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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
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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')
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@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
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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... |
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()
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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... |
def main():
args = parser.parse_args()
world_size = args.gpus
if args.gpus:
assert (world_size <= torch.cuda.device_count()), f'--gpus is too high (specefied {world_size} gpus but only {torch.cuda.device_count()} gpus are available)'
torch.cuda.empty_cache()
if (world_size > 1):
... |
def customize_pipeline_test(config):
config['batching']['bucket_by_sequence_length'] = False
if ('delphes_pf_ttbar' in config['datasets']):
config['train_test_datasets']['physical']['datasets'] = ['delphes_pf_ttbar']
if ('cms_pf_ttbar' in config['datasets']):
config['train_test_datasets'][... |
def submit(config):
crabCommand('submit', config=config)
with open((((config.General.workArea + '/crab_') + config.General.requestName) + '/crab_config.py'), 'w') as fi:
fi.write(config.pythonise_())
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def map_pdgid_to_candid(pdgid, charge):
if (pdgid in [22, 11, 13]):
return pdgid
if (abs(charge) > 0):
return 211
return 130
|
def deltar_pairs(eta_vec, phi_vec, dr_cut):
deta = np.abs(np.subtract.outer(eta_vec, eta_vec))
dphi = (np.mod((np.subtract.outer(phi_vec, phi_vec) + np.pi), (2 * np.pi)) - np.pi)
dr2 = ((deta ** 2) + (dphi ** 2))
dr2 *= np.tri(*dr2.shape)
dr2[(dr2 == 0)] = 999
ind_pairs = np.where((dr2 < dr_cu... |
def get_charge(pid):
abs_pid = abs(pid)
if (pid in [130, 22, 1, 2]):
return 0.0
elif (abs_pid in [11, 13]):
return (- math.copysign(1.0, pid))
elif (abs_pid in [211]):
return math.copysign(1.0, pid)
else:
raise Exception('Unknown pid: ', pid)
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def draw_event(g):
pos = {}
for node in g.nodes:
pos[node] = (g.nodes[node]['eta'], g.nodes[node]['phi'])
fig = plt.figure(figsize=(10, 10))
nodes_to_draw = [n for n in g.nodes if (n[0] == 'elem')]
nx.draw_networkx(g, pos=pos, with_labels=False, node_size=5, nodelist=nodes_to_draw, edgelis... |
def merge_closeby_particles(g, pid=22, deltar_cut=0.001):
photons = [elem for elem in g.nodes if ((g.nodes[elem]['typ'] == pid) and ((elem[0] == 'tp') or (elem[0] == 'sc')))]
phot_eta = [g.nodes[node]['eta'] for node in photons]
phot_phi = [g.nodes[node]['phi'] for node in photons]
merge_pairs = []
... |
def cleanup_graph(g, node_energy_threshold=0.1, edge_energy_threshold=0.05):
g = g.copy()
nodes_to_remove = []
for node in g.nodes:
if ((node[0] == 'sc') or (node[0] == 'tp')):
sw = 0.0
for edge in g.edges(node):
sw += g.edges[edge]['weight']
if ... |
def prepare_normalized_table(g, genparticle_energy_threshold=0.2):
all_genparticles = []
all_elements = []
all_pfcandidates = []
for node in g.nodes:
if (node[0] == 'elem'):
all_elements += [node]
for parent in g.predecessors(node):
all_genparticles += [... |
def make_graph(ev, iev):
element_type = ev['element_type'][iev]
element_pt = ev['element_pt'][iev]
element_e = ev['element_energy'][iev]
element_eta = ev['element_eta'][iev]
element_phi = ev['element_phi'][iev]
element_eta_ecal = ev['element_eta_ecal'][iev]
element_phi_ecal = ev['element_p... |
def gen_e(g):
etot_gen = 0.0
etot_pf = 0.0
for node in g.nodes:
if ((node[0] == 'tp') or (node[0] == 'sc')):
etot_gen += g.nodes[node]['e']
if (node[0] == 'pfcand'):
etot_pf += g.nodes[node]['e']
return (etot_gen, etot_pf)
|
def process(args):
infile = args.input
outpath = os.path.join(args.outpath, os.path.basename(infile).split('.')[0])
tf = uproot.open(infile)
if ('ana' in tf):
tt = tf['ana/pftree']
elif ('pfana' in tf):
tt = tf['pfana/pftree']
else:
raise Exception('Could not find the P... |
def parse_args():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--input', type=str, help='Input file from PFAnalysis', required=True)
parser.add_argument('--outpath', type=str, default='raw', help='output path')
parser.add_argument('--save-full-graph', action='store_true'... |
class ClicEdmQqPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.0.0': 'Initial release.', '1.1.0': 'update stats, move to 380 GeV', '1.2.0': 'sin cos as separate features', '1.3.0': 'Update stats to ~1M events', '1.3.1': 'Update stats to ~2M events', '1.4.0': 'Fix ... |
class ClicEdmTtbarPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.0.0': 'Initial release.', '1.1.0': 'update stats, move to 380 GeV', '1.2.0': 'sin/cos phi separately', '1.3.0': 'Update stats to ~1M events', '1.4.0': 'Fix ycand matching', '1.5.0': 'Regenerate with... |
class ClicEdmTtbarPu10Pf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.3.0': 'Update stats to ~1M events', '1.4.0': 'Fix ycand matching', '1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n For the raw input files in ROOT EDM4HEP form... |
class ClicEdmWwFullhadPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.3.0': 'Update stats to ~1M events', '1.4.0': 'Fix ycand matching', '1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n For the raw input files in ROOT EDM4HEP form... |
class ClicEdmZhTautauPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.3.0': 'First version', '1.4.0': 'Fix ycand matching', '1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n For the raw input files in ROOT EDM4HEP format, please see... |
class ClicEdmQqHitsPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'0.9.0': 'Small stats', '1.0.0': 'Initial release', '1.1.0': 'Remove track referencepoint feature', '1.2.0': 'Keep all interacting genparticles', '1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DO... |
class ClicEdmQqHitsPf10k(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n For the raw input files in ROOT EDM4HEP format, please see the citation above.\n\n The processed tensorflow_dat... |
class ClicEdmSingleElectronHitsPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.1.0': 'Remove track referencepoint feature', '1.2.0': 'Keep all interacting genparticels', '1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n For the raw... |
class ClicEdmSingleGammaHitsPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.1.0': 'Remove track referencepoint feature', '1.2.0': 'Keep all interacting genparticles', '1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n For the raw in... |
class ClicEdmSingleKaon0lHitsPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.1.0': 'Remove track referencepoint feature', '1.2.0': 'Keep all interacting genparticles', '1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n For the raw i... |
class ClicEdmSingleMuonHitsPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.1.0': 'Remove track referencepoint feature', '1.2.0': 'Keep all interacting genparticles', '1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n For the raw inp... |
class ClicEdmSingleNeutronHitsPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.1.0': 'Remove track referencepoint feature', '1.2.0': 'Keep all interacting genparticles', '1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n For the raw ... |
class ClicEdmSinglePiHitsPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.1.0': 'Remove track referencepoint feature', '1.2.0': 'Keep all interacting genparticles', '1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n For the raw input... |
class ClicEdmSinglePi0HitsPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.1.0': 'Remove track referencepoint feature', '1.2.0': 'Keep all interacting genparticles', '1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n For the raw inpu... |
class ClicEdmTtbarHitsPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'0.9.0': 'Small stats', '1.0.0': 'Initial release', '1.1.0': 'Remove track referencepoint feature', '1.2.0': 'Keep all interacting genparticles', '1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL... |
class ClicEdmTtbarHitsPf10k(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.5.0')
RELEASE_NOTES = {'1.5.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n For the raw input files in ROOT EDM4HEP format, please see the citation above.\n\n The processed tensorflow ... |
class CmsPfMultiParticleGun(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf_multi_particle_gun dataset.'
VERSION = tfds.core.Version('1.6.1')
RELEASE_NOTES = {'1.6.0': 'Initial release', '1.6.1': 'Additional stats'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n rsync -r --progress lxplus.... |
class CmsPfQcd(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf_qcd dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.3.0': '12_2_0_pre2 generation with updated caloparticle/trackingparticle', '1.3.1': 'Remove PS again', '1.4.0': 'Add gen jet index information', '1.5.0': 'No p... |
class CmsPfQcdHighPt(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf_qcd_high_pt dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.3.0': '12_2_0_pre2 generation with updated caloparticle/trackingparticle', '1.3.1': 'Remove PS again', '1.4.0': 'Add gen jet index information', ... |
class CmsPfSingleElectron(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf_singleele dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.0.0': 'Initial release.', '1.1.0': 'Initial release.', '1.2.0': '12_1_0_pre3 generation, add corrected energy, cluster flags, 20k events', '1.... |
class CmsPfSingleGamma(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf_singlegamma dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.1.0': 'Initial release', '1.2.0': '12_1_0_pre3 generation, add corrected energy, cluster flags, 20k events', '1.4.0': 'Add gen jet index inform... |
class CmsPfSingleMu(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf_singlemu dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.0.0': 'Initial release.', '1.1.0': 'Add muon type, fix electron GSF association', '1.2.0': '12_1_0_pre3 generation, add corrected energy, cluster fla... |
class CmsPfSingleNeutron(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf_singleneutron dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.1.0': 'Initial release', '1.2.0': '12_1_0_pre3 generation, add corrected energy, cluster flags, 20k events', '1.4.0': 'Add gen jet index in... |
class CmsPfSinglePi(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf_singlepi dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.0.0': 'Initial release.', '1.1.0': 'Add muon type, fix electron GSF association', '1.2.0': '12_1_0_pre3 generation, add corrected energy, cluster fla... |
class CmsPfSinglePi0(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf_singlepi0 dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.1.0': 'Initial release', '1.2.0': '12_1_0_pre3 generation, add corrected energy, cluster flags, 20k events', '1.4.0': 'Add gen jet index informatio... |
class CmsPfSingleProton(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf_singleproton dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.1.0': 'Initial release', '1.2.0': '12_1_0_pre3 generation, add corrected energy, cluster flags, 20k events', '1.4.0': 'Add gen jet index info... |
class CmsPfSingleTau(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf_singletau dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.1.0': 'Add muon type, fix electron GSF association', '1.2.0': '12_1_0_pre3 generation, add corrected energy, cluster flags, 20k events', '1.4.0': '... |
class CmsPfSmsT1tttt(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.6.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n rsync -r --progress lxplus.cern.ch:/eos/user/j/jpata/mlpf/tensorflow_... |
class CmsPfTtbar(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.0.0': 'Initial release.', '1.1.0': 'Add muon type, fix electron GSF association', '1.2.0': '12_1_0_pre3 generation, add corrected energy, cluster flags, 20k even... |
class CmsPfZtt(tfds.core.GeneratorBasedBuilder):
'DatasetBuilder for cms_pf_ztt dataset.'
VERSION = tfds.core.Version('1.6.0')
RELEASE_NOTES = {'1.3.0': '12_2_0_pre2 generation with updated caloparticle/trackingparticle', '1.3.1': 'Remove PS again', '1.4.0': 'Add gen jet index information', '1.5.0': 'No p... |
class DelphesQcdPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.2.0')
RELEASE_NOTES = {'1.0.0': 'Initial release.', '1.1.0': 'Do not pad events to the same size', '1.2.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n Download from https://zenodo.org/record/4559... |
class DelphesTtbarPf(tfds.core.GeneratorBasedBuilder):
VERSION = tfds.core.Version('1.2.0')
RELEASE_NOTES = {'1.0.0': 'Initial release.', '1.1.0': 'Do not pad events to the same size', '1.2.0': 'Regenerate with ARRAY_RECORD'}
MANUAL_DOWNLOAD_INSTRUCTIONS = '\n Download from https://zenodo.org/record/45... |
@numba.njit
def deltaphi(phi1, phi2):
diff = (phi1 - phi2)
return np.arctan2(np.sin(diff), np.cos(diff))
|
@numba.njit
def deltar(eta1, phi1, eta2, phi2):
deta = (eta1 - eta2)
dphi = deltaphi(phi1, phi2)
return np.sqrt(((deta ** 2) + (dphi ** 2)))
|
@numba.njit
def match_jets(jets1, jets2, deltaR_cut):
iev = len(jets1)
jet_inds_1_ev = []
jet_inds_2_ev = []
for ev in range(iev):
j1 = jets1[ev]
j2 = jets2[ev]
jet_inds_1 = []
jet_inds_2 = []
for ij1 in range(len(j1)):
drs = np.zeros(len(j2), dtype=... |
def squeeze_if_one(arr):
if (arr.shape[(- 1)] == 1):
return np.squeeze(arr, axis=(- 1))
else:
return arr
|
def build_dummy_array(num, dtype=np.int64):
return awkward.Array(awkward.contents.ListOffsetArray(awkward.index.Index64(np.zeros((num + 1), dtype=np.int64)), awkward.from_numpy(np.array([], dtype=dtype), highlevel=False)))
|
def match_two_jet_collections(jets_coll, name1, name2, jet_match_dr):
num_events = len(jets_coll[name1])
vec1 = vector.awk(awkward.zip({'pt': jets_coll[name1].pt, 'eta': jets_coll[name1].eta, 'phi': jets_coll[name1].phi, 'energy': jets_coll[name1].energy}))
vec2 = vector.awk(awkward.zip({'pt': jets_coll[n... |
class Expression():
def __init__(self, label, edmtype, eval_list):
self.label = label
self.edmtype = edmtype
self.eval_list = eval_list
self.handle = Handle(self.edmtype)
def get(self, event):
event.getByLabel(self.label, self.handle)
obj = self.handle.product... |
class TFDSDataSource():
def __init__(self, ds):
self.ds = ds
tmp = self.ds.dataset_info
self.ds.dataset_info = SimpleNamespace()
self.ds.dataset_info.name = tmp.name
self.ds.dataset_info.features = tmp.features
self.rep = self.ds.__repr__()
def __getitem__(sel... |
class PFDataset():
'Builds a DataSource from tensorflow datasets.'
def __init__(self, data_dir, name, split, num_samples=None):
'\n Args\n data_dir: path to tensorflow_datasets (e.g. `../data/tensorflow_datasets/`)\n name: sample and version (e.g. `clic_edm_ttbar_pf:1.5.0... |
class PFDataLoader(torch.utils.data.DataLoader):
'\n Copied from https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/loader/dataloader.html#DataLoader\n because we need to implement our own Collater class to load the tensorflow_datasets (see below).\n '
def __init__(self, dat... |
class Collater():
'Based on the Collater found on torch_geometric docs we build our own.'
def __init__(self, keys_to_get, follow_batch=None, exclude_keys=None, pad_bin_size=640, pad_3d=True):
self.follow_batch = follow_batch
self.exclude_keys = exclude_keys
self.keys_to_get = keys_to_... |
class InterleavedIterator(object):
'Will combine DataLoaders of different lengths and batch sizes.'
def __init__(self, data_loaders):
self.idx = 0
self.data_loaders = data_loaders
self.data_loaders_iter = [iter(dl) for dl in data_loaders]
max_loader_size = max([len(dl) for dl ... |
def get_interleaved_dataloaders(world_size, rank, config, use_cuda, pad_3d, use_ray):
loaders = {}
for split in ['train', 'valid']:
loaders[split] = []
for type_ in config[f'{split}_dataset'][config['dataset']]:
dataset = []
for sample in config[f'{split}_dataset'][conf... |
def _logging(rank, _logger, msg):
'Will log the message only on rank 0 or cpu.'
if ((rank == 0) or (rank == 'cpu')):
_logger.info(msg)
|
def _configLogger(name, filename=None, loglevel=logging.INFO):
logger = logging.getLogger(name)
logger.setLevel(loglevel)
if filename:
logfile = logging.FileHandler(filename)
logfile.setLevel(loglevel)
logfile.setFormatter(logging.Formatter('[%(asctime)s] %(levelname)s: %(message)s... |
class ColoredLogger():
color_dict = {'black': '\x1b[0;30m', 'red': '\x1b[0;31m', 'green': '\x1b[0;32m', 'orange': '\x1b[0;33m', 'blue': '\x1b[0;34m', 'purple': '\x1b[0;35m', 'cyan': '\x1b[0;36m', 'lightgray': '\x1b[0;37m', 'darkgray': '\x1b[1;30m', 'lightred': '\x1b[1;31m', 'lightgreen': '\x1b[1;32m', 'yellow': '... |
@lru_cache(10)
def warn_once(msg, logger=_logger):
logger.warning(msg)
|
def main():
args = parser.parse_args()
world_size = (args.gpus if (args.gpus > 0) else 1)
with open(args.config, 'r') as stream:
config = yaml.safe_load(stream)
config = override_config(config, args)
if args.hpo:
run_hpo(config, args)
else:
if args.resume_training:
... |
def set_hps_from_search_space(search_space, config):
varaible_names = ['lr', 'gpu_batch_multiplier']
for var in varaible_names:
if (var in search_space.keys()):
config[var] = search_space[var]
if ('conv_type' in search_space.keys()):
conv_type = search_space['conv_type']
... |
def set_raytune_search_parameters(search_space, config):
if ('layernorm' in search_space.keys()):
config['parameters']['combined_graph_layer']['layernorm'] = bool(search_space['layernorm'])
if ('ffn_dist_hidden_dim' in search_space.keys()):
config['parameters']['combined_graph_layer']['ffn_dis... |
def get_raytune_search_alg(raytune_cfg, seeds=False):
if ((raytune_cfg['sched'] == 'pbt') or (raytune_cfg['sched'] == 'pb2')):
if (raytune_cfg['search_alg'] is not None):
print("INFO: Using schedule '{}' is not compatible with Ray Tune search algorithms.".format(raytune_cfg['sched']))
... |
def get_raytune_schedule(raytune_cfg):
if (raytune_cfg['sched'] == 'asha'):
return AsyncHyperBandScheduler(metric=raytune_cfg['default_metric'], mode=raytune_cfg['default_mode'], time_attr='training_iteration', max_t=raytune_cfg['asha']['max_t'], grace_period=raytune_cfg['asha']['grace_period'], reduction... |
@click.group()
@click.help_option('-h', '--help')
def main():
pass
|
@main.command()
@click.help_option('-h', '--help')
@click.option('-p', '--path', help='path to json file or dir containing json files', type=click.Path())
@click.option('-y', '--ylabel', default=None, help='Y-axis label', type=str)
@click.option('-x', '--xlabel', default='Step', help='X-axis label', type=str)
@click.... |
class CustomTensorBoard(TensorBoard):
'\n Extends tensorflow.keras.callbacks TensorBoard\n\n Custom tensorboard class to make logging of learning rate possible when using\n keras.optimizers.schedules.LearningRateSchedule.\n See https://github.com/tensorflow/tensorflow/pull/37552\n\n Also logs momem... |
class CustomModelCheckpoint(ModelCheckpoint):
'Extends tensorflow.keras.callbacks.ModelCheckpoint to also save optimizer'
def __init__(self, *args, **kwargs):
self.optimizer_to_save = kwargs.pop('optimizer_to_save')
self.optimizer_filepath = kwargs.pop('optimizer_save_filepath')
super... |
class BenchmarkLoggerCallback(tf.keras.callbacks.Callback):
def __init__(self, *args, **kwargs):
self.outdir = kwargs.pop('outdir')
self.steps_per_epoch = kwargs.pop('steps_per_epoch')
self.batch_size_per_gpu = kwargs.pop('batch_size_per_gpu')
self.num_gpus = kwargs.pop('num_gpus'... |
class NpEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
if isinstance(obj, np.floating):
return float(obj)
if isinstance(obj, np.ndarray):
return obj.tolist()
return super(NpEncoder, self).defau... |
def get_model_builder(config, total_steps):
(lr_schedule, optim_callbacks, lr) = get_lr_schedule(config, steps=total_steps)
def model_builder(hp):
node_encoding_hidden_dim = hp.Choice('node_dim', values=[128, 256, 512])
config['parameters']['node_encoding_hidden_dim'] = node_encoding_hidden_d... |
class LRFinder(Callback):
"`Callback` that exponentially adjusts the learning rate after each training batch between `start_lr` and\n `end_lr` for a maximum number of batches: `max_step`. The loss and learning rate are recorded at each step allowing\n visually finding a good learning rate as per https://sgu... |
class ModelOptimizerCheckpoint(tf.keras.callbacks.ModelCheckpoint):
def on_epoch_end(self, epoch, logs=None):
super(ModelOptimizerCheckpoint, self).on_epoch_end(epoch, logs=logs)
weightfile_path = self.opt_path.format(epoch=(epoch + 1), **logs)
weights = {}
self.model.optimizer.sa... |
class CustomCallback(tf.keras.callbacks.Callback):
def __init__(self, outpath, dataset, config, plot_freq=1, horovod_enabled=False, comet_experiment=None, is_hpo_run=False):
super(CustomCallback, self).__init__()
self.plot_freq = plot_freq
self.dataset = dataset
self.outpath = out... |
def epoch_end(self, epoch, logs, comet_experiment=None):
epoch = (epoch + 1)
with open('{}/history_{}.json'.format(self.outpath, epoch), 'w') as fi:
json.dump(logs, fi)
if self.is_hpo_run:
comet_experiment.log_metrics(logs, epoch=epoch)
if (self.plot_freq <= 0):
return
if (... |
def prepare_callbacks(config, outdir, dataset, comet_experiment=None, horovod_enabled=False, benchmark_dir=None, num_train_steps=None, num_cpus=None, num_gpus=None, train_samples=None, is_hpo_run=False):
callbacks = []
callbacks.append(tf.keras.callbacks.TerminateOnNaN())
callbacks += get_checkpoint_histo... |
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