CustomOCR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fee556b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b48aa9a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pdb\n",
    "import six\n",
    "import random\n",
    "import lmdb\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import math\n",
    "from collections import OrderedDict\n",
    "from itertools import chain\n",
    "import logging\n",
    "\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import sampler\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.transforms.functional as TF\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR, StepLR\n",
    "from torch.nn.utils.clip_grad import clip_grad_norm_\n",
    "from torch.utils.data import random_split\n",
    "sys.path.insert(0, '../')\n",
    "# from src.utils.utils import AverageMeter, Eval, OCRLabelConverter\n",
    "# from src.utils.utils import EarlyStopping, gmkdir\n",
    "# from src.optim.optimizer import STLR\n",
    "# from src.utils.utils import gaussian\n",
    "from tqdm import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb9bb349",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8279f044",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SynthDataset(Dataset):\n",
    "    def __init__(self, opt):\n",
    "        super(SynthDataset, self).__init__()\n",
    "        self.path = os.path.join(opt['path'], opt['imgdir'])\n",
    "        self.images = os.listdir(self.path)\n",
    "        self.nSamples = len(self.images)\n",
    "        f = lambda x: os.path.join(self.path, x)\n",
    "        self.imagepaths = list(map(f, self.images))\n",
    "       \ttransform_list =  [transforms.Grayscale(1),\n",
    "                            transforms.ToTensor(), \n",
    "                            transforms.Normalize((0.5,), (0.5,))\n",
    "                            ]\n",
    "        self.transform = transforms.Compose(transform_list)\n",
    "        self.collate_fn = SynthCollator()\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.nSamples\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        assert index <= len(self), 'index range error'\n",
    "        imagepath = self.imagepaths[index]\n",
    "        imagefile = os.path.basename(imagepath)\n",
    "        img = Image.open(imagepath)\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "#             img = TF.autocontrast(img)\n",
    "        item = {'img': img, 'idx':index}\n",
    "        item['label'] = imagefile.split('_')[0]\n",
    "        return item\n",
    "    \n",
    "class SynthCollator(object):\n",
    "    \n",
    "    def __call__(self, batch):\n",
    "        width = [item['img'].shape[2] for item in batch]\n",
    "        indexes = [item['idx'] for item in batch]\n",
    "        imgs = torch.ones([len(batch), batch[0]['img'].shape[0], batch[0]['img'].shape[1], \n",
    "                           max(width)], dtype=torch.float32).to(device)\n",
    "        for idx, item in enumerate(batch):\n",
    "            try:\n",
    "                imgs[idx, :, :, 0:item['img'].shape[2]] = item['img']\n",
    "            except:\n",
    "                print(imgs.shape)\n",
    "        item = {'img': imgs, 'idx':indexes}\n",
    "        if 'label' in batch[0].keys():\n",
    "            labels = [item['label'] for item in batch]\n",
    "            item['label'] = labels\n",
    "        return item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b32567e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BidirectionalLSTM(nn.Module):\n",
    "\n",
    "    def __init__(self, nIn, nHidden, nOut):\n",
    "        super(BidirectionalLSTM, self).__init__()\n",
    "        self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True)\n",
    "        self.embedding = nn.Linear(nHidden * 2, nOut)\n",
    "    def forward(self, input):\n",
    "        self.rnn.flatten_parameters()\n",
    "        recurrent, _ = self.rnn(input)\n",
    "        T, b, h = recurrent.size()\n",
    "        t_rec = recurrent.view(T * b, h)\n",
    "        output = self.embedding(t_rec)  # [T * b, nOut]\n",
    "        output = output.view(T, b, -1)\n",
    "        return output\n",
    "\n",
    "class CRNN(nn.Module):\n",
    "\n",
    "    def __init__(self, opt, leakyRelu=False):\n",
    "        super(CRNN, self).__init__()\n",
    "\n",
    "        assert opt['imgH'] % 16 == 0, 'imgH has to be a multiple of 16'\n",
    "\n",
    "        ks = [3, 3, 3, 3, 3, 3, 2]\n",
    "#         ks = [3, 3, 3, 3, 3, 3, 1]\n",
    "        ps = [1, 1, 1, 1, 1, 1, 0]\n",
    "        ss = [1, 1, 1, 1, 1, 1, 1]\n",
    "        nm = [64, 128, 256, 256, 512, 512, 512]\n",
    "\n",
    "        cnn = nn.Sequential()\n",
    "\n",
    "        def convRelu(i, batchNormalization=False):\n",
    "            nIn = opt['nChannels'] if i == 0 else nm[i - 1]\n",
    "            nOut = nm[i]\n",
    "            cnn.add_module('conv{0}'.format(i),\n",
    "                           nn.Conv2d(nIn, nOut, ks[i], ss[i], ps[i]))\n",
    "            if batchNormalization:\n",
    "                cnn.add_module('batchnorm{0}'.format(i), nn.BatchNorm2d(nOut))\n",
    "            if leakyRelu:\n",
    "                cnn.add_module('relu{0}'.format(i),\n",
    "                               nn.LeakyReLU(0.2, inplace=True))\n",
    "            else:\n",
    "                cnn.add_module('relu{0}'.format(i), nn.ReLU(True))\n",
    "\n",
    "        convRelu(0)\n",
    "        cnn.add_module('pooling{0}'.format(0), nn.MaxPool2d(2, 2))  # 64x16x64\n",
    "        convRelu(1)\n",
    "        cnn.add_module('pooling{0}'.format(1), nn.MaxPool2d(2, 2))  # 128x8x32\n",
    "        convRelu(2, True)\n",
    "        convRelu(3)\n",
    "        cnn.add_module('pooling{0}'.format(2),\n",
    "                       nn.MaxPool2d((2, 2), (2, 1), (0, 1)))  # 256x4x16\n",
    "        convRelu(4, True)\n",
    "        convRelu(5)\n",
    "        cnn.add_module('pooling{0}'.format(3),\n",
    "                       nn.MaxPool2d((2, 2), (2, 1), (0, 1)))  # 512x2x16\n",
    "        convRelu(6, True)  # 512x1x16\n",
    "        self.cnn = cnn\n",
    "        self.rnn = nn.Sequential()\n",
    "        self.rnn = nn.Sequential(\n",
    "            BidirectionalLSTM(opt['nHidden']*2, opt['nHidden'], opt['nHidden']),\n",
    "            BidirectionalLSTM(opt['nHidden'], opt['nHidden'], opt['nClasses']))\n",
    "\n",
    "\n",
    "    def forward(self, input):\n",
    "        # conv features\n",
    "        conv = self.cnn(input)\n",
    "        b, c, h, w = conv.size()\n",
    "        assert h == 1, \"the height of conv must be 1\"\n",
    "        conv = conv.squeeze(2)\n",
    "        conv = conv.permute(2, 0, 1)  # [w, b, c]\n",
    "        # rnn features\n",
    "        output = self.rnn(conv)\n",
    "        output = output.transpose(1,0) #Tbh to bth\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b46f228",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomCTCLoss(torch.nn.Module):\n",
    "    # T x B x H => Softmax on dimension 2\n",
    "    def __init__(self, dim=2):\n",
    "        super().__init__()\n",
    "        self.dim = dim\n",
    "        self.ctc_loss = torch.nn.CTCLoss(reduction='mean', zero_infinity=True)\n",
    "\n",
    "    def forward(self, logits, labels,\n",
    "            prediction_sizes, target_sizes):\n",
    "        EPS = 1e-7\n",
    "        loss = self.ctc_loss(logits, labels, prediction_sizes, target_sizes)\n",
    "        loss = self.sanitize(loss)\n",
    "        return self.debug(loss, logits, labels, prediction_sizes, target_sizes)\n",
    "    \n",
    "    def sanitize(self, loss):\n",
    "        EPS = 1e-7\n",
    "        if abs(loss.item() - float('inf')) < EPS:\n",
    "            return torch.zeros_like(loss)\n",
    "        if math.isnan(loss.item()):\n",
    "            return torch.zeros_like(loss)\n",
    "        return loss\n",
    "\n",
    "    def debug(self, loss, logits, labels,\n",
    "            prediction_sizes, target_sizes):\n",
    "        if math.isnan(loss.item()):\n",
    "            print(\"Loss:\", loss)\n",
    "            print(\"logits:\", logits)\n",
    "            print(\"labels:\", labels)\n",
    "            print(\"prediction_sizes:\", prediction_sizes)\n",
    "            print(\"target_sizes:\", target_sizes)\n",
    "            raise Exception(\"NaN loss obtained. But why?\")\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f59276d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "class OCRTrainer(object):\n",
    "    def __init__(self, opt):\n",
    "        super(OCRTrainer, self).__init__()\n",
    "        self.data_train = opt['data_train']\n",
    "        self.data_val = opt['data_val']\n",
    "        self.model = opt['model']\n",
    "        self.criterion = opt['criterion']\n",
    "        self.optimizer = opt['optimizer']\n",
    "        self.schedule = opt['schedule']\n",
    "        self.converter = OCRLabelConverter(opt['alphabet'])\n",
    "        self.evaluator = Eval()\n",
    "        print('Scheduling is {}'.format(self.schedule))\n",
    "        self.scheduler = CosineAnnealingLR(self.optimizer, T_max=opt['epochs'])\n",
    "        self.batch_size = opt['batch_size']\n",
    "        self.count = opt['epoch']\n",
    "        self.epochs = opt['epochs']\n",
    "        self.cuda = opt['cuda']\n",
    "        self.collate_fn = opt['collate_fn']\n",
    "        self.init_meters()\n",
    "\n",
    "    def init_meters(self):\n",
    "        self.avgTrainLoss = AverageMeter(\"Train loss\")\n",
    "        self.avgTrainCharAccuracy = AverageMeter(\"Train Character Accuracy\")\n",
    "        self.avgTrainWordAccuracy = AverageMeter(\"Train Word Accuracy\")\n",
    "        self.avgValLoss = AverageMeter(\"Validation loss\")\n",
    "        self.avgValCharAccuracy = AverageMeter(\"Validation Character Accuracy\")\n",
    "        self.avgValWordAccuracy = AverageMeter(\"Validation Word Accuracy\")\n",
    "\n",
    "    def forward(self, x):\n",
    "        logits = self.model(x)\n",
    "        return logits.transpose(1, 0)\n",
    "\n",
    "    def loss_fn(self, logits, targets, pred_sizes, target_sizes):\n",
    "        loss = self.criterion(logits, targets, pred_sizes, target_sizes)\n",
    "        return loss\n",
    "\n",
    "    def step(self):\n",
    "        self.max_grad_norm = 0.05\n",
    "        clip_grad_norm_(self.model.parameters(), self.max_grad_norm)\n",
    "        self.optimizer.step()\n",
    "    \n",
    "    def schedule_lr(self):\n",
    "        if self.schedule:\n",
    "            self.scheduler.step()\n",
    "\n",
    "    def _run_batch(self, batch, report_accuracy=False, validation=False):\n",
    "        input_, targets = batch['img'], batch['label']\n",
    "        targets, lengths = self.converter.encode(targets)\n",
    "        logits = self.forward(input_)\n",
    "        logits = logits.contiguous().cpu()\n",
    "        logits = torch.nn.functional.log_softmax(logits, 2)\n",
    "        T, B, H = logits.size()\n",
    "        pred_sizes = torch.LongTensor([T for i in range(B)])\n",
    "        targets= targets.view(-1).contiguous()\n",
    "        loss = self.loss_fn(logits, targets, pred_sizes, lengths)\n",
    "        if report_accuracy:\n",
    "            probs, preds = logits.max(2)\n",
    "            preds = preds.transpose(1, 0).contiguous().view(-1)\n",
    "            sim_preds = self.converter.decode(preds.data, pred_sizes.data, raw=False)\n",
    "            ca = np.mean((list(map(self.evaluator.char_accuracy, list(zip(sim_preds, batch['label']))))))\n",
    "            wa = np.mean((list(map(self.evaluator.word_accuracy, list(zip(sim_preds, batch['label']))))))\n",
    "        return loss, ca, wa\n",
    "\n",
    "    def run_epoch(self, validation=False):\n",
    "        if not validation:\n",
    "            loader = self.train_dataloader()\n",
    "            pbar = tqdm(loader, desc='Epoch: [%d]/[%d] Training'%(self.count, \n",
    "                self.epochs), leave=True)\n",
    "            self.model.train()\n",
    "        else:\n",
    "            loader = self.val_dataloader()\n",
    "            pbar = tqdm(loader, desc='Validating', leave=True)\n",
    "            self.model.eval()\n",
    "        outputs = []\n",
    "        for batch_nb, batch in enumerate(pbar):\n",
    "            if not validation:\n",
    "                output = self.training_step(batch)\n",
    "            else:\n",
    "                output = self.validation_step(batch)\n",
    "            pbar.set_postfix(output)\n",
    "            outputs.append(output)\n",
    "        self.schedule_lr()\n",
    "        if not validation:\n",
    "            result = self.train_end(outputs)\n",
    "        else:\n",
    "            result = self.validation_end(outputs)\n",
    "        return result\n",
    "\n",
    "    def training_step(self, batch):\n",
    "        loss, ca, wa = self._run_batch(batch, report_accuracy=True)\n",
    "        self.optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        self.step()\n",
    "        output = OrderedDict({\n",
    "            'loss': abs(loss.item()),\n",
    "            'train_ca': ca.item(),\n",
    "            'train_wa': wa.item()\n",
    "            })\n",
    "        return output\n",
    "\n",
    "    def validation_step(self, batch):\n",
    "        loss, ca, wa = self._run_batch(batch, report_accuracy=True, validation=True)\n",
    "        output = OrderedDict({\n",
    "            'val_loss': abs(loss.item()),\n",
    "            'val_ca': ca.item(),\n",
    "            'val_wa': wa.item()\n",
    "            })\n",
    "        return output\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        # logging.info('training data loader called')\n",
    "        loader = torch.utils.data.DataLoader(self.data_train,\n",
    "                batch_size=self.batch_size,\n",
    "                collate_fn=self.collate_fn,\n",
    "                shuffle=True)\n",
    "        return loader\n",
    "        \n",
    "    def val_dataloader(self):\n",
    "        # logging.info('val data loader called')\n",
    "        loader = torch.utils.data.DataLoader(self.data_val,\n",
    "                batch_size=self.batch_size,\n",
    "                collate_fn=self.collate_fn)\n",
    "        return loader\n",
    "\n",
    "    def train_end(self, outputs):\n",
    "        for output in outputs:\n",
    "            self.avgTrainLoss.add(output['loss'])\n",
    "            self.avgTrainCharAccuracy.add(output['train_ca'])\n",
    "            self.avgTrainWordAccuracy.add(output['train_wa'])\n",
    "\n",
    "        train_loss_mean = abs(self.avgTrainLoss.compute())\n",
    "        train_ca_mean = self.avgTrainCharAccuracy.compute()\n",
    "        train_wa_mean = self.avgTrainWordAccuracy.compute()\n",
    "\n",
    "        result = {'train_loss': train_loss_mean, 'train_ca': train_ca_mean,\n",
    "        'train_wa': train_wa_mean}\n",
    "        # result = {'progress_bar': tqdm_dict, 'log': tqdm_dict, 'val_loss': train_loss_mean}\n",
    "        return result\n",
    "\n",
    "    def validation_end(self, outputs):\n",
    "        for output in outputs:\n",
    "            self.avgValLoss.add(output['val_loss'])\n",
    "            self.avgValCharAccuracy.add(output['val_ca'])\n",
    "            self.avgValWordAccuracy.add(output['val_wa'])\n",
    "\n",
    "        val_loss_mean = abs(self.avgValLoss.compute())\n",
    "        val_ca_mean = self.avgValCharAccuracy.compute()\n",
    "        val_wa_mean = self.avgValWordAccuracy.compute()\n",
    "\n",
    "        result = {'val_loss': val_loss_mean, 'val_ca': val_ca_mean,\n",
    "        'val_wa': val_wa_mean}\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cc63ce60",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Learner(object):\n",
    "    def __init__(self, model, optimizer, savepath=None, resume=False):\n",
    "        self.model = model\n",
    "        self.optimizer = optimizer\n",
    "        self.savepath = os.path.join(savepath, 'best.ckpt')\n",
    "        self.cuda = torch.cuda.is_available() \n",
    "        self.cuda_count = torch.cuda.device_count()\n",
    "        if self.cuda:\n",
    "            self.model = self.model.cuda()\n",
    "#             self.model = self.model\n",
    "        self.epoch = 0\n",
    "        if self.cuda_count > 1:\n",
    "            print(\"Let's use\", torch.cuda.device_count(), \"GPUs!\")\n",
    "            self.model = nn.DataParallel(self.model)\n",
    "        self.best_score = None\n",
    "        if resume and os.path.exists(self.savepath):\n",
    "            self.checkpoint = torch.load(self.savepath)\n",
    "            self.epoch = self.checkpoint['epoch']\n",
    "            self.best_score=self.checkpoint['best']\n",
    "            self.load()\n",
    "        else:\n",
    "            print('checkpoint does not exist')\n",
    "\n",
    "    def fit(self, opt):\n",
    "        opt['cuda'] = self.cuda\n",
    "        opt['model'] = self.model\n",
    "        opt['optimizer'] = self.optimizer\n",
    "        logging.basicConfig(filename=\"%s/%s.csv\" %(opt['log_dir'], opt['name']), level=logging.INFO)\n",
    "        self.saver = EarlyStopping(self.savepath, patience=15, verbose=True, best_score=self.best_score)\n",
    "        opt['epoch'] = self.epoch\n",
    "        trainer = OCRTrainer(opt)\n",
    "        \n",
    "        for epoch in range(opt['epoch'], opt['epochs']):\n",
    "            train_result = trainer.run_epoch()\n",
    "            val_result = trainer.run_epoch(validation=True)\n",
    "            trainer.count = epoch\n",
    "            info = '%d, %.6f, %.6f, %.6f, %.6f, %.6f, %.6f'%(epoch, train_result['train_loss'], \n",
    "                val_result['val_loss'], train_result['train_ca'],  val_result['val_ca'],\n",
    "                train_result['train_wa'], val_result['val_wa'])\n",
    "            logging.info(info)\n",
    "            self.val_loss = val_result['val_loss']\n",
    "            print(self.val_loss)\n",
    "            if self.savepath:\n",
    "                self.save(epoch)\n",
    "            if self.saver.early_stop:\n",
    "                print(\"Early stopping\")\n",
    "                break\n",
    "\n",
    "    def load(self):\n",
    "        print('Loading checkpoint at {} trained for {} epochs'.format(self.savepath, self.checkpoint['epoch']))\n",
    "        self.model.load_state_dict(self.checkpoint['state_dict'])\n",
    "        if 'opt_state_dict' in self.checkpoint.keys():\n",
    "            print('Loading optimizer')\n",
    "            self.optimizer.load_state_dict(self.checkpoint['opt_state_dict'])\n",
    "\n",
    "    def save(self, epoch):\n",
    "        self.saver(self.val_loss, epoch, self.model, self.optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eea29849",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './data/train'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[12], line 21\u001b[0m\n\u001b[1;32m      1\u001b[0m alphabet \u001b[39m=\u001b[39m \u001b[39m\"\"\"\u001b[39m\u001b[39mOnly thewigsofrcvdampbkuq.$A-210xT5\u001b[39m\u001b[39m'\u001b[39m\u001b[39mMDL,RYHJ\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mISPWENj&BC93VGFKz();#:!7U64Q8?+*ZX/\u001b[39m\u001b[39m%\u001b[39m\u001b[39m\"\"\"\u001b[39m\n\u001b[1;32m      2\u001b[0m args \u001b[39m=\u001b[39m {\n\u001b[1;32m      3\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mname\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m'\u001b[39m\u001b[39monly_good_wo_contrast\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m      4\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mpath\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m'\u001b[39m\u001b[39m./data\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     18\u001b[0m     \n\u001b[1;32m     19\u001b[0m }\n\u001b[0;32m---> 21\u001b[0m data \u001b[39m=\u001b[39m SynthDataset(args)\n\u001b[1;32m     22\u001b[0m args[\u001b[39m'\u001b[39m\u001b[39mcollate_fn\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m SynthCollator()\n\u001b[1;32m     23\u001b[0m train_split \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m(\u001b[39m0.9\u001b[39m\u001b[39m*\u001b[39m\u001b[39mlen\u001b[39m(data))\n",
      "Cell \u001b[0;32mIn[7], line 5\u001b[0m, in \u001b[0;36mSynthDataset.__init__\u001b[0;34m(self, opt)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[39msuper\u001b[39m(SynthDataset, \u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m()\n\u001b[1;32m      4\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpath \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(opt[\u001b[39m'\u001b[39m\u001b[39mpath\u001b[39m\u001b[39m'\u001b[39m], opt[\u001b[39m'\u001b[39m\u001b[39mimgdir\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[0;32m----> 5\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mimages \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39;49mlistdir(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpath)\n\u001b[1;32m      6\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnSamples \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mimages)\n\u001b[1;32m      7\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mlambda\u001b[39;00m x: os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpath, x)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './data/train'"
     ]
    }
   ],
   "source": [
    "alphabet = \"\"\"Only thewigsofrcvdampbkuq.$A-210xT5'MDL,RYHJ\"ISPWENj&BC93VGFKz();#:!7U64Q8?+*ZX/%\"\"\"\n",
    "args = {\n",
    "    'name':'only_good_wo_contrast',\n",
    "    'path':'./data',\n",
    "    'imgdir': 'train',\n",
    "    'imgH':32,\n",
    "    'nChannels':1,\n",
    "    'nHidden':256,\n",
    "    'nClasses':len(alphabet),\n",
    "    'lr':0.001,\n",
    "    'epochs':1200,\n",
    "    'batch_size':128,\n",
    "    'save_dir':'./checkpoints/',\n",
    "    'log_dir':'./logs',\n",
    "    'resume':True,\n",
    "    'cuda':True,\n",
    "    'schedule':False\n",
    "    \n",
    "}\n",
    "\n",
    "data = SynthDataset(args)\n",
    "args['collate_fn'] = SynthCollator()\n",
    "train_split = int(0.9*len(data))\n",
    "val_split = len(data) - train_split\n",
    "args['data_train'], args['data_val'] = random_split(data, (train_split, val_split))\n",
    "print('Traininig Data Size:{}\\nVal Data Size:{}'.format(\n",
    "    len(args['data_train']), len(args['data_val'])))\n",
    "args['alphabet'] = alphabet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf83883c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's use 2 GPUs!\n",
      "checkpoint does not exist\n",
      "None\n",
      "Scheduling is False\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [0]/[1200] Training: 100%|██████████| 7/7 [00:05<00:00,  1.39it/s, loss=4.23, train_ca=0, train_wa=0]    \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.50it/s, val_loss=3.72, val_ca=0, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7185263633728027\n",
      "Validation loss decreased (inf --> 3.718526).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [0]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.51it/s, loss=3.59, train_ca=0, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.98it/s, val_loss=3.39, val_ca=0, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.553903102874756\n",
      "Validation loss decreased (3.718526 --> 3.553903).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [1]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.65it/s, loss=3.39, train_ca=0, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.47it/s, val_loss=3.28, val_ca=0, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.4631349245707193\n",
      "Validation loss decreased (3.553903 --> 3.463135).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [2]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.52it/s, loss=3.43, train_ca=0, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.56it/s, val_loss=3.18, val_ca=0, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.392176628112793\n",
      "Validation loss decreased (3.463135 --> 3.392177).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [3]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.70it/s, loss=3.04, train_ca=11.4, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.99it/s, val_loss=3.25, val_ca=12.3, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3642655849456786\n",
      "Validation loss decreased (3.392177 --> 3.364266).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [4]/[1200] Training: 100%|██████████| 7/7 [00:03<00:00,  1.80it/s, loss=2.96, train_ca=13.9, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.01it/s, val_loss=3.17, val_ca=12.3, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3311362266540527\n",
      "Validation loss decreased (3.364266 --> 3.331136).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [5]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.51it/s, loss=3.16, train_ca=16.4, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.43it/s, val_loss=2.97, val_ca=12.3, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2790446622031078\n",
      "Validation loss decreased (3.331136 --> 3.279045).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [6]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.43it/s, loss=2.96, train_ca=25.7, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.48it/s, val_loss=3.25, val_ca=10.7, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.275797665119171\n",
      "Validation loss decreased (3.279045 --> 3.275798).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [7]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.46it/s, loss=2.63, train_ca=24.3, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.99it/s, val_loss=2.93, val_ca=19.8, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.237477011150784\n",
      "Validation loss decreased (3.275798 --> 3.237477).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [8]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.52it/s, loss=3.23, train_ca=16.8, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.03it/s, val_loss=4.31, val_ca=22.7, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3442399740219115\n",
      "EarlyStopping counter: (-3.237477 1 out of 15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [9]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.49it/s, loss=2.91, train_ca=17.1, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.02it/s, val_loss=2.91, val_ca=18.9, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3046299544247715\n",
      "EarlyStopping counter: (-3.237477 2 out of 15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [10]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.63it/s, loss=2.68, train_ca=21.4, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.00it/s, val_loss=2.79, val_ca=22.5, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2620654702186584\n",
      "EarlyStopping counter: (-3.237477 3 out of 15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [11]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.56it/s, loss=3.17, train_ca=17.1, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.97it/s, val_loss=3.44, val_ca=0.166, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2755481279813328\n",
      "EarlyStopping counter: (-3.237477 4 out of 15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [12]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.49it/s, loss=3.01, train_ca=20, train_wa=0]  \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.99it/s, val_loss=2.81, val_ca=22.7, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.24252074105399\n",
      "EarlyStopping counter: (-3.237477 5 out of 15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [13]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.59it/s, loss=2.79, train_ca=25, train_wa=0]  \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.98it/s, val_loss=2.74, val_ca=23.6, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.209024413426717\n",
      "Validation loss decreased (3.237477 --> 3.209024).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [14]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.57it/s, loss=2.43, train_ca=25.7, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.63it/s, val_loss=4.1, val_ca=2.75, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.264751300215721\n",
      "EarlyStopping counter: (-3.209024 1 out of 15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [15]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.49it/s, loss=2.59, train_ca=22.5, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.52it/s, val_loss=3.31, val_ca=9.36, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2674081325531006\n",
      "EarlyStopping counter: (-3.209024 2 out of 15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [16]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.52it/s, loss=2.51, train_ca=22.9, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.50it/s, val_loss=2.87, val_ca=17.6, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2453475528293185\n",
      "EarlyStopping counter: (-3.209024 3 out of 15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [17]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.46it/s, loss=2.41, train_ca=25, train_wa=0]        \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.46it/s, val_loss=2.59, val_ca=23.8, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.210754733336599\n",
      "EarlyStopping counter: (-3.209024 4 out of 15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [18]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.40it/s, loss=2.74, train_ca=17.1, train_wa=0]\n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.69it/s, val_loss=2.6, val_ca=23.6, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.180412268638611\n",
      "Validation loss decreased (3.209024 --> 3.180412).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [19]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.54it/s, loss=2.33, train_ca=24.6, train_wa=0]    \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.00it/s, val_loss=2.7, val_ca=20.7, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1575115408216203\n",
      "Validation loss decreased (3.180412 --> 3.157512).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [20]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.57it/s, loss=2.52, train_ca=22.9, train_wa=0]      \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.01it/s, val_loss=5.01, val_ca=3.59, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2415631359273736\n",
      "EarlyStopping counter: (-3.157512 1 out of 15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [21]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.52it/s, loss=2.4, train_ca=24.6, train_wa=0]       \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.97it/s, val_loss=2.55, val_ca=23.2, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2114869718966275\n",
      "EarlyStopping counter: (-3.157512 2 out of 15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [22]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.63it/s, loss=2.46, train_ca=19.3, train_wa=0]      \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.49it/s, val_loss=2.63, val_ca=24.6, val_wa=0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.187089115381241\n",
      "EarlyStopping counter: (-3.157512 3 out of 15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.542308557173903\n",
      "Validation loss decreased (0.542454 --> 0.542309).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [1188]/[1200] Training: 100%|██████████| 7/7 [00:05<00:00,  1.39it/s, loss=0.000169, train_ca=100, train_wa=1]     \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.93it/s, val_loss=0.366, val_ca=96.1, val_wa=0.826]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5421605958157227\n",
      "Validation loss decreased (0.542309 --> 0.542161).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [1189]/[1200] Training: 100%|██████████| 7/7 [00:05<00:00,  1.36it/s, loss=5.46e-5, train_ca=100, train_wa=1]     \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.99it/s, val_loss=0.378, val_ca=96.1, val_wa=0.826]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5420231022922859\n",
      "Validation loss decreased (0.542161 --> 0.542023).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [1190]/[1200] Training: 100%|██████████| 7/7 [00:05<00:00,  1.38it/s, loss=2.08e-5, train_ca=100, train_wa=1]     \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.38it/s, val_loss=0.38, val_ca=96.1, val_wa=0.826]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5418867957121973\n",
      "Validation loss decreased (0.542023 --> 0.541887).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [1191]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.48it/s, loss=3.33e-5, train_ca=100, train_wa=1]     \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.98it/s, val_loss=0.38, val_ca=96.1, val_wa=0.814]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5417513148382943\n",
      "Validation loss decreased (0.541887 --> 0.541751).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [1192]/[1200] Training: 100%|██████████| 7/7 [00:05<00:00,  1.37it/s, loss=0.000374, train_ca=100, train_wa=1]    \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.98it/s, val_loss=0.384, val_ca=96, val_wa=0.814]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5416193783183393\n",
      "Validation loss decreased (0.541751 --> 0.541619).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [1193]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.45it/s, loss=0.000166, train_ca=100, train_wa=1]    \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.67it/s, val_loss=0.403, val_ca=96, val_wa=0.826]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5415031808440156\n",
      "Validation loss decreased (0.541619 --> 0.541503).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [1194]/[1200] Training: 100%|██████████| 7/7 [00:05<00:00,  1.29it/s, loss=1.22e-5, train_ca=100, train_wa=1]     \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.96it/s, val_loss=0.423, val_ca=96, val_wa=0.826]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5414040918184762\n",
      "Validation loss decreased (0.541503 --> 0.541404).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [1195]/[1200] Training: 100%|██████████| 7/7 [00:05<00:00,  1.40it/s, loss=5.12e-5, train_ca=100, train_wa=1]     \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.99it/s, val_loss=0.431, val_ca=95.6, val_wa=0.814]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5413121193423307\n",
      "Validation loss decreased (0.541404 --> 0.541312).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [1196]/[1200] Training: 100%|██████████| 7/7 [00:05<00:00,  1.36it/s, loss=0.000101, train_ca=100, train_wa=1]    \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.94it/s, val_loss=0.426, val_ca=95.8, val_wa=0.814]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5412162533387517\n",
      "Validation loss decreased (0.541312 --> 0.541216).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [1197]/[1200] Training: 100%|██████████| 7/7 [00:04<00:00,  1.42it/s, loss=2.31e-5, train_ca=100, train_wa=1]      \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  2.53it/s, val_loss=0.41, val_ca=96.1, val_wa=0.814]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.541106754287866\n",
      "Validation loss decreased (0.541216 --> 0.541107).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: [1198]/[1200] Training: 100%|██████████| 7/7 [00:05<00:00,  1.38it/s, loss=2.08e-5, train_ca=100, train_wa=1]     \n",
      "Validating: 100%|██████████| 1/1 [00:00<00:00,  1.96it/s, val_loss=0.405, val_ca=96.1, val_wa=0.814]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5409934930255016\n",
      "Validation loss decreased (0.541107 --> 0.540993).  Saving model ...\n"
     ]
    }
   ],
   "source": [
    "model = CRNN(args)\n",
    "args['criterion'] = CustomCTCLoss()\n",
    "savepath = os.path.join(args['save_dir'], args['name'])\n",
    "# gmkdir(savepath)\n",
    "# gmkdir(args['log_dir'])\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'])\n",
    "# learner = Learner(model, optimizer, savepath=savepath, resume=args['resume'])\n",
    "# learner.fit(args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "136bc181",
   "metadata": {},
   "source": [
    "# Evaluation and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "556ed2de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from torchvision.utils import make_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e48a5aaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "def get_accuracy(args):\n",
    "    loader = torch.utils.data.DataLoader(args['data'],\n",
    "                batch_size=args['batch_size'],\n",
    "                collate_fn=args['collate_fn'])\n",
    "    model = args['model']\n",
    "    model.eval()\n",
    "    converter = OCRLabelConverter(args['alphabet'])\n",
    "    evaluator = Eval()\n",
    "    labels, predictions, images = [], [], []\n",
    "    for iteration, batch in enumerate(tqdm(loader)):\n",
    "        input_, targets = batch['img'].to(device), batch['label']\n",
    "        images.extend(input_.squeeze().detach())\n",
    "        labels.extend(targets)\n",
    "        targets, lengths = converter.encode(targets)\n",
    "        logits = model(input_).transpose(1, 0)\n",
    "        logits = torch.nn.functional.log_softmax(logits, 2)\n",
    "        logits = logits.contiguous().cpu()\n",
    "        T, B, H = logits.size()\n",
    "        pred_sizes = torch.LongTensor([T for i in range(B)])\n",
    "        probs, pos = logits.max(2)\n",
    "        pos = pos.transpose(1, 0).contiguous().view(-1)\n",
    "        sim_preds = converter.decode(pos.data, pred_sizes.data, raw=False)\n",
    "        predictions.extend(sim_preds)\n",
    "        \n",
    "    make_grid(images[:10], nrow=3)\n",
    "    fig=plt.figure(figsize=(8, 8))\n",
    "    columns = 5\n",
    "    rows = 5\n",
    "    pairs = list(zip(images, predictions))\n",
    "    indices = np.random.permutation(len(pairs))\n",
    "#     print(indices)\n",
    "#     for i in range(1, columns*rows +1):\n",
    "    for i in range(1, len(indices)):\n",
    "        img = images[indices[i]].cpu()\n",
    "        img = (img - img.min())/(img.max() - img.min())\n",
    "        img = np.array(img * 255.0, dtype=np.uint8)\n",
    "        fig.add_subplot(rows, columns, i)\n",
    "        plt.title(predictions[indices[i]])\n",
    "        plt.axis('off')\n",
    "        plt.imshow(img, cmap='gray')\n",
    "    plt.show()\n",
    "    ca = np.mean((list(map(evaluator.char_accuracy, list(zip(predictions, labels))))))\n",
    "    wa = np.mean((list(map(evaluator.word_accuracy_line, list(zip(predictions, labels))))))\n",
    "    return ca, wa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a9a21b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model ./checkpoints/only_good_wo_contrast/best.ckpt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 28.40it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Character Accuracy: 53.00\n",
      "Word Accuracy: 0.16\n"
     ]
    }
   ],
   "source": [
    "args['imgdir'] = 'test'\n",
    "args['data'] = SynthDataset(args)\n",
    "resume_file = os.path.join(args['save_dir'], args['name'], 'best.ckpt')\n",
    "if os.path.isfile(resume_file):\n",
    "    print('Loading model %s'%resume_file)\n",
    "    checkpoint = torch.load(resume_file)\n",
    "#     model.load_state_dict(checkpoint['state_dict'])\n",
    "    model.load_state_dict(checkpoint['state_dict'], strict=False) # If mutli GPU\n",
    "    args['model'] = model\n",
    "    ca, wa = get_accuracy(args)\n",
    "    print(\"Character Accuracy: %.2f\\nWord Accuracy: %.2f\"%(ca, wa))\n",
    "else:\n",
    "    print(\"=> no checkpoint found at '{}'\".format(os.path.join(args['save_dir'], args['name'])))\n",
    "    print('Exiting')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3b493c7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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