ucf101wrapFlow.py 文件源码

def VGG16(inputs, outputs, loss_weight, labels):
    """
    Spatial stream based on VGG16

    """

    with slim.arg_scope([slim.conv2d, slim.fully_connected], 
                        activation_fn=tf.nn.relu,
                        weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
                        weights_regularizer=slim.l2_regularizer(0.0005)):

        # conv1_1 = slim.conv2d(tf.concat(3, [inputs, outputs]), 64, [3, 3], scope='conv1_1')
        conv1_1 = slim.conv2d(inputs, 64, [3, 3], scope='conv1_1')
        conv1_2 = slim.conv2d(conv1_1, 64, [3, 3], scope='conv1_2')
        pool1 = slim.max_pool2d(conv1_2, [2, 2], scope='pool1')

        conv2_1 = slim.conv2d(pool1, 128, [3, 3], scope='conv2_1')
        conv2_2 = slim.conv2d(conv2_1, 128, [3, 3], scope='conv2_2')
        pool2 = slim.max_pool2d(conv2_2, [2, 2], scope='pool2')

        conv3_1 = slim.conv2d(pool2, 256, [3, 3], scope='conv3_1')
        conv3_2 = slim.conv2d(conv3_1, 256, [3, 3], scope='conv3_2')
        conv3_3 = slim.conv2d(conv3_2, 256, [3, 3], scope='conv3_3')
        pool3 = slim.max_pool2d(conv3_3, [2, 2], scope='pool3')

        conv4_1 = slim.conv2d(pool3, 512, [3, 3], scope='conv4_1')
        conv4_2 = slim.conv2d(conv4_1, 512, [3, 3], scope='conv4_2')
        conv4_3 = slim.conv2d(conv4_2, 512, [3, 3], scope='conv4_3')
        pool4 = slim.max_pool2d(conv4_3, [2, 2], scope='pool4')

        conv5_1 = slim.conv2d(pool4, 512, [3, 3], scope='conv5_1')
        conv5_2 = slim.conv2d(conv5_1, 512, [3, 3], scope='conv5_2')
        conv5_3 = slim.conv2d(conv5_2, 512, [3, 3], scope='conv5_3')
        pool5 = slim.max_pool2d(conv5_3, [2, 2], scope='pool5')

        flatten5 = slim.flatten(pool5, scope='flatten5')
        fc6 = slim.fully_connected(flatten5, 4096, scope='fc6')
        dropout6 = slim.dropout(fc6, 0.9, scope='dropout6')
        fc7 = slim.fully_connected(dropout6, 4096, scope='fc7')
        dropout7 = slim.dropout(fc7, 0.9, scope='dropout7')
        fc8 = slim.fully_connected(dropout7, 101, activation_fn=None, scope='fc8')
        prob = tf.nn.softmax(fc8)
        predictions = tf.argmax(prob, 1)

        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(fc8, labels)
        actionLoss = tf.reduce_mean(cross_entropy)

        zeroCon = tf.constant(0)
        losses = [zeroCon, zeroCon, zeroCon, zeroCon, zeroCon, zeroCon, actionLoss]
        flows_all = [zeroCon, zeroCon, zeroCon, zeroCon, zeroCon, zeroCon, prob]

        slim.losses.add_loss(actionLoss)

        return losses, flows_all, predictions