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/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/jasperdr.py | """
Jasper DR (Dense Residual) for ASR, implemented in PyTorch.
Original paper: 'Jasper: An End-to-End Convolutional Neural Acoustic Model,' https://arxiv.org/abs/1904.03288.
"""
__all__ = ['jasperdr10x5_en', 'jasperdr10x5_en_nr']
from .jasper import get_jasper
def jasperdr10x5_en(num_classes=29, **kwargs):... | 2,982 | 30.4 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/segnet.py | """
SegNet for image segmentation, implemented in PyTorch.
Original paper: 'SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,'
https://arxiv.org/abs/1511.00561.
"""
__all__ = ['SegNet', 'segnet_cityscapes']
import os
import torch
import torch.nn as nn
from .common import co... | 7,072 | 31.74537 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/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... | 21,944 | 37.840708 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/fpenet.py | """
FPENet for image segmentation, implemented in PyTorch.
Original paper: 'Feature Pyramid Encoding Network for Real-time Semantic Segmentation,'
https://arxiv.org/abs/1909.08599.
"""
__all__ = ['FPENet', 'fpenet_cityscapes']
import os
import torch
import torch.nn as nn
from .common import conv1x1, conv1... | 12,630 | 28.511682 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/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, caption, paper, ds, img_size, scale, batch, rem) for
... | 94,745 | 110.465882 | 205 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/tresnet.py | """
TResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'TResNet: High Performance GPU-Dedicated Architecture,' https://arxiv.org/abs/2003.13630.
NB: Not tested!
"""
__all__ = ['TResNet', 'tresnet_m', 'tresnet_l', 'tresnet_xl']
import os
import torch
import torch.nn as nn
import torch.nn.fun... | 15,627 | 28.542533 | 117 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/fastseresnet.py | """
Fast-SE-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['FastSEResNet', 'fastseresnet101b']
import os
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1_block, SEBlock
from .resn... | 9,345 | 30.049834 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/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',
... | 23,882 | 35.131619 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/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 ... | 38,588 | 28.502294 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/zfnet.py | """
ZFNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Visualizing and Understanding Convolutional Networks,' https://arxiv.org/abs/1311.2901.
"""
__all__ = ['zfnet', 'zfnetb']
import os
from .alexnet import AlexNet
def get_zfnet(version="a",
model_name=None,
pretrai... | 3,659 | 26.727273 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/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... | 10,823 | 27.710875 | 117 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/erfnet.py | """
ERFNet for image segmentation, implemented in PyTorch.
Original paper: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation,'
http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf.
"""
__all__ = ['ERFNet', 'erfnet_cityscapes', 'FCU']
import os
import to... | 9,330 | 31.175862 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/ibppose_coco.py | """
IBPPose for COCO Keypoint, implemented in PyTorch.
Original paper: 'Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation,'
https://arxiv.org/abs/1911.10529.
"""
__all__ = ['IbpPose', 'ibppose_coco']
import os
import torch
from torch import nn
from .common import ... | 17,476 | 28.521959 | 117 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/xception.py | """
Xception for ImageNet-1K, implemented in PyTorch.
Original paper: 'Xception: Deep Learning with Depthwise Separable Convolutions,' https://arxiv.org/abs/1610.02357.
"""
__all__ = ['Xception', 'xception']
import os
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1_block, conv3x... | 11,572 | 27.717122 | 118 | py |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/mobilenet.py | """
MobileNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,'
https://arxiv.org/abs/1704.04861.
"""
__all__ = ['MobileNet', 'mobilenet_w1', 'mobilenet_w3d4', 'mobilenet_wd2', 'mobilenet_wd4', 'get_mobilenet']
im... | 8,480 | 32.521739 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/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... | 10,908 | 28.563686 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/fastscnn.py | """
Fast-SCNN for image segmentation, implemented in PyTorch.
Original paper: 'Fast-SCNN: Fast Semantic Segmentation Network,' https://arxiv.org/abs/1902.04502.
"""
__all__ = ['FastSCNN', 'fastscnn_cityscapes']
import os
import torch.nn as nn
from .common import conv1x1, conv1x1_block, conv3x3_block, dwconv3x... | 15,264 | 28.814453 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/esnet.py | """
ESNet for image segmentation, implemented in PyTorch.
Original paper: 'ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation,'
https://arxiv.org/abs/1906.09826.
"""
__all__ = ['ESNet', 'esnet_cityscapes']
import os
import torch
import torch.nn as nn
from .common import AsymConvBloc... | 10,912 | 31.002933 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/enet.py | """
ENet for image segmentation, implemented in PyTorch.
Original paper: 'ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation,'
https://arxiv.org/abs/1606.02147.
"""
__all__ = ['ENet', 'enet_cityscapes', 'ENetMixDownBlock']
import os
import torch
import torch.nn as nn
import torch... | 18,480 | 31.14087 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/contextnet.py | """
ContextNet for image segmentation, implemented in PyTorch.
Original paper: 'ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time,'
https://arxiv.org/abs/1805.04554.
"""
__all__ = ['ContextNet', 'ctxnet_cityscapes']
import os
import torch
import torch.nn as nn
from .common im... | 12,923 | 28.239819 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/dicenet.py | """
DiCENet for ImageNet-1K, implemented in PyTorch.
Original paper: 'DiCENet: Dimension-wise Convolutions for Efficient Networks,' https://arxiv.org/abs/1906.03516.
"""
__all__ = ['DiceNet', 'dicenet_wd5', 'dicenet_wd2', 'dicenet_w3d4', 'dicenet_w1', 'dicenet_w5d4', 'dicenet_w3d2',
'dicenet_w7d8', ... | 23,378 | 29.441406 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/nvpattexp.py | """
Neural Voice Puppetry Audio-to-Expression net for speech-driven facial animation, implemented in PyTorch.
Original paper: 'Neural Voice Puppetry: Audio-driven Facial Reenactment,' https://arxiv.org/abs/1912.05566.
"""
__all__ = ['NvpAttExp', 'nvpattexp116bazel76']
import os
import torch
import torch.nn as... | 8,810 | 31.754647 | 116 | py |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/prnet.py | """
PRNet for AFLW2000-3D, implemented in PyTorch.
Original paper: 'Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network,'
https://arxiv.org/abs/1803.07835.
"""
__all__ = ['PRNet', 'prnet']
import os
import torch.nn as nn
from .common import ConvBlock, DeconvBlock, conv1x1... | 13,924 | 30.362613 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/pfpcnet.py | """
PFPCNet for 3D face reconstruction, implemented in PyTorch.
Original paper: 'Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks,'
https://arxiv.org/abs/1609.06536.
"""
__all__ = ['PFPCNet', 'pfpcnet']
import os
import torch.nn as nn
import torch.nn.init as init
from .... | 5,314 | 28.859551 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/espcnet.py | """
ESPNet-C for image segmentation, implemented in PyTorch.
Original paper: 'ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation,'
https://arxiv.org/abs/1803.06815.
"""
__all__ = ['ESPCNet', 'espcnet_cityscapes', 'ESPBlock']
import os
import torch
import torch.nn as nn
fro... | 12,104 | 29.2625 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/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 |
imgclsmob | imgclsmob-master/pytorch/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... | 7,269 | 34.990099 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/wrn.py | """
WRN for ImageNet-1K, implemented in PyTorch.
Original paper: 'Wide Residual Networks,' https://arxiv.org/abs/1605.07146.
"""
__all__ = ['WRN', 'wrn50_2']
import os
import torch.nn as nn
import torch.nn.init as init
class WRNConv(nn.Module):
"""
WRN specific convolution block.
Parameters:
... | 11,401 | 26.474699 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/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', 'MaxPoolBranch', 'AvgPoolBranch', 'Conv1x1Branch', 'ConvSeqBranch']
import os
import torch
im... | 21,472 | 29.807747 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/fdmobilenet.py | """
FD-MobileNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy,'
https://arxiv.org/abs/1802.03750.
"""
__all__ = ['fdmobilenet_w1', 'fdmobilenet_w3d4', 'fdmobilenet_wd2', 'fdmobilenet_wd4', 'get_fdmobilenet']
import os
from .mo... | 4,771 | 29.394904 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/_inceptionresnetv1_.py | __all__ = ['inceptionresnetv1']
import torch
from torch import nn
from common import conv1x1, ConvBlock, conv1x1_block, conv3x3_block, Concurrent
class MaxPoolBranch(nn.Module):
"""
InceptionResNetV2 specific max pooling branch block.
"""
def __init__(self):
super(MaxPoolBranch, self).__init_... | 15,341 | 28.334608 | 108 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/oth_vit.py | from functools import partial
import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self,
dim,
num_heads=8,
qkv_bias=False,
qk_scale=None,
attn_drop=0.,
proj_drop=0.):
super().__init... | 9,413 | 31.129693 | 118 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/_espnet.py | """
ESPNet for image segmentation, implemented in PyTorch.
Original paper: 'ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation,'
https://arxiv.org/abs/1803.06815.
"""
__all__ = ['ESPNet', 'espnet_cityscapes']
import os
import torch
import torch.nn as nn
from common import ... | 8,299 | 29.181818 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/oth_espnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class CBR(nn.Module):
'''
This class defines the convolution layer with batch normalization and PReLU activation
'''
def __init__(self, nIn, nOut, kSize, stride=1):
'''
:param nIn: number of input channels
:para... | 15,567 | 33.90583 | 151 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/oth_quartznet.py | __all__ = ['oth_quartznet5x5_en_ls', 'oth_quartznet15x5_en', 'oth_quartznet15x5_en_nr', 'oth_quartznet15x5_fr',
'oth_quartznet15x5_de', 'oth_quartznet15x5_it', 'oth_quartznet15x5_es', 'oth_quartznet15x5_ca',
'oth_quartznet15x5_pl', 'oth_quartznet15x5_ru', 'oth_jasperdr10x5_en', 'oth_jasperdr10x5_e... | 11,139 | 34.253165 | 111 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/oth_inception_resnet_v1.py | __all__ = ['oth_inceptionresnetv1']
import torch
from torch import nn
class BasicConv2d(nn.Module):
def __init__(self,
in_planes,
out_planes,
kernel_size,
stride,
padding=0):
super().__init__()
self.conv = nn.Co... | 9,240 | 28.336508 | 97 | py |
imgclsmob | imgclsmob-master/keras_/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='kerascv',
version='0.0.40',
description='Image classification models ... | 1,280 | 36.676471 | 119 | py |
imgclsmob | imgclsmob-master/keras_/utils.py | import math
import logging
import os
from keras import backend as K
from keras.utils.np_utils import to_categorical
import mxnet as mx
from keras_.kerascv.model_provider import get_model
def prepare_ke_context(num_gpus,
batch_size):
batch_size *= max(1, num_gpus)
return batch_size
d... | 4,497 | 28.592105 | 91 | py |
imgclsmob | imgclsmob-master/keras_/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/keras_/kerascv/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/keras_/kerascv/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... | 8,493 | 29.335714 | 62 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/shufflenetv2.py | """
ShuffleNet V2 for ImageNet-1K, implemented in Keras.
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... | 11,732 | 29.396373 | 115 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/igcv3.py | """
IGCV3 for ImageNet-1K, implemented in Keras.
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 keras import layers as nn
from ... | 9,422 | 29.495146 | 117 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/preresnet.py | """
PreResNet for ImageNet-1K, implemented in Keras.
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', 'prere... | 26,177 | 31.398515 | 120 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/shufflenetv2b.py | """
ShuffleNet V2 for ImageNet-1K, implemented in Keras. 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', 'sh... | 11,952 | 27.941889 | 115 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/menet.py | """
MENet for ImageNet-1K, implemented in Keras.
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... | 15,495 | 30.054108 | 116 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/efficientnet.py | """
EfficientNet for ImageNet-1K, implemented in Keras.
Original paper: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,'
https://arxiv.org/abs/1905.11946.
"""
__all__ = ['efficientnet_model', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3',
'... | 29,565 | 34.366029 | 120 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/squeezenext.py | """
SqueezeNext for ImageNet-1K, implemented in Keras.
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 keras import ... | 11,973 | 29.390863 | 119 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/resnet.py | """
ResNet for ImageNet-1K, implemented in Keras.
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... | 24,153 | 30.698163 | 118 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/mobilenetv2.py | """
MobileNetV2 for ImageNet-1K, implemented in Keras.
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
from keras import layers as nn
... | 9,328 | 30.305369 | 118 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/squeezenet.py | """
SqueezeNet for ImageNet-1K, implemented in Keras.
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... | 12,000 | 29.693095 | 118 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/vgg.py | """
VGG for ImageNet-1K, implemented in Keras.
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'... | 13,419 | 29.639269 | 117 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/mnasnet.py | """
MnasNet for ImageNet-1K, implemented in Keras.
Original paper: 'MnasNet: Platform-Aware Neural Architecture Search for Mobile,' https://arxiv.org/abs/1807.11626.
"""
__all__ = ['mnasnet_model', 'mnasnet_b1', 'mnasnet_a1', 'mnasnet_small']
import os
from keras import layers as nn
from keras.models import M... | 14,240 | 31.439636 | 118 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/seresnet.py | """
SE-ResNet for ImageNet-1K, implemented in Keras.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['seresnet', 'seresnet10', 'seresnet12', 'seresnet14', 'seresnet16', 'seresnet18', 'seresnet26',
'seresnetbc26b', 'seresnet34', 'seresnetbc38b', 'se... | 17,838 | 31.02693 | 118 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/densenet.py | """
DenseNet for ImageNet-1K, implemented in Keras.
Original paper: 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
"""
__all__ = ['densenet', 'densenet121', 'densenet161', 'densenet169', 'densenet201']
import os
from keras import layers as nn
from keras.models import Model
from ... | 9,837 | 28.722054 | 116 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/seresnext.py | """
SE-ResNeXt for ImageNet-1K, implemented in Keras.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['seresnext', 'seresnext50_32x4d', 'seresnext101_32x4d', 'seresnext101_64x4d']
import os
from keras import layers as nn
from keras.models import Model
from .... | 8,382 | 29.046595 | 115 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/mobilenetv3.py | """
MobileNetV3 for ImageNet-1K, implemented in Keras.
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', 'mobilen... | 18,859 | 32.204225 | 115 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/sepreresnet.py | """
SE-PreResNet for ImageNet-1K, implemented in Keras.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['sepreresnet', 'sepreresnet10', 'sepreresnet12', 'sepreresnet14', 'sepreresnet16', 'sepreresnet18',
'sepreresnet26', 'sepreresnetbc26b', 'seprer... | 18,104 | 31.739602 | 119 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/resnext.py | """
ResNeXt for ImageNet-1K, implemented in Keras.
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_32x4... | 14,656 | 30.45279 | 119 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/senet.py | """
SENet for ImageNet-1K, implemented in Keras.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['senet', 'senet16', 'senet28', 'senet40', 'senet52', 'senet103', 'senet154']
import os
import math
from keras import layers as nn
from keras.models import Model
... | 13,026 | 27.381264 | 115 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/shufflenet.py | """
ShuffleNet for ImageNet-1K, implemented in Keras.
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',
... | 15,527 | 30.689796 | 120 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/common.py | """
Common routines for models in Keras.
"""
__all__ = ['round_channels', 'HSwish', 'is_channels_first', 'get_channel_axis', 'update_keras_shape', 'flatten',
'batchnorm', 'lrn', 'maxpool2d', 'avgpool2d', 'conv2d', 'conv1x1', 'conv3x3', 'depthwise_conv3x3',
'conv_block', 'conv1x1_block', 'conv... | 47,316 | 29.44852 | 120 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/model_store.py | """
Model store which provides pretrained models.
"""
__all__ = ['get_model_file', 'load_model', 'download_model']
import os
import zipfile
import logging
import hashlib
import warnings
import numpy as np
import h5py
from keras import backend as K
from keras.engine.saving import load_attributes_from_hdf5_group
_... | 27,367 | 49.869888 | 116 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/zfnet.py | """
ZFNet for ImageNet-1K, implemented in Keras.
Original paper: 'Visualizing and Understanding Convolutional Networks,' https://arxiv.org/abs/1311.2901.
"""
__all__ = ['zfnet', 'zfnetb']
import os
from .common import is_channels_first
from .alexnet import alexnet_model
def get_zfnet(version="a",
... | 3,608 | 27.872 | 115 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/__init__.py | 0 | 0 | 0 | py | |
imgclsmob | imgclsmob-master/keras_/kerascv/models/darknet53.py | """
DarkNet-53 for ImageNet-1K, implemented in Keras.
Original source: 'YOLOv3: An Incremental Improvement,' https://arxiv.org/abs/1804.02767.
"""
__all__ = ['darknet53_model', 'darknet53']
import os
from keras import layers as nn
from keras.models import Model
from .common import conv1x1_block, conv3x3_block... | 6,500 | 28.684932 | 115 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/mobilenet.py | """
MobileNet & FD-MobileNet for ImageNet-1K, implemented in Keras.
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/... | 11,133 | 32.136905 | 119 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/darknet.py | """
DarkNet for ImageNet-1K, implemented in Keras.
Original source: 'Darknet: Open source neural networks in c,' https://github.com/pjreddie/darknet.
"""
__all__ = ['darknet', 'darknet_ref', 'darknet_tiny', 'darknet19']
import os
from keras import layers as nn
from keras.models import Model
from .common impor... | 8,104 | 29.935115 | 116 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/alexnet.py | """
AlexNet for ImageNet-1K, implemented in Keras.
Original paper: 'One weird trick for parallelizing convolutional neural networks,'
https://arxiv.org/abs/1404.5997.
"""
__all__ = ['alexnet_model', 'alexnet', 'alexnetb']
import os
from keras import layers as nn
from keras.models import Model
from .common... | 9,197 | 27.301538 | 115 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/others/__init__.py | 0 | 0 | 0 | py | |
ZOC | ZOC-main/cifarplus_eval.py | import argparse
import torch
from transformers import BertGenerationTokenizer, BertGenerationDecoder, BertGenerationConfig
import os
from dataloaders.ZO_Clip_loaders import cifarplus_loader
from clip.simple_tokenizer import SimpleTokenizer as clip_tokenizer
from tqdm import tqdm
import copy
import numpy as np
from skle... | 7,866 | 46.969512 | 122 | py |
ZOC | ZOC-main/cifar10_eval.py | import argparse
import torch
import os
from tqdm import tqdm
import numpy as np
from transformers import BertGenerationTokenizer, BertGenerationDecoder, BertGenerationConfig
from dataloaders.ZO_Clip_loaders import cifar10_single_isolated_class_loader
from clip.simple_tokenizer import SimpleTokenizer as clip_tokenizer
f... | 6,916 | 51.007519 | 122 | py |
ZOC | ZOC-main/tinyimagenet_eval.py | import argparse
import torch
from transformers import BertGenerationTokenizer, BertGenerationDecoder, BertGenerationConfig
import os
from dataloaders.ZO_Clip_loaders import tinyimage_single_isolated_class_loader
from clip.simple_tokenizer import SimpleTokenizer as clip_tokenizer
from tqdm import tqdm
import numpy as np... | 5,965 | 44.892308 | 122 | py |
ZOC | ZOC-main/train_decoder.py | import argparse
import torch
from transformers import BertGenerationTokenizer, BertGenerationDecoder, BertGenerationConfig
import os
from dataloaders.coco_full_loader import get_loader
from clip.simple_tokenizer import SimpleTokenizer as clip_tokenizer
from transformers import AdamW
from tqdm import tqdm
def train_de... | 5,162 | 45.513514 | 110 | py |
ZOC | ZOC-main/cifar100_eval.py | import argparse
import torch
from transformers import BertGenerationTokenizer, BertGenerationDecoder, BertGenerationConfig
import os
from dataloaders.ZO_Clip_loaders import cifar100_single_isolated_class_loader
from clip.simple_tokenizer import SimpleTokenizer as clip_tokenizer
from tqdm import tqdm
import numpy as np
... | 6,168 | 45.037313 | 144 | py |
ZOC | ZOC-main/clip/__init__.py | from .clip import *
| 20 | 9.5 | 19 | py |
ZOC | ZOC-main/dataloaders/ZO_Clip_loaders.py | from torch.utils.data import DataLoader, Dataset
import numpy as np
import os
from torchvision.datasets import CIFAR10, CIFAR100
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize, ToPILImage
from PIL import Image
from torchvision.datasets import ImageFolder
import glob
class cifar10_... | 12,638 | 46.515038 | 122 | py |
ZOC | ZOC-main/dataloaders/coco_full_loader.py | from torch.utils.data import DataLoader, TensorDataset
import torch
import numpy as np
import os
from torchvision.datasets import CocoDetection
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from PIL import Image
from tqdm import tqdm
from transformers import BertGenerationTokenizer... | 6,133 | 43.129496 | 137 | py |
ZOC | ZOC-main/dataloaders/__init__.py | 0 | 0 | 0 | py | |
cppflow | cppflow-master/examples/load_frozen_graph/create_model.py | #!/usr/bin/env python
"""
Example for a load frozen tf graph functionality.
"""
# MIT License
#
# Copyright (c) 2021 Daisuke Kato
# Copyright (c) 2021 Paul
# Copyright (c) 2022 Sergio Izquierdo
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated docume... | 2,376 | 36.140625 | 79 | py |
cppflow | cppflow-master/examples/load_model/create_model.py | #!/usr/bin/env python
"""
Example for a load model functionality.
"""
# MIT License
#
# Copyright (c) 2019 Sergio Izquierdo
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without rest... | 1,787 | 34.76 | 79 | py |
cppflow | cppflow-master/examples/efficientnet/create_model.py | #!/usr/bin/env python
"""
Example for create model functionality.
"""
# MIT License
#
# Copyright (c) 2020 Sergio Izquierdo
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without rest... | 1,575 | 34.818182 | 79 | py |
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