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gon_mnist.py
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gon_mnist.py
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import torch
import torch.nn as nn
import torchvision
import numpy as np
import os
plot_dir = 'imgs'
os.makedirs(plot_dir, exist_ok=True)
# image data
dataset_name = 'fashion' # ['mnist', 'fashion']
img_size = 28
n_channels = 1
img_coords = 2
# training info
lr = 1e-4
batch_size = 64
num_latent = 32
hidden_features = 256
num_layers = 4
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# create the GON network (a SIREN as in https://vsitzmann.github.io/siren/)
class SirenLayer(nn.Module):
def __init__(self, in_f, out_f, w0=30, is_first=False, is_last=False):
super().__init__()
self.in_f = in_f
self.w0 = w0
self.linear = nn.Linear(in_f, out_f)
self.is_first = is_first
self.is_last = is_last
self.init_weights()
def init_weights(self):
b = 1 / self.in_f if self.is_first else np.sqrt(6 / self.in_f) / self.w0
with torch.no_grad():
self.linear.weight.uniform_(-b, b)
def forward(self, x):
x = self.linear(x)
return x if self.is_last else torch.sin(self.w0 * x)
def gon_model(dimensions):
first_layer = SirenLayer(dimensions[0], dimensions[1], is_first=True)
other_layers = []
for dim0, dim1 in zip(dimensions[1:-2], dimensions[2:-1]):
other_layers.append(SirenLayer(dim0, dim1))
final_layer = SirenLayer(dimensions[-2], dimensions[-1], is_last=True)
return nn.Sequential(first_layer, *other_layers, final_layer)
# helper functions
def get_mgrid(sidelen, dim=2):
tensors = tuple(dim * [torch.linspace(-1, 1, steps=sidelen)])
mgrid = torch.stack(torch.meshgrid(*tensors), dim=-1)
mgrid = mgrid.reshape(-1, dim)
return mgrid
def cycle(iterable):
while True:
for x in iterable:
yield x
def slerp(a, b, t):
omega = torch.acos(
(a / torch.norm(a, dim=1, keepdim=True) * b / torch.norm(b, dim=1, keepdim=True)).sum(1)).unsqueeze(1)
res = (torch.sin((1.0 - t) * omega) / torch.sin(omega)) * a + (torch.sin(t * omega) / torch.sin(omega)) * b
return res
def slerp_batch(model, z, coords):
lz = z.data.clone().squeeze(1)
col_size = int(np.sqrt(z.size(0)))
src_z = lz.data[:col_size].repeat(col_size, 1)
z1, z2 = lz.data.split(lz.shape[0] // 2)
tgt_z = torch.cat([z2, z1])
tgt_z = tgt_z[:col_size].repeat(col_size, 1)
t = torch.linspace(0, 1, col_size).unsqueeze(1).repeat(1, col_size).contiguous().view(batch_size,
1).contiguous().to(device)
z_slerp = slerp(src_z, tgt_z, t)
z_slerp_rep = z_slerp.unsqueeze(1).repeat(1, coords.size(1), 1)
g_slerp = model(torch.cat((coords, z_slerp_rep), dim=-1))
return g_slerp
def gon_sample(model, recent_zs, coords):
zs = torch.cat(recent_zs, dim=0).squeeze(1).cpu().numpy()
mean = np.mean(zs, axis=0)
cov = np.cov(zs.T)
sample = np.random.multivariate_normal(mean, cov, size=batch_size)
sample = torch.tensor(sample).unsqueeze(1).repeat(1, coords.size(1), 1).to(device).float()
model_input = torch.cat((coords, sample), dim=-1)
return model(model_input)
# load datasets
if dataset_name == 'mnist':
dataset = torchvision.datasets.MNIST('data', train=True, download=True, transform=torchvision.transforms.Compose([
torchvision.transforms.ToTensor()
]))
if dataset_name == 'fashion':
dataset = torchvision.datasets.FashionMNIST('data', train=True, download=True,
transform=torchvision.transforms.Compose([
torchvision.transforms.ToTensor()
]))
train_loader = torch.utils.data.DataLoader(dataset, sampler=None, shuffle=True, batch_size=batch_size, drop_last=True)
train_iterator = iter(cycle(train_loader))
# define GON architecture, for example gon_shape = [34, 256, 256, 256, 256, 1]
gon_shape = [img_coords + num_latent] + [hidden_features] * num_layers + [n_channels]
F = gon_model(gon_shape).to(device)
optim = torch.optim.Adam(lr=lr, params=F.parameters())
c = torch.stack([get_mgrid(img_size, 2) for _ in range(batch_size)]).to(device) # coordinates
print(f'> Number of parameters {len(torch.nn.utils.parameters_to_vector(F.parameters()))}')
recent_zs = []
for step in range(501):
# sample a batch of data
x, t = next(train_iterator)
x, t = x.to(device), t.to(device)
x = x.permute(0, 2, 3, 1).reshape(batch_size, -1, n_channels)
# compute the gradients of the inner loss with respect to zeros (gradient origin)
z = torch.zeros(batch_size, 1, num_latent).to(device).requires_grad_()
z_rep = z.repeat(1, c.size(1), 1)
g = F(torch.cat((c, z_rep), dim=-1))
L_inner = ((g - x) ** 2).sum(1).mean()
z = -torch.autograd.grad(L_inner, [z], create_graph=True, retain_graph=True)[0]
# now with z as our new latent points, optimise the data fitting loss
z_rep = z.repeat(1, c.size(1), 1)
g = F(torch.cat((c, z_rep), dim=-1))
L_outer = ((g - x) ** 2).sum(1).mean()
optim.zero_grad()
L_outer.backward()
optim.step()
# compute sampling statistics
recent_zs.append(z.detach())
recent_zs = recent_zs[-100:]
if step % 100 == 0 and step > 0:
print(f"Step: {step} Loss: {L_outer.item():.3f}")
# plot reconstructions
torchvision.utils.save_image(torch.clamp(g, 0, 1).permute(0, 2, 1).reshape(-1, n_channels, img_size, img_size),
f'imgs/recon_{step}.png', nrow=int(np.sqrt(batch_size)), padding=0)
# plot interpolations
torchvision.utils.save_image(
torch.clamp(slerp_batch(F, z.data, c), 0, 1).permute(0, 2, 1).reshape(-1, n_channels, img_size, img_size),
f'imgs/slerp_{step}.png', nrow=int(np.sqrt(batch_size)), padding=0)
# plot samples
torchvision.utils.save_image(
torch.clamp(gon_sample(F, recent_zs, c), 0, 1).permute(0, 2, 1).reshape(-1, n_channels, img_size, img_size),
f'imgs/sample_{step}.png', nrow=int(np.sqrt(batch_size)), padding=0)