Xfail pytorch tests with conv2d/maxpooling + float16 + CPU

tests/test_backward_conv.py

@@ -10,6 +10,10 @@
 
 
 def test_Decomon_conv_box(data_format, padding, use_bias, mode, floatx, decimal, helpers):
+    # xfail unavailable combinations
+    if floatx == 16 and keras_config.backend() == "torch":
+        pytest.xfail("Pytorch does not implement conv2d for float16")
+
     if data_format == "channels_first" and not helpers.in_GPU_mode():
         pytest.skip("data format 'channels first' is possible only in GPU mode")
 

tests/test_conv.py

@@ -12,6 +12,10 @@
 
 
 def test_Decomon_conv_box(data_format, mode, dc_decomp, floatx, decimal, helpers):
+    # xfail unavailable combinations
+    if floatx == 16 and keras_config.backend() == "torch":
+        pytest.xfail("Pytorch does not implement conv2d for float16")
+
     if data_format == "channels_first" and not helpers.in_GPU_mode():
         pytest.skip("data format 'channels first' is possible only in GPU mode")
 
@@ -63,6 +67,10 @@ def test_Decomon_conv_box(data_format, mode, dc_decomp, floatx, decimal, helpers
 
 
 def test_Decomon_conv_to_decomon_box(shared, floatx, dc_decomp, helpers):
+    # xfail unavailable combinations
+    if floatx == 16 and keras_config.backend() == "torch":
+        pytest.xfail("Pytorch does not implement conv2d for float16")
+
     data_format = "channels_last"
     odd, m_0, m_1 = 0, 0, 1
     dc_decomp = True

tests/test_pooling.py

@@ -1,10 +1,15 @@
 import keras.config as keras_config
+import pytest
 from keras.layers import MaxPooling2D
 
 from decomon.layers.maxpooling import DecomonMaxPooling2D
 
 
 def test_MaxPooling2D_box(mode, floatx, decimal, helpers):
+    # xfail unavailable combinations
+    # if floatx == 16 and keras_config.backend() == "torch":
+    #     pytest.xfail("Pytorch does not implement maxpooling for float16")
+
     odd, m_0, m_1 = 0, 0, 1
     data_format = "channels_last"
     dc_decomp = True

tests/test_utils_conv.py

@@ -1,6 +1,5 @@
 # Test unit for decomon with Dense layers
-
-
+import keras.config
 import keras.ops as K
 import numpy as np
 import pytest
@@ -13,6 +12,10 @@
 
 
 def test_toeplitz_from_Keras(channels, filter_size, strides, flatten, data_format, padding, floatx, decimal, helpers):
+    # xfail unavailable combinations
+    if floatx == 16 and keras.config.backend() == "torch":
+        pytest.xfail("Pytorch does not implement conv2d for float16")
+
     # filter_size, strides, flatten,
     if floatx == 16:
         decimal = 0
@@ -67,6 +70,10 @@ def test_toeplitz_from_Keras(channels, filter_size, strides, flatten, data_forma
 def test_toeplitz_from_Decomon(
     floatx, decimal, mode, channels, filter_size, strides, flatten, data_format, padding, helpers
 ):
+    # xfail unavailable combinations
+    if floatx == 16 and keras.config.backend() == "torch":
+        pytest.xfail("Pytorch does not implement conv2d for float16")
+
     if data_format == "channels_first" and not helpers.in_GPU_mode():
         pytest.skip("data format 'channels first' is possible only in GPU mode")