code stringlengths 501 5.19M | package stringlengths 2 81 | path stringlengths 9 304 | filename stringlengths 4 145 |
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115 Wangpan
===========
|Build| |PyPI version|
115 Wangpan (115网盘 or 115云) is an unofficial Python API and SDK for 115.com. Supported Python verisons are 2.6, 2.7, 3.3, 3.4.
* Documentation: http://115wangpan.readthedocs.org
* GitHub: https://github.com/shichao-an/115wangpan
* PyPI: https://pypi.python.org/pypi/115w... | 115wangpan | /115wangpan-0.7.6.tar.gz/115wangpan-0.7.6/README.rst | README.rst |
Changelog
=========
0.7.6 (2015-08-01)
------------------
- Fixed DRY_RUN message print by using print_msg that handles PY2 and PY3 strings
- Added -F/--files-only option to 115 down
- Fixed files_only parse error
- Fixed unexpected kwargs for get_tasks
- Fixed Task against added 'url' attr
0.7.5 (2015-07-02)
------... | 115wangpan | /115wangpan-0.7.6.tar.gz/115wangpan-0.7.6/CHANGELOG.rst | CHANGELOG.rst |
from __future__ import print_function, absolute_import
import humanize
import inspect
import json
import logging
import os
import re
import requests
import time
from hashlib import sha1
from bs4 import BeautifulSoup
from requests.cookies import RequestsCookieJar
from u115 import conf
from u115.utils import (get_timest... | 115wangpan | /115wangpan-0.7.6.tar.gz/115wangpan-0.7.6/u115/api.py | api.py |
import functools
import os
import pickle
import subprocess
import re
from collections import UserDict
from typing import Callable
from colorit import *
from prompt_toolkit import prompt
from prompt_toolkit.completion import WordCompleter
from prompt_toolkit.shortcuts import yes_no_dialog
from greeting import *
from hel... | 11Team-AssistantBot | /11Team_AssistantBot-1.11.tar.gz/11Team_AssistantBot-1.11/11Team_AssistantBot/Notepad.py | Notepad.py |
import pickle
import re
from datetime import datetime, timedelta
from colorit import *
from prompt_toolkit import prompt
from prompt_toolkit.completion import WordCompleter
from prompt_toolkit.shortcuts import yes_no_dialog
from Notepad import *
from addressbook import *
from greeting import greeting
from help import... | 11Team-AssistantBot | /11Team_AssistantBot-1.11.tar.gz/11Team_AssistantBot-1.11/11Team_AssistantBot/main.py | main.py |
import pickle
import re
from collections import UserDict
from datetime import datetime
from colorit import *
colorit.init_colorit()
class Error(Exception): #власне виключення
pass
# def __str__(self) -> str:
# return "\n \nSomething went wrong\n Try again!\n"
class Field:
def __init... | 11Team-AssistantBot | /11Team_AssistantBot-1.11.tar.gz/11Team_AssistantBot-1.11/11Team_AssistantBot/addressbook.py | addressbook.py |
greeting = """
@@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@@@@@@@@@@@@
#@@@@ @@@@@@@@@@@@@@@@@@@@ ... | 11Team-AssistantBot | /11Team_AssistantBot-1.11.tar.gz/11Team_AssistantBot-1.11/11Team_AssistantBot/greeting.py | greeting.py |
from pathlib import Path
import shutil
import os
from colorit import *
import sys
name_extensions = {
"images": (".jpeg", ".png", ".jpg", ".svg"),
"video": (".avi", ".mp4", ".mov", ".mkv"),
"documents": (".doc", ".docx", ".pdf", ".xlsx", ".pptx", ".txt"),
"music": (".mp3", ".ogg", ".wav", ".amr"),
... | 11Team-AssistantBot | /11Team_AssistantBot-1.11.tar.gz/11Team_AssistantBot-1.11/11Team_AssistantBot/sort.py | sort.py |
from colorit import *
from prettytable import PrettyTable
def pers_assistant_help():
pah_com_list = {"tel_book":"TELEPHONE BOOK", "note_book": "NOTE BOOK", "sorted": "SORTED"}
all_commands = {
"1":[
["show all", "This command shows all contacts in your address book", "show all"],
... | 11Team-AssistantBot | /11Team_AssistantBot-1.11.tar.gz/11Team_AssistantBot-1.11/11Team_AssistantBot/help.py | help.py |
import sys, platform, os, re
if not sys.version_info >= (3, 6):
sys.exit('Python 3.6 or higher is required!')
try:
import eldf
except ImportError:
sys.exit("Module eldf is not installed!\nPlease install it using this command:\n" + (sys.platform == 'win32')*(os.path.dirname(sys.executable) + '\\Scripts\\')... | 11l | /11l-2021.3-py3-none-any.whl/11l.py | 11l.py |
try:
from python_to_11l.tokenizer import Token
import python_to_11l.tokenizer as tokenizer
except ImportError:
from tokenizer import Token
import tokenizer
from typing import List, Tuple, Dict, Callable
from enum import IntEnum
import os, re, eldf
class Scope:
parent : 'Scope'
class Var:
... | 11l | /11l-2021.3-py3-none-any.whl/python_to_11l/parse.py | parse.py |
from typing import List, Tuple
Char = str
from enum import IntEnum
keywords = [ # https://docs.python.org/3/reference/lexical_analysis.html#keywords
'False', 'await', 'else', 'import', 'pass',
'None', 'break', 'except', 'in', 'raise',
'Tru... | 11l | /11l-2021.3-py3-none-any.whl/python_to_11l/tokenizer.py | tokenizer.py |
try:
from tokenizer import Token
import tokenizer
except ImportError:
from .tokenizer import Token
from . import tokenizer
from typing import List, Tuple, Dict, Callable, Set
from enum import IntEnum
import os, eldf
class Error(Exception):
def __init__(self, message, token):
self.message =... | 11l | /11l-2021.3-py3-none-any.whl/_11l_to_cpp/parse.py | parse.py |
R"""
После данной обработки отступы перестают играть роль — границу `scope` всегда определяют фигурные скобки.
Также здесь выполняется склеивание строк, и таким образом границу statement\утверждения задаёт либо символ `;`,
либо символ новой строки (при условии, что перед ним не стоит символ `…`!).
===================... | 11l | /11l-2021.3-py3-none-any.whl/_11l_to_cpp/tokenizer.py | tokenizer.py |
from io import BytesIO
from django.core.files.images import ImageFile
from faker.providers import BaseProvider
from x11x_wagtail_blog.models import AboutTheAuthor
class X11XWagtailBlogProvider(BaseProvider):
"""
Provider for the wonderful faker library. Add `X11XWagtailBlogProvider` to a standard faker to g... | 11x-wagtail-blog | /11x_wagtail_blog-0.2.0-py3-none-any.whl/x11x_wagtail_blog/fakers.py | fakers.py |
from django.conf import settings
from django.db import models
from django.utils import timezone
from modelcluster.fields import ParentalKey
from wagtail.admin.panels import FieldPanel, InlinePanel
from wagtail.fields import StreamField, RichTextField
from wagtail.models import Page
from wagtail.snippets.blocks import S... | 11x-wagtail-blog | /11x_wagtail_blog-0.2.0-py3-none-any.whl/x11x_wagtail_blog/models.py | models.py |
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
import modelcluster.fields
import wagtail.fields
import wagtail.snippets.blocks
import x11x_wagtail_blog.models
class Migration(migrations.Migration):
initial = True
depend... | 11x-wagtail-blog | /11x_wagtail_blog-0.2.0-py3-none-any.whl/x11x_wagtail_blog/migrations/0001_initial.py | 0001_initial.py |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Gaussian(Distribution):
""" Gaussian distribution class for calculating and
visualizing a Gaussian distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing ... | 12-distributions | /12_distributions-0.1.tar.gz/12_distributions-0.1/12_distributions/Gaussiandistribution.py | Gaussiandistribution.py |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Binomial(Distribution):
""" Binomial distribution class for calculating and
visualizing a Binomial distribution.
Attributes:
mean (float) representing the mean value of the distribution
std... | 12-distributions | /12_distributions-0.1.tar.gz/12_distributions-0.1/12_distributions/Binomialdistribution.py | Binomialdistribution.py |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Gaussian(Distribution):
""" Gaussian distribution class for calculating and
visualizing a Gaussian distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing ... | 12-test | /12@test-0.1.tar.gz/12@test-0.1/distributions/Gaussiandistribution.py | Gaussiandistribution.py |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Binomial(Distribution):
""" Binomial distribution class for calculating and
visualizing a Binomial distribution.
Attributes:
mean (float) representing the mean value of the distribution
std... | 12-test | /12@test-0.1.tar.gz/12@test-0.1/distributions/Binomialdistribution.py | Binomialdistribution.py |
# TensorFlow Research Models
This directory contains code implementations and pre-trained models of published research papers.
The research models are maintained by their respective authors.
## Table of Contents
- [TensorFlow Research ... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/README.md | README.md |
"""Build and train mobilenet_v1 with options for quantization."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
from tensorflow.contrib import quantize as contrib_quantize
from datasets import dat... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/mobilenet_v1_train.py | mobilenet_v1_train.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
"""Builds the 35x35 resnet block."""
with tf.variable_scope(scope, ... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/inception_resnet_v2.py | inception_resnet_v2.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
from nets import i3d_utils
# pylint: disable=g-long-lambda
trunc_normal = lambda stddev: tf.truncated_normal_initializer(
0.0, stddev)
conv3d_spati... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/s3dg.py | s3dg.py |
"""Utilities for building I3D network models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
import tf_slim as slim
add_arg_scope = slim.add_arg_scope
layers = slim.layers
def center_initializer():... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/i3d_utils.py | i3d_utils.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import functools
import tensorflow.compat.v1 as tf
import tf_slim as slim
def pix2pix_arg_scope():
"""Returns a default argument scope for isola_net.
Returns:
An arg scope.
"""
... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/pix2pix.py | pix2pix.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import tensorflow.compat.v1 as tf
import tf_slim as slim
class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
"""A named tuple describing a ResNet block.
Its par... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/resnet_utils.py | resnet_utils.py |
"""Contains a factory for building various models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import tf_slim as slim
from nets import alexnet
from nets import cifarnet
from nets import i3d
from nets import inception
from nets import... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/nets_factory.py | nets_factory.py |
"""Contains the definition for inception v2 classification network."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
from nets import inception_utils
# pylint: disable=g-long-lambda
trunc_normal =... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/inception_v2.py | inception_v2.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
def inception_arg_scope(
weight_decay=0.00004,
use_batch_norm=True,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001,
activation_f... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/inception_utils.py | inception_utils.py |
"""Contains the definition for inception v1 classification network."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
from nets import inception_utils
# pylint: disable=g-long-lambda
trunc_normal =... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/inception_v1.py | inception_v1.py |
"""Validate mobilenet_v1 with options for quantization."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import tensorflow.compat.v1 as tf
import tf_slim as slim
from tensorflow.contrib import quantize as contrib_quantize
from datasets impor... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/mobilenet_v1_eval.py | mobilenet_v1_eval.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
from nets import inception_utils
def block_inception_a(inputs, scope=None, reuse=None):
"""Builds Inception-A block for Inception v4 network."""
#... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/inception_v4.py | inception_v4.py |
"""Export quantized tflite model from a trained checkpoint."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
from absl import app
from absl import flags
import tensorflow.compat.v1 as tf
import tensorflow_datasets as tfds
from nets import... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/post_training_quantization.py | post_training_quantization.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
# pylint: disable=g-long-lambda
trunc_normal = lambda stddev: tf.truncated_normal_initializer(
0.0, stddev)
def overfeat_arg_scope(weight_decay=0.... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/overfeat.py | overfeat.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
from nets import i3d_utils
from nets import s3dg
# pylint: disable=g-long-lambda
trunc_normal = lambda stddev: tf.truncated_normal_initializer(
0.0... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/i3d.py | i3d.py |
"""Contains a variant of the CIFAR-10 model definition."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
# pylint: disable=g-long-lambda
trunc_normal = lambda stddev: tf.truncated_normal_initialize... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/cifarnet.py | cifarnet.py |
"""Defines the CycleGAN generator and discriminator networks."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow.compat.v1 as tf
import tf_slim as slim
fro... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/cyclegan.py | cyclegan.py |
"""Contains the definition for inception v3 classification network."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
from nets import inception_utils
# pylint: disable=g-long-lambda
trunc_normal =... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/inception_v3.py | inception_v3.py |
"""Contains a variant of the LeNet model definition."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
def lenet(images, num_classes=10, is_training=False,
dropout_keep_prob=0.5,
... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/lenet.py | lenet.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
from nets import resnet_utils
resnet_arg_scope = resnet_utils.resnet_arg_scope
class NoOpScope(object):
"""No-op context manager."""
def __ente... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/resnet_v1.py | resnet_v1.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
# pylint: disable=g-long-lambda
trunc_normal = lambda stddev: tf.truncated_normal_initializer(
0.0, stddev)
def alexnet_v2_arg_scope(weight_decay=... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/alexnet.py | alexnet.py |
"""DCGAN generator and discriminator from https://arxiv.org/abs/1511.06434."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from math import log
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow.compat.v1 as tf
import tf... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/dcgan.py | dcgan.py |
# Tensorflow mandates these.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import namedtuple
import functools
import tensorflow.compat.v1 as tf
import tf_slim as slim
# Conv and DepthSepConv namedtuple define layers of the MobileNet arch... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/mobilenet_v1.py | mobilenet_v1.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
from nets import resnet_utils
resnet_arg_scope = resnet_utils.resnet_arg_scope
@slim.add_arg_scope
def bottleneck(inputs, depth, depth_bottleneck, st... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/resnet_v2.py | resnet_v2.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
def vgg_arg_scope(weight_decay=0.0005):
"""Defines the VGG arg scope.
Args:
weight_decay: The l2 regularization coefficient.
Returns:
A... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/vgg.py | vgg.py |
"""Convolution blocks for mobilenet."""
import contextlib
import functools
import tensorflow.compat.v1 as tf
import tf_slim as slim
def _fixed_padding(inputs, kernel_size, rate=1):
"""Pads the input along the spatial dimensions independently of input size.
Pads the input such that if it was used in a convolutio... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/mobilenet/conv_blocks.py | conv_blocks.py |
"""Mobilenet Base Class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import contextlib
import copy
import os
import tensorflow.compat.v1 as tf
import tf_slim as slim
@slim.add_arg_scope
def apply_activation(x, name=None, activati... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/mobilenet/mobilenet.py | mobilenet.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import functools
import numpy as np
import tensorflow.compat.v1 as tf
import tf_slim as slim
from nets.mobilenet import conv_blocks as ops
from nets.mobilenet import mobilenet as lib
op = lib.op
... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/mobilenet/mobilenet_v3.py | mobilenet_v3.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import functools
import tensorflow.compat.v1 as tf
import tf_slim as slim
from nets.mobilenet import conv_blocks as ops
from nets.mobilenet import mobilenet as lib
op = lib.op
expand_input = ops... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/mobilenet/mobilenet_v2.py | mobilenet_v2.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import tensorflow.compat.v1 as tf
import tf_slim as slim
from tensorflow.contrib import training as contrib_training
from nets.nasnet import nasnet_utils
arg_scope = slim.arg_scope
# Notes for t... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/nasnet/nasnet.py | nasnet.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tf_slim as slim
arg_scope = slim.arg_scope
DATA_FORMAT_NCHW = 'NCHW'
DATA_FORMAT_NHWC = 'NHWC'
INVALID = 'null'
# The cap for tf.clip_by_value, it's hinted from the ac... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/nasnet/nasnet_utils.py | nasnet_utils.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import tensorflow.compat.v1 as tf
import tf_slim as slim
from tensorflow.contrib import training as contrib_training
from nets.nasnet import nasnet
from nets.nasnet import nasnet_utils
arg_scope =... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/nets/nasnet/pnasnet.py | pnasnet.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
_PADDING = 4
def preprocess_for_train(image,
output_height,
output_width,
padding=_PADDING,
... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/preprocessing/cifarnet_preprocessing.py | cifarnet_preprocessing.py |
"""Contains a factory for building various models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from preprocessing import cifarnet_preprocessing
from preprocessing import inception_preprocessing
from preprocessing import lenet_preprocessing
from prepr... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/preprocessing/preprocessing_factory.py | preprocessing_factory.py |
"""Provides utilities to preprocess images for the Inception networks."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
from tensorflow.python.ops import control_flow_ops
def apply_with_random_selector(x, func, num_cas... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/preprocessing/inception_preprocessing.py | inception_preprocessing.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
_R_MEAN = 123.68
_G_MEAN = 116.78
_B_MEAN = 103.94
_RESIZE_SIDE_MIN = 256
_RESIZE_SIDE_MAX = 512
def _crop(image, offset_height, offset_width, crop_height, crop_width):
... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/preprocessing/vgg_preprocessing.py | vgg_preprocessing.py |
r"""Downloads and converts MNIST data to TFRecords of TF-Example protos.
This module downloads the MNIST data, uncompresses it, reads the files
that make up the MNIST data and creates two TFRecord datasets: one for train
and one for test. Each TFRecord dataset is comprised of a set of TF-Example
protocol buffers, each... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/download_and_convert_mnist.py | download_and_convert_mnist.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow.compat.v1 as tf
import tf_slim as slim
from datasets import dataset_utils
_FILE_PATTERN = 'cifar10_%s.tfrecord'
SPLITS_TO_SIZES = {'train': 50000, 'test': 10000}
_NUM_CLASSES = 1... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/cifar10.py | cifar10.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from six.moves import urllib
import tensorflow.compat.v1 as tf
import tf_slim as slim
from datasets import dataset_utils
# TODO(nsilberman): Add tfrecord file type once the script is updated.
_FILE_... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/imagenet.py | imagenet.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow.compat.v1 as tf
import tf_slim as slim
from datasets import dataset_utils
_FILE_PATTERN = 'mnist_%s.tfrecord'
_SPLITS_TO_SIZES = {'train': 60000, 'test': 10000}
_NUM_CLASSES = 10... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/mnist.py | mnist.py |
r"""Downloads and converts Flowers data to TFRecords of TF-Example protos.
This module downloads the Flowers data, uncompresses it, reads the files
that make up the Flowers data and creates two TFRecord datasets: one for train
and one for test. Each TFRecord dataset is comprised of a set of TF-Example
protocol buffers... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/download_and_convert_flowers.py | download_and_convert_flowers.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import glob
import os.path
import sys
import xml.etree.ElementTree as ET
from six.moves import xrange # pylint: disable=redefined-builtin
class BoundingBox(object):
pass
def GetItem(name, root, index=0):... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/process_bounding_boxes.py | process_bounding_boxes.py |
r"""Downloads and converts VisualWakewords data to TFRecords of TF-Example protos.
This module downloads the COCO dataset, uncompresses it, derives the
VisualWakeWords dataset to create two TFRecord datasets: one for
train and one for test. Each TFRecord dataset is comprised of a set of
TF-Example protocol buffers, ea... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/download_and_convert_visualwakewords.py | download_and_convert_visualwakewords.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow.compat.v1 as tf
import tf_slim as slim
from datasets import dataset_utils
_FILE_PATTERN = '%s.record-*'
_SPLITS_TO_SIZES = {
'train': 82783,
'val': 40504,
}
_ITEMS_TO_D... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/visualwakewords.py | visualwakewords.py |
"""Contains utilities for downloading and converting datasets."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import tarfile
import zipfile
from six.moves import urllib
import tensorflow.compat.v1 as tf
LABELS_FILENAME = 'labels.tx... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/dataset_utils.py | dataset_utils.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow.compat.v1 as tf
import tf_slim as slim
from datasets import dataset_utils
_FILE_PATTERN = 'flowers_%s_*.tfrecord'
SPLITS_TO_SIZES = {'train': 3320, 'validation': 350}
_NUM_CLASSE... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/flowers.py | flowers.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import os
import random
import sys
import threading
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow.compat.v1 as tf
tf.app... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/build_imagenet_data.py | build_imagenet_data.py |
r"""Downloads and converts cifar10 data to TFRecords of TF-Example protos.
This module downloads the cifar10 data, uncompresses it, reads the files
that make up the cifar10 data and creates two TFRecord datasets: one for train
and one for test. Each TFRecord dataset is comprised of a set of TF-Example
protocol buffers... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/download_and_convert_cifar10.py | download_and_convert_cifar10.py |
r"""Helper functions to generate the Visual WakeWords dataset.
It filters raw COCO annotations file to Visual WakeWords Dataset
annotations. The resulting annotations and COCO images are then converted
to TF records.
See download_and_convert_visualwakewords.py for the sample usage.
"""
from __future__ ... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/download_and_convert_visualwakewords_lib.py | download_and_convert_visualwakewords_lib.py |
r"""Process the ImageNet Challenge bounding boxes for TensorFlow model training.
Associate the ImageNet 2012 Challenge validation data set with labels.
The raw ImageNet validation data set is expected to reside in JPEG files
located in the following directory structure.
data_dir/ILSVRC2012_val_00000001.JPEG
data_d... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/datasets/preprocess_imagenet_validation_data.py | preprocess_imagenet_validation_data.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import tensorflow.compat.v1 as tf
import tf_slim as slim
__all__ = ['create_clones',
'deploy',
'optimize_clones',
'DeployedModel',
'DeploymentCo... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/slim/deployment/model_deploy.py | model_deploy.py |
r"""Creates and runs `Estimator` for object detection model on TPUs.
This uses the TPUEstimator API to define and run a model in TRAIN/EVAL modes.
"""
# pylint: enable=line-too-long
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
imp... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/model_tpu_main.py | model_tpu_main.py |
"""Common utility functions for evaluation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import os
import re
import time
import numpy as np
from six.moves import range
import tensorflow.compat.v1 as tf
import tf_slim as slim
from ... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/eval_util.py | eval_util.py |
"""Functions to export object detection inference graph."""
import os
import tempfile
import tensorflow.compat.v1 as tf
import tf_slim as slim
from tensorflow.core.protobuf import saver_pb2
from tensorflow.python.tools import freeze_graph # pylint: disable=g-direct-tensorflow-import
from object_detection.builders imp... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/exporter.py | exporter.py |
r"""Tool to export an object detection model for inference.
Prepares an object detection tensorflow graph for inference using model
configuration and a trained checkpoint. Outputs inference
graph, associated checkpoint files, a frozen inference graph and a
SavedModel (https://tensorflow.github.io/serving/serving_basi... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/export_inference_graph.py | export_inference_graph.py |
r"""Tool to export an object detection model for inference.
Prepares an object detection tensorflow graph for inference using model
configuration and a trained checkpoint. Outputs associated checkpoint files,
a SavedModel, and a copy of the model config.
The inference graph contains one of three input nodes dependin... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/exporter_main_v2.py | exporter_main_v2.py |
import os
import tempfile
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import types_pb2
from tensorflow.core.protobuf import saver_pb2
from object_detection import exporter
from object_detection.builders import graph_rewriter_bu... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/export_tflite_ssd_graph_lib.py | export_tflite_ssd_graph_lib.py |
"""Model input function for tf-learn object detection model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import tensorflow.compat.v1 as tf
from object_detection.builders import dataset_builder
from object_detection.builders import i... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/inputs.py | inputs.py |
r"""Constructs model, inputs, and training environment."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import functools
import os
import tensorflow.compat.v1 as tf
import tensorflow.compat.v2 as tf2
import tf_slim as slim
from object_detec... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/model_lib.py | model_lib.py |
r"""Constructs model, inputs, and training environment."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import os
import pprint
import time
import numpy as np
import tensorflow.compat.v1 as tf
from object_detection import eval_util
from obj... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/model_lib_v2.py | model_lib_v2.py |
r"""Creates and runs TF2 object detection models.
For local training/evaluation run:
PIPELINE_CONFIG_PATH=path/to/pipeline.config
MODEL_DIR=/tmp/model_outputs
NUM_TRAIN_STEPS=10000
SAMPLE_1_OF_N_EVAL_EXAMPLES=1
python model_main_tf2.py -- \
--model_dir=$MODEL_DIR --num_train_steps=$NUM_TRAIN_STEPS \
--sample_1_of... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/model_main_tf2.py | model_main_tf2.py |
r"""Exports an SSD detection model to use with tf-lite.
Outputs file:
* A tflite compatible frozen graph - $output_directory/tflite_graph.pb
The exported graph has the following input and output nodes.
Inputs:
'normalized_input_image_tensor': a float32 tensor of shape
[1, height, width, 3] containing the normalized ... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/export_tflite_ssd_graph.py | export_tflite_ssd_graph.py |
"""Library to export TFLite-compatible SavedModel from TF2 detection models."""
import os
import numpy as np
import tensorflow.compat.v1 as tf1
import tensorflow.compat.v2 as tf
from object_detection.builders import model_builder
from object_detection.builders import post_processing_builder
from object_detection.core ... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/export_tflite_graph_lib_tf2.py | export_tflite_graph_lib_tf2.py |
"""Binary to run train and evaluation on object detection model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
import tensorflow.compat.v1 as tf
from object_detection import model_lib
flags.DEFINE_string(
'model_dir', None... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/model_main.py | model_main.py |
"""Functions to export object detection inference graph."""
import ast
import os
import tensorflow.compat.v2 as tf
from object_detection.builders import model_builder
from object_detection.core import standard_fields as fields
from object_detection.data_decoders import tf_example_decoder
from object_detection.utils i... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/exporter_lib_v2.py | exporter_lib_v2.py |
r"""Exports TF2 detection SavedModel for conversion to TensorFlow Lite.
Link to the TF2 Detection Zoo:
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
The output folder will contain an intermediate SavedModel that can be used with
the TfLite converter.
NOTE: This ... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/export_tflite_graph_tf2.py | export_tflite_graph_tf2.py |
import tensorflow.compat.v1 as tf
from object_detection.core import matcher
from object_detection.utils import shape_utils
class ArgMaxMatcher(matcher.Matcher):
"""Matcher based on highest value.
This class computes matches from a similarity matrix. Each column is matched
to a single row.
To support object... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/matchers/argmax_matcher.py | argmax_matcher.py |
import tensorflow.compat.v1 as tf
from tensorflow.contrib.image.python.ops import image_ops
from object_detection.core import matcher
class GreedyBipartiteMatcher(matcher.Matcher):
"""Wraps a Tensorflow greedy bipartite matcher."""
def __init__(self, use_matmul_gather=False):
"""Constructs a Matcher.
A... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/matchers/bipartite_matcher.py | bipartite_matcher.py |
r"""Utilities for creating TFRecords of TF examples for the Open Images dataset.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
import tensorflow.compat.v1 as tf
from object_detection.core import standard_fields
from object_detection.utils i... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/dataset_tools/oid_tfrecord_creation.py | oid_tfrecord_creation.py |
r"""Convert the Oxford pet dataset to TFRecord for object_detection.
See: O. M. Parkhi, A. Vedaldi, A. Zisserman, C. V. Jawahar
Cats and Dogs
IEEE Conference on Computer Vision and Pattern Recognition, 2012
http://www.robots.ox.ac.uk/~vgg/data/pets/
Example usage:
python object_detection/dataset_t... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/dataset_tools/create_pet_tf_record.py | create_pet_tf_record.py |
r"""Code to download and parse the AVA Actions dataset for TensorFlow models.
The [AVA Actions data set](
https://research.google.com/ava/index.html)
is a dataset for human action recognition.
This script downloads the annotations and prepares data from similar annotations
if local video files are available. The vid... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/dataset_tools/create_ava_actions_tf_record.py | create_ava_actions_tf_record.py |
r"""Convert raw KITTI detection dataset to TFRecord for object_detection.
Converts KITTI detection dataset to TFRecords with a standard format allowing
to use this dataset to train object detectors. The raw dataset can be
downloaded from:
http://kitti.is.tue.mpg.de/kitti/data_object_image_2.zip.
http://kitti.... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/dataset_tools/create_kitti_tf_record.py | create_kitti_tf_record.py |
r"""Convert raw COCO dataset to TFRecord for object_detection.
This tool supports data generation for object detection (boxes, masks),
keypoint detection, and DensePose.
Please note that this tool creates sharded output files.
Example usage:
python create_coco_tf_record.py --logtostderr \
--train_image_dir... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/dataset_tools/create_coco_tf_record.py | create_coco_tf_record.py |
r"""Creates TFRecords of Open Images dataset for object detection.
Example usage:
python object_detection/dataset_tools/create_oid_tf_record.py \
--input_box_annotations_csv=/path/to/input/annotations-human-bbox.csv \
--input_image_label_annotations_csv=/path/to/input/annotations-label.csv \
--input_imag... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/dataset_tools/create_oid_tf_record.py | create_oid_tf_record.py |
r"""Convert raw PASCAL dataset to TFRecord for object_detection.
Example usage:
python object_detection/dataset_tools/create_pascal_tf_record.py \
--data_dir=/home/user/VOCdevkit \
--year=VOC2012 \
--output_path=/home/user/pascal.record
"""
from __future__ import absolute_import
from __fut... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/dataset_tools/create_pascal_tf_record.py | create_pascal_tf_record.py |
r"""An executable to expand image-level labels, boxes and segments.
The expansion is performed using class hierarchy, provided in JSON file.
The expected file formats are the following:
- for box and segment files: CSV file is expected to have LabelName field
- for image-level labels: CSV file is expected to have Lab... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/dataset_tools/oid_hierarchical_labels_expansion.py | oid_hierarchical_labels_expansion.py |
"""Common utility for object detection tf.train.SequenceExamples."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
def context_float_feature(ndarray):
"""Converts a numpy float array to a context flo... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/dataset_tools/seq_example_util.py | seq_example_util.py |
r"""A Beam job to generate detection data for camera trap images.
This tools allows to run inference with an exported Object Detection model in
`saved_model` format and produce raw detection boxes on images in tf.Examples,
with the assumption that the bounding box class label will match the image-level
class label in ... | 123-object-detection | /123_object_detection-0.1.tar.gz/123_object_detection-0.1/object_detection/dataset_tools/context_rcnn/generate_detection_data.py | generate_detection_data.py |
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