[Cherry-Pick]Add darts unittest (#271)

* add darts unittest

demo/darts/search.py

@@ -36,7 +36,6 @@ add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
 add_arg('log_freq',          int,   50,              "Log frequency.")
 add_arg('use_multiprocess',  bool,  True,            "Whether use multiprocess reader.")
-add_arg('data',              str,   'dataset/cifar10',"The dir of dataset.")
 add_arg('batch_size',        int,   64,              "Minibatch size.")
 add_arg('learning_rate',     float, 0.025,            "The start learning rate.")
 add_arg('momentum',          float, 0.9,             "Momentum.")

paddleslim/nas/darts/train_search.py

@@ -206,8 +206,6 @@ class DARTSearch(object):
         if self.use_data_parallel:
             self.train_reader = fluid.contrib.reader.distributed_batch_reader(
                 self.train_reader)
-            self.valid_reader = fluid.contrib.reader.distributed_batch_reader(
-                self.valid_reader)
 
         train_loader = fluid.io.DataLoader.from_generator(
             capacity=64,

tests/test_darts.py

+# 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 paddle
+import unittest
+import paddle.fluid as fluid
+import numpy as np
+from paddleslim.nas.darts import DARTSearch
+from layers import conv_bn_layer
+
+
+class TestDARTS(unittest.TestCase):
+    def test_darts(self):
+        class SuperNet(fluid.dygraph.Layer):
+            def __init__(self):
+                super(SuperNet, self).__init__()
+                self._method = 'DARTS'
+                self._steps = 1
+                self.stem = fluid.dygraph.nn.Conv2D(
+                    num_channels=1, num_filters=3, filter_size=3, padding=1)
+                self.classifier = fluid.dygraph.nn.Linear(
+                    input_dim=2352, output_dim=10)
+                self._multiplier = 4
+                self._primitives = [
+                    'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect',
+                    'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3',
+                    'dil_conv_5x5'
+                ]
+                self._initialize_alphas()
+
+            def _initialize_alphas(self):
+                self.alphas_normal = fluid.layers.create_parameter(
+                    shape=[14, 8], dtype="float32")
+                self.alphas_reduce = fluid.layers.create_parameter(
+                    shape=[14, 8], dtype="float32")
+                self._arch_parameters = [
+                    self.alphas_normal,
+                    self.alphas_reduce,
+                ]
+
+            def arch_parameters(self):
+                return self._arch_parameters
+
+            def forward(self, input):
+                out = self.stem(input) * self.alphas_normal[0][
+                    0] * self.alphas_reduce[0][0]
+                out = fluid.layers.reshape(out, [0, -1])
+                logits = self.classifier(out)
+                return logits
+
+            def _loss(self, input, label):
+                logits = self.forward(input)
+                return fluid.layers.reduce_mean(
+                    fluid.layers.softmax_with_cross_entropy(logits, label))
+
+        def batch_generator(reader):
+            def wrapper():
+                batch_data = []
+                batch_label = []
+                for sample in reader():
+                    image = np.array(sample[0]).reshape(1, 28, 28)
+                    label = np.array(sample[1]).reshape(1)
+                    batch_data.append(image)
+                    batch_label.append(label)
+                    if len(batch_data) == 128:
+                        batch_data = np.array(batch_data, dtype='float32')
+                        batch_label = np.array(batch_label, dtype='int64')
+                        yield [batch_data, batch_label]
+                        batch_data = []
+                        batch_label = []
+
+            return wrapper
+
+        place = fluid.CUDAPlace(0)
+        with fluid.dygraph.guard(place):
+            model = SuperNet()
+            trainset = paddle.dataset.mnist.train()
+            validset = paddle.dataset.mnist.test()
+            train_reader = batch_generator(trainset)
+            valid_reader = batch_generator(validset)
+            searcher = DARTSearch(
+                model, train_reader, valid_reader, place, num_epochs=5)
+            searcher.train()
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_fsp_loss.py

@@ -19,8 +19,8 @@ from paddleslim.dist import merge, fsp_loss
 from layers import conv_bn_layer
 
 
-class TestMerge(unittest.TestCase):
-    def test_merge(self):
+class TestFSPLoss(unittest.TestCase):
+    def test_fsp_loss(self):
         student_main = fluid.Program()
         student_startup = fluid.Program()
         with fluid.program_guard(student_main, student_startup):

tests/test_l2_loss.py

@@ -19,8 +19,8 @@ from paddleslim.dist import merge, l2_loss
 from layers import conv_bn_layer
 
 
-class TestMerge(unittest.TestCase):
-    def test_merge(self):
+class TestL2Loss(unittest.TestCase):
+    def test_l2_loss(self):
         student_main = fluid.Program()
         student_startup = fluid.Program()
         with fluid.program_guard(student_main, student_startup):

tests/test_loss.py

@@ -19,8 +19,8 @@ from paddleslim.dist import merge, loss
 from layers import conv_bn_layer
 
 
-class TestMerge(unittest.TestCase):
-    def test_merge(self):
+class TestLoss(unittest.TestCase):
+    def test_loss(self):
         student_main = fluid.Program()
         student_startup = fluid.Program()
         with fluid.program_guard(student_main, student_startup):

tests/test_soft_label_loss.py

@@ -19,8 +19,8 @@ from paddleslim.dist import merge, soft_label_loss
 from layers import conv_bn_layer
 
 
-class TestMerge(unittest.TestCase):
-    def test_merge(self):
+class TestSoftLabelLoss(unittest.TestCase):
+    def test_soft_label_loss(self):
         student_main = fluid.Program()
         student_startup = fluid.Program()
         with fluid.program_guard(student_main, student_startup):