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| author | Jacob Bohlin <jacob.bohlin@arm.com> | 2020-08-26 18:21:28 +0200 |
|---|---|---|
| committer | tim.hall <tim.hall@arm.com> | 2020-08-27 14:23:03 +0000 |
| commit | a41cd4de2af1e43b76a2a33d78eeb2d90a88b757 (patch) | |
| tree | e7f81ab5fbddca95928e2111fea2f6cff9b75679 /ethosu/vela/operation.py | |
| parent | 2abd3dd75bd3d20e1a3aeaf12362f9872b40fa0a (diff) | |
| download | ethos-u-vela-a41cd4de2af1e43b76a2a33d78eeb2d90a88b757.tar.gz | |
Small fix for Softmax regression
Signed-off-by: Jacob Bohlin <jacob.bohlin@arm.com>
Change-Id: I287c24725126c169afec779b921e43c3ab26f739
Diffstat (limited to 'ethosu/vela/operation.py')
| -rw-r--r-- | ethosu/vela/operation.py | 24 |
1 files changed, 16 insertions, 8 deletions
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py index c1ca3f81..f7a95098 100644 --- a/ethosu/vela/operation.py +++ b/ethosu/vela/operation.py @@ -81,12 +81,12 @@ input and output tensors, as well as an attribute dictionary.""" bias_idx = -1 ofm_idx = -1 npu_block_type = self.attrs.get("npu_block_type", NpuBlockType.Default) - if npu_block_type in set((NpuBlockType.ConvolutionMxN, NpuBlockType.ConvolutionDepthWise)): + if npu_block_type in (NpuBlockType.ConvolutionMxN, NpuBlockType.ConvolutionDepthWise): ifm_idx = 0 weight_idx = 1 ofm_idx = 0 - if self.type in set(("Conv2DBiasAct", "DepthwiseConv2dBiasAct", "TransposeConvAct")): + if self.type in ("Conv2DBiasAct", "DepthwiseConv2dBiasAct", "TransposeConvAct"): if len(self.inputs) >= 3: bias_idx = 2 @@ -101,7 +101,7 @@ input and output tensors, as well as an attribute dictionary.""" weight_idx = 1 ofm_idx = 0 - if self.type in set(("FullyConnectedAct",)): + if self.type == "FullyConnectedAct": if len(self.inputs) >= 3: bias_idx = 2 @@ -116,7 +116,7 @@ input and output tensors, as well as an attribute dictionary.""" ofm_idx = 0 # LeakyRelu, Abs and CLZ have a single IFM - if self.type in set(("LeakyRelu", "Abs", "CLZ")): + if self.type in ("LeakyRelu", "Abs", "CLZ"): ifm2_idx = -1 elif self.type == "Conv2DBackpropInput": @@ -124,7 +124,7 @@ input and output tensors, as well as an attribute dictionary.""" weight_idx = 1 ofm_idx = 0 - elif self.type in set(("Squeeze", "Reshape", "QuantizedReshape", "ExpandDims")): + elif self.type in ("Squeeze", "Reshape", "QuantizedReshape", "ExpandDims"): ifm_idx = 0 ofm_idx = 0 @@ -149,7 +149,7 @@ input and output tensors, as well as an attribute dictionary.""" weight_tensor = None ofm_tensor = None - ifm_idx, ifm2_idx, weight_idx, bias_idx, ofm_idx = self.get_ifm_ifm2_weight_bias_ofm_indices() + ifm_idx, ifm2_idx, weight_idx, _, ofm_idx = self.get_ifm_ifm2_weight_bias_ofm_indices() if ifm_idx != -1: ifm_tensor = self.inputs[ifm_idx] if ifm2_idx != -1: @@ -180,7 +180,7 @@ input and output tensors, as well as an attribute dictionary.""" return ifm_tensor, weight_tensor, bias_tensor, ofm_tensor def is_concat_op(self): - return self.type in set(("Concat", "ConcatV2", "QuantizedConcat", "ConcatTFLite", "PackReshaped")) + return self.type in ("Concat", "ConcatV2", "QuantizedConcat", "ConcatTFLite", "PackReshaped") def get_concat_inputs_axis(self): assert self.is_concat_op() @@ -215,7 +215,7 @@ input and output tensors, as well as an attribute dictionary.""" return dilation_h, dilation_w def is_split_op(self): - return self.type in set(("Split", "SplitV", "StridedSlice", "Slice", "UnpackReshaped")) + return self.type in ("Split", "SplitV", "StridedSlice", "Slice", "UnpackReshaped") def get_split_inputs_axis(self): assert self.is_split_op() @@ -324,3 +324,11 @@ input and output tensors, as well as an attribute dictionary.""" def set_output_tensor(self, tens): tens.ops = [self] self.outputs = [tens] + + def needs_bias(self): + return self.type in ( + "Conv2DBiasAct", + "DepthwiseConv2dBiasAct", + "Conv2DBackpropInputSwitchedBias", + "FullyConnectedAct", + ) |