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import numpy as np
import scipy.io
import h5py
import torch.nn as nn
import operator
from functools import reduce
#################################################
#
# Utilities
#
#################################################
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class MatReader(object):
"""reading data"""
def __init__(self, file_path, to_torch=True, to_cuda=False, to_float=True):
super(MatReader, self).__init__()
self.to_torch = to_torch
self.to_cuda = to_cuda
self.to_float = to_float
self.file_path = file_path
self.data = None
self.old_mat = None
self._load_file()
def _load_file(self):
try:
self.data = scipy.io.loadmat(self.file_path)
self.old_mat = True
except Exception:
self.data = h5py.File(self.file_path)
self.old_mat = False
def load_file(self, file_path):
self.file_path = file_path
self._load_file()
def read_field(self, field):
x = self.data[field] # type: ignore
if not self.old_mat:
x = x[()] # type: ignore
x = np.transpose(x, axes=range(len(x.shape) - 1, -1, -1)) # type: ignore # noqa
if self.to_float:
x = x.astype(np.float32) # type: ignore
if self.to_torch:
x = torch.from_numpy(x)
if self.to_cuda:
x = x.cuda()
return x
def set_cuda(self, to_cuda):
self.to_cuda = to_cuda
def set_torch(self, to_torch):
self.to_torch = to_torch
def set_float(self, to_float):
self.to_float = to_float
# normalization, pointwise gaussian
class UnitGaussianNormalizer(object):
def __init__(self, x, eps=0.00001):
super(UnitGaussianNormalizer, self).__init__()
# x could be in shape of ntrain*n or ntrain*T*n or ntrain*n*T
self.mean = torch.mean(x, 0)
self.std = torch.std(x, 0)
self.eps = eps
def encode(self, x):
x = (x - self.mean) / (self.std + self.eps)
return x
def decode(self, x, sample_idx=None):
if sample_idx is None:
std = self.std + self.eps # n
mean = self.mean
else:
if len(self.mean.shape) == len(sample_idx[0].shape):
std = self.std[sample_idx] + self.eps # batch*n
mean = self.mean[sample_idx]
if len(self.mean.shape) > len(sample_idx[0].shape):
std = self.std[:, sample_idx] + self.eps # T*batch*n
mean = self.mean[:, sample_idx]
# x is in shape of batch*n or T*batch*n
x = (x * std) + mean # type: ignore
return x
def cuda(self):
self.mean = self.mean.cuda()
self.std = self.std.cuda()
def cpu(self):
self.mean = self.mean.cpu()
self.std = self.std.cpu()
# normalization, Gaussian
class GaussianNormalizer(object):
def __init__(self, x, eps=0.00001):
super(GaussianNormalizer, self).__init__()
self.mean = torch.mean(x)
self.std = torch.std(x)
self.eps = eps
def encode(self, x):
x = (x - self.mean) / (self.std + self.eps)
return x
def decode(self, x, sample_idx=None):
x = (x * (self.std + self.eps)) + self.mean
return x
def cuda(self):
self.mean = self.mean.cuda()
self.std = self.std.cuda()
def cpu(self):
self.mean = self.mean.cpu()
self.std = self.std.cpu()
# normalization, scaling by range
class RangeNormalizer(object):
def __init__(self, x, low=0.0, high=1.0):
super(RangeNormalizer, self).__init__()
mymin = torch.min(x, 0)[0].view(-1)
mymax = torch.max(x, 0)[0].view(-1)
self.a = (high - low) / (mymax - mymin)
self.b = -self.a * mymax + high
def encode(self, x):
s = x.size()
x = x.view(s[0], -1)
x = self.a * x + self.b
x = x.view(s)
return x
def decode(self, x):
s = x.size()
x = x.view(s[0], -1)
x = (x - self.b) / self.a
x = x.view(s)
return x
# loss function with rel/abs Lp loss
class LpLoss(object):
def __init__(self, d=2, p=2, size_average=True, reduction=True):
super(LpLoss, self).__init__()
# Dimension and Lp-norm type are postive
assert d > 0 and p > 0
self.d = d
self.p = p
self.reduction = reduction
self.size_average = size_average
def abs(self, x, y):
num_examples = x.size()[0]
# Assume uniform mesh
h = 1.0 / (x.size()[1] - 1.0)
all_norms = (h ** (self.d / self.p)) * torch.norm(
x.view(num_examples, -1) - y.view(num_examples, -1), self.p, 1
)
if self.reduction:
if self.size_average:
return torch.mean(all_norms)
else:
return torch.sum(all_norms)
return all_norms
def rel(self, x, y):
num_examples = x.size()[0]
diff_norms = torch.norm(
x.reshape(num_examples, -1) - y.reshape(num_examples, -1),
self.p,
1,
)
y_norms = torch.norm(y.reshape(num_examples, -1), self.p, 1)
if self.reduction:
if self.size_average:
return torch.mean(diff_norms / y_norms)
else:
return torch.sum(diff_norms / y_norms)
return diff_norms / y_norms
def __call__(self, x, y):
return self.rel(x, y)
# Sobolev norm (HS norm)
# where we also compare the numerical derivatives between the output and target
class HsLoss(object):
def __init__(
self,
d=2,
p=2,
k=1,
a=None,
group=False,
size_average=True,
reduction=True,
):
super(HsLoss, self).__init__()
# Dimension and Lp-norm type are postive
assert d > 0 and p > 0
self.d = d
self.p = p
self.k = k
self.balanced = group
self.reduction = reduction
self.size_average = size_average
if a is None:
a = [
1,
] * k
self.a = a
def rel(self, x, y):
num_examples = x.size()[0]
diff_norms = torch.norm(
x.reshape(num_examples, -1) - y.reshape(num_examples, -1),
self.p,
1,
)
y_norms = torch.norm(y.reshape(num_examples, -1), self.p, 1)
if self.reduction:
if self.size_average:
return torch.mean(diff_norms / y_norms)
else:
return torch.sum(diff_norms / y_norms)
return diff_norms / y_norms
def __call__(self, x, y, a=None):
nx = x.size()[1]
ny = x.size()[2]
k = self.k
balanced = self.balanced
a = self.a
x = x.view(x.shape[0], nx, ny, -1)
y = y.view(y.shape[0], nx, ny, -1)
k_x = (
torch.cat(
(
torch.arange(start=0, end=nx // 2, step=1),
torch.arange(start=-nx // 2, end=0, step=1),
),
0,
)
.reshape(nx, 1)
.repeat(1, ny)
)
k_y = (
torch.cat(
(
torch.arange(start=0, end=ny // 2, step=1),
torch.arange(start=-ny // 2, end=0, step=1),
),
0,
)
.reshape(1, ny)
.repeat(nx, 1)
)
k_x = torch.abs(k_x).reshape(1, nx, ny, 1).to(x.device)
k_y = torch.abs(k_y).reshape(1, nx, ny, 1).to(x.device)
x = torch.fft.fftn(x, dim=[1, 2])
y = torch.fft.fftn(y, dim=[1, 2])
if not balanced:
weight = 1
if k >= 1:
weight += a[0] ** 2 * (k_x**2 + k_y**2)
if k >= 2:
weight += a[1] ** 2 * (
k_x**4 + 2 * k_x**2 * k_y**2 + k_y**4
)
weight = torch.sqrt(weight) # type: ignore
loss = self.rel(x * weight, y * weight)
else:
loss = self.rel(x, y)
if k >= 1:
weight = a[0] * torch.sqrt(k_x**2 + k_y**2)
loss += self.rel(x * weight, y * weight)
if k >= 2:
weight = a[1] * torch.sqrt(
k_x**4 + 2 * k_x**2 * k_y**2 + k_y**4
)
loss += self.rel(x * weight, y * weight)
loss = loss / (k + 1)
return loss
# A simple feedforward neural network
class DenseNet(torch.nn.Module):
def __init__(
self, layers, nonlinearity, out_nonlinearity=None, normalize=False
):
super(DenseNet, self).__init__()
self.n_layers = len(layers) - 1
assert self.n_layers >= 1
self.layers = nn.ModuleList()
for j in range(self.n_layers):
self.layers.append(nn.Linear(layers[j], layers[j + 1]))
if j != self.n_layers - 1:
if normalize:
self.layers.append(nn.BatchNorm1d(layers[j + 1]))
self.layers.append(nonlinearity())
if out_nonlinearity is not None:
self.layers.append(out_nonlinearity())
def forward(self, x):
for _, l in enumerate(self.layers):
x = l(x)
return x
# print the number of parameters
def count_params(model):
c = 0
for p in list(model.parameters()):
c += reduce(
operator.mul, list(p.size() + (2,) if p.is_complex() else p.size())
)
return c
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