{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7cd0d8fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7195af18",
   "metadata": {},
   "source": [
    "## Gradio Pets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "44eb0ad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "import timm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3295ef11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "PILImage mode=RGB size=224x149"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create('basset.jpg')\n",
    "im.thumbnail((224,224))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae2bc6ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6e0bf9da",
   "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-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",
       " TensorBase(14),\n",
       " TensorBase([1.8399e-06, 6.9743e-05, 1.2756e-05, 3.8912e-06, 5.5747e-07, 1.9816e-05,\n",
       "         3.3903e-06, 1.2291e-05, 8.8069e-06, 3.3049e-07, 4.7154e-06, 1.0780e-06,\n",
       "         5.2836e-04, 3.7090e-05, 9.8938e-01, 9.0365e-03, 8.2160e-05, 6.5614e-07,\n",
       "         9.1344e-05, 2.7471e-06, 2.9094e-06, 8.0382e-07, 3.3430e-06, 3.9379e-05,\n",
       "         5.8145e-06, 1.6686e-06, 1.6690e-07, 4.5227e-05, 7.4895e-07, 1.8120e-07,\n",
       "         5.3700e-04, 1.2602e-05, 1.7675e-05, 1.2343e-05, 1.8821e-05, 5.7948e-07,\n",
       "         5.5970e-07]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0419ed3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "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": 7,
   "id": "762dec00",
   "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-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': 1.8398773136141244e-06,\n",
       " 'Bengal': 6.974329153308645e-05,\n",
       " 'Birman': 1.275579688808648e-05,\n",
       " 'Bombay': 3.8912407944735605e-06,\n",
       " 'British_Shorthair': 5.574654551310232e-07,\n",
       " 'Egyptian_Mau': 1.9816458006971516e-05,\n",
       " 'Maine_Coon': 3.3902545055752853e-06,\n",
       " 'Persian': 1.2291030543565284e-05,\n",
       " 'Ragdoll': 8.806910955172498e-06,\n",
       " 'Russian_Blue': 3.3048647196665115e-07,\n",
       " 'Siamese': 4.715384420705959e-06,\n",
       " 'Sphynx': 1.078016225619649e-06,\n",
       " 'american_bulldog': 0.0005283569917082787,\n",
       " 'american_pit_bull_terrier': 3.709043448907323e-05,\n",
       " 'basset_hound': 0.9893821477890015,\n",
       " 'beagle': 0.00903652049601078,\n",
       " 'boxer': 8.215981506509706e-05,\n",
       " 'chihuahua': 6.561360805790173e-07,\n",
       " 'english_cocker_spaniel': 9.134367428487167e-05,\n",
       " 'english_setter': 2.7471251087263227e-06,\n",
       " 'german_shorthaired': 2.9093755529174814e-06,\n",
       " 'great_pyrenees': 8.038204555305128e-07,\n",
       " 'havanese': 3.3430071653128834e-06,\n",
       " 'japanese_chin': 3.937914880225435e-05,\n",
       " 'keeshond': 5.814476935483981e-06,\n",
       " 'leonberger': 1.6686294657120015e-06,\n",
       " 'miniature_pinscher': 1.669043996344044e-07,\n",
       " 'newfoundland': 4.5227454393170774e-05,\n",
       " 'pomeranian': 7.489487643397297e-07,\n",
       " 'pug': 1.812025658409766e-07,\n",
       " 'saint_bernard': 0.0005370019935071468,\n",
       " 'samoyed': 1.2601573871506844e-05,\n",
       " 'scottish_terrier': 1.767518369888421e-05,\n",
       " 'shiba_inu': 1.2342902664386202e-05,\n",
       " 'staffordshire_bull_terrier': 1.88211470231181e-05,\n",
       " 'wheaten_terrier': 5.794777848677768e-07,\n",
       " 'yorkshire_terrier': 5.596969572252419e-07}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "930cf172",
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "image = gr.inputs.Image(shape=(192, 192))\n",
    "label = gr.outputs.Label()\n",
    "examples = ['basset.jpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4f463e23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:3000/\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<fastapi.applications.FastAPI at 0x7f591050c970>,\n",
       " 'http://127.0.0.1:3000/',\n",
       " None)"
      ]
     },
     "execution_count": 9,
     "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-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"
    }
   ],
   "source": [
    "#export\n",
    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "82774c08",
   "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",
       "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\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",
       "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (drop2): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (drop_path): Identity()\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (norm_pre): Identity()\n",
       "      (head): Sequential(\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",
       "        (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): 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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = learn.model\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "10d7900d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Parameter containing:\n",
       " tensor([ 1.2545e+00,  1.9196e+00,  1.2201e+00,  1.0390e+00, -1.6480e-03,\n",
       "          7.6568e-01,  8.8830e-01,  1.6302e+00,  7.0489e-01,  3.2909e+00,\n",
       "          7.8756e-01, -1.2321e-03,  1.0008e+00, -1.1701e-03,  3.2963e+00,\n",
       "          7.5332e-04,  1.9848e+00,  1.0214e+00,  4.4530e+00,  2.5485e-01,\n",
       "          2.7261e+00,  9.2749e-01,  1.2365e+00,  4.6786e-03,  1.7861e+00,\n",
       "          5.4500e-01,  4.6252e+00,  1.1814e-02, -8.0696e-04,  3.4503e+00,\n",
       "          1.3520e+00,  4.1267e+00,  2.6889e+00,  4.1214e+00,  3.4020e+00,\n",
       "          8.4680e-01,  7.3639e-01,  3.9801e+00,  1.2857e+00,  6.4153e-01,\n",
       "          2.6896e+00,  1.1183e+00,  1.1701e+00,  5.5256e-01,  2.3371e+00,\n",
       "          2.6110e-04,  9.7016e-01,  2.1527e-03,  1.1990e+00,  1.7883e+00,\n",
       "          4.0231e-01,  4.4849e-01,  9.7238e-01,  3.9889e+00,  6.5864e-01,\n",
       "          6.8973e-01,  9.8424e-01,  2.7063e+00,  1.2161e+00,  7.5966e-01,\n",
       "          3.3019e+00,  1.6209e+00,  9.5479e-01,  2.1214e+00,  6.2982e-01,\n",
       "          4.0345e+00,  8.9406e-01, -1.5776e-03,  4.0855e+00,  1.0646e+00,\n",
       "          1.3953e+00,  1.6694e+00,  7.7575e-04,  7.6740e-01,  8.8671e-01,\n",
       "          6.4291e-01,  1.3444e+00,  7.1629e-01,  5.4538e-01,  2.0897e+00,\n",
       "          1.1951e+00,  3.0924e-01,  2.9660e-01,  1.4705e+00,  4.0818e+00,\n",
       "         -1.9466e-03,  1.1466e+00,  3.8855e+00,  3.6003e+00,  4.8230e-01,\n",
       "          2.1677e-01,  1.2715e-03,  6.5100e-01,  3.0062e+00,  3.0463e+00,\n",
       "          6.9039e-03], requires_grad=True),\n",
       " Parameter containing:\n",
       " tensor([-9.7076e-02, -4.1602e-02,  4.1634e+00, -1.0902e-02,  2.5195e-03,\n",
       "         -2.6698e-02, -3.1112e-02, -8.0897e-02, -1.3977e-01, -6.1426e-02,\n",
       "          3.2092e-01, -3.3970e-01, -5.7320e-02, -5.0723e-03, -4.5248e-02,\n",
       "         -2.6788e-02, -4.0929e-02, -3.8162e-02,  8.6140e-03, -2.3497e-02,\n",
       "          9.3659e-03, -1.6219e-01, -4.0165e+00,  5.3178e-01, -5.3446e-01,\n",
       "          2.8025e+00,  3.7767e-02, -8.2848e-03, -1.0448e-03, -1.1741e-01,\n",
       "         -1.3899e-01,  1.9646e-02, -9.6837e-02, -1.3024e-01, -1.9224e-01,\n",
       "         -6.6514e-02, -3.5839e-02, -1.2878e-01,  1.5046e-01,  7.7289e-04,\n",
       "         -6.4686e-02,  5.7553e-02, -9.2985e-02, -1.1460e+00, -5.4285e-02,\n",
       "         -5.4245e-03, -1.8200e-01,  2.2406e-02,  3.9471e-02, -5.9339e-02,\n",
       "         -4.1768e-02, -5.6190e-02, -4.3428e-02, -1.2965e-02, -1.1264e-01,\n",
       "          4.9255e-03, -3.7258e-02, -1.5767e-01, -9.7796e-02, -1.8840e-01,\n",
       "         -1.1216e-01, -1.8182e-01, -3.2907e-02, -2.8298e-02,  1.4188e+00,\n",
       "         -3.3801e-02, -4.1863e-02, -2.6832e-01, -4.7449e-02, -4.5676e-05,\n",
       "          2.6773e-01,  1.8772e-01,  6.9789e-01, -3.0746e-01,  8.3766e-02,\n",
       "         -1.0845e+00,  1.5364e-02, -4.4824e-02, -7.7240e-02, -6.7920e-02,\n",
       "         -1.3150e-01, -1.6358e-02, -1.7351e-02, -3.9939e-02, -7.2383e-02,\n",
       "          1.0359e-02, -5.8904e-02, -3.8567e-02, -7.9783e-02, -7.3791e-02,\n",
       "         -1.0318e-02, -3.7690e-01, -9.9740e-03, -2.7374e-02, -6.3630e-02,\n",
       "          1.5712e-03], 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": 26,
   "id": "008537b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Parameter containing:\n",
       " tensor([[ 2.2773e-02, -1.6051e-03,  4.0450e-02,  ...,  1.7370e-03,\n",
       "          -4.5070e-02,  8.0949e-03],\n",
       "         [-1.4383e-01,  1.6965e-02,  2.5983e-02,  ...,  1.2606e-02,\n",
       "          -1.0443e-01,  5.6370e-02],\n",
       "         [-6.5471e-02, -3.2719e-02,  5.6796e-03,  ..., -4.1571e-02,\n",
       "           6.5921e-02, -4.0347e-02],\n",
       "         ...,\n",
       "         [-8.8080e-03,  6.9815e-02,  7.1424e-05,  ...,  4.0177e-03,\n",
       "           4.1478e-02, -1.9052e-02],\n",
       "         [ 2.0792e-03,  3.2267e-02,  2.9801e-02,  ..., -2.9897e-02,\n",
       "          -3.0278e-02,  5.5432e-02],\n",
       "         [ 1.2097e-01, -3.5444e-02, -4.6078e-03,  ..., -6.3829e-03,\n",
       "           2.3691e-02, -1.1242e-02]], requires_grad=True),\n",
       " Parameter containing:\n",
       " tensor([-0.4047, -0.7418, -0.4235, -0.1650, -0.3028, -0.1898, -0.5534, -0.6271,\n",
       "         -0.3008, -0.4254, -0.5997, -0.4107, -0.2172, -1.7935, -0.3170, -0.1163,\n",
       "         -0.4482, -0.2846, -0.4342, -0.4945, -0.4065, -1.1402, -0.6754, -1.7237,\n",
       "         -0.2955, -0.2654, -0.2187, -0.3914, -0.4150, -0.4772,  0.2365, -0.7542,\n",
       "         -0.5852, -0.1820, -1.5272, -0.3626, -2.4689, -2.3461, -0.6109, -0.4115,\n",
       "         -0.6964, -0.5764, -0.5878, -0.0318, -2.0354, -0.2859, -0.3953, -0.8402,\n",
       "         -2.2398, -1.0876, -0.2295, -0.9004, -0.7584, -0.8833, -0.3755, -0.4549,\n",
       "         -0.3835, -0.4047, -2.0231, -1.0263, -0.4106, -1.1564, -0.2224, -0.4250,\n",
       "         -0.2494, -0.4222, -0.0975, -1.4017, -0.6887, -0.4370, -0.2932, -0.4641,\n",
       "         -0.4958, -1.2534, -1.0720, -1.2966, -0.6276, -1.4161, -2.3080, -2.4538,\n",
       "         -0.4259, -0.9987, -0.4638, -0.3147, -0.2416, -0.8744, -0.2829, -1.4208,\n",
       "         -0.3257, -0.3202, -0.0602, -0.1896, -0.2497, -0.6129, -0.2976, -2.1465,\n",
       "         -0.4128, -0.3675, -1.9815, -0.3815, -0.3785, -0.2292, -0.3700, -0.3256,\n",
       "         -0.5584, -2.4192, -0.4590, -1.7748, -0.3996, -0.4092, -0.3518, -0.5332,\n",
       "         -1.6534, -1.8191,  0.6263, -0.4058,  0.5872, -2.2074, -0.2438, -2.4540,\n",
       "         -0.2283, -0.6865,  0.6988,  0.6477, -0.6445, -0.3454, -0.3275, -0.5701,\n",
       "         -0.5173, -0.2774, -0.4090, -0.3018, -0.4874, -0.4954, -0.4073, -0.4356,\n",
       "         -0.5103, -0.4128, -2.0919, -0.2825, -0.5830, -1.5834,  0.6139, -0.8506,\n",
       "         -0.4669, -2.1358, -0.3417, -0.3766, -0.3345, -0.3961, -0.3886, -0.5668,\n",
       "         -0.2224, -1.3059, -0.4601, -0.3928, -0.4665, -0.4214, -0.4755, -0.2865,\n",
       "         -1.5804, -0.1787, -0.4368, -0.3173,  1.5732, -0.4046, -0.4839, -0.2576,\n",
       "         -0.5611, -0.4265, -0.2578, -0.3176, -0.4620, -1.9553, -1.9146, -0.3961,\n",
       "          0.3988, -2.3520, -0.9689, -0.2831, -1.9000, -0.4180,  0.0160, -1.1111,\n",
       "         -0.4924, -0.3177, -1.8912, -0.3101, -0.8137, -2.3345, -0.3843, -0.3847,\n",
       "         -0.1974, -0.4444, -1.6233, -2.5485, -0.3178, -1.2715, -1.1479,  0.6149,\n",
       "         -0.3749, -0.3952, -2.0747, -0.4657, -0.3782, -0.4958, -0.3281, -1.9219,\n",
       "         -2.0018, -0.5307, -0.2555, -1.1161, -0.3516, -2.2185, -1.1394,  0.5366,\n",
       "         -0.3218, -2.0387, -0.4656,  0.1850, -0.5830, -0.3129,  0.6182, -0.2124,\n",
       "         -2.3538, -0.9700, -0.9784, -0.3668, -0.4503, -1.9564, -0.2662, -1.1754,\n",
       "         -0.4200, -0.9024, -0.3604, -0.5172, -1.1882, -0.4191, -0.4770, -1.5558,\n",
       "         -0.4011, -0.6518, -0.4817, -0.2422,  0.6909, -0.5080, -0.4303, -0.6068,\n",
       "         -0.4001, -0.3329, -0.3596, -1.6108, -0.2371, -0.2467, -0.4545,  0.1808,\n",
       "         -0.3225, -0.3918, -0.3514, -0.3756, -1.2178, -0.4000, -0.3578, -0.2883,\n",
       "         -1.7485, -0.2364, -0.1599, -0.2640, -0.9769, -1.3066, -0.4148, -0.2663,\n",
       "         -0.3933, -0.4628, -0.2174,  0.2141, -0.5733, -0.2766, -0.3658, -0.5171,\n",
       "         -0.3484, -0.3365, -0.6445,  0.6866, -0.3738, -0.2902, -2.0863, -0.4882,\n",
       "         -0.2597, -1.0497, -1.6616, -0.3399, -0.5111, -0.5661, -0.3029, -0.5048,\n",
       "         -0.2877, -0.2841, -0.1981, -0.6910, -0.2872, -2.1120, -0.8928, -0.2299,\n",
       "         -1.5010, -0.4734, -2.2293, -0.4020, -0.2925, -0.4198,  0.6646, -0.3047,\n",
       "         -0.1687, -0.3750, -0.6434, -2.3348, -0.3102, -1.2732, -0.8192, -1.0592,\n",
       "         -0.0931, -1.6385,  0.3426, -0.8484, -0.4910, -0.5002, -1.0631, -0.3532,\n",
       "         -1.1562, -0.3843, -0.3172, -0.6432, -0.9083, -0.6567, -0.6489,  0.6336,\n",
       "         -0.2663, -1.3203, -1.1623, -1.2032, -2.0576, -0.3001, -1.3597, -0.4614,\n",
       "         -0.5024, -0.4949, -0.3158, -0.3273, -0.2668, -0.4280, -0.3296, -0.3011,\n",
       "         -1.6635,  0.6434, -0.9455,  0.6097, -0.4234,  0.3918, -0.4943, -0.4285,\n",
       "         -0.2588, -0.4951, -2.1992, -0.2601, -0.3935, -0.4564, -0.5817, -0.3487,\n",
       "         -0.7372, -0.3589, -0.4894, -2.0108,  0.4556, -0.8057, -1.7749, -0.3511,\n",
       "         -0.5359, -0.2100, -0.3956, -0.4780, -1.1457, -0.3976, -2.2114, -0.2840],\n",
       "        requires_grad=True)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = m.get_submodule('0.model.stages.0.blocks.1.mlp.fc1')\n",
    "list(l.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff9d5c73",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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