diff --git a/requirements.txt b/requirements.txt
index 2e75bb44be1dcb1e49f77545d9564604141edbea..b1f36d3786336700530bae6c60bcd7b69a99bfdf 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,10 +9,10 @@ onnx==1.7.0
onnxoptimizer
onnxruntime==1.4.0
pre-commit==2.6.0
+protobuf==3.20.1
pyscaffold==3.2.1
scipy==1.5.2
setupext-janitor>=1.1.2
toposort==1.5
vcdvcd==1.0.5
wget==3.2
-protobuf==3.20.1
\ No newline at end of file
diff --git a/src/finn/builder/build_dataflow_config.py b/src/finn/builder/build_dataflow_config.py
index 807fd706860d7e4667107ddd2ed46ea2b123c3ec..106c221203c6f2a64ce27f2fdf850c1d73c90b4c 100644
--- a/src/finn/builder/build_dataflow_config.py
+++ b/src/finn/builder/build_dataflow_config.py
@@ -340,7 +340,7 @@ class DataflowBuildConfig:
if self.target_fps is None:
return None
else:
- n_clock_cycles_per_sec = 10 ** 9 / self.synth_clk_period_ns
+ n_clock_cycles_per_sec = 10**9 / self.synth_clk_period_ns
n_cycles_per_frame = n_clock_cycles_per_sec / self.target_fps
return int(n_cycles_per_frame)
diff --git a/src/finn/builder/build_dataflow_steps.py b/src/finn/builder/build_dataflow_steps.py
index c977f15e7090f5cae633a013f5eb9e6b3dd34dd2..bf3701f12dc8d95992b31abebcc2b0323ef57135 100644
--- a/src/finn/builder/build_dataflow_steps.py
+++ b/src/finn/builder/build_dataflow_steps.py
@@ -397,7 +397,7 @@ def step_generate_estimate_reports(model: ModelWrapper, cfg: DataflowBuildConfig
model = model.transform(AnnotateCycles())
estimate_network_performance = model.analysis(dataflow_performance)
# add some more metrics to estimated performance
- n_clock_cycles_per_sec = (10 ** 9) / cfg.synth_clk_period_ns
+ n_clock_cycles_per_sec = (10**9) / cfg.synth_clk_period_ns
est_fps = n_clock_cycles_per_sec / estimate_network_performance["max_cycles"]
estimate_network_performance["estimated_throughput_fps"] = est_fps
est_latency_ns = (
@@ -599,7 +599,7 @@ def step_out_of_context_synthesis(model: ModelWrapper, cfg: DataflowBuildConfig)
estimate_network_performance = model.analysis(dataflow_performance)
# add some more metrics to estimated performance
- n_clock_cycles_per_sec = float(ooc_res_dict["fmax_mhz"]) * (10 ** 6)
+ n_clock_cycles_per_sec = float(ooc_res_dict["fmax_mhz"]) * (10**6)
est_fps = n_clock_cycles_per_sec / estimate_network_performance["max_cycles"]
ooc_res_dict["estimated_throughput_fps"] = est_fps
with open(report_dir + "/ooc_synth_and_timing.json", "w") as f:
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index 68cd1ff9ea680e157f59353d0c9d05afc3d9d6d7..896e7c2925e340455f98344d1275d9368f701ed9 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -358,8 +358,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
if noact == 0:
odt = self.get_output_datatype()
B = odt.bitwidth()
- thr_luts = (2 ** B - 1) * acc_bits * math.ceil(self.calc_tmem() / 64)
- comp_luts = (2 ** B - 1) * acc_bits
+ thr_luts = (2**B - 1) * acc_bits * math.ceil(self.calc_tmem() / 64)
+ comp_luts = (2**B - 1) * acc_bits
return int(
c0
diff --git a/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py b/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
index e0f789a8883aad83ed8c8b37a16392308bc720cc..f50c5d1ef61d1677d2c2e394c43ebd6354a5331e 100644
--- a/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
+++ b/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
@@ -694,8 +694,8 @@ class Vector_Vector_Activate_Batch(HLSCustomOp):
if noact == 0:
odt = self.get_output_datatype()
B = odt.bitwidth()
- thr_luts = (2 ** B - 1) * acc_bits * math.ceil(self.calc_tmem() / 64)
- comp_luts = (2 ** B - 1) * acc_bits
+ thr_luts = (2**B - 1) * acc_bits * math.ceil(self.calc_tmem() / 64)
+ comp_luts = (2**B - 1) * acc_bits
return int(c0 + c1 * (P * (mult_luts + acc_luts + thr_luts + comp_luts)) + c2)
diff --git a/src/finn/transformation/fpgadataflow/set_fifo_depths.py b/src/finn/transformation/fpgadataflow/set_fifo_depths.py
index 39eb049565475b462ea0df9d88b46e3598e6cdd9..ce7cf7bc589fae7fb6c8785b51cf45514f49c5a0 100644
--- a/src/finn/transformation/fpgadataflow/set_fifo_depths.py
+++ b/src/finn/transformation/fpgadataflow/set_fifo_depths.py
@@ -222,7 +222,7 @@ class InsertAndSetFIFODepths(Transformation):
fpgapart,
clk_ns=10.0,
max_qsrl_depth=256,
- max_depth=2 ** 14,
+ max_depth=2**14,
swg_exception=True,
vivado_ram_style="auto",
):
diff --git a/src/finn/transformation/qonnx/infer_quant_avg_pool_2d.py b/src/finn/transformation/qonnx/infer_quant_avg_pool_2d.py
index faad31fa06e76b245f25b6f0aa583fec5c0da29a..c234bd38d9679f72b6df73e81df57fba3e8d4554 100644
--- a/src/finn/transformation/qonnx/infer_quant_avg_pool_2d.py
+++ b/src/finn/transformation/qonnx/infer_quant_avg_pool_2d.py
@@ -230,7 +230,7 @@ class AvgPoolAndTruncToQuantAvgPool(Transformation):
# 7c2603a95e90e4de2575020e575c24eab6a15889/src/finn/custom_op/
# general/quantavgpool2d.py#L94
ibits = math.floor(
- math.log(2 ** trunc_in_bits / (k_s * k_s), 2)
+ math.log(2**trunc_in_bits / (k_s * k_s), 2)
)
# Get sign
signed = _get_signed_from_upstream(model, t_node)
diff --git a/src/finn/transformation/qonnx/qonnx_activation_handlers.py b/src/finn/transformation/qonnx/qonnx_activation_handlers.py
index 3336b1eee7fa9d54092cd56b9ba0edaf9d0884b1..c8bde7fea8ae8195001a7eccfd48baa4c48997ae 100644
--- a/src/finn/transformation/qonnx/qonnx_activation_handlers.py
+++ b/src/finn/transformation/qonnx/qonnx_activation_handlers.py
@@ -333,7 +333,7 @@ class QuantReluHandler(QuantActBaseHandler):
# Calculate thersholds, see: https://github.com/Xilinx/brevitas/blob/
# a5bfd6dc5e030f0047ac1ee47932b60e8e873e17/src/brevitas/export/
# onnx/finn/handler/act.py#L21
- num_distinct_values = 2 ** bit_width
+ num_distinct_values = 2**bit_width
num_thresholds = int(num_distinct_values - 1)
flat_scale = quant_scale.flatten().astype(np.float32)
num_scale_channels = flat_scale.shape[0]
@@ -468,9 +468,9 @@ class QuantIdentityHandler(QuantActBaseHandler):
return thresholds
else:
if narrow:
- num_distinct_values = 2 ** bit_width - 1
+ num_distinct_values = 2**bit_width - 1
else:
- num_distinct_values = 2 ** bit_width
+ num_distinct_values = 2**bit_width
num_thresholds = int(num_distinct_values - 1)
flat_scale = quant_scale.flatten()
diff --git a/tests/end2end/test_end2end_bnn_pynq.py b/tests/end2end/test_end2end_bnn_pynq.py
index 1fddc7c1c26a0ba04d5849809ccf59b0a926a509..1d7d5e3e9a939b62a938c6f23e347e3d15a64663 100644
--- a/tests/end2end/test_end2end_bnn_pynq.py
+++ b/tests/end2end/test_end2end_bnn_pynq.py
@@ -765,7 +765,7 @@ class TestEnd2End:
ret = dict()
# try a range of batch sizes, some may fail due to insufficient DMA
# buffers
- bsize_range_in = [8 ** i for i in range(5)]
+ bsize_range_in = [8**i for i in range(5)]
bsize_range = []
for bsize in bsize_range_in:
res = throughput_test_remote(model, bsize)
diff --git a/tests/fpgadataflow/test_fpgadataflow_vvau.py b/tests/fpgadataflow/test_fpgadataflow_vvau.py
index 6f39994bf27594a063a1e66c5bba7867eaabef6e..9eb3a7f4514e610d79bb83cc62a7561a33ced543 100644
--- a/tests/fpgadataflow/test_fpgadataflow_vvau.py
+++ b/tests/fpgadataflow/test_fpgadataflow_vvau.py
@@ -62,8 +62,8 @@ def _infer_sparse_weight_tensor(W_conv, k_h, k_w, channels):
def _calculate_dot_prod_range(dt_a, dt_b, len):
"""Returns the (min,max) values a dot product between two (un)signed vectors of
types dt_a and dt_b of len elements can take."""
- min_prod = 2 ** 30
- max_prod = -(2 ** 30)
+ min_prod = 2**30
+ max_prod = -(2**30)
for a_val in [dt_a.min(), dt_a.max()]:
for b_val in [dt_b.min(), dt_b.max()]:
prod = a_val * b_val * len
diff --git a/tests/fpgadataflow/test_set_folding.py b/tests/fpgadataflow/test_set_folding.py
index 66fd5b43a1b8b8c8986bf9c9b9d0e9efd7a744a6..492f208671f4622d189c48ece874740a68b69072 100644
--- a/tests/fpgadataflow/test_set_folding.py
+++ b/tests/fpgadataflow/test_set_folding.py
@@ -109,7 +109,7 @@ def make_multi_fclayer_model(ch, wdt, adt, tdt, nnodes):
# desired frames per second
-@pytest.mark.parametrize("target_fps", [30, 10 ** 5, 10 ** 7])
+@pytest.mark.parametrize("target_fps", [30, 10**5, 10**7])
# target chip or board
@pytest.mark.parametrize("platform", ["Pynq-Z1", "Ultra96", "U200"])
def test_set_folding(target_fps, platform):
@@ -126,7 +126,7 @@ def test_set_folding(target_fps, platform):
dataflow_model = load_test_checkpoint_or_skip(dataflow_model_filename)
clk_ns = 5
- target_cycles_per_frame = int((10 ** 9 / clk_ns) / target_fps)
+ target_cycles_per_frame = int((10**9 / clk_ns) / target_fps)
dataflow_model = dataflow_model.transform(SetFolding(target_cycles_per_frame))
exp_cycles_dict = dataflow_model.analysis(exp_cycles_per_layer)