135 files changed, 1587 insertions, 1182 deletions

diff --git a/src/.gitattributes b/.gitattributes
index eebe826..eebe826 100644
--- a/src/.gitattributes
+++ b/.gitattributes
diff --git a/notebooks/00-testing-stuff-out.ipynb b/notebooks/00-testing-stuff-out.ipynb
new file mode 100644
index 0000000..becd918
--- /dev/null
+++ b/notebooks/00-testing-stuff-out.ipynb
@@ -0,0 +1,1469 @@
+{
+ "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.nn.functional as F\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "from torchsummary import summary\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 import CNN, TDS2d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tds2d = TDS2d(**{\n",
+    "    \"depth\" : 4,\n",
+    "    \"tds_groups\" : [\n",
+    "      { \"channels\" : 4, \"num_blocks\" : 3, \"stride\" : [2, 2] },\n",
+    "      { \"channels\" : 32, \"num_blocks\" : 3, \"stride\" : [2, 2] },\n",
+    "      { \"channels\" : 64, \"num_blocks\" : 3, \"stride\" : [2, 2] },\n",
+    "      { \"channels\" : 128, \"num_blocks\" : 3, \"stride\" : [2, 1] },\n",
+    "    ],\n",
+    "    \"kernel_size\" : [5, 7],\n",
+    "    \"dropout_rate\" : 0.1\n",
+    "  }, input_dim=32, output_dim=128)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TDS2d(\n",
+       "  (tds): Sequential(\n",
+       "    (0): Conv2d(1, 16, kernel_size=[5, 7], stride=[2, 2], padding=(2, 3))\n",
+       "    (1): ReLU(inplace=True)\n",
+       "    (2): Dropout(p=0.1, inplace=False)\n",
+       "    (3): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "    (4): TDSBlock2d(\n",
+       "      (conv): Sequential(\n",
+       "        (0): Conv3d(4, 4, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (mlp): Sequential(\n",
+       "        (0): Linear(in_features=16, out_features=16, bias=True)\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "        (3): Linear(in_features=16, out_features=16, bias=True)\n",
+       "        (4): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (instance_norm): ModuleList(\n",
+       "        (0): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "        (1): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (5): TDSBlock2d(\n",
+       "      (conv): Sequential(\n",
+       "        (0): Conv3d(4, 4, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (mlp): Sequential(\n",
+       "        (0): Linear(in_features=16, out_features=16, bias=True)\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "        (3): Linear(in_features=16, out_features=16, bias=True)\n",
+       "        (4): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (instance_norm): ModuleList(\n",
+       "        (0): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "        (1): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (6): TDSBlock2d(\n",
+       "      (conv): Sequential(\n",
+       "        (0): Conv3d(4, 4, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (mlp): Sequential(\n",
+       "        (0): Linear(in_features=16, out_features=16, bias=True)\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "        (3): Linear(in_features=16, out_features=16, bias=True)\n",
+       "        (4): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (instance_norm): ModuleList(\n",
+       "        (0): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "        (1): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (7): Conv2d(16, 128, kernel_size=[5, 7], stride=[2, 2], padding=(2, 3))\n",
+       "    (8): ReLU(inplace=True)\n",
+       "    (9): Dropout(p=0.1, inplace=False)\n",
+       "    (10): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "    (11): TDSBlock2d(\n",
+       "      (conv): Sequential(\n",
+       "        (0): Conv3d(32, 32, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (mlp): Sequential(\n",
+       "        (0): Linear(in_features=128, out_features=128, bias=True)\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "        (3): Linear(in_features=128, out_features=128, bias=True)\n",
+       "        (4): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (instance_norm): ModuleList(\n",
+       "        (0): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "        (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (12): TDSBlock2d(\n",
+       "      (conv): Sequential(\n",
+       "        (0): Conv3d(32, 32, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (mlp): Sequential(\n",
+       "        (0): Linear(in_features=128, out_features=128, bias=True)\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "        (3): Linear(in_features=128, out_features=128, bias=True)\n",
+       "        (4): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (instance_norm): ModuleList(\n",
+       "        (0): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "        (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (13): TDSBlock2d(\n",
+       "      (conv): Sequential(\n",
+       "        (0): Conv3d(32, 32, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (mlp): Sequential(\n",
+       "        (0): Linear(in_features=128, out_features=128, bias=True)\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "        (3): Linear(in_features=128, out_features=128, bias=True)\n",
+       "        (4): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (instance_norm): ModuleList(\n",
+       "        (0): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "        (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (14): Conv2d(128, 256, kernel_size=[5, 7], stride=[2, 2], padding=(2, 3))\n",
+       "    (15): ReLU(inplace=True)\n",
+       "    (16): Dropout(p=0.1, inplace=False)\n",
+       "    (17): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "    (18): TDSBlock2d(\n",
+       "      (conv): Sequential(\n",
+       "        (0): Conv3d(64, 64, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (mlp): Sequential(\n",
+       "        (0): Linear(in_features=256, out_features=256, bias=True)\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "        (3): Linear(in_features=256, out_features=256, bias=True)\n",
+       "        (4): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (instance_norm): ModuleList(\n",
+       "        (0): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "        (1): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (19): TDSBlock2d(\n",
+       "      (conv): Sequential(\n",
+       "        (0): Conv3d(64, 64, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (mlp): Sequential(\n",
+       "        (0): Linear(in_features=256, out_features=256, bias=True)\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "        (3): Linear(in_features=256, out_features=256, bias=True)\n",
+       "        (4): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (instance_norm): ModuleList(\n",
+       "        (0): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "        (1): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (20): TDSBlock2d(\n",
+       "      (conv): Sequential(\n",
+       "        (0): Conv3d(64, 64, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (mlp): Sequential(\n",
+       "        (0): Linear(in_features=256, out_features=256, bias=True)\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "        (3): Linear(in_features=256, out_features=256, bias=True)\n",
+       "        (4): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (instance_norm): ModuleList(\n",
+       "        (0): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "        (1): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (21): Conv2d(256, 512, kernel_size=[5, 7], stride=[2, 1], padding=(2, 3))\n",
+       "    (22): ReLU(inplace=True)\n",
+       "    (23): Dropout(p=0.1, inplace=False)\n",
+       "    (24): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "    (25): TDSBlock2d(\n",
+       "      (conv): Sequential(\n",
+       "        (0): Conv3d(128, 128, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (mlp): Sequential(\n",
+       "        (0): Linear(in_features=512, out_features=512, bias=True)\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "        (3): Linear(in_features=512, out_features=512, bias=True)\n",
+       "        (4): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (instance_norm): ModuleList(\n",
+       "        (0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "        (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (26): TDSBlock2d(\n",
+       "      (conv): Sequential(\n",
+       "        (0): Conv3d(128, 128, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (mlp): Sequential(\n",
+       "        (0): Linear(in_features=512, out_features=512, bias=True)\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "        (3): Linear(in_features=512, out_features=512, bias=True)\n",
+       "        (4): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (instance_norm): ModuleList(\n",
+       "        (0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "        (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (27): TDSBlock2d(\n",
+       "      (conv): Sequential(\n",
+       "        (0): Conv3d(128, 128, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (mlp): Sequential(\n",
+       "        (0): Linear(in_features=512, out_features=512, bias=True)\n",
+       "        (1): ReLU(inplace=True)\n",
+       "        (2): Dropout(p=0.1, inplace=False)\n",
+       "        (3): Linear(in_features=512, out_features=512, bias=True)\n",
+       "        (4): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "      (instance_norm): ModuleList(\n",
+       "        (0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "        (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (fc): Linear(in_features=1024, out_features=128, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tds2d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===============================================================================================\n",
+      "Layer (type:depth-idx)                        Output Shape              Param #\n",
+      "===============================================================================================\n",
+      "├─Sequential: 1-1                             [-1, 512, 2, 119]         --\n",
+      "|    └─Conv2d: 2-1                            [-1, 16, 14, 476]         576\n",
+      "|    └─ReLU: 2-2                              [-1, 16, 14, 476]         --\n",
+      "|    └─Dropout: 2-3                           [-1, 16, 14, 476]         --\n",
+      "|    └─InstanceNorm2d: 2-4                    [-1, 16, 14, 476]         32\n",
+      "|    └─TDSBlock2d: 2-5                        [-1, 16, 14, 476]         --\n",
+      "|    |    └─Sequential: 3-1                   [-1, 4, 4, 14, 476]       564\n",
+      "|    |    └─Sequential: 3-2                   [-1, 476, 14, 16]         544\n",
+      "|    └─TDSBlock2d: 2-6                        [-1, 16, 14, 476]         --\n",
+      "|    |    └─Sequential: 3-3                   [-1, 4, 4, 14, 476]       564\n",
+      "|    |    └─Sequential: 3-4                   [-1, 476, 14, 16]         544\n",
+      "|    └─TDSBlock2d: 2-7                        [-1, 16, 14, 476]         --\n",
+      "|    |    └─Sequential: 3-5                   [-1, 4, 4, 14, 476]       564\n",
+      "|    |    └─Sequential: 3-6                   [-1, 476, 14, 16]         544\n",
+      "|    └─Conv2d: 2-8                            [-1, 128, 7, 238]         71,808\n",
+      "|    └─ReLU: 2-9                              [-1, 128, 7, 238]         --\n",
+      "|    └─Dropout: 2-10                          [-1, 128, 7, 238]         --\n",
+      "|    └─InstanceNorm2d: 2-11                   [-1, 128, 7, 238]         256\n",
+      "|    └─TDSBlock2d: 2-12                       [-1, 128, 7, 238]         --\n",
+      "|    |    └─Sequential: 3-7                   [-1, 32, 4, 7, 238]       35,872\n",
+      "|    |    └─Sequential: 3-8                   [-1, 238, 7, 128]         33,024\n",
+      "|    └─TDSBlock2d: 2-13                       [-1, 128, 7, 238]         --\n",
+      "|    |    └─Sequential: 3-9                   [-1, 32, 4, 7, 238]       35,872\n",
+      "|    |    └─Sequential: 3-10                  [-1, 238, 7, 128]         33,024\n",
+      "|    └─TDSBlock2d: 2-14                       [-1, 128, 7, 238]         --\n",
+      "|    |    └─Sequential: 3-11                  [-1, 32, 4, 7, 238]       35,872\n",
+      "|    |    └─Sequential: 3-12                  [-1, 238, 7, 128]         33,024\n",
+      "|    └─Conv2d: 2-15                           [-1, 256, 4, 119]         1,147,136\n",
+      "|    └─ReLU: 2-16                             [-1, 256, 4, 119]         --\n",
+      "|    └─Dropout: 2-17                          [-1, 256, 4, 119]         --\n",
+      "|    └─InstanceNorm2d: 2-18                   [-1, 256, 4, 119]         512\n",
+      "|    └─TDSBlock2d: 2-19                       [-1, 256, 4, 119]         --\n",
+      "|    |    └─Sequential: 3-13                  [-1, 64, 4, 4, 119]       143,424\n",
+      "|    |    └─Sequential: 3-14                  [-1, 119, 4, 256]         131,584\n",
+      "|    └─TDSBlock2d: 2-20                       [-1, 256, 4, 119]         --\n",
+      "|    |    └─Sequential: 3-15                  [-1, 64, 4, 4, 119]       143,424\n",
+      "|    |    └─Sequential: 3-16                  [-1, 119, 4, 256]         131,584\n",
+      "|    └─TDSBlock2d: 2-21                       [-1, 256, 4, 119]         --\n",
+      "|    |    └─Sequential: 3-17                  [-1, 64, 4, 4, 119]       143,424\n",
+      "|    |    └─Sequential: 3-18                  [-1, 119, 4, 256]         131,584\n",
+      "|    └─Conv2d: 2-22                           [-1, 512, 2, 119]         4,588,032\n",
+      "|    └─ReLU: 2-23                             [-1, 512, 2, 119]         --\n",
+      "|    └─Dropout: 2-24                          [-1, 512, 2, 119]         --\n",
+      "|    └─InstanceNorm2d: 2-25                   [-1, 512, 2, 119]         1,024\n",
+      "|    └─TDSBlock2d: 2-26                       [-1, 512, 2, 119]         --\n",
+      "|    |    └─Sequential: 3-19                  [-1, 128, 4, 2, 119]      573,568\n",
+      "|    |    └─Sequential: 3-20                  [-1, 119, 2, 512]         525,312\n",
+      "|    └─TDSBlock2d: 2-27                       [-1, 512, 2, 119]         --\n",
+      "|    |    └─Sequential: 3-21                  [-1, 128, 4, 2, 119]      573,568\n",
+      "|    |    └─Sequential: 3-22                  [-1, 119, 2, 512]         525,312\n",
+      "|    └─TDSBlock2d: 2-28                       [-1, 512, 2, 119]         --\n",
+      "|    |    └─Sequential: 3-23                  [-1, 128, 4, 2, 119]      573,568\n",
+      "|    |    └─Sequential: 3-24                  [-1, 119, 2, 512]         525,312\n",
+      "├─Linear: 1-2                                 [-1, 119, 128]            131,200\n",
+      "===============================================================================================\n",
+      "Total params: 10,272,252\n",
+      "Trainable params: 10,272,252\n",
+      "Non-trainable params: 0\n",
+      "Total mult-adds (G): 5.00\n",
+      "===============================================================================================\n",
+      "Input size (MB): 0.10\n",
+      "Forward/backward pass size (MB): 73.21\n",
+      "Params size (MB): 39.19\n",
+      "Estimated Total Size (MB): 112.50\n",
+      "===============================================================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "===============================================================================================\n",
+       "Layer (type:depth-idx)                        Output Shape              Param #\n",
+       "===============================================================================================\n",
+       "├─Sequential: 1-1                             [-1, 512, 2, 119]         --\n",
+       "|    └─Conv2d: 2-1                            [-1, 16, 14, 476]         576\n",
+       "|    └─ReLU: 2-2                              [-1, 16, 14, 476]         --\n",
+       "|    └─Dropout: 2-3                           [-1, 16, 14, 476]         --\n",
+       "|    └─InstanceNorm2d: 2-4                    [-1, 16, 14, 476]         32\n",
+       "|    └─TDSBlock2d: 2-5                        [-1, 16, 14, 476]         --\n",
+       "|    |    └─Sequential: 3-1                   [-1, 4, 4, 14, 476]       564\n",
+       "|    |    └─Sequential: 3-2                   [-1, 476, 14, 16]         544\n",
+       "|    └─TDSBlock2d: 2-6                        [-1, 16, 14, 476]         --\n",
+       "|    |    └─Sequential: 3-3                   [-1, 4, 4, 14, 476]       564\n",
+       "|    |    └─Sequential: 3-4                   [-1, 476, 14, 16]         544\n",
+       "|    └─TDSBlock2d: 2-7                        [-1, 16, 14, 476]         --\n",
+       "|    |    └─Sequential: 3-5                   [-1, 4, 4, 14, 476]       564\n",
+       "|    |    └─Sequential: 3-6                   [-1, 476, 14, 16]         544\n",
+       "|    └─Conv2d: 2-8                            [-1, 128, 7, 238]         71,808\n",
+       "|    └─ReLU: 2-9                              [-1, 128, 7, 238]         --\n",
+       "|    └─Dropout: 2-10                          [-1, 128, 7, 238]         --\n",
+       "|    └─InstanceNorm2d: 2-11                   [-1, 128, 7, 238]         256\n",
+       "|    └─TDSBlock2d: 2-12                       [-1, 128, 7, 238]         --\n",
+       "|    |    └─Sequential: 3-7                   [-1, 32, 4, 7, 238]       35,872\n",
+       "|    |    └─Sequential: 3-8                   [-1, 238, 7, 128]         33,024\n",
+       "|    └─TDSBlock2d: 2-13                       [-1, 128, 7, 238]         --\n",
+       "|    |    └─Sequential: 3-9                   [-1, 32, 4, 7, 238]       35,872\n",
+       "|    |    └─Sequential: 3-10                  [-1, 238, 7, 128]         33,024\n",
+       "|    └─TDSBlock2d: 2-14                       [-1, 128, 7, 238]         --\n",
+       "|    |    └─Sequential: 3-11                  [-1, 32, 4, 7, 238]       35,872\n",
+       "|    |    └─Sequential: 3-12                  [-1, 238, 7, 128]         33,024\n",
+       "|    └─Conv2d: 2-15                           [-1, 256, 4, 119]         1,147,136\n",
+       "|    └─ReLU: 2-16                             [-1, 256, 4, 119]         --\n",
+       "|    └─Dropout: 2-17                          [-1, 256, 4, 119]         --\n",
+       "|    └─InstanceNorm2d: 2-18                   [-1, 256, 4, 119]         512\n",
+       "|    └─TDSBlock2d: 2-19                       [-1, 256, 4, 119]         --\n",
+       "|    |    └─Sequential: 3-13                  [-1, 64, 4, 4, 119]       143,424\n",
+       "|    |    └─Sequential: 3-14                  [-1, 119, 4, 256]         131,584\n",
+       "|    └─TDSBlock2d: 2-20                       [-1, 256, 4, 119]         --\n",
+       "|    |    └─Sequential: 3-15                  [-1, 64, 4, 4, 119]       143,424\n",
+       "|    |    └─Sequential: 3-16                  [-1, 119, 4, 256]         131,584\n",
+       "|    └─TDSBlock2d: 2-21                       [-1, 256, 4, 119]         --\n",
+       "|    |    └─Sequential: 3-17                  [-1, 64, 4, 4, 119]       143,424\n",
+       "|    |    └─Sequential: 3-18                  [-1, 119, 4, 256]         131,584\n",
+       "|    └─Conv2d: 2-22                           [-1, 512, 2, 119]         4,588,032\n",
+       "|    └─ReLU: 2-23                             [-1, 512, 2, 119]         --\n",
+       "|    └─Dropout: 2-24                          [-1, 512, 2, 119]         --\n",
+       "|    └─InstanceNorm2d: 2-25                   [-1, 512, 2, 119]         1,024\n",
+       "|    └─TDSBlock2d: 2-26                       [-1, 512, 2, 119]         --\n",
+       "|    |    └─Sequential: 3-19                  [-1, 128, 4, 2, 119]      573,568\n",
+       "|    |    └─Sequential: 3-20                  [-1, 119, 2, 512]         525,312\n",
+       "|    └─TDSBlock2d: 2-27                       [-1, 512, 2, 119]         --\n",
+       "|    |    └─Sequential: 3-21                  [-1, 128, 4, 2, 119]      573,568\n",
+       "|    |    └─Sequential: 3-22                  [-1, 119, 2, 512]         525,312\n",
+       "|    └─TDSBlock2d: 2-28                       [-1, 512, 2, 119]         --\n",
+       "|    |    └─Sequential: 3-23                  [-1, 128, 4, 2, 119]      573,568\n",
+       "|    |    └─Sequential: 3-24                  [-1, 119, 2, 512]         525,312\n",
+       "├─Linear: 1-2                                 [-1, 119, 128]            131,200\n",
+       "===============================================================================================\n",
+       "Total params: 10,272,252\n",
+       "Trainable params: 10,272,252\n",
+       "Non-trainable params: 0\n",
+       "Total mult-adds (G): 5.00\n",
+       "===============================================================================================\n",
+       "Input size (MB): 0.10\n",
+       "Forward/backward pass size (MB): 73.21\n",
+       "Params size (MB): 39.19\n",
+       "Estimated Total Size (MB): 112.50\n",
+       "==============================================================================================="
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary(tds2d, (1, 28, 952), device=\"cpu\", depth=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = torch.randn(2,1, 28, 952)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([2, 119, 128])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tds2d(t).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cnn = CNN().cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i = nn.Sequential(nn.Conv2d(1,1,1,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nn.Sequential(i,i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cnn(t).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.networks.vqvae import Encoder, Decoder, VQVAE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vqvae = VQVAE(1, [32, 128, 128, 256], [4, 4, 4, 4], [2, 2, [1, 2], [1, 2]], 2, 32, 256, [[6, 119], [7, 238]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = torch.randn(2, 1, 28, 952)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, l = vqvae(t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "5 * 59 / 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary(vqvae, (1, 28, 952), device=\"cpu\", depth=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "up = nn.Upsample([4, 59])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "up(tt).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tt.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GEGLU(nn.Module):\n",
+    "    def __init__(self, dim_in, dim_out):\n",
+    "        super().__init__()\n",
+    "        self.proj = nn.Linear(dim_in, dim_out * 2)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x, gate = self.proj(x).chunk(2, dim = -1)\n",
+    "        return x * F.gelu(gate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e = GEGLU(256, 2048)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e(t).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emb = nn.Embedding(56, 256)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():\n",
+    "    e = emb(torch.Tensor([55]).long())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from einops import repeat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ee = repeat(e, \"() n -> b n\", b=16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emb.device"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ee"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ee.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = torch.randn(16, 10, 256)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = torch.cat((ee.unsqueeze(1), t, ee.unsqueeze(1)), dim=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.networks.residual_network import IdentityBlock, ResidualBlock, BasicBlock, BottleNeckBlock, ResidualLayer, ResidualNetwork, ResidualNetworkEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.networks import WideResidualNetwork"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wr = WideResidualNetwork(\n",
+    "            in_channels= 1,\n",
+    "            num_classes= 80,\n",
+    "            in_planes=64,\n",
+    "            depth=10,\n",
+    "            num_layers=4,\n",
+    "            width_factor=2,\n",
+    "            num_stages=[64, 128, 256, 256],\n",
+    "            dropout_rate= 0.1,\n",
+    "            activation= \"SELU\",\n",
+    "            use_decoder= False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchsummary import summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "backbone = ResidualNetworkEncoder(1, [64, 65, 66, 67, 68], [2, 2, 2, 2, 2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary(backbone, (1, 28, 952), device=\"cpu\", depth=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "        backbone = nn.Sequential(\n",
+    "            *list(wr.children())[:][:]\n",
+    "        )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "backbone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary(wr, (1, 28, 952), device=\"cpu\", depth=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = torch.rand(1, 1, 28, 952)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = wr(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from einops import rearrange"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = rearrange(b, \"b c h w -> b w c h\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = nn.AdaptiveAvgPool2d((None, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = c(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d.squeeze(3).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch import nn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "32 + 64"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "3 * 112"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col_embed = nn.Parameter(torch.rand(1000, 256 // 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W, H = 196, 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "col_embed[:W].unsqueeze(0).repeat(H, 1, 1).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col_embed[:H].unsqueeze(1).repeat(1, W, 1).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "           torch.cat(\n",
+    "                [\n",
+    "                    col_embed[:W].unsqueeze(0).repeat(H, 1, 1),\n",
+    "                    col_embed[:H].unsqueeze(1).repeat(1, W, 1),\n",
+    "                ],\n",
+    "                dim=-1,\n",
+    "            ).unsqueeze(0).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "4 * 196"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = torch.tensor([1,1,12,1,1,1,1,1,9,9,9,9,9,9])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.nonzero(target == 9, as_tuple=False)[0].item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target[:9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.inf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.networks.transformer.positional_encoding import PositionalEncoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(15, 5))\n",
+    "pe = PositionalEncoding(20, 0)\n",
+    "y = pe.forward(torch.zeros(1, 100, 20))\n",
+    "plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())\n",
+    "plt.legend([\"dim %d\"%p for p in [4,5,6,7]])\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.networks.densenet import DenseNet,_DenseLayer,_DenseBlock"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dnet = DenseNet(12, (6, 12, 10), 1, 24, 80, 4, 0, True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "216 / 8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary(dnet, (1, 28, 952), device=\"cpu\", depth=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "        backbone = nn.Sequential(\n",
+    "            *list(dnet.children())[:][:-4]\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "backbone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.networks import WideResidualNetwork"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = WideResidualNetwork(\n",
+    "        in_channels = 1,\n",
+    "        in_planes = 32,\n",
+    "        num_classes = 80,\n",
+    "        depth = 10,\n",
+    "        width_factor = 1,\n",
+    "        dropout_rate = 0.0,\n",
+    "        num_layers = 5,\n",
+    "        activation = \"relu\",\n",
+    "        use_decoder = False,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary(w, (1, 28, 952), device=\"cpu\", depth=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz= 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask = torch.triu(torch.ones(sz, sz), 1)\n",
+    "mask = mask.masked_fill(mask==1, float('-inf'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "h = torch.rand(1, 256, 10, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h.flatten(2).permute(2, 0, 1).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h.flatten(2).permute(2, 0, 1).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred = torch.Tensor([1,21,2,45,31, 81, 1, 79, 79, 79, 2,1,1,1,1, 81, 1, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79]).long()\n",
+    "target = torch.Tensor([1,1,1,1,1, 81, 79, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79]).long()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask = (target != 79)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred * mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target * mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.models.metrics import accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pad_indcies = torch.nonzero(target == 79, as_tuple=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t1 = torch.nonzero(target == 81, as_tuple=False).squeeze(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t2 = torch.arange(10, target.shape[0] + 1, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for start, stop in zip(t1, t2):\n",
+    "    pred[start+1:stop] = 79"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[pred[start+1:stop] = 79 for start, stop in zip(t1, t2)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "pad_indcies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred[pad_indcies:pad_indcies] = 79"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy(pred, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "acc = (pred == target).sum().float() / target.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "acc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/notebooks/01-look-at-emnist.ipynb b/notebooks/01-look-at-emnist.ipynb
index b70ce12..b70ce12 100644
--- a/src/notebooks/01-look-at-emnist.ipynb
+++ b/notebooks/01-look-at-emnist.ipynb
diff --git a/src/notebooks/02a-sentence-generator.ipynb b/notebooks/02a-sentence-generator.ipynb
index 99aa56a..99aa56a 100644
--- a/src/notebooks/02a-sentence-generator.ipynb
+++ b/notebooks/02a-sentence-generator.ipynb
diff --git a/src/notebooks/02b-emnist-lines-dataset.ipynb b/notebooks/02b-emnist-lines-dataset.ipynb
index f82342b..f82342b 100644
--- a/src/notebooks/02b-emnist-lines-dataset.ipynb
+++ b/notebooks/02b-emnist-lines-dataset.ipynb
diff --git a/src/notebooks/02c-image-patches.ipynb b/notebooks/02c-image-patches.ipynb
index fedea91..fedea91 100644
--- a/src/notebooks/02c-image-patches.ipynb
+++ b/notebooks/02c-image-patches.ipynb
diff --git a/src/notebooks/03a-line-prediction.ipynb b/notebooks/03a-line-prediction.ipynb
index 13f4ff1..13f4ff1 100644
--- a/src/notebooks/03a-line-prediction.ipynb
+++ b/notebooks/03a-line-prediction.ipynb
diff --git a/src/notebooks/04a-look-at-iam-lines.ipynb b/notebooks/04a-look-at-iam-lines.ipynb
index de59a85..de59a85 100644
--- a/src/notebooks/04a-look-at-iam-lines.ipynb
+++ b/notebooks/04a-look-at-iam-lines.ipynb
diff --git a/src/notebooks/04b-look-at-iam-paragraphs-predictions.ipynb b/notebooks/04b-look-at-iam-paragraphs-predictions.ipynb
index 5662eb1..5662eb1 100644
--- a/src/notebooks/04b-look-at-iam-paragraphs-predictions.ipynb
+++ b/notebooks/04b-look-at-iam-paragraphs-predictions.ipynb
diff --git a/src/notebooks/04b-look-at-iam-paragraphs.ipynb b/notebooks/04b-look-at-iam-paragraphs.ipynb
index dc0aef6..dc0aef6 100644
--- a/src/notebooks/04b-look-at-iam-paragraphs.ipynb
+++ b/notebooks/04b-look-at-iam-paragraphs.ipynb
diff --git a/src/notebooks/05-sanity-check-multihead-attention.ipynb b/notebooks/05-sanity-check-multihead-attention.ipynb
index 54f0432..54f0432 100644
--- a/src/notebooks/05-sanity-check-multihead-attention.ipynb
+++ b/notebooks/05-sanity-check-multihead-attention.ipynb
diff --git a/src/notebooks/05a-UNet.ipynb b/notebooks/05a-UNet.ipynb
index 77d895d..77d895d 100644
--- a/src/notebooks/05a-UNet.ipynb
+++ b/notebooks/05a-UNet.ipynb
diff --git a/src/notebooks/05a-test-end-to-end-model.ipynb b/notebooks/05a-test-end-to-end-model.ipynb
index 7723b12..7723b12 100644
--- a/src/notebooks/05a-test-end-to-end-model.ipynb
+++ b/notebooks/05a-test-end-to-end-model.ipynb
diff --git a/src/notebooks/06-try-transformer-model-predictions.ipynb b/notebooks/06-try-transformer-model-predictions.ipynb
index d39e111..d39e111 100644
--- a/src/notebooks/06-try-transformer-model-predictions.ipynb
+++ b/notebooks/06-try-transformer-model-predictions.ipynb
diff --git a/src/notebooks/07-look-at-lexicon.ipynb b/notebooks/07-look-at-lexicon.ipynb
index b7a5a0e..b7a5a0e 100644
--- a/src/notebooks/07-look-at-lexicon.ipynb
+++ b/notebooks/07-look-at-lexicon.ipynb
diff --git a/src/notebooks/07-try-gtn.ipynb b/notebooks/07-try-gtn.ipynb
index 4ef444b..4ef444b 100644
--- a/src/notebooks/07-try-gtn.ipynb
+++ b/notebooks/07-try-gtn.ipynb
diff --git a/src/notebooks/Untitled.ipynb b/notebooks/Untitled.ipynb
index 841a37d..841a37d 100644
--- a/src/notebooks/Untitled.ipynb
+++ b/notebooks/Untitled.ipynb
diff --git a/src/notebooks/g1.png b/notebooks/g1.png
index 09dd49e..09dd49e 100644
--- a/src/notebooks/g1.png
+++ b/notebooks/g1.png
diff --git a/src/notebooks/g2.png b/notebooks/g2.png
index a3cf21e..a3cf21e 100644
--- a/src/notebooks/g2.png
+++ b/notebooks/g2.png
diff --git a/src/notebooks/intersect.png b/notebooks/intersect.png
index 63b7f2f..63b7f2f 100644
--- a/src/notebooks/intersect.png
+++ b/notebooks/intersect.png
diff --git a/src/notebooks/intersection.pdf b/notebooks/intersection.pdf
index c425a9f..c425a9f 100644
--- a/src/notebooks/intersection.pdf
+++ b/notebooks/intersection.pdf
diff --git a/noxfile.py b/noxfile.py
index 60c3923..098a551 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -40,7 +40,7 @@ def install_with_constraints(session: Session, *args: str, **kwargs: Any) -> Non
         session.install(f"--constraint={requirements.name}", *args, **kwargs)
 
 
-@nox.session(python="3.8")
+@nox.session(python="3.9")
 def black(session: Session) -> None:
     """Run black code formatter."""
     args = session.posargs or locations
@@ -48,7 +48,7 @@ def black(session: Session) -> None:
     session.run("black", *args)
 
 
-@nox.session(python=["3.8"])
+@nox.session(python=["3.9"])
 def lint(session: Session) -> None:
     """Lint using flake8."""
     args = session.posargs or locations
@@ -66,7 +66,7 @@ def lint(session: Session) -> None:
     session.run("flake8", *args)
 
 
-@nox.session(python="3.8")
+@nox.session(python="3.9")
 def safety(session: Session) -> None:
     """Scan dependencies for insecure packages."""
     with tempfile.NamedTemporaryFile() as requirements:
@@ -83,7 +83,7 @@ def safety(session: Session) -> None:
         session.run("safety", "check", f"--file={requirements.name}", "--full-report")
 
 
-@nox.session(python=["3.8"])
+@nox.session(python=["3.9"])
 def mypy(session: Session) -> None:
     """Type-check using mypy."""
     args = session.posargs or locations
@@ -91,7 +91,7 @@ def mypy(session: Session) -> None:
     session.run("mypy", *args)
 
 
-@nox.session(python="3.8")
+@nox.session(python="3.9")
 def pytype(session: Session) -> None:
     """Type-check using pytype."""
     args = session.posargs or ["--disable=import-error", *locations]
@@ -99,7 +99,7 @@ def pytype(session: Session) -> None:
     session.run("pytype", *args)
 
 
-@nox.session(python=["3.8"])
+@nox.session(python=["3.9"])
 def tests(session: Session) -> None:
     """Run the test suite."""
     args = session.posargs or ["--cov", "-m", "not e2e"]
@@ -110,7 +110,7 @@ def tests(session: Session) -> None:
     session.run("pytest", *args)
 
 
-@nox.session(python=["3.8"])
+@nox.session(python=["3.9"])
 def typeguard(session: Session) -> None:
     """Runtime type checking using Typeguard."""
     args = session.posargs or ["-m", "not e2e"]
@@ -119,7 +119,7 @@ def typeguard(session: Session) -> None:
     session.run("pytest", f"--typeguard-packages={package}", *args)
 
 
-@nox.session(python=["3.8"])
+@nox.session(python=["3.9"])
 def xdoctest(session: Session) -> None:
     """Run examples with xdoctest."""
     args = session.posargs or ["all"]
@@ -128,7 +128,7 @@ def xdoctest(session: Session) -> None:
     session.run("python", "-m", "xdoctest", package, *args)
 
 
-@nox.session(python="3.8")
+@nox.session(python="3.9")
 def coverage(session: Session) -> None:
     """Upload coverage data."""
     install_with_constraints(session, "coverage[toml]", "codecov")
@@ -136,7 +136,7 @@ def coverage(session: Session) -> None:
     session.run("codecov", *session.posargs)
 
 
-@nox.session(python="3.8")
+@nox.session(python="3.9")
 def docs(session: Session) -> None:
     """Build the documentation."""
     session.run("poetry", "install", "--no-dev", external=True)
diff --git a/poetry.lock b/poetry.lock
index 72da168..78f086e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -244,14 +244,6 @@ optional = false
 python-versions = ">=3.6,<4.0"
 
 [[package]]
-name = "dataclasses"
-version = "0.6"
-description = "A backport of the dataclasses module for Python 3.6"
-category = "main"
-optional = false
-python-versions = "*"
-
-[[package]]
 name = "decorator"
 version = "4.4.2"
 description = "Decorators for Humans"
@@ -432,14 +424,6 @@ python-versions = "*"
 flake8 = "*"
 
 [[package]]
-name = "future"
-version = "0.18.2"
-description = "Clean single-source support for Python 3 and 2"
-category = "main"
-optional = false
-python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
-
-[[package]]
 name = "gitdb"
 version = "4.0.5"
 description = "Git Object Database"
@@ -501,15 +485,17 @@ python-versions = ">=3.5"
 
 [[package]]
 name = "h5py"
-version = "2.10.0"
+version = "3.2.1"
 description = "Read and write HDF5 files from Python"
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 
 [package.dependencies]
-numpy = ">=1.7"
-six = "*"
+numpy = [
+    {version = ">=1.17.5", markers = "python_version == \"3.8\""},
+    {version = ">=1.19.3", markers = "python_version >= \"3.9\""},
+]
 
 [[package]]
 name = "idna"
@@ -995,11 +981,11 @@ test = ["nose", "coverage", "requests", "nose-warnings-filters", "nbval", "nose-
 
 [[package]]
 name = "numpy"
-version = "1.19.4"
+version = "1.20.1"
 description = "NumPy is the fundamental package for array computing with Python."
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [[package]]
 name = "nvidia-ml-py3"
@@ -1029,6 +1015,9 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[package.dependencies]
+numpy = ">=1.19.3"
+
 [[package]]
 name = "packaging"
 version = "20.4"
@@ -1782,15 +1771,13 @@ python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 
 [[package]]
 name = "torch"
-version = "1.7.0"
+version = "1.7.1"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 category = "main"
 optional = false
-python-versions = ">=3.6.1"
+python-versions = ">=3.6.2"
 
 [package.dependencies]
-dataclasses = "*"
-future = "*"
 numpy = "*"
 typing-extensions = "*"
 
@@ -1804,7 +1791,7 @@ python-versions = ">=3.5"
 
 [[package]]
 name = "torchvision"
-version = "0.8.1"
+version = "0.8.2"
 description = "image and video datasets and models for torch deep learning"
 category = "main"
 optional = false
@@ -1813,7 +1800,7 @@ python-versions = "*"
 [package.dependencies]
 numpy = "*"
 pillow = ">=4.1.1"
-torch = "1.7.0"
+torch = "1.7.1"
 
 [package.extras]
 scipy = ["scipy"]
@@ -2006,7 +1993,7 @@ tests = ["pytest", "pytest-cov", "codecov", "scikit-build", "cmake", "ninja", "p
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.8"
-content-hash = "1f194d7de179e9676ef1f8e51b83ff15c001627803008ef8225e8e14ab3acab0"
+content-hash = "c87742a388e1277e84313b4c0ff75681d754c8328db2c488c0aba2a4dafc6a64"
 
 [metadata.files]
 alabaster = [
@@ -2038,6 +2025,8 @@ argon2-cffi = [
     {file = "argon2_cffi-20.1.0-cp37-cp37m-win_amd64.whl", hash = "sha256:6678bb047373f52bcff02db8afab0d2a77d83bde61cfecea7c5c62e2335cb203"},
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-    {file = "torchvision-0.8.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:95b0ce59e631e2c97e6069dff126a43232cca859b18a1b505e5b02dd1a65dd0f"},
-    {file = "torchvision-0.8.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:469e0b831bfe17c46159966b5dc7ba09c87eaeecbed6f9a4d6ec4e691b0c8827"},
-    {file = "torchvision-0.8.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:337820e680e5193872903369d8177d5ea681e7156d370d89d487b0e0f1e56238"},
+    {file = "torchvision-0.8.2-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:86fae370d222f76ad57c57c3bee03f78b8db727743bfb4c1559a3d395159cea8"},
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+    {file = "torchvision-0.8.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:24db8f4c3d812a032273f68563ad5dbd724f5bfbed523d0c6dce8cede26bb153"},
+    {file = "torchvision-0.8.2-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:b068f6bcbe91bdd34dda0a39e8a26392add45a3be82543f6dd523b76484fb56f"},
+    {file = "torchvision-0.8.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:afb76a66b9b0693f758a881a2bf333ed97e3c0c3f15a413c4f49d8dd8bd21307"},
+    {file = "torchvision-0.8.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:cd8817e9197fc60ebae37162a445db90bbf35591314a5767ad3d1490b5d65b0f"},
+    {file = "torchvision-0.8.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:1bd58acc3366ec02266aae56a7a752d43ef07de4a6ba420c4f907d0c9168bb8c"},
+    {file = "torchvision-0.8.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:976750a49db2e23dc5a1ed0b5c31f7af51ed2702eee410ee09ef985c3a3e48cf"},
 ]
 tornado = [
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@@ -3149,19 +3134,28 @@ typed-ast = [
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+    {file = "typed_ast-1.4.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:7e4c9d7658aaa1fc80018593abdf8598bf91325af6af5cce4ce7c73bc45ea53d"},
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 ]
 typeguard = [
diff --git a/pyproject.toml b/pyproject.toml
index 4c674bc..2f774b2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,7 +20,7 @@ marshmallow = "^3.6.0"
 sphinx-autodoc-typehints = "^1.10.3"
 sphinx_rtd_theme = "^0.4.3"
 boltons = "^20.1.0"
-h5py = "^2.10.0"
+h5py = "^3.2.1"
 toml = "^0.10.1"
 torch = "^1.7.0"
 torchvision = "^0.8.1"
diff --git a/src/notebooks/00-testing-stuff-out.ipynb b/src/notebooks/00-testing-stuff-out.ipynb
deleted file mode 100644
index 2d6b43c..0000000
--- a/src/notebooks/00-testing-stuff-out.ipynb
+++ /dev/null
@@ -1,1059 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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.nn.functional as F\n",
-    "import torch\n",
-    "from torch import nn\n",
-    "from torchsummary import summary\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,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.networks import CNN, TDS2d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tds2d = TDS2d(**{\n",
-    "    \"depth\" : 4,\n",
-    "    \"tds_groups\" : [\n",
-    "      { \"channels\" : 4, \"num_blocks\" : 3, \"stride\" : [2, 2] },\n",
-    "      { \"channels\" : 32, \"num_blocks\" : 3, \"stride\" : [2, 2] },\n",
-    "      { \"channels\" : 64, \"num_blocks\" : 3, \"stride\" : [2, 2] },\n",
-    "      { \"channels\" : 128, \"num_blocks\" : 3, \"stride\" : [2, 1] },\n",
-    "    ],\n",
-    "    \"kernel_size\" : [5, 7],\n",
-    "    \"dropout_rate\" : 0.1\n",
-    "  }, input_dim=32, output_dim=128)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tds2d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summary(tds2d, (1, 28, 952), device=\"cpu\", depth=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = torch.randn(2,1, 28, 952)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tds2d(t).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cnn = CNN()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "i = nn.Sequential(nn.Conv2d(1,1,1,1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nn.Sequential(i,i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cnn(t).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.networks.vqvae import Encoder, Decoder, VQVAE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vqvae = VQVAE(1, [32, 128, 128, 256], [4, 4, 4, 4], [2, 2, [1, 2], [1, 2]], 2, 32, 256, [[6, 119], [7, 238]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = torch.randn(2, 1, 28, 952)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x, l = vqvae(t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "5 * 59 / 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summary(vqvae, (1, 28, 952), device=\"cpu\", depth=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "up = nn.Upsample([4, 59])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "up(tt).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tt.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class GEGLU(nn.Module):\n",
-    "    def __init__(self, dim_in, dim_out):\n",
-    "        super().__init__()\n",
-    "        self.proj = nn.Linear(dim_in, dim_out * 2)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x, gate = self.proj(x).chunk(2, dim = -1)\n",
-    "        return x * F.gelu(gate)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "e = GEGLU(256, 2048)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "e(t).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "emb = nn.Embedding(56, 256)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with torch.no_grad():\n",
-    "    e = emb(torch.Tensor([55]).long())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from einops import repeat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ee = repeat(e, \"() n -> b n\", b=16)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "emb.device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ee"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ee.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = torch.randn(16, 10, 256)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = torch.cat((ee.unsqueeze(1), t, ee.unsqueeze(1)), dim=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "e.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.networks.residual_network import IdentityBlock, ResidualBlock, BasicBlock, BottleNeckBlock, ResidualLayer, ResidualNetwork, ResidualNetworkEncoder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.networks import WideResidualNetwork"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wr = WideResidualNetwork(\n",
-    "            in_channels= 1,\n",
-    "            num_classes= 80,\n",
-    "            in_planes=64,\n",
-    "            depth=10,\n",
-    "            num_layers=4,\n",
-    "            width_factor=2,\n",
-    "            num_stages=[64, 128, 256, 256],\n",
-    "            dropout_rate= 0.1,\n",
-    "            activation= \"SELU\",\n",
-    "            use_decoder= False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchsummary import summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "backbone = ResidualNetworkEncoder(1, [64, 65, 66, 67, 68], [2, 2, 2, 2, 2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summary(backbone, (1, 28, 952), device=\"cpu\", depth=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "        backbone = nn.Sequential(\n",
-    "            *list(wr.children())[:][:]\n",
-    "        )\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "backbone"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summary(wr, (1, 28, 952), device=\"cpu\", depth=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = torch.rand(1, 1, 28, 952)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = wr(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from einops import rearrange"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = rearrange(b, \"b c h w -> b w c h\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c = nn.AdaptiveAvgPool2d((None, 1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = c(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d.squeeze(3).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch import nn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "32 + 64"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "3 * 112"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "col_embed = nn.Parameter(torch.rand(1000, 256 // 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "W, H = 196, 4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "col_embed[:W].unsqueeze(0).repeat(H, 1, 1).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "col_embed[:H].unsqueeze(1).repeat(1, W, 1).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "           torch.cat(\n",
-    "                [\n",
-    "                    col_embed[:W].unsqueeze(0).repeat(H, 1, 1),\n",
-    "                    col_embed[:H].unsqueeze(1).repeat(1, W, 1),\n",
-    "                ],\n",
-    "                dim=-1,\n",
-    "            ).unsqueeze(0).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "4 * 196"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target = torch.tensor([1,1,12,1,1,1,1,1,9,9,9,9,9,9])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.nonzero(target == 9, as_tuple=False)[0].item()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target[:9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.inf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.networks.transformer.positional_encoding import PositionalEncoding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(15, 5))\n",
-    "pe = PositionalEncoding(20, 0)\n",
-    "y = pe.forward(torch.zeros(1, 100, 20))\n",
-    "plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())\n",
-    "plt.legend([\"dim %d\"%p for p in [4,5,6,7]])\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.networks.densenet import DenseNet,_DenseLayer,_DenseBlock"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dnet = DenseNet(12, (6, 12, 10), 1, 24, 80, 4, 0, True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "216 / 8"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summary(dnet, (1, 28, 952), device=\"cpu\", depth=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "        backbone = nn.Sequential(\n",
-    "            *list(dnet.children())[:][:-4]\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "backbone"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.networks import WideResidualNetwork"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w = WideResidualNetwork(\n",
-    "        in_channels = 1,\n",
-    "        in_planes = 32,\n",
-    "        num_classes = 80,\n",
-    "        depth = 10,\n",
-    "        width_factor = 1,\n",
-    "        dropout_rate = 0.0,\n",
-    "        num_layers = 5,\n",
-    "        activation = \"relu\",\n",
-    "        use_decoder = False,)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summary(w, (1, 28, 952), device=\"cpu\", depth=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sz= 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mask = torch.triu(torch.ones(sz, sz), 1)\n",
-    "mask = mask.masked_fill(mask==1, float('-inf'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "h = torch.rand(1, 256, 10, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h.flatten(2).permute(2, 0, 1).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h.flatten(2).permute(2, 0, 1).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mask\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred = torch.Tensor([1,21,2,45,31, 81, 1, 79, 79, 79, 2,1,1,1,1, 81, 1, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79]).long()\n",
-    "target = torch.Tensor([1,1,1,1,1, 81, 79, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79]).long()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mask = (target != 79)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mask"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred * mask"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target * mask"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.models.metrics import accuracy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pad_indcies = torch.nonzero(target == 79, as_tuple=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t1 = torch.nonzero(target == 81, as_tuple=False).squeeze(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target.shape[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t2 = torch.arange(10, target.shape[0] + 1, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for start, stop in zip(t1, t2):\n",
-    "    pred[start+1:stop] = 79"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "[pred[start+1:stop] = 79 for start, stop in zip(t1, t2)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "pad_indcies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred[pad_indcies:pad_indcies] = 79"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accuracy(pred, target)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "acc = (pred == target).sum().float() / target.shape[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "acc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.8.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/src/text_recognizer/tests/support/emnist/8.png b/src/text_recognizer/tests/support/emnist/8.png
deleted file mode 100644
index faa29aa..0000000
--- a/src/text_recognizer/tests/support/emnist/8.png
+++ /dev/null
diff --git a/src/text_recognizer/tests/support/emnist/U.png b/src/text_recognizer/tests/support/emnist/U.png
deleted file mode 100644
index 304eaec..0000000
--- a/src/text_recognizer/tests/support/emnist/U.png
+++ /dev/null
diff --git a/src/text_recognizer/tests/support/emnist/e.png b/src/text_recognizer/tests/support/emnist/e.png
deleted file mode 100644
index a03ecd4..0000000
--- a/src/text_recognizer/tests/support/emnist/e.png
+++ /dev/null
diff --git a/src/tasks/build_transitions.py b/tasks/build_transitions.py
index 91f8c1a..91f8c1a 100644
--- a/src/tasks/build_transitions.py
+++ b/tasks/build_transitions.py
diff --git a/src/tasks/create_emnist_lines_datasets.sh b/tasks/create_emnist_lines_datasets.sh
index 6416277..6416277 100755
--- a/src/tasks/create_emnist_lines_datasets.sh
+++ b/tasks/create_emnist_lines_datasets.sh
diff --git a/src/tasks/create_iam_paragraphs.sh b/tasks/create_iam_paragraphs.sh
index fa2bfb0..fa2bfb0 100755
--- a/src/tasks/create_iam_paragraphs.sh
+++ b/tasks/create_iam_paragraphs.sh
diff --git a/src/tasks/download_emnist.sh b/tasks/download_emnist.sh
index 18c8e29..18c8e29 100755
--- a/src/tasks/download_emnist.sh
+++ b/tasks/download_emnist.sh
diff --git a/src/tasks/download_iam.sh b/tasks/download_iam.sh
index e3cf76b..e3cf76b 100755
--- a/src/tasks/download_iam.sh
+++ b/tasks/download_iam.sh
diff --git a/src/tasks/make_wordpieces.py b/tasks/make_wordpieces.py
index 2ac0e2c..2ac0e2c 100644
--- a/src/tasks/make_wordpieces.py
+++ b/tasks/make_wordpieces.py
diff --git a/src/tasks/prepare_experiments.sh b/tasks/prepare_experiments.sh
index 95a538f..95a538f 100755
--- a/src/tasks/prepare_experiments.sh
+++ b/tasks/prepare_experiments.sh
diff --git a/src/tasks/test_functionality.sh b/tasks/test_functionality.sh
index 5ccf0cd..5ccf0cd 100755
--- a/src/tasks/test_functionality.sh
+++ b/tasks/test_functionality.sh
diff --git a/src/tasks/train.sh b/tasks/train.sh
index 60cbd23..60cbd23 100755
--- a/src/tasks/train.sh
+++ b/tasks/train.sh
diff --git a/src/text_recognizer/__init__.py b/text_recognizer/__init__.py
index 3dc1f76..3dc1f76 100644
--- a/src/text_recognizer/__init__.py
+++ b/text_recognizer/__init__.py
diff --git a/src/text_recognizer/character_predictor.py b/text_recognizer/character_predictor.py
index ad71289..ad71289 100644
--- a/src/text_recognizer/character_predictor.py
+++ b/text_recognizer/character_predictor.py
diff --git a/src/text_recognizer/datasets/__init__.py b/text_recognizer/datasets/__init__.py
index a6c1c59..a6c1c59 100644
--- a/src/text_recognizer/datasets/__init__.py
+++ b/text_recognizer/datasets/__init__.py
diff --git a/src/text_recognizer/datasets/dataset.py b/text_recognizer/datasets/dataset.py
index e794605..e794605 100644
--- a/src/text_recognizer/datasets/dataset.py
+++ b/text_recognizer/datasets/dataset.py
diff --git a/src/text_recognizer/datasets/emnist_dataset.py b/text_recognizer/datasets/emnist_dataset.py
index 9884fdf..9884fdf 100644
--- a/src/text_recognizer/datasets/emnist_dataset.py
+++ b/text_recognizer/datasets/emnist_dataset.py
diff --git a/src/text_recognizer/datasets/emnist_essentials.json b/text_recognizer/datasets/emnist_essentials.json
index 2a0648a..2a0648a 100644
--- a/src/text_recognizer/datasets/emnist_essentials.json
+++ b/text_recognizer/datasets/emnist_essentials.json
diff --git a/src/text_recognizer/datasets/emnist_lines_dataset.py b/text_recognizer/datasets/emnist_lines_dataset.py
index 1992446..1992446 100644
--- a/src/text_recognizer/datasets/emnist_lines_dataset.py
+++ b/text_recognizer/datasets/emnist_lines_dataset.py
diff --git a/src/text_recognizer/datasets/iam_dataset.py b/text_recognizer/datasets/iam_dataset.py
index f4a869d..a8998b9 100644
--- a/src/text_recognizer/datasets/iam_dataset.py
+++ b/text_recognizer/datasets/iam_dataset.py
@@ -13,6 +13,7 @@ from text_recognizer.datasets.util import _download_raw_dataset, DATA_DIRNAME
 RAW_DATA_DIRNAME = DATA_DIRNAME / "raw" / "iam"
 METADATA_FILENAME = RAW_DATA_DIRNAME / "metadata.toml"
 EXTRACTED_DATASET_DIRNAME = RAW_DATA_DIRNAME / "iamdb"
+RAW_DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
 
 DOWNSAMPLE_FACTOR = 2  # If images were downsampled, the regions must also be.
 LINE_REGION_PADDING = 0  # Add this many pixels around the exact coordinates.
diff --git a/src/text_recognizer/datasets/iam_lines_dataset.py b/text_recognizer/datasets/iam_lines_dataset.py
index 1cb84bd..1cb84bd 100644
--- a/src/text_recognizer/datasets/iam_lines_dataset.py
+++ b/text_recognizer/datasets/iam_lines_dataset.py
diff --git a/src/text_recognizer/datasets/iam_paragraphs_dataset.py b/text_recognizer/datasets/iam_paragraphs_dataset.py
index 8ba5142..8ba5142 100644
--- a/src/text_recognizer/datasets/iam_paragraphs_dataset.py
+++ b/text_recognizer/datasets/iam_paragraphs_dataset.py
diff --git a/src/text_recognizer/datasets/iam_preprocessor.py b/text_recognizer/datasets/iam_preprocessor.py
index a93eb00..a93eb00 100644
--- a/src/text_recognizer/datasets/iam_preprocessor.py
+++ b/text_recognizer/datasets/iam_preprocessor.py
diff --git a/src/text_recognizer/datasets/sentence_generator.py b/text_recognizer/datasets/sentence_generator.py
index dd76652..dd76652 100644
--- a/src/text_recognizer/datasets/sentence_generator.py
+++ b/text_recognizer/datasets/sentence_generator.py
diff --git a/src/text_recognizer/datasets/transforms.py b/text_recognizer/datasets/transforms.py
index b6a48f5..b6a48f5 100644
--- a/src/text_recognizer/datasets/transforms.py
+++ b/text_recognizer/datasets/transforms.py
diff --git a/src/text_recognizer/datasets/util.py b/text_recognizer/datasets/util.py
index da87756..da87756 100644
--- a/src/text_recognizer/datasets/util.py
+++ b/text_recognizer/datasets/util.py
diff --git a/src/text_recognizer/line_predictor.py b/text_recognizer/line_predictor.py
index 8e348fe..8e348fe 100644
--- a/src/text_recognizer/line_predictor.py
+++ b/text_recognizer/line_predictor.py
diff --git a/src/text_recognizer/models/__init__.py b/text_recognizer/models/__init__.py
index 7647d7e..7647d7e 100644
--- a/src/text_recognizer/models/__init__.py
+++ b/text_recognizer/models/__init__.py
diff --git a/src/text_recognizer/models/base.py b/text_recognizer/models/base.py
index 70f4cdb..70f4cdb 100644
--- a/src/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
diff --git a/src/text_recognizer/models/character_model.py b/text_recognizer/models/character_model.py
index f9944f3..f9944f3 100644
--- a/src/text_recognizer/models/character_model.py
+++ b/text_recognizer/models/character_model.py
diff --git a/src/text_recognizer/models/crnn_model.py b/text_recognizer/models/crnn_model.py
index 1e01a83..1e01a83 100644
--- a/src/text_recognizer/models/crnn_model.py
+++ b/text_recognizer/models/crnn_model.py
diff --git a/src/text_recognizer/models/ctc_transformer_model.py b/text_recognizer/models/ctc_transformer_model.py
index 25925f2..25925f2 100644
--- a/src/text_recognizer/models/ctc_transformer_model.py
+++ b/text_recognizer/models/ctc_transformer_model.py
diff --git a/src/text_recognizer/models/segmentation_model.py b/text_recognizer/models/segmentation_model.py
index 613108a..613108a 100644
--- a/src/text_recognizer/models/segmentation_model.py
+++ b/text_recognizer/models/segmentation_model.py
diff --git a/src/text_recognizer/models/transformer_model.py b/text_recognizer/models/transformer_model.py
index 3f63053..3f63053 100644
--- a/src/text_recognizer/models/transformer_model.py
+++ b/text_recognizer/models/transformer_model.py
diff --git a/src/text_recognizer/models/vqvae_model.py b/text_recognizer/models/vqvae_model.py
index 70f6f1f..70f6f1f 100644
--- a/src/text_recognizer/models/vqvae_model.py
+++ b/text_recognizer/models/vqvae_model.py
diff --git a/src/text_recognizer/networks/__init__.py b/text_recognizer/networks/__init__.py
index 1521355..1521355 100644
--- a/src/text_recognizer/networks/__init__.py
+++ b/text_recognizer/networks/__init__.py
diff --git a/src/text_recognizer/networks/beam.py b/text_recognizer/networks/beam.py
index dccccdb..dccccdb 100644
--- a/src/text_recognizer/networks/beam.py
+++ b/text_recognizer/networks/beam.py
diff --git a/src/text_recognizer/networks/cnn.py b/text_recognizer/networks/cnn.py
index 1807bb9..1807bb9 100644
--- a/src/text_recognizer/networks/cnn.py
+++ b/text_recognizer/networks/cnn.py
diff --git a/src/text_recognizer/networks/cnn_transformer.py b/text_recognizer/networks/cnn_transformer.py
index a2d7926..9150b55 100644
--- a/src/text_recognizer/networks/cnn_transformer.py
+++ b/text_recognizer/networks/cnn_transformer.py
@@ -1,7 +1,7 @@
 """A CNN-Transformer for image to text recognition."""
 from typing import Dict, Optional, Tuple
 
-from einops import rearrange, repeat
+from einops import rearrange
 import torch
 from torch import nn
 from torch import Tensor
diff --git a/src/text_recognizer/networks/crnn.py b/text_recognizer/networks/crnn.py
index 778e232..778e232 100644
--- a/src/text_recognizer/networks/crnn.py
+++ b/text_recognizer/networks/crnn.py
diff --git a/src/text_recognizer/networks/ctc.py b/text_recognizer/networks/ctc.py
index af9b700..af9b700 100644
--- a/src/text_recognizer/networks/ctc.py
+++ b/text_recognizer/networks/ctc.py
diff --git a/src/text_recognizer/networks/densenet.py b/text_recognizer/networks/densenet.py
index 7dc58d9..7dc58d9 100644
--- a/src/text_recognizer/networks/densenet.py
+++ b/text_recognizer/networks/densenet.py
diff --git a/src/text_recognizer/networks/lenet.py b/text_recognizer/networks/lenet.py
index 527e1a0..527e1a0 100644
--- a/src/text_recognizer/networks/lenet.py
+++ b/text_recognizer/networks/lenet.py
diff --git a/src/text_recognizer/networks/loss/__init__.py b/text_recognizer/networks/loss/__init__.py
index b489264..b489264 100644
--- a/src/text_recognizer/networks/loss/__init__.py
+++ b/text_recognizer/networks/loss/__init__.py
diff --git a/src/text_recognizer/networks/loss/loss.py b/text_recognizer/networks/loss/loss.py
index cf9fa0d..cf9fa0d 100644
--- a/src/text_recognizer/networks/loss/loss.py
+++ b/text_recognizer/networks/loss/loss.py
diff --git a/src/text_recognizer/networks/metrics.py b/text_recognizer/networks/metrics.py
index 2605731..2605731 100644
--- a/src/text_recognizer/networks/metrics.py
+++ b/text_recognizer/networks/metrics.py
diff --git a/src/text_recognizer/networks/mlp.py b/text_recognizer/networks/mlp.py
index 1101912..1101912 100644
--- a/src/text_recognizer/networks/mlp.py
+++ b/text_recognizer/networks/mlp.py
diff --git a/src/text_recognizer/networks/residual_network.py b/text_recognizer/networks/residual_network.py
index c33f419..c33f419 100644
--- a/src/text_recognizer/networks/residual_network.py
+++ b/text_recognizer/networks/residual_network.py
diff --git a/src/text_recognizer/networks/stn.py b/text_recognizer/networks/stn.py
index e9d216f..e9d216f 100644
--- a/src/text_recognizer/networks/stn.py
+++ b/text_recognizer/networks/stn.py
diff --git a/src/text_recognizer/networks/transducer/__init__.py b/text_recognizer/networks/transducer/__init__.py
index 8c19a01..8c19a01 100644
--- a/src/text_recognizer/networks/transducer/__init__.py
+++ b/text_recognizer/networks/transducer/__init__.py
diff --git a/src/text_recognizer/networks/transducer/tds_conv.py b/text_recognizer/networks/transducer/tds_conv.py
index 5fb8ba9..5fb8ba9 100644
--- a/src/text_recognizer/networks/transducer/tds_conv.py
+++ b/text_recognizer/networks/transducer/tds_conv.py
diff --git a/src/text_recognizer/networks/transducer/test.py b/text_recognizer/networks/transducer/test.py
index cadcecc..cadcecc 100644
--- a/src/text_recognizer/networks/transducer/test.py
+++ b/text_recognizer/networks/transducer/test.py
diff --git a/src/text_recognizer/networks/transducer/transducer.py b/text_recognizer/networks/transducer/transducer.py
index d7e3d08..d7e3d08 100644
--- a/src/text_recognizer/networks/transducer/transducer.py
+++ b/text_recognizer/networks/transducer/transducer.py
diff --git a/src/text_recognizer/networks/transformer/__init__.py b/text_recognizer/networks/transformer/__init__.py
index 9febc88..9febc88 100644
--- a/src/text_recognizer/networks/transformer/__init__.py
+++ b/text_recognizer/networks/transformer/__init__.py
diff --git a/src/text_recognizer/networks/transformer/attention.py b/text_recognizer/networks/transformer/attention.py
index cce1ecc..cce1ecc 100644
--- a/src/text_recognizer/networks/transformer/attention.py
+++ b/text_recognizer/networks/transformer/attention.py
diff --git a/src/text_recognizer/networks/transformer/positional_encoding.py b/text_recognizer/networks/transformer/positional_encoding.py
index 1ba5537..1ba5537 100644
--- a/src/text_recognizer/networks/transformer/positional_encoding.py
+++ b/text_recognizer/networks/transformer/positional_encoding.py
diff --git a/src/text_recognizer/networks/transformer/transformer.py b/text_recognizer/networks/transformer/transformer.py
index dd180c4..dd180c4 100644
--- a/src/text_recognizer/networks/transformer/transformer.py
+++ b/text_recognizer/networks/transformer/transformer.py
diff --git a/src/text_recognizer/networks/unet.py b/text_recognizer/networks/unet.py
index 510910f..510910f 100644
--- a/src/text_recognizer/networks/unet.py
+++ b/text_recognizer/networks/unet.py
diff --git a/src/text_recognizer/networks/util.py b/text_recognizer/networks/util.py
index 131a6b4..131a6b4 100644
--- a/src/text_recognizer/networks/util.py
+++ b/text_recognizer/networks/util.py
diff --git a/src/text_recognizer/networks/vit.py b/text_recognizer/networks/vit.py
index efb3701..efb3701 100644
--- a/src/text_recognizer/networks/vit.py
+++ b/text_recognizer/networks/vit.py
diff --git a/src/text_recognizer/networks/vq_transformer.py b/text_recognizer/networks/vq_transformer.py
index c673d96..c673d96 100644
--- a/src/text_recognizer/networks/vq_transformer.py
+++ b/text_recognizer/networks/vq_transformer.py
diff --git a/src/text_recognizer/networks/vqvae/__init__.py b/text_recognizer/networks/vqvae/__init__.py
index 763953c..763953c 100644
--- a/src/text_recognizer/networks/vqvae/__init__.py
+++ b/text_recognizer/networks/vqvae/__init__.py
diff --git a/src/text_recognizer/networks/vqvae/decoder.py b/text_recognizer/networks/vqvae/decoder.py
index 8847aba..8847aba 100644
--- a/src/text_recognizer/networks/vqvae/decoder.py
+++ b/text_recognizer/networks/vqvae/decoder.py
diff --git a/src/text_recognizer/networks/vqvae/encoder.py b/text_recognizer/networks/vqvae/encoder.py
index d3adac5..d3adac5 100644
--- a/src/text_recognizer/networks/vqvae/encoder.py
+++ b/text_recognizer/networks/vqvae/encoder.py
diff --git a/src/text_recognizer/networks/vqvae/vector_quantizer.py b/text_recognizer/networks/vqvae/vector_quantizer.py
index f92c7ee..f92c7ee 100644
--- a/src/text_recognizer/networks/vqvae/vector_quantizer.py
+++ b/text_recognizer/networks/vqvae/vector_quantizer.py
diff --git a/src/text_recognizer/networks/vqvae/vqvae.py b/text_recognizer/networks/vqvae/vqvae.py
index 50448b4..50448b4 100644
--- a/src/text_recognizer/networks/vqvae/vqvae.py
+++ b/text_recognizer/networks/vqvae/vqvae.py
diff --git a/src/text_recognizer/networks/wide_resnet.py b/text_recognizer/networks/wide_resnet.py
index b767778..b767778 100644
--- a/src/text_recognizer/networks/wide_resnet.py
+++ b/text_recognizer/networks/wide_resnet.py
diff --git a/src/text_recognizer/paragraph_text_recognizer.py b/text_recognizer/paragraph_text_recognizer.py
index aa39662..aa39662 100644
--- a/src/text_recognizer/paragraph_text_recognizer.py
+++ b/text_recognizer/paragraph_text_recognizer.py
diff --git a/src/text_recognizer/tests/__init__.py b/text_recognizer/tests/__init__.py
index 18ff212..18ff212 100644
--- a/src/text_recognizer/tests/__init__.py
+++ b/text_recognizer/tests/__init__.py
diff --git a/src/text_recognizer/tests/support/__init__.py b/text_recognizer/tests/support/__init__.py
index a265ede..a265ede 100644
--- a/src/text_recognizer/tests/support/__init__.py
+++ b/text_recognizer/tests/support/__init__.py
diff --git a/src/text_recognizer/tests/support/create_emnist_lines_support_files.py b/text_recognizer/tests/support/create_emnist_lines_support_files.py
index 9abe143..9abe143 100644
--- a/src/text_recognizer/tests/support/create_emnist_lines_support_files.py
+++ b/text_recognizer/tests/support/create_emnist_lines_support_files.py
diff --git a/src/text_recognizer/tests/support/create_emnist_support_files.py b/text_recognizer/tests/support/create_emnist_support_files.py
index f9ff030..f9ff030 100644
--- a/src/text_recognizer/tests/support/create_emnist_support_files.py
+++ b/text_recognizer/tests/support/create_emnist_support_files.py
diff --git a/src/text_recognizer/tests/support/create_iam_lines_support_files.py b/text_recognizer/tests/support/create_iam_lines_support_files.py
index 50f9e3d..50f9e3d 100644
--- a/src/text_recognizer/tests/support/create_iam_lines_support_files.py
+++ b/text_recognizer/tests/support/create_iam_lines_support_files.py
diff --git a/src/text_recognizer/tests/support/emnist_lines/Knox Ky<eos>.png b/text_recognizer/tests/support/emnist_lines/Knox Ky<eos>.png
index b7d0618..b7d0618 100644
--- a/src/text_recognizer/tests/support/emnist_lines/Knox Ky<eos>.png
+++ b/text_recognizer/tests/support/emnist_lines/Knox Ky<eos>.png
diff --git a/src/text_recognizer/tests/support/emnist_lines/ancillary beliefs and<eos>.png b/text_recognizer/tests/support/emnist_lines/ancillary beliefs and<eos>.png
index 14a8cf3..14a8cf3 100644
--- a/src/text_recognizer/tests/support/emnist_lines/ancillary beliefs and<eos>.png
+++ b/text_recognizer/tests/support/emnist_lines/ancillary beliefs and<eos>.png
diff --git a/src/text_recognizer/tests/support/emnist_lines/they<eos>.png b/text_recognizer/tests/support/emnist_lines/they<eos>.png
index 7f05951..7f05951 100644
--- a/src/text_recognizer/tests/support/emnist_lines/they<eos>.png
+++ b/text_recognizer/tests/support/emnist_lines/they<eos>.png
diff --git a/src/text_recognizer/tests/support/iam_lines/He rose from his breakfast-nook bench<eos>.png b/text_recognizer/tests/support/iam_lines/He rose from his breakfast-nook bench<eos>.png
index 6eeb642..6eeb642 100644
--- a/src/text_recognizer/tests/support/iam_lines/He rose from his breakfast-nook bench<eos>.png
+++ b/text_recognizer/tests/support/iam_lines/He rose from his breakfast-nook bench<eos>.png
diff --git a/src/text_recognizer/tests/support/iam_lines/and came into the livingroom, where<eos>.png b/text_recognizer/tests/support/iam_lines/and came into the livingroom, where<eos>.png
index 4974cf8..4974cf8 100644
--- a/src/text_recognizer/tests/support/iam_lines/and came into the livingroom, where<eos>.png
+++ b/text_recognizer/tests/support/iam_lines/and came into the livingroom, where<eos>.png
diff --git a/src/text_recognizer/tests/support/iam_lines/his entrance. He came, almost falling<eos>.png b/text_recognizer/tests/support/iam_lines/his entrance. He came, almost falling<eos>.png
index a731245..a731245 100644
--- a/src/text_recognizer/tests/support/iam_lines/his entrance. He came, almost falling<eos>.png
+++ b/text_recognizer/tests/support/iam_lines/his entrance. He came, almost falling<eos>.png
diff --git a/src/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg b/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg
index d9753b6..d9753b6 100644
--- a/src/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg
+++ b/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg
diff --git a/src/text_recognizer/tests/test_character_predictor.py b/text_recognizer/tests/test_character_predictor.py
index 01bda78..01bda78 100644
--- a/src/text_recognizer/tests/test_character_predictor.py
+++ b/text_recognizer/tests/test_character_predictor.py
diff --git a/src/text_recognizer/tests/test_line_predictor.py b/text_recognizer/tests/test_line_predictor.py
index eede4d4..eede4d4 100644
--- a/src/text_recognizer/tests/test_line_predictor.py
+++ b/text_recognizer/tests/test_line_predictor.py
diff --git a/src/text_recognizer/tests/test_paragraph_text_recognizer.py b/text_recognizer/tests/test_paragraph_text_recognizer.py
index 3e280b9..3e280b9 100644
--- a/src/text_recognizer/tests/test_paragraph_text_recognizer.py
+++ b/text_recognizer/tests/test_paragraph_text_recognizer.py
diff --git a/src/text_recognizer/util.py b/text_recognizer/util.py
index b431e22..b431e22 100644
--- a/src/text_recognizer/util.py
+++ b/text_recognizer/util.py
diff --git a/src/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt b/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt
index 344e0a3..344e0a3 100644
--- a/src/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt
+++ b/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt
diff --git a/src/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt b/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt
index f2dfd84..f2dfd84 100644
--- a/src/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt
+++ b/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt
diff --git a/src/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt b/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt
index e1add8d..e1add8d 100644
--- a/src/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt
+++ b/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt
diff --git a/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_FCN_weights.pt b/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_FCN_weights.pt
index d9ca01d..d9ca01d 100644
--- a/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_FCN_weights.pt
+++ b/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_FCN_weights.pt
diff --git a/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_UNet_weights.pt b/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_UNet_weights.pt
index 0af0e57..0af0e57 100644
--- a/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_UNet_weights.pt
+++ b/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_UNet_weights.pt
diff --git a/src/text_recognizer/weights/VQVAEModel_IamLinesDataset_VQVAE_weights.pt b/text_recognizer/weights/VQVAEModel_IamLinesDataset_VQVAE_weights.pt
index b5295c2..b5295c2 100644
--- a/src/text_recognizer/weights/VQVAEModel_IamLinesDataset_VQVAE_weights.pt
+++ b/text_recognizer/weights/VQVAEModel_IamLinesDataset_VQVAE_weights.pt
diff --git a/src/training/experiments/default_config_emnist.yml b/training/experiments/default_config_emnist.yml
index bf2ed0a..bf2ed0a 100644
--- a/src/training/experiments/default_config_emnist.yml
+++ b/training/experiments/default_config_emnist.yml
diff --git a/src/training/experiments/embedding_experiment.yml b/training/experiments/embedding_experiment.yml
index 1e5f941..1e5f941 100644
--- a/src/training/experiments/embedding_experiment.yml
+++ b/training/experiments/embedding_experiment.yml
diff --git a/src/training/experiments/sample_experiment.yml b/training/experiments/sample_experiment.yml
index 8f94475..8f94475 100644
--- a/src/training/experiments/sample_experiment.yml
+++ b/training/experiments/sample_experiment.yml
diff --git a/src/training/gpu_manager.py b/training/gpu_manager.py
index ce1b3dd..ce1b3dd 100644
--- a/src/training/gpu_manager.py
+++ b/training/gpu_manager.py
diff --git a/src/training/prepare_experiments.py b/training/prepare_experiments.py
index 21997af..21997af 100644
--- a/src/training/prepare_experiments.py
+++ b/training/prepare_experiments.py
diff --git a/src/training/run_experiment.py b/training/run_experiment.py
index faafea6..faafea6 100644
--- a/src/training/run_experiment.py
+++ b/training/run_experiment.py
diff --git a/src/training/run_sweep.py b/training/run_sweep.py
index a578592..a578592 100644
--- a/src/training/run_sweep.py
+++ b/training/run_sweep.py
diff --git a/src/training/sweep_emnist.yml b/training/sweep_emnist.yml
index 48d7261..48d7261 100644
--- a/src/training/sweep_emnist.yml
+++ b/training/sweep_emnist.yml
diff --git a/src/training/sweep_emnist_resnet.yml b/training/sweep_emnist_resnet.yml
index 19a3040..19a3040 100644
--- a/src/training/sweep_emnist_resnet.yml
+++ b/training/sweep_emnist_resnet.yml
diff --git a/src/training/trainer/__init__.py b/training/trainer/__init__.py
index de41bfb..de41bfb 100644
--- a/src/training/trainer/__init__.py
+++ b/training/trainer/__init__.py
diff --git a/src/training/trainer/callbacks/__init__.py b/training/trainer/callbacks/__init__.py
index 80c4177..80c4177 100644
--- a/src/training/trainer/callbacks/__init__.py
+++ b/training/trainer/callbacks/__init__.py
diff --git a/src/training/trainer/callbacks/base.py b/training/trainer/callbacks/base.py
index 500b642..500b642 100644
--- a/src/training/trainer/callbacks/base.py
+++ b/training/trainer/callbacks/base.py
diff --git a/src/training/trainer/callbacks/checkpoint.py b/training/trainer/callbacks/checkpoint.py
index a54e0a9..a54e0a9 100644
--- a/src/training/trainer/callbacks/checkpoint.py
+++ b/training/trainer/callbacks/checkpoint.py
diff --git a/src/training/trainer/callbacks/early_stopping.py b/training/trainer/callbacks/early_stopping.py
index 02b431f..02b431f 100644
--- a/src/training/trainer/callbacks/early_stopping.py
+++ b/training/trainer/callbacks/early_stopping.py
diff --git a/src/training/trainer/callbacks/lr_schedulers.py b/training/trainer/callbacks/lr_schedulers.py
index 630c434..630c434 100644
--- a/src/training/trainer/callbacks/lr_schedulers.py
+++ b/training/trainer/callbacks/lr_schedulers.py
diff --git a/src/training/trainer/callbacks/progress_bar.py b/training/trainer/callbacks/progress_bar.py
index 6c4305a..6c4305a 100644
--- a/src/training/trainer/callbacks/progress_bar.py
+++ b/training/trainer/callbacks/progress_bar.py
diff --git a/src/training/trainer/callbacks/wandb_callbacks.py b/training/trainer/callbacks/wandb_callbacks.py
index 552a4f4..552a4f4 100644
--- a/src/training/trainer/callbacks/wandb_callbacks.py
+++ b/training/trainer/callbacks/wandb_callbacks.py
diff --git a/src/training/trainer/train.py b/training/trainer/train.py
index b770c94..b770c94 100644
--- a/src/training/trainer/train.py
+++ b/training/trainer/train.py
diff --git a/src/training/trainer/util.py b/training/trainer/util.py
index 7cf1b45..7cf1b45 100644
--- a/src/training/trainer/util.py
+++ b/training/trainer/util.py
diff --git a/src/wandb/settings b/wandb/settings
index eafb083..eafb083 100644
--- a/src/wandb/settings
+++ b/wandb/settings