repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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CoordFill | CoordFill-master/models/ffc.py | # Fast Fourier Convolution NeurIPS 2020
# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py
# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import ... | 22,247 | 39.014388 | 125 | py |
CoordFill | CoordFill-master/models/models.py | import copy
models = {}
def register(name):
def decorator(cls):
models[name] = cls
return cls
return decorator
def make(model_spec, args=None, load_sd=False):
if args is not None:
model_args = copy.deepcopy(model_spec['args'])
model_args.update(args)
else:
m... | 485 | 19.25 | 54 | py |
CoordFill | CoordFill-master/models/__init__.py | from .models import register, make
from . import gan, modules, coordfill, ffc_baseline
from . import misc
| 106 | 25.75 | 51 | py |
CoordFill | CoordFill-master/models/sync_batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import collections
import contextlib
import... | 16,476 | 43.05615 | 135 | py |
CoordFill | CoordFill-master/models/adv_loss.py | import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch import autograd
import torchvision.models as models
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class AdversarialLoss(nn.Module):
"""
Adversarial loss
https://arxiv.org/abs/1711... | 1,484 | 28.7 | 89 | py |
CoordFill | CoordFill-master/models/coordfill.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from scipy import ndimage
import numpy as np
from .ffc import FFCResNetGenerator
from .modules import CoordFillGenerator
from .ffc import FFCResNetGenerator, FFCResnetBlock, ConcatTupleLayer, FFC_BN_ACT
class AttFFC(nn.Module):
"""Convolutional LR... | 7,669 | 36.598039 | 135 | py |
CoordFill | CoordFill-master/models/bn_helper.py | import torch
import functools
if torch.__version__.startswith('0'):
from .sync_bn.inplace_abn.bn import InPlaceABNSync
BatchNorm2d = functools.partial(InPlaceABNSync, activation='none')
BatchNorm2d_class = InPlaceABNSync
relu_inplace = False
else:
BatchNorm2d_class = BatchNorm2d = torch.nn.SyncBatc... | 451 | 27.25 | 70 | py |
CoordFill | CoordFill-master/models/ffc_baseline.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from scipy import ndimage
import numpy as np
class ResnetBlock_remove_IN(nn.Module):
def __init__(self, dim, dilation=1, use_spectral_norm=True):
super(ResnetBlock_remove_IN, self).__init__()
self.conv_block = nn.Sequential(
... | 4,182 | 32.464 | 164 | py |
CoordFill | CoordFill-master/models/LPIPS/models/base_model.py | import os
import torch
import sys
sys.path.insert(1, './LPIPS/')
# import util.util as util
from torch.autograd import Variable
from pdb import set_trace as st
from IPython import embed
class BaseModel():
def __init__(self):
pass;
def name(self):
return 'BaseModel'
def initializ... | 1,794 | 26.19697 | 78 | py |
CoordFill | CoordFill-master/models/LPIPS/models/pretrained_networks.py | from collections import namedtuple
import torch
from torchvision import models
from IPython import embed
class squeezenet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(squeezenet, self).__init__()
pretrained_features = models.squeezenet1_1(pretrained=pretrained).... | 6,788 | 35.5 | 121 | py |
CoordFill | CoordFill-master/models/LPIPS/models/networks_basic.py |
from __future__ import absolute_import
import sys
sys.path.append('..')
sys.path.append('.')
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable
import numpy as np
from pdb import set_trace as st
from skimage import color
from IPython import embed
from . import pretrain... | 10,730 | 37.188612 | 136 | py |
CoordFill | CoordFill-master/models/LPIPS/models/models.py | from __future__ import absolute_import
def create_model(opt):
model = None
print(opt.model)
from .siam_model import *
model = DistModel()
model.initialize(opt, opt.batchSize, )
print("model [%s] was created" % (model.name()))
return model
| 269 | 21.5 | 52 | py |
CoordFill | CoordFill-master/models/LPIPS/models/__init__.py | 0 | 0 | 0 | py | |
CoordFill | CoordFill-master/models/LPIPS/models/dist_model.py |
from __future__ import absolute_import
import sys
sys.path.append('..')
sys.path.append('.')
import numpy as np
import torch
from torch import nn
import os
from collections import OrderedDict
from torch.autograd import Variable
import itertools
from .base_model import BaseModel
from scipy.ndimage import zoom
import f... | 13,452 | 39.521084 | 278 | py |
CoordFill | CoordFill-master/models/LPIPS/util/html.py | import dominate
from dominate.tags import *
import os
class HTML:
def __init__(self, web_dir, title, image_subdir='', reflesh=0):
self.title = title
self.web_dir = web_dir
# self.img_dir = os.path.join(self.web_dir, )
self.img_subdir = image_subdir
self.img_dir = os.path.jo... | 2,023 | 29.208955 | 91 | py |
CoordFill | CoordFill-master/models/LPIPS/util/visualizer.py | import numpy as np
import os
import time
from . import util
from . import html
# from pdb import set_trace as st
import matplotlib.pyplot as plt
import math
# from IPython import embed
def zoom_to_res(img,res=256,order=0,axis=0):
# img 3xXxX
from scipy.ndimage import zoom
zoom_factor = res/img.shape[1]
... | 8,602 | 38.645161 | 116 | py |
CoordFill | CoordFill-master/models/LPIPS/util/util.py | from __future__ import print_function
import numpy as np
from PIL import Image
import inspect
import re
import numpy as np
import os
import collections
import matplotlib.pyplot as plt
from scipy.ndimage.interpolation import zoom
from skimage.measure import compare_ssim
# from skimage.metrics import
from skimage import... | 14,095 | 29.912281 | 153 | py |
CoordFill | CoordFill-master/models/LPIPS/util/__init__.py | 0 | 0 | 0 | py | |
CoordFill | CoordFill-master/datasets/wrappers.py | import functools
import random
import math
from PIL import Image
import numpy as np
import torch
from torch.utils.data import Dataset
from torchvision import transforms
from datasets import register
def to_mask(mask):
return transforms.ToTensor()(
transforms.Grayscale(num_output_channels=1)(
... | 2,575 | 22.851852 | 77 | py |
CoordFill | CoordFill-master/datasets/datasets.py | import copy
datasets = {}
def register(name):
def decorator(cls):
datasets[name] = cls
return cls
return decorator
def make(dataset_spec, args=None):
if args is not None:
dataset_args = copy.deepcopy(dataset_spec['args'])
dataset_args.update(args)
else:
data... | 432 | 18.681818 | 60 | py |
CoordFill | CoordFill-master/datasets/image_folder.py | import os
import json
from PIL import Image
import pickle
import imageio
import numpy as np
import torch
from torch.utils.data import Dataset
from torchvision import transforms
from datasets import register
@register('image-folder')
class ImageFolder(Dataset):
def __init__(self, path, split_file=None, split_key... | 1,885 | 27.575758 | 107 | py |
CoordFill | CoordFill-master/datasets/__init__.py | from .datasets import register, make
from . import image_folder
from . import wrappers
| 87 | 21 | 36 | py |
cycle-transformer | cycle-transformer-main/test.py | # This code is released under the CC BY-SA 4.0 license.
import glob
import os
import numpy as np
import pandas as pd
import pydicom
import torch
from skimage.metrics import structural_similarity as ssim
from models import create_model
from options.train_options import TrainOptions
@torch.no_grad()
def compute_eval_... | 2,738 | 29.775281 | 86 | py |
cycle-transformer | cycle-transformer-main/train.py | # This code is released under the CC BY-SA 4.0 license.
import time
from options.train_options import TrainOptions
from data import create_dataset
from models import create_model
from util.visualizer import Visualizer
if __name__ == '__main__':
opt = TrainOptions().parse() # get training options
dataset = ... | 3,739 | 57.4375 | 136 | py |
cycle-transformer | cycle-transformer-main/options/train_options.py | from .base_options import BaseOptions
class TrainOptions(BaseOptions):
"""This class includes training options.
It also includes shared options defined in BaseOptions.
"""
def initialize(self, parser):
parser = BaseOptions.initialize(self, parser)
# visdom and HTML visualization para... | 3,916 | 80.604167 | 210 | py |
cycle-transformer | cycle-transformer-main/options/base_options.py | import argparse
import os
from util import util
import torch
import models as models
class BaseOptions:
"""This class defines options used during both training and test time.
It also implements several helper functions such as parsing, printing, and saving the options.
It also gathers additional options ... | 8,414 | 58.680851 | 235 | py |
cycle-transformer | cycle-transformer-main/options/__init__.py | """This package options includes option modules: training options, test options, and basic options (used in both training and test)."""
| 136 | 67.5 | 135 | py |
cycle-transformer | cycle-transformer-main/options/test_options.py | from .base_options import BaseOptions
class TestOptions(BaseOptions):
"""This class includes test options.
It also includes shared options defined in BaseOptions.
"""
def initialize(self, parser):
parser = BaseOptions.initialize(self, parser) # define shared options
# parser.add_arg... | 1,168 | 47.708333 | 110 | py |
cycle-transformer | cycle-transformer-main/models/base_model.py | import os
import torch
from collections import OrderedDict
from abc import ABC, abstractmethod
from . import networks
class BaseModel(ABC):
"""This class is an abstract base class (ABC) for models.
To create a subclass, you need to implement the following five functions:
-- <__init__>: ... | 10,583 | 44.038298 | 260 | py |
cycle-transformer | cycle-transformer-main/models/cytran_model.py | # This code is released under the CC BY-SA 4.0 license.
import torch
import itertools
from util import ImagePool
from models.conv_transformer import ConvTransformer
from .base_model import BaseModel
from . import networks
class CyTranModel(BaseModel):
@staticmethod
def modify_commandline_options(parser, is_t... | 10,350 | 54.352941 | 362 | py |
cycle-transformer | cycle-transformer-main/models/conv_transformer.py | # This code is released under the CC BY-SA 4.0 license.
from einops import rearrange
from torch import nn, einsum
import functools
class Encoder(nn.Module):
def __init__(self, input_nc, ngf=16, norm_layer=nn.BatchNorm2d, n_downsampling=3):
super(Encoder, self).__init__()
if type(norm_layer) == fu... | 6,016 | 34.187135 | 116 | py |
cycle-transformer | cycle-transformer-main/models/networks.py | # This code is released under the CC BY-SA 4.0 license.
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
###############################################################################
# Helper Functions
###############################################... | 28,452 | 45.115073 | 167 | py |
cycle-transformer | cycle-transformer-main/models/__init__.py | """This package contains modules related to objective functions, optimizations, and network architectures.
To add a custom model class called 'dummy', you need to add a file called 'dummy_model.py' and define a subclass DummyModel inherited from BaseModel.
You need to implement the following five functions:
-- <__... | 3,080 | 44.308824 | 250 | py |
cycle-transformer | cycle-transformer-main/models/cycle_gan_model.py | # This code is released under the CC BY-SA 4.0 license.
import torch
import itertools
from util import ImagePool
from .base_model import BaseModel
from . import networks
class CycleGANModel(BaseModel):
"""
This class implements the CycleGAN model, for learning image-to-image translation without paired data.
... | 10,621 | 52.918782 | 362 | py |
cycle-transformer | cycle-transformer-main/util/image_pool.py | import random
import torch
class ImagePool:
"""This class implements an image buffer that stores previously generated images.
This buffer enables us to update discriminators using a history of generated images
rather than the ones produced by the latest generators.
"""
def __init__(self, pool_si... | 2,224 | 39.454545 | 140 | py |
cycle-transformer | cycle-transformer-main/util/html.py | import dominate
from dominate.tags import meta, h3, table, tr, td, p, a, img, br
import os
class HTML:
"""This HTML class allows us to save images and write texts into a single HTML file.
It consists of functions such as <add_header> (add a text header to the HTML file),
<add_images> (add a row of imag... | 3,223 | 36.057471 | 157 | py |
cycle-transformer | cycle-transformer-main/util/visualizer.py | import numpy as np
import os
import sys
import ntpath
import time
from . import util, html
from subprocess import Popen, PIPE
if sys.version_info[0] == 2:
VisdomExceptionBase = Exception
else:
VisdomExceptionBase = ConnectionError
def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
... | 10,460 | 46.121622 | 139 | py |
cycle-transformer | cycle-transformer-main/util/util.py | """This module contains simple helper functions """
from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
def tensor2im(input_image, imtype=np.uint8):
""""Converts a Tensor array into a numpy image array.
Parameters:
input_image (tensor) -- the input i... | 3,175 | 29.538462 | 119 | py |
cycle-transformer | cycle-transformer-main/util/__init__.py | """This package includes a miscellaneous collection of useful helper functions."""
| 83 | 41 | 82 | py |
cycle-transformer | cycle-transformer-main/util/get_data.py | from __future__ import print_function
import os
import tarfile
import requests
from warnings import warn
from zipfile import ZipFile
from bs4 import BeautifulSoup
from os.path import abspath, isdir, join, basename
class GetData(object):
def __init__(self, technique='cyclegan', verbose=True):
url_dict = {
... | 3,093 | 31.229167 | 90 | py |
cycle-transformer | cycle-transformer-main/datasets/combine_A_and_B.py | import os
import numpy as np
import cv2
import argparse
from multiprocessing import Pool
def image_write(path_A, path_B, path_AB):
im_A = cv2.imread(path_A, 1) # python2: cv2.CV_LOAD_IMAGE_COLOR; python3: cv2.IMREAD_COLOR
im_B = cv2.imread(path_B, 1) # python2: cv2.CV_LOAD_IMAGE_COLOR; python3: cv2.IMREAD_COL... | 3,002 | 43.161765 | 181 | py |
cycle-transformer | cycle-transformer-main/datasets/prepare_cityscapes_dataset.py | import os
import glob
from PIL import Image
help_msg = """
The dataset can be downloaded from https://cityscapes-dataset.com.
Please download the datasets [gtFine_trainvaltest.zip] and [leftImg8bit_trainvaltest.zip] and unzip them.
gtFine contains the semantics segmentations. Use --gtFine_dir to specify the path to th... | 4,127 | 40.28 | 142 | py |
cycle-transformer | cycle-transformer-main/datasets/make_dataset_aligned.py | import os
from PIL import Image
def get_file_paths(folder):
image_file_paths = []
for root, dirs, filenames in os.walk(folder):
filenames = sorted(filenames)
for filename in filenames:
input_path = os.path.abspath(root)
file_path = os.path.join(input_path, filename)
... | 2,257 | 34.28125 | 97 | py |
cycle-transformer | cycle-transformer-main/data/colorization_dataset.py | import os
from data.base_dataset import BaseDataset, get_transform
from data import make_dataset
from skimage import color # require skimage
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
class ColorizationDataset(BaseDataset):
"""This dataset class can load a set of natural... | 2,704 | 38.202899 | 141 | py |
cycle-transformer | cycle-transformer-main/data/base_dataset.py | """This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
"""
import random
import numpy as np
import torch.utils.data as data
from PIL import Image
import torchvision.... | 5,400 | 33.183544 | 141 | py |
cycle-transformer | cycle-transformer-main/data/unaligned_dataset.py | import os
from data.base_dataset import BaseDataset, get_transform
from data import make_dataset
from PIL import Image
import random
class UnalignedDataset(BaseDataset):
"""
This dataset class can load unaligned/unpaired datasets.
It requires two directories to host training images from domain A '/path/t... | 3,286 | 44.652778 | 122 | py |
cycle-transformer | cycle-transformer-main/data/ct_dataset.py | # This code is released under the CC BY-SA 4.0 license.
import pickle
import numpy as np
import pydicom
from data.base_dataset import BaseDataset
class CTDataset(BaseDataset):
def __init__(self, opt):
BaseDataset.__init__(self, opt)
self.raw_data = pickle.load(open(opt.dataroot, "rb"))
... | 1,330 | 26.163265 | 69 | py |
cycle-transformer | cycle-transformer-main/data/image_folder.py | """A modified image folder class
We modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py)
so that this class can load images from both current directory and its subdirectories.
"""
import torch.utils.data as data
from PIL import Image
import os
IMG_E... | 1,885 | 27.575758 | 122 | py |
cycle-transformer | cycle-transformer-main/data/aligned_dataset.py | import os
from data.base_dataset import BaseDataset, get_params, get_transform
from data import make_dataset
from PIL import Image
class AlignedDataset(BaseDataset):
"""A dataset class for paired image dataset.
It assumes that the directory '/path/to/data/train' contains image pairs in the form of {A,B}.
... | 2,444 | 39.081967 | 118 | py |
cycle-transformer | cycle-transformer-main/data/__init__.py | """This package includes all the modules related to data loading and preprocessing
To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
You need to implement four functions:
-- <__init__>: ... | 3,270 | 37.034884 | 176 | py |
cycle-transformer | cycle-transformer-main/data/template_dataset.py | from data.base_dataset import BaseDataset, get_transform
# from data.image_folder import make_dataset
# from PIL import Image
class TemplateDataset(BaseDataset):
"""A template dataset class for you to implement custom datasets."""
@staticmethod
def modify_commandline_options(parser, is_train):
"""... | 2,822 | 43.809524 | 156 | py |
cycle-transformer | cycle-transformer-main/data/single_dataset.py | from data.base_dataset import BaseDataset, get_transform
from data import make_dataset
from PIL import Image
class SingleDataset(BaseDataset):
"""This dataset class can load a set of images specified by the path --dataroot /path/to/data.
It can be used for generating CycleGAN results only for one side with t... | 1,482 | 35.170732 | 105 | py |
GreedyAC | GreedyAC-master/main.py | # Import modules
import numpy as np
import environment
import experiment
import pickle
from utils import experiment_utils as exp_utils
import click
import json
from copy import deepcopy
import os
import utils.hypers as hypers
import socket
@click.command(help="Run an experiment outlined by an algorithm and " +
... | 9,203 | 36.721311 | 79 | py |
GreedyAC | GreedyAC-master/environment.py | # Import modules
import gym
from copy import deepcopy
from env.PendulumEnv import PendulumEnv
from env.Acrobot import AcrobotEnv
import env.MinAtar as MinAtar
import numpy as np
class Environment:
"""
Class Environment is a wrapper around OpenAI Gym environments, to ensure
logging can be done as well as t... | 7,743 | 28.444867 | 79 | py |
GreedyAC | GreedyAC-master/experiment.py | #!/usr/bin/env python3
# Import modules
import time
from datetime import datetime
from copy import deepcopy
import numpy as np
class Experiment:
"""
Class Experiment will run a single experiment, which consists of a single
run of agent-environment interaction.
"""
def __init__(self, agent, env, e... | 9,747 | 32.613793 | 79 | py |
GreedyAC | GreedyAC-master/combine.py | #!/usr/bin/env python3
import click
import utils.experiment_utils as exp
import utils.hypers as hypers
import json
import os
import pickle
import signal
import shutil
import sys
from tqdm import tqdm
signal.signal(signal.SIGINT, lambda: exit(0))
@click.command(help="Combine multiple data dictionaries into one")
@c... | 2,542 | 28.229885 | 79 | py |
GreedyAC | GreedyAC-master/env/PendulumEnv.py | #!/usr/bin/env python3
# Adapted from OpenAI Gym Pendulum-v0
# Import modules
import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
from os import path
class PendulumEnv(gym.Env):
"""
PendulumEnv is a modified version of the Pendulum-v0 OpenAI Gym
environment. In this versio... | 8,838 | 31.377289 | 79 | py |
GreedyAC | GreedyAC-master/env/MinAtar.py | from copy import deepcopy
import gym
from gym import spaces
from gym.envs import register
import numpy as np
from minatar import Environment
class GymEnv(gym.Env):
"""
GymEnv wraps MinAtar environments to change their interface to the OpenAI
Gym interface
"""
metadata = {"render.modes": ["human",... | 3,720 | 23.320261 | 79 | py |
GreedyAC | GreedyAC-master/env/Acrobot.py | """classic Acrobot task"""
import numpy as np
from numpy import sin, cos, pi
from gym import spaces
import gym
# TODO: Redo documentation string for class
class AcrobotEnv(gym.Env):
"""
Acrobot is a 2-link pendulum with only the second joint actuated.
Initially, both links point downwards. The goal is ... | 10,907 | 32.155015 | 93 | py |
GreedyAC | GreedyAC-master/env/__init__.py | 0 | 0 | 0 | py | |
GreedyAC | GreedyAC-master/utils/plot_utils.py | # Import modules
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib import ticker, gridspec
import experiment_utils as exp
import numpy as np
from scipy import ndimage
from scipy import stats as st
import seaborn as sns
from collections.abc import Iterable
import pickle
import matplotli... | 23,905 | 37.682848 | 79 | py |
GreedyAC | GreedyAC-master/utils/runs.py | import numpy as np
from copy import deepcopy
TRAIN = "train"
EVAL = "eval"
def episodes_to(in_data, i, type_=TRAIN):
"""
Restricts the number of `type_` episodes to be from episode 0 to the
episode right before episode i.
The input data dictionary is not changed. If `type_` is 'train', then the
t... | 7,984 | 31.72541 | 79 | py |
GreedyAC | GreedyAC-master/utils/plot_mse.py | # Script to plot mean learning curves with standard error
from pprint import pprint
import pickle
import runs
import seaborn as sns
from tqdm import tqdm
import os
from pprint import pprint
import matplotlib.pyplot as plt
import numpy as np
import hypers
import json
import sys
import plot_utils as plot
import matplotli... | 2,814 | 24.36036 | 79 | py |
GreedyAC | GreedyAC-master/utils/experience_replay.py | # Import modules
import numpy as np
import torch
from abc import ABC, abstractmethod
# Class definitions
class ExperienceReplay(ABC):
"""
Abstract base class ExperienceReplay implements an experience replay
buffer. The specific kind of buffer is determined by classes which
implement this base class. F... | 9,362 | 33.422794 | 79 | py |
GreedyAC | GreedyAC-master/utils/hypers.py | from functools import reduce
from collections.abc import Iterable
from copy import deepcopy
import numpy as np
import pickle
from tqdm import tqdm
try:
from utils.runs import expand_episodes
except ModuleNotFoundError:
from runs import expand_episodes
TRAIN = "train"
EVAL = "eval"
def sweeps(parameters, ind... | 28,864 | 33.945521 | 79 | py |
GreedyAC | GreedyAC-master/utils/experiment_utils.py | # Import modules
import numpy as np
import bootstrapped.bootstrap as bs
import bootstrapped.stats_functions as bs_stats
try:
import runs
except ModuleNotFoundError:
import utils.runs
def create_agent(agent, config):
"""
Creates an agent given the agent name and configuration dictionary
Parameters... | 25,362 | 34.37378 | 79 | py |
GreedyAC | GreedyAC-master/agent/Random.py | #!/usr/bin/env python3
# Adapted from https://github.com/pranz24/pytorch-soft-actor-critic
# Import modules
import torch
import numpy as np
from agent.baseAgent import BaseAgent
class Random(BaseAgent):
"""
Random implement a random agent, which is one which samples uniformly from
all available actions.... | 2,248 | 24.556818 | 78 | py |
GreedyAC | GreedyAC-master/agent/baseAgent.py | #!/usr/bin/env python3
# Import modules
from abc import ABC, abstractmethod
# TODO: Given a data dictionary generated by main, create a static
# function to initialize any agent based on this dict. Note that since the
# dict has the agent name, only one function is needed to create ANY agent
# we could also use the e... | 3,716 | 28.975806 | 77 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/VACDiscrete.py | # Import modules
import torch
import inspect
import time
from gym.spaces import Box, Discrete
import numpy as np
import torch.nn.functional as F
from torch.optim import Adam
from agent.baseAgent import BaseAgent
import agent.nonlinear.nn_utils as nn_utils
from agent.nonlinear.policy.MLP import Softmax
from agent.nonlin... | 9,344 | 37.29918 | 78 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/GreedyAC.py | # Import modules
from gym.spaces import Box, Discrete
import torch
import torch.nn.functional as F
from torch.optim import Adam
import numpy as np
from agent.baseAgent import BaseAgent
from utils.experience_replay import TorchBuffer as ExperienceReplay
from agent.nonlinear.value_function.MLP import Q as QMLP
from agent... | 11,905 | 40.340278 | 79 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/GreedyACDiscrete.py | # Import modules
from gym.spaces import Box, Discrete
import inspect
import torch
import torch.nn.functional as F
from torch.optim import Adam
import numpy as np
from agent.baseAgent import BaseAgent
from utils.experience_replay import TorchBuffer as ExperienceReplay
from agent.nonlinear.value_function.MLP import Q as ... | 8,572 | 36.436681 | 78 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/SAC.py | # Import modules
import torch
import numpy as np
import torch.nn.functional as F
from torch.optim import Adam
from agent.baseAgent import BaseAgent
import agent.nonlinear.nn_utils as nn_utils
from agent.nonlinear.policy.MLP import SquashedGaussian, Gaussian
from agent.nonlinear.value_function.MLP import DoubleQ, Q
from... | 20,671 | 35.587611 | 79 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/SACDiscrete.py | #!/usr/bin/env python3
# Import modules
import os
from gym.spaces import Box
import torch
import numpy as np
import torch.nn.functional as F
from torch.optim import Adam
from agent.baseAgent import BaseAgent
import agent.nonlinear.nn_utils as nn_utils
from agent.nonlinear.policy.MLP import Softmax
from agent.nonlinear... | 13,490 | 36.475 | 79 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/VAC.py | # Import modules
import torch
import inspect
from gym.spaces import Box, Discrete
import numpy as np
import torch.nn.functional as F
from torch.optim import Adam
from agent.baseAgent import BaseAgent
import agent.nonlinear.nn_utils as nn_utils
from agent.nonlinear.policy.MLP import Gaussian
from agent.nonlinear.value_f... | 10,808 | 38.021661 | 78 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/nn_utils.py | # Import modules
import torch
import torch.nn as nn
import numpy as np
def weights_init_(layer, init="kaiming", activation="relu"):
"""
Initializes the weights for a fully connected layer of a neural network.
Parameters
----------
layer : torch.nn.Module
The layer to initialize
init : ... | 6,135 | 31.638298 | 79 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/policy/MLP.py | # Import modules
import torch
import time
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal, Independent
from agent.nonlinear.nn_utils import weights_init_
# Global variables
EPSILON = 1e-6
class SquashedGaussian(nn.Module):
"""
Class SquashedGau... | 16,553 | 31.206226 | 79 | py |
GreedyAC | GreedyAC-master/agent/nonlinear/value_function/MLP.py | #!/usr/bin/env python3
# Import modules
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import agent.nonlinear.nn_utils as nn_utils
# Class definitions
class V(nn.Module):
"""
Class V is an MLP for estimating the state value function `v`.
"""
def __init__(self, n... | 8,998 | 30.798587 | 78 | py |
ecdata | ecdata-master/scripts/make_tables.py | import sys
import os
from files import HOME
sys.path.append(os.path.join(HOME, 'lmfdb'))
from lmfdb import db
db.create_table(name='ec_curvedata',
search_columns={
'text': ['Clabel', 'lmfdb_label', 'Ciso', 'lmfdb_iso'],
'numeric': ['regulator', 'absD', 'faltings... | 7,542 | 43.89881 | 179 | py |
ecdata | ecdata-master/scripts/ecdb.py | import os
import sys
sys.path.insert(0, '/home/jec/ecdata/scripts/')
from sage.all import (EllipticCurve, Integer, ZZ, Set, factorial,
mwrank_get_precision, mwrank_set_precision, srange, prod, copy, gcd)
from magma import get_magma
from red_gens import reduce_tgens, reduce_gens
from trace_hash i... | 41,373 | 41.046748 | 189 | py |
ecdata | ecdata-master/scripts/labels.py | # Function to create alllabels file mapping Cremona labels to LMFDB labels
#
# Input: curves file, e.g. curves.230000-239999
#
# Output: alllabels file, e.g. alllabels.230000-239999
#
# Format: conductor iso number conductor lmfdb_iso lmfdb_number
#
# NB we do this by computing the isogeny class and (re)sorting it in
#... | 1,410 | 36.131579 | 74 | py |
ecdata | ecdata-master/scripts/galrep.py | # Sage interface to Sutherland's Magma script for Galois images
GALREP_SCRIPT_DIR = "/home/jec/galrep"
def init_galrep(mag, script_dir=GALREP_SCRIPT_DIR):
"""
Load the 2adic magma script into this magma process
"""
mag.eval('cwd:=GetCurrentDirectory();')
mag.eval('ChangeDirectory("{}");'.format(sc... | 766 | 25.448276 | 70 | py |
ecdata | ecdata-master/scripts/magma.py | # Manage child Magma processes:
#
# User gets a Magma instance using get_magma(), which restarts after
# magma_count uses (default 100). This avoid the possible problems
# with using Magma on hundreds of thousands of curves, without the
# overhead of starting a new one every time.
#
# Also, the scripts for 2adic and m... | 1,106 | 26.675 | 68 | py |
ecdata | ecdata-master/scripts/codec.py | ######################################################################
#
# Utility and coding/decoding functions
#
######################################################################
import re
from sage.all import ZZ, QQ, RR, sage_eval, EllipticCurve, EllipticCurve_from_c4c6
whitespace = re.compile(r'\s+')
def ... | 8,688 | 31.912879 | 118 | py |
ecdata | ecdata-master/scripts/eqn.py | # Custom function to make a latex 'equation' string from a-invariants
#
# assume that [a1,a2,a3] are one of the 12 reduced triples.
# This is the same as latex(EllipticCurve(ainvs)).replace("
# ","").replace("{3}","3").replace("{2}","2"), i.e. the only
# difference is that we have x^3 instead of x^{3} (and x^2 instead... | 893 | 39.636364 | 105 | py |
ecdata | ecdata-master/scripts/twoadic.py | # Sage interface to 2adic Magma script
import os
from codec import parse_twoadic_string
TWOADIC_SCRIPT_DIR = "/home/jec/ecdata/scripts"
def init_2adic(mag, script_dir=TWOADIC_SCRIPT_DIR):
"""
Load the 2adic magma script into this magma process
"""
script = os.path.join(script_dir, "2adic.m")
mag.... | 731 | 21.875 | 68 | py |
ecdata | ecdata-master/scripts/update.py | import os
import sys
from lmfdb import db
HOME = os.getenv("HOME")
UPLOAD_DIR = os.path.join(HOME, "ecq-upload")
sys.path.append(os.path.join(HOME, 'lmfdb'))
all_tables = (db.ec_curvedata, db.ec_localdata, db.ec_mwbsd,
db.ec_classdata, db.ec_galrep,
db.ec_torsion_growth, db.ec_iwasawa)
ma... | 2,737 | 33.225 | 117 | py |
ecdata | ecdata-master/scripts/misc.py | import os
from sage.all import ZZ, QQ, Integer, EllipticCurve, class_to_int
try:
from sage.databases.cremona import cmp_code
except:
pass
from files import read_data, MATSCHKE_DIR, write_curvedata
from moddeg import get_modular_degree
from codec import (parse_int_list, point_to_weighted_proj,
... | 11,920 | 34.373887 | 92 | py |
ecdata | ecdata-master/scripts/summarytable.py | # script used to create table.html from allbsd.* files:
#countrank.awk:
# cat curves.*0000-*9999 |
# gawk -v FIRST=$1 -v LAST=$2 'BEGIN{printf("Curve numbers by rank in the range %d...%d:\nrank:\t0\t1\t2\t3\t4\n",FIRST,LAST);}\
# ($1>=FIRST)&&($1<=LAST){r[$5]+=1;rt+=1;}\
# END {printf("number:\t%d\t%d\t%d\t%d\t%d\nT... | 6,247 | 33.905028 | 207 | py |
ecdata | ecdata-master/scripts/red_gens.py | ######################################################################
#
# Functions for Minkowski-reduction of generators, and naive reduction
# of torsion generators and of generators mod torsion.
#
######################################################################
pt_wt = lambda P: len(str(P))
def reduce_tgens... | 6,804 | 31.099057 | 108 | py |
ecdata | ecdata-master/scripts/ec_utils.py | # elliptic curve utility functions for finding generators, saturating and mapping around an isogeny class
from sage.all import (pari, QQ,
mwrank_get_precision, mwrank_set_precision)
from magma import get_magma, MagmaEffort
mwrank_saturation_precision = 1000 # 500 not enough for 594594bf2
mwrank... | 5,776 | 33.801205 | 148 | py |
ecdata | ecdata-master/scripts/sharanktable.py | # script used to create shas.html from allbsd.* files:
from sage.all import isqrt
# we do not call the output file "shas.html" so we can compare the new
# version with the old
HTML_FILENAME = "newshas.html"
MAX_RANK = 4
SHA_LIST = range(2, 35) + [37, 41, 43, 47, 50, 75]
def make_rankshatable(nmax=30, verbose=False... | 6,552 | 34.61413 | 106 | py |
ecdata | ecdata-master/scripts/aplist.py | # Sage's E.aplist(100) returns a list of the Fourier coefficients for
# p<100. For the aplist files, we want to replace the coefficient for
# p|N with the W-eigenvalue (the root number) and append the
# W-eigenvalues for p|N, p>100. Not relevant for making LMFDBupload
# files.
from sage.all import prime_range
def w... | 1,200 | 25.688889 | 70 | py |
ecdata | ecdata-master/scripts/moddeg.py | def get_modular_degree(E, label):
degphi_magma = 0
degphi_sympow = 0
#return E.modular_degree(algorithm='sympow')
try:
degphi_magma = E.modular_degree(algorithm='magma')
except RuntimeError:
print("{}: degphi via magma failed".format(label))
try:
degphi_sympow = E... | 1,008 | 33.793103 | 89 | py |
ecdata | ecdata-master/scripts/min_quad_twist.py | # May 2023 new function for cmputing minimal quadratic twist for any curve /Q
#
# - for j not 0 or 1728 this only depends on the j-invariant
# - for curves with CM (and j not 0, 1728) we use a lookup table for speed
# - for non-CM curves it's enough to
# (1) find minimal conductor;
# (2) sort into isogeny classes (... | 4,524 | 37.347458 | 103 | py |
ecdata | ecdata-master/scripts/check_gens.py | ######################################################################
#
# Functions to check Minkowksi-reduction of generators, and compare
#
######################################################################
import os
from sage.all import EllipticCurve
from codec import split, parse_int_list, proj_to_point, poin... | 4,640 | 41.972222 | 117 | py |
ecdata | ecdata-master/scripts/intpts.py | # Find integral points in a fail-safe way uing both Sage and Magma,
# comparing, returning the union in all cases and outputting a warning
# message if they disagree.
from sage.all import Set
from magma import get_magma
def get_integral_points_with_sage(E, gens):
return [P[0] for P in E.integral_points(mw_base=ge... | 1,025 | 34.37931 | 97 | py |
ecdata | ecdata-master/scripts/files.py | # Functions to read data from the files:
#
# alllabels.*, allgens.*, alldegphi.*, allisog.*,
# intpts.*, opt_man.*, 2adic.*, galrep.*
#
# and also torsion growth and Iwasawa data files. The latter used to
# be arranged differently; now they are not, but only exist in the
# ranges up to 50000.
import os
from sage.all i... | 67,026 | 38.684429 | 217 | py |
ecdata | ecdata-master/scripts/trace_hash.py | # file copied from lmfdb codebase (lmfdb/lmfdb/utils/trace_hash.py)
# Sage translation of the Magma function TraceHash(), just for elliptic curves /Q and /NF
from sage.all import GF, ZZ, QQ, pari, prime_range
TH_C = [326490430436040986,559705121321738418,1027143540648291608,1614463795034667624,455689193399227776,
8... | 10,937 | 72.409396 | 105 | py |
Vecchia_GPR_var_select | Vecchia_GPR_var_select-master/code/func/reg_tree.py | import numpy as np
from sklearn.tree import DecisionTreeRegressor
def reg_tree_wrap(XTrn, yTrn, XTst, yTst, pIn):
# dataTrn = np.genfromtxt(dataFn + "_train.csv", delimiter=",")
# dataTst = np.genfromtxt(dataFn + "_test.csv", delimiter=",")
# fkMatch = re.search(r'f([0-9]+)', dataFn)
# if fkMatch:
... | 1,508 | 30.4375 | 108 | py |
Vecchia_GPR_var_select | Vecchia_GPR_var_select-master/code/func/SVGP.py | #!/usr/bin/env python
import numpy as np
import gpflow
import tensorflow as tf
from gpflow.ci_utils import ci_niter
class Matern25_aniso(gpflow.kernels.AnisotropicStationary):
def K_d(self, d):
sqrt5 = np.sqrt(5.0)
d = tf.square(d)
d = tf.reduce_sum(d, -1)
d = tf.sqrt(d)
t... | 1,958 | 30.095238 | 76 | py |
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