[circle-mlir/models] Introduce test models for Range op

circle-mlir/circle-mlir/tools-test/circle-impexp-test/test.lst

@@ -129,6 +129,9 @@ AddModel(Pow_F32_R2)
 AddModel(Pow_F32_R2_3)
 AddModel(Pow_F32_R4)
 AddModel(PReLU_F32_R4)
+# AddModel(Range_F32_R0_1) --> Does't support dynamic shape output
+# AddModel(Range_F32_R0_2) --> Does't support dynamic shape output
+# AddModel(Range_F32_R0_3) --> Does't support dynamic shape output
 AddModel(Reciprocal_F32_R4)
 AddModel(ReduceMax_F32_R2)
 AddModel(ReduceMax_F32_R2_d0)

circle-mlir/circle-mlir/tools-test/onnx2circle-models/test.lst

@@ -137,6 +137,10 @@ AddModel(QuantizeLinear_F32_R3_ui8)
 AddModel(QuantizeLinear_F32_R3_ui8_fq)
 AddModel(QuantizeLinear_F32_R4_i16_cw)
 AddModel(QuantizeLinear_F32_R4_ui8_cw)
+# TODO activate after Range op support
+# AddModel(Range_F32_R0_1)
+# AddModel(Range_F32_R0_2)
+# AddModel(Range_F32_R0_3)
 AddModel(Reciprocal_F32_R4)
 AddModel(ReduceMax_F32_R2)
 AddModel(ReduceMax_F32_R2_d0)

circle-mlir/circle-mlir/tools-test/onnx2circle-value-test/test.lst

@@ -137,6 +137,9 @@ AddModel(PReLU_F32_R4)
 # AddModel(QuantizeLinear_F32_R3_ui8_fq)
 # AddModel(QuantizeLinear_F32_R4_i16_cw)
 # AddModel(QuantizeLinear_F32_R4_ui8_cw)
+# AddModel(Range_F32_R0_1) --> Does't support dynamic shape output
+# AddModel(Range_F32_R0_2) --> Does't support dynamic shape output
+# AddModel(Range_F32_R0_3) --> Does't support dynamic shape output
 AddModel(Reciprocal_F32_R4)
 AddModel(ReduceMax_F32_R2)
 AddModel(ReduceMax_F32_R2_d0)

circle-mlir/models/unit/Range_F32_R0_1/__init__.py

@@ -0,0 +1,20 @@
+import torch
+
+
+# Generate Range operator with Float32, scalar
+class net_Range(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, input):
+        limit = input
+        return torch.arange(0, limit, 1, dtype=torch.float32)
+
+    def onnx_opset_version(self):
+        return 11
+
+
+_model_ = net_Range()
+
+# produce float32 scalar with fixed number
+_inputs_ = torch.tensor(10, dtype=torch.float32)

circle-mlir/models/unit/Range_F32_R0_2/__init__.py

@@ -0,0 +1,19 @@
+import torch
+
+
+# Generate Range operator with Float32, scalar
+class net_Range(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, limit, delta):
+        return torch.arange(0, limit, delta, dtype=torch.float32)
+
+    def onnx_opset_version(self):
+        return 11
+
+
+_model_ = net_Range()
+
+# produce float32 scalar with fixed number
+_inputs_ = (torch.tensor(10, dtype=torch.float32), torch.tensor(1, dtype=torch.float32))

circle-mlir/models/unit/Range_F32_R0_3/__init__.py

@@ -0,0 +1,20 @@
+import torch
+
+
+# Generate Range operator with Float32, scalar
+class net_Range(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, start, limit, delta):
+        return torch.arange(start, limit, delta, dtype=torch.float32)
+
+    def onnx_opset_version(self):
+        return 11
+
+
+_model_ = net_Range()
+
+# produce float32 scalar with fixed number
+_inputs_ = (torch.tensor(0, dtype=torch.float32), torch.tensor(10, dtype=torch.float32),
+            torch.tensor(1, dtype=torch.float32))