[Test] remove old MultiThreshold impl, fix test

src/finn/core/multithreshold.py

-import numpy as np
-
-
-def compare(x, y):
-    if x >= y:
-        return 1.0
-    else:
-        return 0.0
-
-
-def execute(v, thresholds, out_scale=None, out_bias=None):
-
-    # the inputs are expected to be in the shape (N,C,H,W)
-    # N : Batch size
-    # C : Number of channels
-    # H : Heigth of the input images
-    # W : Width of the input images
-    #
-    # the thresholds are expected to be in the shape (C, B)
-    # C : Number of channels (must be the same value as C in input tensor or 1
-    #     if all channels use the same threshold value)
-    # B : Desired activation steps => i.e. for 4-bit activation, B=7 (2^(n)-1 and n=4)
-
-    # the output tensor will be scaled by out_scale and biased by out_bias
-
-    # assert threshold shape
-    is_global_threshold = thresholds.shape[0] == 1
-    assert (v.shape[1] == thresholds.shape[0]) or is_global_threshold
-
-    # save the required shape sizes for the loops (N, C and B)
-    num_batch = v.shape[0]
-    num_channel = v.shape[1]
-
-    num_act = thresholds.shape[1]
-
-    # reshape inputs to enable channel-wise reading
-    vr = v.reshape((v.shape[0], v.shape[1], -1))
-
-    # save the new shape size of the images
-    num_img_elem = vr.shape[2]
-
-    # initiate output tensor
-    ret = np.zeros_like(vr)
-
-    # iterate over thresholds channel-wise
-    for t in range(num_channel):
-        channel_thresh = thresholds[0] if is_global_threshold else thresholds[t]
-
-        # iterate over batches
-        for b in range(num_batch):
-
-            # iterate over image elements on which the thresholds should be applied
-            for elem in range(num_img_elem):
-
-                # iterate over the different thresholds that correspond to one channel
-                for a in range(num_act):
-                    # apply successive thresholding to every element of one channel
-                    ret[b][t][elem] += compare(vr[b][t][elem], channel_thresh[a])
-    if out_scale is None:
-        out_scale = 1.0
-    if out_bias is None:
-        out_bias = 0.0
-    return out_scale * ret.reshape(v.shape) + out_bias

tests/test_multi_thresholding.py

 import numpy as np
 
-import finn.core.multithreshold as multi_thresh
+from finn.custom_op.multithreshold import MultiThreshold
 
 
 def test_execute_multi_thresholding():
@@ -194,10 +194,11 @@ def test_execute_multi_thresholding():
         ),
     )
 
-    results = multi_thresh.execute(inputs, thresholds)
+    multi_thresh = MultiThreshold()
+    results = multi_thresh._execute(inputs, thresholds)
 
     assert (results == outputs).all()
 
-    results_scaled = multi_thresh.execute(inputs, thresholds, 2.0, -1.0)
+    results_scaled = multi_thresh._execute(inputs, thresholds, 2.0, -1.0)
     outputs_scaled = 2.0 * outputs - 1.0
     assert (results_scaled == outputs_scaled).all()