[GitHub] [incubator-tvm] apivovarov edited a comment on issue #4298: [TFLite] Support PRelu

[GitBox <gi...@apache.org>]

apivovarov edited a comment on issue #4298: [TFLite] Support PRelu
URL: https://github.com/apache/incubator-tvm/pull/4298#issuecomment-553132406
 
 
   @FrozenGene 
   I tried to compile my model and got the following `unable to unify` errors
   ```
     %0 = nn.pad(%input_1, pad_width=[[0, 0], [0, 1], [0, 1], [0, 0]]);
     %1 = nn.conv2d(%0, %v_param_1, strides=[2, 2], channels=16, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO");
     %2 = nn.bias_add(%1, %v_param_2, axis=3);
     %3 = nn.prelu(%2, %v_param_3, axis=3) tensor type `Tensor[(16), float32]` has 1 dimensions, while `Tensor[(1, 1, 16), float32]` has 3 dimensions; unable to unify: `Tensor[(16), float32]` and `Tensor[(1, 1, 16), float32]`; ;
     %4 = nn.conv2d(%3, %v_param_4, channels=8, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
     %5 = nn.bias_add(%4, %v_param_5, axis=3);
     %6 = nn.prelu(%5, %v_param_6, axis=3) tensor type `Tensor[(8), float32]` has 1 dimensions, while `Tensor[(1, 1, 8), float32]` has 3 dimensions; unable to unify: `Tensor[(8), float32]` and `Tensor[(1, 1, 8), float32]`; ;
     %7 = nn.pad(%6, pad_width=[[0, 0], [1, 1], [1, 1], [0, 0]]);
   ```
   
   ```
     %112 = nn.conv2d(%111, %v_param_89, channels=32, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
     %113 = nn.bias_add(%112, %v_param_90, axis=3);
     %114 = add(%105, %113);
     %115 = nn.prelu(%114, %v_param_91, axis=3) tensor type `Tensor[(32), float32]` has 1 dimensions, while `Tensor[(1, 1, 32), float32]` has 3 dimensions; unable to unify: `Tensor[(32), float32]` and `Tensor[(1, 1, 32), float32]`; ;
     %116 = nn.conv2d(%115, %v_param_92, channels=16, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
     %117 = nn.bias_add(%116, %v_param_93, axis=3);
     %118 = nn.prelu(%117, %v_param_94, axis=3) tensor type `Tensor[(16), float32]` has 1 dimensions, while `Tensor[(1, 1, 16), float32]` has 3 dimensions; unable to unify: `Tensor[(16), float32]` and `Tensor[(1, 1, 16), float32]`; ;
     %119 = nn.pad(%118, pad_width=[[0, 0], [1, 1], [1, 1], [0, 0]]);
   ```
   
   ```
     %538 = nn.conv2d(%537, %v_param_425, channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
     %539 = nn.bias_add(%538, %v_param_426, axis=3);
     %540 = add(%531, %539);
     %541 = nn.prelu(%540, %v_param_427, axis=3) tensor type `Tensor[(256), float32]` has 1 dimensions, while `Tensor[(1, 1, 256), float32]` has 3 dimensions; unable to unify: `Tensor[(256), float32]` and `Tensor[(1, 1, 256), float32]`; ;
     %542 = nn.max_pool2d(%541, pool_size=[2, 2], strides=[2, 2], layout="NHWC");
     %543 = nn.conv2d(%541, %v_param_428, strides=[2, 2], channels=128, kernel_size=[2, 2], data_layout="NHWC", kernel_layout="HWIO");
     %544 = nn.bias_add(%543, %v_param_429, axis=3);
     %545 = nn.prelu(%544, %v_param_430, axis=3) tensor type `Tensor[(128), float32]` has 1 dimensions, while `Tensor[(1, 1, 128), float32]` has 3 dimensions; unable to unify: `Tensor[(128), float32]` and `Tensor[(1, 1, 128), float32]`; ;
     %546 = nn.pad(%545, pad_width=[[0, 0], [1, 1], [1, 1], [0, 0]]);
   ```
   ```
     %620 = nn.bias_add(%619, %v_param_490, axis=3);
     %621 = add(%612, %620);
     %622 = nn.prelu(%621, %v_param_491, axis=3) tensor type `Tensor[(256), float32]` has 1 dimensions, while `Tensor[(1, 1, 256), float32]` has 3 dimensions; unable to unify: `Tensor[(256), float32]` and `Tensor[(1, 1, 256), float32]`; ;
     %623 = nn.conv2d(%622, %v_param_492, channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
     %624 = nn.bias_add(%623, %v_param_493, axis=3);
     %625 = nn.prelu(%624, %v_param_494, axis=3) tensor type `Tensor[(128), float32]` has 1 dimensions, while `Tensor[(1, 1, 128), float32]` has 3 dimensions; unable to unify: `Tensor[(128), float32]` and `Tensor[(1, 1, 128), float32]`; ;
     %626 = nn.pad(%625, pad_width=[[0, 0], [1, 1], [1, 1], [0, 0]]);
     %627 = nn.conv2d(%626, %v_param_495, groups=128, channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI");
     %628 = nn.bias_add(%627, %v_param_496, axis=3);
     %629 = nn.conv2d(%628, %v_param_497, channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO");
     %630 = nn.bias_add(%629, %v_param_498, axis=3);
     %631 = add(%622, %630);
     %632 = nn.prelu(%631, %v_param_499, axis=3) tensor type `Tensor[(256), float32]` has 1 dimensions, while `Tensor[(1, 1, 256), float32]` has 3 dimensions; unable to unify: `Tensor[(256), float32]` and `Tensor[(1, 1, 256), float32]`; ;
     %633 = nn.conv2d(%632, %v_param_502, channels=42, kernel_size=[2, 2], data_layout="NHWC", kernel_layout="HWIO");
   ```

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