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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larq | larq-main/larq/activations_test.py | import numpy as np
import pytest
import tensorflow as tf
import larq as lq
from larq.testing_utils import generate_real_values_with_zeros
@pytest.mark.parametrize("name", ["hard_tanh", "leaky_tanh"])
def test_serialization(name):
fn = tf.keras.activations.get(name)
ref_fn = getattr(lq.activations, name)
... | 1,259 | 29 | 74 | py |
larq | larq-main/larq/conftest_test.py | import pytest
import tensorflow as tf
from larq import context
def test_eager_and_graph_mode_fixture(eager_and_graph_mode):
if eager_and_graph_mode == "eager":
assert tf.executing_eagerly()
else:
assert not tf.executing_eagerly()
assert tf.compat.v1.get_default_session() is not None
... | 788 | 23.65625 | 61 | py |
larq | larq-main/larq/quantized_variable_test.py | import numpy as np
import pytest
import tensorflow as tf
from numpy.testing import assert_almost_equal, assert_array_equal
from packaging import version
from tensorflow.python.distribute.values import DistributedVariable
from larq import context, testing_utils
from larq.quantized_variable import QuantizedVariable
from... | 14,405 | 37.31383 | 94 | py |
larq | larq-main/larq/snapshots/snap_models_test.py | # -*- coding: utf-8 -*-
# snapshottest: v1 - https://goo.gl/zC4yUc
from __future__ import unicode_literals
from snapshottest import Snapshot
snapshots = Snapshot()
snapshots['test_functional_model_summary 2.4+'] = '''+toy_model stats-----------------------------------------------------------------------------------... | 8,529 | 65.640625 | 165 | py |
larq | larq-main/larq/snapshots/snap_quantized_variable_test.py | # -*- coding: utf-8 -*-
# snapshottest: v1 - https://goo.gl/zC4yUc
from __future__ import unicode_literals
from snapshottest import Snapshot
snapshots = Snapshot()
snapshots['test_repr[eager] 1'] = "<QuantizedVariable 'x:0' shape=() dtype=float32 quantizer=<lambda> numpy=0.0>"
snapshots['test_repr[eager] 2'] = "<Qu... | 814 | 39.75 | 114 | py |
larq | larq-main/larq/snapshots/__init__.py | 0 | 0 | 0 | py | |
DAC2018 | DAC2018-master/main.py | ## This program is for DAC HDC contest ######
## 2017/11/22
## xxu8@nd.edu
## University of Notre Dame
import procfunc
import math
import numpy as np
import time
import sys
sys.path.append("./build/lib.linux-aarch64-2.7")
import mypack
#### !!!! you can import any package needed for your program ######
if __name__ ==... | 2,960 | 47.540984 | 150 | py |
DAC2018 | DAC2018-master/setup.py | from distutils.core import setup, Extension
module = Extension('mypack',extra_compile_args=['-std=c++11'], include_dirs=['/usr/local/cuda/include'],
sources = ['Detector.cpp'],extra_objects = ['./plugin.o', './kernel.o'], extra_link_args=['-lnvinfer', '-lnvcaffe_parser', '-lcudnn'])
setup(name = 'mypack', vers... | 388 | 54.571429 | 142 | py |
DAC2018 | DAC2018-master/sender_1_client.py | ## this is for GPU demo with only one FPGA and one computer for computer for display
## xxu8@nd.edu
import socket
import threading
import struct
import time
import cv2
import numpy
import xml.etree.ElementTree as ET
NofClients = 1
class Senders_Carame_Object:
def __init__(self,addr_ports=[("19... | 3,833 | 35.865385 | 95 | py |
DAC2018 | DAC2018-master/val_sample.py | import numpy as np
import sys
import os
import xml.etree.ElementTree as ET
import cv2
class bbox():
def __init__(self, xmin, ymin, xmax, ymax):
self.xmin = xmin
self.ymin = ymin
self.xmax = xmax
self.ymax = ymax
self.width = ymax - ymin
self.height = xmax - xmin
... | 2,744 | 30.193182 | 86 | py |
DAC2018 | DAC2018-master/demo.py | import socket
import cv2
import threading
import struct
import sys
sys.path.append("./build/lib.linux-aarch64-2.7")
import mypack
import procfunc
import math
import numpy as np
import time
mypack.netInit()
if __name__ == "__main__":
teamName = 'ICT-CAS'
DAC = './'
[imgDir, resultDir, timeDir, xmlDi... | 1,237 | 33.388889 | 134 | py |
DAC2018 | DAC2018-master/procfunc.py | import os
import cv2
import time
import numpy as np
import xml.dom.minidom
import random
import sys
sys.path.append("./build/lib.linux-aarch64-2.7")
import mypack
imageSize = (360, 640, 3)
##must be called to creat default directory
def setupDir(homeFolder, teamName):
imgDir = homeFolder + '/images'
resultDi... | 5,024 | 32.278146 | 153 | py |
DAC2018 | DAC2018-master/display.py | import socket
import cv2
import threading
import struct
import numpy
import sys
sys.path.append("./build/lib.linux-aarch64-2.7")
import mypack
mypack.netInit()
###### change your team name here
teamName = "teamName"
windowName = "DAC HDC contest team:"+teamName
class process_display_Object:
def __init... | 3,619 | 44.822785 | 156 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/test.py | import yaml
import os
from train.test import test
config = yaml.safe_load(open('config.yml'))
mode = config['mode']
os.environ["CUDA_VISIBLE_DEVICES"] = str(config['aspect_' + mode + '_model'][config['aspect_' + mode + '_model']['type']]['gpu'])
test(config) | 259 | 31.5 | 129 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/train.py | import yaml
import os
from train.train import train
config = yaml.safe_load(open('config.yml'))
mode = config['mode']
os.environ["CUDA_VISIBLE_DEVICES"] = str(config['aspect_' + mode + '_model'][config['aspect_' + mode + '_model']['type']]['gpu'])
train(config) | 262 | 31.875 | 129 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/preprocess.py | import yaml
from data_process.data_process import data_process
config = yaml.safe_load(open('config.yml'))
data_process(config) | 129 | 20.666667 | 50 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/aspect_category_model/capsnet.py | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import init
from src.module.utils.constants import PAD_INDEX, INF
from src.module.utils.sentence_clip import sentence_clip
from src.module.attention.dot_attention import DotAttention
from src.module.attention.scaled_dot_attention import Sca... | 4,384 | 45.648936 | 119 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/aspect_category_model/recurrent_capsnet.py | import torch
from torch import nn
import torch.nn.functional as F
from src.aspect_category_model.capsnet import CapsuleNetwork
class RecurrentCapsuleNetwork(CapsuleNetwork):
def __init__(self, embedding, aspect_embedding, num_layers, bidirectional, capsule_size, dropout, num_categories):
super(RecurrentCa... | 1,597 | 41.052632 | 118 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/aspect_category_model/bert_capsnet.py | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import init
from src.module.utils.constants import PAD_INDEX, INF
from src.module.utils.sentence_clip import sentence_clip
from src.module.attention.dot_attention import DotAttention
from src.module.attention.scaled_dot_attention import Sca... | 4,772 | 48.206186 | 119 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/aspect_term_model/capsnet.py | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import init
from src.module.utils.constants import PAD_INDEX, INF
from src.module.utils.sentence_clip import sentence_clip
from src.module.attention.dot_attention import DotAttention
from src.module.attention.scaled_dot_attention import Sca... | 4,714 | 46.15 | 119 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/aspect_term_model/recurrent_capsnet.py | import torch
from torch import nn
import torch.nn.functional as F
from src.aspect_term_model.capsnet import CapsuleNetwork
class RecurrentCapsuleNetwork(CapsuleNetwork):
def __init__(self, embedding, num_layers, bidirectional, capsule_size, dropout, num_categories):
super(RecurrentCapsuleNetwork, self).__... | 1,528 | 40.324324 | 100 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/aspect_term_model/bert_capsnet.py | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import init
from src.module.utils.constants import PAD_INDEX, INF
from src.module.utils.sentence_clip import sentence_clip
from src.module.attention.dot_attention import DotAttention
from src.module.attention.scaled_dot_attention import Sca... | 4,780 | 47.785714 | 119 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/attention/concat_attention.py | import torch
from torch import nn
from torch.nn import init
from src.module.attention.attention import Attention
class ConcatAttention(Attention):
def __init__(self, query_size, key_size, dropout=0):
super(ConcatAttention, self).__init__(dropout)
self.query_weights = nn.Parameter(torch.Tensor(quer... | 1,007 | 41 | 101 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/attention/bilinear_attention.py | import torch
from torch import nn
from torch.nn import init
from src.module.attention.attention import Attention
class BilinearAttention(Attention):
def __init__(self, query_size, key_size, dropout=0):
super(BilinearAttention, self).__init__(dropout)
self.weights = nn.Parameter(torch.FloatTensor(q... | 659 | 33.736842 | 76 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/attention/tanh_bilinear_attention.py | import torch
from torch import nn
from torch.nn import init
from src.module.attention.attention import Attention
class TanhBilinearAttention(Attention):
def __init__(self, query_size, key_size, dropout=0):
super(TanhBilinearAttention, self).__init__(dropout)
self.weights = nn.Parameter(torch.Float... | 740 | 36.05 | 94 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/attention/tanh_concat_attention.py | import torch
from torch import nn
from torch.nn import init
from src.module.attention.attention import Attention
class TanhConcatAttention(Attention):
def __init__(self, query_size, key_size, dropout=0):
super(TanhConcatAttention, self).__init__(dropout)
self.query_weights = nn.Parameter(torch.Ten... | 1,049 | 41 | 101 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/attention/multi_head_attention.py | from torch import nn
from torch.nn import init
import math
class MultiHeadAttention(nn.Module):
def __init__(self, attention, num_heads, hidden_size, key_size='default', value_size='default', out_size='default'):
key_size = hidden_size // num_heads if key_size == 'default' else key_size
value_size... | 3,451 | 55.590164 | 120 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/attention/dot_attention.py | from src.module.attention.attention import Attention
class DotAttention(Attention):
def __init__(self, dropout=0):
super(DotAttention, self).__init__(dropout)
def _score(self, query, key):
"""
query: FloatTensor (batch_size, num_queries, query_size)
key: FloatTensor (batch_siz... | 448 | 31.071429 | 64 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/attention/scaled_dot_attention.py | from src.module.attention.attention import Attention
import math
class ScaledDotAttention(Attention):
def __init__(self, dropout=0):
super(ScaledDotAttention, self).__init__(dropout)
def _score(self, query, key):
"""
query: FloatTensor (batch_size, num_queries, query_size)
key... | 499 | 32.333333 | 75 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/attention/attention.py | from torch import nn
import torch.nn.functional as F
from src.module.utils import constants
class Attention(nn.Module):
"""
The base class of attention.
"""
def __init__(self, dropout):
super(Attention, self).__init__()
self.dropout = dropout
def forward(self, query, key, value, m... | 2,355 | 37 | 104 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/attention/mlp_attention.py | import torch
from torch import nn
from torch.nn import init
from src.module.attention.attention import Attention
class MlpAttention(Attention):
def __init__(self, query_size, key_size, out_size=100, dropout=0):
super(MlpAttention, self).__init__(dropout)
self.query_projection = nn.Linear(query_siz... | 1,092 | 44.541667 | 112 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/attention/no_query_attention.py | import torch
from torch import nn
from torch.nn import init
class NoQueryAttention(nn.Module):
def __init__(self, query_size, attention):
super(NoQueryAttention, self).__init__()
self.query_size = query_size
self.query = nn.Parameter(torch.Tensor(1, query_size))
init.xavier_uniform... | 566 | 32.352941 | 62 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/utils/squash.py | import torch
def squash(x, dim=-1):
squared = torch.sum(x * x, dim=dim, keepdim=True)
scale = torch.sqrt(squared) / (1.0 + squared)
return scale * x | 161 | 26 | 53 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/utils/constants.py | PAD = '<pad>'
UNK = '<unk>'
ASPECT = '<aspect>'
PAD_INDEX = 0
UNK_INDEX = 1
ASPECT_INDEX = 2
INF = 1e9 | 104 | 10.666667 | 19 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/utils/loss.py | import torch
from torch import nn
import torch.nn.functional as F
class CapsuleLoss(nn.Module):
def __init__(self, smooth=0.1, lamda=0.6):
super(CapsuleLoss, self).__init__()
self.smooth = smooth
self.lamda = lamda
def forward(self, input, target):
one_hot = torch.zeros_like(i... | 2,309 | 38.152542 | 96 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/src/module/utils/sentence_clip.py | from src.module.utils.constants import PAD_INDEX
def sentence_clip(sentence):
mask = (sentence != PAD_INDEX)
sentence_lens = mask.long().sum(dim=1, keepdim=False)
max_len = sentence_lens.max().item()
return sentence[:, :max_len] | 245 | 34.142857 | 57 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/train/test.py | import torch
import os
from train import make_aspect_term_model, make_aspect_category_model
from train.make_data import make_term_test_data, make_category_test_data
from train.eval import eval
def test(config):
mode = config['mode']
if mode == 'term':
model = make_aspect_term_model.make_model(config)
... | 819 | 38.047619 | 118 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/train/make_aspect_term_model.py | import torch
from torch import nn
import numpy as np
import os
import yaml
from pytorch_pretrained_bert import BertModel
from src.aspect_term_model.recurrent_capsnet import RecurrentCapsuleNetwork
from src.aspect_term_model.bert_capsnet import BertCapsuleNetwork
def make_model(config):
model_type = config['aspect_... | 2,462 | 38.725806 | 83 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/train/make_data.py | import os
from torch.utils.data import DataLoader
from data_process.dataset import ABSADataset
input_list = {
'recurrent_capsnet': ['context', 'aspect'],
'bert_capsnet': ['bert_token', 'bert_segment']
}
def make_term_data(config):
base_path = config['base_path']
train_path = os.path.join(base_path, 'p... | 3,771 | 34.252336 | 93 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/train/make_optimizer.py | from torch import optim
import adabound
def make_optimizer(config, model):
mode = config['mode']
config = config['aspect_' + mode + '_model'][config['aspect_' + mode + '_model']['type']]
lr = config['learning_rate']
weight_decay = config['weight_decay']
opt = {
'sgd': optim.SGD,
'ad... | 831 | 35.173913 | 127 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/train/eval.py | import torch
def eval(model, data_loader, criterion=None):
total_samples = 0
correct_samples = 0
total_loss = 0
model.eval()
with torch.no_grad():
for data in data_loader:
input0, input1, label = data
input0, input1, label = input0.cuda(), input1.cuda(), label.cuda()... | 829 | 35.086957 | 81 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/train/__init__.py | 0 | 0 | 0 | py | |
MAMS-for-ABSA | MAMS-for-ABSA-master/train/make_aspect_category_model.py | import torch
from torch import nn
import numpy as np
import os
import yaml
from pytorch_pretrained_bert import BertModel
from src.aspect_category_model.recurrent_capsnet import RecurrentCapsuleNetwork
from src.aspect_category_model.bert_capsnet import BertCapsuleNetwork
def make_model(config):
model_type = config[... | 2,631 | 40.125 | 89 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/train/train.py | import torch
from torch import nn
from torch import optim
from train import make_aspect_term_model, make_aspect_category_model
from train.make_data import make_term_data, make_category_data
from train.make_optimizer import make_optimizer
from train.eval import eval
import os
import time
import pickle
from src.module.ut... | 3,271 | 42.052632 | 108 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/data_process/data_process.py | import os
import numpy as np
import pickle
import yaml
from data_process.utils import *
def data_process(config):
mode = config['mode']
assert mode in ('term', 'category')
base_path = config['base_path']
raw_train_path = os.path.join(base_path, 'raw/train.xml')
raw_val_path = os.path.join(base_path... | 3,169 | 53.655172 | 122 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/data_process/utils.py | import os
import numpy as np
import random
from xml.etree.ElementTree import parse
from pytorch_pretrained_bert import BertModel, BertTokenizer
from data_process.vocab import Vocab
from src.module.utils.constants import UNK, PAD_INDEX, ASPECT_INDEX
import spacy
import re
import json
url = re.compile('(<url>.*</url>)')... | 10,092 | 36.520446 | 132 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/data_process/dataset.py | import torch
from torch.utils.data import Dataset
import numpy as np
class ABSADataset(Dataset):
def __init__(self, path, input_list):
super(ABSADataset, self).__init__()
data = np.load(path)
self.data = {}
for key, value in data.items():
self.data[key] = torch.tensor(v... | 702 | 28.291667 | 56 | py |
MAMS-for-ABSA | MAMS-for-ABSA-master/data_process/vocab.py | import operator
from src.module.utils.constants import PAD, UNK, ASPECT
class Vocab(object):
def __init__(self):
self._count_dict = dict()
self._predefined_list = [PAD, UNK, ASPECT]
def add(self, word):
if word in self._count_dict:
self._count_dict[word] += 1
else:... | 1,296 | 32.25641 | 97 | py |
OpenFWI | OpenFWI-main/pytorch_ssim.py | # From https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) fo... | 2,722 | 35.306667 | 104 | py |
OpenFWI | OpenFWI-main/test.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 10,383 | 42.814346 | 156 | py |
OpenFWI | OpenFWI-main/gan_train.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 16,662 | 43.553476 | 128 | py |
OpenFWI | OpenFWI-main/network.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 14,861 | 45.15528 | 167 | py |
OpenFWI | OpenFWI-main/vis.py | import os
import torch
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import ListedColormap
# Load colormap for velocity map visualization
rainbow_cmap = ListedColormap(np.load('rainbow256.npy'))
def plot_velocity(output, target, path, vmin=None, vmax... | 4,324 | 38.318182 | 89 | py |
OpenFWI | OpenFWI-main/utils.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 17,006 | 34.804211 | 105 | py |
OpenFWI | OpenFWI-main/dataset.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 3,920 | 37.441176 | 129 | py |
OpenFWI | OpenFWI-main/scheduler.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 2,380 | 35.075758 | 105 | py |
OpenFWI | OpenFWI-main/train.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 14,469 | 41.558824 | 122 | py |
OpenFWI | OpenFWI-main/transforms.py | # © 2022. Triad National Security, LLC. All rights reserved.
# This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
# National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
# Department of Energy/National Nuclear Security Administration. All ri... | 8,236 | 29.394834 | 105 | py |
Desbordante-web-app | Desbordante-web-app/python-consumer/consumer.py | import time
import sys
import json
import logging
import signal
from enum import Enum
import confluent_kafka
import docker
import config
from error_handlers import update_internal_server_error
from error_handlers import update_resource_limit_error
docker_client = docker.from_env()
docker_api_client = docker.APIClient... | 5,672 | 30.516667 | 77 | py |
Desbordante-web-app | Desbordante-web-app/python-consumer/error_handlers.py | import config
import psycopg
def update_error_status(taskID, errorType, error):
# errorType : INTERNAL SERVER ERROR | RESOURCE LIMIT IS REACHED
with psycopg.connect(f"dbname={config.POSTGRES_DBNAME} \
user={config.POSTGRES_USER} password={config.POSTGRES_PASSWORD} \
host={config.POSTGRES_HOST} port={c... | 958 | 35.884615 | 70 | py |
Desbordante-web-app | Desbordante-web-app/python-consumer/config.py | import os
TIMELIMIT = int(os.getenv('TIMELIMIT'))
MAX_RAM = int(os.getenv('MAX_RAM'))
KAFKA_ADDR = os.getenv('KAFKA_HOST') + ':' + os.getenv('KAFKA_PORT')
MAX_ACTIVE_TASKS = int(os.getenv('MAX_ACTIVE_TASKS'))
DOCKER_NETWORK = os.getenv('DOCKER_NETWORK')
POSTGRES_HOST = os.getenv('POSTGRES_HOST')
POSTGRES_PORT = os.get... | 537 | 37.428571 | 68 | py |
clFFT | clFFT-master/src/scripts/perf/plotPerformance.py | # ########################################################################
# Copyright 2013 Advanced Micro Devices, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apach... | 12,413 | 36.504532 | 192 | py |
clFFT | clFFT-master/src/scripts/perf/errorHandler.py | # ########################################################################
# Copyright 2013 Advanced Micro Devices, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apach... | 2,824 | 39.942029 | 100 | py |
clFFT | clFFT-master/src/scripts/perf/measurePerformance.py | # ########################################################################
# Copyright 2013 Advanced Micro Devices, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apach... | 31,972 | 39.116688 | 435 | py |
clFFT | clFFT-master/src/scripts/perf/fftPerformanceTesting.py | # ########################################################################
# Copyright 2013 Advanced Micro Devices, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apach... | 11,307 | 35.714286 | 339 | py |
clFFT | clFFT-master/src/scripts/perf/performanceUtility.py | # ########################################################################
# Copyright 2013 Advanced Micro Devices, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apach... | 3,044 | 30.391753 | 89 | py |
snowboy | snowboy-master/setup.py | import os
import sys
from setuptools import setup, find_packages
from distutils.command.build import build
from distutils.dir_util import copy_tree
from subprocess import call
py_dir = 'Python' if sys.version_info[0] < 3 else 'Python3'
class SnowboyBuild(build):
def run(self):
cmd = ['make']
sw... | 1,814 | 28.274194 | 69 | py |
snowboy | snowboy-master/examples/REST_API/training_service.py | #! /usr/bin/evn python
import sys
import base64
import requests
def get_wave(fname):
with open(fname) as infile:
return base64.b64encode(infile.read())
endpoint = "https://snowboy.kitt.ai/api/v1/train/"
############# MODIFY THE FOLLOWING #############
token = ""
hotword_name = "???"
language = "en"
ag... | 1,276 | 23.09434 | 87 | py |
snowboy | snowboy-master/examples/Python/snowboythreaded.py | import snowboydecoder
import threading
import Queue
class ThreadedDetector(threading.Thread):
"""
Wrapper class around detectors to run them in a separate thread
and provide methods to pause, resume, and modify detection
"""
def __init__(self, models, **kwargs):
"""
Initialize Det... | 3,554 | 35.649485 | 110 | py |
snowboy | snowboy-master/examples/Python/demo4.py | import snowboydecoder
import sys
import signal
import speech_recognition as sr
import os
"""
This demo file shows you how to use the new_message_callback to interact with
the recorded audio after a keyword is spoken. It uses the speech recognition
library in order to convert the recorded audio into text.
Information ... | 2,066 | 25.844156 | 106 | py |
snowboy | snowboy-master/examples/Python/snowboydecoder_arecord.py | #!/usr/bin/env python
import collections
import snowboydetect
import time
import wave
import os
import logging
import subprocess
import threading
logging.basicConfig()
logger = logging.getLogger("snowboy")
logger.setLevel(logging.INFO)
TOP_DIR = os.path.dirname(os.path.abspath(__file__))
RESOURCE_FILE = os.path.join... | 6,573 | 35.120879 | 82 | py |
snowboy | snowboy-master/examples/Python/demo.py | import snowboydecoder
import sys
import signal
interrupted = False
def signal_handler(signal, frame):
global interrupted
interrupted = True
def interrupt_callback():
global interrupted
return interrupted
if len(sys.argv) == 1:
print("Error: need to specify model name")
print("Usage: python... | 757 | 20.055556 | 65 | py |
snowboy | snowboy-master/examples/Python/demo_arecord.py | import snowboydecoder_arecord
import sys
import signal
interrupted = False
def signal_handler(signal, frame):
global interrupted
interrupted = True
def interrupt_callback():
global interrupted
return interrupted
if len(sys.argv) == 1:
print("Error: need to specify model name")
print("Usage... | 781 | 20.722222 | 73 | py |
snowboy | snowboy-master/examples/Python/snowboydetect.py | ../../swig/Python/snowboydetect.py | 34 | 34 | 34 | py |
snowboy | snowboy-master/examples/Python/__init__.py | 0 | 0 | 0 | py | |
snowboy | snowboy-master/examples/Python/snowboydecoder.py | #!/usr/bin/env python
import collections
import pyaudio
import snowboydetect
import time
import wave
import os
import logging
from ctypes import *
from contextlib import contextmanager
logging.basicConfig()
logger = logging.getLogger("snowboy")
logger.setLevel(logging.INFO)
TOP_DIR = os.path.dirname(os.path.abspath(_... | 10,392 | 37.069597 | 82 | py |
snowboy | snowboy-master/examples/Python/demo2.py | import snowboydecoder
import sys
import signal
# Demo code for listening to two hotwords at the same time
interrupted = False
def signal_handler(signal, frame):
global interrupted
interrupted = True
def interrupt_callback():
global interrupted
return interrupted
if len(sys.argv) != 3:
print("... | 1,075 | 24.619048 | 80 | py |
snowboy | snowboy-master/examples/Python/demo_threaded.py | import snowboythreaded
import sys
import signal
import time
stop_program = False
# This a demo that shows running Snowboy in another thread
def signal_handler(signal, frame):
global stop_program
stop_program = True
if len(sys.argv) == 1:
print("Error: need to specify model name")
print("Usage: pyt... | 1,203 | 24.083333 | 76 | py |
snowboy | snowboy-master/examples/Python/demo3.py | import snowboydecoder
import sys
import wave
# Demo code for detecting hotword in a .wav file
# Example Usage:
# $ python demo3.py resources/snowboy.wav resources/models/snowboy.umdl
# Should print:
# Hotword Detected!
#
# $ python demo3.py resources/ding.wav resources/models/snowboy.umdl
# Should print:
# Hotword... | 1,113 | 26.170732 | 98 | py |
snowboy | snowboy-master/examples/Python3/demo4.py | import snowboydecoder
import sys
import signal
import speech_recognition as sr
import os
"""
This demo file shows you how to use the new_message_callback to interact with
the recorded audio after a keyword is spoken. It uses the speech recognition
library in order to convert the recorded audio into text.
Information ... | 2,060 | 26.118421 | 106 | py |
snowboy | snowboy-master/examples/Python3/demo.py | import snowboydecoder
import sys
import signal
interrupted = False
def signal_handler(signal, frame):
global interrupted
interrupted = True
def interrupt_callback():
global interrupted
return interrupted
if len(sys.argv) == 1:
print("Error: need to specify model name")
print("Usage: python... | 757 | 20.055556 | 65 | py |
snowboy | snowboy-master/examples/Python3/snowboydetect.py | ../../swig/Python3/snowboydetect.py | 35 | 35 | 35 | py |
snowboy | snowboy-master/examples/Python3/snowboydecoder.py | #!/usr/bin/env python
import collections
import pyaudio
from . import snowboydetect
import time
import wave
import os
import logging
from ctypes import *
from contextlib import contextmanager
logging.basicConfig()
logger = logging.getLogger("snowboy")
logger.setLevel(logging.INFO)
TOP_DIR = os.path.dirname(os.path.ab... | 10,475 | 36.683453 | 82 | py |
snowboy | snowboy-master/examples/Python3/demo2.py | import snowboydecoder
import sys
import signal
# Demo code for listening to two hotwords at the same time
interrupted = False
def signal_handler(signal, frame):
global interrupted
interrupted = True
def interrupt_callback():
global interrupted
return interrupted
if len(sys.argv) != 3:
print("... | 1,075 | 24.619048 | 80 | py |
snowboy | snowboy-master/examples/Python3/demo3.py | import snowboydecoder
import sys
import wave
# Demo code for detecting hotword in a .wav file
# Example Usage:
# $ python demo3.py resources/snowboy.wav resources/models/snowboy.umdl
# Should print:
# Hotword Detected!
#
# $ python demo3.py resources/ding.wav resources/models/snowboy.umdl
# Should print:
# Hotword... | 1,113 | 26.170732 | 98 | py |
mlj19-iggp | mlj19-iggp-master/specialised_ilasp.py | import asp
import config as cfg
import subprocess
import re
import os
import glob
import common
import prolog
import json
class SPECIALISED_ILASP:
ilasp='./GGP_ILASP'
name='specialised_ilasp'
def __init__(self):
pass
def parse_train(self,datafile,outpath,game,target):
for (bk,modes,e... | 5,853 | 33.233918 | 119 | py |
mlj19-iggp | mlj19-iggp-master/asp.py | import re
def fill_in_fns(arg_list, func_decs, type_decs):
mds = [{"name": "", "body": ""}]
for arg in arg_list:
new_mds = []
if any(td["type"] == arg for td in type_decs):
for md in mds:
new_mds.append({"name": md["name"], "body": (md["body"] + ", +" + arg)})
... | 8,030 | 42.646739 | 166 | py |
mlj19-iggp | mlj19-iggp-master/aleph.py | import common
import prolog
import config as cfg
from os.path import isfile
class Aleph:
name='aleph'
aleph_path='aleph/aleph'
aleph_runner='aleph/runner'
def __init__(self):
pass
def parse_train(self,datafile,outpath,game,target):
for (subtarget,bk,pos,neg) in common.parse_target... | 1,761 | 36.489362 | 110 | py |
mlj19-iggp | mlj19-iggp-master/ilasp.py | import asp
import config as cfg
import subprocess
import re
class ILASP:
ilasp=''
xhail='xhail/xhail_mod.jar'
name='ilasp'
clasp='xhail/clasp-3.1.0-x86_64-linux'
gringo='xhail/gringo3-linux'
def __init__(self):
pass
def parse_train(self,datafile,outpath,game,target):
for ... | 7,805 | 43.605714 | 198 | py |
mlj19-iggp | mlj19-iggp-master/config.py | map_size=8
# learning_timeout=600 # 10 minutes
# learning_timeout=60
learning_timeout=1800
| 91 | 17.4 | 35 | py |
mlj19-iggp | mlj19-iggp-master/runner.py | import aleph
import metagol
import specialised_ilasp
import os
import multiprocessing
import signal
import numpy as np
from os import listdir
from os.path import isfile, join
from multiprocessing import Pool
import config as cfg
import sys
def game_names(path):
# return ['minimal_decay']
return sorted(set('_'.j... | 4,476 | 29.664384 | 121 | py |
mlj19-iggp | mlj19-iggp-master/common.py | import subprocess
def gen_atom(index,x):
syms = ['succ','input','between','true','number','index']
x=x.replace(' ','')[:-1]
(p,args)=x.split('(')
args=list(filter(lambda x: x!='',args.split(',')))
args=[str(index)]+args
for sym in syms:
if sym in p:
p=p.replace(sym,'my_{}'.f... | 1,851 | 31.491228 | 91 | py |
mlj19-iggp | mlj19-iggp-master/prolog.py | import subprocess
def swipl(action,load_files,outfile=None,timeout=None):
call('swipl',action,load_files,outfile,timeout)
def yap(action,load_files,outfile=None,timeout=None):
call('yap',action,load_files,outfile,timeout)
def call(prolog_version,action,load_files,outfile=None,timeout=None):
load_files = ... | 972 | 31.433333 | 98 | py |
mlj19-iggp | mlj19-iggp-master/metagol.py | import common
import subprocess
import prolog
import string
import config as cfg
class Metagol:
name='metagol'
metagol_runner='metagol/runner'
def __init__(self):
pass
def parse_train(self,datafile,outpath,game,target):
for (subtarget,bk,pos,neg) in common.parse_target(datafile):
... | 1,607 | 37.285714 | 127 | py |
mmvae-public | mmvae-public/src/main.py | import argparse
import datetime
import sys
import json
from collections import defaultdict
from pathlib import Path
from tempfile import mkdtemp
import numpy as np
import torch
from torch import optim
import models
import objectives
from utils import Logger, Timer, save_model, save_vars, unpack_data
parser = argpars... | 6,968 | 40.482143 | 93 | py |
mmvae-public | mmvae-public/src/vis.py | # visualisation related functions
import matplotlib.colors as colors
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
from matplotlib.lines import Line2D
from umap import UMAP
def custom_cmap(n):
"""Create customised colormap for scattered latent plot of n... | 2,938 | 39.260274 | 97 | py |
mmvae-public | mmvae-public/src/utils.py | import math
import os
import shutil
import sys
import time
import torch
import torch.distributions as dist
import torch.nn.functional as F
from datasets import CUBImageFt
# Classes
class Constants(object):
eta = 1e-6
log2 = math.log(2)
log2pi = math.log(2 * math.pi)
logceilc = 88 # largest cuda v s... | 6,857 | 32.950495 | 110 | py |
mmvae-public | mmvae-public/src/objectives.py | # objectives of choice
import torch
from numpy import prod
from utils import log_mean_exp, is_multidata, kl_divergence
# helper to vectorise computation
def compute_microbatch_split(x, K):
""" Checks if batch needs to be broken down further to fit in memory. """
B = x[0].size(0) if is_multidata(x) else x.siz... | 9,267 | 40.375 | 95 | py |
mmvae-public | mmvae-public/src/datasets.py | import io
import json
import os
import pickle
from collections import Counter, OrderedDict
from collections import defaultdict
import numpy as np
import torch
import torch.nn as nn
from nltk.tokenize import sent_tokenize, word_tokenize
from torch.utils.data import Dataset
from torchvision import transforms, models, da... | 8,431 | 32.19685 | 101 | py |
mmvae-public | mmvae-public/src/report/analyse_cub.py | """Calculate cross and joint coherence of language and image generation on CUB dataset using CCA."""
import argparse
import os
import sys
import torch
import torch.nn.functional as F
# relative import hack (sorry)
import inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
pa... | 5,427 | 36.694444 | 101 | py |
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