implement instance norm in front

pyinfinitensor/src/pyinfinitensor/onnx.py

@@ -85,7 +85,7 @@ class OnnxStub:
         while len(sorted_nodes) < len(model.graph.node):
             updated = False
             for i, node in enumerate(model.graph.node):
-                if all(t in known_edge for t in node.input):
+                if all(t in known_edge or t == "" for t in node.input):
                     node.name = str(len(sorted_nodes)) + "_" + node.name
                     sorted_nodes.append(i)
                     known_edge.update(node.output)
@@ -280,6 +280,46 @@ class OnnxStub:
                     axis,
                     stash_type,
                 )
+            elif node.op_type == "InstanceNormalization":
+                shape = tensors[node.input[0]].shape()
+                if len(shape) == 4:
+                    (input, scale, bias) = (tensors[node.input[i]] for i in [0, 1, 2])
+                    output = tensors.get(node.output[0])
+                    attributes = _parse_attribute(
+                        node, {"momentum": 1, "training_mode": 0}
+                    )
+                    (momentum,epsilon,training) = (attributes[name] for name in 
+                                                            ["momentum", "epsilon", "training_mode"])
+                    reshape = "{}-reshape".format(node.output[0])
+                    unreshape = "{}-unreshape".format(node.output[0])
+                    shape = tensors[node.input[0]].shape()
+                    mean = self.handler.tensor(
+                        [shape[1]], 1
+                    )
+                    var = self.handler.tensor(
+                        [shape[1]], 1
+                    )
+                    tensors[reshape] = self.handler.reshape(
+                        tensors[node.input[0]],
+                        tensors.get(reshape),
+                        (1,shape[0]*shape[1],shape[2],shape[3])
+                    )
+                    tensors[unreshape] = self.handler.batchNormalization(
+                        tensors[reshape],
+                        tensors.get(unreshape),
+                        mean,
+                        var,
+                        scale,
+                        bias,
+                        momentum,
+                        epsilon,
+                        training != 0
+                    )
+                    tensors[node.output[0]] = self.handler.reshape(
+                        tensors[unreshape],
+                        tensors.get(node.output[0]),
+                        shape
+                    )
             elif node.op_type == "RMSNorm":
                 tensors[node.output[0]] = self.handler.RMSNorm(
                     tensors[node.input[0]],
@@ -601,15 +641,15 @@ class OnnxStub:
                         "nearest_mode",
                     ]
                 )
-                if len(node.input) > 1:
+                if len(node.input) > 1 and node.input[1] in data:
                     roiVal = _parse_data(data[node.input[1]])
                 else:
                     roiVal = []
-                if len(node.input) > 2:
+                if len(node.input) > 2 and node.input[2] in data:
                     scalesVal = _parse_data(data[node.input[2]])
                 else:
                     scalesVal = []
-                if len(node.input) > 3:
+                if len(node.input) > 3 and node.input[3] in data:
                     sizesVal = _parse_data(data[node.input[3]])
                 else:
                     sizesVal = []
@@ -617,9 +657,21 @@ class OnnxStub:
                     tensors[node.input[0]],
                     output,
                     axes,
-                    tensors[node.input[3]] if len(node.input) > 3 else None,
+                    (
-                    tensors[node.input[2]] if len(node.input) > 2 else None,
+                        tensors[node.input[3]]
-                    tensors[node.input[1]] if len(node.input) > 1 else None,
+                        if len(node.input) > 3 and node.input[3] != ""
+                        else None
+                    ),
+                    (
+                        tensors[node.input[2]]
+                        if len(node.input) > 2 and node.input[2] != ""
+                        else None
+                    ),
+                    (
+                        tensors[node.input[1]]
+                        if len(node.input) > 1 and node.input[1] != ""
+                        else None
+                    ),
                     sizesVal,
                     scalesVal,
                     roiVal,