what is the difference between conv2d and Conv2D in Keras?

Question

I am confused with Conv2D and conv2d in Keras. what is the difference between them? I think the first one is a layer and the second one is a backend function, but what does it mean? in Conv2D we send the number of filters, the size of filters and stride ( Conv2D(64,(3,3),stride=(8,8))(input)) but in conv2d we use conv2d(input, kernel, stride=(8,8)) what is kernel is it (64,3,3) and we put number of filter and size together? where should I enter the number of kernels? could you please help me with this issue? Thank you.

code in pytorch

def apply_conv(self, image, filter_type: str):

        if filter_type == 'dct':
            filters = self.dct_conv_weights
        elif filter_type == 'idct':
            filters = self.idct_conv_weights
        else:
            raise('Unknown filter_type value.')

        image_conv_channels = []
        for channel in range(image.shape[1]):
            image_yuv_ch = image[:, channel, :, :].unsqueeze_(1)
            image_conv = F.conv2d(image_yuv_ch, filters, stride=8)
            image_conv = image_conv.permute(0, 2, 3, 1)
            image_conv = image_conv.view(image_conv.shape[0], image_conv.shape[1], image_conv.shape[2], 8, 8)
            image_conv = image_conv.permute(0, 1, 3, 2, 4)
            image_conv = image_conv.contiguous().view(image_conv.shape[0],
                                                  image_conv.shape[1]*image_conv.shape[2],
                                                  image_conv.shape[3]*image_conv.shape[4])

            image_conv.unsqueeze_(1)

            # image_conv = F.conv2d()
            image_conv_channels.append(image_conv)

        image_conv_stacked = torch.cat(image_conv_channels, dim=1)

        return image_conv_stacked

the changed code in Keras

def apply_conv(self, image, filter_type: str):

        if filter_type == 'dct':
            filters = self.dct_conv_weights
        elif filter_type == 'idct':
            filters = self.idct_conv_weights
        else:
            raise('Unknown filter_type value.')
        print(image.shape)

        image_conv_channels = []
        for channel in range(image.shape[1]):
            print(image.shape)
            print(channel)
            image_yuv_ch = K.expand_dims(image[:, channel, :, :],1)
            print( image_yuv_ch.shape)
            print(filters.shape)
            image_conv = Kr.backend.conv2d(image_yuv_ch,filters,strides=(8,8),data_format='channels_first')
           image_conv = Kr.backend.permute_dimensions(image_conv,(0, 2, 3, 1))
            image_conv = Kr.backend.reshape(image_conv,(image_conv.shape[0], image_conv.shape[1], image_conv.shape[2], 8, 8))
            image_conv =  Kr.backend.permute_dimensions(image_conv,(0, 1, 3, 2, 4))
            image_conv = Kr.backend.reshape(image_conv,(image_conv.shape[0],
                                                  image_conv.shape[1]*image_conv.shape[2],
                                                  image_conv.shape[3]*image_conv.shape[4]))

            Kr.backend.expand_dims(image_conv,1)

            # image_conv = F.conv2d()
            image_conv_channels.append(image_conv)

        image_conv_stacked = Kr.backend.concatenate(image_conv_channels, axis=1)

        return image_conv_stacked

but when I execute the code, it produces the following error:

Traceback (most recent call last):

File "", line 383, in decoded_noise=JpegCompression()(act11)#16

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\base_layer.py", line 457, in call output = self.call(inputs, **kwargs)

File "", line 169, in call image_dct = self.apply_conv(noised_image, 'dct')

File "", line 132, in apply_conv image_conv = Kr.backend.conv2d(image_yuv_ch,filters,strides=(8,8),data_format='channels_first')

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\backend\tensorflow_backend.py", line 3650, in conv2d data_format=tf_data_format)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 779, in convolution data_format=data_format)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 839, in init filter_shape[num_spatial_dims]))

ValueError: number of input channels does not match corresponding dimension of filter, 1 != 8

the new code

for channel in range(image.shape[1]):
            image_yuv_ch = K.expand_dims(image[:, channel, :, :],axis=1)
            image_yuv_ch = K.permute_dimensions(image_yuv_ch, (0, 2, 3, 1))
            image_conv = tf.keras.backend.conv2d(image_yuv_ch,kernel=filters,strides=(8,8),padding='same')
            image_conv = tf.keras.backend.reshape(image_conv,(image_conv.shape[0],image_conv.shape[1], image_conv.shape[2],8,8))

the error:

Traceback (most recent call last):

File "", line 263, in decoded_noise=JpegCompression()(act11)#16

File "D:\software\Anaconda3\envs\py36\lib\site-packages\keras\engine\base_layer.py", line 457, in call output = self.call(inputs, **kwargs)

File "", line 166, in call image_dct = self.apply_conv(noised_image, 'dct')

File "", line 128, in apply_conv image_conv = tf.keras.backend.reshape(image_conv,(image_conv.shape[0],image_conv.shape[1], image_conv.shape[2],8,8))

File "D:\software\Anaconda3\envs\py36\lib\site-packages\tensorflow\python\keras\backend.py", line 2281, in reshape return array_ops.reshape(x, shape)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 6482, in reshape "Reshape", tensor=tensor, shape=shape, name=name)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 513, in _apply_op_helper raise err

File "D:\software\Anaconda3\envs\py36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 510, in _apply_op_helper preferred_dtype=default_dtype)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\tensorflow\python\framework\ops.py", line 1146, in internal_convert_to_tensor ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\tensorflow\python\framework\constant_op.py", line 229, in _constant_tensor_conversion_function return constant(v, dtype=dtype, name=name)

File "D:\software\Anaconda3\envs\py36\lib\site-packages\tensorflow\python\framework\constant_op.py", line 208, in constant value, dtype=dtype, shape=shape, verify_shape=verify_shape))

File "D:\software\Anaconda3\envs\py36\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 531, in make_tensor_proto "supported type." % (type(values), values))

TypeError: Failed to convert object of type to Tensor. Contents: (Dimension(None), Dimension(4), Dimension(4), 8, 8). Consider casting elements to a supported type.

Answer 1

Tensorflow and Keras now are using channel_last convention. So first you should permute the channel dim to the last using K.permute_dimension. You might try this code in colab.research.google.com to figure out yourself.

First question:

• conv2d is a function to perform 2D convolution docs
• keras.layers.Conv2D() will return an instance of class Conv2D which perform convolution function. See more here

# The second 
import keras
conv_layer = keras.layers.Conv2D(filters=64, kernel_size=8, strides=(4, 4), padding='same')

Basically, they differ from the way to define and the way to use. K.conv2d is used inside keras.layers.Conv2D when conv_layer apply convolution on some input x such as conv_layer.

The example below may help you to understand it easier the difference between say_hello and SayHello.

def say_hello(word, name):
    print(word, name)


class SayHello():

    def __init__(self, word='Hello'):
        self.word = word
        pass

    def __call__(self, name):
        say_hello(self.word, name)


say_hello('Hello', 'Nadia') #Hello Nadia

sayhello = SayHello(word='Hello') # you will get an instance `sayhello` from class SayHello

sayhello('Nadia') # Hello Nadia

Second question:

• kernel here is a tensor of shape (kernel_size, kernel_size, in_channels, out_channels)
• If you want to get the image_conv of shape (8, 8, 64) then the strides=(4,4).

import tensorflow as tf
import tensorflow.keras.backend as K

image = tf.random_normal((10,3, 32, 32))
print(image.shape) # shape=(10, 3, 32, 32)

channel = 1
image_yuv_ch = K.expand_dims(image[:, channel,:,:], axis=1) # shape=(10, 1, 32, 32)
image_yuv_ch = K.permute_dimensions(image_yuv_ch, (0, 2, 3, 1)) # shape=(10, 32, 32, 1)

# The first K.conv2d
in_channels = 1
out_channels = 64 # same as filters
kernel = tf.random_normal((8, 8, in_channels, out_channels)) # shape=(8, 8, 1, 64)

image_conv = tf.keras.backend.conv2d(image_yuv_ch, kernel=kernel, strides=(4, 4), padding='same')
print(image_conv.shape) #shape=(10, 8, 8, 64)


# The second 
import keras
conv_layer = keras.layers.Conv2D(filters=64, kernel_size=8, strides=(4, 4), padding='same')
image_conv = conv_layer(image_yuv_ch)
print(image_conv.shape) #shape=(10, 8, 8, 64)