Tensorflow-python:語意分割-2-模型訓練(Day 8)

2024 iThome 鐵人賽

DAY 8

AI/ ML & Data

從0開始的影像辨識之路系列第 9 篇

16th鐵人賽

max

2024-08-08 00:13:48

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本次主題是以colab的環境進行學習的，在本篇文章中，我將講解影像辨識的基礎技能在接下來的文章中這些技能將多次出現，先讀過這些語法再繼續去看後面的文章會比較能快速上手喔。依照進度每個禮拜都會記錄不同的影像辨識方法，基本順序會從:

OpenCV
圖片分類(Tensorflow-Image classification)
語意分割(Semantic Segmentation)
生成模仿圖片(CycleGAN and pix2pix in PyTorch)
物件辨識(tensorflow object detection)
額外分享(MediaPipe)

在我們上一篇文章中我們已經把資料集準備好了，接下來我們就回到colab裡面進行模型訓練吧。
雲端硬碟掛載:

from google.colab import drive
drive.mount('/content/drive')

模型訓練:
將雲端硬碟掛載好之後，我們就可以開始訓練模型了。後面文章會再補充模型的介紹以及模型的堆疊。在訓練好模型之後我們會將模型儲存到雲端硬碟，方便下次直接使用模型。

import os
import datetime
from PIL import Image
from IPython.display import display
import PIL
from PIL import ImageOps
import matplotlib.pyplot as plt
import cv2 as cv
import tensorflow as tf 
from tensorflow import keras
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Activation, ReLU
from tensorflow.keras.layers import BatchNormalization, Conv2DTranspose, Concatenate
from tensorflow.keras.models import Model, Sequential
import tensorflow as tf
import numpy as np
import tarfile
from matplotlib import gridspec
from keras.preprocessing.image import load_img
from IPython.display import clear_output
import matplotlib.pyplot as plt
from tensorflow.keras import layers
import matplotlib.image

input_dir = "/content/drive/MyDrive/Segmentation/images"
target_dir = "/content/drive/MyDrive/Segmentation/masks"
num_num_class = 5
batch_size = 1
img_size = (160, 160)

input_img_paths = sorted(
    [ 
        os.path.join(input_dir, fname)
        for fname in os.listdir(input_dir)
        if fname.endswith(".png")
    ]
)


target_img_paths = sorted(
    [
        os.path.join(target_dir, fname)
        for fname in os.listdir(target_dir)
        if fname.endswith(".png") and not fname.startswith(".")
    ]
)

print("樣本數:", len(input_img_paths))

for input_path, target_path in zip(input_img_paths[:10], target_img_paths[:10]):
    print(input_path, "|", target_path)

def convolution_operation(entered_input, filters=64):
    conv1 = Conv2D(filters, kernel_size = (3,3), padding = "same")(entered_input)
    batch_norm1 = BatchNormalization()(conv1)
    act1 = ReLU()(batch_norm1)
    conv2 = Conv2D(filters, kernel_size = (3,3), padding = "same")(act1)
    batch_norm2 = BatchNormalization()(conv2)
    act2 = ReLU()(batch_norm2)
    
    return act2

class data_read(keras.utils.Sequence):
    def __init__(self, batch_size, img_size, input_img_paths, target_img_paths):
        self.batch_size = batch_size
        self.img_size = img_size
        self.input_img_paths = input_img_paths
        self.target_img_paths = target_img_paths

    def __len__(self):
        return len(self.target_img_paths) // self.batch_size

    def __getitem__(self, idx):
        i = idx * self.batch_size
        batch_input_img_paths = self.input_img_paths[i : i + self.batch_size]
        batch_target_img_paths = self.target_img_paths[i : i + self.batch_size]
        x = np.zeros((batch_size,) + self.img_size + (3,), dtype="float32")
        for j, path in enumerate(batch_input_img_paths):
            img = load_img(path, target_size=self.img_size)
            x[j] = img
        y = np.zeros((batch_size,) + self.img_size + (1,), dtype="uint8")
        for j, path in enumerate(batch_target_img_paths):
            img = load_img(path, target_size=self.img_size, color_mode="grayscale")
            img = np.clip(img, 0, 9)
            y[j] = np.expand_dims(img, 2)
        return x, y

def decoder(entered_input, skip, filters=64):
    Upsample = Conv2DTranspose(filters, (2, 2), strides=2, padding="same")(entered_input)
    Connect_Skip = Concatenate()([Upsample, skip])
    out = convolution_operation(Connect_Skip, filters)
    return out

def encoder(entered_input, filters=64):
    enc1 = convolution_operation(entered_input, filters)
    MaxPool1 = MaxPooling2D(strides = (2,2))(enc1)
    return enc1, MaxPool1

def get_model(img_size):
    input1 = Input((160,160,3),batch_size=1)
    skip1, encoder_1 = encoder(input1, 8)
    skip2, encoder_2 = encoder(encoder_1, 8*2)
    skip3, encoder_3 = encoder(encoder_2, 8*4)
    skip4, encoder_4 = encoder(encoder_3, 8*8)
    conv_block = convolution_operation(encoder_4, 8*8)
    decoder_1 = decoder(conv_block, skip4, 8*8)
    decoder_2 = decoder(decoder_1, skip3, 8*4)
    decoder_3 = decoder(decoder_2, skip2, 8*2)
    decoder_4 = decoder(decoder_3, skip1, 8)
    out = Conv2D(10,1, padding="same", activation="sigmoid")(decoder_4)
    model = Model(input1, out)
    return model

keras.backend.clear_session()
model = get_model((160,160))
model.summary()
val_samples = 10
train_input_img_paths = input_img_paths
train_target_img_paths = target_img_paths
val_input_img_paths = input_img_paths
val_target_img_paths = target_img_paths

train_gen = data_read(
    1, img_size, train_input_img_paths, train_target_img_paths
)
val_gen = data_read(1, img_size, val_input_img_paths, val_target_img_paths)

tf.config.run_functions_eagerly(True)
model.compile(optimizer="nAdam", loss="sparse_categorical_crossentropy",
              metrics=['accuracy'])

class DisplayCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs=None):
    clear_output(wait=True)
    show_predictions()
    print ('\nSample Prediction after epoch {}\n'.format(epoch+1))

log_dir = "/content/drive/MyDrive/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")

callbacks = [
    tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
]

log_dir = "/content/drive/MyDrive/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)


print(train_gen)
epochs = 10
model.fit(train_gen,validation_data=val_gen, epochs=epochs)
model.save('/content/drive/MyDrive/fit/model_1.h5')

訓練完成後成果圖: