{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from model import Encoder\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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "out_dim = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training images\n",
      "(5000, 96, 96, 3)\n",
      "(5000,)\n"
     ]
    }
   ],
   "source": [
    "X_train = np.fromfile('data/stl10_binary/train_X.bin', dtype=np.uint8)\n",
    "y_train = np.fromfile('data/stl10_binary/train_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(\"Training images\")\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test images\n",
      "(8000, 96, 96, 3)\n",
      "(8000,)\n"
     ]
    }
   ],
   "source": [
    "X_test = np.fromfile('data/stl10_binary/test_X.bin', dtype=np.uint8)\n",
    "y_test = np.fromfile('data/stl10_binary/test_y.bin', dtype=np.uint8)\n",
    "\n",
    "X_test = np.reshape(X_test, (-1, 3, 96, 96))\n",
    "X_test = np.transpose(X_test, (0, 3, 2, 1))\n",
    "print(\"Test images\")\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test protocol #1 PCA features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA features\n",
      "(5000, 64)\n",
      "(8000, 64)\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=out_dim)\n",
    "X_train_pca = pca.fit_transform(X_train.reshape((X_train.shape[0],-1)))\n",
    "X_test_pca = pca.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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA feature evaluation\n",
      "Train score: 0.3932\n",
      "Test score: 0.353\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/thalles/anaconda3/envs/pytorch/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=0).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": 8,
   "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": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoder(\n",
      "  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (l1): Linear(in_features=64, out_features=64, bias=True)\n",
      "  (l2): Linear(in_features=64, out_features=64, bias=True)\n",
      ")\n",
      "odict_keys(['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias', 'conv3.weight', 'conv3.bias', 'conv4.weight', 'conv4.bias', 'l1.weight', 'l1.bias', 'l2.weight', 'l2.bias'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Encoder(out_dim=out_dim)\n",
    "model.eval()\n",
    "print(model)\n",
    "\n",
    "state_dict = torch.load('model/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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train features\n",
      "(5000, 64)\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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test features\n",
      "(8000, 64)\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/thalles/anaconda3/envs/pytorch/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression(random_state=0).fit(X_train_feature, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SimCLR feature evaluation\n",
      "Train score: 0.415\n",
      "Test score: 0.404625\n"
     ]
    }
   ],
   "source": [
    "print(\"SimCLR feature evaluation\")\n",
    "print(\"Train score:\", clf.score(X_train_feature, y_train))\n",
    "print(\"Test score:\", clf.score(X_test_feature, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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