Hi,
I'm trying to inference "yolov3-tiny" model with input batch_size = 4.
The input shape was (4, 3, 416, 416).
However, the shape of the output is as follows:
module.get_output(0) --> (1, 255, 26, 26)
module.get_output(1) --> (1, 255, 13, 13)
IMHO, the problem has occurred when the following code is executed:
input_shape = (4, 3, 416, 416)
mod, params = relay.frontend.from_darknet(net, dtype=dtype,
shape=input_shape)
when I print out mod["main"], it seems that the reshape func does not support
batch input.
%49 = nn.leaky_relu(%48, alpha=0.1f) /* ty=Tensor[(4, 256, 26, 26),
float32] */;
%50 = nn.conv2d(%49, %LAYERTYPE.CONVOLUTIONAL22_weight, padding=[0, 0, 0,
0], channels=255, kernel_size=[1, 1]) /* ty=Tensor[(4, 255, 26, 26), float32]
*/;
%51 = nn.bias_add(%50, %LAYERTYPE.CONVOLUTIONAL22_bias) /* ty=Tensor[(4,
255, 26, 26), float32] */;
%52 = reshape(%51, newshape=[1, 3, 85, 26, 26]) /* ty=Tensor[(1, 3, 85, 26,
26), float32] */;
%53 = split(%52, indices_or_sections=[2, 4], axis=2) /* ty=(Tensor[(1, 3,
2, 26, 26), float32], Tensor[(1, 3, 2, 26, 26), float32], Tensor[(1, 3, 81, 26,
26), float32]) */;
%54 = %53.0;
%55 = sigmoid(%54) /* ty=Tensor[(1, 3, 2, 26, 26), float32] */;
%56 = %53.1;
%57 = %53.2;
%58 = sigmoid(%57) /* ty=Tensor[(1, 3, 81, 26, 26), float32] */;
%59 = (%55, %56, %58);
%60 = concatenate(%59, axis=2) /* ty=Tensor[(1, 3, 85, 26, 26), float32] */;
%61 = reshape(%60, newshape=[1, 255, 26, 26]) /* ty=Tensor[(1, 255, 26,
26), float32] */;
As shown in the above results,
the output shape of the reshape function in %52 is (1, 3, 85, 26, 26) rather
than (4, 3, 85, 26, 26)
Is there any idea how to resolve such a problem??
best wishes,
R. Kim
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