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
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index bea22a2..56932db 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -1382,7 +1382,7 @@
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 @@
)
# 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 @@
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 @@
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 @@
)
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 @@
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,