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("gitadd2", GitAdd()),
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("changelog", UpdateChangelog()),
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("readme", UpdateReadme()),
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("dist", MakeDist()),
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("testpypi", PushToTestPyPI()),
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("install", InstallFromTestPyPI()),
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("testpkg", TestPackage()),
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("remove_venv", RemoveVenv()),
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("addrelease", GitAddRelease()),
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("pypi", PushToPyPI()),
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("tagfinal", GitTagVersion()),
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# triggers CI to build with cibw and push to PyPI
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("push3", PushToGitHub()),
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("ci3", WaitForCI()),
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]
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context = {}
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context["pkgname"] = get_package_name()
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context["version"] = get_package_version(context["pkgname"])
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skip = True if target else False
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for name, step in procedure:
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if not name == target and skip:
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continue
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skip = False
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step.run(context)
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cprint("\nDone!", color="yellow", style="bright")
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if __name__ == "__main__":
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target = sys.argv[1] if len(sys.argv) > 1 else None
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main(target=target)
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# <FILESEP>
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from __future__ import division
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import math
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import os
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import time
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from config import FLAGS
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from util import data_io
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import matplotlib.pyplot as plt
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from util.custom_ops import *
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tf.flags.DEFINE_integer('scale', 4, 'Upsampling scale for super resolution.')
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def upsample(input_, up_scale):
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with tf.variable_scope("upsample"):
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return tf.keras.layers.UpSampling3D(size=(up_scale, up_scale, up_scale), dtype=tf.float32)(input_)
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def downsample(input_, down_scale):
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with tf.variable_scope("downsample"):
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return tf.keras.layers.AveragePooling3D(pool_size=(down_scale, down_scale, down_scale), padding='valid',
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dtype=tf.float32)(input_)
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def avg_pool(input_, scale):
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with tf.variable_scope("avg_pool"):
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ls = downsample(input_, scale)
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hs = upsample(ls, scale)
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return hs
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def descriptor(inputs, reuse=False):
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with tf.variable_scope('des', reuse=reuse):
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# 64
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conv1 = conv3d(inputs, 200, kernal=(16, 16, 16), strides=(3, 3, 3), padding="SAME", name="conv1")
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conv1 = tf.nn.relu(conv1)
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print conv1
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conv2 = conv3d(conv1, 100, kernal=(6, 6, 6), strides=(2, 2, 2), padding="SAME", name="conv2")
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conv2 = tf.nn.relu(conv2)
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print conv2
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fc = fully_connected(conv2, 1, name="fc")
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print fc
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return fc
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def langevin_dynamics(syn_arg):
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def cond(i, syn):
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return tf.less(i, FLAGS.sample_steps)
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def body(i, syn):
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syn_res = descriptor(syn, reuse=True)
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grad = tf.gradients(syn_res, syn, name='grad_des')[0]
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y = syn - 0.5 * FLAGS.step_size * FLAGS.step_size * (syn / FLAGS.refsig / FLAGS.refsig - grad)
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syn = y - avg_pool(y - syn, FLAGS.scale)
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return tf.add(i, 1), syn
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with tf.name_scope("langevin_dynamics"):
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i = tf.constant(0)
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i, syn = tf.while_loop(cond, body, [i, syn_arg])
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return syn
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def train():
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cube_len = FLAGS.cube_len
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scale = FLAGS.scale
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batch_size = FLAGS.batch_size
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output_dir = os.path.join(FLAGS.output_dir, FLAGS.category)
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checkpoint_dir = os.path.join(output_dir, 'checkpoints')
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