08，hugmodel_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8068f820",
   "metadata": {},
   "source": [
    "# HugModel Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84d3f83",
   "metadata": {},
   "source": [
    "You can install torchkeras using pip:\n",
    "`pip install torchkeras`\n",
    "\n",
    "Here is the mnist example using torchkeras.HugModel. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e915b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys \n",
    "sys.path.append(\"..\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a1920bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset,DataLoader\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e73ca619",
   "metadata": {},
   "source": [
    "### 1, prepare data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bda3c5b9-1211-4bde-bf80-f2c96785d9c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25ab4939",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision \n",
    "from torchvision import transforms\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor()])\n",
    "ds_train = torchvision.datasets.MNIST(root=\"mnist/\",train=True,download=True,transform=transform)\n",
    "ds_train = torch.utils.data.Subset(ds_train,indices=range(0,len(ds_train),30))\n",
    "\n",
    "ds_val = torchvision.datasets.MNIST(root=\"mnist/\",train=False,download=True,transform=transform)\n",
    "ds_val = torch.utils.data.Subset(ds_val,indices=range(0,len(ds_val),30))\n",
    "\n",
    "dl_train =  torch.utils.data.DataLoader(ds_train, batch_size=128, shuffle=True)\n",
    "dl_val =  torch.utils.data.DataLoader(ds_val, batch_size=128, shuffle=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91ec88bd-614b-481e-9c38-2a09a977b82e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85a45abd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([128, 1, 28, 28])\n",
      "torch.Size([128])\n"
     ]
    }
   ],
   "source": [
    "for features,labels in dl_train:\n",
    "    break\n",
    "print(features.shape)\n",
    "print(labels.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c484c0b4",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<Figure size 800x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "#查看部分样本\n",
    "from matplotlib import pyplot as plt \n",
    "\n",
    "plt.figure(figsize=(8,8)) \n",
    "for i in range(9):\n",
    "    img,label = ds_train[i]\n",
    "    img = img.permute(1,2,0)\n",
    "    ax=plt.subplot(3,3,i+1)\n",
    "    ax.imshow(img.numpy())\n",
    "    ax.set_title(\"label = %d\"%label)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([]) \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "485da125",
   "metadata": {},
   "source": [
    "### 2, create the  model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdfe5da6-774a-46a1-998d-b07a3bec4970",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "196d911d-7745-450e-ae1d-ebcc59db600a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_net():\n",
    "    net = nn.Sequential()\n",
    "    net.add_module(\"conv1\",nn.Conv2d(in_channels=1,out_channels=32,kernel_size = 3))\n",
    "    net.add_module(\"pool1\",nn.MaxPool2d(kernel_size = 2,stride = 2))\n",
    "    net.add_module(\"conv2\",nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5))\n",
    "    net.add_module(\"pool2\",nn.MaxPool2d(kernel_size = 2,stride = 2))\n",
    "    net.add_module(\"dropout\",nn.Dropout2d(p = 0.1))\n",
    "    net.add_module(\"adaptive_pool\",nn.AdaptiveMaxPool2d((1,1)))\n",
    "    net.add_module(\"flatten\",nn.Flatten())\n",
    "    net.add_module(\"linear1\",nn.Linear(64,32))\n",
    "    net.add_module(\"relu\",nn.ReLU())\n",
    "    net.add_module(\"linear2\",nn.Linear(32,10))\n",
    "    return net\n",
    "\n",
    "class Accuracy(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        self.correct = nn.Parameter(torch.tensor(0.0),requires_grad=False)\n",
    "        self.total = nn.Parameter(torch.tensor(0.0),requires_grad=False)\n",
    "\n",
    "    def forward(self, preds: torch.Tensor, targets: torch.Tensor):\n",
    "        preds = preds.argmax(dim=-1)\n",
    "        m = (preds == targets).sum()\n",
    "        n = targets.shape[0] \n",
    "        self.correct += m \n",
    "        self.total += n\n",
    "        \n",
    "        return m/n\n",
    "\n",
    "    def compute(self):\n",
    "        return self.correct.float() / self.total \n",
    "    \n",
    "    def reset(self):\n",
    "        self.correct -= self.correct\n",
    "        self.total -= self.total\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9d44f4-3194-4f5e-8a96-c446b476cd4a",
   "metadata": {},
   "source": [
    "### 3, train the  model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cd28353-e6d7-4d64-b887-b537539464c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "net = create_net()\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "metrics_dict = {\"acc\":Accuracy()}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d482bc42-eeda-47fb-a556-a9225156d8f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import reload \n",
    "from torchkeras import hugmodel\n",
    "reload(hugmodel)\n",
    "from torchkeras.hugmodel import HugModel \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f3514c0-1a80-4f8f-a3c8-c23a4a939a09",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from torchkeras import HugModel\n",
    "model = HugModel(net,loss_fn,metrics_dict)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2fb4edb-bfa8-4772-a767-64567d08076a",
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   "outputs": [
    {
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      "/Users/liangyun2/miniforge3/envs/tf26/lib/python3.9/site-packages/transformers/optimization.py:407: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
     ]
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       "      [1500/1600 00:58 < 00:03, 25.44 it/s, Epoch 93/100]\n",
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   "source": [
    "%matplotlib inline \n",
    "model.fit(train_data=dl_train,\n",
    "          val_data=dl_val,\n",
    "          output_dir='output_dir',\n",
    "          epochs=100,\n",
    "          learning_rate=1e-3,\n",
    "          logging_steps=100,\n",
    "          monitor='val_loss',\n",
    "          patience=5,\n",
    "          mode='min',\n",
    "          plot=True,\n",
    "          wandb=False,\n",
    "          #no_cuda=True\n",
    "          #use_mps_device=True\n",
    "         )\n"
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   "cell_type": "code",
   "execution_count": null,
   "id": "43a2b26e-7283-4aa7-88c6-910698e5242a",
   "metadata": {},
   "outputs": [],
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   "metadata": {},
   "source": [
    "### 4, evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "62f8cf7b",
   "metadata": {},
   "outputs": [
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    "model.evaluate(dl_val)\n"
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   "cell_type": "code",
   "execution_count": 22,
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   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 22,
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    "model.evaluate(dl_train)"
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0405a48f-000b-40aa-8869-409d571f512e",
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   "metadata": {},
   "source": [
    "### 5, use the model"
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   "id": "c7b6d2c3-049f-45d8-a63f-86bcb535b9f5",
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": 23,
   "id": "91b21a4e-be1c-4fba-ae99-7fe4962c67b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "y_pred =  3\n",
      "y_prob =  0.9989043474197388\n"
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = model.net\n",
    "net.eval();\n",
    "\n",
    "device = torch.device('cpu')\n",
    "img,label = ds_train[1]\n",
    "tensor = img.to(device)\n",
    "\n",
    "y_pred = torch.argmax(net(tensor[None,...])) \n",
    "y_prob = torch.softmax(net(tensor[None,...]),dim=-1).max() \n",
    "\n",
    "img = img.permute(1,2,0)\n",
    "plt.imshow(img)\n",
    "print('y_pred = ', y_pred.item())\n",
    "print('y_prob = ', y_prob.item())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81da8622",
   "metadata": {},
   "source": [
    "### 6, save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "45089d19",
   "metadata": {},
   "outputs": [],
   "source": [
    "#torch.save(model.net.state_dict(),'checkpoint.pt')\n",
    "model.save_ckpt('checkpoint.pt')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3655c76e-792b-48cc-a02a-cfe0e95ec6d9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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