'int' object has no attribute 'item' on conv shapes calculation (issu…

…e 96) (#101)

* make sure shape is indexed correctly
* Improved and tested convolution shape inference
* Added torch support for _index_tuple
* Updated ruff syntax

---------

Co-authored-by: Jens E. Pedersen <jensegholm@protonmail.com>

.github/workflows/build.yml

@@ -28,9 +28,9 @@ jobs:
       - name: Lint with ruff
         run: |
           # stop the build if there are Python syntax errors or undefined names
-          ruff --output-format=github --select=E9,F63,F7,F82 --target-version=py37 .
+          ruff check . --output-format=github --select=E9,F63,F7,F82 --target-version=py37
           # default set of ruff rules with GitHub Annotations
-          ruff --output-format=github --target-version=py37 --exclude=docs/ --exclude=paper/ .
+          ruff check . --output-format=github --target-version=py37 --exclude=docs/ --exclude=paper/
       - name: Test with pytest
         run: |
           pytest

nir/ir/utils.py

@@ -50,7 +50,7 @@ def calculate_conv_output(
     shapes = []
     for i in range(ndim):
         if isinstance(padding, str) and padding == "same":
-            shape = input_shape[i]
+            shape = _index_tuple(input_shape, i)
         else:
             shape = np.floor(
                 (
@@ -87,19 +87,22 @@ def calc_flatten_output(input_shape: Sequence[int], start_dim: int, end_dim: int
     )
 
 
-def _index_tuple(
-    tuple: Union[int, Sequence[int]], index: int
-) -> Union[int, np.ndarray]:
+def _index_tuple(tuple: Union[int, Sequence[int]], index: int) -> np.ndarray:
     """If the input is a tuple/array, index it.
 
     Otherwise, return it as-is.
     """
-    if isinstance(tuple, np.ndarray) or isinstance(tuple, Sequence):
+    if isinstance(tuple, np.ndarray):
         return tuple[index]
+    elif isinstance(tuple, Sequence):
+        return np.array(tuple[index])
     elif isinstance(tuple, (int, np.integer)):
         return np.array([tuple])
     else:
-        raise TypeError(f"tuple must be int or np.ndarray, not {type(tuple)}")
+        try:
+            return tuple[index]
+        except TypeError:
+            raise TypeError(f"tuple must be int or np.ndarray, not {type(tuple)}")
 
 
 def ensure_str(a: Union[str, bytes]) -> str:

pyproject.toml

@@ -47,5 +47,5 @@ find={include = ["nir*"]}
 
 [tool.ruff]
 line-length = 100
-per-file-ignores = {"docs/conf.py" = ["E402"]}
+lint.per-file-ignores = {"docs/conf.py" = ["E402"]}
 exclude  = ["paper/"]

tests/test_ir.py

@@ -134,6 +134,28 @@ def test_conv2d():
     assert np.allclose(a.output_type["output"], np.array([3, 100, 50]))
 
 
+def test_conv2d_same():
+    # Create a NIR Network
+    conv_weights = np.array([[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]])
+    li_tau = np.array([0.9, 0.8])
+    li_r = np.array([1.0, 1.0])
+    li_v_leak = np.array([0.0, 0.0])
+
+    nir_network = nir.NIRGraph.from_list(
+        nir.Conv2d(
+            input_shape=(3, 3),
+            weight=conv_weights,
+            stride=1,
+            padding="same",
+            dilation=1,
+            groups=1,
+            bias=np.array([0.0] * 9),
+        ),
+        nir.LI(li_tau, li_r, li_v_leak),
+    )
+    assert np.allclose(nir_network.nodes["conv2d"].output_type["output"], [1, 3, 3])
+
+
 def test_cuba_lif():
     a = np.random.randn(10, 10)
     lif = nir.CubaLIF(tau_mem=a, tau_syn=a, r=a, v_leak=a, v_threshold=a)

tests/test_utils.py

@@ -0,0 +1,24 @@
+import numpy as np
+import pytest
+
+from nir.ir.utils import _index_tuple
+
+import importlib
+
+_TORCH_SPEC = importlib.util.find_spec("torch") is not None
+
+
+def test_index_tuple():
+    assert _index_tuple(1, 0) == 1
+    assert _index_tuple([1, 2], 0) == 1
+    assert _index_tuple(np.array([1, 2]), 0) == 1
+    assert np.all(
+        np.equal(_index_tuple(np.array([[1, 2], [3, 4]]), 1), np.array([3, 4]))
+    )
+
+
+@pytest.mark.skipif(_TORCH_SPEC is not None, reason="requires torch")
+def test_index_tuple_torch():
+    torch = _TORCH_SPEC.loader.load_module()
+    assert _index_tuple(torch.tensor([1, 2]), 0) == 1
+    assert _index_tuple(torch.tensor([[1, 2], [3, 4]]), 1).equal(torch.tensor([3, 4]))