Alpha
September 27, 2019, 8:57am
1
I am trying to interpolate x
in the hybrid_forward()
.
I noticed there is a UpSampling()
function, but I found it’s function has learnable prameters, which is not I expected.
F.BilinearResize2D()
can also upsample, but it needs width
and height
. I need to upsample with a scale=2
and I can get height
and width
during hybrid_forward()
in HybridBlock
.
Is there a way to solve my question.
Does mxnet have some methods similar to Pytorch torch.nn.functional.interpolate
Thank you
Hi @Alpha ,
your question is similar to How to upsamle a layer into the shape of another layer in the symbol api? .
I think if you use F.UpSampling() without a num_filter
argument, then it shouldn’t have parameters:
F.UpSampling(x, scale=self.scale, sample_type='bilinear')
You can verify this using the model summary:
mx.viz.print_summary(
symbol,
shape={'data':(1, input_shape[0], input_shape[1], input_shape[2])},
)
_
Layer (type) Output Shape Param # Previous Layer
========================================================================================================================
...
________________________________________________________________________________________________________________________
value_conv0(Convolution) 4x8x8 25600 bc_res_block12_add
________________________________________________________________________________________________________________________
value_bn0(BatchNorm) 4x8x8 8 value_conv0
________________________________________________________________________________________________________________________
value_act0(Activation) 4x8x8 0 value_bn0
________________________________________________________________________________________________________________________
up(UpSampling) 4x16x16 0 value_act0
________________________________________________________________________________________________________________________
value_flatten0(Flatten) 1024 0 up
______________________________________________________________________________________________________________________
...
Greetings,
~QueensGambit
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