Remove references to removed `nnet` and `signal` modules

.github/workflows/test.yml

@@ -72,8 +72,8 @@ jobs:
         float32: [0]
         install-numba: [1]
         part:
-          - "tests --ignore=tests/tensor --ignore=tests/sparse --ignore=tests/tensor/nnet --ignore=tests/tensor/signal"
-          - "tests/tensor tests/sparse --ignore=tests/tensor/test_basic.py --ignore=tests/tensor/test_math.py --ignore=tests/tensor/test_math_scipy.py --ignore=tests/tensor/test_inplace.py --ignore=tests/tensor/test_elemwise.py --ignore=tests/tensor/rewriting/test_basic.py --ignore=tests/tensor/rewriting/test_math.py --ignore=tests/tensor/nnet --ignore=tests/tensor/signal"
+          - "tests --ignore=tests/tensor --ignore=tests/sparse"
+          - "tests/tensor tests/sparse --ignore=tests/tensor/test_basic.py --ignore=tests/tensor/test_math.py --ignore=tests/tensor/test_math_scipy.py --ignore=tests/tensor/test_inplace.py --ignore=tests/tensor/test_elemwise.py --ignore=tests/tensor/rewriting/test_basic.py --ignore=tests/tensor/rewriting/test_math.py"
           - "tests/tensor/test_basic.py tests/tensor/test_math.py tests/tensor/test_math_scipy.py tests/tensor/test_inplace.py"
           - "tests/tensor/test_elemwise.py tests/tensor/rewriting/test_basic.py tests/tensor/rewriting/test_math.py"
           - "tests/tensor/conv/test_abstract_conv.py"
@@ -82,12 +82,12 @@ jobs:
             fast-compile: 1
             float32: 1
             install-numba: 1
-            part: "tests --ignore=tests/tensor/nnet --ignore=tests/tensor/signal"
+            part: "tests"
           - python-version: "3.7"
             fast-compile: 1
             float32: 0
             install-numba: 1
-            part: "tests --ignore=tests/tensor/nnet --ignore=tests/tensor/signal"
+            part: "tests"
     steps:
       - uses: actions/checkout@v3
         with:

tests/tensor/signal/test_conv.py

-import numpy as np
-import pytest
-
-import pytensor
-from pytensor.tensor.math import _allclose
-from pytensor.tensor.signal import conv
-from pytensor.tensor.type import TensorType, dtensor3, dtensor4, dvector, matrix
-from tests import unittest_tools as utt
-
-
-_ = pytest.importorskip("scipy.signal")
-
-
-class TestSignalConv2D:
-    def validate(self, image_shape, filter_shape, out_dim, verify_grad=True):
-
-        image_dim = len(image_shape)
-        filter_dim = len(filter_shape)
-        input = TensorType("float64", shape=(None,) * image_dim)()
-        filters = TensorType("float64", shape=(None,) * filter_dim)()
-
-        bsize = image_shape[0]
-        if image_dim != 3:
-            bsize = 1
-        nkern = filter_shape[0]
-        if filter_dim != 3:
-            nkern = 1
-
-        # PYTENSOR IMPLEMENTATION ############
-        # we create a symbolic function so that verify_grad can work
-        def sym_conv2d(input, filters):
-            return conv.conv2d(input, filters)
-
-        output = sym_conv2d(input, filters)
-        assert output.ndim == out_dim
-        pytensor_conv = pytensor.function([input, filters], output)
-
-        # initialize input and compute result
-        image_data = np.random.random(image_shape)
-        filter_data = np.random.random(filter_shape)
-        pytensor_output = pytensor_conv(image_data, filter_data)
-
-        # REFERENCE IMPLEMENTATION ############
-        out_shape2d = np.array(image_shape[-2:]) - np.array(filter_shape[-2:]) + 1
-        ref_output = np.zeros(tuple(out_shape2d))
-
-        # reshape as 3D input tensors to make life easier
-        image_data3d = image_data.reshape((bsize,) + image_shape[-2:])
-        filter_data3d = filter_data.reshape((nkern,) + filter_shape[-2:])
-        # reshape pytensor output as 4D to make life easier
-        pytensor_output4d = pytensor_output.reshape(
-            (
-                bsize,
-                nkern,
-            )
-            + pytensor_output.shape[-2:]
-        )
-
-        # loop over mini-batches (if required)
-        for b in range(bsize):
-
-            # loop over filters (if required)
-            for k in range(nkern):
-
-                image2d = image_data3d[b, :, :]
-                filter2d = filter_data3d[k, :, :]
-                output2d = np.zeros(ref_output.shape)
-                for row in range(ref_output.shape[0]):
-                    for col in range(ref_output.shape[1]):
-                        output2d[row, col] += (
-                            image2d[
-                                row : row + filter2d.shape[0],
-                                col : col + filter2d.shape[1],
-                            ]
-                            * filter2d[::-1, ::-1]
-                        ).sum()
-
-                assert _allclose(pytensor_output4d[b, k, :, :], output2d)
-
-        # TEST GRADIENT ############
-        if verify_grad:
-            utt.verify_grad(sym_conv2d, [image_data, filter_data])
-
-    @pytest.mark.skipif(
-        pytensor.config.cxx == "",
-        reason="conv2d tests need a c++ compiler",
-    )
-    def test_basic(self):
-        # Basic functionality of nnet.conv.ConvOp is already tested by
-        # its own test suite.  We just have to test whether or not
-        # signal.conv.conv2d can support inputs and filters of type
-        # matrix or tensor3.
-        self.validate((1, 4, 5), (2, 2, 3), out_dim=4, verify_grad=True)
-        self.validate((7, 5), (5, 2, 3), out_dim=3, verify_grad=False)
-        self.validate((3, 7, 5), (2, 3), out_dim=3, verify_grad=False)
-        self.validate((7, 5), (2, 3), out_dim=2, verify_grad=False)
-
-    def test_fail(self):
-        # Test that conv2d fails for dimensions other than 2 or 3.
-
-        with pytest.raises(Exception):
-            conv.conv2d(dtensor4(), dtensor3())
-        with pytest.raises(Exception):
-            conv.conv2d(dtensor3(), dvector())
-
-    def test_bug_josh_reported(self):
-        # Test refers to a bug reported by Josh, when due to a bad merge these
-        # few lines of code failed. See
-        # http://groups.google.com/group/theano-dev/browse_thread/thread/8856e7ca5035eecb
-
-        m1 = matrix()
-        m2 = matrix()
-        conv.conv2d(m1, m2)