Working on fixing decoder transformer

1 files changed, 249 insertions, 182 deletions

diff --git a/notebooks/00-scratch-pad.ipynb b/notebooks/00-scratch-pad.ipynb
index 3c44f2b..8db843c 100644
--- a/notebooks/00-scratch-pad.ipynb
+++ b/notebooks/00-scratch-pad.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -34,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -43,7 +34,7 @@
        "True"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -54,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,187 +54,224 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder = Decoder(dim=128, depth=4, num_heads=8, ff_kwargs={}, attn_kwargs={}, cross_attend=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder.cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.networks.transformer.transformer import Transformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "decoder = Decoder(dim=256, depth=4, num_heads=8, ff_kwargs={}, attn_kwargs={}, cross_attend=True)"
+    "transformer_decoder = Transformer(num_tokens=90, max_seq_len=690, attn_layers=decoder, emb_dim=128, emb_dropout=0.1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Decoder(\n",
-       "  (layers): ModuleList(\n",
-       "    (0): ModuleList(\n",
-       "      (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "      (1): Attention(\n",
-       "        (qkv_fn): Sequential(\n",
-       "          (0): Linear(in_features=256, out_features=49152, bias=False)\n",
-       "          (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "Transformer(\n",
+       "  (attn_layers): Decoder(\n",
+       "    (layers): ModuleList(\n",
+       "      (0): ModuleList(\n",
+       "        (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "        (1): Attention(\n",
+       "          (qkv_fn): Sequential(\n",
+       "            (0): Linear(in_features=128, out_features=24576, bias=False)\n",
+       "            (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "          )\n",
+       "          (dropout): Dropout(p=0.0, inplace=False)\n",
+       "          (fc): Linear(in_features=8192, out_features=128, bias=True)\n",
        "        )\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (fc): Linear(in_features=16384, out_features=256, bias=True)\n",
+       "        (2): Residual()\n",
        "      )\n",
-       "      (2): Residual()\n",
-       "    )\n",
-       "    (1): ModuleList(\n",
-       "      (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "      (1): Attention(\n",
-       "        (qkv_fn): Sequential(\n",
-       "          (0): Linear(in_features=256, out_features=49152, bias=False)\n",
-       "          (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "      (1): ModuleList(\n",
+       "        (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "        (1): Attention(\n",
+       "          (qkv_fn): Sequential(\n",
+       "            (0): Linear(in_features=128, out_features=24576, bias=False)\n",
+       "            (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "          )\n",
+       "          (dropout): Dropout(p=0.0, inplace=False)\n",
+       "          (fc): Linear(in_features=8192, out_features=128, bias=True)\n",
        "        )\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (fc): Linear(in_features=16384, out_features=256, bias=True)\n",
+       "        (2): Residual()\n",
        "      )\n",
-       "      (2): Residual()\n",
-       "    )\n",
-       "    (2): ModuleList(\n",
-       "      (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "      (1): FeedForward(\n",
-       "        (mlp): Sequential(\n",
-       "          (0): GEGLU(\n",
-       "            (fc): Linear(in_features=256, out_features=2048, bias=True)\n",
+       "      (2): ModuleList(\n",
+       "        (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "        (1): FeedForward(\n",
+       "          (mlp): Sequential(\n",
+       "            (0): GEGLU(\n",
+       "              (fc): Linear(in_features=128, out_features=1024, bias=True)\n",
+       "            )\n",
+       "            (1): Dropout(p=0.0, inplace=False)\n",
+       "            (2): Linear(in_features=512, out_features=128, bias=True)\n",
        "          )\n",
-       "          (1): Dropout(p=0.0, inplace=False)\n",
-       "          (2): Linear(in_features=1024, out_features=256, bias=True)\n",
        "        )\n",
+       "        (2): Residual()\n",
        "      )\n",
-       "      (2): Residual()\n",
-       "    )\n",
-       "    (3): ModuleList(\n",
-       "      (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "      (1): Attention(\n",
-       "        (qkv_fn): Sequential(\n",
-       "          (0): Linear(in_features=256, out_features=49152, bias=False)\n",
-       "          (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "      (3): ModuleList(\n",
+       "        (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "        (1): Attention(\n",
+       "          (qkv_fn): Sequential(\n",
+       "            (0): Linear(in_features=128, out_features=24576, bias=False)\n",
+       "            (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "          )\n",
+       "          (dropout): Dropout(p=0.0, inplace=False)\n",
+       "          (fc): Linear(in_features=8192, out_features=128, bias=True)\n",
        "        )\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (fc): Linear(in_features=16384, out_features=256, bias=True)\n",
+       "        (2): Residual()\n",
        "      )\n",
-       "      (2): Residual()\n",
-       "    )\n",
-       "    (4): ModuleList(\n",
-       "      (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "      (1): Attention(\n",
-       "        (qkv_fn): Sequential(\n",
-       "          (0): Linear(in_features=256, out_features=49152, bias=False)\n",
-       "          (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "      (4): ModuleList(\n",
+       "        (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "        (1): Attention(\n",
+       "          (qkv_fn): Sequential(\n",
+       "            (0): Linear(in_features=128, out_features=24576, bias=False)\n",
+       "            (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "          )\n",
+       "          (dropout): Dropout(p=0.0, inplace=False)\n",
+       "          (fc): Linear(in_features=8192, out_features=128, bias=True)\n",
        "        )\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (fc): Linear(in_features=16384, out_features=256, bias=True)\n",
+       "        (2): Residual()\n",
        "      )\n",
-       "      (2): Residual()\n",
-       "    )\n",
-       "    (5): ModuleList(\n",
-       "      (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "      (1): FeedForward(\n",
-       "        (mlp): Sequential(\n",
-       "          (0): GEGLU(\n",
-       "            (fc): Linear(in_features=256, out_features=2048, bias=True)\n",
+       "      (5): ModuleList(\n",
+       "        (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "        (1): FeedForward(\n",
+       "          (mlp): Sequential(\n",
+       "            (0): GEGLU(\n",
+       "              (fc): Linear(in_features=128, out_features=1024, bias=True)\n",
+       "            )\n",
+       "            (1): Dropout(p=0.0, inplace=False)\n",
+       "            (2): Linear(in_features=512, out_features=128, bias=True)\n",
        "          )\n",
-       "          (1): Dropout(p=0.0, inplace=False)\n",
-       "          (2): Linear(in_features=1024, out_features=256, bias=True)\n",
        "        )\n",
+       "        (2): Residual()\n",
        "      )\n",
-       "      (2): Residual()\n",
-       "    )\n",
-       "    (6): ModuleList(\n",
-       "      (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "      (1): Attention(\n",
-       "        (qkv_fn): Sequential(\n",
-       "          (0): Linear(in_features=256, out_features=49152, bias=False)\n",
-       "          (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "      (6): ModuleList(\n",
+       "        (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "        (1): Attention(\n",
+       "          (qkv_fn): Sequential(\n",
+       "            (0): Linear(in_features=128, out_features=24576, bias=False)\n",
+       "            (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "          )\n",
+       "          (dropout): Dropout(p=0.0, inplace=False)\n",
+       "          (fc): Linear(in_features=8192, out_features=128, bias=True)\n",
        "        )\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (fc): Linear(in_features=16384, out_features=256, bias=True)\n",
+       "        (2): Residual()\n",
        "      )\n",
-       "      (2): Residual()\n",
-       "    )\n",
-       "    (7): ModuleList(\n",
-       "      (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "      (1): Attention(\n",
-       "        (qkv_fn): Sequential(\n",
-       "          (0): Linear(in_features=256, out_features=49152, bias=False)\n",
-       "          (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "      (7): ModuleList(\n",
+       "        (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "        (1): Attention(\n",
+       "          (qkv_fn): Sequential(\n",
+       "            (0): Linear(in_features=128, out_features=24576, bias=False)\n",
+       "            (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "          )\n",
+       "          (dropout): Dropout(p=0.0, inplace=False)\n",
+       "          (fc): Linear(in_features=8192, out_features=128, bias=True)\n",
        "        )\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (fc): Linear(in_features=16384, out_features=256, bias=True)\n",
+       "        (2): Residual()\n",
        "      )\n",
-       "      (2): Residual()\n",
-       "    )\n",
-       "    (8): ModuleList(\n",
-       "      (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "      (1): FeedForward(\n",
-       "        (mlp): Sequential(\n",
-       "          (0): GEGLU(\n",
-       "            (fc): Linear(in_features=256, out_features=2048, bias=True)\n",
+       "      (8): ModuleList(\n",
+       "        (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "        (1): FeedForward(\n",
+       "          (mlp): Sequential(\n",
+       "            (0): GEGLU(\n",
+       "              (fc): Linear(in_features=128, out_features=1024, bias=True)\n",
+       "            )\n",
+       "            (1): Dropout(p=0.0, inplace=False)\n",
+       "            (2): Linear(in_features=512, out_features=128, bias=True)\n",
        "          )\n",
-       "          (1): Dropout(p=0.0, inplace=False)\n",
-       "          (2): Linear(in_features=1024, out_features=256, bias=True)\n",
        "        )\n",
+       "        (2): Residual()\n",
        "      )\n",
-       "      (2): Residual()\n",
-       "    )\n",
-       "    (9): ModuleList(\n",
-       "      (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "      (1): Attention(\n",
-       "        (qkv_fn): Sequential(\n",
-       "          (0): Linear(in_features=256, out_features=49152, bias=False)\n",
-       "          (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "      (9): ModuleList(\n",
+       "        (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "        (1): Attention(\n",
+       "          (qkv_fn): Sequential(\n",
+       "            (0): Linear(in_features=128, out_features=24576, bias=False)\n",
+       "            (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "          )\n",
+       "          (dropout): Dropout(p=0.0, inplace=False)\n",
+       "          (fc): Linear(in_features=8192, out_features=128, bias=True)\n",
        "        )\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (fc): Linear(in_features=16384, out_features=256, bias=True)\n",
+       "        (2): Residual()\n",
        "      )\n",
-       "      (2): Residual()\n",
-       "    )\n",
-       "    (10): ModuleList(\n",
-       "      (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "      (1): Attention(\n",
-       "        (qkv_fn): Sequential(\n",
-       "          (0): Linear(in_features=256, out_features=49152, bias=False)\n",
-       "          (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "      (10): ModuleList(\n",
+       "        (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "        (1): Attention(\n",
+       "          (qkv_fn): Sequential(\n",
+       "            (0): Linear(in_features=128, out_features=24576, bias=False)\n",
+       "            (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n",
+       "          )\n",
+       "          (dropout): Dropout(p=0.0, inplace=False)\n",
+       "          (fc): Linear(in_features=8192, out_features=128, bias=True)\n",
        "        )\n",
-       "        (dropout): Dropout(p=0.0, inplace=False)\n",
-       "        (fc): Linear(in_features=16384, out_features=256, bias=True)\n",
+       "        (2): Residual()\n",
        "      )\n",
-       "      (2): Residual()\n",
-       "    )\n",
-       "    (11): ModuleList(\n",
-       "      (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "      (1): FeedForward(\n",
-       "        (mlp): Sequential(\n",
-       "          (0): GEGLU(\n",
-       "            (fc): Linear(in_features=256, out_features=2048, bias=True)\n",
+       "      (11): ModuleList(\n",
+       "        (0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "        (1): FeedForward(\n",
+       "          (mlp): Sequential(\n",
+       "            (0): GEGLU(\n",
+       "              (fc): Linear(in_features=128, out_features=1024, bias=True)\n",
+       "            )\n",
+       "            (1): Dropout(p=0.0, inplace=False)\n",
+       "            (2): Linear(in_features=512, out_features=128, bias=True)\n",
        "          )\n",
-       "          (1): Dropout(p=0.0, inplace=False)\n",
-       "          (2): Linear(in_features=1024, out_features=256, bias=True)\n",
        "        )\n",
+       "        (2): Residual()\n",
        "      )\n",
-       "      (2): Residual()\n",
        "    )\n",
        "  )\n",
+       "  (token_emb): Embedding(90, 128)\n",
+       "  (emb_dropout): Dropout(p=0.1, inplace=False)\n",
+       "  (pos_emb): AbsolutePositionalEmbedding(\n",
+       "    (emb): Embedding(690, 128)\n",
+       "  )\n",
+       "  (project_emb): Identity()\n",
+       "  (norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "  (logits): Linear(in_features=128, out_features=90, bias=True)\n",
        ")"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "decoder.cuda()"
+    "transformer_decoder.cuda()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,16 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from functools import partial"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -275,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,29 +317,95 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "t = torch.randn(32, 1, 576, 640).cuda()"
+    "t = torch.randn(16, 1, 576, 640).cuda()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "v(t).shape"
+    "o = v(t)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from text_recognizer.networks.encoders.efficientnet import EfficientNet"
+    "caption = torch.randint(0, 90, (16, 690)).cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([16, 360, 128])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "o.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([16, 690])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caption.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "forward() missing 2 required positional arguments: 'context' and 'context_mask'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-29-2290911ad81b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtransformer_decoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcaption\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# (1, 1024, 20000)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/text-recognizer/text_recognizer/networks/transformer/transformer.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, mask, return_embeddings, **kwargs)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject_emb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattn_layers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreturn_embeddings\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/text-recognizer/text_recognizer/networks/transformer/layers.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, context, mask, context_mask)\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlayer_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"a\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m                 \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrotary_pos_emb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrotary_pos_emb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mlayer_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m                 \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcontext_mask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: forward() missing 2 required positional arguments: 'context' and 'context_mask'"
+     ]
+    }
+   ],
+   "source": [
+    "transformer_decoder(caption, context = o) # (1, 1024, 20000)"
    ]
   },
   {
@@ -329,7 +414,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "en = EfficientNet()"
+    "from text_recognizer.networks.encoders.efficientnet import EfficientNet"
    ]
   },
   {
@@ -338,7 +423,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "(576, 640) // (8, 8)"
+    "en = EfficientNet()"
    ]
   },
   {
@@ -347,7 +432,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "(576 // 32) ** 2"
+    "en.cuda()"
    ]
   },
   {
@@ -482,24 +567,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from text_recognizer.networks.backbones.efficientnet import EfficientNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "en = EfficientNet()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "datum = torch.randn([2, 1, 576, 640])"
    ]
   },
@@ -536,7 +603,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "en(datum).shape"
+    "en(t).shape"
    ]
   },
   {
@@ -752,7 +819,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,