, 64]         886,272\n",
-       "|    |    └─MaxPool2d: 3-6               [-1, 256, 32, 32]         --\n",
-       "|    └─DownSamplingBlock: 2-4            [-1, 512, 32, 32]         --\n",
-       "|    |    └─ConvBlock: 3-7               [-1, 512, 32, 32]         3,542,016\n",
-       "├─ModuleList: 1                          []                        --\n",
-       "|    └─UpSamplingBlock: 2-5              [-1, 256, 64, 64]         --\n",
-       "|    |    └─Upsample: 3-8                [-1, 512, 64, 64]         --\n",
-       "|    |    └─ConvBlock: 3-9               [-1, 256, 64, 64]         2,360,832\n",
-       "|    └─UpSamplingBlock: 2-6              [-1, 128, 128, 128]       --\n",
-       "|    |    └─Upsample: 3-10               [-1, 256, 128, 128]       --\n",
-       "|    |    └─ConvBlock: 3-11              [-1, 128, 128, 128]       590,592\n",
-       "|    └─UpSamplingBlock: 2-7              [-1, 64, 256, 256]        --\n",
-       "|    |    └─Upsample: 3-12               [-1, 128, 256, 256]       --\n",
-       "|    |    └─ConvBlock: 3-13              [-1, 64, 256, 256]        147,840\n",
-       "├─Conv2d: 1-1                            [-1, 3, 256, 256]         195\n",
-       "==========================================================================================\n",
-       "Total params: 7,787,523\n",
-       "Trainable params: 7,787,523\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (M): 35.93\n",
-       "==========================================================================================\n",
-       "Input size (MB): 0.25\n",
-       "Forward/backward pass size (MB): 1.50\n",
-       "Params size (MB): 29.71\n",
-       "Estimated Total Size (MB): 31.46\n",
-       "=========================================================================================="
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "summary(net, (1, 256, 256), device=\"cpu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}