Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
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null | qimera-main/other_train_scripts/run_imgnet_resnet18_5bit.sh | #!/bin/bash
python main.py --conf_path ./imagenet_resnet18.hocon --multi_label_prob 0.4 --multi_label_num 100 --id 01 --randemb --qw 5 --qa 5 | 143 | 47 | 130 | sh |
null | qimera-main/other_train_scripts/run_imgnet_resnet50_5bit.sh | #!/bin/bash
python main.py --conf_path ./imagenet_resnet50.hocon --multi_label_prob 0.7 --multi_label_num 100 --id 01 --qw 5 --qa 5 | 133 | 43.666667 | 120 | sh |
null | qimera-main/pytorchcv/__init__.py | 0 | 0 | 0 | py | |
null | qimera-main/pytorchcv/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... | 53,356 | 45.236568 | 120 | py |
null | qimera-main/pytorchcv/models/__init__.py | 0 | 0 | 0 | py | |
null | qimera-main/pytorchcv/models/airnet.py | """
AirNet for ImageNet-1K, implemented in PyTorch.
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', 'Air... | 12,525 | 28.612293 | 115 | py |
null | qimera-main/pytorchcv/models/airnext.py | """
AirNeXt for ImageNet-1K, implemented in PyTorch.
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... | 11,535 | 29.041667 | 115 | py |
null | qimera-main/pytorchcv/models/alexnet.py | """
AlexNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'One weird trick for parallelizing convolutional neural networks,'
https://arxiv.org/abs/1404.5997.
"""
__all__ = ['AlexNet', 'alexnet', 'alexnetb']
import os
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as ... | 9,244 | 27.890625 | 115 | py |
null | qimera-main/pytorchcv/models/bagnet.py | """
BagNet for ImageNet-1K, implemented in PyTorch.
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
import torch.nn as nn
import torch... | 10,903 | 29.373259 | 116 | py |
null | qimera-main/pytorchcv/models/bamresnet.py | """
BAM-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'BAM: Bottleneck Attention Module,' https://arxiv.org/abs/1807.06514.
"""
__all__ = ['BamResNet', 'bam_resnet18', 'bam_resnet34', 'bam_resnet50', 'bam_resnet101', 'bam_resnet152']
import os
import torch.nn as nn
import torch.nn.init as in... | 13,297 | 28.420354 | 115 | py |
null | qimera-main/pytorchcv/models/bninception.py | """
BN-Inception for ImageNet-1K, implemented in PyTorch.
Original paper: 'Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,'
https://arxiv.org/abs/1502.03167.
"""
__all__ = ['BNInception', 'bninception']
import os
import torch.nn as nn
import torch.nn.init as i... | 16,280 | 29.488764 | 115 | py |
null | qimera-main/pytorchcv/models/cbamresnet.py | """
CBAM-ResNet for ImageNet-1K, implemented in PyTorch.
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
import torch
import torch.nn as... | 12,908 | 28.405467 | 115 | py |
null | qimera-main/pytorchcv/models/channelnet.py | """
ChannelNet for ImageNet-1K, implemented in PyTorch.
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 torch
import torch.nn as nn
import torch.n... | 18,471 | 29.633499 | 117 | py |
null | qimera-main/pytorchcv/models/common.py | """
Common routines for models in PyTorch.
"""
__all__ = ['round_channels', 'Swish', 'HSigmoid', 'HSwish', 'get_activation_layer', 'conv1x1', 'conv3x3',
'depthwise_conv3x3', 'ConvBlock', 'conv1x1_block', 'conv3x3_block', 'conv7x7_block', 'dwconv3x3_block',
'dwconv5x5_block', 'dwsconv3x3_block... | 39,422 | 29.395528 | 120 | py |
null | qimera-main/pytorchcv/models/condensenet.py | """
CondenseNet for ImageNet-1K, implemented in PyTorch.
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
import torch
import torch.nn as nn
import ... | 14,732 | 28.059172 | 120 | py |
null | qimera-main/pytorchcv/models/darknet.py | """
DarkNet for ImageNet-1K, implemented in PyTorch.
Original source: 'Darknet: Open source neural networks in c,' https://github.com/pjreddie/darknet.
"""
__all__ = ['DarkNet', 'darknet_ref', 'darknet_tiny', 'darknet19']
import os
import torch
import torch.nn as nn
import torch.nn.init as init
from .common i... | 8,529 | 30.360294 | 116 | py |
null | qimera-main/pytorchcv/models/darknet53.py | """
DarkNet-53 for ImageNet-1K, implemented in PyTorch.
Original source: 'YOLOv3: An Incremental Improvement,' https://arxiv.org/abs/1804.02767.
"""
__all__ = ['DarkNet53', 'darknet53']
import os
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1_block, conv3x3_block
class DarkUn... | 6,707 | 29.080717 | 115 | py |
null | qimera-main/pytorchcv/models/darts.py | """
DARTS for ImageNet-1K, implemented in PyTorch.
Original paper: 'DARTS: Differentiable Architecture Search,' https://arxiv.org/abs/1806.09055.
"""
__all__ = ['DARTS', 'darts']
import os
import torch
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1, Identity
from .nasnet import... | 20,225 | 26.668947 | 115 | py |
null | qimera-main/pytorchcv/models/deeplabv3.py | """
DeepLabv3 for image segmentation, implemented in PyTorch.
Original paper: 'Rethinking Atrous Convolution for Semantic Image Segmentation,' https://arxiv.org/abs/1706.05587.
"""
__all__ = ['DeepLabv3', 'deeplabv3_resnetd50b_voc', 'deeplabv3_resnetd101b_voc', 'deeplabv3_resnetd152b_voc',
'deeplabv... | 22,014 | 37.964602 | 120 | py |
null | qimera-main/pytorchcv/models/densenet.py | """
DenseNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
"""
__all__ = ['DenseNet', 'densenet121', 'densenet161', 'densenet169', 'densenet201', 'DenseUnit', 'TransitionBlock']
import os
import torch
import torch.nn as n... | 9,930 | 29.556923 | 116 | py |
null | qimera-main/pytorchcv/models/densenet_cifar.py | """
DenseNet for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
"""
__all__ = ['CIFARDenseNet', 'densenet40_k12_cifar10', 'densenet40_k12_cifar100', 'densenet40_k12_svhn',
'densenet40_k12_bc_cifar10', 'densenet40_k12_... | 29,468 | 36.780769 | 115 | py |
null | qimera-main/pytorchcv/models/diapreresnet.py | """
DIA-PreResNet for ImageNet-1K, implemented in PyTorch.
Original papers: 'DIANet: Dense-and-Implicit Attention Network,' https://arxiv.org/abs/1905.10671.
"""
__all__ = ['DIAPreResNet', 'diapreresnet10', 'diapreresnet12', 'diapreresnet14', 'diapreresnetbc14b', 'diapreresnet16',
'diapreresnet18', ... | 21,166 | 33.814145 | 119 | py |
null | qimera-main/pytorchcv/models/diapreresnet_cifar.py | """
DIA-PreResNet for CIFAR/SVHN, implemented in PyTorch.
Original papers: 'DIANet: Dense-and-Implicit Attention Network,' https://arxiv.org/abs/1905.10671.
"""
__all__ = ['CIFARDIAPreResNet', 'diapreresnet20_cifar10', 'diapreresnet20_cifar100', 'diapreresnet20_svhn',
'diapreresnet56_cifar10', 'diap... | 20,604 | 36.327899 | 120 | py |
null | qimera-main/pytorchcv/models/diaresnet.py | """
DIA-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'DIANet: Dense-and-Implicit Attention Network,' https://arxiv.org/abs/1905.10671.
"""
__all__ = ['DIAResNet', 'diaresnet10', 'diaresnet12', 'diaresnet14', 'diaresnetbc14b', 'diaresnet16', 'diaresnet18',
'diaresnet26', 'diaresnet... | 24,132 | 32.058904 | 116 | py |
null | qimera-main/pytorchcv/models/diaresnet_cifar.py | """
DIA-ResNet for CIFAR/SVHN, implemented in PyTorch.
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', ... | 19,959 | 35.489945 | 120 | py |
null | qimera-main/pytorchcv/models/diracnetv2.py | """
DiracNetV2 for ImageNet-1K, implemented in PyTorch.
Original paper: 'DiracNets: Training Very Deep Neural Networks Without Skip-Connections,'
https://arxiv.org/abs/1706.00388.
"""
__all__ = ['DiracNetV2', 'diracnet18v2', 'diracnet34v2']
import os
import torch.nn as nn
import torch.nn.init as init
cl... | 8,444 | 27.72449 | 115 | py |
null | qimera-main/pytorchcv/models/dla.py | """
DLA for ImageNet-1K, implemented in PyTorch.
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
import torch
import torch.nn as nn
import torch.nn... | 19,884 | 29.734158 | 120 | py |
null | qimera-main/pytorchcv/models/dpn.py | """
DPN for ImageNet-1K, implemented in PyTorch.
Original paper: 'Dual Path Networks,' https://arxiv.org/abs/1707.01629.
"""
__all__ = ['DPN', 'dpn68', 'dpn68b', 'dpn98', 'dpn107', 'dpn131']
import os
import torch
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1, DualPathSequenti... | 18,976 | 27.709531 | 115 | py |
null | qimera-main/pytorchcv/models/drn.py | """
DRN for ImageNet-1K, implemented in PyTorch.
Original paper: 'Dilated Residual Networks,' https://arxiv.org/abs/1705.09914.
"""
__all__ = ['DRN', 'drnc26', 'drnc42', 'drnc58', 'drnd22', 'drnd38', 'drnd54', 'drnd105']
import os
import torch.nn as nn
import torch.nn.init as init
class DRNConv(nn.Module):
... | 18,826 | 28.695584 | 118 | py |
null | qimera-main/pytorchcv/models/efficientnet.py | """
EfficientNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,'
https://arxiv.org/abs/1905.11946.
"""
__all__ = ['EfficientNet', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3',
'effi... | 29,350 | 34.925337 | 120 | py |
null | qimera-main/pytorchcv/models/espnetv2.py | """
ESPNetv2 for ImageNet-1K, implemented in PyTorch.
Original paper: 'ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network,'
https://arxiv.org/abs/1811.11431.
"""
__all__ = ['ESPNetv2', 'espnetv2_wd2', 'espnetv2_w1', 'espnetv2_w5d4', 'espnetv2_w3d2', 'espnetv2_w2']
... | 16,827 | 29.990792 | 118 | py |
null | qimera-main/pytorchcv/models/fbnet.py | """
FBNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search,'
https://arxiv.org/abs/1812.03443.
"""
__all__ = ['FBNet', 'fbnet_cb']
import os
import torch.nn as nn
import torch.nn.init as init
from .common... | 9,969 | 30.352201 | 116 | py |
null | qimera-main/pytorchcv/models/fcn8sd.py | """
FCN-8s(d) for image segmentation, implemented in PyTorch.
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', 'f... | 16,182 | 37.257683 | 120 | py |
null | qimera-main/pytorchcv/models/fishnet.py | """
FishNet for ImageNet-1K, implemented in PyTorch.
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', 'fishnet1... | 20,141 | 29.845329 | 115 | py |
null | qimera-main/pytorchcv/models/fractalnet_cifar.py | """
FractalNet for CIFAR, implemented in PyTorch.
Original paper: 'FractalNet: Ultra-Deep Neural Networks without Residuals,' https://arxiv.org/abs/1605.07648.
"""
__all__ = ['CIFARFractalNet', 'fractalnet_cifar10', 'fractalnet_cifar100']
import os
import numpy as np
import torch
import torch.nn as nn
import ... | 15,952 | 31.034137 | 115 | py |
null | qimera-main/pytorchcv/models/ibnbresnet.py | """
IBN(b)-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net,'
https://arxiv.org/abs/1807.09441.
"""
__all__ = ['IBNbResNet', 'ibnb_resnet50', 'ibnb_resnet101', 'ibnb_resnet152']
import os
import torch.nn as nn
import... | 11,999 | 29 | 115 | py |
null | qimera-main/pytorchcv/models/ibndensenet.py | """
IBN-DenseNet for ImageNet-1K, implemented in PyTorch.
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
impor... | 12,647 | 30.384615 | 115 | py |
null | qimera-main/pytorchcv/models/ibnresnet.py | """
IBN-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net,'
https://arxiv.org/abs/1807.09441.
"""
__all__ = ['IBNResNet', 'ibn_resnet50', 'ibn_resnet101', 'ibn_resnet152']
import os
import torch.nn as nn
import torch.... | 12,570 | 29.002387 | 115 | py |
null | qimera-main/pytorchcv/models/ibnresnext.py | """
IBN-ResNeXt for ImageNet-1K, implemented in PyTorch.
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
import torch.nn as... | 10,749 | 30.341108 | 118 | py |
null | qimera-main/pytorchcv/models/igcv3.py | """
IGCV3 for ImageNet-1K, implemented in PyTorch.
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 torch.nn as nn
import torch... | 9,829 | 30.709677 | 115 | py |
null | qimera-main/pytorchcv/models/inceptionresnetv2.py | """
InceptionResNetV2 for ImageNet-1K, implemented in PyTorch.
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
import torch.nn as nn
import torch.nn.ini... | 18,580 | 28.540541 | 117 | py |
null | qimera-main/pytorchcv/models/inceptionv3.py | """
InceptionV3 for ImageNet-1K, implemented in PyTorch.
Original paper: 'Rethinking the Inception Architecture for Computer Vision,'
https://arxiv.org/abs/1512.00567.
"""
__all__ = ['InceptionV3', 'inceptionv3']
import os
import torch
import torch.nn as nn
import torch.nn.init as init
from .common import... | 20,977 | 28.968571 | 115 | py |
null | qimera-main/pytorchcv/models/inceptionv4.py | """
InceptionV4 for ImageNet-1K, implemented in PyTorch.
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
import torch
import torch.nn as nn
import torch.nn.init as ... | 21,112 | 28.570028 | 115 | py |
null | qimera-main/pytorchcv/models/irevnet.py | """
i-RevNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'i-RevNet: Deep Invertible Networks,' https://arxiv.org/abs/1802.07088.
"""
__all__ = ['IRevNet', 'irevnet301', 'IRevDownscale', 'IRevSplitBlock', 'IRevMergeBlock']
import os
import torch
import torch.nn as nn
import torch.nn.init as init
f... | 15,151 | 29.796748 | 115 | py |
null | qimera-main/pytorchcv/models/isqrtcovresnet.py | """
iSQRT-COV-ResNet for ImageNet-1K, implemented in PyTorch.
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', 'isqrtcovre... | 15,872 | 33.885714 | 120 | py |
null | qimera-main/pytorchcv/models/menet.py | """
MENet for ImageNet-1K, implemented in PyTorch.
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_... | 15,917 | 31.956522 | 116 | py |
null | qimera-main/pytorchcv/models/mixnet.py | """
MixNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'MixConv: Mixed Depthwise Convolutional Kernels,' https://arxiv.org/abs/1907.09595.
"""
__all__ = ['MixNet', 'mixnet_s', 'mixnet_m', 'mixnet_l']
import os
import torch
import torch.nn as nn
import torch.nn.init as init
from .common import rou... | 20,562 | 33.386288 | 116 | py |
null | qimera-main/pytorchcv/models/mnasnet.py | """
MnasNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'MnasNet: Platform-Aware Neural Architecture Search for Mobile,' https://arxiv.org/abs/1807.11626.
"""
__all__ = ['MnasNet', 'mnasnet']
import os
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1_block, conv3x3_b... | 10,012 | 30.990415 | 118 | py |
null | qimera-main/pytorchcv/models/mobilenet.py | """
MobileNet & FD-MobileNet for ImageNet-1K, implemented in PyTorch.
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.or... | 11,801 | 32.816619 | 119 | py |
null | qimera-main/pytorchcv/models/mobilenet_cub.py | """
MobileNet & FD-MobileNet for CUB-200-2011, implemented in torch.
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.org... | 7,540 | 35.08134 | 120 | py |
null | qimera-main/pytorchcv/models/mobilenetv2.py | """
MobileNetV2 for ImageNet-1K, implemented in PyTorch.
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 torch.nn as nn
import ... | 9,653 | 31.18 | 118 | py |
null | qimera-main/pytorchcv/models/mobilenetv3.py | """
MobileNetV3 for ImageNet-1K, implemented in PyTorch.
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', 'mobil... | 18,988 | 33.152878 | 118 | py |
null | qimera-main/pytorchcv/models/model_store.py | """
Model store which provides pretrained models.
"""
__all__ = ['get_model_file', 'load_model', 'download_model', 'calc_num_params']
import os
import zipfile
import logging
import hashlib
_model_sha1 = {name: (error, checksum, repo_release_tag) for name, error, checksum, repo_release_tag in [
('alexnet', '1... | 49,720 | 68.734923 | 115 | py |
null | qimera-main/pytorchcv/models/msdnet.py | """
MSDNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Multi-Scale Dense Networks for Resource Efficient Image Classification,'
https://arxiv.org/abs/1703.09844.
"""
__all__ = ['MSDNet', 'msdnet22', 'MultiOutputSequential', 'MSDFeatureBlock']
import os
import math
import torch
import torch.n... | 19,529 | 30.65316 | 120 | py |
null | qimera-main/pytorchcv/models/msdnet_cifar10.py | """
MSDNet for CIFAR-10, implemented in PyTorch.
Original paper: 'Multi-Scale Dense Networks for Resource Efficient Image Classification,'
https://arxiv.org/abs/1703.09844.
"""
__all__ = ['CIFAR10MSDNet', 'msdnet22_cifar10']
import os
import math
import torch.nn as nn
import torch.nn.init as init
from .co... | 10,172 | 30.691589 | 120 | py |
null | qimera-main/pytorchcv/models/nasnet.py | """
NASNet-A for ImageNet-1K, implemented in PyTorch.
Original paper: 'Learning Transferable Architectures for Scalable Image Recognition,'
https://arxiv.org/abs/1707.07012.
"""
__all__ = ['NASNet', 'nasnet_4a1056', 'nasnet_6a4032', 'nasnet_dual_path_sequential']
import os
import torch
import torch.nn as ... | 38,586 | 28.500765 | 119 | py |
null | qimera-main/pytorchcv/models/nin_cifar.py | """
NIN for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Network In Network,' https://arxiv.org/abs/1312.4400.
"""
__all__ = ['CIFARNIN', 'nin_cifar10', 'nin_cifar100', 'nin_svhn']
import os
import torch.nn as nn
import torch.nn.init as init
class NINConv(nn.Module):
"""
NIN specific convolu... | 8,048 | 29.957692 | 115 | py |
null | qimera-main/pytorchcv/models/ntsnet_cub.py | """
NTS-Net for CUB-200-2011, implemented in PyTorch.
Original paper: 'Learning to Navigate for Fine-grained Classification,' https://arxiv.org/abs/1809.00287.
"""
__all__ = ['NTSNet', 'ntsnet_cub']
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn... | 14,017 | 32.535885 | 115 | py |
null | qimera-main/pytorchcv/models/octresnet.py | """
Oct-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave
Convolution,' https://arxiv.org/abs/1904.05049.
"""
__all__ = ['OctResNet', 'octresnet10_ad2', 'octresnet50b_ad2', 'OctResUnit']
import os
from ... | 27,931 | 32.612515 | 119 | py |
null | qimera-main/pytorchcv/models/peleenet.py | """
PeleeNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Pelee: A Real-Time Object Detection System on Mobile Devices,' https://arxiv.org/abs/1804.06882.
"""
__all__ = ['PeleeNet', 'peleenet']
import os
import torch
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1_b... | 10,827 | 27.419948 | 117 | py |
null | qimera-main/pytorchcv/models/pnasnet.py | """
PNASNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Progressive Neural Architecture Search,' https://arxiv.org/abs/1712.00559.
"""
__all__ = ['PNASNet', 'pnasnet5large']
import os
import torch
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1
from .nasnet import... | 18,176 | 28.945634 | 118 | py |
null | qimera-main/pytorchcv/models/polynet.py | """
PolyNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'PolyNet: A Pursuit of Structural Diversity in Very Deep Networks,'
https://arxiv.org/abs/1611.05725.
"""
__all__ = ['PolyNet', 'polynet']
import os
import torch.nn as nn
import torch.nn.init as init
from .common import ConvBlock, conv1x... | 28,281 | 28.928042 | 119 | py |
null | qimera-main/pytorchcv/models/preresnet.py | """
PreResNet for ImageNet-1K, implemented in PyTorch.
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', 'pre... | 25,857 | 32.024266 | 120 | py |
null | qimera-main/pytorchcv/models/preresnet_cifar.py | """
PreResNet for CIFAR/SVHN, implemented in PyTorch.
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', '... | 24,611 | 35.789238 | 120 | py |
null | qimera-main/pytorchcv/models/proxylessnas.py | """
ProxylessNAS for ImageNet-1K, implemented in PyTorch.
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',
... | 14,555 | 33.492891 | 118 | py |
null | qimera-main/pytorchcv/models/proxylessnas_cub.py | """
ProxylessNAS for CUB-200-2011, implemented in Gluon.
Original paper: 'ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware,'
https://arxiv.org/abs/1812.00332.
"""
__all__ = ['proxylessnas_cpu_cub', 'proxylessnas_gpu_cub', 'proxylessnas_mobile_cub', 'proxylessnas_mobile14_cub']
f... | 4,155 | 32.788618 | 120 | py |
null | qimera-main/pytorchcv/models/pspnet.py | """
PSPNet for image segmentation, implemented in PyTorch.
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', '... | 18,442 | 35.886 | 120 | py |
null | qimera-main/pytorchcv/models/pyramidnet.py | """
PyramidNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Deep Pyramidal Residual Networks,' https://arxiv.org/abs/1610.02915.
"""
__all__ = ['PyramidNet', 'pyramidnet101_a360', 'PyrUnit']
import os
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from .common ... | 11,038 | 28.126649 | 115 | py |
null | qimera-main/pytorchcv/models/pyramidnet_cifar.py | """
PyramidNet for CIFAR/SVHN, implemented in PyTorch.
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', 'pyramidnet... | 23,823 | 32.413745 | 120 | py |
null | qimera-main/pytorchcv/models/resattnet.py | """
ResAttNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Residual Attention Network for Image Classification,' https://arxiv.org/abs/1704.06904.
"""
__all__ = ['ResAttNet', 'resattnet56', 'resattnet92', 'resattnet128', 'resattnet164', 'resattnet200', 'resattnet236',
'resattnet452']
i... | 20,035 | 28.464706 | 117 | py |
null | qimera-main/pytorchcv/models/resdropresnet_cifar.py | """
ResDrop-ResNet for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Deep Networks with Stochastic Depth,' https://arxiv.org/abs/1603.09382.
"""
__all__ = ['CIFARResDropResNet', 'resdropresnet20_cifar10', 'resdropresnet20_cifar100', 'resdropresnet20_svhn']
import os
import torch
import torch.nn as nn
i... | 9,918 | 31.735974 | 119 | py |
null | qimera-main/pytorchcv/models/resnet.py | """
ResNet for ImageNet-1K, implemented in PyTorch.
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', '... | 24,823 | 31.620237 | 120 | py |
null | qimera-main/pytorchcv/models/resnet_cifar.py | """
ResNet for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Deep Residual Learning for Image Recognition,' https://arxiv.org/abs/1512.03385.
"""
__all__ = ['CIFARResNet', 'resnet20_cifar10', 'resnet20_cifar100', 'resnet20_svhn',
'resnet56_cifar10', 'resnet56_cifar100', 'resnet56_svhn',
... | 23,994 | 35.137048 | 120 | py |
null | qimera-main/pytorchcv/models/resnet_cub.py | """
ResNet for CUB-200-2011, implemented in PyTorch.
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', ... | 14,148 | 35.094388 | 117 | py |
null | qimera-main/pytorchcv/models/resnetd.py | """
ResNet(D) with dilation for ImageNet-1K, implemented in PyTorch.
Original paper: 'Deep Residual Learning for Image Recognition,' https://arxiv.org/abs/1512.03385.
"""
__all__ = ['ResNetD', 'resnetd50b', 'resnetd101b', 'resnetd152b']
import os
import torch.nn as nn
import torch.nn.init as init
from .common... | 9,661 | 32.202749 | 120 | py |
null | qimera-main/pytorchcv/models/resnext.py | """
ResNeXt for ImageNet-1K, implemented in PyTorch.
Original paper: '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',
'resnext26_32... | 14,857 | 31.090713 | 119 | py |
null | qimera-main/pytorchcv/models/resnext_cifar.py | """
ResNeXt for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Aggregated Residual Transformations for Deep Neural Networks,' http://arxiv.org/abs/1611.05431.
"""
__all__ = ['CIFARResNeXt', 'resnext20_16x4d_cifar10', 'resnext20_16x4d_cifar100', 'resnext20_16x4d_svhn',
'resnext20_32x2d_cifar10'... | 23,083 | 37.092409 | 116 | py |
null | qimera-main/pytorchcv/models/revnet.py | """
RevNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'The Reversible Residual Network: Backpropagation Without Storing Activations,'
https://arxiv.org/abs/1707.04585.
"""
__all__ = ['RevNet', 'revnet38', 'revnet110', 'revnet164']
import os
from contextlib import contextmanager
import torch
... | 15,590 | 28.142056 | 115 | py |
null | qimera-main/pytorchcv/models/rir_cifar.py | """
RiR for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Resnet in Resnet: Generalizing Residual Architectures,' https://arxiv.org/abs/1603.08029.
"""
__all__ = ['CIFARRiR', 'rir_cifar10', 'rir_cifar100', 'rir_svhn', 'RiRFinalBlock']
import os
import torch
import torch.nn as nn
import torch.nn.init as... | 10,658 | 29.454286 | 119 | py |
null | qimera-main/pytorchcv/models/ror_cifar.py | """
RoR-3 for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Residual Networks of Residual Networks: Multilevel Residual Networks,'
https://arxiv.org/abs/1608.02908.
"""
__all__ = ['CIFARRoR', 'ror3_56_cifar10', 'ror3_56_cifar100', 'ror3_56_svhn', 'ror3_110_cifar10', 'ror3_110_cifar100',
'... | 16,718 | 31.401163 | 118 | py |
null | qimera-main/pytorchcv/models/senet.py | """
SENet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['SENet', 'senet16', 'senet28', 'senet40', 'senet52', 'senet103', 'senet154', 'SEInitBlock']
import os
import math
import torch.nn as nn
import torch.nn.init as... | 13,095 | 28.696145 | 115 | py |
null | qimera-main/pytorchcv/models/sepreresnet.py | """
SE-PreResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['SEPreResNet', 'sepreresnet10', 'sepreresnet12', 'sepreresnet14', 'sepreresnet16', 'sepreresnet18',
'sepreresnet26', 'sepreresnetbc26b', 'sepr... | 18,420 | 32.371377 | 119 | py |
null | qimera-main/pytorchcv/models/sepreresnet_cifar.py | """
SE-PreResNet for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['CIFARSEPreResNet', 'sepreresnet20_cifar10', 'sepreresnet20_cifar100', 'sepreresnet20_svhn',
'sepreresnet56_cifar10', 'sepreresnet56_cifar100',... | 24,663 | 37.298137 | 119 | py |
null | qimera-main/pytorchcv/models/seresnet.py | """
SE-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['SEResNet', 'seresnet10', 'seresnet12', 'seresnet14', 'seresnet16', 'seresnet18', 'seresnet26',
'seresnetbc26b', 'seresnet34', 'seresnetbc38b', '... | 18,211 | 31.579606 | 118 | py |
null | qimera-main/pytorchcv/models/seresnet_cifar.py | """
SE-ResNet for CIFAR/SVHN, implemented in PyTorch.
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',
... | 24,036 | 36.324534 | 120 | py |
null | qimera-main/pytorchcv/models/seresnet_cub.py | """
SE-ResNet for CUB-200-2011, implemented in PyTorch.
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_... | 14,391 | 35.808184 | 120 | py |
null | qimera-main/pytorchcv/models/seresnext.py | """
SE-ResNeXt for ImageNet-1K, implemented in PyTorch.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['SEResNeXt', 'seresnext50_32x4d', 'seresnext101_32x4d', 'seresnext101_64x4d']
import os
import torch.nn as nn
import torch.nn.init as init
from .common im... | 8,721 | 29.929078 | 115 | py |
null | qimera-main/pytorchcv/models/shakedropresnet_cifar.py | """
ShakeDrop-ResNet for CIFAR/SVHN, implemented in PyTorch.
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 tor... | 10,750 | 31.677812 | 119 | py |
null | qimera-main/pytorchcv/models/shakeshakeresnet_cifar.py | """
Shake-Shake-ResNet for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Shake-Shake regularization,' https://arxiv.org/abs/1705.07485.
"""
__all__ = ['CIFARShakeShakeResNet', 'shakeshakeresnet20_2x16d_cifar10', 'shakeshakeresnet20_2x16d_cifar100',
'shakeshakeresnet20_2x16d_svhn', 'shakeshake... | 14,392 | 33.269048 | 120 | py |
null | qimera-main/pytorchcv/models/sharesnet.py | """
ShaResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'ShaResNet: reducing residual network parameter number by sharing weights,'
https://arxiv.org/abs/1702.08782.
"""
__all__ = ['ShaResNet', 'sharesnet18', 'sharesnet34', 'sharesnet50', 'sharesnet50b', 'sharesnet101', 'sharesnet101b',
... | 19,841 | 31.263415 | 117 | py |
null | qimera-main/pytorchcv/models/shufflenet.py | """
ShuffleNet for ImageNet-1K, implemented in PyTorch.
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',
... | 15,779 | 31.875 | 120 | py |
null | qimera-main/pytorchcv/models/shufflenetv2.py | """
ShuffleNet V2 for ImageNet-1K, implemented in PyTorch.
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
... | 11,722 | 30.942779 | 115 | py |
null | qimera-main/pytorchcv/models/shufflenetv2b.py | """
ShuffleNet V2 for ImageNet-1K, implemented in PyTorch. 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', '... | 12,431 | 30.553299 | 115 | py |
null | qimera-main/pytorchcv/models/sknet.py | """
SKNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Selective Kernel Networks,' https://arxiv.org/abs/1903.06586.
"""
__all__ = ['SKNet', 'sknet50', 'sknet101', 'sknet152']
import os
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1, conv1x1_block, conv3x3_block, C... | 10,909 | 28.486486 | 115 | py |
null | qimera-main/pytorchcv/models/sparsenet.py | """
SparseNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Sparsely Aggregated Convolutional Networks,' https://arxiv.org/abs/1801.05895.
"""
__all__ = ['SparseNet', 'sparsenet121', 'sparsenet161', 'sparsenet169', 'sparsenet201', 'sparsenet264']
import os
import math
import torch
import torch.nn ... | 11,645 | 29.566929 | 115 | py |
null | qimera-main/pytorchcv/models/spnasnet.py | """
Single-Path NASNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours,'
https://arxiv.org/abs/1904.02877.
"""
__all__ = ['SPNASNet', 'spnasnet']
import os
import torch.nn as nn
import torch.nn.init as init
from .common ... | 10,388 | 30.10479 | 115 | py |
null | qimera-main/pytorchcv/models/squeezenet.py | """
SqueezeNet for ImageNet-1K, implemented in PyTorch.
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']
imp... | 12,164 | 30.929134 | 118 | py |
null | qimera-main/pytorchcv/models/squeezenext.py | """
SqueezeNext for ImageNet-1K, implemented in PyTorch.
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 torch.nn ... | 12,238 | 30.543814 | 119 | py |
null | qimera-main/pytorchcv/models/superpointnet.py | """
SuperPointNet for HPatches (image matching), implemented in PyTorch.
Original paper: 'SuperPoint: Self-Supervised Interest Point Detection and Description,'
https://arxiv.org/abs/1712.07629.
"""
__all__ = ['SuperPointNet', 'superpointnet']
import os
import torch
import torch.nn as nn
import torch.nn.i... | 11,418 | 31.719198 | 115 | py |
null | qimera-main/pytorchcv/models/vgg.py | """
VGG for ImageNet-1K, implemented in PyTorch.
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_vgg13... | 13,528 | 29.678005 | 117 | py |