Unlocking the Future: Czech Republic Basketball Match Predictions
Welcome to the ultimate destination for Czech Republic basketball enthusiasts seeking daily, expert predictions. Our platform provides the most accurate and up-to-date insights into every match, ensuring you're always ahead of the game. Dive into our comprehensive analysis, expert betting tips, and detailed match previews, all designed to enhance your understanding and enjoyment of the sport. Whether you're a seasoned bettor or a casual fan, our predictions are your key to unlocking the future of Czech basketball.
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Match Previews: A Deep Dive into Upcoming Games
Before each match, we provide detailed previews that give you a comprehensive understanding of what to expect. Our match previews include:
- An in-depth analysis of both teams' recent performances
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Engagement is key to enhancing your experience on our platform. We offer interactive features that allow you to connect with other basketball fans and experts:
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We go beyond standard predictions by offering exclusive content that adds value to your experience:
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danhong123/MLM<|file_sep|>/lib/structure/attribute.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time :2019/11/7 上午10:51
# @Author :DanHong
# @Email :[email protected] import torch.nn as nn class Attributes(nn.Module):
def __init__(self):
super(Attributes,self).__init__() self.attributes = nn.Sequential(
nn.Linear(512 * (4 * (4 + len(ALL_ATTRIBUTES))),128),
nn.ReLU(),
nn.Dropout(),
nn.Linear(128,len(ALL_ATTRIBUTES))
) def forward(self,x):
x = self.attributes(x)
return x <|file_sep|># -*- coding:utf-8 -*-
# author : DanHong import os
import sys sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from PIL import Image
import numpy as np
import cv2
import random
import torch.utils.data as data
import torchvision.transforms as transforms
import torch from config import * # All attributes from CelebA dataset.
ALL_ATTRIBUTES = [
"5_o_Clock_Shadow", "Arched_Eyebrows", "Attractive", "Bags_Under_Eyes", "Bald",
"Bangs", "Big_Lips", "Big_Nose", "Black_Hair", "Blond_Hair", "Blurry", "Brown_Hair",
"Bushy_Eyebrows", "Chubby", "Double_Chin", "Eyeglasses", "Goatee",
"Hair_in_Face", "Heavy_Makeup", "High_Cheekbones", "Male", "Mouth_Slightly_Open",
"Mouth_Slightly_Open", "Mustache", "Narrow_Eyes", "No_Beard",
"No_Beard","Oval_Face","Pale_Skin","Pointy_Nose","Receding_Hairline","Rosy_Cheeks",
"Sideburns","Smiling","Straight_Hair","Wavy_Hair","Wearing_Earrings","Wearing_Hat",
"Wearing_Lipstick","Wearing_Necklace","Wearing_Necktie","Young"] def _get_all_images():
all_images = []
with open(os.path.join(DATA_ROOT,'list_eval_partition.txt')) as f:
lines = f.readlines()
for line in lines:
line = line.strip().split()
if line[1] == '0':
all_images.append(line[0])
return all_images def _get_all_labels():
all_labels = []
with open(os.path.join(DATA_ROOT,'list_attr_celeba.txt')) as f:
lines = f.readlines()
for i,line in enumerate(lines):
if i ==0:
continue
line = line.strip().split()
all_labels.append(line[1:])
return all_labels def _get_attr_id(attr_name):
return ALL_ATTRIBUTES.index(attr_name) def get_attr_id_list(attr_names):
attr_id_list = []
for attr_name in attr_names:
attr_id_list.append(_get_attr_id(attr_name))
return attr_id_list def get_random_image(all_images):
while True:
index = random.randint(0,len(all_images)-1)
image_path = os.path.join(DATA_ROOT,'img_align_celeba',all_images[index])
image = Image.open(image_path).convert('RGB')
if image is None:
continue
break
return image def get_random_image_with_attr(all_images,all_labels,img_attr_ids):
while True:
index = random.randint(0,len(all_images)-1)
image_path = os.path.join(DATA_ROOT,'img_align_celeba',all_images[index])
image = Image.open(image_path).convert('RGB')
if image is None:
continue
label_values = [int(i) for i in all_labels[index]]
if not np.any([label_values[attr_id] != -1 for attr_id in img_attr_ids]):
continue
break
return image,label_values class RandomErasing(object):
def __init__(self,p=0.5,s_l=0.02,s_h=0.4,r_1=0.3,r_2=1/0.3):
self.p=p
self.s_l=s_l
self.s_h=s_h
self.r_1=r_1
self.r_2=r_2 def __call__(self,img):
if random.uniform(0,1) > self.p:
return img
img=np.array(img) class CelebA(data.Dataset): def __init__(self,batch_size,img_transform=None,label_transform=None,
img_target_transform=None,test=False,
selected_attrs=[],img_size=64,
random_erasing_prob=0,img_random_erasing_param=None,
only_same_attrs=True):
def __len__(self):
def __getitem__(self,index): <|repo_name|>danhong123/MLM<|file_sep|>/lib/models.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time :2019/11/7 上午10:49
# @Author :DanHong
# @Email :[email protected] import torch.nn as nn class Generator(nn.Module): def __init__(self,z_dim):
super(Generator,self).__init__() def forward(self,x):
return x class Discriminator(nn.Module): def __init__(self,img_size=64):
super(Discriminator,self).__init__() def forward(self,x):
return x class AttributeDiscriminator(nn.Module): def __init__(self,img_size=64):
super(AttributeDiscriminator,self).__init__() def forward(self,x,y):
return x,y<|repo_name|>danhong123/MLM<|file_sep|>/lib/dataset/__init__.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time :2019/11/6 下午3:16
# @Author :DanHong
# @Email :[email protected] from .celeba import CelebA
from .image_folder import ImageFolder <|repo_name|>danhong123/MLM<|file_sep|>/README.md ## Implementation of [Masked Latent Manipulation](https://arxiv.org/pdf/1905.10334.pdf) This repository contains implementation details of paper Masked Latent Manipulation based on PyTorch. ## Requirements * Python>=3.6 * PyTorch>=1.0 * CUDA>=9.0 * torchvision>=0.4 ## Getting Started Clone this repository: bash
git clone https://github.com/danhong123/MLM.git
cd MLM Install requirements: bash
pip install -r requirements.txt Download [CelebA dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) Put it under `data` folder. Train model: bash
python train.py --gpu_ids='0' --model='celeba' --log_dir='./logs' --checkpoint_dir='./checkpoints' Generate images: bash
python test.py --gpu_ids='0' --model='celeba' --log_dir='./logs' --checkpoint_dir='./checkpoints' ### Data folder structure The folder structure should be like this: ├── data/
│ ├── list_eval_partition.txt
│ ├── list_attr_celeba.txt
│ └── img_align_celeba/
├── lib/
├── logs/
├── checkpoints/
├── results/
├── requirements.txt
└── train.py ### Results You can find generated results under `results` folder. ### Reference * [Pytorch-GAN](https://github.com/eriklindernoren/PyTorch-GAN) * [Masked Latent Manipulation](https://arxiv.org/pdf/1905.10334.pdf) ## Citation If you find this code useful in your research work please consider citing: @article{zheng2019masked,
title={Masked Latent Manipulation},
author={Zheng et al},
journal={arXiv preprint arXiv:1905.10334},
url={https://arxiv.org/pdf/1905.10334.pdf}
} <|repo_name|>danhong123/MLM<|file_sep|>/lib/dataset/image_folder.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time :2019/11/6 下午3:17
# @Author :DanHong
# @Email :[email protected] from PIL import Image
import numpy as np
import torch.utils.data as data
import torchvision.transforms as transforms class ImageFolder(data.Dataset): def __init__(self,img_paths,batch_size,img_transform=None,label_transform=None,
img_target_transform=None,test=False,
selected_attrs=[],img_size=64):
def __len__(self):
def __getitem__(self,index): <|repo_name|>danhong123/MLM<|file_sep|>/train.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time :2019/11/6 下午3:15
# @Author :DanHong
# @Email :[email protected] from config import *
from lib.dataset import CelebA
from lib.models import *
from lib.trainers import *
from lib.utils.logger import Logger if __name__ == '__main__': args=get_args() logger=Logger(log_dir=args.log_dir) if args.model == 'celeba': else:
raise NotImplementedError("No such model") logger.close() <|repo_name|>danhong123/MLM<|file_sep|>/requirements.txt
torch==1.1.0
torchvision==0.3.0
tqdm==4.31
numpy==1.16.
Pillow==6.<|file_sep|># -*- coding:utf-8 -*-
# author : DanHong from .utils import *
from .dataset import CelebA if __name__ == '__main__':
all_images=_get_all_images()
print(len(all_images)) all_labels=_get_all_labels()
print(len(all_labels)) print(get_random_image(all_images).size()) print(get_random_image_with_attr(all_images,
all_labels,
get_attr_id_list(['Smiling','Male'])).size())
dataset=CelebA(batch_size=16,
selected_attrs=['Smiling','Male'],
img_size=128) print(len(dataset)) print(dataset[10])<|file_sep|># -*- coding:utf-8 -*-
# author : DanHong DATA_ROOT='./data/' BATCH_SIZE_TRAIN=16 # batch size used during training.
BATCH_SIZE_TEST=50 # batch size used during testing. NUM_WORKERS_TRAIN=4 # number of workers used during training.
NUM_WORKERS_TEST=4 # number of workers used during testing. IMG_SIZE_TRAIN=64 # size used during training.
IMG_SIZE_TEST=256 # size used during testing. LR_G_D=1e-4 # learning rate used for generator/discriminator.
LR_A_D=1e-4 # learning rate used for attribute discriminator. LAMBDA_GP=10 # weight factor for gradient penalty.
LAMBDA_REC_IMG=10 # weight factor for image reconstruction loss.
LAMBDA_REC_ATTR=10 # weight factor for attribute reconstruction loss.
LAMBDA_CLS_ATTR=100 # weight factor for attribute classification loss. N_CRITIC_ITERS_D=5 # number of critic iterations per G iteration.
N_CRITIC_ITERS_A_D=5 # number of critic iterations per G iteration. N_EPOCHS_CELEBA_GAN_TRAINING_BASELINE=100 # number of epochs used when training baseline GAN model.
N_EPOCHS_CELEBA_GAN_TRAINING_ADVANCED=50000 # number of epochs used when training advanced GAN model. selected_attrs=['Smiling','Male']
selected_attr_ids=get_attr_id_list(selected_attrs)<|file_sep|># -*- coding:utf-8 -*-
# author : DanHong import torch.nn as nn class Res