{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import sys\n",
    "sys.path.insert(1, '../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from models.resnet_simclr import ResNetSimCLR\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "out_dim = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _load_stl10(prefix=\"train\"):\n",
    "    X_train = np.fromfile('../data/stl10_binary/' + prefix + '_X.bin', dtype=np.uint8)\n",
    "    y_train = np.fromfile('../data/stl10_binary/' + prefix + '_y.bin', dtype=np.uint8)\n",
    "\n",
    "    X_train = np.reshape(X_train, (-1, 3, 96, 96))\n",
    "    X_train = np.transpose(X_train, (0, 3, 2, 1))\n",
    "    print(\"{} images\".format(prefix))\n",
    "    print(X_train.shape)\n",
    "    print(y_train.shape)\n",
    "    return X_train, y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train images\n",
      "(5000, 96, 96, 3)\n",
      "(5000,)\n"
     ]
    }
   ],
   "source": [
    "# load STL-10 train data\n",
    "X_train, y_train = _load_stl10(\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test images\n",
      "(8000, 96, 96, 3)\n",
      "(8000,)\n"
     ]
    }
   ],
   "source": [
    "# load STL-10 test data\n",
    "X_test, y_test = _load_stl10(\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test protocol #1 PCA features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA features\n",
      "(5000, 64)\n",
      "(8000, 64)\n"
     ]
    }
   ],
   "source": [
    "scaler = preprocessing.StandardScaler()\n",
    "scaler.fit(X_train.reshape((X_train.shape[0],-1)))\n",
    "\n",
    "pca = PCA(n_components=64)\n",
    "\n",
    "X_train_pca = pca.fit_transform(scaler.transform(X_train.reshape(X_train.shape[0], -1)))\n",
    "X_test_pca = pca.transform(scaler.transform(X_test.reshape(X_test.shape[0], -1)))\n",
    "\n",
    "print(\"PCA features\")\n",
    "print(X_train_pca.shape)\n",
    "print(X_test_pca.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA feature evaluation\n",
      "Train score: 0.396\n",
      "Test score: 0.3565\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=0, max_iter=1000, solver='lbfgs', C=1.0)\n",
    "clf.fit(X_train_pca, y_train)\n",
    "print(\"PCA feature evaluation\")\n",
    "print(\"Train score:\", clf.score(X_train_pca, y_train))\n",
    "print(\"Test score:\", clf.score(X_test_pca, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "data_augment = transforms.Compose([transforms.RandomResizedCrop(96),\n",
    "                                   transforms.ToTensor()])\n",
    "\n",
    "train_dataset = datasets.STL10('../data', split='train', download=True, transform=data_augment)\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, num_workers=1, drop_last=False, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "test_dataset = datasets.STL10('../data', split='test', download=True, transform=data_augment)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, num_workers=1, drop_last=False, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNetSimCLR(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "    (4): Sequential(\n",
      "      (0): BasicBlock(\n",
      "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "      (1): BasicBlock(\n",
      "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (5): Sequential(\n",
      "      (0): BasicBlock(\n",
      "        (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (downsample): Sequential(\n",
      "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): BasicBlock(\n",
      "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (6): Sequential(\n",
      "      (0): BasicBlock(\n",
      "        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (downsample): Sequential(\n",
      "          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): BasicBlock(\n",
      "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (7): Sequential(\n",
      "      (0): BasicBlock(\n",
      "        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (downsample): Sequential(\n",
      "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): BasicBlock(\n",
      "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (relu): ReLU(inplace=True)\n",
      "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (8): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  )\n",
      "  (l1): Linear(in_features=512, out_features=512, bias=True)\n",
      "  (l2): Linear(in_features=512, out_features=64, bias=True)\n",
      ")\n",
      "odict_keys(['features.0.weight', 'features.1.weight', 'features.1.bias', 'features.1.running_mean', 'features.1.running_var', 'features.1.num_batches_tracked', 'features.4.0.conv1.weight', 'features.4.0.bn1.weight', 'features.4.0.bn1.bias', 'features.4.0.bn1.running_mean', 'features.4.0.bn1.running_var', 'features.4.0.bn1.num_batches_tracked', 'features.4.0.conv2.weight', 'features.4.0.bn2.weight', 'features.4.0.bn2.bias', 'features.4.0.bn2.running_mean', 'features.4.0.bn2.running_var', 'features.4.0.bn2.num_batches_tracked', 'features.4.1.conv1.weight', 'features.4.1.bn1.weight', 'features.4.1.bn1.bias', 'features.4.1.bn1.running_mean', 'features.4.1.bn1.running_var', 'features.4.1.bn1.num_batches_tracked', 'features.4.1.conv2.weight', 'features.4.1.bn2.weight', 'features.4.1.bn2.bias', 'features.4.1.bn2.running_mean', 'features.4.1.bn2.running_var', 'features.4.1.bn2.num_batches_tracked', 'features.5.0.conv1.weight', 'features.5.0.bn1.weight', 'features.5.0.bn1.bias', 'features.5.0.bn1.running_mean', 'features.5.0.bn1.running_var', 'features.5.0.bn1.num_batches_tracked', 'features.5.0.conv2.weight', 'features.5.0.bn2.weight', 'features.5.0.bn2.bias', 'features.5.0.bn2.running_mean', 'features.5.0.bn2.running_var', 'features.5.0.bn2.num_batches_tracked', 'features.5.0.downsample.0.weight', 'features.5.0.downsample.1.weight', 'features.5.0.downsample.1.bias', 'features.5.0.downsample.1.running_mean', 'features.5.0.downsample.1.running_var', 'features.5.0.downsample.1.num_batches_tracked', 'features.5.1.conv1.weight', 'features.5.1.bn1.weight', 'features.5.1.bn1.bias', 'features.5.1.bn1.running_mean', 'features.5.1.bn1.running_var', 'features.5.1.bn1.num_batches_tracked', 'features.5.1.conv2.weight', 'features.5.1.bn2.weight', 'features.5.1.bn2.bias', 'features.5.1.bn2.running_mean', 'features.5.1.bn2.running_var', 'features.5.1.bn2.num_batches_tracked', 'features.6.0.conv1.weight', 'features.6.0.bn1.weight', 'features.6.0.bn1.bias', 'features.6.0.bn1.running_mean', 'features.6.0.bn1.running_var', 'features.6.0.bn1.num_batches_tracked', 'features.6.0.conv2.weight', 'features.6.0.bn2.weight', 'features.6.0.bn2.bias', 'features.6.0.bn2.running_mean', 'features.6.0.bn2.running_var', 'features.6.0.bn2.num_batches_tracked', 'features.6.0.downsample.0.weight', 'features.6.0.downsample.1.weight', 'features.6.0.downsample.1.bias', 'features.6.0.downsample.1.running_mean', 'features.6.0.downsample.1.running_var', 'features.6.0.downsample.1.num_batches_tracked', 'features.6.1.conv1.weight', 'features.6.1.bn1.weight', 'features.6.1.bn1.bias', 'features.6.1.bn1.running_mean', 'features.6.1.bn1.running_var', 'features.6.1.bn1.num_batches_tracked', 'features.6.1.conv2.weight', 'features.6.1.bn2.weight', 'features.6.1.bn2.bias', 'features.6.1.bn2.running_mean', 'features.6.1.bn2.running_var', 'features.6.1.bn2.num_batches_tracked', 'features.7.0.conv1.weight', 'features.7.0.bn1.weight', 'features.7.0.bn1.bias', 'features.7.0.bn1.running_mean', 'features.7.0.bn1.running_var', 'features.7.0.bn1.num_batches_tracked', 'features.7.0.conv2.weight', 'features.7.0.bn2.weight', 'features.7.0.bn2.bias', 'features.7.0.bn2.running_mean', 'features.7.0.bn2.running_var', 'features.7.0.bn2.num_batches_tracked', 'features.7.0.downsample.0.weight', 'features.7.0.downsample.1.weight', 'features.7.0.downsample.1.bias', 'features.7.0.downsample.1.running_mean', 'features.7.0.downsample.1.running_var', 'features.7.0.downsample.1.num_batches_tracked', 'features.7.1.conv1.weight', 'features.7.1.bn1.weight', 'features.7.1.bn1.bias', 'features.7.1.bn1.running_mean', 'features.7.1.bn1.running_var', 'features.7.1.bn1.num_batches_tracked', 'features.7.1.conv2.weight', 'features.7.1.bn2.weight', 'features.7.1.bn2.bias', 'features.7.1.bn2.running_mean', 'features.7.1.bn2.running_var', 'features.7.1.bn2.num_batches_tracked', 'l1.weight', 'l1.bias', 'l2.weight', 'l2.bias'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ResNetSimCLR(out_dim=out_dim)\n",
    "model.eval()\n",
    "print(model)\n",
    "\n",
    "state_dict = torch.load('../checkpoints/checkpoint.pth')\n",
    "print(state_dict.keys())\n",
    "\n",
    "model.load_state_dict(state_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Protocol #2 Linear separability evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train features\n",
      "(5000, 512)\n"
     ]
    }
   ],
   "source": [
    "X_train_feature = []\n",
    "\n",
    "for step, (batch_x, batch_y) in enumerate(train_loader):\n",
    "    features, _ = model(batch_x)\n",
    "    X_train_feature.extend(features.detach().numpy())\n",
    "    \n",
    "X_train_feature = np.array(X_train_feature)\n",
    "\n",
    "print(\"Train features\")\n",
    "print(X_train_feature.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test features\n",
      "(8000, 512)\n"
     ]
    }
   ],
   "source": [
    "X_test_feature = []\n",
    "\n",
    "for step, (batch_x, batch_y) in enumerate(test_loader):\n",
    "    features, _ = model(batch_x)\n",
    "    X_test_feature.extend(features.detach().numpy())\n",
    "    \n",
    "X_test_feature = np.array(X_test_feature)\n",
    "\n",
    "print(\"Test features\")\n",
    "print(X_test_feature.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=0, max_iter=1000, solver='lbfgs', C=1.0)\n",
    "\n",
    "scaler = preprocessing.StandardScaler()\n",
    "scaler.fit(X_train_feature)\n",
    "\n",
    "clf.fit(scaler.transform(X_train_feature), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SimCLR feature evaluation\n",
      "Train score: 0.8948\n",
      "Test score: 0.639625\n"
     ]
    }
   ],
   "source": [
    "print(\"SimCLR feature evaluation\")\n",
    "print(\"Train score:\", clf.score(scaler.transform(X_train_feature), y_train))\n",
    "print(\"Test score:\", clf.score(scaler.transform(X_test_feature), y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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