【API 2.0】Add conv1d API

python/paddle/fluid/tests/unittests/test_conv1d_layer.py

@@ -0,0 +1,208 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import paddle
+from paddle import fluid, nn
+import paddle.fluid.dygraph as dg
+import paddle.nn.functional as F
+import paddle.fluid.initializer as I
+import unittest
+
+
+class Conv1dTestCase(unittest.TestCase):
+    def __init__(self,
+                 methodName='runTest',
+                 batch_size=4,
+                 spartial_shape=(16, ),
+                 num_channels=6,
+                 num_filters=8,
+                 filter_size=3,
+                 padding=0,
+                 padding_mode="zeros",
+                 stride=1,
+                 dilation=1,
+                 groups=1,
+                 no_bias=False,
+                 dtype="float32",
+                 data_format="NCL"):
+        super(Conv1dTestCase, self).__init__(methodName)
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.num_filters = num_filters
+        self.spartial_shape = spartial_shape
+        self.filter_size = filter_size
+        self.data_format = data_format
+        self.channel_last = (self.data_format == "NHWC")
+
+        self.padding = padding
+        self.padding_mode = padding_mode
+        self.stride = stride
+        self.dilation = dilation
+        self.groups = groups
+        self.no_bias = no_bias
+        self.dtype = dtype
+
+    def setUp(self):
+        input_shape = (self.batch_size, self.num_channels
+                       ) + self.spartial_shape if not self.channel_last else (
+                           self.batch_size, ) + self.spartial_shape + (
+                               self.num_channels, )
+        self.input = np.random.randn(*input_shape).astype(self.dtype)
+
+        if isinstance(self.filter_size, int):
+            filter_size = [self.filter_size]
+        else:
+            filter_size = self.filter_size
+        self.weight_shape = weight_shape = (self.num_filters, self.num_channels
+                                            // self.groups) + tuple(filter_size)
+        self.weight = np.random.uniform(
+            -1, 1, size=weight_shape).astype(self.dtype)
+        if not self.no_bias:
+            self.bias = np.random.uniform(
+                -1, 1, size=(self.num_filters, )).astype(self.dtype)
+        else:
+            self.bias = None
+
+    def functional(self, place):
+        main = fluid.Program()
+        start = fluid.Program()
+        with fluid.unique_name.guard():
+            with fluid.program_guard(main, start):
+                input_shape = (-1, self.num_channels,
+                               -1) if not self.channel_last else (
+                                   -1, -1, self.num_channels)
+                x_var = fluid.data("input", input_shape, dtype=self.dtype)
+                w_var = fluid.data(
+                    "weight", self.weight_shape, dtype=self.dtype)
+                b_var = fluid.data(
+                    "bias", (self.num_filters, ), dtype=self.dtype)
+                y_var = F.conv1d(
+                    x_var,
+                    w_var,
+                    b_var if not self.no_bias else None,
+                    padding=self.padding,
+                    stride=self.stride,
+                    dilation=self.dilation,
+                    groups=self.groups,
+                    data_format=self.data_format)
+        feed_dict = {"input": self.input, "weight": self.weight}
+        if self.bias is not None:
+            feed_dict["bias"] = self.bias
+        exe = fluid.Executor(place)
+        exe.run(start)
+        y_np, = exe.run(main, feed=feed_dict, fetch_list=[y_var])
+        return y_np
+
+    def paddle_nn_layer(self):
+        x_var = paddle.to_tensor(self.input)
+        conv = nn.Conv1d(
+            self.num_channels,
+            self.num_filters,
+            self.filter_size,
+            padding=self.padding,
+            padding_mode=self.padding_mode,
+            stride=self.stride,
+            dilation=self.dilation,
+            groups=self.groups,
+            data_format=self.data_format)
+        conv.weight.set_value(self.weight)
+        if not self.no_bias:
+            conv.bias.set_value(self.bias)
+        y_var = conv(x_var)
+        y_np = y_var.numpy()
+        return y_np
+
+    def _test_equivalence(self, place):
+        result1 = self.functional(place)
+        with dg.guard(place):
+            result2 = self.paddle_nn_layer()
+        np.testing.assert_array_almost_equal(result1, result2)
+
+    def runTest(self):
+        place = fluid.CPUPlace()
+        self._test_equivalence(place)
+
+        if fluid.core.is_compiled_with_cuda():
+            place = fluid.CUDAPlace(0)
+            self._test_equivalence(place)
+
+
+class Conv1dErrorTestCase(Conv1dTestCase):
+    def runTest(self):
+        place = fluid.CPUPlace()
+        with dg.guard(place):
+            with self.assertRaises(ValueError):
+                self.paddle_nn_layer()
+
+
+def add_cases(suite):
+    suite.addTest(Conv1dTestCase(methodName='runTest'))
+    suite.addTest(Conv1dTestCase(methodName='runTest', stride=[1], dilation=2))
+    suite.addTest(Conv1dTestCase(methodName='runTest', stride=2, dilation=(1)))
+    suite.addTest(
+        Conv1dTestCase(
+            methodName='runTest', padding="same", no_bias=True))
+    suite.addTest(
+        Conv1dTestCase(
+            methodName='runTest', filter_size=3, padding='valid'))
+    suite.addTest(
+        Conv1dTestCase(
+            methodName='runTest', padding=2, data_format='NLC'))
+    suite.addTest(Conv1dTestCase(methodName='runTest', padding=[1]))
+    suite.addTest(Conv1dTestCase(methodName='runTest', padding=2))
+    suite.addTest(Conv1dTestCase(methodName='runTest'))
+    suite.addTest(
+        Conv1dTestCase(
+            methodName='runTest', groups=2, padding="valid"))
+    suite.addTest(
+        Conv1dTestCase(
+            methodName='runTest',
+            num_filters=6,
+            num_channels=3,
+            groups=3,
+            padding="valid",
+            data_format='NLC'))
+
+
+def add_error_cases(suite):
+    suite.addTest(
+        Conv1dErrorTestCase(
+            methodName='runTest', padding_mode="reflect", padding="valid"))
+    suite.addTest(
+        Conv1dErrorTestCase(
+            methodName='runTest', data_format="VALID"))
+    suite.addTest(
+        Conv1dErrorTestCase(
+            methodName='runTest', padding_mode="VALID"))
+    suite.addTest(
+        Conv1dErrorTestCase(
+            methodName='runTest', num_channels=5, groups=2))
+    suite.addTest(
+        Conv1dErrorTestCase(
+            methodName='runTest', num_filters=8, num_channels=15, groups=3))
+    suite.addTest(
+        Conv1dErrorTestCase(
+            methodName='runTest', padding=[1, 2, 3, 4, 5]))
+
+
+def load_tests(loader, standard_tests, pattern):
+    suite = unittest.TestSuite()
+    add_cases(suite)
+    add_error_cases(suite)
+    return suite
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/nn/__init__.py

@@ -93,6 +93,7 @@
 from .layer.common import Dropout3D  #DEFINE_ALIAS
 from .layer.pooling import AdaptiveAvgPool2d  #DEFINE_ALIAS
 from .layer.pooling import AdaptiveAvgPool3d  #DEFINE_ALIAS
+from .layer.conv import Conv1d  #DEFINE_ALIAS
 from .layer.conv import Conv2d  #DEFINE_ALIAS
 from .layer.conv import Conv3d  #DEFINE_ALIAS
 from .layer.conv import ConvTranspose2d  #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -69,6 +69,7 @@
 # from .common import bilinear_tensor_product        #DEFINE_ALIAS
 from .common import assign  #DEFINE_ALIAS
 from .common import interpolate  #DEFINE_ALIAS
+from .conv import conv1d  #DEFINE_ALIAS
 from .conv import conv2d  #DEFINE_ALIAS
 from .conv import conv_transpose2d  #DEFINE_ALIAS
 from .conv import conv3d  #DEFINE_ALIAS