repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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StARformer | StARformer-main/gym/models/decision_transformer.py | import numpy as np
import torch
import torch.nn as nn
import transformers
from models.model import TrajectoryModel
from models.trajectory_gpt2 import GPT2Model
class DecisionTransformer(TrajectoryModel):
"""
This model uses GPT to model (Return_1, state_1, action_1, Return_2, state_2, ...)
"""
def... | 7,017 | 42.8625 | 172 | py |
StARformer | StARformer-main/atari_and_dmc/run_star_atari.py | import argparse
import future, pickle, blosc, os
from datetime import datetime
from collections import deque
import math, random
import numpy as np
import cv2
from PIL import Image
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils.data import Dataset
import torch.optim as optim... | 17,160 | 39.665877 | 270 | py |
StARformer | StARformer-main/atari_and_dmc/utils.py | import random
import numpy as np
import torch
import torch.nn as nn
import os
# source: https://github.com/google-research/batch_rl/blob/master/batch_rl/fixed_replay/replay_memory/fixed_replay_buffer.py
import collections
from concurrent import futures
from dopamine.replay_memory import circular_replay_buffer
import ... | 8,230 | 36.930876 | 146 | py |
StARformer | StARformer-main/atari_and_dmc/starformer.py | from einops import rearrange, repeat
from einops.layers.torch import Rearrange
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_
class GELU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
... | 17,722 | 43.418546 | 141 | py |
StARformer | StARformer-main/atari_and_dmc/run_star_dmc.py | import argparse
import future, pickle, blosc, os
from datetime import datetime
from collections import deque
import math, random
import numpy as np
import cv2
from PIL import Image
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import... | 16,627 | 38.216981 | 270 | py |
NAOMI | NAOMI-master/model_utils.py | import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
######################################################################
########################## MISCELLANEOUS #############################
######################################################################
def... | 4,992 | 30.012422 | 96 | py |
NAOMI | NAOMI-master/model.py | import numpy as np
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn.init as init
from model_utils import *
def num_trainable_params(model):
total = 0
for p in model.parameters():
count = 1
for s in p.size():
... | 11,065 | 36.511864 | 101 | py |
NAOMI | NAOMI-master/helpers.py | from torch.autograd import Variable
from torch import nn
import torch
import numpy as np
import os
import struct
import pickle
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib import animation
from skimage.transform import resize
use_gpu = tor... | 9,940 | 35.413919 | 165 | py |
NAOMI | NAOMI-master/train.py | import argparse
import os
import math
import sys
import pickle
import time
import numpy as np
import shutil
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from model import *
from torch.autograd import Variable
from torch import nn
import torch
import torch.utils
import torch.utils.dat... | 14,011 | 44.054662 | 138 | py |
Information-Restricted-NLMs | Information-Restricted-NLMs-main/setup.py | import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="irnlm",
version="0.0.1",
author="Alexandre Pasquiou",
author_email="alexandre.pasquiou@inria.fr",
description="Python code to reproduce the analyses of the paper: `Inform... | 1,001 | 39.08 | 195 | py |
Information-Restricted-NLMs | Information-Restricted-NLMs-main/src/irnlm/models/utils.py | import os
import re
import glob
import torch
import random
import datetime
import numpy as np
from transformers import GPT2LMHeadModel, WEIGHTS_NAME, CONFIG_NAME
from irnlm.models.gpt2.modeling_hacked_gpt2_integral import GPT2LMHeadModel
from irnlm.models.gpt2.modeling_hacked_gpt2_semantic import GPT2LMHeadModel as G... | 5,601 | 39.890511 | 131 | py |
Information-Restricted-NLMs | Information-Restricted-NLMs-main/src/irnlm/models/gpt2/modeling_hacked_gpt2_semantic_no_relative_pos.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and 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 copy of the License... | 60,611 | 40.232653 | 168 | py |
Information-Restricted-NLMs | Information-Restricted-NLMs-main/src/irnlm/models/gpt2/language_modeling.py | """
"""
import warnings
warnings.simplefilter(action="ignore")
import os
import torch
import pickle
from torch.utils.data import TensorDataset
from torch.utils.data import (
DataLoader,
RandomSampler,
SequentialSampler,
DistributedSampler,
)
from irnlm.models.gpt2.dataset import Dataset
from irnlm.... | 4,340 | 32.651163 | 168 | py |
Information-Restricted-NLMs | Information-Restricted-NLMs-main/src/irnlm/models/gpt2/modeling_hacked_gpt2_semantic.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and 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 copy of the License... | 59,120 | 42.343842 | 168 | py |
Information-Restricted-NLMs | Information-Restricted-NLMs-main/src/irnlm/models/gpt2/modeling_hacked_gpt2_syntactic.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and 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 copy of the License... | 59,145 | 42.36217 | 168 | py |
Information-Restricted-NLMs | Information-Restricted-NLMs-main/src/irnlm/models/gpt2/modeling_hacked_gpt2_integral.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and 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 copy of the License... | 59,145 | 42.36217 | 168 | py |
Information-Restricted-NLMs | Information-Restricted-NLMs-main/src/irnlm/models/gpt2/extract_features_gpt2_syntactic.py | import re
import numpy as np
import pandas as pd
from tqdm import tqdm
import benepar
from spacy.symbols import ORTH
import torch
from irnlm.data.utils import set_nlp_pipeline, get_ids_syntax
from irnlm.data.extract_syntactic_features import integral2syntactic
from irnlm.models.gpt2.extract_features_gpt2_integral im... | 5,558 | 33.74375 | 159 | py |
Information-Restricted-NLMs | Information-Restricted-NLMs-main/src/irnlm/models/gpt2/processors.py | import os
import gc
import time
import math
import torch
import logging
import pandas as pd
from tqdm import tqdm
from torch.nn import CrossEntropyLoss
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader, RandomSampler
from irnlm.models.utils import save_checkpoint, format_time
from irn... | 27,960 | 44.465041 | 211 | py |
Information-Restricted-NLMs | Information-Restricted-NLMs-main/src/irnlm/models/gpt2/extract_features_gpt2_integral.py | import os
import re
import numpy as np
import pandas as pd
from tqdm import tqdm
import torch
from transformers import AutoTokenizer
from irnlm.models.tokenizer import read_bpe
from irnlm.data.text_tokenizer import tokenize
from irnlm.models.gpt2.modeling_hacked_gpt2_integral import GPT2LMHeadModel
from irnlm.models.... | 11,428 | 32.127536 | 196 | py |
Information-Restricted-NLMs | Information-Restricted-NLMs-main/src/irnlm/models/gpt2/extract_features_gpt2_semantic.py | import os
import re
import numpy as np
import pandas as pd
from tqdm import tqdm
import torch
from irnlm.data.extract_semantic_features import integral2semantic
from irnlm.models.gpt2.extract_features_gpt2_integral import batchify_to_truncated_input
def extract_features(
path,
model,
nlp_tokenizer,
... | 3,664 | 32.018018 | 92 | py |
diver-rt | diver-rt-main/run.py | import torch
torch.backends.cudnn.enabled=True
torch.backends.cudnn.benchmark = False
from argparse import ArgumentParser
import moderngl
from scene import DIVeRScene
from orbit_camera import OrbitCamera
import moderngl_window as mglw
from diver import DIVeR
""" camera viewer code"""
class Viewer(mglw.WindowConfig... | 2,820 | 31.056818 | 91 | py |
diver-rt | diver-rt-main/diver.py | import torch
import torch.nn as nn
import torch.nn.functional as NF
from ray_march import aabb_intersect,ray_march
from mlp_evaluation import mlp_eval, upload_weight
""" real-time DIVeR code"""
class DIVeR(nn.Module):
def __init__(self, hparams):
super(DIVeR, self).__init__()
self.voxel_num = hpara... | 3,956 | 43.965909 | 128 | py |
diver-rt | diver-rt-main/orbit_camera.py | import numpy as np
import math
import torch
""" orbit camera model """
class OrbitCamera:
def __init__(self, pivot=[0,0,0], azimuth=0, elevation=60, zoom=400):
"""
Args:
pivot: initial obit center (in voxel grid coordinate system)
azimuth: initial azimuth
elevati... | 3,864 | 27.007246 | 85 | py |
diver-rt | diver-rt-main/ray_march/__init__.py | from torch.utils.cpp_extension import load
from pathlib import Path
_ext_src_root = Path(__file__).parent / 'src'
_ext_include = Path(__file__).parent / 'include'
exts = ['.cpp', '.cu']
_ext_src_files = [
str(f) for f in _ext_src_root.iterdir()
if any([f.name.endswith(ext) for ext in exts])
]
_ext = load(nam... | 496 | 28.235294 | 52 | py |
diver-rt | diver-rt-main/mlp_evaluation/__init__.py | from torch.utils.cpp_extension import load
import torch
from torch.autograd import Function
from pathlib import Path
_ext_src_root = Path(__file__).parent / 'src'
_ext_include = Path(__file__).parent / 'include'
exts = ['.cpp', '.cu']
_ext_src_files = [
str(f) for f in _ext_src_root.iterdir()
if any([f.name.e... | 1,158 | 25.340909 | 52 | py |
MASTER-electric_vehicle_charging_recommendation | MASTER-electric_vehicle_charging_recommendation-main/env.py | import numpy as np
import torch
TorchFloat = None
TorchLong = None
class RunningStat(object):
def __init__(self, shape):
self._n = np.int64(0)
self._M = np.zeros(shape)
self._S = np.zeros(shape)
def push(self, x):
x = np.asarray(x)
n_sample, n_feat = x.shape
as... | 23,320 | 43.085066 | 185 | py |
facenet | facenet-master/src/align/detect_face.py | """ Tensorflow implementation of the face detection / alignment algorithm found at
https://github.com/kpzhang93/MTCNN_face_detection_alignment
"""
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated docu... | 31,695 | 39.531969 | 150 | py |
facenet | facenet-master/tmp/mtcnn_test_pnet_dbg.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
import scipy.io as io
import align.detect_face
#ref = io.loadmat('pnet_dbg.mat')
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
... | 4,550 | 35.408 | 144 | py |
facenet | facenet-master/tmp/mtcnn_test.py | # MIT License
#
# Copyright (c) 2016 David Sandberg
#
# 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, me... | 4,376 | 35.173554 | 119 | py |
hrq-vae | hrq-vae-master/scripts/generate_training_triples.py | import jsonlines, os, json
import numpy as np
from flair.models import SequenceTagger
from flair.data import Sentence
from collections import defaultdict, Counter
from tqdm import tqdm
from copy import deepcopy
import torch
# predictor = Predictor.from_archive(load_archive("https://storage.googleapis.com/allennlp-p... | 15,891 | 37.293976 | 1,024 | py |
FacialGAN | FacialGAN-main/Toolbox/demo.py | import os
import sys
import cv2
import time
import numpy as np
from PIL import Image
import torch
from torchvision import transforms
from torchvision.utils import save_image
# OUR
import copy
import kornia
from munch import Munch
from os.path import join as ospj
import torch
import torch.nn.functional as F
from co... | 13,786 | 34.351282 | 120 | py |
FacialGAN | FacialGAN-main/Toolbox/core/checkpoint.py | import os
import torch
class CheckpointIO(object):
def __init__(self, fname_template, **kwargs):
print(fname_template)
os.makedirs(os.path.dirname(fname_template), exist_ok=True)
self.fname_template = fname_template
self.module_dict = kwargs
def register(self, **kwargs):
... | 1,121 | 33 | 77 | py |
FacialGAN | FacialGAN-main/Toolbox/core/model.py | import copy
import math
from munch import Munch
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import kornia
import torchvision
import cv2
class ResBlk(nn.Module):
def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2),
normalize=False, downsample=Fal... | 11,490 | 33.927052 | 108 | py |
FacialGAN | FacialGAN-main/Toolbox/util/image_pool.py | import random
import torch
from torch.autograd import Variable
class ImagePool():
def __init__(self, pool_size):
self.pool_size = pool_size
if self.pool_size > 0:
self.num_imgs = 0
self.images = []
def query(self, images):
if self.pool_size == 0:
retu... | 1,090 | 33.09375 | 67 | py |
FacialGAN | FacialGAN-main/Toolbox/util/util.py | from __future__ import print_function
print ('?')
import torch
import numpy as np
from PIL import Image
import numpy as np
import os
# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
if isinstance(image_te... | 4,130 | 37.25 | 129 | py |
FacialGAN | FacialGAN-main/Training/main.py | import os
import argparse
from munch import Munch
from torch.backends import cudnn
import torch
from core.data_loader import get_train_loader
from core.data_loader import get_test_loader
from core.solver import Solver
def str2bool(v):
return v.lower() in ('true')
def subdirs(dname):
return [d for d in os.... | 7,689 | 47.36478 | 113 | py |
FacialGAN | FacialGAN-main/Training/core/checkpoint.py | """
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, US... | 1,419 | 32.809524 | 77 | py |
FacialGAN | FacialGAN-main/Training/core/data_loader.py | """
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, US... | 9,902 | 34.367857 | 88 | py |
FacialGAN | FacialGAN-main/Training/core/utils.py | """
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, US... | 5,458 | 33.770701 | 95 | py |
FacialGAN | FacialGAN-main/Training/core/model.py | """
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, US... | 14,050 | 34.662437 | 112 | py |
FacialGAN | FacialGAN-main/Training/core/own_data_loader.py | #from torchvision.datasets.vision import VisionDataset
from core.VisionDataset import VisionDataset
from PIL import Image
import os
import os.path
import imageio
import numpy as np
import random
from torchvision import transforms
import torch
def has_file_allowed_extension(filename, extensions):
"""Checks if... | 10,969 | 35.688963 | 113 | py |
FacialGAN | FacialGAN-main/Training/core/unet.py | from collections import OrderedDict
import torch
import torch.nn as nn
class UNet(nn.Module):
def __init__(self, in_channels=3, out_channels=1, init_features=32):
super(UNet, self).__init__()
features = init_features
self.encoder1 = UNet._block(in_channels, features, name="enc1")
... | 3,740 | 36.787879 | 85 | py |
FacialGAN | FacialGAN-main/Training/core/VisionDataset.py | import os
import torch
import torch.utils.data as data
from typing import Any, Callable, List, Optional, Tuple
class VisionDataset(data.Dataset):
_repr_indent = 7
def __init__(
self,
root: str,
root_masks: str,
attribute: str,
training: bool,
... | 3,518 | 36.83871 | 114 | py |
FacialGAN | FacialGAN-main/Training/core/solver.py | """
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, US... | 14,571 | 39.365651 | 145 | py |
FacialGAN | FacialGAN-main/Training/metrics/lpips.py | """
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, US... | 3,385 | 33.55102 | 81 | py |
FacialGAN | FacialGAN-main/Training/metrics/fid.py | """
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, US... | 3,219 | 34.777778 | 101 | py |
FacialGAN | FacialGAN-main/Training/metrics/eval.py | """
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, US... | 6,537 | 39.608696 | 92 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/dataset/example_config.py | dataset_name = 'C2B'
gpu = 1
total_num = 64000
samples_per_gpu = 16
workers_per_gpu = 1
total_iter = 64000
test_interval = 2000
save_interval = 1000
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Lo... | 8,540 | 34.736402 | 207 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/examples/eval/visualise_inf.py | import torch, torchvision
import mmdet
from mmdet.apis import inference_detector, init_detector, show_result_pyplot
import json
import os
import cv2
# os.chdir("/mnt/hpccs01/home/n11223243/ssod")
os.chdir("/home/nicolas/hpc-home/ssod/")
# Choose to use a config and initialize the detector
config='work_dirs/baseline_s... | 1,220 | 30.307692 | 78 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/examples/eval/eval.py | import argparse
import os
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
from... | 9,075 | 38.290043 | 87 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/examples/train/train.py | import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import get_git_hash
from mmdet import __version__
from mmdet.models import build_detector
from mmdet.dat... | 7,212 | 35.614213 | 83 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/configs/labelmatch/labelmatch_uda_cluster.py | dataset_name = data_template
gpu = gpu_template
total_num = 48000 * 16
samples_per_gpu = 8
workers_per_gpu = 1
total_iter = int(total_num / (samples_per_gpu * gpu))
test_interval = 2000
save_interval = 1000
update_interval = 500
target_thr = 0.6
cluster_method = 'manual_prior'
# # -------------------------dataset------... | 10,563 | 35.302405 | 209 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/configs/labelmatch/labelmatch_uda.py | dataset_name = data_template
gpu = gpu_template
# we desire a batch size of 16
# learning rate is 0.001*16
# run for 32,000 iterations
total_num = 72000 * 16
samples_per_gpu = 8
workers_per_gpu = 1
total_iter = int(total_num / (samples_per_gpu * gpu))
test_interval = 2000
save_interval = 1000
update_interval = 500
# ... | 9,815 | 33.442105 | 209 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/configs/baseline/baseline_uda.py | # hyper-parameter: replace in "bash"
dataset_name = data_template
gpu = gpu_template
total_num = 64000
# 1 GPUs x 16 samples per GPU = 16 batch size. This requires 40Gb memory.
# learning rate then calculated at 0.001*batchsize
# total_iter for pretraining is 64000/batch_size = 4000
# run for longer to assess supervis... | 7,405 | 33.446512 | 209 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/configs/baseline/ema_config/baseline_uda_cls1.py | classes = ('car',)
# # -------------------------model------------------------------
model = dict(
type='FasterRCNN',
pretrained='./pretrained_model/backbone/vgg16_caffe.pth',
backbone=dict(
type='VGG',
depth=16,
out_indices=(4,), # stride=16
with_last_pool=False,
),
... | 3,460 | 32.601942 | 77 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/configs/baseline/ema_config/baseline_uda_cls8.py | classes = ('truck', 'car', 'rider', 'person', 'train', 'motorcycle', 'bicycle', 'bus')
# # -------------------------model------------------------------
model = dict(
type='FasterRCNN',
init_cfg='./pretrained_model/backbone/vgg16_caffe.pth',
backbone=dict(
type='VGG',
depth=16,
out_in... | 3,526 | 33.242718 | 86 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/apis/test.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
"""
modify from mmdet.apis.test: use PrintBar rather than ProgressBar (for AI platform)
"""
import os.path as osp
import time
from shutil import get_terminal_size
import torch
... | 5,688 | 35.235669 | 84 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/apis/train.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
"""
modify from mmdet.apis.train:
1. support ema model
"""
import warnings
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner imp... | 6,403 | 37.812121 | 85 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/core/bbox/samplers/random_sampler_lm.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
import torch
from mmdet.core.bbox.builder import BBOX_SAMPLERS
from mmdet.core.bbox.samplers import RandomSampler, SamplingResult
from mmdet_extension.core.bbox.samplers.samplin... | 6,499 | 46.794118 | 92 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/core/bbox/samplers/sampling_result_lm.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
import torch
from mmdet.core.bbox.samplers import SamplingResult
class SamplingResultLM(SamplingResult):
def __init__(self, pos_inds, ig_inds, neg_inds, bboxes, gt_bboxes,... | 2,678 | 42.209677 | 107 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/core/bbox/assigner/assign_result_lm.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
import torch
from mmdet.core.bbox.assigners import AssignResult
class AssignResultLM(AssignResult):
def add_ig_(self, gt_labels):
# assign as -1 for ignore
... | 683 | 39.235294 | 94 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/core/bbox/assigner/max_iou_assigner_lm.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
import torch
from mmdet.core.bbox.builder import BBOX_ASSIGNERS
from mmdet.core.bbox.assigners import MaxIoUAssigner
from mmdet_extension.core.bbox.assigner.assign_result_lm im... | 4,318 | 43.071429 | 84 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/core/hooks/semi_eval_hooks.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
"""
eval hook for semi-supervised:
1. without ema: same as normal evaluation
2. with ema: 1) only_ema=True: only do evaluation on ema_model
2) only_ema=False: do ev... | 5,701 | 39.728571 | 109 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/core/hooks/labelmatch_cluster_hooks.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
"""
hooks for LabelMatchCluster
"""
import shutil
import os.path as osp
import numpy as np
import pickle
import torch.distributed as dist
from torch.nn.modules.batchnorm import... | 28,514 | 45.899671 | 174 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/core/hooks/stac_hooks.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
"""
hooks for STAC
"""
import os.path as osp
import numpy as np
import torch.distributed as dist
from torch.nn.modules.batchnorm import _BatchNorm
import mmcv
from mmcv.runner... | 8,358 | 38.804762 | 114 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/core/hooks/labelmatch_hooks.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
"""
hooks for LabelMatch
"""
import shutil
import os.path as osp
import numpy as np
import torch.distributed as dist
from torch.nn.modules.batchnorm import _BatchNorm
import m... | 10,970 | 43.417004 | 130 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/core/utils/dist_utils.py | import torch
@torch.no_grad()
def concat_all_gather(tensor, dim=0):
"""Performs all_gather operation on the provided tensors.
*** Warning ***: torch.distributed.all_gather has no gradient.
"""
tensors_gather = [
torch.ones_like(tensor)
for _ in range(torch.distributed.get_world_size()... | 468 | 25.055556 | 72 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/models/detectors/semi_base.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
"""
Base-Detector for semi-supervised learning
"""
import cv2
import os
from collections import OrderedDict
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn
import mmcv
from mmcv.runner import load... | 7,566 | 41.751412 | 110 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/models/detectors/labelmatch_cluster.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
"""
LabelMatchCluster
"""
import numpy as np
import pickle
import torch
from mmcv.runner.dist_utils import get_dist_info
from mmdet.utils import get_root_logger
from mmdet.cor... | 14,434 | 49.296167 | 118 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/models/detectors/labelmatch.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
"""
LabelMatch
"""
import numpy as np
import torch
from mmcv.runner.dist_utils import get_dist_info
from mmdet.utils import get_root_logger
from mmdet.core.bbox.iou_calculator... | 13,339 | 50.505792 | 118 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/models/detectors/semi_two_stage.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
"""
semi-supervised two stage detector
"""
import torch
from mmdet.core import bbox2result
from mmdet.models.detectors import TwoStageDetector
from mmdet_extension.models.dete... | 1,835 | 37.25 | 103 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/models/roi_head/standard_roi_head_lm.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
"""
standard roi_head for LabelMatch
"""
import torch
from mmdet.models.builder import HEADS
from mmdet.core import bbox2roi
from mmdet_extension.models.roi_head import Stand... | 2,417 | 39.3 | 85 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/models/roi_head/standard_roi_head_base.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
"""
standard roi_head for LabelMatch
"""
import torch
from mmdet.models.builder import HEADS
from mmdet.core import bbox2roi
from mmdet.models.roi_heads import StandardRoIHead... | 1,586 | 37.707317 | 84 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/models/roi_head/bbox_heads/convfc_bbox_head_st.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
import torch
from mmdet.core import multi_apply
from mmdet.models.roi_heads.bbox_heads import Shared2FCBBoxHead
from mmdet.models.builder import HEADS
@HEADS.register_module(... | 3,892 | 42.741573 | 90 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/models/roi_head/bbox_heads/convfc_bbox_head_lm.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
import torch
from mmdet.core import multi_apply
from mmdet.models.roi_heads.bbox_heads import Shared2FCBBoxHead
from mmdet.models.builder import HEADS
@HEADS.register_module(... | 4,092 | 44.988764 | 87 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/models/loss/focal_loss.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/facebookresearch/unbiased-teacher
"""
CE version of Focal Loss
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.models.builder import LOSSES
from mmdet.models.losses.... | 1,659 | 37.604651 | 95 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/mmdet_extension/datasets/semi_dataset.py | # Copyright (c) Hangzhou Hikvision Digital Technology Co., Ltd. All rights reserved.
# Modified from https://github.com/open-mmlab/mmdetection
import copy
import random
from torch.utils.data import Dataset
from mmdet.datasets.builder import DATASETS
from mmdet.datasets.pipelines import Compose
from mmdet.datasets impor... | 2,992 | 36.886076 | 91 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/cluster_priors/save_clip_embeddings.py | import os
from multiprocessing import Process
import csv
from PIL import Image
from transformers import CLIPProcessor, CLIPVisionModel
# TODO edit scenario, dataset and DIR
DIR = '/home/nicolas/hpc-home/class_distribution_prediction/'
scenario = 'C2B'
dataset = 'labeled_data'
def run_clip(images, image_names, dataset... | 2,868 | 32.752941 | 166 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/cluster_priors/clip_distances.py | from lib2to3.pytree import LeafPattern
from logging.config import stopListening
from pydoc import cli
from re import L
from tkinter.font import names
import umap
from sklearn.datasets import load_digits
import numpy as np
import pandas as pd
import plotly.express as px
import os
import pickle
import torch
from torch.ut... | 9,686 | 39.701681 | 222 | py |
ClassDistributionPrediction | ClassDistributionPrediction-main/cluster_priors/distance_prior_regression.py | from ast import parse
from lib2to3.pytree import LeafPattern
from logging.config import stopListening
from pydoc import cli
from re import L, X
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.multioutput import MultiOutputClassifier
from tkinter.font import names
from xmlrpc.client im... | 11,815 | 43.089552 | 251 | py |
polish-sentence-evaluation | polish-sentence-evaluation-master/evaluate.py | import logging
import os
from collections import namedtuple
from pathlib import Path
import fire
import json
from typing import Union
from sentevalpl.tasks import get_task_names
from utils.analyzer import PolishAnalyzer
from methods.base import EmbeddingBase
from methods.utils import cached
from sentevalpl.engine im... | 7,103 | 40.788235 | 111 | py |
polish-sentence-evaluation | polish-sentence-evaluation-master/methods/huggingface.py | from typing import List
import numpy as np
import torch.cuda
import torch.nn.functional as F
from transformers import AutoConfig, AutoTokenizer, AutoModel
from methods.base import EmbeddingBase
class HuggingfaceModelEmbedding(EmbeddingBase):
def __init__(self, model_name_or_path: str, layers:str="top", mini_bat... | 2,173 | 41.627451 | 119 | py |
polish-sentence-evaluation | polish-sentence-evaluation-master/methods/roberta.py | import os
import numpy as np
from typing import List
from torch import Tensor, hub
from methods.base import EmbeddingBase
from fairseq.models.roberta import RobertaModel, RobertaHubInterface
from fairseq import hub_utils
class RobertaEmbedding(EmbeddingBase):
def __init__(self, path: str, layers:str="top", bpe:... | 3,165 | 40.116883 | 118 | py |
polish-sentence-evaluation | polish-sentence-evaluation-master/examples/paraphrase_mining/run_mining.py | import logging
import os
import random
import zipfile
import json
from collections import Counter, defaultdict
from typing import List, TextIO, Dict
import fire
import requests
from sentence_transformers import SentenceTransformer, util
from thefuzz import fuzz
from tqdm import tqdm
from lsm import LSM
from examples.pa... | 9,793 | 38.651822 | 116 | py |
polish-sentence-evaluation | polish-sentence-evaluation-master/extensions/LSTMPooling.py | import torch
from torch import Tensor
from torch import nn
from typing import Dict
import os
import json
class LSTMPooling(nn.Module):
def __init__(self, word_embedding_dimension, hidden_size, bidirectional=False):
super(LSTMPooling, self).__init__()
self.config_keys = ['word_embedding_dimension'... | 2,205 | 39.109091 | 126 | py |
polish-sentence-evaluation | polish-sentence-evaluation-master/sentevalpl/engine.py | from __future__ import absolute_import, division, unicode_literals
from senteval import utils
from sentevalpl.pairs_classification import RelatednessEval, EntailmentEval, PPCEval
from sentevalpl.classifier import SentEvalClassifier
from sentevalpl.tasks import get_task_names, get_task_by_name
class SE(object):
d... | 2,213 | 43.28 | 134 | py |
polish-sentence-evaluation | polish-sentence-evaluation-master/sentevalpl/classifier.py | import logging
import os
import numpy as np
from senteval.tools.validation import SplitClassifier
class SentEvalClassifier(object):
def __init__(self, task_path: str, task_name: str, nclasses: int, seed=1111):
self.seed = seed
self.nclasses = nclasses
self.task_name = task_name
lo... | 2,973 | 40.887324 | 120 | py |
polish-sentence-evaluation | polish-sentence-evaluation-master/sentevalpl/pairs_classification.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.
#
'''
Relatedness and Entailment tasks
'''
from __future__ import absolute_import, division, unicode_literals
import os
import i... | 10,482 | 40.109804 | 88 | py |
WPAL-network | WPAL-network-master/tools/test_net.py | #!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or modify
# it under the te... | 4,084 | 33.618644 | 84 | py |
WPAL-network | WPAL-network-master/tools/estimate_param.py | #!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or modify
# it under the ter... | 4,914 | 35.679104 | 112 | py |
WPAL-network | WPAL-network-master/tools/_init_path.py | #!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or modify
# it under the ter... | 1,529 | 29 | 73 | py |
WPAL-network | WPAL-network-master/tools/loc.py | #!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or modify
# it under the te... | 13,043 | 44.291667 | 150 | py |
WPAL-network | WPAL-network-master/tools/test_net_WS_BL.py | #!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or modify
# it under the te... | 4,078 | 33.567797 | 84 | py |
WPAL-network | WPAL-network-master/tools/train_net.py | #!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or modify
# it under the te... | 4,409 | 34.853659 | 117 | py |
WPAL-network | WPAL-network-master/tools/loc_BL.py | #!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or modify
# it under the te... | 6,828 | 38.247126 | 117 | py |
WPAL-network | WPAL-network-master/lib/data_layer/layer.py | #!/usr/bin/env python
#!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or mod... | 7,090 | 37.961538 | 184 | py |
WPAL-network | WPAL-network-master/lib/data_layer/minibatch.py | #!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or modify
# it under the ter... | 3,991 | 34.963964 | 80 | py |
WPAL-network | WPAL-network-master/lib/WS_BL/recog.py | import math
import cv2
import numpy as np
from utils.blob import img_list_to_blob
from config import cfg
class ResizedImageTooLargeException(Exception):
pass
class ResizedSideTooShortException(Exception):
pass
def _get_image_blob(img, neglect):
"""Converts an image into a network input.
Argument... | 4,633 | 32.1 | 93 | py |
WPAL-network | WPAL-network-master/lib/WS_BL/config.py | #!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or modify
# it under the ter... | 6,652 | 28.30837 | 82 | py |
WPAL-network | WPAL-network-master/lib/WS_BL/train.py | #!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or modify
# it under the ter... | 4,383 | 36.152542 | 93 | py |
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