From b3adc0b792cd413fa5c30da951dd6f13a77fda0a Mon Sep 17 00:00:00 2001
From: Nicola Lancellotti <nicola.lancellotti@arm.com>
Date: Tue, 30 Aug 2022 09:53:42 +0000
Subject: [PATCH] [ETHOSN] Fix tests pylint errors

---
 tests/lint/pylint.sh                          |  1 +
 .../contrib/test_ethosn/infrastructure.py     | 50 +++++++++++--------
 .../contrib/test_ethosn/test_concatenate.py   | 10 +++-
 .../test_ethosn/test_constant_duplication.py  | 10 ++--
 .../python/contrib/test_ethosn/test_conv2d.py | 18 ++++---
 .../test_ethosn/test_depth_to_space.py        |  4 ++
 .../test_ethosn/test_fullyconnected.py        | 25 +++++-----
 .../contrib/test_ethosn/test_leaky_relu.py    |  2 +
 tests/python/contrib/test_ethosn/test_mean.py |  2 +
 .../contrib/test_ethosn/test_multiply.py      |  3 ++
 .../contrib/test_ethosn/test_networks.py      | 13 +++--
 .../test_ethosn/test_partition_params.py      | 24 ++++++---
 .../contrib/test_ethosn/test_pooling.py       |  8 ++-
 tests/python/contrib/test_ethosn/test_relu.py |  4 ++
 .../contrib/test_ethosn/test_requantize.py    |  5 ++
 .../python/contrib/test_ethosn/test_resize.py |  4 ++
 .../contrib/test_ethosn/test_sigmoid.py       | 11 ++--
 .../python/contrib/test_ethosn/test_split.py  |  9 +++-
 tests/python/contrib/test_ethosn/test_tanh.py |  4 ++
 .../contrib/test_ethosn/test_topologies.py    | 33 +++++++-----
 20 files changed, 158 insertions(+), 82 deletions(-)

diff --git a/tests/lint/pylint.sh b/tests/lint/pylint.sh
index 2228e110c15e..94fae289b6b9 100755
--- a/tests/lint/pylint.sh
+++ b/tests/lint/pylint.sh
@@ -21,6 +21,7 @@ python3 -m pylint python/tvm --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint vta/python/vta --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint tests/python/unittest/test_tvmscript_type.py --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint tests/python/contrib/test_cmsisnn --rcfile="$(dirname "$0")"/pylintrc
+python3 -m pylint tests/python/contrib/test_ethosn --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint tests/python/relay/aot/*.py --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint tests/python/ci --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint tests/python/integration/ --rcfile="$(dirname "$0")"/pylintrc
diff --git a/tests/python/contrib/test_ethosn/infrastructure.py b/tests/python/contrib/test_ethosn/infrastructure.py
index a1c8ca0a32d2..0071b1a7f52e 100644
--- a/tests/python/contrib/test_ethosn/infrastructure.py
+++ b/tests/python/contrib/test_ethosn/infrastructure.py
@@ -18,17 +18,17 @@
 """Arm(R) Ethos(TM)-N test functions"""
 
 from __future__ import absolute_import, print_function
-import tvm
-from tvm import relay
-from tvm.contrib import utils, graph_executor, download
 from hashlib import md5
 from itertools import zip_longest, combinations
+import os
 import numpy as np
 from PIL import Image
-import os
 
-from . import _infrastructure
+import tvm
+from tvm import relay
+from tvm.contrib import utils, graph_executor, download
 from tvm.relay.op.contrib import partition_for_ethosn
+from . import _infrastructure
 
 
 def get_real_image(im_height, im_width):
@@ -82,23 +82,25 @@ def make_module(func, params):
 
 
 def make_ethosn_composite(ethosn_expr, name):
-    vars = relay.analysis.free_vars(ethosn_expr)
-    inner_vars = [relay.Var(v.name_hint, v.type_annotation) for v in vars]
+    variables = relay.analysis.free_vars(ethosn_expr)
+    inner_vars = [relay.Var(v.name_hint, v.type_annotation) for v in variables]
     func = relay.Function(inner_vars, ethosn_expr)
     func = func.with_attr("Composite", name)
-    call = relay.Call(func, vars)
+    call = relay.Call(func, variables)
     return call
 
 
 def make_ethosn_partition(ethosn_expr):
+    """Make an Ethos(TM)-N partition."""
+
     # Create an Ethos-N global function
     mod = tvm.IRModule({})
-    vars = relay.analysis.free_vars(ethosn_expr)
+    variables = relay.analysis.free_vars(ethosn_expr)
     # NB: it is illegal to reuse variables inside and outside a scope in Relay
     # if you want to duplicate types and names you must re-allocate them.
-    fresh_vars = [relay.Var(v.name_hint, v.type_annotation) for v in vars]
+    fresh_vars = [relay.Var(v.name_hint, v.type_annotation) for v in variables]
     binds = {}
-    for var, fresh_var in zip(vars, fresh_vars):
+    for var, fresh_var in zip(variables, fresh_vars):
         binds[var] = fresh_var
     ethosn_expr_fresh = relay.bind(ethosn_expr, binds)
     func = relay.Function(fresh_vars, ethosn_expr_fresh)
@@ -106,19 +108,21 @@ def make_ethosn_partition(ethosn_expr):
     func = func.with_attr("Inline", tvm.tir.IntImm("int32", 1))
     func = func.with_attr("Compiler", "ethos-n")
     func = func.with_attr("global_symbol", "ethos-n_0")
-    g1 = relay.GlobalVar("ethos-n_0")
-    mod[g1] = func
+    global_var = relay.GlobalVar("ethos-n_0")
+    mod[global_var] = func
     mod = relay.transform.InferType()(mod)
 
     # These are the vars to call the Ethos-N partition with
     more_vars = relay.analysis.free_vars(ethosn_expr)
     # Call the Ethos-N partition in main
-    call_fn1 = g1(*more_vars)
+    call_fn1 = global_var(*more_vars)
     mod["main"] = relay.Function(more_vars, call_fn1)
     return relay.transform.InferType()(mod)
 
 
 def get_host_op_count(mod):
+    """Return the number of host operators."""
+
     class Counter(tvm.relay.ExprVisitor):
         def __init__(self):
             super().__init__()
@@ -219,9 +223,7 @@ def run(lib, inputs, outputs, npu=True):
     return out
 
 
-def build_and_run(
-    mod, inputs, outputs, params, device=tvm.cpu(), npu=True, expected_host_ops=0, npu_partitions=1
-):
+def build_and_run(mod, inputs, outputs, params, npu=True, expected_host_ops=0, npu_partitions=1):
     lib = build(mod, params, npu, expected_host_ops, npu_partitions)
     return run(lib, inputs, outputs, npu)
 
@@ -254,6 +256,8 @@ def inference_result(outputs):
 
 
 def test_error(mod, params, err_msg):
+    """Test an operator error message."""
+
     caught = None
     with tvm.transform.PassContext(
         opt_level=3, config={"relay.ext.ethos-n.options": {"variant": get_ethosn_variant()}}
@@ -262,8 +266,8 @@ def test_error(mod, params, err_msg):
             try:
                 mod = relay.transform.InferType()(mod)
                 relay.build(mod, params=params)
-            except tvm.error.TVMError as e:
-                caught = e.args[0]
+            except tvm.error.TVMError as error:
+                caught = error.args[0]
             finally:
                 relay.backend.te_compiler.get().clear()
 
@@ -275,8 +279,8 @@ def get_conv2d(var, shape, dtype):
     """Standard convolution to test activation functions"""
 
     weight_shape = (1, 1, shape[3], 1)
-    w = tvm.nd.array(np.ones(weight_shape, dtype))
-    weights = relay.const(w, dtype)
+    weights_array = tvm.nd.array(np.ones(weight_shape, dtype))
+    weights = relay.const(weights_array, dtype)
     conv = relay.qnn.op.conv2d(
         var,
         weights,
@@ -300,13 +304,15 @@ def get_conv2d(var, shape, dtype):
         relay.const(0, "int32"),  # output zero point
         out_dtype=dtype,
     )
-    params = {"w": w, "b": b}
+    params = {"w": weights_array, "b": b}
     return req, params
 
 
 def get_conv2d_qnn_params(
     dtype, input_zp, input_sc, kernel_zp, kernel_sc, kernel_h, kernel_w, channels
 ):
+    """Return Conv2D QNN params."""
+
     kernel_sc = (
         kernel_sc.numpy() if isinstance(kernel_sc, tvm.runtime.ndarray.NDArray) else [kernel_sc]
     )
diff --git a/tests/python/contrib/test_ethosn/test_concatenate.py b/tests/python/contrib/test_ethosn/test_concatenate.py
index b2eba6d650e0..cd4ec7a4e4b2 100644
--- a/tests/python/contrib/test_ethosn/test_concatenate.py
+++ b/tests/python/contrib/test_ethosn/test_concatenate.py
@@ -57,6 +57,8 @@ def _get_model(shapes, dtype, axis):
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_concatenate(dtype):
+    """Compare Concatenate output with TVM."""
+
     trials = [
         ([(1, 4), (1, 6)], 1),
         ([(1, 16, 4), (1, 16, 4)], 1),
@@ -78,19 +80,23 @@ def test_concatenate(dtype):
 
 @requires_ethosn
 def test_concatenate_failure():
+    """Check Concatenate error messages."""
+
     trials = [
         ([(1, 4, 4, 4, 4), (1, 4, 4, 4, 4)], "uint8", 1, "dimensions=5, dimensions must be <= 4;"),
         (
             [(1, 4, 4, 4), (1, 4, 4, 4)],
             "uint8",
             3,
-            "Concatenation along the channels dimension (axis 3) requires input tensors with a multiple of 16 channels;",
+            "Concatenation along the channels dimension (axis 3) "
+            "requires input tensors with a multiple of 16 channels;",
         ),
         (
             [(1, 4, 4, 4), (1, 4, 4, 4)],
             "int16",
             2,
-            "dtype='int16', dtype must be either uint8, int8 or int32; dtype='int16', dtype must be either uint8, int8 or int32;",
+            "dtype='int16', dtype must be either uint8, int8 or int32; dtype='int16', "
+            "dtype must be either uint8, int8 or int32;",
         ),
         (
             [(2, 4, 4, 4), (2, 4, 4, 4)],
diff --git a/tests/python/contrib/test_ethosn/test_constant_duplication.py b/tests/python/contrib/test_ethosn/test_constant_duplication.py
index 84956840ecbb..b3cd0046f508 100644
--- a/tests/python/contrib/test_ethosn/test_constant_duplication.py
+++ b/tests/python/contrib/test_ethosn/test_constant_duplication.py
@@ -36,8 +36,10 @@ def _get_model():
     add_const = relay.const(add_const_value, "uint8")
     a = relay.add(a, add_const)
     weight_shape = (kernel_h, kernel_w, shape[3], out_channels)
-    w = tvm.nd.array(np.random.randint(low=0, high=255, size=weight_shape, dtype="uint8"))
-    weights = relay.const(w, "uint8")
+    weights_array = tvm.nd.array(
+        np.random.randint(low=0, high=255, size=weight_shape, dtype="uint8")
+    )
+    weights = relay.const(weights_array, "uint8")
     conv = relay.qnn.op.conv2d(
         a,
         weights,
@@ -66,12 +68,14 @@ def _get_model():
         relay.const(0, "int32"),  # output zero point
         out_dtype="uint8",
     )
-    params = {"w": w, "b": b}
+    params = {"w": weights_array, "b": b}
     return req, params
 
 
 @requires_ethosn
 def test_constant_duplication():
+    """Test that constants are not duplicated."""
+
     np.random.seed(0)
     model, params = _get_model()
     mod = tei.make_module(model, params)
diff --git a/tests/python/contrib/test_ethosn/test_conv2d.py b/tests/python/contrib/test_ethosn/test_conv2d.py
index a411701ea0bc..ffe66f0d2be2 100644
--- a/tests/python/contrib/test_ethosn/test_conv2d.py
+++ b/tests/python/contrib/test_ethosn/test_conv2d.py
@@ -17,9 +17,9 @@
 
 """Arm(R) Ethos(TM)-N integration conv2d tests"""
 
+import math
 import numpy as np
 import pytest
-import math
 import tvm
 from tvm import relay
 from tvm.testing import requires_ethosn
@@ -61,7 +61,7 @@ def _get_model(
 ):
     """Return a model and any parameters it may have"""
     a = relay.var("a", shape=shape, dtype=dtype)
-    if pad == "op" or pad == "both":
+    if pad in ("op", "both"):
         p = _get_same_padding((shape[1], shape[2]), (kernel_h, kernel_w), dilation, strides)
         a = relay.nn.pad(
             a,
@@ -76,12 +76,12 @@ def _get_model(
         weight_shape = (kernel_h, kernel_w, shape[3] // groups, out_channels)
     else:
         weight_shape = (kernel_h, kernel_w, out_channels, 1)
-    w = tvm.nd.array(
+    weights_array = tvm.nd.array(
         np.random.randint(
             np.iinfo(dtype).min, high=np.iinfo(dtype).max + 1, size=weight_shape, dtype=dtype
         )
     )
-    weights = relay.const(w, dtype)
+    weights = relay.const(weights_array, dtype)
     conv = relay.qnn.op.conv2d(
         a,
         weights,
@@ -96,7 +96,7 @@ def _get_model(
         strides=strides,
         groups=groups,
         channels=out_channels,
-        padding=p if pad == "attr" or pad == "both" else (0, 0, 0, 0),
+        padding=p if pad in ("attr", "both") else (0, 0, 0, 0),
         out_dtype="int32",
     )
     b = tvm.nd.array(
@@ -118,7 +118,7 @@ def _get_model(
         relay.const(output_zp, "int32"),  # output zero point
         out_dtype=dtype,
     )
-    params = {"w": w, "b": b}
+    params = {"w": weights_array, "b": b}
     return req, params
 
 
@@ -126,6 +126,8 @@ def _get_model(
 @pytest.mark.parametrize("depthwise", [False, True])
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_conv2d(dtype, depthwise):
+    """Compare Conv2D output with TVM."""
+
     trials = [
         [(1, 17, 20, 26), 4, 3, 1, "attr", (2, 2), (1, 1), False],
         [(1, 30, 27, 30), 5, 5, 3, "none", (1, 1), (1, 1), False],
@@ -208,6 +210,8 @@ def test_conv2d(dtype, depthwise):
 
 @requires_ethosn
 def test_conv2d_failure():
+    """Check Conv2D error messages."""
+
     trials = [
         (
             (1, 4, 4, 4),
@@ -326,7 +330,7 @@ def test_conv2d_failure():
         weight_format,
         err_msg,
     ) in trials:
-        model, params = _get_model(
+        model, _ = _get_model(
             shape,
             kernel_h,
             kernel_w,
diff --git a/tests/python/contrib/test_ethosn/test_depth_to_space.py b/tests/python/contrib/test_ethosn/test_depth_to_space.py
index 1675b82eeace..c071fe00f212 100644
--- a/tests/python/contrib/test_ethosn/test_depth_to_space.py
+++ b/tests/python/contrib/test_ethosn/test_depth_to_space.py
@@ -34,6 +34,8 @@ def _get_model(shape, block, dtype, layout):
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_depth_to_space(dtype):
+    """Compare Depth To Space output with TVM."""
+
     trials = [
         (1, 16, 16, 16),
         (1, 64, 32, 16),
@@ -59,6 +61,8 @@ def test_depth_to_space(dtype):
 
 @requires_ethosn
 def test_depth_to_space_failure():
+    """Check Depth To Space error messages."""
+
     trials = [
         ((2, 16, 16, 16), 2, "uint8", "NHWC", "batch size=2, batch size must = 1"),
         (
diff --git a/tests/python/contrib/test_ethosn/test_fullyconnected.py b/tests/python/contrib/test_ethosn/test_fullyconnected.py
index 2caca9e890a2..d5510bb79d2c 100644
--- a/tests/python/contrib/test_ethosn/test_fullyconnected.py
+++ b/tests/python/contrib/test_ethosn/test_fullyconnected.py
@@ -30,9 +30,9 @@ def _get_model(
 ):
     """Return a model an any parameters it may have"""
     a = relay.var("a", shape=shape, dtype=dtype)
-    w = tvm.nd.array(np.ones(weight_shape, dtype))
-    weights = relay.const(w, dtype)
-    fc = relay.qnn.op.dense(
+    weights_array = tvm.nd.array(np.ones(weight_shape, dtype))
+    weights = relay.const(weights_array, dtype)
+    dense = relay.qnn.op.dense(
         a,
         weights,
         input_zero_point=relay.const(input_zp, "int32"),
@@ -44,7 +44,7 @@ def _get_model(
     )
     b = tvm.nd.array(np.random.randint(0, high=255, size=(weight_shape[0],), dtype="int32"))
     biasc = relay.const(b, "int32")
-    bias = relay.nn.bias_add(fc, biasc)
+    bias = relay.nn.bias_add(dense, biasc)
     req = relay.qnn.op.requantize(
         bias,
         relay.const(input_sc * kernel_sc, "float32"),  # input zero scale
@@ -53,7 +53,7 @@ def _get_model(
         relay.const(output_zp, "int32"),  # output zero point
         out_dtype=dtype,
     )
-    params = {"w": w, "b": b}
+    params = {"w": weights_array, "b": b}
     return req, params
 
 
@@ -76,9 +76,8 @@ def _get_model(
     ],
 )
 def test_fullyconnected(shape, out_channels, dtype, input_zp, input_sc, kernel_zp, kernel_sc):
-    """
-    Test fully connected offloading.
-    """
+    """Compare Fully Connected output with TVM."""
+
     np.random.seed(0)
     inputs = {
         "a": tvm.nd.array(
@@ -116,6 +115,8 @@ def test_fullyconnected(shape, out_channels, dtype, input_zp, input_sc, kernel_z
 
 @requires_ethosn
 def test_fullyconnected_failure():
+    """Check Fully Connected error messages."""
+
     trials = [
         (
             (1, 64),
@@ -139,7 +140,8 @@ def test_fullyconnected_failure():
             0,
             1,
             "uint8",
-            "Weights tensor must have I dimension equal to the number of channels of the input tensor.;",
+            "Weights tensor must have I dimension equal to the number"
+            " of channels of the input tensor.;",
         ),
         ((1024, 64), (1, 64), 0, 1, 0, 1, 0, 1, "uint8", "batch size=1024, batch size must = 1;"),
     ]
@@ -157,10 +159,7 @@ def test_fullyconnected_failure():
         dtype,
         err_msg,
     ) in trials:
-        inputs = {
-            "a": tvm.nd.array(np.random.randint(0, high=255, size=shape, dtype=dtype)),
-        }
-        model, params = _get_model(
+        model, _ = _get_model(
             shape,
             weight_shape,
             input_zp,
diff --git a/tests/python/contrib/test_ethosn/test_leaky_relu.py b/tests/python/contrib/test_ethosn/test_leaky_relu.py
index cdd06f5e73e4..3c3bbc709679 100644
--- a/tests/python/contrib/test_ethosn/test_leaky_relu.py
+++ b/tests/python/contrib/test_ethosn/test_leaky_relu.py
@@ -49,6 +49,7 @@ def _get_model(shape, input_zp, input_sc, output_zp, output_sc, dtype, alpha):
 @pytest.mark.parametrize("alpha", [0.001, 0.5678])
 def test_leaky_relu(dtype, shape, alpha):
     """Compare Leaky ReLU output with TVM."""
+
     np.random.seed(0)
 
     iinfo = np.iinfo(dtype)
@@ -75,6 +76,7 @@ def test_leaky_relu(dtype, shape, alpha):
 @pytest.mark.parametrize("alpha", [-1.34, 2.32, 1, 0])
 def test_leaky_relu_unsupported_alpha(dtype, shape, alpha):
     """Test unsupported values of alpha (<= 0, >= 1) in Leaky ReLU."""
+
     iinfo = np.iinfo(dtype)
     zp_min = iinfo.min
 
diff --git a/tests/python/contrib/test_ethosn/test_mean.py b/tests/python/contrib/test_ethosn/test_mean.py
index 548743fe9548..0ad7e17faed8 100644
--- a/tests/python/contrib/test_ethosn/test_mean.py
+++ b/tests/python/contrib/test_ethosn/test_mean.py
@@ -45,6 +45,7 @@ def _get_model(shape, axis, keepdims, input_zp, input_sc, output_zp, output_sc,
 @pytest.mark.parametrize("shape", [(1, 7, 7, 2048), (1, 8, 8)])
 def test_mean(dtype, shape):
     """Compare Mean output with TVM."""
+
     np.random.seed(0)
 
     zp_min = np.iinfo(dtype).min
@@ -68,6 +69,7 @@ def test_mean(dtype, shape):
 @pytest.mark.parametrize("dtype", ["int8", "uint8"])
 def test_mean_non_equal_quantization(dtype):
     """Test mean is not offloaded when quantization is not equal."""
+
     np.random.seed(0)
 
     shape = (1, 7, 7, 2048)
diff --git a/tests/python/contrib/test_ethosn/test_multiply.py b/tests/python/contrib/test_ethosn/test_multiply.py
index 38d8516b6721..cb95a97db529 100644
--- a/tests/python/contrib/test_ethosn/test_multiply.py
+++ b/tests/python/contrib/test_ethosn/test_multiply.py
@@ -69,6 +69,7 @@ def _get_model(
 @pytest.mark.parametrize("reverse_inputs", [False, True])
 def test_multiply(dtype, shape, constant_shape, reverse_inputs):
     """Compare Multiply output with TVM."""
+
     np.random.seed(0)
 
     iinfo = np.iinfo(dtype)
@@ -106,6 +107,7 @@ def test_multiply(dtype, shape, constant_shape, reverse_inputs):
 @requires_ethosn
 def test_multiply_multiple_inputs_unsupported():
     """Check multiply operator with two inputs is not offloaded."""
+
     np.random.seed(0)
 
     shape = (1, 4, 5, 6)
@@ -151,6 +153,7 @@ def test_multiply_multiple_inputs_unsupported():
 @requires_ethosn
 def test_multiply_unsupported_datatype():
     """Check multiply operator with unsupported datatype is not offloaded."""
+
     np.random.seed(0)
 
     shape = (1, 4, 5, 6)
diff --git a/tests/python/contrib/test_ethosn/test_networks.py b/tests/python/contrib/test_ethosn/test_networks.py
index 11745409d4ea..db1b41244846 100644
--- a/tests/python/contrib/test_ethosn/test_networks.py
+++ b/tests/python/contrib/test_ethosn/test_networks.py
@@ -14,7 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-
+# pylint: disable=wrong-import-position
 """Arm(R) Ethos(TM)-N integration end-to-end network tests"""
 
 import pytest
@@ -22,12 +22,11 @@
 pytest.importorskip("tflite")
 pytest.importorskip("tensorflow")
 
+import tflite.Model
 from tvm import relay
 from tvm.testing import requires_ethosn
 from tvm.contrib import download
-
 import tvm.relay.testing.tf as tf_testing
-import tflite.Model
 from . import infrastructure as tei
 
 
@@ -41,10 +40,10 @@ def _get_tflite_model(tflite_model_path, inputs_dict, dtype):
         tflite_model = tflite.Model.GetRootAsModel(tflite_model_buffer, 0)
     shape_dict = {}
     dtype_dict = {}
-    for input in inputs_dict:
-        input_shape = inputs_dict[input]
-        shape_dict[input] = input_shape
-        dtype_dict[input] = dtype
+    for value in inputs_dict:
+        input_shape = inputs_dict[value]
+        shape_dict[value] = input_shape
+        dtype_dict[value] = dtype
 
     return relay.frontend.from_tflite(
         tflite_model,
diff --git a/tests/python/contrib/test_ethosn/test_partition_params.py b/tests/python/contrib/test_ethosn/test_partition_params.py
index 34e22e6aaba8..e8ac687c04b0 100644
--- a/tests/python/contrib/test_ethosn/test_partition_params.py
+++ b/tests/python/contrib/test_ethosn/test_partition_params.py
@@ -18,19 +18,23 @@
 """Arm(R) Ethos(TM)-N partition parameter tests"""
 
 import pytest
-import tvm
-from tvm import relay
 import numpy as np
 
+import tvm
+from tvm import relay
 from tvm.relay.op.contrib.ethosn import partition_for_ethosn
 from tvm.testing import requires_ethosn
 
 
 @requires_ethosn
 def test_ethosn78_partition_no_error():
+    """Test Arm(R) Ethos(TM)-N78 partition"""
+
     a = relay.var("a", shape=[2, 7, 8, 8], dtype="uint8")
-    w = relay.const(np.random.uniform(-10, 10, (8, 7, 3, 3)).astype("uint8"))
-    res = relay.nn.conv2d(a, w, kernel_size=(3, 3), padding=(1, 1), channels=8, out_dtype="uint8")
+    weights = relay.const(np.random.uniform(-10, 10, (8, 7, 3, 3)).astype("uint8"))
+    res = relay.nn.conv2d(
+        a, weights, kernel_size=(3, 3), padding=(1, 1), channels=8, out_dtype="uint8"
+    )
     b = relay.var("b", shape=[8], dtype="uint8")
     res = relay.nn.bias_add(res, b, axis=1)
 
@@ -41,13 +45,15 @@ def test_ethosn78_partition_no_error():
 
 @requires_ethosn
 def test_ethosn78_partition_undefined_variant():
+    """Test Arm(R) Ethos(TM)-N78 partition with undefined variant"""
+
     with pytest.raises(
         ValueError, match=r".*Please specify a variant in the target string, e.g. -variant=n78.*"
     ):
         a = relay.var("a", shape=[2, 7, 8, 8], dtype="uint8")
-        w = relay.const(np.random.uniform(-10, 10, (8, 7, 3, 3)).astype("uint8"))
+        weights = relay.const(np.random.uniform(-10, 10, (8, 7, 3, 3)).astype("uint8"))
         res = relay.nn.conv2d(
-            a, w, kernel_size=(3, 3), padding=(1, 1), channels=8, out_dtype="uint8"
+            a, weights, kernel_size=(3, 3), padding=(1, 1), channels=8, out_dtype="uint8"
         )
         b = relay.var("b", shape=[8], dtype="uint8")
         res = relay.nn.bias_add(res, b, axis=1)
@@ -58,13 +64,15 @@ def test_ethosn78_partition_undefined_variant():
 
 @requires_ethosn
 def test_ethosn78_partition_invalid_variant():
+    """Test Arm(R) Ethos(TM)-N78 partition with invalid variant"""
+
     with pytest.raises(
         ValueError, match=r".*When targeting Ethos\(TM\)-N78, -variant=n78 should be set.*"
     ):
         a = relay.var("a", shape=[2, 7, 8, 8], dtype="uint8")
-        w = relay.const(np.random.uniform(-10, 10, (8, 7, 3, 3)).astype("uint8"))
+        wwights = relay.const(np.random.uniform(-10, 10, (8, 7, 3, 3)).astype("uint8"))
         res = relay.nn.conv2d(
-            a, w, kernel_size=(3, 3), padding=(1, 1), channels=8, out_dtype="uint8"
+            a, wwights, kernel_size=(3, 3), padding=(1, 1), channels=8, out_dtype="uint8"
         )
         b = relay.var("b", shape=[8], dtype="uint8")
         res = relay.nn.bias_add(res, b, axis=1)
diff --git a/tests/python/contrib/test_ethosn/test_pooling.py b/tests/python/contrib/test_ethosn/test_pooling.py
index 3defaa55e853..e1c7358f71a1 100644
--- a/tests/python/contrib/test_ethosn/test_pooling.py
+++ b/tests/python/contrib/test_ethosn/test_pooling.py
@@ -28,10 +28,10 @@
 def _get_model(shape, typef, sizes, strides, pads, layout, dtype):
     """Return a model and any parameters it may have"""
     req = relay.var("a", shape=shape, dtype=dtype)
-    if typef == relay.nn.avg_pool2d:
+    if typef is relay.nn.avg_pool2d:
         req = relay.cast(req, "int32")
     req = typef(req, pool_size=sizes, strides=strides, padding=pads, ceil_mode=True, layout=layout)
-    if typef == relay.nn.avg_pool2d:
+    if typef is relay.nn.avg_pool2d:
         req = relay.cast(req, dtype)
     return req
 
@@ -39,6 +39,8 @@ def _get_model(shape, typef, sizes, strides, pads, layout, dtype):
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_pooling(dtype):
+    """Compare Pooling output with TVM."""
+
     trials = [
         ((1, 8, 8, 8), relay.nn.max_pool2d, (2, 2), (2, 2), (0, 0, 0, 0), "NHWC"),
         ((1, 9, 9, 9), relay.nn.max_pool2d, (3, 3), (2, 2), (0, 0, 0, 0), "NHWC"),
@@ -65,6 +67,8 @@ def test_pooling(dtype):
 
 @requires_ethosn
 def test_pooling_failure():
+    """Check Pooling error messages."""
+
     trials = [
         (
             (2, 8, 8, 8),
diff --git a/tests/python/contrib/test_ethosn/test_relu.py b/tests/python/contrib/test_ethosn/test_relu.py
index 5d3e8f1e9921..f56a1cd7ad3c 100644
--- a/tests/python/contrib/test_ethosn/test_relu.py
+++ b/tests/python/contrib/test_ethosn/test_relu.py
@@ -35,6 +35,8 @@ def _get_model(shape, dtype, a_min, a_max):
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_relu(dtype):
+    """Compare Relu output with TVM."""
+
     trials = [
         ((1, 4, 4, 4), 65, 178, "uint8"),
         ((1, 8, 4, 2), 1, 254, "uint8"),
@@ -68,6 +70,8 @@ def test_relu(dtype):
 
 @requires_ethosn
 def test_relu_failure():
+    """Check Relu error messages."""
+
     trials = [
         ((1, 4, 4, 4, 4), "uint8", 65, 78, "dimensions=5, dimensions must be <= 4"),
         ((1, 8, 4, 2), "int16", 1, 254, "dtype='int16', dtype must be either uint8, int8 or int32"),
diff --git a/tests/python/contrib/test_ethosn/test_requantize.py b/tests/python/contrib/test_ethosn/test_requantize.py
index e20c3beeabfa..3187c22f3391 100644
--- a/tests/python/contrib/test_ethosn/test_requantize.py
+++ b/tests/python/contrib/test_ethosn/test_requantize.py
@@ -43,6 +43,8 @@ def _get_model(shape, input_zp, input_sc, output_zp, output_sc, in_dtype, out_dt
 @pytest.mark.parametrize("out_dtype", ["int8", "uint8"])
 @pytest.mark.parametrize("shape", [(1, 52, 52, 3)])
 def test_requantize(in_dtype, out_dtype, shape):
+    """Compare Requantize output with TVM."""
+
     np.random.seed(0)
     low = 0 if in_dtype == "uint8" else -5
     high = low + 10
@@ -74,6 +76,7 @@ def test_requantize_mixed_precision_with_following_op():
     Checks a requantize operation that changes precision from uint8 to int8 with a
     following add op.
     """
+
     np.random.seed(0)
     shape = (1, 4, 6, 8)
     in_sc = 0.012566
@@ -133,6 +136,8 @@ def get_model():
 
 @requires_ethosn
 def test_requantize_failure():
+    """Check Requantize error messages."""
+
     input_sc = 0.8
     output_sc = (input_sc / 128) - 0.0001
     model = _get_model(
diff --git a/tests/python/contrib/test_ethosn/test_resize.py b/tests/python/contrib/test_ethosn/test_resize.py
index b9d807d21926..2cc641e63b5c 100644
--- a/tests/python/contrib/test_ethosn/test_resize.py
+++ b/tests/python/contrib/test_ethosn/test_resize.py
@@ -68,6 +68,8 @@ def _get_model(
     ],
 )
 def test_resize(dtype, shape, size, coordinate_transformation_mode, rounding_method):
+    """Compare Resize output with TVM."""
+
     np.random.seed(0)
     zp_min = np.iinfo(dtype).min
     zp_max = np.iinfo(dtype).max
@@ -96,6 +98,8 @@ def test_resize(dtype, shape, size, coordinate_transformation_mode, rounding_met
 
 @requires_ethosn
 def test_resize_failure():
+    """Check Resize error messages."""
+
     trials = [
         (
             (30, 20),
diff --git a/tests/python/contrib/test_ethosn/test_sigmoid.py b/tests/python/contrib/test_ethosn/test_sigmoid.py
index 9947bee3b86b..ae8c301ff01a 100644
--- a/tests/python/contrib/test_ethosn/test_sigmoid.py
+++ b/tests/python/contrib/test_ethosn/test_sigmoid.py
@@ -45,6 +45,8 @@ def _get_model(shape, input_zp, input_sc, output_zp, output_sc, dtype):
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_sigmoid(dtype):
+    """Compare Sigmoid output with TVM."""
+
     trials = [
         (1, 16, 16, 16),
         (1, 8, 8),
@@ -61,7 +63,7 @@ def test_sigmoid(dtype):
         }
         outputs = []
         for npu in [False, True]:
-            for d in range(1, 2):
+            for _ in range(1, 2):
                 if dtype == "uint8":
                     input_zp = 0
                     output_zp = 0
@@ -78,21 +80,22 @@ def test_sigmoid(dtype):
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_sigmoid_failure(dtype):
+    """Check Sigmoid error messages."""
+
     test_zp = 0 if dtype == "uint8" else -128
     trials = [
-        ((2, 4, 4, 4), 64, 0.2, test_zp, 1 / 256, dtype, "batch size=2, batch size must = 1"),
+        ((2, 4, 4, 4), 64, 0.2, test_zp, 1 / 256, "batch size=2, batch size must = 1"),
         (
             (1, 4, 4, 4),
             64,
             0.2,
             3,
             1,
-            dtype,
             f"output quantization params=(3, 1), must = ({test_zp}, 1/256)",
         ),
     ]
 
-    for shape, input_zp, input_sc, output_zp, output_sc, dtype, err_msg in trials:
+    for shape, input_zp, input_sc, output_zp, output_sc, err_msg in trials:
         model = _get_model(shape, input_zp, input_sc, output_zp, output_sc, dtype)
         model = tei.make_ethosn_composite(model, "ethos-n.qnn_sigmoid")
         mod = tei.make_ethosn_partition(model)
diff --git a/tests/python/contrib/test_ethosn/test_split.py b/tests/python/contrib/test_ethosn/test_split.py
index 4d1743d07a32..7f8787afe947 100644
--- a/tests/python/contrib/test_ethosn/test_split.py
+++ b/tests/python/contrib/test_ethosn/test_split.py
@@ -37,6 +37,8 @@ def _get_model(shape, dtype, splits, axis):
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_split(dtype):
+    """Compare Split output with TVM."""
+
     trials = [
         ((1, 16, 16, 32), (2, 7, 10), 2),
         ((1, 12, 8, 16), 3, 1),
@@ -55,7 +57,7 @@ def test_split(dtype):
         for npu in [False, True]:
             model = _get_model(shape, dtype, splits, axis)
             mod = tei.make_module(model, {})
-            output_count = splits if type(splits) == int else len(splits) + 1
+            output_count = splits if isinstance(splits, int) else len(splits) + 1
             outputs.append(tei.build_and_run(mod, inputs, output_count, {}, npu=npu))
 
         tei.verify(outputs, dtype, 0)
@@ -64,6 +66,8 @@ def test_split(dtype):
 @pytest.mark.skip("Split is not supported by the 3.0.1 version of the driver stack.")
 @requires_ethosn
 def test_split_failure():
+    """Check Split error messages."""
+
     trials = [
         ((1, 4, 4, 4, 4), "uint8", 4, 2, "dimensions=5, dimensions must be <= 4;"),
         ((1, 4, 4, 4), "int16", 4, 2, "dtype='int16', dtype must be either uint8, int8 or int32;"),
@@ -74,7 +78,8 @@ def test_split_failure():
             "uint8",
             4,
             3,
-            "Split along the channels dimension (axis 3) requires all output sizes (specified in splitInfo.m_Sizes) to be multiples of 16;",
+            "Split along the channels dimension (axis 3) requires all output sizes "
+            "(specified in splitInfo.m_Sizes) to be multiples of 16;",
         ),
     ]
 
diff --git a/tests/python/contrib/test_ethosn/test_tanh.py b/tests/python/contrib/test_ethosn/test_tanh.py
index 8f44936fdc4f..68170601c5f8 100644
--- a/tests/python/contrib/test_ethosn/test_tanh.py
+++ b/tests/python/contrib/test_ethosn/test_tanh.py
@@ -46,6 +46,8 @@ def _get_model(shape, input_zp, input_sc, output_zp, output_sc, dtype):
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 @pytest.mark.parametrize("shape", [(1, 52, 52, 3)])
 def test_tanh(dtype, shape):
+    """Compare Tanh output with TVM."""
+
     zp_min = np.iinfo(dtype).min
     zp_max = np.iinfo(dtype).max
 
@@ -78,6 +80,8 @@ def test_tanh(dtype, shape):
     ],
 )
 def test_tanh_failure(shape, input_zp, input_sc, output_zp, output_sc, err_msg, dtype):
+    """Check Tanh error messages."""
+
     test_zp = 0 if dtype == "int8" else 128
     model = _get_model(shape, input_zp, input_sc, output_zp, output_sc, dtype)
     model = tei.make_ethosn_composite(model, "ethos-n.qnn_tanh")
diff --git a/tests/python/contrib/test_ethosn/test_topologies.py b/tests/python/contrib/test_ethosn/test_topologies.py
index 970f7dce5cbd..19d7accadb6d 100644
--- a/tests/python/contrib/test_ethosn/test_topologies.py
+++ b/tests/python/contrib/test_ethosn/test_topologies.py
@@ -31,6 +31,8 @@
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_split_add_concat(dtype):
+    """Test a model with split, add and contatenate."""
+
     def get_model(input_shape, dtype, var_names):
         """Return a model"""
 
@@ -148,23 +150,25 @@ def get_model(dtype):
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_output_order(dtype):
+    """Test the output order."""
+
     def get_model(input_shape, dtype, var_names):
         """Return a model"""
 
-        min = np.iinfo(dtype).min
-        max = np.iinfo(dtype).max
+        min_value = np.iinfo(dtype).min
+        max_value = np.iinfo(dtype).max
         a = relay.var(next(var_names), shape=input_shape, dtype=dtype)
 
-        z = relay.op.clip(a, min, max)
-        b = relay.op.clip(z, min, min + 15)
-        c = relay.op.clip(z, min + 16, min + 31)
-        d = relay.op.clip(z, min + 32, min + 47)
-        e = relay.op.clip(z, min + 48, min + 63)
-        f = relay.op.clip(z, min + 64, min + 79)
-        g = relay.op.clip(z, min + 80, min + 95)
-        h = relay.op.clip(z, min + 96, min + 111)
-        i = relay.op.clip(z, min + 112, max)
-        return relay.Tuple((d, c, e, f, i, b, h, g))
+        op_z = relay.op.clip(a, min_value, max_value)
+        op_b = relay.op.clip(op_z, min_value, min_value + 15)
+        op_c = relay.op.clip(op_z, min_value + 16, min_value + 31)
+        op_d = relay.op.clip(op_z, min_value + 32, min_value + 47)
+        op_e = relay.op.clip(op_z, min_value + 48, min_value + 63)
+        op_f = relay.op.clip(op_z, min_value + 64, min_value + 79)
+        op_g = relay.op.clip(op_z, min_value + 80, min_value + 95)
+        op_h = relay.op.clip(op_z, min_value + 96, min_value + 111)
+        op_i = relay.op.clip(op_z, min_value + 112, max_value)
+        return relay.Tuple((op_d, op_c, op_e, op_f, op_i, op_b, op_h, op_g))
 
     np.random.seed(0)
     inputs = {
@@ -190,6 +194,7 @@ def test_output_order_different_sizes(dtype):
     """
     Test the output order when there are multiple outputs of different sizes.
     """
+
     np.random.seed(0)
     input_name = "a"
     input_shape = (1, 8, 8, 4)
@@ -233,6 +238,8 @@ def get_model():
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_split_with_asym_concats(dtype):
+    """Test a model with split and contatenates."""
+
     def get_model(shape, dtype, splits, axis):
         a = relay.var("a", shape=shape, dtype=dtype)
         split = relay.op.split(a, indices_or_sections=splits, axis=axis)
@@ -335,6 +342,8 @@ def get_model(dtype):
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_input_tuples(dtype):
+    """Test a model with a tuple as input."""
+
     def get_model(shapes, dtype, axis):
         tup = []
         for i, shape in enumerate(shapes):