apply black and vsg

playground/00_intro.py

@@ -1,9 +1,9 @@
 import tensorflow as tf
 import larq as lq
 
-(train_images, train_labels), (
- test_images,
- test_labels,
+(
+ (train_images, train_labels),
+ (test_images, test_labels,),
 ) = tf.keras.datasets.mnist.load_data()
 
 train_images = train_images.reshape((60000, 28, 28, 1))

playground/01_binarynet.py

@@ -5,9 +5,9 @@
 
 num_classes = 10
 
-(train_images, train_labels), (
- test_images,
- test_labels,
+(
+ (train_images, train_labels),
+ (test_images, test_labels,),
 ) = tf.keras.datasets.cifar10.load_data()
 
 train_images = train_images.reshape((50000, 32, 32, 3)).astype("float32")

sim/test_adder_tree.py

@@ -61,12 +61,7 @@ def output_data(self) -> int:
  tick = Tick(clock_period=clock_period)
  cocotb.fork(Clock(dut.isl_clk, clock_period, units="ns").start())
  output_mon = ImageMonitor(
- "output",
- dut.oslv_data,
- dut.osl_valid,
- dut.isl_clk,
- 1,
- output_bitwidth,
+ "output", dut.oslv_data, dut.osl_valid, dut.isl_clk, 1, output_bitwidth,
  )
  dut.isl_valid <= 0
  await tick.wait()

sim/test_bnn.py

@@ -42,12 +42,7 @@ async def run_test(dut):
 
  output_bitwitdh = dut.C_OUTPUT_CHANNEL_BITWIDTH.value.integer
  output_mon = ImageMonitor(
- "output",
- dut.oslv_data,
- dut.osl_valid,
- dut.isl_clk,
- 1,
- output_bitwitdh,
+ "output", dut.oslv_data, dut.osl_valid, dut.isl_clk, 1, output_bitwitdh,
  )
 
  # initialize the test

sim/test_window_convolution_activation.py

@@ -49,11 +49,7 @@ async def run_test(dut):
  batch_shape = (1,) + image_shape
  input_ = tf.keras.Input(batch_shape=batch_shape, name="img")
  x = lq.layers.QuantConv2D(
- output_channel,
- kernel_size,
- strides=stride,
- use_bias=False,
- name="test_conv",
+ output_channel, kernel_size, strides=stride, use_bias=False, name="test_conv",
  )(input_)
  if output_channel_bitwidth == 1:
  # Scale is not needed, since we clip afterwards anyway.

sim/test_window_maximum_pooling.py

@@ -106,13 +106,7 @@ def output_data(self) -> int:
 
 # Don't run the full test matrix. Only the most common configs.
 @pytest.mark.parametrize(
- "kernel_size,stride,channel",
- [
- (2, 1, 8),
- (2, 2, 12),
- (3, 1, 16),
- (3, 2, 9),
- ],
+ "kernel_size,stride,channel", [(2, 1, 8), (2, 2, 12), (3, 1, 16), (3, 2, 9),],
 )
 def test_window_maximum_pooling(kernel_size, stride, channel):
  generics = {

src/interface/bnn_uart.vhd

@@ -112,7 +112,8 @@ begin
  case output_state is
 
  when IDLE =>
- assert sl_valid_out_bnn = '0' severity failure;
+ assert sl_valid_out_bnn = '0'
+ severity failure;
 
  int_valid_buffer_values <= 0;
  output_state <= FILL;
@@ -128,7 +129,8 @@ begin
  end if;
 
  when READY_TO_SEND =>
- assert sl_valid_out_bnn = '0' severity failure;
+ assert sl_valid_out_bnn = '0'
+ severity failure;
 
  if (sl_ready_uart_tx = '1') then
  int_valid_buffer_values <= int_valid_buffer_values - 1;

src/util/basic_counter.vhd

@@ -90,7 +90,8 @@ begin
 
  else generate
 
- assert C_MAX mod C_INCREMENT = 0 severity failure;
+ assert C_MAX mod C_INCREMENT = 0
+ severity failure;
 
  gen_power_of_two : if is_power_of_two(C_MAX) generate
 

src/util/serializer.vhd

@@ -35,7 +35,8 @@ begin
  a_data <= a_data(1 to a_data'high) & a_data(0);
 
  if (isl_valid = '1') then
- assert int_output_valid_cycles = 0 severity failure;
+ assert int_output_valid_cycles = 0
+ severity failure;
  int_output_valid_cycles <= C_DATA_COUNT;
  for i in a_data'range loop
  a_data(i) <= get_slice(islv_data, i, C_DATA_BITWIDTH);

src/window_ctrl/window_ctrl.vhd

@@ -80,7 +80,8 @@ begin
  gen_window_buffer : if C_KERNEL_SIZE = 1 generate
 
  -- For 1x1 kernels, there is no buffering needed and no padding supported.
- assert C_PAD = 0 severity failure;
+ assert C_PAD = 0
+ severity failure;
 
  sl_selector_valid_out_d2 <= isl_valid;
  a_selector_data_out(0, 0) <= islv_data;