diff options
Diffstat (limited to 'ethosu/vela/tflite_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 19 |
1 files changed, 16 insertions, 3 deletions
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index b858f648..36c1de5a 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -76,10 +76,10 @@ def create_avg_pool_for_concat(concat_op, name, ifm, ifm_shape: Shape4D, write_o avgpool_op.write_offset = write_offset avgpool_op.write_shape = ifm_shape ofm.ops.append(avgpool_op) - DebugDatabase.add_optimised(concat_op, avgpool_op) avgpool_op.ifm_shapes.append(ifm_shape) avgpool_op.ofm_shapes.append(concat_op.ofm_shapes[0]) avgpool_op.memory_function = Op.ConcatSliceWrite + DebugDatabase.add_optimised(concat_op, avgpool_op) return avgpool_op @@ -279,6 +279,7 @@ def fixup_conv2d_backprop(op, arch, nng): # Update strides op.attrs.update({"stride_w": 1, "stride_h": 1, "strides": (1, 1, 1, 1)}) + DebugDatabase.add_optimised(op, op) return op @@ -301,6 +302,7 @@ def convert_resize_1x1_to_add(op): op.inputs[1] = op.inputs[0] op.inputs[0] = tens op.set_ifm_ofm_shapes() + DebugDatabase.add_optimised(op, op) return op @@ -390,6 +392,7 @@ def convert_resizenn_ac_to_depthwise_conv(op, upscale_factor): # finally update the shape incase we've change the tensor shapes or connections op.set_ifm_ofm_shapes() + DebugDatabase.add_optimised(op, op) return op @@ -433,6 +436,7 @@ def convert_resize_to_upscale_and_average_pool(op): pre_op = scaled_op scaled_op.set_ifm_ofm_shapes() + DebugDatabase.add_optimised(op, scaled_op) # Last x2 upscaling if n > 1: @@ -463,6 +467,7 @@ def convert_resize_to_upscale_and_average_pool(op): scaled_op.outputs = outputs scaled_op.outputs[0].ops = [scaled_op] scaled_op.set_ifm_ofm_shapes() + DebugDatabase.add_optimised(op, scaled_op) return op @@ -531,6 +536,7 @@ def convert_resizebilinear_to_depthwise_convolutions(op, half_pixel_centers=True avgpool_op.add_input_tensor(ifm) avgpool_op.set_output_tensor(intermediate_tens) avgpool_op.set_ifm_ofm_shapes() + DebugDatabase.add_optimised(op, op) dw_conv = Operation(Op.DepthwiseConv2DBias, "depthwise_conv") dw_conv._original_type = Op.ResizeBilinear @@ -592,6 +598,8 @@ def convert_resizebilinear_to_depthwise_convolutions(op, half_pixel_centers=True fixup_bias_tensors(dw_conv, None, None, dtype=DataType.int32) dw_conv.set_ifm_ofm_shapes() + DebugDatabase.add_optimised(op, dw_conv) + dw_conv = dw_conv.clone(f"_{index}") return op @@ -674,6 +682,7 @@ def unfuse_activation_function(op): act_op.add_input_tensor(intermediate_tens) op.set_output_tensor(intermediate_tens) act_op.set_ifm_ofm_shapes() + DebugDatabase.add_optimised(op, act_op) def rewrite_stridedslice_output(op, arch, nng): @@ -955,6 +964,7 @@ def convert_prelu(op, arch, nng): fm_id = ofm.clone(op.name + "_id", set_unique=True) mul_identity.set_output_tensor(fm_id) mul_identity.set_ifm_ofm_shapes() + DebugDatabase.add_optimised(op, mul_identity) # Combine scaled and alpha multiplied values max_op = Operation(Op.Maximum, op.name + "_max") @@ -1389,7 +1399,7 @@ def fuse_activation_function_with_prev(op, arch, nng): prev_op.set_activation_lut(op.activation_lut) # Bypass op prev_op.set_output_tensor(ofm) - DebugDatabase.add_optimised(op, prev_op) + DebugDatabase.add_optimised(prev_op, prev_op) return op @@ -1482,6 +1492,8 @@ def replace_pad_by_hw_pad(op: Operation, arch, nng): op.attrs["padding"] = Padding.EXPLICIT op.attrs["explicit_padding"] = (top, left, bottom, right) op.set_ifm_ofm_shapes() + DebugDatabase.add_optimised(op, op) + return op @@ -1682,6 +1694,7 @@ def convert_mean_to_depthwise_conv_or_avgpool(op, arch, nng): # If the AvgPool version is used, we don't need to do anything else if op.type == Op.AvgPool: + DebugDatabase.add_optimised(op, op) return op # Make unit weight tensor quantization @@ -1712,6 +1725,7 @@ def convert_mean_to_depthwise_conv_or_avgpool(op, arch, nng): # Add bias tensor bias_shape = [shape[-1]] op.inputs.append(create_const_tensor("bias", bias_shape, DataType.int32, np.ones(bias_shape) * bias)) + DebugDatabase.add_optimised(op, op) return op @@ -1803,7 +1817,6 @@ def convert_shape_op_to_constant_tensor(op: Operation, arch, nng): # Convert this SHAPE op to const op.type = Op.Const - DebugDatabase.add_optimised(op, op) # Add size calculation to shape output tensors ofm.values = np.array(ifm.shape) |