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# 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.
import math
from math import log
import torch
import torch.nn.functional as F
from fairseq import metrics, utils
from fairseq.criterions impo... | fairseq/criterions/glat_loss.py | MIT | 8,533 | 3a30bad0dc4620b8de0688670c92df89de5e428c | Python | py | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 11 16:34:10 2019
@author: lingkaikong
"""
from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import os
import argparse
import models
import data_loader
import t... | SVHN/initializartion/2/resnet_bayesian_svhn.py | Apache-2.0 | 5,997 | 651b6c87fc1aea2eb399ca53007db57737872066 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) 2020-2022, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permitted
# provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright notice... | samples/mlp_learning_an_image_pytorch.py | BSD-3-Clause | 7,972 | 3a30d890f3cde74ceff5a92c6771810915a6ad0f | Python | py | |
import os, json
import numpy as np
import networkx as nx
from tqdm import trange
import torch
import torch.utils
from torch.nn import Linear
from torch_scatter import scatter_mean
from torch_geometric.nn import MessagePassing
from torch_geometric.data import DataLoader
from torch_geometric.utils import degree
from tor... | models/LEGS.py | MIT | 8,513 | b2c20653edbbf73e282995211eda440bca39aecb | Python | py | |
# -*- coding: utf-8 -*-
""" Model definition functions and weight loading.
"""
from __future__ import print_function, division, unicode_literals
from os.path import exists
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence, ... | metric/torchMoji/torchmoji/model_def.py | MIT | 14,042 | d7eec96367ec675b34168c651aa49ec610be1cff | Python | py | |
# Author: Vincent Zhang
# Mail: zhyx12@gmail.com
# ----------------------------------------------
import torch
from collections.abc import Sequence
from mmcv.runner import get_dist_info
from mmcv.parallel import MMDistributedDataParallel
import numpy as np
import random
import torch.distributed as dist
from mmcv.utils ... | fastda/utils/basic_utils.py | MIT | 6,695 | 340f8469ae39a9337d9895d140cab4be1408bf83 | Python | py | |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from itertools import count
from typing import Tuple, Union
class StandardScaler:
def __init__(self) -> None:
self.mu = None
self.std = None
def fit(self, data: torch.Tensor) -> None:
self.mu = torc... | callbacks/dynamics/stochastic.py | MIT | 11,642 | b299abc48f834964a7f0e9db082b0b082462dea7 | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import numpy as np
import time
import os
from six.moves import cPickle
import opts3 as opts
import models
from dataloader3 import *
from dataloaderraw import *
import eval_utils
import argparse
im... | eval.py | MIT | 2,650 | 7006c1624a4fbc3fbf539246fac41bd7d1eb221d | Python | py | |
# Copyright (c) 2019 Western Digital Corporation or its affiliates.
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
# from ..registry import NECKS
from ..builder import NECKS
# from mmdet.models.utils import ConvLayer
from mmcv.cnn import xavier_init
from mmcv.runner import load_c... | mmdet/models/necks/yolo_neck.py | Apache-2.0 | 6,570 | 7c4fd4ceebe0444a5e7e74ce4de375a735617fe5 | Python | py | |
from functools import partial
from typing import Callable, Any, List, Optional
import torch
import torch.nn as nn
from torch import Tensor
from ..transforms._presets import ImageClassification
from ..utils import _log_api_usage_once
from ._api import WeightsEnum, Weights
from ._meta import _IMAGENET_CATEGORIES
from .... | torchvision/models/shufflenetv2.py | BSD-3-Clause | 14,320 | 3a314c867ca3268672e0b9291690f43fc1ec588f | Python | py | |
# Display transformed images
from torchvision import models, transforms, datasets
import torch
from PIL import Image
import json
import numpy as np
import os
import csv
import matplotlib.pyplot as plt
from tqdm import tqdm
import scipy.misc
# Transform for input images
transform = transforms.Compose([
transforms.R... | disp_transformed_im.py | MIT | 1,797 | 3a31505174482f8f3a68e72cbb8b84d2e274545d | Python | py | |
import torch
# torch.autograd.set_detect_anomaly(True)
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import cv2
from plyfile import PlyData, PlyElement
import sys
# Misc
img2mse = lambda x, y : torch.mean((x - y) ** 2)
mse2psnr = lambda x : -10. * torch.log(x) / torch.log(torch.Tensor([10.]... | run_nerf_helpers.py | MIT | 15,033 | 3a3158ed147df88fb1cf05ca11f79db4ca370a1e | Python | py | |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1. / np.sqrt(fan_in)
return (-lim, lim)
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... | p2_continuous-control/model.py | MIT | 2,869 | 3a3164076016f3c78b45ee10b97545d09ec32f6d | Python | py | |
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from data import voc as cfg
from ..box_utils import match, match_offset, match_four_corners, match_only_four_corners, log_sum_exp
from ..box_utils import decode, decode_four_corners, decode_fou... | layers/modules/multibox_loss.py | MIT | 46,547 | 3a3173309e1d480c23012e00a4ca72b8d6b493cf | Python | py | |
# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
# Author: adefossez, 2020
import random
import unittest
import torch as th
from julius import LowPassFilter, LowPassFilters, lowpass_filter, resample_frac
from julius.core import pure_tone
def delta(a, b, ref, fraction=0.9):... | tests/test_lowpass.py | MIT | 3,226 | 65496520257aa9d474c780777f54dd854a68997d | Python | py | |
import glob
import os
from shutil import copyfile
import subprocess
import numpy as np
import torch
newpath = '/run/media/rice/DATA/devector_data/'
speakers = set([""])
# if os.path.isfile(newpath):
# print("yes")
# else:
# os.mkdir(newpath)
"""
data_thchs30
"""
def getdata(path,speakers):
length = len(pa... | data.py | BSD-3-Clause | 2,292 | 5f43c5b4f5980868c29b4c9fb5f2480a63812dbf | Python | py | |
import torch
import torch.nn as nn
import detectron2.lib.ops as ops
def get_conv_op(conv="Conv2d", **kwargs):
if len(conv) == 0:
return None
conv_op = {
"Conv2d": nn.Conv2d,
# "Conv2dWS": ops.Conv2dWS,
# "DeformConv": ops.DeformConvPack,
# "MDeformConv": ops.ModulatedD... | detectron2/lib/layers/wrappers.py | Apache-2.0 | 3,174 | 3a31da44d0d135adb56dee7b917c5ad07d02f8ad | Python | py | |
"""Testing recognition module."""
import pytest
import torch
from gpssm.models.components.recognition_model import OutputRecognition, \
ZeroRecognition, NNRecognition, ConvRecognition, LSTMRecognition
@pytest.fixture(params=[OutputRecognition, ZeroRecognition, NNRecognition,
ConvRecognitio... | gpssm/models/components/test/test_recognition.py | MIT | 1,215 | 3a31e0dd452a8d85f5fdb18bb1f7e4610e30573c | Python | py | |
import torch
MAX_RESAMPLE = 10000
class Lipo:
def __init__(self, k, d, a, b):
assert b > a
self.k = k
self.d = d
self.a = a
self.b = b
def scale(self, X):
"Assumes X is a matrix of values in [0, 1]."
return X * (self.b-self.a) +self.a
def sample(s... | src/src/globaloptim/Lipo.py | MIT | 1,173 | f97b4d13e976da6ac1abb7135cd89a7c9caac506 | Python | py | |
import torch
import torch.nn as nn
from torchvision import models, datasets, transforms
from torch.nn import functional as F
def get_backbone(name, pretrained=True, replace_stride_with_dilation=[False, False, False]):
""" Loading backbone, defining names for skip-connections and encoder output. """
# TODO: ... | backboned_unet/unet.py | MIT | 10,491 | 3a3247c4da0f4241dfd88f38a926e40c644d029d | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import logging
import numpy as np
from typing import Dict
import torch
from torch import nn
from detectron2.layers import ShapeSpec
from detectron2.structures import Boxes, Instances, pairwise_iou
from detectron2.utils.events import get_event_stora... | detectron2/modeling/roi_heads/roi_heads.py | Apache-2.0 | 31,041 | 7c9ab4dca764ef4d16cd8e6d2506a60ca7ab3ed6 | Python | py | |
# https://github.com/anandsaha/pytorch.cyclic.learning.rate
import numpy as np
from torch.optim.optimizer import Optimizer
class CyclicLR(object):
"""Sets the learning rate of each parameter group according to
cyclical learning rate policy (CLR). The policy cycles the learning
rate between two boundaries ... | attributes_classify/CyclicLR.py | MIT | 6,735 | 3a3271554082f8d1a5d3dc1338e8bfb14c6dcbfa | Python | py | |
import numpy as np
import torch
import copy
import sys
sys.path.append('../')
from utils.manip import clip_image_values
def unravel_index(index, shape):
out = []
for dim in reversed(shape):
out.append(index % dim)
index = index // dim
return tuple(reversed(out))
def linear_solver(x_0, nor... | fool/linear_solver.py | Apache-2.0 | 1,526 | 45e72de4ba4d1d97cc51cbe4444202a44fbc4f8f | Python | py | |
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
###############################################################################
# Helper Functions
###############################################################################
def get_norm_layer(norm_... | models/networks.py | BSD-3-Clause | 31,699 | ffaa9247eb5333455cabb1ed7c7f0ab862761840 | Python | py | |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import glob
import os
import shutil
from os import path
from setuptools import find_packages, setup
from typing import List
import torch
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
torch_ver = [int(x) for x in to... | setup.py | Apache-2.0 | 7,808 | e64eb42144b9b2a81e890221972c56d84ff35f1c | Python | py | |
"""Common blur operations.
Blur may be caused by unstable camera sensor, dirty lens, relative motion
between the camera and the subject, insufficient illumination, out of focus
settings, imaging while zooming, subject behind a frosted glass window,
or shallow depth of field. The Blur group includes:
1) GaussianBlu... | straug/blur.py | Apache-2.0 | 6,255 | 347682daea727acdca784dec891f7e840a4ebec6 | Python | py | |
# Copyright The PyTorch Lightning team.
#
# 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 i... | flash/core/classification.py | Apache-2.0 | 5,556 | 5fb983983ca730645bc3a01e5b85a8dd04678c22 | Python | py | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2021 The TARTRL 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unle... | dfd/runners/test.py | Apache-2.0 | 2,327 | 12f20444bde199bddb2af6bf13eb7005d73c564c | Python | py | |
import os
import torch
from Utils.Helpers import *
from experiments import *
# Experiments params:
python_env_path = '$HOME/anaconda3/envs/thesis_env/bin/python3'
exps = [exp_0, exp_1, exp_2, exp_3, exp_4, exp_5, exp_6, exp_7, exp_8]
# Run experiments:
working_dir = Path('')
if not Path('experiments').exists():
P... | run_experiments.py | MIT | 852 | d77fd25496896af27185d10d6c0de8f8a909d692 | Python | py | |
from torch import nn
from omegaconf import DictConfig
from models.edge_conv import DynamicEdgeConv, DynamicEdgeConv_DGM, DynamicEdgeConvMINE
class EdgeNet(nn.Module):
"""EdgeNet"""
def __init__(self, cfg: DictConfig):
super().__init__()
self.cfg = cfg
dynamic_edge_type = {
... | models/EdgeNet.py | MIT | 1,645 | 3a32fd880f7b06110191fbcdf40ebfb6c8e3ff17 | Python | py | |
import torch
import gdgrid
import transforms
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from datetime import datetime
np.random.seed(1)
def random_draw(dataset):
now = datetime.now().time().strftime("%H_%M")
num2label = {1:'badge', 2:'offground', 3:'ground', 4:'sa... | data/tst_draw.py | MIT | 1,768 | 7040d6afe533d978f53ca804a77768eed0596dbf | Python | py | |
from typing import List, Tuple
import numpy as np
import torch
from ..builder import BBOX_ASSIGNERS
from .base_assigner import BaseAssigner
from .assign_result import AssignResult
@BBOX_ASSIGNERS.register_module()
class PointKptAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each point... | code/core/bbox/assigners/point_kpt_assigner.py | MIT | 4,773 | 52b619caae92987a2e64c3a189f10d8da2c5d27d | Python | py | |
# Copyright 2021 Google LLC
#
# 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 in writing, ... | corenet/debug_helpers.py | Apache-2.0 | 2,504 | 3a3313f52df18c6d960d7bf7d41eddc6abb3b254 | Python | py | |
import torch
import torch.nn as nn
class BNConvt(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, bias=False, eps=1e-5, momentum=0.1, conv_first=True, relu=False):
super(BNConvt, self).__init__()
if conv_first:
self.main... | models/BNConvt.py | BSD-3-Clause | 1,923 | 03f044658d60579a48068ce9abea9d7c46bb3a4b | Python | py | |
from SAC_Networks import Actor_Critic,ReplayBuffer
from copy import deepcopy
import numpy as np
import torch
from torch.optim import Adam
import gym
import timeit
import itertools
def sac():
polyak_avg=0.995
lr=1e-3
alpha=0.2
gamma=0.99
#(env.observation_space)
#print(env.act... | SAC/SAC_CORE.py | CC0-1.0 | 2,846 | 3a3337260083d3908038dbd955196785027983c9 | Python | py | |
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 8 22:10:13 2019
@author: Yuntian Chen
"""
import numpy as np
from .grid_configuration import config
import torch.utils.data
import torch
import pandas as pd
from scipy import signal
# hyper parameters
use_forcast_weather = 1 # whether to use the forecast d... | src/tgdlf/grid_data_v2.py | MIT | 18,488 | 10bd625c15ac9a79189ffe2af32af1bf884b05f4 | Python | py | |
import torch
import copy
import sys
import time
import pickle
import numpy as np
import warnings
from scipy.interpolate import Rbf
from collections import OrderedDict
from constants import *
def update_progress(index, length, **kwargs):
'''
display progress
Input:
`index`: (i... | functions.py | MIT | 44,039 | f9aaae456e4d822f52f88add625f58b4c2da82c4 | Python | py | |
import torch
from optparse import OptionParser
import rdkit
from rdkit.Chem import Descriptors
from rdkit.Chem import MolFromSmiles, MolToSmiles
from rdkit.Chem import rdmolops
from . import sascorer
import numpy as np
from jtnn.jtnn import *
lg = rdkit.RDLogger.logger()
lg.setLevel(rdkit.RDLogger.CRITICAL)
pars... | jtnn/bo/gen_latent.py | MIT | 2,906 | ff5b5dda06167c2b17ef6e43931a664e9a94f5cd | Python | py | |
# Copyright (C) 2021 Maxime Robeyns <maximerobeyns@gmail.com>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | bdrl/agents/dqn_agent.py | Apache-2.0 | 5,730 | 5e526542b91eae6ccd3aa8be17d60fb5ebc60608 | Python | py | |
import torch
import torch.nn as nn
from utils.debug_tools import save_image_stack, clear_debug_image
class SmoothModel(nn.Module):
def __init__(self, base_model, mean=0, std=0.5, sample_size=20):
super(SmoothModel, self).__init__()
self.base_model = base_model
self.mean = mean
sel... | custom_models/smooth_model.py | MIT | 3,198 | 341c8041c00846a071d26aacdc187aaaee593567 | Python | py | |
# -*- encoding:utf-8 -*-
"""
The optimizer is identical with huggingface's implementation.
See https://github.com/huggingface/pytorch-pretrained-BERT
"""
import math
import torch
from torch.optim import Adam
from torch.optim import Optimizer
from torch.nn.utils import clip_grad_norm_
def warmup_cosine(x, ... | uer/utils/optimizers.py | Apache-2.0 | 6,427 | 3a33eede57d42c4495ea542a2f6275c4bd2a9cf6 | Python | py | |
# flake8: noqa
# @TODO: code formatting issue for 20.07 release
from typing import Dict, List, TYPE_CHECKING
import wandb
from catalyst.core.callback import (
Callback,
CallbackNode,
CallbackOrder,
CallbackScope,
)
from catalyst.utils.misc import split_dict_to_subdicts
if TYPE_CHECKING:
from cata... | catalyst/contrib/callbacks/wandb_logger.py | Apache-2.0 | 6,437 | 3a34013831cf05add5caf024b9814fdca74c83bb | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
BATCH_SIZE = 8
LR = 0.01
EPSILON = 0.95
GAMMA = 0.99
TARGET_REPLACE_ITER = 100 # After how much time you refresh target network
# MEMORY_CAPACITY = 20 # The size of experience replay buffer
# N_ACTIONS = 2
# # N_STATES = len(X_train... | DQN_a_index.py | MIT | 2,916 | f9f7c12dbde7c6fc13bc5da183e4cb530f94618d | Python | py | |
import os
import glob
import json
import pandas as pd
import numpy as np
import csv
import torch
import time
from torch.autograd import Variable
from PIL import Image
import cv2
from torch.nn import functional as F
from opts import parse_opts_online
from model import generate_model
from mean import get_... | online_test_video_depth.py | MIT | 16,070 | 3a340ee0dd8ed64a1c87487c18ff89aa161a9648 | Python | py | |
import numpy as np
import torch
import torch.nn as nn
from skimage import measure
from pytorch3dunet.augment.transforms import LabelToAffinities, StandardLabelToBoundary
from pytorch3dunet.unet3d.losses import GeneralizedDiceLoss, WeightedCrossEntropyLoss, \
DiceLoss, WeightedSmoothL1Loss, _MaskingLossWrapper, Ski... | tests/test_criterion.py | MIT | 6,821 | 5f358dacdf23f86d4626e9bccd87e5f1faff22fe | Python | py | |
'''
AUTHORS:
NORSTRÖM, ARVID 19940206-3193,
HISELIUS, LEO 9402214192
'''
import torch
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
actor = torch.load('models/neural-network-3-actor.pt')
critic = torch.load('models/neural-network-3-critic.pt')
ys = np.linspace(0,1.... | Assignment 3/problem 3/Q_plot.py | MIT | 1,194 | 3a3441aba3352eaceb2e1f0575dcc3e842e18cc1 | Python | py | |
import math
from typing import List, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from kornia.filters import filter2d, gaussian_blur2d
__all__ = [
"PyrDown",
"PyrUp",
"ScalePyramid",
"pyrdown",
"pyrup",
"build_pyramid"
]
def _get_pyramid_gaussian_kernel() -> torc... | kornia/geometry/transform/pyramid.py | ECL-2.0,Apache-2.0 | 12,317 | 3a345372247f6dd1560958cb751774b6db379f29 | Python | py | |
import torch
import torch.nn as nn
from torchvision.ops import nms
import numpy as np
class DecodeBox():
def __init__(self, anchors, num_classes, input_shape, anchors_mask = [[6,7,8], [3,4,5], [0,1,2]]):
super(DecodeBox, self).__init__()
self.anchors = anchors
self.num_class... | utils/utils_bbox.py | MIT | 14,778 | 28e963a7431a0e0eee094ecc0b809416ec1a69a9 | Python | py | |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | tests/python/frontend/onnx/test_forward.py | Apache-2.0 | 156,780 | fdb8d205a244aada5e886f4312c1983bd0b550c8 | Python | py | |
from collections import OrderedDict
from turtle import width
from matplotlib.pyplot import axis
import torch
import torch.nn as nn
from .asff import ASFF
from .rfb import BasicRFB as RFB
from nets.CSPdarknet import darknet53
def conv2d(filter_in, filter_out, kernel_size, stride=1):
pad = (kernel_size - 1) // 2... | nets/yolo.py | MIT | 9,313 | 3a3475fb12fc7639c2e64108bc8907a3eeb18e04 | Python | py | |
# encoding: utf-8
import operator
import torch # isort:skip
import torch.fx.experimental.fx_acc.acc_utils as acc_utils
import torch.nn as nn
from torch.fx.experimental.fx_acc.acc_normalizer import (
register_acc_op,
register_acc_op_mapping,
register_custom_acc_mapper_fn,
)
from torch.fx.passes.shape_prop ... | torch/fx/experimental/fx_acc/acc_ops.py | Intel | 43,973 | fcd60c7d83944b4a57c4f295c41c7216dc2e88a2 | Python | py | |
from typing import Optional, Iterable
import torch
import torch.nn as nn
from rex.modules.dropout import SharedDropout
class FFN(nn.Module):
"""
Multi-layer feed-forward neural networks
Args:
input_dim: input dimension
output_dim: output dimension
mid_dims: middle dimensions, if... | rex/modules/ffn.py | MIT | 4,219 | 03c2bba8e0857e13595973b1b9ba546e857c62cb | Python | py | |
# %%-- Imports
# General
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
import datetime
import time
import imageio
import matplotlib.pyplot as plt
from PIL import Image
# Machine learning
import torch
from torch.autograd import Variable
from torch.utils.data im... | LumiGAN/gcompute.py | MIT | 24,624 | 3a349d98a857e107bc7dacbcd9b71c7304c67c4a | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from collections import Counter
def calculate_prior(
num_classes, img_max=None, prior=None, prior_txt=None, reverse=False
):
if prior_txt:
labels = []
with open(prior_txt) as f:
for line in f:
... | IMU/loss/BalancedCE.py | MIT | 2,030 | 1097922b1bcf4ea5a0ebb3761f13b1696bedd187 | Python | py | |
import torch
from .search_cells import NAS201SearchCell as SearchCell
from .search_model import TinyNetwork as TinyNetwork
from .genotypes import Structure
class TinyNetworkDartsProj(TinyNetwork):
def __init__(self, C, N, max_nodes, num_classes, criterion, search_space, args,
affine=False, track_... | nasbench201/search_model_darts_proj.py | Apache-2.0 | 2,736 | 03a18264f5e1880520510486d185eb4776bf7227 | Python | py | |
from concurrent.futures import ThreadPoolExecutor, as_completed
import torch
import torch.nn.functional as F
try:
import matplotlib.pyplot as plt
except ImportError:
pass
from .box_ops import box_iou
def factor_getter(n, base):
base = base * 0.8 ** (n // 6)
i = n % 6
if i < 3:
f = [0, 0,... | pytorch_mask_rcnn/test.py | MIT | 7,096 | 4215e018c5339e58437d219121a9f3df1be036bb | Python | py | |
# Copyright (c) OpenMMLab. All rights reserved.
import platform
import random
import warnings
from copy import deepcopy
from functools import partial
import numpy as np
import torch
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import TORCH_VERSION, Registry, build_from_cfg, d... | mmgen/datasets/builder.py | Apache-2.0 | 5,066 | 701909950c5ae976f81fa4a5ae2005d32c692f6a | Python | py | |
#!/usr/bin/env python
# encoding: utf-8
# The MIT License (MIT)
# Copyright (c) 2018-2020 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/approaches/triplet_loss.py | MIT | 11,817 | 5ed73f8e3a8fa537bae4df3d4ceb0319673478e3 | Python | py | |
import torch
def padding_mask(lens):
"""Mask out the blank (padding) values
Args:
lens: (bs,)
Return:
mask: (bs, 1, max_len)
"""
bs, max_len = len(lens), max(lens)
mask = torch.zeros(bs, 1, max_len)
for i, l in enumerate(lens):
mask[i, :, :l] = 1
mask = mask > 0... | torchnmt/networks/utils.py | MIT | 611 | 3a353e1aeb787c60720c9e07200605adf07bd2a1 | Python | py | |
import sys, os
import pytest
import torch
import torchxrayvision as xrv
sys.path.insert(0, "../torchxrayvision/")
def test_model_basic():
model = xrv.models.DenseNet()
def test_model_pretrained():
model = xrv.models.DenseNet(weights="densenet121-res224-all")
model = xrv.models.DenseNet(weights="dens... | tests/test_models.py | Apache-2.0 | 2,196 | 5e61657b508238288a7ea7247094006726b2cce6 | Python | py | |
import argparse
import torch
import torch.nn as nn
parser = argparse.ArgumentParser()
parser.add_argument('--max_epoch', type=int, default=30)
parser.add_argument('--dataset', default='prep-amazon') # prep-amazon or fdu-mtl
parser.add_argument('--prep_amazon_file', default='../data/prep-amazon/amazon.pkl')
parser.add... | code/options.py | MIT | 5,459 | 3a35861a684bf04028e7e0bb94c5bc47cf194ac0 | Python | py | |
# Copyright The PyTorch Lightning team.
#
# 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 i... | pytorch_lightning/plugins/precision/deepspeed_precision.py | Apache-2.0 | 3,444 | ff33ccc690ef2fba85dea37a4f69a5adc4462297 | Python | py | |
"""
v.e.s.
Local Learner for the FPC training. Its execution is synchronized with lo_train
MIT License
Copyright (c) 2020 Cesar A. Gomez
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... | ll_train.py | MIT | 8,563 | 107068da47fe5ce8037ce69d03ccdc427fd14e83 | Python | py | |
"""
utils.py
"""
import ast, re
import numpy as np
import os, sys, multiprocessing, logging
from collections import defaultdict
import nltk
import spacy
import lemminflect
import torch
from transformers import GPT2Tokenizer, XLNetTokenizer, TransfoXLTokenizer, OpenAIGPTTokenizer
from transformers import GPT2LMHeadMod... | src/utils.py | MIT | 4,462 | 3a35b7f453e276a67571a68a3d53af99e94e2788 | Python | py | |
import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_p... | imagenet/my_models/doublenorm.py | BSD-3-Clause | 13,320 | 5f15658acc8218609e2a9af7d288925f423b0633 | Python | py | |
# coding: utf-8
# DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited.
#
# This material is based upon work supported by the Assistant Secretary of Defense for Research and
# Engineering under Air Force Contract No. FA8721-05-C-0002 and/or FA8702-15-D-0001. Any opinions,
# findings, conclusio... | models/models.py | MIT | 10,886 | d71d26ee9cb91367a4b0b4ebf984f87d889642a2 | Python | py | |
import IPython
from torch import nn as nn
import nasws.cnn.utils
from nasws.cnn.policy.darts_policy import utils as darts_utils
from .train_search_procedure import _summarize_shared_train
def fairnas_train_model_v1(train_queue, valid_queue, model, criterion, optimizer, lr, args, architect,
to... | nasws/cnn/procedures/fairnas_procedure.py | MIT | 5,438 | 3a35ed822bf3001bc54ccd94707419db86df8bb9 | Python | py | |
"""
Script to print CPU/GPU latency of GeneralGenotypeModel's blocks
"""
import argparse
import os
import sys
import yaml
import torch
from aw_nas.common import get_search_space
from aw_nas.final.general_model import GeneralGenotypeModel
from aw_nas.utils.elapse_utils import analyze_elapses
def profiling(config, d... | scripts/hardware/latency.py | MIT | 2,155 | 3a35f9b2af0c0d61dd26121a4274fca01fc68e06 | Python | py | |
#!/usr/bin/env python3
import math
import warnings
import gpytorch
import torch
from .. import settings
from ..functions._inv_matmul import InvMatmul
from ..functions._inv_quad_log_det import InvQuadLogDet
from ..functions._matmul import Matmul
from ..functions._root_decomposition import RootDecomposition
from ..uti... | gpytorch/lazy/lazy_tensor.py | MIT | 57,504 | f9aed6b0ba905de320d7870c49447c734b4d3e3f | Python | py | |
# coding=utf-8
# Copyright 2021 Intel Corporation. All rights reserved.
# code taken from commit: 35b4582486fe096a5c669b6ca35a3d5c6a1ec56b
# https://github.com/microsoft/DeepSpeedExamples/tree/master/bing_bert
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in comp... | pretraining/dataset/distributed_pretraining_dataset.py | Apache-2.0 | 9,964 | b2fee13f8b556fd5786930a2981453d73634fab7 | Python | py | |
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
from collections import namedtuple
from models import TransGAN_im2im
from models import ViT_8_8
###############################################################################
# Helper Functions
#######... | models/networks.py | BSD-3-Clause | 28,990 | e62191ba806852a16ff9d6d2e4f78e75d53fc866 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
from typing import List
from torch import distributed as dist... | torchrec_dlrm/data/dlrm_dataloader.py | MIT | 4,051 | 03e88d9b1fb97d6f1de60149f4bb86b15b0b7f37 | Python | py | |
# Copyright The PyTorch Lightning team.
#
# 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 i... | torchmetrics/classification/matthews_corrcoef.py | Apache-2.0 | 3,713 | a95a33807085240444fc1ec22a43049846bc2a25 | Python | py | |
import torch
import numpy as np
from .convert import to_var
def normal_logpdf(x, mean, var):
"""
Args:
x: (Variable, FloatTensor) [batch_size, dim]
mean: (Variable, FloatTensor) [batch_size, dim] or [batch_size] or [1]
var: (Variable, FloatTensor) [batch_size, dim]: positive value
... | model/utils/probability.py | MIT | 890 | 3a36e5e704dafab393f21729b44ce67dfdb86780 | Python | py | |
import datetime
import logging
import math
import random
import sys
import time
import psutil
import skopt
import torch
import torch.nn as nn
start = time.time()
process = psutil.Process()
random.seed(0)
logger = logging.getLogger()
logger.addHandler(
logging.FileHandler(datetime.datetime.now().strftime("logs/%... | expr_1hidden.py | MIT | 6,219 | b2d91d72bee02ab2c156443541c7a59af757a80a | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import logging
import re
import torch
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from pyt... | examples/attention_visualization.py | Apache-2.0 | 14,013 | 3a3724c95a094da96f11ae852ac9bb9aba0dc2f0 | Python | py | |
import numpy as np
from scipy.sparse import issparse
from torch.utils.data import Dataset
class MultiYDataset(Dataset):
"""A dataset that group each item in X with its labels for t tasks from Y
Args:
X: an n-dim iterable of inputs
Y: a t-length list of n-dim iterables corresponding to labels ... | metal/multitask/utils.py | Apache-2.0 | 1,768 | 3a373b1e8b8a2f0cefc6b068c8611970a8af6154 | Python | py | |
import torch
class WindowLoss(torch.nn.Module):
def __init__(self, metric, window_size=32, stride=16, batch_reduction='mean'):
super(WindowLoss, self).__init__()
self.window_size = window_size
self.stride = stride
self.metric = metric
# self.metric.batch_reduction = 'mean'
... | losses/composite_losses/window_loss.py | MIT | 1,015 | e98acf6da8d9604fcd0fff9ba4601aef5dc95ac8 | Python | py | |
import torch
import numpy as np
import math
import mmcv
from transforms3d.euler import euler2mat
from core.utils.pose_utils import euler2mat_torch
def aug_poses_normal(poses, std_rot=15, std_trans=[0.01, 0.01, 0.05], max_rot=45, min_z=0.1):
"""
Args:
poses (Tensor): [n,3,4]
std_rot: deg, rando... | core/utils/pose_aug.py | Apache-2.0 | 3,398 | 3a374e36863764546af414eed08c680d2c5b7032 | Python | py | |
import torch
from ..util import barit
class BaseTrainer(object):
"""Generic Base trainer object to inherit functionality from."""
def __init__(self):
self.training = True
self.device = 'cpu'
self._models = {}
self._optimizers = {}
self._losses = {}
self.epoch = 1... | dlt/train/basetrainer.py | BSD-3-Clause-Clear | 3,420 | e9085af4726f48c7262b29ace780073703797bb1 | Python | py | |
import argparse
import os
import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from models.CNN.CNN import ResNet20, train_ResNet, test_ResNet, calculate_intermediate_output
from utils_cifar import load_cifar_2
def main(args):
torch... | models/CNN/train.py | MIT | 3,589 | 1084477316b3c0ab32f67d82e8d2710d80e63fda | Python | py | |
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 29 18:38:40 2020
@author: Arthur
TODO list
balance the weights when mixing data sets
"""
import torch
from torch.utils.data import Dataset, DataLoader, ConcatDataset, Subset
import numpy as np
import os.path
import matplotlib.pyplot as plt
# import mlflow
# from sklearn.... | data/datasets.py | MIT | 32,673 | 12bc0f9752dc99f8299fa50e6b7c4b41736ab090 | Python | py | |
import argparse
import logging
import os
import random
from pathlib import Path
from typing import Any, Dict
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
from transformers import (
AdamW,
AutoConfig,
AutoModel,
Au... | examples/lightning_base.py | Apache-2.0 | 12,378 | 6b04f94328f0ff3b6122369aa192f9b4233749ad | Python | py | |
import numpy
import argparse
import os.path as osp
import random
import nni
import torch
from torch_geometric.utils import dropout_adj, degree, to_undirected
from simple_param.sp import SimpleParam
from pGRACE.model import Encoder, GRACE
from pGRACE.functional import drop_feature, drop_edge_weighted, \
degree_dro... | train.py | MIT | 6,922 | 7c5be9bc14ca7b9cc6abf82ad18874f771ba3991 | Python | py | |
from typing import Dict, List, Optional, Tuple, Union
from typing_extensions import TypedDict
import numpy as np
import torch
from torch.tensor import Tensor
from tqdm import tqdm
from transformers import PreTrainedTokenizer
from neuro_comma.augmentation import AUGMENTATIONS
from neuro_comma.pretrained import TOKEN_I... | src/neuro_comma/dataset.py | MIT | 9,546 | 5f1e62cc47587406b4741d3d7cd5a2aad0b6e1ab | Python | py | |
#!/usr/bin/env python
from __future__ import print_function
from itertools import count
import torch
import torch.autograd
import torch.nn.functional as F
from torch.autograd import Variable
POLY_DEGREE = 4
W_target = torch.randn(POLY_DEGREE, 1) * 5
b_target = torch.randn(1) * 5
def make_features(x):
"""Builds ... | regression/main.py | BSD-3-Clause | 1,690 | 42766b075b4c6232c1a3d8c4c96d17000b84415d | Python | py | |
import torch
import torch.nn as nn
from flask_login import UserMixin
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = nn.Linear(hidden_size, hidden_size)
... | model.py | MIT | 584 | 3a37d0c7626ce2acbed054d4241d0da1d08c4c84 | Python | py | |
import gc
import glob
import hashlib
import itertools
import json
import os
import re
import subprocess
import time
from os.path import join as pjoin
import torch
from multiprocess import Pool
from pytorch_pretrained_bert import BertTokenizer
from others.logging import logger
from others.utils import clean
from prepr... | samples/summarize-bot/BertSum/src/prepro/data_builder.py | MIT | 13,213 | 03c83a3fc20bddcc263d8e8989c592078b0132b6 | Python | py | |
#copy of j25
import numpy as np
from collections import namedtuple
from functools import partial
from PIL import Image
import data_transforms
import data_iterators
import pathfinder
import utils
import app
import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional... | configs/f97-3_pt.py | MIT | 12,376 | fdf499656776cf66f488d690eec012c87da4c4be | Python | py | |
#!/usr/bin/env python
# coding=utf-8
# Copyright The HuggingFace Team and 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.ap... | examples/multiple-choice/run_swag.py | Apache-2.0 | 16,231 | 28b2605f0ca785733c0f6a18ac8d73c6c309e3ad | Python | py | |
#import libraries
import matplotlib.pyplot as plt
import torch
from torchvision import datasets, transforms, models
from torch import nn, optim
import torch.nn.functional as F
from collections import OrderedDict
from PIL import Image
import numpy as np
import seaborn as sb
import json
import argparse
# Define command... | train.py | MIT | 7,972 | 3a37feb635877a45c4051e6e26639327f35c270e | Python | py | |
import torch
import torch.nn as nn
import torch.optim as optim
class PPO:
def __init__(
self,
agent,
clip_param,
ppo_epoch,
num_mini_batch,
value_coef,
entropy_coef,
lr=None,
eps=None,
max_grad_norm=None,
use_clipped_value_los... | rl/ppo.py | MIT | 4,264 | 4b344ab3dd09f714c0895a1066b8ce4e80126d15 | Python | py | |
import torch
from torch2trt_dynamic.torch2trt_dynamic import (get_arg, tensorrt_converter,
trt_)
from .plugins import create_delta2bbox_custom_plugin
@tensorrt_converter(
'mmdet2trt.core.bbox.coder.delta_xywh_bbox_coder.delta2bbox_custom_func')
def convert_delta2b... | mmdet2trt/converters/delta2bbox_custom.py | Apache-2.0 | 1,943 | 5f65982437cc8f321b10f40a19490345dd30d1d4 | Python | py | |
import warnings
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer, build_activation_layer
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import ResLaye... | mmdet/models/backbones/actnn_resnet.py | Apache-2.0 | 24,191 | 3a383fd25b9cd5a27d2a8c17a51aedabab462af2 | Python | py | |
# Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import sys
def uniform_multinomial(batchsize, num_action, use_cuda=True):
''' Sam... | src_py/rlpytorch/sampler/sample_methods.py | BSD-3-Clause | 5,118 | 5fa61e1ad96f29108eca595c7239641f8708feda | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
def get_attention(preds, temp):
""" preds: Bs*C*W*H """
N, C, H, W = preds.shape
value = torch.abs(preds)
# Bs*W*H
fea_map = value.mean(axis=1, keepdim=True)
print("fea_map = ", fea_map.shape)
S_attention = (
H * W ... | code_test.py | Apache-2.0 | 1,258 | 3a386d9626fece53f92bebb1cc670e2c71128b98 | Python | py | |
import torch
import math
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear, Conv1d
from torch_geometric.nn import GCNConv, RGCNConv, global_sort_pool, global_add_pool
from torch_geometric.utils import dropout_adj
from util_functions import *
import pdb
import time
class GNN(torch.nn.Mo... | models.py | MIT | 12,053 | 3a387ff26a191e315d4eab2e8239c3f174863b1b | Python | py | |
import os
import math
import argparse
import random
import logging
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from data.data_sampler import DistIterSampler
import options.options as option
from utils import util
from data import create_dataloader, create_dataset
from models impor... | codes/train.py | Apache-2.0 | 16,488 | e96c561b171e5d750f76000048bdaa1abff538e0 | Python | py | |
import torch
from utils.common_utils import *
import numpy as np
import os
from networks.skip import skip
from networks.fcn import *
from torch.optim.lr_scheduler import MultiStepLR
from SSIM import SSIM
import cv2
from copy import deepcopy
from math import sqrt
INPUT = 'noise' #input type
pad = 'reflection'
impath = ... | deblurring/gradient_prior.py | MIT | 6,463 | b2f046b0dc515a50af0c657bed41c227892ce0ca | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Wrappers around on some nn functions, mainly to support empty tensors.
Ideally, add support directly in PyTorch to empty tensors in those functions.
These can be removed once https://github.com/pytorch/pytorch/issues/12013
is implemented
"""
... | detectron2/layers/wrappers.py | Apache-2.0 | 5,936 | b2eeca4bf7d5869cf880ccaea62cc76f22b16a77 | Python | py |
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