[ECCV 2020] Learning Enriched Features for Real Image Restoration and Enhancement. SOTA results for image denoising, super-resolution, and image enhancement.
def SKFF(self, inputs:list, reduction=8, name='SKFF'):
with tf.variable_scope(name):
ch_n=inputs[0].shape[3]
num=len(inputs)
d=max(ch_n//reduction, 4)
inputs=tf.stack(inputs, 0)
fea=tf.reduce_sum(inputs, 0)
fea=tf.reduce_mean(fea, [1, 2], keep_dims=True)
fea=self.conv_layer(fea, d, 1, name='du')
fea=tf.keras.layers.PReLU()(fea)
vecs=[self.conv_layer(fea, ch_n, 1, name=str(no)) for no in range(num)]
vec=tf.concat(vecs,axis=1)
weight=tf.nn.softmax(vec, axis=1)
weight=tf.transpose(weight, (1, 0, 2, 3))
weight=tf.expand_dims(weight, 2)
out = inputs*weight
out=tf.reduce_sum(out, 0)
return out
Then I used timeline to profile my network. I noticed that there were lots of transpose operations (i.e., convert data from NHWC to NCHW) so the inference speed was actually slower than direct contact different scales.
Is there any way I can optimize the TensorFlow codes? Thanks.
Hi. I tried to implement SKFF with tf 1.15:
Then I used timeline to profile my network. I noticed that there were lots of transpose operations (i.e., convert data from NHWC to NCHW) so the inference speed was actually slower than direct contact different scales.
Is there any way I can optimize the TensorFlow codes? Thanks.