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
value |
|---|---|---|---|---|---|---|
baselines | baselines-master/baselines/acktr/__init__.py | 0 | 0 | 0 | py | |
baselines | baselines-master/baselines/acktr/kfac_utils.py | import tensorflow as tf
def gmatmul(a, b, transpose_a=False, transpose_b=False, reduce_dim=None):
assert reduce_dim is not None
# weird batch matmul
if len(a.get_shape()) == 2 and len(b.get_shape()) > 2:
# reshape reduce_dim to the left most dim in b
b_shape = b.get_shape()
if redu... | 3,389 | 37.965517 | 168 | py |
baselines | baselines-master/baselines/bench/test_monitor.py | from .monitor import Monitor
import gym
import json
def test_monitor():
import pandas
import os
import uuid
env = gym.make("CartPole-v1")
env.seed(0)
mon_file = "/tmp/baselines-test-%s.monitor.csv" % uuid.uuid4()
menv = Monitor(env, mon_file)
menv.reset()
for _ in range(1000):
... | 861 | 25.9375 | 95 | py |
baselines | baselines-master/baselines/bench/benchmarks.py | import re
import os
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
_atari7 = ['BeamRider', 'Breakout', 'Enduro', 'Pong', 'Qbert', 'Seaquest', 'SpaceInvaders']
_atariexpl7 = ['Freeway', 'Gravitar', 'MontezumaRevenge', 'Pitfall', 'PrivateEye', 'Solaris', 'Venture']
_BENCHMARKS = []
remove_version_re = re.comp... | 6,102 | 35.987879 | 129 | py |
baselines | baselines-master/baselines/bench/monitor.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... | 5,741 | 34.012195 | 174 | py |
baselines | baselines-master/baselines/bench/__init__.py | # flake8: noqa F403
from baselines.bench.benchmarks import *
from baselines.bench.monitor import *
| 99 | 24 | 40 | py |
baselines | baselines-master/baselines/her/ddpg.py | from collections import OrderedDict
import numpy as np
import tensorflow as tf
from tensorflow.contrib.staging import StagingArea
from baselines import logger
from baselines.her.util import (
import_function, store_args, flatten_grads, transitions_in_episode_batch, convert_episode_to_batch_major)
from baselines.h... | 21,980 | 47.955457 | 212 | py |
baselines | baselines-master/baselines/her/normalizer.py | import threading
import numpy as np
from mpi4py import MPI
import tensorflow as tf
from baselines.her.util import reshape_for_broadcasting
class Normalizer:
def __init__(self, size, eps=1e-2, default_clip_range=np.inf, sess=None):
"""A normalizer that ensures that observations are approximately distribu... | 5,304 | 36.624113 | 98 | py |
baselines | baselines-master/baselines/her/actor_critic.py | import tensorflow as tf
from baselines.her.util import store_args, nn
class ActorCritic:
@store_args
def __init__(self, inputs_tf, dimo, dimg, dimu, max_u, o_stats, g_stats, hidden, layers,
**kwargs):
"""The actor-critic network and related training code.
Args:
in... | 1,996 | 43.377778 | 92 | py |
baselines | baselines-master/baselines/her/her.py | import os
import click
import numpy as np
import json
from mpi4py import MPI
from baselines import logger
from baselines.common import set_global_seeds, tf_util
from baselines.common.mpi_moments import mpi_moments
import baselines.her.experiment.config as config
from baselines.her.rollout import RolloutWorker
def mp... | 7,498 | 37.654639 | 180 | py |
baselines | baselines-master/baselines/her/util.py | import os
import subprocess
import sys
import importlib
import inspect
import functools
import tensorflow as tf
import numpy as np
from baselines.common import tf_util as U
def store_args(method):
"""Stores provided method args as instance attributes.
"""
argspec = inspect.getfullargspec(method)
def... | 4,038 | 27.64539 | 90 | py |
baselines | baselines-master/baselines/her/__init__.py | 0 | 0 | 0 | py | |
baselines | baselines-master/baselines/her/replay_buffer.py | import threading
import numpy as np
class ReplayBuffer:
def __init__(self, buffer_shapes, size_in_transitions, T, sample_transitions):
"""Creates a replay buffer.
Args:
buffer_shapes (dict of ints): the shape for all buffers that are used in the replay
buffer
... | 3,669 | 32.669725 | 95 | py |
baselines | baselines-master/baselines/her/rollout.py | from collections import deque
import numpy as np
import pickle
from baselines.her.util import convert_episode_to_batch_major, store_args
class RolloutWorker:
@store_args
def __init__(self, venv, policy, dims, logger, T, rollout_batch_size=1,
exploit=False, use_target_net=False, compute_Q=F... | 6,782 | 38.9 | 147 | py |
baselines | baselines-master/baselines/her/her_sampler.py | import numpy as np
def make_sample_her_transitions(replay_strategy, replay_k, reward_fun):
"""Creates a sample function that can be used for HER experience replay.
Args:
replay_strategy (in ['future', 'none']): the HER replay strategy; if set to 'none',
regular DDPG experience replay is u... | 2,822 | 43.109375 | 96 | py |
baselines | baselines-master/baselines/her/experiment/play.py | # DEPRECATED, use --play flag to baselines.run instead
import click
import numpy as np
import pickle
from baselines import logger
from baselines.common import set_global_seeds
import baselines.her.experiment.config as config
from baselines.her.rollout import RolloutWorker
@click.command()
@click.argument('policy_fil... | 1,775 | 27.645161 | 94 | py |
baselines | baselines-master/baselines/her/experiment/config.py | import os
import numpy as np
import gym
from baselines import logger
from baselines.her.ddpg import DDPG
from baselines.her.her_sampler import make_sample_her_transitions
from baselines.bench.monitor import Monitor
DEFAULT_ENV_PARAMS = {
'FetchReach-v1': {
'n_cycles': 10,
},
}
DEFAULT_PARAMS = {
... | 7,705 | 37.148515 | 152 | py |
baselines | baselines-master/baselines/her/experiment/plot.py | # DEPRECATED, use baselines.common.plot_util instead
import os
import matplotlib.pyplot as plt
import numpy as np
import json
import seaborn as sns; sns.set()
import glob2
import argparse
def smooth_reward_curve(x, y):
halfwidth = int(np.ceil(len(x) / 60)) # Halfwidth of our smoothing convolution
k = halfwi... | 3,611 | 28.85124 | 120 | py |
baselines | baselines-master/baselines/her/experiment/__init__.py | 0 | 0 | 0 | py | |
baselines | baselines-master/baselines/her/experiment/data_generation/fetch_data_generation.py | import gym
import numpy as np
"""Data generation for the case of a single block pick and place in Fetch Env"""
actions = []
observations = []
infos = []
def main():
env = gym.make('FetchPickAndPlace-v1')
numItr = 100
initStateSpace = "random"
env.reset()
print("Reset!")
while len(actions) < ... | 3,603 | 27.377953 | 95 | py |
baselines | baselines-master/baselines/ppo1/run_robotics.py | #!/usr/bin/env python3
from mpi4py import MPI
from baselines.common import set_global_seeds
from baselines import logger
from baselines.common.cmd_util import make_robotics_env, robotics_arg_parser
import mujoco_py
def train(env_id, num_timesteps, seed):
from baselines.ppo1 import mlp_policy, pposgd_simple
i... | 1,293 | 30.560976 | 84 | py |
baselines | baselines-master/baselines/ppo1/run_atari.py | #!/usr/bin/env python3
from mpi4py import MPI
from baselines.common import set_global_seeds
from baselines import bench
import os.path as osp
from baselines import logger
from baselines.common.atari_wrappers import make_atari, wrap_deepmind
from baselines.common.cmd_util import atari_arg_parser
def train(env_id, num_... | 1,583 | 31.326531 | 87 | py |
baselines | baselines-master/baselines/ppo1/run_humanoid.py | #!/usr/bin/env python3
import os
from baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser
from baselines.common import tf_util as U
from baselines import logger
import gym
def train(num_timesteps, seed, model_path=None):
env_id = 'Humanoid-v2'
from baselines.ppo1 import mlp_policy, pposgd_simp... | 2,434 | 31.905405 | 120 | py |
baselines | baselines-master/baselines/ppo1/cnn_policy.py | import baselines.common.tf_util as U
import tensorflow as tf
import gym
from baselines.common.distributions import make_pdtype
class CnnPolicy(object):
recurrent = False
def __init__(self, name, ob_space, ac_space, kind='large'):
with tf.variable_scope(name):
self._init(ob_space, ac_space, ... | 2,417 | 41.421053 | 121 | py |
baselines | baselines-master/baselines/ppo1/run_mujoco.py | #!/usr/bin/env python3
from baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser
from baselines.common import tf_util as U
from baselines import logger
def train(env_id, num_timesteps, seed):
from baselines.ppo1 import mlp_policy, pposgd_simple
U.make_session(num_cpu=1).__enter__()
def poli... | 1,025 | 33.2 | 84 | py |
baselines | baselines-master/baselines/ppo1/mlp_policy.py | from baselines.common.mpi_running_mean_std import RunningMeanStd
import baselines.common.tf_util as U
import tensorflow as tf
import gym
from baselines.common.distributions import make_pdtype
class MlpPolicy(object):
recurrent = False
def __init__(self, name, *args, **kwargs):
with tf.variable_scope(na... | 2,842 | 44.854839 | 138 | py |
baselines | baselines-master/baselines/ppo1/pposgd_simple.py | from baselines.common import Dataset, explained_variance, fmt_row, zipsame
from baselines import logger
import baselines.common.tf_util as U
import tensorflow as tf, numpy as np
import time
from baselines.common.mpi_adam import MpiAdam
from baselines.common.mpi_moments import mpi_moments
from mpi4py import MPI
from col... | 9,432 | 42.270642 | 120 | py |
baselines | baselines-master/baselines/ppo1/__init__.py | 0 | 0 | 0 | py | |
baselines | baselines-master/baselines/acer/acer.py | import time
import functools
import numpy as np
import tensorflow as tf
from baselines import logger
from baselines.common import set_global_seeds
from baselines.common.policies import build_policy
from baselines.common.tf_util import get_session, save_variables, load_variables
from baselines.common.vec_env.vec_frame_... | 18,596 | 47.683246 | 179 | py |
baselines | baselines-master/baselines/acer/buffer.py | import numpy as np
class Buffer(object):
# gets obs, actions, rewards, mu's, (states, masks), dones
def __init__(self, env, nsteps, size=50000):
self.nenv = env.num_envs
self.nsteps = nsteps
# self.nh, self.nw, self.nc = env.observation_space.shape
self.obs_shape = env.observati... | 5,881 | 36.464968 | 124 | py |
baselines | baselines-master/baselines/acer/defaults.py | def atari():
return dict(
lrschedule='constant'
)
| 66 | 12.4 | 29 | py |
baselines | baselines-master/baselines/acer/runner.py | import numpy as np
from baselines.common.runners import AbstractEnvRunner
from baselines.common.vec_env.vec_frame_stack import VecFrameStack
from gym import spaces
class Runner(AbstractEnvRunner):
def __init__(self, env, model, nsteps):
super().__init__(env=env, model=model, nsteps=nsteps)
assert... | 2,689 | 42.387097 | 128 | py |
baselines | baselines-master/baselines/acer/policies.py | import numpy as np
import tensorflow as tf
from baselines.common.policies import nature_cnn
from baselines.a2c.utils import fc, batch_to_seq, seq_to_batch, lstm, sample
class AcerCnnPolicy(object):
def __init__(self, sess, ob_space, ac_space, nenv, nsteps, nstack, reuse=False):
nbatch = nenv * nsteps
... | 2,807 | 33.243902 | 95 | py |
baselines | baselines-master/baselines/acer/__init__.py | 0 | 0 | 0 | py | |
mesa-contrib | mesa-contrib-main/hooks/cmd_line_args.py | #!/usr/bin/env python3
#
# Generates the command-line argument hook for MESA, which is saved to
# `$MESA_CONTRIB_DIR/hooks/cmd_line_args.inc`.
#
# You can add or remove the parameters you'd like to control from the
# list `args`, below.
#
# To use the command line arguments in MESA, add the variable
# declarations
#
# ... | 2,202 | 32.892308 | 82 | py |
Noisy_Neighbours | Noisy_Neighbours-main/Global_Fit_Correction/Section_6_3/LISA_utils.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat May 30 08:55:10 2020
@author: aantonelli
LISA utils
"""
import numpy as np
"""
Define the LISA response function -- IMPORTANT: Doppler Shift missing here.
"""
def d_plus(alpha,theta,phi,lam):
sqrt3_64 = np.sqrt(3)/64 #
A = -36 * np... | 2,877 | 28.670103 | 112 | py |
Noisy_Neighbours | Noisy_Neighbours-main/Global_Fit_Correction/Section_6_3/MS_func.py | import numpy as np
def units():
GM_sun = 1.3271244*1e20
c =2.9979246*1e8
M_sun =1.9884099*1e30
G = 6.6743*1e-11
pc= 3.0856776*1e16
pi = np.pi
Mpc = (10**6) * pc
return GM_sun, c, M_sun, G, Mpc, pi
def PowerSpectralDensity(f):
"""
From https://arxiv.org/pdf/1803.01944.pdf.... | 26,129 | 32.414322 | 130 | py |
Input-Specific-Certification | Input-Specific-Certification-main/zipdata.py | import multiprocessing
import os.path as op
from threading import local
from zipfile import ZipFile, BadZipFile
from PIL import Image
from io import BytesIO
import torch.utils.data as data
_VALID_IMAGE_TYPES = ['.jpg', '.jpeg', '.tiff', '.bmp', '.png']
class ZipData(data.Dataset):
_IGNORE_ATTRS = {'_zip_file'}
... | 3,481 | 35.270833 | 110 | py |
Input-Specific-Certification | Input-Specific-Certification-main/certify_iss.py | # evaluate a smoothed classifier on a dataset
import argparse
from time import time
from model import resnet110
from datasets import get_dataset, DATASETS, get_num_classes
import numpy as np
from scipy.stats import norm
from statsmodels.stats.proportion import proportion_confint
import torch
from tqdm import tqdm
pars... | 8,532 | 38.50463 | 219 | py |
Input-Specific-Certification | Input-Specific-Certification-main/model.py | '''
ResNet110 for Cifar-10
References:
[1] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
[2] K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in deep residual networks. In ECCV, 2016.
'''
import torch.nn as nn
import torch.nn.functional as F
import math
def ... | 3,129 | 27.198198 | 101 | py |
Input-Specific-Certification | Input-Specific-Certification-main/datasets.py | import bisect
import os
import pickle
from PIL import Image
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, datasets
from torchvision.datasets.utils import check_integrity
from typing import *
from zipdata import ZipData
# set this environment var... | 14,566 | 36.836364 | 109 | py |
ENCAS | ENCAS-main/nat_api.py | import pickle
import numpy as np
from networks.attentive_nas_dynamic_model import AttentiveNasDynamicModel
from networks.ofa_mbv3_my import OFAMobileNetV3My
from networks.proxyless_my import OFAProxylessNASNetsMy
from search_space.ensemble_ss import EnsembleSearchSpace
from utils import get_metric_complement, get_ne... | 3,511 | 53.030769 | 140 | py |
ENCAS | ENCAS-main/evaluate.py | import time
from collections import defaultdict
import json
import torch
import numpy as np
from ofa.imagenet_classification.elastic_nn.utils import set_running_statistics
from networks.attentive_nas_dynamic_model import AttentiveNasDynamicModel
from networks.ofa_mbv3_my import OFAMobileNetV3My
from networks.proxyles... | 13,651 | 49.940299 | 143 | py |
ENCAS | ENCAS-main/nat.py | import itertools
import os
import time
from concurrent.futures.process import ProcessPoolExecutor
from pathlib import Path
import torch
import torch.nn.functional as F
import torchvision.transforms.functional
from torch.cuda.amp import GradScaler
from ofa.utils import AverageMeter, accuracy
from tqdm import tqdm
from... | 54,638 | 52.672888 | 223 | py |
ENCAS | ENCAS-main/mo_gomea.py | import os
import pandas as pd
import numpy as np
from utils import capture_subprocess_output
from pathlib import Path
class MoGomeaCInterface():
name = 'mo_gomea'
def __init__(self, api_name, path, path_data_for_c_api, n_objectives=2, n_genes=10, alphabet='2',
alphabet_lower_bound_path='0', i... | 2,288 | 47.702128 | 111 | py |
ENCAS | ENCAS-main/utils.py | import atexit
import gzip
import logging
import math
import os
import random
import sys
import yaml
from ofa.utils import count_parameters, measure_net_latency
from pathlib import Path
from ptflops import get_model_complexity_info
from pymoo.factory import get_performance_indicator
from pymoo.util.nds.non_dominated_sor... | 23,267 | 34.577982 | 120 | py |
ENCAS | ENCAS-main/nat_run_many.py | import argparse
import glob
import os
from concurrent.futures.process import ProcessPoolExecutor
from pathlib import Path
import datetime
import torch
from matplotlib import pyplot as plt
import utils
from nat import default_kwargs, main
import yaml
from shutil import copy
import traceback
from concurrent.futures impo... | 9,599 | 41.105263 | 122 | py |
ENCAS | ENCAS-main/dynamic_resolution_collator.py | import random
import copy
import ctypes
import torch
import multiprocessing as mp
import numpy as np
from torchvision import transforms
from utils import onehot, rand_bbox, show_im_from_torch_tensor
class DynamicResolutionCollator:
def __init__(self, n_resolutions_max, if_return_target_idx=True, if_cutmix=Fal... | 4,319 | 43.081633 | 125 | py |
ENCAS | ENCAS-main/fitness_functions.py | import numpy as np
import time
from utils import set_seed
from utils import CsvLogger
from nat_api import NatAPI
from encas.encas_api import EncasAPI
def alphabet_to_list(alphabet, n_variables):
if alphabet.isnumeric():
return [int(alphabet) for _ in range(n_variables)]
file = open(alphabet, 'r')
... | 4,678 | 34.180451 | 108 | py |
ENCAS | ENCAS-main/utils_pareto.py | import json
import os
import numpy as np
from utils import NAT_LOGS_PATH
def is_pareto_efficient(costs): # from https://stackoverflow.com/a/40239615/5126900
"""
Find the pareto-efficient points
:param costs: An (n_points, n_costs) array
:return: A (n_points, ) boolean array, indicating whether each... | 5,740 | 40.302158 | 116 | py |
ENCAS | ENCAS-main/utils_train.py | import random
import numpy as np
import torch
from torch.nn.modules.module import Module
# implementation of CutMixCrossEntropyLoss taken from https://github.com/ildoonet/cutmix
class CutMixCrossEntropyLoss(Module):
def __init__(self, size_average=True):
super().__init__()
self.size_average = siz... | 3,420 | 32.213592 | 112 | py |
ENCAS | ENCAS-main/plot_results/plot_results_imagenet.py | from plotting_functions import *
if __name__ == '__main__':
plt.style.use('ggplot')
plt.rcParams['font.family'] = 'serif'
# plt.rcParams.update({'font.size': 15})
plt.rcParams.update({'font.size': 18})
plt.rcParams['axes.grid'] = True
from cycler import cycler
plt.rcParams['axes.prop_cycl... | 6,002 | 64.967033 | 155 | py |
ENCAS | ENCAS-main/plot_results/plot_results_cifar100.py | from plotting_functions import *
if __name__ == '__main__':
plt.style.use('ggplot')
plt.rcParams['font.family'] = 'serif'
# plt.rcParams.update({'font.size': 15})
plt.rcParams.update({'font.size': 18})
plt.rcParams['axes.grid'] = True
tmp_path = os.path.join(utils.NAT_PATH, '.tmp')
from cy... | 9,012 | 66.261194 | 155 | py |
ENCAS | ENCAS-main/plot_results/timm_pareto.py | '''
find pareto front of timm models, save it 10 times to make my code think there are 10 seeds (this is needed for plotting)
'''
import json
import numpy as np
import os
import yaml
import utils
from utils import NAT_LOGS_PATH
from utils_pareto import is_pareto_efficient
from pathlib import Path
path_test_data = os.... | 1,113 | 31.764706 | 121 | py |
ENCAS | ENCAS-main/plot_results/plot_results_cifar10.py | import matplotlib.pyplot as plt
import utils
from plotting_functions import *
if __name__ == '__main__':
plt.style.use('ggplot')
plt.rcParams['font.family'] = 'serif'
# plt.rcParams.update({'font.size': 15})
plt.rcParams.update({'font.size': 18})
plt.rcParams['axes.grid'] = True
plt.rcParams['... | 9,572 | 64.568493 | 155 | py |
ENCAS | ENCAS-main/plot_results/plot_hv_over_time.py | import itertools
import os
import glob
from pathlib import Path
import matplotlib
import pandas as pd
import numpy as np
from PIL import Image
from matplotlib import pyplot as plt
import utils
from nat import NAT
import yaml
def compute_hypervolumes_over_time(run_path, **kwargs):
csv_path = glob.glob(os.path.join... | 5,163 | 40.98374 | 126 | py |
ENCAS | ENCAS-main/plot_results/stat_test.py | from plotting_functions import *
from scipy.stats import wilcoxon
def get_wilcoxon_p(x, y):
print(x)
print(y)
return wilcoxon(x, y, alternative='greater').pvalue
if __name__ == '__main__':
plt.style.use('ggplot')
plt.rcParams['font.family'] = 'serif'
plt.rcParams.update({'font.size': 15})
... | 13,475 | 73.043956 | 170 | py |
ENCAS | ENCAS-main/plot_results/plotting_functions.py | import re
import os
import json
from collections import defaultdict
import glob
from pathlib import Path
import numpy as np
from matplotlib import pyplot as plt
import itertools
from textwrap import fill
from PIL import Image
import yaml
import hashlib
from pdf2image import convert_from_path
import utils
from util... | 35,222 | 52.287443 | 161 | py |
ENCAS | ENCAS-main/search_space/ensemble_ss.py | import itertools
class EnsembleSearchSpace:
def __init__(self, ss_names_list, ss_kwargs_list):
from search_space import make_search_space
self.search_spaces = [make_search_space(ss_name, **ss_kwargs)
for ss_name, ss_kwargs in zip(ss_names_list, ss_kwargs_list)]
self.n_ss... | 1,496 | 41.771429 | 102 | py |
ENCAS | ENCAS-main/search_space/ofa_ss.py | import numpy as np
import random
import utils
class OFASearchSpace:
def __init__(self, alphabet='2', **kwargs):
self.name = 'ofa'
self.num_blocks = 5
self.encoded_length = 22 #needed for decoding an ensemble
self.if_cascade = False
self.positions = [None]
self.thre... | 9,126 | 42.669856 | 118 | py |
ENCAS | ENCAS-main/search_space/alphanet_ss.py | from copy import copy
import numpy as np
import yaml
import utils
from utils import RecursiveNamespace, alphanet_config_str
class AlphaNetSearchSpace:
def __init__(self, alphabet, **kwargs):
self.supernet_config = RecursiveNamespace(**yaml.safe_load(alphanet_config_str))
self.supernet_config_dic... | 7,457 | 42.109827 | 138 | py |
ENCAS | ENCAS-main/search_space/__init__.py | from .ofa_ss import OFASearchSpace
from .alphanet_ss import AlphaNetSearchSpace
from .proxyless_ss import ProxylessSearchSpace
_name_to_class_dict = {'ofa': OFASearchSpace, 'alphanet': AlphaNetSearchSpace, 'proxyless': ProxylessSearchSpace}
def make_search_space(name, **kwargs):
return _name_to_class_dict[name](*... | 328 | 40.125 | 113 | py |
ENCAS | ENCAS-main/search_space/proxyless_ss.py | import numpy as np
import random
import utils
class ProxylessSearchSpace:
def __init__(self, alphabet='2', **kwargs):
self.name = 'proxyless'
self.num_blocks = 5
self.encoded_length = 22 #needed for decoding an ensemble
self.if_cascade = False
self.positions = [None]
... | 7,851 | 42.142857 | 104 | py |
ENCAS | ENCAS-main/networks/attentive_nas_dynamic_model.py | # taken from https://github.com/facebookresearch/AttentiveNAS
# Difference: images not resized in forward, but beforehand, in the collator (which is faster)
import copy
import random
import collections
import math
import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint_sequential, checkpoint
... | 20,925 | 41.189516 | 142 | py |
ENCAS | ENCAS-main/networks/ofa_mbv3_my.py | import copy
import torch
from ofa.imagenet_classification.elastic_nn.modules import DynamicMBConvLayer, DynamicConvLayer, DynamicLinearLayer
from ofa.imagenet_classification.elastic_nn.networks import OFAMobileNetV3
from ofa.imagenet_classification.networks import MobileNetV3
from ofa.utils import val2list, make_divis... | 9,234 | 48.12234 | 148 | py |
ENCAS | ENCAS-main/networks/proxyless_my.py | from ofa.imagenet_classification.elastic_nn.networks import OFAProxylessNASNets
from ofa.imagenet_classification.networks import ProxylessNASNets
import copy
from ofa.imagenet_classification.elastic_nn.modules import DynamicMBConvLayer
from ofa.utils import val2list, make_divisible, MyNetwork
from ofa.utils.layers imp... | 5,430 | 42.103175 | 131 | py |
ENCAS | ENCAS-main/networks/attentive_nas_static_model.py | # taken from https://github.com/facebookresearch/AttentiveNAS
# Difference: images not resized in forward, but beforehand, in the collator (which is faster)
import torch
import torch.nn as nn
from .modules_alphanet.nn_base import MyNetwork
class AttentiveNasStaticModel(MyNetwork):
def __init__(self, first_conv, ... | 2,694 | 31.083333 | 113 | py |
ENCAS | ENCAS-main/networks/modules_alphanet/dynamic_layers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# adapted from OFA: https://github.com/mit-han-lab/once-for-all
from collections import OrderedDict
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from .static_layers import MBInvertedConvLayer, ConvBnActLayer, Lin... | 15,686 | 38.916031 | 152 | py |
ENCAS | ENCAS-main/networks/modules_alphanet/static_layers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# adapted from OFA: https://github.com/mit-han-lab/once-for-all
from collections import OrderedDict
import torch.nn as nn
from .nn_utils import get_same_padding, build_activation, make_divisible, drop_connect
from .nn_base import MyModule
from .act... | 12,039 | 30.76781 | 131 | py |
ENCAS | ENCAS-main/networks/modules_alphanet/activations.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# adapted from OFA: https://github.com/mit-han-lab/once-for-all
import torch
import torch.nn as nn
import torch.nn.functional as F
# A memory-efficient implementation of Swish function
class SwishImplementation(torch.autograd.Function):
@stati... | 1,405 | 24.107143 | 70 | py |
ENCAS | ENCAS-main/networks/modules_alphanet/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
| 71 | 35 | 70 | py |
ENCAS | ENCAS-main/networks/modules_alphanet/dynamic_ops.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# adapted from OFA: https://github.com/mit-han-lab/once-for-all
from torch.autograd.function import Function
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch
from torch.nn.modules._function... | 10,816 | 39.211896 | 106 | py |
ENCAS | ENCAS-main/networks/modules_alphanet/nn_base.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# adapted from OFA: https://github.com/mit-han-lab/once-for-all
import math
import torch
import torch.nn as nn
try:
from fvcore.common.file_io import PathManager
except:
pass
class MyModule(nn.Module):
def forward(self, x):
... | 4,767 | 30.576159 | 113 | py |
ENCAS | ENCAS-main/networks/modules_alphanet/nn_utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# adapted from OFA: https://github.com/mit-han-lab/once-for-all
import torch.nn as nn
from .activations import *
def make_divisible(v, divisor=8, min_value=1):
"""
forked from slim:
https://github.com/tensorflow/models/blob/\
0344... | 3,431 | 29.918919 | 96 | py |
ENCAS | ENCAS-main/run_manager/run_manager_my.py | from collections import defaultdict
import time
import torch.nn as nn
import torch.nn.parallel
import torch.optim
from sklearn.metrics import balanced_accuracy_score
from tqdm import tqdm
import torchvision
from ofa.utils import AverageMeter, accuracy
class RunManagerMy:
def __init__(self, net, run_config, no_gpu... | 9,055 | 50.748571 | 155 | py |
ENCAS | ENCAS-main/run_manager/run_config_my.py | import math
from ofa.imagenet_classification.run_manager import RunConfig
from ofa.utils import calc_learning_rate
class RunConfigMy(RunConfig):
def __init__(self, n_epochs, init_lr, lr_schedule_type, lr_schedule_param, dataset, train_batch_size,
test_batch_size, valid_size, opt_type, opt_param... | 1,958 | 50.552632 | 120 | py |
ENCAS | ENCAS-main/run_manager/__init__.py | from data_providers.imagenet import *
from data_providers.cifar import CIFAR10DataProvider, CIFAR100DataProvider
from ofa.imagenet_classification.run_manager.run_config import RunConfig
from run_manager.run_config_my import RunConfigMy
class ImagenetRunConfig(RunConfig):
def __init__(self, n_epochs=1, init_lr=1e-... | 4,715 | 45.693069 | 108 | py |
ENCAS | ENCAS-main/acc_predictor/predictor_container.py | import numpy as np
class PredictorContainer:
'''
Contains several predictors
'''
def __init__(self, predictors, name, **kwargs) -> None:
self.predictors = predictors
self.name = name
self.predictor_input_keys = kwargs.get('predictor_input_keys', None)
def fit(self, X, y, ... | 904 | 36.708333 | 114 | py |
ENCAS | ENCAS-main/acc_predictor/rbf_ensemble.py | """
Implementation based on the one provided by the NAT team, their original comment below:
The Ensemble scheme is based on the implementation from:
https://github.com/yn-sun/e2epp/blob/master/build_predict_model.py
https://github.com/HandingWang/RF-CMOCO
"""
import numpy as np
from acc_predictor.rbf import RBF
cla... | 2,879 | 34.555556 | 119 | py |
ENCAS | ENCAS-main/acc_predictor/predictor_subsets.py | import numpy as np
class PredictorSubsets:
'''
Contains several predictors, with each operating on a subset of the input. Outputs are averaged.
'''
def __init__(self, predictor_class, input_sizes, alphabet, alphabet_lb, **kwargs) -> None:
self.n_predictors = len(input_sizes)
self.inpu... | 1,457 | 37.368421 | 100 | py |
ENCAS | ENCAS-main/acc_predictor/rbf.py | from pySOT.surrogate import RBFInterpolant, CubicKernel, TPSKernel, LinearTail, ConstantTail
import numpy as np
class RBF:
""" Radial Basis Function """
def __init__(self, kernel='cubic', tail='linear', alphabet=None, alphabet_lb=None):
self.kernel = kernel
self.tail = tail
self.name =... | 1,458 | 33.738095 | 109 | py |
ENCAS | ENCAS-main/acc_predictor/predictor_subsets_combo_cascade.py | import numpy as np
class PredictorSubsetsComboCascade:
'''
Contains several base predictors, with each operating on a subset of the input.
A meta-predictor combines their outputs.
'''
def __init__(self, predictor_class, predictor_final, input_sizes, alphabet, alphabet_lb, **kwargs) -> None:
... | 2,195 | 46.73913 | 111 | py |
ENCAS | ENCAS-main/acc_predictor/factory.py | import numpy as np
from acc_predictor.predictor_container import PredictorContainer
from acc_predictor.predictor_subsets import PredictorSubsets
from acc_predictor.predictor_subsets_combo_cascade import PredictorSubsetsComboCascade
from acc_predictor.rbf import RBF
from acc_predictor.rbf_ensemble import RBFEnsemble
... | 3,602 | 54.430769 | 146 | py |
ENCAS | ENCAS-main/after_search/symlink_imagenet.py | import glob
import os
import utils
def create_symlinks(experiment_path, **kwargs):
nsga_path = utils.NAT_LOGS_PATH
full_path = os.path.join(nsga_path, experiment_path)
files_to_symlink_all = ['supernet_w1.0', 'supernet_w1.2', 'ofa_proxyless_d234_e346_k357_w1.3',
'attentive_nas_pre... | 912 | 37.041667 | 98 | py |
ENCAS | ENCAS-main/after_search/store_outputs.py | import json
import os
from concurrent.futures import ProcessPoolExecutor
from pathlib import Path
import numpy as np
import yaml
import glob
import utils
from utils import save_gz
from utils_pareto import get_best_pareto_up_and_including_iter
from evaluate import evaluate_many_configs
def store_cumulative_pareto_fr... | 6,451 | 47.149254 | 139 | py |
ENCAS | ENCAS-main/after_search/store_outputs_timm.py | import copy
import utils
from collections import defaultdict
import pandas as pd
import timm
import json
import os
from concurrent.futures import ProcessPoolExecutor
from pathlib import Path
from os.path import join
import numpy as np
from ofa.utils import AverageMeter, accuracy
from timm.data import create_dataset... | 18,030 | 109.619632 | 10,476 | py |
ENCAS | ENCAS-main/after_search/extract_supernet_from_joint.py | import glob
import json
import numpy as np
import os
from os.path import join
from pathlib import Path
from shutil import copy
import re
import yaml
import utils
import utils_pareto
from utils import NAT_LOGS_PATH
def extract(experiment_name, out_experiment_name, idx_snet, if_joint_pareto_only=False, **kwargs): # i... | 4,759 | 45.213592 | 139 | py |
ENCAS | ENCAS-main/after_search/evaluate_stored_outputs.py | import copy
import json
import numpy as np
import os
import torch
import glob
import gzip
import yaml
from matplotlib import pyplot as plt
import utils
from utils import execute_func_for_all_runs_and_combine
labels_path_prefix = utils.NAT_DATA_PATH
def evaluate_stored_one_run(run_path, dataset_type, path_labels, *... | 10,795 | 50.409524 | 269 | py |
ENCAS | ENCAS-main/after_search/average_weights.py | import os
import torch
import utils
def swa(run_path, iters, supernet_name_in, supernet_name_out):
checkpoint_paths = [os.path.join(run_path, f'iter_{i}', supernet_name_in) for i in iters]
# read checkpoints
checkpoints = [torch.load(p, map_location='cpu') for p in checkpoint_paths]
state_dicts = [c[... | 2,015 | 39.32 | 125 | py |
ENCAS | ENCAS-main/after_search/extract_store_eval.py | from plot_results.plotting_functions import compare_val_and_test
from after_search.evaluate_stored_outputs import evaluate_stored_whole_experiment
from after_search.extract_supernet_from_joint import extract_all
from after_search.store_outputs import store_cumulative_pareto_front_outputs
def extract_store_eval(datase... | 1,602 | 56.25 | 122 | py |
ENCAS | ENCAS-main/searcher_wrappers/base_wrapper.py | class BaseSearcherWrapper:
def __init__(self):
pass
def search(self, archive, predictor, iter_current):
pass | 133 | 21.333333 | 55 | py |
ENCAS | ENCAS-main/searcher_wrappers/nsga3_wrapper.py | import os
import time
from pathlib import Path
from pymoo.util.nds.non_dominated_sorting import NonDominatedSorting
from networks.attentive_nas_dynamic_model import AttentiveNasDynamicModel
from networks.ofa_mbv3_my import OFAMobileNetV3My
from networks.proxyless_my import OFAProxylessNASNetsMy
from utils import get_... | 7,978 | 49.821656 | 146 | py |
ENCAS | ENCAS-main/searcher_wrappers/mo_gomea_wrapper.py | import os
import pickle
from pathlib import Path
import numpy as np
from searcher_wrappers.base_wrapper import BaseSearcherWrapper
from mo_gomea import MoGomeaCInterface
from utils import get_metric_complement
from pymoo.util.nds.non_dominated_sorting import NonDominatedSorting
class MoGomeaWrapper(BaseSearcherWrapp... | 6,682 | 47.781022 | 146 | py |
ENCAS | ENCAS-main/searcher_wrappers/random_search_wrapper.py | import os
from pathlib import Path
from networks.attentive_nas_dynamic_model import AttentiveNasDynamicModel
from networks.ofa_mbv3_my import OFAMobileNetV3My
from networks.proxyless_my import OFAProxylessNASNetsMy
from searcher_wrappers.base_wrapper import BaseSearcherWrapper
import numpy as np
from utils import Csv... | 7,158 | 48.034247 | 146 | py |
ENCAS | ENCAS-main/encas/encas_api.py | import glob
import pickle
import gzip
import numpy as np
import os
import torch
from utils import threshold_gene_to_value_moregranular as threshold_gene_to_value
class EncasAPI:
def __init__(self, filename):
self.use_cache = True
kwargs = pickle.load(open(filename, 'rb'))
self.if_allow_... | 6,361 | 42.278912 | 172 | py |
ENCAS | ENCAS-main/encas/greedy_search.py | # implementation of the algorithm from the paper http://proceedings.mlr.press/v80/streeter18a/streeter18a.pdf
import time
from concurrent.futures import ProcessPoolExecutor
import numpy as np
import torch
import utils
class GreedySearchWrapperEnsembleClassification:
def __init__(self, alphabet, subnet_to_output_d... | 9,745 | 50.294737 | 148 | py |
ENCAS | ENCAS-main/encas/post_hoc_search_run_many.py | import glob
import itertools
import os
import json
import gzip
import argparse
import utils_pareto
from encas.mo_gomea_search import MoGomeaWrapperEnsembleClassification
from encas.random_search import RandomSearchWrapperEnsembleClassification
from greedy_search import GreedySearchWrapperEnsembleClassification
# o... | 16,698 | 52.351438 | 154 | py |
ENCAS | ENCAS-main/encas/mo_gomea_search.py | import dill as pickle
import os
import numpy as np
from mo_gomea import MoGomeaCInterface
from utils import threshold_gene_to_value_moregranular as threshold_gene_to_value
def write_np_to_text_file_for_mo_gomea(path, arr):
with open(path, 'wb') as f:
np.savetxt(f, arr, delimiter=' ', newline='\n', heade... | 2,725 | 47.678571 | 147 | py |
ENCAS | ENCAS-main/encas/random_search.py | import dill as pickle
import os
import numpy as np
from encas.encas_api import EncasAPI
from utils import threshold_gene_to_value_moregranular as threshold_gene_to_value, CsvLogger
class RandomSearchWrapperEnsembleClassification:
def __init__(self, alphabet, subnet_to_output_distrs, subnet_to_flops, labels, if_... | 2,675 | 43.6 | 149 | py |
ENCAS | ENCAS-main/subset_selectors/base_subset_selector.py | class BaseSubsetSelector:
def __init__(self):
pass
def select(self, archive, objs_cur):
pass | 117 | 18.666667 | 40 | py |
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