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
value |
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FrechetMotionDistance | FrechetMotionDistance-main/Conv2d.py | from utils import *
from AutoEncoder import *
from fastai.data.all import *
from fastai.vision.all import *
from fastai.torch_basics import *
from fastai.data.load import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
i... | 12,918 | 47.026022 | 377 | py |
FrechetMotionDistance | FrechetMotionDistance-main/AutoEncoder.py | """
This code is freely inspired by https://alanbertl.com/autoencoder-with-fast-ai/
"""
import torch.nn as nn
import torch
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class UpSample(nn.Module):
def __init__(self,feat_in,feat_out,out_shape=None,scale=2):
super().__init__()
... | 1,866 | 30.644068 | 98 | py |
FrechetMotionDistance | FrechetMotionDistance-main/utils.py | import numpy as np
from sklearn.preprocessing import normalize
from scipy import linalg
from torch.utils.data import Dataset
import torch.nn.functional as F
import torch
def get_dir_vec_pairs(dataset, val=None):
norm_bones = val if val is not None else None
#ted gesture
if dataset == 'ted':
... | 11,343 | 38.664336 | 346 | py |
FrechetMotionDistance | FrechetMotionDistance-main/dataset/Human36M.py | import numpy as np
import random
import matplotlib.pyplot as plt
import math
import torch
from torch.utils.data import Dataset
import torchvision.transforms as T
from sklearn.decomposition import PCA
from utils import *
train_subject = ['S1', 'S5', 'S6', 'S7', 'S8', 'S9']
test_subject = ['S11']
all_subject = train... | 12,043 | 35.944785 | 157 | py |
FrechetMotionDistance | FrechetMotionDistance-main/Inception/TED.py | import numpy as np
import random
import matplotlib.pyplot as plt
import math
from sklearn.decomposition import PCA
import torch
from utils import *
class TED(Dataset):
def __init__(self, path, method=None, std=None):
self.path = path
self.method = method
self.std = std
self.d... | 4,039 | 37.47619 | 126 | py |
FrechetMotionDistance | FrechetMotionDistance-main/Inception/test_inception.py | import torch
from InceptionV3 import InceptionV3
block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]
model = InceptionV3([block_idx], pretrained=False)
model.eval()
# input images
img = torch.rand((1, 3, 224, 224)).float()
# img = torch.ones((1, 3, 224, 224)).float()
# forward
feature_maps = model(img)
print(feature_m... | 418 | 23.647059 | 86 | py |
FrechetMotionDistance | FrechetMotionDistance-main/Inception/inception_fmd.py | import warnings
warnings.simplefilter("ignore", UserWarning)
from tqdm import tqdm
import numpy as np
import os
import scipy.stats
import torch
import torch.nn as nn
from torch.nn.functional import adaptive_avg_pool2d
from fastai.data.all import *
from fastai.vision.all import *
from fastai.torch_basics import *
... | 5,173 | 33.959459 | 167 | py |
FrechetMotionDistance | FrechetMotionDistance-main/Inception/utils.py | import numpy as np
from sklearn.preprocessing import normalize
from scipy import linalg
from torch.utils.data import Dataset
import torch.nn.functional as F
import torch
def get_dir_vec_pairs(dataset, val=None):
norm_bones = val if val is not None else None
#ted gesture
if dataset == 'ted':
... | 11,343 | 38.664336 | 346 | py |
FrechetMotionDistance | FrechetMotionDistance-main/Inception/InceptionV3.py |
from numpy import isin
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from IPython.display import HTML
from scipy import linalg
from torch.nn.functional import adaptive_avg_pool2d
class InceptionV3(nn.Module):
DEFAULT_BLOCK_INDEX = 3
BLOCK_INDEX_BY_DIM... | 3,588 | 29.675214 | 72 | py |
FrechetMotionDistance | FrechetMotionDistance-main/Inception/DogLocomotion.py | import torch
from torch.utils.data import Dataset
import numpy as np
import os
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import normalize
from sklearn.decomposition import PCA
from pymo.parsers import BVHParser
from pymo.preprocessing import *
from pymo.viz_too... | 7,183 | 33.705314 | 130 | py |
FrechetMotionDistance | FrechetMotionDistance-main/Inception/Human36M.py | import math
import random
import numpy as np
import einops
import torch
from sklearn.decomposition import PCA
from sklearn.preprocessing import normalize
from utils import *
class Human36M(Dataset):
def __init__(self, path, n_poses=34, method=None, std=None, dataset='test', joint_reord=False):
#Init
... | 9,544 | 38.118852 | 146 | py |
e2cnn | e2cnn-master/setup.py |
from setuptools import setup, find_packages
about = {}
with open("e2cnn/__about__.py") as fp:
exec(fp.read(), about)
install_requires = [
'torch',
'numpy',
'scipy',
'sympy',
]
setup_requires = []
tests_require = ['scikit-learn', 'scikit-image']
extras_require = {
# 'RBF-FD and Gaussians': [... | 1,287 | 23.769231 | 98 | py |
e2cnn | e2cnn-master/examples/e2wrn.py | from typing import Tuple
import torch
import torch.nn.functional as F
import math
import e2cnn.nn as enn
from e2cnn.nn import init
from e2cnn import gspaces
from argparse import ArgumentParser
__all__ = [
"wrn16_8_stl_d8d4d1",
"wrn16_8_stl_d8d4d4",
"wrn16_8_stl_d1d1d1",
"wrn28_10_d8d4d1",
"wrn... | 18,455 | 35.117417 | 131 | py |
e2cnn | e2cnn-master/e2cnn/nn/field_type.py |
from typing import List, Dict
from collections import defaultdict
from e2cnn.group import Group
from e2cnn.group import Representation
from e2cnn.gspaces import GSpace
from e2cnn.group import directsum
import numpy as np
from scipy import sparse
import torch
__all__ = ["FieldType"]
class FieldType:
def... | 17,866 | 36.14553 | 135 | py |
e2cnn | e2cnn-master/e2cnn/nn/geometric_tensor.py |
import torch
from torch import Tensor
from .field_type import FieldType
from typing import List, Union
import itertools
from collections.abc import Iterable
__all__ = ["GeometricTensor", "tensor_directsum"]
class GeometricTensor:
def __init__(self, tensor: Tensor, type: FieldType):
r"""
... | 28,796 | 40.079886 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/init.py |
from e2cnn.nn.modules.r2_conv.basisexpansion import BasisExpansion
from collections import defaultdict
import torch
from scipy import stats
import math
__all__ = ["generalized_he_init", "deltaorthonormal_init"]
def _generalized_he_init_variances(basisexpansion: BasisExpansion):
r"""
Compute the variance... | 5,505 | 33.198758 | 119 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/sequential_module.py |
from e2cnn.nn import GeometricTensor
from .equivariant_module import EquivariantModule
import torch
from typing import List, Tuple, Union, Any
from collections import OrderedDict
__all__ = ["SequentialModule"]
class SequentialModule(EquivariantModule):
def __init__(self,
*args: Equivari... | 4,309 | 30.459854 | 113 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/module_list.py |
from .equivariant_module import EquivariantModule
import torch
TORCH_MAJOR, TORCH_MINOR = map(int, torch.__version__.split('.')[:2])
if TORCH_MAJOR == 1 and TORCH_MINOR <= 8:
from torch._six import container_abcs
else:
import collections.abc as container_abcs
from typing import List, Iterable
__all__ = ["... | 3,157 | 31.556701 | 116 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/multiple_module.py | from e2cnn.nn import GeometricTensor
from e2cnn.nn import FieldType
from e2cnn.gspaces import *
from .equivariant_module import EquivariantModule
from .branching_module import BranchingModule
from .merge_module import MergeModule
from typing import List, Tuple, Union, Any
import torch
import numpy as np
__all__ = ... | 6,943 | 39.372093 | 213 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/identity_module.py | from e2cnn.nn import GeometricTensor
from e2cnn.nn import FieldType
from .equivariant_module import EquivariantModule
import torch
from typing import List, Tuple, Union, Any
__all__ = ["IdentityModule"]
class IdentityModule(EquivariantModule):
def __init__(self,
in_type: FieldType
... | 1,726 | 24.776119 | 104 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/reshuffle_module.py |
import torch
from e2cnn.gspaces import *
from .equivariant_module import EquivariantModule
from e2cnn.nn import GeometricTensor
from e2cnn.nn import FieldType
from typing import List, Tuple, Any
import numpy as np
__all__ = ["ReshuffleModule"]
class ReshuffleModule(EquivariantModule):
def __init__(self,... | 3,556 | 32.242991 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/restriction_module.py |
import torch
import numpy as np
from .equivariant_module import EquivariantModule
from e2cnn.nn import GeometricTensor
from e2cnn.nn import FieldType
from e2cnn.gspaces import *
import torch
from typing import List, Tuple, Any
__all__ = ["RestrictionModule"]
class RestrictionModule(EquivariantModule):
def _... | 3,310 | 31.460784 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/branching_module.py |
from collections import defaultdict
import torch
from e2cnn.nn import GeometricTensor
from e2cnn.nn import FieldType
from .equivariant_module import EquivariantModule
from .reshuffle_module import ReshuffleModule
from e2cnn.gspaces import *
from typing import List, Tuple, Any, Dict
import numpy as np
__all__ =... | 9,907 | 37.254826 | 124 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/merge_module.py |
import torch
from e2cnn.nn import GeometricTensor
from e2cnn.nn import FieldType
from .equivariant_module import EquivariantModule
from typing import List, Tuple, Any, Union, Dict
__all__ = ["MergeModule"]
class MergeModule(EquivariantModule):
def __init__(self,
modules: List[Tuple[Equi... | 4,520 | 34.046512 | 121 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/r2upsampling.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from .equivariant_module import EquivariantModule
from typing import Tuple, Union
import torch
import numpy as np
import math
from torch.nn.functional import interpolate
__all__ = ["R2Upsampling"]
class R2Upsamplin... | 7,649 | 34.581395 | 206 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/disentangle_module.py |
from e2cnn.nn import GeometricTensor
from e2cnn.nn import FieldType
import torch
import numpy as np
from .equivariant_module import EquivariantModule
from .utils import indexes_from_labels
from e2cnn.gspaces import *
from e2cnn.group import disentangle
from collections import defaultdict
from typing import List, T... | 5,492 | 35.377483 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/equivariant_module.py |
from e2cnn.nn import GeometricTensor
from e2cnn.nn import FieldType
from torch.nn import Module
import torch
import numpy as np
from abc import ABC, abstractmethod
from typing import List, Tuple, Any
__all__ = ["EquivariantModule"]
class EquivariantModule(Module, ABC):
def __init__(self):
r"""
... | 5,344 | 38.301471 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/masking_module.py |
from e2cnn.nn import GeometricTensor
from e2cnn.nn import FieldType
from .equivariant_module import EquivariantModule
from typing import Tuple
import torch
import math
__all__ = ["MaskModule"]
def build_mask(s, margin=2, dtype=torch.float32):
mask = torch.zeros(1, 1, s, s, dtype=dtype)
c = (s-1) / 2
... | 2,730 | 33.56962 | 119 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/pooling/pointwise_max.py | from torch.nn import Parameter
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch.nn.functional as F
import torch
from typing import List, Tuple, Any, Union
import math
__all__ = ["PointwiseMaxPool", "Poin... | 9,483 | 34.924242 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/pooling/pointwise_adaptive_max.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
import torch.nn.functional as F
from typing import List, Tuple, Any, Union
__all__ = ["PointwiseAdaptiveMaxPool"]
class PointwiseAdaptiveMaxPool(Equivar... | 3,041 | 30.360825 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/pooling/pointwise_adaptive_avg.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
import torch.nn.functional as F
from typing import List, Tuple, Any, Union
__all__ = ["PointwiseAdaptiveAvgPool"]
class PointwiseAdaptiveAvgPool(Equivar... | 3,035 | 30.298969 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/pooling/pointwise_avg.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch.nn.functional as F
import torch
from typing import List, Tuple, Any, Union
import math
__all__ = ["PointwiseAvgPool", "PointwiseAvgPoolAntialiased"]
cl... | 8,215 | 33.233333 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/pooling/norm_max.py |
from collections import defaultdict
from torch.nn import Parameter
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
import torch.nn.functional as F
from typing import List, Tuple, Any, Union
import math... | 8,998 | 37.293617 | 116 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/invariantmaps/norm.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from e2cnn.nn.modules.equivariant_module import EquivariantModule
from e2cnn.nn.modules.utils import indexes_from_labels
import torch
from typing import List, Tuple, Any
from collections import defaultdict
import numpy a... | 5,205 | 34.903448 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/invariantmaps/gpool.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from e2cnn.nn.modules.equivariant_module import EquivariantModule
from e2cnn.nn.modules.utils import indexes_from_labels
import torch
from torch import nn
from typing import List, Tuple, Any
from collections import defau... | 8,809 | 36.974138 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/invariantmaps/induced_norm.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from e2cnn.nn.modules.equivariant_module import EquivariantModule
from e2cnn.nn.modules.utils import indexes_from_labels
import torch
from typing import List, Tuple, Any
from collections import defaultdict
import numpy a... | 6,736 | 36.427778 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/dropout/field.py |
from collections import defaultdict
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
import torch.nn.functional as F
from torch.nn import Parameter
from typing import List, Tuple, Any
__all__ = ["FieldD... | 5,850 | 32.056497 | 122 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/dropout/pointwise.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch.nn.functional as F
import torch
from typing import List, Tuple, Any
__all__ = ["PointwiseDropout"]
class PointwiseDropout(EquivariantModule):
def... | 2,817 | 29.967033 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/nonlinearities/induced_gated1.py |
from typing import List, Tuple, Any
import numpy as np
from collections import defaultdict
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
from .gated1 import GATED_ID, GATES_ID
import torch
from torch.nn import P... | 11,259 | 36.408638 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/nonlinearities/concatenated.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from e2cnn.group import Representation
from e2cnn.group.representation import build_from_discrete_group_representation
from ..equivariant_module import EquivariantModule
import torch
from typing import List, Tuple, Any
... | 9,612 | 39.733051 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/nonlinearities/norm.py |
from collections import defaultdict
from torch.nn import Parameter
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
from typing import List, Tuple, Any
import numpy as np
__all__ = ["NormNonLinearity"]... | 8,495 | 35.93913 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/nonlinearities/gated1.py |
from typing import List, Tuple, Any
import numpy as np
from collections import defaultdict
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
from torch.nn import Parameter
__all__ = ["GatedNonLinearit... | 11,037 | 37.45993 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/nonlinearities/elu.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
import torch.nn.functional as F
from typing import List, Tuple, Any
import numpy as np
__all__ = ["ELU"]
class ELU(EquivariantModule):
def __in... | 3,718 | 30.516949 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/nonlinearities/gated2.py |
from typing import List, Tuple, Any
import numpy as np
from collections import defaultdict
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
from torch.nn import Parameter
from .gated1 import GATED_ID,... | 8,965 | 36.049587 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/nonlinearities/induced_norm.py |
from collections import defaultdict
from torch.nn import Parameter
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
from typing import List, Tuple, Any
import numpy as np
__all__ = ["InducedNormNonLine... | 9,385 | 37.467213 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/nonlinearities/vectorfield.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
from typing import List, Tuple, Any
import numpy as np
__all__ = ["VectorFieldNonLinearity"]
class VectorFieldNonLinearity(EquivariantModule):
... | 4,759 | 35.615385 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/nonlinearities/relu.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
import torch.nn.functional as F
from typing import List, Tuple, Any
import numpy as np
__all__ = ["ReLU"]
class ReLU(EquivariantModule):
def __... | 3,612 | 30.417391 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/nonlinearities/pointwise.py |
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
import torch.nn.functional as F
from typing import List, Tuple, Any
import numpy as np
__all__ = ["PointwiseNonLinearity"]
class PointwiseNonLinearity(E... | 3,876 | 32.713043 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/r2_conv/r2diffop.py |
import warnings
from e2cnn.diffops.utils import (
load_cache,
store_cache,
required_points,
largest_possible_order,
)
from torch.nn.functional import conv2d, pad
from e2cnn.nn import init
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from e2cnn.gspaces import *
from e2cnn.diffo... | 35,836 | 46.403439 | 193 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/r2_conv/r2convolution.py |
from torch.nn.functional import conv2d, pad
from e2cnn.nn import init
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from e2cnn.gspaces import *
from ..equivariant_module import EquivariantModule
from .basisexpansion import BasisExpansion
from .basisexpansion_blocks import BlocksBasisExpansion
... | 33,759 | 43.246396 | 193 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/r2_conv/basisexpansion_singleblock.py |
from e2cnn.kernels import Basis, EmptyBasisException
from .basisexpansion import BasisExpansion
from typing import Callable, Dict, List, Iterable, Union
import torch
import numpy as np
__all__ = ["SingleBlockBasisExpansion", "block_basisexpansion"]
class SingleBlockBasisExpansion(BasisExpansion):
def __i... | 9,398 | 38.32636 | 124 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/r2_conv/basisexpansion_blocks.py |
from e2cnn.kernels import Basis, EmptyBasisException
from e2cnn.gspaces import *
from e2cnn.group import Representation
from e2cnn.nn import FieldType
from .. import utils
from .basisexpansion import BasisExpansion
from .basisexpansion_singleblock import block_basisexpansion
from collections import defaultdict
from... | 19,386 | 40.963203 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/r2_conv/r2_transposed_convolution.py |
from torch.nn.functional import conv_transpose2d
from e2cnn.nn import init
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from e2cnn.gspaces import *
from ..equivariant_module import EquivariantModule
from .basisexpansion import BasisExpansion
from .basisexpansion_blocks import BlocksBasisExpan... | 24,125 | 39.21 | 193 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/r2_conv/basisexpansion.py |
from abc import ABC, abstractmethod
from torch.nn import Module
import torch
import numpy as np
from typing import List, Iterable, Dict, Union
__all__ = ["BasisExpansion"]
class BasisExpansion(ABC, Module):
def __init__(self):
r"""
Abstract class defining the interface for the different ... | 2,151 | 22.911111 | 101 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/batchnormalization/inner.py |
from typing import List, Tuple, Any
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
from torch.nn import BatchNorm3d
from ..utils import indexes_from_labels
__all__ = ["InnerBatchNorm"]
class InnerBat... | 9,004 | 36.995781 | 120 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/batchnormalization/norm.py |
from collections import defaultdict
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
from torch.nn import Parameter
from typing import List, Tuple, Any
__all__ = ["NormBatchNorm"]
class NormBatchNorm(... | 10,595 | 38.537313 | 125 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/batchnormalization/gnorm.py |
from collections import defaultdict
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
from torch.nn import Parameter
from typing import List, Tuple, Any, Union
import numpy as np
__all__ = ["GNormBatchNor... | 13,442 | 37.852601 | 123 | py |
e2cnn | e2cnn-master/e2cnn/nn/modules/batchnormalization/induced_norm.py |
from collections import defaultdict
from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
from torch.nn import Parameter
from typing import List, Tuple, Any
__all__ = ["InducedNormBatchNorm"]
class Induced... | 11,488 | 39.597173 | 118 | py |
e2cnn | e2cnn-master/test/nn/test_modulelist.py | import unittest
from unittest import TestCase
import e2cnn.nn.init as init
from e2cnn.nn import *
from e2cnn.gspaces import *
import torch
from torch.optim import SGD
from torch import nn
import numpy as np
class TestModuleList(TestCase):
def test_expand(self):
for gs in [Rot2dOnR2(9), Fl... | 5,612 | 30.892045 | 157 | py |
e2cnn | e2cnn-master/test/nn/test_dropout.py | import unittest
from unittest import TestCase
from e2cnn.nn import *
from e2cnn.gspaces import *
import torch
import torch.nn.functional as F
import numpy as np
import random
class TestDropout(TestCase):
def test_pointwise_do_unsorted_inplace(self):
N = 8
g = FlipRot2dOnR2(N)
... | 3,091 | 23.346457 | 121 | py |
e2cnn | e2cnn-master/test/nn/test_reshuffleLayer.py | from unittest import TestCase
import unittest
from e2cnn.nn import *
from e2cnn.gspaces import *
import torch
from random import shuffle
class TestReshuffleLayer(TestCase):
def test_indices_permutation(self):
g = Rot2dOnR2(6)
r = FieldType(g,
[g.representations['irre... | 1,784 | 27.333333 | 111 | py |
e2cnn | e2cnn-master/test/nn/test_geometrictensor.py | import unittest
from unittest import TestCase
from e2cnn.nn import *
from e2cnn.gspaces import *
import torch
import random
class TestGeometricTensor(TestCase):
def test_split(self):
space = Rot2dOnR2(4)
type = FieldType(space, [
space.regular_repr, # size = 4
s... | 21,491 | 35.738462 | 129 | py |
e2cnn | e2cnn-master/test/nn/test_basisexpansion_bk.py | import unittest
from unittest import TestCase
import numpy as np
from e2cnn.nn import *
from e2cnn.gspaces import *
import torch
from random import shuffle
class TestBasisExpansion(TestCase):
def test_cyclicgroup_sorted(self):
N = 8
gc = Rot2dOnR2(N)
reprs = [
... | 8,537 | 27.555184 | 158 | py |
e2cnn | e2cnn-master/test/nn/test_nonlinearities_rotations.py | import unittest
from unittest import TestCase
from e2cnn.nn import *
from e2cnn.gspaces import *
import torch
import numpy as np
import random
class TestNonLinearitiesRotations(TestCase):
def test_cyclic_norm_relu(self):
N = 8
g = Rot2dOnR2(N)
r = FieldType(g, list(g.repr... | 10,794 | 28.175676 | 115 | py |
e2cnn | e2cnn-master/test/nn/test_multiple_module_rotations.py | import unittest
from unittest import TestCase
from e2cnn.nn import *
from e2cnn.gspaces import *
import torch
import random
batchnormalizations = [
([('regular_bnorm', 'pointwise')], InnerBatchNorm),
([('g_bnorm', 'norm')], GNormBatchNorm),
([('norm_bnorm', 'norm')], NormBatchNorm),
([('indnorm_bno... | 30,023 | 30.906482 | 95 | py |
e2cnn | e2cnn-master/test/nn/test_batchnorm.py | import unittest
from unittest import TestCase
from e2cnn.nn import *
from e2cnn.gspaces import *
import torch
import numpy as np
import random
class TestBatchnorms(TestCase):
def test_dihedral_general_bnorm(self):
N = 8
g = FlipRot2dOnR2(N)
r = FieldType(g, list(g.represen... | 3,919 | 27.823529 | 102 | py |
e2cnn | e2cnn-master/test/nn/test_he_init.py | import unittest
from unittest import TestCase
import numpy as np
from e2cnn.nn import *
from e2cnn.nn import init
from e2cnn.gspaces import *
from e2cnn.group import *
import torch
from random import shuffle
class TestGeneralizedHeInit(TestCase):
def test_one_block(self):
N = 8
# gspace = ... | 5,344 | 33.044586 | 127 | py |
e2cnn | e2cnn-master/test/nn/test_export.py | import unittest
from unittest import TestCase
import e2cnn.nn.init as init
from e2cnn.nn import *
from e2cnn.gspaces import *
import torch
from torch.optim import SGD
from torch import nn
import numpy as np
class TestExport(TestCase):
def test_R2Conv(self):
for gs in [Rot2dOnR2(9), FlipRo... | 15,526 | 36.324519 | 157 | py |
e2cnn | e2cnn-master/test/nn/test_convolution_transposed.py | import unittest
from unittest import TestCase
import e2cnn.nn.init as init
from e2cnn.nn import *
from e2cnn.gspaces import *
import numpy as np
import torch
class TestConvolution(TestCase):
def test_cyclic(self):
N = 8
g = Rot2dOnR2(N)
r1 = FieldType(g, list(g.representat... | 4,653 | 28.833333 | 91 | py |
e2cnn | e2cnn-master/test/nn/test_convolution.py | import unittest
from unittest import TestCase
import e2cnn.nn.init as init
from e2cnn.nn import *
from e2cnn.gspaces import *
import numpy as np
import math
import torch
class TestConvolution(TestCase):
def test_cyclic(self):
N = 8
g = Rot2dOnR2(N)
r1 = FieldType(g, list(g... | 6,598 | 30.574163 | 115 | py |
e2cnn | e2cnn-master/test/nn/test_deltaorth_init.py | import unittest
from unittest import TestCase
import numpy as np
from e2cnn.nn import *
from e2cnn.nn import init
from e2cnn.gspaces import *
from e2cnn.group import *
import torch
from random import shuffle
class TestDeltaOrth(TestCase):
def test_one_block(self):
# gspace = FlipRot2dOnR2(6)
... | 2,808 | 31.287356 | 114 | py |
e2cnn | e2cnn-master/test/nn/test_diffop.py | import unittest
from unittest import TestCase
import e2cnn.nn.init as init
from e2cnn.nn import *
from e2cnn.gspaces import *
import numpy as np
import math
import torch
class TestDiffop(TestCase):
def test_cyclic(self):
N = 8
g = Rot2dOnR2(N)
r1 = FieldType(g, list(g.repr... | 7,788 | 25.951557 | 134 | py |
e2cnn | e2cnn-master/test/nn/test_pooling.py | import unittest
from unittest import TestCase
from e2cnn.nn import *
from e2cnn.gspaces import *
import torch
import numpy as np
class TestPooling(TestCase):
def test_pointwise_maxpooling(self):
N = 8
g = Rot2dOnR2(N)
r = FieldType(g, [repr for repr in g.represent... | 5,190 | 32.275641 | 123 | py |
e2cnn | e2cnn-master/test/nn/test_multiple_module_flipsrotations.py | import unittest
from unittest import TestCase
from e2cnn.nn import *
from e2cnn.gspaces import *
import torch
import random
batchnormalizations = [
([('regular_bnorm', 'pointwise')], InnerBatchNorm),
([('g_bnorm', 'norm')], GNormBatchNorm),
([('norm_bnorm', 'norm')], NormBatchNorm),
([('indnorm_bnor... | 32,088 | 31.979445 | 95 | py |
e2cnn | e2cnn-master/test/diffops/test_fd_discretization.py | import itertools
import math
from typing import Tuple
import numpy as np
from scipy.signal import convolve2d, correlate2d
from e2cnn.diffops.utils import *
import unittest
from unittest import TestCase
def polynomial_derivative(coefficients: np.ndarray, diff: Tuple[int, int]) -> np.ndarray:
"""Compute the deriva... | 4,322 | 39.401869 | 109 | py |
e2cnn | e2cnn-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
import sphinx_rtd_theme
# -- Path setup -----------------------------------... | 6,428 | 29.469194 | 124 | py |
e2cnn | e2cnn-master/visualizations/animation.py | import numpy as np
from e2cnn.nn import *
from e2cnn.group import *
from e2cnn.gspaces import *
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import matplotlib.animation as manimation
from skimage.transform import resize
import scipy.ndimage
import torch
from typing import Union
plt.rcParams['im... | 14,994 | 33.313501 | 144 | py |
cbert_aug | cbert_aug-master/aug_dataset_wo_ft.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import os
import shutil
import logging
import argparse
import random
from tqdm import tqdm, trange
import json
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader,... | 17,598 | 40.119159 | 118 | py |
cbert_aug | cbert_aug-master/finetune_dataset.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import os
import shutil
import logging
import argparse
import random
from tqdm import tqdm, trange
import json
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader,... | 15,288 | 38.711688 | 118 | py |
cbert_aug | cbert_aug-master/aug_dataset.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import os
import shutil
import logging
import argparse
import random
from tqdm import tqdm, trange
import json
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader,... | 17,454 | 40.070588 | 118 | py |
cbert_aug | cbert_aug-master/text_classification/text_datasets.py | import csv
import glob
import io
import os
import shutil
import tarfile
import tempfile
import numpy
import chainer
from .nlp_utils import make_vocab
from .nlp_utils import normalize_text
from .nlp_utils import split_text
from .nlp_utils import transform_to_array
import json
URL_DBPEDIA = 'https://github.com/le-scie... | 6,606 | 31.546798 | 102 | py |
Kassiopeia | Kassiopeia-main/Kassiopeia/Documentation/Reference/conf.py | # -*- coding: utf-8 -*-
#
# Kassiopeia documentation build configuration file, created by
# sphinx-quickstart on Tue Oct 18 13:33:10 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
... | 10,991 | 32.410334 | 445 | py |
tensorflow-deeplab-resnet | tensorflow-deeplab-resnet-master/convert.py | #!/usr/bin/env python
# This script belongs to https://github.com/ethereon/caffe-tensorflow
import os
import sys
import numpy as np
import argparse
from kaffe import KaffeError, print_stderr
from kaffe.tensorflow import TensorFlowTransformer
def fatal_error(msg):
print_stderr(msg)
exit(-1)
def validate_arg... | 2,250 | 35.306452 | 89 | py |
tensorflow-deeplab-resnet | tensorflow-deeplab-resnet-master/deeplab_resnet/model.py | # Converted to TensorFlow .caffemodel
# with the DeepLab-ResNet configuration.
# The batch normalisation layer is provided by
# the slim library (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim).
from kaffe.tensorflow import Network
import tensorflow as tf
class DeepLabResNetModel(Network... | 27,003 | 63.602871 | 108 | py |
tensorflow-deeplab-resnet | tensorflow-deeplab-resnet-master/kaffe/transformers.py | '''
A collection of graph transforms.
A transformer is a callable that accepts a graph and returns a transformed version.
'''
import numpy as np
from .caffe import get_caffe_resolver, has_pycaffe
from .errors import KaffeError, print_stderr
from .layers import NodeKind
class DataInjector(object):
'''
Assoc... | 10,811 | 36.154639 | 99 | py |
tensorflow-deeplab-resnet | tensorflow-deeplab-resnet-master/kaffe/graph.py | from google.protobuf import text_format
from .caffe import get_caffe_resolver
from .errors import KaffeError, print_stderr
from .layers import LayerAdapter, LayerType, NodeKind, NodeDispatch
from .shapes import TensorShape
class Node(object):
def __init__(self, name, kind, layer=None):
self.name = name
... | 11,653 | 37.462046 | 99 | py |
tensorflow-deeplab-resnet | tensorflow-deeplab-resnet-master/kaffe/caffe/caffepb.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: caffe.proto
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import... | 237,573 | 42.35292 | 28,178 | py |
tensorflow-deeplab-resnet | tensorflow-deeplab-resnet-master/kaffe/caffe/resolver.py | import sys
SHARED_CAFFE_RESOLVER = None
class CaffeResolver(object):
def __init__(self):
self.import_caffe()
def import_caffe(self):
self.caffe = None
try:
# Try to import PyCaffe first
import caffe
self.caffe = caffe
except ImportError:
... | 1,422 | 28.040816 | 68 | py |
tensorflow-deeplab-resnet | tensorflow-deeplab-resnet-master/kaffe/caffe/__init__.py | from .resolver import get_caffe_resolver, has_pycaffe
| 54 | 26.5 | 53 | py |
tensorflow-deeplab-resnet | tensorflow-deeplab-resnet-master/kaffe/tensorflow/transformer.py | import numpy as np
from ..errors import KaffeError, print_stderr
from ..graph import GraphBuilder, NodeMapper
from ..layers import NodeKind
from ..transformers import (DataInjector, DataReshaper, NodeRenamer, ReLUFuser,
BatchNormScaleBiasFuser, BatchNormPreprocessor, ParameterNamer)
from .... | 10,312 | 35.059441 | 97 | py |
BCI-Attention | BCI-Attention-main/exp_5CV_SEED.py | # -*- coding: utf-8 -*-
"""
Created on Tue Nov 3 09:57:24 2020
@author: dykua
Main script for training with SEED data
For the benchmark purpose - 5cv
"""
#%%
import argparse
from re import sub
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
from scipy.stats import zscore
from tensorfl... | 7,010 | 33.536946 | 117 | py |
BCI-Attention | BCI-Attention-main/DEAP_Plots.py | '''
Make candidate plots and summaries from DEAP experiments
'''
#%%
import tensorflow as tf
# tf.compat.v1.enable_eager_execution()
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat, savemat
from scipy.stats import zscore
from sklearn.metrics import confusion_matrix
from Utils import s... | 37,448 | 34.905081 | 129 | py |
BCI-Attention | BCI-Attention-main/DEAP_test_gen.py | #%%
from random import sample
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import zscore
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint
from sklearn.metrics import c... | 21,808 | 40.383302 | 132 | py |
BCI-Attention | BCI-Attention-main/sigma_effect.py | '''
Poke around sigma's effect with saved models
'''
#%%
from scipy.io import loadmat
from scipy.stats import zscore
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from logging import raiseExceptions
from tensorflow.keras import layers
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import ... | 25,038 | 33.97067 | 129 | py |
BCI-Attention | BCI-Attention-main/Modules.py | '''
custom layers
'''
#%%
from logging import raiseExceptions
# import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
# from tensorflow.python.keras.backend import bias_add
import tensorflow_addons as tfa
from tensorflow import keras
from tensorflow.keras import layers
#########################... | 61,379 | 38.195402 | 127 | py |
BCI-Attention | BCI-Attention-main/Models.py | '''
Candidate models
'''
#%%
from Modules import SwinTransformer, PatchExtract, PatchEmbedding, PatchMerging
from tensorflow.keras import Model, layers, losses, metrics
import tensorflow_addons as tfa
from tensorflow.keras import layers
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.constraints imp... | 34,101 | 47.786838 | 140 | py |
TTUR | TTUR-master/BEGAN_FID_batched/trainer_fid_batched.py | from __future__ import print_function
import os
#import StringIO
import scipy.misc
import numpy as np
from glob import glob
from tqdm import trange
from itertools import chain
from collections import deque
from scipy.linalg import sqrtm
from numpy.linalg import norm
from models import *
from utils import save_image
... | 18,920 | 36.842 | 126 | py |
TTUR | TTUR-master/BEGAN_FID_batched/utils.py | from __future__ import print_function
import os
import math
import json
import logging
import numpy as np
from PIL import Image
from datetime import datetime
def prepare_dirs_and_logger(config):
formatter = logging.Formatter("%(asctime)s:%(levelname)s::%(message)s")
logger = logging.getLogger()
for hdlr ... | 2,783 | 32.142857 | 109 | py |
Instaboost | Instaboost-master/yolact/yolact.py | import torch, torchvision
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.resnet import Bottleneck
import numpy as np
from itertools import product
from math import sqrt
from typing import List
from data.config import cfg, mask_type
from layers import Detect
from layers.interpolate import... | 28,042 | 41.043478 | 137 | py |
Instaboost | Instaboost-master/yolact/backbone.py | import torch
import torch.nn as nn
import pickle
from collections import OrderedDict
class Bottleneck(nn.Module):
""" Adapted from torchvision.models.resnet """
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, norm_layer=nn.BatchNorm2d, dilation=1):
super(Bottleneck, ... | 16,252 | 35.441704 | 117 | py |
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