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import torch.nn as nn
import torch.nn.functional as F
import torch
from torchvision.models import vgg19
import math
class FeatureExtractor(nn.Module):
def __init__(self):
super(FeatureExtractor, self).__init__()
vgg19_model = vgg19(pretrained=True)
# Extracts features at the 11th layer
... | implementations/srgan/models.py | MIT | 3,774 | 5f003cda99e3e25531f1bede58d60a0566ec668b | Python | py | |
import dataclasses
import json
import os
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple
from .file_utils import cached_property, is_torch_available, is_torch_tpu_available, torch_required
from .trainer_utils import EvaluationStrategy
from .utils impor... | src/transformers/training_args.py | Apache-2.0 | 23,860 | ff22be4db444a7739782a9af798c7850c10a66a3 | Python | py | |
#
# Copyright 2018 Analytics Zoo Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | pyzoo/test/zoo/chronos/autots/experimental/test_autotstrainer.py | Apache-2.0 | 15,848 | e97e23acc868bcae3a80c9e8735ae30decf7d8c3 | Python | py | |
import logging
import random
import numpy as np
import torch
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
from hw_asr.base.base_text_encoder import BaseTextEncoder
from hw_asr.utils.parse_config import ConfigParser
logger = logging.getLogger(__name__)
class BaseDataset(Dataset):
... | hw_asr/base/base_dataset.py | MIT | 4,981 | 3ad8118f26474ec1197eec514a7a11a0ab0a7230 | Python | py | |
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.core.datamodule import LightningDataModule
def is_overridden(method_name: str, model: LightningModule) -> bool:
# if you pass DataModule instead of None or a LightningModule, we use LightningDataModule as super
# TODO - refect... | pytorch_lightning/utilities/model_utils.py | Apache-2.0 | 1,306 | d71e1fd5c08a68552beb2bbaa79886423b409acd | Python | py | |
from __future__ import print_function
import datetime
import time
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optim
import codecs
from model.crf import *
from model.lstm_crf import *
import model.utils as utils
from model.evaluator import eval_w
import argpar... | train_w.py | Apache-2.0 | 14,607 | 3ad823671e749373b26ffa5e176d1ae85c4fd56f | Python | py | |
#!/usr/bin/env python3
import time
import sys
from codes.e_utils.names import PROJECT_HOME
sys.path.insert(0,"c:\\users\\wlckd\\anaconda3\\envs\\link_rl\\lib\\site-packages")
import torch
import torch.multiprocessing as mp
from torch import optim
import os
print(torch.__version__)
from dummy_env import dummy_env
fr... | temp/matlab_pendulum_main/MATLAB_pendulum_dqn_main_Test.py | MIT | 5,187 | 3ad82ce39166937c56a7afbd36d731f9ddcb2139 | Python | py | |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
class ASPPModule(nn.ModuleList):
"""Atrous Spatial Pyramid Pooling (ASPP) Module.
Args:
... | mmseg/models/decode_heads/aspp_head.py | Apache-2.0 | 3,947 | 7059aee961183952ec60ca4d6ccea5d3f9ae3f79 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class FleetwoodNet9V1(nn.Module):
def __init__(
self,
num_classes,
init_weights=True,
):
super().__init__()
# Convolutional Layers
self.features = nn.Sequential(
... | src/models/vgg9.py | MIT | 2,358 | 4b884accd8370bdb9a878ead58dcdf56e9519bd5 | Python | py | |
""" Module with examples of common pruning patterns
"""
from .abstract import Pruning
from .utils import get_activations, get_param_gradients, get_gradients
import torch
class ActivationMixin(Pruning):
def update_activations(self, only_prunable=True):
assert self.inputs is not None, \
"Inputs... | shrinkbench/pruning/mixin.py | MIT | 4,333 | e656f68331af1fec02cbbc95d25f541319fadfa3 | Python | py | |
from __future__ import print_function
import torch
import numpy as np
from scipy.ndimage.filters import gaussian_filter
from PIL import Image
import inspect, re
import os
import collections
# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
class util():
def tensor2im(... | util/util.py | MIT | 2,770 | 3ad8df4d80a4c63579b14a9509e273de5a71ab2a | 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.
# Requires vqa-maskrcnn-benchmark to be built and installed. See Readme
# for more details.
import argparse
import os
import time
import cv2
i... | script/gt_t2c_extract_features.py | MIT | 11,018 | 5fbda004ca05dcc3c14636bfdc81988c1b1adbcf | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import math
from functools import partial
import pdb
__all__ = [
'ResNet', 'resnet10', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnet200'
]
def conv3x3x3(in_planes, out_planes, stride... | models/resnet_comp.py | MIT | 11,192 | 248e3395c597c5459c9c84b3916cc11b034d379e | Python | py | |
import os
from comet_ml import Experiment, ExistingExperiment
import sys
import json
import numpy as np
import torch
from torch import nn
from torch import optim
from torchsummary import summary
from torch.optim import lr_scheduler
from opts import parse_opts
from model import generate_model, EmbeddingModel
from mean ... | main.py | MIT | 8,419 | 3ad8fc77f2e0d46adf83230a0e9c029d099836f7 | Python | py | |
import numpy as np
import random
from collections import namedtuple, deque
from double_dqn_model import DoubleQNetwork
from replay_buffer import ReplayBuffer
import torch
import torch.nn.functional as F
import torch.optim as optim
BUFFER_SIZE = int(1e5) # replay buffer size
BATCH_SIZE = 128 # minibatch size... | double_dqn_agent.py | MIT | 4,919 | 3ad9113ec4641d5cd1b81f0fa037a41379d183bb | Python | py | |
#!/usr/bin/env python3
import unittest
import torch
from Lgpytorch.lazy import LowRankRootLazyTensor
from Lgpytorch.test.lazy_tensor_test_case import RectangularLazyTensorTestCase
class TestLowRankRootLazyTensor(RectangularLazyTensorTestCase, unittest.TestCase):
def create_lazy_tensor(self):
root = tor... | test/lazy/test_low_rank_root_lazy_tensor.py | MIT | 1,118 | 3ad91ced48092f18da7e8c90e47e435818570f3e | Python | py | |
from __future__ import print_function
from os import path
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from utils.iterative_trainer import IterativeTrainerConfig, IterativeTrainer
from utils.logger import Logger
from termcolor import colored
from methods impo... | methods/pixelcnn.py | MIT | 9,010 | 3ad91d197bb2df760af0073f1632562b203e78fc | Python | py | |
import torch
import sys
import os
import shutil
from ..constants import OCRD_TOOL
from ocrd import Processor
from ocrd_models.ocrd_page import parse
from ocrd_utils import getLogger, concat_padded
from ocrd_modelfactory import page_from_file
from ocrd_models.ocrd_page import to_xml,parse
import shutil
from pathlib ... | ocrd_anybaseocr/cli/ocrd_anybaseocr_dewarp.py | Apache-2.0 | 3,779 | 7ce8823edf0d5c3e55d46fdf1ed544eaf88c61b1 | Python | py | |
import importlib
import numpy as np
import torch
from scipy.ndimage import rotate, map_coordinates, gaussian_filter
from scipy.ndimage.filters import convolve
from skimage.filters import gaussian
from skimage.segmentation import find_boundaries
from torchvision.transforms import Compose
class RandomFlip:
"""
... | augment/transforms.py | MIT | 20,152 | 7c1b7ce068c6cab5bcfe550aacd0a99f222eb799 | Python | py | |
import argparse
import scipy
from scipy import ndimage
import numpy as np
import sys
import torch
from torch.autograd import Variable
import torchvision.models as models
import torch.nn.functional as F
from torch.utils import data, model_zoo
from model.SEAN import SEANet
from dataset.cityscapes_dataset imp... | evaluation.py | MIT | 3,275 | 4b6c098ae197d7956de8970d2d2b7e858b504c4e | Python | py | |
import torch
from torch import nn
from torch.utils import model_zoo
import pretrainedmodels
from pretrainedmodels.models.dpn import adaptive_avgmax_pool2d
from pretrainedmodels.models.xception import Xception, pretrained_settings as xception_settings
from cnn_finetune.base import ModelWrapperBase, ModelInfo
__all__ ... | cnn_finetune/contrib/pretrainedmodels.py | MIT | 8,884 | 3ad9754f17f976d55e0071b58c077378bb780012 | Python | py | |
import numpy as np
import torch
from torchvision.utils import make_grid
from base import BaseTrainer
from utils import inf_loop
class Trainer(BaseTrainer):
"""
Trainer class
Note:
Inherited from BaseTrainer.
"""
def __init__(self, model, loss, metrics, optimizer, config, data_loader,
... | trainer/trainer.py | MIT | 6,187 | b290fafabe71d85fdc3276dfb4599fc40587d858 | Python | py | |
import os
import warnings
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "4"
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.simplefilter(action='ignore', category=UserWarning)
import dgl
import ... | main/main_clean_model.py | MIT | 2,064 | f9e8b7b91ca8c0429de54449ce1e931459781960 | Python | py | |
"""LSTM Controller."""
import torch
from torch import nn
from torch.nn import Parameter
import numpy as np
class LSTMController(nn.Module):
"""An NTM controller based on LSTM."""
def __init__(self, num_inputs, num_outputs, num_layers):
super(LSTMController, self).__init__()
self.num_inputs = ... | ntm/controller.py | BSD-3-Clause | 1,625 | 3ad9afa863b0951c36cc8664ad7dc1c6b2cbc0de | Python | py | |
# MIT License
# Copyright (c) 2021 yzqin
# 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 limitation the rights
# to use, copy, modify, merge, publish... | dataset_utils/eval_score/eval_utils/evaluation_data_generator.py | MIT | 28,039 | e63196d8701dc6ee8b5fd435c84c6ed4529189f7 | Python | py | |
"""Test script to classify target data."""
from sklearn.metrics import accuracy_score
import torch
import torch.nn as nn
from utils import make_variable
def eval_tgt(encoder, classifier, data_loader):
"""Evaluation for target encoder by source classifier on target dataset."""
# set eval state for Dropout and... | core/test.py | MIT | 1,158 | 800b441833edc48740d8203c3b2c3b886c69c315 | Python | py | |
import os
import numpy as np
import scipy.sparse
from scipy.sparse import csr_matrix
from sklearn.preprocessing import normalize
import torch
import copy
import torch.sparse
from numpy.linalg import multi_dot
from collections import defaultdict
from keynet.dense import random_positive_definite_matrix, random_doubly_st... | keynet/sparse.py | MIT | 36,424 | b23b0dd6af34fd5ad98d636d5218932381b86af9 | Python | py | |
# Copyright (c) 2021, 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 applic... | object_detection/pytorch/tools/train_hvd.py | Apache-2.0 | 12,216 | 7080b702ab2667afa3dd96dc3f0f6ebb2f14b371 | Python | py | |
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## Email: zhanghang0704@gmail.com
## Copyright (c) 2020
##
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
"""ResNet variants"""
im... | code/OCTA-Net/resnet.py | Apache-2.0 | 13,203 | 42255dc4ff324022f0f7ee52b67125262ab6a9b8 | Python | py | |
import os
from env.environment import PortfolioEnv
import numpy as np
import torch as T
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Normal
import multiprocessing as mp
from plot import add_curve, save_plot
class ActorCritic(nn.Module):
def __in... | algorithms/a2c/agent.py | MIT | 6,854 | dfb522319a82f541ac6686d0c22b987086453a64 | Python | py | |
import torch
from torch.autograd import Variable
import utils
# import dataset
from PIL import Image
import glob
import os
import csv
import cv2
# import models.crnn as crnn
def predict_this_box(image, model, alphabet):
converter = utils.strLabelConverter(alphabet)
transformer = dataset.resizeNormalize((200, ... | task2/main.py | MIT | 4,851 | 3ada2aa4fe062c99eceeeceb3204a7da9edff7c0 | Python | py | |
import os
import time
import torch
import pprint
import argparse
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from tianshou.policy import DQNPolicy
from tianshou.utils import BasicLogger
from tianshou.env import SubprocVectorEnv
from tianshou.trainer import offpolicy_trainer
from tianshou.data ... | examples/minigrid/minigrid_dqn.py | MIT | 8,001 | b2a21d23145d73eb4bedae9f5c43865dbcb6e142 | Python | py | |
"""Utilities for interacting with precomputed word representations."""
from typing import Any
from ldp.utils.typing import PathLike
import h5py
import torch
from torch.utils import data
class RepresentationDataset(data.Dataset):
"""Iterates through a dataset of word representations."""
def __init__(self, p... | ldp/parse/representations.py | MIT | 4,125 | 1ea373e610dac2c6c077b7960d8de8b287580af9 | Python | py | |
# 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, software
# distributed under th... | bert/train.py | Apache-2.0 | 977 | f98ff0016bfe99af7396d0faf71d34dbae5c8720 | Python | py | |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.11.1
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# -... | modeling/gbert_classifier.py | MIT | 18,700 | a99790a6c26d47d3f82955db8bb3a0bbe60593d5 | Python | py | |
import os
import gym
import copy
import torch
import pprint
import random
import argparse
import numpy as np
import pandas as pd
import time
from ES import CEM
from TD3 import TD3
from utils import get_output_folder
from Replay_Buffer import ReplayBuffer
from Parameters import Set_parameter
os.environ['CUDA_VISIBLE_DE... | Main.py | MIT | 19,251 | 3ada81bb410dfa0a104859a94e2ae8fac38038aa | Python | py | |
import torch
from torch.utils.data import TensorDataset, DataLoader, random_split
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from numpy import sqrt
# from parameter import HIDDEN_SIZE, NUM_LAYERS, BATCH_SIZE, FEATURE_SIZE, SEQ_SIZE, OUT_SIZE, USE_GPU
class Attn(nn.Module)... | backend/rnn/biLSTM_multi_to_one.py | MIT | 3,807 | f9e9eeac5ace977efdd8f8f508f3180ceb8366c5 | Python | py | |
from torchvision import transforms
class TransformCIFARTrainBaseline(object):
def __init__(self,
norm_mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
norm_std=[x / 255.0 for x in [63.0, 62.1, 66.7]]):
super().__init__()
self.transform = transforms.Compose([
transform... | projects/CIFAR10Demo/scripts/TransformCIFARTrainBaseline.py | MIT | 571 | 24ca6a50501ac069469277a343b77618e6340b55 | Python | py | |
import os
import torch
from gluonnlp.data import SentencepieceTokenizer
from kogpt2.model.sample import sample_sequence
from kogpt2.utils import get_tokenizer
from kogpt2.utils import download, tokenizer
from kogpt2.model.torch_gpt2 import GPT2Config, GPT2LMHeadModel
import gluonnlp
import argparse
parser = argparse.A... | generator.py | Apache-2.0 | 4,160 | 3adac4c7cdc46a3bc0216ab2713c40b9b4c68b01 | Python | py | |
from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import cv2
from util import *
import argparse
import os
import os.path as osp
from DNModel import net
from img_process import preprocess_img, inp_to_image
import pandas as pd
import r... | detect.py | MIT | 6,356 | cc009033ea00a46328a0e22b7853f611e5e0ff8b | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from .operations import *
import random
class MixedOp(nn.Module):
def __init__(self, C, stride):
super(MixedOp, self).__init__()
self._ops = nn.ModuleList()
for primitive in OPS_POOL:
op = OPS[primitive](C, st... | Imagenet_train/timm/models/model_search.py | MIT | 4,623 | 3adacbac0a71c25805ca381c26c332ee446fcf2a | Python | py | |
import torch
def flow_loss_func(flow_preds, flow_gt, valid,
gamma=0.9,
max_flow=400,
**kwargs,
):
n_predictions = len(flow_preds)
flow_loss = 0.0
# exlude invalid pixels and extremely large diplacements
mag = torc... | loss.py | Apache-2.0 | 1,022 | f9f0b0216b2a277f422daab92d5c5b4f53458ae3 | Python | py | |
from torch.utils.data import Dataset
from torchvision import transforms
import torchvision.transforms.functional as TF
import random
import torch
import numpy as np
from PIL import Image
import os
class WeedDataset(Dataset):
def __init__(self, root, random_rotate=True):
self.root = root
self.random... | setup/dataset.py | MIT | 2,993 | 3adaf905bfd5af090b1f0919347188af6a59b3a8 | Python | py | |
from collections import OrderedDict
import numpy as np
import torch
from torch import nn as nn
from torch.nn import init
from rlkit.torch.core import PyTorchModule
from rlkit.misc.eval_util import create_stats_ordered_dict
from rlkit.torch.ddpg import DDPG
import rlkit.torch.pytorch_util as ptu
import rlkit.torch.net... | rlkit/torch/ddpg/easy_v_ql.py | MIT | 5,578 | 3adb0907bd0c50e5f23d53f3181fcc0fabf8b97e | Python | py | |
import numpy as np
from torch.utils.data import Dataset
from .dataset import PairDataset, InstanceDataset
from neuron.config import registry
from neuron import ops
from collections import OrderedDict
__all__ = ['Seq2Pair', 'Image2Pair', 'Seq2Instance', 'Subset', 'Slice',
'RandomConcat']
@registry.regist... | utils/neuron/data/datasets/structure.py | MIT | 14,525 | e93f204e2b33df1e3bfd2d454a5bf351e5343a00 | Python | py | |
import torch
import torch.nn as nn
from dgl.nn.pytorch import SumPooling, AvgPooling, MaxPooling, GlobalAttentionPooling, Set2Set
from conv import GNN_node, GNN_node_Virtualnode
class GNN(nn.Module):
def __init__(self, num_tasks = 1, num_layers = 5, emb_dim = 300, gnn_type = 'gin',
virtual_node... | examples/pytorch/ogb_lsc/PCQM4M/gnn.py | Apache-2.0 | 2,819 | 3adb43eccd1bcce1d10d95f663ae81dc5f3b3afc | Python | py | |
# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is... | transfer_model/data/build.py | Xnet,RSA-MD,X11 | 1,701 | e9ad474fbba91e3a03dd0545b4c2035731bfa24b | Python | py | |
import math
import torch
from torch.optim.optimizer import Optimizer, required
class RAdam(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
self.buffer = [[None, None, None] for in... | pytorch_utilities/optim/radam.py | MIT | 7,961 | 454f32e7bb2c03e1b5196bee8df09cfa6dba9968 | Python | py | |
import os
import torch
import sys
import logging
import time
import tqdm
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import statistics
from os.path import join
#from model.model import EncoderDecoder
def get_logger(name, path, fname):
"""create a logger and return
"""
logg... | eval/eval_utils.py | MIT | 4,275 | e630bc35c7016dfbb2d676bb2f6b5a4768c59ec8 | Python | py | |
import os
import numpy as np
import h5py
import pandas as pd
import spacy
import torch
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data.dataset import Dataset
from torchtext import data
# set common-sense features path
CSPATH = './data/csfeatures/'
def load_multimodal_features_from_h5(feat_h5_video,... | iPerceiveDVC/dataset/dataset.py | MIT | 19,952 | f9e3c33c6b103e96dfa02abd62aafb36b05bede8 | Python | py | |
from functools import partial
import torch
from torch import nn as nn
from torch.nn import functional as F
def conv3d(in_channels, out_channels, kernel_size, bias, padding):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_si... | unet3d/buildingblocks.py | MIT | 15,443 | 1e7332f6f354a67e3fdf23272eef68c3954b5b94 | Python | py | |
from custom_types import *
from constants import CACHE_ROOT, DATA_ROOT
from process_data import mesh_utils
import torch
import os
from options import Options
from process_data.mesh_plotter import plot_mesh
from process_data import files_utils
class MeshHandler:
_mesh_dss: List[Union[mesh_utils.MeshDS, N]] = []
... | models/mesh_handler.py | MIT | 9,624 | 12c78e95bd813f28564d061a349276628dad1e3a | Python | py | |
import h5py
import numpy as np
import torch
import torch.nn as nn
from skimage import measure
from pytorch3dunet.augment.transforms import LabelToAffinities, StandardLabelToBoundary
from pytorch3dunet.embeddings.contrastive_loss import ContrastiveLoss
from pytorch3dunet.unet3d.losses import GeneralizedDiceLoss, Weight... | tests/test_criterion.py | MIT | 7,994 | 3adb86d4839dff61b3fa9eb6865ffdc9c4df8c70 | Python | py | |
from gptlm import GPT2LM
import torch
import argparse
from PackDataset import packDataset_util_bert
def read_data(file_path):
import pandas as pd
data = pd.read_csv(file_path, sep='\t').values.tolist()
sentences = [item[0] for item in data]
labels = [int(item[1]) for item in data]
processed_data... | experiments/test_defense.py | MIT | 5,505 | 3adb9b2e8a48721d0ec5f93f847296e11b5756fa | Python | py | |
'''
Soft Actor-Critic version 2
using target Q instead of V net: 2 Q net, 2 target Q net, 1 policy net
add alpha loss compared with version 1
paper: https://arxiv.org/pdf/1812.05905.pdf
'''
import math
import random
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import ... | sac_v2_multithread.py | Apache-2.0 | 18,359 | 3adbd9a71dbcfaac4e497ef3bb99c7b5950b494f | Python | py | |
# Adopted from a code base from https://github.com/cetinsamet
# --------------------------------------------------
import random
random.seed(123)
import numpy as np
np.random.seed(123)
import torch
torch.manual_seed(123)
criterion = torch.nn.CrossEntropyLoss(reduction='sum') # <-- Loss Function
class Network... | model.py | MIT | 732 | 3adbdac390780fcf3fcd8f494db2ce8c903e0b8d | 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/lite/wrappers.py | Apache-2.0 | 6,982 | ff95e89d1d2cf6f7a805ddf95c2a3067c9599c3e | Python | py | |
import tempfile
import pytest
import torch
from torch import nn
import mmcv
from mmcv.cnn.utils.weight_init import update_init_info
from mmcv.runner import BaseModule, ModuleList, Sequential
from mmcv.utils import Registry, build_from_cfg
COMPONENTS = Registry('component')
FOOMODELS = Registry('model')
@COMPONENTS... | tests/test_runner/test_basemodule.py | Apache-2.0 | 20,735 | f9e36687a95d91b8ba4c27d151a15cc9c4786165 | Python | py | |
# @Author : FederalLab
# @Date : 2021-09-25 16:52:39
# @Last Modified by : Chen Dengsheng
# @Last Modified time: 2021-09-25 16:52:39
# Copyright (c) FederalLab. All rights reserved.
import json
from threading import Lock
from typing import Any, Dict, List, Optional, Tuple
import torch.distribu... | openfed/federated/props.py | MIT | 6,722 | 3adc1ce018c0a14964bd1960d6dc0904bfc00550 | Python | py | |
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | models/ops/modules/ms_deform_attn.py | Apache-2.0 | 6,605 | 12ed0a4f2ead30dc3e20d7682e0f52374c0dc308 | Python | py | |
import argparse
import os
import time
import torch
from filelock import FileLock
# from torch.nn import Linear
from torch.nn.functional import log_softmax, nll_loss
from torch_geometric.nn import JumpingKnowledge
from tools import file_path
from tools import save_data, load_data
class ModelEnsemble(torch.nn.Module)... | AutoGraphEnsemble.py | MIT | 3,884 | 45a0a04d4f306af2e3ab4d7e92a01b72c39e37ba | Python | py | |
# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Lichao Wang, Jianwei Yang, based on code from Ross Girshick, Jiasen Lu, Jianwei Yang
# --------------------------------------------------------
from __future_... | demo.py | MIT | 18,155 | 28ec180b41dcacc5aa40da4e69dbfd7aa6fbbbf8 | Python | py | |
from typing import Callable, List
import numpy as np
import pytest
import torch
from torch import Tensor
from tests.helpers import seed_all
from torchmetrics import Metric
seed_all(1337)
def _compute_sklearn_metric(
metric: Callable, target: List[np.ndarray], preds: List[np.ndarray], behaviour: str, **kwargs
)... | tests/retrieval/helpers.py | Apache-2.0 | 5,286 | 3adc74e0556c2b8d3ac66aab0e9d05a352f1d4c0 | Python | py | |
import torch
from model.model import *
from utils.inference_utils import CropParameters, EventPreprocessor, IntensityRescaler, ImageFilter, ImageDisplay, ImageWriter, UnsharpMaskFilter
from utils.inference_utils import upsample_color_image, merge_channels_into_color_image # for color reconstruction
from utils.timers i... | N-ROD/evrepr/thirdparty/e2vid/image_reconstructor.py | MIT | 5,213 | 1e146e420fed40e75c0131d1b89000552ad1d75c | Python | py | |
from models.losses.GeneralLoss import GeneralLoss
import torch.nn as nn
import torch
from utils.constants import DEVICE
class DefaultDLoss(GeneralLoss):
def __init__(self, weight, **kwargs):
super(DefaultDLoss, self).__init__(weight=weight)
def custom_forward(self, fake_scores, real_scores, smoothin... | models/losses/DefaultDLoss.py | Apache-2.0 | 848 | 6b1c0bce93d99ee03f708fced624ba952d801cc6 | Python | py | |
import torch
import torch.nn as nn
from models.arch_util import ConvGnLelu
from models.stylegan.stylegan2_rosinality import Generator
class Stylegan2LatentBank(nn.Module):
def __init__(self, pretrained_model_file, encoder_nf=64, max_dim=1024, latent_dim=512, encoder_levels=4, decoder_levels=3):
super()._... | codes/models/glean/stylegan2_latent_bank.py | Apache-2.0 | 2,979 | 3adc97359416619f6a6a0a51f6296381d057598c | Python | py | |
import math
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.utils.data import DataLoader, random_split
from torch.utils.tensorboard import SummaryWriter
import torchvision
# from torchvision.models.detection.backbone_utils import resnet_fpn_backbone
# from torchvision.o... | scripts/condinst.py | MIT | 19,324 | 3adcb0e64f1098771d6fe0dddfcf3ecb6a0a3c9a | Python | py | |
from __future__ import division
from __future__ import print_function
import os
import glob
import time
import random
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from utils import load_data, accur... | eval_ppi.py | MIT | 10,192 | 4595fd7a1342792c4b1158da3c43b8fca7bb9771 | Python | py | |
import torch
import torch.nn.functional as F
from torch.nn import Linear
from torch_geometric.nn import (GraphConv, SAGPooling, global_mean_pool,
JumpingKnowledge)
class SAGPool(torch.nn.Module):
def __init__(self, dataset, num_layers, hidden, ratio=0.8):
super().__init__()... | benchmark/kernel/sag_pool.py | MIT | 1,955 | ff36007d4a1228f6626c2df54643fb688b50b11e | Python | py | |
#!/usr/bin/env python
# coding: utf-8
# In[2]:
import os, shutil
from os.path import join as joinpath
from os import listdir
# In[3]:
"""
# Expected input format: an `images` folder with all images in flat structure, i.e. as direct children.
datadir = "/mnt/beegfs/home/vu/Codalab-MetaDL/data/resisc45"
imagedir =... | src/reptile-v4.py | MIT | 25,119 | ff999973613f63901a7ed420a61cf44920044f37 | Python | py | |
"""A few utility function for the pruner."""
import copy
import math
import numpy as np
import torch
from scipy import optimize
def _adapt_uniform_sample_size(probabilities, sample_size_target):
"""Do _adapt_sample_size for a uniform distribution (faster)."""
idx = probabilities.nonzero()
sample_size_tar... | src/torchprune/torchprune/method/base/base_util.py | MIT | 3,738 | 3adcf29a50f9ad918e33d8fe96cb260cd1dbb566 | Python | py | |
import torch.nn as nn
from function import calc_mean_std
def adjust_learning_rate(args, optimizer, iteration_count):
"""Imitating the original implementation"""
lr = args.lr / (1.0 + args.lr_decay * iteration_count)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def calc_conte... | loss.py | Apache-2.0 | 1,279 | 3add18f210145808cb6b2459e1950693fa5e45e9 | Python | py | |
"""
Sandbox for ECE score.
"""
import numpy as np
import torch
from train_joint import get_calibration
def old_get_calibration(y, p_mean, num_bins=10):
"""Compute the calibration.
References:
https://arxiv.org/abs/1706.04599
https://arxiv.org/abs/1807.00263
Args:
y: one-hot encoding of the... | ece_sandbox.py | MIT | 4,799 | 5f159ebcdfac52ee88939e5306ec3f82f3e7a084 | Python | py | |
import pickle
import torch
import numpy as np
import torch.utils.data as data
from torch.utils.data import TensorDataset, DataLoader
import params
import os
import gzip
from torchvision import datasets, transforms
class Descendant_Activations(data.Dataset):
def __init__(self, root, train=True, transform=None, do... | datasets/descendant_activations.py | MIT | 4,994 | 03cf1b9d4a2c2057d91ad4908d01245a861a4568 | Python | py | |
# pylint: disable=no-self-use, invalid-name
import json
import logging
import math
import numpy
import pytest
import torch
import _jsonnet
from allennlp.nn import InitializerApplicator, Initializer
from allennlp.nn.initializers import block_orthogonal, uniform_unit_scaling
from allennlp.common.checks import Configura... | allennlp/tests/nn/initializers_test.py | Apache-2.0 | 4,960 | f9a3e98b992ec83260a13972c818a688487386f0 | Python | py | |
'''GoogLeNet with PyTorch.'''
import torch
import torch.nn as nn
norm_mean, norm_var = 0.0, 1.0
cov_cfg=[(22*i+2) for i in range(1+2+5+2)]
class Inception(nn.Module):
def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes, cp_rate, tmp_name):
super(Inception, self).__init__()
... | hrank/models/googlenet_cifar.py | MIT | 6,582 | 458aedab41694642606f2a6b4a3c50c7d9cc8cdf | Python | py | |
import torch
import geffnet
from torch2trt import torch2trt
import re
from training.zoo.classifiers import DeepFakeClassifier
from trustnet_utils import *
import onnx
print(torch.__version__)
x = torch.randn((128, 3, 600, 600), requires_grad=True)
with torch.no_grad():
model_path = "weights/model.pth"
model = ... | torch2onnx.py | MIT | 1,494 | 7c49e9eeb86bbe25cae72a3a06d84399f0c3bd78 | Python | py | |
import os
import inspect
from functools import partial
import torch.optim as optim
from utils.polylr import PolyLR
from datasets.cityscapes import CityscapesDataset
from datasets.seg_transforms import RandomHorizontalFlip, RandomResize, RandomCrop, ToTensor, Normalize
from torchvision.transforms import ColorJitter, Res... | configs/train/cityscapes_efficientnet_b1_hyperseg-s.py | CC0-1.0 | 2,716 | 3add6761dea1330b91d13e86615dc1cc5c65bccf | Python | py | |
# Copyright (c) 2019, 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 applicabl... | lib/dr_utils/rep/Mesh.py | Apache-2.0 | 42,137 | 3addb00742350308e2f50ae1155e2ff60c98da5d | Python | py | |
#!/usr/bin/env python
import sys, os
import argparse
import pygame
import torch
import numpy as np
from PIL import Image
from splatting import splatting_function
from torch.utils.data.dataloader import default_collate
from geofree import pretrained_models
import imageio
torch.set_grad_enabled(False)
from geofree.modu... | scripts/braindance.py | MIT | 17,010 | b215698843ff290e2d41b54b95f1046e09c3f799 | Python | py | |
import copy
from collections import deque
import gym
import numpy as np
import random
import time
import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
import warnings
from timebudget import timebudget
timebudget.set_quiet()
from typing import List, Union, NamedTuple, Tuple
clas... | rldqn.py | MIT | 9,327 | 3ade124a079f1e305bc708782dfd388a485df819 | Python | py | |
import torch.utils.data
from fnet.data.fnetdataset import FnetDataset
from fnet.data.tifreader import TifReader
import fnet.transforms as transforms
import pandas as pd
import pdb
class TiffDataset(FnetDataset):
"""Dataset for Tif files."""
def __init__(self, dataframe: pd.DataFrame = None, path_csv: str =... | fnet/data/tiffdataset.py | Unlicense | 2,395 | 3ade2d998368d3c8f837822dd1293b2b6ac50946 | Python | py | |
from functools import partial
import torch.nn as nn
from catalyst_rl.utils import metrics
class IoULoss(nn.Module):
"""
Intersection over union (Jaccard) loss
Args:
eps (float): epsilon to avoid zero division
threshold (float): threshold for outputs binarization
activation (str)... | catalyst_rl/contrib/nn/criterion/iou.py | Apache-2.0 | 1,816 | 3ade36cdd09be166ec7455a94a0d5d6d818bb948 | Python | py | |
###########################################################################
# Created by: Hang Zhang
# Email: zhang.hang@rutgers.edu
# Copyright (c) 2017
###########################################################################
from __future__ import division
import torch
import torch.nn as nn
from torch.nn.functio... | models/head/aspp.py | Apache-2.0 | 3,508 | 1e620b0c2ce595b904eceb0ab6df1895ec955b78 | Python | py | |
import torch
import torch.nn as nn
from .utils import _transpose_and_gather_feat
import torch.nn.functional as F
# def _slow_neg_loss(pred, gt):
# '''focal loss from CornerNet'''
# pos_inds = gt.eq(1)
# neg_inds = gt.lt(1)
# neg_weights = torch.pow(1 - gt[neg_inds], 4)
# loss = 0
# pos_pred ... | lib/models/losses.py | MIT | 8,157 | 3ade3ad33f121d84a6a20acc284e3c1a393653e7 | Python | py | |
import copy
from pathlib import Path
import warnings
import numpy as np
import pandas as pd
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateLogger
from pytorch_lightning.loggers import TensorBoardLogger
import torch
from pytorch_forecasting import Baseline, TemporalFu... | examples/test.py | MIT | 2,582 | 3ade3bb52f57edba1c00d6a32f986f188fb7fed4 | 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.
"""
Train a network across multipl... | build/lib.linux-x86_64-3.7/fairseq/trainer.py | BSD-3-Clause | 20,506 | 3ade4856208cd476c435bd5a8ca63c9bc2f035aa | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
import math
import config as cfg
from torch.cuda import amp
# Implementation from https://github.com/HuangYG123/CurricularFace/blob/master/head/metrics.py
class ArcFace(nn.Module):
def __init__(self,in_features,out_... | src/head.py | MIT | 4,180 | 10d5308087d45e7ebfdfae14aa2a269b3c1b9b6b | Python | py | |
from threading import Thread
import torch
import GPUtil
from GPUtil import showUtilization as gpu_usage
__all__ = ["make_hook",
"_add_memory_hooks",
"log_mem",
"Monitor"]
def make_hook(logs, idx, hook_type):
def hook(self, *args):
torch.cuda.synchronize()
logs.... | kirigami/utils/memory_utils.py | MIT | 1,794 | f9afd45e8e8c3282c9f614e7d0349e6d3a01403c | Python | py | |
import random
from typing import List, Tuple
import torch
from torch import nn
from torch.distributions import Normal
from torch.nn import functional as f
from src.data_process import VCTKBatch
from .config import (
DecoderParams, DurationParams, EncoderParams, GaussianUpsampleParams,
ModelParams, PostNetPara... | src/model/non_attentive_tacotron.py | MIT | 16,053 | 42325f1f7c6e43a50ab5374eb0ff7b54ab7e495d | Python | py | |
import copy
import random
from functools import wraps
import torch
from torch import nn
import torch.nn.functional as F
from torchvision import transforms as T
# helper functions
def default(val, def_val):
return def_val if val is None else val
def flatten(t):
return t.reshape(t.shape[0], -1)
def singleto... | byol_pytorch/byol_pytorch.py | MIT | 7,887 | 03cf98148001338566ae7a6a9ee0efab7393440c | Python | py | |
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Dataloaders and dataset utils
"""
import glob
import hashlib
import json
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import Pool, ThreadPool
from pathlib import Path
from threading import Thread
from zipfile im... | utils/datasets.py | MIT | 45,236 | dffbf2dca612937dd81f1d32b7bbf9b75e3b1aeb | Python | py | |
from scipy import spatial
from lib.utils.img_utils import read_depth
from lib.datasets.dataset_catalog import DatasetCatalog
from lib.csrc.nn import nn_utils
from lib.config import cfg
import pycocotools.coco as coco
import numpy as np
from lib.utils.pvnet import pvnet_pose_utils, pvnet_data_utils
import os
from lib.ut... | lib/evaluators/linemod/rdopt.py | Apache-2.0 | 3,902 | b2c93a9673d573dc7cb98f430ae57ff7cd0c4e63 | 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 logging
import random
import time
import numpy as np
import torch
from fairdiplomacy.agents.searchbot_agent import SearchBotAgent
fr... | fairdiplomacy/env.py | MIT | 3,507 | 3adea2bc0ffd133a2b5256f4321f662ed6715c78 | Python | py | |
# -*- coding: utf-8 -*-
"""Linear module for dqn algorithms
- Author: Kh Kim
- Contact: kh.kim@medipixel.io
"""
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class NoisyLinear(nn.Module):
... | rl_algorithms/dqn/linear.py | MIT | 3,859 | 342c2b4db78a6e7f50270916c404552812b091a2 | Python | py | |
import argparse
import os
import numpy as np
import math
import sys
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
i... | projects/wgan/wgan.py | MIT | 5,408 | d7a65e7eb5ce7eac633b83bef9813e536067680b | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import os
import hparams
def to_gpu(x):
x = x.contiguous()
if torch.cuda.is_available():
x = x.cuda(non_blocking=True)
return torch.autograd.Variable(x)
def process_text(train_text_path):
with open(train_... | utils.py | MIT | 3,255 | 3aded2ec7d6f9fe66b75c1c4f79613bda455aad5 | Python | py | |
from __future__ import print_function, division
import numpy as np
import torch
import torch.nn as nn
import networks
class VggEncoder(nn.Module):
def __init__(self, config):
super(VggEncoder, self).__init__()
self.config = config
self.relu = nn.ReLU(True)
self.conv1 = nn.Conv2d(3... | network_nograd/VGGM.py | MIT | 5,421 | f9e0b413befd7b49ef5c9bf1b2090510a459f26b | Python | py | |
#/usr/bin/python
from __future__ import print_function
import argparse
import torch
import pickle
import numpy as np
import os
import math
import random
import sys
import matplotlib.pyplot as plt
import data
import scipy.io
from torch import nn, optim
from torch.nn import functional as F
from etm import ETM
f... | ETM-master/main.py | MIT | 15,708 | 24b87da8e2e73e8fd5adf13381fdbcfdd0472e0a | Python | py | |
#
# Copyright 2018 Analytics Zoo Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | pyzoo/test/zoo/orca/learn/jep/test_pytorch_estimator_for_spark.py | Apache-2.0 | 2,736 | 4b548cfe832096c89295545b6a147fd26e3ff819 | Python | py |
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