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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/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/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/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 |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/resnetd.py | """
ResNet(D) with dilation for ImageNet-1K, implemented in Gluon.
Original paper: 'Deep Residual Learning for Image Recognition,' https://arxiv.org/abs/1512.03385.
"""
__all__ = ['ResNetD', 'resnetd50b', 'resnetd101b', 'resnetd152b']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
fro... | 10,821 | 34.250814 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/quartznet.py | """
QuartzNet for ASR, implemented in Gluon.
Original paper: 'QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions,'
https://arxiv.org/abs/1910.10261.
"""
__all__ = ['quartznet5x5_en_ls', 'quartznet15x5_en', 'quartznet15x5_en_nr', 'quartznet15x5_fr', 'quartznet15x5_de',
... | 14,466 | 42.185075 | 119 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/preresnet.py | """
PreResNet for ImageNet-1K, implemented in Gluon.
Original papers: 'Identity Mappings in Deep Residual Networks,' https://arxiv.org/abs/1603.05027.
"""
__all__ = ['PreResNet', 'preresnet10', 'preresnet12', 'preresnet14', 'preresnetbc14b', 'preresnet16', 'preresnet18_wd4',
'preresnet18_wd2', 'prer... | 31,235 | 34.175676 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/lednet.py | """
LEDNet for image segmentation, implemented in Gluon.
Original paper: 'LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation,'
https://arxiv.org/abs/1905.02423.
"""
__all__ = ['LEDNet', 'lednet_cityscapes']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBl... | 24,449 | 33.48519 | 116 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/superpointnet.py | """
SuperPointNet for HPatches (image matching), implemented in Gluon.
Original paper: 'SuperPoint: Self-Supervised Interest Point Detection and Description,'
https://arxiv.org/abs/1712.07629.
"""
__all__ = ['SuperPointNet', 'superpointnet']
import os
from mxnet import cpu
from mxnet.gluon import nn, Hybr... | 19,321 | 34.323583 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/ibndensenet.py | """
IBN-DenseNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net,'
https://arxiv.org/abs/1807.09441.
"""
__all__ = ['IBNDenseNet', 'ibn_densenet121', 'ibn_densenet161', 'ibn_densenet169', 'ibn_densenet201']
import os
from mx... | 14,757 | 32.848624 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/hardnet.py | """
HarDNet for ImageNet-1K, implemented in Gluon.
Original paper: 'HarDNet: A Low Memory Traffic Network,' https://arxiv.org/abs/1909.00948.
"""
__all__ = ['HarDNet', 'hardnet39ds', 'hardnet68ds', 'hardnet68', 'hardnet85']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
from .common i... | 24,619 | 36.134238 | 116 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/sinet.py | """
SINet for image segmentation, implemented in Gluon.
Original paper: 'SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and
Information Blocking Decoder,' https://arxiv.org/abs/1911.09099.
"""
__all__ = ['SINet', 'sinet_cityscapes']
import os
from mxnet import cpu
f... | 37,954 | 32.888393 | 119 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/shufflenetv2b.py | """
ShuffleNet V2 for ImageNet-1K, implemented in Gluon. The alternative version.
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', 'sh... | 13,269 | 32.00995 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/sparsenet.py | """
SparseNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Sparsely Aggregated Convolutional Networks,' https://arxiv.org/abs/1801.05895.
"""
__all__ = ['SparseNet', 'sparsenet121', 'sparsenet161', 'sparsenet169', 'sparsenet201', 'sparsenet264']
import os
import math
from mxnet import cpu
from mxne... | 13,347 | 31.635697 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/menet.py | """
MENet for ImageNet-1K, implemented in Gluon.
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_12x1_g4... | 17,113 | 33.365462 | 116 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/voca.py | """
VOCA for speech-driven facial animation, implemented in Gluon.
Original paper: 'Capture, Learning, and Synthesis of 3D Speaking Styles,' https://arxiv.org/abs/1905.03079.
"""
__all__ = ['VOCA', 'voca8flame']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
from .common import ConvBl... | 6,959 | 29.79646 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/shakeshakeresnet_cifar.py | """
Shake-Shake-ResNet for CIFAR/SVHN, implemented in Gluon.
Original paper: 'Shake-Shake regularization,' https://arxiv.org/abs/1705.07485.
"""
__all__ = ['CIFARShakeShakeResNet', 'shakeshakeresnet20_2x16d_cifar10', 'shakeshakeresnet20_2x16d_cifar100',
'shakeshakeresnet20_2x16d_svhn', 'shakeshakere... | 16,328 | 35.612108 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/wrn_cifar.py | """
WRN for CIFAR/SVHN, implemented in Gluon.
Original paper: 'Wide Residual Networks,' https://arxiv.org/abs/1605.07146.
"""
__all__ = ['CIFARWRN', 'wrn16_10_cifar10', 'wrn16_10_cifar100', 'wrn16_10_svhn', 'wrn28_10_cifar10',
'wrn28_10_cifar100', 'wrn28_10_svhn', 'wrn40_8_cifar10', 'wrn40_8_cifar10... | 12,476 | 34.245763 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/inceptionresnetv2.py | """
InceptionResNetV2 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__ = ['InceptionResNetV2', 'inceptionresnetv2']
import os
from mxnet import cpu
from mxnet.gluon impo... | 11,625 | 34.772308 | 117 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/ghostnet.py | """
GhostNet for ImageNet-1K, implemented in Gluon.
Original paper: 'GhostNet: More Features from Cheap Operations,' https://arxiv.org/abs/1911.11907.
"""
__all__ = ['GhostNet', 'ghostnet']
import os
import math
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
from .common import round_channels, ... | 15,156 | 33.060674 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/efficientnet.py | """
EfficientNet for ImageNet-1K, implemented in Gluon.
Original papers:
- 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,' https://arxiv.org/abs/1905.11946,
- 'Adversarial Examples Improve Image Recognition,' https://arxiv.org/abs/1911.09665.
"""
__all__ = ['EfficientNet', '... | 44,055 | 37.713533 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/channelnet.py | """
ChannelNet for ImageNet-1K, implemented in Gluon.
Original paper: 'ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions,'
https://arxiv.org/abs/1809.01330.
"""
__all__ = ['ChannelNet', 'channelnet']
import os
from mxnet import cpu
from mxnet.gluon import nn, H... | 19,449 | 30.677524 | 117 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/pnasnet.py | """
PNASNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Progressive Neural Architecture Search,' https://arxiv.org/abs/1712.00559.
"""
__all__ = ['PNASNet', 'pnasnet5large']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
from .common import conv1x1
from .nasnet import nas... | 18,996 | 30.091653 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/efficientnetedge.py | """
EfficientNet-Edge for ImageNet-1K, implemented in Gluon.
Original paper: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,'
https://arxiv.org/abs/1905.11946.
"""
__all__ = ['EfficientNetEdge', 'efficientnet_edge_small_b', 'efficientnet_edge_medium_b', 'efficientnet_edge_large_b... | 16,355 | 37.850356 | 118 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/ibnresnext.py | """
IBN-ResNeXt for ImageNet-1K, implemented in Gluon.
Original paper: 'Aggregated Residual Transformations for Deep Neural Networks,' http://arxiv.org/abs/1611.05431.
"""
__all__ = ['IBNResNeXt', 'ibn_resnext50_32x4d', 'ibn_resnext101_32x4d', 'ibn_resnext101_64x4d']
import os
import math
from mxnet import cp... | 12,457 | 32.853261 | 118 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/squeezenext.py | """
SqueezeNext for ImageNet-1K, implemented in Gluon.
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
from mxnet import ... | 13,315 | 32.124378 | 119 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/xdensenet.py | """
X-DenseNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Deep Expander Networks: Efficient Deep Networks from Graph Theory,'
https://arxiv.org/abs/1711.08757.
"""
__all__ = ['XDenseNet', 'xdensenet121_2', 'xdensenet161_2', 'xdensenet169_2', 'xdensenet201_2', 'pre_xconv3x3_block',
'... | 19,138 | 31.94148 | 117 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/diaresnet_cifar.py | """
DIA-ResNet for CIFAR/SVHN, implemented in Gluon.
Original paper: 'DIANet: Dense-and-Implicit Attention Network,' https://arxiv.org/abs/1905.10671.
"""
__all__ = ['CIFARDIAResNet', 'diaresnet20_cifar10', 'diaresnet20_cifar100', 'diaresnet20_svhn', 'diaresnet56_cifar10',
'diaresnet56_cifar100', 'd... | 21,836 | 36.137755 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/resdropresnet_cifar.py | """
ResDrop-ResNet for CIFAR/SVHN, implemented in Gluon.
Original paper: 'Deep Networks with Stochastic Depth,' https://arxiv.org/abs/1603.09382.
"""
__all__ = ['CIFARResDropResNet', 'resdropresnet20_cifar10', 'resdropresnet20_cifar100', 'resdropresnet20_svhn']
import os
import numpy as np
import mxnet as mx
... | 11,219 | 33.62963 | 119 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/bisenet.py | """
BiSeNet for CelebAMask-HQ, implemented in Gluon.
Original paper: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation,'
https://arxiv.org/abs/1808.00897.
"""
__all__ = ['BiSeNet', 'bisenet_resnet18_celebamaskhq']
import os
from mxnet import cpu
from mxnet.gluon import nn, Hybri... | 13,872 | 30.386878 | 119 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/resnet.py | """
ResNet for ImageNet-1K, implemented in Gluon.
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', 're... | 30,943 | 34.123723 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/simpleposemobile_coco.py | """
SimplePose(Mobile) for COCO Keypoint, implemented in Gluon.
Original paper: 'Simple Baselines for Human Pose Estimation and Tracking,' https://arxiv.org/abs/1804.06208.
"""
__all__ = ['SimplePoseMobile', 'simplepose_mobile_resnet18_coco', 'simplepose_mobile_resnet50b_coco',
'simplepose_mobile_mo... | 15,126 | 40.217984 | 121 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/cbamresnet.py | """
CBAM-ResNet for ImageNet-1K, implemented in Gluon.
Original paper: 'CBAM: Convolutional Block Attention Module,' https://arxiv.org/abs/1807.06521.
"""
__all__ = ['CbamResNet', 'cbam_resnet18', 'cbam_resnet34', 'cbam_resnet50', 'cbam_resnet101', 'cbam_resnet152']
import os
from mxnet import cpu
from mxnet.... | 15,289 | 30.987448 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/diracnetv2.py | """
DiracNetV2 for ImageNet-1K, implemented in Gluon.
Original paper: 'DiracNets: Training Very Deep Neural Networks Without Skip-Connections,'
https://arxiv.org/abs/1706.00388.
"""
__all__ = ['DiracNetV2', 'diracnet18v2', 'diracnet34v2']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridB... | 9,008 | 29.03 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/sepreresnet_cifar.py | """
SE-PreResNet for CIFAR/SVHN, implemented in Gluon.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['CIFARSEPreResNet', 'sepreresnet20_cifar10', 'sepreresnet20_cifar100', 'sepreresnet20_svhn',
'sepreresnet56_cifar10', 'sepreresnet56_cifar100', '... | 26,868 | 37.604885 | 119 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/danet.py | """
DANet for image segmentation, implemented in Gluon.
Original paper: 'Dual Attention Network for Scene Segmentation,' https://arxiv.org/abs/1809.02983.
"""
__all__ = ['DANet', 'danet_resnetd50b_cityscapes', 'danet_resnetd101b_cityscapes', 'ScaleBlock']
import os
import mxnet as mx
from mxnet import cpu
fr... | 14,852 | 32.680272 | 116 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/mobilenetv2.py | """
MobileNetV2 for ImageNet-1K, implemented in Gluon.
Original paper: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks,' https://arxiv.org/abs/1801.04381.
"""
__all__ = ['MobileNetV2', 'mobilenetv2_w1', 'mobilenetv2_w3d4', 'mobilenetv2_wd2', 'mobilenetv2_wd4', 'mobilenetv2b_w1',
'mobilenetv2... | 14,563 | 34.696078 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/squeezenet.py | """
SqueezeNet for ImageNet-1K, implemented in Gluon.
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']
impor... | 12,810 | 32.018041 | 118 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/octresnet_cifar.py | """
Oct-ResNet for CIFAR/SVHN, implemented in Gluon.
Original paper: 'Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave
Convolution,' https://arxiv.org/abs/1904.05049.
"""
__all__ = ['CIFAROctResNet', 'octresnet20_ad2_cifar10', 'octresnet20_ad2_cifar100', 'octresnet20... | 12,716 | 36.293255 | 118 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/nin_cifar.py | """
NIN for CIFAR/SVHN, implemented in Gluon.
Original paper: 'Network In Network,' https://arxiv.org/abs/1312.4400.
"""
__all__ = ['CIFARNIN', 'nin_cifar10', 'nin_cifar100', 'nin_svhn']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
class NINConv(HybridBlock):
"""
NIN speci... | 8,489 | 31.037736 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/vgg.py | """
VGG for ImageNet-1K, implemented in Gluon.
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_vgg13b'... | 15,326 | 31.541401 | 117 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/resnet_cub.py | """
ResNet for CUB-200-2011, implemented in Gluon.
Original paper: 'Deep Residual Learning for Image Recognition,' https://arxiv.org/abs/1512.03385.
"""
__all__ = ['resnet10_cub', 'resnet12_cub', 'resnet14_cub', 'resnetbc14b_cub', 'resnet16_cub', 'resnet18_cub',
'resnet26_cub', 'resnetbc26b_cub', 'r... | 15,639 | 35.627635 | 117 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/bagnet.py | """
BagNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet,'
https://openreview.net/pdf?id=SkfMWhAqYQ.
"""
__all__ = ['BagNet', 'bagnet9', 'bagnet17', 'bagnet33']
import os
from mxnet import cpu
from mxnet.glu... | 12,862 | 32.066838 | 116 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/airnet.py | """
AirNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Attention Inspiring Receptive-Fields Network for Learning Invariant Representations,'
https://ieeexplore.ieee.org/document/8510896.
"""
__all__ = ['AirNet', 'airnet50_1x64d_r2', 'airnet50_1x64d_r16', 'airnet101_1x64d_r2', 'AirBlock', 'AirIn... | 15,893 | 32.461053 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/mnasnet.py | """
MnasNet for ImageNet-1K, implemented in Gluon.
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
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
f... | 16,501 | 34.95207 | 118 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/pyramidnet_cifar.py | """
PyramidNet for CIFAR/SVHN, implemented in Gluon.
Original paper: 'Deep Pyramidal Residual Networks,' https://arxiv.org/abs/1610.02915.
"""
__all__ = ['CIFARPyramidNet', 'pyramidnet110_a48_cifar10', 'pyramidnet110_a48_cifar100', 'pyramidnet110_a48_svhn',
'pyramidnet110_a84_cifar10', 'pyramidnet11... | 25,957 | 33.110381 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/preresnet_cifar.py | """
PreResNet for CIFAR/SVHN, implemented in Gluon.
Original papers: 'Identity Mappings in Deep Residual Networks,' https://arxiv.org/abs/1603.05027.
"""
__all__ = ['CIFARPreResNet', 'preresnet20_cifar10', 'preresnet20_cifar100', 'preresnet20_svhn',
'preresnet56_cifar10', 'preresnet56_cifar100', 'pr... | 26,802 | 36.330084 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/alphapose_coco.py | """
AlphaPose for COCO Keypoint, implemented in Gluon.
Original paper: 'RMPE: Regional Multi-person Pose Estimation,' https://arxiv.org/abs/1612.00137.
"""
__all__ = ['AlphaPose', 'alphapose_fastseresnet101b_coco']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
from mxnet.gluon.contri... | 7,299 | 32.953488 | 116 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/pyramidnet.py | """
PyramidNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Deep Pyramidal Residual Networks,' https://arxiv.org/abs/1610.02915.
"""
__all__ = ['PyramidNet', 'pyramidnet101_a360', 'PyrUnit']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
from .common import pre_conv1x1_bloc... | 13,534 | 31.149644 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/seresnet.py | """
SE-ResNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['SEResNet', 'seresnet10', 'seresnet12', 'seresnet14', 'seresnet16', 'seresnet18', 'seresnet26',
'seresnetbc26b', 'seresnet34', 'seresnetbc38b', 'se... | 20,802 | 33.385124 | 118 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/seresnet_cub.py | """
SE-ResNet for CUB-200-2011, implemented in Gluon.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['seresnet10_cub', 'seresnet12_cub', 'seresnet14_cub', 'seresnetbc14b_cub', 'seresnet16_cub',
'seresnet18_cub', 'seresnet26_cub', 'seresnetbc26b_cu... | 15,666 | 36.037825 | 120 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/densenet.py | """
DenseNet for ImageNet-1K, implemented in Gluon.
Original paper: 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
"""
__all__ = ['DenseNet', 'densenet121', 'densenet161', 'densenet169', 'densenet201', 'DenseUnit', 'TransitionBlock']
import os
from mxnet import cpu
from mxnet.gl... | 11,737 | 32.346591 | 116 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/seresnext.py | """
SE-ResNeXt for ImageNet-1K, implemented in Gluon.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['SEResNeXt', 'seresnext50_32x4d', 'seresnext101_32x4d', 'seresnext101_64x4d']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
from .... | 9,912 | 32.265101 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/darts.py | """
DARTS for ImageNet-1K, implemented in Gluon.
Original paper: 'DARTS: Differentiable Architecture Search,' https://arxiv.org/abs/1806.09055.
"""
__all__ = ['DARTS', 'darts']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
from mxnet.gluon.contrib.nn import Identity
from .common impo... | 21,552 | 27.969086 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/drn.py | """
DRN for ImageNet-1K, implemented in Gluon.
Original paper: 'Dilated Residual Networks,' https://arxiv.org/abs/1705.09914.
"""
__all__ = ['DRN', 'drnc26', 'drnc42', 'drnc58', 'drnd22', 'drnd38', 'drnd54', 'drnd105']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
class DRNConv(Hyb... | 22,644 | 31.35 | 119 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/mixnet.py | """
MixNet for ImageNet-1K, implemented in Gluon.
Original paper: 'MixConv: Mixed Depthwise Convolutional Kernels,' https://arxiv.org/abs/1907.09595.
"""
__all__ = ['MixNet', 'mixnet_s', 'mixnet_m', 'mixnet_l']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
from .common import round_c... | 23,494 | 35.826019 | 119 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/dabnet.py | """
DABNet for image segmentation, implemented in Gluon.
Original paper: 'DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation,'
https://arxiv.org/abs/1907.11357.
"""
__all__ = ['DABNet', 'dabnet_cityscapes']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
f... | 22,095 | 32.682927 | 116 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/cgnet.py | """
CGNet for image segmentation, implemented in Gluon.
Original paper: 'CGNet: A Light-weight Context Guided Network for Semantic Segmentation,'
https://arxiv.org/abs/1811.08201.
"""
__all__ = ['CGNet', 'cgnet_cityscapes']
import os
from mxnet import cpu
from mxnet.gluon import nn, HybridBlock
from .comm... | 17,360 | 32.068571 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/wrn1bit_cifar.py | """
WRN-1bit for CIFAR/SVHN, implemented in Gluon.
Original paper: 'Training wide residual networks for deployment using a single bit for each weight,'
https://arxiv.org/abs/1802.08530.
"""
__all__ = ['CIFARWRN1bit', 'wrn20_10_1bit_cifar10', 'wrn20_10_1bit_cifar100', 'wrn20_10_1bit_svhn',
'wrn20... | 28,170 | 32.657109 | 115 | py |
imgclsmob | imgclsmob-master/gluon/gluoncv2/models/condensenet.py | """
CondenseNet for ImageNet-1K, implemented in Gluon.
Original paper: 'CondenseNet: An Efficient DenseNet using Learned Group Convolutions,'
https://arxiv.org/abs/1711.09224.
"""
__all__ = ['CondenseNet', 'condensenet74_c4_g4', 'condensenet74_c8_g8']
import os
from mxnet import cpu
from mxnet.gluon impor... | 17,278 | 30.245931 | 120 | py |
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