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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
}
|
