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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "7c02ae76-b540-4b16-9492-e9210b3b9249",
"metadata": {
"tags": []
},
"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",
"\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": {
"tags": []
},
"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": {
"tags": []
},
"outputs": [],
"source": [
"path = \"../training/conf/network/convnext.yaml\""
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "e52ecb01-c975-4e55-925d-1182c7aea473",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"with open(path, \"rb\") as f:\n",
" cfg = OmegaConf.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "f939aa37-7b1d-45cc-885c-323c4540bda1",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'_target_': 'text_recognizer.network.convnext.convnext.ConvNext', 'dim': 8, 'dim_mults': [2, 8], 'depths': [2, 2], 'attn': {'_target_': 'text_recognizer.network.convnext.transformer.Transformer', 'attn': {'_target_': 'text_recognizer.network.convnext.transformer.Attention', 'dim': 64, 'heads': 4, 'dim_head': 64, 'scale': 8}, 'ff': {'_target_': 'text_recognizer.network.convnext.transformer.FeedForward', 'dim': 64, 'mult': 4}}}"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cfg"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "c9589350",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"net = instantiate(cfg)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from torchinfo import summary"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "25759b7b-8deb-4163-b75d-a1357c9fe88f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"====================================================================================================\n",
"Layer (type:depth-idx) Output Shape Param #\n",
"====================================================================================================\n",
"ConvNext [2, 64, 14, 256] --\n",
"├─Conv2d: 1-1 [2, 8, 56, 1024] 400\n",
"├─ModuleList: 1-2 -- 30,512\n",
"├─Transformer: 1-3 [2, 64, 14, 256] 98,688\n",
"├─LayerNorm: 1-4 [2, 64, 14, 256] 64\n",
"====================================================================================================\n",
"Total params: 129,664\n",
"Trainable params: 129,664\n",
"Non-trainable params: 0\n",
"Total mult-adds (G): 2.00\n",
"====================================================================================================\n",
"Input size (MB): 0.46\n",
"Forward/backward pass size (MB): 260.57\n",
"Params size (MB): 0.52\n",
"Estimated Total Size (MB): 261.55\n",
"===================================================================================================="
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(net, (2, 1, 56, 1024), device=\"cpu\", depth=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "05c1d499",
"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
}
|
