While implementing SEGNET, getting error of "python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py", line 293, in _get_input_channel raise ValueError('The channel dimension of the inputs ' ValueError: The channel dimension of the inputs should be defined. Found `None`."

[pankajkasar]

I am implementing SEGNET segmentation Network in python but getting the following error,

_Traceback (most recent call last): File "/scratch/pkasar.dbatu/training/NEW_SEGNET_updated_on_16_11_20.py", line 370, in model=segnet(input_shape=(256,256,3),n_labels=1) File "/scratch/pkasar.dbatu/training/NEW_SEGNET_updated_on_16_11_20.py", line 161, in segnet conv_14 = Convolution2D(512, (kernel, kernel), padding="same")(unpool_1) File "/home/pkasar.dbatu/.conda/envs/dl/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 897, in call self._maybe_build(inputs) File "/home/pkasar.dbatu/.conda/envs/dl/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 2416, in _maybe_build self.build(input_shapes) # pylint:disable=not-callable File "/home/pkasar.dbatu/.conda/envs/dl/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py", line 153, in build input_channel = self._get_input_channel(input_shape) File "/home/pkasar.dbatu/.conda/envs/dl/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py", line 293, in _get_inputchannel raise ValueError('The channel dimension of the inputs ' ValueError: The channel dimension of the inputs should be defined. Found None.

Tensorflow image is:- tensorflow 2.2.0 tensorflow-gpu 2.2.0 keras-base 2.4.3
keras-gpu 2.4.3
python 3.7.9 Please help me out Thank you in advance

@tensorflow-copybara , @tensorflow-jenkins @TensorFlow-MKL @tensorflowbutler @tensorflower-gardener

The code snippet is as follows:-

from keras.layers.convolutional import Convolution2D
from keras.layers.core import Activation, Reshape
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras import backend as K
from keras.layers import Layer
class MaxPoolingWithArgmax2D(Layer):
    def __init__(self, pool_size=(2, 2), strides=(2, 2), padding="same", **kwargs):
        super(MaxPoolingWithArgmax2D, self).__init__(**kwargs)
        self.padding = padding
        self.pool_size = pool_size
        self.strides = strides

    def call(self, inputs, **kwargs):
        padding = self.padding
        pool_size = self.pool_size
        strides = self.strides
        if K.backend() == "tensorflow":
            ksize = [1, pool_size[0], pool_size[1], 1]
            padding = padding.upper()
            strides = [1, strides[0], strides[1], 1]
            output, argmax = K.tf.nn.max_pool_with_argmax(
                inputs, ksize=ksize, strides=strides, padding=padding
            )
        else:
            errmsg = "{} backend is not supported for layer {}".format(
                K.backend(), type(self).__name__
            )
            raise NotImplementedError(errmsg)
        argmax = K.cast(argmax, K.floatx())
        return [output, argmax]

    def compute_output_shape(self, input_shape):
        ratio = (1, 2, 2, 1)
        output_shape = [
            dim // ratio[idx] if dim is not None else None
            for idx, dim in enumerate(input_shape)
        ]
        output_shape = tuple(output_shape)
        return [output_shape, output_shape]

    def compute_mask(self, inputs, mask=None):
        return 2 * [None]
class MaxUnpooling2D(Layer):
    def __init__(self, size=(2, 2), **kwargs):
        super(MaxUnpooling2D, self).__init__(**kwargs)
        self.size = size
    def call(self, inputs, output_shape=None):
        updates, mask = inputs[0], inputs[1]
        with K.tf.variable_scope(self.name):
            mask = K.cast(mask, "int32")
            input_shape = K.tf.shape(updates, out_type="int32")
            #  calculation new shape
            if output_shape is None:
                output_shape = (
                    input_shape[0],
                    input_shape[1] * self.size[0],
                    input_shape[2] * self.size[1],
                    input_shape[3],
                )
            self.output_shape1 = output_shape
            # calculation indices for batch, height, width and feature maps
            one_like_mask = K.ones_like(mask, dtype="int32")
            batch_shape = K.concatenate([[input_shape[0]], [1], [1], [1]], axis=0)
            batch_range = K.reshape(
                K.tf.range(output_shape[0], dtype="int32"), shape=batch_shape
            )
            b = one_like_mask * batch_range
            y = mask // (output_shape[2] * output_shape[3])
            x = (mask // output_shape[3]) % output_shape[2]
            feature_range = K.tf.range(output_shape[3], dtype="int32")
            f = one_like_mask * feature_range
            # transpose indices & reshape update values to one dimension
            updates_size = K.tf.size(updates)
            indices = K.transpose(K.reshape(K.stack([b, y, x, f]), [4, updates_size]))
            values = K.reshape(updates, [updates_size])
            ret = K.tf.scatter_nd(indices, values, output_shape)
            return ret
    def compute_output_shape(self, input_shape):
        mask_shape = input_shape[1]
        return (
            mask_shape[0],
            mask_shape[1] * self.size[0],
            mask_shape[2] * self.size[1],
            mask_shape[3],
        )
def segnet(input_shape, n_labels, kernel=3, pool_size=(2, 2), output_mode="softmax"):
    # encoder
    inputs = Input(shape=input_shape)

    conv_1 = Convolution2D(64, (kernel, kernel), padding="same")(inputs)
    conv_1 = BatchNormalization()(conv_1)
    conv_1 = Activation("relu")(conv_1)
    conv_2 = Convolution2D(64, (kernel, kernel), padding="same")(conv_1)
    conv_2 = BatchNormalization()(conv_2)
    conv_2 = Activation("relu")(conv_2)
    pool_1, mask_1 = MaxPoolingWithArgmax2D(pool_size)(conv_2)
    conv_3 = Convolution2D(128, (kernel, kernel), padding="same")(pool_1)
    conv_3 = BatchNormalization()(conv_3)
    conv_3 = Activation("relu")(conv_3)
    conv_4 = Convolution2D(128, (kernel, kernel), padding="same")(conv_3)
    conv_4 = BatchNormalization()(conv_4)
    conv_4 = Activation("relu")(conv_4)

    pool_2, mask_2 = MaxPoolingWithArgmax2D(pool_size)(conv_4)

    conv_5 = Convolution2D(256, (kernel, kernel), padding="same")(pool_2)
    conv_5 = BatchNormalization()(conv_5)
    conv_5 = Activation("relu")(conv_5)
    conv_6 = Convolution2D(256, (kernel, kernel), padding="same")(conv_5)
    conv_6 = BatchNormalization()(conv_6)
    conv_6 = Activation("relu")(conv_6)
    conv_7 = Convolution2D(256, (kernel, kernel), padding="same")(conv_6)
    conv_7 = BatchNormalization()(conv_7)
    conv_7 = Activation("relu")(conv_7)

    pool_3, mask_3 = MaxPoolingWithArgmax2D(pool_size)(conv_7)

    conv_8 = Convolution2D(512, (kernel, kernel), padding="same")(pool_3)
    conv_8 = BatchNormalization()(conv_8)
    conv_8 = Activation("relu")(conv_8)
    conv_9 = Convolution2D(512, (kernel, kernel), padding="same")(conv_8)
    conv_9 = BatchNormalization()(conv_9)
    conv_9 = Activation("relu")(conv_9)
    conv_10 = Convolution2D(512, (kernel, kernel), padding="same")(conv_9)
    conv_10 = BatchNormalization()(conv_10)
    conv_10 = Activation("relu")(conv_10)

    pool_4, mask_4 = MaxPoolingWithArgmax2D(pool_size)(conv_10)

    conv_11 = Convolution2D(512, (kernel, kernel), padding="same")(pool_4)
    conv_11 = BatchNormalization()(conv_11)
    conv_11 = Activation("relu")(conv_11)
    conv_12 = Convolution2D(512, (kernel, kernel), padding="same")(conv_11)
    conv_12 = BatchNormalization()(conv_12)
    conv_12 = Activation("relu")(conv_12)
    conv_13 = Convolution2D(512, (kernel, kernel), padding="same")(conv_12)
    conv_13 = BatchNormalization()(conv_13)
    conv_13 = Activation("relu")(conv_13)

    pool_5, mask_5 = MaxPoolingWithArgmax2D(pool_size)(conv_13)
    print("Build enceder done..")

    # decoder

    unpool_1 = MaxUnpooling2D(pool_size)([pool_5, mask_5])

    conv_14 = Convolution2D(512, (kernel, kernel), padding="same")(unpool_1)
    conv_14 = BatchNormalization()(conv_14)
    conv_14 = Activation("relu")(conv_14)
    conv_15 = Convolution2D(512, (kernel, kernel), padding="same")(conv_14)
    conv_15 = BatchNormalization()(conv_15)
    conv_15 = Activation("relu")(conv_15)
    conv_16 = Convolution2D(512, (kernel, kernel), padding="same")(conv_15)
    conv_16 = BatchNormalization()(conv_16)
    conv_16 = Activation("relu")(conv_16)

    unpool_2 = MaxUnpooling2D(pool_size)([conv_16, mask_4])

    conv_17 = Convolution2D(512, (kernel, kernel), padding="same")(unpool_2)
    conv_17 = BatchNormalization()(conv_17)
    conv_17 = Activation("relu")(conv_17)
    conv_18 = Convolution2D(512, (kernel, kernel), padding="same")(conv_17)
    conv_18 = BatchNormalization()(conv_18)
    conv_18 = Activation("relu")(conv_18)
    conv_19 = Convolution2D(256, (kernel, kernel), padding="same")(conv_18)
    conv_19 = BatchNormalization()(conv_19)
    conv_19 = Activation("relu")(conv_19)

    unpool_3 = MaxUnpooling2D(pool_size)([conv_19, mask_3])

    conv_20 = Convolution2D(256, (kernel, kernel), padding="same")(unpool_3)
    conv_20 = BatchNormalization()(conv_20)
    conv_20 = Activation("relu")(conv_20)
    conv_21 = Convolution2D(256, (kernel, kernel), padding="same")(conv_20)
    conv_21 = BatchNormalization()(conv_21)
    conv_21 = Activation("relu")(conv_21)
    conv_22 = Convolution2D(128, (kernel, kernel), padding="same")(conv_21)
    conv_22 = BatchNormalization()(conv_22)
    conv_22 = Activation("relu")(conv_22)

    unpool_4 = MaxUnpooling2D(pool_size)([conv_22, mask_2])

    conv_23 = Convolution2D(128, (kernel, kernel), padding="same")(unpool_4)
    conv_23 = BatchNormalization()(conv_23)
    conv_23 = Activation("relu")(conv_23)
    conv_24 = Convolution2D(64, (kernel, kernel), padding="same")(conv_23)
    conv_24 = BatchNormalization()(conv_24)
    conv_24 = Activation("relu")(conv_24)

    unpool_5 = MaxUnpooling2D(pool_size)([conv_24, mask_1])

    conv_25 = Convolution2D(64, (kernel, kernel), padding="same")(unpool_5)
    conv_25 = BatchNormalization()(conv_25)
    conv_25 = Activation("relu")(conv_25)

    conv_26 = Convolution2D(n_labels, (1, 1), padding="valid")(conv_25)
    conv_26 = BatchNormalization()(conv_26)
    conv_26 = Reshape(
        (input_shape[0] * input_shape[1], n_labels),
        input_shape=(input_shape[0], input_shape[1], n_labels),
    )(conv_26)

    outputs = Activation(output_mode)(conv_26)
    print("Build decoder done..")

    model = Model(inputs=inputs, outputs=outputs, name="SegNet")

    return model

model=segnet(input_shape=(256,256,3),n_labels=1)```

[Saduf2019]

@pankajkasar Please provide with indented code such that we can replicate the issue or if possible share a colab gist with the error, can you try with tf nightly or 2.4 and let us know if the issue exists.

[pankajkasar]

The following only line which I forget to add: model=segnet(input_shape=(256,256,3),n_labels=1)

I also added in above code. Thanks for reply.

[Saduf2019]

@pankajkasar Please move the issue to closed status is resolved.

[pankajkasar]

@Saduf2019 TF 2.4.0 is not yet available to us for download and use. What else option?? I will immediately close issue if i get all answers.

[pankajkasar]

gist:93f2dafae3cb5a0da41746960bc6718a

Created public gist

[pankajkasar]

Finally I found solution on above error. We need to change class MaxUnpooling2D definition as follows:-

class MaxUnpooling2D(Layer):
def __init__(self, size=(2, 2), **kwargs):
    super(MaxUnpooling2D, self).__init__(**kwargs)
    self.size = size

def call(self, inputs, output_shape=None):
    updates, mask = inputs[0], inputs[1]
    with tf.compat.v1.variable_scope(self.name):
        mask = K.cast(mask, 'int32')
        input_shape = tf.shape(updates, out_type='int32')
        #print(updates.shape)
        #print(mask.shape)
        if output_shape is None:
            output_shape = (
                input_shape[0],
                input_shape[1] * self.size[0],
                input_shape[2] * self.size[1],
                input_shape[3])

        ret = tf.scatter_nd(K.expand_dims(K.flatten(mask)),
                              K.flatten(updates),
                              [K.prod(output_shape)])

        input_shape = updates.shape
        out_shape = [-1,
                     input_shape[1] * self.size[0],
                     input_shape[2] * self.size[1],
                     input_shape[3]]
    return K.reshape(ret, out_shape)

def get_config(self):
    config = super().get_config().copy()
    config.update({
        'size': self.size
    })
    return config

def compute_output_shape(self, input_shape):
    mask_shape = input_shape[1]
    return (
            mask_shape[0],
            mask_shape[1]*self.size[0],
            mask_shape[2]*self.size[1],
            mask_shape[3]
            )

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