Update Clip in opset 11 to support min/max as inputs instead of attri…

…butes (#2096)

Update Clip in opset 11 to support min/max as inputs instead of attributes.

docs/Changelog.md

@@ -10335,6 +10335,41 @@ This version of the operator has been available since version 11 of the default
 <dd>Constrain input and output types to integer tensors.</dd>
 </dl>
 
+### <a name="Clip-11"></a>**Clip-11**</a>
+
+  Clip operator limits the given input within an interval. The interval is
+  specified by the inputs 'min' and 'max'. They default to
+  numeric_limits::lowest() and numeric_limits::max(), respectively.
+
+#### Version
+
+This version of the operator has been available since version 11 of the default ONNX operator set.
+
+#### Inputs (1 - 3)
+
+<dl>
+<dt><tt>input</tt> : T</dt>
+<dd>Input tensor whose elements to be clipped</dd>
+<dt><tt>min</tt> (optional) : T</dt>
+<dd>Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).</dd>
+<dt><tt>max</tt> (optional) : T</dt>
+<dd>Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Output tensor with clipped input elements</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
 ### <a name="CumSum-11"></a>**CumSum-11**</a>
 
   Performs cumulative sum of the input elements along the given axis.

docs/Operators.md

@@ -2029,29 +2029,24 @@ expect(node, inputs=[x], outputs=[y],
 ### <a name="Clip"></a><a name="clip">**Clip**</a>
 
   Clip operator limits the given input within an interval. The interval is
-  specified with arguments 'min' and 'max'. They default to
-  numeric_limits::lowest() and numeric_limits::max() respectively.
+  specified by the inputs 'min' and 'max'. They default to
+  numeric_limits::lowest() and numeric_limits::max(), respectively.
 
 #### Version
 
-This version of the operator has been available since version 6 of the default ONNX operator set.
+This version of the operator has been available since version 11 of the default ONNX operator set.
 
-Other versions of this operator: <a href="Changelog.md#Clip-1">Clip-1</a>
+Other versions of this operator: <a href="Changelog.md#Clip-1">Clip-1</a>, <a href="Changelog.md#Clip-6">Clip-6</a>
 
-#### Attributes
-
-<dl>
-<dt><tt>max</tt> : float (default is 3.4028234663852886e+38)</dt>
-<dd>Maximum value, above which element is replaced by max</dd>
-<dt><tt>min</tt> : float (default is -3.4028234663852886e+38)</dt>
-<dd>Minimum value, under which element is replaced by min</dd>
-</dl>
-
-#### Inputs
+#### Inputs (1 - 3)
 
 <dl>
 <dt><tt>input</tt> : T</dt>
 <dd>Input tensor whose elements to be clipped</dd>
+<dt><tt>min</tt> (optional) : T</dt>
+<dd>Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).</dd>
+<dt><tt>max</tt> (optional) : T</dt>
+<dd>Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).</dd>
 </dl>
 
 #### Outputs
@@ -2077,42 +2072,43 @@ Other versions of this operator: <a href="Changelog.md#Clip-1">Clip-1</a>
 ```python
 node = onnx.helper.make_node(
     'Clip',
-    inputs=['x'],
+    inputs=['x', 'min', 'max'],
     outputs=['y'],
-    min=-1.0,
-    max=1.0
 )
 
 x = np.array([-2, 0, 2]).astype(np.float32)
-y = np.clip(x, -1, 1)  # expected output [-1., 0., 1.]
-expect(node, inputs=[x], outputs=[y],
+min_val = np.float32(-1)
+max_val = np.float32(1)
+y = np.clip(x, min_val, max_val)  # expected output [-1., 0., 1.]
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
        name='test_clip_example')
 
 x = np.random.randn(3, 4, 5).astype(np.float32)
-y = np.clip(x, -1.0, 1.0)
-expect(node, inputs=[x], outputs=[y],
+y = np.clip(x, min_val, max_val)
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
        name='test_clip')
 node = onnx.helper.make_node(
     'Clip',
-    inputs=['x'],
+    inputs=['x', 'min', 'max'],
     outputs=['y'],
-    min=-5.0,
-    max=5.0,
 )
 
+min_val = np.float32(-5)
+max_val = np.float32(5)
+
 x = np.array([-1, 0, 1]).astype(np.float32)
 y = np.array([-1, 0, 1]).astype(np.float32)
-expect(node, inputs=[x], outputs=[y],
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
        name='test_clip_inbounds')
 
 x = np.array([-6, 0, 6]).astype(np.float32)
 y = np.array([-5, 0, 5]).astype(np.float32)
-expect(node, inputs=[x], outputs=[y],
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
        name='test_clip_outbounds')
 
 x = np.array([-1, 0, 6]).astype(np.float32)
 y = np.array([-1, 0, 5]).astype(np.float32)
-expect(node, inputs=[x], outputs=[y],
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
        name='test_clip_splitbounds')
 ```
 
@@ -2125,28 +2121,31 @@ expect(node, inputs=[x], outputs=[y],
 ```python
 node = onnx.helper.make_node(
     'Clip',
-    inputs=['x'],
+    inputs=['x', 'min'],
     outputs=['y'],
-    min=0.0
 )
+min_val = np.float32(0)
 x = np.random.randn(3, 4, 5).astype(np.float32)
-y = np.clip(x, 0.0, np.inf)
-expect(node, inputs=[x], outputs=[y],
+y = np.clip(x, min_val, np.inf)
+expect(node, inputs=[x, min_val], outputs=[y],
        name='test_clip_default_min')
 
+no_min = ""  # optional input, not supplied
 node = onnx.helper.make_node(
     'Clip',
-    inputs=['x'],
+    inputs=['x', no_min, 'max'],
     outputs=['y'],
-    max=0.0
 )
+max_val = np.float32(0)
 x = np.random.randn(3, 4, 5).astype(np.float32)
-y = np.clip(x, -np.inf, 0.0)
-expect(node, inputs=[x], outputs=[y],
+y = np.clip(x, -np.inf, max_val)
+expect(node, inputs=[x, max_val], outputs=[y],
        name='test_clip_default_max')
+
+no_max = ""  # optional input, not supplied
 node = onnx.helper.make_node(
     'Clip',
-    inputs=['x'],
+    inputs=['x', no_min, no_max],
     outputs=['y'],
 )
 

docs/TestCoverage.md

@@ -1167,42 +1167,43 @@ There are 2 test cases, listed as following:
 ```python
 node = onnx.helper.make_node(
     'Clip',
-    inputs=['x'],
+    inputs=['x', 'min', 'max'],
     outputs=['y'],
-    min=-1.0,
-    max=1.0
 )
 
 x = np.array([-2, 0, 2]).astype(np.float32)
-y = np.clip(x, -1, 1)  # expected output [-1., 0., 1.]
-expect(node, inputs=[x], outputs=[y],
+min_val = np.float32(-1)
+max_val = np.float32(1)
+y = np.clip(x, min_val, max_val)  # expected output [-1., 0., 1.]
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
        name='test_clip_example')
 
 x = np.random.randn(3, 4, 5).astype(np.float32)
-y = np.clip(x, -1.0, 1.0)
-expect(node, inputs=[x], outputs=[y],
+y = np.clip(x, min_val, max_val)
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
        name='test_clip')
 node = onnx.helper.make_node(
     'Clip',
-    inputs=['x'],
+    inputs=['x', 'min', 'max'],
     outputs=['y'],
-    min=-5.0,
-    max=5.0,
 )
 
+min_val = np.float32(-5)
+max_val = np.float32(5)
+
 x = np.array([-1, 0, 1]).astype(np.float32)
 y = np.array([-1, 0, 1]).astype(np.float32)
-expect(node, inputs=[x], outputs=[y],
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
        name='test_clip_inbounds')
 
 x = np.array([-6, 0, 6]).astype(np.float32)
 y = np.array([-5, 0, 5]).astype(np.float32)
-expect(node, inputs=[x], outputs=[y],
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
        name='test_clip_outbounds')
 
 x = np.array([-1, 0, 6]).astype(np.float32)
 y = np.array([-1, 0, 5]).astype(np.float32)
-expect(node, inputs=[x], outputs=[y],
+expect(node, inputs=[x, min_val, max_val], outputs=[y],
        name='test_clip_splitbounds')
 ```
 
@@ -1213,28 +1214,31 @@ expect(node, inputs=[x], outputs=[y],
 ```python
 node = onnx.helper.make_node(
     'Clip',
-    inputs=['x'],
+    inputs=['x', 'min'],
     outputs=['y'],
-    min=0.0
 )
+min_val = np.float32(0)
 x = np.random.randn(3, 4, 5).astype(np.float32)
-y = np.clip(x, 0.0, np.inf)
-expect(node, inputs=[x], outputs=[y],
+y = np.clip(x, min_val, np.inf)
+expect(node, inputs=[x, min_val], outputs=[y],
        name='test_clip_default_min')
 
+no_min = ""  # optional input, not supplied
 node = onnx.helper.make_node(
     'Clip',
-    inputs=['x'],
+    inputs=['x', no_min, 'max'],
     outputs=['y'],
-    max=0.0
 )
+max_val = np.float32(0)
 x = np.random.randn(3, 4, 5).astype(np.float32)
-y = np.clip(x, -np.inf, 0.0)
-expect(node, inputs=[x], outputs=[y],
+y = np.clip(x, -np.inf, max_val)
+expect(node, inputs=[x, max_val], outputs=[y],
        name='test_clip_default_max')
+
+no_max = ""  # optional input, not supplied
 node = onnx.helper.make_node(
     'Clip',
-    inputs=['x'],
+    inputs=['x', no_min, no_max],
     outputs=['y'],
 )
 

onnx/backend/test/case/node/clip.py

@@ -16,70 +16,74 @@ class Clip(Base):
     def export():  # type: () -> None
         node = onnx.helper.make_node(
             'Clip',
-            inputs=['x'],
+            inputs=['x', 'min', 'max'],
             outputs=['y'],
-            min=-1.0,
-            max=1.0
         )
 
         x = np.array([-2, 0, 2]).astype(np.float32)
-        y = np.clip(x, -1, 1)  # expected output [-1., 0., 1.]
-        expect(node, inputs=[x], outputs=[y],
+        min_val = np.float32(-1)
+        max_val = np.float32(1)
+        y = np.clip(x, min_val, max_val)  # expected output [-1., 0., 1.]
+        expect(node, inputs=[x, min_val, max_val], outputs=[y],
                name='test_clip_example')
 
         x = np.random.randn(3, 4, 5).astype(np.float32)
-        y = np.clip(x, -1.0, 1.0)
-        expect(node, inputs=[x], outputs=[y],
+        y = np.clip(x, min_val, max_val)
+        expect(node, inputs=[x, min_val, max_val], outputs=[y],
                name='test_clip')
         node = onnx.helper.make_node(
             'Clip',
-            inputs=['x'],
+            inputs=['x', 'min', 'max'],
             outputs=['y'],
-            min=-5.0,
-            max=5.0,
         )
 
+        min_val = np.float32(-5)
+        max_val = np.float32(5)
+
         x = np.array([-1, 0, 1]).astype(np.float32)
         y = np.array([-1, 0, 1]).astype(np.float32)
-        expect(node, inputs=[x], outputs=[y],
+        expect(node, inputs=[x, min_val, max_val], outputs=[y],
                name='test_clip_inbounds')
 
         x = np.array([-6, 0, 6]).astype(np.float32)
         y = np.array([-5, 0, 5]).astype(np.float32)
-        expect(node, inputs=[x], outputs=[y],
+        expect(node, inputs=[x, min_val, max_val], outputs=[y],
                name='test_clip_outbounds')
 
         x = np.array([-1, 0, 6]).astype(np.float32)
         y = np.array([-1, 0, 5]).astype(np.float32)
-        expect(node, inputs=[x], outputs=[y],
+        expect(node, inputs=[x, min_val, max_val], outputs=[y],
                name='test_clip_splitbounds')
 
     @staticmethod
     def export_clip_default():  # type: () -> None
         node = onnx.helper.make_node(
             'Clip',
-            inputs=['x'],
+            inputs=['x', 'min'],
             outputs=['y'],
-            min=0.0
         )
+        min_val = np.float32(0)
         x = np.random.randn(3, 4, 5).astype(np.float32)
-        y = np.clip(x, 0.0, np.inf)
-        expect(node, inputs=[x], outputs=[y],
+        y = np.clip(x, min_val, np.inf)
+        expect(node, inputs=[x, min_val], outputs=[y],
                name='test_clip_default_min')
 
+        no_min = ""  # optional input, not supplied
         node = onnx.helper.make_node(
             'Clip',
-            inputs=['x'],
+            inputs=['x', no_min, 'max'],
             outputs=['y'],
-            max=0.0
         )
+        max_val = np.float32(0)
         x = np.random.randn(3, 4, 5).astype(np.float32)
-        y = np.clip(x, -np.inf, 0.0)
-        expect(node, inputs=[x], outputs=[y],
+        y = np.clip(x, -np.inf, max_val)
+        expect(node, inputs=[x, max_val], outputs=[y],
                name='test_clip_default_max')
+
+        no_max = ""  # optional input, not supplied
         node = onnx.helper.make_node(
             'Clip',
-            inputs=['x'],
+            inputs=['x', no_min, no_max],
             outputs=['y'],
         )