MLBEDSW-2654: Convert Resizebilinear to a number of 2x2 pools
Signed-off-by: Charles Xu <charles.xu@arm.com>
Change-Id: Ida307afc33cd7963bdeb505df400732a3efcc846
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index 8d920d8..9f92e75 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -210,14 +210,70 @@
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 @@
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 @@
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:
diff --git a/ethosu/vela/register_command_stream_generator.py b/ethosu/vela/register_command_stream_generator.py
index 7b1e9a6..5a14cb4 100644
--- a/ethosu/vela/register_command_stream_generator.py
+++ b/ethosu/vela/register_command_stream_generator.py
@@ -666,6 +666,11 @@
ifm_scale_f64 = np.double(cmd.ifm_tensor.quantization.scale_f32)
ofm_scale_f64 = np.double(cmd.ofm_tensor.quantization.scale_f32)
scale, shift = scaling.quantise_scale(ifm_scale_f64 / ofm_scale_f64)
+ elif primary_op.type == "ResizeBilinear" and "rescale" in primary_op.attrs:
+ rescale = primary_op.attrs["rescale"]
+ rescale_bits = len(bin(round_up_to_int(rescale))) - 2 + 1
+ scale, shift = scaling.quantise_pooling_scale(k_height * k_width, rescale_bits)
+ scale = int(round_away_zero(scale * rescale))
else:
# In case avg pool fused with concat or other memory operation, rescaling might be needed.
# k_height == k_width == 1 is allways true in this case
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index 65588bf..ab7f2db 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -15,6 +15,8 @@
# limitations under the License.
# Description:
# The SupportedOperators class which is a collection of all supported operators and parameter checks.
+import numpy as np
+
from .data_type import BaseType
from .data_type import DataType
@@ -287,13 +289,15 @@
return True
if op.inputs[0].shape == op.outputs[0].shape:
return True
- upscaled_shape = [op.inputs[0].shape[1] * 2, op.inputs[0].shape[2] * 2]
- out_shape = op.outputs[0].shape[1:3]
- if not op.attrs["align_corners"] and out_shape != upscaled_shape:
- return False
- elif op.attrs["align_corners"] and out_shape != [upscaled_shape[0] - 1, upscaled_shape[1] - 1]:
- return False
- return True
+ upscaled_shape = np.array(op.inputs[0].shape[1:3])
+ out_shape = np.array(op.outputs[0].shape[1:3])
+ while (upscaled_shape < out_shape).all():
+ upscaled_shape *= 2
+ if op.attrs["align_corners"]:
+ upscaled_shape -= 1
+ if np.array_equal(out_shape, upscaled_shape):
+ return True
+ return False
def check_vector_product_restrictions(self, op):
# check data type