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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FBNETGEN | FBNETGEN-main/main.py | from pathlib import Path
import argparse
import yaml
import torch
from model import FBNETGEN, GNNPredictor, SeqenceModel, BrainNetCNN
from train import BasicTrain, BiLevelTrain, SeqTrain, GNNTrain, BrainCNNTrain
from datetime import datetime
from dataloader import init_dataloader
def main(args):
with open(args.... | 3,687 | 35.514851 | 109 | py |
FBNETGEN | FBNETGEN-main/dataloader.py |
import numpy as np
import torch
import torch.utils.data as utils
from sklearn import preprocessing
import pandas as pd
from scipy.io import loadmat
import pathlib
class StandardScaler:
"""
Standard the input
"""
def __init__(self, mean, std):
self.mean = mean
self.std = std
def t... | 6,468 | 30.556098 | 104 | py |
FBNETGEN | FBNETGEN-main/train.py | from typing import overload
import torch
from numpy.lib import save
from util import Logger, accuracy, TotalMeter
import numpy as np
from pathlib import Path
import torch.nn.functional as F
from sklearn.metrics import roc_auc_score
from sklearn.metrics import precision_recall_fscore_support
from util.prepossess import ... | 16,952 | 34.690526 | 98 | py |
FBNETGEN | FBNETGEN-main/util/prepossess.py | import torch
import numpy as np
import random
def mixup_data(x, nodes, y, alpha=1.0, device='cuda'):
'''Returns mixed inputs, pairs of targets, and lambda'''
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1
batch_size = x.size()[0]
index = torch.randperm(batch_size).... | 2,388 | 27.783133 | 90 | py |
FBNETGEN | FBNETGEN-main/util/loss.py | import torch
def inner_loss(label, matrixs):
loss = 0
if torch.sum(label == 0) > 1:
loss += torch.mean(torch.var(matrixs[label == 0], dim=0))
if torch.sum(label == 1) > 1:
loss += torch.mean(torch.var(matrixs[label == 1], dim=0))
return loss
def intra_loss(label, matrixs):
a,... | 1,451 | 24.928571 | 70 | py |
FBNETGEN | FBNETGEN-main/util/logger.py | import logging
class Logger:
def __init__(self):
self.logger = logging.getLogger()
self.logger.setLevel(logging.INFO)
for handler in self.logger.handlers:
handler.close()
self.logger.handlers.clear()
formatter = logging.Formatter(
'[%(asctime)s][%(f... | 579 | 28 | 82 | py |
FBNETGEN | FBNETGEN-main/util/__init__.py | from .logger import Logger
from .meter import AverageMeter, TotalMeter, accuracy
| 81 | 26.333333 | 53 | py |
FBNETGEN | FBNETGEN-main/util/meter.py | from typing import List
import torch
def accuracy(output: torch.Tensor, target: torch.Tensor, top_k=(1,)) -> List[float]:
max_k = max(top_k)
batch_size = target.size(0)
_, predict = output.topk(max_k, 1, True, True)
predict = predict.t()
correct = predict.eq(target.view(1, -1).expand_as(predict))... | 1,699 | 22.943662 | 84 | py |
FBNETGEN | FBNETGEN-main/util/FCNet/fc_net_label_generation.py | from sklearn.cluster import AffinityPropagation
import numpy as np
import argparse
import random
import pathlib
def main(args):
final_fc = np.load(args.data_path, allow_pickle=True)
if args.dataset == 'PNC':
final_fc = final_fc.item()
final_fc = final_fc['data']
column_idxs = []
lab... | 1,893 | 29.548387 | 116 | py |
FBNETGEN | FBNETGEN-main/util/FCNet/infer.py | import torch
import argparse
import yaml
from model import SeqenceModel, FCNet
from dataloader import infer_dataloader
from pathlib import Path
import numpy as np
from sklearn.linear_model import ElasticNet
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import roc_... | 2,775 | 22.726496 | 119 | py |
FBNETGEN | FBNETGEN-main/util/analysis/extract_info_from_log.py | import argparse
import re
def main(args):
table = []
with open(args.path, 'r') as f:
lines = f.readlines()
for l in lines:
value = re.findall(r'.*Epoch\[(\d+)/500\].*Train Loss: (\d+\.\d+).*Test Loss: (\d+\.\d+)', l)
table.append(value[0])
s = f'|Epoch|'
fo... | 853 | 25.6875 | 124 | py |
FBNETGEN | FBNETGEN-main/util/abide/03-generate_abide_dataset.py | import deepdish as dd
import os.path as osp
import os
import numpy as np
import argparse
from pathlib import Path
import pandas as pd
def main(args):
data_dir = os.path.join(args.root_path, 'ABIDE_pcp/cpac/filt_noglobal/raw')
timeseires = os.path.join(args.root_path, 'ABIDE_pcp/cpac/filt_noglobal/')
met... | 2,040 | 26.958904 | 194 | py |
FBNETGEN | FBNETGEN-main/util/abide/02-process_data.py | # Copyright (c) 2019 Mwiza Kunda
# Modified by Xuan Kan
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program... | 3,874 | 37.366337 | 124 | py |
FBNETGEN | FBNETGEN-main/util/abide/01-fetch_data.py | # Copyright (c) 2019 Mwiza Kunda
# Copyright (C) 2017 Sarah Parisot <s.parisot@imperial.ac.uk>, , Sofia Ira Ktena <ira.ktena@imperial.ac.uk>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, eit... | 4,095 | 39.156863 | 133 | py |
FBNETGEN | FBNETGEN-main/util/abide/preprocess_data.py | # Copyright (c) 2019 Mwiza Kunda
# Copyright (C) 2017 Sarah Parisot <s.parisot@imperial.ac.uk>, Sofia Ira Ktena <ira.ktena@imperial.ac.uk>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, eithe... | 11,247 | 40.201465 | 118 | py |
FBNETGEN | FBNETGEN-main/model/GSL.py | import torch
import torch.nn as nn
from torch.nn import functional as F
from model.cell import DCGRUCell
import numpy as np
from .model import GNNPredictor, ConvKRegion, Embed2GraphByLinear, GruKRegion, Embed2GraphByProduct
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def count_parameters(mod... | 16,048 | 35.894253 | 119 | py |
FBNETGEN | FBNETGEN-main/model/model.py | from turtle import forward
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Conv1d, MaxPool1d, Linear, GRU
import math
def sample_gumbel(shape, eps=1e-20):
U = torch.rand(shape).cuda()
return -torch.autograd.Variable(torch.log(-torch.log(U + eps) + ep... | 13,552 | 29.050998 | 97 | py |
FBNETGEN | FBNETGEN-main/model/__init__.py | from .GSL import BrainGSLModel, TSConstruction
from .model import FBNETGEN, GNNPredictor, SeqenceModel, BrainNetCNN | 115 | 57 | 68 | py |
FBNETGEN | FBNETGEN-main/model/cell.py | import numpy as np
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class LayerParams:
def __init__(self, rnn_network: torch.nn.Module, layer_type: str):
self._rnn_network = rnn_network
self._params_dict = {}
self._biases_dict = {}
self._type = lay... | 6,299 | 38.873418 | 105 | py |
dynet | dynet-master/setup.py | import distutils.sysconfig
import logging as log
import platform
import zipfile
import sys
from distutils.command.build import build as _build
from distutils.command.build_py import build_py as _build_py
from distutils.command.install_data import install_data as _install_data
from distutils.errors import DistutilsSetup... | 16,189 | 39.173697 | 289 | py |
dynet | dynet-master/examples/variational-autoencoder/basic-image-recon/utils.py | import os, struct
import numpy as np
import math
# adapted from https://github.com/clab/dynet/blob/master/examples/mnist/mnist-autobatch.py
def load_mnist(dataset, path):
"""
wget -O - http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz | gunzip > train-images-idx3-ubyte
wget -O - http://yann.lecu... | 4,525 | 39.053097 | 108 | py |
dynet | dynet-master/examples/variational-autoencoder/basic-image-recon/vae.py | from __future__ import print_function
from utils import load_mnist, make_grid, pre_pillow_float_img_process, save_image
import numpy as np
import argparse
import dynet as dy
import os
if not os.path.exists('results'):
os.makedirs('results')
parser = argparse.ArgumentParser(description='VAE MNIST Example')
parser... | 6,690 | 31.639024 | 118 | py |
dynet | dynet-master/examples/python-utils/util.py | import mmap
class Vocab:
def __init__(self, w2i):
self.w2i = dict(w2i)
self.i2w = {i:w for w,i in w2i.items()}
@classmethod
def from_corpus(cls, corpus):
w2i = {}
for sent in corpus:
for word in sent:
w2i.setdefault(word, len(w2i))
return... | 1,731 | 28.355932 | 111 | py |
dynet | dynet-master/examples/rnnlm/lstmlm-auto.py | from __future__ import print_function
from collections import defaultdict
import math
import random
import time
import dynet as dy
# path to Mikolov PTB train.txt and valid.txt
FLAGS_train = 'train.txt'
FLAGS_valid = 'valid.txt'
FLAGS_layers = 1
FLAGS_hidden_dim = 128
FLAGS_batch_size = 16
FLAGS_word_dim = 64
def s... | 4,126 | 31.242188 | 79 | py |
dynet | dynet-master/examples/rnnlm/rnnlm.py | import dynet as dy
import time
import random
LAYERS = 2
INPUT_DIM = 256 #50 #256
HIDDEN_DIM = 256 # 50 #1024
VOCAB_SIZE = 0
from collections import defaultdict
from itertools import count
import argparse
import sys
import util
class RNNLanguageModel:
def __init__(self, model, LAYERS, INPUT_DIM, HIDDEN_DIM, VOC... | 4,334 | 31.593985 | 105 | py |
dynet | dynet-master/examples/rnnlm/rnnlm_transduce.py | # a version rnnlm.py using the transduce() interface.
import dynet as dy
import time
import random
LAYERS = 2
INPUT_DIM = 50 #256
HIDDEN_DIM = 50 #1024
VOCAB_SIZE = 0
import argparse
import sys
import util
try:
from itertools import izip as zip
except ImportError:
pass
class RNNLanguageModel:
def __ini... | 3,338 | 30.205607 | 102 | py |
dynet | dynet-master/examples/mnist/mnist-autobatch.py | #! /usr/bin/env python3
import time
import random
import os
import struct
import argparse
import numpy as np
import dynet as dy
# To run this, download the four files from http://yann.lecun.com/exdb/mnist/
# using the --download option.
# Pass the path where the data should be stored (or is already stored)
# to the ... | 6,853 | 35.26455 | 78 | py |
dynet | dynet-master/examples/mnist/basic-mnist-benchmarks/mnist_dynet_autobatch.py | from __future__ import division
import os
import struct
import argparse
import random
import time
import numpy as np
# import dynet as dy
# import dynet_config
# dynet_config.set_gpu()
import dynet as dy
# First, download the MNIST dataset from the official website and decompress it.
# wget -O - http://yann.lecun.com/... | 6,044 | 38.509804 | 106 | py |
dynet | dynet-master/examples/mnist/basic-mnist-benchmarks/mnist_pytorch.py | from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import time
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNI... | 4,645 | 38.372881 | 95 | py |
dynet | dynet-master/examples/mnist/basic-mnist-benchmarks/mnist_dynet_minibatch.py | from __future__ import division
import os
import struct
import argparse
import random
import time
import numpy as np
# import dynet as dy
# import dynet_config
# dynet_config.set_gpu()
import dynet as dy
# First, download the MNIST dataset from the official website and decompress it.
# wget -O - http://yann.lecun.com/... | 6,215 | 38.341772 | 106 | py |
dynet | dynet-master/examples/tensorboard/rnnlm-batch.py | import dynet as dy
import time
import random
from pycrayon import CrayonClient
LAYERS = 2
INPUT_DIM = 256 #50 #256
HIDDEN_DIM = 256 # 50 #1024
VOCAB_SIZE = 0
MB_SIZE = 50 # mini batch size
import argparse
from collections import defaultdict
from itertools import count
import sys
import util
class RNNLanguageMode... | 5,527 | 32.707317 | 105 | py |
dynet | dynet-master/examples/tensorboard/util.py | import mmap
class Vocab:
def __init__(self, w2i):
self.w2i = dict(w2i)
self.i2w = {i:w for w,i in w2i.items()}
@classmethod
def from_corpus(cls, corpus):
w2i = {}
for sent in corpus:
for word in sent:
w2i.setdefault(word, len(w2i))
return... | 1,731 | 28.355932 | 111 | py |
dynet | dynet-master/examples/treelstm/main.py | from __future__ import print_function
import dynet as dy
dyparams = dy.DynetParams()
dyparams.from_args()
import sys
import time
import os
import argparse
import warnings
import zipfile
from six.moves import urllib
from model import TreeLSTMClassifier
from utils import get_embeds, acc_eval
from scheduler import Sche... | 5,324 | 35.472603 | 108 | py |
dynet | dynet-master/examples/treelstm/dataloader.py | import re
import codecs
import random
from collections import Counter
def read_dataset(filename):
return [Tree.from_sexpr(line.strip()) for line in codecs.open(filename, "r")]
def get_vocabs(trees):
label_vocab = Counter()
word_vocab = Counter()
for tree in trees:
label_vocab.update([n.label... | 2,717 | 26.454545 | 87 | py |
dynet | dynet-master/examples/treelstm/utils.py | import codecs
import numpy as np
import dynet as dy
def acc_eval(dataset, model):
dataset.reset(shuffle=False)
good = bad = 0.0
for tree in dataset:
dy.renew_cg()
pred = np.argmax(model.predict_for_tree(tree, decorate=False, training=False))
if pred == tree.label:
good ... | 845 | 26.290323 | 86 | py |
dynet | dynet-master/examples/treelstm/model.py | import dynet as dy
import numpy as np
import os
class TreeLSTMBuilder(object):
def __init__(self, pc_param, pc_embed, word_vocab, wdim, hdim, word_embed=None):
self.WS = [pc_param.add_parameters((hdim, wdim)) for _ in "iou"]
self.US = [pc_param.add_parameters((hdim, 2 * hdim)) for _ in "iou"]
... | 5,341 | 45.859649 | 112 | py |
dynet | dynet-master/examples/treelstm/scheduler.py | import time
import dynet as dy
import numpy as np
from utils import acc_eval
class Scheduler:
def __init__(self, model, train, dev, params):
self.train, self.dev = train, dev
self.model = model
self.params = params
self.trainer_param = getattr(dy, params['trainer'])(model.pc_param... | 2,726 | 39.701493 | 125 | py |
dynet | dynet-master/examples/treelstm/filter_glove.py | import codecs
import re
import os
data_dir = 'trees'
datasets = ['train', 'dev', 'test']
glove_origin_path = 'glove.840B.300d.txt'
glove_filtered_path = 'glove_filtered.txt'
def get_vocab(file_path):
vocab = set()
tokker = re.compile(r'([^ ()]+)\)')
with codecs.open(file_path) as f:
for line in f... | 954 | 24.810811 | 65 | py |
dynet | dynet-master/examples/devices/xor-multidevice.py | # Usage:
# python xor-multidevice.py --dynet-devices CPU,GPU:0,GPU:1
# or python xor-multidevice.py --dynet-gpus 2
import sys
import dynet as dy
#xsent = True
xsent = False
HIDDEN_SIZE = 8
ITERATIONS = 2000
m = dy.Model()
trainer = dy.SimpleSGDTrainer(m)
pW1 = m.add_parameters((HIDDEN_SIZE, 2), device="GPU:1")
... | 2,082 | 22.404494 | 66 | py |
dynet | dynet-master/examples/devices/cpu_vs_gpu.py | # Usage: python cpu_vs_gpu.py
import time
from multiprocessing import Process
def do_cpu():
import _dynet as C
C.init()
cm = C.Model()
cpW = cm.add_parameters((1000,1000))
s = time.time()
C.renew_cg()
W = C.parameter(cpW)
W = W*W*W*W*W*W*W
z = C.squared_distance(W,W)
z.value()
z.backward()
pri... | 866 | 18.266667 | 38 | py |
dynet | dynet-master/examples/reinforcement-learning/ddpg.py | import dynet as dy
import numpy as np
from memory import Memory
from network import MLP
# Deep Deterministic Policy Gradient: https://arxiv.org/abs/1509.02971
# An reinforcement learning agent to learn in environments which have continuous action spaces.
class DDPG:
def __init__(self, obs_dim, action_dim, hidden... | 4,171 | 39.901961 | 143 | py |
dynet | dynet-master/examples/reinforcement-learning/reduce_tree.py | import operator
import numpy as np
# A simple binary tree structure to calculate some statistics from leaves.
class ReduceTree(object):
def __init__(self, size, op):
if size & (size - 1) != 0:
raise ValueError("size mush be a power of 2.")
self.size = size
self.values = np.zero... | 1,743 | 27.590164 | 86 | py |
dynet | dynet-master/examples/reinforcement-learning/memory.py | import numpy as np
from math import log, ceil
from reduce_tree import ReduceTree, SumTree
# A simple memory to store and sample experiences.
class Memory(object):
def __init__(self, size):
self.size = size
self.idx = 0
self.memory = np.zeros(size, dtype=object)
def store(self, exp):
... | 1,997 | 31.225806 | 102 | py |
dynet | dynet-master/examples/reinforcement-learning/network.py | import dynet as dy
class Network(object):
def __init__(self, pc):
self.pc = dy.ParameterCollection() if pc is None else pc
def update(self, other, soft=False, tau=0.1):
params_self, params_other = self.pc.parameters_list(), other.pc.parameters_list()
for x, y in zip(params_self, param... | 2,989 | 38.866667 | 101 | py |
dynet | dynet-master/examples/reinforcement-learning/dqn.py | import dynet as dy
import numpy as np
from memory import Memory, PrioritizedMemory
# DeepQNetwork: https://arxiv.org/abs/1312.5602
# An reinforcement learning agent to learn in environments which have discrete action spaces.
# Double Q-Learning: https://arxiv.org/abs/1509.06461
# Prioritized Replay: https://arxiv.or... | 4,403 | 37.631579 | 115 | py |
dynet | dynet-master/examples/reinforcement-learning/train_test_utils.py | import numpy as np
def train_pipeline_progressive(env, player, score_threshold, batch_size, n_episode, learn_start=100, print_every=10):
rewards, losses = [], []
for i_episode in range(n_episode):
obs = env.reset()
reward = 0
for t in range(env._max_episode_steps):
action =... | 3,261 | 39.271605 | 117 | py |
dynet | dynet-master/examples/reinforcement-learning/main_ddpg.py | import argparse
import gym
from ddpg import DDPG
from train_test_utils import train_pipeline_conservative, test
def establish_args():
parser = argparse.ArgumentParser()
parser.add_argument("--env_name", default="Walker2d-v2", type=str)
parser.add_argument("--memory_size", default=1e6, type=float)
pars... | 1,094 | 39.555556 | 111 | py |
dynet | dynet-master/examples/reinforcement-learning/main_dqn.py | import argparse
import gym
from dqn import DeepQNetwork
from network import MLP, Header
from train_test_utils import train_pipeline_progressive, train_pipeline_conservative, test
def establish_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dynet-gpus', default=0, type=int)
parser.add_... | 1,846 | 33.849057 | 119 | py |
dynet | dynet-master/examples/tagger/bilstmtagger.py | import dynet as dy
from collections import Counter
import random
import util
# format of files: each line is "word<TAB>tag<newline>", blank line is new sentence.
train_file="/Users/yogo/Vork/Research/corpora/pos/WSJ.TRAIN"
test_file="/Users/yogo/Vork/Research/corpora/pos/WSJ.TEST"
MLP=True
def read(fname):
sen... | 3,719 | 24.655172 | 84 | py |
dynet | dynet-master/examples/transformer/wrap-data.py | import sys
import collections
import itertools
def threshold_vocab(fname, threshold):
word_counts = collections.Counter()
with open(fname) as fin:
for line in fin:
for token in line.split():
word_counts[token] += 1
ok = set()
for word, count in sorted(word_counts.it... | 4,419 | 43.646465 | 370 | py |
dynet | dynet-master/examples/batching/rnnlm-batch.py | import dynet as dy
import time
import random
LAYERS = 2
INPUT_DIM = 256 #50 #256
HIDDEN_DIM = 256 # 50 #1024
VOCAB_SIZE = 0
MB_SIZE = 50 # mini batch size
import argparse
from collections import defaultdict
from itertools import count
import sys
import util
class RNNLanguageModel:
def __init__(self, model, LA... | 4,910 | 32.182432 | 102 | py |
dynet | dynet-master/examples/batching/minibatch.py | import dynet as dy
import numpy as np
m = dy.Model()
lp = m.add_lookup_parameters((100,10))
# regular lookup
a = lp[1].npvalue()
b = lp[2].npvalue()
c = lp[3].npvalue()
# batch lookup instead of single elements.
# two ways of doing this.
abc1 = dy.lookup_batch(lp, [1,2,3])
print(abc1.npvalue())
abc2 = lp.batch([1,2... | 875 | 23.333333 | 89 | py |
dynet | dynet-master/examples/sequence-to-sequence/attention.py | import dynet as dy
import random
EOS = "<EOS>"
characters = list("abcdefghijklmnopqrstuvwxyz ")
characters.append(EOS)
int2char = list(characters)
char2int = {c:i for i,c in enumerate(characters)}
VOCAB_SIZE = len(characters)
LSTM_NUM_OF_LAYERS = 2
EMBEDDINGS_SIZE = 32
STATE_SIZE = 32
ATTENTION_SIZE = 32
model = d... | 5,302 | 31.533742 | 113 | py |
dynet | dynet-master/examples/xor/xor.py | import sys
import dynet as dy
#xsent = True
xsent = False
HIDDEN_SIZE = 8
ITERATIONS = 2000
m = dy.Model()
trainer = dy.SimpleSGDTrainer(m)
W = m.add_parameters((HIDDEN_SIZE, 2))
b = m.add_parameters(HIDDEN_SIZE)
V = m.add_parameters((1, HIDDEN_SIZE))
a = m.add_parameters(1)
if len(sys.argv) == 2:
m.populate_fro... | 1,367 | 17.739726 | 47 | py |
dynet | dynet-master/python/dynet_config.py | def set(mem="512", random_seed=0, autobatch=0,
profiling=0, weight_decay=0, shared_parameters=0,
requested_gpus=0, gpu_mask=None):
if "__DYNET_CONFIG" in __builtins__:
(mem, random_seed, auto_batch, profiling) = (
__builtins__["__DYNET_CONFIG"]["mem"] if __builtins__["__DYNET_CO... | 2,199 | 51.380952 | 159 | py |
dynet | dynet-master/python/dynet_viz.py | from __future__ import print_function
import sys
import re
from collections import defaultdict
if sys.version_info.major > 2:
# alias dict.items() as dict.iteritems() in python 3+
class compat_dict(defaultdict):
pass
compat_dict.iteritems = defaultdict.items
defaultdict = compat_dict
# add xrange to ... | 39,058 | 35.640713 | 188 | py |
dynet | dynet-master/python/model_test.py | """
Tests for model saving and loading, including for user-defined models.
"""
from __future__ import print_function
import dynet as dy
import numpy
import os
# first, define three user-defined classes
class Transfer(Saveable):
def __init__(self, nin, nout, act, model):
self.act = act
self.W = mod... | 6,174 | 30.829897 | 98 | py |
dynet | dynet-master/tests/python/test.py | import dynet as dy
import numpy as np
import unittest
import gc
def npvalue_callable(x):
return x.npvalue()
def gradient_callable(x):
return x.gradient()
class TestInput(unittest.TestCase):
def setUp(self):
self.input_vals = np.arange(81)
self.squared_norm = (self.input_vals**2).sum()... | 26,802 | 32.970849 | 88 | py |
dynet | dynet-master/bench/sequence_transduction.py | import dynet as dy
import random
import time
import sys
random.seed(1)
SEQ_LENGTH=2
BATCH_SIZE=2
HIDDEN=1
NCLASSS=2
EMBED_SIZE=1
N_SEQS=1000
autobatching=True
dy.renew_cg()
random_seq = lambda ln,t: [random.randint(0,t-1) for _ in xrange(ln)]
seq_lengths = [SEQ_LENGTH for _ in range(N_SEQS)]
#seq_lengths = [random.r... | 1,763 | 23.84507 | 72 | py |
dynet | dynet-master/doc/source/doc_util.py | from __future__ import print_function
import re
INDENT = 1
NAME = 2
INHERIT = 3
ARGUMENTS = 3
PASS=' pass\n'
def pythonize_arguments(arg_str):
"""
Remove types from function arguments in cython
"""
out_args = []
# If there aren't any arguments return the empty string
if arg_str is None:
... | 3,948 | 33.043103 | 164 | py |
dynet | dynet-master/doc/source/conf.py | # -*- coding: utf-8 -*-
#
# DyNet documentation build configuration file, created by
# sphinx-quickstart on Thu Oct 13 16:13:12 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All... | 9,095 | 30.583333 | 83 | py |
pose_refinement | pose_refinement-master/src/training/loaders.py | import numpy as np
from torch.utils.data import DataLoader, SequentialSampler
from itertools import chain
import torch
from databases.datasets import pose_grid_from_index, Mpi3dTrainDataset, PersonStackedMucoTempDataset, ConcatPoseDataset
class ConcatSampler(torch.utils.data.Sampler):
""" Concatenates two sampl... | 6,259 | 41.297297 | 136 | py |
pose_refinement | pose_refinement-master/src/training/callbacks.py | import math
import numpy as np
import torch
from training.loaders import UnchunkedGenerator
from training.torch_tools import eval_results
from util.pose import remove_root, mrpe, optimal_scaling, r_mpjpe
class BaseCallback(object):
def on_itergroup_end(self, iter_cnt, epoch_loss):
pass
def on_epoch... | 13,217 | 38.57485 | 117 | py |
pose_refinement | pose_refinement-master/src/training/torch_tools.py | import numpy as np
from torch.utils.data import DataLoader, TensorDataset
from itertools import zip_longest, chain
import torch
from util.misc import assert_shape
from inspect import signature
import time
from torch import optim
from util.pose import mrpe
def exp_decay(params):
def f(epoch):
return params... | 13,551 | 35.926431 | 136 | py |
pose_refinement | pose_refinement-master/src/training/__init__.py | 0 | 0 | 0 | py | |
pose_refinement | pose_refinement-master/src/training/preprocess.py | import numpy as np
import torch
from databases.datasets import PoseDataset
from databases.joint_sets import Common14Joints, CocoExJoints, MuPoTSJoints
from util.misc import assert_shape, load
from util.pose import remove_root, remove_root_keepscore, combine_pose_and_trans
def preprocess_2d(data, fx, cx, fy, cy, join... | 16,694 | 33.853862 | 139 | py |
pose_refinement | pose_refinement-master/src/util/mx_tools.py | import numpy as np
def project_points(calib, points3d):
"""
Projects 3D points using a calibration matrix.
Parameters:
points3d: ndarray of shape (nPoints, 3)
"""
assert points3d.ndim == 2 and points3d.shape[1] == 3
p = np.empty((len(points3d), 2))
p[:, 0] = points3d[:, 0] / poin... | 1,694 | 32.235294 | 97 | py |
pose_refinement | pose_refinement-master/src/util/misc.py | import json
import os
import pickle
import numpy as np
import scipy.io
def ensuredir(path):
"""
Creates a folder if it doesn't exists.
:param path: path to the folder to create
"""
if len(path) == 0:
return
if not os.path.exists(path):
os.makedirs(path)
def load(path, pkl_p... | 3,651 | 31.035088 | 128 | py |
pose_refinement | pose_refinement-master/src/util/pose.py | import numpy as np
from databases.joint_sets import CocoExJoints
from util.misc import assert_shape
def harmonic_mean(a, b, eps=1e-6):
return 2 / (1 / (a + eps) + 1 / (b + eps))
def _combine(data, target, a, b):
"""
Modifies data by combining (taking average) joints at index a and b at position target.... | 7,952 | 30.939759 | 113 | py |
pose_refinement | pose_refinement-master/src/util/viz.py | """Functions to visualize human poses"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, ImageMagickWriter
from mpl_toolkits.mplot3d import proj3d
import cv2
def get_3d_axes(*subplot):
"""
Creates a 3D Matplotlib axis. The arguments are the same as of the `... | 12,276 | 35.322485 | 129 | py |
pose_refinement | pose_refinement-master/src/util/__init__.py | 0 | 0 | 0 | py | |
pose_refinement | pose_refinement-master/src/scripts/generate_muco_temp.py | """
generates the muco_temp synthetic dataset. In order to use this script, you already have to have to have generated
the sequence meta data files in 'sequence_meta.pkl' and the ground-truth poses. The scripts can be found in mpi_inf_3dhp.ipynb
"""
from databases import mpii_3dhp, muco_temp
from databases.joint_sets i... | 2,445 | 41.912281 | 126 | py |
pose_refinement | pose_refinement-master/src/scripts/maskrcnn_bboxes.py | """ Generates Mask-RCNN bounding boxes. """
import argparse
from detectron2.utils.logger import setup_logger
setup_logger()
# import some common libraries
import numpy as np
import cv2
# import some common detectron2 utilities
from detectron2 import model_zoo
from detectron2.config import get_cfg
import detectron2.... | 3,049 | 29.19802 | 101 | py |
pose_refinement | pose_refinement-master/src/scripts/hrnet_predict.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
sys.path.append('../hrnet/lib')
from scripts import hrnet_dataset
# ------------------------------------------------------------------------------
# pose.pytorch
# Copyright (c) 2018-present Micros... | 7,588 | 33.03139 | 95 | py |
pose_refinement | pose_refinement-master/src/scripts/hrnet_dataset.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Modified by Marton Veges
# ------------------------------------------------------------------------------
from __future__ impor... | 10,625 | 32.415094 | 128 | py |
pose_refinement | pose_refinement-master/src/scripts/eval.py | #!/usr/bin/python3
"""
Evaluates a (not end2end) model on MuPo-TS
"""
import argparse
import os
import numpy as np
import torch
from util.misc import load
from databases import mupots_3d
from databases.datasets import PersonStackedMuPoTsDataset
from databases.joint_sets import MuPoTSJoints, CocoExJoints
from model.po... | 4,512 | 34.81746 | 127 | py |
pose_refinement | pose_refinement-master/src/scripts/__init__.py | 0 | 0 | 0 | py | |
pose_refinement | pose_refinement-master/src/scripts/predict.py | import argparse
import cv2
import numpy as np
import os
from databases.datasets import FlippableDataset
from databases.joint_sets import MuPoTSJoints, CocoExJoints
from model.pose_refinement import optimize_poses
from scripts.eval import load_model, LOG_PATH
from training.callbacks import TemporalTestEvaluator
from tr... | 6,812 | 38.842105 | 132 | py |
pose_refinement | pose_refinement-master/src/scripts/train.py | import argparse
import os
from databases.datasets import Mpi3dTestDataset, Mpi3dTrainDataset, PersonStackedMucoTempDataset, ConcatPoseDataset
from model.videopose import TemporalModel, TemporalModelOptimized1f
from training.callbacks import preds_from_logger, ModelCopyTemporalEvaluator
from training.loaders import Chu... | 7,059 | 38.222222 | 122 | py |
pose_refinement | pose_refinement-master/src/databases/mupots_3d.py | import glob
import os
import cv2
import numpy as np
from databases.joint_sets import MuPoTSJoints
from util.misc import load, assert_shape
from util.mx_tools import calibration_matrix
MUPO_TS_PATH = '../datasets/MuPoTS'
def _decode_sequence(sequence):
assert isinstance(sequence, (int, np.int32, str)), "sequenc... | 11,443 | 36.768977 | 119 | py |
pose_refinement | pose_refinement-master/src/databases/joint_sets.py | import numpy as np
from util.misc import assert_shape
# SIDEDNESS
# 0 - right
# 1 - left
# 2 - center
class JointSet:
def index_of(self, joint_name):
joint_inds = np.where(self.NAMES == joint_name)[0]
assert len(joint_inds) > 0, "No joint called " + joint_name
return joint_inds[0]
de... | 4,916 | 39.636364 | 123 | py |
pose_refinement | pose_refinement-master/src/databases/muco_temp.py | import os
import cv2
from util.misc import load
MUCO_TEMP_PATH = '../datasets/MucoTemp'
def get_frame(cam, vid_id, frame_ind, rgb=True):
path = os.path.join(MUCO_TEMP_PATH, 'frames/cam_%d/vid_%d' % (cam, vid_id), 'img_%04d.jpg' % frame_ind)
img = cv2.imread(path)
if rgb:
img = cv2.cvtColor(img,... | 830 | 27.655172 | 130 | py |
pose_refinement | pose_refinement-master/src/databases/datasets.py | import os
import h5py
import numpy as np
from torch.utils.data import Dataset
from databases import mupots_3d, mpii_3dhp, muco_temp
from databases.joint_sets import CocoExJoints, OpenPoseJoints, MuPoTSJoints
class PoseDataset(Dataset):
""" Subclasses should have the attributes poses2d/3d, pred_cdepths, pose[2|3... | 27,399 | 42.149606 | 132 | py |
pose_refinement | pose_refinement-master/src/databases/mpii_3dhp.py | import os
import cv2
import numpy as np
from databases.joint_sets import MuPoTSJoints
from util.misc import load
MPII_3DHP_PATH = '../datasets/Mpi3DHP'
def test_frames(seq):
frames = sorted(os.listdir(os.path.join(MPII_3DHP_PATH, 'mpi_inf_3dhp_test_set', 'TS%d' % seq, 'imageSequence')))
# In TS3/TS4 last ... | 6,001 | 34.099415 | 136 | py |
pose_refinement | pose_refinement-master/src/databases/__init__.py | 0 | 0 | 0 | py | |
pose_refinement | pose_refinement-master/src/model/pose_refinement.py | import numpy as np
import torch
from scipy import ndimage
from databases.joint_sets import MuPoTSJoints
from training.callbacks import BaseMPJPECalculator
from training.torch_tools import get_optimizer
from util.misc import assert_shape
from util.pose import remove_root, insert_zero_joint
def pose_error(pred, init):... | 7,511 | 34.267606 | 127 | py |
pose_refinement | pose_refinement-master/src/model/__init__.py | 0 | 0 | 0 | py | |
pose_refinement | pose_refinement-master/src/model/videopose.py | # Based on https://github.com/facebookresearch/VideoPose3D
#
# Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
class TemporalModelBase(nn.Module):
""... | 9,265 | 40.927602 | 116 | py |
UltraNest | UltraNest-master/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
try:
from setuptools import setup
except:
from distutils.core import setup
from Cython.Build import cythonize
from distutils.extension import Extension
from Cython.Distutils import build_ext
extra_include_dirs = ['.']
try:
import numpy
extra_include_dirs ... | 2,347 | 29.102564 | 97 | py |
UltraNest | UltraNest-master/languages/c++/runcppsimple.py | import numpy as np
import ctypes
from ultranest import ReactiveNestedSampler
# this version uses one parameter vector per function call
# because function calls are expensive, the runcpp.py way is more efficient and recommended
mycpplib = ctypes.CDLL("mycpplib.so")
# define the arguments of the functions and return ... | 1,039 | 29.588235 | 94 | py |
UltraNest | UltraNest-master/languages/c++/runcpp.py | import numpy as np
import ctypes
from ultranest import ReactiveNestedSampler
mycpplib = ctypes.CDLL("mycpplib.so")
# define the arguments of the functions and return values
mycpplib.my_cpp_transform_vectorized.argtypes = [
np.ctypeslib.ndpointer(dtype=np.float64, ndim=2, flags='C_CONTIGUOUS'),
ctypes.c_size_t... | 1,108 | 30.685714 | 111 | py |
UltraNest | UltraNest-master/languages/python/runpy.py | import numpy as np
from ultranest import ReactiveNestedSampler
def mytransform(cube):
return cube * 2 - 1
def mylikelihood(params):
centers = 0.1 * np.arange(params.shape[1]).reshape((1, -1))
return -0.5 * (((params - centers) / 0.01)**2).sum(axis=1)
paramnames = ["a", "b", "c"]
sampler = ReactiveNestedS... | 446 | 26.9375 | 97 | py |
UltraNest | UltraNest-master/languages/c/runcsimple.py | import numpy as np
import ctypes
from ultranest import ReactiveNestedSampler
# this version uses one parameter vector per function call
# because function calls are expensive, the runc.py way is more efficient and recommended
myclib = ctypes.CDLL("mylib.so")
# define the arguments of the functions and return value... | 1,013 | 29.727273 | 94 | py |
UltraNest | UltraNest-master/languages/c/runc.py | import numpy as np
import ctypes
from ultranest import ReactiveNestedSampler
myclib = ctypes.CDLL("mylib.so")
# define the arguments of the functions and return values
myclib.my_c_transform_vectorized.argtypes = [
np.ctypeslib.ndpointer(dtype=np.float64, ndim=2, flags='C_CONTIGUOUS'),
ctypes.c_size_t,
... | 1,089 | 30.142857 | 111 | py |
UltraNest | UltraNest-master/languages/fortran/runfort.py | import numpy as np
import ctypes
from ultranest import ReactiveNestedSampler
myfortlib = ctypes.CDLL("myfortlib.so")
# define the arguments of the functions and return values
myfortlib.my_fort_transform.argtypes = [
np.ctypeslib.ndpointer(dtype=np.float64, ndim=1, flags='C_CONTIGUOUS'),
ctypes.POINTER(ctypes.... | 1,249 | 31.051282 | 94 | py |
UltraNest | UltraNest-master/examples/testfunnel.py | import argparse
import numpy as np
from numpy import log
def main(args):
np.random.seed(2)
ndim = args.x_dim
sigma = args.sigma
centers = np.sin(np.arange(ndim) / 2.)
data = np.random.normal(centers, sigma).reshape((1, -1))
def loglike(theta):
sigma = 10**theta[:,0]
like = -0.5... | 1,890 | 33.381818 | 125 | py |
UltraNest | UltraNest-master/examples/rundirichlet.py | #!/usr/bin/env python3
"""
This script tests the UltraNest stepsamplers
in a few configurations with a real model.
"""
import numpy as np
import ultranest, ultranest.stepsampler
# velocity dispersions of dwarf galaxies by van Dokkum et al., Nature, 555, 629 https://arxiv.org/abs/1803.10237v1
values = np.array([15, ... | 2,378 | 33.478261 | 114 | py |
UltraNest | UltraNest-master/examples/testslantedeggbox.py | import os
import sys
import argparse
import numpy as np
from numpy import cos, pi
def main(args):
def loglike(z):
chi = (2. + (cos(z[:,:2] / 2.)).prod(axis=1))**5
chi2 = -np.abs((z - 5 * pi) / 0.5).sum(axis=1)
return chi + chi2
def transform(x):
return x * 100
import ... | 1,676 | 31.25 | 82 | py |
UltraNest | UltraNest-master/examples/test.py | import os
import sys
import argparse
import numpy as np
def main(args):
from ultranest import NestedSampler
#def loglike(z):
# return np.array([-sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0) for x in z])
def loglike_(z):
return np.array([-sum(100.0 * (x[1::2] - x[::2] **... | 2,192 | 38.872727 | 107 | py |
UltraNest | UltraNest-master/examples/testrosenbrock.py | import argparse
import numpy as np
def main(args):
ndim = args.x_dim
adaptive_nsteps = args.adapt_steps
if adaptive_nsteps is None:
adaptive_nsteps = False
def loglike(theta):
a = theta[:,:-1]
b = theta[:,1:]
return -2 * (100 * (b - a**2)**2 + (1 - a)**2).sum(axis=1)
... | 6,260 | 39.655844 | 136 | py |
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