add unittest

tests/test_analyze_helper.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 sys
+sys.path.append("../")
+import os
+import unittest
+import paddle
+import paddle.fluid as fluid
+import paddle.dataset.mnist as reader
+from paddleslim.common import get_distribution, pdf
+sys.path.append("../demo")
+from models import MobileNet
+
+
+class TestAnalysisHelper(unittest.TestCase):
+    def get_model(self):
+        image = fluid.layers.data(
+            name='image', shape=[1, 28, 28], dtype='float32')
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+        train_loader = fluid.io.DataLoader.from_generator(
+            feed_list=[image, label],
+            capacity=512,
+            use_double_buffer=True,
+            iterable=True)
+        model = MobileNet()
+        out = model.net(input=image, class_dim=10)
+        cost = fluid.layers.cross_entropy(input=out, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+        startup_prog = fluid.default_startup_program()
+        train_prog = fluid.default_main_program()
+        return startup_prog, train_prog, train_loader
+
+    def test_analysishelper(self):
+        startup_prog, train_prog, train_loader = self.get_model()
+        place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        train_reader = reader.train()
+        train_reader = paddle.fluid.io.batch(train_reader, batch_size=64)
+        train_loader.set_sample_list_generator(train_reader, place)
+        exe = fluid.Executor(place)
+        exe.run(startup_prog)
+        var_names = [
+            var.name for var in list(train_prog.list_vars())
+            if 'generated_var' not in var.name and '@GRAD' not in var.name
+        ][:50]
+        var_dist = get_distribution(train_prog, var_names, exe, train_loader)
+        pdf(var_dist, pdf_save_dir='var_dist_pdf')
+
+        # test get_distribution
+        self.assertTrue(isinstance(var_dist, dict))
+        # test pdf
+        self.assertTrue(os.path.exists('./var_dist_pdf'))
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_quant_utility.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 sys
+sys.path.append("../")
+import os
+import unittest
+import paddle
+import paddle.fluid as fluid
+import paddle.dataset.mnist as reader
+from paddleslim.common import get_distribution
+from paddleslim.quant import pact_thres
+sys.path.append("../demo")
+from models import MobileNet
+
+
+class TestQuantUtility(unittest.TestCase):
+    def get_model(self):
+        image = fluid.layers.data(
+            name='image', shape=[1, 28, 28], dtype='float32')
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+        train_loader = fluid.io.DataLoader.from_generator(
+            feed_list=[image, label],
+            capacity=512,
+            use_double_buffer=True,
+            iterable=True)
+        model = MobileNet()
+        out = model.net(input=image, class_dim=10)
+        cost = fluid.layers.cross_entropy(input=out, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+        optimizer = fluid.optimizer.Momentum(learning_rate=0.1, momentum=0.9)
+        optimizer.minimize(avg_cost)
+        startup_prog = fluid.default_startup_program()
+        train_prog = fluid.default_main_program()
+        return startup_prog, train_prog, train_loader
+
+    def test_pact_thres(self):
+        startup_prog, train_prog, train_loader = self.get_model()
+        place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        train_reader = reader.train()
+        train_reader = paddle.fluid.io.batch(train_reader, batch_size=64)
+        train_loader.set_sample_list_generator(train_reader, place)
+        exe = fluid.Executor(place)
+        exe.run(startup_prog)
+        var_names = [
+            var.name for var in list(train_prog.list_vars())
+            if 'generated_var' not in var.name and '@GRAD' not in var.name
+        ]
+        var_dist = get_distribution(train_prog, var_names, exe, train_loader)
+        pact_alphas = pact_thres(var_dist)
+        self.assertTrue(isinstance(pact_alphas, dict))
+        self.assertTrue(isinstance(list(pact_alphas.values())[0], float))
+
+
+if __name__ == '__main__':
+    unittest.main()