fix linting

Author: maltanar

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__()

src/qonnx/util/inference_cost.py

@@ -44,6 +44,7 @@
 from qonnx.transformation.infer_datatypes import InferDataTypes
 from qonnx.transformation.infer_shapes import InferShapes
 
+
 def compute_bops_and_macs(inf_cost_dict):
     total_bops = 0.0
     total_macs = 0.0
@@ -56,6 +57,7 @@ def compute_bops_and_macs(inf_cost_dict):
             total_macs += v
     return total_bops, total_macs
 
+
 def compute_mem_bits_and_elems(inf_cost_dict, filter_string="mem_w"):
     total_mem_bits = 0.0
     total_mem_elems = 0.0
@@ -67,6 +69,7 @@ def compute_mem_bits_and_elems(inf_cost_dict, filter_string="mem_w"):
             total_mem_elems += v
     return total_mem_bits, total_mem_elems
 
+
 def assign_mem_bits_and_elems(res_dict):
     mem_w_bits, mem_w_elems = compute_mem_bits_and_elems(res_dict, "mem_w")
     mem_o_bits, mem_o_elems = compute_mem_bits_and_elems(res_dict, "mem_o")
@@ -76,6 +79,7 @@ def assign_mem_bits_and_elems(res_dict):
     res_dict["total_mem_o_elems"] = mem_o_elems
     return res_dict
 
+
 def inference_cost(
     model_filename_or_wrapper,
     *,
@@ -96,7 +100,7 @@ def inference_cost(
         datatype inference and constant folding. Strongly recommended.
     :param discount_sparsity: If set, will discount op cost of MAC ops with a
         constant zero weight, and the mem cost of constant zero weights."""
-    
+
     combined_results = {}
     if isinstance(model_filename_or_wrapper, ModelWrapper):
         model = model_filename_or_wrapper
@@ -117,8 +121,7 @@ def inference_cost(
     model = model.transform(GiveReadableTensorNames())
     if output_onnx is not None:
         model.save(output_onnx)
-    ret = model.analysis(lambda x: infca.inference_cost(x, discount_sparsity,
-                                                        cost_breakdown))
+    ret = model.analysis(lambda x: infca.inference_cost(x, discount_sparsity, cost_breakdown))
     for i, res in ret.items():
         if i == "total_cost":
             bops, macs = compute_bops_and_macs(res)
@@ -148,9 +151,11 @@ def inference_cost(
                 per_node_breakdown[node_name] = node_res
             combined_results[i] = per_node_breakdown
     return combined_results
-    
+
+
 def main():
     clize.run(inference_cost)
 
+
 if __name__ == "__main__":
     main()

tests/analysis/test_matmul_mac_cost.py

@@ -27,19 +27,19 @@
 # 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."