Code clean-up using black and flake8
- No functional change
Signed-off-by: Tim Hall <tim.hall@arm.com>
Change-Id: I5ab1198b9d092cd041fa9b85b2dee9900d299bfc
diff --git a/ethosu/vela/high_level_command_stream_generator.py b/ethosu/vela/high_level_command_stream_generator.py
index ab72fbc..d02fd85 100644
--- a/ethosu/vela/high_level_command_stream_generator.py
+++ b/ethosu/vela/high_level_command_stream_generator.py
@@ -33,14 +33,13 @@
in_tensor = dma_op.inputs[0]
yield DMA(in_tensor, tensor, box)
+
def match_tensor(source, derived):
if source == derived:
return True
ops = derived.ops
- return (ops != [] and
- len(ops) ==1 and
- ops[0].type == "SplitSliceRead" and
- source == ops[0].inputs[0])
+ return ops != [] and len(ops) == 1 and ops[0].type == "SplitSliceRead" and source == ops[0].inputs[0]
+
def generate_high_level_command_stream_for_pass(strat, passes, block_configs, idx):
is_first = idx == 0
@@ -59,8 +58,7 @@
if len(ps.inputs) == 2 and npu_block_type == NpuBlockType.ElementWise:
# Ensure correct imf and ifm2 order
- if (match_tensor(ps.inputs[0], ps.primary_op.inputs[1]) and
- match_tensor(ps.inputs[1], ps.primary_op.inputs[0])):
+ if match_tensor(ps.inputs[0], ps.primary_op.inputs[1]) and match_tensor(ps.inputs[1], ps.primary_op.inputs[0]):
ps.ifm_tensor, ps.ifm2_tensor = ps.ifm2_tensor, ps.ifm_tensor
split_offsets[0], split_offsets[1] = split_offsets[1], split_offsets[0]
@@ -117,21 +115,46 @@
if ifm_tensor.shape != []:
ifm_box, _, _ = ofm_box.transform_with_strides_and_skirt(
- strides, skirt, ifm_tensor.shape, npu_block_type, concat_axis, concat_offset, split_offsets[0], upscaling
+ strides,
+ skirt,
+ ifm_tensor.shape,
+ npu_block_type,
+ concat_axis,
+ concat_offset,
+ split_offsets[0],
+ upscaling,
)
else:
ifm_box = Box([], [])
if ifm2_tensor is not None and ifm2_tensor.shape != []:
ifm2_box, _, _ = ofm_box.transform_with_strides_and_skirt(
- strides, skirt, ifm2_tensor.shape, npu_block_type, concat_axis, concat_offset, split_offsets[1], upscaling
+ strides,
+ skirt,
+ ifm2_tensor.shape,
+ npu_block_type,
+ concat_axis,
+ concat_offset,
+ split_offsets[1],
+ upscaling,
)
else:
ifm2_box = Box([], [])
for intermediate in ps.intermediates:
- if intermediate != None and intermediate.shape != [] and intermediate.purpose == TensorPurpose.FeatureMap:
+ if (
+ intermediate is not None
+ and intermediate.shape != []
+ and intermediate.purpose == TensorPurpose.FeatureMap
+ ):
intermediate_box, _, _ = ofm_box.transform_with_strides_and_skirt(
- strides, skirt, intermediate.shape, npu_block_type, concat_axis, concat_offset, split_offsets[0], upscaling
+ strides,
+ skirt,
+ intermediate.shape,
+ npu_block_type,
+ concat_axis,
+ concat_offset,
+ split_offsets[0],
+ upscaling,
)
yield from dma_if_necessary(ps, intermediate_box, intermediate)
@@ -218,13 +241,32 @@
k_height = weight_tensor.shape[0]
ifm_box, pad_top, pad_bottom = ofm_box.transform_with_strides_and_skirt(
- strides, skirt, ifm_tensor.shape, npu_block_type, concat_axis, concat_offset, split_offsets[0], k_height, upscaling
+ strides,
+ skirt,
+ ifm_tensor.shape,
+ npu_block_type,
+ concat_axis,
+ concat_offset,
+ split_offsets[0],
+ k_height,
+ upscaling,
)
for intermediate in ps.intermediates:
- if intermediate != None and intermediate.shape != [] and intermediate.purpose == TensorPurpose.FeatureMap:
+ if (
+ intermediate is not None
+ and intermediate.shape != []
+ and intermediate.purpose == TensorPurpose.FeatureMap
+ ):
intermediate_box, _, _ = ofm_box.transform_with_strides_and_skirt(
- strides, skirt, intermediate.shape, npu_block_type, concat_axis, concat_offset, split_offsets[0], upscaling
+ strides,
+ skirt,
+ intermediate.shape,
+ npu_block_type,
+ concat_axis,
+ concat_offset,
+ split_offsets[0],
+ upscaling,
)
yield from dma_if_necessary(ps, intermediate_box, intermediate)