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import os
import argparse
import glob
from PIL import Image
import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from sklearn.datasets import make_circles
import torch
import torch.nn as nn
import torch.optim as optim
parser = argparse.ArgumentParser()
parser.add_argument('--adjoint', action='store_true')
parser.add_argument('--viz', action='store_true', default=True)
parser.add_argument('--niters', type=int, default=1)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--num_samples', type=int, default=512)
parser.add_argument('--width', type=int, default=64)
parser.add_argument('--hidden_dim', type=int, default=32)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--train_dir', type=str, default=None)
parser.add_argument('--results_dir', type=str, default="./results")
args = parser.parse_args()
if args.adjoint:
from torchdiffeq import odeint_adjoint as odeint
else:
from torchdiffeq import odeint
class CNF(nn.Module):
"""Adapted from the NumPy implementation at:
https://gist.github.com/rtqichen/91924063aa4cc95e7ef30b3a5491cc52
"""
def __init__(self, in_out_dim, hidden_dim, width):
super().__init__()
self.in_out_dim = in_out_dim
self.hidden_dim = hidden_dim
self.width = width
self.hyper_net = HyperNetwork(in_out_dim, hidden_dim, width)
def forward(self, t, states):
z = states[0]
logp_z = states[1]
batchsize = z.shape[0]
with torch.set_grad_enabled(True):
z.requires_grad_(True)
W, B, U = self.hyper_net(t)
Z = torch.unsqueeze(z, 0).repeat(self.width, 1, 1)
h = torch.tanh(torch.matmul(Z, W) + B)
dz_dt = torch.matmul(h, U).mean(0)
dlogp_z_dt = -trace_df_dz(dz_dt, z).view(batchsize, 1)
return (dz_dt, dlogp_z_dt)
def trace_df_dz(f, z):
"""Calculates the trace of the Jacobian df/dz.
Stolen from: https://github.com/rtqichen/ffjord/blob/master/lib/layers/odefunc.py#L13
"""
sum_diag = 0.
for i in range(z.shape[1]):
sum_diag += torch.autograd.grad(f[:, i].sum(), z, create_graph=True)[0].contiguous()[:, i].contiguous()
return sum_diag.contiguous()
class HyperNetwork(nn.Module):
"""Hyper-network allowing f(z(t), t) to change with time.
Adapted from the NumPy implementation at:
https://gist.github.com/rtqichen/91924063aa4cc95e7ef30b3a5491cc52
"""
def __init__(self, in_out_dim, hidden_dim, width):
super().__init__()
blocksize = width * in_out_dim
self.fc1 = nn.Linear(1, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, 3 * blocksize + width)
self.in_out_dim = in_out_dim
self.hidden_dim = hidden_dim
self.width = width
self.blocksize = blocksize
def forward(self, t):
# predict params
params = t.reshape(1, 1)
params = torch.tanh(self.fc1(params))
params = torch.tanh(self.fc2(params))
params = self.fc3(params)
# restructure
params = params.reshape(-1)
W = params[:self.blocksize].reshape(self.width, self.in_out_dim, 1)
U = params[self.blocksize:2 * self.blocksize].reshape(self.width, 1, self.in_out_dim)
G = params[2 * self.blocksize:3 * self.blocksize].reshape(self.width, 1, self.in_out_dim)
U = U * torch.sigmoid(G)
B = params[3 * self.blocksize:].reshape(self.width, 1, 1)
return [W, B, U]
class RunningAverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, momentum=0.99):
self.momentum = momentum
self.reset()
def reset(self):
self.val = None
self.avg = 0
def update(self, val):
if self.val is None:
self.avg = val
else:
self.avg = self.avg * self.momentum + val * (1 - self.momentum)
self.val = val
def get_batch(num_samples):
points, _ = make_circles(n_samples=num_samples, noise=0.06, factor=0.5)
x = torch.tensor(points).type(torch.float32).to(device)
logp_diff_t1 = torch.zeros(num_samples, 1).type(torch.float32).to(device)
return(x, logp_diff_t1)
if __name__ == '__main__':
t0 = 0
t1 = 10
device = torch.device('cuda:' + str(args.gpu)
if torch.cuda.is_available() else 'cpu')
# model
func = CNF(in_out_dim=2, hidden_dim=args.hidden_dim, width=args.width).to(device)
# for param in func.parameters():
# nn.init.constant_(param, 0.1)
# func(torch.tensor(0.).to(device), (torch.tensor([[0., 1.], [2., 3.], [4., 5.]]).to(device), torch.zeros((2, 1)).to(device)))
optimizer = optim.Adam(func.parameters(), lr=args.lr)
p_z0 = torch.distributions.MultivariateNormal(
loc=torch.tensor([0.0, 0.0]).to(device),
covariance_matrix=torch.tensor([[0.1, 0.0], [0.0, 0.1]]).to(device)
)
loss_meter = RunningAverageMeter()
if args.train_dir is not None:
if not os.path.exists(args.train_dir):
os.makedirs(args.train_dir)
ckpt_path = os.path.join(args.train_dir, 'ckpt.pth')
if os.path.exists(ckpt_path):
checkpoint = torch.load(ckpt_path)
func.load_state_dict(checkpoint['func_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
print('Loaded ckpt from {}'.format(ckpt_path))
try:
for itr in range(1, args.niters + 1):
optimizer.zero_grad()
x, logp_diff_t1 = get_batch(args.num_samples)
z_t, logp_diff_t = odeint(
func,
(x, logp_diff_t1),
torch.tensor([t1, t0]).type(torch.float32).to(device),
atol=1e-5,
rtol=1e-5,
method='dopri5',
)
z_t0, logp_diff_t0 = z_t[-1], logp_diff_t[-1]
logp_x = p_z0.log_prob(z_t0).to(device) - logp_diff_t0.view(-1)
loss = -logp_x.mean(0)
loss.backward()
optimizer.step()
loss_meter.update(loss.item())
print('Iter: {}, running avg loss: {:.4f}'.format(itr, loss))
except KeyboardInterrupt:
if args.train_dir is not None:
ckpt_path = os.path.join(args.train_dir, 'ckpt.pth')
torch.save({
'func_state_dict': func.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, ckpt_path)
print('Stored ckpt at {}'.format(ckpt_path))
print('Training complete after {} iters.'.format(itr))
if True:
viz_samples = 30000
viz_timesteps = 41
target_sample, _ = get_batch(viz_samples)
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
with torch.no_grad():
# Generate evolution of samples
z_t0 = p_z0.sample([viz_samples]).to(device)
logp_diff_t0 = torch.zeros(viz_samples, 1).type(torch.float32).to(device)
z_t_samples, _ = odeint(
func,
(z_t0, logp_diff_t0),
torch.tensor(np.linspace(t0, t1, viz_timesteps)).to(device),
atol=1e-5,
rtol=1e-5,
method='dopri5',
)
# Generate evolution of density
x = np.linspace(-1.5, 1.5, 100)
y = np.linspace(-1.5, 1.5, 100)
points = np.vstack(np.meshgrid(x, y)).reshape([2, -1]).T
z_t1 = torch.tensor(points).type(torch.float32).to(device)
logp_diff_t1 = torch.zeros(z_t1.shape[0], 1).type(torch.float32).to(device)
z_t_density, logp_diff_t = odeint(
func,
(z_t1, logp_diff_t1),
torch.tensor(np.linspace(t1, t0, viz_timesteps)).to(device),
atol=1e-5,
rtol=1e-5,
method='dopri5',
)
# Create plots for each timestep
for (t, z_sample, z_density, logp_diff) in zip(
np.linspace(t0, t1, viz_timesteps),
z_t_samples, z_t_density, logp_diff_t
):
fig = plt.figure(figsize=(12, 4), dpi=200)
plt.tight_layout()
plt.axis('off')
plt.margins(0, 0)
fig.suptitle(f'{t:.2f}s')
ax1 = fig.add_subplot(1, 3, 1)
ax1.set_title('Target')
ax1.get_xaxis().set_ticks([])
ax1.get_yaxis().set_ticks([])
ax2 = fig.add_subplot(1, 3, 2)
ax2.set_title('Samples')
ax2.get_xaxis().set_ticks([])
ax2.get_yaxis().set_ticks([])
ax3 = fig.add_subplot(1, 3, 3)
ax3.set_title('Log Probability')
ax3.get_xaxis().set_ticks([])
ax3.get_yaxis().set_ticks([])
ax1.hist2d(*target_sample.detach().cpu().numpy().T, bins=300, density=True,
range=[[-1.5, 1.5], [-1.5, 1.5]])
ax2.hist2d(*z_sample.detach().cpu().numpy().T, bins=300, density=True,
range=[[-1.5, 1.5], [-1.5, 1.5]])
logp = p_z0.log_prob(z_density) - logp_diff.view(-1)
ax3.tricontourf(*z_t1.detach().cpu().numpy().T,
np.exp(logp.detach().cpu().numpy()), 200)
plt.savefig(os.path.join(args.results_dir, f"cnf-viz-{int(t*1000):05d}.jpg"),
pad_inches=0.2, bbox_inches='tight')
plt.close()
img, *imgs = [Image.open(f) for f in sorted(glob.glob(os.path.join(args.results_dir, f"cnf-viz-*.jpg")))]
img.save(fp=os.path.join(args.results_dir, "cnf-viz.gif"), format='GIF', append_images=imgs,
save_all=True, duration=250, loop=0)
print('Saved visualization animation at {}'.format(os.path.join(args.results_dir, "cnf-viz.gif"))) |