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author | Dwight Lidman <dwight.lidman@arm.com> | 2021-03-26 10:53:28 +0100 |
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committer | patrik.gustavsson <patrik.gustavsson@arm.com> | 2021-04-07 10:50:39 +0000 |
commit | 95b279f1454d58a93238851cb5ff394c7782ad32 (patch) | |
tree | eb2e8f4db229f0581a894084c75f44b55877a05b /ethosu/vela/graph_optimiser.py | |
parent | fe368bc231fb680ebfa48e2c35e92dec5639df5e (diff) | |
download | ethos-u-vela-95b279f1454d58a93238851cb5ff394c7782ad32.tar.gz |
MEAN implementation changed to Average Pool
This is a small commit which changes one of
the four MEAN implementations to a simpler
one, using an AvgPool instead of a
DepthwiseConv.
Signed-off-by: Dwight Lidman <dwight.lidman@arm.com>
Change-Id: I9e8af071e8b820796577ee4792b4812a1212602b
Diffstat (limited to 'ethosu/vela/graph_optimiser.py')
-rw-r--r-- | ethosu/vela/graph_optimiser.py | 25 |
1 files changed, 14 insertions, 11 deletions
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py index bea22a23..56932dbe 100644 --- a/ethosu/vela/graph_optimiser.py +++ b/ethosu/vela/graph_optimiser.py @@ -1382,7 +1382,7 @@ def fixup_bias_tensors(op, arch, nng): return op -def convert_mean_to_depthwise_conv(op, arch, nng): +def convert_mean_to_depthwise_conv_or_avgpool(op, arch, nng): if op.type == Op.Mean and op.run_on_npu: keep_dims = op.attrs.get("keep_dims", False) inp, axis = op.inputs @@ -1422,8 +1422,6 @@ 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() @@ -1509,14 +1507,11 @@ def convert_mean_to_depthwise_conv(op, arch, nng): op.set_output_tensor(intermediate) op.set_ifm_ofm_shapes() elif ifmq.zero_point == ofmq.zero_point and ifmq.scale_f32 == ofmq.scale_f32: + # Here we can just use a simple AvgPool with truncating rounding, + # as we're emulating simple integer division. op.rounding_mode = NpuRoundingMode.TRUNCATE - weight_scale = 1 / (h * w) - foq = ofmq.clone() - foq.zero_point = 0 - op.forced_output_quantization = foq - fiq = ifmq.clone() - fiq.zero_point = 0 - op.forced_input_quantization = fiq + op.type = Op.AvgPool + op.attrs.update({"ksize": (1, h, w, 1), "filter_height": h, "filter_width": w}) else: op.rounding_mode = NpuRoundingMode.NATURAL weight_scale = 1 / (h * w) @@ -1537,6 +1532,12 @@ def convert_mean_to_depthwise_conv(op, arch, nng): shape = [shape[0], 1, h * w, shape[3]] op.ifm_shapes[0] = Shape4D(shape) inp.avoid_NHCWB16 = True + if h > 256 and op.type == Op.AvgPool: + op.attrs.update({"ksize": (1, 1, h * w, 1), "filter_height": 1, "filter_width": h * w}) + + # If the AvgPool version is used, we don't need to do anything else + if op.type == Op.AvgPool: + return op # Make unit weight tensor quantization weight_quant = ifmq.clone() @@ -1561,6 +1562,8 @@ def convert_mean_to_depthwise_conv(op, arch, nng): ) op.weights.quant_values = np.reshape(op.inputs[1].quant_values, weight_shape) + # Add None bias tensor + op.inputs.append(None) # Add bias tensor if bias: bias_shape = [shape[-1]] @@ -1643,7 +1646,7 @@ def optimise_graph_a(nng, arch, verbose_graph=False): op_rewrite_list = [ set_tensor_equivalence, - convert_mean_to_depthwise_conv, + convert_mean_to_depthwise_conv_or_avgpool, convert_depthwise_to_conv, convert_conv_to_fc, convert_softmax, |