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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linpde-gp | linpde-gp-main/experiments/experiment_utils/_setup.py | import pathlib
import jax
import matplotlib.pyplot as plt
from matplotlib_inline.backend_inline import set_matplotlib_formats
# Use custom matplotlib style file
plt.style.use(pathlib.Path(__file__).parent / "linpde-gp.mplstyle")
# Set output formats for matplotlib inline backend
set_matplotlib_formats("svg")
# Jax ... | 376 | 24.133333 | 67 | py |
cac-openset | cac-openset-master/train_cacOpenset.py | """
Train an open set classifier with CAC Loss on the datasets.
The overall setup of this training script has been adapted from https://github.com/kuangliu/pytorch-cifar
Dimity Miller, 2020
"""
import torch
import torch.nn as nn
import torch.optim as optim
import json
import torchvision
import torchvision.transfo... | 7,391 | 31.279476 | 141 | py |
cac-openset | cac-openset-master/eval_closedSet.py | """
Evaluate average performance for a standard closed set classifier on a given dataset.
Dimity Miller, 2020
"""
import argparse
import json
import torchvision
import torchvision.transforms as tf
import torch
import torch.nn as nn
from networks import closedSetClassifier
import datasets.utils as dataHelper
fro... | 3,022 | 30.821053 | 136 | py |
cac-openset | cac-openset-master/utils.py | """
Helper functions for training and evaluation.
progress_bar and format_time function was taken from https://github.com/kuangliu/pytorch-cifar which mimics xlua.progress
Dimity Miller, 2020
"""
import os
import sys
import time
import math
import numpy as np
import torch
import datasets.utils as dataHel... | 6,578 | 28.904545 | 147 | py |
cac-openset | cac-openset-master/train_closedSet.py | """
Train a closed-set classifier on the datasets.
This training script has been adapted from https://github.com/kuangliu/pytorch-cifar
Dimity Miller, 2020
"""
import torch
import torch.nn as nn
import torch.optim as optim
import json
import torchvision
import torchvision.transforms as tf
import argparse
impo... | 4,757 | 28.7375 | 137 | py |
cac-openset | cac-openset-master/eval_cacOpenset.py | """
Evaluate average performance for our proposed CAC open-set classifier on a given dataset.
Dimity Miller, 2020
"""
import argparse
import json
import torchvision
import torchvision.transforms as tf
import torch
import torch.nn as nn
from networks import openSetClassifier
import datasets.utils as dataHelper
fr... | 3,148 | 38.3625 | 140 | py |
cac-openset | cac-openset-master/networks/closedSetClassifier.py | """
Network definition for standard closed set classifier.
Dimity Miller, 2020
"""
import torch
import torchvision
import torch.nn as nn
class closedSetClassifier(nn.Module):
def __init__(self, num_classes = 20, num_channels = 3, im_size = 64, init_weights = False, dropout = 0.3, **kwargs):
sup... | 5,115 | 32.657895 | 120 | py |
cac-openset | cac-openset-master/networks/openSetClassifier.py | """
Network definition for our proposed CAC open set classifier.
Dimity Miller, 2020
"""
import torch
import torchvision
import torch.nn as nn
class openSetClassifier(nn.Module):
def __init__(self, num_classes = 20, num_channels = 3, im_size = 64, init_weights = False, dropout = 0.3, **kwargs):
super(openSetC... | 5,072 | 26.873626 | 117 | py |
cac-openset | cac-openset-master/datasets/utils.py | """
Functions useful for creating experiment datasets and dataloaders.
Dimity Miller, 2020
"""
import torch
import torchvision
import torchvision.transforms as tf
import json
from torch.autograd import Variable
import numpy as np
import random
random.seed(1000)
def get_train_loaders(datasetName, trial_num, cfg):
... | 11,899 | 34.628743 | 133 | py |
cac-openset | cac-openset-master/datasets/generate_trainval_splits.py | """
Randomly select train and validation subsets from training datasets.
80/20 split ratio used for all datasets except TinyImageNet, which will use 90/10.
Dimity Miller, 2020
"""
import json
import random
import torchvision
import numpy as np
random.seed(1000)
def save_trainval_split(dataset, train_idxs, val_id... | 2,075 | 31.4375 | 91 | py |
cac-openset | cac-openset-master/datasets/generate_class_splits.py | """
Randomly selects 5 trials of known and unknown classes for each dataset and saves for reference.
1. MNIST, SVHN, CIFAR10 - 6 known, 4 unknown
2. CIFAR+M - 4 known from non-animal subset of CIFAR10, M unknown from animal subset of CIFAR100
3. TinyImageNet - 20 known, 180 unknown
Dimity Miller, 2020
"""
import... | 2,349 | 36.301587 | 107 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/examples/speed_tests/timeitcwt_1d.py | import torch
import ptwt
import pywt
import time
import numpy as np
import matplotlib.pyplot as plt
import tikzplotlib
from ptwt.continuous_transform import _ShannonWavelet
def _to_jit_cwt(sig):
widths = torch.arange(1, 31)
wavelet = _ShannonWavelet("shan0.1-0.4")
sampling_period = (4 / 800) * np.pi
... | 2,991 | 30.166667 | 96 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/examples/speed_tests/timeitconv_1d.py | import pywt
import ptwt
import torch
import numpy as np
import time
from typing import NamedTuple
import matplotlib.pyplot as plt
# import tikzplotlib
class WaveletTuple(NamedTuple):
"""Replaces namedtuple("Wavelet", ("dec_lo", "dec_hi", "rec_lo", "rec_hi"))."""
dec_lo: torch.Tensor
dec_hi: torch.Tensor
... | 3,752 | 32.212389 | 99 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/examples/speed_tests/timeitconv_3d.py | from typing import NamedTuple
import pywt
import ptwt
import torch
import numpy as np
import time
import matplotlib.pyplot as plt
# import tikzplotlib
class WaveletTuple(NamedTuple):
"""Replaces namedtuple("Wavelet", ("dec_lo", "dec_hi", "rec_lo", "rec_hi"))."""
dec_lo: torch.Tensor
dec_hi: torch.Tenso... | 3,770 | 31.791304 | 87 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/examples/speed_tests/timeitconv_2d.py | from typing import NamedTuple
import pywt
import ptwt
import torch
import numpy as np
import time
from pytorch_wavelets import DWTForward
import matplotlib.pyplot as plt
# import tikzplotlib
class WaveletTuple(NamedTuple):
"""Replaces namedtuple("Wavelet", ("dec_lo", "dec_hi", "rec_lo", "rec_hi"))."""
dec_... | 4,683 | 33.441176 | 103 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/examples/continuous_signal_analysis/cwt_chirp_analysis.py | import torch
import numpy as np
import ptwt
import matplotlib.pyplot as plt
import scipy.signal as signal
if __name__ == "__main__":
t = np.linspace(-2, 2, 800, endpoint=False)
sig = signal.chirp(t, f0=1, f1=12, t1=2, method="linear")
widths = np.arange(1, 31)
cwtmatr_pt, freqs = ptwt.cwt(
torc... | 808 | 26.896552 | 80 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/examples/wavelet_packet_chirp_analysis/chirp_analysis.py | import torch
import pywt
import numpy as np
import scipy.signal
import matplotlib.pyplot as plt
# use from src.ptwt.packets if you cloned the repo instead of using pip.
from ptwt import WaveletPacket
fs = 1000
t = np.linspace(0, 2, int(2//(1/fs)))
w = np.sin(256*np.pi*t**2)
wavelet = pywt.Wavelet("sym8")
wp = Wavele... | 1,171 | 23.93617 | 81 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/examples/network_compression/wavelet_linear.py | # Originally created by moritz (wolter@cs.uni-bonn.de)
# at https://github.com/v0lta/Wavelet-network-compression/blob/master/wavelet_learning/wavelet_linear.py
import torch
import numpy as np
from torch.nn.parameter import Parameter
import pywt
from ptwt.conv_transform import wavedec, waverec
class WaveletLayer(torch... | 4,444 | 34.56 | 104 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/examples/network_compression/mnist_compression.py | # Originally created by moritz (wolter@cs.uni-bonn.de) on 17/12/2019
# at https://github.com/v0lta/Wavelet-network-compression/blob/master/mnist_compression.py
# based on https://github.com/pytorch/examples/blob/master/mnist/main.py
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.... | 10,195 | 30.962382 | 91 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/examples/deepfake_analysis/packet_plot.py | import os
from itertools import product
from tqdm import tqdm
from PIL import Image
import numpy as np
import torch
import ptwt
import pywt
import matplotlib.pyplot as plt
def get_freq_order(level: int):
"""Get the frequency order for a given packet decomposition level.
Adapted from:
https://github.com/... | 5,330 | 32.31875 | 87 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/src/ptwt/conv_transform_2.py | """This module implements two-dimensional padded wavelet transforms.
The implementation relies on torch.nn.functional.conv2d and
torch.nn.functional.conv_transpose2d under the hood.
"""
from typing import List, Optional, Tuple, Union
import pywt
import torch
from ._util import Wavelet, _as_wavelet, _get_len, _is_d... | 9,062 | 32.817164 | 87 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/src/ptwt/matmul_transform_3.py | """Implement 3D separable boundary transforms."""
import sys
from functools import partial
from typing import Dict, List, NamedTuple, Optional, Tuple, Union
import numpy as np
import torch
from ._util import (
Wavelet,
_as_wavelet,
_is_boundary_mode_supported,
_is_dtype_supported,
)
from .matmul_trans... | 16,716 | 37.254005 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/src/ptwt/matmul_transform.py | """Implement matrix based fwt and ifwt.
This module uses boundary filters instead of padding.
The implementation is based on the description
in Strang Nguyen (p. 32), as well as the description
of boundary filters in "Ripples in Mathematics" section 10.3 .
"""
# Created by moritz (wolter@cs.uni-bonn.de) at 14.04.20
i... | 22,734 | 35.728595 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/src/ptwt/_util.py | """Utility methods to compute wavelet decompositions from a dataset."""
from typing import Optional, Protocol, Sequence, Tuple, Union
import pywt
import torch
class Wavelet(Protocol):
"""Wavelet object interface, based on the pywt wavelet object."""
name: str
dec_lo: Sequence[float]
dec_hi: Sequence... | 1,989 | 28.701493 | 85 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/src/ptwt/separable_conv_transform.py | """Implement separable convolution based transforms.
Under the hood code in this module transforms all dimensions
individually using torch.nn.functional.conv1d and it's
transpose.
"""
from typing import Dict, List, Optional, Union
import numpy as np
import pywt
import torch
from ._util import _as_wavelet
from .conv_... | 13,612 | 33.638677 | 85 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/src/ptwt/continuous_transform.py | """PyTorch compatible cwt code.
This module is based on pywt's cwt implementation.
"""
from typing import Any, Tuple, Union
import numpy as np
import torch
from pywt import ContinuousWavelet, DiscreteContinuousWavelet, Wavelet
from pywt._functions import scale2frequency
from torch.fft import fft, ifft
def _next_fas... | 11,257 | 34.968051 | 107 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/src/ptwt/matmul_transform_2.py | """Two dimensional matrix based fast wavelet transform implementations.
This module uses boundary filters to minimize padding.
"""
# Written by moritz ( @ wolter.tech ) in 2021
import sys
from typing import List, Optional, Tuple, Union, cast
import numpy as np
import torch
from ._util import (
Wavelet,
_as_w... | 32,359 | 39 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/src/ptwt/sparse_math.py | """Efficiently construct fwt operations using sparse matrices."""
# Written by moritz ( @ wolter.tech ) 17.09.21
from itertools import product
from typing import List
import torch
def _dense_kron(
sparse_tensor_a: torch.Tensor, sparse_tensor_b: torch.Tensor
) -> torch.Tensor:
"""Faster than sparse_kron.
... | 20,237 | 32.39604 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/src/ptwt/packets.py | """Compute analysis wavelet packet representations."""
# Created on Fri Apr 6 2021 by moritz (wolter@cs.uni-bonn.de)
import collections
from functools import partial
from itertools import product
from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple, Union, cast
import numpy as np
import pywt
import... | 21,343 | 37.807273 | 89 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/src/ptwt/conv_transform_3.py | """Code for three dimensional padded transforms.
The functions here are based on torch.nn.functional.conv3d and it's transpose.
"""
from typing import Dict, List, Optional, Sequence, Union, cast
import pywt
import torch
from ._util import Wavelet, _as_wavelet, _get_len, _is_dtype_supported, _outer
from .conv_transf... | 9,342 | 33.603704 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/src/ptwt/wavelets_learnable.py | """Experimental code for adaptive wavelet learning.
See https://arxiv.org/pdf/2004.09569.pdf for more information.
"""
# Created by moritz wolter@cs.uni-bonn.de, 14.05.20
# Inspired by Ripples in Mathematics, Jensen and La Cour-Harbo, Chapter 7.7
# import pywt
from abc import ABC, abstractmethod
from typing import Tup... | 10,275 | 33.02649 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/src/ptwt/conv_transform.py | """Fast wavelet transformations based on torch.nn.functional.conv1d and its transpose.
This module treats boundaries with edge-padding.
"""
# Created by moritz wolter, 14.04.20
from typing import List, Optional, Sequence, Tuple, Union
import pywt
import torch
from ._util import Wavelet, _as_wavelet, _get_len, _is_dt... | 11,634 | 32.921283 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/tests/_mackey_glass.py | """Generate artificial time-series data for debugging purposes."""
from typing import Optional, Union
import torch
def generate_mackey(
batch_size: int = 100,
tmax: float = 200,
delta_t: float = 1.0,
rnd: bool = True,
device: Union[torch.device, str] = "cuda",
) -> torch.Tensor:
"""Generate s... | 3,296 | 31.643564 | 85 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/tests/test_sparse_math.py | """Test the sparse math code from ptwt.sparse_math."""
# Written by moritz ( @ wolter.tech ) in 2021
import numpy as np
import pytest
import scipy.signal
import torch
from scipy import datasets
from src.ptwt.sparse_math import (
batch_mm,
construct_conv2d_matrix,
construct_conv_matrix,
construct_stride... | 10,862 | 32.018237 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/tests/test_matrix_fwt_2.py | """Test code for the boundary wavelets."""
# Created by moritz ( wolter@cs.uni-bonn.de ), 08.09.21
import numpy as np
import pytest
import pywt
import scipy.signal
import torch
from src.ptwt.conv_transform import _flatten_2d_coeff_lst
from src.ptwt.matmul_transform import MatrixWavedec, MatrixWaverec
from src.ptwt.mat... | 8,091 | 37.717703 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/tests/test_separable_conv_fwt.py | """Separable transform test code."""
import numpy as np
import pytest
import pywt
import torch
from src.ptwt.conv_transform import wavedec
from src.ptwt.matmul_transform_2 import MatrixWavedec2
from src.ptwt.matmul_transform_3 import MatrixWavedec3
from src.ptwt.separable_conv_transform import (
_fswavedec,
_... | 5,702 | 36.032468 | 87 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/tests/test_matrix_fwt.py | """Test the fwt and ifwt matrices."""
# Written by moritz ( @ wolter.tech ) in 2021
import numpy as np
import pytest
import pywt
import torch
from src.ptwt.matmul_transform import (
MatrixWavedec,
MatrixWaverec,
_construct_a,
_construct_s,
construct_boundary_a,
construct_boundary_s,
)
from test... | 7,206 | 38.598901 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/tests/test_convolution_fwt.py | """Test the conv-fwt code."""
# Written by moritz ( @ wolter.tech ) in 2021
import numpy as np
import pytest
import pywt
import torch
from scipy import datasets
from src.ptwt._util import _outer
from src.ptwt.conv_transform import (
_flatten_2d_coeff_lst,
_translate_boundary_strings,
wavedec,
waverec,
... | 8,187 | 37.990476 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/tests/test_matrix_fwt_3.py | """Test the 3d matrix-fwt code."""
import numpy as np
import pytest
import pywt
import torch
from src.ptwt.matmul_transform import construct_boundary_a
from src.ptwt.matmul_transform_3 import MatrixWavedec3, MatrixWaverec3
from src.ptwt.sparse_math import _batch_dim_mm
@pytest.mark.parametrize("axis", [1, 2, 3])
@p... | 3,090 | 35.364706 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/tests/test_jit.py | """Ensure pytorch's torch.jit.trace feature works properly."""
from typing import NamedTuple
import numpy as np
import pytest
import pywt
import torch
from scipy import signal
import src.ptwt as ptwt
from ptwt.continuous_transform import _ShannonWavelet
from tests._mackey_glass import MackeyGenerator
class WaveletT... | 6,477 | 33.457447 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/tests/test_packets.py | """Test the wavelet packet code."""
# Created on Fri Apr 6 2021 by moritz (wolter@cs.uni-bonn.de)
from itertools import product
import numpy as np
import pytest
import pywt
import torch
from scipy import datasets
from src.ptwt.packets import WaveletPacket, WaveletPacket2D, get_freq_order
def _compare_trees1(
wa... | 11,918 | 32.574648 | 88 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/tests/test_wavelet.py | """Test the adaptive wavelet cost functions."""
import pytest
import pywt
import torch
from src.ptwt.wavelets_learnable import SoftOrthogonalWavelet
@pytest.mark.parametrize(
"lst, is_orth",
[
(pywt.wavelist(family="db"), True),
(pywt.wavelist(family="sym"), True),
(pywt.wavelist(fami... | 1,316 | 29.627907 | 65 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/tests/test_cwt.py | """Test the continuous transformation code."""
from typing import Union
import numpy as np
import pytest
import pywt
import torch
from scipy import signal
from src.ptwt.continuous_transform import _ShannonWavelet, cwt
continuous_wavelets = [
"cgau1",
"cgau2",
"cgau3",
"cgau4",
"cgau5",
"cgau6... | 3,961 | 33.452174 | 81 | py |
PyTorch-Wavelet-Toolbox | PyTorch-Wavelet-Toolbox-main/tests/test_convolution_fwt_3.py | """Test our 3d for loop-convolution based fwt code."""
from typing import List
import numpy as np
import pytest
import pywt
import torch
import src.ptwt as ptwt
def _expand_dims(batch_list: List) -> List:
for pos, bel in enumerate(batch_list):
if type(bel) is np.ndarray:
batch_list[pos] = n... | 2,637 | 30.404762 | 85 | py |
adaptive-selfsupervision-pinns | adaptive-selfsupervision-pinns-main/train.py | """
train PINNs
"""
import os, sys, time
import copy
import numpy as np
import argparse
import random
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import wandb
import matplotlib.pyplot as plt
from datetime import datetime
import logging
from... | 25,848 | 43.261986 | 296 | py |
adaptive-selfsupervision-pinns | adaptive-selfsupervision-pinns-main/models/ffn.py | import torch
import torch.nn as nn
import numpy as np
from utils.misc_utils import set_activation
class FeedForward(nn.Module):
''' An n-layer-feed-forward-layer module '''
def __init__(self, in_dim=2, out_dim=1, depth=5, hidden_dim=50, activation='tanh'):
super().__init__()
self.depth = depth
... | 1,588 | 36.833333 | 102 | py |
adaptive-selfsupervision-pinns | adaptive-selfsupervision-pinns-main/utils/domains.py | '''
domain classes
'''
import torch
import random
import numpy as np
from utils.pde_sols import compute_sol
from utils.misc_utils import normalize, softmax, show
import matplotlib.pyplot as plt
def fake_mask(nt, nx, n=100):
mask = np.zeros((nt,nx))
offset = 20
mask[nt//4-offset:3*nt//4-offset, nx//4:3*nx... | 17,941 | 46.845333 | 164 | py |
adaptive-selfsupervision-pinns | adaptive-selfsupervision-pinns-main/utils/logging_utils.py | import os
import logging
_format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
def config_logger(log_level=logging.INFO):
logging.basicConfig(format=_format, level=log_level)
def log_to_file(logger_name=None, log_level=logging.INFO, log_filename='tensorflow.log'):
if not os.path.exists(os.path.dirnam... | 1,042 | 30.606061 | 97 | py |
adaptive-selfsupervision-pinns | adaptive-selfsupervision-pinns-main/utils/misc_utils.py | """
misc utils
"""
import numpy as np
import scipy as sc
import scipy.ndimage as nd
import torch
import torch.nn as nn
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import Normalize
from matplotlib import cm
def get_velocity(vel_type... | 9,928 | 33.595819 | 138 | py |
adaptive-selfsupervision-pinns | adaptive-selfsupervision-pinns-main/utils/loss_utils.py | """
loss functions
"""
import torch
import torch.nn as nn
import logging
import numpy as np
from torch.autograd import Variable
import time
from utils.misc_utils import gaussian, poisson_source, get_diff_tensor, diffusion_coef, show, get_velocity
import matplotlib.pyplot as plt
#from functorch import vmap
class Loss... | 12,316 | 43.305755 | 165 | py |
adaptive-selfsupervision-pinns | adaptive-selfsupervision-pinns-main/utils/optimizer_utils.py | import os, sys
import logging
import torch
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.lr_scheduler import ReduceLROnPlateau
from utils.mod_lbfgs import ModLBFGS
class EarlyStopping:
""" https://github.com/Bjarten/early-stoppin... | 2,787 | 38.267606 | 136 | py |
adaptive-selfsupervision-pinns | adaptive-selfsupervision-pinns-main/utils/data_utils.py | """
data loaders
"""
import re
import time
import os, sys
import logging
import glob
import torch
import random
import numpy as np
from torch.utils.data import DataLoader, Dataset, TensorDataset
from torch.utils.data.distributed import DistributedSampler
import torchvision.transforms as transforms
from utils.pde_sols... | 6,612 | 48.350746 | 140 | py |
adaptive-selfsupervision-pinns | adaptive-selfsupervision-pinns-main/utils/mod_lbfgs.py | import torch
from functools import reduce
from utils.optimizer_base import Optimizer
def _cubic_interpolate(x1, f1, g1, x2, f2, g2, bounds=None):
# ported from https://github.com/torch/optim/blob/master/polyinterp.lua
# Compute bounds of interpolation area
if bounds is not None:
xmin_bound, xmax_bo... | 18,218 | 36.257669 | 124 | py |
adaptive-selfsupervision-pinns | adaptive-selfsupervision-pinns-main/utils/optimizer_base.py | from collections import defaultdict, abc as container_abcs
import torch
from copy import deepcopy
from itertools import chain
import warnings
import functools
class _RequiredParameter(object):
"""Singleton class representing a required parameter for an Optimizer."""
def __repr__(self):
return "<requi... | 11,818 | 42.774074 | 115 | py |
adaptive-selfsupervision-pinns | adaptive-selfsupervision-pinns-main/utils/pde_sols.py | """
solutions to different PDE systems
"""
import time
import os
import numpy as np
import scipy as sc
import torch
import torch.fft
import logging
import matplotlib.pyplot as plt
from scipy.sparse.linalg import LinearOperator
from utils.misc_utils import gaussian, poisson_source, diffusion_coef, show, get_diff_tens... | 7,599 | 32.480176 | 147 | py |
FewShotDetection | FewShotDetection-master/test.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import sys
import numpy as np
import argparse
import time
import cv2
import pickle
import torch
from torch.autograd import Variable
from roi_data_layer.roidb import combined_roidb
... | 17,250 | 44.278215 | 129 | py |
FewShotDetection | FewShotDetection-master/train.py | import _init_paths
import os
import sys
import numpy as np
import argparse
import pprint
import pdb
import time
import collections
import torch
import torch.nn as nn
import torch.optim as optim
import random
from tensorboardX import SummaryWriter
import torchvision.transforms as transforms
from torch.utils.data.sample... | 25,214 | 42.700173 | 129 | py |
FewShotDetection | FewShotDetection-master/lib/roi_data_layer/roibatchLoader.py |
"""The data layer used during training to train a Fast R-CNN network.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.utils.data as data
from PIL import Image
import torch
from model.utils.config import cfg
from roi_data_layer.minibatch i... | 9,043 | 39.556054 | 109 | py |
FewShotDetection | FewShotDetection-master/lib/roi_data_layer/minibatch.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Xinlei Chen
# --------------------------------------------------------
"""Compute minibatch blobs for training a Fast R-CNN ne... | 3,033 | 31.978261 | 113 | py |
FewShotDetection | FewShotDetection-master/lib/datasets/metadata.py | # --------------------------------------------------------
# Pytorch Meta R-CNN
# Written by Anny Xu, Xiaopeng Yan, based on the code from Jianwei Yang
# --------------------------------------------------------
import os
import os.path
import sys
import torch.utils.data as data
import cv2
import torch
import random
imp... | 5,159 | 38.090909 | 104 | py |
FewShotDetection | FewShotDetection-master/lib/datasets/metadata_TFA.py | import torch.utils.data as data
import cv2
import torch
import collections
import time
import os
import numpy as np
import json
import os.path as osp
from model.utils.config import cfg
from pycocotools.coco import COCO
class MetaDatasetTFA(data.Dataset):
def __init__(self, root, image_set, year, img_size, shots=... | 6,141 | 38.625806 | 112 | py |
FewShotDetection | FewShotDetection-master/lib/datasets/metadata_3d.py | from __future__ import print_function
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import datasets
import datasets.o... | 6,916 | 33.758794 | 106 | py |
FewShotDetection | FewShotDetection-master/lib/datasets/pascal_voc_rbg.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ i... | 13,812 | 34.692506 | 85 | py |
FewShotDetection | FewShotDetection-master/lib/datasets/custom_metadata.py | import os, sys
import numpy as np
import pandas as pd
import cv2
import collections
import random
import time
import torch
import torch.utils.data as data
from model.utils.config import cfg
import datasets
from datasets.imdb import imdb
import datasets.custom
class MetaDatasetCustom(data.Dataset):
def __init__(... | 6,426 | 33.740541 | 108 | py |
FewShotDetection | FewShotDetection-master/lib/datasets/metadata_coco.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future... | 9,807 | 36.292776 | 110 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_crop/build.py | from __future__ import print_function
import os
import torch
from torch.utils.ffi import create_extension
#this_file = os.path.dirname(__file__)
sources = ['src/roi_crop.c']
headers = ['src/roi_crop.h']
defines = []
with_cuda = False
if torch.cuda.is_available():
print('Including CUDA code.')
sources += ['sr... | 881 | 22.837838 | 75 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_crop/functions/gridgen.py | # functions/add.py
import torch
from torch.autograd import Function
import numpy as np
class AffineGridGenFunction(Function):
def __init__(self, height, width,lr=1):
super(AffineGridGenFunction, self).__init__()
self.lr = lr
self.height, self.width = height, width
self.grid = np.ze... | 2,233 | 46.531915 | 171 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_crop/functions/crop_resize.py | # functions/add.py
import torch
from torch.autograd import Function
from .._ext import roi_crop
from cffi import FFI
ffi = FFI()
class RoICropFunction(Function):
def forward(self, input1, input2):
self.input1 = input1
self.input2 = input2
self.device_c = ffi.new("int *")
output = to... | 1,545 | 39.684211 | 126 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_crop/functions/roi_crop.py | # functions/add.py
import torch
from torch.autograd import Function
from .._ext import roi_crop
import pdb
class RoICropFunction(Function):
def forward(self, input1, input2):
self.input1 = input1.clone()
self.input2 = input2.clone()
output = input2.new(input2.size()[0], input1.size()[1], in... | 1,002 | 44.590909 | 122 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_crop/modules/gridgen.py | from torch.nn.modules.module import Module
import torch
from torch.autograd import Variable
import numpy as np
from ..functions.gridgen import AffineGridGenFunction
import pyximport
pyximport.install(setup_args={"include_dirs":np.get_include()},
reload_support=True)
class _AffineGridGen(Module):
... | 16,532 | 38.838554 | 170 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_crop/modules/roi_crop.py | from torch.nn.modules.module import Module
from ..functions.roi_crop import RoICropFunction
class _RoICrop(Module):
def __init__(self, layout = 'BHWD'):
super(_RoICrop, self).__init__()
def forward(self, input1, input2):
return RoICropFunction()(input1, input2)
| 287 | 31 | 48 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_crop/_ext/roi_crop/__init__.py |
from torch.utils.ffi import _wrap_function
from ._roi_crop import lib as _lib, ffi as _ffi
__all__ = []
def _import_symbols(locals):
for symbol in dir(_lib):
fn = getattr(_lib, symbol)
if callable(fn):
locals[symbol] = _wrap_function(fn, _ffi)
else:
locals[symbol] =... | 382 | 22.9375 | 53 | py |
FewShotDetection | FewShotDetection-master/lib/model/faster_rcnn/faster_rcnn.py | # --------------------------------------------------------
# Pytorch Meta R-CNN
# Written by Anny Xu, Xiaopeng Yan, based on the code from Jianwei Yang
# --------------------------------------------------------
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import V... | 13,518 | 45.778547 | 129 | py |
FewShotDetection | FewShotDetection-master/lib/model/faster_rcnn/resnet.py | # --------------------------------------------------------
# Pytorch Meta R-CNN
# Written by Anny Xu, Xiaopeng Yan, based on code from Jianwei Yang
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
fro... | 11,588 | 32.787172 | 109 | py |
FewShotDetection | FewShotDetection-master/lib/model/faster_rcnn/vgg16.py | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ impor... | 2,018 | 31.047619 | 89 | py |
FewShotDetection | FewShotDetection-master/lib/model/faster_rcnn/trail.py | # --------------------------------------------------------
# Pytorch multi-GPU Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick
# --------------------------------------------------------
import _init_paths
import os
import sys... | 15,360 | 37.116625 | 117 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_align/build.py | from __future__ import print_function
import os
import torch
from torch.utils.ffi import create_extension
# sources = ['src/roi_align.c']
# headers = ['src/roi_align.h']
sources = []
headers = []
defines = []
with_cuda = False
if torch.cuda.is_available():
print('Including CUDA code.')
sources += ['src/roi_al... | 872 | 22.594595 | 75 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_align/functions/roi_align.py | import torch
from torch.autograd import Function
from .._ext import roi_align
# TODO use save_for_backward instead
class RoIAlignFunction(Function):
def __init__(self, aligned_height, aligned_width, spatial_scale):
self.aligned_width = int(aligned_width)
self.aligned_height = int(aligned_height)
... | 1,760 | 35.6875 | 102 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_align/modules/roi_align.py | from torch.nn.modules.module import Module
from torch.nn.functional import avg_pool2d, max_pool2d
from ..functions.roi_align import RoIAlignFunction
class RoIAlign(Module):
def __init__(self, aligned_height, aligned_width, spatial_scale):
super(RoIAlign, self).__init__()
self.aligned_width = int(... | 1,672 | 37.906977 | 74 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_align/_ext/roi_align/__init__.py |
from torch.utils.ffi import _wrap_function
from ._roi_align import lib as _lib, ffi as _ffi
__all__ = []
def _import_symbols(locals):
for symbol in dir(_lib):
fn = getattr(_lib, symbol)
if callable(fn):
locals[symbol] = _wrap_function(fn, _ffi)
else:
locals[symbol] ... | 383 | 23 | 53 | py |
FewShotDetection | FewShotDetection-master/lib/model/rpn/proposal_layer.py | from __future__ import absolute_import
# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
# ---------------... | 7,001 | 38.559322 | 105 | py |
FewShotDetection | FewShotDetection-master/lib/model/rpn/rpn_region.py | from __future__ import absolute_import
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from model.utils.config import cfg
from .proposal_layer_region import _ProposalLayer
from .anchor_target_layer import _AnchorTargetLayer
from model.utils.net_utils import _smoot... | 4,338 | 37.39823 | 109 | py |
FewShotDetection | FewShotDetection-master/lib/model/rpn/proposal_layer_region.py | from __future__ import absolute_import
# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
# ---------------... | 7,199 | 39 | 105 | py |
FewShotDetection | FewShotDetection-master/lib/model/rpn/bbox_transform.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
# --------------------------------------------------------
# Reorganized... | 9,288 | 35.003876 | 100 | py |
FewShotDetection | FewShotDetection-master/lib/model/rpn/rpn.py | from __future__ import absolute_import
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from model.utils.config import cfg
from .proposal_layer import _ProposalLayer
from .anchor_target_layer import _AnchorTargetLayer
from model.utils.net_utils import _smooth_l1_lo... | 8,248 | 39.043689 | 109 | py |
FewShotDetection | FewShotDetection-master/lib/model/rpn/anchor_target_layer.py | from __future__ import absolute_import
# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
# ---------------... | 8,917 | 39.721461 | 128 | py |
FewShotDetection | FewShotDetection-master/lib/model/rpn/proposal_target_layer_cascade_region.py | from __future__ import absolute_import
# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
# ---------------... | 9,977 | 44.561644 | 114 | py |
FewShotDetection | FewShotDetection-master/lib/model/rpn/proposal_target_layer_cascade.py | from __future__ import absolute_import
# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
# ---------------... | 9,308 | 43.971014 | 108 | py |
FewShotDetection | FewShotDetection-master/lib/model/utils/config.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path as osp
import numpy as np
# `pip install easydict` if you don't have it
from easydict import EasyDict as edict
__C = edict()
# Consumers can get config by:
# from fast_rcnn_config im... | 14,255 | 30.964126 | 91 | py |
FewShotDetection | FewShotDetection-master/lib/model/utils/net_utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import torchvision.models as models
from model.utils.config import cfg
from model.roi_crop.functions.roi_crop import RoICropFunction
import cv2
import pdb
import random
def save_net(fname, net):
... | 8,904 | 34.763052 | 110 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_pooling/build.py | from __future__ import print_function
import os
import torch
from torch.utils.ffi import create_extension
sources = ['src/roi_pooling.c']
headers = ['src/roi_pooling.h']
defines = []
with_cuda = False
if torch.cuda.is_available():
print('Including CUDA code.')
sources += ['src/roi_pooling_cuda.c']
header... | 848 | 22.583333 | 75 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_pooling/functions/roi_pool.py | import torch
from torch.autograd import Function
from .._ext import roi_pooling
import pdb
class RoIPoolFunction(Function):
def __init__(ctx, pooled_height, pooled_width, spatial_scale):
ctx.pooled_width = pooled_width
ctx.pooled_height = pooled_height
ctx.spatial_scale = spatial_scale
... | 1,773 | 44.487179 | 108 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_pooling/modules/roi_pool.py | from torch.nn.modules.module import Module
from ..functions.roi_pool import RoIPoolFunction
class _RoIPooling(Module):
def __init__(self, pooled_height, pooled_width, spatial_scale):
super(_RoIPooling, self).__init__()
self.pooled_width = int(pooled_width)
self.pooled_height = int(pooled_... | 524 | 34 | 105 | py |
FewShotDetection | FewShotDetection-master/lib/model/roi_pooling/_ext/roi_pooling/__init__.py |
from torch.utils.ffi import _wrap_function
from ._roi_pooling import lib as _lib, ffi as _ffi
__all__ = []
def _import_symbols(locals):
for symbol in dir(_lib):
fn = getattr(_lib, symbol)
if callable(fn):
locals[symbol] = _wrap_function(fn, _ffi)
else:
locals[symbol... | 385 | 23.125 | 53 | py |
FewShotDetection | FewShotDetection-master/lib/model/nms/nms_wrapper.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import torch
from model.utils.config import cfg
if torch.cuda.is_availab... | 757 | 33.454545 | 81 | py |
FewShotDetection | FewShotDetection-master/lib/model/nms/nms_gpu.py | from __future__ import absolute_import
import torch
import numpy as np
from ._ext import nms
import pdb
def nms_gpu(dets, thresh):
keep = dets.new(dets.size(0), 1).zero_().int()
num_out = dets.new(1).zero_().int()
nms.nms_cuda(keep, dets, num_out, thresh)
keep = keep[:num_out[0]]
return keep
| 299 | 22.076923 | 47 | py |
FewShotDetection | FewShotDetection-master/lib/model/nms/nms_cpu.py | from __future__ import absolute_import
import numpy as np
import torch
def nms_cpu(dets, thresh):
dets = dets.cpu().numpy()
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
kee... | 5,777 | 27.185366 | 93 | py |
FewShotDetection | FewShotDetection-master/lib/model/nms/build.py | from __future__ import print_function
import os
import torch
from torch.utils.ffi import create_extension
#this_file = os.path.dirname(__file__)
sources = []
headers = []
defines = []
with_cuda = False
if torch.cuda.is_available():
print('Including CUDA code.')
sources += ['src/nms_cuda.c']
headers += ['... | 850 | 21.394737 | 75 | py |
FewShotDetection | FewShotDetection-master/lib/model/nms/_ext/nms/__init__.py |
from torch.utils.ffi import _wrap_function
from ._nms import lib as _lib, ffi as _ffi
__all__ = []
def _import_symbols(locals):
for symbol in dir(_lib):
fn = getattr(_lib, symbol)
if callable(fn):
locals[symbol] = _wrap_function(fn, _ffi)
else:
locals[symbol] = fn
... | 377 | 22.625 | 53 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/BERT/run_re.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 28,742 | 42.748858 | 150 | py |
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