.. _l-onnx-doc-Ceil: ==== Ceil ==== .. contents:: :local: .. _l-onnx-op-ceil-13: Ceil - 13 ========= **Version** * **name**: `Ceil (GitHub) `_ * **domain**: **main** * **since_version**: **13** * **function**: False * **support_level**: SupportType.COMMON * **shape inference**: True This version of the operator has been available **since version 13**. **Summary** Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor elementwise. **Inputs** * **X** (heterogeneous) - **T**: Input tensor **Outputs** * **Y** (heterogeneous) - **T**: Output tensor **Type Constraints** * **T** in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors. **Examples** **default** :: node = onnx.helper.make_node( "Ceil", inputs=["x"], outputs=["y"], ) x = np.array([-1.5, 1.2]).astype(np.float32) y = np.ceil(x) # expected output [-1., 2.] expect(node, inputs=[x], outputs=[y], name="test_ceil_example") x = np.random.randn(3, 4, 5).astype(np.float32) y = np.ceil(x) expect(node, inputs=[x], outputs=[y], name="test_ceil") **Differences** .. raw:: html
00Ceil takes one input data (Tensor) and produces one output dataCeil takes one input data (Tensor) and produces one output data
11(Tensor) where the ceil is, y = ceil(x), is applied to(Tensor) where the ceil is, y = ceil(x), is applied to
22the tensor elementwise.the tensor elementwise.
33
44**Inputs****Inputs**
55
66* **X** (heterogeneous) - **T**:* **X** (heterogeneous) - **T**:
77 Input tensor Input tensor
88
99**Outputs****Outputs**
1010
1111* **Y** (heterogeneous) - **T**:* **Y** (heterogeneous) - **T**:
1212 Output tensor Output tensor
1313
1414**Type Constraints****Type Constraints**
1515
1616* **T** in (* **T** in (
17 tensor(bfloat16),
1718 tensor(double), tensor(double),
1819 tensor(float), tensor(float),
1920 tensor(float16) tensor(float16)
2021 ): ):
2122 Constrain input and output types to float tensors. Constrain input and output types to float tensors.
.. _l-onnx-op-ceil-6: Ceil - 6 ======== **Version** * **name**: `Ceil (GitHub) `_ * **domain**: **main** * **since_version**: **6** * **function**: False * **support_level**: SupportType.COMMON * **shape inference**: True This version of the operator has been available **since version 6**. **Summary** Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor elementwise. **Inputs** * **X** (heterogeneous) - **T**: Input tensor **Outputs** * **Y** (heterogeneous) - **T**: Output tensor **Type Constraints** * **T** in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors. **Differences** .. raw:: html
00Ceil takes one input data (Tensor) and produces one output dataCeil takes one input data (Tensor) and produces one output data
11(Tensor) where the ceil is, y = ceil(x), is applied to(Tensor) where the ceil is, y = ceil(x), is applied to
22the tensor elementwise.the tensor elementwise.
33
4**Attributes**
5
6* **consumed_inputs**:
7 legacy optimization attribute.
8
94**Inputs****Inputs**
105
116* **X** (heterogeneous) - **T**:* **X** (heterogeneous) - **T**:
127 Input tensor Input tensor
138
149**Outputs****Outputs**
1510
1611* **Y** (heterogeneous) - **T**:* **Y** (heterogeneous) - **T**:
1712 Output tensor Output tensor
1813
1914**Type Constraints****Type Constraints**
2015
2116* **T** in (* **T** in (
2217 tensor(double), tensor(double),
2318 tensor(float), tensor(float),
2419 tensor(float16) tensor(float16)
2520 ): ):
2621 Constrain input and output types to float tensors. Constrain input and output types to float tensors.
.. _l-onnx-op-ceil-1: Ceil - 1 ======== **Version** * **name**: `Ceil (GitHub) `_ * **domain**: **main** * **since_version**: **1** * **function**: False * **support_level**: SupportType.COMMON * **shape inference**: False This version of the operator has been available **since version 1**. **Summary** Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor elementwise. **Attributes** * **consumed_inputs**: legacy optimization attribute. **Inputs** * **X** (heterogeneous) - **T**: Input tensor **Outputs** * **Y** (heterogeneous) - **T**: Output tensor **Type Constraints** * **T** in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.