代码中无padding和stride的具体设置信息，这种情况下怎么看padding和stride大小呢？

此代码是tensorflow利用CNN对MNIST进行分类的，来源是CSDN中某篇博文，暂时没找到链接，等找到了再贴上来。
我想计算卷积之后的Feature map维度，看了一下网上的公式，都是要padding和stride的，但是这个代码里面没写，百度无果，这种情况下，padding和stride是默认的嘛？要怎么看呢？

import os
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
from keras.datasets import mnist
class CNN(object):
    def __init__(self):
        model = models.Sequential()
        # 第1层卷积，卷积核大小为3*3，32个，28*28为待训练图片的大小
        model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
        model.add(layers.MaxPooling2D((2, 2)))
        # 第2层卷积，卷积核大小为3*3，64个
        model.add(layers.Conv2D(64, (3, 3), activation='relu'))
        model.add(layers.MaxPooling2D((2, 2)))
        # 第3层卷积，卷积核大小为3*3，64个
        model.add(layers.Conv2D(64, (3, 3), activation='relu'))

        model.add(layers.Flatten())
        model.add(layers.Dense(64, activation='relu'))
        model.add(layers.Dense(10, activation='softmax'))

        model.summary()

        self.model = model
class DataSource(object):
    def __init__(self):
        # mnist数据集存储的位置，如何不存在将自动下载
        (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
        # 6万张训练图片，1万张测试图片
        train_images = train_images.reshape((60000, 28, 28, 1))
        test_images = test_images.reshape((10000, 28, 28, 1))
        # 像素值映射到 0 - 1 之间
        train_images, test_images = train_images / 255.0, test_images / 255.0

        self.train_images, self.train_labels = train_images, train_labels
        self.test_images, self.test_labels = test_images, test_labels
class Train:
    def __init__(self):
        self.cnn = CNN()
        self.data = DataSource()

    def train(self):
        check_path = './ckpt/cp-{epoch:04d}.ckpt'
        # period 每隔5epoch保存一次
      save_model_cb=tf.keras.callbacks.ModelCheckpoint(check_path,save_weights_only=True, verbose=2, period=5)

        self.cnn.model.compile(optimizer='adam',
                               loss='sparse_categorical_crossentropy',
                               metrics=['accuracy'])
        self.cnn.model.fit(self.data.train_images, self.data.train_labels, epochs=20, batch_size=128, callbacks=[save_model_cb])
        test_loss, test_acc = self.cnn.model.evaluate(self.data.test_images, self.data.test_labels)
        print("准确率: %.4f，共测试了%d张图片 " % (test_acc, len(self.data.test_labels)))
if __name__ == "__main__":
    app = Train()
    app.train()