VoVAllen

#!/usr/bin/python

import h5py
import psycopg2
import psycopg2.extras
from math import sqrt
import ipdb
import numpy as np
from tqdm import tqdm

VoVAllen

float:
  any:
  - base_args: ['@metric']
    constructor: PGEmbedding
    disabled: false
    docker_tag: ann-benchmarks-pg_embedding_hnsw
    module: ann_benchmarks.algorithms.pg_embedding_hnsw
    name: pg_embedding_hnsw
    run_groups:
      M-12:

VoVAllen

import torch
from torch.nn.utils.rnn import PackedSequence
from typing import overload, Optional

class Base(torch.nn.Module):
    def __init__(self):
        super().__init__()
    @overload
    @torch._jit_internal._overload_method
    def forward(self, inputs, hx=None):

VoVAllen

#!/bin/bash

set -x

if [ $# -ne 2 ]; then
    REPO=dmlc
    BRANCH=master
else
    REPO=$1
    BRANCH=$2

VoVAllen

Rule:
- DOMAIN-SUFFIX,digicert.com,DIRECT
- DOMAIN-SUFFIX,mzstatic.com,Domestic
- DOMAIN-SUFFIX,akadns.net,Domestic
- DOMAIN-SUFFIX,aaplimg.com,Domestic
- DOMAIN-SUFFIX,cdn-apple.com,Domestic
- DOMAIN-SUFFIX,apple.com, Domestic
- DOMAIN-SUFFIX,icloud.com,Domestic
- DOMAIN-SUFFIX,icloud-content.com,Domestic
- DOMAIN,e.crashlytics.com,REJECT

VoVAllen

def att_animation(maps_array, mode, src, tgt, head_id):
    weights = [maps[mode2id[mode]][head_id] for maps in maps_array]
    fig, axes = plt.subplots(1, 2)
    axes[0].set_yticks(np.arange(len(src)))
    axes[0].set_xticks(np.arange(len(tgt)))
    axes[0].set_yticklabels(src)
    axes[0].set_xticklabels(tgt)
    plt.setp(axes[0].get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor")
    

VoVAllen

# This part for jupyter notebook setting (if you wants to save, don't use this)
# %matplotlib inline
# %config InlineBackend.figure_format = 'svg'
# import numpy as np
# import matplotlib.pyplot as plt
# plt.rcParams["animation.html"] = "jshtml"


import networkx as nx
from networkx.algorithms import bipartite

VoVAllen

class Flatten(nn.Module):
    def __init__(self):
        super(Flatten, self).__init__()

    def forward(self, x):
        return x.view(x.size(0), -1)

VoVAllen

def is_cuda(operation):
    if isinstance(operation, th.nn.Module):
        return 'cuda' in str(type(operation.parameters().next()))
    elif isinstance(operation, th._TensorBase):
        return 'cuda' in str(type(operation))
    elif isinstance(operation, Variable):
        return 'cuda' in str(type(operation.data))
    else:
        raise Exception("Operation is not nn.Module or Variable or Tensor")