1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
|
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
from trainer import Trainer
class UNet(nn.Module):
def __init__(self):
super().__init__()
# Input
# The input to the model is a 10 vector which represents the input image.
# The Input is passed through layers to generate a 1x28x28, 1x14x14, 1x7x7 tensor.
# -------
# input: 1x10
self.inconv1 = nn.Linear(10, 28 * 28)
self.inconv2 = nn.Linear(10, 14 * 14)
self.inconv3 = nn.Linear(10, 7 * 7)
# Encoder
# In the encoder, convolutional layers with the Conv2d function are used to extract features from the input image.
# Each block in the encoder consists of two convolutional layers followed by a max-pooling layer,
# with the exception of the last block which does not include a max-pooling layer.
# -------
# input: 28x28x1
self.e11 = nn.Conv2d(1, 64, kernel_size=3, padding=1) # output: 28x28x64
self.e12 = nn.Conv2d(64, 64, kernel_size=3, padding=1) # output: 28x28x64
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) # output: 14x14x64
# input: 14x14x64
self.e21 = nn.Conv2d(64, 128, kernel_size=3, padding=1) # output: 14x14x128
self.e22 = nn.Conv2d(128, 128, kernel_size=3, padding=1) # output: 14x14x128
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) # output: 7x7x128
# input: 7x7x128
self.e31 = nn.Conv2d(129, 256, kernel_size=3, padding=1) # output: 7x7x256
self.e32 = nn.Conv2d(257, 256, kernel_size=3, padding=1) # output: 7x7x256
# Decoder
# In the decoder, the output of the encoder is upsampled using the ConvTranspose2d function.
# Each block in the decoder consists of two convolutional layers followed by an upsampling layer,
# with the exception of the last block which does not include an upsampling layer.
# -------
# input: 7x7x256
self.upconv1 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2) # output: 14x14x128
self.d11 = nn.Conv2d(256, 128, kernel_size=3, padding=1) # output: 14x14x(128x2)
self.d12 = nn.Conv2d(128, 128, kernel_size=3, padding=1) # output: 14x14x128
# input: 14x14x128
self.upconv2 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2) # output: 28x28x64
self.d21 = nn.Conv2d(128, 64, kernel_size=3, padding=1) # output: 28x28x(64x2)
self.d22 = nn.Conv2d(64, 64, kernel_size=3, padding=1) # output: 28x28x64
# Output
# The output of the decoder is passed through a convolutional layer with the Conv2d function to obtain the final output.
# -------
# input: 28x28x64
self.outconv = nn.Conv2d(64, 1, kernel_size=1) # output: 28x28x1
def forward(self, x, y):
# Input
y = self.inconv3(y)
y = y.view(-1, 1, 7, 7)
# Encoder
x = F.relu(self.e11(x))
x1 = F.relu(self.e12(x))
x = self.pool1(x1)
x = F.relu(self.e21(x))
x2 = F.relu(self.e22(x))
x = self.pool2(x2)
x = torch.cat([x, y], dim=1)
x = F.relu(self.e31(x))
x = torch.cat([x, y], dim=1)
x = F.relu(self.e32(x))
# Decoder
x = self.upconv1(x)
x = torch.cat([x, x], dim=1)
x = F.relu(self.d11(x))
x = F.relu(self.d12(x))
x = self.upconv2(x)
x = torch.cat([x, x1], dim=1)
x = F.relu(self.d21(x))
x = F.relu(self.d22(x))
# Output
x = self.outconv(x)
return x
DIFFU_STEPS = 10
class MNISTDiffusionDataset(Dataset):
def __init__(self, train=True):
super().__init__()
self.mnist_data = datasets.MNIST(root='./data', train=train, download=True, transform=transforms.ToTensor())
def __getitem__(self, index):
# Get the image and the label.
img, label = self.mnist_data[index]
# Add noise to the image.
noise = np.random.normal(0, 1, (28, 28))
alpha = np.random.uniform(1 / DIFFU_STEPS, 1.0)
# The target is the image with the noise.
target = img * alpha + noise * (1 - alpha)
# The input is the image with more noise.
input = img * (alpha - 1 / DIFFU_STEPS) + noise * (1 - alpha + 1 / DIFFU_STEPS)
# Convert the label to a one-hot vector.
vector = torch.nn.functional.one_hot(torch.tensor(label), num_classes=10)
return (input.clone().detach().to(dtype=torch.float32),
vector.clone().detach().to(dtype=torch.float32),
target.clone().detach().to(dtype=torch.float32))
def __len__(self):
return len(self.mnist_data)
############
# Training #
############
# Define the device.
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Load the model.
model = UNet().to(device)
# model.load_state_dict(torch.load('model.pth'))
# Define the optimizer.
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
# Define the training dataset.
train_dataset = MNISTDiffusionDataset(train=True)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
trainer = Trainer()
# Train the model.
trainer.train(model, train_loader, 2, optimizer, F.mse_loss)
# Save the model.
torch.save(model.state_dict(), 'model.pth')
##############
# Evaluation #
##############
# Load the model.
model = UNet().to(device)
model.load_state_dict(torch.load('model.pth'))
# Set the model to evaluation mode.
model.eval()
n = 10
fig = plt.figure(figsize=(2 * 2 * n, 3 * 2))
gs = plt.GridSpec(nrows=3, ncols=2*2*n)
for i in range(n):
# Get the i-th input and its label.
input, vector, label = train_dataset[i]
# Plot the input.
ax = fig.add_subplot(gs[0:1, 1 + 4 * i:3 + 4 * i])
ax.imshow(input[0], cmap='gray')
ax.axis('off')
# Plot the label.
ax = fig.add_subplot(gs[1:2, 4 * i:2 + 4 * i])
ax.imshow(label[0], cmap='gray')
ax.axis('off')
# Get the model output.
output = model(input.unsqueeze(0).to(device), vector.unsqueeze(0).to(device))
# Plot the model output.
ax = fig.add_subplot(gs[1:2, 2 + 4 * i:4 + 4 * i])
ax.imshow(output[0, 0].cpu().detach(), cmap='gray')
ax.axis('off')
# Plot the difference between the label and the model output.
ax = fig.add_subplot(gs[2:3, 1 + 4 * i:3 + 4 * i])
ax.imshow((label - output[0, 0].cpu().detach())[0], cmap='coolwarm')
ax.axis('off')
fig.tight_layout()
fig.savefig('diff.tmp.png')
# Plot the evolution of the noise.
fig = plt.figure(figsize=(n, DIFFU_STEPS))
noises = np.random.normal(0, 1, (n, 1, 28, 28))
noises = torch.Tensor(noises).to(device)
vector = torch.nn.functional.one_hot(torch.tensor(range(n)), num_classes=10).to(device)
vector = vector.clone().detach().to(dtype=torch.float32)
# Apply the model multiple times.
for i in range(DIFFU_STEPS):
noises = model(noises, vector)
for j in range(n):
ax = fig.add_subplot(DIFFU_STEPS, n, i * n + j + 1)
ax.imshow(noises[j, 0].cpu().detach(), cmap='gray')
ax.axis('off')
fig.tight_layout()
fig.savefig('diffu.tmp.png')
# Plot bench of generated images.
fig = plt.figure(figsize=(n, n))
noises = np.random.normal(0, 1, (n * n, 1, 28, 28))
noises = torch.Tensor(noises).to(device)
vector = torch.nn.functional.one_hot(torch.tensor([range(n)] * n), num_classes=10).to(device)
vector = vector.clone().detach().to(dtype=torch.float32)
# Apply the model multiple times.
for i in range(DIFFU_STEPS):
noises = model(noises, vector)
for i in range(n * n):
ax = fig.add_subplot(n, n, i + 1)
ax.imshow(noises[i, 0].cpu().detach(), cmap='gray')
ax.axis('off')
fig.tight_layout()
fig.savefig('bench.tmp.png')
plt.close('all')
|
