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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|---|---|---|---|---|---|---|
XFL | XFL-master/test/common/crypto/one_time_pad/test_one_time_add.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 10,297 | 34.388316 | 135 | py |
XFL | XFL-master/test/common/fedavg/otp/test_trainer3.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 2,072 | 29.485294 | 87 | py |
XFL | XFL-master/test/common/fedavg/otp/test_trainer1.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 2,075 | 29.985075 | 87 | py |
XFL | XFL-master/test/common/fedavg/otp/random_input.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 2,199 | 24.882353 | 74 | py |
XFL | XFL-master/test/common/fedavg/otp/test_trainer2.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 2,072 | 29.485294 | 87 | py |
XFL | XFL-master/test/common/fedavg/otp/test_scheduler.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 2,756 | 28.967391 | 87 | py |
XFL | XFL-master/test/common/utils/test_model_preserver.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 1,425 | 35.564103 | 140 | py |
XFL | XFL-master/test/common/utils/test_config_parser.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 1,168 | 28.225 | 74 | py |
XFL | XFL-master/test/algorithm/core/test_optimizer.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 801 | 31.08 | 74 | py |
XFL | XFL-master/test/algorithm/framework/horizontal/test_h_poisson_regression.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 8,853 | 39.063348 | 117 | py |
XFL | XFL-master/test/algorithm/framework/horizontal/test_h_vgg_jax.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 7,515 | 41.948571 | 130 | py |
XFL | XFL-master/test/algorithm/framework/horizontal/test_h_nbafl.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 10,433 | 37.360294 | 130 | py |
XFL | XFL-master/test/algorithm/framework/transfer/test_transfer_logistic_regression.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 6,386 | 36.133721 | 106 | py |
XFL | XFL-master/test/algorithm/framework/vertical/test_kmeans.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 23,214 | 35.330203 | 123 | py |
XFL | XFL-master/test/algorithm/framework/vertical/test_logistic_regression.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 30,427 | 39.952894 | 120 | py |
XFL | XFL-master/test/algorithm/framework/vertical/test_xgboost.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 33,748 | 48.053779 | 557 | py |
XFL | XFL-master/test/algorithm/framework/vertical/test_xgb.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 24,834 | 36.402108 | 117 | py |
XFL | XFL-master/test/algorithm/framework/vertical/test_xgb2.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 43,089 | 43.560496 | 118 | py |
XFL | XFL-master/demo/horizontal/chatglm/chatglm-demo/quantization.py | from torch.nn import Linear
from torch.nn.parameter import Parameter
import bz2
import torch
import base64
import ctypes
from transformers.utils import logging
from typing import List
from functools import partial
logger = logging.get_logger(__name__)
try:
from cpm_kernels.kernels.base import LazyKernelCModule,... | 15,054 | 73.529703 | 7,375 | py |
XFL | XFL-master/demo/horizontal/chatglm/chatglm-demo/modeling_chatglm.py | """ PyTorch ChatGLM model. """
import math
import copy
import os
import warnings
import re
import sys
import torch
import torch.utils.checkpoint
import torch.nn.functional as F
from torch import nn
from torch.nn import CrossEntropyLoss, LayerNorm
from torch.nn.utils import skip_init
from typing import Optional, Tuple... | 57,568 | 39.089833 | 121 | py |
XFL | XFL-master/docs/en/source/conf.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 3,375 | 32.76 | 79 | py |
XFL | XFL-master/docs/zh_CN/source/conf.py | # Copyright 2022 The XFL Authors. 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 copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 3,378 | 32.79 | 79 | py |
ultrasound-nerve-segmentation | ultrasound-nerve-segmentation-master/train.py | from __future__ import print_function
import os
from skimage.transform import resize
from skimage.io import imsave
import numpy as np
from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.optimizers import Adam
from keras.callbacks import ModelChec... | 5,352 | 33.75974 | 107 | py |
deep-video-mvs | deep-video-mvs-master/dataset/scannet-export/scannet-export.py | import os
import random
import numpy as np
from multiprocessing import Pool
import copy
import os
import struct
import zlib
from itertools import groupby
import cv2
import imageio
import numpy as np
import torch
COMPRESSION_TYPE_COLOR = {-1: 'unknown', 0: 'raw', 1: 'png', 2: 'jpeg'}
COMPRESSION_TYPE_DEPTH = {-1: 'unkn... | 12,059 | 40.586207 | 124 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/losses.py | from __future__ import division
import torch
from torch import nn
class LossMeter(object):
def __init__(self):
self.count = 0.0
self.sum = 0.0
self.avg = 0.0
self.item_average = 0.0
def update(self, loss, count):
self.sum += loss
self.count += count
s... | 3,529 | 41.53012 | 146 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/utils.py | from __future__ import division
import os
import zipfile
import cv2
import kornia
import numpy as np
import torch
from path import Path
from pytorch3d import structures, renderer
from dvmvs.errors import compute_errors
# GEOMETRIC UTILS
def pose_distance(reference_pose, measurement_pose):
"""
:param refere... | 19,349 | 47.014888 | 156 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/layers.py | import torch
def down_conv_layer(input_channels, output_channels, kernel_size):
return torch.nn.Sequential(
torch.nn.Conv2d(
input_channels,
output_channels,
kernel_size,
padding=(kernel_size - 1) // 2,
stride=1,
bias=False),
... | 1,984 | 29.075758 | 84 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/dataset_loader.py | import copy
import random
from functools import partial
from multiprocessing import Manager
from multiprocessing.pool import Pool
import cv2
import numpy as np
import torch
from kornia import adjust_brightness, adjust_gamma, adjust_contrast
from path import Path
from torch.utils.data import Dataset, DataLoader
from d... | 21,202 | 38.192237 | 128 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/convlstm.py | import torch
import torch.nn as nn
from dvmvs.utils import warp_frame_depth
class MVSLayernormConvLSTMCell(nn.Module):
def __init__(self, input_dim, hidden_dim, kernel_size, activation_function=None):
super(MVSLayernormConvLSTMCell, self).__init__()
self.activation_function = activation_functio... | 2,554 | 38.307692 | 116 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/train.py | import torch
import torchvision
from tqdm import tqdm
from dvmvs.config import Config
from dvmvs.losses import LossMeter
from dvmvs.utils import save_checkpoint, save_optimizer, freeze_batchnorm
def switch_mode(model, mode):
if mode == 'train':
for module in model:
module.train()
... | 8,579 | 56.583893 | 153 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/mvdepthnet/encoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from dvmvs.utils import freeze_batchnorm
def down_conv_layer(input_channels, output_channels, kernel_size):
return nn.Sequential(
nn.Conv2d(
input_channels,
output_channels,
... | 3,979 | 28.051095 | 77 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/mvdepthnet/run-testing.py | import cv2
import numpy as np
import torch
from path import Path
from tqdm import tqdm
from dvmvs.baselines.mvdepthnet.decoder import Decoder
from dvmvs.baselines.mvdepthnet.encoder import Encoder
from dvmvs.config import Config
from dvmvs.dataset_loader import PreprocessImage, load_image
from dvmvs.utils import cost_... | 9,324 | 48.078947 | 138 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/mvdepthnet/decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from dvmvs.utils import freeze_batchnorm
def down_conv_layer(input_channels, output_channels, kernel_size):
return nn.Sequential(
nn.Conv2d(
input_channels,
output_channels,
kernel_size,
padd... | 3,920 | 28.044444 | 80 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/base_model.py | import collections
from abc import ABCMeta, abstractmethod
from torch import nn
def dict_update(d, u):
"""Improved update for nested dictionaries.
Arguments:
d: The dictionary to be updated.
u: The update dictionary.
Returns:
The updated dictionary.
"""
d = d.copy()
... | 1,752 | 24.042857 | 75 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/utils.py | import torch
def reorder_desc(desc, batch_sz):
"""Reorders Descriptors"""
b, c, h, w = desc.shape
desc = desc.view(-1, batch_sz, c, h, w)
desc = desc.transpose(1, 0)
return desc
def pose_square(pose):
"""Converts pose matrix of size 3x4 to a square matrix of size 4x4"""
pose_sh = pose.... | 1,006 | 24.175 | 73 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/superpoint.py | import torch
import torchvision
from .base_model import BaseModel
def simple_nms(scores, radius):
"""Performs non maximum suppression on the heatmap using max-pooling.
This method does not suppress contiguous points that have the same score.
Arguments:
scores: the score heatmap, with shape `[B, H... | 9,883 | 38.536 | 153 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/densedepth.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_model import BaseModel
from .resnet_s2d import resnet50
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
... | 13,604 | 37.109244 | 160 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/run-testing.py | import argparse
import cv2
import numpy as np
import torch.backends.cudnn as cudnn
import torch.utils.data
from path import Path
from tqdm import tqdm
from dvmvs.baselines.deltas import superpoint, triangulation, densedepth
from dvmvs.baselines.deltas.utils import *
from dvmvs.config import Config
from dvmvs.dataset_... | 15,377 | 46.462963 | 170 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/resnet_s2d.py | import torch.nn as nn
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=di... | 9,946 | 36.394737 | 106 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/triangulation.py | import numpy as np
import torch
from torch import svd
from .base_model import BaseModel
def homogeneous_to_euclidean(points):
"""Converts homogeneous points to euclidean
Args:
points numpy array or torch tensor of shape (N, M + 1): N homogeneous points of dimension M
Returns:
numpy arra... | 22,576 | 37.527304 | 157 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/gpmvs/encoder.py | import torch
import torch.nn as nn
from dvmvs.utils import freeze_batchnorm
def down_conv_layer(input_channels, output_channels, kernel_size):
return nn.Sequential(
nn.Conv2d(
input_channels,
output_channels,
kernel_size,
padding=(kernel_size - 1) // 2,
... | 2,588 | 26.542553 | 66 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/gpmvs/run-testing.py | from copy import deepcopy
import cv2
import numpy as np
import torch
from path import Path
from scipy.linalg import expm
from tqdm import tqdm
from dvmvs.baselines.gpmvs.decoder import Decoder
from dvmvs.baselines.gpmvs.encoder import Encoder
from dvmvs.baselines.gpmvs.gplayer import GPlayer
from dvmvs.config import ... | 10,783 | 45.283262 | 153 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/gpmvs/decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from dvmvs.utils import freeze_batchnorm
def down_conv_layer(input_channels, output_channels, kernel_size):
return nn.Sequential(
nn.Conv2d(
input_channels,
output_channels,
kernel_size,
padd... | 3,920 | 28.044444 | 80 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/gpmvs/gplayer.py | import math
import torch
from dvmvs.utils import freeze_batchnorm
class GPlayer(torch.nn.Module):
def __init__(self, device):
super(GPlayer, self).__init__()
self.gamma2 = torch.nn.Parameter(torch.randn(1).to(device).float(), requires_grad=True)
self.ell = torch.nn.Parameter(torch.randn(... | 1,637 | 37.093023 | 134 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/dpsnet/run-testing.py | import cv2
import numpy as np
import torch
from path import Path
from tqdm import tqdm
from dvmvs.baselines.dpsnet.dpsnet import PSNet
from dvmvs.config import Config
from dvmvs.dataset_loader import PreprocessImage, load_image
from dvmvs.utils import save_results, InferenceTimer, visualize_predictions
def predict()... | 7,607 | 46.849057 | 138 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/dpsnet/dpsnet.py | from __future__ import print_function
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.utils.data
from torch.autograd import Variable
from dvmvs.utils import freeze_batchnorm
pixel_coords = None
def set_id_grid(depth):
globa... | 16,381 | 40.684478 | 157 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/pairnet/run-training.py | import datetime
import itertools
import os
import numpy as np
from path import Path
from tensorboardX import SummaryWriter
from torch.backends import cudnn
from torch.utils.data import DataLoader
from dvmvs.dataset_loader import MVSDataset
from dvmvs.losses import LossMeter, update_losses
from dvmvs.pairnet.model imp... | 12,924 | 45.160714 | 132 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/pairnet/model.py | from collections import OrderedDict
import torch
from torchvision import models
from torchvision.ops import FeaturePyramidNetwork
from dvmvs.config import Config
from dvmvs.layers import conv_layer, depth_layer_3x3
fpn_output_channels = 32
hyper_channels = 32
class StandardLayer(torch.nn.Module):
def __init__(... | 14,393 | 46.193443 | 127 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/pairnet/run-testing.py | import cv2
import numpy as np
import torch
from path import Path
from tqdm import tqdm
from dvmvs.config import Config
from dvmvs.dataset_loader import PreprocessImage, load_image
from dvmvs.pairnet.model import FeatureExtractor, FeatureShrinker, CostVolumeEncoder, CostVolumeDecoder
from dvmvs.utils import cost_volume... | 10,187 | 50.715736 | 138 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/pairnet/run-testing-online.py | import cv2
import numpy as np
import torch
from path import Path
from tqdm import tqdm
from dvmvs.config import Config
from dvmvs.dataset_loader import PreprocessImage, load_image
from dvmvs.keyframe_buffer import KeyframeBuffer
from dvmvs.pairnet.model import FeatureExtractor, FeatureShrinker, CostVolumeEncoder, Cost... | 9,280 | 48.897849 | 138 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/fusionnet/run-training.py | import datetime
import itertools
import os
import numpy as np
from path import Path
from tensorboardX import SummaryWriter
from torch.backends import cudnn
from torch.utils.data import DataLoader
from dvmvs.dataset_loader import MVSDataset
from dvmvs.fusionnet.model import *
from dvmvs.losses import LossMeter, update... | 13,088 | 44.290657 | 144 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/fusionnet/model.py | from collections import OrderedDict
import torch
from torchvision import models
from torchvision.ops import FeaturePyramidNetwork
from dvmvs.config import Config
from dvmvs.convlstm import MVSLayernormConvLSTMCell
from dvmvs.layers import conv_layer, depth_layer_3x3
fpn_output_channels = 32
hyper_channels = 32
cla... | 15,992 | 46.316568 | 127 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/fusionnet/run-testing.py | import cv2
import numpy as np
import torch
from dvmvs.config import Config
from dvmvs.dataset_loader import PreprocessImage, load_image
from dvmvs.fusionnet.model import FeatureExtractor, FeatureShrinker, CostVolumeEncoder, LSTMFusion, CostVolumeDecoder
from dvmvs.utils import cost_volume_fusion, save_results, visualiz... | 12,702 | 53.055319 | 149 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/fusionnet/run-testing-online.py | import cv2
import numpy as np
import torch
from path import Path
from tqdm import tqdm
from dvmvs.config import Config
from dvmvs.dataset_loader import PreprocessImage, load_image
from dvmvs.fusionnet.model import FeatureExtractor, FeatureShrinker, CostVolumeEncoder, LSTMFusion, CostVolumeDecoder
from dvmvs.keyframe_b... | 12,118 | 50.351695 | 149 | py |
featuretools | featuretools-main/featuretools/tests/testing_utils/mock_ds.py | from datetime import datetime
import numpy as np
import pandas as pd
from woodwork.logical_types import (
URL,
Boolean,
Categorical,
CountryCode,
Datetime,
Double,
EmailAddress,
Filepath,
Integer,
IPAddress,
LatLong,
NaturalLanguage,
Ordinal,
PersonFullName,
... | 47,055 | 61.741333 | 2,445 | py |
scientific-re | scientific-re-main/main.py | import os
import random
import torch
import numpy
import torch.backends.cudnn
from src.data.prepare_data import Preparedata
from src.model.CNN import CNN
from src.model.run import Run
from src.parameters.parameters import Parameters
class Controller(Parameters):
def __init__(self):
# prepare the data
... | 1,999 | 26.027027 | 73 | py |
scientific-re | scientific-re-main/src/model/CNN.py | import torch
from torch import nn
import torch.nn.functional as F
class CNN(torch.nn.Module):
def __init__(self, params):
super().__init__()
self.device = params.device
self.dropout = nn.Dropout(params.dropout)
self.embedding_size = params.bert_emb_size + 2 * params.position_emb... | 1,788 | 33.403846 | 125 | py |
scientific-re | scientific-re-main/src/model/run.py | import torch
from sklearn.metrics import f1_score
from torch import optim, nn
from torch.utils.data import Dataset, DataLoader
from transformers import AutoTokenizer, AutoModel
class DatasetMaper(Dataset):
def __init__(self, s, p1, p2, y):
self.s = s
self.p1 = p1
self.p2 = p2
self.... | 9,545 | 45.565854 | 200 | py |
scientific-re | scientific-re-main/src/parameters/parameters.py | from dataclasses import dataclass
import torch
@dataclass
class Parameters:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
seeds = [3828, 3152, 2396]
# Files
train = 'sample-data/sample-train.txt'
train_relations = 'sample-data/sample-train-rel.txt'
dev = 'sample-data/... | 1,140 | 25.534884 | 106 | py |
taskgrouping | taskgrouping-master/taskonomy_loader.py | import torch.utils.data as data
from PIL import Image, ImageOps
import os
import os.path
import zipfile as zf
import io
import logging
import random
import copy
import numpy as np
import time
import torch
import multiprocessing
import warnings
import torchvision.transforms as transforms
from multiprocessing import M... | 10,600 | 29.81686 | 131 | py |
taskgrouping | taskgrouping-master/taskonomy_losses.py | import torch
import collections
sl=0
nl=0
nl2=0
nl3=0
dl=0
el=0
rl=0
kl=0
tl=0
al=0
cl=0
popular_offsets=collections.defaultdict(int)
batch_number=0
def segment_semantic_loss(output,target,mask):
global sl
sl = torch.nn.functional.cross_entropy(output.float(),target.long().squeeze(dim=1),ignore_index=0,reduct... | 6,961 | 27.650206 | 119 | py |
taskgrouping | taskgrouping-master/train_taskonomy.py | import argparse
import os
import shutil
import time
import platform
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torchvision.datasets as datasets
from taskonomy_losses import *
from taskonomy_loader import TaskonomyLoader
from apex.parallel impo... | 26,764 | 35.917241 | 160 | py |
taskgrouping | taskgrouping-master/read_training_history.py | import argparse
import os
import torch
from collections import defaultdict
from train_taskonomy import print_table
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--model_file', '-m', default='', type=str, metavar='PATH',
help='path to latest checkpoin... | 2,820 | 30.696629 | 83 | py |
taskgrouping | taskgrouping-master/sync_batchnorm/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.da... | 3,226 | 32.968421 | 115 | py |
taskgrouping | taskgrouping-master/sync_batchnorm/unittest.py | # -*- coding: utf-8 -*-
# File : unittest.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import unittest
import torch
class TorchTes... | 746 | 23.9 | 59 | py |
taskgrouping | taskgrouping-master/sync_batchnorm/batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import collections
import contextlib
import... | 15,829 | 39.075949 | 116 | py |
taskgrouping | taskgrouping-master/sync_batchnorm/batchnorm_reimpl.py | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File : batchnorm_reimpl.py
# Author : acgtyrant
# Date : 11/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import torch
import torch.nn as nn
import torch... | 2,385 | 30.813333 | 95 | py |
taskgrouping | taskgrouping-master/model_definitions/ozan_rep_fun.py | import torch.autograd
import sys
import math
from .ozan_min_norm_solvers import MinNormSolver
import statistics
class OzanRepFunction(torch.autograd.Function):
# def __init__(self,copies,noop=False):
# super(OzanRepFunction,self).__init__()
# self.copies=copies
# self.noop=noop
n=5
d... | 7,442 | 34.442857 | 155 | py |
taskgrouping | taskgrouping-master/model_definitions/xception_taskonomy_small.py | """
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Keras implementation. Achieves the following performance on the validation set:
Loss:0.9173 Prec@1:78.892 Prec@5:94.292
REMEM... | 29,185 | 34.37697 | 317 | py |
taskgrouping | taskgrouping-master/model_definitions/resnet_taskonomy.py | import torch.nn as nn
import math
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch.nn import init
import torch
from .ozan_rep_fun import ozan_rep_function,trevor_rep_function,OzanRepFunction,TrevorRepFunction
#from .utils import load_state_dict_from_url
__all__ = ['resnet18_taskon... | 16,822 | 36.301552 | 154 | py |
taskgrouping | taskgrouping-master/model_definitions/ozan_min_norm_solvers.py | import numpy as np
import torch
import math
class MinNormSolver:
MAX_ITER = 250
STOP_CRIT = 1e-5
def _min_norm_element_from2(v1v1, v1v2, v2v2):
"""
Analytical solution for min_{c} |cx_1 + (1-c)x_2|_2^2
d is the distance (objective) optimzed
v1v1 = <x1,x1>
v1v2 = <x... | 7,628 | 36.214634 | 147 | py |
taskgrouping | taskgrouping-master/model_definitions/xception_taskonomy_joined_decoder.py | """
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Keras implementation. Achieves the following performance on the validation set:
Loss:0.9173 Prec@1:78.892 Prec@5:94.292
REMEM... | 13,355 | 31.183133 | 251 | py |
taskgrouping | taskgrouping-master/model_definitions/xception_taskonomy_new.py | """
"""
import math
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch.nn import init
import torch
from .ozan_rep_fun import ozan_rep_function,trevor_rep_function,OzanRepFunction,TrevorRepFunction
__all__ = ['xception_taskonomy_new','xception_taskonomy_new_fifth... | 14,950 | 32.979545 | 181 | py |
vae_lesion_deficit | vae_lesion_deficit-main/utils.py | import numpy as np
import random
from torch.utils.data import Dataset, DataLoader
from monai.transforms import *
def resize(volume, target_size):
resize_transform = Compose([Resize((target_size[0],
target_size[1],
target_size[2]))])
... | 4,837 | 35.104478 | 111 | py |
vae_lesion_deficit | vae_lesion_deficit-main/model.py | import math
import torch
import torch.nn as nn
import torch.distributions as D
import torch.nn.functional as F
# Define two globals
bce_fn = nn.BCELoss(reduction='none')
Tensor = torch.cuda.FloatTensor
def add_coords(x, just_coords=False):
'''
This just the Uber CoordConv method extended to 3D. Definitely u... | 13,659 | 36.322404 | 132 | py |
vae_lesion_deficit | vae_lesion_deficit-main/train.py | import numpy as np
import os
import argparse
import torch
import torch.optim as optim
import datetime
import torch as tc
from model import ModelWrapper
from utils import create_train_val_cal_loaders
Tensor = torch.cuda.FloatTensor
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if... | 6,905 | 34.234694 | 107 | py |
GCNH | GCNH-main/main.py | """
Perform training and testing of GCNH on the 10 available splits
"""
import torch
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from utils import *
from datetime import datetime
from copy import deepcopy
from scipy.sparse import coo_matrix
from models import GCNH
from tqdm import tq... | 5,717 | 34.7375 | 161 | py |
GCNH | GCNH-main/utils.py | import scipy.sparse as sp
import torch
import numpy as np
import pickle as pkl
import sys
import networkx as nx
from dataset import CustomDataset
import argparse
import random
from os import path as path
"""
READ ARGUMENTS
"""
def parse_boolean(value):
"""Parse boolean values passed as argument"""
value = v... | 12,193 | 38.980328 | 127 | py |
GCNH | GCNH-main/layers.py | """
GCNH Layer
"""
import torch
import torch.nn as nn
from torch.nn.modules.module import Module
from torch_scatter import scatter
class GCNH_layer(Module):
def __init__(self, nfeat, nhid, maxpool):
super(GCNH_layer, self).__init__()
self.nhid = nhid
self.maxpool = maxpool
... | 1,967 | 28.373134 | 84 | py |
GCNH | GCNH-main/models.py | """
Define GCNH model
"""
import torch.nn as nn
import torch.nn.functional as F
from layers import GCNH_layer
import torch
from utils import *
class GCNH(nn.Module):
def __init__(self, nfeat, nclass, nhid, dropout, nlayers, maxpool):
super(GCNH, self).__init__()
self.nhid = nhid
s... | 1,619 | 28.454545 | 109 | py |
GCNH | GCNH-main/main_syn.py | """
Perform training and testing of GCNH on the synthetic dataset
"""
import torch
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from utils import *
import os
from tqdm import tqdm
from copy import deepcopy
from models import GCNH
from scipy.sparse import coo_matrix
if __name__ == "_... | 4,675 | 36.111111 | 141 | py |
merc2020 | merc2020-master/feature_extract.py | """
train code
"""
import os
import tensorflow as tf
import keras
from keras.layers import Dense, Lambda, AveragePooling1D
import numpy as np
from keras import backend as K
from keras.models import load_model, Model
os.environ["CUDA_VISIBLE_DEVICES"]='0'
def attention_pooling(model_input):
"""
attention pooli... | 2,279 | 28.230769 | 117 | py |
merc2020 | merc2020-master/train.py | """
train code
"""
import os
import tensorflow as tf
import keras
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Conv2D, Dropout, MaxPooling2D, Input, Flatten, Lambda, AveragePooling1D, Activation, TimeDistributed, LSTM, Bidirectional, BatchNormalization
import numpy as np
fr... | 4,570 | 34.434109 | 173 | py |
CLMR | CLMR-master/main.py | import argparse
import pytorch_lightning as pl
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning import Trainer
from pytorch_lightning.loggers import TensorBoardLogger
from torch.utils.data import DataLoader
# Audio Augmentations
from torchaudio_augmentations import (
Rand... | 5,117 | 28.583815 | 88 | py |
CLMR | CLMR-master/export.py | """
This script will extract a pre-trained CLMR PyTorch model to an ONNX model.
"""
import argparse
import os
import torch
from collections import OrderedDict
from copy import deepcopy
from clmr.models import SampleCNN, Identity
from clmr.utils import load_encoder_checkpoint, load_finetuner_checkpoint
def convert_en... | 2,321 | 28.025 | 87 | py |
CLMR | CLMR-master/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Note: To use the 'upload' functionality of this file, you must:
# $ pipenv install twine --dev
import io
import os
import sys
from shutil import rmtree
from setuptools import find_packages, setup, Command
# Package meta-data.
NAME = "clmr"
DESCRIPTION = "Contrastive... | 3,919 | 27.405797 | 86 | py |
CLMR | CLMR-master/linear_evaluation.py | import os
import argparse
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from torchaudio_augmentations import Compose, RandomResizedCrop
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.loggers import TensorBoardLogger
from clmr.... | 4,555 | 28.393548 | 87 | py |
CLMR | CLMR-master/tests/test_spectogram.py | import unittest
import torchaudio
import torch.nn as nn
from torchaudio_augmentations import *
from clmr.datasets import AUDIO
class TestAudioSet(unittest.TestCase):
sample_rate = 16000
def get_audio_transforms(self, num_samples):
transform = Compose(
[
RandomResizedCrop(... | 1,659 | 29.740741 | 86 | py |
CLMR | CLMR-master/tests/test_audioset.py | import unittest
import torchaudio
from torchaudio_augmentations import (
Compose,
RandomApply,
RandomResizedCrop,
PolarityInversion,
Noise,
Gain,
Delay,
PitchShift,
Reverb,
)
from clmr.datasets import AUDIO
class TestAudioSet(unittest.TestCase):
sample_rate = 16000
def get... | 1,500 | 29.632653 | 86 | py |
CLMR | CLMR-master/clmr/data.py | """Wrapper for Torch Dataset class to enable contrastive training
"""
import torch
from torch import Tensor
from torch.utils.data import Dataset
from torchaudio_augmentations import Compose
from typing import Tuple, List
class ContrastiveDataset(Dataset):
def __init__(self, dataset: Dataset, input_shape: List[int... | 1,258 | 27.613636 | 85 | py |
CLMR | CLMR-master/clmr/evaluation.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
from tqdm import tqdm
from sklearn import metrics
def evaluate(
encoder: nn.Module,
finetuned_head: nn.Module,
test_dataset: Dataset,
dataset_name: str,
audio_length: int,
device,
) -> dict:... | 1,921 | 30.508197 | 95 | py |
CLMR | CLMR-master/clmr/modules/callbacks.py | import matplotlib
import matplotlib.pyplot as plt
matplotlib.use("Agg")
from pytorch_lightning.callbacks import Callback
class PlotSpectogramCallback(Callback):
def on_train_start(self, trainer, pl_module):
if not pl_module.hparams.time_domain:
x, y = trainer.train_dataloader.dataset[0]
... | 725 | 24.928571 | 61 | py |
CLMR | CLMR-master/clmr/modules/linear_evaluation.py | import torch
import torch.nn as nn
import torchmetrics
from copy import deepcopy
from pytorch_lightning import LightningModule
from torch import Tensor
from torch.utils.data import DataLoader, Dataset, TensorDataset
from typing import Tuple
from tqdm import tqdm
class LinearEvaluation(LightningModule):
def __init... | 3,975 | 31.590164 | 98 | py |
CLMR | CLMR-master/clmr/modules/supervised_learning.py | import torch
import torchmetrics
import torch.nn as nn
from pytorch_lightning import LightningModule
class SupervisedLearning(LightningModule):
def __init__(self, args, encoder: nn.Module, output_dim: int):
super().__init__()
self.save_hyperparameters(args)
self.encoder = encoder
... | 2,082 | 29.632353 | 75 | py |
CLMR | CLMR-master/clmr/modules/contrastive_learning.py | import torch
import torch.nn as nn
from pytorch_lightning import LightningModule
from torch import Tensor
from simclr import SimCLR
from simclr.modules import NT_Xent, LARS
class ContrastiveLearning(LightningModule):
def __init__(self, args, encoder: nn.Module):
super().__init__()
self.save_hyper... | 2,587 | 35.450704 | 87 | py |
CLMR | CLMR-master/clmr/models/sample_cnn.py | import torch
import torch.nn as nn
from .model import Model
class SampleCNN(Model):
def __init__(self, strides, supervised, out_dim):
super(SampleCNN, self).__init__()
self.strides = strides
self.supervised = supervised
self.sequential = [
nn.Sequential(
... | 1,881 | 26.676471 | 84 | py |
CLMR | CLMR-master/clmr/models/sample_cnn_xl.py | import torch
import torch.nn as nn
from .model import Model
class SampleCNNXL(Model):
def __init__(self, strides, supervised, out_dim):
super(SampleCNN, self).__init__()
self.strides = strides
self.supervised = supervised
self.sequential = [
nn.Sequential(
... | 1,892 | 26.838235 | 84 | py |
CLMR | CLMR-master/clmr/models/shortchunk_cnn.py | import torch.nn as nn
class ShortChunkCNN_Res(nn.Module):
"""
Short-chunk CNN architecture with residual connections.
"""
def __init__(self, n_channels=128, n_classes=50):
super(ShortChunkCNN_Res, self).__init__()
self.spec_bn = nn.BatchNorm2d(1)
# CNN
self.layer1 = ... | 2,845 | 28.340206 | 85 | py |
CLMR | CLMR-master/clmr/models/model.py | import torch.nn as nn
import numpy as np
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
def initialize(self, m):
if isinstance(m, (nn.Conv1d)):
# nn.init.xavier_uniform_(m.weight)
# if m.bias is not None:
# nn.init.xavier_unif... | 555 | 22.166667 | 82 | py |
CLMR | CLMR-master/clmr/models/sinc_net.py | import numpy as np
import torch
import torch.nn.functional as F
import torch.nn as nn
import sys
from torch.autograd import Variable
import math
def flip(x, dim):
xsize = x.size()
dim = x.dim() + dim if dim < 0 else dim
x = x.contiguous()
x = x.view(-1, *xsize[dim:])
x = x.view(x.size(0), x.size(1... | 16,565 | 28.902527 | 226 | py |
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