预测图像中的点序列

Question

我的训练集是一组图像（3 通道或 1 ofc 我只使用一种类型的通道）。标签是我想从图像中预测的特定顺序的一系列点。

我正在使用受 tensorflow 网站上的图像字幕示例启发的模型。这也是本文采用的方法 https://arxiv.org/pdf/1901.03781.pdf

class CNN_Encoder(tf.keras.Model):
    # Since you have already extracted the features and dumped it using pickle
    # This encoder passes those features through a Fully connected layer
    def __init__(self, embedding_dim):
        super(CNN_Encoder, self).__init__()
        self.fc = tf.keras.layers.Dense(embedding_dim)

    def call(self, x):
        x = self.fc(x)
        x = tf.nn.relu(x)
        return x

class RNN_Decoder(tf.keras.Model):
    def __init__(self, embedding_dim, units, output_dim):
        super(RNN_Decoder, self).__init__()
        self.units = units


        self.gru = tf.keras.layers.GRU(self.units,
                                       return_sequences=True,
                                       return_state=True,
                                       recurrent_initializer='glorot_uniform')
        self.fc1 = tf.keras.layers.Dense(self.units)
        self.fc2 = tf.keras.layers.Dense(output_dim)

    def call(self, x, features, hidden):


        x = tf.concat((features, x), axis=-1)
        output, state = self.gru(x)
        x = self.fc1(state)
        x = self.fc2(x)
        return x

    def reset_state(self, batch_size):
        return tf.zeros((batch_size, self.units))

@tf.function
def train_step(img_tensor, target):
    loss = 0


    hidden = decoder.reset_state(batch_size=target.shape[0])
    dec_input = tf.expand_dims([[0., 0.]] * target.shape[0], 1)
    with tf.GradientTape() as tape:

        features = encoder(img_tensor)
        for i in (range(1, target.shape[1])):
            predictions = decoder(dec_input, features, hidden)
            loss += loss_function(target[:, i], predictions)

            # using teacher forcing
            dec_input = tf.expand_dims(target[:, i], 1)
    total_loss = (loss / int(target.shape[1]))
    trainable_variables = encoder.trainable_variables + decoder.trainable_variables
    gradients = tape.gradient(loss, trainable_variables)
    optimizer.apply_gradients(zip(gradients, trainable_variables))
    return loss, total_loss

EPOCHS = 20
batch_size = 8
for epoch in tqdm(range(start_epoch, EPOCHS)):
    start = time.time()
    total_loss = 0

    for (batch, (img_tensor, target)) in enumerate((data_generator(preds_t, labels_t))):
        img_tensor = img_tensor.reshape((-1, 1, 128*128))
        batch_loss, t_loss = train_step(img_tensor, target)
        total_loss += t_loss

        if batch % 100 == 0:
            print ('Epoch {} Batch {} Loss {:.4f}'.format(
              epoch + 1, batch, batch_loss.numpy() / int(target.shape[1])))
        if batch == 10000:

            break
    # storing the epoch end loss value to plot later
    #loss_plot.append(total_loss / num_steps)

    if epoch % 5 == 0:
        ckpt_manager.save()

    print ('Epoch {} Loss {:.6f}'.format(epoch + 1,
                                         total_loss/num_steps))
    print ('Time taken for 1 epoch {} sec\n'.format(time.time() - start))

对于特征向量。我正在提取 unet 的最后一层。所以每张图片的大小都是 1x128x128。我将它重塑为 1x1x128*128。然后我通过一个完全连接的层。然后形状变成 1x1x256

我想预测的标签是图像坐标 (x, y)。 gru层的输入是串联 1x1x256 , 1x1x2 （t-1 坐标）。然后我进一步通过一个 2 层 fc 层，输出维度为 2 为 2 坐标。我暂时删除了注意力以获得更简单的模型。我规范化我的图像。我用 0,0 填充坐标序列，开始 -1，-1 结束，-2，-2 常规填充，以获得 350x2 的统一序列长度。

网络好像学的不多。我只是在图像上沿对角线散布了几个点。我看到的图像字幕模型最大的不同是单词可以转换为嵌入，然后你有一个 128 个图像特征和 128 个单词特征被连接起来并输入到 lstm 中。在我的例子中，序列信息只有 1 个条目。这可能是网络学习不多的原因。

如果有人对我应该更改的内容有任何见解那就太好了

Answer 1

你的问题需要一定的经验和深入的调查。我只会针对欠拟合问题提出一般性建议。这是 things to try.

的列表

就我个人而言，我会先尝试对单个批次进行过度拟合。

预测图像中的点序列

Predicting point sequence in image

python

deep-learning

tensorflow

recurrent-neural-network

seq2seq