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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YOSO | YOSO-main/projects/YOSO/yoso/data/dataset_mappers/yoso_panoptic_lsj_dataset_mapper.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/d2/detr/dataset_mapper.py
import copy
import logging
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.data import detection_utils as utils
fr... | 5,786 | 33.861446 | 109 | py |
YOSO | YOSO-main/projects/YOSO/yoso/data/dataset_mappers/yoso_panoptic_dataset_mapper.py | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import numpy as np
import torch
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
from detectron2.structures impo... | 6,193 | 36.313253 | 98 | py |
YOSO | YOSO-main/projects/YOSO/yoso/data/dataset_mappers/yoso_instance_dataset_mapper.py | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import numpy as np
import pycocotools.mask as mask_util
import torch
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.data import detection_utils as utils
from detectron2.data import transforms... | 9,547 | 33.345324 | 97 | py |
YOSO | YOSO-main/demo/predictor.py | # Copyright (c) Facebook, Inc. and its affiliates.
import atexit
import bisect
import multiprocessing as mp
from collections import deque
import cv2
import torch
from detectron2.data import MetadataCatalog
from detectron2.engine.defaults import DefaultPredictor
from detectron2.utils.video_visualizer import VideoVisual... | 7,897 | 34.576577 | 96 | py |
mmMOT | mmMOT-master/main.py | import argparse
import logging
import os
import pprint
import time
import torch
import torch.backends.cudnn as cudnn
import torch.optim
import yaml
from easydict import EasyDict
from kitti_devkit.evaluate_tracking import evaluate
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
# from mod... | 9,624 | 33.873188 | 78 | py |
mmMOT | mmMOT-master/test.py | import argparse
import logging
import os
import pprint
import time
import torch
import torch.backends.cudnn as cudnn
import torch.optim
import yaml
from easydict import EasyDict
from torch.utils.data import DataLoader
from tracking_model import TrackingModule
from utils.build_util import build_augmentation, build_data... | 4,546 | 29.931973 | 79 | py |
mmMOT | mmMOT-master/eval_seq.py | import argparse
import logging
import os
import pprint
import time
import torch
import torch.backends.cudnn as cudnn
import torch.optim
import yaml
from easydict import EasyDict
from kitti_devkit.evaluate_tracking import evaluate
from torch.utils.data import DataLoader
from tracking_model import TrackingModule
from ut... | 6,797 | 32.160976 | 79 | py |
mmMOT | mmMOT-master/cost.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class CostLoss(nn.Module):
def __init__(self, p=1):
super(CostLoss, self).__init__()
self.distance = nn.L1Loss(reduction='mean')
def forward(self, y, gt_y, cost):
distance = self.distance(y, gt_y)
loss = cost.... | 6,936 | 36.295699 | 79 | py |
mmMOT | mmMOT-master/solvers.py | from __future__ import print_function
import numpy as np
import scipy.optimize as optimize
import torch
from ortools.linear_solver import pywraplp
def ortools_solve(det_score,
link_score,
new_score,
end_score,
det_split,
gt=Non... | 13,666 | 38.386167 | 79 | py |
mmMOT | mmMOT-master/tracking_model.py | import numpy as np
import torch
from solvers import ortools_solve
from utils.data_util import get_start_gt_anno
class TrackingModule(object):
def __init__(self, model, optimizer, criterion, det_type='3D'):
self.model = model
self.optimizer = optimizer
self.criterion = criterion
se... | 14,204 | 39.355114 | 79 | py |
mmMOT | mmMOT-master/modules/ghm_loss.py | """
The Mopdified implementation of GHM-C and GHM-R losses.
Details can be found in the paper `Gradient Harmonized Single-stage Detector`:
https://arxiv.org/abs/1811.05181
Copyright (c) 2018 Multimedia Laboratory, CUHK.
Licensed under the MIT License (see LICENSE for details)
Written by Buyu Li
"""
import torch
import... | 3,899 | 32.050847 | 78 | py |
mmMOT | mmMOT-master/modules/fusion_net.py | import torch
import torch.nn as nn
# Common fusion module
class fusion_module_C(nn.Module):
def __init__(self, appear_len, point_len, out_channels):
super(fusion_module_C, self).__init__()
print(
"Fusion Module C: split sigmoid weight gated point, image fusion")
self.appear_le... | 3,073 | 32.053763 | 78 | py |
mmMOT | mmMOT-master/modules/dropblock.py | import torch
import torch.nn.functional as F
from torch import nn
class DropBlock2D(nn.Module):
r"""Randomly zeroes 2D spatial blocks of the input tensor.
As described in the paper
`DropBlock: A regularization method for convolutional networks`_ ,
dropping whole blocks of feature map allows to remove ... | 4,462 | 30.20979 | 94 | py |
mmMOT | mmMOT-master/modules/tracking_net.py | from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from .appear_net import AppearanceNet
from .fusion_net import * # noqa
from .gcn import affinity_module
from .new_end import * # noqa
from .point_net import * # noqa
from .score_net import * # noqa
class TrackingNet... | 6,791 | 34.010309 | 71 | py |
mmMOT | mmMOT-master/modules/vgg.py | import math
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = [
'VGG',
'vgg11',
'vgg11_bn',
'vgg13',
'vgg13_bn',
'vgg16',
'vgg16_bn',
'vgg19_bn',
'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': '... | 6,952 | 29.495614 | 78 | py |
mmMOT | mmMOT-master/modules/new_end.py | import torch.nn as nn
import torch.nn.functional as F
class NewEndIndicator_v1(nn.Module):
def __init__(self, in_channels, kernel_size, reduction, mode='avg'):
super(NewEndIndicator_v1, self).__init__()
self.mode = mode
self.w_end_conv = nn.Sequential(
nn.GroupNorm(1, in_chann... | 3,081 | 36.13253 | 77 | py |
mmMOT | mmMOT-master/modules/score_net.py | import torch.nn as nn
import torchvision
class ScoringNet(nn.Module):
def __init__(self, arch='resnet18'):
super(ScoringNet, self).__init__()
self.out_channels = 1
if 'vgg' in arch:
self.arch = 'vgg'
self.features = torchvision.models.vgg16_bn(
pret... | 1,971 | 30.301587 | 69 | py |
mmMOT | mmMOT-master/modules/gcn.py | import torch
import torch.nn as nn
# Similarity function
def batch_multiply(objs, dets):
"""
:param objs: BxDxN
:param dets: BxDxM
:return:BxDxNxM
"""
x = torch.einsum('bci,bcj->bcij', objs, dets)
return x
def batch_minus_abs(objs, dets):
"""
:param objs: BxDxN
... | 2,427 | 28.253012 | 74 | py |
mmMOT | mmMOT-master/modules/point_net.py | import torch
import torch.nn as nn
class PointNet_v1(nn.Module):
def __init__(self, in_channels, out_channels=512, use_dropout=False):
super(PointNet_v1, self).__init__()
self.feat = PointNetfeatGN(in_channels, out_channels)
reduction = 512 // out_channels
self.reduction = reducti... | 5,884 | 37.214286 | 78 | py |
mmMOT | mmMOT-master/modules/appear_net.py | import torch
import torch.nn as nn
import torchvision
from .dropblock import DropBlock2D
from .vgg import vgg16_bn_128, vgg16_bn_256, vgg16_bn_512 # noqa
class SkipPool(nn.Module):
def __init__(self, channels, reduction, out_channels, dropblock_size=5):
super(SkipPool, self).__init__()
self.cha... | 8,633 | 32.595331 | 77 | py |
mmMOT | mmMOT-master/dataset/patchwise_dataset.py | import numpy as np
import io
from PIL import Image
import pickle
import csv
import random
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
from functools import partial
# For Point Cloud
from point_cloud.preprocess import read_and_prep_point... | 9,441 | 40.412281 | 134 | py |
mmMOT | mmMOT-master/dataset/common.py | import numpy as np
import io
from PIL import Image
import torch
import torchvision
from utils.data_util import generate_seq_dets, generate_seq_gts, generate_seq_dets_rrc, LABEL, LABEL_VERSE, \
get_rotate_mat, align_pos, align_points, get_frame_det_info, get_transform_mat
TRAIN_SEQ_ID = ['00... | 3,924 | 32.262712 | 109 | py |
mmMOT | mmMOT-master/dataset/test_seq_dataset.py | import numpy as np
import io
from PIL import Image
import pickle
import csv
import random
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
from functools import partial
# For Point Cloud
from point_cloud.preprocess import read_and_prep_point... | 10,450 | 41.311741 | 111 | py |
mmMOT | mmMOT-master/point_cloud/point_cloud_ops.py | import time
import numba
import numpy as np
@numba.jit(nopython=True)
def _points_to_voxel_reverse_kernel(points,
voxel_size,
coors_range,
num_points_per_voxel,
coor_to_voxe... | 7,465 | 39.139785 | 92 | py |
mmMOT | mmMOT-master/utils/data_util.py | import csv
import os
import pickle
from collections import OrderedDict
import numpy as np
import pyproj
import torch
from point_cloud.box_np_ops import (camera_to_lidar, imu_to_lidar,
lidar_to_camera, lidar_to_imu)
from .kitti_util import read_calib_file
LABEL = {
'Car': 0,
... | 21,521 | 33.161905 | 88 | py |
mmMOT | mmMOT-master/utils/build_util.py | import numpy as np
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from cost import TrackingLoss
from dataset import PatchwiseDataset, TestSequenceDataset
from modules import TrackingNet
def children(m: nn.Module):
"Get children of `m`."
return list(m.children())
def num_child... | 7,767 | 32.339056 | 74 | py |
mmMOT | mmMOT-master/utils/learning_schedules_fastai.py | from functools import partial
import numpy as np
from .optim_util import OptimWrapper
class LRSchedulerStep(object):
def __init__(self, fai_optimizer: OptimWrapper, total_step, lr_phases,
mom_phases):
# if not isinstance(fai_optimizer, OptimWrapper):
# raise TypeError('{} i... | 3,852 | 33.401786 | 75 | py |
mmMOT | mmMOT-master/utils/optim_util.py | from collections import Iterable
from functools import partial
import torch
from torch import nn
from torch._utils import _unflatten_dense_tensors
from torch.nn.utils import parameters_to_vector
bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.GroupNorm)
def split_bn_bias(layer_groups):
"Split the... | 8,413 | 34.804255 | 108 | py |
mmMOT | mmMOT-master/utils/train_util.py | import logging
import os
import shutil
import numpy as np
import torch
from scipy.stats import truncnorm
from torch.utils.data.sampler import Sampler
def create_logger(name, log_file, level=logging.INFO):
l = logging.getLogger(name)
formatter = logging.Formatter(
'[%(asctime)s][%(filename)10s][line:%... | 6,241 | 29.009615 | 93 | py |
learning-rocbfs | learning-rocbfs-main/ctrl_perception_based_cbf.py | import cvxpy as cp
import jax
import jax.numpy as jnp
import haiku as hk
import pickle
import json
import os
from core.dynamics.carla_4state import CarlaDynamics
ROOT = '../old_trained_results/0922/'
CBF_PATH = os.path.join(ROOT, 'trained_cbf.npy')
ARGS_PATH = os.path.join(ROOT, 'args.json')
META_DATA_PATH = os.path.... | 3,276 | 27.495652 | 104 | py |
learning-rocbfs | learning-rocbfs-main/main.py | import pandas as pd
import haiku as hk
import jax.numpy as jnp
import optax
import jax
import jax.nn as jnn
import wandb
import os
import pickle
from core.utils.parse_args import parse_args
from core.utils.viz import Visualizer
from core.data.load import load_data_v2
from core.losses.new_cbf_loss import CBFLoss
from c... | 4,296 | 33.376 | 121 | py |
learning-rocbfs | learning-rocbfs-main/learning_cte.py | from haiku.nets import ResNet18
import haiku as hk
import os
import pandas as pd
import optax
import argparse
import jax.numpy as jnp
import jax.random as jrand
import jax
import pickle
import os
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from tqdm import tqdm
from core.data.images import... | 4,797 | 36.193798 | 104 | py |
learning-rocbfs | learning-rocbfs-main/ctrl_state_based_cbf.py | import cvxpy as cp
import jax
import jax.numpy as jnp
import haiku as hk
import pickle
import json
import os
from core.dynamics.carla_4state import CarlaDynamics
ROOT = '../old_trained_results/0720/results-less-robust-low-margins-all-data/'
CBF_PATH = os.path.join(ROOT, 'trained_cbf.npy')
ARGS_PATH = os.path.join(ROO... | 3,321 | 27.886957 | 104 | py |
learning-rocbfs | learning-rocbfs-main/core/dynamics/carla_4state.py | import jax.numpy as jnp
class CarlaDynamics:
def __init__(self, T_x):
self._state_dim = 4
self._input_dim = 1
self._Tx = T_x
self._inv_Tx = jnp.linalg.inv(self._Tx)
@property
def state_dim(self):
return self._state_dim
@property
def input_dim(self):
... | 1,120 | 30.138889 | 77 | py |
learning-rocbfs | learning-rocbfs-main/core/dynamics/carla.py | import jax.numpy as jnp
class CarlaNoConstraints:
def __init__(self):
self._state_dim = 5
self._input_dim = 1
@property
def state_dim(self):
return self._state_dim
@property
def input_dim(self):
return self._input_dim
def f(self, state):
x, y, v, θ... | 679 | 20.935484 | 56 | py |
learning-rocbfs | learning-rocbfs-main/core/output_maps/img_to_cte.py | import jax.numpy as jnp
import numpy as np
import os
import pickle
from haiku.nets import ResNet18
import haiku as hk
import pandas as pd
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from functools import partial
import jax
from core.data.images import NumpyLoader
class ImgToCTE:
def _... | 2,620 | 28.122222 | 75 | py |
learning-rocbfs | learning-rocbfs-main/core/output_maps/pos_to_velocity.py | import jax.numpy as jnp
import numpy as np
class PosToVelocity:
def __init__(self):
pass
@property
def state_cols(self):
return ['cte', 'v_est', 'theta_e', 'd']
def map(self, df):
df['x_diff'] = df['x-loc-center(m)'] - df['x-loc-center(m)'].shift(1)
df['y_diff'] = df[... | 801 | 26.655172 | 83 | py |
learning-rocbfs | learning-rocbfs-main/core/utils/viz.py | import seaborn as sns
import pandas as pd
import os
import matplotlib.pyplot as plt
import wandb
import numpy as np
import jax
import jax.numpy as jnp
sns.set(style='darkgrid', font_scale=2.5)
class Visualizer:
def __init__(self, net, results_path, data_dict, cbf_fn):
self._net = net
self._resul... | 5,738 | 38.040816 | 169 | py |
learning-rocbfs | learning-rocbfs-main/core/data/images.py | import numpy as np
import jax.numpy as jnp
from torch.utils.data import DataLoader, Dataset
import torch
import os
import pandas as pd
# DATA_ROOT = 'data/carla/images/Dataset_with_image/left_turn_state_space_sampling'
# DATA_DIRS = [
# 'random_noise_driving',
# 'straight_lane_driving',
# 'start_of_turn',
... | 3,399 | 30.192661 | 83 | py |
learning-rocbfs | learning-rocbfs-main/core/data/load.py | import pandas as pd
import numpy as np
import jax.numpy as jnp
from sklearn.neighbors import KDTree
from prettytable import PrettyTable
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import json
import os
from viz import plot_peanut, double_grid
def load_data_v2(args, output_m... | 6,608 | 35.313187 | 150 | py |
learning-rocbfs | learning-rocbfs-main/core/losses/cbf_loss.py | import jax
import jax.numpy as jnp
import jax.nn as jnn
from jax.flatten_util import ravel_pytree
from functools import partial
class CBFLoss:
def __init__(self, hparams, network, dynamics, alpha, dual_vars):
self._hparams = hparams
self._network = network
self._dynamics = dynamics
... | 5,105 | 38.890625 | 99 | py |
learning-rocbfs | learning-rocbfs-main/core/losses/new_cbf_loss.py | import jax
import jax.numpy as jnp
import jax.nn as jnn
from jax.flatten_util import ravel_pytree
from functools import partial
class CBFLoss:
def __init__(self, hparams, network, dynamics, alpha, dual_vars, T_x):
self._hparams = hparams
self._network = network
self._dynamics = dynamics
... | 4,643 | 41.218182 | 114 | py |
learning-rocbfs | learning-rocbfs-main/data/carla/automatic_control_state_based_cbf_test_location.py | """Example of automatic vehicle control from client side with state based cbf."""
from __future__ import print_function
import argparse
import collections
import datetime
import glob
import logging
import math
import os
import random
import re
import sys
import weakref
import pandas as pd
import cvxpy as cp
import jax... | 39,225 | 40.247108 | 114 | py |
learning-rocbfs | learning-rocbfs-main/data/carla/automatic_control_perception_based_cbf_train_location.py | """Example of automatic vehicle control from client side with perception based cbf."""
from __future__ import print_function
import argparse
import collections
import datetime
import glob
import logging
import math
import os
import random
import re
import sys
import weakref
import math
import pandas as pd
from copy im... | 40,166 | 40.366632 | 125 | py |
learning-rocbfs | learning-rocbfs-main/data/carla/automatic_control_state_based_cbf_train_location.py | """Example of automatic vehicle control from client side with state based cbf."""
from __future__ import print_function
import argparse
import collections
import datetime
import glob
import logging
import math
import os
import random
import re
import sys
import weakref
import pandas as pd
import cvxpy as cp
import jax... | 39,263 | 40.287066 | 114 | py |
learning-rocbfs | learning-rocbfs-main/data/carla/automatic_control_state_based_cbf_train_location_server_version.py | """Example of automatic vehicle control from client side with state based cbf."""
from __future__ import print_function
import argparse
import collections
import datetime
import glob
import logging
import math
import os
import random
import re
import sys
import weakref
import pandas as pd
import cvxpy as cp
import jax... | 41,176 | 40.761663 | 118 | py |
learning-rocbfs | learning-rocbfs-main/data/carla/automatic_control_perception_based_cbf_test_location.py | """Example of automatic vehicle control from client side with perception based cbf."""
from __future__ import print_function
import argparse
import collections
import datetime
import glob
import logging
import math
import os
import random
import re
import sys
import weakref
import math
import pandas as pd
from copy im... | 40,055 | 40.294845 | 118 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/train_avalanche.py | from torch.cuda import current_device
from avalanche.benchmarks.generators import dataset_benchmark
from avalanche.benchmarks.utils import AvalancheDataset
from avalanche.evaluation.metrics import forgetting_metrics, accuracy_metrics,\
loss_metrics, timing_metrics, cpu_usage_metrics, StreamConfusionMatrix,\
dis... | 7,423 | 42.670588 | 163 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/train_OCDVAE.py | ########################
# Importing libraries
########################
# System libraries
import os
import random
from time import gmtime, strftime
import numpy as np
import pickle
import copy
# Tensorboard for PyTorch logging and visualization
from torch.utils.tensorboard import SummaryWriter
# Torch libraries
impo... | 24,481 | 49.06544 | 140 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/eval_openset.py | """
Stand alone evaluation script for open set recognition and plotting of different datasets
Uses the same command line parser as main.py
The attributes that need to be specified are the number of variational samples (should be greater than one if prediction
uncertainties are supposed to be calculated and compared),... | 19,383 | 60.148265 | 122 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/cmdparser.py | """
Command line argument options parser.
Adopted and modified from https://github.com/pytorch/examples/blob/master/imagenet/main.py
Usage with two minuses "- -". Options are written with a minus "-" in command line, but
appear with an underscore "_" in the attributes' list.
"""
import argparse
parser = argparse.Arg... | 10,853 | 65.588957 | 119 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Training/evaluate.py | import math
import torch
import numpy as np
import torch.nn as nn
def get_latent_embedding(model, data_loader, num_classes, device):
"""
Computes the latent embedding, i.e. z for each element of a dataset. The corresponding z values are directly
organized by classes, such that they can readily be used for... | 17,680 | 42.44226 | 120 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Training/loss_functions.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def get_kl(m, v, m0, v0):
# adapted from: https://github.com/bunkerj/mlmi4-vcl/blob/master/src/KL.py
# numerical value for stability of log computation
eps = 1e-8
constTerm = -0.5 * m.numel()
logStdDiff = 0.5 * torch.sum(torch.log... | 15,399 | 45.95122 | 135 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Training/validate.py | import time
import math
import numpy as np
import torch
import torch.nn.functional as F
from lib.Utility.metrics import AverageMeter
from lib.Utility.metrics import ConfusionMeter, SegConfusionMeter
from lib.Utility.metrics import accuracy, iou_class_condtitional, iou_to_accuracy, get_seg_confusion
from lib.Utility.met... | 44,919 | 61.562674 | 156 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Training/train.py | import time
import torch
from lib.Utility.metrics import AverageMeter
from lib.Utility.metrics import accuracy, iou_class_condtitional, iou_to_accuracy
from lib.Utility.metrics import to_one_hot
from lib.Training.loss_functions import loss_fn_kd, loss_fn_kd_multihead, loss_fn_kd_2d
from lib.Utility.visualization import... | 25,096 | 55.145414 | 145 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Datasets/incremental_dataset.py | import math
import os
import torchvision
import torch.utils.data
from tqdm import tqdm
from tqdm import trange
import lib.OpenSet.meta_recognition as mr
from lib.Training.evaluate import sample_per_class_zs
import lib.Datasets.datasets as all_datasets
from lib.Training.evaluate import eval_dataset
from lib.Utility.metr... | 148,748 | 59.027845 | 138 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Datasets/datasets.py | import os
import errno
import wget
import zipfile
import glob
import librosa
import scipy
from tqdm import tqdm
import torch
import torch.utils.data
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import numpy as np
import scipy.io.wavfile as wavf
from lib.Datasets.Custom.incremneta... | 45,491 | 39.836625 | 118 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Datasets/Custom/incremnetal_instance_set.py | import torch
import torch.utils.data as data
import torchvision.transforms as transforms
import lib.Utility.transforms as custom_transforms
import csv
import json
import os
import os.path
import sys
from pathlib import Path
import numpy as np
import cv2
from PIL import Image
from tqdm import tqdm
class Classificat... | 15,633 | 34.857798 | 148 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Models/pixelcnn.py | """"
The below two classes of this file's PixelCNN code are originally adapted from
https://github.com/pbloem/pixel-models and have been modified to fit the rest of our code.
MIT License
Copyright (c) 2018 Peter Bloem
Permission is hereby granted, free of charge, to any person obtaining a copy
... | 6,925 | 37.477778 | 119 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Models/architectures_avalanche.py | from collections import OrderedDict
import torch
import torch.nn as nn
from avalanche.models.dynamic_modules import IncrementalClassifier
def get_feat_size(block, spatial_size, ncolors=3):
"""
Function to infer spatial dimensionality in intermediate stages of a model after execution of the specified block.
... | 3,669 | 36.070707 | 128 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Models/architectures.py | from collections import OrderedDict
import torch
import torch.nn as nn
import lib.Models.si as SI
def grow_classifier(device, classifier, class_increment, weight_initializer):
"""
Function to grow the units of a classifier an initializing only the newly added units while retaining old knowledge.
Paramete... | 62,328 | 48.428232 | 129 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Models/initialization.py | import torch
import torch.nn as nn
from torch.nn import init
class WeightInit:
"""
Class for weight-initialization. Would have been nice to just inherit
but PyTorch does not have a class for weight initialization. However methods
for weight initialization are imported and used from the following scrip... | 3,347 | 32.818182 | 92 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Models/si.py | import torch
import torch.nn as nn
class SI_StorageUnit(nn.Module):
"""
An empty module, used to store SI buffers
"""
def __init__(self):
super(SI_StorageUnit, self).__init__()
self.epsilon = 1e-3
# Init SI containers
self.W = {}
self.p_old = {}
# Flag... | 7,457 | 44.2 | 112 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/OpenSet/meta_recognition.py | import torch
import numpy as np
import libmr
def get_means(tensors_list):
"""
Calculate the mean of a list of tensors for each tensor in the list. In our case the list typically contains
a tensor for each class, such as the per class z values.
Parameters:
tensors_list (list): List of Tensors
... | 11,082 | 41.140684 | 120 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Utility/visualization.py | import torch
import torchvision
import os
import math
import seaborn as sns
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.colors import ListedColormap
# matplotlib backend, required for plotting of images to tensorboard
matplotlib.use('Agg')
# ... | 20,646 | 41.8361 | 118 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Utility/utils.py | import torch
import shutil
import os
def save_checkpoint(state, is_best, file_path, file_name='checkpoint.pth.tar'):
"""
Saves the current state of the model. Does a copy of the file
in case the model performed better than previously.
Parameters:
state (dict): Includes optimizer and model sta... | 1,202 | 33.371429 | 103 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Utility/metrics.py | import torch
import numpy as np
def to_one_hot(targets, num_classes):
labels = targets.unsqueeze_(1).long()
#print("labels", labels.shape)
one_hot = torch.zeros(labels.shape[0], num_classes, labels.shape[2], labels.shape[3]).to(targets.device)
#print("one_hot", one_hot.shape)
one_hot.scatter_(1, l... | 10,161 | 33.447458 | 108 | py |
OCDVAEContinualLearning | OCDVAEContinualLearning-master/lib/Utility/transforms.py | import torch
import torch.nn as nn
import math
class IlluminationInvariant():
def __call__(self, x):
eps = 1e-7
r = x[0,:,:]
g = x[1,:,:]
b = x[2,:,:]
c_1 = torch.atan((r + eps)/(torch.max(g,b) +eps)).unsqueeze_(0)
c_2 = torch.atan((g + eps)/(torch.max(r,b) +eps)).un... | 3,587 | 35.242424 | 138 | py |
AdaInt | AdaInt-main/tools/test.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import mmcv
import torch
from mmcv.parallel import MMDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from mmedit.apis import multi_gpu_test, set_random_seed, single_gpu_test
from mmedit.core.distributed_wrapper im... | 4,548 | 31.963768 | 79 | py |
AdaInt | AdaInt-main/tools/onnx2tensorrt.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from typing import Iterable, Optional
import cv2
import mmcv
import numpy as np
import onnxruntime as ort
import torch
from mmcv.ops import get_onnxruntime_op_path
from mmcv.tensorrt import (TRTWrapper, is_tensorrt_plugin_l... | 8,913 | 33.820313 | 79 | py |
AdaInt | AdaInt-main/tools/publish_model.py | # Copyright (c) OpenMMLab. All rights reserved.
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', h... | 1,196 | 28.925 | 78 | py |
AdaInt | AdaInt-main/tools/pytorch2onnx.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import mmcv
import numpy as np
import onnx
import onnxruntime as rt
import torch
from mmcv.onnx import register_extra_symbolics
from mmcv.runner import load_checkpoint
from mmedit.datasets.pipelines import Compose
from mmedit.models import bui... | 7,443 | 35.135922 | 79 | py |
AdaInt | AdaInt-main/tools/deploy_test.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import warnings
from typing import Any
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.parallel import MMDataParallel
from torch import nn
from mmedit.apis import single_gpu_test
from mmedit.core.export import ONNXRuntimeEditing
fr... | 5,467 | 31.164706 | 79 | py |
AdaInt | AdaInt-main/tools/train.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv import Config
from mmcv.runner import init_dist
from mmedit import __version__
from mmedit.apis import set_random_seed, train_model
from mmedit.datasets import bui... | 4,806 | 31.70068 | 77 | py |
AdaInt | AdaInt-main/tools/deployment/mmedit_handler.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import random
import string
from io import BytesIO
import PIL.Image as Image
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmedit.apis import init_model, restoration_inference
from mmedit.core import tensor2img
class MMEditHandler(B... | 2,099 | 34 | 79 | py |
AdaInt | AdaInt-main/tools/deployment/mmedit2torchserve.py | # Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
try:
from model_archiver.model_packaging import package_model
from model_archiver.model_packaging_utils import ModelExportUtils
except Imp... | 3,725 | 32.567568 | 76 | py |
AdaInt | AdaInt-main/tests/test_inference.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmedit.apis import init_model, restoration_video_inference
def test_restoration_video_inference():
if torch.cuda.is_available():
# recurrent framework (BasicVSR)
model = init_model(
'./configs/restorers/ba... | 2,133 | 39.264151 | 77 | py |
AdaInt | AdaInt-main/tests/test_runtime/test_optimizer.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmedit.core import build_optimizers
class ExampleModel(nn.Module):
def __init__(self):
super().__init__()
self.model1 = nn.Conv2d(3, 8, kernel_size=3)
self.model2 = nn.Conv2d(3, 4, kernel_size=3)
... | 4,279 | 40.960784 | 78 | py |
AdaInt | AdaInt-main/tests/test_runtime/test_visual_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest.mock import MagicMock
import mmcv
import numpy as np
import pytest
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from mmedit.core import VisualizationHook
from mmedit.utils import ... | 2,480 | 28.891566 | 79 | py |
AdaInt | AdaInt-main/tests/test_runtime/test_dataset_builder.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from torch.utils.data import ConcatDataset, RandomSampler, SequentialSampler
from mmedit.datasets import (DATASETS, RepeatDataset, build_dataloader,
build_dataset)
from mmedit.datasets.samplers import DistributedSampler
@DATASE... | 4,660 | 32.056738 | 78 | py |
AdaInt | AdaInt-main/tests/test_runtime/test_eval_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import tempfile
from unittest.mock import MagicMock
import mmcv.runner
import pytest
import torch
import torch.nn as nn
from mmcv.runner import obj_from_dict
from torch.utils.data import DataLoader, Dataset
from mmedit.core import EvalIterHook
class Exa... | 2,150 | 28.067568 | 79 | py |
AdaInt | AdaInt-main/tests/test_runtime/test_ema_hook.py | from copy import deepcopy
import pytest
import torch
import torch.nn as nn
from torch.nn.parallel import DataParallel
from mmedit.core.hooks import ExponentialMovingAverageHook
class SimpleModule(nn.Module):
def __init__(self):
super().__init__()
self.a = nn.Parameter(torch.tensor([1., 2.]))
... | 8,180 | 33.518987 | 79 | py |
AdaInt | AdaInt-main/tests/test_models/test_base_model.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest.mock import patch
import pytest
import torch
from mmedit.models import BaseModel
class TestBaseModel(unittest.TestCase):
@patch.multiple(BaseModel, __abstractmethods__=set())
def test_parse_losses(self):
self.base_model =... | 1,059 | 29.285714 | 72 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_generation_backbones/test_generators.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import numpy as np
import pytest
import torch
from mmedit.models import build_backbone
from mmedit.models.common import (ResidualBlockWithDropout,
UnetSkipConnectionBlock)
def test_unet_skip_connection_block():
_cfg = ... | 10,368 | 27.17663 | 66 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_sr_backbones/test_sr_backbones.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmedit.models.backbones import EDSR, SRCNN, MSRResNet, RRDBNet
from mmedit.models.components import ModifiedVGG
def test_srresnet_backbone():
"""Test SRResNet backbone."""
# x2 model
MSRResNet(
in_... | 6,182 | 26.726457 | 79 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_sr_backbones/test_duf.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmedit.models.backbones.sr_backbones.duf import DynamicUpsamplingFilter
def test_dynamic_upsampling_filter():
"""Test DynamicUpsamplingFilter."""
with pytest.raises(TypeError):
# The type of filter_size must be tuple
... | 1,223 | 33 | 76 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_sr_backbones/test_liif_net.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmedit.models import build_backbone
def test_liif_edsr():
model_cfg = dict(
type='LIIFEDSR',
encoder=dict(
type='EDSR',
in_channels=3,
out_channels=3,
mid_channels=64,
nu... | 2,884 | 25.227273 | 49 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_sr_backbones/test_iconvsr.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmedit.models.backbones.sr_backbones.iconvsr import IconVSR
def test_iconvsr():
"""Test IconVSR."""
# gpu (since IconVSR contains DCN, only GPU mode is available)
if torch.cuda.is_available():
iconvsr = IconVSR(
... | 1,695 | 30.407407 | 66 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_sr_backbones/test_tdan_net.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmedit.models.backbones.sr_backbones.tdan_net import TDANNet
def test_tdan_net():
"""Test TDANNet."""
# gpu (DCN is available only on GPU)
if torch.cuda.is_available():
tdan = TDANNet().cuda()
input_tensor = ... | 772 | 29.92 | 69 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_sr_backbones/test_basicvsr_net.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmedit.models.backbones.sr_backbones.basicvsr_net import BasicVSRNet
def test_basicvsr_net():
"""Test BasicVSR."""
# cpu
basicvsr = BasicVSRNet(
mid_channels=64, num_blocks=30, spynet_pretrained=None)
input_tenso... | 1,137 | 30.611111 | 74 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_sr_backbones/test_dic_net.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
import torch.nn as nn
from mmedit.models import build_backbone
from mmedit.models.backbones.sr_backbones.dic_net import (
FeedbackBlock, FeedbackBlockCustom, FeedbackBlockHeatmapAttention)
def test_feedback_block():
x1 = torch.rand(2,... | 2,496 | 24.222222 | 70 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_sr_backbones/test_glean_net.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmedit.models.backbones.sr_backbones.glean_styleganv2 import \
GLEANStyleGANv2
class TestGLEANNet:
@classmethod
def setup_class(cls):
cls.default_cfg = dict(in_size=16, out_size=256, style_channels=512)
cls.s... | 1,834 | 32.981481 | 78 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_sr_backbones/test_edvr_net.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmedit.models.backbones.sr_backbones.edvr_net import (EDVRNet,
PCDAlignment,
TSAFusion)
def test_pcd_alignment():
"... | 2,717 | 28.868132 | 76 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_sr_backbones/test_tof.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmedit.models.backbones import TOFlow
def test_tof():
"""Test TOFlow."""
# cpu
tof = TOFlow(adapt_official_weights=True)
input_tensor = torch.rand(1, 7, 3, 32, 32)
tof.init_weights(pretrained=None)
output = tof(i... | 937 | 26.588235 | 57 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_sr_backbones/test_rdn.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmedit.models import build_backbone
def test_rdn():
scale = 4
model_cfg = dict(
type='RDN',
in_channels=3,
out_channels=3,
mid_channels=64,
num_blocks=16,
upscale_factor=s... | 1,353 | 22.344828 | 56 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_sr_backbones/test_basicvsr_plusplus.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmedit.models.backbones.sr_backbones.basicvsr_pp import BasicVSRPlusPlus
def test_basicvsr_plusplus():
"""Test BasicVSR++."""
# cpu
model = BasicVSRPlusPlus(
mid_channels=64,
num_blocks=7,
is_low_res_... | 2,698 | 30.752941 | 77 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_encoder_decoders/test_gl_model.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmedit.models import build_backbone, build_component
from mmedit.models.backbones import GLDilationNeck
from mmedit.models.common import SimpleGatedConvModule
def test_gl_encdec():
input_x = torch.randn(1, 4, 256, 256)
template_c... | 2,945 | 30.010526 | 75 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_encoder_decoders/test_deepfill_encoder.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmedit.models.backbones import ContextualAttentionNeck, DeepFillEncoder
from mmedit.models.common import SimpleGatedConvModule
def test_deepfill_enc():
encoder = DeepFillEncoder()
x = torch.randn((2, 5, 256, 256))
outputs = encoder(x)
... | 3,966 | 34.738739 | 76 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_encoder_decoders/test_deepfill_decoder.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmedit.models.backbones import DeepFillDecoder
def test_deepfill_dec():
decoder = DeepFillDecoder(128, out_act_cfg=None)
assert not decoder.with_out_activation
decoder = DeepFillDecoder(128)
x = torch.randn((2, 128, 64, 64))
inpu... | 1,533 | 34.674419 | 68 | py |
AdaInt | AdaInt-main/tests/test_models/test_backbones/test_encoder_decoders/test_decoders.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmedit.models.backbones import (VGG16, FBADecoder, IndexedUpsample,
IndexNetDecoder, IndexNetEncoder,
PlainDecoder, ResGCADecoder,
... | 8,503 | 33.710204 | 79 | py |
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