Add paper files. Add notebooks.

Author: evertonaleixo

drawio/adapters.drawio

@@ -0,0 +1,150 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ef3ea784-8148-4b92-98ae-bd4a41422cba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "# Adiciona o diretório pai ao caminho de pesquisa de módulos\n",
+    "sys.path.append(os.path.abspath(os.path.join('..')))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "59152fa1-060b-407c-b960-681046bd7a60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchvision.models import vgg19\n",
+    "from continuous_lora.models.lora_vgg19 import LoraVGG19"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "277ba4d8-bd72-4abc-9e50-e81fb7693aa7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_model = vgg19()\n",
+    "model = LoraVGG19(\n",
+    "    model=base_model,\n",
+    "    masks=[[0,1,2,3,4], [5,6,7,8,9]],\n",
+    "    adapt_last_n_conv=2,\n",
+    "    adapt_last_n_linear=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6799c67f-fe0d-471d-91e6-17571aba243c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LoraVGG19(\n",
+       "  (features): Sequential(\n",
+       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (1): ReLU(inplace=True)\n",
+       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (3): ReLU(inplace=True)\n",
+       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (6): ReLU(inplace=True)\n",
+       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (8): ReLU(inplace=True)\n",
+       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (11): ReLU(inplace=True)\n",
+       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (13): ReLU(inplace=True)\n",
+       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (15): ReLU(inplace=True)\n",
+       "    (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (17): ReLU(inplace=True)\n",
+       "    (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (20): ReLU(inplace=True)\n",
+       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (22): ReLU(inplace=True)\n",
+       "    (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (24): ReLU(inplace=True)\n",
+       "    (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (26): ReLU(inplace=True)\n",
+       "    (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (29): ReLU(inplace=True)\n",
+       "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (31): ReLU(inplace=True)\n",
+       "    (32): ContinuousConvLoRALayer(\n",
+       "      (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    )\n",
+       "    (33): ReLU(inplace=True)\n",
+       "    (34): ContinuousConvLoRALayer(\n",
+       "      (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    )\n",
+       "    (35): ReLU(inplace=True)\n",
+       "    (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "  )\n",
+       "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
+       "  (classifier): Sequential(\n",
+       "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
+       "    (1): ReLU(inplace=True)\n",
+       "    (2): Dropout(p=0.5, inplace=False)\n",
+       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
+       "    (4): ReLU(inplace=True)\n",
+       "    (5): Dropout(p=0.5, inplace=False)\n",
+       "    (6): ContinuousLinearLoRALayer(in_features=4096, out_features=1000, bias=True)\n",
+       "  )\n",
+       "  (softmax): Softmax(dim=-1)\n",
+       ")"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b59e6b06-a339-43a8-a2b5-900c17c884a3",
+   "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": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}