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'''
Following distilaltion distills knowledge from a normal teacher to
a self distill student model. This enables us with the option of
where (meaning at what FC layer) to transfer the KD loss, instead of
just one option of tranferring to the final FC layer of the original
student model.
'''
# train a student network... | train_kd_to_SD.py | MIT | 16,935 | 10bf01e3769a5a62e2610397258b5414802090e6 | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import numpy as np
from progress.bar import Bar
import time
import torch
from models.model import create_model, load_model
from utils.image import get_affine_transform
from utils.debugger import Deb... | src/lib/detectors/base_detector.py | MIT | 5,050 | a93f7c3adc5feef3b4ceec877207f32a898e36af | Python | py | |
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmpose.models.backbones.utils import (InvertedResidual, SELayer,
channel_shuffle, make_divisible)
def is_norm(modules):
"""Check if is one ... | tests/test_backbones/test_backbones_utils.py | Apache-2.0 | 3,988 | fee72321f984726585c28b7d25d44ad101e6e955 | Python | py | |
import torch
def sequence_mask(seq):
batch_size, seq_len = seq.size()
mask = torch.triu(torch.ones((seq_len, seq_len), dtype=torch.uint8),
diagonal=1)
mask = mask.unsqueeze(0).expand(batch_size, -1, -1) # [B, L, L]
return mask | model/tool/sequence_mask.py | Apache-2.0 | 265 | 52b13984b40678228c38e6780a9e04f3f859e541 | Python | py | |
import torch
import random
from .default import NormalNN
class L2(NormalNN):
"""
@article{kirkpatrick2017overcoming,
title={Overcoming catastrophic forgetting in neural networks},
author={Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and... | agents/regularization.py | MIT | 14,056 | fee73925193ae13ebd9e28cc4bb7913c2f7f8377 | Python | py | |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
from torch import jit, nn
from pycave.bayes.core import covariance_shape, CovarianceType
from pycave.bayes.core._jit import jit_log_normal, jit_sample_normal
from pycave.core import ConfigModule
@dataclass
class GaussianMixture... | pycave/bayes/gmm/model.py | MIT | 5,405 | 4250e4921436593bd16aeb816c4b1b9182a126af | Python | py | |
import torch
import torch.nn as nn
"""Define new activation for VQA"""
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
def forward(self, x):
return torch.mul(x, torch.sigmoid(x)) | src/activation.py | MIT | 231 | dfc43b4e26f19f5bf711ed47305f6375a0cf5b8b | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# import argparse
# import itertools
import json
import os
import subprocess
# import sys
# import time
import unittest
import uuid
import torch
# import datasets
# import hparams_registry
# import algorithms
# import networks
# from parameterize... | domainbed/test/scripts/test_train.py | MIT | 1,654 | fee7530ec18d87fbf0803988096a26535416ce48 | Python | py | |
import argparse
import os
import shutil
import time
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.tra... | docker_classify.py | Apache-2.0 | 17,636 | fee75bea34e7ea676b3afa3fe0470f7f2ac65d47 | Python | py | |
import torch
from torch import nn
import torch.nn.functional as F
from tamil_tech.torch.layers import *
from tamil_tech.torch.utils import *
class Decoder(nn.Module):
''' A decoder model with self attention mechanism. '''
def __init__(
self, sos_id, eos_id,
n_tgt_vocab, d_word_vec,
... | tamil_tech/torch/decoders.py | Apache-2.0 | 10,036 | fee75dc8515fa5bdd20dab853081f18de09bc90a | Python | py | |
import os
import sys
import json
import torch
import torch.nn as nn
from torchvision import transforms, datasets
import torch.optim as optim
from tqdm import tqdm
from model import GoogLeNet
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("using {} device.".format(d... | pytorch_classification/GoogLeNet/train_pre.py | MIT | 4,907 | fee79bb224341713cfcc2d053d9451f607a02b35 | Python | py | |
import os
import torch
from torchtext.data.field import Field, RawField
from torchtext.data.iterator import BucketIterator
from torchtext.vocab import Vectors
from datasets.castor_dataset import CastorPairDataset
class WikiQA(CastorPairDataset):
NAME = 'wikiqa'
NUM_CLASSES = 2
ID_FIELD = Field(sequentia... | datasets/wikiqa.py | Apache-2.0 | 2,777 | fee7a6a0ceb303692b6ec58108f9ace97d6edeba | Python | py | |
import argparse
from multiprocessing import Pool
import os
from time import time
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch.utils.data import Dataset
from utils.data_utils import DatasetHandler, load_mr_scan
from utils import evaluation, utils
class DataPreloader(Dataset):
def __... | baseline.py | MIT | 4,617 | 7c2a073c2402c5957b8c4e8dbd886439c8a48eac | Python | py | |
"""dataset.py"""
import os
import numpy as np
from tqdm import tqdm
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision.datasets import ImageFolder
from torchvision import transforms
class CustomImageFolder(ImageFolder):
def __init__(self, root, transform=None):
super(CustomIm... | gan_training/dvae/dataset.py | RSA-MD | 2,742 | 45e7a24a8a15686b125c8d939438dcc97b119172 | Python | py | |
from torch import nn
import torch
import torch.nn.functional as F
from detro.networks.components import BiFPN, Center_layer, Offset_layer, Reg_layer, Heatmap_layer, BasicBlock
from detro.networks.losslib import center_loss, distance_loss
from detro.networks.backbones.resnet_backbone import resnet18
class FeatureFusion... | detro/packages/fcosdet/network.py | MIT | 1,880 | fee7eb72919db9a3e293dcb1856698b507092ff2 | Python | py | |
from data import *
from utils.augmentations import SSDAugmentation, BaseTransform
from utils.functions import MovingAverage, SavePath
from layers.modules import MultiBoxLoss
from yolact import Yolact
import os
import sys
import time
import math
from pathlib import Path
import torch
from torch.autograd import Variable
i... | train.py | MIT | 15,891 | fee7f2ace7f34ac5b560ad895db224be66cec613 | Python | py | |
import torch
from fastscript import *
from fastbench.utils import show_install
from fastbench.vision import train_imagenette
from fastbench.tabular import train_tabular
from fastbench.imdb import train_imdb
"""Compute image benchmarks on imagenette"""
welcome_str="""Welcome to fastbench! your friendly benchmark for ... | bench.py | Apache-2.0 | 1,269 | 12a14cd549ebdd9a9f133e27f5548cb9deb7aba3 | Python | py | |
import torch_audiomentations
from torch import Tensor
from hw_asr.augmentations.base import AugmentationBase
class Gain(AugmentationBase):
def __init__(self, sample_rate, p=0.2, mode="per_example", p_mode="per_example", *args, **kwargs):
self._aug = torch_audiomentations.Gain(sample_rate=sample_rate, p=p... | hw_asr/augmentations/wave_augmentations/Gain.py | MIT | 473 | fee7fce16e90530f99707bdd31b40080ae548e5a | Python | py | |
import torch.nn.functional as F
from utils.utils import *
def make_divisible(v, divisor):
# Function ensures all layers have a channel number that is divisible by 8
# https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
return math.ceil(v / divisor) * divisor
class... | ultralytics_newest_yolov3/utils/layers.py | Apache-2.0 | 4,538 | fee81ca101141b611c8e335c17240c32aa21a8e7 | Python | py | |
import os
import itertools
import torch
import numpy as np
import pandas as pd
from scipy.stats import special_ortho_group, pearsonr
from sklearn.decomposition import PCA
class Constants(object):
eta = 1e-6
class CustomBatch:
def __init__(self, batch):
self.batch = torch.stack(batch, dim=1)
de... | task_dyva/utils.py | MIT | 9,565 | 5e5336f8fe83adf405e5a1180407fe85f8b27baa | Python | py | |
from torch_geometric.nn import radius_graph
class RadiusGraph(object):
r"""Creates edges based on node positions :obj:`pos` to all points within a
given distance.
Args:
r (float): The distance.
loop (bool, optional): If :obj:`True`, the graph will contain
self-loops. (default:... | torch_geometric/transforms/radius_graph.py | MIT | 1,416 | fee8414b8fe69b088757362328c1a24509732ad1 | Python | py | |
import random
import numpy as np
import torch
from torch.utils.data import Dataset
from purano.util import tokenize
from purano.training.fasttext_hnsw import FasttextHnsw
class Text2TitleDataset(Dataset):
def __init__(self, data, ft_model, min_words=2, max_words=150):
self.ft_hnsw = FasttextHnsw(ft_mode... | purano/training/datasets/text2title.py | Apache-2.0 | 1,815 | fee8705a014b75291358b4ce334665e97caf9274 | Python | py | |
import unittest
from unittest import TestCase
import torch
from src.signals.outliers import MomentOutlierSignal
class MomentOutlierTest(TestCase):
def setUp(self):
self.eps = 1e-2 # threshold for differences in data evaluation
def test_scalar_mean(self):
Signal = MomentOu... | tests.py | BSD-3-Clause | 1,697 | fee871e4d588db3332a3f127507c3dc3746ea042 | Python | py | |
#!/usr/bin/env python3
# encoding: utf-8
import os
import cv2
import argparse
import numpy as np
import torch
import torch.multiprocessing as mp
from config import config
from utils.pyt_utils import ensure_dir, link_file, load_model, parse_devices
from utils.visualize import print_iou, show_img
from engine.evaluator ... | model/dfn/cityscapes.dfn.R101_v1c/eval.py | MIT | 3,703 | f95867a1ff6a16e823d1ce55a0b733bd89064d07 | Python | py | |
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# ------------------------------------------------------------------------
from typing import Optional
... | models/transformer_decoder.py | MIT | 14,590 | fee89790a193ea737b0ebac3edfd1e728944d44d | Python | py | |
"""Common image segmentation losses.
"""
import torch
from torch.nn import functional as F
def bce_loss(true, logits, mask=None, pos_weight=None):
"""Computes the weighted binary cross-entropy loss.
Args:
true: a tensor of shape [B, 1, H, W].
logits: a tensor of shape [B, 1, H, W]. Correspo... | lib/junno/nnet/torch/loss.py | BSD-3-Clause | 6,650 | fee8aae2bddb39feae17ada11b92b18382c83da5 | Python | py | |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. 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 r... | examples/pytorch/eager/huggingface_models/examples/text-classification/run_glue_no_trainer_prune.py | Apache-2.0 | 20,958 | 3452720866fd40c0469d40387dd273d28c0e5b74 | Python | py | |
import os
import argparse
import numpy as np
import torch
import model
import data
import train
parser = argparse.ArgumentParser(
description=r"""Denoising MLP autoencoder""")
parser.add_argument('--mode', type=str, default='train',
help='mode to run main (train, generate)')
parser.ad... | main.py | Apache-2.0 | 6,394 | fee8c8bbfcfe75d676e2c9fb58125dbd53c110db | Python | py | |
"""
Use the output of gaze_estimation to calibrate (train) screen_gaze
"""
import logging
import math
import time
from random import random
from typing import List, Tuple
import screeninfo
import cv2
import numpy as np
import torch.nn
import yacs.config
from gaze2screen_model import Gaze2Screen
from gaz... | screen_gaze.py | MIT | 8,042 | 45a1f4478ba4209f962c1407763a559f32b5cb0e | Python | py | |
"""The data layer used during training to train a Fast R-CNN network.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.utils.data as data
from PIL import Image
import torch
from model.utils.config import cfg
from roi_da_data_layer.minibatc... | lib/roi_da_data_layer/roibatchLoader.py | MIT | 9,067 | fee8d74d718c95ec6382a1fe92ce520ebd20e21e | Python | py | |
"""
This script was made by Nick at 19/07/20.
To implement code for inference with your model.
"""
from argparse import ArgumentParser, Namespace
import os
import matplotlib.pyplot as plt
import numpy as np
import pytorch_lightning as pl
import torch
from src.utils import Config, get_dataloader
pl.seed_every... | infer.py | MIT | 2,098 | 127ff7df37ff42b84b26a441171af0497fd7e3f8 | Python | py | |
from typing import Optional
import torch
from catalyst.metrics.functional import process_multilabel_components
def average_precision(
outputs: torch.Tensor,
targets: torch.Tensor,
weights: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Computes the average precision.
Args:
outputs... | catalyst/metrics/precision.py | Apache-2.0 | 2,152 | 45e47c6a2072a302e75d095e2813fd8d45efff58 | Python | py | |
# -*- coding: utf-8 -*-
import torch
import numpy as np
from ..aggregators_base import AggregatorBase
from ...registry import AGGREGATORS
from ....index.utils import feature_loader
from typing import Dict
@AGGREGATORS.register
class PWA(AggregatorBase):
"""
Part-based Weighting Aggregation.
c.f. https:/... | pyretri/extract/aggregator/aggregators_impl/pwa.py | Apache-2.0 | 3,165 | fee907b49fb72949b0454cd024c4c1eaf2824b6e | Python | py | |
import torch
import torch.nn.functional as F
import numpy as np
from scipy import interpolate
class InputPadder:
""" Pads images such that dimensions are divisible by 8 """
def __init__(self, dims, mode='sintel'):
self.ht, self.wd = dims[-2:]
pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8
... | core/utils/utils.py | BSD-3-Clause | 2,477 | fee90888145858cfd00bab9f01cb631acb48fddd | Python | py | |
from typing import Any, Dict, Mapping, Union
import copy
import os
import torch
import torch.distributed as dist
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
from catalyst.core.engine import IEngine
from catalyst.typing import RunnerCriterion, RunnerModel, RunnerOptimizer, RunnerSchedul... | catalyst/engines/torch.py | Apache-2.0 | 15,063 | 5ece2b919922690c19e7ca78357c60316390cd93 | Python | py | |
#!/usr/bin/env python
# encoding: utf-8
# The MIT License (MIT)
# Copyright (c) 2017-2018 CNRS
# 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 Software without restriction, including without limita... | pyannote/audio/embedding/models/clopinet.py | MIT | 11,434 | 5ed4f65c1272d213490c71a79633fab900597924 | Python | py | |
import os
import torch
from torch.utils.ffi import create_extension
sources = ['src/lib_cffi.cpp']
headers = ['src/lib_cffi.h']
extra_objects = ['src/bn.cu.obj'] #['src/bn.o']
with_cuda = True
this_file = os.path.dirname(os.path.realpath(__file__))
extra_objects = [os.path.join(this_file, fname) for fname in extra_o... | modules/build_windows.py | BSD-3-Clause | 658 | 12f17021b3cb74b5a9d7b8dc51ce59ababca79ad | Python | py | |
# The based unit of graph convolutional networks.
import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvTemporalGraphical(nn.Module):
r"""The basic module for applying a graph convolution.
Args:
in_channels (int): Number of channels in the input sequence data
out_channel... | net/utils/tgcn_origin_mask_softmax.py | BSD-2-Clause | 2,912 | fee938d119bbe55d1ff17562027b70e1f0abf156 | Python | py | |
# coding: utf-8
import argparse
import time
import math
import os
import torch
import torch.nn as nn
import torch.onnx
import data
import model
parser = argparse.ArgumentParser(description='PyTorch Wikitext-2 RNN/LSTM Language Model')
parser.add_argument('--data', type=str, default='./data/wikitext-2',
... | word_language_model/main.py | BSD-3-Clause | 9,985 | 03bfaeb87eef2585aca6084ccae24b77d128d3c8 | Python | py | |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.ops.multi_scale_deform_attn import (
MultiScaleDeformableAttention, MultiScaleDeformableAttnFunction,
multi_scale_deformable_attn_pytorch)
_USING_PARROTS = True
try:
from parrots.autograd import gradcheck
except ImportErr... | tests/test_ops/test_ms_deformable_attn.py | Apache-2.0 | 6,657 | fee94c575cf107565ebbf355d220910897efd2db | Python | py | |
import pytest
import torch
from torch.autograd import gradcheck
import kornia
import kornia.testing as utils # test utils
from kornia.testing import assert_close
@pytest.mark.parametrize("batch_size", [1, 2, 5])
def test_get_perspective_transform(batch_size, device, dtype):
# generate input data
h_max, w_ma... | test/geometry/transform/test_imgwarp.py | ECL-2.0,Apache-2.0 | 24,846 | df9fe54749873420acb91ab5748aef7ac4cb73fa | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 ... | ai_ct_scans/dino/eval_video_segmentation.py | MIT | 12,672 | fee96aeef3b82a4afb6dda035e13fa807a9c2250 | Python | py | |
import warnings
from typing import Optional, Tuple
import torch
from kornia.utils import _extract_device_dtype, safe_inverse_with_mask
from .conversions import convert_points_from_homogeneous
from .epipolar import normalize_points
from .linalg import transform_points
TupleTensor = Tuple[torch.Tensor, torch.Tensor]
... | kornia/geometry/homography.py | ECL-2.0,Apache-2.0 | 7,555 | fee972b59b3c99c8819c4c446bbe8239b3f89b44 | Python | py | |
# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... | nnunet/utilities/nd_fotmax_for_inference.py | Apache-2.0 | 815 | d726ece1bc96a65e8b9f8234807eddf387aeccbf | Python | py | |
from optparse import OptionParser
import os, time, random, pickle
import torch
import numpy as np
from src.util.data_operator import read_conll, write_conll, vocab
from src.util.config_reader import Configurable
import importlib
import pdb
if __name__ == '__main__':
random.seed(666)
np.random.seed(666)
t... | gap/test_driver.py | BSD-3-Clause | 3,143 | 6b4018be8bd7489b34ec234483f6b7e8c4c65c04 | Python | py | |
# Copyright (c) 2020 NVIDIA CORPORATION.
# Copyright (c) 2018-2020 Chris Choy (chrischoy@ai.stanford.edu).
#
# 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 Software without restriction, including wit... | models_dev/mink_transformer_voxel.py | MIT | 14,799 | fee98467ad47141b32e94ea8525cbe8a3e107988 | Python | py | |
import os
import time
import numpy as np
import torch
import torchvision
from torchvision.transforms import ToTensor
from appfl.config import *
from appfl.misc.data import *
from appfl.misc.utils import *
from models.cnn import *
import appfl.run_serial as rs
import appfl.run_mpi as rm
from mpi4py import MPI
impor... | examples/mnist.py | MIT | 6,291 | 45d5db5076c5f95ffa268a542aab9a4dc649cd52 | Python | py | |
from torch.optim.optimizer import Optimizer, required
import torch
import math
import numpy as np
class LRangerMod(Optimizer):
# AMSGrad/Adam + AdaMod + QH Momentum + Iterate Averaging + Lookahead + Rule of Thumb Linear Warmup (instead of RAdam Rectification) + P from PAdam
def __init__(self, params, lr=1e-... | models/optim/optimizers.py | MIT | 56,673 | 106a295d4157c965d3a731ed518fb3fbf65915b7 | Python | py | |
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension
setup(
name='deep_hough',
ext_modules=[
CUDAExtension('deep_hough', [
'deep_hough_cuda.cpp',
'deep_hough_cuda_kernel.cu',
],
extra_compile_args={'cxx': ... | utils/_cdht/setup.py | MIT | 419 | fee9a54c890ae04dd39ea5c888e95f0df7edea27 | Python | py | |
import typing as t
from typing import TYPE_CHECKING
import torch
from simple_di import inject
from simple_di import Provide
from bentoml import Tag
from bentoml import Model
from bentoml.exceptions import BentoMLException
from bentoml.exceptions import MissingDependencyException
from ..models import PT_EXT
from ..mo... | bentoml/_internal/frameworks/pytorch_lightning.py | Apache-2.0 | 7,526 | b26b861b83d8a9c204852ecf3e0dcfc3717012fa | Python | py | |
import unittest
from torchtext.data.metrics import bleu_score
class TestTorchtext(unittest.TestCase):
def test_bleu_score(self):
candidate = [['I', 'love', 'Kaggle', 'Notebooks']]
refs = [[['Completely', 'Different']]]
self.assertEqual(0, bleu_score(candidate, refs))
| tests/test_torchtext.py | Apache-2.0 | 301 | f9fbf76f48f629dfed65985f8afa1c2c2edfe082 | Python | py | |
import torch
import torch.nn as nn
from marl.utils import init
"""
Modify standard PyTorch distributions so they are compatible with this code.
"""
FixedCategorical = torch.distributions.Categorical
old_sample = FixedCategorical.sample
FixedCategorical.sample = lambda self: old_sample(self).unsqueeze(-1)
log_prob_c... | marl/rlcore/distributions.py | MIT | 961 | feea030eb20117e3867ffdfc7b66ce84a5c65425 | Python | py | |
# -*- coding: utf-8 -*-
"""“TextLSTM-Torch.ipynb”的副本
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1K75NsbkuejOzp2tfsXGDJxP-nQl9V0DC
"""
'''
code by Tae Hwan Jung(Jeff Jung) @graykode, modify by wmathor
'''
import torch
import numpy as np
import to... | 3-2.TextLSTM/TextLSTM-Torch.py | MIT | 2,735 | ff74ce2fbeb33e03c27cb45adea7ec9bd33ec358 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Loss functions."""
import torch.nn as nn
from pycls.core.config import cfg
# Supported loss functions
_loss_funs... | pycls/core/losses.py | MIT | 714 | feea0b6f684ca7e60b9c20aae77b56fc307b205e | Python | py | |
import torch
import torch.nn as nn
import numpy as np
from torch.optim import AdamW
import torch.optim as optim
import itertools
from model.warplayer import warp
from torch.nn.parallel import DistributedDataParallel as DDP
from model.IFNet2F import *
import torch.nn.functional as F
from model.loss import *
device = to... | model/RIFE2F.py | MIT | 9,667 | feea1df72f089809e4124bbf6b051afa4b9c5cd5 | Python | py | |
"""From the project root directory (containing data files), this can be run with:
Boolean logic evaluation:
python -m spinn.models.fat_classifier --training_data_path ../bl-data/pbl_train.tsv \
--eval_data_path ../bl-data/pbl_dev.tsv
SST sentiment (Demo only, model needs a full GloVe embeddings file to do well... | python/spinn/models/fat_classifier.py | Apache-2.0 | 30,757 | 804d6983d0ab86b72ae3f189cb33c0e8fff19bb3 | Python | py | |
# -*- encoding: utf-8 -*-
"""
@File : DQN_brain.py
@Time : 2020/12/4
@Author : Yuan Yifu
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
MEMORY_CAPACITY = 2000
N_STATE = 4
N_ACTIONS = 2
EPSILON = 0.9
LR = 0.01
REPLACE_COUNTER = 100
BATCH_SIZE = 32
GAMMA = 0.9
class Ne... | DQN/CartPole/DQN_brain.py | MIT | 2,753 | feea298fda9086d758aa991fce9281751e0298f3 | Python | py | |
## Portions of Code from, copyright 2018 Jochen Gast
from __future__ import absolute_import, division, print_function
import os
import socket
import re
#from pytz import timezone
from datetime import datetime
import fnmatch
import itertools
import argparse
import sys
import six
import unicodedata
import json
import i... | tools.py | Apache-2.0 | 15,246 | feea3dd241d956d227c4f00aa1ad125875d5106a | Python | py | |
#!/usr/bin/env python3
#
# # Copyright (c) 2021 Facebook, inc. and its affiliates. All Rights Reserved
#
#
from ast import dump
import copy
import multiprocessing as mp
import argparse
import pickle
import numpy as np
import random
import torch
import torch.distributed as dist
import sys
import os
import time
import re... | uimnet/workers/mog.py | MIT | 5,191 | 03f3df4f8f67adfdc0faa5cc70ab431db46cfcc1 | Python | py | |
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import MultiStepLR, StepLR
from resnet_with_block import cifar_resnet32, cifar_se_resnet32,\
cifar_srm_resnet32, cifar_srm_with_corr_matrix_resnet32, srm_resnet50, se_resnet50, resnet50, oursrm_resnet50, cifar_srm_with_median_resnet32
im... | experiments.py | MIT | 7,165 | feeaad1d6e1f36f48b379131a42de400cf41efa3 | Python | py | |
import torch.nn as nn
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmedit.models.common import MaskConvModule
from mmedit.models.registry import COMPONENTS
@COMPONENTS.register_module()
class PConvEncoder(nn.Module):
"""Encoder with partial conv.
About the details for this archetecture, pls see:
... | mmedit/models/backbones/encoder_decoders/encoders/pconv_encoder.py | Apache-2.0 | 4,187 | 52f3c2c16e83672b8ffd5f4f4d633e17bd5bc035 | Python | py | |
# -*- coding: utf-8 -*-
# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the... | built-in/TensorFlow/Research/cv/image_classification/Cars_for_TensorFlow/automl/vega/search_space/networks/pytorch/utils/anchor_utils/anchor_target.py | Apache-2.0 | 8,923 | 6b10dd34e33d4af7d5e8f6f9d90c285879c524fe | Python | py | |
import time
from datetime import timedelta
import pytest
import numpy as np
from torch.optim import Adam
from flights_time_series_dataset import FlightSeriesDataset, FlightsDataset
from skorch.callbacks import EarlyStopping
from skorch.dataset import CVSplit
from sklearn.metrics import r2_score # mean_squared_error
f... | tests/test_quantum_lstm.py | Unlicense | 3,305 | 45f63e96bbc2b4435ef68a0d26b2ef139b417198 | Python | py | |
import sys
sys.path.append('..')
import fewshot_re_kit
import torch
from torch import autograd, optim, nn
from torch.autograd import Variable
from torch.nn import functional as F
import numpy as np
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
sup... | models/snail.py | MIT | 5,430 | fdf88f4426ed836307e84fbcc77f7db74c2d80c7 | Python | py | |
from torch.utils.data import Subset
from PIL import Image
from torchvision.datasets import MNIST
from base.torchvision_dataset import TorchvisionDataset
from .preprocessing import create_semisupervised_setting
import torch
import torchvision.transforms as transforms
import random
class MNIST_Dataset(TorchvisionDatas... | src/datasets/mnist.py | MIT | 3,695 | feead5eead461ab2c38f6a05f004ec17e76c4b81 | Python | py | |
"""
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
import sys
import json
import logging
import os
impor... | transformers/file_utils.py | Apache-2.0 | 11,684 | feead9ed742260a9807f0fd91824c50499e996b1 | Python | py | |
import mmcv
import numpy as np
import torch
from torch.nn.modules.utils import _pair
from .builder import ANCHOR_GENERATORS
@ANCHOR_GENERATORS.register_module()
class AnchorGenerator(object):
"""Standard anchor generator for 2D anchor-based detectors.
Args:
strides (list[int] | list[tuple[int, int]]... | mmdet/core/anchor/anchor_generator.py | Apache-2.0 | 31,208 | 12d6f50b2de43788dad888d6c7aef1c9e8e1c8a9 | Python | py | |
# Copyright (c) 2020 Graphcore Ltd. All rights reserved.
import pytest
import os
import subprocess
import torch
import numpy as np
import poptorch
from torchvision import transforms
from pathlib import Path
import shutil
import sys
import re
import random
sys.path.append('..')
from datasets.preprocess import NormalizeT... | applications/pytorch/cnns/datasets/test_data.py | MIT | 15,639 | feeb24d89063a2a99f82e15b3f0979acb8b428f1 | Python | py | |
# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
from typing import Dict
from typing import List
from typing import Optional
import math
import k2
import torch
def _intersect_device(a_fsas: k2.Fsa, b_fsas: k2.Fsa, b_to_a_map: torch.Tensor,
sorted_match_a: bool):
'''This ... | snowfall/decoding/lm_rescore.py | Apache-2.0 | 12,581 | 1edef233c9760a7dcf5221f171d565e7721df116 | Python | py | |
from __future__ import division
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from inverse_warp import inverse_warp
def photometric_reconstruction_loss(tgt_img, ref_imgs, intrinsics,
depth, explainability_mask, pose,
... | loss_functions.py | MIT | 5,674 | 24cbefa4d038de57e007efbfac67d68c10b50e5e | Python | py | |
import warnings
from collections import OrderedDict
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer, constant_init, trunc_normal_init
from mmcv.cnn.bricks.transformer import FFN, build_dropout
from mm... | mmdet/models/backbones/swin.py | Apache-2.0 | 30,173 | fce01664ad04db71de8607e3651ec247dadff94c | Python | py | |
# -*- coding: utf-8 -*-
__author__ = 'ooo'
__date__ = '2018/12/15 12:17'
import time
import argparse
import shutil
import os
import random
from datetime import datetime
from collections import namedtuple
from PIL import Image
import warnings
import torch
from torch import nn
from torch.utils import data
import torchv... | factory/data_factory.py | MIT | 20,254 | e982d73b7a49f7cc47bf80b5e838fd45c7172c2d | Python | py | |
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
class Brain:
def __init__(self, net, config):
self.net = net
self.value_loss_coef = config.brain.value_loss_coef
self.entropy_coef = config.brain.entropy_coef
self.max_grad_norm = config.brain.max_gra... | 2_PG/2-2_A2C/brain.py | MIT | 1,821 | 5e364bf006880d9390316f5acc3bf866fc39aac4 | Python | py | |
import argparse
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch_geometric.utils import negative_sampling
import torch_geometric.transforms as T
from torch_geometric.nn import GCNConv, SAGEConv
from ogb.linkproppred import PygLinkPropPredDataset, Evaluator
from logger ... | baseline_gcn.py | MIT | 10,400 | 0398b1a9246ab9dd96030b7828dbaf8a20a6ffa6 | Python | py | |
"""
Copyright 2020 The Microsoft DeepSpeed Team
"""
import torch
from .builder import CUDAOpBuilder
class TransformerBuilder(CUDAOpBuilder):
BUILD_VAR = "DS_BUILD_TRANSFORMER"
NAME = "transformer"
def __init__(self, name=None):
name = self.NAME if name is None else name
super().__init__(n... | op_builder/transformer.py | MIT | 1,263 | a94c6da62e399937b917e0f41b8323317d4b1b46 | Python | py | |
import os.path as osp
import warnings
from collections import OrderedDict
import mmcv
import numpy as np
from mmcv.utils import print_log
from terminaltables import AsciiTable
from torch.utils.data import Dataset
from mmdet.core import eval_map, eval_recalls
from .builder import DATASETS
from .pipelines import Compos... | mmdet/datasets/custom.py | Apache-2.0 | 13,691 | feeba09b0f97a28dab293bbd33a768dcfc81c68b | Python | py | |
import asyncio
import websockets
import json
import sys
import plotly.graph_objs as go
import plotly.offline as offline
import numpy as np
from pytorch_pretrained_bert.tokenization import load_vocab, BertTokenizer
from pytorch_pretrained_bert.modeling import BertForPreTraining, BertConfig, BertForMaskedLM
fr... | BERTRAM/mi_mask_prediction.py | MIT | 4,271 | 4bc3d3fd38e94eeed13ea364fe1d4943c9e44138 | Python | py | |
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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
#
# U... | openfold/model/template.py | Apache-2.0 | 9,755 | dfcc67571d43c428606fa18ad09566c1fbac5975 | Python | py | |
import torch.nn as nn
import torch
import torch.nn.functional as F
# *********************A（特征）量化（二值)***********************
class BinActive(torch.autograd.Function):
def forward(self, input):
self.save_for_backward(input)
size = input.size()
mean = torch.mean(input.abs(), 1, keepdim=True)... | quantization/WbWtAb/models/nin_bn_conv.py | MIT | 3,870 | 24ece1e6a073a05187a4dd530e90e3595f7e494c | Python | py | |
import random
from collections import namedtuple, deque
import numpy as np
import torch
Transition = namedtuple('Transition',
('state', 'action', 'next_state', 'reward', 'is_done'))
class ReplayMemory(object):
def __init__(self, maxlen):
self.memory = deque([], maxlen=maxlen)
... | Agents/LearningAgents/Memory.py | MIT | 8,884 | 529f146a377d7e6fc514731164f589f3dcee63f8 | Python | py | |
from __future__ import print_function
import argparse
import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from models import vgg, resnet, densenet
import numpy as np
import os
import sys
from tqdm import tqdm
from utils import *
if __name__ == '__main__':
parser = argpa... | imagenet/eval.py | MIT | 4,014 | 5e8ede2b1076cf31f56630b9fdde06a8ef7d890f | Python | py | |
"""
Author: yvon
Date: 2021-08-05 17:41:49
LastEditTime: 2021-12-06 15:31:10
LastEditors: Please set LastEditors
Description: 批量测试文件夹中的图片 12.6号验证无误，后续应该不会再更新。
FilePath: /lanenet-lane-detection-pytorch/test_lanenet.py
"""
# import argparse
import os
import os.path as ops
# import time
import cv2
# from sklearn import ... | test_lanenet.py | MIT | 5,992 | feebcde9c3433de47ff5b4a8582d3e48f5c15078 | Python | py | |
import argparse
import atexit
import copy
import datetime
import numpy as np
import os
import torch
import tensorboardX
from functools import partial
from prob_mbrl import utils, models, algorithms, envs
if __name__ == '__main__':
parser = argparse.ArgumentParser("Deep-PILCO with moment matching")
parser.add_... | examples/deep_pilco_mm.py | MIT | 11,148 | 45e78a6ef22baf9fbbb3878b1ccd10c7947afa8f | Python | py | |
import os
import logging
from PIL import Image
import numpy as np
import sklearn.datasets
import joblib
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, datasets
import skorch.dataset
from skorch.utils import to_numpy
from utils.helpers import set_attributes
from .helpers import t... | utils/data/datasets.py | MIT | 13,146 | 24fc3ebcbfec638eaf5880a13f0a25c6a95cd2f1 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import numpy as np
import torch
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
class BufferList(nn.Module):
"""
Similar to nn.ParameterList, but for buffers
"""
def __init__(self... | maskrcnn_benchmark/modeling/rpn/anchor_generator.py | Xnet,X11 | 8,907 | feebedbf54bfb53006467c0c761aec2692ad455d | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import copy
import logging
import numpy as np
from typing import List, Union
import torch
import detectron2.data.detection_utils as utils
import detectron2.data.transforms as T
from detectron2.config import configurable
from .detection_utils impor... | projects/PointSup/point_sup/dataset_mapper.py | Apache-2.0 | 4,813 | 52b9bd4ce19d51e07f98aa9adf36c41f6ddc22af | Python | py | |
import torch
from torch.nn import functional as F
from openpose.model.matcher import Matcher
from openpose.model.balanced_positive_negative_sampler import (
BalancedPositiveNegativeSampler,
)
from openpose.structures.boxlist_ops import boxlist_iou
from openpose.modeling.utils import cat
from openpose.layers impor... | openpose/model/roi_heads/vpose_head/vpose_loss.py | MIT | 7,025 | feec08bdec128863b4bcbcb84f6676204b70935d | Python | py | |
import os
import torch
import models
def set_seed(seed, cuda):
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.manual_seed(seed)
if cuda: torch.cuda.manual_seed(seed)
def SSE(logits, label):
target = torch.zeros_like(logits)
target[torch.arange(target.size(0)).l... | examples/WAGE/utils.py | MIT | 2,973 | feec1558c82b3373e1448e3c0a14b761f164409e | Python | py | |
"""Segmentation task interface module.
This module contains a class that defines the objectives of models/trainers for segmentation tasks.
"""
import logging
from typing import Optional # noqa: F401
import numpy as np
import PIL.Image
import torch
import tqdm
import thelper.concepts
import thelper.utils
from thelpe... | thelper/tasks/segm.py | Apache-2.0 | 8,891 | feec22ecb3665d0f23dd4a31e2d945f4898b74a9 | Python | py | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
import torch
from torch import nn, autograd
from torch.utils.data import DataLoader, Dataset
import numpy as np
import random
from sklearn import metrics
import sys
import os
from timeit import default_timer as timer
class DatasetSplit(Dataset):
... | models/Update.py | MIT | 2,869 | 2486c0567e063aef73a99c2e05dcabfa5aa90e84 | Python | py | |
#!/usr/bin/env python
# coding: utf-8
#
# Author: Kazuto Nakashima
# URL: https://kazuto1011.github.io
# Date: 08 February 2019
from __future__ import absolute_import, print_function
import os.path as osp
import cv2
import numpy as np
import torch
from PIL import Image
from torch.utils import data
from .base i... | libs/datasets/voc.py | MIT | 4,766 | 7c36c3ce6d3700cede361a9dbee146e7880078cc | Python | py | |
from __future__ import print_function
from six.moves import range
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
from PIL import Image
from tqdm import tqdm
import pickle
import os
import numpy as np
import glob
import scipy.misc... | OP-GAN/code/trainer.py | MIT | 25,808 | feec41681fea71e37708f27ef55ac87e2e175ce6 | Python | py | |
# Copyright xmuspeech (Author: Snowdar 2020-02-05)
import sys
import os
import torch
import torch.nn.functional as F
subtools = '/data/lijianchen/workspace/sre/subtools'
# subtools = os.getenv('SUBTOOLS')
sys.path.insert(0, '{}/pytorch'.format(subtools))
from libs.nnet import *
import libs.support.utils as utils
c... | pytorch/model/xvector.py | Apache-2.0 | 5,469 | feec4d8d98894a2e15fbf2e1112632b39d5cba31 | Python | py | |
from escnn.nn.modules.basismanager import BasisManager
from collections import defaultdict
import torch
from scipy import stats
import math
__all__ = ["generalized_he_init", "deltaorthonormal_init"]
def _generalized_he_init_variances(basismanager: BasisManager):
r"""
Compute the variances of the weigh... | escnn/nn/init.py | BSD-3-Clause | 5,451 | 6b0cdca8b38aa0de50d93a2580d6a8924358c4b1 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as func
class DepthWiseSeparableConv1d(nn.Module):
r"""Depthwise separable 1D convolution.
Analogous functionality to :class:`torch.nn.Conv1d`.
Args:
in_channels (int): number of input channels.
out_channels (int): number of output channels.... | torchsupport/modules/separable.py | MIT | 1,862 | 804911a4e4c25e12c6674f4894351301903c0444 | Python | py | |
from typing import Any, Dict, Optional, Tuple, Type
import pytest
import torch
from torch.autograd import gradcheck
import kornia
import kornia.testing as utils # test utils
from kornia.augmentation import (
AugmentationSequential,
CenterCrop,
ColorJiggle,
ColorJitter,
Denormalize,
LongestMax... | test/augmentation/test_augmentation.py | ECL-2.0,Apache-2.0 | 124,458 | 28bd7771a56dce256a273403446b27a9fefa05c9 | Python | py | |
import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
import time
import re
import os
import sys
import cv2
import bdcn
from datasets.dataset import Data
import argparse
import cfg
from matplotlib import pyplot as plt
... | test.py | MIT | 3,370 | 24c171fb6f4bd452e860e193527863990c7a1419 | Python | py | |
from argparse import Namespace
import csv
from logging import Logger
import pickle
import random
from typing import List, Set, Tuple
import os
from rdkit import Chem
import numpy as np
from tqdm import tqdm
from joblib import Parallel, delayed
from chemprop.data.data import MoleculeDatapoint, MoleculeDataset
from che... | dglt/contrib/moses/moses/model/pred_mpnn/utils.py | MIT | 13,952 | feeca11b719300a1a7d183dfdc22b2c343a86e50 | Python | py | |
from typing import List
import torch
import torch.nn as nn
from tqdm import tqdm
from chemprop.data import MoleculeDataLoader, MoleculeDataset, StandardScaler
def predict(model: nn.Module,
data_loader: MoleculeDataLoader,
disable_progress_bar: bool = False,
scaler: StandardScaler... | chemprop/train/predict.py | MIT | 1,413 | 5f0e71011f23a211a87515f980255b2d9a373b03 | Python | py | |
# Copyright 2021 Faranak Shamsafar
#
# 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 a... | models/submodule.py | Apache-2.0 | 10,865 | feecaea15da95321d89bb0d8062393aa631b517a | Python | py |
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