lantiga

# mypy: ignore-errors
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.

from typing import Optional, Tuple
from dataclasses import dataclass
import math

import torch
from torch import nn

lantiga

from transformers import BertForQuestionAnswering
import torch

bert_name = "bert-large-uncased-whole-word-masking-finetuned-squad"

model = BertForQuestionAnswering.from_pretrained(bert_name, torchscript=True)
model.eval()

inputs = [torch.ones(1, 2, dtype=torch.int64),
          torch.ones(1, 2, dtype=torch.int64),

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# This script saves the ultralytics/yolov3 model to TorchScript
# so that it can be used in RedisAI (or from other C/C++ runtimes)
#
# git clone https://github.com/ultralytics/yolov3.git
# cd yolov3
# conda create -n pytorch python=3.7
# conda install -yc pytorch pytorch torchvision
# conda install -y numpy opencv
#
# Copy this script to the root of the repo (yolov3) and run

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sudo apt update
sudo apt install -y git build-essential ninja-build cmake python3-pip python3-cffi redis unzip wget

git clone https://github.com/RedisAI/RedisAI.git

cd RedisAI

mkdir build

WITH_PT=0 WITH_TF=0 WITH_TFLITE=0 WITH_ORT=0 bash get_deps.sh

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import torch
import torch.nn as nn


class ResnetBlock(nn.Module):

    def __init__(self, dim):
        super(ResnetBlock, self).__init__()
        self.conv_block = self.build_conv_block(dim)

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Indexed convolutions

A convolution operator over a 1D tensor (BxCxL), where a list of neighbors for each element is provided through a indices tensor (LxK), where K is the size of the convolution kernel. Each row of indices specifies the indices of the K neighbors of the corresponding element in the input. A -1 is handled like for zero padding.

Note that the neighbors specified in indices are not relative, but rather absolute. They have to be specified for each of the elements of the output.

A use case is for convolutions over non-square lattices, such as images on hexagonal lattices coming from Cherenkov telescopes (http://www.isdc.unige.ch/%7Elyard/FirstLight/FirstLight_slowHD.mov).

Example:

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import torch
import torch.nn as nn
from torch.autograd import Variable

from graphviz import Digraph


def name(node, annotation=None):
    kind = node.kind()

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class Vmtk < Formula
  desc "The Vascular Modeling Toolkit"
  homepage "http://www.vmtk.org"
  url "https://github.com/vmtk/vmtk/archive/v1.3.2.tar.gz"
  version "1.3.2"
  sha256 "2632a74341605bd3ddd97971fad70941329e77a112f9363bc5053a1e2ba7c30e"
  head "https://github.com/vmtk/vmtk.git"
  revision 1

  # bottle do

lantiga

### Keybase proof

I hereby claim:

  * I am lantiga on github.
  * I am lantiga (https://keybase.io/lantiga) on keybase.
  * I have a public key whose fingerprint is C364 40F7 E6D0 2B02 AF9C  FC34 BE4B 71AA 57A8 4B6A

To claim this, I am signing this object:

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(ns carla.core
  (:require [clojure.math.combinatorics :as combo]))

(defn- fact-matches? [fact match]
  (= match (select-keys fact (keys match))))

(defn make-rules [] [])

(defn make-session [] {:facts #{}})