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| author | Dwight Lidman <dwight.lidman@arm.com> | 2021-03-15 19:06:10 +0100 |
|---|---|---|
| committer | patrik.gustavsson <patrik.gustavsson@arm.com> | 2021-03-16 16:29:01 +0000 |
| commit | 9b3791817c32529dfeddae57c29c2abe19311fc4 (patch) | |
| tree | 866c769bb8627b686f7d25ddce3413380c34cbdb /ethosu/vela/graph_optimiser.py | |
| parent | c822d62ba27b874a130e9d8d434c12b419d10d62 (diff) | |
| download | ethos-u-vela-9b3791817c32529dfeddae57c29c2abe19311fc4.tar.gz | |
MLBEDSW-4215: Add support for MEAN to match QuantizedMeanOrSum implementation
This commit adds support for emulating the behavior
of the QuantizedMeanOrSum implementation of MEAN in
TensorFlow Lite.
Signed-off-by: Dwight Lidman <dwight.lidman@arm.com>
Change-Id: Ifd24e0e678e2f85cd66ab82deeaaf010d5351b1e
Diffstat (limited to 'ethosu/vela/graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/graph_optimiser.py | 34 |
1 files changed, 28 insertions, 6 deletions
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py index 30841175..e8218fcc 100644 --- a/ethosu/vela/graph_optimiser.py +++ b/ethosu/vela/graph_optimiser.py @@ -1422,9 +1422,12 @@ def convert_mean_to_depthwise_conv(op, arch, nng): ) # Change op type op.type = Op.DepthwiseConv2DBias + # Add None bias tensor + op.inputs.append(None) # Set IFM/OFM shapes after changing op type op.set_ifm_ofm_shapes() + weight_scale, bias = 1, None ofmq, ifmq = op.ofm.quantization, inp.quantization # Set rounding mode, scaling and zero point based on which reference implementation to match if len(shape) == 4 and axis == [1, 2] and keep_dims: @@ -1442,7 +1445,6 @@ def convert_mean_to_depthwise_conv(op, arch, nng): assert inp.dtype == DataType.int8 # Use a depthwise to calculate the sum, # followed by a multiplication with 1/N to get the MEAN - op.type = Op.DepthwiseConv2DBias weight_scale = 1 intermediate = op.ofm.clone(suffix="_intermediate", set_unique=True) intermediate.dtype = DataType.int16 @@ -1482,7 +1484,13 @@ def convert_mean_to_depthwise_conv(op, arch, nng): fiq.zero_point = 0 op.forced_input_quantization = fiq else: - raise UnsupportedFeatureError("Mean operators with these attributes are currently not supported") + op.rounding_mode = NpuRoundingMode.NATURAL + weight_scale = 1 / (h * w) + # Input zero point is adjusted after mean calculation, so we emulate that with a bias + bias = -ifmq.zero_point * h * w + fiq = ifmq.clone() + fiq.zero_point = 0 + op.forced_input_quantization = fiq # Change dimensions to 4 if dims < 4: @@ -1496,10 +1504,8 @@ def convert_mean_to_depthwise_conv(op, arch, nng): op.ifm_shapes[0] = Shape4D(shape) inp.avoid_NHCWB16 = True - # Add None bias tensor - op.inputs.append(None) # Make unit weight tensor quantization - weight_quant = inp.quantization.clone() + weight_quant = ifmq.clone() weight_quant.min = 0 weight_quant.max = 255 weight_quant.scale_f32 = weight_scale @@ -1519,7 +1525,23 @@ def convert_mean_to_depthwise_conv(op, arch, nng): ), 1, ) - op.inputs[1].quant_values = np.reshape(op.inputs[1].quant_values, weight_shape) + op.weights.quant_values = np.reshape(op.inputs[1].quant_values, weight_shape) + + # Add bias tensor + if bias: + bias_shape = [shape[-1]] + op.set_input_tensor( + create_const_tensor( + "bias", + bias_shape, + inp.dtype, + np.ones(bias_shape) * bias, + value_dtype=np.int32, + quant_value_dtype=np.int32, + quantization=None, + ), + 2, + ) return op |