Fixes

Author: Alejandro Gaston Alvarez Franceschi

coremltools/converters/mil/frontend/torch/ops.py

@@ -217,7 +217,10 @@ def get_bindings(alist) -> List[Any]:
 
         for i in alist:
             if isinstance(i, str):
-                results.append(context[i])
+                try:
+                    results.append(context[i])
+                except ValueError:
+                    results.append(None)
             elif isinstance(i, (list, tuple)) and all(isinstance(j, int) for j in i):
                 results.append(mb.const(val=i))
             elif isinstance(i, (list, tuple)):
@@ -980,11 +983,25 @@ def _convolution(context, node):
     # we require a (2 * n)-tuple, where n is the number of spatial dimensions, start and end for each spatial dimension
     pad = inputs[4].val
 
-    if len(weight.shape) in (3, 4):
-        # 1D and 2D: Need to explicitly state L-R, T-B pad
+    if type(pad) == str:
+        if pad == "same":
+            pad = 1
+        elif pad == "valid":
+            pad = 0
+        else:
+            raise ValueError(f"Unkown padding string value: '{pad}'")
+
+    if len(weight.shape) == 3:
+        # 1D padding: needs explicitly state L-R for x dim
         pad = _np.repeat(pad, 2)
+    elif len(weight.shape) == 4:
+        # 2D padding: needs explicitly state L-R for x,y dims
+        if type(pad) == int:
+            pad = _np.repeat(pad, 4)
+        elif len(pad) == 2:
+            pad = _np.repeat(pad, 2)
     elif len(weight.shape) == 5:
-        # 3D: Need to explicitly state F-Bk, L-R, T-B pad
+        # 3D padding: needs explicitly state L-R for x,y,z dims
         if type(pad) == int:
             pad = _np.repeat(pad, 6)
         elif len(pad) == 3:

coremltools/converters/mil/frontend/torch/test/test_torch_ops.py

@@ -1433,6 +1433,7 @@ class TestConv(TorchBaseTest):
             [
                 "compute_unit",
                 "backend",
+                "scripting",
                 "padding",
                 "stride",
                 "length",
@@ -1444,10 +1445,11 @@ class TestConv(TorchBaseTest):
             ]
         ),
         [
-            (compute_unit, backend, padding, stride, *param)
-            for compute_unit, backend, padding, stride, param in itertools.product(
+            (compute_unit, backend, scripting, padding, stride, *param)
+            for compute_unit, backend, scripting, padding, stride, param in itertools.product(
                 [ct.ComputeUnit.CPU_ONLY],
                 backends,
+                [True, False],
                 ["same", "valid", 0, 1],
                 [1, 2, 3],
                 [
@@ -1467,6 +1469,7 @@ def test_convolution1d(
         self,
         compute_unit,
         backend,
+        scripting,
         padding,
         stride,
         length,
@@ -1495,13 +1498,15 @@ def test_convolution1d(
             model,
             backend=backend,
             compute_unit=compute_unit,
+            use_scripting=scripting,
         )
 
     @pytest.mark.parametrize(
         ",".join(
             [
                 "compute_unit",
                 "backend",
+                "scripting",
                 "padding",
                 "stride",
                 "height",
@@ -1514,10 +1519,11 @@ def test_convolution1d(
             ]
         ),
         [
-            (compute_unit, backend, padding, stride, *param)
-            for compute_unit, backend, padding, stride, param in itertools.product(
+            (compute_unit, backend, scripting, padding, stride, *param)
+            for compute_unit, backend, scripting, padding, stride, param in itertools.product(
                 [ct.ComputeUnit.CPU_ONLY],
                 backends,
+                [True, False],
                 ["same", "valid", 1, 0],
                 [1, 2, 3],
                 [
@@ -1537,6 +1543,7 @@ def test_convolution2d(
         self,
         compute_unit,
         backend,
+        scripting,
         padding,
         stride,
         height,
@@ -1550,6 +1557,7 @@ def test_convolution2d(
     ):
         if padding == "same" and stride != 1:
             return
+            # pytest.skip("iOS16 target not available on macOS 13")
         model = nn.Conv2d(
             in_channels=in_channels,
             out_channels=out_channels,
@@ -1565,13 +1573,15 @@ def test_convolution2d(
             model,
             backend=backend,
             compute_unit=compute_unit,
+            use_scripting=scripting,
         )
 
     @pytest.mark.parametrize(
         ",".join(
             [
                 "compute_unit",
                 "backend",
+                "scripting",
                 "padding",
                 "stride",
                 "depth",
@@ -1585,10 +1595,11 @@ def test_convolution2d(
             ]
         ),
         [
-            (compute_unit, backend, padding, stride, *param)
-            for compute_unit, backend, padding, stride, param in itertools.product(
+            (compute_unit, backend, scripting, padding, stride, *param)
+            for compute_unit, backend, scripting, padding, stride, param in itertools.product(
                 [ct.ComputeUnit.CPU_ONLY],
                 backends,
+                [True, False],
                 ["same", "valid", 1, 0],
                 [1, 2, 3],
                 [
@@ -1608,6 +1619,7 @@ def test_convolution3d(
         self,
         compute_unit,
         backend,
+        scripting,
         padding,
         stride,
         depth,
@@ -1637,6 +1649,7 @@ def test_convolution3d(
             model,
             backend=backend,
             compute_unit=compute_unit,
+            use_scripting=scripting,
         )