python_code stringlengths 0 456k |
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#!/usr/bin/env python
'''
example to detect upright people in images using HOG features
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
def inside(r, q):
rx, ry, rw, rh = r
qx, qy, qw, qh = q
return rx > qx and ry > qy and rx + rw < qx + qw and r... |
#!/usr/bin/env python
'''
face detection using haar cascades
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
def detect(img, cascade):
rects = cascade.detectMultiScale(img, scaleFactor=1.275, minNeighbors=4, minSize=(30, 30),
... |
#!/usr/bin/env python
'''
===============================================================================
QR code detect and decode pipeline.
===============================================================================
'''
import os
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
class ... |
###############################################################################
#
# IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
#
# By downloading, copying, installing or using the software you agree to this license.
# If you do not agree to this license, do not download, install,
# copy or us... |
###############################################################################
#
# IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
#
# By downloading, copying, installing or using the software you agree to this license.
# If you do not agree to this license, do not download, install,
# copy or us... |
###############################################################################
#
# IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
#
# By downloading, copying, installing or using the software you agree to this license.
# If you do not agree to this license, do not download, install,
# copy or us... |
#!/usr/bin/env python
from __future__ import print_function
import sys, os, re
classes_ignore_list = (
'OpenCV(Test)?Case',
'OpenCV(Test)?Runner',
'CvException',
)
funcs_ignore_list = (
'\w+--HashCode',
'Mat--MatLong',
'\w+--Equals',
'Core--MinMaxLocResult',
)
class JavaParser:
def _... |
#!/usr/bin/env python
import sys, re, os.path, errno, fnmatch
import json
import logging
import codecs
from shutil import copyfile
from pprint import pformat
from string import Template
if sys.version_info[0] >= 3:
from io import StringIO
else:
import io
class StringIO(io.StringIO):
def write(self... |
#!/usr/bin/env python
import cv2 as cv
from tests_common import NewOpenCVTests
class knearest_test(NewOpenCVTests):
def test_load(self):
k_nearest = cv.ml.KNearest_load(self.find_file("ml/opencv_ml_knn.xml"))
self.assertFalse(k_nearest.empty())
self.assertTrue(k_nearest.isTrained())
if __... |
#!/usr/bin/env python
'''
SVM and KNearest digit recognition.
Sample loads a dataset of handwritten digits from '../data/digits.png'.
Then it trains a SVM and KNearest classifiers on it and evaluates
their accuracy.
Following preprocessing is applied to the dataset:
- Moment-based image deskew (see deskew())
- Dig... |
#!/usr/bin/env python
# Python 2/3 compatibility
from __future__ import print_function
import cv2 as cv
import numpy as np
from tests_common import NewOpenCVTests
class TestGoodFeaturesToTrack_test(NewOpenCVTests):
def test_goodFeaturesToTrack(self):
arr = self.get_sample('samples/data/lena.jpg', 0)
... |
#!/usr/bin/env python
'''
The sample demonstrates how to train Random Trees classifier
(or Boosting classifier, or MLP, or Knearest, or Support Vector Machines) using the provided dataset.
We use the sample database letter-recognition.data
from UCI Repository, here is the link:
Newman, D.J. & Hettich, S. & Blake, C.... |
# This script is used to estimate an accuracy of different face detection models.
# COCO evaluation tool is used to compute an accuracy metrics (Average Precision).
# Script works with different face detection datasets.
import os
import json
from fnmatch import fnmatch
from math import pi
import cv2 as cv
import argpar... |
from __future__ import print_function
import sys
import argparse
import cv2 as cv
import tensorflow as tf
import numpy as np
import struct
if sys.version_info > (3,):
long = int
from tensorflow.python.tools import optimize_for_inference_lib
from tensorflow.tools.graph_transforms import TransformGraph
from tensorf... |
#!/usr/bin/env python
import os
import cv2 as cv
import numpy as np
from tests_common import NewOpenCVTests, unittest
def normAssert(test, a, b, msg=None, lInf=1e-5):
test.assertLess(np.max(np.abs(a - b)), lInf, msg)
def inter_area(box1, box2):
x_min, x_max = max(box1[0], box2[0]), min(box1[2], box2[2])
... |
import numpy as np
import sys
import os
import argparse
import tensorflow as tf
from tensorflow.python.platform import gfile
from imagenet_cls_test_alexnet import MeanValueFetch, DnnCaffeModel, Framework, ClsAccEvaluation
try:
import cv2 as cv
except ImportError:
raise ImportError('Can\'t find OpenCV Python mod... |
import numpy as np
import sys
import os
import fnmatch
import argparse
try:
import cv2 as cv
except ImportError:
raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
'configure environment variable PYTHONPATH to "opencv_build_dir/li... |
import numpy as np
import sys
import os
import argparse
from imagenet_cls_test_alexnet import MeanChannelsFetch, CaffeModel, DnnCaffeModel, ClsAccEvaluation
try:
import caffe
except ImportError:
raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '
... |
from __future__ import print_function
from abc import ABCMeta, abstractmethod
import numpy as np
import sys
import os
import argparse
import time
try:
import caffe
except ImportError:
raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '
... |
from __future__ import print_function
from abc import ABCMeta, abstractmethod
import numpy as np
import sys
import argparse
import time
from imagenet_cls_test_alexnet import CaffeModel, DnnCaffeModel
try:
import cv2 as cv
except ImportError:
raise ImportError('Can\'t find OpenCV Python module. If you\'ve built... |
# Iterate all GLSL shaders (with suffix '.comp') in current directory.
#
# Use glslangValidator to compile them to SPIR-V shaders and write them
# into .cpp files as unsigned int array.
#
# Also generate a header file 'spv_shader.hpp' to extern declare these shaders.
import re
import os
import sys
dir = "./"
license_... |
#!/usr/bin/env python
from __future__ import print_function
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
class Bindings(NewOpenCVTests):
def check_name(self, name):
#print(name)
self.assertFalse(name == None)
self.assertFalse(name == "")
def test_regis... |
#!/usr/bin/env python
'''
Feature homography
==================
Example of using features2d framework for interactive video homography matching.
ORB features and FLANN matcher are used. The actual tracking is implemented by
PlaneTracker class in plane_tracker.py
'''
# Python 2/3 compatibility
from __future__ import ... |
#!/usr/bin/env python
from __future__ import print_function
import collections
import re
import os.path
import sys
from xml.dom.minidom import parse
if sys.version_info > (3,):
long = int
def cmp(a, b): return (a>b)-(a<b)
class TestInfo(object):
def __init__(self, xmlnode):
self.fixture = xmlnod... |
#!/usr/bin/env python
from __future__ import print_function
import xml.etree.ElementTree as ET
from glob import glob
from pprint import PrettyPrinter as PP
LONG_TESTS_DEBUG_VALGRIND = [
('calib3d', 'Calib3d_InitUndistortRectifyMap.accuracy', 2017.22),
('dnn', 'Reproducibility*', 1000), # large DNN models
... |
#!/usr/bin/env python
import os
import argparse
import logging
import datetime
from run_utils import Err, CMakeCache, log, execute
from run_suite import TestSuite
from run_android import AndroidTestSuite
epilog = '''
NOTE:
Additional options starting with "--gtest_" and "--perf_" will be passed directly to the test e... |
#!/usr/bin/env python
import math, os, sys
webcolors = {
"indianred": "#cd5c5c",
"lightcoral": "#f08080",
"salmon": "#fa8072",
"darksalmon": "#e9967a",
"lightsalmon": "#ffa07a",
"red": "#ff0000",
"crimson": "#dc143c",
"firebrick": "#b22222",
"darkred": "#8b0000",
"pink": "#ffc0cb",
"lightpink": "#ffb6c1",
"hotpink": ... |
#!/usr/bin/env python
from optparse import OptionParser
import glob, sys, os, re
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("-o", "--output", dest="output", help="output file name", metavar="FILENAME", default=None)
(options, args) = parser.parse_args()
if not options.output... |
#!/usr/bin/env python
import testlog_parser, sys, os, xml, re
from table_formatter import *
from optparse import OptionParser
cvsize_re = re.compile("^\d+x\d+$")
cvtype_re = re.compile("^(CV_)(8U|8S|16U|16S|32S|32F|64F)(C\d{1,3})?$")
def keyselector(a):
if cvsize_re.match(a):
size = [int(d) for d in a.sp... |
#!/usr/bin/env python
import sys
import os
import platform
import re
import tempfile
import glob
import logging
import shutil
from subprocess import check_call, check_output, CalledProcessError, STDOUT
def initLogger():
logger = logging.getLogger("run.py")
logger.setLevel(logging.DEBUG)
ch = logging.Strea... |
#!/usr/bin/env python
import testlog_parser, sys, os, xml, glob, re
from table_formatter import *
from optparse import OptionParser
numeric_re = re.compile("(\d+)")
cvtype_re = re.compile("(8U|8S|16U|16S|32S|32F|64F)C(\d{1,3})")
cvtypes = { '8U': 0, '8S': 1, '16U': 2, '16S': 3, '32S': 4, '32F': 5, '64F': 6 }
convert... |
#!/usr/bin/env python
from __future__ import print_function
import sys, re, os.path, cgi, stat, math
from optparse import OptionParser
from color import getColorizer, dummyColorizer
class tblCell(object):
def __init__(self, text, value = None, props = None):
self.text = text
self.value = value
... |
#!/usr/bin/env python
import os
import re
import getpass
from run_utils import Err, log, execute, isColorEnabled, hostos
from run_suite import TestSuite
def exe(program):
return program + ".exe" if hostos == 'nt' else program
class ApkInfo:
def __init__(self):
self.pkg_name = None
self.pkg_t... |
#!/usr/bin/env python
from __future__ import print_function
import testlog_parser, sys, os, xml, glob, re
from table_formatter import *
from optparse import OptionParser
from operator import itemgetter, attrgetter
from summary import getSetName, alphanum_keyselector
import re
if __name__ == "__main__":
usage = "%... |
#!/usr/bin/env python
import os
import re
import sys
from run_utils import Err, log, execute, getPlatformVersion, isColorEnabled, TempEnvDir
from run_long import LONG_TESTS_DEBUG_VALGRIND, longTestFilter
class TestSuite(object):
def __init__(self, options, cache, id):
self.options = options
self.c... |
#!/usr/bin/env python
"""
This script can generate XLS reports from OpenCV tests' XML output files.
To use it, first, create a directory for each machine you ran tests on.
Each such directory will become a sheet in the report. Put each XML file
into the corresponding directory.
Then, create your ... |
from __future__ import print_function
import os
import sys
import csv
from pprint import pprint
from collections import deque
try:
long # Python 2
except NameError:
long = int # Python 3
# trace.hpp
REGION_FLAG_IMPL_MASK = 15 << 16
REGION_FLAG_IMPL_IPP = 1 << 16
REGION_FLAG_IMPL_OPENCL = 2 << 16
DEB... |
#!/usr/bin/env python
import testlog_parser, sys, os, xml, re, glob
from table_formatter import *
from optparse import OptionParser
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("-o", "--output", dest="format", help="output results in text format (can be 'txt', 'html' or 'auto' - defaul... |
#!/usr/bin/env python
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
class solvepnp_test(NewOpenCVTests):
def test_regression_16040(self):
obj_points = np.array([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]], dty... |
#!/usr/bin/env python
'''
camera calibration for distorted images with chess board samples
reads distorted images, calculates the calibration and write undistorted images
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
... |
#!/usr/bin/python
# Copyright 2016 Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of con... |
#!/usr/bin/env python
# Copyright (c) 2012 Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this lis... |
#!/usr/bin/env python
# Copyright (c) 2012 Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this lis... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import os
import os.path as osp
import argparse
import math
from tqdm import tqdm
import torch.nn as nn
import t... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import argparse
import numpy as np
import os
import random
import horovod.torch as hvd
import torch
from ofa.im... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import os
import torch
import argparse
from ofa.imagenet_classification.data_providers.imagenet import ImagenetD... |
#!/usr/bin/env python
import os, sys
import shutil
import datetime
from setuptools import setup, find_packages
from setuptools.command.install import install
# readme = open('README.md').read()
readme = """
# Once for All: Train One Network and Specialize it for Efficient Deployment [[arXiv]](https://arxiv.org/abs/19... |
dependencies = ['torch', 'torchvision']
from functools import partial
from ofa.model_zoo import ofa_net, ofa_specialized
# general model
ofa_supernet_resnet50 = partial(ofa_net, net_id="ofa_resnet50", pretrained=True)
ofa_supernet_mbv3_w10 = partial(ofa_net, net_id="ofa_mbv3_d234_e346_k357_w1.0", pretrained=True)
ofa... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import json
import torch
from ofa.utils import download_url
from ofa.imagenet_classification.networks import get... |
import yaml
from ofa.utils import download_url
class LatencyEstimator(object):
def __init__(
self,
local_dir="~/.hancai/latency_tools/",
url="https://hanlab.mit.edu/files/proxylessNAS/LatencyTools/mobile_trim.yaml",
):
if url.startswith("http"):
fname = download_url... |
import copy
import random
from tqdm import tqdm
import numpy as np
__all__ = ["EvolutionFinder"]
class ArchManager:
def __init__(self):
self.num_blocks = 20
self.num_stages = 5
self.kernel_sizes = [3, 5, 7]
self.expand_ratios = [3, 4, 6]
self.depths = [2, 3, 4]
sel... |
from .accuracy_predictor import AccuracyPredictor
from .flops_table import FLOPsTable
from .latency_table import LatencyTable
from .evolution_finder import EvolutionFinder
from .imagenet_eval_helper import evaluate_ofa_subnet, evaluate_ofa_specialized
|
import torch.nn as nn
import torch
import copy
from ofa.utils import download_url
# Helper for constructing the one-hot vectors.
def construct_maps(keys):
d = dict()
keys = list(set(keys))
for k in keys:
if k not in d:
d[k] = len(list(d.keys()))
return d
ks_map = construct_maps... |
import time
import copy
import torch
import torch.nn as nn
import numpy as np
from ofa.utils.layers import *
__all__ = ["FLOPsTable"]
def rm_bn_from_net(net):
for m in net.modules():
if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
m.forward = lambda x: x
class FLOPsTable:... |
import os.path as osp
import numpy as np
import math
from tqdm import tqdm
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.utils.data
from torchvision import transforms, datasets
from ofa.utils import AverageMeter, accuracy
from ofa.model_zoo import ofa_specialized
from ofa.imagenet_classifica... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import os
import copy
from .latency_lookup_table import *
class BaseEfficiencyModel:
def __init__(self, ofa... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import yaml
from ofa.utils import download_url, make_divisible, MyNetwork
__all__ = [
"count_conv_flop",
... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import copy
import random
import numpy as np
from tqdm import tqdm
__all__ = ["EvolutionFinder"]
class Evoluti... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
from .evolution import *
|
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
from .acc_dataset import *
from .acc_predictor import *
from .arch_encoder import *
|
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import os
import numpy as np
import torch
import torch.nn as nn
__all__ = ["AccuracyPredictor"]
class Accuracy... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import os
import json
import numpy as np
from tqdm import tqdm
import torch
import torch.utils.data
from ofa.uti... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import random
import numpy as np
from ofa.imagenet_classification.networks import ResNets
__all__ = ["MobileNet... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import math
import torch.nn as nn
import torch.nn.functional as F
from .common_tools import min_divisible_value
... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import numpy as np
import os
import sys
import torch
try:
from urllib import urlretrieve
except ImportError:... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import math
import copy
import time
import torch
import torch.nn as nn
__all__ = [
"mix_images",
"mix_la... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
from .pytorch_modules import *
from .pytorch_utils import *
from .my_modules import *
from .flops_counter import ... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import torch
import torch.nn as nn
from .my_modules import MyConv2d
__all__ = ["profile"]
def count_convNd(m,... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from .my_m... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import torch
import torch.nn as nn
from collections import OrderedDict
from ofa.utils import get_same_padding, m... |
r"""Definition of the DataLoader and associated iterators that subclass _BaseDataLoaderIter
To support these two classes, in `./_utils` we define many utility methods and
functions to be run in multiprocessing. E.g., the data loading worker loop is
in `./_utils/worker.py`.
"""
import threading
import itertools
import... |
from .my_data_loader import *
from .my_data_worker import *
from .my_distributed_sampler import *
from .my_random_resize_crop import *
|
r""""Contains definitions of the methods used by the _BaseDataLoaderIter workers.
These **needs** to be in global scope since Py2 doesn't support serializing
static methods.
"""
import torch
import random
import os
from collections import namedtuple
# from torch._six import queue
from torch.multiprocessing import Que... |
import time
import random
import math
import os
from PIL import Image
import torchvision.transforms.functional as F
import torchvision.transforms as transforms
__all__ = ["MyRandomResizedCrop", "MyResizeRandomCrop", "MyResize"]
_pil_interpolation_to_str = {
Image.NEAREST: "PIL.Image.NEAREST",
Image.BILINEAR:... |
import math
import torch
from torch.utils.data.distributed import DistributedSampler
__all__ = ["MyDistributedSampler", "WeightedDistributedSampler"]
class MyDistributedSampler(DistributedSampler):
"""Allow Subset Sampler in Distributed Training"""
def __init__(
self, dataset, num_replicas=None, ran... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import copy
import torch.nn.functional as F
import torch.nn as nn
import torch
from ofa.utils import AverageMete... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
from .progressive_shrinking import *
|
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import torch.nn as nn
import random
import time
import torch
import torch.nn.functional as F
from tqdm import tqd... |
import random
from ofa.imagenet_classification.elastic_nn.modules.dynamic_layers import (
DynamicConvLayer,
DynamicLinearLayer,
)
from ofa.imagenet_classification.elastic_nn.modules.dynamic_layers import (
DynamicResNetBottleneckBlock,
)
from ofa.utils.layers import IdentityLayer, ResidualBlock
from ofa.im... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import copy
import random
from ofa.utils import make_divisible, val2list, MyNetwork
from ofa.imagenet_classifica... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
from .ofa_proxyless import OFAProxylessNASNets
from .ofa_mbv3 import OFAMobileNetV3
from .ofa_resnets import OFAR... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import copy
import random
from ofa.imagenet_classification.elastic_nn.modules.dynamic_layers import (
Dynami... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import copy
import torch
import torch.nn as nn
from collections import OrderedDict
from ofa.utils.layers import ... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
from .dynamic_layers import *
from .dynamic_op import *
|
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import torch.nn.functional as F
import torch.nn as nn
import torch
from torch.nn.parameter import Parameter
from... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
from .run_config import *
from .run_manager import *
from .distributed_run_manager import *
|
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
from ofa.utils import calc_learning_rate, build_optimizer
from ofa.imagenet_classification.data_providers import ... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import os
import json
import time
import random
import torch
import torch.nn as nn
import torch.nn.functional as ... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import os
import random
import time
import json
import numpy as np
import torch.nn as nn
import torch.nn.function... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
from .proxyless_nets import *
from .mobilenet_v3 import *
from .resnets import *
def get_net_by_name(name):
... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import copy
import torch.nn as nn
from ofa.utils.layers import (
set_layer_from_config,
MBConvLayer,
... |
import torch.nn as nn
from ofa.utils.layers import (
set_layer_from_config,
ConvLayer,
IdentityLayer,
LinearLayer,
)
from ofa.utils.layers import ResNetBottleneckBlock, ResidualBlock
from ofa.utils import make_divisible, MyNetwork, MyGlobalAvgPool2d
__all__ = ["ResNets", "ResNet50", "ResNet50D"]
cla... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import json
import torch.nn as nn
from ofa.utils.layers import (
set_layer_from_config,
MBConvLayer,
... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import numpy as np
import torch
__all__ = ["DataProvider"]
class DataProvider:
SUB_SEED = 937162211 # ran... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import warnings
import os
import math
import numpy as np
import torch.utils.data
import torchvision.transforms as... |
# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
from .imagenet import *
|
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