Hi,
I tried one script on alter_op_layout as follows. For the first layer, input is
(1,3,224,244), infer type generates the layout_transform as 'ty=Tensor[(1,
**0**, 224, 224, 4), float32]'
@register_alter_op_layout("nn.conv2d", level=102)
def alter_conv2d(attrs, inputs, tinfos):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs['data_layout'] = 'NCHW4c'
new_attrs['kernel_layout'] = 'OIHW4o4i'
return relay.nn.conv2d(data, weight, **new_attrs)
x = relay.var("x", shape=(1, 3, 224, 224))
weight = relay.var("weight")
y = relay.nn.conv2d(x, weight, channels=64, kernel_size=(3, 3), padding=(1,
1))
y = run_opt_pass(y, [transform.CanonicalizeOps(),
transform.AlterOpLayout()])
print(y)
output:
free_var %x: Tensor[(1, 3, 224, 224), float32]
%0 = layout_transform(%x, src_layout="NCHW", dst_layout="NCHW4c") /*
ty=Tensor[(1, 0, 224, 224, 4), float32] */;
free_var %weight: Tensor[(64, 3, 3, 3), float32]
%1 = layout_transform(%weight, src_layout="OIHW", dst_layout="OIHW4o4i") /*
ty=Tensor[(16, 0, 3, 3, 4, 4), float32] */;
%2 = nn.conv2d(%0, %1, padding=[1, 1], channels=64, kernel_size=[3, 3],
data_layout="NCHW4c", kernel_layout="OIHW4o4i") /* ty=Tensor[(1, 16, 224, 224,
4), float32] */;
layout_transform(%2, src_layout="NCHW4c", dst_layout="NCHW") /* ty=Tensor[(1,
64, 224, 224), float32] */
my question is:
1) is 'ty=Tensor[(1, **0**, 224, 224, 4), float32]' correct ?
2) Need pre-process for input image to make it have shape (4, 224,224) ?
Thank you.
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Tianqi Chen, UW, Seattle, WA, 98105, United States
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