def update(args): clean(args) if args.image == 'all' or args.image == 'exabgp': ExaBGP.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'exabgp_mrtparse': ExaBGP_MRTParse.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'gobgp': GoBGP.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'quagga': Quagga.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'bird': BIRD.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'mirage': if args.checkout == "HEAD": args.checkout = "bgperf" MIRAGE.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'mirage_st': if args.checkout == "HEAD": args.checkout = "bgperf" MIRAGE_ST.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'throughput': if args.checkout == "HEAD": args.checkout = "feature-integrate-test" ThroughputTarget.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'frr': FRRouting.build_image(True, checkout=args.checkout, nocache=args.no_cache)
def prepare(args): ExaBGP.build_image(args.force, nocache=args.no_cache) ExaBGP_MRTParse.build_image(args.force, nocache=args.no_cache) GoBGP.build_image(args.force, nocache=args.no_cache) Quagga.build_image(args.force, checkout='quagga-1.0.20160309', nocache=args.no_cache) BIRD.build_image(args.force, nocache=args.no_cache)
def prepare(args): ExaBGP.build_image(args.force, nocache=args.no_cache) ExaBGP_MRTParse.build_image(args.force, nocache=args.no_cache) GoBGP.build_image(args.force, nocache=args.no_cache) BIRD.build_image(args.force, nocache=args.no_cache) FRRouting.build_image(args.force, checkout='stable/3.0', nocache=args.no_cache)
def update(args): if args.image == 'all' or args.image == 'exabgp': ExaBGP.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'gobgp': GoBGP.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'quagga': Quagga.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'bird': BIRD.build_image(True, checkout=args.checkout, nocache=args.no_cache)
def prepare(args): ExaBGP.build_image(args.force, nocache=args.no_cache) ExaBGP_MRTParse.build_image(args.force, nocache=args.no_cache) GoBGP.build_image(args.force, nocache=args.no_cache) Quagga.build_image(args.force, checkout='quagga-1.0.20160309', nocache=args.no_cache) BIRD.build_image(args.force, nocache=args.no_cache) MIRAGE.build_image(args.force, checkout='bgperf', nocache=args.no_cache) MIRAGE_ST.build_image(args.force, checkout='bgperf', nocache=args.no_cache) FRRouting.build_image(args.force, checkout='stable/3.0', nocache=args.no_cache)
def update(args): if args.image == 'all' or args.image == 'exabgp': ExaBGP.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'exabgp_mrtparse': ExaBGP_MRTParse.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'gobgp': GoBGP.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'bird': BIRD.build_image(True, checkout=args.checkout, nocache=args.no_cache) if args.image == 'all' or args.image == 'frr': FRRouting.build_image(True, checkout=args.checkout, nocache=args.no_cache)
def __getitem__(self, idx): ys, meta, _, xs = self.mix[idx] xs = torch.from_numpy(xs) ys = torch.from_numpy(ys) X1s = torchaudio.functional.spectrogram(waveform=torch.transpose( xs[0, :, :], 0, 1), pad=0, window=torch.hann_window(400), n_fft=512, hop_length=256, win_length=400, power=None, normalized=False) X2s = torchaudio.functional.spectrogram(waveform=torch.transpose( xs[1, :, :], 0, 1), pad=0, window=torch.hann_window(400), n_fft=512, hop_length=256, win_length=400, power=None, normalized=False) Ys = torchaudio.functional.spectrogram(waveform=torch.transpose( ys, 0, 1), pad=0, window=torch.hann_window(400), n_fft=512, hop_length=256, win_length=400, power=None, normalized=False) M1s = (X1s[0, :, :, 0]**2 + X1s[0, :, :, 1]**2) / (X1s[0, :, :, 0]**2 + X1s[0, :, :, 1]**2 + X2s[0, :, :, 0]**2 + X2s[0, :, :, 1]**2) M2s = (X1s[1, :, :, 0]**2 + X1s[1, :, :, 1]**2) / (X1s[1, :, :, 0]**2 + X1s[1, :, :, 1]**2 + X2s[1, :, :, 0]**2 + X2s[1, :, :, 1]**2) Ms = torch.cat( (torch.unsqueeze(M1s, dim=0), torch.unsqueeze(M2s, dim=0)), 0) tau = BIRD.getTDOA(meta)[0] return Ys, Ms, tau
def __getitem__(self, idx): ys, meta, _, _ = self.mix[idx] ys = torch.from_numpy(ys) Ys = torchaudio.functional.spectrogram(waveform=torch.transpose( ys, 0, 1), pad=0, window=torch.hann_window(400), n_fft=512, hop_length=256, win_length=400, power=None, normalized=False) rt60 = BIRD.getRT60(meta) return Ys, rt60
from torchaudio.datasets import LIBRISPEECH from augment import RT60 parser = argparse.ArgumentParser() parser.add_argument('--root', default='', type=str, help='Root to save the datasets') parser.add_argument('--folds_train', default=[1,2,3,4,5,6,7], type=int, nargs='+', help='List of BIRD folds for training') parser.add_argument('--folds_eval', default=[8,9], type=int, nargs='+', help='List of BIRD folds for validation') parser.add_argument('--folds_test', default=[10], type=int, nargs='+', help='List of BIRD folds for test') args = parser.parse_args() # This holds the RIR dataset for training, validation and testing folder_in_archive_rir = 'Bird' rir_train = BIRD(root=args.root, folder_in_archive=folder_in_archive_rir, folds=args.folds_train) rir_eval = BIRD(root=args.root, folder_in_archive=folder_in_archive_rir, folds=args.folds_eval) rir_test = BIRD(root=args.root, folder_in_archive=folder_in_archive_rir, folds=args.folds_test) # This holds the speech dataset for training, validation and testing folder_in_archive_speech = 'LibriSpeech' speech_train = LIBRISPEECH(root=args.root, folder_in_archive=folder_in_archive_speech, url='train-clean-100', download=True) speech_eval = LIBRISPEECH(root=args.root, folder_in_archive=folder_in_archive_speech, url='dev-clean', download=True) speech_test = LIBRISPEECH(root=args.root, folder_in_archive=folder_in_archive_speech, url='test-clean', download=True) # We can simply create augmented data with this training dataset augmented_train = RT60(rir=rir_train, speech=speech_train, samples_count=10000) Ys, rt60 = augmented_train[5]
# Author: Francois Grondin # Date: October 19, 2020 # Affiliation: Universite de Sherbrooke # Contact: [email protected] import argparse import os, sys, inspect current_dir = os.path.dirname( os.path.abspath(inspect.getfile(inspect.currentframe()))) parent_dir = os.path.dirname(current_dir) sys.path.insert(0, parent_dir) from bird import BIRD parser = argparse.ArgumentParser() parser.add_argument('--root', default='', type=str, help='Root to save the datasets') parser.add_argument('--folds', default=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], type=int, nargs='+', help='List of folds') args = parser.parse_args() rir = BIRD(root=args.root, folder_in_archive='Bird', folds=args.folds)
def prepare(args): ExaBGP.build_image(args.force, nocache=args.no_cache) GoBGP.build_image(args.force, nocache=args.no_cache) Quagga.build_image(args.force, nocache=args.no_cache) BIRD.build_image(args.force, nocache=args.no_cache)
nargs='+', help='Alpha range') parser.add_argument('--c', default=[335.0, 355.0], type=float, nargs='+', help='Speed of sound range') parser.add_argument('--d', default=[0.01, 0.30], type=float, nargs='+', help='Microphone spacing range') parser.add_argument('--r', default=[0.0, 22.0, 0.0, 22.0, 0.0, 22.0, 0.0, 22.0], type=float, nargs='+', help='Distance between source and microphones') args = parser.parse_args() rir = BIRD(root=args.root, folder_in_archive='Bird', folds=args.folds, room=args.room, alpha=args.alpha, c=args.c, d=args.d, r=args.r) print(rir._df)
def enter(): global bird bird = BIRD() grass = Grass() game_world.add_object(grass, 0) game_world.add_object(bird, 1)