add Conv2D/Conv2DTranspose/Conv3D/Conv3DTranspose in paddle.nn.layer (#23488)

* add Conv/ConvTranspose layers in paddle.nn.layer, test=develop

* add example code in docstring, test=develop

* update unittests to work with test_runner, test=develop

python/paddle/fluid/dygraph/nn.py

@@ -651,12 +651,11 @@ class Conv3DTranspose(layers.Layer):
                         ] + self._filter_size
         self.weight = self.create_parameter(
             dtype=self._dtype, shape=filter_shape, attr=self._param_attr)
-        if self._bias_attr:
-            self.bias = self.create_parameter(
-                attr=self._bias_attr,
-                shape=[self._num_filters],
-                dtype=self._dtype,
-                is_bias=True)
+        self.bias = self.create_parameter(
+            attr=self._bias_attr,
+            shape=[self._num_filters],
+            dtype=self._dtype,
+            is_bias=True)
 
     def forward(self, input):
         pre_bias = self._helper.create_variable_for_type_inference(

python/paddle/fluid/tests/unittests/test_conv2d_layer.py

@@ -0,0 +1,247 @@
+# 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
+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 Conv2DTestCase(unittest.TestCase):
+    def __init__(self,
+                 methodName='runTest',
+                 batch_size=4,
+                 spartial_shape=(16, 16),
+                 num_channels=6,
+                 num_filters=8,
+                 filter_size=3,
+                 padding=0,
+                 stride=1,
+                 dilation=1,
+                 groups=1,
+                 act=None,
+                 no_bias=False,
+                 use_cudnn=True,
+                 data_format="NCHW",
+                 dtype="float32"):
+        super(Conv2DTestCase, 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.padding = padding
+        self.stride = stride
+        self.dilation = dilation
+        self.groups = groups
+        self.act = act
+        self.no_bias = no_bias
+        self.use_cudnn = use_cudnn
+        self.data_format = data_format
+        self.dtype = dtype
+
+    def setUp(self):
+        self.channel_last = self.data_format == "NHWC"
+        if self.channel_last:
+            input_shape = (self.batch_size, ) + self.spartial_shape + (
+                self.num_channels, )
+        else:
+            input_shape = (self.batch_size, self.num_channels
+                           ) + self.spartial_shape
+        self.input = np.random.randn(*input_shape).astype(self.dtype)
+
+        if isinstance(self.filter_size, int):
+            filter_size = [self.filter_size] * 2
+        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 fluid_layer(self, place):
+        main = fluid.Program()
+        start = fluid.Program()
+        with fluid.unique_name.guard():
+            with fluid.program_guard(main, start):
+                input_shape = (-1, -1, -1,self.num_channels) \
+                    if self.channel_last else (-1, self.num_channels, -1, -1)
+                x_var = fluid.data("input", input_shape, dtype=self.dtype)
+                weight_attr = I.NumpyArrayInitializer(self.weight)
+                if self.bias is None:
+                    bias_attr = False
+                else:
+                    bias_attr = I.NumpyArrayInitializer(self.bias)
+                y_var = fluid.layers.conv2d(
+                    x_var,
+                    self.num_filters,
+                    self.filter_size,
+                    padding=self.padding,
+                    stride=self.stride,
+                    dilation=self.dilation,
+                    groups=self.groups,
+                    param_attr=weight_attr,
+                    bias_attr=bias_attr,
+                    use_cudnn=self.use_cudnn,
+                    act=self.act,
+                    data_format=self.data_format)
+        feed_dict = {"input": self.input}
+        exe = fluid.Executor(place)
+        exe.run(start)
+        y_np, = exe.run(main, feed=feed_dict, fetch_list=[y_var])
+        return y_np
+
+    def functional(self, place):
+        main = fluid.Program()
+        start = fluid.Program()
+        with fluid.unique_name.guard():
+            with fluid.program_guard(main, start):
+                input_shape = (-1, -1, -1,self.num_channels) \
+                    if self.channel_last else (-1, self.num_channels, -1, -1)
+                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.conv2d(
+                    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,
+                    act=self.act,
+                    use_cudnn=self.use_cudnn,
+                    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 = dg.to_variable(self.input)
+        conv = nn.Conv2D(
+            self.num_channels,
+            self.num_filters,
+            self.filter_size,
+            padding=self.padding,
+            stride=self.stride,
+            dilation=self.dilation,
+            groups=self.groups,
+            act=self.act,
+            use_cudnn=self.use_cudnn,
+            data_format=self.data_format,
+            dtype=self.dtype)
+        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):
+        place = fluid.CPUPlace()
+        result1 = self.fluid_layer(place)
+        result2 = self.functional(place)
+        with dg.guard(place):
+            result3 = self.paddle_nn_layer()
+        np.testing.assert_array_almost_equal(result1, result2)
+        np.testing.assert_array_almost_equal(result2, result3)
+
+    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 Conv2DErrorTestCase(Conv2DTestCase):
+    def runTest(self):
+        place = fluid.CPUPlace()
+        with dg.guard(place):
+            with self.assertRaises(ValueError):
+                self.paddle_nn_layer()
+
+
+def add_cases(suite):
+    suite.addTest(Conv2DTestCase(methodName='runTest'))
+    suite.addTest(
+        Conv2DTestCase(
+            methodName='runTest', stride=[1, 2], dilation=2))
+    suite.addTest(
+        Conv2DTestCase(
+            methodName='runTest', stride=2, dilation=(2, 1)))
+    suite.addTest(
+        Conv2DTestCase(
+            methodName='runTest', padding="same", no_bias=True, act="sigmoid"))
+    suite.addTest(
+        Conv2DTestCase(
+            methodName='runTest', filter_size=(3, 3), padding='valid'))
+    suite.addTest(Conv2DTestCase(methodName='runTest', padding=(2, 3)))
+    suite.addTest(Conv2DTestCase(methodName='runTest', padding=[1, 2, 2, 1]))
+    suite.addTest(
+        Conv2DTestCase(
+            methodName='runTest', padding=[[0, 0], [0, 0], [1, 2], [2, 1]]))
+    suite.addTest(Conv2DTestCase(methodName='runTest', data_format="NHWC"))
+    suite.addTest(
+        Conv2DTestCase(
+            methodName='runTest',
+            data_format="NHWC",
+            padding=[[0, 0], [1, 1], [2, 2], [0, 0]]))
+    suite.addTest(
+        Conv2DTestCase(
+            methodName='runTest', groups=2, padding="valid"))
+    suite.addTest(
+        Conv2DTestCase(
+            methodName='runTest',
+            num_filters=6,
+            num_channels=3,
+            groups=3,
+            use_cudnn=False,
+            act="sigmoid",
+            padding="valid"))
+
+
+def add_error_cases(suite):
+    suite.addTest(
+        Conv2DErrorTestCase(
+            methodName='runTest', use_cudnn="not_valid"))
+    suite.addTest(
+        Conv2DErrorTestCase(
+            methodName='runTest', num_channels=5, groups=2))
+
+
+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

@@ -14,7 +14,7 @@
 
 # TODO: import all neural network related api under this directory,
 # including layers, linear, conv, rnn etc.
-__all__ = []
+# __all__ = []
 
 # TODO: define alias in nn directory
 # from .clip import ErrorClipByValue   #DEFINE_ALIAS
@@ -58,6 +58,8 @@ __all__ = []
 # from .layer.loss import MSELoss   #DEFINE_ALIAS
 from .layer.loss import L1Loss  #DEFINE_ALIAS
 from .layer import loss  #DEFINE_ALIAS
+from .layer import conv  #DEFINE_ALIAS
+from .layer.conv import Conv2D, Conv2DTranspose, Conv3D, Conv3DTranspose  #DEFINE_ALIAS
 # from .layer.loss import NLLLoss   #DEFINE_ALIAS
 # from .layer.loss import BCELoss   #DEFINE_ALIAS
 # from .layer.learning_rate import CosineDecay   #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -17,6 +17,7 @@
 # __all__ = [ ]
 
 # TODO: define alias in functional directory
+from . import conv
 from .conv import conv2d  #DEFINE_ALIAS
 from .conv import conv2d_transpose  #DEFINE_ALIAS
 from .conv import conv3d  #DEFINE_ALIAS

python/paddle/nn/layer/__init__.py

@@ -12,7 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# TODO: define activation functions of neural network  
+# TODO: define activation functions of neural network
 
 from . import loss
-__all__ = [loss]
+from . import conv
+
+from .loss import *
+from .conv import *