Datasets:
jblitzar
/
github-python-corpus

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args = "" if nbs else "--ignore=tests/test_sample_notebooks.py"
c.run(f"pytest {args}", pty=True)
c.run("flake8")
@task
def setup(c, version=None):
"""
Setup dev environment, requires conda
"""
version = version or "3.9"
suffix = "" if version == "3.9" else version.replace(".", "")
env_name = f"soorgeon{suffix}"
c.run(f"conda create --name {env_name} python={version} --yes")
c.run(
'eval "$(conda shell.bash hook)" '
f"&& conda activate {env_name} "
"&& pip install --editable .[dev]"
)
print(f"Done! Activate your environment with:\nconda activate {env_name}")
@task(aliases=["v"])
def version(c):
"""Create a new version of this project"""
from pkgmt import versioneer
versioneer.version(project_root=".", tag=True)
@task(aliases=["r"])
def release(c, tag, production=True):
"""Upload to PyPI (prod by default): inv upload {tag}"""
from pkgmt import versioneer
versioneer.upload(tag, production=production)
@task
def install_git_hook(c, force=False):
"""Installs pre-push git hook"""
path = Path(".git/hooks/pre-push")
hook_exists = path.is_file()
if hook_exists:
if force:
path.unlink()
else:
sys.exit(
"Error: pre-push hook already exists. "
'Run: "invoke install-git-hook -f" to force overwrite.'
)
shutil.copy(".githooks/pre-push", ".git/hooks")
print(f"pre-push hook installed at {str(path)}")
@task
def uninstall_git_hook(c):
"""Uninstalls pre-push git hook"""
path = Path(".git/hooks/pre-push")
hook_exists = path.is_file()
if hook_exists:
path.unlink()
print(f"Deleted {str(path)}.")
else:
print("Hook doesn't exist, nothing to delete.")
# <FILESEP>
import gc, os, glob, argparse, h5py
import numpy as np
import tflearn
import tensorflow as tf
import tensorflow.contrib.slim as slim
from functools import reduce # for calculating PSNR
from operator import mul # for calculating the num of parameters
import net_MFCNN
def transformer(batch, chan, flow, U , out_size, name='SpatialTransformer', **kwargs):
def _repeat(x, n_repeats):
with tf.variable_scope('_repeat'):
rep = tf.transpose(
tf.expand_dims(tf.ones(shape=tf.stack([n_repeats, ])), 1), [1, 0])
rep = tf.cast(rep, 'int32')
x = tf.matmul(tf.reshape(x, (-1, 1)), rep)
return tf.reshape(x, [-1])
def _repeat2(x, n_repeats):
with tf.variable_scope('_repeat'):
rep = tf.expand_dims(tf.ones(shape=tf.stack([n_repeats, ])), 1)
rep = tf.cast(rep, 'int32')
x = tf.matmul(rep, tf.reshape(x, (1, -1)))
return tf.reshape(x, [-1])
def _interpolate(im, x, y, out_size):