Add pre-commit support for sanity checks

Use pre-commit framework [1] to run black and flake8 before the commit.
black and flake8 are managed by the pre-commit framework and they can be
run manually by the user using `pre-commit run` command.

Fix the code base with the help of black and flake8.
Fix import statements according to PEP8 guidelines [1]
Both tools have the following settings (specified in the pre-commit
configuration file):
* line length: 120 characters
* directory to exclude: ethosu/vela/tflite/ and ethosu/vela/ethos_u55_regs

Updated README.md on how to install pre-commit and how to run sanity checks.
Pipenv files have been updated including new dependencies for pre-commit.

[1]: https://www.python.org/dev/peps/pep-0008/#imports
[2]: https://github.com/pre-commit/pre-commit

Change-Id: I304d9fffdf019d390ffa396a529c8a7c2437f63d
Signed-off-by: Diego Russo <diego.russo@arm.com>

1 files changed, 7 insertions, 8 deletions

diff --git a/ethosu/vela/npu_performance.py b/ethosu/vela/npu_performance.py
index 84cc4931..11f1e92b 100644
--- a/ethosu/vela/npu_performance.py
+++ b/ethosu/vela/npu_performance.py
@@ -23,12 +23,13 @@
 # estimate.
 
 import enum
-from . import numeric_util
+
 import numpy as np
-from .tensor import TensorPurpose, MemArea, TensorFormat, shape_num_elements, Tensor, TensorBlockTraversal
-from .operation import Operation
-from .data_type import DataType, BaseType
-from .nn_graph import PassPlacement, NpuBlockType, SchedulerRewrite, Pass
+
+from . import numeric_util
+from .tensor import TensorPurpose, MemArea, shape_num_elements, TensorBlockTraversal
+from .nn_graph import PassPlacement, SchedulerRewrite
+from .operation import NpuBlockType
 from .architecture_features import Block, Kernel
 
 
@@ -357,9 +358,7 @@ def performance_metrics_for_pass(arch, ps, block_config=None, rewrite_list=[], f
                     n_kernel_xy, 4
                 )  # need at least 4, as this is the minimum duty cycle for secondary accumulator writes
                 if weight_tensor is not None:
-                    n_kernel_xy = numeric_util.round_up(
-                        n_kernel_xy, 4
-                    )  # weights need to be read in blocks of 4
+                    n_kernel_xy = numeric_util.round_up(n_kernel_xy, 4)  # weights need to be read in blocks of 4
 
             num_mac_ops = 0
             for n_blocks_for_size, block_size in block_setup: