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為了加深理解，我對SSD項目進行了復現，基於原版，有按照自己理解的修改，

項目見github：SSD_Realization_TensorFlow、SSD_Realization_MXNet

構建思路按照訓練主函數的步驟順序，文末貼了出來，下面我們按照這個順序簡要介紹一下各個流程的重點，想要詳細了解的建議看一看之前的解讀源碼的對應篇章（tf），或者看看李沐博士的ssd介紹視頻（雖然不太詳細，不過結合講義思路很清晰，參見：『MXNet』第十彈_物體檢測SSD）。

重點說明

SSD架構主要有四個部分，網絡設計、搜索框設計、學習目標處理、損失函數實現。

網絡設計

重點在於正常前向網絡中挑選出的特征層分別添加兩個卷積出口：分類和回歸出口，用於對應後面的每個搜索框的各個類別得分、以及4個坐標值。

搜索框設計

對應網絡的特征層：每個層有若幹搜索框，我們需要搜索框位置形狀信息。對於tf版本我們保存了每個框的中心點以及HW信息，而mx版本我們保存的是左上右下兩個的4個坐標數值，mx更為直觀，但是tf版本節省空間：一組框對應同一個中心點，不過搜索框信息量不大，b無傷大雅。

學習目標處理

個人感覺最為繁瑣，我們需要的的信息包含（此時已經獲得了）：一組搜索框（實際上指的是全部搜索框的n4個坐標值），圖片的label、圖片的真實框坐標(對應label數目4)，我們需要的就是找到搜索框和真是圖片的標簽聯系，
獲取：
每個搜索框對應的分類（和哪個真實框的IOU最大就選真實框的類別標註給該搜索，也就是說會出現大量的0 class搜索框）

每個搜索框的坐標的回歸目標（同上的尋找方法，空位也為0）
負類掩碼，雖然每張圖片裏面通常只有幾個標註的邊框，但SSD會生成大量的錨框。可以想象很多錨框都不會框住感興趣的物體，就是說跟任何對應感興趣物體的表框的IoU都小於某個閾值。這樣就會產生大量的負類錨框，或者說對應標號為0的錨框。對於這類錨框有兩點要考慮的：
1、邊框預測的損失函數不應該包括負類錨框，因為它們並沒有對應的真實邊框
2、因為負類錨框數目可能遠多於其他，我們可以只保留其中的一些。而且是保留那些目前預測最不確信它是負類的，就是對類0預測值排序，選取數值最小的哪一些困難的負類錨框
所以需要使用掩碼，抑制一部分計算出來的loss。

損失函數

可講的不多，按照公式實現即可，重點也在上一步計算出來的掩碼處理損失函數值一步。

MXNet訓練主函數

if __name__ == ‘__main__‘:
    batch_size = 4
    ctx = mx.cpu(0)
    # ctx = mx.gpu(0)
    # box_metric = mx.MAE()
    cls_metric = mx.metric.Accuracy()
    ssd = ssd_mx.SSDNet()
    ssd.initialize(ctx=ctx)  # mx.init.Xavier(magnitude=2)

    cls_loss = util_mx.FocalLoss()
    box_loss = util_mx.SmoothL1Loss()

    trainer = mx.gluon.Trainer(ssd.collect_params(),
                               ‘sgd‘, {‘learning_rate‘: 0.01, ‘wd‘: 5e-4})

    data = get_iterators(data_shape=304, batch_size=batch_size)
    for epoch in range(30):
        # reset data iterators and metrics
        data.reset()
        cls_metric.reset()
        # box_metric.reset()
        tic = time.time()
        for i, batch in enumerate(data):
            start_time = time.time()
            x = batch.data[0].as_in_context(ctx)
            y = batch.label[0].as_in_context(ctx)
            # 將-1占位符改為背景標簽0，對應坐標框記錄為[0,0,0,0]
            y = nd.where(y < 0, nd.zeros_like(y), y)
            with mx.autograd.record():
                # anchors, 檢測框坐標，[1，n，4]
                # class_preds, 各圖片各檢測框分類情況，[bs，n，num_cls + 1]
                # box_preds, 各圖片檢測框坐標預測情況，[bs, n * 4]
                anchors, class_preds, box_preds = ssd(x, True)

                # box_target, 檢測框的收斂目標，[bs, n * 4]
                # box_mask, 隱藏不需要的背景類，[bs, n * 4]
                # cls_target, 記錄全檢測框的真實類別，[bs，n]
                box_target, box_mask, cls_target = ssd_mx.training_targets(anchors, class_preds, y)

                loss1 = cls_loss(class_preds, cls_target)
                loss2 = box_loss(box_preds, box_target, box_mask)
                loss = loss1 + loss2
            loss.backward()
            trainer.step(batch_size)
            if i % 1 == 0:
                duration = time.time() - start_time
                examples_per_sec = batch_size / duration
                sec_per_batch = float(duration)
                format_str = "[*] step %d,  loss=%.2f (%.1f examples/sec; %.3f sec/batch)"
                print(format_str % (i, nd.sum(loss).asscalar(), examples_per_sec, sec_per_batch))
            if i % 500 == 0:
ssd.model.save_parameters(‘model_mx_{}.params‘.format(epoch))

TensorFlow訓練主函數

def main():

    max_steps = 1500
    batch_size = 32
    adam_beta1 = 0.9
    adam_beta2 = 0.999
    opt_epsilon = 1.0
    num_epochs_per_decay = 2.0
    num_samples_per_epoch = 17125
    moving_average_decay = None

    tf.logging.set_verbosity(tf.logging.DEBUG)
    with tf.Graph().as_default():

        # Create global_step.
        with tf.device("/device:CPU:0"):
            global_step = tf.train.create_global_step()

        ssd = SSDNet()
        ssd_anchors = ssd.anchors

        # tfr解析操作放在GPU下有加速，效果不穩定
        dataset =             tfr_data_process.get_split(‘./TFR_Data‘,
                                       ‘voc2012_*.tfrecord‘,
                                       num_classes=21,
                                       num_samples=num_samples_per_epoch)

        with tf.device("/device:CPU:0"):  # 僅CPU支持隊列操作
            image, glabels, gbboxes =                 tfr_data_process.tfr_read(dataset)

            image, glabels, gbboxes =                 preprocess_img_tf.preprocess_image(image, glabels, gbboxes, out_shape=(300, 300))

            gclasses, glocalisations, gscores =                 ssd.bboxes_encode(glabels, gbboxes, ssd_anchors)

            batch_shape = [1] + [len(ssd_anchors)] * 3  # (1,f層,f層,f層)
            # Training batches and queue.
            r = tf.train.batch(  # 圖片，中心點類別，真實框坐標，得分
                util_tf.reshape_list([image, gclasses, glocalisations, gscores]),
                batch_size=batch_size,
                num_threads=4,
                capacity=5 * batch_size)
            batch_queue = slim.prefetch_queue.prefetch_queue(
                r,  # <-----輸入格式實際上並不需要調整
                capacity=2 * 1)

        # Dequeue batch.
        b_image, b_gclasses, b_glocalisations, b_gscores =             util_tf.reshape_list(batch_queue.dequeue(), batch_shape)  # 重整list

        predictions, localisations, logits, end_points =             ssd.net(b_image, is_training=True, weight_decay=0.00004)

        ssd.losses(logits, localisations,
                   b_gclasses, b_glocalisations, b_gscores,
                   match_threshold=.5,
                   negative_ratio=3,
                   alpha=1,
                   label_smoothing=.0)

        update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
        # =================================================================== #
        # Configure the moving averages.
        # =================================================================== #
        if moving_average_decay:
            moving_average_variables = slim.get_model_variables()
            variable_averages = tf.train.ExponentialMovingAverage(
                moving_average_decay, global_step)
        else:
            moving_average_variables, variable_averages = None, None

        # =================================================================== #
        # Configure the optimization procedure.
        # =================================================================== #
        with tf.device("/device:CPU:0"):  # learning_rate節點使用CPU（不明）
            decay_steps = int(num_samples_per_epoch / batch_size * num_epochs_per_decay)
            learning_rate = tf.train.exponential_decay(0.01,
                                                       global_step,
                                                       decay_steps,
                                                       0.94,  # learning_rate_decay_factor,
                                                       staircase=True,
                                                       name=‘exponential_decay_learning_rate‘)
            optimizer = tf.train.AdamOptimizer(
                learning_rate,
                beta1=adam_beta1,
                beta2=adam_beta2,
                epsilon=opt_epsilon)
            tf.summary.scalar(‘learning_rate‘, learning_rate)

        if moving_average_decay:
            # Update ops executed locally by trainer.
            update_ops.append(variable_averages.apply(moving_average_variables))

        # Variables to train.
        trainable_scopes = None
        if trainable_scopes is None:
            variables_to_train = tf.trainable_variables()
        else:
            scopes = [scope.strip() for scope in trainable_scopes.split(‘,‘)]
            variables_to_train = []
            for scope in scopes:
                variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)
                variables_to_train.extend(variables)

        losses = tf.get_collection(tf.GraphKeys.LOSSES)
        regularization_losses = tf.get_collection(
            tf.GraphKeys.REGULARIZATION_LOSSES)
        regularization_loss = tf.add_n(regularization_losses)
        loss = tf.add_n(losses)
        tf.summary.scalar("loss", loss)
        tf.summary.scalar("regularization_loss", regularization_loss)

        grad = optimizer.compute_gradients(loss, var_list=variables_to_train)
        grad_updates = optimizer.apply_gradients(grad,
                                                 global_step=global_step)
        update_ops.append(grad_updates)
        # update_op = tf.group(*update_ops)

        with tf.control_dependencies(update_ops):
            total_loss = tf.add_n([loss, regularization_loss])
        tf.summary.scalar("total_loss", total_loss)

        # =================================================================== #
        # Kicks off the training.
        # =================================================================== #
        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
        config = tf.ConfigProto(log_device_placement=False,
                                gpu_options=gpu_options)
        saver = tf.train.Saver(max_to_keep=5,
                               keep_checkpoint_every_n_hours=1.0,
                               write_version=2,
                               pad_step_number=False)

        if True:
            import os
            import time

            print(‘start......‘)
            model_path = ‘./logs‘
            batch_size = batch_size
            with tf.Session(config=config) as sess:
                summary = tf.summary.merge_all()
                coord = tf.train.Coordinator()
                threads = tf.train.start_queue_runners(sess=sess, coord=coord)
                writer = tf.summary.FileWriter(model_path, sess.graph)

                init_op = tf.group(tf.global_variables_initializer(),
                                   tf.local_variables_initializer())
                init_op.run()
                for step in range(max_steps):
                    start_time = time.time()
                    loss_value = sess.run(total_loss)
                    # loss_value, summary_str = sess.run([train_tensor, summary_op])
                    # writer.add_summary(summary_str, step)

                    duration = time.time() - start_time
                    if step % 10 == 0:
                        summary_str = sess.run(summary)
                        writer.add_summary(summary_str, step)

                        examples_per_sec = batch_size / duration
                        sec_per_batch = float(duration)
                        format_str = "[*] step %d,  loss=%.2f (%.1f examples/sec; %.3f sec/batch)"
                        print(format_str % (step, loss_value, examples_per_sec, sec_per_batch))
                    # if step % 100 == 0:
                    #     accuracy_step = test_cifar10(sess, training=False)
                    #     acc.append(‘{:.3f}‘.format(accuracy_step))
                    #     print(acc)
                    if step % 500 == 0 and step != 0:
                        saver.save(sess, os.path.join(model_path, "ssd_tf.model"), global_step=step)

                coord.request_stop()
coord.join(threads)

『TensorFlow × MXNet』SSD項目復現經驗