demo_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "executionInfo": {
     "elapsed": 1989,
     "status": "ok",
     "timestamp": 1622662467198,
     "user": {
      "displayName": "Jingxiao Liu",
      "photoUrl": "",
      "userId": "03475133506377629126"
     },
     "user_tz": 420
    },
    "id": "2BlTsMqhuGlX"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from Dataset import *\n",
    "from model import *\n",
    "import torch.utils.data as util_data\n",
    "from torchvision import transforms,datasets\n",
    "from PIL import Image, ImageOps\n",
    "import yaml\n",
    "from train import *\n",
    "import random\n",
    "from easydict import EasyDict as edict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1622662467201,
     "user": {
      "displayName": "Jingxiao Liu",
      "photoUrl": "",
      "userId": "03475133506377629126"
     },
     "user_tz": 420
    },
    "id": "FmoK3y89-w-i"
   },
   "outputs": [],
   "source": [
    "source_train = []\n",
    "source_test = []\n",
    "target_train = []\n",
    "target_test = []\n",
    "\n",
    "image_size=28\n",
    "bt_sz = 128\n",
    "img_transform = transforms.Compose([\n",
    "    transforms.RandomCrop((image_size)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.5,0.5,0.5),\n",
    "                         std=(0.5,0.5,0.5))\n",
    "])\n",
    "# 'dataset', 'mnist_m', 'mnist_m_train'\n",
    "# 'dataset', 'mnist_m', 'mnist_m_test'\n",
    "train_list = os.path.join('dataset', 'mnist_m', 'mnist_m_train_labels.txt')\n",
    "mnist_m_train = GetLoader(\n",
    "    data_root=os.path.join('dataset', 'mnist_m', 'mnist_m_train'),\n",
    "    data_list=train_list,\n",
    "    transform=img_transform\n",
    ")\n",
    "test_list = os.path.join('dataset', 'mnist_m', 'mnist_m_test_labels.txt')\n",
    "mnist_m_test= GetLoader(\n",
    "    data_root=os.path.join('dataset', 'mnist_m', 'mnist_m_test'),\n",
    "    data_list=test_list,\n",
    "    transform=img_transform\n",
    ")\n",
    "\n",
    "source_train = [torch.utils.data.DataLoader(\n",
    "        datasets.MNIST('./dataset/', train=True, download=False,\n",
    "                      transform=transforms.Compose([\n",
    "                          transforms.ToTensor(),\n",
    "                          transforms.Normalize((0.5,), (0.5,)),\n",
    "                          transforms.Lambda(lambda x: x.repeat(3, 1, 1))\n",
    "                      ])),\n",
    "        batch_size=bt_sz, shuffle=True,drop_last=True),\n",
    "        torch.utils.data.DataLoader(\n",
    "        datasets.MNIST('./dataset/', train=True, download=False,\n",
    "                      transform=transforms.Compose([\n",
    "                          transforms.ToTensor(),\n",
    "                          transforms.Normalize((0.5,), (0.5,)),\n",
    "                          transforms.Lambda(lambda x: x.repeat(3, 1, 1))\n",
    "                      ])),\n",
    "        batch_size=bt_sz, shuffle=True,drop_last=True)]\n",
    "\n",
    "target_train = [torch.utils.data.DataLoader(\n",
    "        mnist_m_train,batch_size=bt_sz, shuffle=True,drop_last=True),\n",
    "        torch.utils.data.DataLoader(\n",
    "        mnist_m_train,batch_size=bt_sz, shuffle=True,drop_last=True)]\n",
    "\n",
    "source_test = [torch.utils.data.DataLoader(\n",
    "        datasets.MNIST('./dataset/', train=False, download=False,\n",
    "                      transform=transforms.Compose([\n",
    "                          transforms.ToTensor(),\n",
    "                          transforms.Normalize((0.5,), (0.5,)),\n",
    "                          transforms.Lambda(lambda x: x.repeat(3, 1, 1))\n",
    "                      ])),\n",
    "        batch_size=bt_sz, shuffle=False,drop_last=True),\n",
    "        torch.utils.data.DataLoader(\n",
    "        datasets.MNIST('./dataset/', train=False, download=False,\n",
    "                      transform=transforms.Compose([\n",
    "                          transforms.ToTensor(),\n",
    "                          transforms.Normalize((0.5,), (0.5,)),\n",
    "                          transforms.Lambda(lambda x: x.repeat(3, 1, 1))\n",
    "                      ])),\n",
    "        batch_size=bt_sz, shuffle=False,drop_last=True)]\n",
    "\n",
    "target_test = [torch.utils.data.DataLoader(\n",
    "        mnist_m_test,batch_size=bt_sz, shuffle=False,drop_last=True),\n",
    "        torch.utils.data.DataLoader(\n",
    "        mnist_m_test,batch_size=bt_sz, shuffle=False,drop_last=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1622662467201,
     "user": {
      "displayName": "Jingxiao Liu",
      "photoUrl": "",
      "userId": "03475133506377629126"
     },
     "user_tz": 420
    },
    "id": "eIuJVRjKyvPO",
    "outputId": "4b8d51f3-3783-487f-fa8e-fabc5d47c40e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "init_lr: 0.01\n",
      "optim: {'type': 'sgd', 'params': {'lr': 0.01, 'momentum': 0.9, 'weight_decay': 0.001, 'nesterov': True}}\n",
      "lr_scheduler: {'type': 'inv', 'gamma': 0.001, 'decay_rate': 0.75}\n"
     ]
    }
   ],
   "source": [
    "with open('dann.yml','r') as f:\n",
    "  cfg = edict(yaml.load(f))\n",
    "for x in cfg:\n",
    "  print('{}: {}'.format(x, cfg[x]))"
   ]
  },
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    "executionInfo": {
     "elapsed": 861874,
     "status": "ok",
     "timestamp": 1622666457775,
     "user": {
      "displayName": "Jingxiao Liu",
      "photoUrl": "",
      "userId": "03475133506377629126"
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    "id": "bMniwKrcyzYd",
    "outputId": "b2df5e86-bde5-4c82-c2a5-0f6aa897846b"
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   "outputs": [
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      "{'Tgt accuracy': array(0.784375, dtype=float32)}\n",
      "{'Tgt accuracy 2': array(0.85725445, dtype=float32)}\n",
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      "{'Tgt accuracy': array(0.7959821, dtype=float32)}\n",
      "{'Tgt accuracy 2': array(0.87165177, dtype=float32)}\n",
      "{'Src accuracy': array(0.9872129, dtype=float32)}\n",
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     "output_type": "stream",
     "text": [
      "{'Tgt accuracy': array(0.8257812, dtype=float32)}\n",
      "{'Tgt accuracy 2': array(0.8953125, dtype=float32)}\n",
      "{'Src accuracy': array(0.9660624, dtype=float32)}\n",
      "{'Src accuracy 2': array(0.9787494, dtype=float32)}\n"
     ]
    },
    {
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       "HBox(children=(FloatProgress(value=0.0, description='Train epoch = 31', layout=Layout(flex='2'), max=460.0, st…"
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    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finish train\n"
     ]
    }
   ],
   "source": [
    "max_iter = 15000.0\n",
    "width = 128\n",
    "base_net = 'mnist_net'\n",
    "class_num = [5,2]\n",
    "\n",
    "model_instance = MUD(base_net_sha=base_net,\\\n",
    "                    width=width, use_gpu=True, class_num=class_num,\\\n",
    "                    max_iter=max_iter, alpha=10., \\\n",
    "                    low_value=0., high_value=1.,\\\n",
    "                    gamma=1., weight=1.)\n",
    "\n",
    "param_groups = model_instance.get_parameter_list()\n",
    "group_ratios = [group['lr'] for group in param_groups]\n",
    "\n",
    "optimizer = torch.optim.SGD(param_groups, **cfg.optim.params)\n",
    "lr_scheduler = INVScheduler(gamma=cfg.lr_scheduler.gamma,\n",
    "                decay_rate=cfg.lr_scheduler.decay_rate,\n",
    "                init_lr=cfg.init_lr)\n",
    "\n",
    "all_probs_tgt,all_probs_tgt_2,all_probs_src,all_probs_src_2,\\\n",
    "cls_losses,tsf_losses,oth_diffs_s,oth_diffs_t,target_acc1,\\\n",
    "target_acc2, target_acc2_ = train(model_instance,\\\n",
    "              source_train, target_train, target_test,\\\n",
    "              group_ratios, max_iter=max_iter, optimizer=optimizer,\\\n",
    "              lr_scheduler=lr_scheduler, eval_interval=100)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1622666457775,
     "user": {
      "displayName": "Jingxiao Liu",
      "photoUrl": "",
      "userId": "03475133506377629126"
     },
     "user_tz": 420
    },
    "id": "vzWFflsayYwL",
    "outputId": "d0a032fa-8a6f-4d78-f80f-8fb08abfcb77"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HMD(\n",
       "  (base_network_sha): mynet(\n",
       "    (conv1): Conv2d(3, 64, kernel_size=(5, 5), stride=(1, 1))\n",
       "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu1): ReLU()\n",
       "    (mp1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (conv2): Conv2d(64, 48, kernel_size=(5, 5), stride=(1, 1))\n",
       "    (relu2): ReLU()\n",
       "    (mp2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (hier_extractor_layer): Sequential(\n",
       "    (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "    (1): BatchNorm1d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "    (3): Dropout(p=0.25, inplace=False)\n",
       "  )\n",
       "  (classifier_layer): ModuleList(\n",
       "    (0): Sequential(\n",
       "      (0): Linear(in_features=768, out_features=128, bias=True)\n",
       "      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU()\n",
       "      (3): Dropout(p=0.25, inplace=False)\n",
       "      (4): Linear(in_features=128, out_features=128, bias=True)\n",
       "      (5): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (6): ReLU()\n",
       "      (7): Dropout(p=0.25, inplace=False)\n",
       "      (8): Linear(in_features=128, out_features=5, bias=True)\n",
       "    )\n",
       "    (1): Sequential(\n",
       "      (0): Linear(in_features=768, out_features=128, bias=True)\n",
       "      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU()\n",
       "      (3): Dropout(p=0.25, inplace=False)\n",
       "      (4): Linear(in_features=128, out_features=128, bias=True)\n",
       "      (5): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (6): ReLU()\n",
       "      (7): Dropout(p=0.25, inplace=False)\n",
       "      (8): Linear(in_features=128, out_features=2, bias=True)\n",
       "    )\n",
       "  )\n",
       "  (domain_classifier_layer): Sequential(\n",
       "    (0): Linear(in_features=768, out_features=128, bias=True)\n",
       "    (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "    (3): Dropout(p=0.25, inplace=False)\n",
       "    (4): Linear(in_features=128, out_features=2, bias=True)\n",
       "  )\n",
       "  (domain_classifier_layer2): Sequential(\n",
       "    (0): Linear(in_features=768, out_features=128, bias=True)\n",
       "    (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "    (3): Dropout(p=0.25, inplace=False)\n",
       "    (4): Linear(in_features=128, out_features=2, bias=True)\n",
       "  )\n",
       "  (softmax): Softmax(dim=1)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_instance.c_net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "executionInfo": {
     "elapsed": 422,
     "status": "ok",
     "timestamp": 1622666458195,
     "user": {
      "displayName": "Jingxiao Liu",
      "photoUrl": "",
      "userId": "03475133506377629126"
     },
     "user_tz": 420
    },
    "id": "r_sleCGSkZ-W",
    "outputId": "bc64bfe1-7be4-4297-fc06-add1291f4b7a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,5))\n",
    "plt.plot(np.arange(1,15200,100),target_acc1,'k',label='digit comparison task')\n",
    "plt.plot(np.arange(1,15200,100),target_acc2,'r',label='odd-even task')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1622666458196,
     "user": {
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