feat: rewrite tests

2026-04-17 03:00:54 -04:00 · 2022-04-04 13:32:24 +02:00
parent f5431be602
commit c45b9ffa43
41 changed files with 4977 additions and 4 deletions
--- a/tests/execution/test_convolution.py
+++ b/tests/execution/test_convolution.py
@@ -0,0 +1,206 @@
+"""
+Tests of execution of convolution operation.
+"""
+
+import numpy as np
+import pytest
+
+import concrete.numpy as cnp
+
+
+@pytest.mark.parametrize(
+    "input_shape,weight_shape",
+    [
+        pytest.param(
+            (1, 1, 4, 4),
+            (1, 1, 2, 2),
+        ),
+        pytest.param(
+            (4, 3, 4, 4),
+            (2, 3, 2, 2),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "strides",
+    [
+        (2, 2),
+    ],
+)
+@pytest.mark.parametrize(
+    "dilations",
+    [
+        (1, 1),
+    ],
+)
+@pytest.mark.parametrize(
+    "has_bias",
+    [
+        True,
+        False,
+    ],
+)
+def test_conv2d(input_shape, weight_shape, strides, dilations, has_bias, helpers):
+    """
+    Test conv2d.
+    """
+
+    configuration = helpers.configuration()
+
+    weight = np.random.randint(0, 4, size=weight_shape)
+
+    if has_bias:
+        bias = np.random.randint(0, 4, size=(weight_shape[0],))
+    else:
+        bias = None
+
+    @cnp.compiler({"x": "encrypted"}, configuration=configuration)
+    def function(x):
+        return cnp.conv2d(x, weight, bias, strides=strides, dilations=dilations)
+
+    inputset = [np.random.randint(0, 4, size=input_shape) for i in range(100)]
+    circuit = function.compile(inputset)
+
+    sample = np.random.randint(0, 4, size=input_shape, dtype=np.uint8)
+    helpers.check_execution(circuit, function, sample)
+
+
+@pytest.mark.parametrize(
+    "input_shape,weight_shape,bias_shape,pads,strides,dilations,auto_pad,"
+    "expected_error,expected_message",
+    [
+        pytest.param(
+            (1, 1, 4, 4),
+            (1, 1, 2, 2),
+            (1,),
+            (0, 0, 0, 0),
+            (1, 1),
+            (1, 1),
+            "VALID",
+            ValueError,
+            "Auto pad should be in {'NOTSET'} but it's 'VALID'",
+        ),
+        pytest.param(
+            (1, 1, 4, 4),
+            (1, 1, 2, 2),
+            (1,),
+            (),
+            (1, 1),
+            (1, 1),
+            "NOTSET",
+            ValueError,
+            "Pads should be of form "
+            "(height_begin_pad, width_begin_pad, height_end_pad, width_end_pad) "
+            "but it's ()",
+        ),
+        pytest.param(
+            (1, 1, 4, 4),
+            (1, 1, 2, 2),
+            (1,),
+            (0, 0, 0, 0),
+            (),
+            (1, 1),
+            "NOTSET",
+            ValueError,
+            "Strides should be of form (height_stride, width_stride) but it's ()",
+        ),
+        pytest.param(
+            (1, 1, 4, 4),
+            (1, 1, 2, 2),
+            (1,),
+            (0, 0, 0, 0),
+            (1, 1),
+            (),
+            "NOTSET",
+            ValueError,
+            "Dilations should be of form (height_dilation, width_dilation) but it's ()",
+        ),
+        pytest.param(
+            (),
+            (1, 1, 2, 2),
+            (1,),
+            (0, 0, 0, 0),
+            (1, 1),
+            (1, 1),
+            "NOTSET",
+            ValueError,
+            "Input should be of shape (N, C, H, W) but it's of shape ()",
+        ),
+        pytest.param(
+            (1, 1, 4, 4),
+            (),
+            (1,),
+            (0, 0, 0, 0),
+            (1, 1),
+            (1, 1),
+            "NOTSET",
+            ValueError,
+            "Weight should be of shape (F, C, H, W) but it's of shape ()",
+        ),
+        pytest.param(
+            (1, 1, 4, 4),
+            (1, 1, 2, 2),
+            (),
+            (0, 0, 0, 0),
+            (1, 1),
+            (1, 1),
+            "NOTSET",
+            ValueError,
+            "Bias should be of shape (F,) but it's of shape ()",
+        ),
+    ],
+)
+def test_bad_conv2d_tracing(
+    input_shape,
+    weight_shape,
+    bias_shape,
+    pads,
+    strides,
+    dilations,
+    auto_pad,
+    expected_error,
+    expected_message,
+    helpers,
+):
+    """
+    Test conv2d with bad parameters.
+    """
+
+    configuration = helpers.configuration()
+
+    weight = np.random.randint(0, 4, size=weight_shape)
+
+    if bias_shape is not None:
+        bias = np.random.randint(0, 4, size=bias_shape)
+    else:
+        bias = None
+
+    @cnp.compiler({"x": "encrypted"}, configuration=configuration)
+    def function(x):
+        return cnp.conv2d(x, weight, bias, pads, strides, dilations, auto_pad)
+
+    inputset = [np.random.randint(0, 4, size=input_shape) for i in range(100)]
+    with pytest.raises(expected_error) as excinfo:
+        function.compile(inputset)
+
+    assert str(excinfo.value) == expected_message
+
+
+def test_bad_conv2d_evaluation():
+    """
+    Test conv2d evaluation with bad parameters.
+    """
+
+    x = np.random.randint(0, 4, size=(1, 1, 4, 4))
+
+    with pytest.raises(ValueError) as excinfo:
+        cnp.conv2d(x, "abc")
+
+    assert str(excinfo.value) == "Weight should be of type np.ndarray for evaluation"
+
+    weight = np.random.randint(0, 4, size=(1, 1, 2, 2))
+
+    with pytest.raises(ValueError) as excinfo:
+        cnp.conv2d(x, weight, "abc")
+
+    assert str(excinfo.value) == "Bias should be of type np.ndarray for evaluation"