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（我尝试了这两种 API）来执行多类语义分割。 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（大小为 400x400）。 对于每个图像，我有 3 个类的相应标签。 最后，我有形状为 (100, 400, 400) 的图像和形状为 (100,400,400,3) 的标签。 （如此处所述： 如何实现多类语义分割？ ）

为了能够使用语义分割，我使用了一种热编码（例如： 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，但没有什么是很清楚的。

有没有人有想法或建议？

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 。 对于输入图像和标签，输入形状必须分别为 (100, 400, 400, 1) 和 (100, 400, 400, 3)。

然而，问题在于模型和模型的输出形状必须与模型的输入匹配。 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")

这产生了一个输出形状 (?,400,400,2)，因此与用于评估的标签形状（即 (100, 400, 400, 3) 不匹配。解决方案是模型输出通道数量的变化如：

- 对于 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)

希望它会帮助某人。

感谢您的评论和阅读。