bnn toplevel: check multiple frames

sim/test_bnn.py

@@ -25,6 +25,7 @@ async def run_test(dut):
  height = dut.C_INPUT_HEIGHT.value.integer
  width = dut.C_INPUT_WIDTH.value.integer
  input_image = np.random.randint(0, 255, (1, height, width, 1), dtype=np.uint8)
+ # input_image = np.full((1, height, width, 1), 0, dtype=np.uint8)
 
  # TODO: How to disable the custom gradient warning?
  model = tf.keras.models.load_model("../../models/test")
@@ -55,22 +56,28 @@ async def run_test(dut):
  cocotb.fork(Clock(dut.isl_clk, clock_period, units="ns").start())
 
  dut.isl_valid <= 0
+ dut.isl_start <= 1
+ await tick.wait()
+ dut.isl_start <= 0
  await tick.wait()
 
- for pixel in input_image.flat:
- dut.isl_valid <= 1
- dut.islv_data <= int(pixel)
- await tick.wait()
- dut.isl_valid <= 0
- await tick.wait()
+ for _ in range(2):
+ for pixel in input_image.flat:
+ dut.isl_valid <= 1
+ dut.islv_data <= int(pixel)
+ await tick.wait()
+ dut.isl_valid <= 0
+ await tick.wait()
 
- await tick.wait_multiple(height * width)
+  await tick.wait_multiple(height * width)
 
- np.testing.assert_almost_equal(
- np.resize(np.array(output_mon.output), class_scores_pos.shape),
- class_scores_pos,
- decimal=0,
- )
+ np.testing.assert_almost_equal(
+ np.resize(np.array(output_mon.output), class_scores_pos.shape),
+ class_scores_pos,
+ decimal=0,
+ )
+
+ output_mon.clear()
 
 
 def test_bnn():