dhruvbird

elem = zf.infolist()[0]
archive_name = elem.filename.split("/")[0]
print(archive_name)

AddTensorsModel

dhruvbird

zf2 = zipfile.ZipFile("AddTensorsModel.ptl", "a")
contents = "Both tensors can be any dtype as long as they can be added"
zf2.writestr("{}/extra/dtype_info.txt".format(archive_name), contents)
zf2.close()

dhruvbird

zf = zipfile.ZipFile("AddTensorsModel.ptl")
zf.infolist()

[<ZipInfo filename='AddTensorsModel/extra/model_info.txt' file_size=70>,
 <ZipInfo filename='AddTensorsModel/data.pkl' file_size=86>,
 <ZipInfo filename='AddTensorsModel/code/__torch__.py' compress_type=deflate file_size=247 compress_size=166>,
 <ZipInfo filename='AddTensorsModel/code/__torch__.py.debug_pkl' file_size=145>,
 <ZipInfo filename='AddTensorsModel/constants.pkl' file_size=4>,
 <ZipInfo filename='AddTensorsModel/bytecode.pkl' file_size=452>,
 <ZipInfo filename='AddTensorsModel/version' file_size=2>,

dhruvbird

#include <torch/csrc/jit/mobile/import.h>
#include <torch/csrc/jit/mobile/module.h>

int main() {
  std::string model_path = "AddTensorsModel.ptl";
  torch::jit::ExtraFilesMap extra_files;

  // Add keys into extra_files to indicate which metadata files
  // we wish to fetch.
  extra_files["model_info.txt"] = "";

dhruvbird

include_all_non_op_selectives: false
build_features: []
operators:
  aten::__getitem__.t:
    is_used_for_training: false
    is_root_operator: true
    include_all_overloads: false
  aten::_set_item.str:
    is_used_for_training: false
    is_root_operator: true

dhruvbird

import torch
from torch import nn
from torch.nn import functional as F
from matplotlib import pyplot as plt

n_out_channels = 1
mat = torch.arange(0, 36).reshape((6, 6)).float()
print(f"mat:\n{mat}")

patches = []