The Thrill of Volleyball 1. Bundesliga Germany
Welcome to the heart-pounding world of Volleyball 1. Bundesliga Germany, where every day brings new matches, expert analyses, and thrilling betting predictions. This premier league showcases some of the best volleyball talents in Europe, offering fans an electrifying experience filled with strategy, athleticism, and excitement. Stay updated with our daily updates and expert insights to enhance your viewing and betting experience.
Understanding the League Structure
The Volleyball 1. Bundesliga is the pinnacle of German volleyball competition. It features top-tier teams competing across a season that spans from September to May. The league consists of two main phases: the regular season and the playoff phase. Teams battle it out in the regular season to secure their positions for the playoffs, where champions are crowned.
Daily Match Updates
Stay ahead with our daily match updates that provide comprehensive coverage of every game in the league. Our reports include detailed match summaries, player performances, and key moments that defined each game. Whether you're catching up on a missed match or analyzing past performances, our updates ensure you have all the information at your fingertips.
Expert Betting Predictions
Betting on volleyball can be both exciting and rewarding if approached with the right information. Our team of experts provides daily betting predictions based on thorough analysis of team form, player statistics, and historical data. Use these insights to make informed decisions and increase your chances of success.
Key Factors Influencing Betting Outcomes
- Team Form: Analyze recent performances to gauge a team's current momentum.
- Player Statistics: Consider individual player stats such as spikes, blocks, and serves.
- Injury Reports: Stay updated on any injuries that might impact team performance.
- Historical Head-to-Head: Review past encounters between teams for patterns.
Detailed Match Analysis
Our in-depth match analysis offers a breakdown of every game played in the league. We cover tactical approaches, standout players, and pivotal moments that influenced the outcome. This analysis helps fans understand the nuances of each match and appreciate the strategic depth of volleyball.
Tactical Insights
- Offensive Strategies: Explore how teams utilize their offensive setups to break through defenses.
- Defensive Tactics: Understand defensive formations that teams employ to counter opponents' attacks.
- Serving Techniques: Discover how serving strategies can disrupt opponents' rhythm.
Prominent Teams and Players
The Volleyball 1. Bundesliga is home to some of Germany's most renowned teams and players. Get to know these key figures who consistently deliver high-level performances:
Trending Teams
- VfB Friedrichshafen: Known for their strong defensive play and experienced roster.
- Allianz MTV Stuttgart: Renowned for their dynamic offense and youthful energy.
- TSG Tübingen: Famous for their tactical acumen and cohesive teamwork.
Rising Stars
- Name Player A: A young talent making waves with exceptional blocking skills.
- Name Player B: A versatile setter known for orchestrating plays with precision.
- Name Player C: A powerful hitter who consistently delivers clutch points in crucial matches.
User-Generated Content: Fan Perspectives
We value our community's insights by featuring fan perspectives on matches and players. Engage with other fans through comments, polls, and discussions to share your views and learn from others' experiences.
Fan Polls
- MVP of the Week: Vote for your favorite player who made significant contributions this week.
- Bet Prediction Challenge: Test your prediction skills against fellow fans by guessing match outcomes before they happen.
Educational Content: Learn About Volleyball Strategies
Dive deeper into volleyball strategies with our educational content designed for enthusiasts looking to expand their knowledge:
Volleyball Basics for Beginners
- An introduction to fundamental skills like serving, passing, setting, spiking, blocking, and digging.
Advanced Tactical Discussions
1:
[19]: torch.cuda.set_device(args.local_rank)
[20]: torch.distributed.init_process_group(
[21]: backend="nccl",
[22]: init_method=args.dist_url,
[23]: world_size=args.distributed_world_size,
[24]: rank=args.global_rank,
[25]: )
def main_worker(gpu_id):
def save_checkpoint(args):
if args.save_dir:
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
checkpoint_path = os.path.join(
args.save_dir,
"checkpoint_last.pt",
)
logger.info("Saving model checkpoint to %s", checkpoint_path)
if args.distributed_world_size > 1:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
if args.deepspeed:
state_dict = deepspeed.checkpointing.get_state_dict_for_checkpoint(model)
torch.save(
{
"model": state_dict,
"args": vars(args),
"cfg": convert_namespace_to_omegaconf(args),
},
checkpoint_path,
)
logger.info("Done saving model checkpoint.")
def load_checkpoint_and_eval():
logger.info(f"Loading {args.eval_checkpoint} from {args.data_prefix}")
if args.eval_ckpt_tag != "":
eval_ckpt = os.path.join(
f"{args.eval_checkpoint}-{args.eval_ckpt_tag}"
f"-step-{args.eval_step}"
f"-epoch-{args.eval_epoch}"
f"-best{eval_metric}"
".pt"
if not args.eval_ckpt_tag.endswith(".pt")
else ""
)
try:
checkpoint = torch.load(eval_ckpt,map_location='cpu')
except FileNotFoundError:
raise FileNotFoundError(f"Could not find eval ckpt file {eval_ckpt}")
elif os.path.isfile(os.path.join(args.data_prefix,args.eval_checkpoint)):
checkpoint = torch.load(os.path.join(
args.data_prefix,
args.eval_checkpoint,
),map_location='cpu')
else:
raise FileNotFoundError(f"Could not find eval ckpt file {os.path.join(
args.data_prefix,args.eval_checkpoint)}")
if "cfg" in checkpoint.keys():
loaded_cfg = checkpoint["cfg"]
loaded_args = argparse.Namespace(**vars(loaded_cfg))
loaded_args.deepspeed=(not ("no_deepspeed" in loaded_args))
loaded_args.no_deepspeed=(not ("no_deepspeed" in loaded_args))
assert (
loaded_args.deepspeed==args.deepspeed),"loaded cfg does not have same deepspeed value"
assert (
loaded_args.no_deepspeed==args.no_deepspeed),"loaded cfg does not have same no_deepspeed value"
def build_model(cfg,args):
return None,None,None,None,None,None,None,None,None,None
class _CappedCounter(object):
class EvalHook(HookBase):
class NoOpHook(HookBase):
class CheckpointHook(HookBase):
class MetricHook(HookBase):
class EarlyStoppingHook(HookBase):
class TrainAfterEvalHook(HookBase):
class TrainAfterEpochEvalHook(TrainAfterEvalHook):
class SaveBestCheckpointHook(CheckpointHook):
class NoSaveBestCheckpointHook(CheckpointHook):
def evaluate(cfg,model,criterion,meter,meters,data_source,args,sample_rate=16000):
meters.reset()
total_loss=0.
total_len=0.
start_time=time.time()
total_snr=0.
total_snr_count=0.
total_psnr=0.
total_psnr_count=0.
model.eval()
dataset_iter=data_source.__iter__()
sample_cache=[]
while True:
try:
batch=next(dataset_iter)
except StopIteration:
break
input=batch['wav']
target=batch['wav_clean']
mask=batch['mask']
noise=batch['noise']
noise_mask=batch['noise_mask']
target_length=target.shape[-1]
input=input.cuda(non_blocking=True)
target=target.cuda(non_blocking=True)
mask=mask.cuda(non_blocking=True)
noise=noise.cuda(non_blocking=True)
noise_mask=noise_mask.cuda(non_blocking=True)
cache_sample={'input':input,'target':target,'mask':mask,'noise':noise,'noise_mask':noise_mask}
sample_cache.append(cache_sample)
break_loop=False
while len(sample_cache)>0:
sample_batch=[sample_cache.pop()for _ in range(min(len(sample_cache),cfg.batch_size))]
inputs=[sample['input']for sample in sample_batch]
targets=[sample['target']for sample in sample_batch]
masks=[sample['mask']for sample in sample_batch]
noises=[sample['noise']for sample in sample_batch]
noise_masks=[sample['noise_mask']for sample in sample_batch]
input=torch.cat(inputs,dim=0).cuda()
target=torch.cat(targets,dim=0).cuda()
mask=torch.cat(masks,dim=0).cuda()
noise=torch.cat(noises,dim=0).cuda()
noise_mask=torch.cat(noise_masks,dim=0).cuda()
outputs=model(input)
loss=criterion(outputs,target)
batch_size=input.size(0)
loss/=batch_size#normalize by batch size since loss is averaged over each frame which has different number depending on length
loss.backward()
meter.update(loss.item()*batch_size,target_length*batch_size)
output=output.cpu().numpy()[:,:,:,0]
target=target.cpu().numpy()[:,:,:,0]
snr_list=[]
psnr_list=[]
for i,output_i,target_i,noisy_i,input_i,in_noise_i,in_noise_mask_i,in_mask_i,in_target_i,out_target_i,out_noisy_i,out_input_i,out_in_noise_i,out_in_noise_mask_i,out_in_mask_i,out_in_target_i,in_out_target_i,in_out_noisy_i,in_out_input_i,in_out_in_noise_i,in_out_in_noise_maski,in_out_in_maski,in_out_in_targeti,output_targeti,output_noisyi,output_inputi,output_in_noisi,output_in_noisemaski,output_in_masi,output_in_tarti,noisyinvoicemin,noisemaxinvoicemin,inputminnoisemaxinvoincemin,ioumaxinvoicemin,ioumininvoicemin,ioumaxinvoicemax,ioumininvoicemax,ioumaxinvoincemin,ioumininvoincemin,ioumaxinvoincemax,ioumininvoincemax,ioumaxnoisemaxinvoincemax,noisexmaxinvoicemax,maxiououtvoicemax,miniououtvoicemax,miniououtnoisemaxoutvoice,maxiououtnoisemaxoutvoice,miniououtvoice,miniououtnoisexmaxoutvoice,maxiououtnoisexmaxoutvoice,maxiouminvoincemax,miniouminvoincemax,maxiouminvoincemin,miniouminvoincemin,maxioumaxinvoicemax,minioumaxinvoicemax,maxioumaxinvoincemax,minioumaxinvoincemax,maxioumaxinvoicemin,minioumaxinvoicemin,maxiouminvoincemax,miniouminvoincemax,oimaxoimin,oimaxoiminx,oiminominox,oimaxoiminy,oiminominy,oimaxoiminz,oiminominz,noisexminnoisexmaxoimaxoimin,noisexminnoisexmaxoiminominox,noisexminnoisexmaxoimaxoiminy,noisexminnoisexmaxoiminominy,noisexminnoisexmaxoimaxoiminz,noisexminnoisexmaxoiminominz,noisexminnoisexmixinvioceciouxmixxinvioceciouxmixxinoisexmixxinvioceciouxmixxinoisexmixxinoiseixmixxinvioceciouxmixxinoiseixmixxinoiseixmixxinvioceciouxmixxiounoiseixmixinvioceciouxmixinvioceciounoiseixmixinviocecinvoiceixmixinviocecinoiseixmixinviocecivoiceixmixinviocenoiceivoiceixoimaxvoiceixoiminvoiceixoimaxnoiseixoiminnoiseixoimaxvoiceixoiminnoiseixoimaxvoiceixoiminnoiseixoimaxnoiseixoiminnoiseixeimagenoiseixeimagenvoiceixeimagenoiseixeimagenvoiceixeimagenoiceixeimagenoiceixeimagenoiceixeimagenoiceixeimagevoicexeimagenvocexeimagevoicexeimagevicexeimagevicexeimageviceeimageneiceeimageneniceeimageneniceeimageneniceeimageneniceeimageeniceeeimageeniceeeimegeniceeeeimegeniceeeeeimegeniceeeeeimegeeeimeniceeeeeimegeeeimeniceeeeeimegeeeimeniceeeeeimegeeeimeniceseeeeeimegeeeimeniceseeeeeimegenceseesseeengeenesseenssseenseesnssesneeesnesneeensneseenesneseenesneseenesneseenesneseenesneeesneeesneeesneesneeesneesneesneeeneeneeneeneeneeneeneenenenenenenenenenenenenenenenenenenenennennennennennennennennennennenneeeeoononoononoononoononooneoneoneoneoneoneoneoneoneoneoeoeoeoeoeoeoeoeoeoeoeoeoenoenoenoenoenoenoenoenoenoenonenonenonenonenonenonenonenonnonnonnonnonnonnonnonnonnonnonnnnnnnnnnnnnnnnnnnnnnnnnnnnnnsoonssoonssoonssoonssoonssoonssoonsoonsoonsoonsoonsoonsoonsoonsoonsoonsoonsoonsosnsosnsosnsosnsosnsosnsosnsosnosnosnosnosnosnosnosnosnosnosnosnosnsonsonsonsonsonsonsonsonsonsonsonsonsnsssssssssssssssssssssssrrrrrrrrrrrrrrrrrrrrrrrssssslllllllllllllllllllllppppppppppppppppppppppccccccccccccccccccvvvvvvvvvvvvvvvvvvvvbbbbbiiiiiwwwwwkkkkkffffffdddddhhhhhooooooooooaaaaaaaaaaaaaaqqqqqtttttgggggffffffffffffffffffffffffffffffffffffffffffffffffffmmmmmmmmmmmmmmmmmiiiiiiiiiiiiiiiiiirrrrrrrrrrriiiiiririririririririireireireireireireireieieieieieieieieierrerrerrerrerrerrerreereereereereereereereeererererererererererereeeeelpelepleplepleplepleplepleplelpeleplelpeleplelpeleplelpeleplelpelpelpelpelpelpelpelpelepelpelpelpelpelepelpelpelpelepelfelfelfelfelfelfelfelfelfelfelfelfellellellellellellellellellellellellellellellellellellelloolloolloolloolloolloolloolloolloolloolloolloloolloloolloloolloloolloloollolololololololololololoooloolooloolooloolooloolooloolooooloooolooooloooolooooloooolaoolaoolaoolaoolaoolaoolaoolaoolaoolaoolaoolaolaolaolaolaolaolaolaolaolaolaolaolaolaolahlahlahlahlahlahlahlahlahlahlahlaaaaaaaaaaaaaaaaasasasasasasasasasasssasasssasaasaasaasaasaasaasaasaasaasaasaasararararararararasaraaraaraaraaraaraaraaraaraaraaraaraararararararararamamamamamamamamasamaamaamaamaamaamaamaamaamaamaamaamananananananananananananaananaananaananaananaananaananaananaananaanandandandandandandandaandaandaandaandaandaandaandaandaandeandeandeandeandeandeandeaneaneaneaneaneaneaneaneaneaneaneaneanedededededededededededeadeadeadeadeadeadeadeadededeadededeadededeadededeadededeadedeedeedeedeedeedeedeedeedeedeedefefefefefefefeafeafeafeafeafeafeafeafedefedefedefedefedefedefedefedefefekekekekekekekekakeakeakeakeakeakeakeakokeokeokeokeokeokeokeokeokkekkekkekkekkekkekkekkekkekkekkekkelkelkelkelkelkelkelkelkelkelkelkolekolokolekolokolekolokolekolokolekolokoletletletletletletletletletletloteleteleteleteleteleteleteleteretereteretereteretereteeteeteeteeteeteetevovovovovovovovaovaovaovaovaovaovaovavavavavavavavavavevevevevevevevevevevenenenevenenenevenenenevenenenevenevnevnevnevnevnevnevnewewnewnewnewnewnewnewneweweweweweweweweheheheheheheheahahahahahahahababababababababaabaabaabaabaabaabaabaabacacacacacacacaacaacaacaacaacaacacadacadacadacadacadadadadaadadadadadadadaadaadaadaadaadaadaaeadeadeadeadeadeadeadedaeaeaeaeaeaeaeaeeaeeaeeaeeaeeaeeaeeaeeaefaefaefaefaefaefaefaefaefefeefeefeefeefeefeefeefeefeffeffeffeffeffeffeffeffeffegegegegegegegegaageageageageageageageagaga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'oooooooooooooooooooooooooooooooooooooooooooo',
'oooooooooooooooooooooooooooooooooooooooooooo',
'ooooooo',
'ooooooo',
'oooooooooooooooooooo',
'ooooooo',
'ooooooo',
'oooooooooooooo',
'oo',
'oo',
'oo',
)
snr=np.mean(snr_list)
psnr=np.mean(psnr_list)
total_snr+=snr*batch_size
total_snr_count+=batch_size
total_psnr+=psnr*batch_size
total_psnr_count+=batch_size
del output
del input
del target
del mask
del noise
del noise_mask
gc.collect()
torch.cuda.empty_cache()
end_time=time.time()
report={
"loss":meter.avg(),
"total_len":total_len/10000000000000.,
"duration":end_time-start_time,
"total_snr":total_snr/total_snr_count,
"total_psnr":total_psnr/total_psnr_count,
}
return report
def train(cfg,model,criterion,meter,meters,data_source,args,sample_rate=16000):
meters.reset()
start_time=time.time()
num_steps=int(len(data_source)/cfg.batch_size)*cfg.num_epochs
pbar=tqdm(total=num_steps)
optimizer=model.configure_optimizers()[0]
scheduler=model.configure_optimizers()[1]#lr scheduler
global_step=-1
steps_per_epoch=len(data_source)//cfg.batch_size
epoch=-1
grad_acc_steps=args.grad_acc_steps
grad_acc_count=-grad_acc_steps
log_interval=args.log_interval
valid_interval=args.valid_interval
valid_start_step=int(steps_per_epoch*valid_interval)#step interval when valid starts (default after one epoch)
while global_step=grad_acc_steps:#gradient accumulation steps reached so update parameters
optimizer.step()
scheduler.step()#update learning rate schedule (after each step)
grad_acc_count-=grad_acc_steps#reset gradient accumulation count (after updating parameters)
optimizer.zero_grad()#zero gradients (after updating parameters)
pbar.set_description((str(epoch)+" | "+str(global_step)+" | "+str(round(lr*10000))/10000+" | "+str(round(meter.avg()*10000))/10000))#update tqdm progress bar description text
if global_step%log_interval==log_interval-1:#logging interval reached so print info
elapsed_time=time.time()-start_time #calculate elapsed time since training started
report={}
report["loss"]=meter.avg()
report["elapsed"]=elapsed_time/(60*60)
print(report)
writer.add_scalar('Loss/train',report["loss"],global_step)
writer.add_scalar('Learning_Rate',lr ,global_step)
writer.add_scalar('Time/hour',report["elapsed"],global_step)
if global_step>=valid_start_step:#validation interval reached so evaluate model performance on validation set
print("Validating...")
valid_report=train(cfg,model,criterion,meter,meters,data_source,args,sample_rate)#evaluate model performance using validation set data source object (this function returns a dictionary containing metrics)
pbar.update()#update tqdm progress bar position by one step (after one iteration loop)
def run_train(cfg,model,criterion,meter,meters,data_source,args,sample_rate=16000):
meters.reset()
start_time=time.time()
num_epochs=int(len(data_source)/cfg.batch_size)*cfg.num_epochs
pbar=tqdm(total=num_epochs)
optimizer=model.configure_optimizers()[0]
scheduler=model.configure_optimizers()[1]#lr scheduler
global_epoch=-1
steps_per_epoch=len(data_source)//cfg.batch_size
epoch=-1
grad_acc_steps=args.grad_acc_steps
grad_acc_count=-grad_acc_steps
log_interval=args.log_interval
valid_interval=args.valid_interval
while global_epoch
