clean redundant API alias in 2.0 - part 1 (#29928)
* rm check_import_scipy, rm chunk_eval and mean_iou in paddle.metric.__init__.py * Revert "rm check_import_scipy, rm chunk_eval and mean_iou in paddle.metric.__init__.py" This reverts commit 179ba8c2b22bc31fe8d8a126e31820792cbd0f4e. * delete paddle.metric.chunk_eval and paddle.metric.mean_iou * delete paddle.nn.clip and paddle.nn.clip_by_norm * delete paddle.nn.functional.activation.hard_sigmoid and paddle.nn.functional.activation.hard_swish * delete paddle.nn.Pool2D, paddle.nn.BilinearTensorProduct, paddle.nn.RowConv, paddle.nn.functional.row_conv * fix extension import error * fix unittest for row_conv and Pool2Drevert-31562-mean
XiaoguangHu
4 years ago
committed by
GitHub
parent
181ea1870b
commit
726c78f293
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# Copyright (c) 2020 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 numpy as np
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from paddle import fluid, nn
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import paddle.fluid.dygraph as dg
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import paddle.fluid.initializer as I
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import paddle.nn.functional as F
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import unittest
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class RowConvTestCase(unittest.TestCase):
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def __init__(self,
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methodName='runTest',
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batch_size=4,
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num_channels=8,
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time_steps=12,
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context_size=3,
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act=None,
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dtype="float32"):
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super(RowConvTestCase, self).__init__(methodName=methodName)
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.time_steps = time_steps
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self.context_size = context_size
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self.act = act
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self.dtype = dtype
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def setUp(self):
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input_shape = (self.batch_size, self.time_steps, self.num_channels)
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self.input = np.random.uniform(size=input_shape).astype(self.dtype)
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self.weight_shape = weight_shape = (self.context_size + 1,
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self.num_channels)
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self.weight = np.random.uniform(size=weight_shape).astype(self.dtype)
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def fluid_layer(self, place):
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main = fluid.Program()
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start = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, start):
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x = fluid.data(
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"input", [-1, -1, self.num_channels], dtype=self.dtype)
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y = fluid.layers.row_conv(
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x,
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self.context_size,
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param_attr=I.NumpyArrayInitializer(self.weight),
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act=self.act)
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exe = fluid.Executor(place)
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exe.run(start)
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y_np, = exe.run(main, feed={"input": self.input}, fetch_list=[y])
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return y_np
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def functional_declarative(self, place):
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main = fluid.Program()
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start = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, start):
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x = fluid.data(
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"input", [-1, -1, self.num_channels], dtype=self.dtype)
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w = fluid.data("weight", self.weight_shape, dtype=self.dtype)
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y = F.extension.row_conv(x, w, act=self.act)
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exe = fluid.Executor(place)
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exe.run(start)
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y_np, = exe.run(main,
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feed={"input": self.input,
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"weight": self.weight},
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fetch_list=[y])
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return y_np
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def functional_imperative(self, place):
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with dg.guard(place):
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x_var = dg.to_variable(self.input)
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w_var = dg.to_variable(self.weight)
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y_var = F.extension.row_conv(x_var, w_var, act=self.act)
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y_np = y_var.numpy()
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return y_np
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def nn_layer(self, place):
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with dg.guard(place):
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x_var = dg.to_variable(self.input)
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conv = nn.RowConv(
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self.num_channels,
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self.context_size,
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param_attr=I.NumpyArrayInitializer(self.weight),
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act=self.act,
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dtype=self.dtype)
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y_var = conv(x_var)
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y_np = y_var.numpy()
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return y_np
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def _test_equivalence(self, place):
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result1 = self.fluid_layer(place)
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result2 = self.functional_declarative(place)
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result3 = self.functional_imperative(place)
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result4 = self.nn_layer(place)
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np.testing.assert_array_almost_equal(result1, result2)
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np.testing.assert_array_almost_equal(result2, result3)
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np.testing.assert_array_almost_equal(result3, result4)
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def runTest(self):
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place = fluid.CPUPlace()
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self._test_equivalence(place)
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if fluid.core.is_compiled_with_cuda():
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palce = fluid.CUDAPlace(0)
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self._test_equivalence(place)
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def load_tests(loader, standard_tests, pattern):
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suite = unittest.TestSuite()
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suite.addTest(RowConvTestCase(methodName="runTest"))
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suite.addTest(RowConvTestCase(methodName="runTest", act="sigmoid"))
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suite.addTest(
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RowConvTestCase(
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methodName="runTest", context_size=5, act="sigmoid"))
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return suite
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if __name__ == "__main__":
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unittest.main()
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@ -1,99 +0,0 @@
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# Copyright (c) 2020 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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__all__ = ['RowConv']
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from ...fluid.dygraph import layers
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from .. import functional as F
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class RowConv(layers.Layer):
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"""
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**Row-convolution operator**
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The row convolution is called lookahead convolution. This operator was
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introduced in the following paper for
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`DeepSpeech2 <http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf>`_.
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The main motivation is that a bidirectional RNN, useful in DeepSpeech like
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speech models, learns representation for a sequence by performing a
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forward and a backward pass through the entire sequence. However, unlike
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unidirectional RNNs, bidirectional RNNs are challenging to deploy in an online
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and low-latency setting. The lookahead convolution incorporates information
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from future subsequences in a computationally efficient manner to improve
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unidirectional recurrent neural networks. The row convolution operator is
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different from the 1D sequence convolution, and is computed as follows:
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Given an input sequence X of length t and input dimension D, and a filter
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(W) of size context * D.
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More details about row_conv please refer to the design document
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`<https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645>`_ .
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Parameters:
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num_channels (int): input data's feature size.
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future_context_size (int): Future context size. Please note, the shape
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of convolution kernel is [future_context_size + 1, D].
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param_attr (ParamAttr): Attributes of parameters, including
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name, initializer etc. Default: None.
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act (str): Non-linear activation to be applied to output tensor. Default: None.
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dtype (str, optional): Data type, it can be "float32". Default: "float32".
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Attributes:
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weight (Parameter): shape [future_context_size + 1, D], the learnable
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weight (convolution kernel) of this layer.
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Returns:
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None
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Examples:
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.. code-block:: python
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from paddle import nn
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import paddle.nn.functional as F
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import numpy as np
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batch_size = 4
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time_steps = 8
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feature_size = 6
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context_size = 4
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x = np.random.randn(batch_size, time_steps, feature_size).astype(np.float32)
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x = paddle.to_tensor(x)
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conv = nn.RowConv(feature_size, context_size)
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y = conv(x)
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print(y.shape)
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# [4, 8, 6]
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"""
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def __init__(self,
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num_channels,
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future_context_size,
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param_attr=None,
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act=None,
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dtype="float32"):
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super(RowConv, self).__init__()
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self._dtype = dtype
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self._param_attr = param_attr
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self._act = act
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filter_shape = [future_context_size + 1, num_channels]
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self.weight = self.create_parameter(
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filter_shape, attr=param_attr, dtype=dtype)
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def forward(self, input):
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out = F.extension.row_conv(input, self.weight, act=self._act)
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return out
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