app.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e0730bd3-124e-47f3-bcfa-6117a8f43d90",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "import timm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2517d91-4985-4df6-af24-221afa57abd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "PILImage mode=RGB size=224x191"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create('basset.jpg')\n",
    "im.thumbnail((224, 224))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7af8e17b-a24e-4f9a-9499-7c9244eaaeae",
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = load_learner('breed_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a3fc6a3-2827-41c4-b2d8-5fe5a7d642c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "('basset_hound',\n",
       " tensor(14),\n",
       " tensor([1.5111e-07, 9.8931e-09, 2.0148e-10, 3.3501e-10, 4.5111e-09, 1.7550e-08,\n",
       "         1.0715e-08, 4.5443e-09, 8.9104e-10, 3.7553e-09, 7.5826e-08, 4.6479e-09,\n",
       "         1.0448e-06, 5.2671e-08, 9.9943e-01, 2.6312e-04, 1.9668e-10, 1.7658e-08,\n",
       "         3.0304e-04, 1.4419e-08, 3.0731e-08, 8.8281e-10, 2.2223e-07, 3.8956e-08,\n",
       "         1.9416e-08, 5.1098e-10, 7.3089e-10, 1.1494e-09, 3.0407e-09, 1.3461e-08,\n",
       "         8.7606e-07, 2.0719e-08, 1.7117e-08, 1.2087e-09, 1.1160e-09, 1.5923e-08,\n",
       "         1.9659e-10]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.predict('basset.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae9a654e-d454-4826-a23a-6920507db17a",
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = learn.dls.vocab\n",
    "\n",
    "def classify_image(img):\n",
    "    pred, idx, probs = learn.predict(img)\n",
    "    return dict(zip(categories, map(float, probs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d9fde98c-0443-4166-ad5a-1abe256664b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'Abyssinian': 1.5110809670204617e-07,\n",
       " 'Bengal': 9.893088126489147e-09,\n",
       " 'Birman': 2.0147801482739425e-10,\n",
       " 'Bombay': 3.3501063034790945e-10,\n",
       " 'British_Shorthair': 4.511082174474268e-09,\n",
       " 'Egyptian_Mau': 1.754956002741892e-08,\n",
       " 'Maine_Coon': 1.0714641618392307e-08,\n",
       " 'Persian': 4.544295606478954e-09,\n",
       " 'Ragdoll': 8.910370774550813e-10,\n",
       " 'Russian_Blue': 3.755329380794592e-09,\n",
       " 'Siamese': 7.582563910091267e-08,\n",
       " 'Sphynx': 4.647874085605963e-09,\n",
       " 'american_bulldog': 1.0448227385495557e-06,\n",
       " 'american_pit_bull_terrier': 5.267115810170253e-08,\n",
       " 'basset_hound': 0.9994311928749084,\n",
       " 'beagle': 0.00026312374393455684,\n",
       " 'boxer': 1.9668469630751417e-10,\n",
       " 'chihuahua': 1.7658045692314772e-08,\n",
       " 'english_cocker_spaniel': 0.0003030433726962656,\n",
       " 'english_setter': 1.4418649385561366e-08,\n",
       " 'german_shorthaired': 3.073101950690216e-08,\n",
       " 'great_pyrenees': 8.828088815526769e-10,\n",
       " 'havanese': 2.2223359508188878e-07,\n",
       " 'japanese_chin': 3.89556049640305e-08,\n",
       " 'keeshond': 1.941592486787158e-08,\n",
       " 'leonberger': 5.109763723254446e-10,\n",
       " 'miniature_pinscher': 7.308866289967852e-10,\n",
       " 'newfoundland': 1.149376815945402e-09,\n",
       " 'pomeranian': 3.0406710482822064e-09,\n",
       " 'pug': 1.3460747183557942e-08,\n",
       " 'saint_bernard': 8.760604259805405e-07,\n",
       " 'samoyed': 2.071886306964643e-08,\n",
       " 'scottish_terrier': 1.7117116613007965e-08,\n",
       " 'shiba_inu': 1.2086767142704957e-09,\n",
       " 'staffordshire_bull_terrier': 1.1159536628113642e-09,\n",
       " 'wheaten_terrier': 1.5922530849366012e-08,\n",
       " 'yorkshire_terrier': 1.9658942529421353e-10}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
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       "<IPython.core.display.HTML object>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "output_type": "display_data"
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      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
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     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
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       "<IPython.core.display.HTML object>"
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    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
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       "<IPython.core.display.HTML object>"
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classify_image('basset.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c6bfebd9-68e6-4252-a3a5-155211644bfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nifdi Guliyev\\anaconda3\\lib\\site-packages\\gradio\\inputs.py:259: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
      "  warnings.warn(\n",
      "C:\\Users\\Nifdi Guliyev\\anaconda3\\lib\\site-packages\\gradio\\inputs.py:262: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
      "  super().__init__(\n",
      "C:\\Users\\Nifdi Guliyev\\anaconda3\\lib\\site-packages\\gradio\\outputs.py:197: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
      "  warnings.warn(\n",
      "C:\\Users\\Nifdi Guliyev\\anaconda3\\lib\\site-packages\\gradio\\outputs.py:200: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
      "  super().__init__(num_top_classes=num_top_classes, type=type, label=label)\n"
     ]
    }
   ],
   "source": [
    "image = gr.inputs.Image((192, 192))\n",
    "label = gr.outputs.Label()\n",
    "examples = ['basset.jpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35644be6-7e82-4cab-ba36-e6545e328066",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ce5c7107-548f-486c-a7ab-7f856ca88e71",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = learn.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "55f00a09-4d80-4bed-afc8-74363e66659e",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): TimmBody(\n",
       "    (model): ConvNeXt(\n",
       "      (stem): Sequential(\n",
       "        (0): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))\n",
       "        (1): LayerNorm2d((96,), eps=1e-06, elementwise_affine=True)\n",
       "      )\n",
       "      (stages): Sequential(\n",
       "        (0): ConvNeXtStage(\n",
       "          (downsample): Identity()\n",
       "          (blocks): Sequential(\n",
       "            (0): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)\n",
       "              (norm): LayerNorm((96,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (1): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)\n",
       "              (norm): LayerNorm((96,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (2): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)\n",
       "              (norm): LayerNorm((96,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ConvNeXtStage(\n",
       "          (downsample): Sequential(\n",
       "            (0): LayerNorm2d((96,), eps=1e-06, elementwise_affine=True)\n",
       "            (1): Conv2d(96, 192, kernel_size=(2, 2), stride=(2, 2))\n",
       "          )\n",
       "          (blocks): Sequential(\n",
       "            (0): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)\n",
       "              (norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (1): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)\n",
       "              (norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (2): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)\n",
       "              (norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (2): ConvNeXtStage(\n",
       "          (downsample): Sequential(\n",
       "            (0): LayerNorm2d((192,), eps=1e-06, elementwise_affine=True)\n",
       "            (1): Conv2d(192, 384, kernel_size=(2, 2), stride=(2, 2))\n",
       "          )\n",
       "          (blocks): Sequential(\n",
       "            (0): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (1): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (2): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (3): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (4): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (5): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (6): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (7): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (8): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): ConvNeXtStage(\n",
       "          (downsample): Sequential(\n",
       "            (0): LayerNorm2d((384,), eps=1e-06, elementwise_affine=True)\n",
       "            (1): Conv2d(384, 768, kernel_size=(2, 2), stride=(2, 2))\n",
       "          )\n",
       "          (blocks): Sequential(\n",
       "            (0): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)\n",
       "              (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (1): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)\n",
       "              (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "            (2): ConvNeXtBlock(\n",
       "              (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)\n",
       "              (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "              (mlp): Mlp(\n",
       "                (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (act): GELU()\n",
       "                (drop1): Dropout(p=0.0, inplace=False)\n",
       "                (norm): Identity()\n",
       "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (shortcut): Identity()\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (norm_pre): Identity()\n",
       "      (head): NormMlpClassifierHead(\n",
       "        (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Identity())\n",
       "        (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True)\n",
       "        (flatten): Flatten(start_dim=1, end_dim=-1)\n",
       "        (pre_logits): Identity()\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "        (fc): Identity()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (1): Sequential(\n",
       "    (0): AdaptiveConcatPool2d(\n",
       "      (ap): AdaptiveAvgPool2d(output_size=1)\n",
       "      (mp): AdaptiveMaxPool2d(output_size=1)\n",
       "    )\n",
       "    (1): fastai.layers.Flatten(full=False)\n",
       "    (2): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.25, inplace=False)\n",
       "    (4): Linear(in_features=1536, out_features=512, bias=False)\n",
       "    (5): ReLU(inplace=True)\n",
       "    (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): Dropout(p=0.5, inplace=False)\n",
       "    (8): Linear(in_features=512, out_features=37, bias=False)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "06e53cca-78bd-4732-9b39-5b111d6d1348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Parameter containing:\n",
       " tensor([ 1.2555e+00,  1.9174e+00,  1.2195e+00,  1.0376e+00, -1.1278e-03,\n",
       "          7.6647e-01,  8.8708e-01,  1.6304e+00,  7.0573e-01,  3.2893e+00,\n",
       "          7.8528e-01, -1.4520e-03,  1.0007e+00, -2.2499e-03,  3.2972e+00,\n",
       "         -9.1179e-04,  1.9841e+00,  1.0208e+00,  4.4523e+00,  2.5571e-01,\n",
       "          2.7257e+00,  9.2508e-01,  1.2365e+00,  3.4305e-03,  1.7867e+00,\n",
       "          5.4309e-01,  4.6252e+00,  1.1504e-02, -5.8160e-04,  3.4505e+00,\n",
       "          1.3516e+00,  4.1271e+00,  2.6875e+00,  4.1213e+00,  3.4007e+00,\n",
       "          8.5015e-01,  7.3581e-01,  3.9801e+00,  1.2861e+00,  6.4055e-01,\n",
       "          2.6906e+00,  1.1189e+00,  1.1700e+00,  5.5229e-01,  2.3347e+00,\n",
       "          1.0214e-03,  9.6856e-01,  2.1676e-03,  1.1995e+00,  1.7876e+00,\n",
       "          4.0176e-01,  4.5011e-01,  9.7088e-01,  3.9889e+00,  6.5831e-01,\n",
       "          6.8824e-01,  9.8559e-01,  2.7048e+00,  1.2154e+00,  7.6268e-01,\n",
       "          3.3011e+00,  1.6199e+00,  9.5533e-01,  2.1204e+00,  6.2942e-01,\n",
       "          4.0345e+00,  8.9299e-01, -3.6488e-03,  4.0870e+00,  1.0654e+00,\n",
       "          1.3960e+00,  1.6692e+00,  2.3845e-04,  7.6707e-01,  8.8677e-01,\n",
       "          6.4232e-01,  1.3445e+00,  7.1611e-01,  5.4651e-01,  2.0899e+00,\n",
       "          1.1967e+00,  3.0855e-01,  2.9692e-01,  1.4698e+00,  4.0844e+00,\n",
       "         -1.1224e-03,  1.1465e+00,  3.8853e+00,  3.5999e+00,  4.8298e-01,\n",
       "          2.1720e-01,  7.3199e-05,  6.4918e-01,  3.0065e+00,  3.0471e+00,\n",
       "          4.9698e-03], requires_grad=True),\n",
       " Parameter containing:\n",
       " tensor([-9.6642e-02, -4.0686e-02,  4.1643e+00, -1.0328e-02,  4.7689e-03,\n",
       "         -2.5639e-02, -3.1176e-02, -8.1273e-02, -1.4149e-01, -6.2183e-02,\n",
       "          3.2314e-01, -3.3804e-01, -5.7259e-02, -5.2809e-03, -4.7954e-02,\n",
       "         -2.6594e-02, -4.0845e-02, -3.9094e-02,  9.9132e-03, -2.2877e-02,\n",
       "          8.6934e-03, -1.6410e-01, -4.0166e+00,  5.2939e-01, -5.3498e-01,\n",
       "          2.8044e+00,  3.7683e-02, -9.3950e-03, -2.4858e-03, -1.1685e-01,\n",
       "         -1.3918e-01,  2.0053e-02, -9.5154e-02, -1.3076e-01, -1.9308e-01,\n",
       "         -6.9149e-02, -3.7693e-02, -1.2886e-01,  1.5147e-01,  2.6744e-03,\n",
       "         -6.5373e-02,  5.7756e-02, -9.1995e-02, -1.1464e+00, -5.3775e-02,\n",
       "         -5.6114e-03, -1.8407e-01,  2.3684e-02,  3.9073e-02, -6.0639e-02,\n",
       "         -4.0841e-02, -5.6427e-02, -4.3854e-02, -1.2499e-02, -1.1132e-01,\n",
       "          6.4012e-03, -3.8068e-02, -1.5796e-01, -9.9871e-02, -1.8460e-01,\n",
       "         -1.0955e-01, -1.8181e-01, -3.2966e-02, -2.6645e-02,  1.4193e+00,\n",
       "         -3.2943e-02, -4.2977e-02, -2.6869e-01, -4.7920e-02, -1.0038e-03,\n",
       "          2.6788e-01,  1.8717e-01,  6.9689e-01, -3.0790e-01,  8.2215e-02,\n",
       "         -1.0852e+00,  1.6964e-02, -4.5317e-02, -8.0172e-02, -6.7286e-02,\n",
       "         -1.3020e-01, -1.6759e-02, -1.8897e-02, -3.9436e-02, -7.0088e-02,\n",
       "          1.2184e-02, -5.8705e-02, -3.7720e-02, -7.9094e-02, -7.3969e-02,\n",
       "         -9.9879e-03, -3.7708e-01, -1.0369e-02, -2.5772e-02, -6.2676e-02,\n",
       "         -5.5537e-04], requires_grad=True)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = m.get_submodule('0.model.stem.1')\n",
    "list(l.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ee966ef-1db9-462e-b442-a3c7f6f909a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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