add unittest of dygraph RL models. (#17550)

* test=develop

* test=develop

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

+# Copyright (c) 2018 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.
+
+from __future__ import print_function
+
+import contextlib
+import unittest
+import numpy as np
+import six
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import core
+from paddle.fluid.optimizer import SGDOptimizer
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
+import paddle.fluid.dygraph.nn as nn
+from paddle.fluid.dygraph.base import to_variable
+from test_imperative_base import new_program_scope
+
+
+class Policy(fluid.dygraph.Layer):
+    def __init__(self, name_scope):
+        super(Policy, self).__init__(name_scope)
+
+        self.affine1 = nn.FC(self.full_name(), size=128)
+        self.affine2 = nn.FC(self.full_name(), size=2)
+        self.dropout_ratio = 0.6
+
+        self.saved_log_probs = []
+        self.rewards = []
+
+    def forward(self, inputs):
+        x = fluid.layers.reshape(inputs, shape=[-1, 4])
+        x = self.affine1(x)
+        x = fluid.layers.dropout(x, self.dropout_ratio)
+        x = fluid.layers.relu(x)
+        action_scores = self.affine2(x)
+        return fluid.layers.softmax(action_scores, axis=1)
+
+
+class TestImperativeMnist(unittest.TestCase):
+    def test_mnist_float32(self):
+        seed = 90
+        epoch_num = 1
+
+        state = np.random.normal(size=4).astype("float32")
+        state_list = state.tolist()
+        reward = np.random.random(size=[1, 1]).astype("float32")
+        reward_list = reward.tolist()
+        action_list = [1]
+        action = np.array(action_list).astype("float32")
+        mask_list = [[0, 1]]
+        mask = np.array(mask_list).astype("float32")
+
+        with fluid.dygraph.guard():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+            policy = Policy("PolicyModel")
+
+            dy_state = fluid.dygraph.base.to_variable(state)
+            dy_state.stop_gradient = True
+            loss_probs = policy(dy_state)
+
+            dy_mask = fluid.dygraph.base.to_variable(mask)
+            dy_mask.stop_gradient = True
+
+            loss_probs = fluid.layers.log(loss_probs)
+            loss_probs = fluid.layers.elementwise_mul(loss_probs, dy_mask)
+            loss_probs = fluid.layers.reduce_sum(loss_probs, dim=-1)
+
+            dy_reward = fluid.dygraph.base.to_variable(reward)
+            dy_reward.stop_gradient = True
+
+            loss_probs = fluid.layers.elementwise_mul(dy_reward, loss_probs)
+            loss = fluid.layers.reduce_sum(loss_probs)
+
+            sgd = SGDOptimizer(learning_rate=1e-3)
+
+            dy_param_init_value = {}
+
+            dy_out = loss.numpy()
+
+            for param in policy.parameters():
+                dy_param_init_value[param.name] = param.numpy()
+
+            loss.backward()
+            sgd.minimize(loss)
+            policy.clear_gradients()
+
+            dy_param_value = {}
+            for param in policy.parameters():
+                dy_param_value[param.name] = param.numpy()
+
+        with new_program_scope():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
+
+            policy = Policy("PolicyModel")
+
+            st_sgd = SGDOptimizer(learning_rate=1e-3)
+
+            st_state = fluid.layers.data(
+                name='st_state', shape=[4], dtype='float32')
+            st_reward = fluid.layers.data(
+                name='st_reward', shape=[1], dtype='float32')
+            st_mask = fluid.layers.data(
+                name='st_mask', shape=[2], dtype='float32')
+
+            st_loss_probs = policy(st_state)
+
+            st_loss_probs = fluid.layers.log(st_loss_probs)
+            st_loss_probs = fluid.layers.elementwise_mul(st_loss_probs, st_mask)
+            st_loss_probs = fluid.layers.reduce_sum(st_loss_probs, dim=-1)
+
+            st_loss_probs = fluid.layers.elementwise_mul(st_reward,
+                                                         st_loss_probs)
+            st_loss = fluid.layers.reduce_sum(st_loss_probs)
+
+            st_sgd.minimize(st_loss)
+
+            # initialize params and fetch them
+            static_param_init_value = {}
+            static_param_name_list = []
+            for param in policy.parameters():
+                static_param_name_list.append(param.name)
+
+            out = exe.run(fluid.default_startup_program(),
+                          fetch_list=static_param_name_list)
+
+            for i in range(len(static_param_name_list)):
+                static_param_init_value[static_param_name_list[i]] = out[i]
+
+            fetch_list = [st_loss.name]
+            fetch_list.extend(static_param_name_list)
+
+            out = exe.run(
+                fluid.default_main_program(),
+                feed={"st_state": state,
+                      "st_reward": reward,
+                      "st_mask": mask},
+                fetch_list=fetch_list)
+
+            static_param_value = {}
+            static_out = out[0]
+            for i in range(1, len(out)):
+                static_param_value[static_param_name_list[i - 1]] = out[i]
+
+        #self.assertTrue(np.allclose(dy_x_data.all(), static_x_data.all()))
+
+        for key, value in six.iteritems(static_param_init_value):
+            self.assertTrue(np.equal(value, dy_param_init_value[key]).all())
+
+        self.assertTrue(np.equal(static_out, dy_out).all())
+
+        for key, value in six.iteritems(static_param_value):
+            self.assertTrue(np.equal(value, dy_param_value[key]).all())
+
+
+if __name__ == '__main__':
+    unittest.main()