import cv2

import numpy as np

import tensorflow as tf

import os

from tensorflow.python.framework import graph_util

# 参考连接 https://blog.csdn.net/tensorflowforum/article/details/112352764 代码

# 参考连接 参数详解：https://blog.csdn.net/weixin_43529465/article/details/124721583

# https://blog.csdn.net/rain6789/article/details/78754516

class SingleCnn(tf.keras.Model):

    def __init__(self):

        super(SingleCnn, self).__init__()

        # filters=1 卷积核数目，相当于卷积核的channel

        self.conv = tf.keras.layers.Conv2D(filters=1,

                                           kernel_size=[1, 1],

                                           # valid表示不填充， same表示合理填充

                                           padding='valid',

                                        # data_format='channels_last',-> 表示HWC，输入可以定义批次

                                           data_format='channels_last',

                                           use_bias=False,

                                           kernel_initializer=tf.keras.initializers.he_uniform(seed=None),

                                           name="conv")

    def call(self, inputs):

        x = self.conv(inputs)

        return x

if __name__ == "__main__":

    # 图像数据

    imagefile = r"catanddog\cat\5.JPG"

    img = cv2.imread(imagefile)

    img = cv2.resize(img, (64, 64))

    img = np.expand_dims(img, axis=0)

    print(img.shape, type(img), img.dtype)

    img = img.astype(np.uint8)

    singlecnn = SingleCnn()

    output = singlecnn(img)

    imagefile = r"catanddog\cat\5.JPG"

    img = cv2.imread(imagefile)

    img = cv2.resize(img, (64, 64))

    img = np.expand_dims(img, axis=0)

    print(img.shape, type(img), img.dtype)

    img = img.astype(np.uint8)

Value for attr 'T' of uint8 is not in the list of allowed values: half, bfloat16, float, double, int32

	; NodeDef: {{node Conv2D}}; Op<name=Conv2D; signature=input:T, filter:T -> output:T; attr=T:type,allowed=[DT_HALF, DT_BFLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32]; attr=strides:list(int); attr=use_cudnn_on_gpu:bool,default=true; attr=padding:string,allowed=["SAME", "VALID", "EXPLICIT"]; attr=explicit_paddings:list(int),default=[]; attr=data_format:string,default="NHWC",allowed=["NHWC", "NCHW"]; attr=dilations:list(int),default=[1, 1, 1, 1]> [Op:Conv2D]

Call arguments received by layer 'conv' (type Conv2D):

  • inputs=tf.Tensor(shape=(1, 64, 64, 3), dtype=uint8)

    # 未量化的model不支持int32和int8

    # img = img.astype(np.int32)

    img = tf.convert_to_tensor(img, np.float32)

    print(img.shape, type(img), img.dtype)

    # =========== ckpt保存 with session的写法tf2 已不再使用 ===========

    # with tf.Session(graph=tf.Graph()) as sess:

    #     constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['op_to_store'])

    # 保存参考 https://zhuanlan.zhihu.com/p/146243327

    # save_format='tf' 代表保存pb

    singlecnn.save('./pbmodel/singlecnn.pb', save_format='tf')

    # 加载模型 验证可以加载

    new_model = tf.keras.models.load_model('./pbmodel/singlecnn.pb', compile=False)

    output_ = new_model(img)

    # print(output_.shape, output_[0][2:6][2:6])

    print(output_.shape)