# 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. import contextlib import unittest import numpy as np import six import sys import paddle import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.optimizer import SGDOptimizer from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC from test_imperative_base import new_program_scope from paddle.fluid.imperative.base import to_variable class Discriminator(fluid.imperative.Layer): def __init__(self): super(Discriminator, self).__init__() self._fc1 = FC(size=32, act='elu', name="d_fc1") self._fc2 = FC(size=1, name="d_fc2") def parameters(self): return self._fc1.parameters() + self._fc2.parameters() def forward(self, inputs): x = self._fc1(inputs) return self._fc2(x) class Generator(fluid.imperative.Layer): def __init__(self): super(Generator, self).__init__() self._fc1 = FC(size=64, act='elu', name="g_fc1") self._fc2 = FC(size=64, act='elu', name="g_fc2") self._fc3 = FC(size=1, name="g_fc3") def parameters(self): return self._fc1.parameters() + self._fc2.parameters( ) + self._fc3.parameters() def forward(self, inputs): x = self._fc1(inputs) x = self._fc2(x) return self._fc3(x) class TestImperativeMnist(unittest.TestCase): def test_gan_float32(self): seed = 90 startup = fluid.Program() startup.random_seed = seed discriminate_p = fluid.Program() generate_p = fluid.Program() discriminate_p.random_seed = seed generate_p.random_seed = seed scope = fluid.core.Scope() with new_program_scope( main=discriminate_p, startup=startup, scope=scope): discriminator = Discriminator() generator = Generator() img = fluid.layers.data( name="img", shape=[2, 1], append_batch_size=False) noise = fluid.layers.data( name="noise", shape=[2, 2], append_batch_size=False) d_real = discriminator(img) d_loss_real = fluid.layers.reduce_mean( fluid.layers.sigmoid_cross_entropy_with_logits( x=d_real, label=fluid.layers.fill_constant( shape=[2, 1], dtype='float32', value=1.0))) d_fake = discriminator(generator(noise)) d_loss_fake = fluid.layers.reduce_mean( fluid.layers.sigmoid_cross_entropy_with_logits( x=d_fake, label=fluid.layers.fill_constant( shape=[2, 1], dtype='float32', value=0.0))) d_loss = d_loss_real + d_loss_fake sgd = SGDOptimizer(learning_rate=1e-3) sgd.minimize(d_loss) with new_program_scope(main=generate_p, startup=startup, scope=scope): discriminator = Discriminator() generator = Generator() noise = fluid.layers.data( name="noise", shape=[2, 2], append_batch_size=False) d_fake = discriminator(generator(noise)) g_loss = fluid.layers.reduce_mean( fluid.layers.sigmoid_cross_entropy_with_logits( x=d_fake, label=fluid.layers.fill_constant( shape=[2, 1], dtype='float32', value=1.0))) sgd = SGDOptimizer(learning_rate=1e-3) sgd.minimize(g_loss) exe = fluid.Executor(fluid.CPUPlace() if not core.is_compiled_with_cuda( ) else fluid.CUDAPlace(0)) static_params = dict() with fluid.scope_guard(scope): img = np.ones([2, 1], np.float32) noise = np.ones([2, 2], np.float32) exe.run(startup) static_d_loss = exe.run(discriminate_p, feed={'img': img, 'noise': noise}, fetch_list=[d_loss])[0] static_g_loss = exe.run(generate_p, feed={'noise': noise}, fetch_list=[g_loss])[0] # generate_p contains all parameters needed. for param in generate_p.global_block().all_parameters(): static_params[param.name] = np.array( scope.find_var(param.name).get_tensor()) dy_params = dict() with fluid.imperative.guard(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed discriminator = Discriminator() generator = Generator() sgd = SGDOptimizer(learning_rate=1e-3) d_real = discriminator(to_variable(np.ones([2, 1], np.float32))) d_loss_real = fluid.layers.reduce_mean( fluid.layers.sigmoid_cross_entropy_with_logits( x=d_real, label=to_variable(np.ones([2, 1], np.float32)))) d_fake = discriminator( generator(to_variable(np.ones([2, 2], np.float32)))) d_loss_fake = fluid.layers.reduce_mean( fluid.layers.sigmoid_cross_entropy_with_logits( x=d_fake, label=to_variable(np.zeros([2, 1], np.float32)))) d_loss = d_loss_real + d_loss_fake d_loss._backward() sgd.minimize(d_loss) discriminator.clear_gradients() generator.clear_gradients() d_fake = discriminator( generator(to_variable(np.ones([2, 2], np.float32)))) g_loss = fluid.layers.reduce_mean( fluid.layers.sigmoid_cross_entropy_with_logits( x=d_fake, label=to_variable(np.ones([2, 1], np.float32)))) g_loss._backward() sgd.minimize(g_loss) for p in discriminator.parameters(): dy_params[p.name] = p._numpy() for p in generator.parameters(): dy_params[p.name] = p._numpy() dy_g_loss = g_loss._numpy() dy_d_loss = d_loss._numpy() self.assertEqual(dy_g_loss, static_g_loss) self.assertEqual(dy_d_loss, static_d_loss) for k, v in six.iteritems(dy_params): self.assertTrue(np.allclose(v, static_params[k])) if __name__ == '__main__': unittest.main()