mirror of
https://github.com/sthalles/SimCLR.git
synced 2025-06-03 15:03:00 +08:00
473 lines
18 KiB
Plaintext
473 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import sys\n",
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"sys.path.insert(1, '../')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from models.resnet_simclr import ResNetSimCLR\n",
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"import torchvision.transforms as transforms\n",
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"from torch.utils.data import DataLoader\n",
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"from torchvision import datasets\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"batch_size = 256\n",
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"out_dim = 64"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def _load_stl10(prefix=\"train\"):\n",
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" X_train = np.fromfile('../data/stl10_binary/' + prefix + '_X.bin', dtype=np.uint8)\n",
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" y_train = np.fromfile('../data/stl10_binary/' + prefix + '_y.bin', dtype=np.uint8)\n",
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"\n",
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" X_train = np.reshape(X_train, (-1, 3, 96, 96))\n",
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" X_train = np.transpose(X_train, (0, 3, 2, 1))\n",
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" print(\"{} images\".format(prefix))\n",
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" print(X_train.shape)\n",
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" print(y_train.shape)\n",
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" return X_train, y_train"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"train images\n",
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"(5000, 96, 96, 3)\n",
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"(5000,)\n"
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]
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}
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],
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"source": [
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"# load STL-10 train data\n",
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"X_train, y_train = _load_stl10(\"train\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"test images\n",
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"(8000, 96, 96, 3)\n",
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"(8000,)\n"
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]
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}
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],
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"source": [
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"# load STL-10 test data\n",
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"X_test, y_test = _load_stl10(\"test\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Test protocol #1 PCA features"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.decomposition import PCA\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn import preprocessing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"PCA features\n",
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"(5000, 64)\n",
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"(8000, 64)\n"
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]
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}
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],
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"source": [
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"scaler = preprocessing.StandardScaler()\n",
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"scaler.fit(X_train.reshape((X_train.shape[0],-1)))\n",
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"\n",
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"pca = PCA(n_components=64)\n",
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"\n",
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"X_train_pca = pca.fit_transform(scaler.transform(X_train.reshape(X_train.shape[0], -1)))\n",
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"X_test_pca = pca.transform(scaler.transform(X_test.reshape(X_test.shape[0], -1)))\n",
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"\n",
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"print(\"PCA features\")\n",
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"print(X_train_pca.shape)\n",
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"print(X_test_pca.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"PCA feature evaluation\n",
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"Train score: 0.396\n",
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"Test score: 0.3565\n"
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]
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}
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],
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"source": [
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"clf = LogisticRegression(random_state=0, max_iter=1000, solver='lbfgs', C=1.0)\n",
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"clf.fit(X_train_pca, y_train)\n",
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"print(\"PCA feature evaluation\")\n",
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"print(\"Train score:\", clf.score(X_train_pca, y_train))\n",
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"print(\"Test score:\", clf.score(X_test_pca, y_test))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Files already downloaded and verified\n"
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]
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}
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],
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"source": [
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"data_augment = transforms.Compose([transforms.RandomResizedCrop(96),\n",
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" transforms.ToTensor()])\n",
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"\n",
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"train_dataset = datasets.STL10('../data', split='train', download=True, transform=data_augment)\n",
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"train_loader = DataLoader(train_dataset, batch_size=batch_size, num_workers=1, drop_last=False, shuffle=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Files already downloaded and verified\n"
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]
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}
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],
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"source": [
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"test_dataset = datasets.STL10('../data', split='test', download=True, transform=data_augment)\n",
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"test_loader = DataLoader(test_dataset, batch_size=batch_size, num_workers=1, drop_last=False, shuffle=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ResNetSimCLR(\n",
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" (features): Sequential(\n",
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" (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
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" (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (2): ReLU(inplace=True)\n",
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" (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
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" (4): Sequential(\n",
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" (0): BasicBlock(\n",
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" (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (relu): ReLU(inplace=True)\n",
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" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" (1): BasicBlock(\n",
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" (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (relu): ReLU(inplace=True)\n",
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" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" )\n",
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" (5): Sequential(\n",
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" (0): BasicBlock(\n",
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" (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
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" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (relu): ReLU(inplace=True)\n",
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" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (downsample): Sequential(\n",
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" (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
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" (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" )\n",
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" (1): BasicBlock(\n",
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" (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (relu): ReLU(inplace=True)\n",
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" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" )\n",
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" (6): Sequential(\n",
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" (0): BasicBlock(\n",
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" (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
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" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (relu): ReLU(inplace=True)\n",
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" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (downsample): Sequential(\n",
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" (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
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" (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" )\n",
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" (1): BasicBlock(\n",
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" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (relu): ReLU(inplace=True)\n",
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" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" )\n",
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" (7): Sequential(\n",
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" (0): BasicBlock(\n",
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" (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
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" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (relu): ReLU(inplace=True)\n",
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" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (downsample): Sequential(\n",
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" (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
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" (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" )\n",
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" (1): BasicBlock(\n",
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" (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (relu): ReLU(inplace=True)\n",
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" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" )\n",
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" (8): AdaptiveAvgPool2d(output_size=(1, 1))\n",
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" )\n",
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" (l1): Linear(in_features=512, out_features=512, bias=True)\n",
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" (l2): Linear(in_features=512, out_features=64, bias=True)\n",
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")\n",
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"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"
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]
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},
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{
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"data": {
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"text/plain": [
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"<All keys matched successfully>"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model = ResNetSimCLR(out_dim=out_dim)\n",
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"model.eval()\n",
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"print(model)\n",
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"\n",
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"state_dict = torch.load('../checkpoints/checkpoint.pth')\n",
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"print(state_dict.keys())\n",
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"\n",
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"model.load_state_dict(state_dict)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Protocol #2 Linear separability evaluation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"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": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "pytorch",
|
|
"language": "python",
|
|
"name": "pytorch"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.6.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|