From bae777550300866eaf8f656065b925386d547e3f Mon Sep 17 00:00:00 2001 From: MUSA YILMAZ <95527400+yilmazmusa08@users.noreply.github.com> Date: Mon, 5 Jun 2023 15:48:55 +0300 Subject: [PATCH 01/15] Add files via upload --- trainer/GPU_clear.py | 3 + trainer/trainer.ipynb | 333 ++++++++++++++++++++++++++++++++++++++++-- trainer/trainer.py | 68 +++++++++ 3 files changed, 394 insertions(+), 10 deletions(-) create mode 100644 trainer/GPU_clear.py create mode 100644 trainer/trainer.py diff --git a/trainer/GPU_clear.py b/trainer/GPU_clear.py new file mode 100644 index 000000000..ed8ac5b89 --- /dev/null +++ b/trainer/GPU_clear.py @@ -0,0 +1,3 @@ +import torch + +torch.cuda.empty_cache() \ No newline at end of file diff --git a/trainer/trainer.ipynb b/trainer/trainer.ipynb index 712bf8334..08fa6bd21 100644 --- a/trainer/trainer.ipynb +++ b/trainer/trainer.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-07-23T04:19:23.488642Z", @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-07-23T04:19:23.885144Z", @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-07-23T04:19:24.119144Z", @@ -56,7 +56,7 @@ " if opt.lang_char == 'None':\n", " characters = ''\n", " for data in opt['select_data'].split('-'):\n", - " csv_path = os.path.join(opt['train_data'], data, 'labels.csv')\n", + " csv_path = os.path.join(opt['train'], data, 'labels.csv')\n", " df = pd.read_csv(csv_path, sep='^([^,]+),', engine='python', usecols=['filename', 'words'], keep_default_na=False)\n", " all_char = ''.join(df['words'])\n", " characters += ''.join(set(all_char))\n", @@ -68,19 +68,330 @@ " return opt" ] }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "opt = get_config(\"config_files/train.yaml\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "# Create a device object to represent the GPU\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n" + ] + }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if torch.cuda.is_available():\n", + " \n", + " print(\"CUDA is available\")\n", + "else:\n", + " print(\"CUDA is not available\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2021-07-23T04:49:07.045060Z", "start_time": "2021-07-23T04:20:15.050992Z" } }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtering the images containing characters which are not in opt.character\n", + "Filtering the images whose label is longer than opt.batch_max_length\n", + "--------------------------------------------------------------------------------\n", + "dataset_root: /home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data\n", + "opt.select_data: ['/home/musa/Desktop/Work/Mina/Project3/EasyOCR', 'master/trainer/all_data/train']\n", + "opt.batch_ratio: ['10162', '2591']\n", + "--------------------------------------------------------------------------------\n", + "dataset_root: /home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data\t dataset: /home/musa/Desktop/Work/Mina/Project3/EasyOCR\n", + "/home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data/val\n", + " filename words\n", + "0 10445.jpg 1813\n", + "1 10714.jpg 1801\n", + "2 10819.jpg 22\n", + "3 11635.jpg 1806\n", + "4 11720.jpg 1808\n", + "... ... ...\n", + "2627 11010.jpg 1819\n", + "2628 11949.jpg 1827\n", + "2629 11498.jpg 1823\n", + "2630 11557.jpg 1801\n", + "2631 10504.jpg 1801\n", + "\n", + "[2632 rows x 2 columns]\n", + "sub-directory:\t/val\t num samples: 2607\n", + "/home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data/train\n", + " filename words\n", + "0 4046.jpg 1816\n", + "1 6996.jpg 1804\n", + "2 2601.jpg 1832\n", + "3 232.jpg 1824\n", + "4 6493.jpg 1841\n", + "... ... ...\n", + "10224 4189.jpg 1837\n", + "10225 8945.jpg 1826\n", + "10226 img005-055.png 4\n", + "10227 67.jpg 1837\n", + "10228 1326.jpg 1837\n", + "\n", + "[10229 rows x 2 columns]\n", + "sub-directory:\t/train\t num samples: 10166\n", + "num total samples of /home/musa/Desktop/Work/Mina/Project3/EasyOCR: 12773 x 1.0 (total_data_usage_ratio) = 12773\n", + "num samples of /home/musa/Desktop/Work/Mina/Project3/EasyOCR per batch: 32 x 10162.0 (batch_ratio) = 325184\n", + "--------------------------------------------------------------------------------\n", + "dataset_root: /home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data\t dataset: master/trainer/all_data/train\n", + "/home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data/train\n", + " filename words\n", + "0 4046.jpg 1816\n", + "1 6996.jpg 1804\n", + "2 2601.jpg 1832\n", + "3 232.jpg 1824\n", + "4 6493.jpg 1841\n", + "... ... ...\n", + "10224 4189.jpg 1837\n", + "10225 8945.jpg 1826\n", + "10226 img005-055.png 4\n", + "10227 67.jpg 1837\n", + "10228 1326.jpg 1837\n", + "\n", + "[10229 rows x 2 columns]\n", + "sub-directory:\t/train\t num samples: 10166\n", + "num total samples of master/trainer/all_data/train: 10166 x 1.0 (total_data_usage_ratio) = 10166\n", + "num samples of master/trainer/all_data/train per batch: 32 x 2591.0 (batch_ratio) = 82912\n", + "--------------------------------------------------------------------------------\n", + "Total_batch_size: 325184+82912 = 408096\n", + "--------------------------------------------------------------------------------\n", + "dataset_root: /home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data/val\t dataset: /\n", + "/home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data/val/\n", + " filename words\n", + "0 10445.jpg 1813\n", + "1 10714.jpg 1801\n", + "2 10819.jpg 22\n", + "3 11635.jpg 1806\n", + "4 11720.jpg 1808\n", + "... ... ...\n", + "2627 11010.jpg 1819\n", + "2628 11949.jpg 1827\n", + "2629 11498.jpg 1823\n", + "2630 11557.jpg 1801\n", + "2631 10504.jpg 1801\n", + "\n", + "[2632 rows x 2 columns]\n", + "sub-directory:\t/.\t num samples: 2607\n", + "--------------------------------------------------------------------------------\n", + "No Transformation module specified\n", + "model input parameters 64 600 20 1 256 256 12 5 None VGG BiLSTM CTC\n", + "Model:\n", + "DataParallel(\n", + " (module): Model(\n", + " (FeatureExtraction): VGG_FeatureExtractor(\n", + " (ConvNet): Sequential(\n", + " (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (1): ReLU(inplace=True)\n", + " (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (4): ReLU(inplace=True)\n", + " (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (6): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (7): ReLU(inplace=True)\n", + " (8): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (9): ReLU(inplace=True)\n", + " (10): MaxPool2d(kernel_size=(2, 1), stride=(2, 1), padding=0, dilation=1, ceil_mode=False)\n", + " (11): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (13): ReLU(inplace=True)\n", + " (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (16): ReLU(inplace=True)\n", + " (17): MaxPool2d(kernel_size=(2, 1), stride=(2, 1), padding=0, dilation=1, ceil_mode=False)\n", + " (18): Conv2d(256, 256, kernel_size=(2, 2), stride=(1, 1))\n", + " (19): ReLU(inplace=True)\n", + " )\n", + " )\n", + " (AdaptiveAvgPool): AdaptiveAvgPool2d(output_size=(None, 1))\n", + " (SequenceModeling): Sequential(\n", + " (0): BidirectionalLSTM(\n", + " (rnn): LSTM(256, 256, batch_first=True, bidirectional=True)\n", + " (linear): Linear(in_features=512, out_features=256, bias=True)\n", + " )\n", + " (1): BidirectionalLSTM(\n", + " (rnn): LSTM(256, 256, batch_first=True, bidirectional=True)\n", + " (linear): Linear(in_features=512, out_features=256, bias=True)\n", + " )\n", + " )\n", + " (Prediction): Linear(in_features=256, out_features=12, bias=True)\n", + " )\n", + ")\n", + "Modules, Parameters\n", + "module.FeatureExtraction.ConvNet.0.weight 288\n", + "module.FeatureExtraction.ConvNet.0.bias 32\n", + "module.FeatureExtraction.ConvNet.3.weight 18432\n", + "module.FeatureExtraction.ConvNet.3.bias 64\n", + "module.FeatureExtraction.ConvNet.6.weight 73728\n", + "module.FeatureExtraction.ConvNet.6.bias 128\n", + "module.FeatureExtraction.ConvNet.8.weight 147456\n", + "module.FeatureExtraction.ConvNet.8.bias 128\n", + "module.FeatureExtraction.ConvNet.11.weight 294912\n", + "module.FeatureExtraction.ConvNet.12.weight 256\n", + "module.FeatureExtraction.ConvNet.12.bias 256\n", + "module.FeatureExtraction.ConvNet.14.weight 589824\n", + "module.FeatureExtraction.ConvNet.15.weight 256\n", + "module.FeatureExtraction.ConvNet.15.bias 256\n", + "module.FeatureExtraction.ConvNet.18.weight 262144\n", + "module.FeatureExtraction.ConvNet.18.bias 256\n", + "module.SequenceModeling.0.rnn.weight_ih_l0 262144\n", + "module.SequenceModeling.0.rnn.weight_hh_l0 262144\n", + "module.SequenceModeling.0.rnn.bias_ih_l0 1024\n", + "module.SequenceModeling.0.rnn.bias_hh_l0 1024\n", + "module.SequenceModeling.0.rnn.weight_ih_l0_reverse 262144\n", + "module.SequenceModeling.0.rnn.weight_hh_l0_reverse 262144\n", + "module.SequenceModeling.0.rnn.bias_ih_l0_reverse 1024\n", + "module.SequenceModeling.0.rnn.bias_hh_l0_reverse 1024\n", + "module.SequenceModeling.0.linear.weight 131072\n", + "module.SequenceModeling.0.linear.bias 256\n", + "module.SequenceModeling.1.rnn.weight_ih_l0 262144\n", + "module.SequenceModeling.1.rnn.weight_hh_l0 262144\n", + "module.SequenceModeling.1.rnn.bias_ih_l0 1024\n", + "module.SequenceModeling.1.rnn.bias_hh_l0 1024\n", + "module.SequenceModeling.1.rnn.weight_ih_l0_reverse 262144\n", + "module.SequenceModeling.1.rnn.weight_hh_l0_reverse 262144\n", + "module.SequenceModeling.1.rnn.bias_ih_l0_reverse 1024\n", + "module.SequenceModeling.1.rnn.bias_hh_l0_reverse 1024\n", + "module.SequenceModeling.1.linear.weight 131072\n", + "module.SequenceModeling.1.linear.bias 256\n", + "module.Prediction.weight 3072\n", + "module.Prediction.bias 12\n", + "Total Trainable Params: 3759500\n", + "Trainable params num : 3759500\n", + "Optimizer:\n", + "Adadelta (\n", + "Parameter Group 0\n", + " differentiable: False\n", + " eps: 1e-08\n", + " foreach: None\n", + " lr: 1.0\n", + " maximize: False\n", + " rho: 0.95\n", + " weight_decay: 0\n", + ")\n", + "------------ Options -------------\n", + "number: 0123456789\n", + "symbol: .\n", + "lang_char: \n", + "experiment_name: custom_model\n", + "train_data: /home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data\n", + "valid_data: /home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data/val\n", + "manualSeed: 1111\n", + "workers: 6\n", + "batch_size: 408096\n", + "num_iter: 300000\n", + "valInterval: 20000\n", + "saved_model: \n", + "FT: False\n", + "optim: False\n", + "lr: 1.0\n", + "beta1: 0.9\n", + "rho: 0.95\n", + "eps: 1e-08\n", + "grad_clip: 5\n", + "select_data: ['/home/musa/Desktop/Work/Mina/Project3/EasyOCR', 'master/trainer/all_data/train']\n", + "batch_ratio: ['10162', '2591']\n", + "total_data_usage_ratio: 1.0\n", + "batch_max_length: 5\n", + "imgH: 64\n", + "imgW: 600\n", + "rgb: False\n", + "sensitive: True\n", + "PAD: True\n", + "contrast_adjust: 0.0\n", + "data_filtering_off: False\n", + "Transformation: None\n", + "FeatureExtraction: VGG\n", + "SequenceModeling: BiLSTM\n", + "Prediction: CTC\n", + "num_fiducial: 20\n", + "input_channel: 1\n", + "output_channel: 256\n", + "hidden_size: 256\n", + "decode: greedy\n", + "new_prediction: False\n", + "freeze_FeatureFxtraction: False\n", + "freeze_SequenceModeling: False\n", + "character: 0123456789.\n", + "num_class: 12\n", + "---------------------------------------\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/musa/anaconda3/lib/python3.10/site-packages/torch/cuda/amp/grad_scaler.py:120: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n", + " warnings.warn(\"torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\")\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "Caught FileNotFoundError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n File \"/home/musa/anaconda3/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loop\n data = fetcher.fetch(index)\n File \"/home/musa/anaconda3/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 51, in fetch\n data = [self.dataset[idx] for idx in possibly_batched_index]\n File \"/home/musa/anaconda3/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 51, in \n data = [self.dataset[idx] for idx in possibly_batched_index]\n File \"/home/musa/anaconda3/lib/python3.10/site-packages/torch/utils/data/dataset.py\", line 298, in __getitem__\n return self.dataset[self.indices[idx]]\n File \"/home/musa/anaconda3/lib/python3.10/site-packages/torch/utils/data/dataset.py\", line 243, in __getitem__\n return self.datasets[dataset_idx][sample_idx]\n File \"/home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/dataset.py\", line 182, in __getitem__\n img = Image.open(img_fpath).convert('L')\n File \"/home/musa/anaconda3/lib/python3.10/site-packages/PIL/Image.py\", line 3227, in open\n fp = builtins.open(filename, \"rb\")\nFileNotFoundError: [Errno 2] No such file or directory: '/home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data/train/57.28.png'\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mamp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(device)\n", + "File \u001b[0;32m~/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/train.py:203\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(opt, show_number, amp)\u001b[0m\n\u001b[1;32m 201\u001b[0m scaler\u001b[38;5;241m.\u001b[39mupdate()\n\u001b[1;32m 202\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 203\u001b[0m image_tensors, labels \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 204\u001b[0m image \u001b[38;5;241m=\u001b[39m image_tensors\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 205\u001b[0m text, length \u001b[38;5;241m=\u001b[39m converter\u001b[38;5;241m.\u001b[39mencode(labels, batch_max_length\u001b[38;5;241m=\u001b[39mopt\u001b[38;5;241m.\u001b[39mbatch_max_length)\n", + "File \u001b[0;32m~/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/dataset.py:101\u001b[0m, in \u001b[0;36mBatch_Balanced_Dataset.get_batch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, data_loader_iter \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataloader_iter_list):\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 101\u001b[0m image, text \u001b[38;5;241m=\u001b[39m \u001b[43mdata_loader_iter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__next__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 102\u001b[0m balanced_batch_images\u001b[38;5;241m.\u001b[39mappend(image)\n\u001b[1;32m 103\u001b[0m balanced_batch_texts \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m text\n", + "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 631\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 632\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m 633\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 634\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 635\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 636\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 637\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 638\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", + "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1346\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1344\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1345\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_task_info[idx]\n\u001b[0;32m-> 1346\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1372\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._process_data\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 1370\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_try_put_index()\n\u001b[1;32m 1371\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ExceptionWrapper):\n\u001b[0;32m-> 1372\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1373\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n", + "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/torch/_utils.py:644\u001b[0m, in \u001b[0;36mExceptionWrapper.reraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 641\u001b[0m \u001b[38;5;66;03m# If the exception takes multiple arguments, don't try to\u001b[39;00m\n\u001b[1;32m 642\u001b[0m \u001b[38;5;66;03m# instantiate since we don't know how to\u001b[39;00m\n\u001b[1;32m 643\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[0;32m--> 644\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: Caught FileNotFoundError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n File \"/home/musa/anaconda3/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loop\n data = fetcher.fetch(index)\n File \"/home/musa/anaconda3/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 51, in fetch\n data = [self.dataset[idx] for idx in possibly_batched_index]\n File \"/home/musa/anaconda3/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 51, in \n data = [self.dataset[idx] for idx in possibly_batched_index]\n File \"/home/musa/anaconda3/lib/python3.10/site-packages/torch/utils/data/dataset.py\", line 298, in __getitem__\n return self.dataset[self.indices[idx]]\n File \"/home/musa/anaconda3/lib/python3.10/site-packages/torch/utils/data/dataset.py\", line 243, in __getitem__\n return self.datasets[dataset_idx][sample_idx]\n File \"/home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/dataset.py\", line 182, in __getitem__\n img = Image.open(img_fpath).convert('L')\n File \"/home/musa/anaconda3/lib/python3.10/site-packages/PIL/Image.py\", line 3227, in open\n fp = builtins.open(filename, \"rb\")\nFileNotFoundError: [Errno 2] No such file or directory: '/home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data/train/57.28.png'\n" + ] + } + ], + "source": [ + "train(opt, amp=False).to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ - "opt = get_config(\"config_files/en_filtered_config.yaml\")\n", - "train(opt, amp=False)" + "\"\"\"# Move the model to the GPU\n", + "model = train(opt, amp=False).to(device)\n", + "\n", + "# Move the data to the GPU\n", + "# Here you should replace this with your own data loading and preprocessing code\n", + "data = torch.randn(32, 3, 224, 224).to(device)\n", + "\n", + "# Train the model on the GPU\n", + "model.train()\n", + "for i in range(100):\n", + " output = model(data)\n", + " loss = output.mean()\n", + " loss.backward()\"\"\"" ] }, { @@ -88,12 +399,14 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "print(\"finish\")" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -107,9 +420,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.11" + "version": "3.10.9" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/trainer/trainer.py b/trainer/trainer.py new file mode 100644 index 000000000..d76b74e83 --- /dev/null +++ b/trainer/trainer.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[2]: + + +import os +import torch.backends.cudnn as cudnn +import yaml +from train import train +from utils import AttrDict +import pandas as pd + + +# In[3]: + + +cudnn.benchmark = True +cudnn.deterministic = False + + +# In[4]: + + +def get_config(file_path): + with open(file_path, 'r', encoding="utf8") as stream: + opt = yaml.safe_load(stream) + opt = AttrDict(opt) + if opt.lang_char == 'None': + characters = '' + for data in opt['select_data'].split('-'): + csv_path = os.path.join(opt['train'], data, 'labels.csv') + df = pd.read_csv(csv_path, sep='^([^,]+),', engine='python', usecols=['filename', 'words'], keep_default_na=False) + all_char = ''.join(df['words']) + characters += ''.join(set(all_char)) + characters = sorted(set(characters)) + opt.character= ''.join(characters) + else: + opt.character = opt.number + opt.symbol + opt.lang_char + os.makedirs(f'./saved_models/{opt.experiment_name}', exist_ok=True) + return opt + + +# In[5]: + + +opt = get_config("config_files/train.yaml") + + +import torch + +# Create a device object to represent the GPU +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + + +if torch.cuda.is_available(): + + print("CUDA is available") +else: + print("CUDA is not available") + + +train(opt, amp=True).to(device) + + +print("finish") + From a5ab83e6412ec9939fc6e56cb244f0cafc71da79 Mon Sep 17 00:00:00 2001 From: MUSA YILMAZ <95527400+yilmazmusa08@users.noreply.github.com> Date: Mon, 5 Jun 2023 15:50:07 +0300 Subject: [PATCH 02/15] Add files via upload --- trainer/config_files/model.ini | 21 +++++++++++++++ trainer/config_files/train.yaml | 45 +++++++++++++++++++++++++++++++++ 2 files changed, 66 insertions(+) create mode 100644 trainer/config_files/model.ini create mode 100644 trainer/config_files/train.yaml diff --git a/trainer/config_files/model.ini b/trainer/config_files/model.ini new file mode 100644 index 000000000..1f53556ea --- /dev/null +++ b/trainer/config_files/model.ini @@ -0,0 +1,21 @@ + Transformation: Bu parametre, modelin resimlere uygulayacağı ön işleme işlevini belirler. Bu işlevler, resimleri boyutlandırma, kesme, renk düzenleme vb. işlemler olabilir. Örnek olarak, resimlere boyutlandırma işlevini eklemek isteyebilirsiniz. + + FeatureExtraction: Bu parametre, resim özelliklerini çıkarmak için kullanılan ağ mimarisini belirler. Bu ağ mimarisi, özellikle Convolutional Neural Networks (CNN) gibi evrişimli sinir ağlarından oluşabilir. Örnek olarak, ResNet, EfficientNet, Inception, VGG gibi farklı mimariler deneyebilirsiniz. + + SequenceModeling: Bu parametre, karakter seçimini ve modelin karakterleri tanıma yöntemini belirler. Bu aşamada, genellikle LSTM gibi recurrent neural networks kullanılır. Örnek olarak, GRU veya Convolutional Sequence to Sequence Learning gibi farklı modelleri deneyebilirsiniz. + + Prediction: Bu parametre, modelin karakterlerin belirlenmesinde kullanacağı yöntemi belirler. Connectionist Temporal Classification (CTC) veya Attn gibi farklı yöntemler kullanılabilir. + + num_fiducial: Bu parametre, resimlerin geometrik dönüşümünü hesaplamak için kullanılan sabit sayıdır. + + input_channel: Bu parametre, girdi resminin kaç kanaldan oluştuğunu belirler. Genellikle, tek kanallı siyah-beyaz resimler için 1 ve renkli resimler için 3 olarak ayarlanır. + + output_channel: Bu parametre, FeatureExtraction'dan çıkan çıktı kanal sayısını belirler. + + hidden_size: Bu parametre, SequenceModeling'de kullanılan gizli katman boyutunu belirler. + + decode: Bu parametre, Prediction aşamasında kullanılan karakter tahmin yöntemini belirler. + + freeze_FeatureFxtraction: Bu parametre, eğitim sırasında FeatureExtraction ağındaki ağırlıkların dondurulup dondurulmayacağını belirler. Örnek olarak, eğitim süresini azaltmak için önceden eğitilmiş bir VGG ağının ağırlıklarını kullanabilir ve dondurabilirsiniz. + + freeze_SequenceModeling: Bu parametre, eğitim sırasında SequenceModeling ağındaki ağırlıkların dondurulup dondurulmayacağını belirler. Örnek olarak, daha hızlı eğitim için önceden eğitilmiş bir LSTM ağının ağırlıklarını kullanabilir ve dondurabilirsiniz. \ No newline at end of file diff --git a/trainer/config_files/train.yaml b/trainer/config_files/train.yaml new file mode 100644 index 000000000..90584a2c5 --- /dev/null +++ b/trainer/config_files/train.yaml @@ -0,0 +1,45 @@ +number: '0123456789' +symbol: "!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ €" +lang_char: 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' +experiment_name: 'custom_model' +train_data: '/home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data' +valid_data: '/home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data/val' +manualSeed: 1111 +workers: 10 +batch_size: 32 +num_iter: 200000 +valInterval: 1000 +saved_model: '/home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/saved_models/iter_130000.pth' +FT: False +optim: False # default is Adadelta +lr: 0.1 +beta1: 0.9 +rho: 0.95 +eps: 0.00000001 +grad_clip: 5 +# Data processing +select_data: '/home/musa/Desktop/Work/Mina/Project3/EasyOCR-master/trainer/all_data/train' # veri kümesi adı +batch_ratio: '876-443' +total_data_usage_ratio: 0.15 +batch_max_length: 6 # sayıların toplam hanesi + nokta + virgül +imgH: 72 +imgW: 400 +rgb: False +sensitive: True +PAD: True +contrast_adjust: 0.0 +data_filtering_off: False +# Model Architecture +Transformation: 'None' +FeatureExtraction: 'VGG' +SequenceModeling: 'BiLSTM' +Prediction: 'CTC' +num_fiducial: 16 +input_channel: 1 +output_channel: 256 +hidden_size: 256 +decode: 'greedy' +new_prediction: False +freeze_FeatureFxtraction: False +freeze_SequenceModeling: False + From e59e71256056e3ff6878eb1551e09b976e95c98e Mon Sep 17 00:00:00 2001 From: MUSA YILMAZ <95527400+yilmazmusa08@users.noreply.github.com> Date: Mon, 5 Jun 2023 15:53:05 +0300 Subject: [PATCH 03/15] Add files via upload --- trainer/saved_models/best_model.pth | Bin 0 -> 15143051 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 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