Pre-commit fixes

src/qonnx/core/datatype.py

@@ -144,6 +144,7 @@ def to_numpy_dt(self):
     def get_canonical_name(self):
         return "FLOAT32"
 
+
 class Float16Type(BaseDataType):
     def bitwidth(self):
         return 16
@@ -175,6 +176,7 @@ def to_numpy_dt(self):
     def get_canonical_name(self):
         return "FLOAT16"
 
+
 class IntType(BaseDataType):
     def __init__(self, bitwidth, signed):
         super().__init__()

tests/analysis/test_matmul_mac_cost.py

@@ -26,20 +26,19 @@
 # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-
-import pytest
-import qonnx
 from pkgutil import get_data
+
 import qonnx.util.inference_cost as infc
-from qonnx.util.cleanup import cleanup_model
 from qonnx.core.modelwrapper import ModelWrapper
+from qonnx.util.cleanup import cleanup_model
 
 
 def test_matmul_mac_cost():
-    raw_model = get_data("qonnx","data/onnx/matmul_update/sdp.onnx")
+    raw_model = get_data("qonnx", "data/onnx/matmul_update/sdp.onnx")
     model = ModelWrapper(raw_model)
     cleaned_model = cleanup_model(model)
-    # Two Matmul layers with shape (i_shape, w_shape, o_shape), L1: ([4, 64, 32], [4, 32, 64], [4, 64, 64]) and L2: ([4, 64, 64], [4, 64, 32], [4, 64, 32])
+    # Two Matmul layers with shape (i_shape, w_shape, o_shape),
+    # L1: ([4, 64, 32], [4, 32, 64], [4, 64, 64]) and L2: ([4, 64, 64], [4, 64, 32], [4, 64, 32])
     inf_cost_dict = infc.inference_cost(cleaned_model, discount_sparsity=False)
-    mac_cost = inf_cost_dict['op_mac_FLOAT32_FLOAT32'] # Expected mac cost 4*32*64*64 + 4*64*64*32 = 1048576
+    mac_cost = inf_cost_dict["op_mac_FLOAT32_FLOAT32"]  # Expected mac cost 4*32*64*64 + 4*64*64*32 = 1048576
     assert mac_cost == 1048576.0, "Error: discrepancy in mac cost."

tests/transformation/test_nn_resize_to_deconv.py

@@ -94,7 +94,7 @@ def test_resize_conv_to_deconv_quant_model(maintain_bit_width: bool):
     # shouldn't be expected to maintain reasonable functional similarity
     if not maintain_bit_width:
         assert np.isclose(
-            output_deconv, output_resize_conv, atol=1 / 255.0, rtol=1.
+            output_deconv, output_resize_conv, atol=1 / 255.0, rtol=1.0
         ).all(), "Error: expected output does not match the produced output."
 
 
@@ -108,7 +108,7 @@ def create_nn_resize_conv_model(
     ifm_dim = input_dim
     ofm_dim = ifm_dim * upscale_factor
     ofm_ch = out_channels
-    scales = np.array([1., 1., upscale_factor, upscale_factor], dtype=np.float32)
+    scales = np.array([1.0, 1.0, upscale_factor, upscale_factor], dtype=np.float32)
 
     resize = oh.make_node(
         "Resize",