# 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 unittest import numpy as np import six import paddle import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.optimizer import SGDOptimizer from paddle.fluid import Linear from test_imperative_base import new_program_scope from paddle.fluid.dygraph.base import to_variable from paddle.fluid.framework import _test_eager_guard class Discriminator(fluid.Layer): def __init__(self): super(Discriminator, self).__init__() self._fc1 = Linear(1, 32, act='elu') self._fc2 = Linear(32, 1) def forward(self, inputs): x = self._fc1(inputs) x = self._fc2(x) return x class Generator(fluid.Layer): def __init__(self): super(Generator, self).__init__() self._fc1 = Linear(2, 64, act='elu') self._fc2 = Linear(64, 64, act='elu') self._fc3 = Linear(64, 1) def forward(self, inputs): x = self._fc1(inputs) x = self._fc2(x) x = self._fc3(x) return x class TestDygraphGAN(unittest.TestCase): def func_test_gan_float32(self): seed = 90 paddle.seed(1) paddle.framework.random._manual_program_seed(1) startup = fluid.Program() discriminate_p = fluid.Program() generate_p = fluid.Program() 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.dygraph.guard(): paddle.seed(1) paddle.framework.random._manual_program_seed(1) discriminator = Discriminator() generator = Generator() sgd = SGDOptimizer(learning_rate=1e-3, parameter_list=(discriminator.parameters() + generator.parameters())) 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() dy_params2 = dict() with fluid.dygraph.guard(): fluid.set_flags({'FLAGS_sort_sum_gradient': True}) paddle.seed(1) paddle.framework.random._manual_program_seed(1) discriminator2 = Discriminator() generator2 = Generator() sgd2 = SGDOptimizer(learning_rate=1e-3, parameter_list=(discriminator2.parameters() + generator2.parameters())) d_real2 = discriminator2(to_variable(np.ones([2, 1], np.float32))) d_loss_real2 = fluid.layers.reduce_mean( fluid.layers.sigmoid_cross_entropy_with_logits( x=d_real2, label=to_variable(np.ones([2, 1], np.float32)))) d_fake2 = discriminator2( generator2(to_variable(np.ones([2, 2], np.float32)))) d_loss_fake2 = fluid.layers.reduce_mean( fluid.layers.sigmoid_cross_entropy_with_logits( x=d_fake2, label=to_variable(np.zeros([2, 1], np.float32)))) d_loss2 = d_loss_real2 + d_loss_fake2 d_loss2.backward() sgd2.minimize(d_loss2) discriminator2.clear_gradients() generator2.clear_gradients() d_fake2 = discriminator2( generator2(to_variable(np.ones([2, 2], np.float32)))) g_loss2 = fluid.layers.reduce_mean( fluid.layers.sigmoid_cross_entropy_with_logits( x=d_fake2, label=to_variable(np.ones([2, 1], np.float32)))) g_loss2.backward() sgd2.minimize(g_loss2) for p in discriminator2.parameters(): dy_params2[p.name] = p.numpy() for p in generator.parameters(): dy_params2[p.name] = p.numpy() dy_g_loss2 = g_loss2.numpy() dy_d_loss2 = d_loss2.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): np.testing.assert_allclose(v, static_params[k], rtol=1e-05) self.assertEqual(dy_g_loss2, static_g_loss) self.assertEqual(dy_d_loss2, static_d_loss) for k, v in six.iteritems(dy_params2): np.testing.assert_allclose(v, static_params[k], rtol=1e-05) def test_gan_float32(self): with _test_eager_guard(): self.func_test_gan_float32() self.func_test_gan_float32() if __name__ == '__main__': unittest.main()