code stringlengths 17 6.64M |
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def agent(agent_id, config, game, tm_subset, model_weights_queue, experience_queue):
random_state = np.random.RandomState(seed=agent_id)
network = Network(config, game.state_dims, game.action_dim, game.max_moves, master=False)
model_weights = model_weights_queue.get()
network.model.set_weights(model_w... |
def main(_):
tf.config.experimental.set_visible_devices([], 'GPU')
tf.get_logger().setLevel('INFO')
config = (get_config(FLAGS) or FLAGS)
env = Environment(config, is_training=True)
game = CFRRL_Game(config, env)
model_weights_queues = []
experience_queues = []
if ((FLAGS.num_agents ==... |
class SiamRPN(nn.Module):
def __init__(self, size=2, feature_out=512, anchor=5):
configs = [3, 96, 256, 384, 384, 256]
configs = list(map((lambda x: (3 if (x == 3) else (x * size))), configs))
feat_in = configs[(- 1)]
super(SiamRPN, self).__init__()
self.featureExtract = n... |
class SiamRPNBIG(SiamRPN):
def __init__(self):
super(SiamRPNBIG, self).__init__(size=2)
self.cfg = {'lr': 0.295, 'window_influence': 0.42, 'penalty_k': 0.055, 'instance_size': 271, 'adaptive': True}
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class SiamRPNvot(SiamRPN):
def __init__(self):
super(SiamRPNvot, self).__init__(size=1, feature_out=256)
self.cfg = {'lr': 0.45, 'window_influence': 0.44, 'penalty_k': 0.04, 'instance_size': 271, 'adaptive': False}
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class SiamRPNotb(SiamRPN):
def __init__(self):
super(SiamRPNotb, self).__init__(size=1, feature_out=256)
self.cfg = {'lr': 0.3, 'window_influence': 0.4, 'penalty_k': 0.22, 'instance_size': 271, 'adaptive': False}
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def track_video(model, video):
(toc, regions) = (0, [])
(image_files, gt) = (video['image_files'], video['gt'])
for (f, image_file) in enumerate(image_files):
im = cv2.imread(image_file)
tic = cv2.getTickCount()
if (f == 0):
(target_pos, target_sz) = rect_2_cxy_wh(gt[f]... |
def load_dataset(dataset):
base_path = join(realpath(dirname(__file__)), 'data', dataset)
if (not exists(base_path)):
print('Please download OTB dataset into `data` folder!')
exit()
json_path = join(realpath(dirname(__file__)), 'data', (dataset + '.json'))
info = json.load(open(json_pa... |
def main():
global args, v_id
args = parser.parse_args()
net = SiamRPNotb()
net.load_state_dict(torch.load(join(realpath(dirname(__file__)), 'SiamRPNOTB.model')))
net.eval().cuda()
dataset = load_dataset(args.dataset)
fps_list = []
for (v_id, video) in enumerate(dataset.keys()):
... |
def track_video(model, video):
image_save = 0
(toc, regions) = (0, [])
(image_files, gt) = (video['image_files'], video['gt'])
for (f, image_file) in enumerate(image_files):
im = cv2.imread(image_file)
tic = cv2.getTickCount()
if (f == 0):
(target_pos, target_sz) = ... |
def load_dataset(dataset):
base_path = join(realpath(dirname(__file__)), 'data', dataset)
if (not exists(base_path)):
print('Please download OTB dataset into `data` folder!')
exit()
json_path = join(realpath(dirname(__file__)), 'data', (dataset + '.json'))
info = json.load(open(json_pa... |
def main():
global args, v_id
args = parser.parse_args()
net = SiamRPNotb()
net.load_state_dict(torch.load(join(realpath(dirname(__file__)), 'SiamRPNOTB.model')))
net.eval().cuda()
dataset = load_dataset(args.dataset)
fps_list = []
for (v_id, video) in enumerate(dataset.keys()):
... |
def track_video(model, video):
image_save = 0
(toc, regions) = (0, [])
(image_files, gt) = (video['image_files'], video['gt'])
for (f, image_file) in enumerate(image_files):
im = cv2.imread(image_file)
tic = cv2.getTickCount()
if (f == 0):
(target_pos, target_sz) = ... |
def load_dataset(dataset):
base_path = join(realpath(dirname(__file__)), 'data', dataset)
if (not exists(base_path)):
print('Please download OTB dataset into `data` folder!')
exit()
json_path = join(realpath(dirname(__file__)), 'data', (dataset + '.json'))
info = json.load(open(json_pa... |
def main():
global args, v_id
args = parser.parse_args()
net = SiamRPNotb()
net.load_state_dict(torch.load(join(realpath(dirname(__file__)), 'SiamRPNOTB.model')))
net.eval().cuda()
dataset = load_dataset(args.dataset)
fps_list = []
for (v_id, video) in enumerate(dataset.keys()):
... |
def recode_cc_data(frame):
' Recodes numeric categorical variables into categorical character variables\n with more transparent values. \n \n Args:\n frame: Pandas DataFrame version of UCI credit card default data.\n \n Returns: \n H2OFrame with recoded values.\n \n '
... |
def generate_local_sample(row, frame, X, N=1000):
' Generates a perturbed sample around a row of interest.\n \n Args:\n row: Row of H2OFrame to be explained.\n frame: H2OFrame in which row is stored.\n X: List of model input variables.\n N: Number of samples to generate.\n \n ... |
def plot_local_contrib(row, model, X, g_pred=None, scale=False):
' Plots reason codes in a bar chart. \n \n Args:\n \n row: Row of H2OFrame to be explained.\n model: H2O linear model used for generating reason codes.\n X: List of model input variables.\n g_pred: Prediction of ... |
def recode_cc_data(frame):
' Recodes numeric categorical variables into categorical character variables\n with more transparent values. \n \n Args:\n frame: Pandas DataFrame version of UCI credit card default data.\n \n Returns: \n H2OFrame with recoded values.\n \n '
... |
def get_percentile_dict(yhat, id_, frame):
' Returns the minimum, the maximum, and the deciles of a column, yhat, \n as the indices based on another column id_.\n \n Args:\n yhat: Column in which to find percentiles.\n id_: Id column that stores indices for percentiles of yhat.\n ... |
def dataloader_msrvtt_train(args, tokenizer):
msrvtt_dataset = MSRVTTDataset(subset='train', anno_path=args.anno_path, video_path=args.video_path, max_words=args.max_words, tokenizer=tokenizer, max_frames=args.max_frames, video_framerate=args.video_framerate, config=args)
try:
train_sampler = torch.ut... |
def dataloader_msrvtt_test(args, tokenizer, subset='test'):
msrvtt_testset = MSRVTTDataset(subset=subset, anno_path=args.anno_path, video_path=args.video_path, max_words=args.max_words, tokenizer=tokenizer, max_frames=args.max_frames, video_framerate=args.video_framerate, config=args)
try:
test_sample... |
def dataloader_activity_train(args, tokenizer):
activity_dataset = ActivityNetDataset(subset='train', data_path=args.anno_path, features_path=args.video_path, max_words=args.max_words, feature_framerate=args.video_framerate, tokenizer=tokenizer, max_frames=args.max_frames)
train_sampler = torch.utils.data.dis... |
def dataloader_activity_test(args, tokenizer, subset='test'):
activity_testset = ActivityNetDataset(subset=subset, data_path=args.anno_path, features_path=args.video_path, max_words=args.max_words, feature_framerate=args.video_framerate, tokenizer=tokenizer, max_frames=args.max_frames)
try:
test_sampl... |
def dataloader_didemo_train(args, tokenizer):
didemo_dataset = DiDeMoDataset(subset='train', data_path=args.anno_path, features_path=args.video_path, max_words=args.max_words, feature_framerate=args.video_framerate, tokenizer=tokenizer, max_frames=args.max_frames)
train_sampler = torch.utils.data.distributed.... |
def dataloader_didemo_test(args, tokenizer, subset='test'):
didemo_testset = DiDeMoDataset(subset=subset, data_path=args.anno_path, features_path=args.video_path, max_words=args.max_words, feature_framerate=args.video_framerate, tokenizer=tokenizer, max_frames=args.max_frames)
try:
test_sampler = torc... |
class MSRVTTDataset(RetrievalDataset):
'MSRVTT dataset.'
def __init__(self, subset, anno_path, video_path, tokenizer, max_words=32, max_frames=12, video_framerate=1, image_resolution=224, mode='all', config=None):
super(MSRVTTDataset, self).__init__(subset, anno_path, video_path, tokenizer, max_words... |
def _interpolation(kwargs):
interpolation = kwargs.pop('resample', Image.BILINEAR)
if isinstance(interpolation, (list, tuple)):
return random.choice(interpolation)
else:
return interpolation
|
def _check_args_tf(kwargs):
if (('fillcolor' in kwargs) and (_PIL_VER < (5, 0))):
kwargs.pop('fillcolor')
kwargs['resample'] = _interpolation(kwargs)
|
def shear_x(img, factor, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, factor, 0, 0, 1, 0), **kwargs)
|
def shear_y(img, factor, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, 0, factor, 1, 0), **kwargs)
|
def translate_x_rel(img, pct, **kwargs):
pixels = (pct * img.size[0])
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
|
def translate_y_rel(img, pct, **kwargs):
pixels = (pct * img.size[1])
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
|
def translate_x_abs(img, pixels, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
|
def translate_y_abs(img, pixels, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
|
def rotate(img, degrees, **kwargs):
_check_args_tf(kwargs)
if (_PIL_VER >= (5, 2)):
return img.rotate(degrees, **kwargs)
elif (_PIL_VER >= (5, 0)):
(w, h) = img.size
post_trans = (0, 0)
rotn_center = ((w / 2.0), (h / 2.0))
angle = (- math.radians(degrees))
m... |
def auto_contrast(img, **__):
return ImageOps.autocontrast(img)
|
def invert(img, **__):
return ImageOps.invert(img)
|
def equalize(img, **__):
return ImageOps.equalize(img)
|
def solarize(img, thresh, **__):
return ImageOps.solarize(img, thresh)
|
def solarize_add(img, add, thresh=128, **__):
lut = []
for i in range(256):
if (i < thresh):
lut.append(min(255, (i + add)))
else:
lut.append(i)
if (img.mode in ('L', 'RGB')):
if ((img.mode == 'RGB') and (len(lut) == 256)):
lut = ((lut + lut) + l... |
def posterize(img, bits_to_keep, **__):
if (bits_to_keep >= 8):
return img
return ImageOps.posterize(img, bits_to_keep)
|
def contrast(img, factor, **__):
return ImageEnhance.Contrast(img).enhance(factor)
|
def color(img, factor, **__):
return ImageEnhance.Color(img).enhance(factor)
|
def brightness(img, factor, **__):
return ImageEnhance.Brightness(img).enhance(factor)
|
def sharpness(img, factor, **__):
return ImageEnhance.Sharpness(img).enhance(factor)
|
def _randomly_negate(v):
'With 50% prob, negate the value'
return ((- v) if (random.random() > 0.5) else v)
|
def _rotate_level_to_arg(level, _hparams):
level = ((level / _MAX_LEVEL) * 30.0)
level = _randomly_negate(level)
return (level,)
|
def _enhance_level_to_arg(level, _hparams):
return ((((level / _MAX_LEVEL) * 1.8) + 0.1),)
|
def _enhance_increasing_level_to_arg(level, _hparams):
level = ((level / _MAX_LEVEL) * 0.9)
level = (1.0 + _randomly_negate(level))
return (level,)
|
def _shear_level_to_arg(level, _hparams):
level = ((level / _MAX_LEVEL) * 0.3)
level = _randomly_negate(level)
return (level,)
|
def _translate_abs_level_to_arg(level, hparams):
translate_const = hparams['translate_const']
level = ((level / _MAX_LEVEL) * float(translate_const))
level = _randomly_negate(level)
return (level,)
|
def _translate_rel_level_to_arg(level, hparams):
translate_pct = hparams.get('translate_pct', 0.45)
level = ((level / _MAX_LEVEL) * translate_pct)
level = _randomly_negate(level)
return (level,)
|
def _posterize_level_to_arg(level, _hparams):
return (int(((level / _MAX_LEVEL) * 4)),)
|
def _posterize_increasing_level_to_arg(level, hparams):
return ((4 - _posterize_level_to_arg(level, hparams)[0]),)
|
def _posterize_original_level_to_arg(level, _hparams):
return ((int(((level / _MAX_LEVEL) * 4)) + 4),)
|
def _solarize_level_to_arg(level, _hparams):
return (int(((level / _MAX_LEVEL) * 256)),)
|
def _solarize_increasing_level_to_arg(level, _hparams):
return ((256 - _solarize_level_to_arg(level, _hparams)[0]),)
|
def _solarize_add_level_to_arg(level, _hparams):
return (int(((level / _MAX_LEVEL) * 110)),)
|
class AugmentOp():
'\n Apply for video.\n '
def __init__(self, name, prob=0.5, magnitude=10, hparams=None):
hparams = (hparams or _HPARAMS_DEFAULT)
self.aug_fn = NAME_TO_OP[name]
self.level_fn = LEVEL_TO_ARG[name]
self.prob = prob
self.magnitude = magnitude
... |
def _select_rand_weights(weight_idx=0, transforms=None):
transforms = (transforms or _RAND_TRANSFORMS)
assert (weight_idx == 0)
rand_weights = _RAND_CHOICE_WEIGHTS_0
probs = [rand_weights[k] for k in transforms]
probs /= np.sum(probs)
return probs
|
def rand_augment_ops(magnitude=10, hparams=None, transforms=None):
hparams = (hparams or _HPARAMS_DEFAULT)
transforms = (transforms or _RAND_TRANSFORMS)
return [AugmentOp(name, prob=0.5, magnitude=magnitude, hparams=hparams) for name in transforms]
|
class RandAugment():
def __init__(self, ops, num_layers=2, choice_weights=None):
self.ops = ops
self.num_layers = num_layers
self.choice_weights = choice_weights
def __call__(self, img):
ops = np.random.choice(self.ops, self.num_layers, replace=(self.choice_weights is None), ... |
def rand_augment_transform(config_str, hparams):
"\n RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719\n\n Create a RandAugment transform\n :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by\n dashe... |
class RawVideoExtractorCV2():
def __init__(self, centercrop=False, size=224, framerate=(- 1), subset='test'):
self.centercrop = centercrop
self.size = size
self.framerate = framerate
self.transform = self._transform(self.size)
self.subset = subset
self.tsfm_dict = ... |
def url_to_filename(url: str, etag: str=None) -> str:
"\n Convert `url` into a hashed filename in a repeatable way.\n If `etag` is specified, append its hash to the url's, delimited\n by a period.\n "
url_bytes = url.encode('utf-8')
url_hash = sha256(url_bytes)
filename = url_hash.hexdiges... |
def filename_to_url(filename: str, cache_dir: Union[(str, Path)]=None) -> Tuple[(str, str)]:
'\n Return the url and etag (which may be ``None``) stored for `filename`.\n Raise ``FileNotFoundError`` if `filename` or its stored metadata do not exist.\n '
if (cache_dir is None):
cache_dir = PYTO... |
def cached_path(url_or_filename: Union[(str, Path)], cache_dir: Union[(str, Path)]=None) -> str:
"\n Given something that might be a URL (or might be a local path),\n determine which. If it's a URL, download the file and cache it, and\n return the path to the cached file. If it's already a local path,\n ... |
def split_s3_path(url: str) -> Tuple[(str, str)]:
'Split a full s3 path into the bucket name and path.'
parsed = urlparse(url)
if ((not parsed.netloc) or (not parsed.path)):
raise ValueError('bad s3 path {}'.format(url))
bucket_name = parsed.netloc
s3_path = parsed.path
if s3_path.star... |
def s3_request(func: Callable):
'\n Wrapper function for s3 requests in order to create more helpful error\n messages.\n '
@wraps(func)
def wrapper(url: str, *args, **kwargs):
try:
return func(url, *args, **kwargs)
except ClientError as exc:
if (int(exc.re... |
@s3_request
def s3_etag(url: str) -> Optional[str]:
'Check ETag on S3 object.'
s3_resource = boto3.resource('s3')
(bucket_name, s3_path) = split_s3_path(url)
s3_object = s3_resource.Object(bucket_name, s3_path)
return s3_object.e_tag
|
@s3_request
def s3_get(url: str, temp_file: IO) -> None:
'Pull a file directly from S3.'
s3_resource = boto3.resource('s3')
(bucket_name, s3_path) = split_s3_path(url)
s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
|
def http_get(url: str, temp_file: IO) -> None:
req = requests.get(url, stream=True)
content_length = req.headers.get('Content-Length')
total = (int(content_length) if (content_length is not None) else None)
progress = tqdm(unit='B', total=total)
for chunk in req.iter_content(chunk_size=1024):
... |
def get_from_cache(url: str, cache_dir: Union[(str, Path)]=None) -> str:
"\n Given a URL, look for the corresponding dataset in the local cache.\n If it's not there, download it. Then return the path to the cached file.\n "
if (cache_dir is None):
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
... |
def read_set_from_file(filename: str) -> Set[str]:
'\n Extract a de-duped collection (set) of text from a file.\n Expected file format is one item per line.\n '
collection = set()
with open(filename, 'r', encoding='utf-8') as file_:
for line in file_:
collection.add(line.rstri... |
def get_file_extension(path: str, dot=True, lower: bool=True):
ext = os.path.splitext(path)[1]
ext = (ext if dot else ext[1:])
return (ext.lower() if lower else ext)
|
class LayerNorm(nn.LayerNorm):
"Subclass torch's LayerNorm to handle fp16."
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
|
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return (x * torch.sigmoid((1.702 * x)))
|
class ResidualAttentionBlock(nn.Module):
def __init__(self, d_model: int, n_head: int, attn_mask=None):
super(ResidualAttentionBlock, self).__init__()
self.attn = nn.MultiheadAttention(d_model, n_head)
self.ln_1 = LayerNorm(d_model)
self.mlp = nn.Sequential(OrderedDict([('c_fc', n... |
class Transformer(nn.Module):
def __init__(self, width: int, layers: int, heads: int, attn_mask=None):
super(Transformer, self).__init__()
self.width = width
self.layers = layers
self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads) for _ in range(layers)])
de... |
def warmup_cosine(x, warmup=0.002):
if (x < warmup):
return (x / warmup)
return (0.5 * (1.0 + math.cos((math.pi * x))))
|
def warmup_constant(x, warmup=0.002):
' Linearly increases learning rate over `warmup`*`t_total` (as provided to BertAdam) training steps.\n Learning rate is 1. afterwards. '
if (x < warmup):
return (x / warmup)
return 1.0
|
def warmup_linear(x, warmup=0.002):
' Specifies a triangular learning rate schedule where peak is reached at `warmup`*`t_total`-th (as provided to BertAdam) training step.\n After `t_total`-th training step, learning rate is zero. '
if (x < warmup):
return (x / warmup)
return max(((x - 1.0)... |
class BertAdam(Optimizer):
"Implements BERT version of Adam algorithm with weight decay fix.\n Params:\n lr: learning rate\n warmup: portion of t_total for the warmup, -1 means no warmup. Default: -1\n t_total: total number of training steps for the learning\n rate schedule, -1... |
@lru_cache()
def default_bpe():
return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'bpe_simple_vocab_16e6.txt.gz')
|
@lru_cache()
def bytes_to_unicode():
"\n Returns list of utf-8 byte and a corresponding list of unicode strings.\n The reversible bpe codes work on unicode strings.\n This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.\n When you're at something like a 10B toke... |
def get_pairs(word):
'Return set of symbol pairs in a word.\n Word is represented as tuple of symbols (symbols being variable-length strings).\n '
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
|
def basic_clean(text):
text = ftfy.fix_text(text)
text = html.unescape(html.unescape(text))
return text.strip()
|
def whitespace_clean(text):
text = re.sub('\\s+', ' ', text)
text = text.strip()
return text
|
class SimpleTokenizer(object):
def __init__(self, bpe_path: str=default_bpe()):
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for (k, v) in self.byte_encoder.items()}
merges = gzip.open(bpe_path).read().decode('utf-8').split('\n')
merges = merges[1:(((49152 - 25... |
class PretrainedConfig(object):
pretrained_model_archive_map = {}
config_name = ''
weights_name = ''
@classmethod
def get_config(cls, pretrained_model_name, cache_dir, type_vocab_size, state_dict, task_config=None):
archive_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), p... |
def get_world_size():
if (not dist.is_available()):
return 1
if (not dist.is_initialized()):
return 1
return dist.get_world_size()
|
def get_rank():
if (not dist.is_available()):
return 0
if (not dist.is_initialized()):
return 0
return dist.get_rank()
|
def is_main_process():
return (get_rank() == 0)
|
def synchronize():
'\n Helper function to synchronize (barrier) among all processes when\n using distributed training\n '
if (not dist.is_available()):
return
if (not dist.is_initialized()):
return
world_size = dist.get_world_size()
if (world_size == 1):
return
... |
def all_gather(data):
'\n Run all_gather on arbitrary picklable data (not necessarily tensors)\n Args:\n data: any picklable object\n Returns:\n list[data]: list of data gathered from each rank\n '
world_size = get_world_size()
if (world_size == 1):
return [data]
buff... |
def reduce_dict(input_dict, average=True):
'\n Args:\n input_dict (dict): all the values will be reduced\n average (bool): whether to do average or sum\n Reduce the values in the dictionary from all processes so that process with rank\n 0 has the averaged results. Returns a dict with the sa... |
def setup_logger(name, save_dir, dist_rank, filename='log.txt'):
logger = logging.getLogger(name)
logger.setLevel(logging.ERROR)
if (dist_rank > 0):
return logger
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler(stream=sys.stdout)
ch.setLevel(logging.DEBUG)
formatter = log... |
class SmoothedValue(object):
'Track a series of values and provide access to smoothed values over a\n window or the global series average.\n '
def __init__(self, window_size=20):
self.deque = deque(maxlen=window_size)
self.series = []
self.total = 0.0
self.count = 0
... |
class MetricLogger(object):
def __init__(self, delimiter='\t'):
self.meters = defaultdict(SmoothedValue)
self.delimiter = delimiter
def update(self, **kwargs):
for (k, v) in kwargs.items():
if isinstance(v, torch.Tensor):
v = v.item()
assert is... |
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