Updating tests

Signed-off-by: Priyanka Dangi <quic_pdangi@quicinc.com>

TrainingExtensions/torch/test/python/experimental/v2/test_encoding_analyzer.py

@@ -38,7 +38,7 @@
 import pytest
 import numpy as np
 import random
-from aimet_torch.experimental.v2.quantization.encoding_analyzer import MseEncodingAnalyzer, SqnrEncodingAnalyzer, PercentileEncodingAnalyzer, MinMaxEncodingAnalyzer, _HistogramObserver
+from aimet_torch.experimental.v2.quantization.encoding_analyzer import SqnrEncodingAnalyzer, PercentileEncodingAnalyzer, MinMaxEncodingAnalyzer, _HistogramObserver
 
 @pytest.fixture(autouse=True)
 def set_seed():
@@ -50,10 +50,9 @@ class TestEncodingAnalyzer():
     def encoding_analyzers(self):
         min_max_encoding_analyzer = MinMaxEncodingAnalyzer((1,))
         # TODO: Uncomment after implementation is complete
-        # mse_encoding_analyzer = MseEncodingAnalyzer()
         # percentile_encoding_analyzer = PercentileEncodingAnalyzer()
         # sqnr_encoding_analyzer = SqnrEncodingAnalyzer()
-        # encoding_analyzer_list = [min_max_encoding_analyzer, mse_encoding_analyzer, percentile_encoding_analyzer, sqnr_encoding_analyzer]
+        # encoding_analyzer_list = [min_max_encoding_analyzer, percentile_encoding_analyzer, sqnr_encoding_analyzer]
         encoding_analyzer_list = [min_max_encoding_analyzer]
         yield encoding_analyzer_list
 
@@ -182,17 +181,14 @@ def histogram_based_encoding_analyzers(self, request):
         min_max_shape = request.param[0]
         num_bins = request.param[1]
 
-        mse_encoding_analyzer = MseEncodingAnalyzer(min_max_shape, num_bins)
         percentile_encoding_analyzer = PercentileEncodingAnalyzer(min_max_shape, num_bins)
         sqnr_encoding_analyzer = SqnrEncodingAnalyzer(min_max_shape, num_bins)
-        encoding_analyzer_list = [mse_encoding_analyzer, percentile_encoding_analyzer, sqnr_encoding_analyzer]
+        encoding_analyzer_list = [percentile_encoding_analyzer, sqnr_encoding_analyzer]
         yield encoding_analyzer_list
 
     @pytest.mark.parametrize('num_bins', [-1, 0])
     def test_invalid_bin_value(self, num_bins):
         min_max_shape = (1,)
-        with pytest.raises(ValueError):
-            MseEncodingAnalyzer(min_max_shape, num_bins)
 
         with pytest.raises(ValueError):
             PercentileEncodingAnalyzer(min_max_shape, num_bins)
@@ -370,8 +366,6 @@ def test_collect_stats_multidimensional(self):
             m = i % 4
             assert torch.equal(histograms[i].min,  x[j,k,m].min())
             assert torch.equal(histograms[i].max,  x[j,k,m].max())
-
-        assert False
 
 
     def test_histogram_during_merging(self):