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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ray | ray-master/release/lightning_tests/workloads/test_tuner.py | import os
import time
import json
from pytorch_lightning.loggers.csv_logs import CSVLogger
import ray
import ray.tune as tune
from ray.air.config import CheckpointConfig, ScalingConfig
from ray.train.lightning import LightningTrainer, LightningConfigBuilder
from ray.tune.schedulers import ASHAScheduler
from lightning... | 2,621 | 26.893617 | 84 | py |
rainbow-iqn-apex | rainbow-iqn-apex-master/rainbowiqn/compute_loss_iqn.py | """
Copyright 2018 The Dopamine Authors. All rights reserved.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"Licen... | 18,269 | 49.891365 | 98 | py |
rainbow-iqn-apex | rainbow-iqn-apex-master/rainbowiqn/launch_learner.py | import logging
import os
import time
from datetime import datetime
from multiprocessing import Process, Queue
import numpy as np
import redis
import torch
import rainbowiqn.constants as cst
from rainbowiqn.learner import Learner
from rainbowiqn.args import return_args
from rainbowiqn.env import Env
from rainbowiqn.re... | 7,728 | 34.292237 | 99 | py |
rainbow-iqn-apex | rainbow-iqn-apex-master/rainbowiqn/actor.py | import random
import io
import math
import numpy as np
import torch
import rainbowiqn.constants as cst
from rainbowiqn.agent import Agent
class Actor(Agent):
"""This class just handle actor specific methods"""
# Acts based on single state (no batch)
def act(self, state_buffer):
state = torch.fro... | 5,041 | 39.336 | 99 | py |
rainbow-iqn-apex | rainbow-iqn-apex-master/rainbowiqn/args.py | import argparse
import os
import random
import torch
def return_args():
parser = argparse.ArgumentParser(description="Rainbow-IQN")
parser.add_argument("--seed", type=int, default=123, help="Random seed")
parser.add_argument("--disable-cuda", action="store_true", help="Disable CUDA")
parser.add_argum... | 14,117 | 33.773399 | 99 | py |
rainbow-iqn-apex | rainbow-iqn-apex-master/rainbowiqn/learner.py | import io
import torch
import rainbowiqn.constants as cst
from rainbowiqn.agent import Agent
class Learner(Agent):
"""This class just handle learner specific methods"""
def __init__(self, args, action_space, redis_servor):
super().__init__(args, action_space, redis_servor)
def learn(self, mem_r... | 1,169 | 30.621622 | 83 | py |
rainbow-iqn-apex | rainbow-iqn-apex-master/rainbowiqn/utils.py | import csv
import os
from datetime import datetime
import plotly
import torch
from plotly.graph_objs import Scatter
from plotly.graph_objs.scatter import Line
import rainbowiqn.constants as cst
# Simple ISO 8601 timestamped logger
def log(s):
print("[" + str(datetime.now().strftime("%Y-%m-%dT%H:%M:%S")) + "] " ... | 6,369 | 34 | 99 | py |
rainbow-iqn-apex | rainbow-iqn-apex-master/rainbowiqn/model.py | import math
import torch
from torch import nn
from torch.nn import functional as F
# Factorised NoisyLinear layer with bias
class NoisyLinear(nn.Module):
def __init__(self, in_features, out_features, std_init, disable_cuda=False):
super().__init__()
self.disable_cuda = disable_cuda
self.i... | 6,487 | 38.803681 | 99 | py |
rainbow-iqn-apex | rainbow-iqn-apex-master/rainbowiqn/redis_memory.py | import random
import time
from collections import namedtuple
import numpy as np
import redlock
import torch
import rainbowiqn.constants as cst
Transition = namedtuple("Transition", ("timestep", "state", "action", "reward", "nonterminal"))
blank_trans = Transition(0, np.zeros((84, 84), dtype=np.uint8), None, 0, False... | 25,244 | 42.980836 | 103 | py |
rainbow-iqn-apex | rainbow-iqn-apex-master/rainbowiqn/agent.py | import os
import torch
from torch import optim
import rainbowiqn.compute_loss_iqn as compute_loss_iqn
from rainbowiqn.model import DQN
class Agent:
"""This class handle both actor and learner because most of their methods are shared"""
def __init__(self, args, action_space, redis_servor):
self.acti... | 7,339 | 42.952096 | 99 | py |
rainbow-iqn-apex | rainbow-iqn-apex-master/rainbowiqn/test_multiple_seed.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 21 12:20:12 2018
@author: toromanoff
"""
import random
import time
import numpy as np
import torch
from rainbowiqn.actor import Actor
from rainbowiqn.args import return_args
from rainbowiqn.env import Env
# Test the input snapshot (args --model... | 2,966 | 30.56383 | 93 | py |
rainbow-iqn-apex | rainbow-iqn-apex-master/rainbowiqn/launch_actor.py | import logging
import random
import time
import numpy as np
import redis
from torch.multiprocessing import Process
import rainbowiqn.constants as cst
from rainbowiqn.actor import Actor
from rainbowiqn.args import return_args
from rainbowiqn.env import Env
from rainbowiqn.redis_memory import ReplayRedisMemory
from rai... | 8,191 | 38.384615 | 99 | py |
rainbow-iqn-apex | rainbow-iqn-apex-master/rainbowiqn/env.py | import math
from collections import deque
import atari_py
import cv2 # Note that importing cv2 before torch may cause segfaults?
import numpy as np
class Env:
def __init__(self, args):
self.device = args.device
self.ale = atari_py.ALEInterface()
self.ale.setInt("random_seed", args.seed)
... | 4,988 | 38.595238 | 99 | py |
rsna-resnet10 | rsna-resnet10-main/working/validation.py | import os
import monai
import numpy as np
import pandas as pd
import torch
from sklearn.metrics import roc_auc_score
from dataset import BrainRSNADataset
data = pd.read_csv("../input/train.csv")
targets = data.MGMT_value.values
device = torch.device("cuda")
model = monai.networks.nets.resnet10(spatial_dims=3, n_in... | 1,981 | 31.491803 | 128 | py |
rsna-resnet10 | rsna-resnet10-main/working/dataset.py | import glob
import os
import re
import joblib
import numpy as np
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
import config
import utils
class BrainRSNADataset(Dataset):
def __init__(
self, data, transform=None, target="MGMT_value", mri_type="FLAIR", is_train=True, ds_type="fo... | 3,481 | 32.480769 | 120 | py |
rsna-resnet10 | rsna-resnet10-main/working/predict.py | import glob
import os
import random
import re
import albumentations as A
import cv2
import monai
import numpy as np
import pandas as pd
import pydicom
import torch
import torch.nn as nn
import torch.optim as optim
from albumentations.pytorch import ToTensorV2
from pydicom.pixel_data_handlers.util import apply_voi_lut
... | 510 | 22.227273 | 58 | py |
rsna-resnet10 | rsna-resnet10-main/working/train.py | import argparse
import os
import monai
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.metrics import roc_auc_score
from torch.optim import lr_scheduler
from tqdm import tqdm
import config
from dataset import BrainRSNADataset
parser = argparse.Argume... | 4,548 | 31.963768 | 176 | py |
SAITS | SAITS-main/Simple_RNN_on_imputed_data.py | """
The simple RNN classification model for imputed dataset PhysioNet-2012.
If you use code in this repository, please cite our paper as below. Many thanks.
@article{DU2023SAITS,
title = {{SAITS: Self-Attention-based Imputation for Time Series}},
journal = {Expert Systems with Applications},
volume = {219},
pages = {... | 8,806 | 34.65587 | 186 | py |
SAITS | SAITS-main/run_models.py | """
The script for running (including training and testing) all models in this repo.
If you use code in this repository, please cite our paper as below. Many thanks.
@article{DU2023SAITS,
title = {{SAITS: Self-Attention-based Imputation for Time Series}},
journal = {Expert Systems with Applications},
volume = {219},
... | 26,083 | 37.134503 | 143 | py |
SAITS | SAITS-main/modeling/utils.py | """
Utility functions are stored here.
If you use code in this repository, please cite our paper as below. Many thanks.
@article{DU2023SAITS,
title = {{SAITS: Self-Attention-based Imputation for Time Series}},
journal = {Expert Systems with Applications},
volume = {219},
pages = {119619},
year = {2023},
issn = {0957-... | 9,209 | 32.860294 | 186 | py |
SAITS | SAITS-main/modeling/unified_dataloader.py | """
The unified dataloader for all models' dataset loading.
If you use code in this repository, please cite our paper as below. Many thanks.
@article{DU2023SAITS,
title = {{SAITS: Self-Attention-based Imputation for Time Series}},
journal = {Expert Systems with Applications},
volume = {219},
pages = {119619},
year = ... | 15,712 | 39.812987 | 186 | py |
SAITS | SAITS-main/modeling/mrnn.py | """
Our implementation of MRNN model for time-series imputation.
If you use code in this repository, please cite our paper as below. Many thanks.
@article{DU2023SAITS,
title = {{SAITS: Self-Attention-based Imputation for Time Series}},
journal = {Expert Systems with Applications},
volume = {219},
pages = {119619},
ye... | 6,084 | 36.331288 | 186 | py |
SAITS | SAITS-main/modeling/layers.py | """
Layer modules for self-attention models (Transformer and SAITS).
If you use code in this repository, please cite our paper as below. Many thanks.
@article{DU2023SAITS,
title = {{SAITS: Self-Attention-based Imputation for Time Series}},
journal = {Expert Systems with Applications},
volume = {219},
pages = {119619}... | 6,193 | 32.846995 | 186 | py |
SAITS | SAITS-main/modeling/transformer.py | """
Transformer model for time-series imputation.
If you use code in this repository, please cite our paper as below. Many thanks.
@article{DU2023SAITS,
title = {{SAITS: Self-Attention-based Imputation for Time Series}},
journal = {Expert Systems with Applications},
volume = {219},
pages = {119619},
year = {2023},
is... | 5,024 | 34.638298 | 186 | py |
SAITS | SAITS-main/modeling/brits.py | """
Our implementation of BRITS model for time-series imputation.
If you use code in this repository, please cite our paper as below. Many thanks.
@article{DU2023SAITS,
title = {{SAITS: Self-Attention-based Imputation for Time Series}},
journal = {Expert Systems with Applications},
volume = {219},
pages = {119619},
y... | 9,538 | 35.132576 | 186 | py |
SAITS | SAITS-main/modeling/saits.py | """
SAITS model for time-series imputation.
If you use code in this repository, please cite our paper as below. Many thanks.
@article{DU2023SAITS,
title = {{SAITS: Self-Attention-based Imputation for Time Series}},
journal = {Expert Systems with Applications},
volume = {219},
pages = {119619},
year = {2023},
issn = {... | 8,512 | 36.835556 | 186 | py |
QMUPD | QMUPD-master/test.py | """General-purpose test script for image-to-image translation.
Once you have trained your model with train.py, you can use this script to test the model.
It will load a saved model from --checkpoints_dir and save the results to --results_dir.
It first creates model and dataset given the option. It will hard-code some... | 4,263 | 59.056338 | 145 | py |
QMUPD | QMUPD-master/train.py | """General-purpose training script for image-to-image translation.
This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and
different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
You need to specify the dataset ('--dataroot'), e... | 5,295 | 59.873563 | 181 | py |
QMUPD | QMUPD-master/options/base_options.py | import argparse
import os
from util import util
import torch
import models
import data
class BaseOptions():
"""This class defines options used during both training and test time.
It also implements several helper functions such as parsing, printing, and saving the options.
It also gathers additional opti... | 8,437 | 56.794521 | 235 | py |
QMUPD | QMUPD-master/models/base_model.py | import os
import torch
from collections import OrderedDict
from abc import ABCMeta, abstractmethod
from . import networks
import pdb
class BaseModel():
__metaclass__ = ABCMeta
"""This class is an abstract base class (ABC) for models.
To create a subclass, you need to implement the following five functions... | 11,142 | 43.931452 | 260 | py |
QMUPD | QMUPD-master/models/networks.py | #coding:utf-8
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import pdb
###############################################################################
# Helper Functions
###############################################################################... | 66,109 | 38.586826 | 193 | py |
QMUPD | QMUPD-master/models/pretrained_networks.py | from collections import namedtuple
import torch
from torchvision import models
from IPython import embed
class squeezenet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(squeezenet, self).__init__()
pretrained_features = models.squeezenet1_1(pretrained=pretrained).... | 6,559 | 35.043956 | 109 | py |
QMUPD | QMUPD-master/models/networks_basic.py |
from __future__ import absolute_import
import sys
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable
import numpy as np
from pdb import set_trace as st
from skimage import color
from IPython import embed
from . import pretrained_networks as pn
from util import util
d... | 7,514 | 38.973404 | 134 | py |
QMUPD | QMUPD-master/models/test_model.py | from .base_model import BaseModel
from . import networks
import torch
import pdb
class TestModel(BaseModel):
""" This TesteModel can be used to generate CycleGAN results for only one direction.
This model will automatically set '--dataset_mode single', which only loads the images from one collection.
See ... | 5,148 | 52.082474 | 160 | py |
QMUPD | QMUPD-master/models/dist_model.py |
from __future__ import absolute_import
import sys
sys.path.append('..')
sys.path.append('.')
import numpy as np
import torch
from torch import nn
from collections import OrderedDict
from torch.autograd import Variable
from .base_model import BaseModel
from scipy.ndimage import zoom
import skimage.transform
from . im... | 13,695 | 41.271605 | 281 | py |
QMUPD | QMUPD-master/models/cycle_gan_cls_model.py | import torch
import itertools
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import models.dist_model as dm # numpy==1.14.3
import torchvision.transforms as transforms
import os
from util.util import tensor2im, tensor2im2, save_image
def truncate(fake_B,a=127.5):#[-1,1]
... | 34,356 | 59.701413 | 360 | py |
QMUPD | QMUPD-master/util/image_pool.py | import random
import torch
class ImagePool():
"""This class implements an image buffer that stores previously generated images.
This buffer enables us to update discriminators using a history of generated images
rather than the ones produced by the latest generators.
"""
def __init__(self, pool_... | 2,226 | 39.490909 | 140 | py |
QMUPD | QMUPD-master/util/util.py | """This module contains simple helper functions """
from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
import pdb
from scipy.io import savemat
def tensor2im(input_image, imtype=np.uint8):
""""Converts a Tensor array into a numpy image array.
Parameters:
... | 4,625 | 33.522388 | 119 | py |
QMUPD | QMUPD-master/data/base_dataset.py | """This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
"""
import random
import numpy as np
import torch.utils.data as data
from PIL import Image
import torchvision.... | 6,615 | 34.379679 | 141 | py |
QMUPD | QMUPD-master/data/image_folder.py | """A modified image folder class
We modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py)
so that this class can load images from both current directory and its subdirectories.
"""
import torch.utils.data as data
from PIL import Image
import os
import... | 1,893 | 27.268657 | 122 | py |
QMUPD | QMUPD-master/data/__init__.py | """This package includes all the modules related to data loading and preprocessing
To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
You need to implement four functions:
-- <__init__>: ... | 3,554 | 36.819149 | 176 | py |
QMUPD | QMUPD-master/data/single_dataset.py | from data.base_dataset import BaseDataset, get_transform, get_params, get_transform_mask
from data.image_folder import make_dataset
from PIL import Image
import torch
import os, glob
class SingleDataset(BaseDataset):
"""This dataset class can load a set of images specified by the path --dataroot /path/to/data.
... | 3,147 | 41.540541 | 105 | py |
QMUPD | QMUPD-master/data/unaligned_mask_stylecls_dataset.py | import os.path
from data.base_dataset import BaseDataset, get_params, get_transform, get_transform_mask
from data.image_folder import make_dataset
from PIL import Image
import random
import torch
import torchvision.transforms as transforms
import numpy as np
class UnalignedMaskStyleClsDataset(BaseDataset):
"""
... | 7,723 | 47.275 | 126 | py |
b4msa | b4msa-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 5,407 | 29.902857 | 90 | py |
FLAGS-FL | FLAGS-FL-main/deprecated.py | def neighborhood_divergence(self, nodeset, cfl_model, div_metric = 'L2', div_mode ='cfl_div', normalize = False):
div_dict = {node:None for node in self.neighborhood}
total_div_dict = copy.deepcopy(div_dict)
conv_div_dict = copy.deepcopy(div_dict)
fc_div_dict = copy.deepcopy(div_dict)
... | 4,049 | 48.390244 | 123 | py |
FLAGS-FL | FLAGS-FL-main/env_sysmodel.py | import numpy as np
import random
import networkx as nx
import matplotlib.pyplot as plt
import copy
import heapq
import pickle
import sys, gc
import torch
import torchvision
import torchvision.models as models
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import Dataset,... | 15,051 | 45.313846 | 168 | py |
FLAGS-FL | FLAGS-FL-main/utils.py | import random
import torch
import pickle
import os
def constrained_sum(n, total):
"""Return a randomly chosen list of n positive integers summing to total.
Each such list is equally likely to occur.
"""
divider = []
while 1 in divider or len(divider) == 0:
dividers = sorted(random.sample(r... | 5,189 | 48.428571 | 295 | py |
FLAGS-FL | FLAGS-FL-main/data_utils.py | import copy
import numpy as np
import torch
import torchvision
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader, TensorDataset, IterableDataset
from DNN import *
class DataSubset(Dataset):
"""
Takes the dataset, distribution list and node as arguments.
"""
... | 4,708 | 42.201835 | 141 | py |
FLAGS-FL | FLAGS-FL-main/data_dist.py | import copy
import numpy as np
import math
import random
from itertools import chain
import torch
import torchvision
from torchvision import datasets, transforms
def data_iid(dataset, num_classes, num_nodes):
"""
Sample I.I.D. client data for the selected dataset
:param dataset:
:param num_users:
:... | 3,917 | 36.314286 | 108 | py |
FLAGS-FL | FLAGS-FL-main/Main-Fed.py | import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID";
# #The GPU id to use, usually either "0" or "1";
os.environ["CUDA_VISIBLE_DEVICES"]="2"
import sys, argparse
import pickle
import time
import gc
from get_args import arg_parser
from utils import dataset_approve, save_file, model_size
from data_utils import * # Re... | 11,612 | 49.056034 | 203 | py |
FLAGS-FL | FLAGS-FL-main/devices.py | from DNN import *
import heapq
import numpy as np
from data_utils import DataSubset
import copy, gc
import torch
import torchvision
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader, TensorDataset, IterableDataset
class Nodes:
"""
Generat... | 8,364 | 43.026316 | 190 | py |
FLAGS-FL | FLAGS-FL-main/DNN.py | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import networkx as nx
import random
import copy
import heapq
import sys, gc
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.models as models
from utils import optimizer_to, schedu... | 11,656 | 39.196552 | 154 | py |
byteps | byteps-master/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Note: To use the 'upload' functionality of this file, you must:
# $ pip install twine
import io
import os
import sys
import re
import shutil
from shutil import rmtree
import textwrap
import shlex
import subprocess
from setuptools import find_packages, setup, Command,... | 43,211 | 36.838879 | 133 | py |
byteps | byteps-master/byteps/mxnet/compression.py | # Copyright 2019 Bytedance Inc. All Rights Reserved.
# Copyright 2018 Uber Technologies, Inc. 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.or... | 5,470 | 32.157576 | 90 | py |
byteps | byteps-master/byteps/mxnet/__init__.py | # Copyright 2019 Bytedance Inc. or its affiliates. All Rights Reserved.
# Copyright 2018 Amazon.com, Inc. or its affiliates. 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 ... | 15,017 | 40.601108 | 103 | py |
byteps | byteps-master/byteps/mxnet/ops.py | # Copyright 2019 Bytedance Inc. or its affiliates. All Rights Reserved.
# Copyright 2018 Amazon.com, Inc. or its affiliates. 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 ... | 4,372 | 34.266129 | 152 | py |
byteps | byteps-master/byteps/torch/cross_barrier.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from byteps.torch.compression import Compression
from byteps.torch.ops import push_pull_async_inplace as byteps_push_pull
from byteps.torch.ops import poll, synchronize
from byteps.torch.ops import init, shutdo... | 17,594 | 40.302817 | 116 | py |
byteps | byteps-master/byteps/torch/compression.py | # Copyright 2019 Bytedance Inc. All Rights Reserved.
# Copyright 2018 Uber Technologies, Inc. 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.or... | 2,485 | 31.710526 | 90 | py |
byteps | byteps-master/byteps/torch/__init__.py | # Copyright 2019 Bytedance Inc. All Rights Reserved.
# Copyright 2019 Uber Technologies, Inc. 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.or... | 19,967 | 41.75803 | 107 | py |
byteps | byteps-master/byteps/torch/ops.py | # Copyright 2019 ByteDance, Inc. All Rights Reserved.
# Copyright 2019 Uber Technologies, Inc. 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.o... | 9,763 | 40.198312 | 100 | py |
byteps | byteps-master/byteps/torch/parallel/distributed.py | import torch
from torch.nn.modules import Module
from byteps.torch.ops import push_pull_group_sync_inplace as byteps_push_pull_group
from byteps.torch.ops import push_pull_async_inplace as byteps_push_pull
from byteps.torch.ops import poll, synchronize, declare, byteps_torch_set_num_grads
from byteps.torch.ops import s... | 12,762 | 43.315972 | 94 | py |
byteps | byteps-master/byteps/_keras/__init__.py | # Copyright 2019 Bytedance Inc. or its affiliates. All Rights Reserved.
# Copyright 2017 Uber Technologies, Inc. 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
#
# h... | 5,479 | 43.918033 | 102 | py |
byteps | byteps-master/byteps/misc/imagenet18/__init__.py | # Copyright 2019 Bytedance Inc. All Rights Reserved.
# Copyright 2019 Uber Technologies, Inc. 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.or... | 22,494 | 41.363465 | 124 | py |
byteps | byteps-master/byteps/keras/callbacks.py | # Copyright 2019 Bytedance Inc. or its affiliates. All Rights Reserved.
# Copyright 2018 Uber Technologies, Inc. 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
#
# h... | 7,090 | 49.29078 | 109 | py |
byteps | byteps-master/byteps/keras/__init__.py | # Copyright 2019 Bytedance Inc. or its affiliates. All Rights Reserved.
# Copyright 2017 Uber Technologies, Inc. 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
#
# h... | 5,619 | 44.322581 | 114 | py |
byteps | byteps-master/byteps/tensorflow/__init__.py | # Copyright 2019 Bytedance Inc. All Rights Reserved.
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
# Modifications copyright (C) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may o... | 18,667 | 43.660287 | 95 | py |
byteps | byteps-master/byteps/tensorflow/keras/callbacks.py | # Copyright 2018 Uber Technologies, Inc. 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 applica... | 7,292 | 48.612245 | 109 | py |
byteps | byteps-master/byteps/tensorflow/keras/__init__.py | # Copyright 2017 Uber Technologies, Inc. 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 applica... | 5,850 | 43.325758 | 114 | py |
byteps | byteps-master/tests/meta_test.py | # Copyright 2020 Amazon Technologies, Inc. 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 appli... | 2,989 | 33.767442 | 80 | py |
byteps | byteps-master/tests/test_mxnet.py | # Copyright 2020 Amazon Technologies, Inc. All Rights Reserved.
# Copyright 2019 ByteDance Technologies, Inc. All Rights Reserved.
# Copyright 2018 Uber Technologies, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the L... | 5,019 | 38.527559 | 97 | py |
byteps | byteps-master/tests/utils.py | import mxnet as mx
import mxnet.ndarray as nd
import numpy as np
from numba import jit
def fake_data(dtype="float32", batch_size=32, height=224, width=224, depth=3, num_classes=1000):
image_list = []
label_list = []
for _ in range(8):
image = mx.ndarray.random.normal(-1, 1,
... | 1,537 | 28.018868 | 96 | py |
byteps | byteps-master/tests/test_tensorflow_keras.py | # Copyright 2018 Uber Technologies, Inc. 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 applica... | 3,717 | 34.75 | 80 | py |
byteps | byteps-master/tests/test_dithering.py | # Copyright 2020 Amazon Technologies, Inc. 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 appli... | 5,787 | 31.335196 | 136 | py |
byteps | byteps-master/tests/test_onebit.py | # Copyright 2020 Amazon Technologies, Inc. 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 appli... | 3,778 | 30.231405 | 80 | py |
byteps | byteps-master/tests/test_topk.py | # Copyright 2020 Amazon Technologies, Inc. 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 appli... | 3,718 | 29.991667 | 80 | py |
byteps | byteps-master/tests/test_randomk.py | # Copyright 2020 Amazon Technologies, Inc. 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 appli... | 4,164 | 31.286822 | 100 | py |
byteps | byteps-master/example/mxnet/train_cifar100_byteps_gc.py | # Copyright 2019 Bytedance Inc. or its affiliates. All Rights Reserved.
# Copyright 2018 Amazon.com, Inc. or its affiliates. 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 ... | 12,981 | 39.823899 | 103 | py |
byteps | byteps-master/example/mxnet/train_gluon_mnist_byteps.py | # Copyright 2019 Bytedance Inc. or its affiliates. All Rights Reserved.
# Copyright 2018 Amazon.com, Inc. or its affiliates. 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 ... | 5,952 | 34.646707 | 116 | py |
byteps | byteps-master/example/mxnet/train_gluon_mnist_byteps_gc.py | # Copyright 2019 Bytedance Inc. or its affiliates. All Rights Reserved.
# Copyright 2018 Amazon.com, Inc. or its affiliates. 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 ... | 7,876 | 34.481982 | 79 | py |
byteps | byteps-master/example/mxnet/train_imagenet_byteps.py | #!/usr/bin/env python
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "L... | 2,218 | 32.119403 | 92 | py |
byteps | byteps-master/example/mxnet/train_gluon_imagenet_byteps_gc.py | import argparse
import logging
import math
import os
import subprocess
import time
import gluoncv as gcv
import mxnet as mx
import numpy as np
from gluoncv.data import imagenet
from gluoncv.model_zoo import get_model
from gluoncv.utils import LRScheduler, LRSequential, makedirs
from mxnet import autograd as ag
from mx... | 23,900 | 42.377495 | 119 | py |
byteps | byteps-master/example/mxnet/symbols/inception-resnet-v2.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 6,943 | 42.672956 | 117 | py |
byteps | byteps-master/example/mxnet/symbols/resnet.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 9,630 | 47.888325 | 147 | py |
byteps | byteps-master/example/mxnet/symbols/mobilenetv2.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 7,343 | 32.534247 | 119 | py |
byteps | byteps-master/example/mxnet/symbols/vgg.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 3,634 | 46.207792 | 154 | py |
byteps | byteps-master/example/mxnet/symbols/inception-v3.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 10,858 | 54.974227 | 152 | py |
byteps | byteps-master/example/mxnet/symbols/mlp.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 1,434 | 42.484848 | 84 | py |
byteps | byteps-master/example/mxnet/symbols/googlenet.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 4,170 | 56.136986 | 141 | py |
byteps | byteps-master/example/mxnet/symbols/resnetv1.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 10,040 | 48.955224 | 147 | py |
byteps | byteps-master/example/mxnet/symbols/resnext.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 9,928 | 46.056872 | 159 | py |
byteps | byteps-master/example/mxnet/symbols/inception-v4.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 8,706 | 39.310185 | 152 | py |
byteps | byteps-master/example/mxnet/symbols/lenet.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 2,957 | 44.507692 | 97 | py |
byteps | byteps-master/example/mxnet/symbols/mobilenet.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 8,457 | 57.331034 | 174 | py |
byteps | byteps-master/example/mxnet/symbols/resnet-v1.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 10,040 | 48.955224 | 147 | py |
byteps | byteps-master/example/mxnet/symbols/inception-bn.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 7,598 | 51.406897 | 141 | py |
byteps | byteps-master/example/mxnet/symbols/alexnet.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 3,218 | 45.652174 | 181 | py |
byteps | byteps-master/example/mxnet/common/fit.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 14,826 | 42.737463 | 132 | py |
byteps | byteps-master/example/mxnet/common/find_mxnet.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 995 | 38.84 | 63 | py |
byteps | byteps-master/example/mxnet/common/data.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 10,469 | 49.57971 | 103 | py |
byteps | byteps-master/example/mxnet/common/fit_byteps.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 13,661 | 40.274924 | 132 | py |
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