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Utils.py
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import torch
import torch.nn.init
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import cv2
import numpy as np
# resize image to size 32x32
cv2_scale = lambda x: cv2.resize(x, dsize=(32, 32),
interpolation=cv2.INTER_LINEAR)
# reshape image
np_reshape32 = lambda x: np.reshape(x, (32, 32, 1))
np_reshape64 = lambda x: np.reshape(x, (64, 64, 1))
def zeros_like(x):
assert x.__class__.__name__.find('Variable') != -1 or x.__class__.__name__.find('Tensor') != -1, "Object is neither a Tensor nor a Variable"
y = torch.zeros(x.size())
if x.is_cuda:
y = y.cuda()
if x.__class__.__name__ == 'Variable':
return torch.autograd.Variable(y, requires_grad=x.requires_grad)
elif x.__class__.__name__.find('Tensor') != -1:
return torch.zeros(y)
def ones_like(x):
assert x.__class__.__name__.find('Variable') != -1 or x.__class__.__name__.find('Tensor') != -1, "Object is neither a Tensor nor a Variable"
y = torch.ones(x.size())
if x.is_cuda:
y = y.cuda()
if x.__class__.__name__ == 'Variable':
return torch.autograd.Variable(y, requires_grad=x.requires_grad)
elif x.__class__.__name__.find('Tensor') != -1:
return torch.ones(y)
def batched_forward(model, data, batch_size, **kwargs):
n_patches = len(data)
if n_patches > batch_size:
bs = batch_size
n_batches = int(n_patches / bs + 1)
for batch_idx in range(n_batches):
st = batch_idx * bs
if batch_idx == n_batches - 1:
if (batch_idx + 1) * bs > n_patches:
end = n_patches
else:
end = (batch_idx + 1) * bs
else:
end = (batch_idx + 1) * bs
if st >= end:
continue
if batch_idx == 0:
first_batch_out = model(data[st:end], kwargs)
out_size = torch.Size([n_patches] + list(first_batch_out.size()[1:]))
#out_size[0] = n_patches
out = torch.zeros(out_size);
if data.is_cuda:
out = out.cuda()
out = Variable(out)
out[st:end] = first_batch_out
else:
out[st:end,:,:] = model(data[st:end], kwargs)
return out
else:
return model(data, kwargs)
class L2Norm(nn.Module):
def __init__(self):
super(L2Norm,self).__init__()
self.eps = 1e-10
def forward(self, x):
norm = torch.sqrt(torch.sum(x * x, dim = 1) + self.eps)
x= x / norm.unsqueeze(-1).expand_as(x)
return x
class L1Norm(nn.Module):
def __init__(self):
super(L1Norm,self).__init__()
self.eps = 1e-10
def forward(self, x):
norm = torch.sum(torch.abs(x), dim = 1) + self.eps
x= x / norm.expand_as(x)
return x
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
def CircularGaussKernel(kernlen=None, circ_zeros = False, sigma = None, norm = True):
assert ((kernlen is not None) or sigma is not None)
if kernlen is None:
kernlen = int(2.0 * 3.0 * sigma + 1.0)
if (kernlen % 2 == 0):
kernlen = kernlen + 1;
halfSize = kernlen / 2;
halfSize = kernlen / 2;
r2 = float(halfSize*halfSize)
if sigma is None:
sigma2 = 0.9 * r2;
sigma = np.sqrt(sigma2)
else:
sigma2 = 2.0 * sigma * sigma
x = np.linspace(-halfSize,halfSize,kernlen)
xv, yv = np.meshgrid(x, x, sparse=False, indexing='xy')
distsq = (xv)**2 + (yv)**2
kernel = np.exp(-( distsq/ (sigma2)))
if circ_zeros:
kernel *= (distsq <= r2).astype(np.float32)
if norm:
kernel /= np.sum(kernel)
return kernel
def generate_2dgrid(h,w, centered = True):
if centered:
x = torch.linspace(-w/2+1, w/2, w)
y = torch.linspace(-h/2+1, h/2, h)
else:
x = torch.linspace(0, w-1, w)
y = torch.linspace(0, h-1, h)
grid2d = torch.stack([y.repeat(w,1).t().contiguous().view(-1), x.repeat(h)],1)
return grid2d
def generate_3dgrid(d, h, w, centered = True):
if type(d) is not list:
if centered:
z = torch.linspace(-d/2+1, d/2, d)
else:
z = torch.linspace(0, d-1, d)
dl = d
else:
z = torch.FloatTensor(d)
dl = len(d)
grid2d = generate_2dgrid(h,w, centered = centered)
grid3d = torch.cat([z.repeat(w*h,1).t().contiguous().view(-1,1), grid2d.repeat(dl,1)],dim = 1)
return grid3d
def zero_response_at_border(x, b):
if (b < x.size(3)) and (b < x.size(2)):
x[:, :, 0:b, :] = 0
x[:, :, x.size(2) - b: , :] = 0
x[:, :, :, 0:b] = 0
x[:, :, :, x.size(3) - b: ] = 0
else:
return x * 0
return x
class GaussianBlur(nn.Module):
def __init__(self, sigma=1.6):
super(GaussianBlur, self).__init__()
weight = self.calculate_weights(sigma)
self.register_buffer('buf', weight)
return
def calculate_weights(self, sigma):
kernel = CircularGaussKernel(sigma = sigma, circ_zeros = False)
h,w = kernel.shape
halfSize = float(h) / 2.;
self.pad = int(np.floor(halfSize))
return torch.from_numpy(kernel.astype(np.float32)).view(1,1,h,w);
def forward(self, x):
w = Variable(self.buf)
if x.is_cuda:
w = w.cuda()
return F.conv2d(F.pad(x, (self.pad,self.pad,self.pad,self.pad), 'replicate'), w, padding = 0)
def batch_eig2x2(A):
trace = A[:,0,0] + A[:,1,1]
delta1 = (trace*trace - 4 * ( A[:,0,0]* A[:,1,1] - A[:,1,0]* A[:,0,1]))
mask = delta1 > 0
delta = torch.sqrt(torch.abs(delta1))
l1 = mask.float() * (trace + delta) / 2.0 + 1000. * (1.0 - mask.float())
l2 = mask.float() * (trace - delta) / 2.0 + 0.0001 * (1.0 - mask.float())
return l1,l2
def line_prepender(filename, line):
with open(filename, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write(line.rstrip('\r\n') + '\n' + content)
return