import  tensorflow as tf
from    tensorflow.keras import layers, optimizers, datasets, Sequential
import  os
from    resnet import resnet18

os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
tf.random.set_seed(2345)





def preprocess(x, y):
    # 将数据映射到-1~1
    x = 2*tf.cast(x, dtype=tf.float32) / 255. - 1
    y = tf.cast(y, dtype=tf.int32) # 类型转换
    return x,y


(x,y), (x_test, y_test) = datasets.cifar10.load_data() # 加载数据集
y = tf.squeeze(y, axis=1) # 删除不必要的维度
y_test = tf.squeeze(y_test, axis=1) # 删除不必要的维度
print(x.shape, y.shape, x_test.shape, y_test.shape)


train_db = tf.data.Dataset.from_tensor_slices((x,y)) # 构建训练集
# 随机打散，预处理，批量化
train_db = train_db.shuffle(1000).map(preprocess).batch(512)

test_db = tf.data.Dataset.from_tensor_slices((x_test,y_test)) #构建测试集
# 随机打散，预处理，批量化
test_db = test_db.map(preprocess).batch(512)
# 采样一个样本
sample = next(iter(train_db))
print('sample:', sample[0].shape, sample[1].shape,
      tf.reduce_min(sample[0]), tf.reduce_max(sample[0]))


def main():

    # [b, 32, 32, 3] => [b, 1, 1, 512]
    model = resnet18() # ResNet18网络
    model.build(input_shape=(None, 32, 32, 3))
    model.summary() # 统计网络参数
    optimizer = optimizers.Adam(lr=1e-4) # 构建优化器

    for epoch in range(100): # 训练epoch

        for step, (x,y) in enumerate(train_db):

            with tf.GradientTape() as tape:
                # [b, 32, 32, 3] => [b, 10],前向传播
                logits = model(x)
                # [b] => [b, 10],one-hot编码
                y_onehot = tf.one_hot(y, depth=10)
                # 计算交叉熵
                loss = tf.losses.categorical_crossentropy(y_onehot, logits, from_logits=True)
                loss = tf.reduce_mean(loss)
            # 计算梯度信息
            grads = tape.gradient(loss, model.trainable_variables)
            # 更新网络参数
            optimizer.apply_gradients(zip(grads, model.trainable_variables))

            if step %50 == 0:
                print(epoch, step, 'loss:', float(loss))



        total_num = 0
        total_correct = 0
        for x,y in test_db:

            logits = model(x)
            prob = tf.nn.softmax(logits, axis=1)
            pred = tf.argmax(prob, axis=1)
            pred = tf.cast(pred, dtype=tf.int32)

            correct = tf.cast(tf.equal(pred, y), dtype=tf.int32)
            correct = tf.reduce_sum(correct)

            total_num += x.shape[0]
            total_correct += int(correct)

        acc = total_correct / total_num
        print(epoch, 'acc:', acc)



if __name__ == '__main__':
    main()