repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/mnist.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/mnist.py | import numpy
import os
import urllib
import gzip
import cPickle as pickle
import pdb
def mnist_generator(data, batch_size, n_labelled, limit=None, selecting_label = None, bias = None):
images, targets = data
if bias is not None :
images = images[targets!=bias]
targets = targets[targets!=bias]... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/__init__.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/__init__.py | import numpy as np
import tensorflow as tf
#import locale
#locale.setlocale(locale.LC_ALL, '')
_params = {}
_param_aliases = {}
def param(name, *args, **kwargs):
"""
A wrapper for `tf.Variable` which enables parameter sharing in models.
Creates and returns theano shared variables similarly to `tf.Va... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/lsun.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/lsun.py | import numpy as np
import scipy.misc
import time
import cv2
from os import listdir
def make_generator(path, n_files, batch_size, image_size):
epoch_count = [1]
images_name = listdir(path)
if n_files == 0:
n_files = len(images_name)
else:
n_files = n_files
def get_epoch():
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/save_images.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/save_images.py | """
Image grid saver, based on color_grid_vis from github.com/Newmu
"""
import numpy as np
import scipy.misc
from scipy.misc import imsave
def save_images(X, save_path):
# [0, 1] -> [0,255]
if isinstance(X.flatten()[0], np.floating):
X = (255.99*X).astype('uint8')
n_samples = X.shape[0]
rows ... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/mnist_mask_digit.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/mnist_mask_digit.py |
import numpy
import os
import urllib
import gzip
import cPickle as pickle
import pdb
def mnist_generator(data, batch_size, n_labelled, limit=None, selecting_label = None):
images, targets = data
# if selecting_label is None:
# rng_state = numpy.random.get_state()
# numpy.random.shuffle(images)
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/layernorm.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/layernorm.py | import tflib as lib
import numpy as np
import tensorflow as tf
def Layernorm(name, norm_axes, inputs):
mean, var = tf.nn.moments(inputs, norm_axes, keep_dims=True)
# Assume the 'neurons' axis is the first of norm_axes. This is the case for fully-connected and BCHW conv layers.
n_neurons = inputs.get_shap... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/deconv2d.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/deconv2d.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
_weights_stdev = None
def set_weights_stdev(weights_stdev):
global _weights_stdev
_weights_stdev = weights_stdev
def unset... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/__init__.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/__init__.py | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false | |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/conv1d.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/conv1d.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
def Conv1D(name, input_dim, output_dim, filter_size, inputs, he_init=True, mask_type=None, stride=1, weightnorm=None, biases=True, ... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/conv2d.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/conv2d.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
_weights_stdev = None
def set_weights_stdev(weights_stdev):
global _weights_stdev
_weights_stdev = weights_stdev
def unset... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/batchnorm.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/batchnorm.py | import tflib as lib
import numpy as np
import tensorflow as tf
def Batchnorm(name, axes, inputs, is_training=None, stats_iter=None, update_moving_stats=True, fused=True):
if ((axes == [0,2,3]) or (axes == [0,2])) and fused==True:
if axes==[0,2]:
inputs = tf.expand_dims(inputs, 3)
# Old... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/cond_batchnorm.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/cond_batchnorm.py | import tflib as lib
import numpy as np
import tensorflow as tf
def Batchnorm(name, axes, inputs, is_training=None, stats_iter=None, update_moving_stats=True, fused=True, labels=None, n_labels=None):
"""conditional batchnorm (dumoulin et al 2016) for BCHW conv filtermaps"""
if axes != [0,2,3]:
raise Ex... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/linear.py | MNISTtf/MNISTtf_old/conditional_one_hot/miner/tflib/ops/linear.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
def disable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = False
_weights_stdev = None
def set_wei... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/teacher_output_d.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/teacher_output_d.py |
import os, sys
sys.path.append(os.getcwd())
import numpy as np
import tensorflow as tf
import tflib as lib
import tflib.ops.linear
import tflib.ops.conv2d
import tflib.ops.batchnorm
import tflib.ops.deconv2d
import tflib.save_images
import tflib.mnist
import tflib.plot
import pdb
def teacher_model(noise, fake_dat... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/cgan_super_g_d_two_class_unbalance_one_D.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/cgan_super_g_d_two_class_unbalance_one_D.py | import os, sys
sys.path.append(os.getcwd())
import time
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import sklearn.datasets
import tensorflow as tf
import tflib as lib
import tflib.ops.linear
import tflib.ops.conv2d
import tflib.ops.batchnorm
import tflib.ops.deconv2d
i... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/teacher.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/teacher.py |
import os, sys
sys.path.append(os.getcwd())
import numpy as np
import tensorflow as tf
import tflib as lib
import tflib.ops.linear
import tflib.ops.conv2d
import tflib.ops.batchnorm
import tflib.ops.deconv2d
import tflib.save_images
import tflib.mnist
import tflib.plot
import pdb
def teacher_model(noise, fake_data... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/cgan_mnist_knowledge_distillation_adaptor_step1.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/cgan_mnist_knowledge_distillation_adaptor_step1.py | import os, sys
sys.path.append(os.getcwd())
import time
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import sklearn.datasets
import tensorflow as tf
import tflib as lib
import tflib.ops.linear
import tflib.ops.conv2d
import tflib.ops.batchnorm
import tflib.ops.deconv2d
i... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/mnist.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/mnist.py | import numpy
import os
import urllib
import gzip
import cPickle as pickle
import pdb
def mnist_generator(data, batch_size, n_labelled, limit=None, selecting_label = None, bias = None):
images, targets = data
if bias is not None :
images = images[targets!=bias]
targets = targets[targets!=bias]... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/cgan_super_g_d_two_class_unbalance_one_D_no_share_latent.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/cgan_super_g_d_two_class_unbalance_one_D_no_share_latent.py | import os, sys
sys.path.append(os.getcwd())
import time
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import sklearn.datasets
import tensorflow as tf
import tflib as lib
import tflib.ops.linear
import tflib.ops.conv2d
import tflib.ops.batchnorm
import tflib.ops.deconv2d
i... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/lsun_label.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/lsun_label.py |
from os import listdir
import numpy as np
import scipy.misc
import time
import pdb
Label={'bedroom':0,
'kitchen':1,
'dining_room':2,
'conference_room':3,
'living_room':4,
'bridge':5,
'tower':6,
'classroom':7,
'church_outdoor':8,
'restaurant':9}
def make_g... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/plot.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/plot.py | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import collections
import time
import cPickle as pickle
_since_beginning = collections.defaultdict(lambda: {})
_since_last_flush = collections.defaultdict(lambda: {})
_iter = [0]
def tick():
_iter[0] += 1
def plot(name, valu... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/mnist_step1.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/mnist_step1.py | import numpy
import os
import urllib
import gzip
import cPickle as pickle
import pdb
def mnist_generator(data, batch_size, n_labelled, limit=None, selecting_label = None, bias = None):
images, targets = data
if bias is not None :
images = images[targets!=bias]
targets = targets[targets!=bias]... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/mnist.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/mnist.py | import numpy
import os
import urllib
import gzip
import cPickle as pickle
import pdb
def mnist_generator(data, batch_size, n_labelled, limit=None, selecting_label = None, bias = None):
images, targets = data
if bias is not None :
images = images[targets!=bias]
targets = targets[targets!=bias]... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/__init__.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/__init__.py | import numpy as np
import tensorflow as tf
#import locale
#locale.setlocale(locale.LC_ALL, '')
_params = {}
_param_aliases = {}
def param(name, *args, **kwargs):
"""
A wrapper for `tf.Variable` which enables parameter sharing in models.
Creates and returns theano shared variables similarly to `tf.Va... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/lsun.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/lsun.py | import numpy as np
import scipy.misc
import time
import cv2
from os import listdir
def make_generator(path, n_files, batch_size, image_size):
epoch_count = [1]
images_name = listdir(path)
if n_files == 0:
n_files = len(images_name)
else:
n_files = n_files
def get_epoch():
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/save_images.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/save_images.py | """
Image grid saver, based on color_grid_vis from github.com/Newmu
"""
import numpy as np
import scipy.misc
from scipy.misc import imsave
def save_images(X, save_path):
# [0, 1] -> [0,255]
if isinstance(X.flatten()[0], np.floating):
X = (255.99*X).astype('uint8')
n_samples = X.shape[0]
rows ... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/mnist_mask_digit.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/mnist_mask_digit.py |
import numpy
import os
import urllib
import gzip
import cPickle as pickle
import pdb
def mnist_generator(data, batch_size, n_labelled, limit=None, selecting_label = None):
images, targets = data
# if selecting_label is None:
# rng_state = numpy.random.get_state()
# numpy.random.shuffle(images)
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/layernorm.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/layernorm.py | import tflib as lib
import numpy as np
import tensorflow as tf
def Layernorm(name, norm_axes, inputs):
mean, var = tf.nn.moments(inputs, norm_axes, keep_dims=True)
# Assume the 'neurons' axis is the first of norm_axes. This is the case for fully-connected and BCHW conv layers.
n_neurons = inputs.get_shap... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/deconv2d.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/deconv2d.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
_weights_stdev = None
def set_weights_stdev(weights_stdev):
global _weights_stdev
_weights_stdev = weights_stdev
def unset... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/__init__.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/__init__.py | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false | |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/conv1d.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/conv1d.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
def Conv1D(name, input_dim, output_dim, filter_size, inputs, he_init=True, mask_type=None, stride=1, weightnorm=None, biases=True, ... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/conv2d.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/conv2d.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
_weights_stdev = None
def set_weights_stdev(weights_stdev):
global _weights_stdev
_weights_stdev = weights_stdev
def unset... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/batchnorm.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/batchnorm.py | import tflib as lib
import numpy as np
import tensorflow as tf
def Batchnorm(name, axes, inputs, is_training=None, stats_iter=None, update_moving_stats=True, fused=True):
if ((axes == [0,2,3]) or (axes == [0,2])) and fused==True:
if axes==[0,2]:
inputs = tf.expand_dims(inputs, 3)
# Old... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/cond_batchnorm.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/cond_batchnorm.py | import tflib as lib
import numpy as np
import tensorflow as tf
def Batchnorm(name, axes, inputs, is_training=None, stats_iter=None, update_moving_stats=True, fused=True, labels=None, n_labels=None):
"""conditional batchnorm (dumoulin et al 2016) for BCHW conv filtermaps"""
if axes != [0,2,3]:
raise Ex... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/linear.py | MNISTtf/MNISTtf_old/conditional_one_hot/on_manifold/tflib/ops/linear.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
def disable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = False
_weights_stdev = None
def set_wei... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/plot_loss.py | MNISTtf/off_manifold/plot_loss.py | import os, sys
sys.path.append(os.getcwd())
import time
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import tflib as lib
import tflib.plot
import pickle
if(len(sys.argv)==1):
sys.argv=[sys.argv[0],7,16]
BIAS_DIGIT = int(sys.argv[1]) #default is 9
NOISE_LEN = int(... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/gan_mnist_knowledge_distillation_adaptor_step2_eccv.py | MNISTtf/off_manifold/gan_mnist_knowledge_distillation_adaptor_step2_eccv.py | import os, sys
import numpy as np
sys.path.append(os.getcwd())
if(len(sys.argv)==1):
sys.argv=[sys.argv[0],'0,1,2,4,5,6,8,9','16','0','-1','3'] #sys.argv=[sys.argv[0],'3,7','16','0','.3,.7','3,7']
if(len(sys.argv)>2):
BIAS_DIGIT = np.array(sys.argv[1].split(',')).astype(int) #default is 9
NOISE_LEN = int(sy... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/gan_mnist_knowledge_distillation_adaptor_step2_scratch.py | MNISTtf/off_manifold/gan_mnist_knowledge_distillation_adaptor_step2_scratch.py | import os, sys
import numpy as np
sys.path.append(os.getcwd())
if(len(sys.argv)==1):
sys.argv=[sys.argv[0],'0,1,2,4,5,6,8,9','16','0','-1','3'] #sys.argv=[sys.argv[0],'3,7','16','0','.3,.7','3,7']
if(len(sys.argv)>2):
BIAS_DIGIT = np.array(sys.argv[1].split(',')).astype(int) #default is 9
NOISE_LEN = int(sy... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/gan_mnist_knowledge_distillation_adaptor_step4.py | MNISTtf/off_manifold/gan_mnist_knowledge_distillation_adaptor_step4.py | import os, sys
sys.path.append(os.getcwd())
BIAS_DIGIT = int(sys.argv[1]) #default is 9
NOISE_LEN = int(sys.argv[2]) #default is 128
import time
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import sklearn.datasets
import tensorflow as tf
import tflib as lib
import tflib.... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/gan_mnist_knowledge_distillation_adaptor_step2.py | MNISTtf/off_manifold/gan_mnist_knowledge_distillation_adaptor_step2.py | import os, sys
import numpy as np
sys.path.append(os.getcwd())
if(len(sys.argv)==1):
sys.argv=[sys.argv[0],'3,7','16','1','.3,.7','3,7'] #sys.argv=[sys.argv[0],'0,1,2,4,5,6,8,9','16','0','-1','3']
if(len(sys.argv)>2):
BIAS_DIGIT = np.array(sys.argv[1].split(',')).astype(int) #default is 9
NOISE_LEN = int(sy... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/gan_mnist_knowledge_distillation_adaptor_step1.py | MNISTtf/off_manifold/gan_mnist_knowledge_distillation_adaptor_step1.py | import os, sys
import numpy as np
sys.path.append(os.getcwd())
if(len(sys.argv)==1):
sys.argv=[sys.argv[0],'7','16','1','-1'] #sys.argv=[sys.argv[0],'3,7','16','0','.3,.7','-1']
if(len(sys.argv)>2):
BIAS_DIGIT = np.array(sys.argv[1].split(',')).astype(int) #default is 9
NOISE_LEN = int(sys.argv[2]) #default... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/gan_mnist_knowledge_distillation_adaptor_step3.py | MNISTtf/off_manifold/gan_mnist_knowledge_distillation_adaptor_step3.py | import os, sys
import numpy as np
sys.path.append(os.getcwd())
if(len(sys.argv)==1):
sys.argv=[sys.argv[0],'0,1,2,4,5,6,8,9','16','0','-1','3'] #sys.argv=[sys.argv[0],'3,7','16','0','.3,.7','3,7']
if(len(sys.argv)>2):
BIAS_DIGIT = np.array(sys.argv[1].split(',')).astype(int) #default is 9
NOISE_LEN = int(sy... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/lsun_label.py | MNISTtf/off_manifold/tflib/lsun_label.py |
from os import listdir
import numpy as np
import scipy.misc
import time
import pdb
Label={'bedroom':0,
'kitchen':1,
'dining_room':2,
'conference_room':3,
'living_room':4,
'bridge':5,
'tower':6,
'classroom':7,
'church_outdoor':8,
'restaurant':9}
def make_g... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/plot.py | MNISTtf/off_manifold/tflib/plot.py | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import collections
import time
#import cPickle as pickle
import pickle
_since_beginning = collections.defaultdict(lambda: {})
_since_last_flush = collections.defaultdict(lambda: {})
_iter = [0]
def tick():
_iter[0] += 1
def ... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/mnist_allsteps.py | MNISTtf/off_manifold/tflib/mnist_allsteps.py | import numpy
import os
import urllib
import gzip
#import cPickle as pickle
import pickle
import pdb
import math
def mnist_generator(data, batch_size, n_labelled, limit=None, selecting_label = None, bias = None, portion = 1):
images, targets = data
if bias is not None :
images = images[targets!=bias]
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/mnist_step1.py | MNISTtf/off_manifold/tflib/mnist_step1.py | import numpy
import os
import urllib
import gzip
#import cPickle as pickle
import pickle
import pdb
import math
def mnist_generator(data, batch_size, n_labelled, limit=None, selecting_label = None, bias = None):
images, targets = data
if bias is not None :
images = images[targets!=bias]
targe... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/mnist_original.py | MNISTtf/off_manifold/tflib/mnist_original.py | import numpy
import os
import urllib
import gzip
#import cPickle as pickle
import pickle
import pdb
import math
def mnist_generator(data, batch_size, n_labelled, limit=None, selecting_label = None, bias = None):
images, targets = data
if bias is not None :
images = images[targets!=bias]
targe... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/mnist_step2.py | MNISTtf/off_manifold/tflib/mnist_step2.py | import numpy
import os
import urllib
import gzip
#import cPickle as pickle
import pickle
import pdb
import os
from scipy.misc import imsave
def mnist_generator(data, batch_size, n_labelled, limit=None, selecting_label = None):
images, targets = data
#for index, i in enumerate(targets):
# if not os.path... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/mnist.py | MNISTtf/off_manifold/tflib/mnist.py | import numpy
import os
import urllib
import gzip
#import cPickle as pickle
import pickle
import pdb
import math
import numpy as np
def rounddown(x):
return int(math.floor(x / 100.0)) * 100
def mnist_generator(data, batch_size, n_labelled, limit=None, selecting_label = None, bias = None, portions = None):
im... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/__init__.py | MNISTtf/off_manifold/tflib/__init__.py | import numpy as np
import tensorflow as tf
#import locale
#locale.setlocale(locale.LC_ALL, '')
_params = {}
_param_aliases = {}
def param(name, *args, **kwargs):
"""
A wrapper for `tf.Variable` which enables parameter sharing in models.
Creates and returns theano shared variables similarly to `tf.Va... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/lsun.py | MNISTtf/off_manifold/tflib/lsun.py | import numpy as np
import scipy.misc
import time
import cv2
from os import listdir
def make_generator(path, n_files, batch_size, image_size):
epoch_count = [1]
images_name = listdir(path)
if n_files == 0:
n_files = len(images_name)
else:
n_files = n_files
def get_epoch():
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/save_images.py | MNISTtf/off_manifold/tflib/save_images.py | """
Image grid saver, based on color_grid_vis from github.com/Newmu
"""
import numpy as np
import scipy.misc
from scipy.misc import imsave
def save_images(X, save_path):
# [0, 1] -> [0,255]
if isinstance(X.flatten()[0], np.floating):
X = (255.99*X).astype('uint8')
n_samples = X.shape[0]
rows ... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/ops/layernorm.py | MNISTtf/off_manifold/tflib/ops/layernorm.py | import tflib as lib
import numpy as np
import tensorflow as tf
def Layernorm(name, norm_axes, inputs):
mean, var = tf.nn.moments(inputs, norm_axes, keep_dims=True)
# Assume the 'neurons' axis is the first of norm_axes. This is the case for fully-connected and BCHW conv layers.
n_neurons = inputs.get_shap... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/ops/deconv2d.py | MNISTtf/off_manifold/tflib/ops/deconv2d.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
_weights_stdev = None
def set_weights_stdev(weights_stdev):
global _weights_stdev
_weights_stdev = weights_stdev
def unset... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/ops/__init__.py | MNISTtf/off_manifold/tflib/ops/__init__.py | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false | |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/ops/conv1d.py | MNISTtf/off_manifold/tflib/ops/conv1d.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
def Conv1D(name, input_dim, output_dim, filter_size, inputs, he_init=True, mask_type=None, stride=1, weightnorm=None, biases=True, ... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/ops/conv2d.py | MNISTtf/off_manifold/tflib/ops/conv2d.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
_weights_stdev = None
def set_weights_stdev(weights_stdev):
global _weights_stdev
_weights_stdev = weights_stdev
def unset... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/ops/batchnorm.py | MNISTtf/off_manifold/tflib/ops/batchnorm.py | import tflib as lib
import numpy as np
import tensorflow as tf
def Batchnorm(name, axes, inputs, is_training=None, stats_iter=None, update_moving_stats=True, fused=True):
if ((axes == [0,2,3]) or (axes == [0,2])) and fused==True:
if axes==[0,2]:
inputs = tf.expand_dims(inputs, 3)
# Old... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/ops/cond_batchnorm.py | MNISTtf/off_manifold/tflib/ops/cond_batchnorm.py | import tflib as lib
import numpy as np
import tensorflow as tf
def Batchnorm(name, axes, inputs, is_training=None, stats_iter=None, update_moving_stats=True, fused=True, labels=None, n_labels=None):
"""conditional batchnorm (dumoulin et al 2016) for BCHW conv filtermaps"""
if axes != [0,2,3]:
raise Ex... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/off_manifold/tflib/ops/linear.py | MNISTtf/off_manifold/tflib/ops/linear.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
def disable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = False
_weights_stdev = None
def set_wei... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/plot_loss.py | MNISTtf/on_manifold/plot_loss.py | import os, sys
sys.path.append(os.getcwd())
import time
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import tflib as lib
import tflib.plot
import pickle
if(len(sys.argv)==1):
sys.argv=[sys.argv[0],7,16]
BIAS_DIGIT = int(sys.argv[1]) #default is 9
NOISE_LEN = int(... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/gan_mnist_knowledge_distillation_adaptor_step4.py | MNISTtf/on_manifold/gan_mnist_knowledge_distillation_adaptor_step4.py | import os, sys
import numpy as np
sys.path.append(os.getcwd())
if(len(sys.argv)==1):
sys.argv=[sys.argv[0],'3,7','16','0','.3,.7']
if(len(sys.argv)>2):
BIAS_DIGIT = np.array(sys.argv[1].split(',')).astype(int) #default is 9
NOISE_LEN = int(sys.argv[2]) #default is 128
if(len(sys.argv)>3):
os.environ["CU... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/gan_mnist_knowledge_distillation_adaptor_step2.py | MNISTtf/on_manifold/gan_mnist_knowledge_distillation_adaptor_step2.py | import os, sys
import numpy as np
sys.path.append(os.getcwd())
if(len(sys.argv)==1):
sys.argv=[sys.argv[0],'3,7','16','0','.3,.7'] #3,7 16 1 .1,.9
if(len(sys.argv)>2):
BIAS_DIGIT = np.array(sys.argv[1].split(',')).astype(int) #default is 9
NOISE_LEN = int(sys.argv[2]) #default is 128
if(len(sys.argv)>3):
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/gan_mnist_knowledge_distillation_adaptor_step1.py | MNISTtf/on_manifold/gan_mnist_knowledge_distillation_adaptor_step1.py | import os, sys
import numpy as np
sys.path.append(os.getcwd())
if(len(sys.argv)==1):
sys.argv=[sys.argv[0],'-1','16','0','-1']
if(len(sys.argv)>2):
BIAS_DIGIT = np.array(sys.argv[1].split(',')).astype(int) #default is 9
NOISE_LEN = int(sys.argv[2]) #default is 128
if(len(sys.argv)>3):
os.environ["CUDA_D... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/gan_mnist_knowledge_distillation_adaptor_step3.py | MNISTtf/on_manifold/gan_mnist_knowledge_distillation_adaptor_step3.py | import os, sys
import numpy as np
sys.path.append(os.getcwd())
if(len(sys.argv)==1):
sys.argv=[sys.argv[0],'3,7','16','0','.3,.7'] #3,7 16 1 .1,.9
if(len(sys.argv)>2):
BIAS_DIGIT = np.array(sys.argv[1].split(',')).astype(int) #default is 9
NOISE_LEN = int(sys.argv[2]) #default is 128
if(len(sys.argv)>3):
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/lsun_label.py | MNISTtf/on_manifold/tflib/lsun_label.py |
from os import listdir
import numpy as np
import scipy.misc
import time
import pdb
Label={'bedroom':0,
'kitchen':1,
'dining_room':2,
'conference_room':3,
'living_room':4,
'bridge':5,
'tower':6,
'classroom':7,
'church_outdoor':8,
'restaurant':9}
def make_g... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/plot.py | MNISTtf/on_manifold/tflib/plot.py | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import collections
import time
import cPickle as pickle
_since_beginning = collections.defaultdict(lambda: {})
_since_last_flush = collections.defaultdict(lambda: {})
_iter = [0]
def tick():
_iter[0] += 1
def plot(name, valu... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/mnist.py | MNISTtf/on_manifold/tflib/mnist.py | import numpy
import os
import urllib
import gzip
#import cPickle as pickle
import pickle
import pdb
import math
import numpy as np
def rounddown(x):
return int(math.floor(x / 100.0)) * 100
def mnist_generator(data, batch_size, n_labelled, limit=None, selecting_label = None, bias = None, portions = None):
im... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/__init__.py | MNISTtf/on_manifold/tflib/__init__.py | import numpy as np
import tensorflow as tf
#import locale
#locale.setlocale(locale.LC_ALL, '')
_params = {}
_param_aliases = {}
def param(name, *args, **kwargs):
"""
A wrapper for `tf.Variable` which enables parameter sharing in models.
Creates and returns theano shared variables similarly to `tf.Va... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/lsun.py | MNISTtf/on_manifold/tflib/lsun.py | import numpy as np
import scipy.misc
import time
import cv2
from os import listdir
def make_generator(path, n_files, batch_size, image_size):
epoch_count = [1]
images_name = listdir(path)
if n_files == 0:
n_files = len(images_name)
else:
n_files = n_files
def get_epoch():
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/save_images.py | MNISTtf/on_manifold/tflib/save_images.py | """
Image grid saver, based on color_grid_vis from github.com/Newmu
"""
import numpy as np
import scipy.misc
from scipy.misc import imsave
def save_images(X, save_path):
# [0, 1] -> [0,255]
if isinstance(X.flatten()[0], np.floating):
X = (255.99*X).astype('uint8')
n_samples = X.shape[0]
rows ... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/ops/layernorm.py | MNISTtf/on_manifold/tflib/ops/layernorm.py | import tflib as lib
import numpy as np
import tensorflow as tf
def Layernorm(name, norm_axes, inputs):
mean, var = tf.nn.moments(inputs, norm_axes, keep_dims=True)
# Assume the 'neurons' axis is the first of norm_axes. This is the case for fully-connected and BCHW conv layers.
n_neurons = inputs.get_shap... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/ops/deconv2d.py | MNISTtf/on_manifold/tflib/ops/deconv2d.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
_weights_stdev = None
def set_weights_stdev(weights_stdev):
global _weights_stdev
_weights_stdev = weights_stdev
def unset... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/ops/__init__.py | MNISTtf/on_manifold/tflib/ops/__init__.py | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false | |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/ops/conv1d.py | MNISTtf/on_manifold/tflib/ops/conv1d.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
def Conv1D(name, input_dim, output_dim, filter_size, inputs, he_init=True, mask_type=None, stride=1, weightnorm=None, biases=True, ... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/ops/conv2d.py | MNISTtf/on_manifold/tflib/ops/conv2d.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
_weights_stdev = None
def set_weights_stdev(weights_stdev):
global _weights_stdev
_weights_stdev = weights_stdev
def unset... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/ops/batchnorm.py | MNISTtf/on_manifold/tflib/ops/batchnorm.py | import tflib as lib
import numpy as np
import tensorflow as tf
def Batchnorm(name, axes, inputs, is_training=None, stats_iter=None, update_moving_stats=True, fused=True):
if ((axes == [0,2,3]) or (axes == [0,2])) and fused==True:
if axes==[0,2]:
inputs = tf.expand_dims(inputs, 3)
# Old... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/ops/cond_batchnorm.py | MNISTtf/on_manifold/tflib/ops/cond_batchnorm.py | import tflib as lib
import numpy as np
import tensorflow as tf
def Batchnorm(name, axes, inputs, is_training=None, stats_iter=None, update_moving_stats=True, fused=True, labels=None, n_labels=None):
"""conditional batchnorm (dumoulin et al 2016) for BCHW conv filtermaps"""
if axes != [0,2,3]:
raise Ex... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/MNISTtf/on_manifold/tflib/ops/linear.py | MNISTtf/on_manifold/tflib/ops/linear.py | import tflib as lib
import numpy as np
import tensorflow as tf
_default_weightnorm = False
def enable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = True
def disable_default_weightnorm():
global _default_weightnorm
_default_weightnorm = False
_weights_stdev = None
def set_wei... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/precompute_acts.py | styleGAN/precompute_acts.py | import argparse
import pickle
import random
import numpy as np
from tqdm import tqdm
import torch
from torchvision import transforms
from dataset import MultiResolutionDataset
from train import sample_data
from metric.inception import InceptionV3
if __name__ == '__main__':
parser = argparse.ArgumentParser(descript... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/train.py | styleGAN/train.py | import argparse
import random
import math
from tqdm import tqdm
import numpy as np
from PIL import Image
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.autograd import Variable, grad
from torch.utils.data import DataLoader
from torchvision import datasets, transforms, utils
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/finetune.py | styleGAN/finetune.py | import os
import argparse
import pickle
import math
import random
import numpy as np
from tqdm import tqdm
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.autograd import grad
from torchvision import transforms, utils
from torch.utils.tensorboard import SummaryWriter
from data... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/model.py | styleGAN/model.py | import torch
from torch import nn
from torch.nn import init
from torch.nn import functional as F
from torch.autograd import Function
from math import sqrt
import random
import pdb
def init_linear(linear):
init.xavier_normal(linear.weight)
linear.bias.data.zero_()
def init_conv(conv, glu=True):
init.... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/generate.py | styleGAN/generate.py | import argparse
import math
import torch
from torchvision import utils
from model import StyledGenerator
@torch.no_grad()
def get_mean_style(generator, device):
mean_style = None
for i in range(10):
style = generator.mean_style(torch.randn(1024, 512).to(device))
if mean_style is None:
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/dataset.py | styleGAN/dataset.py | from io import BytesIO
import lmdb
from PIL import Image
from torch.utils.data import Dataset
class MultiResolutionDataset(Dataset):
def __init__(self, path, transform, resolution=8):
self.env = lmdb.open(
path,
max_readers=32,
readonly=True,
lock=False,
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/test.py | styleGAN/test.py | import os
import argparse
import pickle
import math
import random
import numpy as np
from tqdm import tqdm
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.autograd import grad
from torchvision import transforms, utils
from torch.utils.tensorboard import SummaryWriter
from data... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/prepare_data.py | styleGAN/prepare_data.py | import argparse
from io import BytesIO
import multiprocessing
from functools import partial
from PIL import Image
import lmdb
from tqdm import tqdm
from torchvision import datasets
from torchvision.transforms import functional as trans_fn
import pdb
def resize_and_convert(img, size, quality=100):
img = trans_fn.... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/loss/AdaBIGGANLoss.py | styleGAN/loss/AdaBIGGANLoss.py | import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from .Vgg16PerceptualLoss import Vgg16PerceptualLoss
class AdaBIGGANLoss(nn.Module):
def __init__(self,perceptual_loss = "vgg",
scale_per=0.001,
scale_emd=0.1,
scale_reg=0.02,
... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/loss/Vgg16PerceptualLoss.py | styleGAN/loss/Vgg16PerceptualLoss.py | import torch
from torchvision import models
import torch.nn.functional as F
class Vgg16PerceptualLoss(torch.nn.Module):
def __init__(self, perceptual_indices = [1,3,6,8,11,13,15,18,20,22] ,loss_func="l1",requires_grad = False):
'''
perceptual_indices: indices to use for perceptural loss
los... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/loss/__init__.py | styleGAN/loss/__init__.py | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false | |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/metric/inception.py | styleGAN/metric/inception.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
try:
from torchvision.models.utils import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
# Inception weights ported to Pytorch from
# http://do... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/metric/fid_score.py | styleGAN/metric/fid_score.py | #!/usr/bin/env python3
"""Calculates the Frechet Inception Distance (FID) to evalulate GANs
The FID metric calculates the distance between two distributions of images.
Typically, we have summary statistics (mean & covariance matrix) of one
of these distributions, while the 2nd distribution is given by a GAN.
When run a... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/metric/metric.py | styleGAN/metric/metric.py | import time
import functools
import numpy as np
from tqdm import tqdm
import torch
from torch.utils.data import TensorDataset, DataLoader
from .fid_score import calculate_frechet_distance
# from .kid_score import polynomial_mmd_averages
from .swd_score import calculate_swd
def get_fake_images_and_acts(inception, g_... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/metric/swd_score.py | styleGAN/metric/swd_score.py | # https://github.com/koshian2/swd-pytorch/blob/master/swd.py
from PIL import Image
import math
import numpy as np
import torch
import torch.nn.functional as F
import torchvision
# Gaussian blur kernel
def get_gaussian_kernel(device="cpu"):
kernel = np.array([
[1, 4, 6, 4, 1],
[4, 16, 24, 16, 4],
[6, 24, 36, 24... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/styleGAN/metric/kid_score.py | styleGAN/metric/kid_score.py | # https://github.com/mbinkowski/MMD-GAN/blob/master/gan/compute_scores.py
"""Calculates the Kernel Inception Distance (KID) to evalulate GANs
"""
import os
import sys
import numpy as np
from sklearn.metrics.pairwise import polynomial_kernel
def polynomial_mmd_averages(codes_r, codes_g, n_subsets=100, subset_size=1000... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/sync_batchnorm/batchnorm_reimpl.py | sync_batchnorm/batchnorm_reimpl.py | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File : batchnorm_reimpl.py
# Author : acgtyrant
# Date : 11/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import torch
import torch.nn as nn
import torch... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/sync_batchnorm/replicate.py | sync_batchnorm/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.da... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/sync_batchnorm/unittest.py | sync_batchnorm/unittest.py | # -*- coding: utf-8 -*-
# File : unittest.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import unittest
import torch
class TorchTes... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/sync_batchnorm/comm.py | sync_batchnorm/comm.py | # -*- coding: utf-8 -*-
# File : comm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import queue
import collections
import threading... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
yaxingwang/MineGAN | https://github.com/yaxingwang/MineGAN/blob/a810f2d77f36ea9cf6993dede958b6f5d458f4b6/sync_batchnorm/__init__.py | sync_batchnorm/__init__.py | # -*- coding: utf-8 -*-
# File : __init__.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
from .batchnorm import SynchronizedBatchNorm... | python | MIT | a810f2d77f36ea9cf6993dede958b6f5d458f4b6 | 2026-01-05T07:08:28.063149Z | false |
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