repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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imgclsmob | imgclsmob-master/other/chainer_/cifar1.py | """
CIFAR/SVHN dataset routines.
"""
import numpy as np
import chainer
from chainer import iterators
from chainer import Chain
from chainer.dataset import DatasetMixin
from chainer.datasets import cifar, svhn
__all__ = ['add_dataset_parser_arguments', 'get_val_data_iterator', 'get_data_iterators', 'CIFARPredictor... | 4,156 | 27.868056 | 107 | py |
imgclsmob | imgclsmob-master/other/chainer_/train_ch_cifar.py | import argparse
import numpy as np
import chainer
from chainer import cuda
from chainer import training
from chainer.training import extensions
from chainer.serializers import save_npz
from common.logger_utils import initialize_logging
from chainer_.utils import prepare_model
from chainer_.cifar1 import add_dataset_p... | 9,190 | 28.744337 | 115 | py |
imgclsmob | imgclsmob-master/other/chainer_/seg_utils1.py | """
Segmentation datasets (VOC2012/ADE20K/Cityscapes/COCO) routines.
"""
__all__ = ['add_dataset_parser_arguments', 'get_test_dataset', 'get_metainfo', 'SegPredictor']
import numpy as np
import chainer
from chainer import Chain
from chainer_.datasets.voc_seg_dataset import VOCSegDataset
from chainer_.datasets.ade... | 5,374 | 31.575758 | 94 | py |
imgclsmob | imgclsmob-master/other/chainer_/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/other/datasets/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/other/gluon/seg_utils1.py | """
Segmentation datasets (VOC2012/ADE20K/Cityscapes/COCO) routines.
"""
__all__ = ['add_dataset_parser_arguments', 'batch_fn', 'get_test_data_source', 'get_num_training_samples', 'validate1',
'get_metainfo']
from tqdm import tqdm
from mxnet import gluon
from mxnet.gluon.data.vision import transforms
f... | 5,809 | 31.640449 | 119 | py |
imgclsmob | imgclsmob-master/other/gluon/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/other/gluon/khpa/khpa_utils.py | """
KHPA dataset routines.
"""
__all__ = ['add_dataset_parser_arguments', 'get_batch_fn', 'get_train_data_source', 'get_val_data_source', 'validate']
import math
from mxnet import gluon
from gluon.weighted_random_sampler import WeightedRandomSampler
from other.gluon.khpa.khpa_cls_dataset import KHPA
def add_dat... | 6,499 | 33.210526 | 118 | py |
imgclsmob | imgclsmob-master/other/gluon/khpa/khpa_cls_dataset.py | """
KHPA classification dataset.
"""
import os
import json
import logging
import numpy as np
import pandas as pd
import mxnet as mx
from mxnet.gluon.data import Dataset
from imgaug import augmenters as iaa
from imgaug import parameters as iap
class KHPA(Dataset):
"""
Load the KHPA classification dataset.... | 20,192 | 41.511579 | 104 | py |
imgclsmob | imgclsmob-master/other/gluon/khpa/eval_gl_khpa.py | import argparse
import time
import logging
import mxnet as mx
from common.logger_utils import initialize_logging
from gluon.utils import prepare_mx_context, prepare_model, calc_net_weight_count
from other.gluon.khpa.khpa_utils import add_dataset_parser_arguments
from other.gluon.khpa.khpa_utils import get_batch_fn
fr... | 4,686 | 27.23494 | 101 | py |
imgclsmob | imgclsmob-master/other/gluon/khpa/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/other/gluon/khpa/train_gl_khpa.py | import argparse
import time
import logging
import os
import numpy as np
import random
import mxnet as mx
from mxnet import gluon
from mxnet import autograd as ag
from common.logger_utils import initialize_logging
from common.train_log_param_saver import TrainLogParamSaver
from gluon.lr_scheduler import LRScheduler
fr... | 19,879 | 31.012882 | 105 | py |
imgclsmob | imgclsmob-master/other/pytorch/imagenet1k1.py | import math
import os
import cv2
import numpy as np
from PIL import Image
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
__all__ = ['add_dataset_parser_arguments', 'get_train_data_loader', 'get_val_data_loader']
def add_dataset_parser_arguments(parser):
... | 5,152 | 27.469613 | 90 | py |
imgclsmob | imgclsmob-master/other/pytorch/cub200_2011_utils1.py | """
CUB-200-2011 fine-grained classification dataset routines.
"""
__all__ = ['add_dataset_parser_arguments', 'get_train_data_loader', 'get_val_data_loader']
import math
import torch.utils.data
import torchvision.transforms as transforms
from pytorch.datasets.cub200_2011_cls_dataset import CUB200_2011
def add_d... | 3,014 | 26.409091 | 90 | py |
imgclsmob | imgclsmob-master/other/pytorch/cifar1.py | """
CIFAR/SVHN dataset routines.
"""
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
__all__ = ['add_dataset_parser_arguments', 'get_train_data_loader', 'get_val_data_loader']
def add_dataset_parser_arguments(parser,
da... | 4,409 | 28.205298 | 90 | py |
imgclsmob | imgclsmob-master/other/pytorch/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/other/pytorch/seg_utils.py | """
Segmentation datasets (VOC2012/ADE20K/Cityscapes/COCO) routines.
"""
__all__ = ['add_dataset_parser_arguments', 'get_test_data_loader', 'validate1', 'get_metainfo']
from tqdm import tqdm
import torch.utils.data
import torchvision.transforms as transforms
from pytorch.datasets.voc_seg_dataset import VOCSegData... | 5,401 | 31.347305 | 95 | py |
imgclsmob | imgclsmob-master/tensorflow_/setup.py | from setuptools import setup, find_packages
from os import path
from io import open
here = path.abspath(path.dirname(__file__))
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
setup(
name='tensorflowcv',
version='0.0.38',
description='Image classification mo... | 1,267 | 37.424242 | 120 | py |
imgclsmob | imgclsmob-master/tensorflow_/utils.py | import numpy as np
import tensorflow as tf
from .tensorflowcv.model_provider import get_model
from .tensorflowcv.models.common import is_channels_first
def save_model_params(sess,
file_path):
# assert file_path.endswith('.npz')
param_dict = {v.name: v.eval(sess) for v in tf.global_varia... | 1,997 | 33.448276 | 99 | py |
imgclsmob | imgclsmob-master/tensorflow_/utils_tp.py | import math
import logging
import os
import multiprocessing
import numpy as np
import cv2
import tensorflow as tf
from tensorpack.models import regularize_cost
from tensorpack.tfutils.summary import add_moving_summary
# from tensorpack.tfutils.summary import add_tensor_summary
from tensorpack import ModelDesc, get_cur... | 12,668 | 36.482249 | 108 | py |
imgclsmob | imgclsmob-master/tensorflow_/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/model_provider.py | from .models.alexnet import *
from .models.zfnet import *
from .models.vgg import *
from .models.resnet import *
from .models.preresnet import *
from .models.resnext import *
from .models.seresnet import *
from .models.sepreresnet import *
from .models.seresnext import *
from .models.senet import *
from .models.densene... | 8,612 | 29.010453 | 96 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/shufflenetv2.py | """
ShuffleNet V2 for ImageNet-1K, implemented in TensorFlow.
Original paper: 'ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design,'
https://arxiv.org/abs/1807.11164.
"""
__all__ = ['ShuffleNetV2', 'shufflenetv2_wd2', 'shufflenetv2_w1', 'shufflenetv2_w3d2', 'shufflenetv2_w2']
import ... | 14,880 | 29.745868 | 117 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/igcv3.py | """
IGCV3 for ImageNet-1K, implemented in TensorFlow.
Original paper: 'IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks,'
https://arxiv.org/abs/1806.00178.
"""
__all__ = ['IGCV3', 'igcv3_w1', 'igcv3_w3d4', 'igcv3_wd2', 'igcv3_wd4']
import os
import tensorflow as tf
from .c... | 12,086 | 30.313472 | 116 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/preresnet.py | """
PreResNet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'Identity Mappings in Deep Residual Networks,' https://arxiv.org/abs/1603.05027.
"""
__all__ = ['PreResNet', 'preresnet10', 'preresnet12', 'preresnet14', 'preresnetbc14b', 'preresnet16', 'preresnet18_wd4',
'preresnet18_wd2', '... | 31,739 | 30.645065 | 120 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/shufflenetv2b.py | """
ShuffleNet V2 for ImageNet-1K, implemented in TensorFlow. The alternative variant.
Original paper: 'ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design,'
https://arxiv.org/abs/1807.11164.
"""
__all__ = ['ShuffleNetV2b', 'shufflenetv2b_wd2', 'shufflenetv2b_w1', 'shufflenetv2b_w3d2'... | 15,582 | 29.980119 | 116 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/menet.py | """
MENet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications,'
https://arxiv.org/abs/1803.09127.
"""
__all__ = ['MENet', 'menet108_8x1_g3', 'menet128_8x1_g4', 'menet160_8x1_g8', 'menet228_12x1_g3', 'menet256_12... | 19,323 | 29.86901 | 116 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/channelnet.py | """
ChannelNet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions,'
https://arxiv.org/abs/1809.01330.
"""
__all__ = ['ChannelNet', 'channelnet']
import os
import tensorflow as tf
from .common import co... | 24,927 | 30.16 | 117 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/squeezenext.py | """
SqueezeNext for ImageNet-1K, implemented in TensorFlow.
Original paper: 'SqueezeNext: Hardware-Aware Neural Network Design,' https://arxiv.org/abs/1803.10615.
"""
__all__ = ['SqueezeNext', 'sqnxt23_w1', 'sqnxt23_w3d2', 'sqnxt23_w2', 'sqnxt23v5_w1', 'sqnxt23v5_w3d2', 'sqnxt23v5_w2']
import os
import tensor... | 15,382 | 29.704591 | 119 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/resnet.py | """
ResNet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'Deep Residual Learning for Image Recognition,' https://arxiv.org/abs/1512.03385.
"""
__all__ = ['ResNet', 'resnet10', 'resnet12', 'resnet14', 'resnetbc14b', 'resnet16', 'resnet18_wd4', 'resnet18_wd2',
'resnet18_w3d4', 'resnet18'... | 29,772 | 29.85285 | 118 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/mobilenetv2.py | """
MobileNetV2 for ImageNet-1K, implemented in TensorFlow.
Original paper: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks,' https://arxiv.org/abs/1801.04381.
"""
__all__ = ['MobileNetV2', 'mobilenetv2_w1', 'mobilenetv2_w3d4', 'mobilenetv2_wd2', 'mobilenetv2_wd4']
import os
import tensorflow as tf
fr... | 12,232 | 30.939948 | 118 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/squeezenet.py | """
SqueezeNet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,'
https://arxiv.org/abs/1602.07360.
"""
__all__ = ['SqueezeNet', 'squeezenet_v1_0', 'squeezenet_v1_1', 'squeezeresnet_v1_0', 'squeezeresnet_v1_1']
... | 14,788 | 29.810417 | 118 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/vgg.py | """
VGG for ImageNet-1K, implemented in TensorFlow.
Original paper: 'Very Deep Convolutional Networks for Large-Scale Image Recognition,'
https://arxiv.org/abs/1409.1556.
"""
__all__ = ['VGG', 'vgg11', 'vgg13', 'vgg16', 'vgg19', 'bn_vgg11', 'bn_vgg13', 'bn_vgg16', 'bn_vgg19', 'bn_vgg11b',
'bn_vg... | 15,566 | 30.576065 | 117 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/mnasnet.py | """
MnasNet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'MnasNet: Platform-Aware Neural Architecture Search for Mobile,' https://arxiv.org/abs/1807.11626.
"""
__all__ = ['MnasNet', 'mnasnet_b1', 'mnasnet_a1', 'mnasnet_small']
import os
import tensorflow as tf
from .common import is_channels_fi... | 17,642 | 32.478178 | 118 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/seresnet.py | """
SE-ResNet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['SEResNet', 'seresnet10', 'seresnet12', 'seresnet14', 'seresnet16', 'seresnet18', 'seresnet26',
'seresnetbc26b', 'seresnet34', 'seresnetbc38b'... | 21,991 | 30.194326 | 118 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/densenet.py | """
DenseNet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
"""
__all__ = ['DenseNet', 'densenet121', 'densenet161', 'densenet169', 'densenet201']
import os
import tensorflow as tf
from .common import pre_conv1x1_block,... | 13,065 | 29.816038 | 116 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/seresnext.py | """
SE-ResNeXt for ImageNet-1K, implemented in TensorFlow.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['SEResNeXt', 'seresnext50_32x4d', 'seresnext101_32x4d', 'seresnext101_64x4d']
import os
import tensorflow as tf
from .common import conv1x1_block, se_b... | 10,990 | 30.048023 | 116 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/mobilenetv3.py | """
MobileNetV3 for ImageNet-1K, implemented in TensorFlow.
Original paper: 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
"""
__all__ = ['MobileNetV3', 'mobilenetv3_small_w7d20', 'mobilenetv3_small_wd2', 'mobilenetv3_small_w3d4',
'mobilenetv3_small_w1', 'mobilenetv3_small_w5d4', 'mo... | 22,437 | 33.048558 | 116 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/sepreresnet.py | """
SE-PreResNet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['SEPreResNet', 'sepreresnet10', 'sepreresnet12', 'sepreresnet14', 'sepreresnet16', 'sepreresnet18',
'sepreresnet26', 'sepreresnetbc26b', 's... | 22,299 | 30.766382 | 119 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/resnext.py | """
ResNeXt for ImageNet-1K, implemented in TensorFlow.
Original papers: 'Aggregated Residual Transformations for Deep Neural Networks,' http://arxiv.org/abs/1611.05431.
"""
__all__ = ['ResNeXt', 'resnext14_16x4d', 'resnext14_32x2d', 'resnext14_32x4d', 'resnext26_16x4d', 'resnext26_32x2d',
'resnext2... | 18,384 | 30.320273 | 119 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/senet.py | """
SENet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['SENet', 'senet16', 'senet28', 'senet40', 'senet52', 'senet103', 'senet154']
import os
import math
import tensorflow as tf
from .common import conv1x1_block... | 16,887 | 28.16753 | 116 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/shufflenet.py | """
ShuffleNet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,'
https://arxiv.org/abs/1707.01083.
"""
__all__ = ['ShuffleNet', 'shufflenet_g1_w1', 'shufflenet_g2_w1', 'shufflenet_g3_w1', 'shufflenet_g4_w1',
... | 19,344 | 30.151369 | 120 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/common.py | """
Common routines for models in TensorFlow.
"""
__all__ = ['round_channels', 'hswish', 'is_channels_first', 'get_channel_axis', 'flatten', 'batchnorm', 'maxpool2d',
'avgpool2d', 'conv2d', 'conv1x1', 'conv3x3', 'depthwise_conv3x3', 'conv_block', 'conv1x1_block',
'conv3x3_block', 'conv7x7_blo... | 39,625 | 28.265879 | 120 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/model_store.py | """
Model store which provides pretrained models.
"""
__all__ = ['get_model_file', 'load_state_dict', 'download_state_dict', 'init_variables_from_state_dict']
import os
import zipfile
import logging
import hashlib
_model_sha1 = {name: (error, checksum, repo_release_tag) for name, error, checksum, repo_release_ta... | 21,784 | 51.748184 | 115 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/zfnet.py | """
ZFNet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'Visualizing and Understanding Convolutional Networks,' https://arxiv.org/abs/1311.2901.
"""
__all__ = ['zfnet', 'zfnetb']
import os
import tensorflow as tf
from .common import is_channels_first
from .alexnet import AlexNet
def get_zfnet(... | 4,297 | 30.372263 | 116 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/darknet53.py | """
DarkNet-53 for ImageNet-1K, implemented in TensorFlow.
Original source: 'YOLOv3: An Incremental Improvement,' https://arxiv.org/abs/1804.02767.
"""
__all__ = ['DarkNet53', 'darknet53']
import os
import tensorflow as tf
from .common import conv1x1_block, conv3x3_block, is_channels_first, flatten
def dark... | 8,796 | 31.223443 | 116 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/mobilenet.py | """
MobileNet & FD-MobileNet for ImageNet-1K, implemented in TensorFlow.
Original papers:
- 'MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,'
https://arxiv.org/abs/1704.04861.
- 'FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy,' https://arxiv... | 14,167 | 31.645161 | 119 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/darknet.py | """
DarkNet for ImageNet-1K, implemented in TensorFlow.
Original source: 'Darknet: Open source neural networks in c,' https://github.com/pjreddie/darknet.
"""
__all__ = ['DarkNet', 'darknet_ref', 'darknet_tiny', 'darknet19']
import os
import tensorflow as tf
from .common import conv2d, maxpool2d, conv1x1_bloc... | 10,892 | 31.038235 | 116 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/alexnet.py | """
AlexNet for ImageNet-1K, implemented in TensorFlow.
Original paper: 'One weird trick for parallelizing convolutional neural networks,'
https://arxiv.org/abs/1404.5997.
"""
__all__ = ['AlexNet', 'alexnet', 'alexnetb']
import os
import tensorflow as tf
from .common import maxpool2d, conv_block, is_chann... | 11,485 | 28.603093 | 116 | py |
imgclsmob | imgclsmob-master/tensorflow_/tensorflowcv/models/others/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/tests/convert_gl2tf2_conv2d_b.py | import numpy as np
import tensorflow as tf
import tensorflow.keras.layers as nn
def is_channels_first(data_format):
"""
Is tested data format channels first.
Parameters:
----------
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
Returns:
----... | 3,744 | 26.947761 | 94 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2tf_batchnorm.py | import numpy as np
import mxnet as mx
import tensorflow as tf
LENGTH = 64
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.bn = mx.gluon.nn.BatchNorm(
moment... | 3,380 | 25.414063 | 78 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2tf_dense.py | import numpy as np
import mxnet as mx
import tensorflow as tf
# import tensorflow.contrib.slim as slim
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.dense = mx.gluon.nn.De... | 2,765 | 27.8125 | 75 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2tf_maxpool2d.py | import math
import numpy as np
import mxnet as mx
import tensorflow as tf
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.pool = mx.gluon.nn.MaxPool2D(
pool_... | 3,444 | 23.607143 | 85 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2tf2_lstm.py | import numpy as np
import tensorflow as tf
import tensorflow.keras.layers as nn
def _calc_width(net):
import numpy as np
net_params = net.collect_params()
weight_count = 0
for param in net_params.values():
if (param.shape is None) or (not param._differentiable):
continue
we... | 4,631 | 29.675497 | 96 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2tf2_batchnorm.py | import numpy as np
import mxnet as mx
import tensorflow as tf
import tensorflow.keras.layers as nn
LENGTH = 64
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.bn = mx.gluon... | 4,796 | 26.568966 | 93 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2pt_batchnorm.py | import numpy as np
import mxnet as mx
import torch
from torch.autograd import Variable
LENGTH = 64
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.bn = mx.gluon.nn.BatchNor... | 2,622 | 25.494949 | 77 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2pt_conv2d.py | import numpy as np
import mxnet as mx
import torch
from torch.autograd import Variable
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.conv = mx.gluon.nn.Conv2D(
... | 2,352 | 24.576087 | 77 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2tf_conv2d.py | import numpy as np
import mxnet as mx
import tensorflow as tf
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.conv = mx.gluon.nn.Conv2D(
channels=64,
... | 4,243 | 24.566265 | 77 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2tf2_conv2d.py | import numpy as np
import tensorflow as tf
import tensorflow.keras.layers as nn
def is_channels_first(data_format):
"""
Is tested data format channels first.
Parameters:
----------
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
Returns:
----... | 4,851 | 27.209302 | 94 | py |
imgclsmob | imgclsmob-master/tests/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/tests/convert_gl2tf2_avgpool2d.py | import math
import numpy as np
import mxnet as mx
import tensorflow as tf
import tensorflow.keras.layers as nn
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.pool = mx.gluo... | 4,921 | 30.961039 | 99 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2pt_dense.py | import numpy as np
import mxnet as mx
import torch
from torch.autograd import Variable
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.dense = mx.gluon.nn.Dense(
... | 2,369 | 25.333333 | 72 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2tf_gconv2d.py | import numpy as np
import mxnet as mx
import tensorflow as tf
GROUPS = 8
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.g_conv = mx.gluon.nn.Conv2D(
channe... | 5,334 | 27.37766 | 94 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2tf2_dwconv2d.py | import numpy as np
import tensorflow as tf
import tensorflow.keras.layers as nn
channels = 12
def is_channels_first(data_format):
"""
Is tested data format channels first.
Parameters:
----------
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
Re... | 3,875 | 27.291971 | 108 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2tf_avgpool2d.py | # import math
import numpy as np
import mxnet as mx
import tensorflow as tf
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.pool = mx.gluon.nn.AvgPool2D(
poo... | 3,395 | 24.343284 | 87 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2tf_dwconv2d.py | import numpy as np
import mxnet as mx
import tensorflow as tf
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.dw_conv = mx.gluon.nn.Conv2D(
channels=32,
... | 4,434 | 25.716867 | 83 | py |
imgclsmob | imgclsmob-master/tests/convert_gl2tf_conv1x1.py | import numpy as np
import mxnet as mx
import tensorflow as tf
class GluonModel(mx.gluon.HybridBlock):
def __init__(self,
**kwargs):
super(GluonModel, self).__init__(**kwargs)
with self.name_scope():
self.conv = mx.gluon.nn.Conv2D(
channels=64,
... | 4,267 | 24.710843 | 77 | py |
imgclsmob | imgclsmob-master/gluon/lr_scheduler.py | from math import pi, cos
from mxnet import lr_scheduler
class LRScheduler(lr_scheduler.LRScheduler):
"""
Learning Rate Scheduler
For mode='step', we multiply lr with `step_factor` at each epoch in `step`.
For mode='poly'::
lr = targetlr + (baselr - targetlr) * (1 - iter / maxiter) ^ power
... | 4,213 | 33.260163 | 107 | py |
imgclsmob | imgclsmob-master/gluon/losses.py | """
Loss functions.
"""
__all__ = ['SegSoftmaxCrossEntropyLoss', 'MixSoftmaxCrossEntropyLoss']
from mxnet.gluon.loss import Loss, _reshape_like
class SegSoftmaxCrossEntropyLoss(Loss):
"""
SoftmaxCrossEntropyLoss with ignore labels (for segmentation task).
Parameters:
----------
axis : int, ... | 3,478 | 33.79 | 102 | py |
imgclsmob | imgclsmob-master/gluon/weighted_random_sampler.py | """
Dataset weighted random sampler.
"""
__all__ = ['WeightedRandomSampler']
import numpy as np
import mxnet as mx
from mxnet.gluon.data import Sampler
class WeightedRandomSampler(Sampler):
"""
Samples elements from [0, length) randomly without replacement.
Parameters:
----------
length : i... | 969 | 23.871795 | 100 | py |
imgclsmob | imgclsmob-master/gluon/dataset_utils.py | """
Dataset routines.
"""
__all__ = ['get_dataset_metainfo', 'get_train_data_source', 'get_val_data_source', 'get_test_data_source',
'get_batch_fn']
from .datasets.imagenet1k_cls_dataset import ImageNet1KMetaInfo
from .datasets.imagenet1k_rec_cls_dataset import ImageNet1KRecMetaInfo
from .datasets.cub2... | 9,354 | 33.648148 | 117 | py |
imgclsmob | imgclsmob-master/gluon/model_stats.py | """
Routines for model statistics calculation.
"""
import logging
import numpy as np
import mxnet as mx
from mxnet.gluon import nn
from mxnet.gluon.contrib.nn import Identity, PixelShuffle2D
from .gluoncv2.models.common import ReLU6, ChannelShuffle, ChannelShuffle2, PReLU2, HSigmoid, HSwish,\
InterpolationBloc... | 11,415 | 36.552632 | 111 | py |
imgclsmob | imgclsmob-master/gluon/setup.py | from setuptools import setup, find_packages
from os import path
from io import open
here = path.abspath(path.dirname(__file__))
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
setup(
name='gluoncv2',
version='0.0.64',
description='Image classification and se... | 1,566 | 42.527778 | 120 | py |
imgclsmob | imgclsmob-master/gluon/utils.py | """
Main routines shared between training and evaluation scripts.
"""
__all__ = ['prepare_mx_context', 'get_initializer', 'prepare_model', 'calc_net_weight_count', 'validate',
'validate_asr', 'report_accuracy', 'get_composite_metric', 'get_metric_name', 'get_loss']
import os
import re
import logging
im... | 12,230 | 27.444186 | 116 | py |
imgclsmob | imgclsmob-master/gluon/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/gluon/distillation.py | """
DNN distillation routines.
"""
__all__ = ['MealDiscriminator', 'MealAdvLoss']
from mxnet.gluon import nn, HybridBlock
from .gluoncv2.models.common import conv1x1, conv1x1_block
from mxnet.gluon.loss import SigmoidBinaryCrossEntropyLoss
class MealDiscriminator(HybridBlock):
"""
MEALv2 discriminator.
... | 3,585 | 28.393443 | 98 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/gluon/gluoncv2/model_provider.py | from .models.alexnet import *
from .models.zfnet import *
from .models.vgg import *
from .models.bninception import *
from .models.resnet import *
from .models.preresnet import *
from .models.resnext import *
from .models.seresnet import *
from .models.sepreresnet import *
from .models.seresnext import *
from .models.s... | 47,262 | 35.666408 | 95 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/airnext.py | """
AirNeXt for ImageNet-1K, implemented in Gluon.
Original paper: 'Attention Inspiring Receptive-Fields Network for Learning Invariant Representations,'
https://ieeexplore.ieee.org/document/8510896.
"""
__all__ = ['AirNeXt', 'airnext50_32x4d_r2', 'airnext101_32x4d_r2', 'airnext101_32x4d_r16']
import os
i... | 13,827 | 31.845606 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/pspnet.py | """
PSPNet for image segmentation, implemented in Gluon.
Original paper: 'Pyramid Scene Parsing Network,' https://arxiv.org/abs/1612.01105.
"""
__all__ = ['PSPNet', 'pspnet_resnetd50b_voc', 'pspnet_resnetd101b_voc', 'pspnet_resnetd50b_coco',
'pspnet_resnetd101b_coco', 'pspnet_resnetd50b_ade20k', 'ps... | 19,131 | 37.035785 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/dla.py | """
DLA for ImageNet-1K, implemented in Gluon.
Original paper: 'Deep Layer Aggregation,' https://arxiv.org/abs/1707.06484.
"""
__all__ = ['DLA', 'dla34', 'dla46c', 'dla46xc', 'dla60', 'dla60x', 'dla60xc', 'dla102', 'dla102x', 'dla102x2', 'dla169']
import os
from mxnet import cpu
from mxnet.gluon import nn, Hy... | 23,814 | 32.401122 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/proxylessnas.py | """
ProxylessNAS for ImageNet-1K, implemented in Gluon.
Original paper: 'ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware,'
https://arxiv.org/abs/1812.00332.
"""
__all__ = ['ProxylessNAS', 'proxylessnas_cpu', 'proxylessnas_gpu', 'proxylessnas_mobile', 'proxylessnas_mobile14',
... | 16,517 | 35.788419 | 118 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/isqrtcovresnet.py | """
iSQRT-COV-ResNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root
Normalization,' https://arxiv.org/abs/1712.01034.
"""
__all__ = ['iSQRTCOVResNet', 'isqrtcovresnet18', 'isqrtcovresnet34', 'isqrtcovresn... | 17,607 | 35.683333 | 118 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/shufflenetv2.py | """
ShuffleNet V2 for ImageNet-1K, implemented in Gluon.
Original paper: 'ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design,'
https://arxiv.org/abs/1807.11164.
"""
__all__ = ['ShuffleNetV2', 'shufflenetv2_wd2', 'shufflenetv2_w1', 'shufflenetv2_w3d2', 'shufflenetv2_w2']
import os
fr... | 12,524 | 32.4 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/fishnet.py | """
FishNet for ImageNet-1K, implemented in Gluon.
Original paper: 'FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction,'
http://papers.nips.cc/paper/7356-fishnet-a-versatile-backbone-for-image-region-and-pixel-level-prediction.pdf.
"""
__all__ = ['FishNet', 'fishnet99', 'fishnet150... | 23,458 | 33.097384 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/hrnet.py | """
HRNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Deep High-Resolution Representation Learning for Visual Recognition,'
https://arxiv.org/abs/1908.07919.
"""
__all__ = ['hrnet_w18_small_v1', 'hrnet_w18_small_v2', 'hrnetv2_w18', 'hrnetv2_w30', 'hrnetv2_w32', 'hrnetv2_w40',
'hrnetv... | 26,230 | 35.381415 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/fcn8sd.py | """
FCN-8s(d) for image segmentation, implemented in Gluon.
Original paper: 'Fully Convolutional Networks for Semantic Segmentation,' https://arxiv.org/abs/1411.4038.
"""
__all__ = ['FCN8sd', 'fcn8sd_resnetd50b_voc', 'fcn8sd_resnetd101b_voc', 'fcn8sd_resnetd50b_coco',
'fcn8sd_resnetd101b_coco', 'fcn... | 16,570 | 38.267773 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/selecsls.py | """
SelecSLS for ImageNet-1K, implemented in Gluon.
Original paper: 'XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera,'
https://arxiv.org/abs/1907.00837.
"""
__all__ = ['SelecSLS', 'selecsls42', 'selecsls42b', 'selecsls60', 'selecsls60b', 'selecsls84']
import os
from mxnet i... | 14,256 | 33.943627 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/inceptionv4.py | """
InceptionV4 for ImageNet-1K, implemented in Gluon.
Original paper: 'Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,'
https://arxiv.org/abs/1602.07261.
"""
__all__ = ['InceptionV4', 'inceptionv4']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock... | 23,613 | 33.573939 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/regnet.py | """
RegNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
"""
__all__ = ['RegNet', 'regnetx002', 'regnetx004', 'regnetx006', 'regnetx008', 'regnetx016', 'regnetx032', 'regnetx040',
'regnetx064', 'regnetx080', 'regnetx120', ... | 30,188 | 34.896552 | 118 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/icnet.py | """
ICNet for image segmentation, implemented in Gluon.
Original paper: 'ICNet for Real-Time Semantic Segmentation on High-Resolution Images,'
https://arxiv.org/abs/1704.08545.
"""
__all__ = ['ICNet', 'icnet_resnetd50b_cityscapes']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
fr... | 14,177 | 31.668203 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/mobilenetb.py | """
MobileNet(B) with simplified depthwise separable convolution block for ImageNet-1K, implemented in Gluon.
Original paper: 'MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,'
https://arxiv.org/abs/1704.04861.
"""
__all__ = ['mobilenetb_w1', 'mobilenetb_w3d4', 'mobilenet... | 4,189 | 33.916667 | 113 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/shakedropresnet_cifar.py | """
ShakeDrop-ResNet for CIFAR/SVHN, implemented in Gluon.
Original paper: 'ShakeDrop Regularization for Deep Residual Learning,' https://arxiv.org/abs/1802.02375.
"""
__all__ = ['CIFARShakeDropResNet', 'shakedropresnet20_cifar10', 'shakedropresnet20_cifar100', 'shakedropresnet20_svhn']
import os
import numpy... | 12,306 | 33.570225 | 119 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/inceptionresnetv1.py | """
InceptionResNetV1 for ImageNet-1K, implemented in Gluon.
Original paper: 'Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,'
https://arxiv.org/abs/1602.07261.
"""
__all__ = ['InceptionResNetV1', 'inceptionresnetv1', 'InceptionAUnit', 'InceptionBUnit', 'InceptionCUnit',
... | 21,298 | 34.204959 | 117 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/scnet.py | """
SCNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Improving Convolutional Networks with Self-Calibrated Convolutions,'
http://mftp.mmcheng.net/Papers/20cvprSCNet.pdf.
"""
__all__ = ['SCNet', 'scnet50', 'scnet101', 'scneta50', 'scneta101']
import os
from mxnet import cpu
from mxnet.gluon im... | 19,878 | 33.814361 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/igcv3.py | """
IGCV3 for ImageNet-1K, implemented in Gluon.
Original paper: 'IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks,'
https://arxiv.org/abs/1806.00178.
"""
__all__ = ['IGCV3', 'igcv3_w1', 'igcv3_w3d4', 'igcv3_wd2', 'igcv3_wd4']
import os
from mxnet import cpu
from mxnet.glu... | 11,243 | 33.280488 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/seresnet_cifar.py | """
SE-ResNet for CIFAR/SVHN, implemented in Gluon.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['CIFARSEResNet', 'seresnet20_cifar10', 'seresnet20_cifar100', 'seresnet20_svhn',
'seresnet56_cifar10', 'seresnet56_cifar100', 'seresnet56_svhn',
... | 25,848 | 36.846266 | 120 | py |
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