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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
CS224N 2018-19: Homework 4
model_embeddings.py: Embeddings for the NMT model
Pencheng Yin <pcyin@cs.cmu.edu>
Sahil Chopra <schopra8@stanford.edu>
Anand Dhoot <anandd@stanford.edu>
"""
import torch.nn as nn
import logging_utils
import logging
LOGLEVEL = logging.DEBU... | courses/cs224n/hw/a4/model_embeddings.py | MIT | 2,087 | 8052cd0b8f5fac5cd9f510ef3b8d3cec047f3a94 | Python | py | |
import os
import math
import time
import pprint
from tqdm import tqdm
import dill as pickle
import numpy as np
from numpy import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils import Beam, GNMTGlobalScorer, constant, tile, top_k_top_p_filtering
from models import RNNEncoder, RNN... | models/seq2seq.py | MIT | 8,180 | d7b9891fd5e5ff075600b1f0a29b21cc63e6cb45 | Python | py | |
from __future__ import print_function
import torch.utils.data as data
from PIL import Image
import os
import os.path
import errno
import numpy as np
import torch
import codecs
class MovingMNIST(data.Dataset):
"""`MovingMNIST <http://www.cs.toronto.edu/~nitish/unsupervised_video/>`_ Dataset.
Args:
roo... | scripts/moving_mnist.py | Apache-2.0 | 6,064 | 29d0839fc6efe723a5aa2be08b26bf136e8c2c31 | Python | py | |
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and the HuggingFace Inc. 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
#
# U... | src/transformers/modeling_albert.py | Apache-2.0 | 50,595 | 29d0b58226a64ba0f0c0e38a9d35a435964ac3a9 | Python | py | |
# -*- coding: utf-8 -*-
# @Time : 2019/8/23 21:58
# @Author : zhoujun
import time
import torch
import torchvision.utils as vutils
from tqdm import tqdm
from base import BaseTrainer
from utils import WarmupPolyLR, runningScore, cal_text_score
class Trainer(BaseTrainer):
def __init__(self, config, model, crit... | trainer/trainer.py | Apache-2.0 | 10,129 | 29d0c6f250e078e9680104a36619da59f559cd8f | Python | py | |
from typing import List
from typing import Optional
from typing import Union
import pandas as pd
from etna import SETTINGS
from etna.datasets.tsdataset import TSDataset
from etna.loggers import tslogger
from etna.models.base import Model
from etna.models.base import log_decorator
from etna.transforms import PytorchFo... | etna/models/nn/tft.py | Apache-2.0 | 5,415 | 52b01513da59536c1b9c981c54b5b6054d7de561 | Python | py | |
from abc import ABC, abstractmethod
import numpy as np
import torch
from typing_extensions import Protocol
from l5kit.data import get_agents_slice_from_frames
from l5kit.evaluation import error_functions
from l5kit.evaluation import metrics as l5metrics
from l5kit.rasterization import EGO_EXTENT_LENGTH, EGO_EXTENT_WI... | l5kit/l5kit/cle/metrics.py | Apache-2.0 | 10,397 | 29d0fd468ca62fc25dcd886acd2dff12aba3045e | Python | py | |
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import math
import torch
from fa... | fairseq/sequence_generator.py | BSD-3-Clause | 26,593 | 7c11589894edeba4c6957ef4e7c80b283f8d2c21 | Python | py | |
import random
import os
import time
import hashlib
import torch
import pandas as pd
import numpy as np
import pathlib
from torch_geometric.utils import remove_self_loops
from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score
from sklearn.metrics import mean_squared_error, mean_absolute... | src_2gi_ddi/utils.py | MIT | 40,124 | cc042c476e87a4120c42fa54376d4dff4154510e | Python | py | |
from collections import defaultdict
import logging
import os
import sys
import time
from typing import Type, Union
import h5py
import numpy as np
from sklearn.metrics import f1_score
import torch
from torch.utils.data import DataLoader
from omegaconf import DictConfig, OmegaConf, ListConfig
from torch import nn
from t... | deepethogram/feature_extractor/inference.py | FSFAP | 25,316 | 29d121df2f7a15607ee47ad426a10c15d5eb44ca | Python | py | |
import warnings
import torch
from torch import Tensor
from torch.nn.functional import grid_sample, conv2d, interpolate, pad as torch_pad
from torch.jit.annotations import BroadcastingList2
from typing import Optional, Tuple, List
def _is_tensor_a_torch_image(x: Tensor) -> bool:
return x.ndim >= 2
def _assert_i... | torchvision/transforms/functional_tensor.py | BSD-3-Clause | 35,317 | a95e29fda0dae749efe689b23d6116e35c079bbd | Python | py | |
import torch
def assert_output_has_nan(x: torch.Tensor) -> None:
assert torch.isnan(x).any() == False, "Output contains NaN."
def assert_output_shape_wrong(x: torch.Tensor, expected_shape: tuple) -> None:
assert (
x.shape == expected_shape
), f"Output's shape: {tuple(x.shape)} != Expected shape: {... | tests/utils.py | Apache-2.0 | 337 | f960e17e7a4804bd248a9e4866f9fe09aabb64d9 | Python | py | |
#!/usr/bin/env python3
r"""
Sampler modules to be used with MC-evaluated acquisition functions.
"""
import random
from abc import ABC, abstractmethod
from typing import Optional
import torch
from torch import Tensor
from torch.nn import Module
from torch.quasirandom import SobolEngine
from ..exceptions import Unsup... | botorch/acquisition/sampler.py | MIT | 9,031 | 29d13649d380a662958b5027aa3eb910ceba2f46 | Python | py | |
import math
import torch
import torch.nn as nn
from torch.optim.optimizer import Optimizer
from .types import Betas2, OptFloat, OptLossClosure, Params
__all__ = ('Yogi',)
class Yogi(Optimizer):
r"""Implements Yogi Optimizer Algorithm.
It has been proposed in `Adaptive methods for Nonconvex Optimization`__.... | torch_optimizer/yogi.py | Apache-2.0 | 5,363 | 29d14aef7010ae5516ad2fa86f742b3e2b5869df | Python | py | |
import json
import logging
import sys
import pandas as pd
import numpy as np
import torch
from task_config import SuperGLUE_LABEL_MAPPING
from snorkel.mtl.data import MultitaskDataset
sys.path.append("..") # Adds higher directory to python modules path.
logger = logging.getLogger(__name__)
TASK_NAME = "SWAG"
d... | superglue_parsers/swag.py | Apache-2.0 | 4,132 | 29d15b423dae1b9b0a06da7395efc6f6e9935574 | Python | py | |
import math
import numpy as np
import torch
from numba import jit
from scipy import sparse
from pagnn.types import SparseMat
@jit(nopython=True)
def conv1d_shape(in_channels, kernel_size, stride=1, padding=0, dilation=1):
return math.floor((in_channels + 2 * padding - dilation * (kernel_size - 1) - 1) / stride ... | src/pagnn/utils/array_ops.py | MIT | 3,606 | b2b4bc2ee12a24e2c69934872bb74985ba2f8a2a | Python | py | |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import copy
import types
import logging
import torch
from nni.common.graph_utils import build_module_graph
from nni.compression.pytorch.quantization.literal import QuantType, BN_FOLD_OP, BN_FOLD_TAG
from nni.compression.pytorch.quantization.obser... | nni/compression/pytorch/compressor.py | MIT | 40,636 | 7049504d721bad8a466de16979f53549fcba3028 | Python | py | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# pylint: disable-msg=import-error
"""
data_util.py is writen for creating iterator and save field
"""
import logging
import hazm
import torch
import numpy as np
import pandas as pd
import pickle as pkl
from torchtext import data
from torchtext.vocab import Vectors
from ... | src/emoji_prediction/utils/data_util.py | MIT | 13,448 | 7cb8cf86734ad59253c64dbcfc657dec5778bd2d | Python | py | |
import logging
from typing import Type, Tuple, Dict, List
import itertools
from collections import OrderedDict
import torch
import dace
from dace import data as dt
from daceml.autodiff.backward_pass_generator import BackwardPassGenerator
from daceml.autodiff.base_abc import AutoDiffException, BackwardResult
from dac... | daceml/autodiff/pytorch.py | BSD-3-Clause | 5,718 | 29d19be2319a47e009a5737f9a5530030ff375ef | Python | py | |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from model.utils.config import cfg
from model.deconv.deconv import _Deconv
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import ... | lib/model/deconv/resnet.py | MIT | 20,109 | fd6910cc71d7dbbf3054b36ccb43f3dd3ad84dc6 | Python | py | |
# Copyright (c) 2022, 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/nlp/modules/common/megatron/language_model.py | Apache-2.0 | 24,152 | 29d1ac3adf36379f451279751cd3dbafaf6b9170 | Python | py | |
# Copyright 2020 Toyota Research Institute. All rights reserved.
import cv2
import numpy as np
import random
import torchvision.transforms as transforms
from PIL import Image
import os
from packnet_sfm.utils.misc import filter_dict
####################################################################################... | packnet_sfm/datasets/augmentations_woodscape_fisheye.py | MIT | 7,596 | 29d1b3475a7706f7f63e68db785dc9f7a93fe99b | Python | py | |
import copy
import random
from functools import partial
import numpy as np
from mmdet.cv_core.parallel import collate
from mmdet.cv_core.utils import Registry, build_from_cfg
from torch.utils.data import DataLoader
from .samplers import GroupSampler
# only linux
# 用于查询或修改当前系统资源限制设置,可以通过代码控制进程内存,不知道纠结有多少影响？ TODO
imp... | mmdet/datasets/builder.py | MIT | 4,115 | 4265459142e02f66beaeef6e720e2a241513a0ed | Python | py | |
"""PyTorch compatible samplers.
These determine the order of iteration through a dataset.
Authors:
* Aku Rouhe 2020
* Samuele Cornell 2020
* Ralf Leibold 2020
"""
import torch
import logging
from operator import itemgetter
from torch.utils.data import (
RandomSampler,
WeightedRandomSampler,
Distribu... | speechbrain/dataio/sampler.py | Apache-2.0 | 22,878 | 345c01e9dcac525d1b3d8db26818885f6b23da93 | Python | py | |
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import transforms as T
import torch.nn.functional as F
from torchvision import models
from torchvision.datasets import ImageFolder
import pytorch_lightning as pl
import torchmetrics
from tqdm import tqdm
import matplotlib.pyplot... | ComputerVision/TransferLearning/code/train.py | MIT | 7,817 | 29d1e09bcfd8c82d5ec6f7e5032130dca7144749 | Python | py | |
import abc
import torch
import torchaudio
import numpy as np
from omegaconf.listconfig import ListConfig
from omegaconf.dictconfig import DictConfig
import torchvision.transforms as transforms
from torchvision.transforms import Compose, ToTensor, Normalize
from torchaudio.transforms import FrequencyMasking, TimeMaskin... | cvap/data/audio/transform.py | MIT | 9,230 | f9763e77d1e73ff7c094ce284c22f2c096072fc2 | Python | py | |
# Cheney - Mon Sept 18 2017
# PyTorch implementation for Lp-WGAN
# Reference: WGAN
from __future__ import print_function
import argparse
import random
import os
import gc
import resource
import numpy as np
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import t... | negative.py | BSD-3-Clause | 14,862 | 5ec61626761dca118087b4129504aa9a7e6e66e5 | Python | py | |
from __future__ import print_function
import numpy as np
import os
import sys
import cv2
import random
import pickle
import torch
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
import torch.optim as optim
from torch.optim import lr_scheduler
import torch.utils.data as data
import torch.nn.ini... | lib/ssds_train.py | MIT | 26,254 | 7c2540edabda758fbc14518b444fbf1ba17ef849 | Python | py | |
import numpy as np
from numba import jit
from collections import deque
import itertools
import os
import os.path as osp
import time
import torch
import cv2
import torch.nn.functional as F
from models.model import create_model, load_model
from models.decode import mot_decode
from tracking_utils.utils import *
from trac... | src/lib/tracker/multitracker.py | MIT | 17,994 | dfb10db68d9511398fc0443648afe2cb75caf1a9 | Python | py | |
import os
from collections import defaultdict
import numpy as np
import torch
import tqdm
from .agent import SACAgent
from .config import get_args
def test(args, agent, env):
score_list = []
for _ in range(args.test_times):
obs = env.reset()
score = 0
for __ in range(args.max_episode... | drl/sac/main.py | MIT | 4,132 | 5f831f19c3da8a0c10e19ef1fa1554ecf2a10b66 | Python | py | |
from __future__ import print_function
from PIL import Image
import os
import os.path
import numpy as np
import sys
import pickle
import torch
import torch.utils.data as data
from itertools import permutations
class VisionDataset(data.Dataset):
_repr_indent = 4
def __init__(self, root, transforms=None, trans... | dataset/cifar.py | MIT | 9,888 | b27ae40be0df32ab7d9ef1a9e06b83a9ca31b99c | Python | py | |
"""
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 torch
from torch import nn
from torch.nn import functional as F
from models.unet.unet_model import UnetModel
class ConvBlock(nn.Modu... | models/uncertainty/unet_vae/vae_2.py | MIT | 4,539 | cc03b4f8d2fb16e67da129603ca2d034d19a7fcb | Python | py | |
from collections import namedtuple
import numpy as np
import torch
# a snapshot of state to be stored in replay memory
Transition = namedtuple('Transition', ('observation_list', 'quest_list', 'possible_words', 'word_indices', 'reward', 'is_final'))
class PrioritizedReplayMemory(object):
def __init__(self, capa... | command_generation_memory.py | MIT | 5,513 | e65c98892d9101b9ed885e6c69716950f9394e30 | Python | py | |
#!/usr/bin/env python3
# use pytorch for dataloader
# https://github.com/pytorch/examples/blob/master/imagenet/main.py
import argparse
import os
import random
import shutil
import time
import warnings
import numpy as np
import hashlib
import torch
import torch.nn as nn
import torch.utils.data
import torchvision.trans... | python/cvi_toolkit/eval_on_board/eval_imagenet_pytorch.py | Apache-2.0 | 9,276 | 3afbfc757af33d83d7edb49a03059d6f61b3a266 | Python | py | |
# WarpCTC PyTorch bindings (c) 2018 by Thomas Viehmann <tv@lernapparat.de>
# All rights reserved. Licensed under the
# Apache License, Version 2.0, January 2004
# see LICENSE in root directory
from setuptools import setup, find_packages, Command, distutils
from torch.utils.cpp_extension import BuildExtension, CppExte... | pytorch_binding/setup.py | Apache-2.0 | 2,630 | 12d3d8e67303355241623ea4f2976767ffc593ad | Python | py | |
'''VGG11/13/16/19 in Pytorch.'''
import torch
import torch.nn as nn
cfg = {
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512,... | models/vgg.py | MIT | 1,469 | 10780c8dcfaa5dfc05518ae4c9e17ffbfd79333c | Python | py | |
import sys
sys.path.append('../')
import torch
import torch.nn as nn
import math, random, sys
import argparse
from fast_jtnn import *
import rdkit
from tqdm import tqdm
def load_model(vocab, model_path, hidden_size=450, latent_size=56, depthT=20, depthG=3):
vocab = [x.strip("\r\n ") for x in open(vocab)]
voc... | fast_molvae/sample.py | MIT | 1,880 | 29d29d03e546e5d8fe1a0349404663dcdda4be9d | Python | py | |
# -*- coding: utf-8 -*-
import os
import numpy as np
import logging
import pickle
import torch
import torch.nn as nn
from torch import optim
from torch import multiprocessing as mp
from rlmodule import MultiDiscretePolicy, Value, Memory, UserAction2, VanillaTransition
# from estimator import RewardEstimator
from uti... | vanilla_ppo.py | MIT | 31,898 | 29d29fb624eae1b8c155e6a18734fd91542a347c | Python | py | |
import torch
import torch.nn as nn
from torch.nn import init
import math
import numpy as np
from .correlation_package.modules.correlation import Correlation
from .submodules import *
'Parameter count , 39,175,298 '
class FlowNetC(nn.Module):
def __init__(self,args, batchNorm=True, div_flow = 20):
... | networks/FlowNetC.py | Apache-2.0 | 4,893 | 29d2a032983b9960fdf273f9bff0ccebc85ae09c | Python | py | |
import math
import torch
import torch.nn as nn
from src.modules.base_generator import GeneratorAbstract
class MBConv(nn.Module):
"""MBConvBlock used in Efficientnet.
Reference:
https://github.com/narumiruna/efficientnet-pytorch/blob/master/efficientnet/models/efficientnet.py
Note:
Drop co... | src/modules/mbconv.py | MIT | 5,255 | 4ba3f823cd11e449ff4b66492b7ea5572840ed06 | Python | py | |
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .resnet import ResNet,ResNetSimple
@BACKBONES.register_module()
class ResNet_Tiny(ResNetSimple):
"""ResNet backbone for CIFAR.
Compared to standard ResNet, it uses `kernel_size=3` and `stride=1`... | mmpose/models/backbones/resnet_tiny.py | Apache-2.0 | 3,746 | 03c67fe4ab8a2e2326d2eb9cab6f0e6fc526463f | Python | py | |
"""Computer vision example on Transfer Learning.
This computer vision example illustrates how one could fine-tune a pre-trained
network (by default, a ResNet50 is used) using pytorch-lightning. For the sake
of this example, the 'cats and dogs dataset' (~60MB, see `DATA_URL` below) and
the proposed network (denoted by ... | pl_examples/domain_templates/computer_vision_fine_tuning.py | Apache-2.0 | 16,964 | 703f1c9b024194a10432b8cbf3333c526856f491 | 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/regression/mape.py | Apache-2.0 | 3,583 | d71cc8f6719deadaa3efb5a0931aac5c62868be0 | Python | py | |
import torch
from random import randrange
from math import floor
from torch.nn.utils.rnn import pack_padded_sequence, PackedSequence
from .config import PAD
def _limit_lengths(seqs, max_length=None, max_tokens=None):
max_length = max_length or float('inf')
lengths = [min(s.nelement(), max_length) for s in seq... | seq2seq/tools/__init__.py | MIT | 2,351 | 29d2f3075fedadfd803750b971278af1518c0e94 | Python | py | |
import json
import torch
from diora.logging.configuration import get_logger
class ParsePredictor(object):
def __init__(self, net, word2idx):
super(ParsePredictor, self).__init__()
self.net = net
self.word2idx = word2idx
self.idx2word = {v: k for k, v in word2idx.items()}
... | pytorch/diora/analysis/cky.py | Apache-2.0 | 3,464 | 29d30662c53dab67b6a92643f2a07f47ddb88106 | Python | py | |
#!/usr/bin/env python3
from typing import Iterable, Union
import torch
class NGD(torch.optim.Optimizer):
r"""Implements a natural gradient descent step.
It **can only** be used in conjunction with a :obj:`~gpytorch.variational._NaturalVariationalDistribution`.
.. seealso::
- :obj:`gpytorch.vari... | gpytorch/optim/ngd.py | MIT | 1,392 | 03d1bf20735527abfc3f32bd2fbd23f227a0f78a | Python | py | |
import torch.nn as nn
import cv2
import torch
from copy import deepcopy
import numpy as np
from torch.autograd import Variable
from torch.autograd import Function as F
from parameters import Parameters
import math
p = Parameters()
###############################################################
##
## visualize
##
###... | util.py | MIT | 10,996 | 29d3314f24829e89a982f37176b128ebd28877db | Python | py | |
import numpy as np
import pytest
import torch
from PIL import Image
import clip
@pytest.mark.parametrize('model_name', clip.available_models())
def test_consistency(model_name):
device = "cpu"
jit_model, transform = clip.load(model_name, device=device)
py_model, _ = clip.load(model_name, device=device, j... | tests/test_consistency.py | MIT | 802 | 29d343d01391bdaf7772dfb2be29e0ef653ec313 | Python | py | |
import pytest
import torch
from syft.frameworks.torch.pointers import PointerTensor
from syft.exceptions import InvalidTensorForRemoteGet
def test___str__(workers):
bob = workers["bob"]
tensor = torch.Tensor([1, 2, 3, 4])
assert isinstance(tensor.__str__(), str)
tensor_ptr = tensor.send(bob)
as... | test/torch/tensors/test_native.py | Apache-2.0 | 4,022 | 7c71ca2cbedf7a16cb8cf62c327f27e8823857bb | Python | py | |
from typing import Tuple
import torch
from torch import Tensor
from .geometry import get_theta, unit
def project_ideal(focal_length: float, view_points: Tensor, model: str) \
-> Tensor:
'''
Ideal projection
'''
n = unit(view_points)
theta = get_theta(n, normed=True)
if model == 'equi... | omnicalib/simulation.py | MIT | 2,643 | e661b14becdd3bd802d58d0f7c8fd84cd9ee25ce | Python | py | |
from pathlib import Path
from typing import Union
from pytorch_lightning.plugins.environments import ClusterEnvironment
from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
from pytorch_lightning.plugins.io.torch_plugin import TorchCheckpointIO
from pytorch_lightning.plugins.io.xla_plugin import XLA... | pytorch_lightning/plugins/__init__.py | Apache-2.0 | 3,566 | 29d3926b6d294b6968145fd4dd194c16532f2f5b | Python | py | |
import gradio
import numpy as np
import torch
from PIL import Image
from fba.anonymizer import build_anonymizer
from fba import utils
from fba.config import Config
import gradio.inputs
def anonymize(img: Image.Image, truncation_value: float):
img = img.convert("RGB")
img = np.array(img)
img = np.rollaxis(im... | app.py | MIT | 1,219 | 29d3ae95f692773a0f832986b963d6bbf4e5a686 | Python | py | |
# 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 json
import argparse
import numpy as np
import os
from os import path
import itertools
from scipy.stats import entropy
import collection... | src/zoo/dialog/test_noise.py | MIT | 12,355 | 5f1143b097426197ed44cdba6add04d23b55ee0a | Python | py | |
from math import sqrt
import numpy as np
import torch as t
import torch.nn as nn
from gensim.models.keyedvectors import KeyedVectors
from torch.autograd import Variable
class Embeddings(nn.Module):
def __init__(self, path, vocab_size, max_len, h_size):
super(Embeddings, self).__init__()
self.voc... | nn/embedding/embedding.py | MIT | 2,362 | 29d3ea957df9c0aae268444adcd8460d4784fb39 | Python | py | |
import torch
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, in_sz, h_szs, out_sz,
hiddens_nonlinearity, final_nonlinearity):
# in_sz (int, input sz)
# h_szs (list of hidden layer szs)
# out_sz (int, output sz)
# hiddens_nonlinearity ('relu', 'tanh')
# final_nonlinea... | src/test/py/ltprg/game/synthetic_nom_ref/network_components.py | MIT | 1,959 | 03d07e0479ec9a2f385b354e6eaa5757efb5d761 | Python | py | |
import torch_geometric
from models import CompareFeaSt, CompareSAGE
from transforms import InletGeodesics, RemoveFlowExtensions, HeatSamplingCluster, FeatureDescriptors
from .template import Experiment
def fit(device):
# Neural network
model = CompareFeaSt()
# Training data
dataset = 'single_arteries... | experiments/compare.py | MIT | 891 | 29d42dc89b7d89530d1a8365375730d6ce12e6dd | Python | py | |
import torch
import torch.nn as nn
import numpy as np
from pruners.Pruner import Pruner
from worldmodel.VAE import Flatten
# Prunes p% of remaining weights
# prunes only weights
# sparsity in layer depends on layer size
# supposed to use with linear layers only!!!
class ERPruner(Pruner):
def __init__(self, net, ... | rl/pruners/ERPruner.py | MIT | 1,913 | 10f82e2a1338fddcd458b353bc9ca444132ab662 | Python | py | |
'''
Copyright 2019 The Microsoft DeepSpeed Team
'''
import os
import torch
import warnings
import hashlib
import torch.distributed as dist
from torch.nn.modules import Module
from torch.distributed.distributed_c10d import _get_global_rank
from tensorboardX import SummaryWriter
from deepspeed.runtime.utils import see... | deepspeed/runtime/engine.py | MIT | 71,958 | f965eb688d16db78153d63f1402865bbad9ce4f1 | Python | py | |
import os
import pickle
import numpy as np
import torch
from osim.env import ProstheticsEnv
from osim.http.client import Client
from pros_ai import get_policy_observation
from robo_rl.obsvail import get_obsvail_parser, get_logfile_name
from robo_rl.sac import SAC, SigmoidSquasher
from tensorboardX import SummaryWriter... | obsvail/pros_play.py | MIT | 4,474 | 29d4d9974783bd693508ff8e753ff2e41c8e601e | Python | py | |
import math
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architectu... | fairseq/models/position_transformer.py | MIT | 46,990 | 5e6a6c3321fc0ecb2b28ff487f365c6b5f8d0983 | Python | py | |
import torch
import torch.nn as nn
import numpy as np
# import use_package
# import qiqi_package as qiqi
import torch.utils.data as Data
import sys
import torchvision
import torchvision.transforms as transforms
import time
import math
# 起始位置
x = torch.empty(5, 3)
print(x)
x = torch.rand(5, 3)
print(x)
x = torch.zer... | Chinese_5/Day_1/01.py | Apache-2.0 | 1,639 | 12ffa3dc8d4f0042efd8303592bc20c5865c1b78 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pathlib
import pickle
import warnings
from typing import List, Optional, Sequence, Tuple, Union
import torch
from ... | mbrl/models/gaussian_mlp.py | MIT | 18,616 | 29d4f69e26e3d8bba2c88220799aede1b3fd391e | Python | py | |
import argparse
import json
import math
import logging
from urllib.request import urlopen
import sys
import os
import subprocess
import json
import yaml
import glob
from braceexpand import braceexpand
from types import SimpleNamespace
import os.path
from omegaconf import OmegaConf
import hashlib
import time
import ... | pixray.py | MIT | 86,560 | 4b2f85675a92ab9b3557069840f11e4bb172a85c | Python | py | |
import torch
from torch.optim.lr_scheduler import CosineAnnealingLR, LambdaLR
import numpy as np
import matplotlib.pyplot as plt
import pdb
from models.worker.macro_cnn_worker import MacroCNN
class Trainer :
def __init__(self, args, data, controller, worker):
self.args = args
self.trainData = data.train
self.... | trainer/trainer.py | MIT | 14,399 | 422366dce9f2d623ff876d1a67b5cc8e1a1e0dc3 | Python | py | |
#!/usr/bin/python3
#coding=utf-8
import os
import cv2
import json
import torch
import numpy as np
import transform
from torch.utils.data import Dataset
class Config(object):
def __init__(self, **kwargs):
self.kwargs = kwargs
self.mean = np.array([[[115.75, 120.83, 93.49]]])
s... | fine/dataset.py | MIT | 5,556 | 6b073aa9899108871c661e8a13a095e19ecf43b5 | Python | py | |
from argparse import ArgumentParser, Namespace
from pathlib import Path
try:
from spatial_correlation_sampler import SpatialCorrelationSampler
except ModuleNotFoundError:
from ptlflow.utils.correlation import IterSpatialCorrelationSampler as SpatialCorrelationSampler
import torch
import torch.nn as nn
from .p... | ptlflow/models/irr/pwcnet_irr.py | Apache-2.0 | 4,862 | 5f2c17520efb5036448178d4b90f153917d09bf3 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from .utils import *
####################################################################
class seqOnly(nn.Module):
def __init__(self, args):
super(seqOnly, self).__init__()
self.input_size = args.in... | structureimpute/models/seqOnly.py | MIT | 3,105 | ff27e819da4539fb43ee1268786d61b89a85f887 | Python | py | |
# Copyright (C) 2020-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
#
from __future__ import absolute_import, division, print_function
import numpy as np
import torch
from torch import nn
from torch.cuda.amp import GradScaler, autocast
from torchreid import metrics
from torchreid.engine.engine import E... | torchreid/engine/image/multihead.py | MIT | 13,107 | 29d572202f6bb8335888ba2b18a297f239e85c75 | Python | py | |
import argparse
import logging
from tqdm import trange
import torch
import torch.nn.functional as F
import numpy as np
from torch import nn
from torch.autograd import Variable
from transformers import GPT2Config
from transformers import GPT2LMHeadModel, GPT2Tokenizer, BertTokenizer
from DataLoader import *
from Model i... | GPT2-coarse-to-fine.py | MIT | 17,255 | 29d57d6df1669fdb7bb7318e9c71ff113ff3c6bc | Python | py | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as utils
import torchvision.transforms as transforms
from pygments.formatters import img
from scipy import io
import numpy as np
import mathematical_operations as mo
from datetime im... | venv/drafts/old_models/4/model.py | MIT | 10,479 | 29d580455c0fa63e6da6276e9137afd6126cc4c4 | Python | py | |
import re
import glob
import math
from tqdm import tqdm
import numpy as np
import torch
import random
import requests, zipfile, io
import os
from .pytorch_pretrained_bert.tokenization import BertTokenizer
from .pytorch_pretrained_bert.modeling import BertForSeq2SeqDecoder
from .biunilm import seq2seq_loader
import t... | text2text/abstractor.py | MIT | 7,341 | 29d58828ee4285d6934ac46e632fdbdac09a0eca | Python | py | |
#
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
import copy
from typing import Optional, Any, List
import torch
import numpy as np
import av
from torch import Tensor
import random
import argparse
from PIL import Image
from torchvision.transforms import functional... | data/video_reader/base_video_reader.py | AML | 8,266 | 29d58c053e27e29d22ade318582b16d97b3537ee | Python | py | |
# coding=utf-8
# Copyright 2021 The Fairseq Authors, Microsoft Research, 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... | src/transformers/models/wavlm/modeling_wavlm.py | Apache-2.0 | 79,902 | 7c99a5d7808ccd4a2ca5ad9f6e04647f77900c3e | Python | py | |
import torch
import numpy as np
from model.mgcvae import ASubmodel, BQSubmodel
from model.dataset import get_timesteps_data
from model.model_utils import mutual_inf_mc, sigmoid_anneal
from model.components import GMM2D
import model.dynamics as dynamic_module
import torch.distributions as td
import model.anneal_scheduli... | mats/model/mats.py | MIT | 46,410 | e60d6dd8d460a3d0e6c6c4e04c97178151da3bfa | Python | py | |
from collections import OrderedDict
import torch
from torch import nn
class IntermediateLayerGetter(nn.ModuleDict):
"""
Module wrapper that returns intermediate layers from a model
It has a strong assumption that the modules have been registered
into the model in the same order as they are used.
... | torchvision/models/_utils.py | BSD-3-Clause | 2,476 | 4b337a53f7e4ee69c796d51124cfb67107ee6442 | Python | py | |
#!/usr/bin/env python3
import gym
import ptan
import time
import random
import numpy as np
import argparse
from tensorboardX import SummaryWriter
import torch
import torch.nn as nn
import torch.nn.utils as nn_utils
import torch.nn.functional as F
import torch.optim as optim
from lib import common
import os
GAMMA = ... | z_externals/handson_second_edition/Chapter12/04_pong_r2.py | MIT | 8,388 | fdc96576a24429eeb592dda74fb88fc3bbd4d597 | Python | py | |
# Copyright 2019 Lorna Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable l... | nets/densenet_test.py | Apache-2.0 | 8,708 | e60ec184f92b25f653c70f66324a4dc5f7a8762f | 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... | network_architecture/generic_modular_residual_UNet.py | Apache-2.0 | 24,357 | 5f6c03f318a588c453876307874fce92a339eab4 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class ActionEncoder(nn.Module):
def __init__(self, horizon, state_embed_size, road_state_num, road_embed_size, skill_embed_size=9,
state_num=4, action_num=2, hidden_size=400):
super(ActionEncoder, self).__init__()
... | Pre-skills/action_encoder.py | MIT | 2,482 | 29d61805b933a24c322ecb6b07f162fc49aa43dc | Python | py | |
# Copyright 2020 The HuggingFace 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 required by applicabl... | src/transformers/trainer_seq2seq.py | Apache-2.0 | 10,369 | 5e533a62a65947ee8a0830a0f1526518111e1e03 | Python | py | |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
def fixed_padding(inputs, kernel_size, dilation):
kernel_size_effective = kernel_size + (kernel_size - 1) * (dilation - 1)
pa... | modeling/backbone/xception.py | MIT | 10,425 | 29d642d9917d843b0b6453ff76b09b68ae89d16a | Python | py | |
import random
import warnings
from itertools import islice
from typing import List, Iterator, Optional
import humanize
from tqdm import trange
from torch.utils.data import Dataset, DataLoader
from .subject import Subject
from .sampler import PatchSampler
from .dataset import SubjectsDataset
class Queue(Dataset):
... | torchio/data/queue.py | Apache-2.0 | 12,399 | 5f9126e9ef892714fea710452156c980b5663928 | Python | py | |
# global
import torch
from typing import Union, Tuple, List
# local
import ivy
def can_cast(from_: Union[torch.dtype, torch.Tensor], to: torch.dtype) -> bool:
if isinstance(from_, torch.Tensor):
from_ = from_.dtype
from_str = str(from_)
to_str = str(to)
if ivy.dtype_bits(to) < ivy.dtype_bits(... | ivy/functional/backends/torch/data_type.py | Apache-2.0 | 4,309 | 29d65839b23f56babae1a7e76e29a0b52ea60922 | Python | py | |
import json
from os.path import abspath, dirname, exists, join
import argparse
import logging
from tqdm import trange
import tqdm
import torch
import torch.nn.functional as F
import numpy as np
import socket
import os, sys
import re
import logging
from functools import partial
from demo_utils import download_model_fold... | interact.py | MIT | 8,494 | 29d65c9afc18a48776cd149cfa81e957f41e7b17 | Python | py | |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless req... | transformers/tests/modeling_xlnet_test.py | MIT | 14,912 | cc1ddfd8c1f59afd84e326ec3cc4b3c440e4614f | Python | py | |
# coding: utf-8
import argparse
import time
import math
import os, sys
import itertools
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from DEQModel.data_utils import get_lm_corpus
from DEQModel.models.trellisnets.deq_trellisnet import DEQTrellisNetLM
from DEQModel.modules import ra... | DEQModel/train_trellisnet.py | MIT | 15,315 | 29d67684a33f0c3c1488378c32fb34290c1e2afb | Python | py | |
#!/usr/bin/python
# -*- encoding: utf-8 -*-
import torch
import torch.nn as nn
import torch.utils.model_zoo as modelzoo
import torch.nn.functional as F
import torchvision
import torch.utils.checkpoint as ckpt
from modules import InPlaceABNSync as BatchNorm2d
class ConvBNReLU(nn.Module):
def __init__(self,
... | models/xception.py | MIT | 9,132 | 29d67faf667c9116ad0ea81f1108c1c1138023ff | Python | py | |
from sys import exit
from os import path, walk, makedirs
from numpy import inf, array, where, partition, std as np_std
from torch.optim import lr_scheduler, SGD, Adam, AdamW
from torch import nn, no_grad, cuda, flatten, utils, cat, stack
from torch.multiprocessing import set_start_method
from tqdm import tqdm
from li... | src/lib/pcapAE.py | MIT | 25,763 | 29d6c9817c830af063ac3c96752b17774282362e | Python | py | |
import os
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
cls_to_idx = {
'plane': 0, 'car': 1, 'bird': 2, 'cat': 3, 'deer': 4, 'dog': 5, 'frog': 6, 'horse': 7, 'ship': 8, 'truck': 9
}
def get_train_transforms():
normalize = transforms.Normalize(mean=(0.491, 0.482... | data.py | MIT | 1,886 | 5f4e7516a2880c1bce4f56ac334dffcdd256d442 | Python | py | |
# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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... | official/cv/pwcnet/src/utils/ckpt_convert.py | Apache-2.0 | 1,606 | 4b7b01fd3ab0945b2ff3a1a185e267cd91dfc8a6 | Python | py | |
"""
This code is adapted from: https://github.com/ZJULearning/RMI
The implementation of the paper:
Region Mutual Information Loss for Semantic Segmentation.
"""
# python 2.X, 3.X compatibility
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
import torch
im... | loss/rmi.py | BSD-3-Clause | 9,614 | 1086a0b001499679c2075883e1a0b64606d088df | Python | py | |
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 12 17:50:52 2020
@author: Mat Tun
"""
import numpy as np
import os
import argparse
import time
import cv2
import copy
from imitrob_dataset import imitrob_dataset
from torch.utils.data import DataLoader
from object_detector import find_objects
from cuboid_PNP_solver import... | trainer.py | BSD-3-Clause | 17,788 | 29d70e51663931890544b360713c3e5d2d2c704e | Python | py | |
import torchvision.models as models
print('Checking models...')
print(models.resnet18())
print(models.alexnet())
print(models.vgg16())
print(models.squeezenet1_0())
print('Checking pretrained models...')
# print(models.resnet18(pretrained=True))
# print(models.alexnet(pretrained=True))
print(models.squeezenet1_0(pret... | dl/pytorch/tests/vision.py | Apache-2.0 | 334 | ff00ff9481a8189a826e010a9c13fa05e2eac21f | Python | py | |
import csv
import gzip
import importlib
import logging
import math
import os
import sys
from collections import defaultdict
from glob import glob
from os import makedirs
from pathlib import Path
from typing import Dict, List, Callable
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seabor... | utils.py | MIT | 14,945 | 1ece16f8d4756282bfaf610cb8b0e42ebc4195d3 | Python | py | |
import numpy as np
from collections import deque
import torch
import torch.nn.functional as F
from opendr.perception.object_tracking_2d.fair_mot.algorithm.lib.models.decode import (
mot_decode,
)
from opendr.perception.object_tracking_2d.fair_mot.algorithm.lib.tracking_utils.kalman_filter import (
KalmanFilte... | src/opendr/perception/object_tracking_2d/fair_mot/algorithm/lib/tracker/multitracker.py | Apache-2.0 | 15,434 | 29d74bedf2e313c8e16e6ea74243a46101e45a52 | Python | py | |
import os
import time
import numpy as np
import torch.multiprocessing
import architectures as archs
import batchminer as bmine
import criteria as criteria
import metric
from modules.xbm import XBM
from utilities import logger
from utilities import misc
from scripts._share import set_seed, get_dataloaders, train_one_ep... | scripts/standard.py | MIT | 8,359 | 29d74c9eadc50886b640b2ea421749c7cdfbcb9f | Python | py | |
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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 compliance with the License.
# You may obtain a cop... | src/transformers/configuration_utils.py | Apache-2.0 | 43,115 | fd8786692caaef62f0a199793585af4817f45833 | Python | py | |
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
import torchvision.utils as vutils
import torch.nn.functional as F
import numpy as np
import copy
def make_iterative_func(func):
def wrapper(vars)... | utils/experiment.py | MIT | 4,519 | 340f9215b0da4d3b7ea888296353270b2d3e2076 | Python | py | |
import torch
from torch import nn
from torch.nn import utils
from .init_filters import NN_CONV_LAYERS, NN_LINEAR_LAYERS
def _check_module(model):
if not isinstance(model, nn.Module):
raise RuntimeError(
f'apply_spectral_norm expected an nn.Module, got {torch.typename(model)}'
)
_DEFA... | boardom/models/spectral_norm.py | BSD-2-Clause | 1,434 | 42b0c3eafae6a68975be3af8b8e98fad49c71fd1 | Python | py | |
from __future__ import absolute_import
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from model.utils.config import cfg
from .proposal_layer import _ProposalLayer
from .anchor_target_layer import _AnchorTargetLayer
from model.utils.net_utils import _smooth_l1_lo... | lib/model/rpn/rpn.py | MIT | 5,622 | 28d1fd7cea747bbea49adf82b783b930fcdc8eb2 | Python | py |
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