ReduceL2:ReduceL2_forward (#55)

* fixes to work with verigram models

* ReduceL2 op is added to __init__ file

* revert Clip operation

Author: zhumakhan

onnx2pytorch/convert/operations.py

@@ -214,6 +214,8 @@ def convert_operations(onnx_graph, opset_version, batch_dim=0, enable_pruning=Tr
             op = partial(torch.prod, **kwargs)
         elif node.op_type == "ReduceSum":
             op = ReduceSum(opset_version=opset_version, **extract_attributes(node))
+        elif node.op_type == "ReduceL2":
+            op = ReduceL2(opset_version=opset_version, **extract_attributes(node))
         elif node.op_type == "Relu":
             op = nn.ReLU(inplace=True)
         elif node.op_type == "Reshape":

onnx2pytorch/operations/__init__.py

@@ -22,6 +22,7 @@
 from .range import Range
 from .reducemax import ReduceMax
 from .reducesum import ReduceSum
+from .reducel2 import ReduceL2
 from .reshape import Reshape
 from .resize import Resize, Upsample
 from .scatter import Scatter
@@ -63,6 +64,7 @@
     "Range",
     "ReduceMax",
     "ReduceSum",
+    "ReduceL2",
     "Reshape",
     "Resize",
     "Scatter",

onnx2pytorch/operations/reducel2.py

@@ -0,0 +1,31 @@
+import torch
+from torch import nn
+
+class ReduceL2(nn.Module):
+    def __init__(
+        self, opset_version, dim=None, keepdim=True, noop_with_empty_axes=False
+    ):
+        self.opset_version = opset_version
+        self.dim = dim
+        self.keepdim = keepdim
+        self.noop_with_empty_axes = noop_with_empty_axes
+        super().__init__()
+
+    def forward(self, data: torch.Tensor, axes: torch.Tensor = None):
+        if self.opset_version < 13:
+            dims = self.dim
+        else:
+            dims = axes
+        if dims is None:
+            if self.noop_with_empty_axes:
+                return data
+            else:
+                dims = tuple(range(data.ndim))
+        
+        if isinstance(dims, int):
+            dim = dims
+        else:
+            dim=tuple(list(dims))
+
+        ret = torch.sqrt(torch.sum(torch.square(data), dim=dim, keepdim=self.keepdim))
+        return ret