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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reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/dqn_algos/demo.py | import numpy as np
from arguments import get_args
from models import net
import torch
from rl_utils.env_wrapper.atari_wrapper import make_atari, wrap_deepmind
def get_tensors(obs):
obs = np.transpose(obs, (2, 0, 1))
obs = np.expand_dims(obs, 0)
obs = torch.tensor(obs, dtype=torch.float32)
return obs
i... | 1,134 | 30.527778 | 90 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/dqn_algos/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# the convolution layer of deepmind
class deepmind(nn.Module):
def __init__(self):
super(deepmind, self).__init__()
self.conv1 = nn.Conv2d(4, 32, 8, stride=4)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
self.conv3 = ... | 2,596 | 37.761194 | 88 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/dqn_algos/train.py | import sys
from arguments import get_args
from rl_utils.env_wrapper.create_env import create_single_env
from rl_utils.logger import logger, bench
from rl_utils.seeds.seeds import set_seeds
from dqn_agent import dqn_agent
import os
import numpy as np
if __name__ == '__main__':
# get arguments
args = get_args()... | 589 | 24.652174 | 61 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/dqn_algos/dqn_agent.py | import sys
import numpy as np
from models import net
from utils import linear_schedule, select_actions, reward_recorder
from rl_utils.experience_replay.experience_replay import replay_buffer
import torch
from datetime import datetime
import os
import copy
# define the dqn agent
class dqn_agent:
def __init__(self, ... | 5,646 | 43.81746 | 144 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/trpo/trpo_agent.py | import torch
import numpy as np
import os
from models import network
from rl_utils.running_filter.running_filter import ZFilter
from utils import select_actions, eval_actions, conjugated_gradient, line_search, set_flat_params_to
from datetime import datetime
class trpo_agent:
def __init__(self, env, args):
... | 9,299 | 52.142857 | 129 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/trpo/arguments.py | import argparse
def get_args():
parse = argparse.ArgumentParser()
parse.add_argument('--gamma', type=float, default=0.99, help='the discount factor of the RL')
parse.add_argument('--env-name', type=str, default='Walker2d-v2', help='the training environment')
parse.add_argument('--seed', type=int, defau... | 1,521 | 62.416667 | 117 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/trpo/utils.py | import numpy as np
import torch
from torch.distributions.normal import Normal
# select actions
def select_actions(pi):
mean, std = pi
normal_dist = Normal(mean, std)
return normal_dist.sample().detach().numpy().squeeze()
# evaluate the actions
def eval_actions(pi, actions):
mean, std = pi
normal_d... | 2,026 | 33.355932 | 125 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/trpo/demo.py | import numpy as np
import torch
import gym
from arguments import get_args
from models import network
def denormalize(x, mean, std, clip=10):
x -= mean
x /= (std + 1e-8)
return np.clip(x, -clip, clip)
def get_tensors(x):
return torch.tensor(x, dtype=torch.float32).unsqueeze(0)
if __name__ == '__main__... | 1,383 | 31.952381 | 94 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/trpo/models.py | import torch
from torch import nn
from torch.nn import functional as F
class network(nn.Module):
def __init__(self, num_states, num_actions):
super(network, self).__init__()
# define the critic
self.critic = critic(num_states)
self.actor = actor(num_states, num_actions)
def for... | 1,376 | 28.297872 | 66 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/trpo/train.py | from arguments import get_args
from rl_utils.seeds.seeds import set_seeds
from rl_utils.env_wrapper.create_env import create_single_env
from trpo_agent import trpo_agent
if __name__ == '__main__':
args = get_args()
# make environemnts
env = create_single_env(args)
# set the random seeds
set_seeds(a... | 461 | 26.176471 | 61 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/a2c/a2c_agent.py | import numpy as np
import torch
from models import net
from datetime import datetime
from utils import select_actions, evaluate_actions, discount_with_dones
import os
class a2c_agent:
def __init__(self, envs, args):
self.envs = envs
self.args = args
# define the network
self.net = n... | 6,370 | 47.633588 | 135 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/a2c/arguments.py | import argparse
def get_args():
parse = argparse.ArgumentParser()
parse.add_argument('--gamma', type=float, default=0.99, help='the discount factor of RL')
parse.add_argument('--seed', type=int, default=123, help='the random seeds')
parse.add_argument('--env-name', type=str, default='BreakoutNoFrameski... | 1,881 | 66.214286 | 109 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/a2c/utils.py | import torch
import numpy as np
from torch.distributions.categorical import Categorical
# select - actions
def select_actions(pi, deterministic=False):
cate_dist = Categorical(pi)
if deterministic:
return torch.argmax(pi, dim=1).item()
else:
return cate_dist.sample().unsqueeze(-1)
# get th... | 749 | 29 | 92 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/a2c/demo.py | from arguments import get_args
from models import net
import torch
from utils import select_actions
import cv2
import numpy as np
from rl_utils.env_wrapper.frame_stack import VecFrameStack
from rl_utils.env_wrapper.atari_wrapper import make_atari, wrap_deepmind
# update the current observation
def get_tensors(obs):
... | 1,193 | 33.114286 | 95 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/a2c/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# the convolution layer of deepmind
class deepmind(nn.Module):
def __init__(self):
super(deepmind, self).__init__()
self.conv1 = nn.Conv2d(4, 32, 8, stride=4)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
self.conv3 = ... | 1,959 | 37.431373 | 88 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/a2c/train.py | from arguments import get_args
from a2c_agent import a2c_agent
from rl_utils.env_wrapper.create_env import create_multiple_envs
from rl_utils.seeds.seeds import set_seeds
from a2c_agent import a2c_agent
import os
if __name__ == '__main__':
# set signle thread
os.environ['OMP_NUM_THREADS'] = '1'
os.environ[... | 612 | 25.652174 | 64 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/ddpg/arguments.py | import argparse
def get_args():
parse = argparse.ArgumentParser(description='ddpg')
parse.add_argument('--env-name', type=str, default='Pendulum-v0', help='the training environment')
parse.add_argument('--lr-actor', type=float, default=1e-4, help='the lr of the actor')
parse.add_argument('--lr-critic',... | 2,113 | 69.466667 | 105 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/ddpg/utils.py | import numpy as np
import torch
# add ounoise here
class ounoise():
def __init__(self, std, action_dim, mean=0, theta=0.15, dt=1e-2, x0=None):
self.std = std
self.mean = mean
self.action_dim = action_dim
self.theta = theta
self.dt = dt
self.x0 = x0
# reset t... | 686 | 27.625 | 84 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/ddpg/demo.py | from arguments import get_args
import gym
from models import actor
import torch
import numpy as np
def normalize(obs, mean, std, clip):
return np.clip((obs - mean) / std, -clip, clip)
if __name__ == '__main__':
args = get_args()
env = gym.make(args.env_name)
# get environment infos
obs_dims = env.... | 1,518 | 34.325581 | 90 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/ddpg/ddpg_agent.py | import numpy as np
from models import actor, critic
import torch
import os
from datetime import datetime
from mpi4py import MPI
from rl_utils.mpi_utils.normalizer import normalizer
from rl_utils.mpi_utils.utils import sync_networks, sync_grads
from rl_utils.experience_replay.experience_replay import replay_buffer
from... | 8,833 | 45.494737 | 142 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/ddpg/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# define the actor network
class actor(nn.Module):
def __init__(self, obs_dims, action_dims):
super(actor, self).__init__()
self.fc1 = nn.Linear(obs_dims, 400)
self.fc2 = nn.Linear(400, 300)
self.action_out = nn.Line... | 950 | 28.71875 | 53 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/ddpg/train.py | from ddpg_agent import ddpg_agent
from arguments import get_args
from rl_utils.seeds.seeds import set_seeds
from rl_utils.env_wrapper.create_env import create_single_env
from mpi4py import MPI
import os
if __name__ == '__main__':
# set thread and mpi stuff
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['MK... | 717 | 28.916667 | 61 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/ppo/arguments.py | import argparse
def get_args():
parse = argparse.ArgumentParser()
parse.add_argument('--gamma', type=float, default=0.99, help='the discount factor of RL')
parse.add_argument('--seed', type=int, default=123, help='the random seeds')
parse.add_argument('--num-workers', type=int, default=8, help='the num... | 2,274 | 72.387097 | 116 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/ppo/utils.py | import numpy as np
import torch
from torch.distributions.normal import Normal
from torch.distributions.beta import Beta
from torch.distributions.categorical import Categorical
import random
def select_actions(pi, dist_type, env_type):
if env_type == 'atari':
actions = Categorical(pi).sample()
else:
... | 1,370 | 35.078947 | 78 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/ppo/demo.py | from arguments import get_args
from models import cnn_net, mlp_net
import torch
import cv2
import numpy as np
import gym
from rl_utils.env_wrapper.frame_stack import VecFrameStack
from rl_utils.env_wrapper.atari_wrapper import make_atari, wrap_deepmind
# denormalize
def normalize(x, mean, std, clip=10):
x -= mean
... | 2,641 | 35.694444 | 112 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/ppo/models.py | import torch
from torch import nn
from torch.nn import functional as F
"""
this network also include gaussian distribution and beta distribution
"""
class mlp_net(nn.Module):
def __init__(self, state_size, num_actions, dist_type):
super(mlp_net, self).__init__()
self.dist_type = dist_type
... | 3,913 | 37 | 88 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/ppo/ppo_agent.py | import numpy as np
import torch
from torch import optim
from rl_utils.running_filter.running_filter import ZFilter
from models import cnn_net, mlp_net
from utils import select_actions, evaluate_actions
from datetime import datetime
import os
import copy
class ppo_agent:
def __init__(self, envs, args):
self... | 11,143 | 50.592593 | 144 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/ppo/train.py | from arguments import get_args
from ppo_agent import ppo_agent
from rl_utils.env_wrapper.create_env import create_multiple_envs, create_single_env
from rl_utils.seeds.seeds import set_seeds
import os
if __name__ == '__main__':
# set signle thread
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THRE... | 757 | 28.153846 | 83 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/sac/arguments.py | import argparse
# define the arguments that will be used in the SAC
def get_args():
parse = argparse.ArgumentParser()
parse.add_argument('--env-name', type=str, default='HalfCheetah-v2', help='the environment name')
parse.add_argument('--cuda', action='store_true', help='use GPU do the training')
parse... | 3,080 | 82.27027 | 123 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/sac/utils.py | import numpy as np
import torch
from torch.distributions.normal import Normal
from torch.distributions import Distribution
"""
the tanhnormal distributions from rlkit may not stable
"""
class tanh_normal(Distribution):
def __init__(self, normal_mean, normal_std, epsilon=1e-6, cuda=False):
self.normal_mean... | 2,841 | 34.08642 | 118 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/sac/demo.py | from arguments import get_args
import gym
import torch
import numpy as np
from models import tanh_gaussian_actor
if __name__ == '__main__':
args = get_args()
env = gym.make(args.env_name)
# get environment infos
obs_dims = env.observation_space.shape[0]
action_dims = env.action_space.shape[0]
a... | 1,433 | 35.769231 | 112 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/sac/sac_agent.py | import numpy as np
import torch
from models import flatten_mlp, tanh_gaussian_actor
from rl_utils.experience_replay.experience_replay import replay_buffer
from utils import get_action_info
from datetime import datetime
import copy
import os
import gym
"""
2019-Nov-12 - start to add the automatically tempature tuning
... | 10,871 | 49.803738 | 166 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/sac/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# the flatten mlp
class flatten_mlp(nn.Module):
#TODO: add the initialization method for it
def __init__(self, input_dims, hidden_size, action_dims=None):
super(flatten_mlp, self).__init__()
self.fc1 = nn.Linear(input_dims, hidd... | 1,745 | 38.681818 | 130 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/sac/train.py | from arguments import get_args
from sac_agent import sac_agent
from rl_utils.seeds.seeds import set_seeds
from rl_utils.env_wrapper.create_env import create_single_env
if __name__ == '__main__':
args = get_args()
# build the environment
env = create_single_env(args)
# set the seeds
set_seeds(args)
... | 450 | 25.529412 | 61 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/__init__.py | 0 | 0 | 0 | py | |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/seeds/seeds.py | import numpy as np
import random
import torch
# set random seeds for the pytorch, numpy and random
def set_seeds(args, rank=0):
# set seeds for the numpy
np.random.seed(args.seed + rank)
# set seeds for the random.random
random.seed(args.seed + rank)
# set seeds for the pytorch
torch.manual_see... | 407 | 26.2 | 52 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/experience_replay/experience_replay.py | import numpy as np
import random
"""
define the replay buffer and corresponding algorithms like PER
"""
class replay_buffer:
def __init__(self, memory_size):
self.storge = []
self.memory_size = memory_size
self.next_idx = 0
# add the samples
def add(self, obs, action, reward,... | 1,380 | 31.880952 | 103 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/logger/logger.py | import os
import sys
import shutil
import os.path as osp
import json
import time
import datetime
import tempfile
from collections import defaultdict
from contextlib import contextmanager
DEBUG = 10
INFO = 20
WARN = 30
ERROR = 40
DISABLED = 50
class KVWriter(object):
def writekvs(self, kvs):
raise NotImpl... | 14,802 | 28.429423 | 122 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/logger/bench.py | __all__ = ['Monitor', 'get_monitor_files', 'load_results']
from gym.core import Wrapper
import time
from glob import glob
import csv
import os.path as osp
import json
class Monitor(Wrapper):
EXT = "monitor.csv"
f = None
def __init__(self, env, filename, allow_early_resets=False, reset_keywords=(), info_k... | 5,704 | 34 | 174 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/logger/plot.py | import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
from rl_utils.bench import load_results
sns.set(style="dark")
sns.set_context("poster", font_scale=2, rc={"lines.linewidth": 2})
sns.set(rc={"figure.figsize": (15, 8)})
colors = sns.color_palette(palette='muted')
X_TIMESTEPS = 'timesteps'
... | 3,962 | 38.237624 | 115 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/logger/__init__.py | 0 | 0 | 0 | py | |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/mpi_utils/normalizer.py | import threading
import numpy as np
from mpi4py import MPI
class normalizer:
def __init__(self, size, eps=1e-2, default_clip_range=np.inf):
self.size = size
self.eps = eps
self.default_clip_range = default_clip_range
# some local information
self.local_sum = np.zeros(self.si... | 2,777 | 38.126761 | 145 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/mpi_utils/utils.py | from mpi4py import MPI
import numpy as np
import torch
# sync_networks across the different cores
def sync_networks(network):
"""
netowrk is the network you want to sync
"""
comm = MPI.COMM_WORLD
flat_params = _get_flat_params_or_grads(network, mode='params')
comm.Bcast(flat_params, root=0)
... | 1,427 | 31.454545 | 119 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/mpi_utils/__init__.py | 0 | 0 | 0 | py | |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/running_filter/__init__.py | 0 | 0 | 0 | py | |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/running_filter/running_filter.py | from collections import deque
import numpy as np
# this is from the https://github.com/ikostrikov/pytorch-trpo/blob/master/running_state.py
# from https://github.com/joschu/modular_rl
# http://www.johndcook.com/blog/standard_deviation/
class RunningStat(object):
def __init__(self, shape):
self._n = 0
... | 1,715 | 23.169014 | 90 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/env_wrapper/create_env.py | from rl_utils.env_wrapper.atari_wrapper import make_atari, wrap_deepmind
from rl_utils.env_wrapper.multi_envs_wrapper import SubprocVecEnv
from rl_utils.env_wrapper.frame_stack import VecFrameStack
from rl_utils.logger import logger, bench
import os
import gym
"""
this functions is to create the environments
"""
def... | 2,239 | 34.555556 | 104 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/env_wrapper/multi_envs_wrapper.py | import multiprocessing as mp
import numpy as np
from rl_utils.env_wrapper import VecEnv, CloudpickleWrapper, clear_mpi_env_vars
def worker(remote, parent_remote, env_fn_wrapper):
parent_remote.close()
env = env_fn_wrapper.x()
try:
while True:
cmd, data = remote.recv()
if cmd... | 4,074 | 34.12931 | 128 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/env_wrapper/frame_stack.py | from rl_utils.env_wrapper import VecEnvWrapper
import numpy as np
from gym import spaces
class VecFrameStack(VecEnvWrapper):
def __init__(self, venv, nstack):
self.venv = venv
self.nstack = nstack
wos = venv.observation_space # wrapped ob space
low = np.repeat(wos.low, self.nstack... | 1,162 | 36.516129 | 94 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/env_wrapper/__init__.py | import os
from abc import ABC, abstractmethod
import contextlib
class AlreadySteppingError(Exception):
"""
Raised when an asynchronous step is running while
step_async() is called again.
"""
def __init__(self):
msg = 'already running an async step'
Exception.__init__(self, msg)
c... | 5,877 | 26.596244 | 219 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_utils/env_wrapper/atari_wrapper.py | import numpy as np
import os
os.environ.setdefault('PATH', '')
from collections import deque
import gym
from gym import spaces
import cv2
cv2.ocl.setUseOpenCL(False)
"""
the wrapper is taken from the openai baselines
"""
class NoopResetEnv(gym.Wrapper):
def __init__(self, env, noop_max=30):
"""Sample ini... | 10,334 | 32.125 | 130 | py |
gcn-over-pruned-trees | gcn-over-pruned-trees-master/eval.py | """
Run evaluation with saved models.
"""
import random
import argparse
from tqdm import tqdm
import torch
from data.loader import DataLoader
from model.trainer import GCNTrainer
from utils import torch_utils, scorer, constant, helper
from utils.vocab import Vocab
parser = argparse.ArgumentParser()
parser.add_argumen... | 2,130 | 30.80597 | 97 | py |
gcn-over-pruned-trees | gcn-over-pruned-trees-master/prepare_vocab.py | """
Prepare vocabulary and initial word vectors.
"""
import json
import pickle
import argparse
import numpy as np
from collections import Counter
from utils import vocab, constant, helper
def parse_args():
parser = argparse.ArgumentParser(description='Prepare vocab for relation extraction.')
parser.add_argume... | 4,313 | 35.252101 | 102 | py |
gcn-over-pruned-trees | gcn-over-pruned-trees-master/train.py | """
Train a model on TACRED.
"""
import os
import sys
from datetime import datetime
import time
import numpy as np
import random
import argparse
from shutil import copyfile
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from data.loader import DataLoader
from model.... | 8,638 | 44.708995 | 156 | py |
gcn-over-pruned-trees | gcn-over-pruned-trees-master/utils/constant.py | """
Define constants.
"""
EMB_INIT_RANGE = 1.0
# vocab
PAD_TOKEN = '<PAD>'
PAD_ID = 0
UNK_TOKEN = '<UNK>'
UNK_ID = 1
VOCAB_PREFIX = [PAD_TOKEN, UNK_TOKEN]
# hard-coded mappings from fields to ids
SUBJ_NER_TO_ID = {PAD_TOKEN: 0, UNK_TOKEN: 1, 'ORGANIZATION': 2, 'PERSON': 3}
OBJ_NER_TO_ID = {PAD_TOKEN: 0, UNK_TOKEN: ... | 3,049 | 100.666667 | 1,091 | py |
gcn-over-pruned-trees | gcn-over-pruned-trees-master/utils/scorer.py | #!/usr/bin/env python
"""
Score the predictions with gold labels, using precision, recall and F1 metrics.
"""
import argparse
import sys
from collections import Counter
NO_RELATION = "no_relation"
def parse_arguments():
parser = argparse.ArgumentParser(description='Score a prediction file using the gold labels.... | 4,324 | 37.616071 | 137 | py |
gcn-over-pruned-trees | gcn-over-pruned-trees-master/utils/helper.py | """
Helper functions.
"""
import os
import subprocess
import json
import argparse
### IO
def check_dir(d):
if not os.path.exists(d):
print("Directory {} does not exist. Exit.".format(d))
exit(1)
def check_files(files):
for f in files:
if f is not None and not os.path.exists(f):
... | 1,698 | 24.742424 | 69 | py |
gcn-over-pruned-trees | gcn-over-pruned-trees-master/utils/vocab.py | """
A class for basic vocab operations.
"""
from __future__ import print_function
import os
import random
import numpy as np
import pickle
from utils import constant
random.seed(1234)
np.random.seed(1234)
def build_embedding(wv_file, vocab, wv_dim):
vocab_size = len(vocab)
emb = np.random.uniform(-1, 1, (vo... | 3,714 | 35.782178 | 112 | py |
gcn-over-pruned-trees | gcn-over-pruned-trees-master/utils/torch_utils.py | """
Utility functions for torch.
"""
import torch
from torch import nn, optim
from torch.optim import Optimizer
### class
class MyAdagrad(Optimizer):
"""My modification of the Adagrad optimizer that allows to specify an initial
accumulater value. This mimics the behavior of the default Adagrad implementation ... | 5,681 | 33.858896 | 106 | py |
gcn-over-pruned-trees | gcn-over-pruned-trees-master/data/loader.py | """
Data loader for TACRED json files.
"""
import json
import random
import torch
import numpy as np
from utils import constant, helper, vocab
class DataLoader(object):
"""
Load data from json files, preprocess and prepare batches.
"""
def __init__(self, filename, batch_size, opt, vocab, evaluation=F... | 5,487 | 36.848276 | 121 | py |
gcn-over-pruned-trees | gcn-over-pruned-trees-master/model/tree.py | """
Basic operations on trees.
"""
import numpy as np
from collections import defaultdict
class Tree(object):
"""
Reused tree object from stanfordnlp/treelstm.
"""
def __init__(self):
self.parent = None
self.num_children = 0
self.children = list()
def add_child(self,child)... | 4,951 | 25.623656 | 75 | py |
gcn-over-pruned-trees | gcn-over-pruned-trees-master/model/gcn.py | """
GCN model for relation extraction.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from model.tree import Tree, head_to_tree, tree_to_adj
from utils import constant, torch_utils
class GCNClassifier(nn.Module):
""" A wrapper classif... | 7,886 | 39.239796 | 131 | py |
gcn-over-pruned-trees | gcn-over-pruned-trees-master/model/trainer.py | """
A trainer class.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from model.gcn import GCNClassifier
from utils import constant, torch_utils
class Trainer(object):
def __init__(self, opt, emb_matrix=None):
raise NotImplemen... | 3,659 | 32.888889 | 101 | py |
SwinMR | SwinMR-main/main_test_swinmr_CC.py | '''
# -----------------------------------------
Main Program for Testing
SwinMR for MRI_Recon
Dataset: CC
by Jiahao Huang (j.huang21@imperial.ac.uk)
# -----------------------------------------
'''
import argparse
import cv2
import csv
import sys
import numpy as np
from collections import OrderedDict
import os
import t... | 11,599 | 40.281139 | 134 | py |
SwinMR | SwinMR-main/main_train_swinmr.py | '''
# -----------------------------------------
Main Program for Training
SwinMR for MRI_Recon
by Jiahao Huang (j.huang21@imperial.ac.uk)
# -----------------------------------------
'''
import os
import sys
import math
import argparse
import random
import cv2
import numpy as np
import logging
import time
import torch... | 15,434 | 43.353448 | 176 | py |
SwinMR | SwinMR-main/models/model_base.py | import os
import torch
import torch.nn as nn
from utils.utils_bnorm import merge_bn, tidy_sequential
from torch.nn.parallel import DataParallel, DistributedDataParallel
class ModelBase():
def __init__(self, opt):
self.opt = opt # opt
self.save_dir = opt['path']['models'] #... | 7,442 | 33.299539 | 148 | py |
SwinMR | SwinMR-main/models/select_network.py | '''
# -----------------------------------------
Define Training Network
by Jiahao Huang (j.huang21@imperial.ac.uk)
# -----------------------------------------
'''
import functools
import torch
import torchvision.models
from torch.nn import init
# --------------------------------------------
# Recon Generator, netG, ... | 5,220 | 35.006897 | 113 | py |
SwinMR | SwinMR-main/models/network_swinmr.py | '''
# -----------------------------------------
Network
SwinMR m.1.3
by Jiahao Huang (j.huang21@imperial.ac.uk)
Thanks:
https://github.com/JingyunLiang/SwinIR
https://github.com/microsoft/Swin-Transformer
# -----------------------------------------
'''
import math
import torch
import torch.nn as nn
import torch.nn.fu... | 41,096 | 41.631743 | 175 | py |
SwinMR | SwinMR-main/models/loss.py | import torch
import torch.nn as nn
import torchvision
from torch.nn import functional as F
from torch import autograd as autograd
import math
"""
Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace)
(2*): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1... | 14,821 | 37.299742 | 150 | py |
SwinMR | SwinMR-main/models/network_feature.py | import torch
import torch.nn as nn
import torchvision
"""
# --------------------------------------------
# VGG Feature Extractor
# --------------------------------------------
"""
# --------------------------------------------
# VGG features
# Assume input range is [0, 1]
# ------------------------------------------... | 1,594 | 32.93617 | 93 | py |
SwinMR | SwinMR-main/models/basicblock.py | from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
'''
# --------------------------------------------
# Advanced nn.Sequential
# https://github.com/xinntao/BasicSR
# --------------------------------------------
'''
def sequential(*args):
"""Advanced nn.Sequent... | 24,138 | 39.775338 | 160 | py |
SwinMR | SwinMR-main/models/select_model.py | '''
# -----------------------------------------
Define Training Model
by Jiahao Huang (j.huang21@imperial.ac.uk)
# -----------------------------------------
'''
def define_Model(opt):
model = opt['model']
# --------------------------------------------------------
# SwinMR
# ---------------------------... | 731 | 26.111111 | 79 | py |
SwinMR | SwinMR-main/models/select_mask.py | '''
# -----------------------------------------
Define Undersampling Mask
by Jiahao Huang (j.huang21@imperial.ac.uk)
# -----------------------------------------
'''
import os
import scipy
import scipy.fftpack
from scipy.io import loadmat
import cv2
import numpy as np
def define_Mask(opt):
mask_name = opt['mask']... | 5,773 | 47.521008 | 112 | py |
SwinMR | SwinMR-main/models/model_swinmr_pi.py | '''
# -----------------------------------------
Model
SwinMR (PI) m.1.3
by Jiahao Huang (j.huang21@imperial.ac.uk)
Thanks:
https://github.com/JingyunLiang/SwinIR
https://github.com/microsoft/Swin-Transformer
# -----------------------------------------
'''
from collections import OrderedDict
import torch
import torch.... | 14,836 | 39.649315 | 176 | py |
SwinMR | SwinMR-main/models/model_swinmr.py | '''
# -----------------------------------------
Model
SwinMR m.1.3
by Jiahao Huang (j.huang21@imperial.ac.uk)
Thanks:
https://github.com/JingyunLiang/SwinIR
https://github.com/microsoft/Swin-Transformer
# -----------------------------------------
'''
from collections import OrderedDict
import torch
import torch.nn as... | 14,546 | 39.520891 | 176 | py |
SwinMR | SwinMR-main/utils/utils_early_stopping.py | """
# --------------------------------------------
# Early Stopping
# --------------------------------------------
# Jiahao Huang (j.huang21@imperial.uk.ac)
# 30/Jan/2022
# --------------------------------------------
"""
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a ... | 1,297 | 27.217391 | 108 | py |
SwinMR | SwinMR-main/utils/utils_image.py | import os
import math
import random
import numpy as np
import torch
import cv2
from numpy import Inf
from torchvision.utils import make_grid
from datetime import datetime
# import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import skimage.metrics
import S... | 38,657 | 31.595278 | 120 | py |
SwinMR | SwinMR-main/utils/utils_dist.py | # Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py # noqa: E501
import functools
import os
import subprocess
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
# ----------------------------------
# init
# ----------------------------------
def init... | 5,275 | 25.118812 | 102 | py |
SwinMR | SwinMR-main/utils/utils_option.py | import os
from collections import OrderedDict
from datetime import datetime
import json
import re
import glob
'''
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 03/Mar/2019
# --------------------------------------------
# https://github.com/xinntao/BasicSR
# -----------... | 7,982 | 31.583673 | 84 | py |
SwinMR | SwinMR-main/utils/utils_logger.py | import sys
import datetime
import logging
'''
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 03/Mar/2019
# --------------------------------------------
# https://github.com/xinntao/BasicSR
# --------------------------------------------
'''
def log(*args, **kwargs):
... | 1,686 | 24.179104 | 107 | py |
SwinMR | SwinMR-main/utils/utils_swinmr.py | import torch
from torch import nn
import os
import cv2
import gc
import numpy as np
from scipy.io import *
from scipy.fftpack import *
"""
# --------------------------------------------
# Jiahao Huang (j.huang21@imperial.uk.ac)
# 30/Jan/2022
# --------------------------------------------
"""
# Fourier Transform
def... | 455 | 15.888889 | 46 | py |
SwinMR | SwinMR-main/utils/utils_model.py | # -*- coding: utf-8 -*-
import numpy as np
import torch
from utils import utils_image as util
import re
import glob
import os
'''
# --------------------------------------------
# Model
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 03/Mar/2019
# ------------------------... | 9,837 | 28.902736 | 148 | py |
SwinMR | SwinMR-main/utils/utils_regularizers.py | import torch
import torch.nn as nn
'''
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 03/Mar/2019
# --------------------------------------------
'''
# --------------------------------------------
# SVD Orthogonal Regularization
# --------------------------------------... | 3,416 | 31.542857 | 87 | py |
SwinMR | SwinMR-main/utils/utils_bnorm.py | import torch
import torch.nn as nn
"""
# --------------------------------------------
# Batch Normalization
# --------------------------------------------
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# 01/Jan/2019
# --------------------------------------------
"""
# --------------------------------... | 3,132 | 33.054348 | 187 | py |
SwinMR | SwinMR-main/data/dataset_CCsagpi.py | '''
# -----------------------------------------
Data Loader
CC-SAG-PI d.1.1
by Jiahao Huang (j.huang21@imperial.ac.uk)
# -----------------------------------------
'''
import random
import torch.utils.data as data
import utils.utils_image as util
from utils.utils_swinmr import *
from models.select_mask import define_Ma... | 5,361 | 34.045752 | 116 | py |
SwinMR | SwinMR-main/data/select_dataset.py | '''
# -----------------------------------------
Select Dataset
by Jiahao Huang (j.huang21@imperial.ac.uk)
# -----------------------------------------
'''
def define_Dataset(dataset_opt):
dataset_type = dataset_opt['dataset_type'].lower()
# ------------------------------------------------
# CC-359 Calgary... | 904 | 30.206897 | 102 | py |
SwinMR | SwinMR-main/data/dataset_CCsagnpi.py | '''
# -----------------------------------------
Data Loader
CC-SAG-NPI d.1.1
by Jiahao Huang (j.huang21@imperial.ac.uk)
# -----------------------------------------
'''
import random
import torch.utils.data as data
import utils.utils_image as util
from utils.utils_swinmr import *
from models.select_mask import define_M... | 4,898 | 32.554795 | 105 | py |
risk-slim | risk-slim-master/setup.py | #! /usr/bin/env python
#
# Copyright (C) 2017 Berk Ustun
import os
import sys
from setuptools import setup, find_packages, dist
from setuptools.extension import Extension
#resources
#setuptools http://setuptools.readthedocs.io/en/latest/setuptools.html
#setuptools + Cython: http://stackoverflow.com/questions/32528560... | 2,322 | 25.397727 | 70 | py |
risk-slim | risk-slim-master/examples/ex_02_advanced_options.py | import os
import numpy as np
import pprint
import riskslim
# data
data_name = "breastcancer" # name of the data
data_dir = os.getcwd() + '/examples/data/' # directory where datasets are stored
data_csv_file = data_dir + data_name + '_data.csv' # csv file for t... | 6,526 | 53.848739 | 217 | py |
risk-slim | risk-slim-master/examples/ex_01_quickstart.py | import os
import pprint
import numpy as np
import riskslim
# data
data_name = "breastcancer" # name of the data
data_dir = os.getcwd() + '/examples/data/' # directory where datasets are stored
data_csv_file = data_dir + data_name + '_data.csv' # csv file for t... | 3,223 | 46.411765 | 217 | py |
risk-slim | risk-slim-master/examples/ex_03_constraints.py | import os
import numpy as np
import cplex as cplex
import pprint
import riskslim
# data
import riskslim.coefficient_set
data_name = "breastcancer" # name of the data
data_dir = os.getcwd() + '/examples/data/' # directory where datasets are stored
data_csv_file = data_... | 4,811 | 41.584071 | 217 | py |
risk-slim | risk-slim-master/riskslim/lattice_cpa.py | import time
import numpy as np
from cplex.callbacks import HeuristicCallback, LazyConstraintCallback
from cplex.exceptions import CplexError
from .bound_tightening import chained_updates
from .defaults import DEFAULT_LCPA_SETTINGS
from .utils import print_log, validate_settings
from .heuristics import discrete_descent,... | 37,697 | 42.834884 | 169 | py |
risk-slim | risk-slim-master/riskslim/coefficient_set.py | import numpy as np
from prettytable import PrettyTable
from .defaults import INTERCEPT_NAME
class CoefficientSet(object):
"""
Class used to represent and manipulate constraints on individual coefficients
including upper bound, lower bound, variable type, and regularization.
Coefficient Set is composed... | 13,648 | 27.978769 | 134 | py |
risk-slim | risk-slim-master/riskslim/utils.py | import logging
import sys
from pathlib import Path
import time
import warnings
import numpy as np
import pandas as pd
import prettytable as pt
from .defaults import INTERCEPT_NAME
# DATA
def load_data_from_csv(dataset_csv_file, sample_weights_csv_file = None, fold_csv_file = None, fold_num = 0):
"""
Parameter... | 12,518 | 37.051672 | 126 | py |
risk-slim | risk-slim-master/riskslim/defaults.py | import numpy as np
INTERCEPT_NAME = '(Intercept)'
# Settings
DEFAULT_LCPA_SETTINGS = {
#
'c0_value': 1e-6,
'w_pos': 1.00,
#
# MIP Formulation
'drop_variables': True, #drop variables
'tight_formulation': True, #use a slightly tighter MIP formulation
'include_auxillary_variab... | 5,018 | 47.728155 | 130 | py |
risk-slim | risk-slim-master/riskslim/initialization.py | import time
import numpy as np
from cplex import Cplex, SparsePair, infinity as CPX_INFINITY
from .setup_functions import setup_penalty_parameters
from .mip import create_risk_slim, set_cplex_mip_parameters
from .solution_pool import SolutionPool
from .bound_tightening import chained_updates, chained_updates_for_lp
fro... | 21,391 | 37.613718 | 150 | py |
risk-slim | risk-slim-master/riskslim/bound_tightening.py | import numpy as np
def chained_updates(bounds, C_0_nnz, new_objval_at_feasible = None, new_objval_at_relaxation = None, MAX_CHAIN_COUNT = 20):
new_bounds = dict(bounds)
# update objval_min using new_value (only done once)
if new_objval_at_relaxation is not None:
if new_bounds['objval_min'] < new... | 6,773 | 42.146497 | 130 | py |
risk-slim | risk-slim-master/riskslim/solution_pool.py | import numpy as np
import prettytable as pt
class SolutionPool(object):
"""
Helper class used to store solutions to the risk slim optimization problem
"""
def __init__(self, obj):
if isinstance(obj, SolutionPool):
self._P = obj.P
self._objvals = obj.objvals
... | 9,755 | 29.776025 | 111 | py |
risk-slim | risk-slim-master/riskslim/heuristics.py | import numpy as np
#todo: finish specifications
#todo: add input checking (with ability to turn off)
#todo: Cython implementation
def sequential_rounding(rho, Z, C_0, compute_loss_from_scores_real, get_L0_penalty, objval_cutoff = float('Inf')):
"""
Parameters
----------
rho: ... | 11,885 | 39.155405 | 150 | py |
risk-slim | risk-slim-master/riskslim/setup_functions.py | import numpy as np
from .coefficient_set import CoefficientSet, get_score_bounds
from .utils import print_log
def setup_loss_functions(data, coef_set, L0_max = None, loss_computation = None, w_pos = 1.0):
"""
Parameters
----------
data
coef_set
L0_max
loss_computation
w_pos
Retur... | 10,101 | 35.469314 | 122 | py |
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