train1.py

from skimage import io,transform
import glob
import os
import tensorflow as tf
import numpy as np
import time
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

#读取花朵图片
def read_img(path):
    cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)]
    otherpath = cate[1]
    flowerpath = cate[0]
    imgs=[]
    labels = []
    flowerCate = [flowerpath+'/'+x for x in os.listdir(flowerpath) if os.path.isdir(flowerpath+'/'+x)]
    # 读取花朵数据
    for idx,folder in enumerate(flowerCate):
        print('reading the dirs :%s' % (folder))
        for im in glob.glob(folder+'/*.jpg'):
            img=io.imread(im)
            img=transform.resize(img,(w,h))
            imgs.append(img)
            labels.append(0)

    print("reading the dirs :%s" % (otherpath))
    for im in glob.glob(otherpath + '/*.jpg'):
        try:
            img = io.imread(im)
            try:
                if img.shape[2] == 3:
                    img = transform.resize(img, (w, h))
                    imgs.append(img)
                    labels.append(1)
            except:
                continue
        except ValueError:
            print("error=================================================>" + im)
            continue

    print(labels)
    return np.asarray(imgs,np.float32),np.asarray(labels,np.int32)

def inference(input_tensor, train, regularizer):
    with tf.variable_scope('layer1-conv1'):
        conv1_weights = tf.get_variable("weight",[5,5,3,32],initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0))
        conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))

    with tf.name_scope("layer2-pool1"):
        pool1 = tf.nn.max_pool(relu1, ksize = [1,2,2,1],strides=[1,2,2,1],padding="VALID")

    with tf.variable_scope("layer3-conv2"):
        conv2_weights = tf.get_variable("weight",[5,5,32,64],initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0))
        conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))

    with tf.name_scope("layer4-pool2"):
        pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

    with tf.variable_scope("layer5-conv3"):
        conv3_weights = tf.get_variable("weight",[3,3,64,128],initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv3_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))
        conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases))

    with tf.name_scope("layer6-pool3"):
        pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

    with tf.variable_scope("layer7-conv4"):
        conv4_weights = tf.get_variable("weight",[3,3,128,128],initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv4_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))
        conv4 = tf.nn.conv2d(pool3, conv4_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu4 = tf.nn.relu(tf.nn.bias_add(conv4, conv4_biases))

    with tf.name_scope("layer8-pool4"):
        pool4 = tf.nn.max_pool(relu4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
        nodes = 6*6*128
        reshaped = tf.reshape(pool4,[-1,nodes])

    with tf.variable_scope('layer9-fc1'):
        fc1_weights = tf.get_variable("weight", [nodes, 1024],
                                      initializer=tf.truncated_normal_initializer(stddev=0.1))
        if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))
        fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1))

        fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)
        if train: fc1 = tf.nn.dropout(fc1, 0.5)

    with tf.variable_scope('layer10-fc2'):
        fc2_weights = tf.get_variable("weight", [1024, 512],
                                      initializer=tf.truncated_normal_initializer(stddev=0.1))
        if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))
        fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1))

        fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)
        if train: fc2 = tf.nn.dropout(fc2, 0.5)

    with tf.variable_scope('layer11-fc3'):
        fc3_weights = tf.get_variable("weight", [512, 5],
                                      initializer=tf.truncated_normal_initializer(stddev=0.1))
        if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights))
        fc3_biases = tf.get_variable("bias", [5], initializer=tf.constant_initializer(0.1))
        logit = tf.matmul(fc2, fc3_weights) + fc3_biases
    return logit

def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
        assert len(inputs) == len(targets)
        if shuffle:
            indices = np.arange(len(inputs))
            np.random.shuffle(indices)
        for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
            if shuffle:
                excerpt = indices[start_idx:start_idx + batch_size]
            else:
                excerpt = slice(start_idx, start_idx + batch_size)
            yield inputs[excerpt], targets[excerpt]

def semantic_alignment(src_feature, tgt_feature, src_label, tgt_label, num_classes=2):
    '''
    input:
    src_feature: feature from source domain
    tgt_feature: feature from target somain
    src_label: source label(one-hot encoding)
    tgt_label: target label(one-hot encoding)
    num_classes : the number of class(e.g., 2)

    output:
    semantic_loss : the semantic loss between domains.
    '''

    source_result = tf.argmax(src_label, 1)  # source label
    target_result = tf.argmax(tgt_label, 1)  # target label
    ones = tf.ones_like(src_feature)#得到一个与源域数据格式一致的全1的张量
    print('ones',ones.shape)
    print('source_result', source_result.shape)
    print('target_result', target_result.shape)
    current_source_count = tf.unsorted_segment_sum(ones, source_result, num_classes)#计算出当前源域数据
    current_target_count = tf.unsorted_segment_sum(ones, target_result, num_classes)#计算出当前目标域数据
    current_positive_source_count = tf.maximum(current_source_count, tf.ones_like(current_source_count))#返回当前源域数据与之间的最大值
    current_positive_target_count = tf.maximum(current_target_count, tf.ones_like(current_target_count))#返回当前目标域数据与之间的最大值
    current_source_centroid = tf.divide(tf.unsorted_segment_sum(data=src_feature, segment_ids= \
    source_result, num_segments=num_classes), current_positive_source_count)
    current_target_centroid = tf.divide(tf.unsorted_segment_sum(data=tgt_feature, segment_ids= \
    target_result, num_segments=num_classes), current_positive_target_count)
    semantic_loss = tf.reduce_mean((tf.square(current_source_centroid - current_target_centroid)))
    return semantic_loss

if __name__ == '__main__':
    # 数据集地址
    path = 'D:/python/workspace/flower/data/'
    # 模型保存地址
    model_path = 'D:/python/workspace/flower/model1/model.ckpt'

    # 将所有的图片resize成100*100
    w = 100
    h = 100
    c = 3

    data, label = read_img(path)

    newlabel = []
    # 打乱顺序
    num_example = data.shape[0]
    arr = np.arange(num_example)
    np.random.shuffle(arr)
    data = data[arr]
    label = label[arr]

    # 将所有数据分为训练集和验证集
    ratio = 0.8
    s = np.int(num_example * ratio)
    x_train = data[:s]
    y_train = label[:s]
    x_val = data[s:]
    y_val = label[s:]

    # -----------------构建网络----------------------
    # 占位符
    x = tf.placeholder(tf.float32, shape=[None, w, h, c], name='x')
    y_ = tf.placeholder(tf.int32, shape=[None, ], name='y_')


    def inference(input_tensor, train, regularizer):
        with tf.variable_scope('layer1-conv1'):
            conv1_weights = tf.get_variable("weight", [5, 5, 3, 32],
                                            initializer=tf.truncated_normal_initializer(stddev=0.1))
            conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0))
            conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')
            relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))

        with tf.name_scope("layer2-pool1"):
            pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")

        with tf.variable_scope("layer3-conv2"):
            conv2_weights = tf.get_variable("weight", [5, 5, 32, 64],
                                            initializer=tf.truncated_normal_initializer(stddev=0.1))
            conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0))
            conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')
            relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))

        with tf.name_scope("layer4-pool2"):
            pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

        with tf.variable_scope("layer5-conv3"):
            conv3_weights = tf.get_variable("weight", [3, 3, 64, 128],
                                            initializer=tf.truncated_normal_initializer(stddev=0.1))
            conv3_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))
            conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME')
            relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases))

        with tf.name_scope("layer6-pool3"):
            pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

        with tf.variable_scope("layer7-conv4"):
            conv4_weights = tf.get_variable("weight", [3, 3, 128, 128],
                                            initializer=tf.truncated_normal_initializer(stddev=0.1))
            conv4_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))
            conv4 = tf.nn.conv2d(pool3, conv4_weights, strides=[1, 1, 1, 1], padding='SAME')
            relu4 = tf.nn.relu(tf.nn.bias_add(conv4, conv4_biases))

        with tf.name_scope("layer8-pool4"):
            pool4 = tf.nn.max_pool(relu4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
            nodes = 6 * 6 * 128
            reshaped = tf.reshape(pool4, [-1, nodes])

        with tf.variable_scope('layer9-fc1'):
            fc1_weights = tf.get_variable("weight", [nodes, 1024],
                                          initializer=tf.truncated_normal_initializer(stddev=0.1))
            if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))
            fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1))

            fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)
            if train: fc1 = tf.nn.dropout(fc1, 0.5)

        with tf.variable_scope('layer10-fc2'):
            fc2_weights = tf.get_variable("weight", [1024, 512],
                                          initializer=tf.truncated_normal_initializer(stddev=0.1))
            if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))
            fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1))

            fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)
            if train: fc2 = tf.nn.dropout(fc2, 0.5)

        with tf.variable_scope('layer11-fc3'):
            fc3_weights = tf.get_variable("weight", [512, 2],
                                          initializer=tf.truncated_normal_initializer(stddev=0.1))
            if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights))
            fc3_biases = tf.get_variable("bias", [2], initializer=tf.constant_initializer(0.1))
            logit = tf.matmul(fc2, fc3_weights) + fc3_biases
        return logit


    # ---------------------------网络结束---------------------------
    regularizer = tf.contrib.layers.l2_regularizer(0.0001)
    logits = inference(x, False, regularizer)

    # (小处理)将logits乘以1赋值给logits_eval，定义name，方便在后续调用模型时通过tensor名字调用输出tensor
    b = tf.constant(value=1, dtype=tf.float32)
    logits_eval = tf.multiply(logits, b, name='logits_eval')

    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_)
    train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
    correct_prediction = tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), y_)
    acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


    # 定义一个函数，按批次取数据
    def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
        assert len(inputs) == len(targets)
        if shuffle:
            indices = np.arange(len(inputs))
            np.random.shuffle(indices)
        for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
            if shuffle:
                excerpt = indices[start_idx:start_idx + batch_size]
            else:
                excerpt = slice(start_idx, start_idx + batch_size)
            yield inputs[excerpt], targets[excerpt]


    # 训练和测试数据，可将n_epoch设置更大一些

    n_epoch = 10
    batch_size = 64
    saver = tf.train.Saver()
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())
    for epoch in range(n_epoch):
        start_time = time.time()

        # training
        train_loss, train_acc, n_batch = 0, 0, 0
        for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True):
            _, err, ac = sess.run([train_op, loss, acc], feed_dict={x: x_train_a, y_: y_train_a})
            train_loss += err
            train_acc += ac
            n_batch += 1
        print("%d  epoch" % epoch)
        print("   train loss: %f" % (np.sum(train_loss) / n_batch))
        print("   train acc: %f" % (np.sum(train_acc) / n_batch))

        # validation
        val_loss, val_acc, n_batch = 0, 0, 0
        for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False):
            err, ac = sess.run([loss, acc], feed_dict={x: x_val_a, y_: y_val_a})
            val_loss += err
            val_acc += ac
            n_batch += 1
        print("   validation loss: %f" % (np.sum(val_loss) / n_batch))
        print("   validation acc: %f" % (np.sum(val_acc) / n_batch))
        print("============================================================ ")
    saver.save(sess, model_path)
    sess.close()