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| author | Fredrik Svedberg <fredrik.svedberg@arm.com> | 2020-11-18 11:30:21 +0100 |
|---|---|---|
| committer | patrik.gustavsson <patrik.gustavsson@arm.com> | 2020-11-19 09:29:25 +0000 |
| commit | bdf09f9eca75fab14298a94a5402efc15f698e68 (patch) | |
| tree | 6a894b819de7b28320c1f0386839926ce142b99b | |
| parent | 4bb989b0e0f82ef2bd25c6848cebf51d253e22ae (diff) | |
| download | ethos-u-vela-bdf09f9eca75fab14298a94a5402efc15f698e68.tar.gz | |
[MLBEDSW-3300] Fix DepthwiseConv2D fails when bias tensor quant_values are None
Fixed DepthwiseConv2D fails when bias tensor quant_values are None.
Also fixed DepthwiseConv2D fails with implicit depth multiplier.
Signed-off-by: Fredrik Svedberg <fredrik.svedberg@arm.com>
Change-Id: I799a565eefa498ccf7ac626fcd472b8cbd908931
| -rw-r--r-- | ethosu/vela/supported_operators.py | 2 | ||||
| -rw-r--r-- | ethosu/vela/tflite_reader.py | 6 |
2 files changed, 7 insertions, 1 deletions
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py index 4429238..91fcb5a 100644 --- a/ethosu/vela/supported_operators.py +++ b/ethosu/vela/supported_operators.py @@ -490,7 +490,7 @@ class SupportedOperators: def constraint_bias_40bit(op): "Optional Bias tensor values must fit within 40-bits" bias = op.bias - if bias and bias.dtype == DataType.int64: + if bias and bias.dtype == DataType.int64 and bias.quant_values is not None: valid = all(len(bin(quant_value)[2:]) <= 40 for quant_value in bias.quant_values) return valid, f"Tensor '{bias.name}' has values larger than 40-bits" return True, "Op has no bias tensor, or it fits in 40-bit" diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py index c190f7e..9e20215 100644 --- a/ethosu/vela/tflite_reader.py +++ b/ethosu/vela/tflite_reader.py @@ -188,6 +188,12 @@ class TFLiteSubgraph: if "depth_multiplier" in op.attrs: op.attrs["channel_multiplier"] = op.attrs["depth_multiplier"] + if op_type == Op.DepthwiseConv2DBias and op.attrs["depth_multiplier"] == 0: + # The depth multiplier is implicit and is calculated as weight channels / ifm channels + # Note however that the weights have been reshaped above. + # The original value is cached above in channel_multiplier + op.attrs["depth_multiplier"] = op.weights.shape[2] // op.ifm.shape[-1] + faf = op.attrs.pop("fused_activation_function", None) if faf is not None: op.activation = create_activation_function(faf) |