{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import torch\n",
    "from torch import nn\n",
    "from importlib.util import find_spec\n",
    "if find_spec(\"text_recognizer\") is None:\n",
    "    import sys\n",
    "    sys.path.append('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from text_recognizer.networks.unet import UNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = UNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.rand(1, 1, 256, 256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModuleList(\n",
       "  (0): _DilationBlock(\n",
       "    (activation): ELU(alpha=1.0, inplace=True)\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n",
       "      (1): ELU(alpha=1.0, inplace=True)\n",
       "    )\n",
       "    (conv1): Sequential(\n",
       "      (0): Conv2d(1, 32, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (1): ELU(alpha=1.0, inplace=True)\n",
       "    )\n",
       "    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (down_sampling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (1): _DilationBlock(\n",
       "    (activation): ELU(alpha=1.0, inplace=True)\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n",
       "      (1): ELU(alpha=1.0, inplace=True)\n",
       "    )\n",
       "    (conv1): Sequential(\n",
       "      (0): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (1): ELU(alpha=1.0, inplace=True)\n",
       "    )\n",
       "    (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (down_sampling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (2): _DilationBlock(\n",
       "    (activation): ELU(alpha=1.0, inplace=True)\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n",
       "      (1): ELU(alpha=1.0, inplace=True)\n",
       "    )\n",
       "    (conv1): Sequential(\n",
       "      (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (1): ELU(alpha=1.0, inplace=True)\n",
       "    )\n",
       "    (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (down_sampling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (3): _DilationBlock(\n",
       "    (activation): ELU(alpha=1.0, inplace=True)\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n",
       "      (1): ELU(alpha=1.0, inplace=True)\n",
       "    )\n",
       "    (conv1): Sequential(\n",
       "      (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (1): ELU(alpha=1.0, inplace=True)\n",
       "    )\n",
       "    (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.encoder_blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModuleList(\n",
       "  (0): _UpSamplingBlock(\n",
       "    (conv_block): _ConvBlock(\n",
       "      (activation): ReLU(inplace=True)\n",
       "      (block): Sequential(\n",
       "        (0): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (up_sampling): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "  )\n",
       "  (1): _UpSamplingBlock(\n",
       "    (conv_block): _ConvBlock(\n",
       "      (activation): ReLU(inplace=True)\n",
       "      (block): Sequential(\n",
       "        (0): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (up_sampling): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "  )\n",
       "  (2): _UpSamplingBlock(\n",
       "    (conv_block): _ConvBlock(\n",
       "      (activation): ReLU(inplace=True)\n",
       "      (block): Sequential(\n",
       "        (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (up_sampling): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.decoder_blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Conv2d(64, 3, kernel_size=(1, 1), stride=(1, 1))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "yy = net(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = (torch.randn(1, 256, 256) > 0).long()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 3, 256, 256])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[1, 0, 1,  ..., 0, 1, 0],\n",
       "         [1, 0, 1,  ..., 0, 1, 0],\n",
       "         [1, 1, 0,  ..., 1, 1, 0],\n",
       "         ...,\n",
       "         [1, 0, 0,  ..., 0, 1, 1],\n",
       "         [0, 0, 1,  ..., 1, 1, 0],\n",
       "         [0, 0, 1,  ..., 0, 0, 0]]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "loss = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.2502, grad_fn=<NllLoss2DBackward>)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss(yy, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[[-0.1692,  0.1223,  0.1750,  ..., -0.1869, -0.0585,  0.0462],\n",
       "          [-0.1302, -0.0230,  0.3185,  ..., -0.3760,  0.0204, -0.0686],\n",
       "          [-0.1062, -0.0216,  0.4592,  ...,  0.0990,  0.0808, -0.1419],\n",
       "          ...,\n",
       "          [ 0.1386, -0.2856,  0.3074,  ..., -0.3874, -0.0322,  0.0503],\n",
       "          [ 0.3562, -0.0960,  0.0815,  ...,  0.1893,  0.1438,  0.2804],\n",
       "          [-0.2106, -0.1988,  0.0016,  ..., -0.0031, -0.2820,  0.0113]],\n",
       "\n",
       "         [[-0.1542, -0.1322, -0.3917,  ..., -0.2297, -0.2328,  0.0103],\n",
       "          [ 0.1040,  0.2189, -0.3661,  ...,  0.4818, -0.3737,  0.1117],\n",
       "          [ 0.0735, -0.6487, -0.1899,  ...,  0.2213, -0.1529, -0.1020],\n",
       "          ...,\n",
       "          [-0.2046, -0.1477,  0.2941,  ...,  0.0652, -0.7276,  0.1676],\n",
       "          [ 0.0413, -0.2013, -0.3192,  ..., -0.4947, -0.1179, -0.1000],\n",
       "          [-0.4108,  0.0199,  0.2238,  ..., -0.4482, -0.2370,  0.0119]],\n",
       "\n",
       "         [[ 0.0834,  0.1303,  0.0629,  ...,  0.4766, -0.0481,  0.2538],\n",
       "          [ 0.1218,  0.1324,  0.2464,  ...,  0.0081,  0.4444,  0.4583],\n",
       "          [ 0.1155,  0.1417,  0.2248,  ...,  0.6365, -0.0040,  0.3144],\n",
       "          ...,\n",
       "          [ 0.0744, -0.0751, -0.5654,  ..., -0.2890, -0.0437,  0.2719],\n",
       "          [ 0.1057, -0.1093, -0.3803,  ...,  0.0229,  0.1403,  0.0944],\n",
       "          [-0.0958, -0.3931, -0.0186,  ...,  0.2102, -0.0842,  0.1909]]]],\n",
       "       grad_fn=<MkldnnConvolutionBackward>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "Layer (type:depth-idx)                   Output Shape              Param #\n",
      "==========================================================================================\n",
      "├─ModuleList: 1                          []                        --\n",
      "|    └─DownSamplingBlock: 2-1            [-1, 64, 128, 128]        --\n",
      "|    |    └─ConvBlock: 3-1               [-1, 64, 256, 256]        37,824\n",
      "|    |    └─MaxPool2d: 3-2               [-1, 64, 128, 128]        --\n",
      "|    └─DownSamplingBlock: 2-2            [-1, 128, 64, 64]         --\n",
      "|    |    └─ConvBlock: 3-3               [-1, 128, 128, 128]       221,952\n",
      "|    |    └─MaxPool2d: 3-4               [-1, 128, 64, 64]         --\n",
      "|    └─DownSamplingBlock: 2-3            [-1, 256, 32, 32]         --\n",
      "|    |    └─ConvBlock: 3-5               [-1, 256, 64, 64]         886,272\n",
      "|    |    └─MaxPool2d: 3-6               [-1, 256, 32, 32]         --\n",
      "|    └─DownSamplingBlock: 2-4            [-1, 512, 32, 32]         --\n",
      "|    |    └─ConvBlock: 3-7               [-1, 512, 32, 32]         3,542,016\n",
      "├─ModuleList: 1                          []                        --\n",
      "|    └─UpSamplingBlock: 2-5              [-1, 256, 64, 64]         --\n",
      "|    |    └─Upsample: 3-8                [-1, 512, 64, 64]         --\n",
      "|    |    └─ConvBlock: 3-9               [-1, 256, 64, 64]         2,360,832\n",
      "|    └─UpSamplingBlock: 2-6              [-1, 128, 128, 128]       --\n",
      "|    |    └─Upsample: 3-10               [-1, 256, 128, 128]       --\n",
      "|    |    └─ConvBlock: 3-11              [-1, 128, 128, 128]       590,592\n",
      "|    └─UpSamplingBlock: 2-7              [-1, 64, 256, 256]        --\n",
      "|    |    └─Upsample: 3-12               [-1, 128, 256, 256]       --\n",
      "|    |    └─ConvBlock: 3-13              [-1, 64, 256, 256]        147,840\n",
      "├─Conv2d: 1-1                            [-1, 3, 256, 256]         195\n",
      "==========================================================================================\n",
      "Total params: 7,787,523\n",
      "Trainable params: 7,787,523\n",
      "Non-trainable params: 0\n",
      "Total mult-adds (M): 35.93\n",
      "==========================================================================================\n",
      "Input size (MB): 0.25\n",
      "Forward/backward pass size (MB): 1.50\n",
      "Params size (MB): 29.71\n",
      "Estimated Total Size (MB): 31.46\n",
      "==========================================================================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "├─ModuleList: 1                          []                        --\n",
       "|    └─DownSamplingBlock: 2-1            [-1, 64, 128, 128]        --\n",
       "|    |    └─ConvBlock: 3-1               [-1, 64, 256, 256]        37,824\n",
       "|    |    └─MaxPool2d: 3-2               [-1, 64, 128, 128]        --\n",
       "|    └─DownSamplingBlock: 2-2            [-1, 128, 64, 64]         --\n",
       "|    |    └─ConvBlock: 3-3               [-1, 128, 128, 128]       221,952\n",
       "|    |    └─MaxPool2d: 3-4               [-1, 128, 64, 64]         --\n",
       "|    └─DownSamplingBlock: 2-3            [-1, 256, 32, 32]         --\n",
       "|    |    └─ConvBlock: 3-5               [-1, 256, 64, 64]         886,272\n",
       "|    |    └─MaxPool2d: 3-6               [-1, 256, 32, 32]         --\n",
       "|    └─DownSamplingBlock: 2-4            [-1, 512, 32, 32]         --\n",
       "|    |    └─ConvBlock: 3-7               [-1, 512, 32, 32]         3,542,016\n",
       "├─ModuleList: 1                          []                        --\n",
       "|    └─UpSamplingBlock: 2-5              [-1, 256, 64, 64]         --\n",
       "|    |    └─Upsample: 3-8                [-1, 512, 64, 64]         --\n",
       "|    |    └─ConvBlock: 3-9               [-1, 256, 64, 64]         2,360,832\n",
       "|    └─UpSamplingBlock: 2-6              [-1, 128, 128, 128]       --\n",
       "|    |    └─Upsample: 3-10               [-1, 256, 128, 128]       --\n",
       "|    |    └─ConvBlock: 3-11              [-1, 128, 128, 128]       590,592\n",
       "|    └─UpSamplingBlock: 2-7              [-1, 64, 256, 256]        --\n",
       "|    |    └─Upsample: 3-12               [-1, 128, 256, 256]       --\n",
       "|    |    └─ConvBlock: 3-13              [-1, 64, 256, 256]        147,840\n",
       "├─Conv2d: 1-1                            [-1, 3, 256, 256]         195\n",
       "==========================================================================================\n",
       "Total params: 7,787,523\n",
       "Trainable params: 7,787,523\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (M): 35.93\n",
       "==========================================================================================\n",
       "Input size (MB): 0.25\n",
       "Forward/backward pass size (MB): 1.50\n",
       "Params size (MB): 29.71\n",
       "Estimated Total Size (MB): 31.46\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "summary(net, (1, 256, 256), device=\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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