Tensorflow2 自定義數據集圖片完成圖片分類任務

對於自定義數據集的圖片任務，通用流程通常分爲如下幾個步驟：

• Load data

• Train-Val-Test

• Build model

• Transfer Learning

其中大部分精力會花在數據的準備和預處理上，本文用一種較爲通用的數據處理手段，並經過手動構建，簡單模型， 層數較深的resnet網絡，和基於VGG19的遷移學習。

你能夠經過這個例子，快速搭建網絡，並訓練處一個較爲滿意的結果。

1. Load data

數據集來自Pokemon的5分類數據， 每一種的圖片數量爲200多張，是一個較小型的數據集。

官方項目連接：

https://www.pyimagesearch.com/2018/04/16/keras-and-convolutional-neural-networks-cnns/

1.1 數據集介紹

Pokemon文件夾中包含5個子文件，其中每一個子文件夾名爲對應的類別名。文件夾中包含有png， jpeg的圖片文件。

1.2 解題思路

• 因爲文件夾中沒有劃分，訓練集和測試集，因此須要構建一個csv文件讀取全部的文件，及其類別

• shuffle數據集之後，劃分Train_val_test

• 對數據進行預處理, 數據標準化，數據加強， 可視化處理

"""python
# 建立數字編碼表

import os
  import glob
  import random
  import csv
  import tensorflow as tf
  from tensorflow import keras
  import matplotlib.pyplot as plt
  import time
  
  
  def load_csv(root, filename, name2label):
      """
      將分散在各文件夾中的圖片, 轉換爲圖片和label對應的一個dataset文件, 格式爲csv
      :param root: 文件路徑(每一個子文件夾中的文件屬於一類)
      :param filename: 文件名
      :param name2label: 類名編碼表  {'類名1':0, '類名2':1..}
      :return: images, labels
      """
      # 判斷是否csv文件已經生成
      if not os.path.exists(os.path.join(root, filename)):  # join-將路徑與文件名何爲一個路徑並返回(沒有會生成新路徑)
          images = []  # 存的是文件路徑
          for name in name2label.keys():
              # pokemon\pikachu\00000001.png
              # glob.glob() 利用通配符檢索路徑內的文件,相似於正則表達式
              images += glob.glob(os.path.join(root, name, '*'))  # png, jpg, jpeg
          print(name2label)
          print(len(images), images)
  
          random.shuffle(images)
  
          with open(os.path.join(root, filename), 'w', newline='') as f:
              writer = csv.writer(f)
              for img in images:
                  name = img.split(os.sep)[1]  # os.sep 表示分隔符 window-'\\' , linux-'/'
                  label = name2label[name]  # 0, 1, 2..
                  # 'pokemon\\bulbasaur\\00000000.png', 0
                  writer.writerow([img, label])  # 若是不設定newline='', 2個數據會分爲2行寫
              print('write into csv file:', filename)
  
      # 讀取現有文件
      images, labels = [], []
      with open(os.path.join(root, filename)) as f:
          reader = csv.reader(f)
          for row in reader:
              # 'pokemon\\bulbasaur\\00000000.png', 0
              img, label = row
              label = int(label)  # str-> int
              images.append(img)
              labels.append(label)
  
      assert len(images) == len(labels)
  
      return images, labels
  
  
  def load_pokemon(root, mode='train'):
      """
      # 建立數字編碼表
      :param root: root path
      :param mode: train, valid, test
      :return: images, labels, name2label
      """
  
      name2label = {}  # {'bulbasaur': 0, 'charmander': 1, 'mewtwo': 2, 'pikachu': 3, 'squirtle': 4}
      for name in sorted(os.listdir(os.path.join(root))):
          # sorted() 是爲了復現結果的一致性
          # os.listdir - 返回路徑下的全部文件(文件夾,文件)列表
          if not os.path.isdir(os.path.join(root, name)):  # 是否爲文件夾且是否存在
              continue
          # 每一個類別編碼一個數字
          name2label[name] = len(name2label)
  
      # 讀取label
      images, labels = load_csv(root, 'images.csv', name2label)
  
      # 劃分數據集 [6:2:2]
      if mode == 'train':
          images = images[:int(0.6 * len(images))]
          labels = labels[:int(0.6 * len(labels))]  # len(images) == len(labels)
  
      elif mode == 'valid':
          images = images[int(0.6 * len(images)):int(0.8 * len(images))]
          labels = labels[int(0.6 * len(labels)):int(0.8 * len(labels))]
  
      else:
          images = images[int(0.8 * len(images)):]
          labels = labels[int(0.8 * len(labels)):]
  
      return images, labels, name2label
  
  
  # imagenet 數據集均值, 方差
  img_mean = tf.constant([0.485, 0.456, 0.406])  # 3 channel
  img_std = tf.constant([0.229, 0.224, 0.225])
  
  def normalization(x, mean=img_mean, std=img_std):
      # [224, 224, 3]
      x = (x - mean) / std
      return x
  
  def denormalization(x, mean=img_mean, std=img_std):
      x = x * std + mean
      return x
  
  
  def preprocess(x, y):
      # x: path, y: label
      x = tf.io.read_file(x)  # 2進制
      # x = tf.image.decode_image(x)
      x = tf.image.decode_jpeg(x, channels=3)  # RGBA
      x = tf.image.resize(x, [244, 244])
  
      # data augmentation
      # x = tf.image.random_flip_up_down(x)
      x = tf.image.random_flip_left_right(x)
      x = tf.image.random_crop(x, [224, 224, 3])  # 模型縮減比例不宜過大,不然會增大訓練難度
  
      x = tf.cast(x, dtype=tf.float32) / 255. # unit8 -> float32
      # U[0,1] -> N(0,1)  # 提升訓練準確度
      x = normalization(x)
  
      y = tf.convert_to_tensor(y)
  
      return x, y
  
  def main():
      images, labels, name2label = load_pokemon('pokemon', 'train')
      print('images:', len(images), images)
      print('labels:', len(labels), labels)
      # print(name2label)
  
      # .map()函數要位於.batch()以前, 不然 x=tf.io.read_file()會一次讀取一個batch的圖片,從而報錯
      db = tf.data.Dataset.from_tensor_slices((images, labels)).map(preprocess).shuffle(1000).batch(32)
  
      # tf.summary()
      # 提供了各種方法（支持各類多種格式）用於保存訓練過程當中產生的數據（好比loss_value、accuracy、整個variable），
      # 這些數據以日誌文件的形式保存到指定的文件夾中。
  
      # 數據可視化：而tensorboard能夠將tf.summary()
      # 記錄下來的日誌可視化，根據記錄的數據格式，生成折線圖、統計直方圖、圖片列表等多種圖。
      # tf.summary()
      # 經過遞增的方式更新日誌，這讓咱們能夠邊訓練邊使用tensorboard讀取日誌進行可視化，從而實時監控訓練過程。
      writer = tf.summary.create_file_writer('logs')
      for step, (x, y) in enumerate(db):
          with writer.as_default():
              x = denormalization(x)
              tf.summary.image('img', x, step=step, max_outputs=9)  # STEP：默認選項，指的是橫軸顯示的是訓練迭代次數
  
              time.sleep(5)
  
  
  
  if __name__ == '__main__':
      main()

"""

2. 構建模型進行訓練

2.1 自定義小型網絡

因爲數據集數量較少，大型網絡的訓練中每每會出現過擬合狀況，這裏就定義了一個2層卷積的小型網絡。
引入early_stopping回調函數後，3個epoch沒有較大變化的狀況下，模型訓練的準確率爲0.8547

"""
# 1. 自定義小型網絡
model = keras.Sequential([
layers.Conv2D(16, 5, 3),
layers.MaxPool2D(3, 3),
layers.ReLU(),
layers.Conv2D(64, 5, 3),
layers.MaxPool2D(2, 2),
layers.ReLU(),
layers.Flatten(),
layers.Dense(64),
layers.ReLU(),
layers.Dense(5)
])

model.build(input_shape=(None, 224, 224, 3))  
  model.summary()
  
  early_stopping = EarlyStopping(
      monitor='val_loss',
      patience=3,
      min_delta=0.001
  )
  
  
  model.compile(optimizer=optimizers.Adam(lr=1e-3),
                 loss=losses.CategoricalCrossentropy(from_logits=True),
                 metrics=['accuracy'])
  model.fit(db_train, validation_data=db_val, validation_freq=1, epochs=100,
             callbacks=[early_stopping])
  model.evaluate(db_test)

"""

2.2 自定義的Resnet網絡

resnet 網絡對於層次較深的網絡的可訓練型提高很大，主要是經過一個identity layer保證了深層次網絡的訓練效果不會弱於淺層網絡。
其餘文章中有詳細介紹resnet的搭建，這裏就不作贅述, 這裏構建了一個resnet18網絡， 準確率0.7607。

"""
import os

import numpy as np
  import tensorflow as tf
  from tensorflow import keras
  from tensorflow.keras import layers
  
  tf.random.set_seed(22)
  np.random.seed(22)
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
  assert tf.__version__.startswith('2.')
  
  
  class ResnetBlock(keras.Model):
  
      def __init__(self, channels, strides=1):
          super(ResnetBlock, self).__init__()
  
          self.channels = channels
          self.strides = strides
  
          self.conv1 = layers.Conv2D(channels, 3, strides=strides,
                                     padding=[[0, 0], [1, 1], [1, 1], [0, 0]])
          self.bn1 = keras.layers.BatchNormalization()
          self.conv2 = layers.Conv2D(channels, 3, strides=1,
                                     padding=[[0, 0], [1, 1], [1, 1], [0, 0]])
          self.bn2 = keras.layers.BatchNormalization()
  
          if strides != 1:
              self.down_conv = layers.Conv2D(channels, 1, strides=strides, padding='valid')
              self.down_bn = tf.keras.layers.BatchNormalization()
  
      def call(self, inputs, training=None):
          residual = inputs
  
          x = self.conv1(inputs)
          x = tf.nn.relu(x)
          x = self.bn1(x, training=training)
          x = self.conv2(x)
          x = tf.nn.relu(x)
          x = self.bn2(x, training=training)
  
          # 殘差鏈接
          if self.strides != 1:
              residual = self.down_conv(inputs)
              residual = tf.nn.relu(residual)
              residual = self.down_bn(residual, training=training)
  
          x = x + residual
          x = tf.nn.relu(x)
          return x
  
  
  class ResNet(keras.Model):
  
      def __init__(self, num_classes, initial_filters=16, **kwargs):
          super(ResNet, self).__init__(**kwargs)
  
          self.stem = layers.Conv2D(initial_filters, 3, strides=3, padding='valid')
  
          self.blocks = keras.models.Sequential([
              ResnetBlock(initial_filters * 2, strides=3),
              ResnetBlock(initial_filters * 2, strides=1),
              # layers.Dropout(rate=0.5),
  
              ResnetBlock(initial_filters * 4, strides=3),
              ResnetBlock(initial_filters * 4, strides=1),
  
              ResnetBlock(initial_filters * 8, strides=2),
              ResnetBlock(initial_filters * 8, strides=1),
  
              ResnetBlock(initial_filters * 16, strides=2),
              ResnetBlock(initial_filters * 16, strides=1),
          ])
  
          self.final_bn = layers.BatchNormalization()
          self.avg_pool = layers.GlobalMaxPool2D()
          self.fc = layers.Dense(num_classes)
  
      def call(self, inputs, training=None):
          # print('x:',inputs.shape)
          out = self.stem(inputs, training = training)
          out = tf.nn.relu(out)
  
          # print('stem:',out.shape)
  
          out = self.blocks(out, training=training)
          # print('res:',out.shape)
  
          out = self.final_bn(out, training=training)
          # out = tf.nn.relu(out)
  
          out = self.avg_pool(out)
  
          # print('avg_pool:',out.shape)
          out = self.fc(out)
  
          # print('out:',out.shape)
  
          return out
  
  
  def main():
      num_classes = 5
  
      resnet18 = ResNet(5)
      resnet18.build(input_shape=(None, 224, 224, 3))
      resnet18.summary()
  
  
  if __name__ == '__main__':
      main()

"""

"""
# 2.resnet18訓練, 圖片數量較小,訓練結果不是特別好
# resnet = ResNet(5) # 0.7607
# resnet.build(input_shape=(None, 224, 224, 3))
# resnet.summary()
"""

2.3 VGG19遷移學習

遷移學習利用了數據集之間的類似性，對於數據集數量較少的時候，訓練效果會遠優於其餘。
在訓練過程當中，使用include_top=False, 去掉最後分類的基層Dense, 從新構建並訓練就能夠了。準確率0.9316

"""
# 3. VGG19遷移學習,遷移學習利用數據集之間的類似性, 結果遠好於其餘2種
# 爲了方便，這裏仍然使用resnet命名
net = tf.keras.applications.VGG19(weights='imagenet', include_top=False, pooling='max' )
net.trainable = False
resnet = keras.Sequential([
net,
layers.Dense(5)
])
resnet.build(input_shape=(None, 224, 224, 3)) # 0.9316
resnet.summary()

early_stopping = EarlyStopping(
      monitor='val_loss',
      patience=3,
      min_delta=0.001
  )
  
  
  resnet.compile(optimizer=optimizers.Adam(lr=1e-3),
                 loss=losses.CategoricalCrossentropy(from_logits=True),
                 metrics=['accuracy'])
  resnet.fit(db_train, validation_data=db_val, validation_freq=1, epochs=100,
             callbacks=[early_stopping])
  resnet.evaluate(db_test)

"""

附錄：

train_scratch.py 代碼

"""

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers, optimizers, losses
from tensorflow.keras.callbacks import EarlyStopping

tf.random.set_seed(22)
np.random.seed(22)
assert tf.__version__.startswith('2.')

# 設置GPU顯存按需分配
# gpus = tf.config.experimental.list_physical_devices('GPU')
# if gpus:
#     try:
#         # Currently, memory growth needs to be the same across GPUs
#         for gpu in gpus:
#             tf.config.experimental.set_memory_growth(gpu, True)
#         logical_gpus = tf.config.experimental.list_logical_devices('GPU')
#         print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
#     except RuntimeError as e:
#         # Memory growth must be set before GPUs have been initialized
#         print(e)

from pokemon import load_pokemon, normalization
from resnet import ResNet


def preprocess(x, y):
    # x: 圖片的路徑，y：圖片的數字編碼
    x = tf.io.read_file(x)
    x = tf.image.decode_jpeg(x, channels=3)  # RGBA
    # 圖片縮放
    # x = tf.image.resize(x, [244, 244])
    # 圖片旋轉
    # x = tf.image.rot90(x,2)
    # 隨機水平翻轉
    x = tf.image.random_flip_left_right(x)
    # 隨機豎直翻轉
    # x = tf.image.random_flip_up_down(x)

    # 圖片先縮放到稍大尺寸
    x = tf.image.resize(x, [244, 244])
    # 再隨機裁剪到合適尺寸
    x = tf.image.random_crop(x, [224, 224, 3])

    # x: [0,255]=> -1~1
    x = tf.cast(x, dtype=tf.float32) / 255.
    x = normalization(x)
    y = tf.convert_to_tensor(y)
    y = tf.one_hot(y, depth=5)

    return x, y


batchsz = 32

# create train db
images1, labels1, table = load_pokemon('pokemon', 'train')
db_train = tf.data.Dataset.from_tensor_slices((images1, labels1))
db_train = db_train.shuffle(1000).map(preprocess).batch(batchsz)
# create validation db
images2, labels2, table = load_pokemon('pokemon', 'valid')
db_val = tf.data.Dataset.from_tensor_slices((images2, labels2))
db_val = db_val.map(preprocess).batch(batchsz)
# create test db
images3, labels3, table = load_pokemon('pokemon', mode='test')
db_test = tf.data.Dataset.from_tensor_slices((images3, labels3))
db_test = db_test.map(preprocess).batch(batchsz)


# 1. 自定義小型網絡
# resnet = keras.Sequential([
#     layers.Conv2D(16, 5, 3),
#     layers.MaxPool2D(3, 3),
#     layers.ReLU(),
#     layers.Conv2D(64, 5, 3),
#     layers.MaxPool2D(2, 2),
#     layers.ReLU(),
#     layers.Flatten(),
#     layers.Dense(64),
#     layers.ReLU(),
#     layers.Dense(5)
# ])  # 0.8547


# 2.resnet18訓練, 圖片數量較小,訓練結果不是特別好
# resnet = ResNet(5)  # 0.7607
# resnet.build(input_shape=(None, 224, 224, 3))
# resnet.summary()


# 3. VGG19遷移學習,遷移學習利用數據集之間的類似性, 結果遠好於其餘2種
net = tf.keras.applications.VGG19(weights='imagenet', include_top=False, pooling='max' )
net.trainable = False
resnet = keras.Sequential([
    net,
    layers.Dense(5)
])
resnet.build(input_shape=(None, 224, 224, 3))   # 0.9316
resnet.summary()

early_stopping = EarlyStopping(
    monitor='val_loss',
    patience=3,
    min_delta=0.001
)


resnet.compile(optimizer=optimizers.Adam(lr=1e-3),
               loss=losses.CategoricalCrossentropy(from_logits=True),
               metrics=['accuracy'])
resnet.fit(db_train, validation_data=db_val, validation_freq=1, epochs=100,
           callbacks=[early_stopping])
resnet.evaluate(db_test)

"""