trocr_seq2seq_playground.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "94a78791",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import TrOCRProcessor, VisionEncoderDecoderModel\n",
    "from utils import OCRTokenizer, OCRDataset, collate_batch, save_experiment_info\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torchinfo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f84086",
   "metadata": {},
   "source": [
    "TrOCR Captcha finetuning \\\n",
    "https://colab.research.google.com/drive/14MfFkhgPS63RJcP7rpBOK6OII_y34jx_?usp=sharing#scrollTo=3VazhIpdx7mW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6c9262d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(' ', 10061), ('8', 3028), ('S', 3012), ('b', 3006), ('V', 2992)]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = OCRTokenizer('./synthetic_dataset/train/labels.txt')\n",
    "tokenizer.counter.most_common(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "8d76957c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 1500, 5000, 5000)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset = OCRDataset('./synthetic_dataset/train/', tokenizer, do_train_transform=True, image_size=(384, 384))  # need quadratic images for vit, 64 h for others\n",
    "val_dataset = OCRDataset('./synthetic_dataset/val/', tokenizer, do_train_transform=False, image_size=(384, 384))\n",
    "test_dataset = OCRDataset('./synthetic_dataset/test_clean/', tokenizer, do_train_transform=False, image_size=(384, 384))\n",
    "test_captchas_dataset = OCRDataset('./synthetic_dataset/test_captchas/', tokenizer, do_train_transform=False, image_size=(384, 384))\n",
    "len(train_dataset), len(val_dataset), len(test_dataset), len(test_captchas_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "19fd399f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.\n",
      "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-printed and are newly initialized: ['encoder.pooler.dense.bias', 'encoder.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "VisionEncoderDecoderModel(\n",
       "  (encoder): ViTModel(\n",
       "    (embeddings): ViTEmbeddings(\n",
       "      (patch_embeddings): ViTPatchEmbeddings(\n",
       "        (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
       "      )\n",
       "      (dropout): Dropout(p=0.0, inplace=False)\n",
       "    )\n",
       "    (encoder): ViTEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): ViTLayer(\n",
       "          (attention): ViTAttention(\n",
       "            (attention): ViTSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): ViTSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ViTIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): ViTOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (1): ViTLayer(\n",
       "          (attention): ViTAttention(\n",
       "            (attention): ViTSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): ViTSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ViTIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): ViTOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (2): ViTLayer(\n",
       "          (attention): ViTAttention(\n",
       "            (attention): ViTSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): ViTSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ViTIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): ViTOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (3): ViTLayer(\n",
       "          (attention): ViTAttention(\n",
       "            (attention): ViTSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): ViTSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ViTIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): ViTOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (4): ViTLayer(\n",
       "          (attention): ViTAttention(\n",
       "            (attention): ViTSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): ViTSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ViTIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): ViTOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (5): ViTLayer(\n",
       "          (attention): ViTAttention(\n",
       "            (attention): ViTSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): ViTSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ViTIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): ViTOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (6): ViTLayer(\n",
       "          (attention): ViTAttention(\n",
       "            (attention): ViTSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): ViTSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ViTIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): ViTOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (7): ViTLayer(\n",
       "          (attention): ViTAttention(\n",
       "            (attention): ViTSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): ViTSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ViTIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): ViTOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (8): ViTLayer(\n",
       "          (attention): ViTAttention(\n",
       "            (attention): ViTSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): ViTSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ViTIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): ViTOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (9): ViTLayer(\n",
       "          (attention): ViTAttention(\n",
       "            (attention): ViTSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): ViTSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ViTIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): ViTOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (10): ViTLayer(\n",
       "          (attention): ViTAttention(\n",
       "            (attention): ViTSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): ViTSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ViTIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): ViTOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (11): ViTLayer(\n",
       "          (attention): ViTAttention(\n",
       "            (attention): ViTSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (output): ViTSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ViTIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): ViTOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "          )\n",
       "          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    (pooler): ViTPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (decoder): TrOCRForCausalLM(\n",
       "    (model): TrOCRDecoderWrapper(\n",
       "      (decoder): TrOCRDecoder(\n",
       "        (embed_tokens): Embedding(50265, 1024, padding_idx=1)\n",
       "        (embed_positions): TrOCRLearnedPositionalEmbedding(514, 1024)\n",
       "        (layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "        (layers): ModuleList(\n",
       "          (0): TrOCRDecoderLayer(\n",
       "            (self_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (activation_fn): GELUActivation()\n",
       "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (encoder_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "          (1): TrOCRDecoderLayer(\n",
       "            (self_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (activation_fn): GELUActivation()\n",
       "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (encoder_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "          (2): TrOCRDecoderLayer(\n",
       "            (self_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (activation_fn): GELUActivation()\n",
       "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (encoder_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "          (3): TrOCRDecoderLayer(\n",
       "            (self_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (activation_fn): GELUActivation()\n",
       "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (encoder_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "          (4): TrOCRDecoderLayer(\n",
       "            (self_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (activation_fn): GELUActivation()\n",
       "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (encoder_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "          (5): TrOCRDecoderLayer(\n",
       "            (self_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (activation_fn): GELUActivation()\n",
       "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (encoder_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "          (6): TrOCRDecoderLayer(\n",
       "            (self_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (activation_fn): GELUActivation()\n",
       "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (encoder_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "          (7): TrOCRDecoderLayer(\n",
       "            (self_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (activation_fn): GELUActivation()\n",
       "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (encoder_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "          (8): TrOCRDecoderLayer(\n",
       "            (self_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (activation_fn): GELUActivation()\n",
       "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (encoder_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "          (9): TrOCRDecoderLayer(\n",
       "            (self_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (activation_fn): GELUActivation()\n",
       "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (encoder_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "          (10): TrOCRDecoderLayer(\n",
       "            (self_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (activation_fn): GELUActivation()\n",
       "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (encoder_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "          (11): TrOCRDecoderLayer(\n",
       "            (self_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (activation_fn): GELUActivation()\n",
       "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (encoder_attn): TrOCRAttention(\n",
       "              (k_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (v_proj): Linear(in_features=768, out_features=1024, bias=True)\n",
       "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            )\n",
       "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (output_projection): Linear(in_features=1024, out_features=50265, bias=False)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-printed')\n",
    "model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-printed')\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "f208be90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "=====================================================================================\n",
       "Layer (type:depth-idx)                                       Param #\n",
       "=====================================================================================\n",
       "VisionEncoderDecoderModel                                    --\n",
       "├─ViTModel: 1-1                                              --\n",
       "│    └─ViTEmbeddings: 2-1                                    443,904\n",
       "│    │    └─ViTPatchEmbeddings: 3-1                          590,592\n",
       "│    │    └─Dropout: 3-2                                     --\n",
       "│    └─ViTEncoder: 2-2                                       --\n",
       "│    │    └─ModuleList: 3-3                                  85,026,816\n",
       "│    └─LayerNorm: 2-3                                        1,536\n",
       "│    └─ViTPooler: 2-4                                        --\n",
       "│    │    └─Linear: 3-4                                      590,592\n",
       "│    │    └─Tanh: 3-5                                        --\n",
       "├─TrOCRForCausalLM: 1-2                                      --\n",
       "│    └─TrOCRDecoderWrapper: 2-5                              --\n",
       "│    │    └─TrOCRDecoder: 3-6                                247,268,352\n",
       "│    └─Linear: 2-6                                           51,471,360\n",
       "=====================================================================================\n",
       "Total params: 385,393,152\n",
       "Trainable params: 385,393,152\n",
       "Non-trainable params: 0\n",
       "====================================================================================="
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torchinfo.summary(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c3c94740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 384, 384)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor((test_dataset[1]['image']*255).to(torch.uint8)).pixel_values[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "c0f7f09d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7217aad810>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_image = test_captchas_dataset[np.random.randint(0, len(test_captchas_dataset))]['image']\n",
    "plt.imshow(test_image.permute(1, 2, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "fc2b6ed2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 384, 384])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset[0]['image'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "cf74bdfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PreTrainedTokenizerFast(name_or_path='microsoft/trocr-base-printed', vocab_size=50265, model_max_len=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'eos_token': AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'unk_token': AddedToken(\"<unk>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'sep_token': AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'pad_token': AddedToken(\"<pad>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'cls_token': AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'mask_token': AddedToken(\"<mask>\", rstrip=False, lstrip=True, single_word=False, normalized=True)})"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "a668d382",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
     ]
    }
   ],
   "source": [
    "generated_ids = model.generate(test_image.unsqueeze(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "35120ec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['KOSQ42RL3']"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)\n",
    "generated_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3770814",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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