repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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mmpretrain | mmpretrain-master/tests/test_data/test_datasets/test_common.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import pickle
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import torch
from mmcls.datasets import DATASETS
from mmcls.datasets import BaseDataset as _BaseDataset
from mmcls.datasets imp... | 33,212 | 35.417763 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_data/test_pipelines/test_transform.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import random
import mmcv
import numpy as np
import pytest
import torch
import torchvision
from mmcv.utils import build_from_cfg
from numpy.testing import assert_array_almost_equal, assert_array_equal
from PIL import Image
from torchvisi... | 48,968 | 36.610599 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_utils/test_device.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import patch
import mmcv
from mmcls.utils import auto_select_device
class TestAutoSelectDevice(TestCase):
@patch.object(mmcv, '__version__', '1.6.0')
@patch('mmcv.device.get_device', create=True)
def test_m... | 831 | 27.689655 | 57 | py |
mmpretrain | mmpretrain-master/tests/data/retinanet.py | # Copyright (c) OpenMMLab. All rights reserved.
# small RetinaNet
num_classes = 3
# model settings
model = dict(
type='RetinaNet',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_g... | 2,477 | 28.5 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_metrics/test_losses.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcls.models import build_loss
def test_asymmetric_loss():
# test asymmetric_loss
cls_score = torch.Tensor([[5, -5, 0], [5, -5, 0]])
label = torch.Tensor([[1, 0, 1], [0, 1, 0]])
weight = torch.tensor([0.5, 0.5])
loss... | 12,587 | 33.677686 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_metrics/test_metrics.py | # Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
import pytest
import torch
from mmcls.core import average_performance, mAP
from mmcls.models.losses.accuracy import Accuracy, accuracy_numpy
def test_mAP():
target = torch.Tensor([[1, 1, 0, -1], [1, 1, 0, -1], [0, -1, 1, -1],
... | 3,587 | 37.170213 | 75 | py |
mmpretrain | mmpretrain-master/tests/test_metrics/test_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcls.models.losses.utils import convert_to_one_hot
def ori_convert_to_one_hot(targets: torch.Tensor, classes) -> torch.Tensor:
assert (torch.max(targets).item() <
classes), 'Class Index must be less than number of classe... | 1,937 | 37.76 | 75 | py |
mmpretrain | mmpretrain-master/configs/vision_transformer/vit-large-p32_ft-64xb64_in1k-384.py | # Refer to pytorch-image-models
_base_ = [
'../_base_/models/vit-large-p32.py',
'../_base_/datasets/imagenet_bs64_pil_resize_autoaug.py',
'../_base_/schedules/imagenet_bs4096_AdamW.py',
'../_base_/default_runtime.py'
]
model = dict(backbone=dict(img_size=384))
img_norm_cfg = dict(
mean=[127.5, 127... | 1,172 | 29.868421 | 71 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/seresnet50.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='SEResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
i... | 426 | 22.722222 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet101.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in... | 425 | 22.666667 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnext101_32x4d.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeXt',
depth=101,
num_stages=4,
out_indices=(3, ),
groups=32,
width_per_group=4,
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='Linea... | 472 | 22.65 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet152.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=152,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in... | 425 | 22.666667 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet34_cifar.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=34,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
... | 406 | 22.941176 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnetv1d50.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNetV1d',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
... | 427 | 22.777778 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnext152_32x4d.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeXt',
depth=152,
num_stages=4,
out_indices=(3, ),
groups=32,
width_per_group=4,
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='Linea... | 472 | 22.65 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/seresnet101.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='SEResNet',
depth=101,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
... | 427 | 22.777778 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnetv1d101.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNetV1d',
depth=101,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
... | 428 | 22.833333 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet50_cifar_cutmix.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='MultiLabelLinearClsHead',
num_classes=1... | 549 | 27.947368 | 76 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet18_cifar.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=18,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
... | 406 | 22.941176 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet18.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_... | 423 | 22.555556 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet50_cifar.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
... | 407 | 23 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet34_gem.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=34,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GeneralizedMeanPooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
i... | 425 | 22.666667 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet50_mixup.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='MultiLabelLinearClsHead',
num_classes=1000,
... | 545 | 27.736842 | 76 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnetv1d152.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNetV1d',
depth=152,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
... | 428 | 22.833333 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnetv1c50.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
... | 427 | 22.777778 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/wide-resnet50.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
stem_channels=64,
base_channels=128,
expansion=2,
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head... | 498 | 22.761905 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnest101.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeSt',
depth=101,
num_stages=4,
stem_channels=128,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
... | 646 | 24.88 | 57 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnext101_32x8d.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeXt',
depth=101,
num_stages=4,
out_indices=(3, ),
groups=32,
width_per_group=8,
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='Linea... | 472 | 22.65 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet34.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=34,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_... | 423 | 22.555556 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet101_cifar.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=101,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
... | 408 | 23.058824 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnext50_32x4d.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeXt',
depth=50,
num_stages=4,
out_indices=(3, ),
groups=32,
width_per_group=4,
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='Linear... | 471 | 22.6 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet50.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_... | 424 | 22.611111 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/seresnext50_32x4d.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='SEResNeXt',
depth=50,
num_stages=4,
out_indices=(3, ),
groups=32,
width_per_group=4,
se_ratio=16,
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dic... | 494 | 22.571429 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet50_label_smooth.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_... | 458 | 23.157895 | 75 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnest50.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeSt',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in... | 618 | 24.791667 | 57 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnest269.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeSt',
depth=269,
num_stages=4,
stem_channels=128,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
... | 646 | 24.88 | 57 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/seresnext101_32x4d.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='SEResNeXt',
depth=101,
num_stages=4,
out_indices=(3, ),
groups=32,
width_per_group=4,
se_ratio=16,
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=di... | 495 | 22.619048 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet152_cifar.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=152,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
... | 408 | 23.058824 | 60 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnest200.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeSt',
depth=200,
num_stages=4,
stem_channels=128,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
... | 646 | 24.88 | 57 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet50_cutmix.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='MultiLabelLinearClsHead',
num_classes=1000,
... | 538 | 27.368421 | 76 | py |
mmpretrain | mmpretrain-master/configs/_base_/models/resnet50_cifar_mixup.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='MultiLabelLinearClsHead',
num_classes=1... | 524 | 28.166667 | 77 | py |
mmpretrain | mmpretrain-master/configs/_base_/datasets/imagenet_bs64_mixer_224.py | # dataset settings
dataset_type = 'ImageNet'
# change according to https://github.com/rwightman/pytorch-image-models/blob
# /master/timm/models/mlp_mixer.py
img_norm_cfg = dict(
mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
# training is not supported for now
train_pipeline = [
dict(type... | 1,601 | 31.693878 | 79 | py |
mmpretrain | mmpretrain-master/configs/_base_/datasets/pipelines/rand_aug.py | # Refers to `_RAND_INCREASING_TRANSFORMS` in pytorch-image-models
rand_increasing_policies = [
dict(type='AutoContrast'),
dict(type='Equalize'),
dict(type='Invert'),
dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, ... | 1,430 | 31.522727 | 79 | py |
mmpretrain | mmpretrain-master/configs/csra/resnet101-csra_1xb16_voc07-448px.py | _base_ = ['../_base_/datasets/voc_bs16.py', '../_base_/default_runtime.py']
# Pre-trained Checkpoint Path
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth' # noqa
# If you want to use the pre-trained weight of ResNet101-CutMix from
# the originary repo... | 2,484 | 31.697368 | 123 | py |
mmpretrain | mmpretrain-master/configs/mobilenet_v3/mobilenet-v3-small_8xb32_in1k.py | # Refer to https://pytorch.org/blog/ml-models-torchvision-v0.9/#classification
# ----------------------------
# -[x] auto_augment='imagenet'
# -[x] batch_size=128 (per gpu)
# -[x] epochs=600
# -[x] opt='rmsprop'
# -[x] lr=0.064
# -[x] eps=0.0316
# -[x] alpha=0.9
# -[x] weight_decay=1e-05
# -[x] mome... | 4,607 | 27.981132 | 78 | py |
mmpretrain | mmpretrain-master/configs/mobilenet_v3/mobilenet-v3-large_8xb32_in1k.py | # Refer to https://pytorch.org/blog/ml-models-torchvision-v0.9/#classification
# ----------------------------
# -[x] auto_augment='imagenet'
# -[x] batch_size=128 (per gpu)
# -[x] epochs=600
# -[x] opt='rmsprop'
# -[x] lr=0.064
# -[x] eps=0.0316
# -[x] alpha=0.9
# -[x] weight_decay=1e-05
# -[x] mome... | 4,607 | 27.981132 | 78 | py |
mmpretrain | mmpretrain-master/docs/en/conf.py | # flake8: noqa
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------... | 8,620 | 32.544747 | 158 | py |
mmpretrain | mmpretrain-master/docs/zh_CN/conf.py | # flake8: noqa
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------... | 7,890 | 31.607438 | 164 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/losses.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autograd
##############################################################################################
# Losses
# DCGAN loss
def loss_dcgan_dis(dis_real, dis_fake, weights):
L1 = torch.mean(F.softplus(-dis_real * weights))
... | 3,439 | 29.175439 | 147 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/utils.py | import torch
import torch.nn as nn
import copy
from torch.optim.lr_scheduler import _LRScheduler
import torch.nn.functional as F
class ConfigMapper(object):
def __init__(self, args):
for key in args:
self.__dict__[key] = args[key]
def toggle_grad(model, on_or_off):
for param in model.param... | 4,296 | 32.310078 | 157 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/datasets.py | # Dataset factory
import torch
import dataset_lib as datasets
import torchvision.transforms as T
import utils
import os
from random import shuffle
from PIL import Image
import numpy as np
import math
import pickle
import random
def resize_and_crop(img, size):
img_w, img_h = img.size
scale_factor = max((float... | 9,259 | 36.188755 | 118 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/evaluation.py | # Code for evaluating trained GAN models.
import torch
import utils
import losses
import os.path as osp
from pathlib import Path
import torchvision.utils as vutils
import math
import numpy as np
import copy
import models
from datasets import dataset_factory
import torch.nn as nn
import os
import json
_ATTR_CLS_MODEL... | 8,547 | 30.083636 | 146 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/trainer.py | import torch.optim as optim
import torch
import torch.nn as nn
import utils
from datasets import dataset_factory
import models
import losses
import os.path as osp
from pathlib import Path
import torchvision.utils as vutils
import math
#import inception
import numpy as np
import torch.nn.functional as F
import copy
impo... | 22,178 | 41.165399 | 122 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/dataset_lib/celeba_with_attributes.py | from .vision import VisionDataset
from PIL import Image
from random import shuffle
import os
import os.path
import sys
import numpy as np
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
Args:
filename (string): path to a file
extensions (tu... | 10,397 | 33.54485 | 108 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/dataset_lib/phototour.py | import os
import numpy as np
from PIL import Image
import torch
from .vision import VisionDataset
from .utils import download_url
class PhotoTour(VisionDataset):
"""`Learning Local Image Descriptors Data <http://phototour.cs.washington.edu/patches/default.htm>`_ Dataset.
Args:
root (string): Root ... | 7,263 | 33.590476 | 113 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/dataset_lib/video_utils.py | import bisect
import math
import torch
from torchvision.io import read_video_timestamps, read_video
from .utils import tqdm
def unfold(tensor, size, step, dilation=1):
"""
similar to tensor.unfold, but with the dilation
and specialized for 1d tensors
Returns all consecutive windows of `size` element... | 8,343 | 37.62963 | 100 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/dataset_lib/utils.py | import os
import os.path
import hashlib
import gzip
import errno
import tarfile
import zipfile
import torch
from torch.utils.model_zoo import tqdm
def gen_bar_updater():
pbar = tqdm(total=None)
def bar_update(count, block_size, total_size):
if pbar.total is None and total_size:
pbar.tota... | 8,536 | 29.27305 | 109 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/dataset_lib/svhn.py | from __future__ import print_function
from .vision import VisionDataset
from PIL import Image
import os
import os.path
import numpy as np
from .utils import download_url, check_integrity, verify_str_arg
class SVHN(VisionDataset):
"""`SVHN <http://ufldl.stanford.edu/housenumbers/>`_ Dataset.
Note: The SVHN dat... | 4,533 | 38.426087 | 95 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/dataset_lib/vision.py | import os
import torch
import torch.utils.data as data
class VisionDataset(data.Dataset):
_repr_indent = 4
def __init__(self, root, transforms=None, transform=None, target_transform=None):
if isinstance(root, torch._six.string_classes):
root = os.path.expanduser(root)
self.root = ... | 2,950 | 35.432099 | 86 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/dataset_lib/celeba.py | from functools import partial
import torch
import os
import PIL
from .vision import VisionDataset
from .utils import download_file_from_google_drive, check_integrity, verify_str_arg
class CelebA(VisionDataset):
"""`Large-scale CelebFaces Attributes (CelebA) Dataset <http://mmlab.ie.cuhk.edu.hk/projects/CelebA.htm... | 7,437 | 47.934211 | 120 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/dataset_lib/folder.py | from .vision import VisionDataset
from PIL import Image
import os
import os.path
import sys
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
Args:
filename (string): path to a file
extensions (tuple of strings): extensions to consider (lower... | 7,457 | 34.345972 | 113 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/dataset_lib/imagenet.py | from __future__ import print_function
import os
import shutil
import tempfile
import torch
from .folder import ImageFolder
from .utils import check_integrity, download_and_extract_archive, extract_archive, \
verify_str_arg
ARCHIVE_DICT = {
'train': {
'url': 'http://www.image-net.org/challenges/LSVRC/20... | 6,660 | 37.726744 | 100 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/dataset_lib/coco.py | from .vision import VisionDataset
from PIL import Image
import os
import os.path
class CocoCaptions(VisionDataset):
"""`MS Coco Captions <http://mscoco.org/dataset/#captions-challenge2015>`_ Dataset.
Args:
root (string): Root directory where images are downloaded to.
annFile (string): Path to... | 4,530 | 35.540323 | 109 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/dataset_lib/mnist.py | from __future__ import print_function
from .vision import VisionDataset
import warnings
from PIL import Image
import os
import os.path
import numpy as np
import torch
import codecs
from .utils import download_url, download_and_extract_archive, extract_archive, \
makedir_exist_ok, verify_str_arg
class MNIST(Vision... | 19,093 | 40.872807 | 119 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/inception/intra_fid.py | #!/usr/bin/env python3
''' Calculates the Frechet Inception Distance (FID) to evalulate GANs.
The FID metric calculates the distance between two distributions of images.
Typically, we have summary statistics (mean & covariance matrix) of one
of these distributions, while the 2nd distribution is given by a GAN.
When r... | 13,247 | 37.964706 | 111 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/models/base.py | import torch
import torch.nn as nn
from .layers import *
import utils
class BaseDiscriminator(nn.Module):
def __init__(self, config):
super(BaseDiscriminator, self).__init__()
self.config = config
linear_layer = linear_layers[config.D_linear]
if self.config.conditioning == 'proje... | 1,448 | 39.25 | 89 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/models/resnet.py | import torch
import torch.nn as nn
import utils
from .layers import *
from .base import *
class ResBlockGenerator(nn.Module):
def __init__(self, config, in_channels, out_channels, stride=1):
super(ResBlockGenerator, self).__init__()
conv_layer = cond_conv_layers[config.G_conv]
norm_layer ... | 7,720 | 34.74537 | 117 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/models/layers.py | import torch
import torch.nn as nn
###############################################################################
# Unconditional layers
class SpectralConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1,
bias=T... | 8,576 | 30.649446 | 119 | py |
robust-OT | robust-OT-main/robust CIFAR/GAN/models/DCGAN.py | import torch
import torch.nn as nn
from .layers import *
from .base import *
import utils
import torch.nn.functional as F
class Generator(nn.Module):
def __init__(self, config):
super(Generator, self).__init__()
self.ngpu = int(config.ngpu)
nz = int(config.nz)
ngf = int(config.ngf)... | 6,777 | 35.637838 | 117 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/main.py | """
---
title: StyleGAN 2 Model Training
summary: >
An annotated PyTorch implementation of StyleGAN2 model training code.
---
# [StyleGAN 2](index.html) Model Training
This is the training code for [StyleGAN 2](index.html) model.
*<small>These are $64 \times 64$ images generat... | 18,195 | 36.286885 | 122 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/hypernetworks/hyper_lstm.py | """
---
title: HyperNetworks - HyperLSTM
summary: A PyTorch implementation/tutorial of HyperLSTM introduced in paper HyperNetworks.
---
# HyperNetworks - HyperLSTM
We have implemented HyperLSTM introduced in paper
[HyperNetworks](https://arxiv.org/abs/1609.09106), with annotations
using [PyTorch](https://pytorch.org)... | 11,626 | 41.746324 | 180 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/hypernetworks/experiment.py | import torch
import torch.nn as nn
from labml import experiment
from labml.configs import option
from labml.utils.pytorch import get_modules
from labml_helpers.module import Module
from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
from labml_nn.hypernetworks.hyper_lstm import HyperLSTM
from ... | 2,825 | 26.436893 | 80 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/sketch_rnn/__init__.py | """
---
title: Sketch RNN
summary: >
This is an annotated PyTorch implementation of the Sketch RNN from paper A Neural Representation of Sketch Drawings.
Sketch RNN is a sequence-to-sequence model that generates sketches of objects such as bicycles, cats, etc.
---
# Sketch RNN
This is an annotated [PyTorch](https... | 25,107 | 38.540157 | 118 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/capsule_networks/__init__.py | """
---
title: Capsule Networks
summary: >
PyTorch implementation and tutorial of Capsule Networks.
Capsule network is a neural network architecture that embeds features
as capsules and routes them with a voting mechanism to next layer of capsules.
---
# Capsule Networks
This is a [PyTorch](https://pytorch.org)... | 7,602 | 39.441489 | 178 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/capsule_networks/mnist.py | """
---
title: Classify MNIST digits with Capsule Networks
summary: Code for training Capsule Networks on MNIST dataset
---
# Classify MNIST digits with Capsule Networks
This is an annotated PyTorch code to classify MNIST digits with PyTorch.
This paper implements the experiment described in paper
[Dynamic Routing B... | 6,498 | 35.307263 | 109 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/normalization/batch_norm/cifar10.py | """
---
title: CIFAR10 Experiment to try Group Normalization
summary: >
This trains is a simple convolutional neural network that uses group normalization
to classify CIFAR10 images.
---
# CIFAR10 Experiment for Group Normalization
"""
import torch.nn as nn
from labml import experiment
from labml.configs import ... | 1,629 | 24.076923 | 99 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/normalization/batch_norm/__init__.py | """
---
title: Batch Normalization
summary: >
A PyTorch implementation/tutorial of batch normalization.
---
# Batch Normalization
This is a [PyTorch](https://pytorch.org) implementation of Batch Normalization from paper
[Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](h... | 9,671 | 41.421053 | 186 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/normalization/batch_norm/mnist.py | """
---
title: MNIST Experiment to try Batch Normalization
summary: >
This trains is a simple convolutional neural network that uses batch normalization
to classify MNIST digits.
---
# MNIST Experiment for Batch Normalization
"""
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from ... | 2,425 | 27.880952 | 91 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/normalization/group_norm/experiment.py | """
---
title: CIFAR10 Experiment to try Group Normalization
summary: >
This trains is a simple convolutional neural network that uses group normalization
to classify CIFAR10 images.
---
# CIFAR10 Experiment for Group Normalization
"""
import torch.nn as nn
from labml import experiment
from labml.configs import ... | 2,308 | 25.54023 | 95 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/normalization/group_norm/__init__.py | """
---
title: Group Normalization
summary: >
A PyTorch implementation/tutorial of group normalization.
---
# Group Normalization
This is a [PyTorch](https://pytorch.org) implementation of
the [Group Normalization](https://arxiv.org/abs/1803.08494) paper.
[Batch Normalization](../batch_norm/index.html) works well f... | 6,451 | 35.451977 | 191 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/normalization/weight_standardization/conv2d.py | """
---
title: 2D Convolution Layer with Weight Standardization
summary: >
A PyTorch implementation/tutorial of a 2D Convolution Layer with Weight Standardization.
---
# 2D Convolution Layer with Weight Standardization
This is an implementation of a 2 dimensional convolution layer with [Weight Standardization](./ind... | 1,879 | 29.819672 | 107 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/normalization/weight_standardization/experiment.py | """
---
title: CIFAR10 Experiment to try Weight Standardization and Batch-Channel Normalization
summary: >
This trains is a VGG net that uses weight standardization and batch-channel normalization
to classify CIFAR10 images.
---
# CIFAR10 Experiment to try Weight Standardization and Batch-Channel Normalization
""... | 1,818 | 26.560606 | 99 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/normalization/weight_standardization/__init__.py | """
---
title: Weight Standardization
summary: >
A PyTorch implementation/tutorial of Weight Standardization.
---
# Weight Standardization
This is a [PyTorch](https://pytorch.org) implementation of Weight Standardization from the paper
[Micro-Batch Training with Batch-Channel Normalization and Weight Standardizatio... | 3,780 | 42.45977 | 203 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/normalization/instance_norm/experiment.py | """
---
title: CIFAR10 Experiment to try Instance Normalization
summary: >
This trains is a simple convolutional neural network that uses instance normalization
to classify CIFAR10 images.
---
# CIFAR10 Experiment for Instance Normalization
This demonstrates the use of an instance normalization layer in a convolu... | 1,824 | 25.838235 | 99 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/normalization/instance_norm/__init__.py | """
---
title: Instance Normalization
summary: >
A PyTorch implementation/tutorial of instance normalization.
---
# Instance Normalization
This is a [PyTorch](https://pytorch.org) implementation of
[Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022).
Instance norm... | 4,232 | 33.137097 | 114 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/normalization/batch_channel_norm/__init__.py | """
---
title: Batch-Channel Normalization
summary: >
A PyTorch implementation/tutorial of Batch-Channel Normalization.
---
# Batch-Channel Normalization
This is a [PyTorch](https://pytorch.org) implementation of Batch-Channel Normalization from the paper
[Micro-Batch Training with Batch-Channel Normalization and W... | 10,047 | 41.218487 | 203 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/normalization/layer_norm/__init__.py | """
---
title: Layer Normalization
summary: >
A PyTorch implementation/tutorial of layer normalization.
---
# Layer Normalization
This is a [PyTorch](https://pytorch.org) implementation of
[Layer Normalization](https://arxiv.org/abs/1607.06450).
### Limitations of [Batch Normalization](../batch_norm/index.html)
* ... | 5,224 | 35.284722 | 103 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/recurrent_highway_networks/__init__.py | """
---
title: Recurrent Highway Networks
summary: A simple PyTorch implementation/tutorial of Recurrent Highway Networks.
---
# Recurrent Highway Networks
This is a [PyTorch](https://pytorch.org) implementation of [Recurrent Highway Networks](https://arxiv.org/abs/1607.03474).
"""
from typing import Optional
import... | 5,685 | 34.098765 | 122 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/lstm/__init__.py | """
---
title: Long Short-Term Memory (LSTM)
summary: A simple PyTorch implementation/tutorial of Long Short-Term Memory (LSTM) modules.
---
# Long Short-Term Memory (LSTM)
This is a [PyTorch](https://pytorch.org) implementation of Long Short-Term Memory.
"""
from typing import Optional, Tuple
import torch
from tor... | 5,990 | 36.679245 | 103 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/graphs/gatv2/experiment.py | """
---
title: Train a Graph Attention Network v2 (GATv2) on Cora dataset
summary: >
This trains is a Graph Attention Network v2 (GATv2) on Cora dataset
---
# Train a Graph Attention Network v2 (GATv2) on Cora dataset
[](https://app.labml.ai/run... | 3,796 | 31.452991 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/graphs/gatv2/__init__.py | """
---
title: Graph Attention Networks v2 (GATv2)
summary: >
A PyTorch implementation/tutorial of Graph Attention Networks v2.
---
# Graph Attention Networks v2 (GATv2)
This is a [PyTorch](https://pytorch.org) implementation of the GATv2 operator from the paper
[How Attentive are Graph Attention Networks?](https://ar... | 11,024 | 44.9375 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/graphs/gat/experiment.py | """
---
title: Train a Graph Attention Network (GAT) on Cora dataset
summary: >
This trains is a Graph Attention Network (GAT) on Cora dataset
---
# Train a Graph Attention Network (GAT) on Cora dataset
[](https://app.labml.ai/run/d6c636cadf3511... | 10,542 | 32.683706 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/graphs/gat/__init__.py | """
---
title: Graph Attention Networks (GAT)
summary: >
A PyTorch implementation/tutorial of Graph Attention Networks.
---
# Graph Attention Networks (GAT)
This is a [PyTorch](https://pytorch.org) implementation of the paper
[Graph Attention Networks](https://arxiv.org/abs/1710.10903).
GATs work on graph data.
A g... | 9,047 | 44.014925 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/experiments/nlp_autoregression.py | """
---
title: NLP auto-regression trainer
summary: >
This is a reusable trainer for auto-regressive tasks
---
# Auto-regressive NLP model trainer
"""
from typing import Callable
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from labml import lab, monit, logger, tracker
from labml.con... | 9,188 | 28.834416 | 118 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/experiments/nlp_classification.py | """
---
title: NLP classification trainer
summary: >
This is a reusable trainer for classification tasks
---
# NLP model trainer for classification
"""
from collections import Counter
from typing import Callable
import torch
import torchtext
from torch import nn
from torch.utils.data import DataLoader
from torchte... | 8,615 | 29.338028 | 113 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/experiments/cifar10.py | """
---
title: CIFAR10 Experiment
summary: >
This is a reusable trainer for CIFAR10 dataset
---
# CIFAR10 Experiment
"""
from typing import List
import torch.nn as nn
from labml import lab
from labml.configs import option
from labml_helpers.datasets.cifar10 import CIFAR10Configs as CIFAR10DatasetConfigs
from labml... | 3,522 | 29.903509 | 84 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/experiments/mnist.py | """
---
title: MNIST Experiment
summary: >
This is a reusable trainer for MNIST dataset
---
# MNIST Experiment
"""
import torch.nn as nn
import torch.utils.data
from labml_helpers.module import Module
from labml import tracker
from labml.configs import option
from labml_helpers.datasets.mnist import MNISTConfigs a... | 3,383 | 27.923077 | 88 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/optimizers/ada_belief.py | """
---
title: AdaBelief optimizer
summary: A simple PyTorch implementation/tutorial of AdaBelief optimizer.
---
# AdaBelief Optimizer
This is based from AdaBelief
[official implementation](https://github.com/juntang-zhuang/Adabelief-Optimizer)
of the paper
[AdaBelief Optimizer: Adapting Stepsizes by the Belief in Ob... | 6,774 | 41.34375 | 128 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/optimizers/amsgrad.py | """
---
title: AMSGrad Optimizer
summary: A simple PyTorch implementation/tutorial of AMSGrad optimizer.
---
# AMSGrad
This is a [PyTorch](https://pytorch.org) implementation of the paper
[On the Convergence of Adam and Beyond](https://arxiv.org/abs/1904.09237).
We implement this as an extension to our [Adam optimiz... | 8,022 | 38.136585 | 156 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/optimizers/noam.py | """
---
title: Noam optimizer from Attention is All You Need paper
summary: >
This is a tutorial/implementation of Noam optimizer.
Noam optimizer has a warm-up period and then an exponentially decaying learning rate.
---
# Noam Optimizer
This is the [PyTorch](https://pytorch.org) implementation of optimizer intro... | 3,380 | 36.988764 | 117 | py |
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