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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 torch\n",
- "import numpy as np\n",
- "from omegaconf import OmegaConf\n",
- "\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": 3,
- "id": "3cf50475-39f2-4642-a7d1-5bcbc0a036f7",
- "metadata": {},
- "outputs": [],
- "source": [
- "path = \"../training/conf/experiment/conv_transformer_paragraphs.yaml\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 86,
- "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": 87,
- "id": "f939aa37-7b1d-45cc-885c-323c4540bda1",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'dim': 192, 'num_heads': 4, 'dim_head': 64, 'dropout_rate': 0.05, '_target_': 'text_recognizer.networks.transformer.attention.Attention', 'causal': False}"
- ]
- },
- "execution_count": 87,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "cfg.network.decoder.cross_attn"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 88,
- "id": "aaeab329-aeb0-4a1b-aa35-5a2aab81b1d0",
- "metadata": {},
- "outputs": [],
- "source": [
- "net = instantiate(cfg.network)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 89,
- "id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
- "metadata": {},
- "outputs": [],
- "source": [
- "from torchinfo import summary"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 91,
- "id": "66118c10-2e59-469f-99d6-ddea4bfd0d73",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "====================================================================================================\n",
- "Layer (type:depth-idx) Output Shape Param #\n",
- "====================================================================================================\n",
- "ConvTransformer -- --\n",
- "├─EfficientNet: 1 -- --\n",
- "│ └─ModuleList: 2-1 -- --\n",
- "├─Decoder: 1 -- --\n",
- "│ └─ModuleList: 2-2 -- --\n",
- "│ │ └─ModuleList: 3-1 -- --\n",
- "│ │ └─ModuleList: 3-2 -- --\n",
- "│ │ └─ModuleList: 3-3 -- --\n",
- "│ │ └─ModuleList: 3-4 -- --\n",
- "│ │ └─ModuleList: 3-5 -- --\n",
- "│ │ └─ModuleList: 3-6 -- --\n",
- "│ │ └─ModuleList: 3-7 -- --\n",
- "│ │ └─ModuleList: 3-8 -- --\n",
- "│ │ └─ModuleList: 3-9 -- --\n",
- "├─EfficientNet: 1-1 [2, 1280, 18, 20] --\n",
- "│ └─Sequential: 2-3 [2, 32, 288, 320] --\n",
- "│ │ └─ZeroPad2d: 3-10 [2, 1, 577, 641] --\n",
- "│ │ └─Conv2d: 3-11 [2, 32, 288, 320] 288\n",
- "│ │ └─BatchNorm2d: 3-12 [2, 32, 288, 320] 64\n",
- "│ │ └─Mish: 3-13 [2, 32, 288, 320] --\n",
- "│ └─ModuleList: 2-1 -- --\n",
- "│ │ └─MBConvBlock: 3-14 [2, 16, 288, 320] --\n",
- "│ │ │ └─Sequential: 4-1 [2, 32, 288, 320] 352\n",
- "│ │ │ └─Sequential: 4-2 [2, 32, 288, 320] 552\n",
- "│ │ │ └─Sequential: 4-3 [2, 16, 288, 320] 544\n",
- "│ │ └─MBConvBlock: 3-15 [2, 24, 144, 160] --\n",
- "│ │ │ └─Sequential: 4-4 [2, 96, 288, 320] 1,728\n",
- "│ │ │ └─Sequential: 4-5 [2, 96, 144, 160] 1,056\n",
- "│ │ │ └─Sequential: 4-6 [2, 96, 144, 160] 4,728\n",
- "│ │ │ └─Sequential: 4-7 [2, 24, 144, 160] 2,352\n",
- "│ │ └─MBConvBlock: 3-16 [2, 24, 144, 160] --\n",
- "│ │ │ └─Sequential: 4-8 [2, 144, 144, 160] 3,744\n",
- "│ │ │ └─Sequential: 4-9 [2, 144, 144, 160] 1,584\n",
- "│ │ │ └─Sequential: 4-10 [2, 144, 144, 160] 10,548\n",
- "│ │ │ └─Sequential: 4-11 [2, 24, 144, 160] 3,504\n",
- "│ │ └─MBConvBlock: 3-17 [2, 40, 72, 80] --\n",
- "│ │ │ └─Sequential: 4-12 [2, 144, 144, 160] 3,744\n",
- "│ │ │ └─Sequential: 4-13 [2, 144, 72, 80] 3,888\n",
- "│ │ │ └─Sequential: 4-14 [2, 144, 72, 80] 10,548\n",
- "│ │ │ └─Sequential: 4-15 [2, 40, 72, 80] 5,840\n",
- "│ │ └─MBConvBlock: 3-18 [2, 40, 72, 80] --\n",
- "│ │ │ └─Sequential: 4-16 [2, 240, 72, 80] 10,080\n",
- "│ │ │ └─Sequential: 4-17 [2, 240, 72, 80] 6,480\n",
- "│ │ │ └─Sequential: 4-18 [2, 240, 72, 80] 29,100\n",
- "│ │ │ └─Sequential: 4-19 [2, 40, 72, 80] 9,680\n",
- "│ │ └─MBConvBlock: 3-19 [2, 80, 36, 40] --\n",
- "│ │ │ └─Sequential: 4-20 [2, 240, 72, 80] 10,080\n",
- "│ │ │ └─Sequential: 4-21 [2, 240, 36, 40] 2,640\n",
- "│ │ │ └─Sequential: 4-22 [2, 240, 36, 40] 29,100\n",
- "│ │ │ └─Sequential: 4-23 [2, 80, 36, 40] 19,360\n",
- "│ │ └─MBConvBlock: 3-20 [2, 80, 36, 40] --\n",
- "│ │ │ └─Sequential: 4-24 [2, 480, 36, 40] 39,360\n",
- "│ │ │ └─Sequential: 4-25 [2, 480, 36, 40] 5,280\n",
- "│ │ │ └─Sequential: 4-26 [2, 480, 36, 40] 115,800\n",
- "│ │ │ └─Sequential: 4-27 [2, 80, 36, 40] 38,560\n",
- "│ │ └─MBConvBlock: 3-21 [2, 80, 36, 40] --\n",
- "│ │ │ └─Sequential: 4-28 [2, 480, 36, 40] 39,360\n",
- "│ │ │ └─Sequential: 4-29 [2, 480, 36, 40] 5,280\n",
- "│ │ │ └─Sequential: 4-30 [2, 480, 36, 40] 115,800\n",
- "│ │ │ └─Sequential: 4-31 [2, 80, 36, 40] 38,560\n",
- "│ │ └─MBConvBlock: 3-22 [2, 112, 36, 40] --\n",
- "│ │ │ └─Sequential: 4-32 [2, 480, 36, 40] 39,360\n",
- "│ │ │ └─Sequential: 4-33 [2, 480, 36, 40] 12,960\n",
- "│ │ │ └─Sequential: 4-34 [2, 480, 36, 40] 115,800\n",
- "│ │ │ └─Sequential: 4-35 [2, 112, 36, 40] 53,984\n",
- "│ │ └─MBConvBlock: 3-23 [2, 112, 36, 40] --\n",
- "│ │ │ └─Sequential: 4-36 [2, 672, 36, 40] 76,608\n",
- "│ │ │ └─Sequential: 4-37 [2, 672, 36, 40] 18,144\n",
- "│ │ │ └─Sequential: 4-38 [2, 672, 36, 40] 226,632\n",
- "│ │ │ └─Sequential: 4-39 [2, 112, 36, 40] 75,488\n",
- "│ │ └─MBConvBlock: 3-24 [2, 112, 36, 40] --\n",
- "│ │ │ └─Sequential: 4-40 [2, 672, 36, 40] 76,608\n",
- "│ │ │ └─Sequential: 4-41 [2, 672, 36, 40] 18,144\n",
- "│ │ │ └─Sequential: 4-42 [2, 672, 36, 40] 226,632\n",
- "│ │ │ └─Sequential: 4-43 [2, 112, 36, 40] 75,488\n",
- "│ │ └─MBConvBlock: 3-25 [2, 192, 18, 20] --\n",
- "│ │ │ └─Sequential: 4-44 [2, 672, 36, 40] 76,608\n",
- "│ │ │ └─Sequential: 4-45 [2, 672, 18, 20] 18,144\n",
- "│ │ │ └─Sequential: 4-46 [2, 672, 18, 20] 226,632\n",
- "│ │ │ └─Sequential: 4-47 [2, 192, 18, 20] 129,408\n",
- "│ │ └─MBConvBlock: 3-26 [2, 192, 18, 20] --\n",
- "│ │ │ └─Sequential: 4-48 [2, 1152, 18, 20] 223,488\n",
- "│ │ │ └─Sequential: 4-49 [2, 1152, 18, 20] 31,104\n",
- "│ │ │ └─Sequential: 4-50 [2, 1152, 18, 20] 664,992\n",
- "│ │ │ └─Sequential: 4-51 [2, 192, 18, 20] 221,568\n",
- "│ │ └─MBConvBlock: 3-27 [2, 192, 18, 20] --\n",
- "│ │ │ └─Sequential: 4-52 [2, 1152, 18, 20] 223,488\n",
- "│ │ │ └─Sequential: 4-53 [2, 1152, 18, 20] 31,104\n",
- "│ │ │ └─Sequential: 4-54 [2, 1152, 18, 20] 664,992\n",
- "│ │ │ └─Sequential: 4-55 [2, 192, 18, 20] 221,568\n",
- "│ │ └─MBConvBlock: 3-28 [2, 192, 18, 20] --\n",
- "│ │ │ └─Sequential: 4-56 [2, 1152, 18, 20] 223,488\n",
- "│ │ │ └─Sequential: 4-57 [2, 1152, 18, 20] 31,104\n",
- "│ │ │ └─Sequential: 4-58 [2, 1152, 18, 20] 664,992\n",
- "│ │ │ └─Sequential: 4-59 [2, 192, 18, 20] 221,568\n",
- "│ │ └─MBConvBlock: 3-29 [2, 320, 18, 20] --\n",
- "│ │ │ └─Sequential: 4-60 [2, 1152, 18, 20] 223,488\n",
- "│ │ │ └─Sequential: 4-61 [2, 1152, 18, 20] 12,672\n",
- "│ │ │ └─Sequential: 4-62 [2, 1152, 18, 20] 664,992\n",
- "│ │ │ └─Sequential: 4-63 [2, 320, 18, 20] 369,280\n",
- "│ └─Sequential: 2-4 [2, 1280, 18, 20] --\n",
- "│ │ └─Conv2d: 3-30 [2, 1280, 18, 20] 409,600\n",
- "│ │ └─BatchNorm2d: 3-31 [2, 1280, 18, 20] 2,560\n",
- "├─Sequential: 1-2 [2, 192, 360] --\n",
- "│ └─Conv2d: 2-5 [2, 192, 18, 20] 245,952\n",
- "│ └─AxialPositionalEmbedding: 2-6 [2, 192, 18, 20] 7,296\n",
- "│ └─Flatten: 2-7 [2, 192, 360] --\n",
- "├─Embedding: 1-3 [1, 682, 192] 11,136\n",
- "├─Decoder: 1-4 [2, 682, 192] --\n",
- "│ └─ModuleList: 2-2 -- --\n",
- "│ │ └─ModuleList: 3-1 -- --\n",
- "│ │ │ └─ScaleNorm: 4-64 [1, 682, 192] 1\n",
- "│ │ │ └─LocalAttention: 4-65 [1, 682, 192] 196,800\n",
- "│ │ │ └─Residual: 4-66 [1, 682, 192] --\n",
- "│ │ └─ModuleList: 3-2 -- --\n",
- "│ │ │ └─ScaleNorm: 4-67 [1, 682, 192] 1\n",
- "│ │ │ └─Attention: 4-68 [2, 682, 192] 196,800\n",
- "│ │ │ └─Residual: 4-69 [2, 682, 192] --\n",
- "│ │ └─ModuleList: 3-3 -- --\n",
- "│ │ │ └─ScaleNorm: 4-70 [2, 682, 192] 1\n",
- "│ │ │ └─FeedForward: 4-71 [2, 682, 192] 444,096\n",
- "│ │ │ └─Residual: 4-72 [2, 682, 192] --\n",
- "│ │ └─ModuleList: 3-4 -- --\n",
- "│ │ │ └─ScaleNorm: 4-73 [2, 682, 192] 1\n",
- "│ │ │ └─LocalAttention: 4-74 [2, 682, 192] 196,800\n",
- "│ │ │ └─Residual: 4-75 [2, 682, 192] --\n",
- "│ │ └─ModuleList: 3-5 -- --\n",
- "│ │ │ └─ScaleNorm: 4-76 [2, 682, 192] 1\n",
- "│ │ │ └─Attention: 4-77 [2, 682, 192] 196,800\n",
- "│ │ │ └─Residual: 4-78 [2, 682, 192] --\n",
- "│ │ └─ModuleList: 3-6 -- --\n",
- "│ │ │ └─ScaleNorm: 4-79 [2, 682, 192] 1\n",
- "│ │ │ └─FeedForward: 4-80 [2, 682, 192] 444,096\n",
- "│ │ │ └─Residual: 4-81 [2, 682, 192] --\n",
- "│ │ └─ModuleList: 3-7 -- --\n",
- "│ │ │ └─ScaleNorm: 4-82 [2, 682, 192] 1\n",
- "│ │ │ └─Attention: 4-83 [2, 682, 192] 196,800\n",
- "│ │ │ └─Residual: 4-84 [2, 682, 192] --\n",
- "│ │ └─ModuleList: 3-8 -- --\n",
- "│ │ │ └─ScaleNorm: 4-85 [2, 682, 192] 1\n",
- "│ │ │ └─Attention: 4-86 [2, 682, 192] 196,800\n",
- "│ │ │ └─Residual: 4-87 [2, 682, 192] --\n",
- "│ │ └─ModuleList: 3-9 -- --\n",
- "│ │ │ └─ScaleNorm: 4-88 [2, 682, 192] 1\n",
- "│ │ │ └─FeedForward: 4-89 [2, 682, 192] 444,096\n",
- "│ │ │ └─Residual: 4-90 [2, 682, 192] --\n",
- "├─Linear: 1-5 [2, 682, 58] 11,194\n",
- "====================================================================================================\n",
- "Total params: 9,930,947\n",
- "Trainable params: 9,930,947\n",
- "Non-trainable params: 0\n",
- "Total mult-adds (G): 11.45\n",
- "====================================================================================================\n",
- "Input size (MB): 2.95\n",
- "Forward/backward pass size (MB): 2048.49\n",
- "Params size (MB): 39.72\n",
- "Estimated Total Size (MB): 2091.17\n",
- "===================================================================================================="
- ]
- },
- "execution_count": 91,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "summary(net, ((2, 1, 576, 640), (1, 682)), device=\"cpu\", depth=4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "b13ac47c-322d-47d4-bcee-43e5341f74a7",
- "metadata": {},
- "outputs": [],
- "source": [
- "start_tokens = torch.ones(1, 1).long()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "55a16f5d-2b27-4a12-b5bb-eb079784b0ae",
- "metadata": {},
- "outputs": [],
- "source": [
- "num_dims = len(start_tokens.shape)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "46c65400-fa47-4c10-9edd-8416e6a1185a",
- "metadata": {},
- "outputs": [],
- "source": [
- "if num_dims == 1:\n",
- " start_tokens = start_tokens[None, :]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "1dfa0b95-a075-4121-b2bf-f1a8100b10fd",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "torch.Size([1, 1])"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "start_tokens.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "c85a357c-f6af-42c5-b714-89df024c29e3",
- "metadata": {},
- "outputs": [],
- "source": [
- "b, t = start_tokens.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "0ba293f4-e08d-4aaa-94d5-da4899f9b592",
- "metadata": {},
- "outputs": [],
- "source": [
- "out = start_tokens"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "a8225f98-c5e9-4da2-b756-75599fa8e044",
- "metadata": {},
- "outputs": [],
- "source": [
- "input_mask = torch.full_like(out, True, dtype=torch.bool, device=out.device)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "63dfcfd7-6b93-49ac-a0ab-59be53fa0853",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor([[True]])"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "input_mask"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "a752bfcf-f323-43bc-a910-fec4695150e0",
- "metadata": {},
- "outputs": [],
- "source": [
- "x = out[:, -200:]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "4b1b1989-930a-48c5-a7b3-746289107b97",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor([[1]])"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "x"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "513f27bb-2ae1-42a0-8de9-9ae39fdfff32",
- "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": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-09-20 08:43:15 +0000