Conditional GAN

所为Conditional GAN，即在一定条件下的对抗生成模型，其应用包括pix2pix等

1. 准备训练数据和超参数

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

# torch.manual_seed(1)    # reproducible
# np.random.seed(1)

# Hyper Parameters
BATCH_SIZE = 64
LR_G = 0.0001           # learning rate for generator
LR_D = 0.0001           # learning rate for discriminator
N_IDEAS = 5             # think of this as number of ideas for generating an art work (Generator)
ART_COMPONENTS = 15     # it could be total point G can draw in the canvas
PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])

# show our beautiful painting range
# plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')
# plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')
# plt.legend(loc='upper right')
# plt.show()

2. 构建Conditional

def artist_works_with_labels():     # painting from the famous artist (real target)
    a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]
    paintings = a * np.power(PAINT_POINTS, 2) + (a-1)
    labels = (a-1) > 0.5            # upper paintings (1), lower paintings (0), two classes
    paintings = torch.from_numpy(paintings).float()
    labels = torch.from_numpy(labels.astype(np.float32))
    return paintings, labels

3. 构建GAN模型

G = nn.Sequential(                      # Generator
    nn.Linear(N_IDEAS+1, 128),          # random ideas (could from normal distribution) + class label
    nn.ReLU(),
    nn.Linear(128, ART_COMPONENTS),     # making a painting from these random ideas
)

D = nn.Sequential(                      # Discriminator
    nn.Linear(ART_COMPONENTS+1, 128),   # receive art work either from the famous artist or a newbie like G with label
    nn.ReLU(),
    nn.Linear(128, 1),
    nn.Sigmoid(),                       # tell the probability that the art work is made by artist
)

4. 选择优化器

opt_D = torch.optim.Adam(D.parameters(), lr=LR_D)
opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)

5. 训练和优化

for step in range(10000):
    artist_paintings, labels = artist_works_with_labels()           # real painting, label from artist
    G_ideas = torch.randn(BATCH_SIZE, N_IDEAS)                      # random ideas
    G_inputs = torch.cat((G_ideas, labels), 1)                      # ideas with labels
    G_paintings = G(G_inputs)                                       # fake painting w.r.t label from G

    D_inputs0 = torch.cat((artist_paintings, labels), 1)            # all have their labels
    D_inputs1 = torch.cat((G_paintings, labels), 1)
    prob_artist0 = D(D_inputs0)                 # D try to increase this prob
    prob_artist1 = D(D_inputs1)                 # D try to reduce this prob

    D_score0 = torch.log(prob_artist0)          # maximise this for D
    D_score1 = torch.log(1. - prob_artist1)     # maximise this for D

    D_loss = - torch.mean(D_score0 + D_score1)  # minimise the negative of both two above for D
    G_loss = torch.mean(D_score1)               # minimise D score w.r.t G

    opt_D.zero_grad()
    D_loss.backward(retain_graph=True)      # reusing computational graph
    opt_D.step()

    opt_G.zero_grad()
    G_loss.backward()
    opt_G.step()

6. 可视化训练过程和结果

plt.ion()   # something about continuous plotting
    if step % 200 == 0:  # plotting
        plt.cla()
        plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)
        bound = [0, 0.5] if labels.data[0, 0] == 0 else [0.5, 1]
        plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + bound[1], c='#74BCFF', lw=3, label='upper bound')
        plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + bound[0], c='#FF9359', lw=3, label='lower bound')
        plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 13})
        plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 13})
        plt.text(-.5, 1.7, 'Class = %i' % int(labels.data[0, 0]), fontdict={'size': 13})
        plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.draw();plt.pause(0.1)

plt.ioff()
plt.show()

# plot a generated painting for upper class
z = torch.randn(1, N_IDEAS)
label = torch.FloatTensor([[1.]])     # for upper class
G_inputs = torch.cat((z, label), 1)
G_paintings = G(G_inputs)
plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='G painting for upper class',)
plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + bound[1], c='#74BCFF', lw=3, label='upper bound (class 1)')
plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + bound[0], c='#FF9359', lw=3, label='lower bound (class 1)')
plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.show()