Unet upsampling block execution time: TransposeConv vs. Upsample+Conv2d - keras

Question

I'am trying to implement a binary segmentation model using unet in keras. Here are the upsampling blocks of my networks.
In one model i used normal transpose convolution, while in the second one i used resize bilinear+ conv2d.
# Convolution block with Transpose Convolution
def deconv_block(tensor, nfilters, size=3, padding='same', kernel_initializer = 'he_normal'):
y = Conv2DTranspose(filters=nfilters, kernel_size=size, strides=2, padding = padding, kernel_initializer = kernel_initializer)(tensor)
y = BatchNormalization()(y)
y = Dropout(0.5)(y)
y = Activation("relu")(y)
return y
# Convolution block with Upsampling+Conv2D
def deconv_block_rez(tensor, nfilters, size=3, padding='same', kernel_initializer = 'he_normal'):
y = UpSampling2D(size = (2,2),interpolation='bilinear')(tensor)
y = Conv2D(filters=nfilters, kernel_size=(size,size), padding = 'same', kernel_initializer = kernel_initializer)(y)
y = BatchNormalization()(y)
y = Dropout(0.5)(y)
y = Activation("relu")(y)
return y
Are they equivalent in terms of quality and execution time?
Quality-wise they were found to be almost same(i used 128*128 input); but in terms of execution time they were different.I read in a blog that upsampling+conv2d will not suffer from the problem of checkerboard artifacts; but does it happen at the cost of execution time?
Here is a typical block of my network....
Upsample:-
up_sampling2d_2 (UpSampling2D) (None, 16, 16, 800) 0
concatenate_1[0][0]
__________________________________________________________________________________________________ batch_normalization_2 (Conv2D) (None, 16, 16, 256) 1843456
up_sampling2d_2[0][0]
__________________________________________________________________________________________________ dropout_2 (Dropout) (None, 16, 16, 256) 0
batch_normalization_2[0][0]
__________________________________________________________________________________________________ activation_2 (Activation) (None, 16, 16, 256) 0
dropout_2[0][0]
Transpose conv:-
conv2d_transpose_2 (Conv2DTrans (None, 16, 16, 256) 1843456
concatenate_1[0][0]
__________________________________________________________________________________________________ batch_normalization_2 (BatchNor (None, 16, 16, 256) 1024
conv2d_transpose_2[0][0]
__________________________________________________________________________________________________ dropout_2 (Dropout) (None, 16, 16, 256) 0
batch_normalization_2[0][0]
__________________________________________________________________________________________________ activation_2 (Activation) (None, 16, 16, 256) 0
dropout_2[0][0]
Even though both of them have almost same parameters; the block with upsample+conv2d has more execution time(resize-bilinear is taking negligible time) i.e conv vs. transpose-conv => 154ms vs 35ms).
Clearly the conv2d has stride 1 in the resize+normal-conv block and transpose-conv has stride 2; but both uses 3x3 kernels.And, recently people have begun to use the resize+conv blocks instead of normal transpose convolution.
Does this speed difference happen always or can we use any variation of the upsample+conv2d (say stride 2 or kernel 1x1 )so that execution time is same(also quality shouldn't be degraded) or is it any bug in my code.