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class Mask():
' Parent class for masks\n the output mask will be <mask_type>.mask\n channels: 1, 3 or 4:\n 1 - Returns a single channel mask\n 3 - Returns a 3 channel mask\n 4 - Returns the original image with the mask in the alpha channel '
... |
class dfl_full(Mask):
' DFL facial mask '
def build_mask(self):
mask = np.zeros((self.face.shape[0:2] + (1,)), dtype=np.float32)
nose_ridge = (self.landmarks[27:31], self.landmarks[33:34])
jaw = (self.landmarks[0:17], self.landmarks[48:68], self.landmarks[0:1], self.landmarks[8:9], se... |
class components(Mask):
' Component model mask '
def build_mask(self):
mask = np.zeros((self.face.shape[0:2] + (1,)), dtype=np.float32)
r_jaw = (self.landmarks[0:9], self.landmarks[17:18])
l_jaw = (self.landmarks[8:17], self.landmarks[26:27])
r_cheek = (self.landmarks[17:20], ... |
class extended(Mask):
' Extended mask\n Based on components mask. Attempts to extend the eyebrow points up the forehead\n '
def build_mask(self):
mask = np.zeros((self.face.shape[0:2] + (1,)), dtype=np.float32)
landmarks = self.landmarks.copy()
ml_pnt = ((landmarks[36] + lan... |
class facehull(Mask):
' Basic face hull mask '
def build_mask(self):
mask = np.zeros((self.face.shape[0:2] + (1,)), dtype=np.float32)
hull = cv2.convexHull(np.array(self.landmarks).reshape(((- 1), 2)))
cv2.fillConvexPoly(mask, hull, 255.0, lineType=cv2.LINE_AA)
return mask
|
class random_components(Mask):
' Extended mask\n Based on components mask. Attempts to extend the eyebrow points up the forehead\n '
def build_mask(self):
mask = np.zeros((self.face.shape[0:2] + (1,)), dtype=np.float32)
landmarks = self.landmarks.copy()
ml_pnt = ((landmarks[... |
def simple_transform():
t = Compose([Resize(256, 256)])
return t
|
def strong_aug_pixel(p=0.5):
print('[DATA]: strong aug pixel')
from albumentations import Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue, MultiplicativeNoise, IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, RandomBrightnessContrast, IAAPiecewiseAffine, I... |
def pixel_aug(p=0.5):
print('[DATA]: pixel aug')
from albumentations import JpegCompression, Blur, Downscale, CLAHE, HueSaturationValue, RandomBrightnessContrast, IAAAdditiveGaussianNoise, GaussNoise, GaussianBlur, MedianBlur, MotionBlur, Compose, OneOf
from random import sample, randint, uniform
retu... |
def spatial_aug(p=0.5):
print('[DATA] spatial aug')
from albumentations import GridDropout, RandomResizedCrop, Rotate, HorizontalFlip, Compose
aug = Compose([GridDropout(holes_number_x=3, holes_number_y=3, random_offset=True, p=0.5), RandomResizedCrop(256, 256, scale=(0.7, 1.0), p=1.0), HorizontalFlip(p=0... |
def pixel_aug_mild(p=0.5):
print('[DATA]: pixel aug mild')
from albumentations import JpegCompression, Blur, Downscale, CLAHE, HueSaturationValue, RandomBrightnessContrast, IAAAdditiveGaussianNoise, GaussNoise, GaussianBlur, MedianBlur, MotionBlur, Compose, OneOf
from random import sample, randint, unifor... |
class Augmentator():
def __init__(self, augment_fn=''):
if (augment_fn == 'pixel_aug'):
self.augment_fn = pixel_aug()
elif (augment_fn == 'simple'):
self.augment_fn = simple_transform()
elif (augment_fn == 'pixel_mild'):
self.augment_fn = pixel_aug_mild... |
def data_transform(size=256, normalize=True):
if normalize:
t = Compose([Resize(size, size), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ToTensor()])
else:
t = Compose([Resize(size, size), ToTensor()])
return t
|
def color_transfer(source, target, clip=True, preserve_paper=True, mask=None):
'\n\tTransfers the color distribution from the source to the target\n\timage using the mean and standard deviations of the L*a*b*\n\tcolor space.\n\tThis implementation is (loosely) based on to the "Color Transfer\n\tbetween Images" pa... |
def image_stats(image, mask=None):
'\n\tParameters:\n\t-------\n\timage: NumPy array\n\t\tOpenCV image in L*a*b* color space\n\tReturns:\n\t-------\n\tTuple of mean and standard deviations for the L*, a*, and b*\n\tchannels, respectively\n\t'
(l, a, b) = cv2.split(image)
if (mask is not None):
(l,... |
def _min_max_scale(arr, new_range=(0, 255)):
'\n\tPerform min-max scaling to a NumPy array\n\tParameters:\n\t-------\n\tarr: NumPy array to be scaled to [new_min, new_max] range\n\tnew_range: tuple of form (min, max) specifying range of\n\t\ttransformed array\n\tReturns:\n\t-------\n\tNumPy array that has been sc... |
def _scale_array(arr, clip=True):
'\n\tTrim NumPy array values to be in [0, 255] range with option of\n\tclipping or scaling.\n\tParameters:\n\t-------\n\tarr: array to be trimmed to [0, 255] range\n\tclip: should array be scaled by np.clip? if False then input\n\t\tarray will be min-max scaled to range\n\t\t[max... |
def colorTransfer(src, dst, mask):
transferredDst = np.copy(dst)
maskIndices = np.where((mask != 0))
maskedSrc = src[(maskIndices[0], maskIndices[1])].astype(np.int32)
maskedDst = dst[(maskIndices[0], maskIndices[1])].astype(np.int32)
meanSrc = np.mean(maskedSrc, axis=0)
meanDst = np.mean(mask... |
def color_transfer(source, target, clip=None, preserve_paper=None, mask=None):
return colorTransfer(src=source, dst=target, mask=mask)
|
def mkdir_p(path):
try:
os.makedirs(os.path.abspath(path))
except OSError as exc:
if ((exc.errno == errno.EEXIST) and os.path.isdir(path)):
pass
else:
raise
|
def files(path, exts=None, r=False):
if os.path.isfile(path):
if ((exts is None) or ((exts is not None) and (splitext(path)[(- 1)] in exts))):
(yield path)
elif os.path.isdir(path):
for (p, _, fs) in os.walk(path):
for f in sorted(fs):
if (exts is not No... |
def rect_to_bb(rect):
x = rect.left()
y = rect.top()
w = (rect.right() - x)
h = (rect.bottom() - y)
return (x, y, w, h)
|
def shape_to_np(shape, dtype='int'):
if isinstance(shape, np.ndarray):
return shape.astype(dtype)
coords = np.zeros((68, 2), dtype=dtype)
for i in range(0, 68):
coords[i] = (shape.part(i).x, shape.part(i).y)
return coords
|
def shape_to_np(shape, dtype='int'):
coords = np.zeros((68, 2), dtype=dtype)
for i in range(0, 68):
coords[i] = (shape.part(i).x, shape.part(i).y)
return coords
|
def rot90(v):
return np.array([(- v[1]), v[0]])
|
def find_face_cvhull(im):
gray = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
rects = detector(gray, 1)
if (not rects):
return None
shape = predictor(gray, rects[0])
shape = shape_to_np(shape)
hull = cv2.convexHull(shape)
return hull
|
def find_face_landmark(im):
gray = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
rects = detector(gray, 1)
if (not rects):
return None
shape = predictor(gray, rects[0])
shape = shape_to_np(shape)
return shape
|
class Masks4D(object):
def __call__(self, masks):
first_w = True
first_h = True
first_c = True
for (k, mask) in enumerate(masks):
(h, w) = mask.shape
real_mask = torch.unsqueeze(torch.unsqueeze(torch.unsqueeze(mask, 0), 0), 0)
for (i, mask_h) in... |
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', help='path to pretrained model')
parser.add_argument('--pretrained', help='downloads pretrained model [celebahq]')
parser.add_argument('--output_path', required=True, help='path to save generated samples')
par... |
def sample(opt):
tf.InteractiveSession()
assert (opt.model_path or opt.pretrained), 'specify weights path or pretrained model'
if opt.model_path:
raise NotImplementedError
elif opt.pretrained:
assert (opt.pretrained == 'celebahq')
sys.path.append('resources/glow/demo')
... |
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', required=True, help='path to pretrained model')
parser.add_argument('--output_path', required=True, help='path to save generated samples')
parser.add_argument('--num_samples', type=int, default=100, help='number o... |
def sample(opt):
tf.InteractiveSession()
with open(opt.model_path, 'rb') as file:
(G, D, Gs) = pickle.load(file)
rng = np.random.RandomState(opt.seed)
for batch_start in tqdm(range(0, opt.num_samples, opt.batch_size)):
bs = (min(opt.num_samples, (batch_start + opt.batch_size)) - batch_... |
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', help='path to pretrained model')
parser.add_argument('--pretrained', help='downloads pretrained model [ffhq, celebahq]')
parser.add_argument('--output_path', required=True, help='path to save generated samples')
... |
def sample(opt):
tf.InteractiveSession()
assert (opt.model_path or opt.pretrained), 'specify weights path or pretrained model'
if opt.model_path:
with open(opt.model_path, 'rb') as file:
(G, D, Gs) = pickle.load(file)
elif opt.pretrained:
urls = dict(ffhq='https://drive.goo... |
def get_transform(opt, for_val=False):
transform_list = []
if for_val:
transform_list.append(transforms.Resize(opt.loadSize, interpolation=PIL.Image.LANCZOS))
transform_list.append(transforms.CenterCrop(opt.loadSize))
transform_list.append(transforms.ToTensor())
else:
trans... |
def get_mask_transform(opt, for_val=False):
transform_list = []
transform_list.append(transforms.ToTensor())
transform = transforms.Compose(transform_list)
return transform
|
class AllAugmentations(object):
def __init__(self):
import albumentations
self.transform = albumentations.Compose([albumentations.Blur(blur_limit=3), albumentations.JpegCompression(quality_lower=30, quality_upper=100, p=0.5), albumentations.RandomBrightnessContrast(), albumentations.augmentations... |
class JPEGCompression(object):
def __init__(self, level):
import albumentations as A
self.level = level
self.transform = A.augmentations.transforms.JpegCompression(p=1)
def __call__(self, image):
image_np = np.array(image)
image_out = self.transform.apply(image_np, qu... |
class Blur(object):
def __init__(self, level):
import albumentations as A
self.level = level
self.transform = A.Blur(blur_limit=(self.level, self.level), always_apply=True)
def __call__(self, image):
image_np = np.array(image)
augmented = self.transform(image=image_np... |
class Gamma(object):
def __init__(self, level):
import albumentations as A
self.level = level
self.transform = A.augmentations.transforms.RandomGamma(p=1)
def __call__(self, image):
image_np = np.array(image)
image_out = self.transform.apply(image_np, gamma=(self.leve... |
class UnpairedMaskDataset(data.Dataset):
'A dataset class for loading images within a single folder\n '
def __init__(self, opt, im_path, label, is_val=False):
'Initialize this dataset class.\n\n Parameters:\n opt -- experiment options\n im_path -- path to folder of ima... |
class Struct():
def __init__(self, **entries):
self.__dict__.update(entries)
|
def find_model_using_name(model_name):
model_filename = (('models.' + model_name) + '_model')
modellib = importlib.import_module(model_filename)
model = None
target_model_name = (model_name.replace('_', '') + 'model')
for (name, cls) in modellib.__dict__.items():
if ((name.lower() == targe... |
def get_option_setter(model_name):
model_class = find_model_using_name(model_name)
return model_class.modify_commandline_options
|
def create_model(opt, **kwargs):
model = find_model_using_name(opt.model)
instance = model(opt, **kwargs)
print(('model [%s] was created' % instance.name()))
return instance
|
class BaseModel():
@staticmethod
def modify_commandline_options(parser):
networks.modify_commandline_options(parser)
return parser
def __init__(self, opt):
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.device = (torch.device('cu... |
def compute_mhsa(q, k, v, scale_factor=1, mask=None):
scaled_dot_prod = (torch.einsum('... i d , ... j d -> ... i j', q, k) * scale_factor)
if (mask is not None):
assert (mask.shape == scaled_dot_prod.shape[2:])
scaled_dot_prod = scaled_dot_prod.masked_fill(mask, (- np.inf))
attention = to... |
class MultiHeadSelfAttention(nn.Module):
def __init__(self, dim, heads=8, dim_head=None):
"\n Implementation of multi-head attention layer of the original transformer model.\n einsum and einops.rearrange is used whenever possible\n Args:\n dim: token's dimension, i.e. word... |
class NLBlockND(nn.Module):
def __init__(self, in_channels=256):
"Implementation of Non-Local Block with 4 different pairwise functions but doesn't include subsampling trick\n args:\n in_channels: original channel size (1024 in the paper)\n inter_channels: channel size inside... |
def make_patch_resnet(depth, layername, num_classes=2, extra_output=None):
def change_out(layers):
(ind, layer) = [(i, l) for (i, (n, l)) in enumerate(layers) if (n == layername)][0]
if layername.startswith('layer'):
bn = list(layer.modules())[((- 1) if (depth < 50) else (- 2))]
... |
def make_patch_xceptionnet(layername, num_classes=2, extra_output=None):
def change_out(layers):
(ind, layer) = [(i, l) for (i, (n, l)) in enumerate(layers) if (n == layername)][0]
if layername.startswith('block'):
module_list = list(layer.modules())
bn = module_list[(- 1)... |
def make_pcl(backbone='xception', layername='block3', input_size=128):
if (backbone == 'xception'):
channels = [128, 256, 728, 728, 728, 728, 728, 728, 728, 728, 728, 1024]
(b1, b2, b3, b12) = (int((input_size / 4)), int((input_size / 8)), int((input_size / 16)), int((input_size / 32)))
ou... |
def make_xceptionnet_long():
from . import xception
def change_out(layers):
channels = [3, 32, 64, 128, 256, 728, 728, 728, 728, 728, 728, 728, 728, 728, 1024, 1536, 2048]
(ind, layer) = [(i, l) for (i, (n, l)) in enumerate(layers) if (n == 'block2')][0]
new_layers = [('pblock3', xcep... |
class CustomResNet(nn.Module):
"\n Customizable ResNet, compatible with pytorch's resnet, but:\n * The top-level sequence of modules can be modified to add\n or remove or alter layers.\n * Extra outputs can be produced, to allow backprop and access\n to internal features.\n * Pooling is... |
class CustomXceptionNet(nn.Module):
'\n Customizable Xceptionnet, compatible with https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/xception.py\n but:\n * The top-level sequence of modules can be modified to add\n or remove or alter layers.\n * Extra outpu... |
class Vectorize(nn.Module):
def __init__(self):
super(Vectorize, self).__init__()
def forward(self, x):
x = x.view(x.size(0), int(numpy.prod(x.size()[1:])))
return x
|
class GlobalAveragePool2d(nn.Module):
def __init__(self):
super(GlobalAveragePool2d, self).__init__()
def forward(self, x):
x = torch.mean(x.view(x.size(0), x.size(1), (- 1)), dim=2)
return x
|
def get_scheduler(optimizer, opt):
if (opt.lr_policy == 'plateau'):
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.1, threshold=0.0001, patience=opt.patience, eps=1e-06)
elif (opt.lr_policy == 'constant'):
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='... |
def init_weights(net, init_type='xavier', gain=0.02):
def init_func(m):
classname = m.__class__.__name__
if (hasattr(m, 'weight') and ((classname.find('Conv') != (- 1)) or (classname.find('Linear') != (- 1)))):
if (init_type == 'normal'):
init.normal_(m.weight.data, 0.... |
def init_net(net, init_type='xavier', gpu_ids=[]):
if (len(gpu_ids) > 0):
assert torch.cuda.is_available()
net.to(gpu_ids[0])
net = torch.nn.DataParallel(net, gpu_ids)
if (init_type is None):
return net
init_weights(net, init_type)
return net
|
def modify_commandline_options(parser):
(opt, _) = parser.parse_known_args()
if ('xception' in opt.which_model_netD):
parser.set_defaults(loadSize=333, fineSize=299)
elif ('resnet' in opt.which_model_netD):
parser.set_defaults(loadSize=256, fineSize=224)
else:
raise NotImplemen... |
def define_D(which_model_netD, init_type, gpu_ids=[]):
if ('resnet' in which_model_netD):
from torchvision.models import resnet
model = getattr(resnet, which_model_netD)
netD = model(pretrained=False, num_classes=2)
elif ('xception' in which_model_netD):
from . import xception
... |
def define_patch_D(which_model_netD, init_type, gpu_ids=[]):
if which_model_netD.startswith('resnet'):
from . import customnet
splits = which_model_netD.split('_')
depth = int(splits[0][6:])
layer = splits[1]
if (len(splits) == 2):
netD = customnet.make_patch_re... |
def define_PCL(which_model_netD, init_type, gpu_ids=[], input_size=128):
if which_model_netD.startswith('resnet'):
from . import customnet
backbone = which_model_netD.split('_')[0]
layer = which_model_netD.split('_')[1]
(netPCL, out_ch) = customnet.make_pcl(backbone=backbone, layer... |
class WideNet(nn.Module):
def __init__(self, kernel_size=7, dilation=1):
super().__init__()
sequence = [nn.Conv2d(3, 256, kernel_size=kernel_size, dilation=dilation, stride=2, padding=(kernel_size // 2), bias=False), nn.BatchNorm2d(256), nn.ReLU(inplace=True), nn.MaxPool2d(3, stride=2, padding=1)... |
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=False):
super(SeparableConv2d, self).__init__()
self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=bi... |
class PixelBlock(nn.Module):
def __init__(self, in_filters, out_filters, reps, strides=1, start_with_relu=True, grow_first=True):
super(PixelBlock, self).__init__()
assert (strides == 1)
if ((out_filters != in_filters) or (strides != 1)):
self.skip = nn.Conv2d(in_filters, out_... |
class Block(nn.Module):
def __init__(self, in_filters, out_filters, reps, strides=1, start_with_relu=True, grow_first=True):
super(Block, self).__init__()
if ((out_filters != in_filters) or (strides != 1)):
self.skip = nn.Conv2d(in_filters, out_filters, 1, stride=strides, bias=False)
... |
class Xception(nn.Module):
'\n Xception optimized for the ImageNet dataset, as specified in\n https://arxiv.org/pdf/1610.02357.pdf\n '
def __init__(self, num_classes=1000):
' Constructor\n Args:\n num_classes: number of classes\n '
super(Xception, self).__ini... |
def xception(num_classes=1000, pretrained='imagenet'):
model = Xception(num_classes=num_classes)
if pretrained:
settings = pretrained_settings['xception'][pretrained]
model = Xception(num_classes=num_classes)
pretrained_state = model_zoo.load_url(settings['url'])
model_state = ... |
class BaseOptions(options.Options):
def __init__(self, print_opt=True):
options.Options.__init__(self)
self.isTrain = False
self.print_opt = print_opt
parser = self.parser
parser.add_argument('--model', type=str, default='basic_discriminator', help='chooses which model to ... |
class TestOptions(BaseOptions):
def __init__(self):
BaseOptions.__init__(self, print_opt=False)
parser = self.parser
parser.add_argument('--train_config', type=argparse.FileType(mode='r'), required=True, help='config file saved from model training')
parser.add_argument('--partitio... |
class TrainOptions(BaseOptions):
def __init__(self, print_opt=True):
BaseOptions.__init__(self, print_opt)
parser = self.parser
parser.add_argument('--display_freq', type=int, default=1000, help='frequency of showing training results visualization')
parser.add_argument('--print_fr... |
def train(opt):
torch.manual_seed(opt.seed)
if (opt.model == 'patch_inconsistency_discriminator'):
WITH_MASK = True
else:
WITH_MASK = False
if (not WITH_MASK):
dset = PairedDataset(opt, os.path.join(opt.real_im_path, 'train'), os.path.join(opt.fake_im_path, 'train'), with_mask=... |
def validate(model, opt):
logging.info('Starting evaluation loop ...')
model.reset()
assert (not model.net_D.training)
if (opt.model == 'patch_inconsistency_discriminator'):
WITH_MASK = True
else:
WITH_MASK = False
if (not WITH_MASK):
val_dset = PairedDataset(opt, os.pa... |
def train(opt):
torch.manual_seed(opt.seed)
dset = I2GDataset(opt, os.path.join(opt.real_im_path, 'train'))
dset.get32frames()
dl = DataLoader(dset, batch_size=opt.batch_size, num_workers=opt.nThreads, pin_memory=False, shuffle=True)
assert (opt.fake_class_id in [0, 1])
fake_label = opt.fake_c... |
def validate(model, opt):
logging.info('Starting evaluation loop ...')
model.reset()
assert (not model.net_D.training)
val_dset = I2GDataset(opt, os.path.join(opt.real_im_path, 'val'), is_val=True)
val_dset.get32frames()
val_dl = DataLoader(val_dset, batch_size=opt.batch_size, num_workers=opt.... |
class TqdmLoggingHandler(logging.Handler):
def __init__(self, level=logging.NOTSET):
super(self.__class__, self).__init__(level)
def emit(self, record):
try:
msg = self.format(record)
tqdm.tqdm.write(msg)
self.flush()
except (KeyboardInterrupt, Sys... |
class MultiLineFormatter(logging.Formatter):
def __init__(self, fmt=None, datefmt=None, style='%'):
assert (style == '%')
super(MultiLineFormatter, self).__init__(fmt, datefmt, style)
self.multiline_fmt = fmt
def format(self, record):
"\n This is mostly the same as log... |
def handle_exception(exc_type, exc_value, exc_traceback):
if issubclass(exc_type, KeyboardInterrupt):
sys.__excepthook__(exc_type, exc_value, exc_traceback)
return
logging.error('Uncaught exception', exc_info=(exc_type, exc_value, exc_traceback))
|
def configure(logging_file, log_level=logging.INFO, level_prefix='', prefix='', write_to_stdout=True, append=True):
logging.getLogger().setLevel(logging.INFO)
sys.excepthook = handle_exception
handlers = []
if write_to_stdout:
handlers.append(TqdmLoggingHandler())
delayed_logging = []
... |
@contextlib.contextmanager
def disable(level):
prev_level = logging.getLogger().getEffectiveLevel()
logging.disable(level)
(yield)
logging.disable(prev_level)
|
class Options():
def __init__(self):
self.parser = parser = argparse.ArgumentParser()
self.parser.add_argument('config_file', nargs='?', type=argparse.FileType(mode='r'))
self.parser.add_argument('--overwrite_config', action='store_true', help='overwrite config files if they exist')
... |
def verbose(verbose):
'\n Sets default verbosity level. Set to True to see progress bars.\n '
global default_verbosity
default_verbosity = verbose
|
def post(**kwargs):
'\n When within a progress loop, pbar.post(k=str) will display\n the given k=str status on the right-hand-side of the progress\n status bar. If not within a visible progress bar, does nothing.\n '
innermost = innermost_tqdm()
if innermost:
innermost.set_postfix(**k... |
def desc(desc):
'\n When within a progress loop, pbar.desc(str) changes the\n left-hand-side description of the loop toe the given description.\n '
innermost = innermost_tqdm()
if innermost:
innermost.set_description(str(desc))
|
def descnext(desc):
'\n Called before starting a progress loop, pbar.descnext(str)\n sets the description text that will be used in the following loop.\n '
global next_description
if ((not default_verbosity) or (tqdm is None)):
return
next_description = desc
|
def print(*args):
'\n When within a progress loop, will print above the progress loop.\n '
global next_description
next_description = None
if default_verbosity:
msg = ' '.join((str(s) for s in args))
if (tqdm is None):
print(msg)
else:
tqdm.write(m... |
def tqdm_terminal(it, *args, **kwargs):
'\n Some settings for tqdm that make it run better in resizable terminals.\n '
return tqdm(it, *args, dynamic_ncols=True, ascii=True, leave=(not innermost_tqdm()), **kwargs)
|
def in_notebook():
'\n True if running inside a Jupyter notebook.\n '
try:
shell = get_ipython().__class__.__name__
if (shell == 'ZMQInteractiveShell'):
return True
elif (shell == 'TerminalInteractiveShell'):
return False
else:
return F... |
def innermost_tqdm():
'\n Returns the innermost active tqdm progress loop on the stack.\n '
if (hasattr(tqdm, '_instances') and (len(tqdm._instances) > 0)):
return max(tqdm._instances, key=(lambda x: x.pos))
else:
return None
|
def __call__(x, *args, **kwargs):
'\n Invokes a progress function that can wrap iterators to print\n progress messages, if verbose is True.\n \n If verbose is False or tqdm is unavailable, then a quiet\n non-printing identity function is used.\n\n verbose can also be set to a spefific progress fu... |
class CallableModule(types.ModuleType):
def __init__(self):
types.ModuleType.__init__(self, __name__)
self.__dict__.update(sys.modules[__name__].__dict__)
def __call__(self, x, *args, **kwargs):
return __call__(x, *args, **kwargs)
|
def exit_if_job_done(directory, redo=False, force=False, verbose=True):
if pidfile_taken(os.path.join(directory, 'lockfile.pid'), force=force, verbose=verbose):
sys.exit(0)
donefile = os.path.join(directory, 'done.txt')
if os.path.isfile(donefile):
with open(donefile) as f:
msg... |
def mark_job_done(directory):
with open(os.path.join(directory, 'done.txt'), 'w') as f:
f.write(('done by %d@%s %s at %s' % (os.getpid(), socket.gethostname(), os.getenv('STY', ''), time.strftime('%c'))))
|
def pidfile_taken(path, verbose=False, force=False):
"\n Usage. To grab an exclusive lock for the remaining duration of the\n current process (and exit if another process already has the lock),\n do this:\n\n if pidfile_taken('job_423/lockfile.pid', verbose=True):\n sys.exit(0)\n\n To do a ... |
def delete_pidfile(lockfile, path):
'\n Runs at exit after pidfile_taken succeeds.\n '
if (lockfile is not None):
try:
lockfile.close()
except:
pass
try:
os.unlink(path)
except:
pass
|
def blocks(obj, space=''):
return IPython.display.HTML(space.join(blocks_tags(obj)))
|
def rows(obj, space=''):
return IPython.display.HTML(space.join(rows_tags(obj)))
|
def rows_tags(obj):
if isinstance(obj, dict):
obj = obj.items()
results = []
results.append('<table style="display:inline-table">')
for row in obj:
results.append('<tr style="padding:0">')
for item in row:
results.append(('<td style="text-align:left; vertical-align:... |
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