Discover the Thrill of Kazakhstan's Ice-Hockey Championship
Welcome to the heart of Kazakhstan's ice-hockey action! Our platform provides you with the most up-to-date information on fresh matches from the Kazakhstan Ice-Hockey Championship. Whether you're a seasoned fan or new to the sport, our expert betting predictions will enhance your viewing experience and help you stay ahead of the game. Dive into the world of ice-hockey in Kazakhstan and explore everything you need to know about this exhilarating sport.
Understanding Kazakhstan's Ice-Hockey Landscape
Kazakhstan has been carving out a significant niche in the world of ice-hockey, showcasing a blend of emerging talent and seasoned professionals. The championship is not just a display of athletic prowess but also a cultural event that brings together communities across the nation. With teams from various regions competing fiercely, each match is a spectacle of skill, strategy, and passion.
Key Features of Our Platform
- Real-Time Match Updates: Get live updates on every game as they happen, ensuring you never miss a moment of the action.
- Expert Betting Predictions: Benefit from insights provided by our team of seasoned analysts who offer predictions based on comprehensive data analysis.
- Detailed Team Profiles: Explore in-depth profiles of each team, including player stats, historical performance, and upcoming strategies.
- User-Friendly Interface: Navigate our platform with ease, thanks to an intuitive design that makes accessing information simple and efficient.
Why Follow Kazakhstan's Ice-Hockey Championship?
The championship is more than just a series of games; it's an opportunity to witness the growth of ice-hockey in a region known for its rich sporting traditions. As Kazakhstan continues to develop its sports infrastructure, the quality and competitiveness of its ice-hockey matches are reaching new heights. This makes it an exciting time for fans and bettors alike.
Insights into Kazakhstan's Top Teams
Each team in the championship brings its unique strengths to the ice. From powerhouse clubs with storied histories to rising stars making their mark, understanding these teams is key to appreciating the dynamics of each match.
- Barys Astana: Known for its strong defensive play and strategic prowess, Barys Astana has consistently been at the forefront of Kazakhstan's ice-hockey scene.
- Aktobe Toros: With a focus on speed and agility, Aktobe Toros is a team that thrives on quick transitions and dynamic offensive strategies.
- Kazakhmys Satpayev: Renowned for its robust physical play and resilience, Kazakhmys Satpayev is a formidable opponent on any given day.
The Role of Betting in Enhancing Your Experience
Betting adds an extra layer of excitement to watching ice-hockey. Our expert predictions are crafted using advanced algorithms and deep statistical analysis, providing you with insights that can inform your betting decisions. Whether you're looking to place small bets or go all-in on your favorite team, our platform ensures you have all the information you need.
How Our Expert Predictions Are Made
Our predictions are not based on mere speculation; they are the result of rigorous analysis. We consider various factors such as team form, head-to-head statistics, player injuries, and even weather conditions that might affect outdoor training sessions. This comprehensive approach ensures that our predictions are as accurate as possible.
Detailed Match Analysis and Previews
Before each match, we provide detailed analyses and previews to give you a better understanding of what to expect. These include:
- Team Form: An overview of how each team has performed in recent matches.
- Key Players to Watch: Insights into players who could be game-changers in upcoming matches.
- Tactical Breakdown: An analysis of potential strategies that teams might employ during the game.
The Cultural Impact of Ice-Hockey in Kazakhstan
Beyond the rink, ice-hockey plays a significant role in shaping cultural identity in Kazakhstan. It serves as a unifying force, bringing together people from diverse backgrounds to celebrate their shared passion for the sport. The championship is not just about winning or losing; it's about community spirit and national pride.
Future Prospects for Kazakhstan's Ice-Hockey Scene
With continued investment in sports infrastructure and youth development programs, the future looks bright for ice-hockey in Kazakhstan. The country is nurturing young talent through specialized training academies and international collaborations. This focus on development promises to elevate Kazakhstan's standing on the global ice-hockey stage.
Engaging with Fans: Social Media and Community Events
We believe in fostering a strong community around our platform. Engage with other fans through our social media channels where you can share your thoughts, predictions, and experiences. Additionally, we host community events that allow fans to meet players, participate in interactive sessions, and immerse themselves fully in the world of ice-hockey.
Tips for Newcomers to Ice-Hockey Betting
If you're new to betting on ice-hockey, here are some tips to get you started:
- Start Small: Begin with modest bets as you familiarize yourself with the nuances of betting.
- Educate Yourself: Take time to understand betting terms and strategies before placing your bets.
- Analyze Data: Use data-driven insights from our platform to make informed decisions.
- Maintain Discipline: Set limits for yourself to ensure that betting remains a fun and responsible activity.
The Technical Aspects of Ice-Hockey Matches
zhaozhuolun/Pyramid-of-Pyramids<|file_sep|>/POPC-Net/train.py
import os
import sys
import torch
import random
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
sys.path.append('../')
from utils.utils import *
from utils.metrics import *
from utils.lr_scheduler import *
from utils.logger import Logger
from utils.dataset import Dataset
from models.popc_net import POPC_Net
device = 'cuda' if torch.cuda.is_available() else 'cpu'
def main():
# set random seed
torch.manual_seed(123)
torch.cuda.manual_seed_all(123)
random.seed(123)
# set hyperparameters
batch_size = 4 # (B)
num_workers = 8 # (w)
num_epochs = 200 # (E)
learning_rate = 1e-4 # (lr)
weight_decay = 1e-4 # (wd)
dataset_path = '../datasets/POP_dataset/'
log_dir = './logs'
model_save_path = './checkpoints/POPC_Net.pth'
input_height = output_height = 256 # (H)
pyramid_levels = [0] + [i for i in range(1, int(math.log(input_height/8)/math.log(2))+1)]
input_channels = [3] + [64*(4**i) for i in pyramid_levels[1:]]
output_channels = [64*(4**i) for i in pyramid_levels[:-1]] + [3]
total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('Total params: %d' % total_params)
logger = Logger(log_dir=log_dir)
train_dataset = Dataset(dataset_path=dataset_path,
mode='train',
pyramid_levels=pyramid_levels,
input_channels=input_channels,
output_channels=output_channels,
crop_size=[input_height,input_height])
train_loader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True,
drop_last=True)
test_dataset = Dataset(dataset_path=dataset_path,
mode='test',
pyramid_levels=pyramid_levels,
input_channels=input_channels,
output_channels=output_channels,
crop_size=[input_height,input_height])
test_loader = DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True)
model = POPC_Net(pyramid_levels=pyramid_levels,
input_channels=input_channels,
output_channels=output_channels).to(device)
criterion = nn.MSELoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
scheduler = StepLR(optimizer=optimizer,
step_size=int(num_epochs*0.75),
gamma=0.5)
# scheduler_warmup = GradualWarmupScheduler(optimizer=optimizer,
# multiplier=10,
# total_epoch=int(num_epochs*0.05),
# after_scheduler=scheduler)
# scheduler_warmup.step()
# print('learning rate schedule:')
# for epoch in range(num_epochs):
# print('Epoch {}/{}: {}'.format(epoch+1,num_epochs,scheduler_warmup.get_lr()))
# scheduler_warmup.last_epoch += num_epochs
# scheduler_warmup.step()
# print('learning rate schedule:')
# for epoch in range(num_epochs):
# print('Epoch {}/{}: {}'.format(epoch+1,num_epochs,scheduler_warmup.get_lr()))
# scheduler_warmup.last_epoch += num_epochs
# scheduler_warmup.step()
# print('learning rate schedule:')
# for epoch in range(num_epochs):
# print('Epoch {}/{}: {}'.format(epoch+1,num_epochs,scheduler_warmup.get_lr()))
# scheduler_warmup.last_epoch += num_epochs
# scheduler_warmup.step()
# print('learning rate schedule:')
# for epoch in range(num_epochs):
# print('Epoch {}/{}: {}'.format(epoch+1,num_epochs,scheduler_warmup.get_lr()))
best_mse_loss = float('inf')
mse_losses_train_hist = []
mse_losses_test_hist = []
start_time_epoch_all = time.time()
model.train()
for epoch in range(num_epochs):
print('n')
best_mse_loss_index_test = mse_losses_test_hist.index(min(mse_losses_test_hist))
print('nBest MSE Loss Test:',round(mse_losses_test_hist[best_mse_loss_index_test],6))
print('Epoch:',best_mse_loss_index_test+1,'n')
best_mse_loss_index_train = mse_losses_train_hist.index(min(mse_losses_train_hist))
print('nBest MSE Loss Train:',round(mse_losses_train_hist[best_mse_loss_index_train],6))
print('Epoch:',best_mse_loss_index_train+1,'n')
end_time_epoch_all = time.time()
time_epoch_all_spend_time_all_hours_all_minutes_all_seconds_all_milliseconds_all_microseconds_all_nanoseconds_all_billion_seconds_all_billion_nanoseconds
= str(end_time_epoch_all-start_time_epoch_all)
.split('.')[0] + '.' + str(end_time_epoch_all-start_time_epoch_all)
.split('.')[1][:13]
print('nTime Epoch All Spend Time All Hours All Minutes All Seconds All Milliseconds All Microseconds All Nanoseconds All Billion Seconds All Billion Nanoseconds:n',time_epoch_all_spend_time_all_hours_all_minutes_all_seconds_all_milliseconds_all_microseconds_all_nanoseconds_all_billion_seconds_all_billion_nanoseconds)
model_save_dirname_base,_model_save_dirname_extention
= os.path.splitext(model_save_path)
model_load_path
= model_save_dirname_base+'_E'+str(best_mse_loss_index_test+1)+_model_save_dirname_extention
model.load_state_dict(torch.load(model_load_path))
mse_loss_test_final
,psnr_loss_test_final,msssim_loss_test_final,msssim_95th_percentile_loss_test_final
,_,_,_,_
,_,_,_,_
,_,_,_
,_,_,_
,_
,_
,_= test(test_loader,test_dataset,model,criterion,batch_size,num_workers)
logger.plot_curve(x_label='Epoch',
y_label='MSE Loss Train',
y_data=mse_losses_train_hist,
title='MSE Loss Train',
save_dir=log_dir+'/mse_loss_train.png')
logger.plot_curve(x_label='Epoch',
y_label='MSE Loss Test',
y_data=mse_losses_test_hist,
title='MSE Loss Test',
save_dir=log_dir+'/mse_loss_test.png')
logger.save_model(model=model_save_path)
if __name__ == '__main__':
main()<|file_sep|># Pyramid-of-Pyramids
This repository contains code for "Pyramid-of-Pyramids Network (POPC-Net): A Multi-Scale Network Architecture For Single Image Super-Resolution" paper.
## Introduction
We propose a novel multi-scale network architecture named Pyramid-of-Pyramids Network (POPC-Net), which can obtain high-resolution details by exploiting hierarchical image representations at different scales.
## Requirements
* Python==3.6.*
* PyTorch==1.*
* torchvision==0.*
## Usage
shell
python train.py
shell
python test.py
## Datasets
The POP dataset can be downloaded from [Google Drive](https://drive.google.com/file/d/15q7SxX9EjGQo7XGZvLcTgR4KJWgAaBYC/view?usp=sharing).
## Citing
If you find this work useful for your research please consider citing:
@article{zhu2020popcnet,
title={Pyramid-of-Pyramids Network (POPC-Net): A Multi-Scale Network Architecture For Single Image Super-Resolution},
author={Zhuo-Lun Zhao},
journal={arXiv preprint arXiv:2010.01293},
year={2020}
}
## License
This project is licensed under MIT license.
## Acknowledgement
The code is adapted from [SRResNet](https://github.com/yulunzhang/SRResNet).
<|repo_name|>zhaozhuolun/Pyramid-of-Pyramids<|file_sep|>/POPC-Net/utils/metrics.py
import torch
import numpy as np
import math
def mse(y_pred,y_true):
y_pred_mean_per_image,y_true_mean_per_image,y_pred_mean,y_true_mean
=(torch.mean(y_pred,dim=[d for d in range(1,len(y_pred.shape))]),
torch.mean(y_true,dim=[d for d in range(1,len(y_true.shape))]),
torch.mean(y_pred),
torch.mean(y_true))
y_pred_centered_per_image=y_pred-y_pred_mean_per_image.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
y_true_centered_per_image=y_true-y_true_mean_per_image.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
y_pred_centered=y_pred-y_pred_mean
y_true_centered=y_true-y_true_mean
numerator=torch.sum(torch.mul(y_pred_centered_per_image,y_true_centered_per_image),dim=[d for d in range(1,len(y_pred.shape))])
denominator=torch.sqrt(torch.sum(torch.pow(y_pred_centered_per_image,dim=2),dim=[
