如何使用tensorflow实现多类语义分割

Question

我正在尝试使用 tensorflow 和 tflearn 或 Keras 执行多类语义分割（我尝试了这两个 API）。与此处类似的问题 (How to load Image Masks (Labels) for Image Segmentation in Keras)

我必须用 3 个不同的类别分割图像的不同部分：海（0 类）、船（1 类）、天空（2 类）。

我有 100 张灰度图像（尺寸 400x400）。对于每张图片，我都有对应的标签和 3 个类别。最后，我得到了形状为 (100, 400, 400) 的图像和形状为 (100,400,400,3) 的标签。 （此处解释为：How to implement multi-class semantic segmentation?）

为了能够使用语义分割，我使用了一种热编码（比如这里：https://www.jeremyjordan.me/semantic-segmentation/），我最终得到了这个：

train_images.shape: (100,400,400,1)
train_labels.shape: (100,400,400,3)

其中标签如下：sea [1,0,0]；船 [0,1,0]，天空 [0,0,1]

但是，每次我尝试训练时都会收到此错误：

ValueError: Cannot feed value of shape (22, 240, 240, 3) for Tensor 'TargetsData/Y:0', which has shape '(?, 240, 240, 2)'

我用这个加载模型：

model = TheNet(input_shape=(None, 400, 40, 1))

编辑：这是我使用的模型

• 使用 TFlearn：

  def TheNet(input_size = (80, 400, 400, 2), feature_map=8, kernel_size=5, keep_rate=0.8, lr=0.001, log_dir ="logs",savedir="Results/Session_Dump"):
  
  
  # level 0 input
  layer_0a_input  = tflearn.layers.core.input_data(input_size) #shape=[None,n1,n2,n3,1])
  
  # level 1 down
  layer_1a_conv   = tflearn_conv_2d(net=layer_0a_input, nb_filter=feature_map, kernel=5, stride=1, activation=False)
  layer_1a_stack  = tflearn_merge_2d([layer_0a_input]*feature_map, "concat")
  layer_1a_stack  = tflearn.activations.prelu(layer_1a_stack)
  layer_1a_add    = tflearn_merge_2d([layer_1a_conv,layer_1a_stack], "elemwise_sum")
  layer_1a_down   = tflearn_conv_2d(net=layer_1a_add, nb_filter=feature_map*2, kernel=2, stride=2, dropout=keep_rate)
  
  # level 2 down
  layer_2a_conv   = tflearn_conv_2d(net=layer_1a_down, nb_filter=feature_map*2, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_2a_conv   = tflearn_conv_2d(net=layer_2a_conv, nb_filter=feature_map*2, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_2a_add    = tflearn_merge_2d([layer_1a_down,layer_2a_conv], "elemwise_sum")
  layer_2a_down   = tflearn_conv_2d(net=layer_2a_add, nb_filter=feature_map*4, kernel=2, stride=2, dropout=keep_rate)
  
  # level 3 down
  layer_3a_conv   = tflearn_conv_2d(net=layer_2a_down, nb_filter=feature_map*4, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_3a_conv   = tflearn_conv_2d(net=layer_3a_conv, nb_filter=feature_map*4, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_3a_conv   = tflearn_conv_2d(net=layer_3a_conv, nb_filter=feature_map*4, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_3a_add    = tflearn_merge_2d([layer_2a_down,layer_3a_conv], "elemwise_sum")
  layer_3a_down   = tflearn_conv_2d(net=layer_3a_add, nb_filter=feature_map*8, kernel=2, stride=2, dropout=keep_rate)
  
  # level 4 down
  layer_4a_conv   = tflearn_conv_2d(net=layer_3a_down, nb_filter=feature_map*8, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_4a_conv   = tflearn_conv_2d(net=layer_4a_conv, nb_filter=feature_map*8, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_4a_conv   = tflearn_conv_2d(net=layer_4a_conv, nb_filter=feature_map*8, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_4a_add    = tflearn_merge_2d([layer_3a_down,layer_4a_conv], "elemwise_sum")
  layer_4a_down   = tflearn_conv_2d(net=layer_4a_add, nb_filter=feature_map*16,kernel=2,stride=2,dropout=keep_rate)
  
  # level 5
  layer_5a_conv   = tflearn_conv_2d(net=layer_4a_down, nb_filter=feature_map*16, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_5a_conv   = tflearn_conv_2d(net=layer_5a_conv, nb_filter=feature_map*16, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_5a_conv   = tflearn_conv_2d(net=layer_5a_conv, nb_filter=feature_map*16, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_5a_add    = tflearn_merge_2d([layer_4a_down,layer_5a_conv], "elemwise_sum")
  layer_5a_up     = tflearn_deconv_2d(net=layer_5a_add, nb_filter=feature_map*8, kernel=2, stride=2, dropout=keep_rate)
  
  # level 4 up
  layer_4b_concat = tflearn_merge_2d([layer_4a_add,layer_5a_up], "concat")
  layer_4b_conv   = tflearn_conv_2d(net=layer_4b_concat, nb_filter=feature_map*16, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_4b_conv   = tflearn_conv_2d(net=layer_4b_conv, nb_filter=feature_map*16, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_4b_conv   = tflearn_conv_2d(net=layer_4b_conv, nb_filter=feature_map*16, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_4b_add    = tflearn_merge_2d([layer_4b_conv,layer_4b_concat], "elemwise_sum")
  layer_4b_up     = tflearn_deconv_2d(net=layer_4b_add, nb_filter=feature_map*4, kernel=2, stride=2, dropout=keep_rate)
  
  # level 3 up
  layer_3b_concat = tflearn_merge_2d([layer_3a_add,layer_4b_up], "concat")
  layer_3b_conv   = tflearn_conv_2d(net=layer_3b_concat, nb_filter=feature_map*8, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_3b_conv   = tflearn_conv_2d(net=layer_3b_conv, nb_filter=feature_map*8, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_3b_conv   = tflearn_conv_2d(net=layer_3b_conv, nb_filter=feature_map*8, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_3b_add    = tflearn_merge_2d([layer_3b_conv,layer_3b_concat], "elemwise_sum")
  layer_3b_up     = tflearn_deconv_2d(net=layer_3b_add, nb_filter=feature_map*2, kernel=2, stride=2, dropout=keep_rate)
  
  # level 2 up
  layer_2b_concat = tflearn_merge_2d([layer_2a_add,layer_3b_up], "concat")
  layer_2b_conv   = tflearn_conv_2d(net=layer_2b_concat, nb_filter=feature_map*4, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_2b_conv   = tflearn_conv_2d(net=layer_2b_conv, nb_filter=feature_map*4, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_2b_add    = tflearn_merge_2d([layer_2b_conv,layer_2b_concat], "elemwise_sum")
  layer_2b_up     = tflearn_deconv_2d(net=layer_2b_add, nb_filter=feature_map, kernel=2, stride=2, dropout=keep_rate)
  
  # level 1 up
  layer_1b_concat = tflearn_merge_2d([layer_1a_add,layer_2b_up], "concat")
  layer_1b_conv   = tflearn_conv_2d(net=layer_1b_concat, nb_filter=feature_map*2, kernel=kernel_size, stride=1, dropout=keep_rate)
  layer_1b_add    = tflearn_merge_2d([layer_1b_conv,layer_1b_concat], "elemwise_sum")
  
  # level 0 classifier
  layer_0b_conv   = tflearn_conv_2d(net=layer_1b_add, nb_filter=2, kernel=5, stride=1, dropout=keep_rate)
  layer_0b_clf    = tflearn.layers.conv.conv_2d(layer_0b_conv, 2, 1, 1, activation="softmax")
  
  # Optimizer
  regress = tflearn.layers.estimator.regression(layer_0b_clf, optimizer='adam', loss=dice_loss_2d, learning_rate=lr) # categorical_crossentropy/dice_loss_3d
  
  model   = tflearn.models.dnn.DNN(regress, tensorboard_dir=log_dir)
  
  # Saving the model
  if not os.path.lexists(savedir+"weights"):
      os.makedirs(savedir+"weights")
  model.save(savedir+"weights/weights_session")
  
  return model
  

• 使用 Keras：

  def TheNet(input_shape, nb_kernel, kernel_size, dropout, lr, log_dir ="logs",savedir="Results/Session_Dump"):
  
  layer_0 = keras.Input(shape = input_shape)
  
  #LVL 1 Down
  layer_1_conv = Cust_2D_Conv(layer_0, nb_kernel, kernel_size, stride=1)
  layer_1_stak = keras.layers.concatenate([layer_0,layer_0,layer_0,layer_0,layer_0,layer_0,layer_0,layer_0])
  layer_1_stak = keras.layers.PReLU()(layer_1_stak)
  layer_1_addd = keras.layers.Multiply()([layer_1_conv,layer_1_stak])
  layer_1_down = Cust_2D_Conv(layer_1_addd, nb_kernel=nb_kernel*2, kernel_size=3, stride=2, dropout=0.2)
  
  #LVL 2 Down
  layer_2_conv = Cust_2D_Conv(layer_1_down, nb_kernel=nb_kernel*2, kernel_size=5, stride=1, dropout=0.2)
  layer_2_conv = Cust_2D_Conv(layer_2_conv, nb_kernel=nb_kernel*2, kernel_size=5, stride=1, dropout=0.2)
  layer_2_addd = keras.layers.Multiply()([layer_2_conv,layer_1_down])
  layer_2_down = Cust_2D_Conv(layer_2_addd, nb_kernel=nb_kernel*4, kernel_size=3, stride=2, dropout=0.2)  
  #LVL 3 Down
  layer_3_conv = Cust_2D_Conv(layer_2_down, nb_kernel=nb_kernel*4, kernel_size=5, stride=1, dropout=0.2)
  layer_3_conv = Cust_2D_Conv(layer_3_conv, nb_kernel=nb_kernel*4, kernel_size=5, stride=1, dropout=0.2)
  layer_3_conv = Cust_2D_Conv(layer_3_conv, nb_kernel=nb_kernel*4, kernel_size=5, stride=1, dropout=0.2)
  layer_3_addd = keras.layers.Multiply()([layer_3_conv,layer_2_down])
  layer_3_down = Cust_2D_Conv(layer_3_addd, nb_kernel=nb_kernel*8, kernel_size=3, stride=2, dropout=0.2)
  
  #LVL 4 Down
  layer_4_conv = Cust_2D_Conv(layer_3_down, nb_kernel=nb_kernel*8, kernel_size=5, stride=1, dropout=0.2)
  layer_4_conv = Cust_2D_Conv(layer_4_conv, nb_kernel=nb_kernel*8, kernel_size=5, stride=1, dropout=0.2)
  layer_4_conv = Cust_2D_Conv(layer_4_conv, nb_kernel=nb_kernel*8, kernel_size=5, stride=1, dropout=0.2)
  layer_4_addd = keras.layers.Multiply()([layer_4_conv,layer_3_down])
  layer_4_down = Cust_2D_Conv(layer_4_addd, nb_kernel=nb_kernel*16, kernel_size=3, stride=2, dropout=0.2)
  
  #LVL 5 Down
  layer_5_conv = Cust_2D_Conv(layer_4_down, nb_kernel=nb_kernel*16, kernel_size=5, stride=1, dropout=0.2)
  layer_5_conv = Cust_2D_Conv(layer_5_conv, nb_kernel=nb_kernel*16, kernel_size=5, stride=1, dropout=0.2)
  layer_5_conv = Cust_2D_Conv(layer_5_conv, nb_kernel=nb_kernel*16, kernel_size=5, stride=1, dropout=0.2)
  layer_5_addd = keras.layers.Multiply()([layer_5_conv,layer_4_down])
  layer_5_up = Cust_2D_DeConv(layer_5_addd, nb_kernel=nb_kernel*8, kernel_size=3, stride=2, dropout=0.2)
  
  #LVL 4 Up
  layer_4b_concat = keras.layers.concatenate([layer_5_up, layer_4_addd])
  layer_4b_conv = Cust_2D_Conv(layer_4b_concat, nb_kernel=nb_kernel*16, kernel_size=5, stride=1, dropout=0.2)
  layer_4b_conv = Cust_2D_Conv(layer_4b_conv, nb_kernel=nb_kernel*16, kernel_size=5, stride=1, dropout=0.2)
  layer_4b_conv = Cust_2D_Conv(layer_4b_conv, nb_kernel=nb_kernel*16, kernel_size=5, stride=1, dropout=0.2)
  layer_4b_addd = keras.layers.Multiply()([layer_4b_conv,layer_4b_concat])
  layer_4b_up = Cust_2D_DeConv(layer_4b_addd, nb_kernel=nb_kernel*4, kernel_size=3, stride=2, dropout=0.2)
  
  #LVL 3 Up
  layer_3b_concat = keras.layers.concatenate([layer_4b_up, layer_3_addd])
  layer_3b_conv = Cust_2D_Conv(layer_3b_concat, nb_kernel=nb_kernel*8, kernel_size=5, stride=1, dropout=0.2)
  layer_3b_conv = Cust_2D_Conv(layer_3b_conv, nb_kernel=nb_kernel*8, kernel_size=5, stride=1, dropout=0.2)
  layer_3b_conv = Cust_2D_Conv(layer_3b_conv, nb_kernel=nb_kernel*8, kernel_size=5, stride=1, dropout=0.2)
  layer_3b_addd = keras.layers.Multiply()([layer_3b_conv,layer_3b_concat])
  layer_3b_up = Cust_2D_DeConv(layer_3b_addd, nb_kernel=nb_kernel*2, kernel_size=3, stride=2, dropout=0.2)
  
  #LVL 2 Up
  layer_2b_concat = keras.layers.concatenate([layer_3b_up, layer_2_addd])
  layer_2b_conv = Cust_2D_Conv(layer_2b_concat, nb_kernel=nb_kernel*4, kernel_size=5, stride=1, dropout=0.2)
  layer_2b_conv = Cust_2D_Conv(layer_2b_conv, nb_kernel=nb_kernel*4, kernel_size=5, stride=1, dropout=0.2)
  layer_2b_addd = keras.layers.Multiply()([layer_2b_conv,layer_2b_concat])
  layer_2b_up = Cust_2D_DeConv(layer_2b_addd, nb_kernel=nb_kernel, kernel_size=3, stride=2, dropout=0.2)
  
  #LVL 1 Up
  layer_1b_concat = keras.layers.concatenate([layer_2b_up, layer_1_addd])
  layer_1b_conv = Cust_2D_Conv(layer_1b_concat, nb_kernel=nb_kernel*2, kernel_size=5, stride=1, dropout=0.2)
  layer_1b_addd = keras.layers.Multiply()([layer_1b_conv,layer_1b_concat])
  
  #LVL 0
  layer_0b_conv = Cust_2D_Conv(layer_1b_addd, nb_kernel=2, kernel_size=5, stride=1, dropout=0.2)
  layer_0b_clf= keras.layers.Conv2D(2, 1, 1, activation="softmax")(layer_0b_conv)
  
  model = keras.Model(inputs=layer_0, outputs=layer_0b_clf, name='Keras_model')
  
  model.compile(loss=dice_loss_2d,
            optimizer=keras.optimizers.Adam(),
            metrics=['accuracy','categorical_accuracy'])
  
  return model
  

我一直在四处寻找解决方案，但没有什么很清楚。

有人有什么想法或建议吗？

Answer 1

对于谁可能面临同样的问题，我找到了解决方案

问题不在于输入形状per-say。输入图像和标签的输入形状必须分别为 (100, 400, 400, 1) 和 (100, 400, 400, 3)。

但是，问题在于模型和模型的输出形状必须与模型的输入相匹配。在原始帖子中显示的代码中，输出形状直接来自这一行：

layer_0b_clf    = tflearn.layers.conv.conv_2d(layer_0b_conv, 2, 1, 1, activation="softmax")

产生输出形状 (?,400,400,2)，因此与评估的标签形状不匹配（即 (100, 400, 400, 3)。解决方案是输出通道数的变化来自以下模型：

- 用于 TFlearn：conv_2d(layer_0b_conv, 3, 1, 1, activation="softmax")

    layer_0b_clf    = tflearn.layers.conv.conv_2d(layer_0b_conv, 3, 1, 1, activation="softmax")

- 对于 Keras：Conv2D(3, 1, 1, activation="softmax")

    layer_0b_clf= keras.layers.Conv2D(3, 1, 1, activation="softmax")(layer_0b_conv)

希望对某人有所帮助。

感谢您的 cmets 和阅读。