Google Colaboratory を使って、Chainer で 犬、猫の分類に挑戦する！

概要

Connpassの「Chainer x Azure ML Hackathon CV編」に参加してきた際に、
「Cats and Dogs」の分類にをしてみてはどうでしょう と頂いたので、挑戦してみました。
GPUを持っていないので、「Google Colaboratory」を使いました。

以下の点で勉強になりました。
・Chainerを利用し、CNN用の画像を読み込ませる方法
・画像のリサイズ、リシェイプ
・Google Colaboratoryと、GoogleDriveの連携

DeepLearning は、理解できていない部分もあるので、間違っているかもしれません。
その時は、指摘いただけると、ありがたいです！

環境構築

Google Colaboratory で、ノートブックを新規作成する

「ファイル 」ｰ 「Python3のノートブックを新規作成」をクリックします。

colabをGPUインスタンスにする。

ランタイム - ランタイムのタイプを選択 より、ハードウェアアクセラレータをGPUに変更し、保存します。

Chainerのインストール

ここを参考にしました。

!pip uninstall chainer -y
!pip uninstall cupy-cuda80 -y
!apt -y install libcusparse8.0 libnvrtc8.0 libnvtoolsext1
!ln -snf /usr/lib/x86_64-linux-gnu/libnvrtc-builtins.so.8.0 /usr/lib/x86_64-linux-gnu/libnvrtc-builtins.so
!pip install 'chainer==4.0.0b4' 'cupy-cuda80==4.0.0b4'

[33mSkipping chainer as it is not installed.[0m
[33mSkipping cupy-cuda80 as it is not installed.[0m
Reading package lists... Done
Building dependency tree       
Reading state information... Done
The following NEW packages will be installed:
  libcusparse8.0 libnvrtc8.0 libnvtoolsext1
0 upgraded, 3 newly installed, 0 to remove and 0 not upgraded.
Need to get 28.9 MB of archives.
After this operation, 71.6 MB of additional disk space will be used.
Get:1 http://archive.ubuntu.com/ubuntu artful/multiverse amd64 libcusparse8.0 amd64 8.0.61-1 [22.6 MB]
Get:2 http://archive.ubuntu.com/ubuntu artful/multiverse amd64 libnvrtc8.0 amd64 8.0.61-1 [6,225 kB]
Get:3 http://archive.ubuntu.com/ubuntu artful/multiverse amd64 libnvtoolsext1 amd64 8.0.61-1 [32.2 kB]
Fetched 28.9 MB in 1s (16.4 MB/s)

7[0;23r8[1ASelecting previously unselected package libcusparse8.0:amd64.
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7[0;24r8[1A[JCollecting chainer==4.0.0b4
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!pip install chainer -U
!pip install cupy-cuda80 -U

Collecting chainer
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Collecting cupy-cuda80==4.1.0 (from chainer)
[?25l  Downloading https://files.pythonhosted.org/packages/3f/23/c051fb66543a48b44376eed0320350a04750521d588553bac508ba66bcc7/cupy_cuda80-4.1.0-cp36-cp36m-manylinux1_x86_64.whl (194.0MB)
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Building wheels for collected packages: chainer
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  Found existing installation: cupy-cuda80 4.0.0b4
    Uninstalling cupy-cuda80-4.0.0b4:
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    Uninstalling chainer-4.0.0b4:
      Successfully uninstalled chainer-4.0.0b4
Successfully installed chainer-4.1.0 cupy-cuda80-4.1.0
Requirement already up-to-date: cupy-cuda80 in /usr/local/lib/python3.6/dist-packages (4.1.0)
Requirement not upgraded as not directly required: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from cupy-cuda80) (1.11.0)
Requirement not upgraded as not directly required: fastrlock>=0.3 in /usr/local/lib/python3.6/dist-packages (from cupy-cuda80) (0.3)
Requirement not upgraded as not directly required: numpy>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from cupy-cuda80) (1.14.3)

GPUが有効になっていることを確認する

★ランタイムの再起動が必要かも

以下のようになっていれば、有効になっているようです
GPU availability: True
cuDNN availablility: True

import chainer
import cupy
chainer.print_runtime_info()
print('GPU availability:', chainer.cuda.available)
print('cuDNN availablility:', chainer.cuda.cudnn_enabled)

Chainer: 4.1.0
NumPy: 1.14.3
CuPy:
  CuPy Version          : 4.1.0
  CUDA Root             : None
  CUDA Build Version    : 8000
  CUDA Driver Version   : 9000
  CUDA Runtime Version  : 8000
  cuDNN Build Version   : 7102
  cuDNN Version         : 7102
  NCCL Build Version    : 2104
GPU availability: True
cuDNN availablility: True

犬、猫のデータを取得、解凍する

データのダウンロード

import os
import requests
import zipfile

file_name = "dogscats.zip"
url='http://files.fast.ai/data/dogscats.zip'
response = requests.get(url)
with open(os.path.join("./", file_name), 'wb') as f:
    f.write(response.content)

#ダウンロードできた確認する
%%bash
ls

datalab
dogscats.zip

マイクロソフトのサイトからダウンロードしたら、「unzip」に失敗したので、この記事を見て、ここからダウンロードすることにしました。

解凍する

学習データは、「dogscats/train/cats/」、「dogscats/train/dogs/」というような感じで、
検証データは、「dogscats/valid/cats/」、「dogscats/valid/dogs/」というような感じで入っているようです。

!unzip dogscats.zip

Archive:  dogscats.zip
   creating: dogscats/
   creating: dogscats/sample/
   creating: dogscats/sample/train/
   creating: dogscats/sample/train/cats/
  inflating: dogscats/sample/train/cats/cat.2921.jpg  
  inflating: dogscats/sample/train/cats/cat.394.jpg  
  inflating: dogscats/sample/train/cats/cat.4865.jpg  
  inflating: dogscats/sample/train/cats/cat.3570.jpg  
  inflating: dogscats/sample/train/cats/cat.2266.jpg  
  inflating: dogscats/sample/train/cats/cat.9021.jpg  
  inflating: dogscats/sample/train/cats/cat.11737.jpg  
  inflating: dogscats/sample/train/cats/cat.4600.jpg  
   creating: dogscats/sample/train/dogs/
  inflating: dogscats/sample/train/dogs/dog.1402.jpg  
  inflating: dogscats/sample/train/dogs/dog.1614.jpg  
  inflating: dogscats/sample/train/dogs/dog.8643.jpg  
  inflating: dogscats/sample/train/dogs/dog.6391.jpg  
  inflating: dogscats/sample/train/dogs/dog.2423.jpg  
  inflating: dogscats/sample/train/dogs/dog.9077.jpg  
  inflating: dogscats/sample/train/dogs/dog.8091.jpg  
  inflating: dogscats/sample/train/dogs/dog.6768.jpg  
   creating: dogscats/sample/valid/
  inflating: dogscats/sample/valid/features.npy  
   creating: dogscats/sample/valid/cats/
  inflating: dogscats/sample/valid/cats/cat.10435.jpg  
  inflating: dogscats/sample/valid/cats/cat.4319.jpg  
  inflating: dogscats/sample/valid/cats/cat.5202.jpg  
  inflating: dogscats/sample/valid/cats/cat.4785.jpg  
   creating: dogscats/sample/valid/dogs/
  inflating: dogscats/sample/valid/dogs/dog.11314.jpg  
  inflating: dogscats/sample/valid/dogs/dog.4090.jpg  
  inflating: dogscats/sample/valid/dogs/dog.10459.jpg  
  inflating: dogscats/sample/valid/dogs/dog.5697.jpg  
  inflating: dogscats/sample/valid/labels.npy  
   creating: dogscats/train/
   creating: dogscats/train/cats/
  inflating: dogscats/train/cats/cat.2921.jpg  
  inflating: dogscats/train/cats/cat.10435.jpg  
  inflating: dogscats/train/cats/cat.394.jpg  
  inflating: dogscats/train/cats/cat.4319.jpg  
  inflating: dogscats/train/cats/cat.5202.jpg  
  inflating: dogscats/train/cats/cat.4865.jpg  
  inflating: dogscats/train/cats/cat.3570.jpg  
  inflating: dogscats/train/cats/cat.2266.jpg  
  inflating: dogscats/train/cats/cat.9021.jpg  
  inflating: dogscats/train/cats/cat.4785.jpg  

データを、Chainerで利用可能な形に成型する

• 画像サイズを統一する
• shapeを「チャンネル，縦，横」にする（ChainerでCNNを利用する場合、左記のようなshapeにする必要がある）
• TransformDataset型にする

データを確認する

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
img_path = './dogscats/train/cats/cat.9021.jpg'
Image.open(img_path)

画像サイズを変更する

def img_resize(img, out_size, is_file_path=True):
  if (is_file_path):
    img = Image.open(img)
  else:
    #Chainerの「チャネル、縦、横」→「縦、横、チャネル」にする
    #後で、TransformDatasetを作ると気にこちらを使います
    #※image_dataset.pyの中で、transpose(2, 0, 1)をしてくれているので、
    #  リサイズするなら、transpose(1, 2, 0)が必要なようです
    img = Image.fromarray(img.transpose(1, 2, 0))

  resized_img = img.resize(out_size, Image.BICUBIC)

  return resized_img

resized_img = img_resize('./dogscats/train/cats/cat.9021.jpg', (120, 120))
print(resized_img)
resized_img

<PIL.Image.Image image mode=RGB size=120x120 at 0x7F3F6F281240>

shapeを「チャンネル，縦，横」にする

#ndarray型に変換(縦、横がわかりやすいように、サイズを変えてます)
resized_img = img_resize('./dogscats/train/cats/cat.9021.jpg', (120, 200))
img_array = np.array(resized_img)

#shapeを確認する
img_array.shape

(200, 120, 3)

*※image_dataset.pyの中で、transpose(2, 0, 1)をしてくれているので、
リサイズをしないのであれば、不要な処理です。 *

#shapeを「チャンネル，縦，横」にする
def img_to_chainer_shape(img):
  img_array = np.array(img)
  return img_array.transpose(2, 0, 1)

#変わっていることを確認する
img = np.array(img_to_chainer_shape(img_resize('./dogscats/train/cats/cat.9021.jpg', (120, 200))))
img.shape

(3, 200, 120)

TransformDataset型にする

メモリに展開しないで、データセットを作ります。ここを参考にしました。

from chainer import datasets
cats_train_path = 'dogscats/train/cats/'
dogs_train_path = 'dogscats/train/dogs/'
cats_valid_path = 'dogscats/valid/cats/'
dogs_valid_path = 'dogscats/valid/dogs/'

filepath_and_label_list = []

import os

def get_image_filepath_list(dir, label):
  filepath_list = []
  files = os.listdir(dir)
  for file in files:
    filepath_list.append((dir + file, label))
  return filepath_list

#学習と検証データを一緒にしてます
filepath_and_label_list.extend(get_image_filepath_list(cats_train_path, 0))
filepath_and_label_list.extend(get_image_filepath_list(dogs_train_path, 1))
filepath_and_label_list.extend(get_image_filepath_list(cats_valid_path, 0))
filepath_and_label_list.extend(get_image_filepath_list(dogs_valid_path, 1))

# 各データに行う変換
def transform(inputs):
  img , label = inputs
  #サイズ変換
  img = img_resize(img.astype(np.uint8), (120, 120), False)
  #Chainer用のChapeにする
  img = img_to_chainer_shape(img)
  #スケーリング(データを0～1の間にする)
  img = img.astype(np.float32) / 255
  # ランダムに左右反転
  if np.random.rand() > 0.5:
      img = img[..., ::-1]
  return img, label

from chainer.datasets import LabeledImageDataset

# データセット作成
d = LabeledImageDataset(filepath_and_label_list)

from chainer.datasets import TransformDataset
#変換をメソッドを設定する
td = TransformDataset(d, transform)

学習する

データセットを学習、検証で分ける

from chainer import datasets

train, valid = datasets.split_dataset_random(td, int(len(d) * 0.8), seed=0)

モデルを定義する

ここを参考にしました。

import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training,serializers,Chain,datasets,sequential,optimizers,iterators
from chainer.training import extensions,Trainer
from chainer.dataset import concat_examples

class CNN(Chain):
  def __init__(self):
    super(CNN, self).__init__()

    with self.init_scope():
      self.conv1 = L.Convolution2D(None, out_channels=32, ksize=3, stride=1, pad=1)
      self.conv2 = L.Convolution2D(in_channels=32, out_channels=64, ksize=3, stride=1, pad=1)
      self.conv3 = L.Convolution2D(in_channels=64, out_channels=128, ksize=3, stride=1, pad=1)
      self.conv4 = L.Convolution2D(in_channels=128, out_channels=256, ksize=3, stride=1, pad=1)
      self.l1 = L.Linear(None, 1000)
      self.l2 = L.Linear(1000, 2)

  def __call__(self, x):
    f = F.max_pooling_2d(F.relu(self.conv1(x)), ksize=2, stride=2)
    f = F.max_pooling_2d(F.relu(self.conv2(f)), ksize=2, stride=2)
    f = F.max_pooling_2d(F.relu(self.conv3(f)), ksize=2, stride=2)
    f = F.max_pooling_2d(F.relu(self.conv4(f)), ksize=2, stride=2)
    f = F.dropout(F.relu(self.l1(f)), ratio=0.75)
    f = self.l2(f)
    return f

batchsize = 32
max_epoch = 10
gpu_id = 0

model = L.Classifier(CNN())
model.to_gpu(gpu_id)

<chainer.links.model.classifier.Classifier at 0x7f3f090b3e10>

train_iter = iterators.MultiprocessIterator(train, batchsize)
valid_iter = iterators.MultiprocessIterator(valid, batchsize, False, False)

optimaizer = optimizers.Adam().setup(model)
updater = training.StandardUpdater(train_iter, optimaizer, device=gpu_id)
trainer = Trainer(updater, stop_trigger=(max_epoch, 'epoch'))

trainer.extend(extensions.LogReport())
trainer.extend(extensions.Evaluator(valid_iter, model, device=gpu_id), name='val')
trainer.extend(extensions.PrintReport(['epoch', 'main/loss', 'main/accuracy', 'val/main/loss', 'val/main/accuracy', 'elapsed_time']))
trainer.extend(extensions.snapshot(filename='snapshot_epoch-{.updater.epoch}'))
trainer.extend(extensions.PlotReport(['main/loss', 'val/main/loss'], x_key='epoch', file_name='loss.png'))
trainer.extend(extensions.PlotReport(['main/accuracy', 'val/main/accuracy'], x_key='epoch', file_name='accuracy.png'))
trainer.extend(extensions.ProgressBar())
trainer.extend(extensions.dump_graph('main/loss'))

学習スタート！

ProgressBarも、１エポックごとに表示できたらいいに、、、って思います。
終わる時間が知りたいだけです。

trainer.run()

epoch       main/loss   main/accuracy  val/main/loss  val/main/accuracy  elapsed_time
[J     total [..................................................]  1.60%
this epoch [########..........................................] 16.00%
       100 iter, 0 epoch / 10 epochs
       inf iters/sec. Estimated time to finish: 0:00:00.
[4A[J     total [#.................................................]  3.20%
this epoch [################..................................] 32.00%
       200 iter, 0 epoch / 10 epochs
     6.177 iters/sec. Estimated time to finish: 0:16:19.434320.
[4A[J     total [##................................................]  4.80%
this epoch [########################..........................] 48.00%
       300 iter, 0 epoch / 10 epochs
     6.133 iters/sec. Estimated time to finish: 0:16:10.154462.
[4A[J     total [###...............................................]  6.40%
this epoch [################################..................] 64.00%
       400 iter, 0 epoch / 10 epochs
    6.1124 iters/sec. Estimated time to finish: 0:15:57.076340.
[4A[J     total [####..............................................]  8.00%
this epoch [########################################..........] 80.00%
       500 iter, 0 epoch / 10 epochs
    6.1181 iters/sec. Estimated time to finish: 0:15:39.832635.
[4A[J     total [####..............................................]  9.60%
this epoch [################################################..] 96.00%
       600 iter, 0 epoch / 10 epochs
    6.1134 iters/sec. Estimated time to finish: 0:15:24.198442.
[4A[J1           0.674315    0.57565        0.619502       0.646696           138.422       





[4A[J     total [#################################################.] 99.20%
this epoch [##############################################....] 92.00%
      6200 iter, 9 epoch / 10 epochs
    4.6714 iters/sec. Estimated time to finish: 0:00:10.703371.
[4A[J10          0.178794    0.92595        0.214504       0.909435           1366.39       
[J

学習結果を確認する

Image.open('result/loss.png')

Image.open('result/accuracy.png')

学習済みモデルを保存する

Google Driveに出力します。ここを参考にしました。

#保存するフォルダを確認する
%%bash
ls

datalab
dogscats
dogscats.zip
result

#resultの中に、snapshotができているので、確認する
%%bash
ls result

accuracy.png
cg.dot
log
loss.png
snapshot_epoch-1
snapshot_epoch-10
snapshot_epoch-2
snapshot_epoch-3
snapshot_epoch-4
snapshot_epoch-5
snapshot_epoch-6
snapshot_epoch-7
snapshot_epoch-8
snapshot_epoch-9

GoogleDriveへの接続

!pip install -U -q PyDrive

from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials

auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)

最後のエポックのスナップをGoogleDriveに保存する

snap_file_name = "result/snapshot_epoch-" + str(max_epoch)
upload_file = drive.CreateFile()
upload_file.SetContentFile(snap_file_name)
upload_file.Upload()

検証する

GoogleDriveへ接続する（上を参照　既に接続済みなら不要です）

GoogleDriveからファイルを取得する

上で、GoogleDriveにアップロードしたファイルに対し、GoogleDriveで「共有可能なリンクを取得」する必要があります。

downloaded = drive.CreateFile({'id':'ここは、GoogleDriveの共有可能なリンクのIDです'})
downloaded.GetContentFile('snapshot_epoch-'+ str(max_epoch))

学習済みモデルを読み込む

model = L.Classifier(CNN())

infer_net = model
serializers.load_npz('snapshot_epoch-' + str(max_epoch), infer_net, path='updater/model:main/')

infer_net.to_gpu(0)

<chainer.links.model.classifier.Classifier at 0x7f3f0543c320>

※ infer_net.xp.asarray(x)がないと、以下のエラーになります。ここを参考にしました。

※ x[None, ...]がないと、以下のエラーになります。 ここを参考にしました。

def get_x_test(file_path, size, show=True):
  if show:
    plt.imshow(Image.open(file_path))
  #リサイズ
  x = img_resize(file_path, size, True)
  #リシェイプ
  x = img_to_chainer_shape(x)
  #型変換
  #float32にする
  x = x.astype(np.float32)
  # ネットワークと同じデバイス上にデータを送る
  x = infer_net.xp.asarray(x)
  #[ミニバッチ、チャネル、高さ、幅]にする
  x = x[None, ...]
  return x

from chainer.cuda import to_cpu

x_test = get_x_test('dogscats/test1/5011.jpg', (120, 120))

with chainer.using_config('train', False), chainer.using_config('enable_backprop', False):
    y = infer_net.predictor(x_test)

y = to_cpu(y.array)

print(y)

y = y.argmax(axis=1)[0]

print(y)

if y == 0:
  y = '猫'
else:
  y = '犬'

print('予測ラベル:', y)

[[-324.16788  299.3006 ]]
1
予測ラベル: 犬

まとめ

画像取得から、検証までの流れができて面白ったです。
これで、いろんなデータセットに挑戦できるという気がしてきたので、
ほかのデータセットにチャレンジしていきます！