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============================= test session starts ============================== | |
platform linux -- Python 3.6.4, pytest-3.5.0, py-1.5.3, pluggy-0.6.0 | |
rootdir: /home/tmain/dvc, inifile: | |
plugins: sugar-0.9.1, remotedata-0.2.0, openfiles-0.2.0, doctestplus-0.1.2, arraydiff-0.2 | |
collected 1 item | |
tests/test_install.py F [100%] | |
=================================== FAILURES =================================== | |
_______________________________ TestInstall.test _______________________________ |
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============================= test session starts ============================== | |
platform linux -- Python 3.6.4, pytest-3.5.0, py-1.5.3, pluggy-0.6.0 | |
rootdir: /home/tmain/Lyrebird/dvc, inifile: | |
plugins: sugar-0.9.1, remotedata-0.2.0, openfiles-0.2.0, doctestplus-0.1.2, arraydiff-0.2 | |
collected 1 item | |
tests/test_install.py F [100%] | |
=================================== FAILURES =================================== | |
_______________________________ TestInstall.test _______________________________ |
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#################################### | |
# Apt libraries | |
#################################### | |
sudo apt-get update && sudo apt-get install -y --no-install-recommends\ | |
build-essential \ | |
vim \ | |
numactl \ | |
git \ | |
curl \ | |
git \ |
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import math | |
import numpy as np | |
import tensorflow as tf | |
log_sigma1 = 0. | |
log_sigma2 = -0.5 | |
prior_pi = 0.25 | |
sigma1 = math.exp(log_sigma1) | |
sigma2 = math.exp(log_sigma2) |
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def extract_patches(imgs, landmarks, patch_shape): | |
""" Extracts patches from an image. | |
Args: | |
imgs: a numpy array of dimensions [batch_size, width, height, channels] | |
landmarks: a numpy array of dimensions [num_patches, 2] | |
patch_shape: (width, height) | |
Returns: | |
a numpy array [num_patches, width, height, channels] | |
""" |
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import numpy as np | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from torch.autograd import Variable | |
import torch.optim as optim | |
from yellowfin_tuner import YFOptimizer | |
from tqdm import tqdm |
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import torch | |
from torch.autograd import Variable | |
import torch.nn as nn | |
import numpy as np | |
import torch.optim as optim | |
import torch.nn.functional as F | |
import time | |
from torchvision.models import vgg | |
# Initialize network |
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import pandas as pd | |
import numpy as np | |
from tsfresh.feature_extraction import feature_calculators as fc | |
from time import time | |
def count_above_mean(x): | |
x = np.asarray(x) | |
m = np.mean(x) |
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from __future__ import print_function | |
import numpy as np | |
from keras.models import Sequential | |
from keras.layers.core import Dense, Dropout, Activation, Flatten | |
from keras.layers.convolutional import Convolution2D, MaxPooling2D | |
from keras.utils import np_utils | |
from keras.utils import generic_utils | |
import multiprocessing | |
import os | |
import time |
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print 1 |