wehub-resource-sync
161 lines
4.8 KiB
Python
161 lines
4.8 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import paddle
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from paddle import base
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paddle.disable_static()
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SEED = 2020
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np.random.seed(SEED)
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paddle.seed(SEED)
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class Generator(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.conv1 = paddle.nn.Conv2D(3, 3, 3, padding=1)
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def forward(self, x):
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x = self.conv1(x)
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x = paddle.tanh(x)
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return x
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class Discriminator(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.convd = paddle.nn.Conv2D(6, 3, 1)
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def forward(self, x):
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x = self.convd(x)
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return x
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class TestRetainGraph(unittest.TestCase):
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def cal_gradient_penalty(
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self,
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netD,
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real_data,
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fake_data,
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edge_data=None,
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type='mixed',
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constant=1.0,
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lambda_gp=10.0,
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):
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if lambda_gp > 0.0:
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if type == 'real':
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interpolatesv = real_data
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elif type == 'fake':
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interpolatesv = fake_data
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elif type == 'mixed':
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alpha = paddle.rand((real_data.shape[0], 1))
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alpha = paddle.expand(
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alpha,
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[
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real_data.shape[0],
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np.prod(real_data.shape) // real_data.shape[0],
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],
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)
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alpha = paddle.reshape(alpha, real_data.shape)
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interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
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else:
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raise NotImplementedError(f'{type} not implemented')
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interpolatesv.stop_gradient = False
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real_data.stop_gradient = True
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fake_AB = paddle.concat((real_data.detach(), interpolatesv), 1)
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disc_interpolates = netD(fake_AB)
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outs = paddle.tensor.fill_constant(
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disc_interpolates.shape, disc_interpolates.dtype, 1.0
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)
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gradients = paddle.grad(
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outputs=disc_interpolates,
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inputs=fake_AB,
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grad_outputs=outs,
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create_graph=True,
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retain_graph=True,
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only_inputs=True,
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)
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gradients = paddle.reshape(gradients[0], [real_data.shape[0], -1])
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gradient_penalty = (
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paddle.mean(
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(paddle.norm(gradients + 1e-16, 2, 1) - constant) ** 2
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)
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* lambda_gp
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) # added eps
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return gradient_penalty, gradients
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else:
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return 0.0, None
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def run_retain(self, need_retain):
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g = Generator()
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d = Discriminator()
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optim_g = paddle.optimizer.Adam(parameters=g.parameters())
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optim_d = paddle.optimizer.Adam(parameters=d.parameters())
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gan_criterion = paddle.nn.MSELoss()
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l1_criterion = paddle.nn.L1Loss()
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A = np.random.rand(2, 3, 32, 32).astype('float32')
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B = np.random.rand(2, 3, 32, 32).astype('float32')
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realA = paddle.to_tensor(A)
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realB = paddle.to_tensor(B)
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fakeB = g(realA)
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optim_d.clear_gradients()
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fake_AB = paddle.concat((realA, fakeB), 1)
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G_pred_fake = d(fake_AB.detach())
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false_target = paddle.tensor.fill_constant(
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G_pred_fake.shape, 'float32', 0.0
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)
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G_gradient_penalty, _ = self.cal_gradient_penalty(
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d, realA, fakeB, lambda_gp=10.0
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)
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loss_d = gan_criterion(G_pred_fake, false_target) + G_gradient_penalty
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loss_d.backward(retain_graph=need_retain)
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optim_d.minimize(loss_d)
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optim_g.clear_gradients()
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fake_AB = paddle.concat((realA, fakeB), 1)
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G_pred_fake = d(fake_AB)
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true_target = paddle.tensor.fill_constant(
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G_pred_fake.shape, 'float32', 1.0
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)
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loss_g = l1_criterion(fakeB, realB) + gan_criterion(
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G_pred_fake, true_target
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)
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loss_g.backward()
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optim_g.minimize(loss_g)
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def test_retain(self):
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self.run_retain(need_retain=True)
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if not base.framework.in_dygraph_mode():
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self.assertRaises(RuntimeError, self.run_retain, need_retain=False)
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if __name__ == '__main__':
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unittest.main()
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