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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URUST | URUST-main/F-LSeSim/models/util.py | import numpy as np
import torch
from scipy import signal
def gkern(kernlen=1, std=3):
"""Returns a 2D Gaussian kernel array."""
gkern1d = signal.gaussian(kernlen, std=std).reshape(kernlen, 1)
gkern2d = np.outer(gkern1d, gkern1d)
return gkern2d
def get_kernel(padding=1, gaussian_std=3, mode="constant... | 740 | 26.444444 | 67 | py |
URUST | URUST-main/F-LSeSim/models/generator.py | import torch
import torch.nn as nn
from models.downsample import Downsample
from models.normalization import make_norm_layer
from models.upsample import Upsample
class ResnetBlock(nn.Module):
def __init__(self, features, norm_cfg=None):
super().__init__()
self.norm_cfg = norm_cfg or {'type': 'in'... | 10,607 | 31.64 | 85 | py |
URUST | URUST-main/F-LSeSim/models/downsample.py | import torch.nn as nn
class Downsample(nn.Module):
def __init__(self, features):
super().__init__()
self.reflectionpad = nn.ReflectionPad2d(1)
self.conv = nn.Conv2d(features, features, kernel_size=3, stride=2)
def forward(self, x):
x = self.reflectionpad(x)
x = self.co... | 343 | 23.571429 | 74 | py |
URUST | URUST-main/F-LSeSim/models/normalization.py | from copy import deepcopy
from typing import Any, Dict
import torch.nn as nn
from models.kin import KernelizedInstanceNorm
from models.tin import ThumbInstanceNorm
# TODO: To be deprecated
def get_normalization_layer(num_features, normalization="kin"):
if normalization == "kin":
return KernelizedInstanc... | 1,641 | 29.407407 | 89 | py |
URUST | URUST-main/F-LSeSim/models/cycle_gan_model.py | import itertools
import torch
from util.image_pool import ImagePool
from . import losses, networks
from .base_model import BaseModel
class CycleGANModel(BaseModel):
"""
This class implements the CycleGAN model, for learning image-to-image translation without paired data.
The model training requires '-... | 11,701 | 39.351724 | 300 | py |
URUST | URUST-main/F-LSeSim/models/upsample.py | import torch.nn as nn
class Upsample(nn.Module):
def __init__(self, features):
super().__init__()
layers = [
nn.ReplicationPad2d(1),
nn.ConvTranspose2d(features, features, kernel_size=4, stride=2, padding=3),
]
self.model = nn.Sequential(*layers)
def fo... | 373 | 23.933333 | 87 | py |
URUST | URUST-main/F-LSeSim/models/sc_model.py | import itertools
import torch
from models.kin import (
init_kernelized_instance_norm,
)
from models.tin import (
init_thumbnail_instance_norm,
not_use_thumbnail_instance_norm,
use_thumbnail_instance_norm,
)
from util.image_pool import ImagePool
from . import losses, networks
from .base_model import B... | 16,588 | 35.459341 | 120 | py |
URUST | URUST-main/F-LSeSim/evaluations/inception.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
try:
from torchvision.models.utils import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
# Inception weights ported to Pytorch from
# http://download.tenso... | 11,946 | 35.647239 | 126 | py |
URUST | URUST-main/F-LSeSim/evaluations/DC.py | import os
import pathlib
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
import numpy as np
import torch
from PIL import Image
from scipy import linalg
from torch.nn.functional import adaptive_avg_pool2d
try:
from tqdm import tqdm
except ImportError:
# If not tqdm is not available, provide ... | 4,521 | 28.174194 | 100 | py |
URUST | URUST-main/F-LSeSim/evaluations/fid_score.py | """Calculates the Frechet Inception Distance (FID) to evalulate GANs
The FID metric calculates the distance between two distributions of images.
Typically, we have summary statistics (mean & covariance matrix) of one
of these distributions, while the 2nd distribution is given by a GAN.
When run as a stand-alone program... | 8,986 | 34.38189 | 100 | py |
URUST | URUST-main/F-LSeSim/util/image_pool.py | import random
import torch
class ImagePool:
"""This class implements an image buffer that stores previously generated images.
This buffer enables us to update discriminators using a history of generated images
rather than the ones produced by the latest generators.
"""
def __init__(self, pool_s... | 2,336 | 36.693548 | 131 | py |
URUST | URUST-main/F-LSeSim/util/util.py | """This module contains simple helper functions """
from __future__ import print_function
import argparse
import importlib
import os
from argparse import Namespace
import cv2
import numpy as np
import torch
import torch.nn.functional as F
import torchvision
from PIL import Image
def str2bool(v):
if isinstance(v... | 5,213 | 28.292135 | 97 | py |
URUST | URUST-main/F-LSeSim/scripts/edges/batch_hed.py | # HED batch processing script; modified from https://github.com/s9xie/hed/blob/master/examples/hed/HED-tutorial.ipynb
# Step 1: download the hed repo: https://github.com/s9xie/hed
# Step 2: download the models and protoxt, and put them under {caffe_root}/examples/hed/
# Step 3: put this script under {caffe_root}/exampl... | 3,733 | 32.63964 | 141 | py |
URUST | URUST-main/F-LSeSim/scripts/eval_cityscapes/evaluate.py | import argparse
import os
import caffe
import numpy as np
import scipy.misc
from cityscapes import cityscapes
from PIL import Image
from util import fast_hist, get_scores, segrun
parser = argparse.ArgumentParser()
parser.add_argument(
"--cityscapes_dir",
type=str,
required=True,
help="Path to the ori... | 3,392 | 31.314286 | 88 | py |
URUST | URUST-main/F-LSeSim/data/colorization_dataset.py | import os
import numpy as np
import torchvision.transforms as transforms
from PIL import Image
from skimage import color # require skimage
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
class ColorizationDataset(BaseDataset):
"""This dataset class can load a... | 2,719 | 36.777778 | 141 | py |
URUST | URUST-main/F-LSeSim/data/base_dataset.py | """This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
"""
import random
from abc import ABC, abstractmethod
import numpy as np
import torch.utils.data as data
impo... | 8,615 | 30.445255 | 141 | py |
URUST | URUST-main/F-LSeSim/data/unaligned_dataset.py | import os
import random
import torchvision.transforms as transforms
from PIL import Image
import util.util as util
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
def remove_file(files, file_name):
try:
files.remove(file_name)
except Exception:
... | 4,548 | 35.98374 | 104 | py |
URUST | URUST-main/F-LSeSim/data/dataset.py | import os
import random
from pathlib import Path
import albumentations as A
import numpy as np
from albumentations.pytorch import ToTensorV2
from PIL import Image
from torch.utils.data import Dataset
test_transforms = A.Compose(
[
A.Resize(width=512, height=512),
A.Normalize(mean=[0.5, 0.5, 0.5], ... | 2,753 | 27.989474 | 84 | py |
URUST | URUST-main/F-LSeSim/data/image_folder.py | """A modified image folder class
We modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py)
so that this class can load images from both current directory and its subdirectories.
"""
import os
import torch.utils.data as data
from PIL import Image
IMG_E... | 1,970 | 23.949367 | 122 | py |
URUST | URUST-main/F-LSeSim/data/__init__.py | """This package includes all the modules related to data loading and preprocessing
To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
You need to implement four functions:
-- <__init__>: ... | 3,585 | 35.222222 | 160 | py |
URUST | URUST-main/metrics/calculate_fid.py | """Calculates the Frechet Inception Distance (FID) to evalulate GANs
The FID metric calculates the distance between two distributions of images.
Typically, we have summary statistics (mean & covariance matrix) of one
of these distributions, while the 2nd distribution is given by a GAN.
When run as a stand-alone program... | 10,019 | 31.745098 | 85 | py |
URUST | URUST-main/metrics/inception.py | """
Source:
https://github.com/mseitzer/pytorch-fid/blob/master/src/pytorch_fid/inception.py
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
try:
from torchvision.models.utils import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_ur... | 12,102 | 35.236527 | 140 | py |
URUST | URUST-main/utils/dataset.py | import os
import random
from pathlib import Path
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from utils.util import get_transforms
def remove_file(files, file_name):
try:
files.remove(file_name)
except Exception:
pass
class XYDataset(Dataset):
def __in... | 6,497 | 30.090909 | 79 | py |
URUST | URUST-main/utils/util.py | import random
import albumentations as A
import cv2
import numpy as np
import torch
import yaml
from albumentations.pytorch import ToTensorV2
from scipy import signal
from yaml.loader import SafeLoader
def gkern(kernlen=1, std=3):
"""Returns a 2D Gaussian kernel array."""
gkern1d = signal.gaussian(kernlen, s... | 6,370 | 29.629808 | 76 | py |
osm-changeset-classification | osm-changeset-classification-master/download-osmch.py | import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import numpy as np
import osmcsclassify
import csv
import pickle
import sqlite3
import random
import datetime
import urllib.request
import json
from keras.preprocessing.sequence import pad_sequences
from keras.models import load_model
labels_index ={}
... | 2,390 | 26.170455 | 156 | py |
osm-changeset-classification | osm-changeset-classification-master/classify.py | import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import numpy as np
import osmcsclassify
import csv
import pickle
import sqlite3
from keras.preprocessing.sequence import pad_sequences
from keras.models import load_model
writeToReviewFile = True
changesets = []
changeSetCollection = None
texts = [] #... | 4,356 | 30.121429 | 105 | py |
osm-changeset-classification | osm-changeset-classification-master/findchangesets.py | import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import numpy as np
import osmcsclassify
import csv
import pickle
import sqlite3
import random
import datetime
from keras.preprocessing.sequence import pad_sequences
from keras.models import load_model
labels_index ={}
maximumSeqLength = 0
tokenizer = {... | 2,774 | 35.513158 | 171 | py |
osm-changeset-classification | osm-changeset-classification-master/train.py | import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import numpy as np
import osmcsclassify
import csv
import pickle
import random
import re
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, GlobalMaxPooling1D
from ... | 8,163 | 30.521236 | 134 | py |
L2G-neurips2021 | L2G-neurips2021-master/main_Unrolling.py |
import torch.optim as optim
from src.models import *
from src.utils import *
from src.utils_data import *
import argparse
import time
import logging
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#%%
# parser for hyper-parameters
parser = argparse.ArgumentParser()
# synthetic data:
parse... | 5,700 | 29.005263 | 145 | py |
L2G-neurips2021 | L2G-neurips2021-master/main_L2G.py |
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from src.models import *
from src.utils import *
from src.utils_data import *
import argparse
import time
import logging
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#%%
# parser for hyper-parameters... | 7,280 | 33.023364 | 145 | py |
L2G-neurips2021 | L2G-neurips2021-master/src/baselines.py |
import torch
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
import math
from src.utils import *
#%%
class ADMM():
def __init__(self, l2_penalty, log_penalty, step_size=1e-02, relaxation_factor = 1.8):
self.alpha = log_penalty # the penalty before log barrier
self.beta ... | 3,445 | 28.965217 | 90 | py |
L2G-neurips2021 | L2G-neurips2021-master/src/utils_data.py |
from torch.utils.data import TensorDataset, DataLoader
from torch.utils.data import random_split
import networkx as nx
import scipy
import pickle
import multiprocess
from functools import partial
from src.utils import *
#%%
def data_loading(dir_dataset, batch_size = None, train_prop=0.8):
with open(dir_dataset... | 10,304 | 32.241935 | 119 | py |
L2G-neurips2021 | L2G-neurips2021-master/src/utils.py |
import numpy as np
import math
import torch
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
from scipy.spatial.distance import squareform
from sklearn.metrics.pairwise import euclidean_distances
import scipy.sparse as sparse
from sklearn import metrics
import scipy.stats
#%%
def halfvec_to... | 6,501 | 24.398438 | 115 | py |
L2G-neurips2021 | L2G-neurips2021-master/src/models.py |
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")
from src.utils import *
#%%
class GraphConvLayer(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super(GraphConvLayer, self).__init__()
... | 12,162 | 30.107417 | 113 | py |
premise-selection-nn | premise-selection-nn-master/rnn_network_model.py | #!/usr/bin/env python3.7
"""Premise selection RNN model."""
# -- Build-in modules --
from argparse import ArgumentParser
from random import shuffle
import os
# -- Third-party modules --
import numpy as np
import pickle
import tensorflow as tf
from tensorflow.keras.layers import (Add, BatchNormalization, Bidirectiona... | 12,798 | 36.755162 | 119 | py |
premise-selection-nn | premise-selection-nn-master/token_embedding.py | #!/usr/bin/env python3.7
"""Token embedding for functional signatures, using probabilistic distribution of features."""
# Build-in modules
import argparse
import os
# Third-party modules
import numpy as np
import pickle
from tensorflow.keras.initializers import he_uniform
from tensorflow.keras.layers import Input, D... | 5,335 | 35.29932 | 115 | py |
premise-selection-nn | premise-selection-nn-master/plot_and_evaluate.py | #!/usr/bin/env python3.7
"""Plot training and validation losses and accuracy."""
# -- Built-in modules --
import ast
import os
from argparse import ArgumentParser
# -- Third-party modules --
import matplotlib.pyplot as plt
import numpy as np
import pickle
from tensorflow.keras.models import load_model
from tensorflo... | 7,929 | 35.883721 | 116 | py |
premise-selection-nn | premise-selection-nn-master/network_model.py | #!/usr/bin/env python3.7
"""Premise selection model."""
# -- Build-in modules --
from argparse import ArgumentParser
import os
# -- Third-party modules --
import numpy as np
import pickle
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.layers import Add,... | 7,698 | 35.316038 | 120 | py |
premise-selection-nn | premise-selection-nn-master/formating.py | #!/usr/bin/env python3.7
"""Tools for premise selection NN framework."""
# Built-in modules
import os
from random import sample
# Third-party modules
import numpy as np
from tensorflow.keras.models import load_model, Model
# Proprietary modules
from token_embedding import embed_functions
from utils import (calculat... | 6,561 | 45.539007 | 120 | py |
GraphEmbedding | GraphEmbedding-master/ge/models/sdne.py | # -*- coding:utf-8 -*-
"""
Author:
Weichen Shen,weichenswc@163.com
Reference:
[1] Wang D, Cui P, Zhu W. Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2016: 1225-1234.(https://www.kdd.org/kdd2016/papers/file... | 6,123 | 33.994286 | 252 | py |
GraphEmbedding | GraphEmbedding-master/ge/models/line.py | # -*- coding:utf-8 -*-
"""
Author:
Weichen Shen,weichenswc@163.com
Reference:
[1] Tang J, Qu M, Wang M, et al. Line: Large-scale information network embedding[C]//Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2015: 1067-... | 7,272 | 33.14554 | 272 | py |
bfsinkhorn | bfsinkhorn-main/setup.py | # Copyright (c) 2022 Derk P. Kooi
# 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, distrib... | 2,363 | 43.603774 | 178 | py |
bfsinkhorn | bfsinkhorn-main/bfsinkhorn/utils.py | from jax import jit, vmap
import jax.numpy as jnp
@jit
def log1mexp(a):
"""Computes log(1 - exp(-a)) for a > 0
This should be stable whether or not a is above or below log(2)
Parameters
----------
a : float
The argument to the exponential
Returns
-------
log1mexp : float
... | 2,093 | 23.068966 | 78 | py |
bfsinkhorn | bfsinkhorn-main/bfsinkhorn/fermion.py | from jax import jit, vmap, lax
import jax.numpy as jnp
from functools import partial
from jax.scipy.special import xlogy
from .utils import summinexp_vmap
@partial(jit, static_argnums=(1))
def compute_partition_function(eps, N, beta):
"""Compute the fermionic partition function ratios
Parameters
--------... | 10,491 | 28.806818 | 123 | py |
bfsinkhorn | bfsinkhorn-main/bfsinkhorn/boson.py | from jax import jit, vmap, lax
import jax.numpy as jnp
from functools import partial
from jax.scipy.special import xlogy
from .utils import minlogsumminexp, minlogsumminexp_vmap
@partial(jit, static_argnums=(1))
def compute_free_energy(eps, N, beta):
"""Compute bosonic free energies
Parameters
----------... | 10,570 | 28.69382 | 123 | py |
bfsinkhorn | bfsinkhorn-main/tests/test_fermion.py | # Import bfsinkhorn
import bfsinkhorn
# Import fermionic package
import bfsinkhorn.fermion
# Import jax config and numpy and set floats to 64-bit
from jax.config import config
import jax.numpy as jnp
config.update("jax_enable_x64", True)
def test_fermionic_sinkhorn():
# Fake orbital energies -> fake occupatio... | 799 | 22.529412 | 79 | py |
bfsinkhorn | bfsinkhorn-main/tests/test_import.py | # Import bfsinkhorn
import bfsinkhorn
# Import bosonic package
import bfsinkhorn.boson
# Import fermionic package
import bfsinkhorn.fermion
# Import jax config and numpy and set floats to 64-bit
from jax.config import config
import jax.numpy as jnp
config.update("jax_enable_x64", True)
def test_import():
asse... | 439 | 16.6 | 54 | py |
bfsinkhorn | bfsinkhorn-main/tests/test_boson.py | # Import bfsinkhorn
import bfsinkhorn
# Import bosonic package
import bfsinkhorn.boson
# Import jax config and numpy and set floats to 64-bit
from jax.config import config
import jax.numpy as jnp
config.update("jax_enable_x64", True)
def test_bosonic_sinkhorn():
# Fake orbital energies -> fake occupations
... | 787 | 22.176471 | 77 | py |
detpro | detpro-main/setup.py | #!/usr/bin/env python
import os
from setuptools import find_packages, setup
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
CUDAExtension)
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return co... | 5,864 | 35.203704 | 125 | py |
detpro | detpro-main/tools/test.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)
fro... | 8,315 | 38.042254 | 79 | py |
detpro | detpro-main/tools/benchmark.py | import argparse
import time
import torch
from mmcv import Config
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint, wrap_fp16_model
from mmdet.datasets import (build_dataloader, build_dataset,
replace_ImageToTensor)
from mmde... | 3,176 | 30.455446 | 79 | py |
detpro | detpro-main/tools/analysis_differ.py | import matplotlib.pyplot as plt
import numpy as np
import cv2
from os.path import join as ospj
import torch
import torch.nn.functional as F
analysis_results_path = 'analysis_results_fcos'
# feature_type = 'feature' # fpn
# feature_type = 'cls_feature' # cls
# feature_type = 'reg_feature' # reg
# feature_type = 'cls'... | 2,526 | 35.1 | 119 | py |
detpro | detpro-main/tools/get_flops.py | import argparse
import torch
from mmcv import Config
from mmdet.models import build_detector
try:
from mmcv.cnn import get_model_complexity_info
except ImportError:
raise ImportError('Please upgrade mmcv to >0.6.2')
def parse_args():
parser = argparse.ArgumentParser(description='Train a detector')
... | 1,932 | 27.426471 | 79 | py |
detpro | detpro-main/tools/publish_model.py | import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = par... | 1,125 | 27.15 | 77 | py |
detpro | detpro-main/tools/regnet2mmdet.py | import argparse
from collections import OrderedDict
import torch
def convert_stem(model_key, model_weight, state_dict, converted_names):
new_key = model_key.replace('stem.conv', 'conv1')
new_key = new_key.replace('stem.bn', 'bn1')
state_dict[new_key] = model_weight
converted_names.add(model_key)
... | 3,015 | 32.511111 | 77 | py |
detpro | detpro-main/tools/pytorch2onnx.py | import argparse
import os.path as osp
import numpy as np
import onnx
import onnxruntime as rt
import torch
from mmdet.core import (build_model_from_cfg, generate_inputs_and_wrap_model,
preprocess_example_input)
def pytorch2onnx(config_path,
checkpoint_path,
... | 6,991 | 33.44335 | 78 | py |
detpro | detpro-main/tools/upgrade_model_version.py | import argparse
import re
import tempfile
from collections import OrderedDict
import torch
from mmcv import Config
def is_head(key):
valid_head_list = [
'bbox_head', 'mask_head', 'semantic_head', 'grid_head', 'mask_iou_head'
]
return any(key.startswith(h) for h in valid_head_list)
def parse_co... | 6,794 | 31.357143 | 79 | py |
detpro | detpro-main/tools/test_analysis.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)
fro... | 8,804 | 38.662162 | 103 | py |
detpro | detpro-main/tools/test_robustness.py | import argparse
import copy
import os
import os.path as osp
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
from pycocotools.coco import COCO
from pycocotools.cocoe... | 14,711 | 37.920635 | 79 | py |
detpro | detpro-main/tools/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.apis import set_random_seed, train_detector... | 7,553 | 36.77 | 118 | py |
detpro | detpro-main/prompt/lr_scheduler.py | """
Modified from https://github.com/KaiyangZhou/deep-person-reid
"""
import torch
from torch.optim.lr_scheduler import _LRScheduler
AVAI_SCHEDS = ['single_step', 'multi_step', 'cosine']
class _BaseWarmupScheduler(_LRScheduler):
def __init__(
self,
optimizer,
successor,
warmup_ep... | 2,521 | 23.72549 | 76 | py |
detpro | detpro-main/prompt/backup_run.py | import os, sys
from trainer import test_embedding
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset, ConcatDataset
from classname import *
from config import configs
import coop_mini
from trainer import test_embedding, test_embedding_neg
from lr_scheduler import build_lr_scheduler... | 8,484 | 36.378855 | 98 | py |
detpro | detpro-main/prompt/backup_coop_mini.py | import os.path as osp
import os
import torch
import torch.nn as nn
from torch.nn import functional as F
from clip import clip
from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer
_tokenizer = _Tokenizer()
def load_clip_to_cpu():
url = clip._MODELS["ViT-B/32"]
model_path = clip._download(url, os.... | 8,147 | 35.538117 | 102 | py |
detpro | detpro-main/prompt/sim.py | import torch
import sys
_, a, b = sys.argv
x, y = torch.load(a), torch.load(b)
x, y = x.squeeze(), y.squeeze()
x = x / x.norm(dim = -1, keepdim = True)
y = y / y.norm(dim = -1, keepdim = True)
x = x[:1203]
y = y[:1203]
sim = (x*y).sum(dim=-1)
print(sim)
print(sim.mean()) | 275 | 16.25 | 40 | py |
detpro | detpro-main/prompt/gather.py | import os, sys
import torch
path = sys.argv[1]
save_name = os.path.join(path, sys.argv[2])
if os.path.exists(save_name):
print('Data: target already exists!')
exit(0)
feats = []
labels = []
ious = []
files = []
for splt in os.listdir(path):
print(splt)
files += [os.path.join(path, splt, f) for f in o... | 820 | 23.147059 | 90 | py |
detpro | detpro-main/prompt/run.py | import os, sys
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset, ConcatDataset
from classname import *
import coop_mini
from trainer import test_embedding, test_embedding_neg, train_epoch, get_embedding, checkpoint, accuracy1, accuracy5
from lr_scheduler import build_lr_scheduler... | 12,643 | 39.919094 | 156 | py |
detpro | detpro-main/prompt/coop_mini.py | import os.path as osp
import os
import torch
import torch.nn as nn
from torch.nn import functional as F
from clip import clip
from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer
_tokenizer = _Tokenizer()
def load_clip_to_cpu():
url = clip._MODELS["ViT-B/32"]
model_path = clip._download(url, os.... | 10,451 | 37.426471 | 142 | py |
detpro | detpro-main/prompt/gen_cls_embedding.py | import coop_mini
from classname import *
# from trainer import checkpoint
import sys, torch
_, prompt_path, save_name, dataset = sys.argv
def checkpoint(model, name, class_names):
with torch.no_grad():
prompts, tokenized_prompts = model.prompt_learner.forward_for_classes(class_names)
text_features... | 1,405 | 39.171429 | 90 | py |
detpro | detpro-main/prompt/trainer.py | from classname import *
import torch
from torch.nn import functional as F
import time
from lr_scheduler import build_lr_scheduler
from torch.optim import SGD
import random
from config import temperature
from torch.cuda.amp import autocast as autocast
torch.cuda.empty_cache()
def get_embedding(model, class_names):
... | 9,457 | 37.921811 | 128 | py |
detpro | detpro-main/configs/transfer/mask_rcnn_r50_fpn_sample1e-3_mstrain_coco_pretrain.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.000025)
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
... | 3,995 | 34.052632 | 107 | py |
detpro | detpro-main/configs/transfer/mask_rcnn_r50_fpn_sample1e-3_mstrain_objects365_pretrain.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/obj365_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.000025)
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe... | 2,313 | 31.138889 | 77 | py |
detpro | detpro-main/configs/transfer/transfer_objects365.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/obj365_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.000025)
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe... | 3,237 | 32.040816 | 77 | py |
detpro | detpro-main/configs/transfer/transfer_voc.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/voc0712.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.000025)
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
... | 2,534 | 30.6875 | 84 | py |
detpro | detpro-main/configs/transfer/faster_rcnn_r50_fpn_sample1e-3_mstrain_voc_pretrain.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/voc0712.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.000025)
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
... | 2,895 | 31.177778 | 77 | py |
detpro | detpro-main/configs/transfer/transfer_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.000025)
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
... | 3,087 | 31.505263 | 80 | py |
detpro | detpro-main/configs/_base_/models/retinanet_r50_fpn.py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 1,657 | 26.180328 | 56 | py |
detpro | detpro-main/configs/_base_/models/faster_rcnn_r50_fpn.py | model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch'... | 3,415 | 29.5 | 77 | py |
detpro | detpro-main/configs/_base_/models/retinanet_r50_fpn_analysis.py | # model settings
model = dict(
type='RetinaNetAnalysis',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,... | 1,680 | 26.557377 | 56 | py |
detpro | detpro-main/configs/_base_/models/cascade_rcnn_r50_fpn.py | # model settings
model = dict(
type='CascadeRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 6,001 | 31.619565 | 79 | py |
detpro | detpro-main/configs/_base_/models/rpn_r50_caffe_c4.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=dict(type... | 1,655 | 27.067797 | 72 | py |
detpro | detpro-main/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py | # model settings
model = dict(
type='CascadeRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 6,610 | 31.890547 | 79 | py |
detpro | detpro-main/configs/_base_/models/fast_rcnn_r50_fpn.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 1,932 | 29.68254 | 77 | py |
detpro | detpro-main/configs/_base_/models/mask_rcnn_r50_fpn.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 3,891 | 29.888889 | 78 | py |
detpro | detpro-main/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
strides=(1, 2, 2, 1),
dilations=(1, 1, 1, 2),
out_indic... | 3,266 | 29.25 | 77 | py |
detpro | detpro-main/configs/_base_/models/rpn_r50_fpn.py | # model settings
model = dict(
type='RPN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style... | 1,699 | 26.868852 | 72 | py |
detpro | detpro-main/configs/_base_/models/ssd300.py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 1,395 | 26.92 | 60 | py |
detpro | detpro-main/configs/_base_/models/faster_rcnn_r50_caffe_c4.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2,... | 3,481 | 28.760684 | 78 | py |
detpro | detpro-main/configs/_base_/models/mask_rcnn_r50_caffe_c4.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, )... | 3,840 | 29.007813 | 78 | py |
detpro | detpro-main/configs/lvis/cascade_mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1_pretrain.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'LVISV1Dataset'
data_root = 'data/lvis_v1/'
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.000025)
... | 6,751 | 36.303867 | 108 | py |
detpro | detpro-main/configs/lvis/cascade_mask_rcnn_r50_fpn_sample1e-3_mstrain_20e_lvis_v1_pretrain_ens.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
]
dataset_type = 'LVISV1Dataset'
data_root = 'data/lvis_v1/'
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.000025)
... | 6,800 | 36.574586 | 108 | py |
detpro | detpro-main/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1_pretrain.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'LVISV1Dataset'
data_root = 'data/lvis_v1/'
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.000025)
# evalua... | 4,240 | 34.341667 | 108 | py |
detpro | detpro-main/configs/lvis/cascade_mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1_pretrain_ens.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'LVISV1Dataset'
data_root = 'data/lvis_v1/'
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.000025)
#... | 6,779 | 36.458564 | 108 | py |
detpro | detpro-main/mmdet/apis/inference.py | import warnings
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import torch
from mmcv.ops import RoIPool
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint
from mmdet.core import get_classes
from mmdet.datasets.pipelines import Compose
from mmdet.models import build_det... | 7,478 | 33.465438 | 79 | py |
detpro | detpro-main/mmdet/apis/train_iter.py | import random
import warnings
import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
Fp16OptimizerHook, OptimizerHook, build_optimizer,
build_runner)
fro... | 6,378 | 36.304094 | 79 | py |
detpro | detpro-main/mmdet/apis/test.py | import os.path as osp
import pickle
import shutil
import tempfile
import time
import mmcv
import torch
import torch.distributed as dist
from mmcv.image import tensor2imgs
from mmcv.runner import get_dist_info
from mmdet.core import encode_mask_results
def single_gpu_test(model,
data_loader,
... | 6,826 | 34.743455 | 79 | py |
detpro | detpro-main/mmdet/apis/test_analysis.py | import os.path as osp
import pickle
import shutil
import tempfile
import time
import mmcv
import torch
import torch.distributed as dist
from mmcv.image import tensor2imgs
from mmcv.runner import get_dist_info
from mmdet.core import encode_mask_results
def single_gpu_test_analysis(model,
... | 7,167 | 35.20202 | 110 | py |
detpro | detpro-main/mmdet/apis/train.py | import random
import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
Fp16OptimizerHook, OptimizerHook, build_optimizer)
from mmcv.utils import build_from_cfg
from mmdet.core imp... | 5,700 | 36.506579 | 79 | py |
detpro | detpro-main/mmdet/core/evaluation/eval_hooks.py | import os.path as osp
import warnings
from math import inf
import mmcv
from mmcv.runner import Hook
from torch.utils.data import DataLoader
from mmdet.utils import get_root_logger
class EvalHook(Hook):
"""Evaluation hook.
Notes:
If new arguments are added for EvalHook, tools/test.py,
tools/... | 10,652 | 40.613281 | 79 | py |
detpro | detpro-main/mmdet/core/post_processing/merge_augs.py | import numpy as np
import torch
from mmcv.ops import nms
from ..bbox import bbox_mapping_back
def merge_aug_proposals(aug_proposals, img_metas, rpn_test_cfg):
"""Merge augmented proposals (multiscale, flip, etc.)
Args:
aug_proposals (list[Tensor]): proposals from different testing
scheme... | 4,286 | 35.330508 | 78 | py |
detpro | detpro-main/mmdet/core/post_processing/bbox_nms.py | import torch
from mmcv.ops.nms import batched_nms
from mmdet.core.bbox.iou_calculators import bbox_overlaps
def multiclass_nms(multi_bboxes,
multi_scores,
score_thr,
nms_cfg,
max_num=-1,
score_factors=None,
... | 5,488 | 35.111842 | 79 | py |
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