Move hw params to pytest product

run/param_test.py

@@ -1,5 +1,7 @@
 import numpy as np
 import os
+import pytest
+import itertools
 from qkeras import *
 from tensorflow.keras.layers import Input
 import sys
@@ -20,41 +22,45 @@
 keras.utils.set_random_seed(0)
 type_d = { 'np': {8: np.int8, 16: np.int16, 32: np.int32, 64: np.int64} }
 
-'''
-0. Specify Hardware
-'''
-hw = Hardware (
-        processing_elements  = (8,24),
-        frequency_mhz        = 250,
-        bits_input           = 4,
-        bits_weights         = 4,
-        bits_sum             = 24,
-        bits_bias            = 16,
-        max_batch_size       = 64, 
-        max_channels_in      = 2048,
-        max_kernel_size      = (13, 13),
-        max_image_size       = (512,512),
-        ram_weights_depth    = 20,
-        ram_edges_depth      = 288,
-        axi_width            = 64,
-        target_cpu_int_bits  = 32,
-        valid_prob           = 0.1,
-        ready_prob           = 0.1,
-        data_dir             = 'vectors',
-     )
-hw.export_json()
-hw = Hardware.from_json('hardware.json')
-hw.export() # Generates: config_hw.svh, config_hw.tcl
-hw.export_vivado_tcl(board='zcu104')
-
-
-def test_dnn_engine():
+def product_dict(**kwargs):
+    for instance in itertools.product(*(kwargs.values())):
+        yield dict(zip(kwargs.keys(), instance))
+
+@pytest.mark.parametrize("PARAMS", list(product_dict(
+                                        processing_elements  = [(8,24)   ],
+                                        frequency_mhz        = [ 250     ],
+                                        bits_input           = [ 4       ],
+                                        bits_weights         = [ 4       ],
+                                        bits_sum             = [ 16      ],
+                                        bits_bias            = [ 16      ],
+                                        max_batch_size       = [ 64      ], 
+                                        max_channels_in      = [ 2048    ],
+                                        max_kernel_size      = [ 13      ],
+                                        max_image_size       = [ 512     ],
+                                        ram_weights_depth    = [ 20      ],
+                                        ram_edges_depth      = [ 288     ],
+                                        axi_width            = [ 64      ],
+                                        target_cpu_int_bits  = [ 32      ],
+                                        valid_prob           = [ 0.1     ],
+                                        ready_prob           = [ 0.1     ],
+                                        data_dir             = ['vectors'],
+                                    )))
+def test_dnn_engine(PARAMS):
+    '''
+    0. Specify Hardware
+    '''
+    hw = Hardware (**PARAMS)
+    hw.export_json()
+    hw = Hardware.from_json('hardware.json')
+    hw.export() # Generates: config_hw.svh, config_hw.tcl
+    hw.export_vivado_tcl(board='zcu104')
+
 
     xq, kq, bq = f'quantized_bits({hw.X_BITS},0,False,True,1)', f'quantized_bits({hw.K_BITS},0,False,True,1)', f'quantized_bits({hw.B_BITS},0,False,True,1)'
     inp        = {'bits':hw.X_BITS, 'frac':hw.X_BITS-1}
 
     '''
-    Build Model
+    1. Build Model
     '''
     input_shape = (8,18,18,3) # (XN, XH, XW, CI)
     x = x_in = Input(input_shape[1:], name='input')