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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end-to-end-asd | end-to-end-asd-main/experiment_config.py | import torch.nn as nn
import torch.optim as optim
import models.graph_models as g3d
EASEE_R3D_18_inputs = {
# input files
'csv_train_full': '/Dataset/ava_active_speaker/csv/ava_activespeaker_train_augmented.csv',
'csv_val_full': '/Dataset/ava_active_speaker/csv/ava_activespeaker_val_augmented.csv',
'... | 2,020 | 27.871429 | 94 | py |
end-to-end-asd | end-to-end-asd-main/easee_R3D50.py | import os
import torch
import experiment_config as exp_conf
import util.custom_transforms as ct
from torchvision import transforms
from torch_geometric.loader import DataLoader
from torch.optim.lr_scheduler import MultiStepLR
from ez_io.logging import setup_optim_outputs
from datasets.graph_datasets import Independe... | 4,566 | 43.77451 | 108 | py |
end-to-end-asd | end-to-end-asd-main/easee_R3D18.py | import os
import sys
import torch
import experiment_config as exp_conf
import util.custom_transforms as ct
from torchvision import transforms
from torch_geometric.loader import DataLoader
from torch.optim.lr_scheduler import MultiStepLR
from ez_io.logging import setup_optim_outputs
from datasets.graph_datasets impor... | 4,585 | 43.524272 | 108 | py |
end-to-end-asd | end-to-end-asd-main/models/shared_3d.py | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
def get_inplanes():
return [64, 128, 256, 512]
def conv3x3x3(in_planes, out_planes, stride=1):
return nn.Conv3d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, b... | 2,293 | 23.666667 | 73 | py |
end-to-end-asd | end-to-end-asd-main/models/shared_2d.py | import torch.nn as nn
from torch import Tensor
from typing import Type, Any, Callable, Union, List, Optional
def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3,... | 3,514 | 33.80198 | 111 | py |
end-to-end-asd | end-to-end-asd-main/models/graph_models.py | import torch
import torch.nn as nn
import torch.nn.parameter
from functools import partial
from torch_geometric.nn import EdgeConv
from models.graph_layouts import generate_av_mask
from models.shared_2d import BasicBlock2D, conv1x1
from models.shared_3d import BasicBlock3D, Bottleneck3D, conv1x1x1, get_inplanes
try... | 15,486 | 39.225974 | 146 | py |
end-to-end-asd | end-to-end-asd-main/util/custom_transforms.py | from torchvision import transforms
video_train = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.3729, 0.2850, 0.2439), (0.2286, 0.2008, 0.1911))
])
video_val = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.3729, 0.2850, 0.2439), (0.2286, 0.2008, 0.1911))
]) | 318 | 28 | 76 | py |
end-to-end-asd | end-to-end-asd-main/util/augmentations.py | import random
from PIL import Image
from torchvision.transforms import RandomCrop
from torchvision.transforms.functional import hflip
def video_temporal_crop(video_data, crop_ratio):
# random flip
if bool(random.getrandbits(1)):
video_data = [s.transpose(Image.FLIP_LEFT_RIGHT) for s in video_data]
... | 814 | 27.103448 | 98 | py |
end-to-end-asd | end-to-end-asd-main/datasets/graph_datasets.py | import os
import math
import torch
import random
import numpy as np
import ez_io.io_ava as io
import util.clip_utils as cu
from torch_geometric.data import Data, Dataset
from ez_io.file_util import csv_to_list, postprocess_speech_label, postprocess_entity_label
from util.augmentations import video_temporal_crop, vide... | 14,316 | 40.259366 | 176 | py |
end-to-end-asd | end-to-end-asd-main/optimization/losses.py | import torch
import torch.nn as nn
class assignation_loss_audio(torch.nn.Module):
def __init__(self, graph_size):
super(assignation_loss_audio, self).__init__()
self.graph_size = graph_size
self.softmax_layer = torch.nn.Softmax(dim=1)
def forward(self, outputs, audio_targets):
... | 749 | 33.090909 | 71 | py |
end-to-end-asd | end-to-end-asd-main/optimization/optimization_amp.py | import os
import torch
from torch.cuda.amp import autocast
from models.graph_layouts import generate_av_mask
from sklearn.metrics import average_precision_score
from models.graph_layouts import generate_temporal_video_center_mask, generate_temporal_video_mask
def optimize_easee(model, dataloader_train, data_loader_... | 7,919 | 39.615385 | 128 | py |
JEMPP | JEMPP-master/eval_jempp.py | # coding=utf-8
# Copyright 2019 The Google Research Authors.
#
# 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 applicab... | 25,406 | 37.730183 | 147 | py |
JEMPP | JEMPP-master/train_jempp.py | # coding=utf-8
# Copyright 2019 The Google Research Authors.
#
# 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 applicab... | 17,931 | 43.606965 | 166 | py |
JEMPP | JEMPP-master/utils.py | import os
import torch
import torch as t
import torch.nn as nn
import torchvision as tv
import torchvision.transforms as tr
from torch.utils.data import DataLoader, Dataset
import numpy as np
from torch.nn.modules.loss import _Loss
from ExpUtils import AverageMeter
class Hamiltonian(_Loss):
def __init__(self, la... | 7,385 | 32.572727 | 139 | py |
JEMPP | JEMPP-master/ExpUtils.py | import os
import sys
import json
import time
import socket
import shutil
import signal
import logging
from functools import partial
import torch
import numpy as np
import tensorboardX as tbX
import matplotlib.pyplot as plt
logging.basicConfig(level=logging.INFO, format="%(asctime)s: %(filename)s[%(lineno)d]: %(messa... | 12,387 | 31.686016 | 131 | py |
JEMPP | JEMPP-master/Task/data.py | from tensorflow.python.platform import flags
from tensorflow.contrib.data.python.ops import batching
import tensorflow as tf
import json
from torch.utils.data import Dataset
import pickle
import os.path as osp
import os
import numpy as np
import time
from scipy.misc import imread, imresize
from torchvision.datasets imp... | 19,913 | 33.512998 | 135 | py |
JEMPP | JEMPP-master/Task/calibration.py | import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
def expected_calibration_error(predictions, truths, confidences, bin_size=0.1, title='demo'):
upper_bounds = np.arange(bin_size, 1 + bin_size, bin_size)
ac... | 8,434 | 39.748792 | 169 | py |
JEMPP | JEMPP-master/Task/eval_buffer.py | import os
import torch as t
import numpy as np
from torch.utils.data import DataLoader
from tqdm import tqdm
def norm_ip(img, min, max):
temp = t.clamp(img, min=min, max=max)
temp = (temp + -min) / (max - min + 1e-5)
return temp
def eval_fid(f, replay_buffer, args):
from Task.inception import get_in... | 1,753 | 28.728814 | 120 | py |
JEMPP | JEMPP-master/models/jem_models.py | import torch as t
import torch.nn as nn
from models import wideresnet
import models
from models import wideresnet_yopo
im_sz = 32
n_ch = 3
class F(nn.Module):
def __init__(self, depth=28, width=2, norm=None, dropout_rate=0.0, n_classes=10, model='wrn', args=None):
super(F, self).__init__()
# defau... | 2,494 | 32.716216 | 130 | py |
JEMPP | JEMPP-master/models/norms.py | # coding=utf-8
# Copyright 2019 The Google Research Authors.
#
# 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 applicab... | 5,894 | 33.881657 | 107 | py |
JEMPP | JEMPP-master/models/wideresnet_yopo.py | # coding=utf-8
# Copyright 2019 The Google Research Authors.
#
# 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 applicab... | 4,536 | 35.58871 | 98 | py |
JEMPP | JEMPP-master/models/wideresnet.py | # coding=utf-8
# Copyright 2019 The Google Research Authors.
#
# 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 applicab... | 4,325 | 35.974359 | 105 | py |
DiT | DiT-main/sample.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Sample new images from a pre-trained DiT.
"""
import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends... | 3,269 | 37.928571 | 120 | py |
DiT | DiT-main/download.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Functions for downloading pre-trained DiT models
"""
from torchvision.datasets.utils import download_url
import torch
... | 1,713 | 32.607843 | 111 | py |
DiT | DiT-main/sample_ddp.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Samples a large number of images from a pre-trained DiT model using DDP.
Subsequently saves a .npz file that can be us... | 7,411 | 43.383234 | 120 | py |
DiT | DiT-main/models.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# GLIDE: https://github.com/openai/glide-text2im
#... | 14,995 | 39.420485 | 113 | py |
DiT | DiT-main/train.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
A minimal training script for DiT using PyTorch DDP.
"""
import torch
# the first flag below was False when we tested ... | 10,949 | 39.555556 | 132 | py |
DiT | DiT-main/diffusion/timestep_sampler.py | # Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_d... | 6,013 | 38.827815 | 108 | py |
DiT | DiT-main/diffusion/gaussian_diffusion.py | # Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_d... | 34,326 | 38.275744 | 129 | py |
DiT | DiT-main/diffusion/diffusion_utils.py | # Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_d... | 3,189 | 34.842697 | 108 | py |
DiT | DiT-main/diffusion/respace.py | # Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_d... | 5,485 | 41.2 | 108 | py |
FATE | FATE-master/examples/pipeline/homo_nn/pipeline_homo_nn_train_binary.py | #
# Copyright 2019 The FATE 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 appli... | 3,330 | 34.817204 | 120 | py |
FATE | FATE-master/examples/pipeline/homo_nn/pipeline_homo_nn_train_regression.py | #
# Copyright 2019 The FATE 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 appli... | 3,333 | 35.637363 | 120 | py |
FATE | FATE-master/examples/pipeline/homo_nn/pipeline_homo_nn_train_multi.py | #
# Copyright 2019 The FATE 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 appli... | 3,628 | 37.2 | 120 | py |
FATE | FATE-master/examples/pipeline/homo_nn/pipeline_homo_nn_aggregate_n_epoch.py | #
# Copyright 2019 The FATE 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 appli... | 3,357 | 35.107527 | 120 | py |
FATE | FATE-master/examples/pipeline/hetero_ftl/pipeline-hetero-ftl-with-predict.py | #
# Copyright 2019 The FATE 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 appli... | 3,842 | 37.818182 | 103 | py |
FATE | FATE-master/examples/pipeline/hetero_ftl/pipeline-hetero-ftl-encrypted.py | #
# Copyright 2019 The FATE 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 appli... | 3,286 | 38.60241 | 103 | py |
FATE | FATE-master/examples/pipeline/hetero_ftl/pipeline-hetero-ftl-communication-efficient.py | #
# Copyright 2019 The FATE 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 appli... | 3,471 | 38.908046 | 103 | py |
FATE | FATE-master/examples/pipeline/hetero_ftl/pipeline-hetero-ftl-plain.py | #
# Copyright 2019 The FATE 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 appli... | 3,282 | 38.554217 | 103 | py |
FATE | FATE-master/examples/pipeline/hetero_nn/pipeline-hetero-nn-train-binary-selective-bp.py | #
# Copyright 2019 The FATE 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 appli... | 4,307 | 35.201681 | 117 | py |
FATE | FATE-master/examples/pipeline/hetero_nn/pipeline-hetero-nn-train-binary-drop-out.py | #
# Copyright 2019 The FATE 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 appli... | 4,241 | 34.949153 | 116 | py |
FATE | FATE-master/examples/pipeline/hetero_nn/pipeline-hetero-nn-train-with-early-stop.py | #
# Copyright 2019 The FATE 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 appli... | 5,533 | 39.101449 | 110 | py |
FATE | FATE-master/examples/pipeline/hetero_nn/pipeline-hetero-nn-train-binary.py | #
# Copyright 2019 The FATE 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 appli... | 4,154 | 35.130435 | 116 | py |
FATE | FATE-master/examples/pipeline/hetero_nn/pipeline-hetero-nn-train-binary-coae.py | #
# Copyright 2019 The FATE 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 appli... | 4,957 | 36.560606 | 118 | py |
FATE | FATE-master/examples/pipeline/hetero_nn/pipeline-hetero-nn-train-binary-multi-host.py | #
# Copyright 2019 The FATE 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 appli... | 4,613 | 37.45 | 116 | py |
FATE | FATE-master/examples/pipeline/hetero_nn/pipeline-hetero-nn-train-with-check-point.py | #
# Copyright 2019 The FATE 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 appli... | 4,374 | 35.458333 | 110 | py |
FATE | FATE-master/examples/pipeline/hetero_nn/pipeline-hetero-nn-train-with-warm_start.py | #
# Copyright 2019 The FATE 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 appli... | 4,500 | 36.508333 | 117 | py |
FATE | FATE-master/examples/pipeline/hetero_nn/pipeline-hetero-nn-train-multi.py | #
# Copyright 2019 The FATE 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 appli... | 4,593 | 37.283333 | 117 | py |
FATE | FATE-master/examples/pipeline/homo_graph/pipeline_homo_graph_sage.py | #
# Copyright 2019 The FATE 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 appli... | 3,919 | 38.59596 | 117 | py |
FATE | FATE-master/examples/benchmark_quality/homo_nn/local-homo_nn.py | #
# Copyright 2019 The FATE 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 appli... | 3,738 | 27.761538 | 76 | py |
FATE | FATE-master/examples/benchmark_quality/homo_nn/fate-homo_nn.py | #
# Copyright 2019 The FATE 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 appli... | 7,460 | 34.870192 | 120 | py |
FATE | FATE-master/examples/benchmark_quality/hetero_nn/local-hetero_nn.py | import argparse
import numpy as np
import os
import pandas
from sklearn import metrics
from pipeline.utils.tools import JobConfig
import torch as t
from torch import nn
from pipeline import fate_torch_hook
from torch.utils.data import DataLoader, TensorDataset
from federatedml.nn.backend.utils.common import global_se... | 4,651 | 27.365854 | 79 | py |
FATE | FATE-master/examples/benchmark_quality/hetero_nn/fate-hetero_nn.py | #
# Copyright 2019 The FATE 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 appli... | 7,650 | 32.853982 | 81 | py |
FATE | FATE-master/examples/benchmark_quality/hetero_nn_pytorch/local-hetero_nn.py | import argparse
import numpy as np
import os
from tensorflow import keras
import pandas
import tensorflow as tf
from tensorflow.keras.utils import to_categorical
from tensorflow.keras import optimizers
from sklearn import metrics
from pipeline.utils.tools import JobConfig
from sklearn.preprocessing import LabelEncoder
... | 4,792 | 30.741722 | 97 | py |
FATE | FATE-master/examples/benchmark_quality/hetero_nn_pytorch/fate-hetero_nn.py | import argparse
from collections import OrderedDict
from pipeline.backend.pipeline import PipeLine
from pipeline.component import DataTransform
from pipeline.component import HeteroNN
from pipeline.component import Intersection
from pipeline.component import Reader
from pipeline.component import Evaluation
from pipelin... | 6,139 | 40.768707 | 116 | py |
FATE | FATE-master/python/fate_test/fate_test/scripts/data_cli.py | import os
import re
import sys
import time
import uuid
import json
from datetime import timedelta
import click
from pathlib import Path
from ruamel import yaml
from fate_test import _config
from fate_test._config import Config
from fate_test._client import Clients
from fate_test._io import LOGGER, echo
from fate_test... | 19,039 | 43.176334 | 119 | py |
FATE | FATE-master/python/fate_client/setup.py | # -*- coding: utf-8 -*-
from setuptools import setup
packages = [
"flow_client",
"flow_client.flow_cli",
"flow_client.flow_cli.commands",
"flow_client.flow_cli.utils",
"flow_sdk",
"flow_sdk.client",
"flow_sdk.client.api",
"pipeline",
"pipeline.backend",
"pipeline.component",
... | 2,995 | 43.058824 | 1,418 | py |
FATE | FATE-master/python/fate_client/pipeline/__init__.py | try:
from pipeline.component.nn.backend.torch.import_hook import fate_torch_hook
from pipeline.component.nn.backend import torch as fate_torch
except ImportError:
fate_torch_hook, fate_torch = None, None
except ValueError:
fate_torch_hook, fate_torch = None, None
__all__ = ['fate_torch_hook', 'fate_tor... | 325 | 31.6 | 79 | py |
FATE | FATE-master/python/fate_client/pipeline/param/ftl_param.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2019 The FATE 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/lic... | 7,810 | 47.216049 | 127 | py |
FATE | FATE-master/python/fate_client/pipeline/param/hetero_nn_param.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2019 The FATE 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/lic... | 12,330 | 41.37457 | 139 | py |
FATE | FATE-master/python/fate_client/pipeline/param/boosting_param.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2019 The FATE 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/lic... | 35,176 | 47.253772 | 134 | py |
FATE | FATE-master/python/fate_client/pipeline/param/homo_nn_param.py | from pipeline.param.base_param import BaseParam
class TrainerParam(BaseParam):
def __init__(self, trainer_name=None, **kwargs):
super(TrainerParam, self).__init__()
self.trainer_name = trainer_name
self.param = kwargs
def check(self):
if self.trainer_name is not None:
... | 2,340 | 31.971831 | 107 | py |
FATE | FATE-master/python/fate_client/pipeline/component/hetero_ftl.py | #
# Copyright 2019 The FATE 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 appli... | 3,526 | 35.739583 | 95 | py |
FATE | FATE-master/python/fate_client/pipeline/component/homo_nn.py | #
# Copyright 2019 The FATE 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 appli... | 8,452 | 41.691919 | 136 | py |
FATE | FATE-master/python/fate_client/pipeline/component/__init__.py | from pipeline.component.column_expand import ColumnExpand
from pipeline.component.data_statistics import DataStatistics
from pipeline.component.dataio import DataIO
from pipeline.component.data_transform import DataTransform
from pipeline.component.evaluation import Evaluation
from pipeline.component.hetero_data_split ... | 3,628 | 37.606383 | 141 | py |
FATE | FATE-master/python/fate_client/pipeline/component/hetero_nn.py | #
# Copyright 2019 The FATE 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 appli... | 11,564 | 43.141221 | 134 | py |
FATE | FATE-master/python/fate_client/pipeline/component/nn/models/sequantial.py | #
# Copyright 2019 The FATE 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 appli... | 3,465 | 34.731959 | 112 | py |
FATE | FATE-master/python/fate_client/pipeline/component/nn/models/keras_interface.py | #
# Copyright 2019 The FATE 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 appli... | 2,295 | 28.435897 | 91 | py |
FATE | FATE-master/python/fate_client/pipeline/component/nn/backend/torch/import_hook.py | try:
from pipeline.component.nn.backend.torch import nn as nn_
from pipeline.component.nn.backend.torch import init as init_
from pipeline.component.nn.backend.torch import optim as optim_
from pipeline.component.nn.backend.torch.cust import CustModel, CustLoss
from pipeline.component.nn.backend.tor... | 1,920 | 36.666667 | 101 | py |
FATE | FATE-master/python/fate_client/pipeline/component/nn/backend/torch/base.py | import json
import torch as t
from torch.nn import Sequential as tSequential
from pipeline.component.nn.backend.torch.operation import OpBase
class FateTorchLayer(object):
def __init__(self):
t.nn.Module.__init__(self)
self.param_dict = dict()
self.initializer = {'weight': None, 'bias': N... | 4,209 | 26.880795 | 81 | py |
FATE | FATE-master/python/fate_client/pipeline/component/nn/backend/torch/optim.py | from torch import optim
from pipeline.component.nn.backend.torch.base import FateTorchOptimizer
class ASGD(optim.ASGD, FateTorchOptimizer):
def __init__(
self,
params=None,
lr=0.01,
lambd=0.0001,
alpha=0.75,
t0=1000000.0,
weight_decay=0,
foreach=Non... | 12,959 | 30.228916 | 118 | py |
FATE | FATE-master/python/fate_client/pipeline/component/nn/backend/torch/cust.py | from torch import nn
import importlib
from pipeline.component.nn.backend.torch.base import FateTorchLayer, FateTorchLoss
import difflib
MODEL_PATH = None
LOSS_PATH = None
def str_simi(str_a, str_b):
return difflib.SequenceMatcher(None, str_a, str_b).quick_ratio()
def get_class(module_name, class_name, param, ... | 4,188 | 36.738739 | 119 | py |
FATE | FATE-master/python/fate_client/pipeline/component/nn/backend/torch/init.py | import copy
import torch as t
from torch.nn import init as torch_init
import functools
from pipeline.component.nn.backend.torch.base import FateTorchLayer
from pipeline.component.nn.backend.torch.base import Sequential
str_init_func_map = {
"uniform": torch_init.uniform_,
"normal": torch_init.normal_,
"con... | 6,775 | 25.677165 | 89 | py |
FATE | FATE-master/python/fate_client/pipeline/component/nn/backend/torch/nn.py | from pipeline.component.nn.backend.torch.base import FateTorchLayer, FateTorchLoss
from pipeline.component.nn.backend.torch.base import Sequential
from torch import nn
class Bilinear(nn.modules.linear.Bilinear, FateTorchLayer):
def __init__(
self,
in1_features,
in2_features,
... | 81,792 | 32.412173 | 82 | py |
FATE | FATE-master/python/fate_client/pipeline/component/nn/backend/torch/interactive.py | import torch as t
from torch.nn import ReLU, Linear, LazyLinear, Tanh, Sigmoid, Dropout, Sequential
from pipeline.component.nn.backend.torch.base import FateTorchLayer
class InteractiveLayer(t.nn.Module, FateTorchLayer):
r"""A :class: InteractiveLayer.
An interface for InteractiveLayer. In interactive... | 5,522 | 34.178344 | 113 | py |
FATE | FATE-master/python/fate_client/pipeline/component/nn/backend/torch/__init__.py | try:
from pipeline.component.nn.backend.torch import nn, init, operation, optim, serialization
except ImportError:
nn, init, operation, optim, serialization = None, None, None, None, None
__all__ = ['nn', 'init', 'operation', 'optim', 'serialization']
| 261 | 36.428571 | 93 | py |
FATE | FATE-master/python/fate_client/pipeline/component/nn/backend/torch/operation.py | import torch
import torch as t
import copy
from torch.nn import Module
class OpBase(object):
def __init__(self):
self.param_dict = {}
def to_dict(self):
ret = copy.deepcopy(self.param_dict)
ret['op'] = type(self).__name__
return ret
class Astype(Module, OpBase):
def __... | 3,488 | 23.570423 | 89 | py |
FATE | FATE-master/python/fate_client/pipeline/component/nn/backend/torch/serialization.py | import copy
import inspect
from collections import OrderedDict
try:
from torch.nn import Sequential as tSeq
from pipeline.component.nn.backend.torch import optim, init, nn
from pipeline.component.nn.backend.torch import operation
from pipeline.component.nn.backend.torch.base import Sequential, get_torch... | 4,867 | 37.03125 | 100 | py |
FATE | FATE-master/python/fate_client/flow_client/flow_cli/commands/model.py | #
# Copyright 2019 The FATE 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 appli... | 17,583 | 35.941176 | 120 | py |
FATE | FATE-master/python/federatedml/nn/model_zoo/graphsage.py | import torch as t
from torch import nn
from torch.nn import Module
import torch_geometric.nn as pyg
class Sage(nn.Module):
def __init__(self, in_channels, hidden_channels, class_num):
super().__init__()
self.model = nn.ModuleList([
pyg.SAGEConv(in_channels=in_channels, out_channels=hid... | 702 | 29.565217 | 94 | py |
FATE | FATE-master/python/federatedml/nn/model_zoo/homographsage.py |
import torch as t
from torch import nn
from torch.nn import Module
import torch_geometric.nn as pyg
class Sage(nn.Module):
def __init__(self, in_channels, hidden_channels, class_num):
super().__init__()
self.model = nn.ModuleList([
pyg.SAGEConv(in_channels=in_channels, out_channels=hi... | 703 | 28.333333 | 94 | py |
FATE | FATE-master/python/federatedml/nn/model_zoo/vision.py | import torch as t
from torchvision.models import get_model
class TorchVisionModels(t.nn.Module):
"""
This Class provides ALL torchvision classification models,
instantiate models and using pretrained weights by providing string model name and weight names
Parameters
----------
vision_model_na... | 1,062 | 38.37037 | 114 | py |
FATE | FATE-master/python/federatedml/nn/model_zoo/pretrained_bert.py | from transformers.models.bert import BertModel
from torch.nn import Module
from federatedml.util import LOGGER
class PretrainedBert(Module):
def __init__(self, pretrained_model_name_or_path: str = 'bert-base-uncased', freeze_weight=False):
"""
A pretrained Bert Model based on transformers
... | 1,566 | 38.175 | 117 | py |
FATE | FATE-master/python/federatedml/nn/dataset/base.py | from torch.utils.data import Dataset as Dataset_
from federatedml.nn.backend.utils.common import ML_PATH, LLM_PATH
import importlib
import abc
import numpy as np
class Dataset(Dataset_):
def __init__(self, **kwargs):
super(Dataset, self).__init__()
self._type = 'local' # train/predict
se... | 5,430 | 28.677596 | 119 | py |
FATE | FATE-master/python/federatedml/nn/dataset/image.py | import torch
from federatedml.nn.dataset.base import Dataset
from torchvision.datasets import ImageFolder
from torchvision import transforms
import numpy as np
class ImageDataset(Dataset):
"""
A basic Image Dataset built on pytorch ImageFolder, supports simple image transform
Given a folder path, ImageD... | 3,837 | 35.552381 | 119 | py |
FATE | FATE-master/python/federatedml/nn/dataset/graph.py | import numpy as np
import pandas as pd
from federatedml.statistic.data_overview import with_weight
from federatedml.nn.dataset.base import Dataset
try:
from torch_geometric.data import Data
except BaseException:
pass
import torch
from federatedml.util import LOGGER
class GraphDataset(Dataset):
"""
A... | 4,680 | 39.353448 | 145 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/import_hook.py | try:
from federatedml.component.nn.backend.torch import nn as nn_
from federatedml.component.nn.backend.torch import init as init_
from federatedml.component.nn.backend.torch import optim as optim_
from federatedml.component.nn.backend.torch.cust import CustModel, CustLoss
from federatedml.nn.backen... | 1,925 | 36.764706 | 101 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/base.py | import json
import torch as t
from torch.nn import Sequential as tSequential
from federatedml.nn.backend.torch.operation import OpBase
class FateTorchLayer(object):
def __init__(self):
t.nn.Module.__init__(self)
self.param_dict = dict()
self.initializer = {'weight': None, 'bias': None}
... | 4,203 | 26.657895 | 81 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/optim.py | from torch import optim
from federatedml.nn.backend.torch.base import FateTorchLayer, Sequential
from federatedml.nn.backend.torch.base import FateTorchOptimizer
class ASGD(optim.ASGD, FateTorchOptimizer):
def __init__(
self,
params=None,
lr=0.01,
lambd=0.0001,
alpha=0.75,... | 13,025 | 30.3125 | 118 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/cust_model.py | import importlib
from torch import nn
from federatedml.nn.backend.torch.base import FateTorchLayer
from federatedml.nn.backend.utils.common import ML_PATH
PATH = '{}.model_zoo'.format(ML_PATH)
class CustModel(FateTorchLayer, nn.Module):
def __init__(self, module_name, class_name, **kwargs):
super(Cust... | 1,984 | 34.446429 | 97 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/cust.py | from torch import nn
import importlib
from federatedml.nn.backend.torch.base import FateTorchLayer, FateTorchLoss
from federatedml.nn.backend.utils.common import ML_PATH, LLM_PATH
import difflib
LLM_MODEL_PATH = '{}.model_zoo'.format(LLM_PATH)
MODEL_PATH = '{}.model_zoo'.format(ML_PATH)
LOSS_PATH = '{}.loss'.format(M... | 4,907 | 34.057143 | 118 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/init.py | import copy
import torch as t
from torch.nn import init as torch_init
import functools
from federatedml.nn.backend.torch.base import FateTorchLayer
from federatedml.nn.backend.torch.base import Sequential
str_init_func_map = {
"uniform": torch_init.uniform_,
"normal": torch_init.normal_,
"constant": torch_... | 6,761 | 25.622047 | 89 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/nn.py | from torch import nn
from federatedml.nn.backend.torch.base import FateTorchLayer, FateTorchLoss
from federatedml.nn.backend.torch.base import Sequential
class Bilinear(nn.modules.linear.Bilinear, FateTorchLayer):
def __init__(
self,
in1_features,
in2_features,
out... | 81,778 | 32.406454 | 79 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/interactive.py | import torch as t
from torch.nn import ReLU, Linear, LazyLinear, Tanh, Sigmoid, Dropout, Sequential
from federatedml.nn.backend.torch.base import FateTorchLayer
class InteractiveLayer(t.nn.Module, FateTorchLayer):
r"""A :class: InteractiveLayer.
An interface for InteractiveLayer. In interactive layer... | 5,516 | 33.917722 | 113 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/__init__.py | try:
from federatedml.nn.backend.torch import nn, init, operation, optim, serialization
except ImportError:
nn, init, operation, optim, serialization = None, None, None, None, None
__all__ = ['nn', 'init', 'operation', 'optim', 'serialization']
| 254 | 35.428571 | 86 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/operation.py | import torch as t
import copy
from torch.nn import Module
class OpBase(object):
def __init__(self):
self.param_dict = {}
def to_dict(self):
ret = copy.deepcopy(self.param_dict)
ret['op'] = type(self).__name__
return ret
class Astype(Module, OpBase):
def __init__(self, ... | 3,475 | 23.652482 | 89 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/serialization.py | import copy
import inspect
from collections import OrderedDict
try:
from torch.nn import Sequential as tSeq
from federatedml.nn.backend.torch import optim, init, nn
from federatedml.nn.backend.torch import operation
from federatedml.nn.backend.torch.base import Sequential, get_torch_instance
from fe... | 4,832 | 36.757813 | 100 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/torch_modules_extract/extract_pytorch_modules.py | import inspect
from torch.nn.modules import linear, activation, rnn, dropout, sparse, pooling, conv, transformer, batchnorm
from torch.nn.modules import padding, pixelshuffle
from torch.nn.modules import loss
class Required(object):
def __init__(self):
pass
def __repr__(self):
return '(Requi... | 3,479 | 27.064516 | 108 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/torch_modules_extract/extract_pytorch_optim.py | import inspect
from torch import optim
from federatedml.nn.backend.torch.torch_modules_extract.extract_pytorch_modules import extract_init_param, Required
from torch.optim.optimizer import required
def code_assembly(param, nn_class):
para_str = ""
non_default_param = ""
init_str = """"""
special_param... | 2,304 | 27.45679 | 121 | py |
FATE | FATE-master/python/federatedml/nn/backend/torch/test/test_cust_model.py | from federatedml.nn.backend.torch import nn, init
import json
from federatedml.nn.backend.torch import serialization as s
import torch as t
from federatedml.nn.backend.torch.import_hook import fate_torch_hook
from federatedml.nn.backend.torch.cust import CustModel
fate_torch_hook(t)
cust_resnet = CustModel(name='resn... | 713 | 31.454545 | 77 | py |
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