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Merge pull request #316 from Zeref996/zeref-onnx-cases
add 10 api cases to tests
2 parents 327acfd + e70ae1f commit b8afe4a

10 files changed

Lines changed: 490 additions & 2 deletions

‎tests/onnxbase.py‎

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@@ -64,8 +64,8 @@ def __init__(self, func, name, ver_list, delta=1e-6, rtol=1e-5):
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self.seed = 33
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np.random.seed(self.seed)
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paddle.seed(self.seed)
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self.delta = 1e-6
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self.rtol = 1e-5
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self.delta = 1e-10
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self.rtol = 1e-11
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self.func = func
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if paddle.device.is_compiled_with_cuda() is True:
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self.places = ['gpu', 'cpu']

‎tests/test_broadcast_to.py‎

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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from onnxbase import APIOnnx
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from onnxbase import randtool
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class Net(paddle.nn.Layer):
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"""
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simplr Net
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"""
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def __init__(self):
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super(Net, self).__init__()
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def forward(self, inputs):
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"""
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forward
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"""
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x = paddle.broadcast_to(inputs, shape=[2, 3])
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return x
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def test_broadcast_to_base():
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"""
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api: paddle.broadcast_to
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op version: 9, 10, 11, 12
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"""
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op = Net()
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op.eval()
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# net, name, ver_list, delta=1e-10, rtol=1e-11
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obj = APIOnnx(op, 'broadcast_to', [9, 10, 11, 12])
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obj.set_input_data("input_data", paddle.to_tensor([1, 2, 3], dtype='int32'))
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obj.run()

‎tests/test_floor_divide.py‎

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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from onnxbase import APIOnnx
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from onnxbase import randtool
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class Net(paddle.nn.Layer):
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"""
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simplr Net
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"""
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def __init__(self):
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super(Net, self).__init__()
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def forward(self, inputs, inputs_):
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"""
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forward
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"""
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x = paddle.floor_divide(inputs, inputs_)
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return x
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def test_floor_divide_base():
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"""
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api: paddle.floor_divide
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op version: 10
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"""
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op = Net()
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op.eval()
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# net, name, ver_list, delta=1e-10, rtol=1e-11
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obj = APIOnnx(op, 'floor_divide', [9, 10, 11, 12])
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obj.set_input_data("input_data",
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paddle.to_tensor([2, 3, 8, 7]),
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paddle.to_tensor([1, 5, 3, 3]))
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obj.run()

‎tests/test_mod.py‎

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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from onnxbase import APIOnnx
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from onnxbase import randtool
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class Net(paddle.nn.Layer):
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"""
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simplr Net
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"""
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def __init__(self):
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super(Net, self).__init__()
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def forward(self, inputs, inputs_):
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"""
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forward
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"""
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x = paddle.mod(inputs, inputs_)
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return x
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def test_mod_10():
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"""
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api: paddle.mod
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op version: 10
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"""
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op = Net()
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op.eval()
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# net, name, ver_list, delta=1e-6, rtol=1e-5
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obj = APIOnnx(op, 'mod', [10])
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obj.set_input_data("input_data",
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paddle.to_tensor([2, 3, 8, 7]),
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paddle.to_tensor([1, 5, 3, 3]))
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obj.run()
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def test_mod_11():
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"""
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api: paddle.mod
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op version: 11
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"""
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op = Net()
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op.eval()
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# net, name, ver_list, delta=1e-6, rtol=1e-5
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obj = APIOnnx(op, 'mod', [11])
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obj.set_input_data("input_data",
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paddle.to_tensor([2, 3, 8, 7]),
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paddle.to_tensor([1, 5, 3, 3]))
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obj.run()
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def test_mod_12():
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"""
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api: paddle.mod
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op version: 12
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"""
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op = Net()
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op.eval()
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# net, name, ver_list, delta=1e-6, rtol=1e-5
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obj = APIOnnx(op, 'mod', [12])
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obj.set_input_data("input_data",
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paddle.to_tensor([2, 3, 8, 7]),
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paddle.to_tensor([1, 5, 3, 3]))
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obj.run()

‎tests/test_nn_GRU.py‎

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
10+
# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
13+
# limitations under the License.
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15+
import paddle
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from onnxbase import APIOnnx
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from onnxbase import randtool
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19+
20+
class Net(paddle.nn.Layer):
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"""
22+
simplr Net
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"""
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25+
def __init__(self):
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super(Net, self).__init__()
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self._gru = paddle.nn.GRU(16, 32, 2)
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def forward(self, inputs, inputs_):
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"""
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forward
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"""
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x, h = self._gru(inputs, inputs_)
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return x
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def test_GRU_base():
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"""
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api: paddle.nn.GRU
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op version: 9, 10, 11, 12
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"""
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op = Net()
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op.eval()
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# net, name, ver_list, delta=1e-10, rtol=1e-11
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obj = APIOnnx(op, 'nn_GRU', [9, 10, 11, 12])
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obj.set_input_data("input_data",
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paddle.randn((4, 23, 16)), paddle.randn((2, 4, 32)))
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obj.run()

‎tests/test_nn_MaxPool1D.py‎

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12+
# See the License for the specific language governing permissions and
13+
# limitations under the License.
14+
15+
import paddle
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from onnxbase import APIOnnx
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from onnxbase import randtool
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19+
20+
class Net(paddle.nn.Layer):
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"""
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simplr Net
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"""
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25+
def __init__(self):
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super(Net, self).__init__()
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self._max_pool = paddle.nn.MaxPool1D(
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kernel_size=2,
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stride=None,
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padding=0,
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ceil_mode=False,
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return_mask=False,
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name=None)
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def forward(self, inputs):
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"""
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forward
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"""
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x = self._max_pool(inputs)
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return x
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def test_MaxPool1D_base():
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"""
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api: paddle.MaxPool1D
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op version: 9, 10, 11, 12
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"""
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op = Net()
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op.eval()
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# net, name, ver_list, delta=1e-6, rtol=1e-5
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obj = APIOnnx(op, 'nn_MaxPool1D', [9, 10, 11, 12])
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obj.set_input_data(
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"input_data",
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paddle.to_tensor(
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randtool("float", -1, 1, [3, 1, 10]).astype('float32')))
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obj.run()

‎tests/test_nn_MaxPool2D.py‎

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
10+
# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12+
# See the License for the specific language governing permissions and
13+
# limitations under the License.
14+
15+
import paddle
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from onnxbase import APIOnnx
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from onnxbase import randtool
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19+
20+
class Net(paddle.nn.Layer):
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"""
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simplr Net
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"""
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def __init__(self):
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super(Net, self).__init__()
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self._max_pool = paddle.nn.MaxPool2D(
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kernel_size=2,
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stride=None,
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padding=0,
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ceil_mode=False,
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return_mask=False,
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data_format='NCHW',
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name=None)
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def forward(self, inputs):
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"""
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forward
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"""
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x = self._max_pool(inputs)
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return x
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def test_MaxPool2D_base():
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"""
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api: paddle.MaxPool2D
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op version: 9, 10, 11, 12
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"""
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op = Net()
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op.eval()
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# net, name, ver_list, delta=1e-6, rtol=1e-5
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obj = APIOnnx(op, 'nn_MaxPool2D', [9, 10, 11, 12])
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obj.set_input_data(
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"input_data",
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paddle.to_tensor(
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randtool("float", -1, 1, [3, 1, 10, 10]).astype('float32')))
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obj.run()

‎tests/test_nn_PixelShuffle.py‎

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
5+
# You may obtain a copy of the License at
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#
7+
# http://www.apache.org/licenses/LICENSE-2.0
8+
#
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# Unless required by applicable law or agreed to in writing, software
10+
# distributed under the License is distributed on an "AS IS" BASIS,
11+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12+
# See the License for the specific language governing permissions and
13+
# limitations under the License.
14+
15+
import paddle
16+
from onnxbase import APIOnnx
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from onnxbase import randtool
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19+
20+
class Net(paddle.nn.Layer):
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"""
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simplr Net
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"""
24+
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def __init__(self):
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super(Net, self).__init__()
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self.shuffle = paddle.nn.PixelShuffle(
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upscale_factor=3, data_format='NCHW', name=None)
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def forward(self, inputs):
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"""
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forward
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"""
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x = self.shuffle(inputs)
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return x
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def test_PixelShuffle_base():
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"""
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api: paddle.nn.PixelShuffle
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op version: 9, 10, 11, 12
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"""
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op = Net()
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op.eval()
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# net, name, ver_list, delta=1e-10, rtol=1e-11
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obj = APIOnnx(op, 'nn_PixelShuffle', [9, 10, 11, 12])
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obj.set_input_data("input_data", paddle.rand((2, 9, 4, 4)))
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obj.run()

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