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-rw-r--r--notebooks/00-scratch-pad.ipynb431
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,
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-11-10 08:11:54 +0000