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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StreamingTransformer | StreamingTransformer-master/espnet/utils/dataset.py | #!/usr/bin/env python
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""pytorch dataset and dataloader implementation for chainer training."""
import torch
import torch.utils.data
class TransformDataset(torch.utils.data.Dataset):
"""Transf... | 2,561 | 26.548387 | 80 | py |
StreamingTransformer | StreamingTransformer-master/espnet/utils/fill_missing_args.py | # -*- coding: utf-8 -*-
# Copyright 2018 Nagoya University (Tomoki Hayashi)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import argparse
import logging
def fill_missing_args(args, add_arguments):
"""Fill missing arguments in args.
Args:
args (Namespace or None): Namesapce containing ... | 1,426 | 29.361702 | 84 | py |
StreamingTransformer | StreamingTransformer-master/espnet/utils/deterministic_utils.py | import logging
import torch
def set_deterministic_pytorch(args):
"""Ensures pytorch produces deterministic results depending on the program arguments
:param Namespace args: The program arguments
"""
# seed setting
torch.manual_seed(args.seed)
# debug mode setting
# 0 would be fastest, bu... | 869 | 30.071429 | 88 | py |
StreamingTransformer | StreamingTransformer-master/espnet/transform/spec_augment.py | """Spec Augment module for preprocessing i.e., data augmentation"""
import random
import numpy
from PIL import Image
from PIL.Image import BICUBIC
from espnet.transform.functional import FuncTrans
def time_warp(x, max_time_warp=80, inplace=False, mode="PIL"):
"""time warp for spec augment
move random cent... | 5,924 | 28.187192 | 88 | py |
StreamingTransformer | StreamingTransformer-master/utils/average_checkpoints.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import json
import os
import numpy as np
def main():
if args.log is not None:
with open(args.log) as f:
logs = json.load(f)
val_scores = []
for log in logs:
if "validation/main/acc" in log.keys():
... | 2,516 | 30.074074 | 81 | py |
combo | combo-master/examples/classifier_multiple_libs_example.py | # -*- coding: utf-8 -*-
"""Example of combining the models from different ML libraries. The example
shows the combination of scikit-learn, xgboost, and LightGBM models.
"""
# Author: Yue Zhao <zhaoy@cmu.edu>
# License: BSD 2 clause
import os
import sys
# temporary solution for relative imports in case combo is not ... | 2,531 | 35.695652 | 76 | py |
CodeGen | CodeGen-main/codegen2/sample.py | from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("checkpoints/codegen2-6B")
model = CodeGenForCausalLM.from_pretrained("checkpoints/codegen2-6B", torch_dtype=torch.float16, revision="sharded")
inputs = tokenizer("# this function prints hello world", return_tensors=... | 466 | 57.375 | 116 | py |
CodeGen | CodeGen-main/codegen1/jaxformer/hf/sample.py | # Copyright (c) 2022, salesforce.com, inc.
# All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
import os
import re
import time
import random
import argparse
import torch
from transformers import ... | 6,949 | 26.254902 | 224 | py |
CodeGen | CodeGen-main/codegen1/jaxformer/hf/train_deepspeed.py | # Minimal example of training the 16B checkpoint on GPU with CPU offloading using deepspeed.
'''
apt install python3.8 python3.8-venv python3.8-dev
python3.8 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip setuptools
pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
pip inst... | 5,560 | 26.666667 | 452 | py |
CodeGen | CodeGen-main/codegen1/jaxformer/hf/codegen/modeling_codegen.py | # coding=utf-8
# Copyright 2021 The EleutherAI and HuggingFace Teams. 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
#... | 27,568 | 38.955072 | 160 | py |
CodeGen | CodeGen-main/codegen1/benchmark/mtpb_sample.py | # Copyright (c) 2022, salesforce.com, inc.
# All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
python3.9 -m venv .venv
source .venv/bin/activate
pip3 install --upgrade pip
pip3 install --upgrad... | 11,246 | 28.912234 | 209 | py |
HTRbyMatching | HTRbyMatching-main/htr_utils.py | import torch
from tqdm import tqdm
from torchvision.transforms import functional as Fsupp
import os
import numpy as np
from configs import getOptions
from PIL import Image, ImageFont, ImageDraw, ImageEnhance
import editdistance
import random
options = getOptions().parse()
alphabet_path = options.alphabet
resizing = ... | 8,610 | 28.489726 | 146 | py |
HTRbyMatching | HTRbyMatching-main/test.py | import torch
import torchvision
from src.faster_rcnn import FastRCNNPredictor ,TwoMLPHead
import torchvision
from src.faster_rcnn import FasterRCNN
from src.rpn import AnchorGenerator
import torchvision
import src.transforms as T
import cv2
import os
import random
import numpy as np
from configs import getOptions
... | 4,237 | 28.227586 | 113 | py |
HTRbyMatching | HTRbyMatching-main/train_progressive.py | import os
import random
import numpy as np
import PIL
import torch
from PIL import Image
import pickle
from torch import nn
import torchvision
from src.faster_rcnn import FastRCNNPredictor ,TwoMLPHead
import torchvision
from src.faster_rcnn import FasterRCNN
from src.rpn import AnchorGenerator
from torchvision.tra... | 26,067 | 29.77686 | 200 | py |
HTRbyMatching | HTRbyMatching-main/utils.py | from __future__ import print_function
from collections import defaultdict, deque
import datetime
import pickle
import time
import torch
import torch.distributed as dist
import errno
import os
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the... | 9,162 | 28.36859 | 94 | py |
HTRbyMatching | HTRbyMatching-main/load_data.py | from PIL import Image
import os
import torch
import sys
import cv2
import transforms as T
import torchvision.transforms as torchT
import torchvision.transforms.functional as TF
import utils
from configs import getOptions
import random
options = getOptions().parse()
cipher = options.cipher #"synthetic"#
alphabet = o... | 6,895 | 37.311111 | 167 | py |
HTRbyMatching | HTRbyMatching-main/train.py | import torch
import torchvision
from src.faster_rcnn import FastRCNNPredictor ,TwoMLPHead
import torchvision
from src.faster_rcnn import FasterRCNN
from src.rpn import AnchorGenerator
import torchvision
from src.engine import train_one_epoch
import os
from load_data import load_data
from configs import getOptions
imp... | 4,100 | 25.62987 | 90 | py |
HTRbyMatching | HTRbyMatching-main/transforms.py | import random
import torch
from torchvision.transforms import functional as F
def _flip_coco_person_keypoints(kps, width):
flip_inds = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
flipped_data = kps[:, flip_inds]
flipped_data[..., 0] = width - flipped_data[..., 0]
# Maintain COCO conven... | 1,534 | 29.098039 | 74 | py |
HTRbyMatching | HTRbyMatching-main/src/engine.py | import math
import sys
import time
import torch
import torchvision.models.detection.mask_rcnn
from .coco_utils import get_coco_api_from_dataset
from src.coco_eval import CocoEvaluator
import src.utils
import utils
def train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq):
model.train()
me... | 3,989 | 34.625 | 97 | py |
HTRbyMatching | HTRbyMatching-main/src/util_fc.py | from PIL import Image, ImageFont, ImageDraw, ImageEnhance
import numpy as np
import os
import PIL
import torch
def drawprobs(img1,shots,st_ch,en_ch):
mat_size = 100
image_hline = Image.new('RGB', (img1.size()[2]+5+mat_size, 5), (0, 0, 255))
image1 = Image.fromarray(img1.mul(255).permute(1, 2, ... | 2,944 | 35.358025 | 146 | py |
HTRbyMatching | HTRbyMatching-main/src/faster_rcnn.py | from collections import OrderedDict
import torch
from torch import nn
import torch.nn.functional as F
from torchvision.ops import misc as misc_nn_ops
from torchvision.ops import MultiScaleRoIAlign
from torchvision.models.utils import load_state_dict_from_url
from .generalized_rcnn import GeneralizedRCNN
from .rpn i... | 16,622 | 44.667582 | 112 | py |
HTRbyMatching | HTRbyMatching-main/src/generalized_rcnn.py | """
Implements the Generalized R-CNN framework
"""
from collections import OrderedDict
import torch
from torch import nn
class GeneralizedRCNN(nn.Module):
"""
Main class for Generalized R-CNN.
Arguments:
backbone (nn.Module):
rpn (nn.Module):
heads (nn.Module): takes the features... | 2,562 | 31.858974 | 134 | py |
HTRbyMatching | HTRbyMatching-main/src/utils.py | from __future__ import print_function
from collections import defaultdict, deque
import datetime
import pickle
import time
import torch
import torch.distributed as dist
import errno
import os
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the... | 9,161 | 28.459807 | 94 | py |
HTRbyMatching | HTRbyMatching-main/src/rpn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch.nn import functional as F
from torch import nn
from torchvision.ops import boxes as box_ops
from torchvision.models.detection import _utils as det_utils
class AnchorGenerator(nn.Module):
"""
Module that generates... | 17,453 | 39.21659 | 111 | py |
HTRbyMatching | HTRbyMatching-main/src/roi_heads.py | import torch
import torch.nn.functional as F
from torch import nn
from torchvision.ops import boxes as box_ops
from torchvision.ops import misc as misc_nn_ops
from torchvision.ops import roi_align
from torchvision.models.detection import _utils as det_utils
def contrastive_loss_torch(y_pred, y_gt):
... | 24,437 | 35.150888 | 165 | py |
HTRbyMatching | HTRbyMatching-main/src/coco_utils.py | import copy
import os
from PIL import Image
import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask
from pycocotools.coco import COCO
import src.transforms as T
class FilterAndRemapCocoCategories(object):
def __init__(self, categories, remap=True):
self.categor... | 8,736 | 33.670635 | 102 | py |
HTRbyMatching | HTRbyMatching-main/src/coco_eval.py | import json
import tempfile
import numpy as np
import copy
import time
import torch
import torch._six
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from collections import defaultdict
import src.utils
class CocoEvaluator(object):
def __init__(... | 11,988 | 33.254286 | 107 | py |
HTRbyMatching | HTRbyMatching-main/src/transforms.py | import random
import torch
from torchvision.transforms import functional as F
def _flip_coco_person_keypoints(kps, width):
flip_inds = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
flipped_data = kps[:, flip_inds]
flipped_data[..., 0] = width - flipped_data[..., 0]
# Maintain COCO conven... | 1,534 | 29.098039 | 74 | py |
personalized-breath | personalized-breath-master/src/main.py | import os
import argparse
import pickle
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import StepLR
from matplotlib import pyplot as plt
from training.trainer import train, test, EarlyStopping
from utils.process_d... | 9,351 | 48.481481 | 227 | py |
personalized-breath | personalized-breath-master/src/evaluation/verification_one_vs_all.py | import numpy as np
from scipy.optimize import brentq
from scipy.interpolate import interp1d
from sklearn.metrics import roc_curve
from scipy.spatial import distance
from sklearn.mixture import GaussianMixture
import torch
import torch.nn as nn
def get_predicted_set(loader, models):
Y_true, Y_out=[],[]
for batc... | 2,206 | 37.051724 | 82 | py |
personalized-breath | personalized-breath-master/src/evaluation/verification.py | import numpy as np
from scipy.optimize import brentq
from scipy.interpolate import interp1d
from sklearn.metrics import roc_curve
from scipy.spatial import distance
from sklearn.mixture import GaussianMixture
import torch
def get_embedded_set(loader,model):
X_out, Y_out=[],[]
for batch_idx, (types, samples,lab... | 3,076 | 35.630952 | 71 | py |
personalized-breath | personalized-breath-master/src/evaluation/plot_tsne.py | import os
import numpy as np
import pickle
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from matplotlib import pyplot as plt
import random
from scipy.optimize import brentq
from scipy.interpolate import interp1d
from ... | 2,836 | 40.720588 | 139 | py |
personalized-breath | personalized-breath-master/src/training/dataloader.py | import numpy as np
from torch.utils.data import Dataset
class Audio_Dataset(Dataset):
def __init__(self,root_dir,filenamelist,old_new_name_map, object_id = None):
self.filenamelist = filenamelist
self.root_dir=root_dir
self.old_new_name_map=old_new_name_map
self.type_name = "Audio"
... | 3,469 | 37.555556 | 111 | py |
personalized-breath | personalized-breath-master/src/training/trainer.py | import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
from utils.plot_grad_flow import plot_grad_flow
class EarlyStopping:
def __init__(self, checkpoint_name, lr_scheduler, patiences=[], delta=0):
self.checkpoint_name = checkpoint_name
self.lr_scheduler = lr_sche... | 4,129 | 34.913043 | 77 | py |
personalized-breath | personalized-breath-master/src/modeling/triplet_tristou.py | import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import PackedSequence
from torch.nn.utils.rnn import pad_packed_sequence
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from sklearn.neighbors impor... | 22,819 | 45.288032 | 160 | py |
personalized-breath | personalized-breath-master/src/modeling/cnn_lstm.py | import numpy as np
import torch
import torch.nn as nn
class Audio_CNN_LSTM(nn.Module):
def __init__(self,no_outer,one_vs_all = False):
super(Audio_CNN_LSTM, self).__init__()
self.audio_layers = nn.Sequential(
nn.Conv1d(in_channels=20, out_channels=32, kernel_size=8),
nn.ReLU... | 3,080 | 37.5125 | 76 | py |
personalized-breath | personalized-breath-master/src/modeling/tcn.py | import numpy as np
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x):
return x[:, :, :-self.chomp_size].contiguous()
clas... | 4,753 | 44.27619 | 142 | py |
BachProp | BachProp-master/src/BachProp.py | import utils
import numpy as np
import pickle
from tqdm import tqdm
import os, sys
from sys import stdout
import keras
from keras.utils import np_utils
from keras.preprocessing.sequence import pad_sequences
from keras.models import Model, load_model
from keras.layers import Input, Masking, TimeDistributed, Dense, Con... | 34,032 | 43.721419 | 183 | py |
Cophy-PGNN | Cophy-PGNN-master/loss_surface_vis/helper_electromagnetic.py | import numpy as np
import matplotlib.pyplot as plt
import torch
import pandas as pd
import torch.nn.functional as f
import sys
import os
sys.path.append(os.path.abspath('../loss_surface_vis'))
sys.path.append(os.path.abspath('../scripts'))
from loss_functions_electromagnetic import multiply_Eg_C
# Calculates | HC - E... | 5,641 | 37.121622 | 122 | py |
Cophy-PGNN | Cophy-PGNN-master/loss_surface_vis/loss_functions.py | import torch
from math import sqrt
def inverse_norm(batch, scale, mean):
return batch * scale + mean
def phy_loss(batchPred, batchReal, batchInput, norm=False):
num_data = batchPred.size(0)
if batchInput.dim() == 2:
H_height = int(sqrt(batchInput.size(1)))
H_width = H_height
elif batch... | 2,025 | 31.15873 | 94 | py |
Cophy-PGNN | Cophy-PGNN-master/loss_surface_vis/data_loader.py | import torch
import numpy as np
from torch.autograd import Variable
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import os
# restore hamiltonian from nonzeros
def restore_h(nonzero, nonzero_loc, dim):
data_count = nonzero.shape[0]
H = torch.zeros((data_count... | 15,995 | 38.399015 | 108 | py |
Cophy-PGNN | Cophy-PGNN-master/loss_surface_vis/helper.py | import numpy as np
import matplotlib.pyplot as plt
import torch
from scipy import interp
def getFriendlyName(DNN_type):
name = "NN"
if DNN_type == "PGNN_OnlyDTr":
name = r'\emph{CoPhy}-PGNN (only-$\mathcal{D}_{Tr}$)'
elif DNN_type == "PGNN_LF":
name = r'\emph{CoPhy}-PGNN (Label-free)'
e... | 3,283 | 35.488889 | 91 | py |
Cophy-PGNN | Cophy-PGNN-master/loss_surface_vis/loadModel.py | from DNN import DNN
import glob
import os
import pandas as pd
import torch
import loss_landscapes
import numpy as np
def LoadModel(datasetLoader, model_path, DNN_type, H, Depth, device, initialModel=False):
D_in = datasetLoader.x_dim
D_out = datasetLoader.y_dim
model_final = DNN(D_in, H, D_out, Depth).to... | 2,197 | 42.098039 | 148 | py |
Cophy-PGNN | Cophy-PGNN-master/loss_surface_vis/lossCalculator.py | import torch
import math
def inverse_norm(batch, scale, mean):
return batch * scale + mean
def energy_loss(batchPred, batchInput):
H_height = int(math.sqrt(batchInput.size(1)))
H_width = H_height
batchEg = batchPred[:, -1]
loss_e = torch.exp(batchEg)
return loss_e
def phy_loss(batchPred, batc... | 3,152 | 26.181034 | 115 | py |
Cophy-PGNN | Cophy-PGNN-master/eigensolver_comparison/util/solvers.py |
# support libraries
import time
import torch
import numpy as np
# linear algebra libraries
import scipy.linalg
import scipy.sparse.linalg
# ---------------------- Base Class --------------------------
class EigenSolver(object):
def __init__(self, smallest_eigen=True):
self.solvers = {}
if smalle... | 6,024 | 31.392473 | 140 | py |
Cophy-PGNN | Cophy-PGNN-master/scripts/data_loader_electromagnetic.py |
import torch
import numpy as np
from torch.autograd import Variable
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import os
from scipy.io import loadmat
import h5py
def readMatFile(file_path, name):
try:
return loadmat(file_path)[name]
except Excepti... | 9,309 | 34.807692 | 78 | py |
Cophy-PGNN | Cophy-PGNN-master/scripts/evaluations.py | import torch
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
def grad_cosine(grad_1, grad_2):
cos = np.zeros(len(grad_1))
for i in range(len(grad_1)):
cos_arr = grad_1[i] * grad_2[i]
cos_arr /= np.sqrt(np.sum(grad_1[i] ** 2))
cos_arr /= np.sqrt(np.sum(... | 3,111 | 36.493976 | 107 | py |
Cophy-PGNN | Cophy-PGNN-master/scripts/training_record_grad.py | # visualization
import matplotlib.pyplot as plt
import seaborn as sns
# pytorch
import torch
from torch import optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
# sklearn
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
# scipy
from scipy... | 38,672 | 34.414835 | 140 | py |
Cophy-PGNN | Cophy-PGNN-master/scripts/loss_functions.py | import torch
from math import sqrt
def inverse_norm(batch, scale, mean):
return batch * scale + mean
def phy_loss(batchPred, batchReal, batchInput, norm=False):
num_data = batchPred.size(0)
if batchInput.dim() == 2:
H_height = int(sqrt(batchInput.size(1)))
H_width = H_height
elif batch... | 2,026 | 30.671875 | 94 | py |
Cophy-PGNN | Cophy-PGNN-master/scripts/loss_functions_electromagnetic.py | import torch
from math import sqrt
import numpy as np
# The batch of this file is vectors of the form [Real Imaginary].
# Real takes the first half of the vector, and Imaginary takes the second half.
# Last two elements are eignevalue (real, img)
def inverse_norm(batch, scale, mean):
return batch * scale + mean
... | 2,430 | 32.763889 | 142 | py |
Cophy-PGNN | Cophy-PGNN-master/scripts/data_loader.py |
import torch
import numpy as np
from torch.autograd import Variable
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import os
# restore hamiltonian from nonzeros
def restore_h(nonzero, nonzero_loc, dim):
data_count = nonzero.shape[0]
H = torch.zeros((data_coun... | 15,586 | 37.9675 | 108 | py |
Cophy-PGNN | Cophy-PGNN-master/scripts/training.py | # visualization
import matplotlib.pyplot as plt
import seaborn as sns
# pytorch
import torch
from torch import optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
# sklearn
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
# scipy
from scipy... | 31,770 | 33.4962 | 140 | py |
Cophy-PGNN | Cophy-PGNN-master/scripts/early_stopping.py | # This implementation of early stopping is inspired by:
# https://github.com/Bjarten/early-stopping-pytorch/blob/master/pytorchtools.py
import numpy as np
import torch
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience=7, ... | 2,020 | 38.627451 | 111 | py |
Cophy-PGNN | Cophy-PGNN-master/scripts/training_electromagnetic.py | # visualization
import matplotlib.pyplot as plt
import seaborn as sns
# pytorch
import torch
from torch import optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
# sklearn
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
# scipy
from scipy... | 40,476 | 35.797273 | 181 | py |
Cophy-PGNN | Cophy-PGNN-master/scripts/parameters.py | import torch
from torch import optim
import numpy as np
# ========================= The Parameter Class =========================
class Params(object):
def __init__(
self,
nn_params={},
train_params={},
io_params={},
data_params={},
loss_params={},
name='d... | 3,290 | 31.584158 | 95 | py |
Cophy-PGNN | Cophy-PGNN-master/scripts/gradient.py | import torch
import numpy as np
import os
import pickle
import pandas as pd
import glob
def load_grad(grad_path, epoch):
load = lambda path: pickle.load(open(grad_path + path % epoch, 'rb'))
grad_dict = {
'train_mse': load('train_mse_%d.pkl'),
'train_s_norm': load('train_train_s_norm_%d.pkl'),
... | 6,168 | 35.076023 | 87 | py |
Cophy-PGNN | Cophy-PGNN-master/scripts/DNN.py | import torch
from collections import OrderedDict
# Multi-layer Perceptron
class DNN(torch.nn.Module):
def __init__(
self,
input_size,
hidden_size,
output_size,
depth,
act=torch.nn.Tanh,
softmax=False
):
super(DNN, self).__init__()
... | 973 | 28.515152 | 76 | py |
mia-3dcnn | mia-3dcnn-main/train_mia_9a.py | import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
import tensorflow_addons as tfa
import sklearn
import sklearn.metrics
import argparse
import cv2
import imageio
import imgaug as ia
from imgaug import augmenters as iaa
class DataGenerator(tf.keras.utils.Sequence):
def __ini... | 15,013 | 37.497436 | 123 | py |
mia-3dcnn | mia-3dcnn-main/valid_mia_9a.py | import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
import argparse
import cv2
import pandas as pd
import imageio
import imgaug as ia
from imgaug import augmenters as iaa
class DataGenerator(tf.keras.utils.Sequence):
def __init__(
self, list_IDs, labels, batch_size=1... | 9,905 | 33.395833 | 123 | py |
mia-3dcnn | mia-3dcnn-main/preprocess_images.py | import os
import numpy as np
from scipy import ndimage
import cv2
import re
from tqdm import tqdm
def read_image_folder(folderpath):
ct_names = os.listdir(folderpath)
ct_names = [file_name.zfill(7) for file_name in ct_names]
ct_names.sort()
matrix = []
for filename in ct_names:
filepath... | 6,236 | 34.64 | 120 | py |
mia-3dcnn | mia-3dcnn-main/detection-task/train_mia_9a.py | import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
import tensorflow_addons as tfa
import sklearn
import sklearn.metrics
import argparse
import cv2
import imageio
import imgaug as ia
from imgaug import augmenters as iaa
class DataGenerator(tf.keras.utils.Sequence):
def __ini... | 15,013 | 37.497436 | 123 | py |
mia-3dcnn | mia-3dcnn-main/detection-task/valid_mia_9a.py | import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
import argparse
import cv2
import pandas as pd
import imageio
import imgaug as ia
from imgaug import augmenters as iaa
class DataGenerator(tf.keras.utils.Sequence):
def __init__(
self, list_IDs, labels, batch_size=1... | 9,905 | 33.395833 | 123 | py |
mia-3dcnn | mia-3dcnn-main/severity-task/train_sev_6.py | import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers
import tensorflow_addons as tfa
from scipy import ndimage
import PIL
from PIL import Image
import sklearn
import sklearn.metrics
import argparse
import cv2
import math
import copy
import random
impo... | 19,256 | 36.684932 | 123 | py |
mia-3dcnn | mia-3dcnn-main/severity-task/sev-inference.py | import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
import argparse
import cv2
from collections import defaultdict
import pandas as pd
import imageio
import imgaug as ia
from imgaug import augmenters as iaa
class DataGenerator(tf.keras.utils.Sequence):
def __init__(
s... | 9,106 | 31.758993 | 123 | py |
blob | blob-master/simulate_abtest_with_bandit.py | from recogym import env_1_args
from models.models_organic_bandit import RecoModelRTAEWithBanditTF, RecoModelRTAEWithBanditTF_Full
from models.models_organic import RecoModelRTAE, RecoModelItemKNN
from models.liang_multivae import MultiVAE
from recogym.agents import organic_user_count_args
from recogym.agents import Ra... | 10,531 | 40.793651 | 217 | py |
blob | blob-master/models/liang_multivae.py | import seaborn as sn
sn.set()
from pandas.util import hash_pandas_object
import tensorflow as tf
from tensorflow.contrib.layers import apply_regularization, l2_regularizer
from tqdm import tqdm
from torch.utils.data import DataLoader
from utils.utils import *
# from https://github.com/dawenl/vae_cf/blob/master/VA... | 13,484 | 37.528571 | 142 | py |
blob | blob-master/models/model_based_agents.py | from recogym.agents import Agent
import torch
import pandas as pd
import numpy as np
class ModelBasedAgent(Agent):
def __init__(self, config):
super(ModelBasedAgent, self).__init__(config)
self.data = {
't': [],
'u': [],
'z': [],
'v': [],
... | 2,732 | 33.594937 | 124 | py |
blob | blob-master/models/models_organic_bandit.py | import tensorflow as tf
from tensorflow_probability import distributions as tfd
from tensorflow.math import softplus as sp
import os
import sys
import datetime
import numpy as np
import pandas as pd
from pandas.util import hash_pandas_object
import torch
from torch import optim
from torch.utils.data import DataLoade... | 33,237 | 40.70389 | 173 | py |
blob | blob-master/models/models_bandit.py | import numpy as np
from torch.autograd import Variable
from torch.nn import functional as F
from recogym.agents import (
AbstractFeatureProvider,
Model,
ModelBasedAgent,
ViewsFeaturesProvider
)
from recogym import Configuration
pytorch_mlr_args = {
'n_epochs': 30,
'learning_rate': 0.01,
'r... | 51,589 | 37.994709 | 128 | py |
blob | blob-master/models/models_organic.py | import os
import sys
import pdb
import datetime
import numpy as np
import pandas as pd
from pandas.util import hash_pandas_object
import torch
from torch import optim
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
from tqdm import tqdm
from utils.utils import PandasDataset, shrink_wrap... | 9,434 | 40.381579 | 190 | py |
blob | blob-master/utils/utils_vae.py | import torch
from pylab import *
# The KL
def EQ_diag_standard_normal_logpdf(p_inv_Sigmaq_diag, p_muq):
assert(p_inv_Sigmaq_diag.shape[0] == p_muq.shape[0])
assert(p_inv_Sigmaq_diag.shape[1] == p_muq.shape[1])
MB, K = p_inv_Sigmaq_diag.shape
return -0.5 * ( (p_muq * p_muq).sum(1).reshape(MB,1,1) + (1... | 2,153 | 43.875 | 314 | py |
blob | blob-master/utils/utils.py | import pandas as pd
from pylab import *
from torch.utils.data import Dataset
from utils.utils_vae import block_torch_update
import torch
import torch.nn.functional as F
import torch.nn as nn
def to_categorical(y, num_classes=None, dtype='float32'): # from keras
y = np.array(y.cpu(), dtype='int')
input_shap... | 6,992 | 34.140704 | 139 | py |
MeLT | MeLT-main/main.py | from modeling.encoder import MeLT
from test_tube import HyperOptArgumentParser
from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
import torch
import torch.nn as nn
import pytorch_lightning as pl
import sys
import os
import random
from pytorch_lightning import Callback
import optuna
from optuna.inte... | 5,456 | 40.656489 | 153 | py |
MeLT | MeLT-main/modeling/encoder.py | """
Defines the MeLT encoder which leverages building blocks from encoder_layers.py
"""
import math
import torch.nn as nn
import torch
from modeling.attn import MultiHeadedAttn
from modeling.neural import PositionalFeedForward, PositionalEncoding, batch_by_usr, batch_by_usr_no_mask, metrics, calc_avg_preds
from m... | 13,760 | 45.489865 | 167 | py |
MeLT | MeLT-main/modeling/encoder_layers.py | """
Defines the individual layers to be used for a transformer encoder
"""
import math
import torch.nn as nn
import torch
from modeling.attn import MultiHeadPooling, MultiHeadedAttn
from modeling.neural import PositionalEncoding, PositionalFeedForward
class TransformerEncoderLayer(nn.Module):
"""
A singl... | 1,586 | 32.0625 | 79 | py |
MeLT | MeLT-main/modeling/attn.py | import math
import torch
import torch.nn as nn
import sys
class MultiHeadedAttn(nn.Module):
"""
Multi-Headed Attn
Args:
num_heads (int): amount of parallel heads
model_dim (int): dimension of K,V,Q (must be divisible by head count)
dropout (float): dropout rate
"""
def __i... | 5,200 | 36.417266 | 116 | py |
MeLT | MeLT-main/modeling/data_handler.py | import logging
logging.disable(logging.CRITICAL)
import torch
import pandas as pd
from transformers import BertTokenizer
from transformers import DistilBertTokenizer, DistilBertTokenizerFast, AlbertTokenizerFast, RobertaTokenizerFast
from transformers import AutoTokenizer
from torch.utils.data import TensorDataset, Dat... | 6,353 | 40.25974 | 185 | py |
MeLT | MeLT-main/modeling/neural.py | import math
import torch
import torch.nn as nn
import numpy as np
from scipy.stats import pearsonr, zscore
from sklearn.metrics.pairwise import cosine_similarity
from collections import defaultdict
import sys
def metrics(loss, preds, metric_name, labels=None, other_data=None):
"""
Determines the values of... | 13,707 | 37.505618 | 135 | py |
w2ot | w2ot-main/scripts/eval-conj-solver-benchmarks.py | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
import argparse
import functools
import glob
import os
import yaml
import time
from collections import defaultdict
import numpy as np
import pickle as pkl
import pandas as pd
pd.set_option("display.precision", 2)
import jax
import jax.nump... | 9,617 | 31.60339 | 88 | py |
w2ot | w2ot-main/scripts/prof-conj.py | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
import argparse
import jax
import jax.numpy as jnp
import numpy as np
import pickle as pkl
import os
import functools
from collections import defaultdict
import time
from w2ot import conjugate_solver, utils
import matplotlib.pyplot as p... | 2,942 | 27.028571 | 86 | py |
w2ot | w2ot-main/scripts/eval-conj-solver-lbfgs.py | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
import argparse
import functools
import glob
import os
import yaml
import time
from collections import namedtuple
import numpy as np
import pickle as pkl
import pandas as pd
pd.set_option("display.precision", 2)
import jax
import jax.numpy... | 4,996 | 31.032051 | 94 | py |
w2ot | w2ot-main/scripts/vis-2d-transport.py | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
import argparse
import jax
import jax.numpy as jnp
import numpy as np
import pickle as pkl
import os
import shutil
import functools
from w2ot import conjugate_solver, utils
import matplotlib.pyplot as plt
from matplotlib.collections impor... | 20,756 | 31.688189 | 156 | py |
w2ot | w2ot-main/scripts/eval-conj-solver.py | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
import argparse
import jax
import jax.numpy as jnp
import numpy as np
import pickle as pkl
import os
import functools
from w2ot import conjugate_solver, utils
import sys
import w2ot.run_train as train
sys.modules['train'] = train # Legacy... | 3,784 | 27.458647 | 88 | py |
w2ot | w2ot-main/scripts/vis-image-transport.py | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
import argparse
import jax
import jax.numpy as jnp
import numpy as np
import pickle as pkl
import os
import shutil
import functools
from w2ot import conjugate_solver, utils
import matplotlib.pyplot as plt
from matplotlib.collections impor... | 7,222 | 31.536036 | 97 | py |
w2ot | w2ot-main/scripts/vis-2d-grid-warp.py | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
import argparse
import jax
import jax.numpy as jnp
import numpy as np
import pickle as pkl
import os
import shutil
import functools
from w2ot import conjugate_solver, utils
import matplotlib.pyplot as plt
from matplotlib.collections impor... | 4,788 | 28.93125 | 95 | py |
w2ot | w2ot-main/w2ot/dual_trainer.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
import copy
import functools
import jax
import jax.numpy as jnp
from jax.lax import stop_gradient
from flax import linen as nn
from flax.training import train_state
from dataclasses import dataclass
from typing import Tuple, Sequence, Optional
import optax
from ... | 12,679 | 36.076023 | 90 | py |
w2ot | w2ot-main/w2ot/utils.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
import jax
import jax.numpy as jnp
from jax import dtypes
from flax import linen as nn
from functools import partial
batch_dot = jax.vmap(jnp.dot)
class RunningAverageMeter(object):
def __init__(self, momentum=0.999):
self.momentum = momentum
... | 2,492 | 27.011236 | 75 | py |
w2ot | w2ot-main/w2ot/data.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
import random
import copy
import jax
import jax.numpy as jnp
import sys
import gc
import numpy as np
import numpy.random as npr
import sklearn.datasets
import torch
from torch.utils.data import IterableDataset
from torchvision import transforms
from torchvision... | 22,476 | 32.953172 | 139 | py |
w2ot | w2ot-main/w2ot/run_train.py | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
import hydra
import csv
import time
import warnings
warnings.filterwarnings('ignore')
from collections import defaultdict
import copy
import numpy as np
import pickle as pkl
import jax
import jax.numpy as jnp
import optax
import os
impo... | 8,791 | 33.614173 | 114 | py |
w2ot | w2ot-main/w2ot/conjugate_solver.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
import functools
import jax
from jax import lax
import jax.numpy as jnp
import optax
from dataclasses import dataclass, field
from collections import namedtuple
from typing import Optional
import copy
from w2ot.external.jax_lbfgs import _minimize_lbfgs
from w2ot... | 5,692 | 29.44385 | 105 | py |
w2ot | w2ot-main/w2ot/external/jaxopt_lbfgs.py | # This file is modified from:
# https://github.com/google/jaxopt/blob/418bce35ff7410a86dc5edb64ee5d3716b3bc132/jaxopt/_src/lbfgs.py
# and remains under the original licensing.
#
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in complian... | 14,723 | 36.370558 | 101 | py |
w2ot | w2ot-main/w2ot/external/jax_lbfgs.py | # This file is modified from
# https://github.com/google/jax/blob/ba557d5/jax/_src/scipy/optimize/_lbfgs.py
# and remains under the original licensing.
#
#
# Copyright 2020 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Lice... | 12,258 | 31.517241 | 103 | py |
w2ot | w2ot-main/w2ot/external/ott_icnn.py | # This file is modified from the following files and remains under the
# original licensing.
#
# https://github.com/ott-jax/ott/blob/main/ott/core/icnn.py
# and
# https://github.com/ott-jax/ott/blob/main/ott/core/layers.py
#
#
# Copyright 2022 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License... | 3,334 | 27.504274 | 83 | py |
w2ot | w2ot-main/w2ot/external/jaxopt_backtracking_linesearch.py | # This file is modified from:
# https://github.com/google/jaxopt/blob/418bce35ff7410a86dc5edb64ee5d3716b3bc132/jaxopt/_src/backtracking_linesearch.py
# and remains under the original licensing.
#
#
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | 7,275 | 35.38 | 119 | py |
w2ot | w2ot-main/w2ot/external/benchmark/distributions.py | # This file is from
# https://github.com/iamalexkorotin/Wasserstein1Benchmark/commit/647a1acc85f88e207733d087cbe87987cc0dea06
# and remains under the original licensing.
import torch
import numpy as np
from scipy.linalg import sqrtm
import sklearn.datasets
import random
from .potentials import BasePotential
def symme... | 12,163 | 31.524064 | 119 | py |
w2ot | w2ot-main/w2ot/external/benchmark/resnet2.py | # This file is from
# https://github.com/iamalexkorotin/Wasserstein1Benchmark/commit/647a1acc85f88e207733d087cbe87987cc0dea06
# and remains under the original licensing.
import numpy as np
import torch
from torch import nn
def weights_init_G(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1... | 5,356 | 31.271084 | 105 | py |
w2ot | w2ot-main/w2ot/external/benchmark/inception.py | # This file is from
# https://github.com/iamalexkorotin/Wasserstein1Benchmark/commit/647a1acc85f88e207733d087cbe87987cc0dea06
# and remains under the original licensing.
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
try:
from torchvision.models.utils import load_state_dict_... | 12,349 | 36.087087 | 126 | py |
w2ot | w2ot-main/w2ot/external/benchmark/plotters.py | # This file is from
# https://github.com/iamalexkorotin/Wasserstein1Benchmark/commit/647a1acc85f88e207733d087cbe87987cc0dea06
# and remains under the original licensing.
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from .tools import ewma, freeze
import torch
import gc
def plot_benchmark_emb(... | 6,286 | 39.824675 | 114 | py |
w2ot | w2ot-main/w2ot/external/benchmark/potentials.py | # This file is from
# https://github.com/iamalexkorotin/Wasserstein1Benchmark/commit/647a1acc85f88e207733d087cbe87987cc0dea06
# and remains under the original licensing.
import torch
import torch.nn as nn
import torch.autograd as autograd
import numpy as np
class BasePotential(nn.Module):
def __init__(self, batch... | 4,662 | 36.910569 | 120 | py |
w2ot | w2ot-main/w2ot/external/benchmark/tools.py | # This file is from
# https://github.com/iamalexkorotin/Wasserstein1Benchmark/commit/647a1acc85f88e207733d087cbe87987cc0dea06
# and remains under the original licensing.
import pandas as pd
import numpy as np
import os
import itertools
import torch
from torch import nn
import torch.nn.functional as F
from tqdm impo... | 5,316 | 28.703911 | 122 | py |
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