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utils.py
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utils.py
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import torch
import torch.nn as nn
import numpy as np
import torchvision.transforms as T
import itertools
import matplotlib.pyplot as plt
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
#https://github.com/sthalles/SimCLR/blob/master/data_aug/view_generator.py
class ContrastiveLearningViewGenerator(object):
"""Take two random crops of one image as the query and key."""
def __init__(self, base_transform, n_views=2):
self.base_transform = base_transform
self.n_views = n_views
def __call__(self, x):
return [self.base_transform(x) for i in range(self.n_views)]
#https://github.com/sthalles/SimCLR/blob/master/data_aug/gaussian_blur.py
class GaussianBlur(object):
"""blur a single image on CPU"""
def __init__(self, kernel_size):
radias = kernel_size // 2
kernel_size = radias * 2 + 1
self.blur_h = nn.Conv2d(3, 3, kernel_size=(kernel_size, 1),
stride=1, padding=0, bias=False, groups=3)
self.blur_v = nn.Conv2d(3, 3, kernel_size=(1, kernel_size),
stride=1, padding=0, bias=False, groups=3)
self.k = kernel_size
self.r = radias
self.blur = nn.Sequential(
nn.ReflectionPad2d(radias),
self.blur_h,
self.blur_v
)
self.pil_to_tensor = T.ToTensor()
self.tensor_to_pil = T.ToPILImage()
def __call__(self, img):
img = self.pil_to_tensor(img).unsqueeze(0)
sigma = np.random.uniform(0.1, 2.0)
x = np.arange(-self.r, self.r + 1)
x = np.exp(-np.power(x, 2) / (2 * sigma * sigma))
x = x / x.sum()
x = torch.from_numpy(x).view(1, -1).repeat(3, 1)
self.blur_h.weight.data.copy_(x.view(3, 1, self.k, 1))
self.blur_v.weight.data.copy_(x.view(3, 1, 1, self.k))
with torch.no_grad():
img = self.blur(img)
img = img.squeeze()
img = self.tensor_to_pil(img)
return img
def plot_confusion_matrix_2(cm,
target_names,
title='Confusion matrix',
cmap=None,
normalize=False):
"""
given a sklearn confusion matrix (cm), make a nice plot
Arguments
---------
cm: confusion matrix from sklearn.metrics.confusion_matrix
target_names: given classification classes such as [0, 1, 2]
the class names, for example: ['high', 'medium', 'low']
title: the text to display at the top of the matrix
cmap: the gradient of the values displayed from matplotlib.pyplot.cm
see http://matplotlib.org/examples/color/colormaps_reference.html
plt.get_cmap('jet') or plt.cm.Blues
normalize: If False, plot the raw numbers
If True, plot the proportions
Citiation
---------
http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
"""
FONT_SIZE = 8
accuracy = np.trace(cm) / float(np.sum(cm))
misclass = 1 - accuracy
if cmap is None:
cmap = plt.get_cmap('viridis')
plt.figure(figsize=(8*2, 6*2)) # 8, 6
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
if target_names is not None:
tick_marks = np.arange(len(target_names))
plt.xticks(tick_marks, target_names, rotation=90, fontsize=FONT_SIZE)
plt.yticks(tick_marks, target_names, fontsize=FONT_SIZE)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 1.5 if normalize else cm.max() / 2
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
if normalize:
plt.text(j, i, "{:0.4f}".format(cm[i, j]),
horizontalalignment="center",
fontsize=FONT_SIZE,
color="white" if cm[i, j] > thresh else "black")
else:
plt.text(j, i, "{:,}".format(cm[i, j]),
horizontalalignment="center",
fontsize=FONT_SIZE,
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
plt.show()
def unsigned_correlation_coefficient(variables):
'''
Estimate the Unsigned Correlation Coefficient (UCC) based on
Multivariate Correlation Theorem from https://arxiv.org/pdf/1401.4827.pdf
Input:
variables: a torch tensor of size (N, hidden_dim) where,
N : number of samples for each variable, generally corresponds to batch_size
hidden_dim: number of variables in multivariate correlation calculation
Output: a scalar representing UCC(variables[:, 0];variables[:, 1];...variables[:, N-1])
'''
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Correlation Matrix needs rows to be variables and columns to be observations
# Hidden dimension of a layer are variables. Their values in a batch are samples.
variables = torch.transpose(variables, 0, 1)
hidden_dim, N = variables.size()
corr_matrix = torch.corrcoef(variables)
uic_squared = torch.det(corr_matrix)
ucc_squared = 1 - uic_squared
ucc = torch.sqrt(ucc_squared)
return ucc