content stringlengths 200 198k | repo stringlengths 4 186 | path stringclasses 1
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import torch
import numpy as np
import utils as u
from argparse import Namespace
from torch.nn.parameter import Parameter
import torch.nn as nn
import math
class Sp_GCN(torch.nn.Module):
def __init__(self, args, activation):
super().__init__()
self.activation = activation
self.num_layers =... | experiment/models/egcn_components.py | Apache-2.0 | 8,391 | 703acda094063a9753dfbf20f39fe63c446c3200 | Python | py | |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import mmcv
import pytest
import torch
from mmgen.apis import (init_model, sample_ddpm_model, sample_img2img_model,
sample_unconditional_model)
class TestSampleUnconditionalModel:
@classmethod
def setup_class(cls):
pr... | tests/test_apis/test_inference.py | Apache-2.0 | 4,700 | 80067a452fa870b692422ddf10450a6e2f946390 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class VectorQuantizer(nn.Module):
def __init__(self, num_embeddings, embedding_dim, commitment_cost):
super(VectorQuantizer, self).__init__()
self._embedding_dim = embedding_dim
self._num_embeddings = num_... | models/vq.py | MIT | 5,117 | 29c8de5193f979b7c2e3aed07b06c435ef7d8358 | Python | py | |
"""Helper functions to modify models to have multiple gradient paths."""
import functools
import torch
def introspect_model(model, input_data_shape, modality="vision"):
"""Compute model feature shapes."""
feature_shapes = dict()
if modality == "vision":
setup = dict(device=next(iter(model.paramete... | breaching/cases/malicious_modifications/parameter_utils.py | MIT | 4,260 | 29c8fd49ef4ca55c1720666065464b1dbaeb45d2 | Python | py | |
from typing import Dict # isort:skip
from collections import defaultdict
import torch
from torch.optim import Optimizer
class Lookahead(Optimizer):
def __init__(self, optimizer: Optimizer, k: int = 5, alpha: float = 0.5):
"""
Taken from: https://github.com/alphadl/lookahead.pytorch
"""
... | catalyst_rl/contrib/nn/optimizers/lookahead.py | Apache-2.0 | 3,019 | ff7c4e020707de111d1a7b59c1f7d1c54a817571 | Python | py | |
import time
import torch
import wjc_core
import argparse
from tensorboardX import SummaryWriter
from attention_unet import AttU_Net
from segnet import SegNet
from unet import Unet
from Unet_plus_plus import NestedUNet
from Res_net import ResNet50, ResNet101
from MDOAU_net import MDOAU_net
from new_model impo... | main.py | MIT | 5,813 | 29c961eef20045d82c1d158a48f894403bf3f467 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Train a video classification model."""
import numpy as np
import pprint
import torch
from fvcore.nn.precise_bn import get_bn_modules, update_bn_stats
import slowfast.models.losses as losses
import slowfast.models.optimiz... | tools/train_net.py | Apache-2.0 | 17,761 | 29c98c9e93439e81f21bef32b61dee2b64df09f5 | Python | py | |
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | nemo/collections/asr/parts/rnnt_beam_decoding.py | Apache-2.0 | 29,286 | 29c9960ffe14fd99bff9e9cd061c5aa4b1c3511e | Python | py | |
import unittest
import os
import mock
import torchtext
from seq2seq.data import SourceField, TargetField
from seq2seq.trainer import SupervisedTrainer
class TestSupervisedTrainer(unittest.TestCase):
def setUp(self):
test_path = os.path.dirname(os.path.realpath(__file__))
src = SourceField()
... | seq2seq/tests/test_supervised_trainer.py | Apache-2.0 | 1,805 | 24d274b7e2dbd31aa10daec97b9c9a10ee04cffa | Python | py | |
import os
import sys
import json
import subprocess
import numpy as np
import torch
from torch import nn
from opts import parse_opts
from model import generate_model
from mean import get_mean
from classify import classify_video
import glob
from tqdm import tqdm
import pdb
import pickle as pkl
def convert_to_np(resul... | video-classification-3d-cnn-pytorch/main.py | MIT | 2,405 | 29c9d3535c0c856d9cc26dbd11db02ac7cdfba01 | Python | py | |
import os
import sys
import json
import random
import numpy as np
from PIL import Image
import collections
import torch
import torchvision.transforms as T
from skimage import io
from torchvision.transforms import functional as F
from pathlib import Path
if sys.version_info < (3, 3):
sequence = collections.Sequence... | naslib/utils/load_ops.py | Apache-2.0 | 15,458 | 29c9dcab51a4b3598e2475dee4b42d542b108019 | Python | py | |
from typing import List, Union
import numpy as np
import torch
from numpy import ndarray
from transformers import *
class BertParent(object):
"""
Base handler for BERT models.
"""
MODELS = {
'bert-base-uncased': (BertModel, BertTokenizer),
'bert-large-uncased': (BertModel, BertToken... | summarizer/bert_parent.py | MIT | 5,766 | 1038f8f7d05a5821b3d113cbe4bae5bc481e5824 | Python | py | |
import math
import random
import torch
from torch import nn
from torch.nn import functional as F
if torch.cuda.is_available():
from op.fused_act import FusedLeakyReLU, fused_leaky_relu
from op.upfirdn2d import upfirdn2d
else:
from op.fused_act_cpu import FusedLeakyReLU, fused_leaky_relu
from op.upfirdn... | e4e/models/stylegan2/model.py | MIT | 18,729 | fcb12af85669ab6fd7f79cb14ddbdf80b2fbd83d | Python | py | |
import os
import time
import random
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
from dataset.md_seq import MoDaSeq, paired_collate_fn
from dataset.md_seq_ac import MoDaSeqAC
from utils.log import Logger
from utils.functional import str2bool, load_data, load_... | actor_critic.py | MIT | 32,393 | 29c9f5519c3a6894f95b71f8dd4328c6631e9aa8 | Python | py | |
from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
import numpy as np
import torch as th
from hmlf import spaces
from hmlf.algorithms.td3.policies import TD3Policy
from hmlf.algorithms.td3.td3 import TD3
from hmlf.common.noise import ActionNoise
from hmlf.common.off_policy_algorithm import OffPolicy... | hmlf/algorithms/paddpg/paddpg.py | MIT | 8,866 | 650f078c17d28bcb7fafab11e968ceab55bcfd62 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from mmdet.core import (auto_fp16, bbox_target, delta2bbox, force_fp32,
multiclass_nms)
from ..builder import build_loss
from ..losses import accuracy
from ..registry import HEADS
@HEADS.register_module
class... | mmdet/models/track_heads/track_head.py | Apache-2.0 | 6,697 | f996cfbdd33ced8d566b70911d4f362419268041 | Python | py | |
"""
trying to resolve:
- https://intellij-support.jetbrains.com/hc/en-us/requests/3764538
You will need to run
pip install transformers
pip install fairseq
On mac
pip3 install torch torchvision torchaudio
on linux
pip3 install torch==1.10.1+cpu torchvision==0.11.2+cpu torchaudio==0.10.1+cpu -f https:... | tutorials_for_myself/try_catch_pycharm_issues/try_catch_with_pickle.py | MIT | 3,168 | e621119a87c44175c1d81820b550a2a48182dd0a | Python | py | |
import os
import librosa.display as lbd
import matplotlib.pyplot as plt
import sounddevice
import soundfile
import torch
import torchaudio
import configparser
import soundfile as sf
from Preprocessing.AudioPreprocessor import AudioPreprocessor
from Preprocessing.TextFrontend import TextFrontend
from speechbrain.pretr... | InferenceInterfaces/aridialect_Tacotron2.py | Apache-2.0 | 9,929 | 42192edf6ecbfabbfe5a31cefbcb3b9f6890d8d9 | Python | py | |
# Owner(s): ["oncall: quantization"]
import torch
import math
from typing import Tuple
from torch.ao.quantization import (
FakeQuantize,
MovingAverageMinMaxObserver,
default_observer,
default_affine_fixed_qparams_fake_quant,
)
from torch.ao.quantization._learnable_fake_quantize import _LearnableFakeQu... | test/quantization/core/test_workflow_ops.py | Intel | 58,785 | 28de0540877f7bf0341b2377dbeab6de32246309 | Python | py | |
from .camera_geometry import CameraGeometry
import numpy as np
import cv2
import torch
from fastseg import MobileV3Small
class LaneDetector():
def __init__(self, cam_geom=CameraGeometry(), model_path='./fastai_model.pth'):
self.cg = cam_geom
self.cut_v, self.grid = self.cg.precompute_gri... | code/solutions/lane_detection/lane_detector.py | CC-BY-4.0 | 2,634 | ff011bbf911eba1f3dbeb536a002234c8535353f | Python | py | |
import torch
from torch import nn
class FuzzBuzzModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
"""
2 layer network for prediction fiz or buz
:param int input_size: size of the input layer
:param int hidden_size: size of the hidden linear laye... | FuzzBuzzNet.py | MIT | 918 | 29ca7a089ac9fa07f66636838e1a5d6bf3f31d15 | Python | py | |
import warnings
from typing import Dict, List, Optional, Tuple
import torch
from torch.utils.data import Dataset
from lhotse import validate
from lhotse.cut import Cut, CutSet, MonoCut
from lhotse.utils import EPSILON
class SourceSeparationDataset(Dataset):
"""
.. warning: Speech separation datasets are not... | lhotse/dataset/source_separation.py | Apache-2.0 | 6,601 | 247ce4e66125d4182c051248347393e4d3b8011b | Python | py | |
# we already use multiprocessing, multithreading will not improve anything here so disable it
import os
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'
import torch.nn as nn
import torch
import trw
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self... | tutorials/recipes/segmentation_cityscapes.py | MIT | 1,231 | d75c87b0aa48899d3f7a59f5f0a918fcfd831cf4 | Python | py | |
"""
CHAED includes
1) 1000 images, 10 for each of 100 Chinese characters
2) including 20 characters with single element and
3) 80 with multi components in different structure types including horizontal composition, vertical composition,
half surrounding compos... | dataset/chaed.py | Apache-2.0 | 10,301 | 7c84da9fb31ffda029ec297f3eb3e8dea43be73c | Python | py | |
"""
Contains pieces taken from https://github.com/pytorch/examples/issues/70
and https://github.com/pytorch/examples/tree/master/vae
"""
import torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self):
nc = 3
ndf = 128
ngf = 128
nz = 100
super().__init__()
... | demo/fishvae.py | MIT | 3,721 | 5f0a581cab1a0bc31a3e55d97bf72ae5f8ee8aac | Python | py | |
import math
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import _standard_normal, broadcast_all
from scipy.stats import gennorm
import numpy as np
class GeneralisedNormal(ExponentialFami... | nnest/distributions/generalised_normal.py | MIT | 2,870 | 29cab4c961b2937ebd8ba5570e717d3ca75ea4ce | Python | py | |
#!/usr/bin/env python
import logging
import argparse
import numpy
import torch
import pyro
import pyro.infer
import pyro.infer.mcmc
import pyro.distributions as dist
def get_arguments():
parser = argparse.ArgumentParser(description='Bayesian Calibration arguments')
parser.add_argument('--logging-level', def... | run.py | BSD-3-Clause | 3,112 | 29cabb18faffc8c6e81b74262da3a89ee7c76709 | Python | py | |
import os, sys
sys.path.append(os.getcwd())
import time
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import sklearn.datasets
import tflib as lib
import tflib.save_images
import tflib.mnist
import tflib.plot
import torch
import torch.autograd as autograd
import torch.nn ... | gan_mnist.py | MIT | 7,897 | 29cac132574a88fad302c9496c1fddc4225ec57d | Python | py | |
import torch
import warpctc_pytorch as warp_ctc
import pytest
@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU")
def test_simple():
probs = torch.FloatTensor([[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]]]).transpose(0, 1).contiguous()
grads = torch.zeros(probs.size())
labe... | pytorch_binding/tests/test_gpu.py | Apache-2.0 | 3,891 | fcee5901015ececb2bdd86e930783631bc755564 | Python | py | |
# -*- coding: utf-8 -*-
#
# sampler.py
#
# Copyright 2020 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 at
#
# http://www.apache.or... | entity_name_align/dataloader/sampler.py | Apache-2.0 | 41,901 | fcf10be3073803a176ed88e1700363ce87a73d95 | Python | py | |
import torch
import torch.nn.functional as F
import numpy as np
from PIL import Image
import morphology
import network
import os
import math
from tqdm import tqdm ###
idx = 0
def save_img(img, output_path):
result = Image.fromarray(
(img.data.cpu().numpy().transpose((1, 2, 0)) * 255).astype(np.uint8))
... | inference.py | MIT | 30,176 | 29cadda836c61bd5fe5aec34cb80e6fba67391d7 | Python | py | |
# flake8: noqa
import numpy as np
import torch
from catalyst.metrics.functional._hitrate import hitrate
def test_hitrate():
"""
Tests for catalyst.metrics.hitrate metric.
"""
y_pred = [0.5, 0.2, 0.1]
y_true = [1.0, 0.0, 1.0]
k = [1, 2, 3]
hitrate_at1, hitrate_at2, hitrate_at3 = hitrate(... | tests/catalyst/metrics/functional/test_hitrate.py | Apache-2.0 | 1,175 | 7c464798fdac0c3f7c13305ae4fb20f5e77ce681 | Python | py | |
# -*- coding: utf-8 -*-
'''
Author: Ioannis Kakogeorgiou
Email: gkakogeorgiou@gmail.com
Python Version: 3.7.10
Description: evaluation.py includes the code in order to produce
the evaluation for each class as well as the prediction
masks for the pixel-level semantic segmentation.
'''
... | semantic_segmentation/unet/evaluation.py | MIT | 7,939 | 29caebd1538de52b3018c176bd7b4d33e4f2ce6d | Python | py | |
from __future__ import absolute_import, division, print_function
import numbers
import torch
import math
def _compute_chain_variance_stats(input):
# compute within-chain variance and variance estimator
# input has shape N x C x sample_shape
N = input.size(0)
chain_var = input.var(dim=0)
var_with... | pyro/ops/stats.py | MIT | 15,570 | 3414389735c4a837d6a9cc0ce02f5a13bc1ed9c4 | Python | py | |
# -*- coding: utf-8 -*-
# file: run_glove_test.py
# time: 2021/12/4
# author: yangheng <yangheng@m.scnu.edu.cn>
# github: https://github.com/yangheng95
# Copyright (C) 2021. All Rights Reserved.
import shutil
from torch import cuda
from pyabsa import APCModelList, BERTBaselineAPCModelList, GloVeAPCModelList, \
AT... | basic_test/run_glove_test.py | MIT | 4,009 | 45cf0eeb9178cbf1cf37b6ac7dbed185d7f842b7 | Python | py | |
"""Neural net classes."""
import fnmatch
from itertools import chain
from collections import OrderedDict
import tempfile
import warnings
import numpy as np
from sklearn.base import BaseEstimator
import torch
from torch.utils.data import DataLoader
from skorch.callbacks import EpochTimer
from skorch.callbacks import ... | skorch/net.py | BSD-3-Clause | 60,946 | 7c798ebdc6ada3734387a212d4e5e5b54dd64fe2 | Python | py | |
import torchvision.models as vision_models
import torch
from torchvision import transforms
def load_model():
model = vision_models.googlenet(pretrained=True)
model.eval()
model = {
"model": model,
"preprocessing": transforms.Compose([
transforms.Resize(256),
transf... | api_ml/models/googlenet.py | Apache-2.0 | 632 | 29cb13deb0085d89241b0c1ed3e5803fce7e93be | Python | py | |
import torch
import unittest
from summarize.modules.coverage_matrix_attention import MLPCoverageAttention
class TestMLPCoverageAttention(unittest.TestCase):
def test_mlp_coverage_attention(self):
batch_size = 3
num_encoder_tokens = 7
num_decoder_tokens = 9
encoder_dim = 11
... | summarize/tests/modules/coverage_matrix_attention/mlp_test.py | Apache-2.0 | 1,850 | 29cb203792208463f38070f2061b12a29e99a09f | Python | py | |
from collections import deque
import numpy as np
import torch
from torch import optim
from torch._C import device
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from QL.DQN import DQN
from QL.ReplayBuffer import ReplayBuffer
from SetteEMezzoGame import SetteEMezzo
import matplotlib.pyplot a... | SetteEMezzoDQL.py | MIT | 8,094 | 7cadcded2f6081bd3908737105c9159f1bfcff98 | Python | py | |
#import needed library
import os
import logging
import random
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
from utils import net_builder, get_logger, count_paramet... | train.py | MIT | 15,340 | 7c29059dffdbd1e1c916e56d6d52b8329aa81243 | Python | py | |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import os.path as osp
import mmcv
import torch
from mmcv import DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
... | tools/analysis_tools/test_robustness.py | Apache-2.0 | 15,222 | 29cb41da657005ee8fbf772fda03603a81442983 | Python | py | |
import os
import sys
sys.path.append('../../')
from dependencies import *
from settings import *
from reproducibility import *
from models.TGS_salt.Unet34_scSE_hyper import Unet_scSE_hyper as Net
import pickle
TUNE = False
SIZE = 101
PAD = 27
Y0, Y1, X0, X1 = PAD,PAD+SIZE,PAD,PAD+SIZE,
FOLD=["0","1","2","3","9"]
RAN... | projects/TGS_salt/obsolete/inference_kfolds.py | Apache-2.0 | 16,127 | 4bb89f8a136e8762efbdb70d563358bdf76e86ab | Python | py | |
import torch
import os
from PIL import Image
from torch.utils.data import Dataset
from natsort import natsorted
# define custom dataset for TrojAI competition
class CustomDataSet(Dataset):
def __init__(self, main_dir, transform, triggered_classes, label_specific=False):
self.main_dir = main_dir
s... | dataset.py | MIT | 1,311 | 1e702f9a77ff5fbbb2255cc5504bb0d975267ce8 | Python | py | |
from __future__ import division
import torch
import onmt
"""
Class for managing the internals of the beam search process.
hyp1-hyp1---hyp1 -hyp1
\ /
hyp2 \-hyp2 /-hyp2hyp2
/ \
hyp3-hyp3---hyp3 -hyp3
================... | onmt/Beam.py | MIT | 3,298 | 4b6603e165e8776102b05e829cf0060f6cd4e981 | Python | py | |
#!/usr/bin/env python3
import inf_common as IC
import torch
from torch import Tensor
import time
from typing import Dict, List, Tuple, Optional
from collections import defaultdict
from collections import ChainMap
import sys,random,itertools
import numpy as np
import math
from multiprocessing import Pool
import... | model_debugger_pieces.py | MIT | 8,653 | 5ea164c824ec5a95922fb0d777e10b355a89dbeb | Python | py | |
"Callback support for half precision (fp16) training. Increases training speed."
from ..torch_core import *
from ..callback import *
from ..basic_train import *
from torch._utils import _unflatten_dense_tensors
from torch.nn.utils import parameters_to_vector
__all__ = ['MixedPrecision']
def get_master(layer_groups:Mo... | fastai_do_not_use/callbacks/fp16.py | Apache-2.0 | 5,471 | 29cbc52038794815c64f65c4427d098876fbbb75 | Python | py | |
import random
from collections import namedtuple
import numpy as np
import torch
from src.config import BATCH_SIZE, BUFFER_SIZE, GAMMA, TAU, CHECKPOINT_SAVE_PATH, \
NOISE_STD, ACTOR_LEARNING_RATE, CRITIC_LEARNING_RATE
from src.memory import ReplayBuffer
from src.network import ActorNet
from src.network import Cri... | src/agent.py | Apache-2.0 | 4,397 | 2484eec6304f9f2534c77e77de1a81b0027b0a4c | Python | py | |
# /*
#
# MIT License
#
# Copyright (c) 2021 AI4Finance
#
# Author: Berend Gort
#
# Year: 2021
#
# GitHub_link_author: https://github.com/Burntt
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Softwa... | finrl_meta/env_crypto_trading/alpaca_paper_trade_multicrypto.py | MIT | 10,870 | 29cbd9721025f8168c04ffdef8485991c21335eb | Python | py | |
import math
import torch
import numpy as np
import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib.collections import EllipseCollection
import sys
import os
import pickle
def save(simu, frames, attributes, attributes_alltimes, Nsaved=None,
save_file=True, file_name="data"):
"""Basi... | sisyphe/display.py | MIT | 23,641 | 7075e22401621713be70eddda935145605f53e97 | Python | py | |
#
# Copyright (c) 2019 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | distiller/apputils/image_classifier.py | Apache-2.0 | 51,382 | cc0a2ca1473f8e522acd22cecef7936e6f275129 | Python | py | |
#!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in c... | examples/pplm_clickbait/run_gen_while.py | Apache-2.0 | 9,951 | 5f21fa4952efbcfbb3de7c65f8e55e0f64227046 | Python | py | |
import numpy as np
import torch
import PIL.Image
from collections import OrderedDict
# global variable for holding a persistent single copy of a model
netNetwork = None
class Network(torch.nn.Module):
def __init__(self, pretrained_hed=True):
super(Network, self).__init__()
self.netVggOne = torch.... | lib/hed_pytorch/hed.py | Apache-2.0 | 6,551 | 650cbefbdf30ab5cf55c02d9236da486868acfd4 | Python | py | |
import re
from itertools import chain
from torch.nn import Module, Conv2d, ConvTranspose2d
def count_parameters(module: Module):
"""
Counts the number of learnable parameters in a Module
"""
return sum(p.numel() for p in module.parameters() if p.requires_grad)
def count_conv2d(module: Module):
... | dense/utils.py | MIT | 1,041 | 1e0ec2bd3b7d545b7b4f31ab917fe12f64f2bd62 | Python | py | |
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## ECE Department, Rutgers University
## Email: zhang.hang@rutgers.edu
## Copyright (c) 2017
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this sou... | hzproc/__init__.py | MIT | 1,792 | 29cc86fa56deaedc00f364d338d6b1e47139eb63 | Python | py | |
import getopt
import random
import sys
from collections import deque
# make sure the root path is in system path
from pathlib import Path
from flatland.envs.malfunction_generators import malfunction_from_params, MalfunctionParameters
base_dir = Path(__file__).resolve().parent.parent
sys.path.append(str(base_dir))
im... | baselines/torch_training/training_navigation.py | MIT | 8,536 | 5ed2bd1e3bcbafbda72aff86932cc15e318b1789 | Python | py | |
import torch
import utils
class Agent:
"""An abstraction of the behavior of an agent. The agent is able:
- to choose an action given an observation,
- to analyze the feedback (i.e. reward and done state) of its action."""
def __init__(self, env_id, obs_space, model_dir, argmax=False, num_envs=1):
... | utils/agent.py | MIT | 2,077 | 29ccbefa3e6a41c5c43b1731c6577edfcd059ec3 | Python | py | |
import pickle
import shutil
import pytest
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.loggers import TensorBoardLogger
from test_models.conftest import make_dataloaders
from torch.optim import AdamW
from pytorch_forecasting.metrics import MAE, CrossEntro... | tests/test_models/test_mlp.py | MIT | 3,324 | e92d2855d4e61d3a12531fdd35e7c4571ce4a0e6 | Python | py | |
import math
import numbers
import warnings
from enum import Enum
from typing import List, Tuple, Any, Optional
import numpy as np
import torch
from PIL import Image
from torch import Tensor
try:
import accimage
except ImportError:
accimage = None
from ..utils import _log_api_usage_once
from . import function... | torchvision/transforms/functional.py | BSD-3-Clause | 62,349 | 7c6aee1f3767c3abd6a9bd0c26e76062bb77cb6a | Python | py | |
''' Now that we have trained a VAE model wioth normalizing flows, we want to evalueate it in some way.
We estimate the probability of data under the model using an importance sampling technique.
We can write the marginal likelihood of a datapoint as:
log p_theta(x) = log E_q [p_theta(x,z) / q_phi(z|... | evaluation_trained_model.py | MIT | 4,144 | 80181ad3e0230fe42c78e770313817a103b80a2c | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
COCO dataset which returns image_id for evaluation.
Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py
"""
from pathlib import Path
import torch
import torch.utils.data
import torchvision
f... | detr/datasets/coco.py | Apache-2.0 | 5,258 | 29cceedc67c9eed3ac960ee145a46a0acfeb0644 | Python | py | |
#!/usr/bin/env python
# coding=utf-8
# This file is copied from torchvision.models
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.nn as nn
from . import utils
class Segnet(nn.Module):
def __init__(self, in_channels, num_classes, stride, ... | libs/nets/segnet.py | Apache-2.0 | 1,230 | 29cd023d601ee36df2ae784026a952552d008a6a | Python | py | |
import torch
import torch.nn as nn
from torch.distributions import MultivariateNormal
import gym
import numpy as np
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class Memory:
def __init__(self):
self.actions = []
self.states = []
self.logprobs = []
self.r... | PPO_continuous.py | MIT | 8,169 | 29cd21e22f4a6c7f67358f9577a9d32a49e9cc0d | Python | py | |
import os
import sys
sys.path.append('./../')
import common.pytorch as pt
import torch
import torchvision
def main():
#define the imagenet resnet50
ori_save_pth = '/gmodel/admin/PyTorch_IR/vv1/resnet50.pth'
# model = torchvision.models.resnet50(pretrained=True)
# torch.save(model, ori_save_pth)
#co... | model-exchange/test/pytorch_test.py | OML | 1,671 | 1e171f11534da6642e5d080ae1b5d0f0a1c4d937 | Python | py | |
import logging
import torch
class DropBlock(torch.nn.Module):
def __init__(self,
block_size=7,
keep_prob=0.9,
current_step=0.,
train_steps=1.):
super(DropBlock, self).__init__()
self.block_size = block_size
self.keep_prob... | layers/dropblock.py | MIT | 2,573 | 29cd41e194e0d0d4e3c0354f52732b45085fbbb2 | Python | py | |
import cv2
import torch
import tqdm
import os
import numpy as np
import pickle
from data.coco_pose.ref import ref_dir, flipRef
from utils.misc import get_transform, kpt_affine, resize
from utils.group import HeatmapParser
from util import draw_limbs
import matplotlib.pyplot as plt
valid_filepath = ref_dir + '/validati... | test.py | BSD-3-Clause | 9,471 | 1e78cffcb8713da99b18ff0b2dc0e18b0bb2d764 | Python | py | |
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.utils.data as data
from torch import optim
from torchvision import datasets, transforms
import numpy as np
import random
import sys
import argparse
import logging
import os
import time
from dataset.cifar10 import get_dataset
sys.pa... | cifar10/train.py | MIT | 21,553 | 29cd447bf6c8a174a03c2ba1f356332427dec332 | Python | py | |
import torch
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pad_sequence
import pickle
import pandas as pd
from tqdm import tqdm
class IEMOCAPDataset(Dataset):
def __init__(self, dataset_path, mode='train'):
self.videoIDs, self.videoSpeakers, self.videoLabels, self.videoT... | bclstm/dataloader.py | MIT | 5,034 | 29cd46befe926b6ee628c17e835895634c9be1b2 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from dataclasses import dataclass
from typing import Union
import torch
@dataclass
class DensePoseEmbeddingPredictorOutput:
"""
Predictor output that contains embedding and coarse segmentation data:
* embedding: float tensor of size ... | projects/DensePose/densepose/structures/cse.py | Apache-2.0 | 1,398 | fceeea5f8212b52df96a0433942e660f0b031d30 | Python | py | |
from typing import Dict, Optional, Set, Union
from pathlib import Path
import torch
from torch import quantization
from catalyst.core import IRunner
from catalyst.core.callback import Callback, CallbackOrder
from catalyst.dl.utils import save_quantized_model
class DynamicQuantizationCallback(Callback):
"""
... | catalyst/dl/callbacks/quantization.py | Apache-2.0 | 4,916 | 29cd4ec53c12c23b92e2a396b85faa0a15f0babc | Python | py | |
import numpy as np
import random
import json
import sys
import torch
import config
from env import make_env
import time
import os
import ann_th as ann
import argparse
from gym.wrappers import Monitor
np.set_printoptions(precision=2)
np.set_printoptions(linewidth=160)
final_mode = True
render_mode = False
#final_m... | model_th.py | Apache-2.0 | 8,570 | d7c136e076504afbb57dd56c1635b56441eae197 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import json
import math
import numpy as np
class Normalize():
def __init__(self, mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225)):
self.mean = mean
self.std = std
def __call__(self, img):
imgarr = np.asarray(... | network/scalenet.py | MIT | 7,318 | 6b1137f746ea487e381804e14b48c48390a23e94 | Python | py | |
import datetime, time
import os
import numpy as np
import torch
import torchvision.utils as vutils
from . import utils
class Logger(object):
def __init__(self, args):
self.times = {'init': time.time()}
self._checkPath(args)
self.args = args
self.printArgs()
def printArgs(self):... | utils/logger.py | MIT | 2,836 | 29cd5667d00d6f05e684db912ef461e977ff7deb | Python | py | |
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (ConvModule, NonLocal3d, build_activation_layer,
constant_init, kaiming_init)
from mmcv.runner import _load_checkpoint, load_checkpoint
from mmcv.utils import _BatchNorm
from torch.nn.modules.utils import _ntuple, _trip... | mmaction/models/backbones/resnet3d.py | Apache-2.0 | 32,623 | 29cd93394be074f38a40405313549b8c395faebe | Python | py | |
import argparse
import json
import os
import pickle
import sys
import sagemaker_containers
import pandas as pd
import torch
import torch.optim as optim
import torch.utils.data
from model import LSTMClassifier
def model_fn(model_dir):
"""Load the PyTorch model from the `model_dir` directory."""
print("Loading ... | Project/train/train.py | MIT | 6,217 | 29cdb57faf5fd2738fc600f0e3569fd10fb8e2f3 | Python | py | |
# -*- coding: iso-8859-15 -*-
import collections
import random
import modeling, tokenization
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
import matplotlib.pyplot as plt
class TrainingInstance(ob... | pytorch_viz/transfer.py | Apache-2.0 | 16,621 | 29cdbb01e8919a12bcae73275cef655324e1ca81 | Python | py | |
import copy
import tempfile
import unittest
import torch
from transformers import (
ADAPTER_CONFIG_MAP,
ADAPTERFUSION_CONFIG_MAP,
AdapterType,
BertModel,
DistilBertModel,
PfeifferConfig,
RobertaModel,
XLMRobertaModel,
)
from transformers.testing_utils import require_torch
from .test_m... | tests/test_adapter_fusion_loading.py | Apache-2.0 | 6,343 | 4b5de5b9c9801aec880664291bae8420f47aec72 | Python | py | |
from os.path import join as pjoin
from os import mkdir
import pytest
import numpy as np
from hangar import Repository
try:
import torch
from torch.utils.data import DataLoader
from hangar import make_torch_dataset
skipTorch = False
except ImportError:
skipTorch = True
@pytest.mark.skipif(skipTor... | tests/test_dataloaders.py | Apache-2.0 | 16,692 | 038e72282e8e35fd7095368e5b6c5a5e244aa90f | Python | py | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#------------------------------------------------------
# @ File : demo_pipeline.py
# @ Description:
# @ Author : Alex Chung
# @ Contact : yonganzhong@outlook.com
# @ License : Copyright (c) 2017-2018
# @ Time : 2021/2/3 上午10:40
# @ Software : PyCh... | docs/demo_pipeline.py | Apache-2.0 | 8,742 | a93b2026b39e41a92144f209c2f1e047acbcfa15 | Python | py | |
import torch
import torch.nn as nn
from uer.layers import *
from uer.layers.transformer import TransformerDecoderLayer
from uer.layers.layer_norm import LayerNorm, T5LayerNorm
from uer.layers.relative_position_embedding import RelativePositionEmbedding
class TransformerDecoder(nn.Module):
"""
BERT encoder exp... | uer/decoders/transformer_decoder.py | MIT | 3,300 | 10366372da9197784deb61d83e926ee4eb8b08c9 | Python | py | |
# @Time : 2019-08-10 11:20
# @Author : Lee_zix
# @Email : Lee_zix@163.com
# @File : main.py
# @Software: PyCharm
"""
The entry of the KGEvolve
"""
import argparse
import itertools
import os
import sys
import time
import pickle
import copy
import dgl
import numpy as np
import torch
from tqdm import tqdm
impor... | src/ensemble.py | Apache-2.0 | 39,049 | 29cded363b6d74c4cd575b9410e89998cadc52cb | Python | py | |
# coding: utf-8
import argparse
import time
import math
import os
import torch
import torch.nn as nn
import torch.onnx
import sys
sys.path.append(os.path.abspath(".."))
from utils import data_train
from model import model
parser = argparse.ArgumentParser(description='LSTM Reverse Language Model')
parser.add_argument... | train/train_revLM.py | BSD-3-Clause | 10,539 | 1ec30ec75b0ddf60e737370e7b80610ace81d60c | Python | py | |
import copy
import numpy as np
import torch
import nemo.collections.asr as nemo_asr
from omegaconf import OmegaConf
from torch.utils.data import DataLoader
from TheSoundOfAIOSR.stt.nemo.audio_data_layer import AudioDataLayer
from TheSoundOfAIOSR.stt.nemo.audio_framer import FrameASR
from TheSoundOfAIOSR.stt.nemo.st... | TheSoundOfAIOSR/stt/nemo/streaming_model.py | MIT | 4,554 | 29ce1277b2b4f7260c68478c0d666e09086bd45f | Python | py | |
''' Incremental-Classifier Learning
Authors : Khurram Javed, Muhammad Talha Paracha
Maintainer : Khurram Javed
Lab : TUKL-SEECS R&D Lab
Email : 14besekjaved@seecs.edu.pk '''
from torchvision import datasets, transforms
import torch
import numpy as np
# To incdude a new Dataset, inherit from Dataset an... | myData/iDataset.py | Apache-2.0 | 5,405 | 1e1a3f1f995695710eff1e60597c8d709aa09537 | Python | py | |
# ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# -------------------------------------------------------------------... | InnerEye/ML/configs/histo_configs/classification/BaseMIL.py | MIT | 5,928 | 29ce29da8fef2b73e55640e3e287b1c168d5443e | Python | py | |
from __future__ import annotations
import abc
import torch
import torch.nn as nn
import transformers
from jiant.ext.allennlp import SelfAttentiveSpanExtractor
from jiant.shared.model_resolution import ModelArchitectures
from jiant.tasks.core import TaskTypes
from typing import Callable
from typing import List
"""
... | jiant/proj/main/modeling/heads.py | MIT | 12,398 | 7c548d5e00a2b1f70a2db6c22ea436915d6a77a9 | Python | py | |
# --------------------------------------------------------
# ScaleConvLSTM
# Copyright (c) 2018 Fudan-VTS
# Licensed under The MIT License [see LICENSE for details]
# Written by liwenxi
# --------------------------------------------------------
import torch
from torch import nn, optim
from torchvision import models
fro... | Deep-Learning/ConvLSTM/ScaleConvLSTM/test.py | Apache-2.0 | 4,211 | 1e6b22ecdc453d1427d3d874999e760f6e5174c8 | Python | py | |
import os
import json
import random
from collections import defaultdict, Counter
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader, Sampler
from torchtext.vocab import Vocab, Vectors
from torch.distributions.categorical import Categorical
class MasterData():
def __init__(self, fil... | src/data/pubmed.py | MIT | 7,028 | 7cb8637299413d5e345238ccd4c81595c9d6b741 | Python | py | |
import torch
import torch.distributions
from torch.utils.data import DataLoader, Dataset
import numpy as np
import os
from torchvision.datasets.folder import default_loader
from scipy.io import loadmat
import paths_config
from .imagenet_augmentation import get_imageNet_augmentation
FLOWERS_LABELS = [
... | utils/dataloaders/flowers.py | MIT | 4,538 | 5e6314a41477b4b1715aa8d5b4a5505c9894ea74 | Python | py | |
import numpy as np
import torchvision
import torch
import torch.nn as nn
from torch.autograd import Variable
import dataset
import models
import helpers
from config import *
assert(file_name == 'gan')
writer, save_dir = helpers.init(gpu, file_name, experiment_name)
discriminator = models.Discriminator().cuda()
gener... | gan.py | MIT | 4,534 | b25844e81d4aff97c0abf11525cbd79a9d4a5656 | Python | py | |
from six.moves import cPickle
import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
import pandas as pd
from tqdm import tqdm
from joblib import Parallel, delayed
def get_data(data_table,i):
return np.load(data_table.loc[i]['input'])
def load_dataset(data_path):
... | asr/util/librispeech_dataset.py | MIT | 5,666 | 29cf2e4692420c5016746de5d4c5cf67ebe4093b | Python | py | |
from __future__ import absolute_import
# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
# ---------------... | lib/model/rpn/proposal_layer.py | MIT | 8,416 | 454c6c3dbc8085d741c51853b43585953b7058d9 | Python | py | |
"""Regression trainer/evaluator implementation module."""
import logging
from typing import AnyStr # noqa: F401
import numpy as np
import torch
import torch.optim
import thelper.concepts
import thelper.typedefs as typ # noqa: F401
import thelper.utils
from thelper.train.base import Trainer
logger = logging.getLogg... | thelper/train/regr.py | Apache-2.0 | 8,096 | 1ec5b1f96f8f73837be58a4cbbdc7e132e98e732 | Python | py | |
# Copyright (c) 2019 Microsoft Corporation.
# Copyright (c) 2018 Ricky Tian Qi Chen
# Licensed under the MIT license.
import torch
import lib.dist as dist
from lib.adapters import LpNestedAdapter
import vae_quant
import ast
def parseISA(isalist):
print(isalist)
pnested = []
pnested.append(isalist[0])
... | lpnested-vae-learn-exponents/metric_helpers/loader.py | MIT | 2,909 | 29cf74bbf9fc9829a4863993e4beb94ca289e482 | Python | py | |
"""
as for test_rnn_memorization.py, but this time the sender, not the receiver
same idea: train supervised, and compare compositional vs non-compoisitoinal grammars
"""
import time
import random
import torch
from torch import optim
import torch.nn.functional as F
import numpy as np
from ulfs import rl_common
from u... | mll/run_mem_send.py | MIT | 14,370 | 29cf8450127153834feb8ac76fc2a3760ac29e0c | Python | py | |
'''
Goal1: a GRU where the weight matrices have a block structure so that information flow is constrained
Data is assumed to come in [block1, block2, ..., block_n].
Goal2: Dynamic parameter sharing between blocks (RIMs)
'''
import torch
import torch.nn as nn
from .GroupLinearLayer import GroupLinearLayer
from .... | modular_dynamics/utilities/BlockGRU.py | MIT | 5,005 | 80550b91556e6f467f9b7c717176825f6ed44e8b | Python | py | |
#!/usr/bin/env python
# coding=utf-8
################################################################################
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical
################################################################################
class MLP... | model.py | MIT | 3,002 | 108c428d69075e67b62939e9ad65f95edf37ef10 | Python | py | |
from typing import Any, Dict, List, Optional, Tuple, Type, Union
import numpy as np
import gym
import torch as th
import torch.nn.functional as F
from torch import nn
from torch.distributions import MultivariateNormal
from stable_baselines3.common.policies import BaseModel, BasePolicy, ContinuousCritic, regi... | custom_algos/d3pg/policies.py | MIT | 16,597 | 7cdb372ded8f8abab69394b54f919f2c359427f9 | Python | py | |
from torch.autograd import Variable
import warnings
warnings.filterwarnings("ignore")
import torch
import torch.optim as optim
from datetime import datetime, timedelta
from torch.utils.data import DataLoader
import torch.nn as nn
from data.dataset import NCPDataset, NCP2DDataset, NCPJPGDataset, NCPJPGDataset_new, NCP... | validation.py | MIT | 7,370 | 29cfbedc007e763b33c5b66bc3cfe0ed28a40c2a | Python | py | |
from __future__ import print_function, division
import sys
import os
import torch
import numpy as np
import random
import csv
import itertools
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from torch.utils.data.sampler import Sampler
from pycocotools.coco import COCO
impo... | retinanet/dataloader.py | Apache-2.0 | 19,097 | 7018d322e38debbf63ca62c810bf8422e307e252 | Python | py | |
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
"""
CNNモデルクラス
"""
def __init__(self, num_classes=10, **kwargs):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
self.... | nn_lesson/model/cnn.py | MIT | 864 | ff826e93b7845a3a682fe6d7ab843d0aba35bec6 | Python | py |
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