takedarts
浮世絵作者予測のPyTorchによる実装（LB: 0.660）
はじめに
PyTorchを使った学習/推論コード（LB: 0.660）です。
- google colaboratoryで実行
- モデルとしてResNet18を使用
- データ拡張はhorizontal flipのみ
セットアップ
学習データとテストデータをGoogle Driveから読み込みます。
このコードを実行する前に、これらのデータファイルをGoogle Driveの ProbSpace/ukiyoe/ 以下に保存しておいてください。
from google.colab import drive
drive.mount('/content/drive')Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
画像の重複を確認するために imagehash をインストールします。画像の重複確認を行わないなら以下のインストールは不要です。
!pip install imagehashRequirement already satisfied: imagehash in /usr/local/lib/python3.6/dist-packages (4.0) Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from imagehash) (1.16.5) Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from imagehash) (1.3.1) Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from imagehash) (1.12.0) Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from imagehash) (4.3.0) Requirement already satisfied: pywavelets in /usr/local/lib/python3.6/dist-packages (from imagehash) (1.0.3) Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->imagehash) (0.46)
import google.colab
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from sklearn.model_selection import train_test_split
import torch.cuda
import torch.nn as nn
import torch.optim as optim
import torch.autograd as autograd
import torch.utils.data as dataset
import torchvision.models as models
import albumentations
import imagehash
import argparse
import os
import tqdm
import random
import csv
DATA_DIR = 'drive/My Drive/ProbSpace/ukiyoe'
RANDOM_SEED = 2019
random.seed(RANDOM_SEED)
os.environ['PYTHONHASHSEED'] = str(RANDOM_SEED)
np.random.seed(RANDOM_SEED)
torch.manual_seed(RANDOM_SEED)
torch.cuda.manual_seed(RANDOM_SEED)
torch.torch.backends.cudnn.benchmark = True
torch.torch.backends.cudnn.enabled = Trueデータの読み込み
それぞれのデータをndarrayとして読み込みます。画像のデータ形式は (width, height, channel) となっているようです。
train_labels = np.load(os.path.join(DATA_DIR, 'ukiyoe-train-labels.npz'))['arr_0']
train_images = np.load(os.path.join(DATA_DIR, 'ukiyoe-train-imgs.npz'))['arr_0']
test_images = np.load(os.path.join(DATA_DIR, 'ukiyoe-test-imgs.npz'))['arr_0']
print('train-labels: shape={}, dtype={}'.format(train_labels.shape, train_labels.dtype))
print('train-images: shape={}, dtype={}'.format(train_images.shape, train_images.dtype))
print('test-images: shape={}, dtype={}'.format(test_images.shape, test_images.dtype))train-labels: shape=(3158,), dtype=int64 train-images: shape=(3158, 224, 224, 3), dtype=uint8 test-images: shape=(397, 224, 224, 3), dtype=uint8
データの確認
学習データに含まれる各ラベルのデータ数を確認。0, 1, 4, 6は多いですが、5, 7, 8, 9は少なめです。
print('train-labels: min={}, max={}'.format(np.min(train_labels), np.max(train_labels)))
axis = plt.figure().add_subplot(1, 1, 1)
axis.set_xlabel('label')
axis.set_ylabel('# of images')
axis.hist(train_labels)
plt.show()train-labels: min=0, max=9
images = [[] for _ in range(10)]
for image, label in zip(train_images, train_labels):
images[label].append(image)
figure = plt.figure(figsize=(8, 18))
for i in range(10):
for j, img in enumerate(images[i][:5]):
axis = figure.add_subplot(10, 5, i * 5 + j + 1)
axis.imshow(img)
axis.tick_params(labelbottom=False, labelleft=False, bottom=False, left=False)
axis.set_xlabel(f'class={i}')
plt.show()
重複画像の確認
非常に似ている画像が存在していますが、模様の位置やコントラストが異なります（出典元が違うかも）。テストデータの中にも重複していると思われる画像を確認できます。
data = [[f'train_{t}_{i}', x, None] for i, (x, t) in enumerate(zip(train_images, train_labels))]
data.extend([f'test_{i}', x, None] for i, x in enumerate(test_images))
for i in tqdm.tqdm(range(len(data)), desc='hashing'):
data[i][2] = imagehash.phash(Image.fromarray(data[i][1]))
threshold = 10
clusters = []
for x in tqdm.tqdm(data, desc='clustering'):
for c in clusters:
for _, _, h in c:
if h - x[2] < threshold:
c.append(x)
x = None
break
if x is None:
break
if x is not None:
clusters.append([x])hashing: 100%|██████████| 3555/3555 [00:03<00:00, 958.86it/s] clustering: 100%|██████████| 3555/3555 [00:22<00:00, 159.81it/s]
dups = [c for c in clusters if len(c) > 2]
print(f'{len(dups)} duplications are found')
figure = plt.figure(figsize=(8, 30))
for i, dup in enumerate(dups):
for j, (n, x, _) in enumerate(dup[:5]):
axis = figure.add_subplot(len(dups), 5, i * 5 + j + 1)
axis.imshow(x)
axis.tick_params(labelbottom=False, labelleft=False, bottom=False, left=False)
axis.set_xlabel(n)
plt.show()17 duplications are found
モデル。
モデルはResNet18を使用。このコンペではImageNetなどの学習済みパラメータを使えないので、ネットワークの定義だけを使っています。
model = models.resnet18(pretrained=False, num_classes=10)
model.cuda() ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=512, out_features=10, bias=True)
)データの前処理
データ拡張はhorizontal flipのみを使用。今回の画像データは(width, height, channels)の形式になっていますが、PyTorchの入力データとするために(channels, width, height)の形式に変更しています。
class Dataset(dataset.Dataset):
def __init__(self, images, labels, train=False):
super().__init__()
transforms = []
if train:
transforms.append(albumentations.HorizontalFlip(p=0.5))
transforms.append(albumentations.ToFloat())
self.transform = albumentations.Compose(transforms)
self.images = images
self.labels = labels
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
image = np.rollaxis(self.transform(image=self.images[idx])['image'], 2, 0)
label = self.labels[idx]
return image, label
train_x, valid_x, train_y, valid_y = train_test_split(
train_images, train_labels, test_size=0.2, random_state=RANDOM_SEED)
train_loader = dataset.DataLoader(
Dataset(train_x, train_y, train=True), batch_size=64, shuffle=True)
valid_loader = dataset.DataLoader(
Dataset(valid_x, valid_y), batch_size=64, shuffle=False)
学習
optimizerはadam（学習係数は0.001）を使用しています。
def perform(model, loader, optimizer):
loss_total = 0
accuracy_total = 0
count = 0
for images, labels in loader:
images = images.cuda()
labels = labels.cuda()
preds = model(images)
loss = nn.functional.cross_entropy(preds, labels)
with torch.no_grad():
accuracy = torch.mean((torch.max(preds, dim=1)[1] == labels).float())
if optimizer is not None:
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_total += float(loss.detach()) * len(images)
accuracy_total += float(accuracy.detach()) * len(images)
count += len(images)
return loss_total / count, accuracy_total / count
optimizer = optim.Adam(model.parameters(), lr=0.001)
log = []
for epoch in range(10):
model.train()
with autograd.detect_anomaly():
train_loss, train_accuracy = perform(model, train_loader, optimizer)
model.eval()
with torch.no_grad():
valid_loss, valid_accuracy = perform(model, valid_loader, None)
print('[{}] train(loss/accuracy)={:.2f}/{:.2f}, valid(loss/accuracy)={:.2f}/{:.2f}'.format(
epoch + 1, train_loss, train_accuracy, valid_loss, valid_accuracy))
log.append((epoch + 1, train_loss, train_accuracy, valid_loss, valid_accuracy))[1] train(loss/accuracy)=1.56/0.46, valid(loss/accuracy)=1.69/0.41 [2] train(loss/accuracy)=1.22/0.55, valid(loss/accuracy)=1.40/0.47 [3] train(loss/accuracy)=1.10/0.61, valid(loss/accuracy)=4.75/0.30 [4] train(loss/accuracy)=1.04/0.63, valid(loss/accuracy)=1.25/0.55 [5] train(loss/accuracy)=0.92/0.67, valid(loss/accuracy)=1.51/0.47 [6] train(loss/accuracy)=0.87/0.68, valid(loss/accuracy)=1.56/0.54 [7] train(loss/accuracy)=0.78/0.71, valid(loss/accuracy)=2.48/0.45 [8] train(loss/accuracy)=0.71/0.74, valid(loss/accuracy)=1.64/0.54 [9] train(loss/accuracy)=0.70/0.75, valid(loss/accuracy)=0.98/0.68 [10] train(loss/accuracy)=0.60/0.78, valid(loss/accuracy)=1.10/0.62
figure = plt.figure(figsize=(8, 3))
axis = figure.add_subplot(1, 2, 1)
axis.plot([x[0] for x in log], [x[1] for x in log], label='train')
axis.plot([x[0] for x in log], [x[3] for x in log], label='valid')
axis.set_xlabel('epoch')
axis.set_ylabel('loss')
axis.legend()
axis = figure.add_subplot(1, 2, 2)
axis.plot([x[0] for x in log], [x[2] for x in log], label='train')
axis.plot([x[0] for x in log], [x[4] for x in log], label='valid')
axis.set_xlabel('epoch')
axis.set_ylabel('accuracy')
axis.legend()
plt.show()
推論
dummy_labels = np.zeros((test_images.shape[0], 1), dtype=np.int64)
test_loader = dataset.DataLoader(
Dataset(test_images, dummy_labels), batch_size=64, shuffle=False)
test_labels = []
model.eval()
for images, _ in test_loader:
images = images.cuda()
with torch.no_grad():
preds = model(images)
preds = torch.max(preds, dim=1)[1]
test_labels.extend(int(x) for x in preds)提出ファイルの作成
with open('submission.csv', 'w') as writer:
csv_writer = csv.writer(writer)
csv_writer.writerow(('id', 'y'))
csv_writer.writerows((i + 1, x) for i, x in enumerate(test_labels))
google.colab.files.download('submission.csv')