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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "7c02ae76-b540-4b16-9492-e9210b3b9249",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.environ['CUDA_VISIBLE_DEVICE'] = ''\n",
+ "import random\n",
+ "\n",
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import numpy as np\n",
+ "from omegaconf import OmegaConf\n",
+ "import torch\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "from importlib.util import find_spec\n",
+ "if find_spec(\"text_recognizer\") is None:\n",
+ " import sys\n",
+ " sys.path.append('..')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "ccdb6dde-47e5-429a-88f2-0764fb7e259a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from hydra import compose, initialize\n",
+ "from omegaconf import OmegaConf\n",
+ "from hydra.utils import instantiate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "3cf50475-39f2-4642-a7d1-5bcbc0a036f7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = \"../training/conf/network/mammut_lines.yaml\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "e52ecb01-c975-4e55-925d-1182c7aea473",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(path, \"rb\") as f:\n",
+ " cfg = OmegaConf.load(f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "f939aa37-7b1d-45cc-885c-323c4540bda1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'_target_': 'text_recognizer.network.mammut.MaMMUT', 'encoder': {'_target_': 'text_recognizer.network.vit.Vit', 'image_height': 56, 'image_width': 1024, 'patch_height': 56, 'patch_width': 8, 'dim': 512, 'encoder': {'_target_': 'text_recognizer.network.transformer.encoder.Encoder', 'dim': 512, 'heads': 12, 'dim_head': 64, 'ff_mult': 4, 'depth': 4, 'dropout_rate': 0.1}, 'channels': 1}, 'image_attn_pool': {'_target_': 'text_recognizer.network.transformer.attention.Attention', 'dim': 512, 'heads': 8, 'causal': False, 'dim_head': 64, 'ff_mult': 4, 'dropout_rate': 0.0, 'use_flash': True, 'norm_context': True, 'rotary_emb': None}, 'decoder': {'_target_': 'text_recognizer.network.transformer.decoder.Decoder', 'dim': 512, 'ff_mult': 4, 'heads': 12, 'dim_head': 64, 'depth': 6, 'dropout_rate': 0.1}, 'dim': 512, 'dim_latent': 512, 'num_tokens': 58, 'pad_index': 3, 'num_image_queries': 256}"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cfg"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "aaeab329-aeb0-4a1b-aa35-5a2aab81b1d0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "net = instantiate(cfg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from torchinfo import summary"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "7daf1f49",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "====================================================================================================\n",
+ "Layer (type:depth-idx) Output Shape Param #\n",
+ "====================================================================================================\n",
+ "MaMMUT [1, 89, 58] 627,712\n",
+ "├─Vit: 1-1 [1, 128, 512] --\n",
+ "│ └─Sequential: 2-1 [1, 128, 512] --\n",
+ "│ │ └─Rearrange: 3-1 [1, 128, 448] --\n",
+ "│ │ └─LayerNorm: 3-2 [1, 128, 448] 896\n",
+ "│ │ └─Linear: 3-3 [1, 128, 512] 229,888\n",
+ "│ │ └─LayerNorm: 3-4 [1, 128, 512] 1,024\n",
+ "│ └─Encoder: 2-2 [1, 128, 512] --\n",
+ "│ │ └─ModuleList: 3-5 -- --\n",
+ "│ │ │ └─Attention: 4-1 [1, 128, 512] 4,724,224\n",
+ "│ │ │ └─Attention: 4-2 [1, 128, 512] 4,724,224\n",
+ "│ │ │ └─Attention: 4-3 [1, 128, 512] 4,724,224\n",
+ "│ │ │ └─Attention: 4-4 [1, 128, 512] 4,724,224\n",
+ "│ │ └─LayerNorm: 3-6 [1, 128, 512] 1,024\n",
+ "├─Attention: 1-2 [1, 257, 512] --\n",
+ "│ └─LayerNorm: 2-3 [1, 257, 512] 1,024\n",
+ "│ └─Linear: 2-4 [1, 257, 512] 262,144\n",
+ "│ └─LayerNorm: 2-5 [1, 128, 512] 1,024\n",
+ "│ └─Linear: 2-6 [1, 128, 1024] 524,288\n",
+ "│ └─Attend: 2-7 [1, 8, 257, 64] --\n",
+ "│ └─Linear: 2-8 [1, 257, 512] 262,144\n",
+ "│ └─Sequential: 2-9 [1, 257, 512] --\n",
+ "│ │ └─Linear: 3-7 [1, 257, 4096] 2,101,248\n",
+ "│ │ └─SwiGLU: 3-8 [1, 257, 2048] --\n",
+ "│ │ └─Linear: 3-9 [1, 257, 512] 1,049,088\n",
+ "├─LayerNorm: 1-3 [1, 257, 512] 1,024\n",
+ "├─Embedding: 1-4 [1, 89, 512] 29,696\n",
+ "├─Decoder: 1-5 [1, 89, 512] --\n",
+ "│ └─ModuleList: 2-10 -- --\n",
+ "│ │ └─ModuleList: 3-10 -- --\n",
+ "│ │ │ └─Attention: 4-5 [1, 89, 512] 4,724,224\n",
+ "│ │ │ └─Attention: 4-6 [1, 89, 512] 4,724,224\n",
+ "│ │ └─ModuleList: 3-11 -- --\n",
+ "│ │ │ └─Attention: 4-7 [1, 89, 512] 4,724,224\n",
+ "│ │ │ └─Attention: 4-8 [1, 89, 512] 4,724,224\n",
+ "│ │ └─ModuleList: 3-12 -- --\n",
+ "│ │ │ └─Attention: 4-9 [1, 89, 512] 4,724,224\n",
+ "│ │ │ └─Attention: 4-10 [1, 89, 512] 4,724,224\n",
+ "│ │ └─ModuleList: 3-13 -- --\n",
+ "│ │ │ └─Attention: 4-11 [1, 89, 512] 4,724,224\n",
+ "│ │ │ └─Attention: 4-12 [1, 89, 512] 4,724,224\n",
+ "│ │ └─ModuleList: 3-14 -- --\n",
+ "│ │ │ └─Attention: 4-13 [1, 89, 512] 4,724,224\n",
+ "│ │ │ └─Attention: 4-14 [1, 89, 512] 4,724,224\n",
+ "│ │ └─ModuleList: 3-15 -- --\n",
+ "│ │ │ └─Attention: 4-15 [1, 89, 512] 4,724,224\n",
+ "│ │ │ └─Attention: 4-16 [1, 89, 512] 4,724,224\n",
+ "│ └─LayerNorm: 2-11 [1, 89, 512] 1,024\n",
+ "├─Sequential: 1-6 [1, 89, 58] --\n",
+ "│ └─LayerNorm: 2-12 [1, 89, 512] 1,024\n",
+ "│ └─Linear: 2-13 [1, 89, 58] 29,696\n",
+ "====================================================================================================\n",
+ "Total params: 80,711,552\n",
+ "Trainable params: 80,711,552\n",
+ "Non-trainable params: 0\n",
+ "Total mult-adds (M): 80.08\n",
+ "====================================================================================================\n",
+ "Input size (MB): 0.23\n",
+ "Forward/backward pass size (MB): 131.05\n",
+ "Params size (MB): 320.34\n",
+ "Estimated Total Size (MB): 451.61\n",
+ "===================================================================================================="
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "summary(net, ((1, 1, 56, 1024), (1, 89)), device=\"cpu\", depth=4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "166bf656-aba6-4654-a530-dfce12666297",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t = net(torch.randn(1, 1, 56, 1024), torch.randint(1, 4, (1, 4)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "43d9af25-9872-497d-8796-4835a65262ed",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([1, 4, 58])"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "t.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "63ac7f1b-0eb1-4625-96b8-467846eb7ae6",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-09-20 01:32:00 +0000