pre-commit applied

Signed-off-by: Michal Danilowicz <[email protected]>

src/finn/qnn-data/templates/driver/driver_base.py

@@ -145,7 +145,7 @@ def load_external_weights(self):
                 # weight_buf.sync_to_device()
                 weight_buf.flush()
 
-                input_shape = self._io_shape_dict['external_weights_input_shapes'][idma_name]
+                input_shape = self._io_shape_dict["external_weights_input_shapes"][idma_name]
                 # NHWC input?
                 if len(input_shape) == 4:
                     num_repeats = input_shape[1] * input_shape[2]

src/finn/qnn-data/templates/driver/validate.py

@@ -100,7 +100,7 @@
         driver.copy_output_data_from_device(obuf_normal)
         batch_ok = (obuf_normal.flatten() == exp.flatten()).sum()
         ok += batch_ok
-        nok += (bsize - batch_ok)
+        nok += bsize - batch_ok
         print("batch %d / %d : total OK %d NOK %d" % (i + 1, n_batches, ok, nok))
 
     acc = 100.0 * ok / (total)

src/finn/transformation/fpgadataflow/make_pynq_driver.py

@@ -242,7 +242,9 @@ def apply(self, model):
                     dma_target_model = ModelWrapper(dma_target_sdp.get_nodeattr("model"))
                     iodma_output_tensor = iodma_node.onnx_node.output[0]
                     dma_consumer = dma_target_model.find_consumer(iodma_output_tensor)
-                    ext_weight_shapes_dict[idma_name] = dma_target_model.get_tensor_shape(dma_consumer.output[0])
+                    ext_weight_shapes_dict[idma_name] = dma_target_model.get_tensor_shape(
+                        dma_consumer.output[0]
+                    )
 
                     init_tensor = df_model.get_initializer(iodma_node.onnx_node.input[0])
                     ext_weight_dma_cnt += 1

tests/end2end/test_ext_weights.py

@@ -29,11 +29,11 @@
 
 import pytest
 
+import json
 import os
 import shutil
 import subprocess
 import wget
-import json
 
 import finn.builder.build_dataflow as build
 import finn.builder.build_dataflow_config as build_cfg
@@ -67,7 +67,7 @@ def get_checkpoint_name(step, topology):
     else:
         # other checkpoints are onnx files
         return build_dir + "/end2end_" + topology + "_ext_weights_%s.onnx" % (step)
-    
+
 
 def verify_runtime_weights(folding_config_file, runtime_weights_dir):
     with open(folding_config_file) as file:
@@ -79,7 +79,7 @@ def verify_runtime_weights(folding_config_file, runtime_weights_dir):
             num_ext_weights += 1
     runtime_weights_files = os.listdir(runtime_weights_dir)
     for idx in range(num_ext_weights):
-        expected_file = 'idma{}.npy'.format(idx)
+        expected_file = "idma{}.npy".format(idx)
         assert expected_file in runtime_weights_files