diff options
Diffstat (limited to 'ethosu/vela/graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/graph_optimiser.py | 68 |
1 files changed, 61 insertions, 7 deletions
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py index 8d920d83..9f92e755 100644 --- a/ethosu/vela/graph_optimiser.py +++ b/ethosu/vela/graph_optimiser.py @@ -210,14 +210,70 @@ def convert_resizebilinear_1x1_to_add(op): return op +# Convert ResizeBilinear to a number of 2x2 pool ops +def convert_resizebilinear_to_2x2_pool(op): + count = 0 + pre_op = op + outputs = op.outputs + + op.attrs.update({"strides": (1, 1, 1, 1), "ksize": (1, 2, 2, 1)}) + if op.attrs["align_corners"]: + shape_modifier = 1 + op.attrs["padding"] = b"VALID" + else: + shape_modifier = 0 + op.attrs["padding"] = b"SAME" + op.inputs[0].resampling_mode = resampling_mode.NEAREST + + upscaled_shape = np.array(op.inputs[0].shape[1:3]) + out_shape = np.array(op.outputs[0].shape[1:3]) + if (upscaled_shape == upscaled_shape * 2 - shape_modifier).all(): + return op + + while (upscaled_shape < out_shape).all(): + if count == 0: + scaled_op = pre_op + else: + scaled_op = op.clone("_{}".format(count)) + scaled_op.inputs[0] = pre_op.outputs[0] + + upscaled_shape = upscaled_shape * 2 - shape_modifier + + if (upscaled_shape == out_shape).all(): + scaled_op.outputs = outputs + scaled_op.outputs[0].ops = [scaled_op] + else: + shape = outputs[0].shape.copy() + shape[1:3] = upscaled_shape[0:2] + out_tens = Tensor(shape, DataType.int16, "{}_{}".format(op.outputs[0].name, count)) + out_tens.quantization = op.outputs[0].quantization.clone() + out_tens.quantization.quant_min = np.iinfo(np.int16).min + out_tens.quantization.quant_max = np.iinfo(np.int16).max + scaled_op.set_output_tensor(out_tens) + pre_op = scaled_op + count += 1 + + # Setup the scale value + if scaled_op.inputs[0].dtype.bits == 8 and scaled_op.outputs[0].dtype.bits == 16: + scaled_op.attrs["rescale"] = 128 + elif scaled_op.inputs[0].dtype.bits == 16 and scaled_op.outputs[0].dtype.bits == 8: + scaled_op.attrs["rescale"] = 1 / 128 + elif "rescale" in scaled_op.attrs: + del scaled_op.attrs["rescale"] + + return op + + def fixup_resizebilinear(op, arch): - if op.type == "ResizeBilinear": - if op.inputs[0].shape[1] == 1 and op.inputs[0].shape[2] == 1: - convert_resizebilinear_1x1_to_add(op) - elif op.inputs[0].shape == op.outputs[0].shape: + if op.type == "ResizeBilinear" and op.run_on_npu: + if op.inputs[0].shape == op.outputs[0].shape: # Bypass nop resizebilinear op.inputs = op.inputs[:1] op.type = "Identity" + elif op.inputs[0].shape[1] == 1 and op.inputs[0].shape[2] == 1: + convert_resizebilinear_1x1_to_add(op) + else: + convert_resizebilinear_to_2x2_pool(op) return op @@ -822,8 +878,6 @@ def optimise_graph_a(nng, arch, verbose_graph=False): fixup_pack_input, fixup_conv2d_backprop, fixup_act_reorder, - add_attrs_to_resizebilinear, - add_padding_fields, mark_npu_block_type, fixup_elementwise_with_scalars, reorder_depthwise_weights, @@ -842,7 +896,7 @@ def optimise_graph_a(nng, arch, verbose_graph=False): for idx, sg in enumerate(nng.subgraphs): # remove passthrough tensors and attempt further optimizations nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( - sg, arch, [remove_passthrough_tensor], [fuse_activation_function_with_prev] + sg, arch, [remove_passthrough_tensor], [fuse_activation_function_with_prev, add_padding_fields] ) if verbose_graph: |