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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SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/rpn/fcos/loss.py | """
This file contains specific functions for computing losses of FCOS
file
"""
import torch
from torch.nn import functional as F
from torch import nn
import os
from ..utils import concat_box_prediction_layers
from fcos_core.layers import IOULoss
from fcos_core.layers import SigmoidFocalLoss
from fcos_core.modeling.ma... | 11,308 | 38.267361 | 96 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/rpn/fcos/fcos.py | import math
import torch
import torch.nn.functional as F
from torch import nn
from .inference import make_fcos_postprocessor
from .loss import make_fcos_loss_evaluator
from fcos_core.layers import Scale
from fcos_core.layers import DFConv2d
class FCOSHead(torch.nn.Module):
def __init__(self, cfg, in_channels):
... | 7,488 | 34.159624 | 89 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/rpn/retinanet/inference.py | import torch
from ..inference import RPNPostProcessor
from ..utils import permute_and_flatten
from fcos_core.modeling.box_coder import BoxCoder
from fcos_core.modeling.utils import cat
from fcos_core.structures.bounding_box import BoxList
from fcos_core.structures.boxlist_ops import cat_boxlist
from fcos_core.structu... | 6,869 | 34.230769 | 79 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/rpn/retinanet/loss.py | """
This file contains specific functions for computing losses on the RetinaNet
file
"""
import torch
from torch.nn import functional as F
from ..utils import concat_box_prediction_layers
from fcos_core.layers import smooth_l1_loss
from fcos_core.layers import SigmoidFocalLoss
from fcos_core.modeling.matcher import ... | 3,421 | 30.685185 | 83 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/rpn/retinanet/retinanet.py | import math
import torch
import torch.nn.functional as F
from torch import nn
from .inference import make_retinanet_postprocessor
from .loss import make_retinanet_loss_evaluator
from ..anchor_generator import make_anchor_generator_retinanet
from fcos_core.modeling.box_coder import BoxCoder
class RetinaNetHead(torc... | 5,292 | 33.594771 | 88 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/rpn/atss/inference.py | import torch
from ..utils import permute_and_flatten
from fcos_core.structures.bounding_box import BoxList
from fcos_core.structures.boxlist_ops import cat_boxlist
from fcos_core.structures.boxlist_ops import boxlist_ml_nms
from fcos_core.structures.boxlist_ops import remove_small_boxes
class ATSSPostProcessor(torch.... | 5,227 | 36.884058 | 97 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/rpn/atss/loss.py | import torch
from torch import nn
import os
from ..utils import concat_box_prediction_layers
from fcos_core.layers import SigmoidFocalLoss
from fcos_core.modeling.matcher import Matcher
from fcos_core.structures.boxlist_ops import boxlist_iou
from fcos_core.structures.boxlist_ops import cat_boxlist
INF = 100000000
... | 16,433 | 51.33758 | 117 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/rpn/atss/atss.py | import math
import torch
import torch.nn.functional as F
from torch import nn
from .inference import make_atss_postprocessor
from .loss import make_atss_loss_evaluator
from fcos_core.layers import Scale
from fcos_core.layers import DFConv2d
from ..anchor_generator import make_anchor_generator_atss
class BoxCoder(ob... | 9,242 | 38.840517 | 93 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/roi_heads.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .box_head.box_head import build_roi_box_head
from .mask_head.mask_head import build_roi_mask_head
from .keypoint_head.keypoint_head import build_roi_keypoint_head
class CombinedROIHeads(torch.nn.ModuleDict):
"""
Combine... | 3,269 | 41.467532 | 96 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/mask_head/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import torch
from torch import nn
from fcos_core.layers.misc import interpolate
from fcos_core.structures.bounding_box import BoxList
# TODO check if want to return a single BoxList or a composite
# object
class MaskPostProces... | 6,545 | 30.931707 | 87 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from torch import nn
from torch.nn import functional as F
from ..box_head.roi_box_feature_extractors import ResNet50Conv5ROIFeatureExtractor
from fcos_core.modeling import registry
from fcos_core.modeling.poolers import Pooler
from fcos_core.model... | 2,475 | 32.917808 | 82 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/mask_head/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch.nn import functional as F
from fcos_core.layers import smooth_l1_loss
from fcos_core.modeling.matcher import Matcher
from fcos_core.structures.boxlist_ops import boxlist_iou
from fcos_core.modeling.utils import cat
def pr... | 5,331 | 36.286713 | 80 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/mask_head/roi_mask_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from torch import nn
from torch.nn import functional as F
from fcos_core.layers import Conv2d
from fcos_core.layers import ConvTranspose2d
from fcos_core.modeling import registry
@registry.ROI_MASK_PREDICTOR.register("MaskRCNNC4Predictor")
class... | 2,202 | 36.982759 | 83 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/mask_head/mask_head.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from fcos_core.structures.bounding_box import BoxList
from .roi_mask_feature_extractors import make_roi_mask_feature_extractor
from .roi_mask_predictors import make_roi_mask_predictor
from .inference import make_... | 3,117 | 36.119048 | 86 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/box_head/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from fcos_core.structures.bounding_box import BoxList
from fcos_core.structures.boxlist_ops import boxlist_nms
from fcos_core.structures.boxlist_ops import cat_boxlist
from fcos_cor... | 6,658 | 37.491329 | 88 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/box_head/roi_box_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.nn import functional as F
from fcos_core.modeling import registry
from fcos_core.modeling.backbone import resnet
from fcos_core.modeling.poolers import Pooler
from fcos_core.modeling.make_layers import ... | 5,359 | 34.263158 | 81 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/box_head/box_head.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from .roi_box_feature_extractors import make_roi_box_feature_extractor
from .roi_box_predictors import make_roi_box_predictor
from .inference import make_roi_box_post_processor
from .loss import make_roi_box_loss_... | 2,765 | 37.416667 | 96 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/box_head/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch.nn import functional as F
from fcos_core.layers import smooth_l1_loss
from fcos_core.modeling.box_coder import BoxCoder
from fcos_core.modeling.matcher import Matcher
from fcos_core.structures.boxlist_ops import boxlist_iou... | 7,012 | 35.149485 | 90 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/box_head/roi_box_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from fcos_core.modeling import registry
from torch import nn
@registry.ROI_BOX_PREDICTOR.register("FastRCNNPredictor")
class FastRCNNPredictor(nn.Module):
def __init__(self, config, in_channels):
super(FastRCNNPredictor, self).__init_... | 2,286 | 35.301587 | 87 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/keypoint_head/inference.py | import torch
from torch import nn
class KeypointPostProcessor(nn.Module):
def __init__(self, keypointer=None):
super(KeypointPostProcessor, self).__init__()
self.keypointer = keypointer
def forward(self, x, boxes):
mask_prob = x
scores = None
if self.keypointer:
... | 4,450 | 34.325397 | 102 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/keypoint_head/roi_keypoint_feature_extractors.py | from torch import nn
from torch.nn import functional as F
from fcos_core.modeling import registry
from fcos_core.modeling.poolers import Pooler
from fcos_core.layers import Conv2d
@registry.ROI_KEYPOINT_FEATURE_EXTRACTORS.register("KeypointRCNNFeatureExtractor")
class KeypointRCNNFeatureExtractor(nn.Module):
de... | 1,865 | 35.588235 | 87 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/keypoint_head/loss.py | import torch
from torch.nn import functional as F
from fcos_core.modeling.matcher import Matcher
from fcos_core.modeling.balanced_positive_negative_sampler import (
BalancedPositiveNegativeSampler,
)
from fcos_core.structures.boxlist_ops import boxlist_iou
from fcos_core.modeling.utils import cat
from fcos_core.l... | 7,041 | 37.271739 | 90 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/keypoint_head/keypoint_head.py | import torch
from .roi_keypoint_feature_extractors import make_roi_keypoint_feature_extractor
from .roi_keypoint_predictors import make_roi_keypoint_predictor
from .inference import make_roi_keypoint_post_processor
from .loss import make_roi_keypoint_loss_evaluator
class ROIKeypointHead(torch.nn.Module):
def __i... | 2,057 | 38.576923 | 86 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/modeling/roi_heads/keypoint_head/roi_keypoint_predictors.py | from torch import nn
from fcos_core import layers
from fcos_core.modeling import registry
@registry.ROI_KEYPOINT_PREDICTOR.register("KeypointRCNNPredictor")
class KeypointRCNNPredictor(nn.Module):
def __init__(self, cfg, in_channels):
super(KeypointRCNNPredictor, self).__init__()
input_features =... | 1,255 | 31.205128 | 81 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/structures/image_list.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from __future__ import division
import torch
class ImageList(object):
"""
Structure that holds a list of images (of possibly
varying sizes) as a single tensor.
This works by padding the images to the same size,
and storing in... | 2,485 | 33.054795 | 87 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/structures/segmentation_mask.py | import cv2
import copy
import torch
import numpy as np
from fcos_core.layers.misc import interpolate
import pycocotools.mask as mask_utils
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
""" ABSTRACT
Segmentations come in either:
1) Binary masks
2) Polygons
Binary masks can be represented in a contiguous array... | 17,276 | 31.173184 | 94 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/structures/bounding_box.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class BoxList(object):
"""
This class represents a set of bounding boxes.
The bounding boxes are represented as a Nx4 Tensor.
In order to uniquely determine the bou... | 9,646 | 35.131086 | 92 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/structures/boxlist_ops.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .bounding_box import BoxList
from fcos_core.layers import nms as _box_nms
from fcos_core.layers import ml_nms as _box_ml_nms
def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores"):
"""
Performs no... | 4,558 | 28.038217 | 97 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos_core/structures/keypoint.py | import torch
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class Keypoints(object):
def __init__(self, keypoints, size, mode=None):
# FIXME remove check once we have better integration with device
# in my version this would consistently return a CPU tensor
device = keypoints.device ... | 6,555 | 33.687831 | 97 | py |
SA-AutoAug | SA-AutoAug-master/FCOS/fcos/fcos.py | import cv2, os
import torch
from fcos_core.config import cfg as base_cfg
from torchvision import transforms as T
from fcos_core.modeling.detector import build_detection_model
from fcos_core.utils.checkpoint import DetectronCheckpointer
from fcos_core.structures.image_list import to_image_list
from fcos_core.structures.... | 15,053 | 34.588652 | 119 | py |
MMCE | MMCE-master/20ng_mmce.py | from __future__ import print_function
import os
import sys
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from keras.layers import Dense, Input, GlobalMaxPooling1D
from keras.layers import Conv1D, MaxPoolin... | 14,543 | 40.673352 | 83 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/export.py | import torch, os, cv2
from model.model import parsingNet
from utils.common import merge_config
from utils.dist_utils import dist_print
import torch
import scipy.special, tqdm
import numpy as np
import torchvision.transforms as transforms
from data.dataset import LaneTestDataset
from data.constant import culane_row_anch... | 1,430 | 29.446809 | 96 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/test.py | import torch, os
from model.model import parsingNet
from utils.common import merge_config
from utils.dist_utils import dist_print
from evaluation.eval_wrapper import eval_lane
import torch
if __name__ == "__main__":
torch.backends.cudnn.benchmark = True
args, cfg = merge_config()
distributed = False
i... | 1,711 | 34.666667 | 125 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/speed_real.py | # Thanks for the contribution of KopiSoftware https://github.com/KopiSoftware
import torch
import time
import numpy as np
from model.model import parsingNet
import torchvision.transforms as transforms
import cv2
from matplotlib import pyplot as plt
from PIL import Image
img_transforms = transforms.Compose([
tran... | 4,359 | 27.496732 | 101 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/demo.py | import torch, os, cv2
from model.model import parsingNet
from utils.common import merge_config
from utils.dist_utils import dist_print
import torch
import scipy.special, tqdm
import numpy as np
import torchvision.transforms as transforms
from data.dataset import LaneTestDataset
from data.constant import culane_row_anch... | 3,853 | 40.44086 | 192 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/speed_simple.py | import torch
import time
import numpy as np
from model.model import parsingNet
# torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
net = parsingNet(pretrained = False, backbone='18',cls_dim = (100+1,56,4),use_aux=False).cuda()
# net = parsingNet(pretrained = False, backbone='18',cls_dim... | 802 | 24.09375 | 97 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/train.py | import torch, os, datetime
import numpy as np
from model.model import parsingNet
from data.dataloader import get_train_loader
from utils.dist_utils import dist_print, dist_tqdm, is_main_process, DistSummaryWriter
from utils.factory import get_metric_dict, get_loss_dict, get_optimizer, get_scheduler
from utils.metrics... | 5,618 | 35.251613 | 155 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/evaluation/eval_wrapper.py |
from data.dataloader import get_test_loader
from evaluation.tusimple.lane import LaneEval
from utils.dist_utils import is_main_process, dist_print, get_rank, get_world_size, dist_tqdm, synchronize
import os, json, torch, scipy
import numpy as np
import platform
def generate_lines(out, shape, names, output_path, gridi... | 11,756 | 48.39916 | 157 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/utils/loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class OhemCELoss(nn.Module):
def __init__(self, thresh, n_min, ignore_lb=255, *args, **kwargs):
super(OhemCELoss, self).__init__()
self.thresh = -torch.log(torch.tensor(thresh, dtype=torch.float)).cuda()
... | 2,506 | 32.426667 | 86 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/utils/dist_utils.py | import torch
import torch.distributed as dist
import pickle
def get_world_size():
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
return dist.get_world_size()
def to_python_float(t):
if hasattr(t, 'item'):
return t.item()
else:
return t... | 4,623 | 25.574713 | 77 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/utils/common.py | import os, argparse
from utils.dist_utils import is_main_process, dist_print, DistSummaryWriter
from utils.config import Config
import torch
import time
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'fal... | 5,268 | 43.652542 | 260 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/utils/factory.py | from utils.loss import SoftmaxFocalLoss, ParsingRelationLoss, ParsingRelationDis
from utils.metrics import MultiLabelAcc, AccTopk, Metric_mIoU
from utils.dist_utils import DistSummaryWriter
import torch
def get_optimizer(net,cfg):
training_params = filter(lambda p: p.requires_grad, net.parameters())
if cfg.o... | 5,179 | 39.155039 | 160 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/utils/metrics.py | import numpy as np
import torch
import time,pdb
def converter(data):
if isinstance(data,torch.Tensor):
data = data.cpu().data.numpy().flatten()
return data.flatten()
def fast_hist(label_pred, label_true,num_classes):
#pdb.set_trace()
hist = np.bincount(num_classes * label_true.astype(int) + lab... | 3,280 | 30.854369 | 111 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/data/mytransforms.py | import numbers
import random
import numpy as np
from PIL import Image, ImageOps, ImageFilter
#from config import cfg
import torch
import pdb
import cv2
# ===============================img tranforms============================
class Compose2(object):
def __init__(self, transforms):
self.transforms = trans... | 5,085 | 29.27381 | 129 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/data/dataloader.py | import torch, os
import numpy as np
import torchvision.transforms as transforms
import data.mytransforms as mytransforms
from data.constant import tusimple_row_anchor, culane_row_anchor
from data.dataset import LaneClsDataset, LaneTestDataset
def get_train_loader(batch_size, data_root, griding_num, dataset, use_aux, ... | 4,784 | 42.899083 | 121 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/data/dataset.py | import torch
from PIL import Image
import os
import pdb
import numpy as np
import cv2
from data.mytransforms import find_start_pos
def loader_func(path):
return Image.open(path)
class LaneTestDataset(torch.utils.data.Dataset):
def __init__(self, path, list_path, img_transform=None):
super(LaneTestDa... | 5,672 | 33.174699 | 142 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/model/model.py | import torch
from model.backbone import resnet
import numpy as np
class conv_bn_relu(torch.nn.Module):
def __init__(self,in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1,bias=False):
super(conv_bn_relu,self).__init__()
self.conv = torch.nn.Conv2d(in_channels,out_channels, ker... | 4,814 | 39.125 | 167 | py |
Ultra-Fast-Lane-Detection | Ultra-Fast-Lane-Detection-master/model/backbone.py | import torch,pdb
import torchvision
import torch.nn.modules
class vgg16bn(torch.nn.Module):
def __init__(self,pretrained = False):
super(vgg16bn,self).__init__()
model = list(torchvision.models.vgg16_bn(pretrained=pretrained).features.children())
model = model[:33]+model[34:43]
self... | 2,086 | 35.614035 | 92 | py |
deepSI | deepSI-master/testing/validation/validation-uxeyey-deepSI-transfer.py | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import uxyeye
import deepSI
from matplotlib import pyplot as plt
# In[2]:
# sys_data_deepSI =
sys_data_deepSI = deepSI.datasets.WienerHammerBenchMark(split_data=False)#sys_data_deepSI[:134020], sys_data_deepSI[134020:]
sys_data_uxyeye = uxyeye.data_sets.WienerHamme... | 3,638 | 16.246445 | 173 | py |
deepSI | deepSI-master/examples/making-your-own-pytorch-estimator.py | import deepSI
import numpy as np
from matplotlib import pyplot as plt
from torch import nn
import torch
class NARX_basic(deepSI.fit_systems.System_torch):
"""docstring for NARX"""
def __init__(self, na=20, nb=20):
super(NARX_basic, self).__init__()
self.na, self.nb = na, nb
self.k0 = ma... | 5,263 | 37.144928 | 139 | py |
deepSI | deepSI-master/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 3,033 | 31.276596 | 142 | py |
deepSI | deepSI-master/deepSI/datasets/dataset_utils.py |
import urllib.request
from urllib import request
import os
import os.path
from pathlib import Path
from sys import platform
import shutil
def get_work_dirs():
'''A utility function which gets the utility directories for each OS
It creates a working directory called deepSI
in LOCALAPPDATA for windo... | 6,782 | 30.995283 | 119 | py |
deepSI | deepSI-master/deepSI/systems/system.py | from deepSI.system_data import System_data, System_data_list, System_data_norm
import deepSI
import numpy as np
import pickle
from secrets import token_urlsafe
import copy
import gym
from gym.spaces import Box
from matplotlib import pyplot as plt
def load_system(file):
"""This is not a safe function, only use on t... | 26,487 | 40.130435 | 197 | py |
deepSI | deepSI-master/deepSI/system_data/system_data.py |
import deepSI
import numpy as np
from matplotlib import pyplot as plt
from tqdm.auto import tqdm
from torch.utils.data import Dataset, DataLoader, ConcatDataset
def load_system_data(file):
'''Load System_data from .npz file'''
outfile = dict(np.load(file,allow_pickle=True))
def get_sys_data(data):
... | 39,216 | 40.455603 | 214 | py |
deepSI | deepSI-master/deepSI/exp_design/first.py |
from deepSI.system_data import System_data, System_data_list, System_data_norm
from deepSI.systems.system import System, System_gym
from deepSI.fit_systems.torch_io import Torch_io
from deepSI.fit_systems.encoders import SS_encoder
import numpy as np
from tqdm.auto import tqdm
from matplotlib import pyplot as plt
# ... | 2,955 | 36.417722 | 110 | py |
deepSI | deepSI-master/deepSI/utils/__init__.py | from deepSI.utils.torch_nets import simple_res_net, feed_forward_nn, general_koopman_forward_layer, \
CNN_chained_upscales, CNN_encoder, complete_MLP_res_net,\
Shotgun_MLP, Shotgun_encoder, integrator_RK4, time_integrators, integrator_euler
import deepSI.utils.sklearn_regs
from deepSI.utils.fitting... | 413 | 68 | 101 | py |
deepSI | deepSI-master/deepSI/utils/torch_nets.py | import torch
from torch import nn, optim
import numpy as np
class feed_forward_nn(nn.Module): #a simple MLP
def __init__(self,n_in=6, n_out=5, n_nodes_per_layer=64, n_hidden_layers=2, activation=nn.Tanh):
super(feed_forward_nn,self).__init__()
self.n_in = n_in
self.n_out = n_out
se... | 27,739 | 44.032468 | 177 | py |
deepSI | deepSI-master/deepSI/utils/lyapunov.py | import torch
import numpy as np
from torch.autograd.functional import jacobian
from matplotlib import pyplot as plt
def get_lyapunov_exponent(sys, test, nsteps = 15, n_samp=100, verbose=1 ):
test_p = sys.apply_experiment(test, save_state=True)
xt = torch.tensor(test_p.x,dtype=torch.float32) #this is not norm... | 1,287 | 30.414634 | 75 | py |
deepSI | deepSI-master/deepSI/fit_systems/fit_system.py |
from deepSI.systems.system import System, System_io, System_data, load_system
import numpy as np
from deepSI.datasets import get_work_dirs
import deepSI
import torch
from torch import nn, optim
from tqdm.auto import tqdm
import time
from pathlib import Path
import os.path
from torch.utils.data import Dataset, DataLoad... | 34,303 | 48.287356 | 211 | py |
deepSI | deepSI-master/deepSI/fit_systems/hyperparameter_optimization.py | import numpy as np
from tqdm.auto import tqdm
def process_dict(search_dict):
#this is not foul proof so watch out. (e.g. passing lists as items when the whole list should be passed to the function)
new_dict = {}
for key,item in search_dict.items():
if isinstance(item,range):
new_dict[ke... | 6,268 | 46.854962 | 248 | py |
deepSI | deepSI-master/deepSI/fit_systems/__init__.py | from deepSI.fit_systems.fit_system import System_fittable, System_torch
from deepSI.fit_systems.hyperparameter_optimization import random_search, grid_search
from deepSI.fit_systems.sklearn_io import Sklearn_io, Sklearn_io_linear
from deepSI.fit_systems.encoders import SS_encoder, SS_encoder_general, \
SS_par... | 673 | 73.888889 | 103 | py |
deepSI | deepSI-master/deepSI/fit_systems/io_autoencoder.py |
from deepSI.fit_systems.fit_system import System_fittable, System_torch
from deepSI.system_data import System_data
import torch
from torch import nn
import numpy as np
class IO_autoencoder(System_torch):
"""docstring for IO_autoencoder"""
def __init__(self, nz=4, na=5, nb=5):
super(IO_autoencoder, s... | 4,678 | 50.417582 | 192 | py |
deepSI | deepSI-master/deepSI/fit_systems/torch_io.py |
from deepSI.fit_systems.fit_system import System_fittable, System_torch
from deepSI.systems.system import System_io
import deepSI
import torch
from torch import nn
class Torch_io(System_torch, System_io):
def __init__(self, na=5, nb=5, feedthrough=False):
assert feedthrough==False
super(Torch_io,... | 4,167 | 40.267327 | 224 | py |
deepSI | deepSI-master/deepSI/fit_systems/encoders.py |
from deepSI.fit_systems.fit_system import System_fittable, System_torch
from deepSI.system_data.system_data import System_data_norm, System_data, System_data_list
import deepSI
import torch
from torch import nn
import numpy as np
import time
class SS_encoder(System_torch):
'''The basic implementation of the subsp... | 36,336 | 48.037787 | 179 | py |
downscaling-rnn-gan | downscaling-rnn-gan-master/dsrnngan/main.py | import argparse
import json
import os
import numpy as np
import pandas as pd
from tensorflow.keras.optimizers import SGD
import plots
import train
path = os.path.dirname(os.path.abspath(__file__))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('mode', type=str, help="tr... | 5,385 | 38.028986 | 81 | py |
downscaling-rnn-gan | downscaling-rnn-gan-master/dsrnngan/gan.py | import gc
import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from tensorflow.keras import backend as K
from tensorflow.keras.optimizers import Adam
from tensorflow.python.keras.utils import generic_utils
from layers import GradientPenalty, RandomWeightedAverage
from... | 6,681 | 35.714286 | 78 | py |
downscaling-rnn-gan | downscaling-rnn-gan-master/dsrnngan/layers.py | import numpy as np
import tensorflow as tf
from tensorflow.python.keras.engine import InputSpec
from tensorflow.python.keras.engine import base_layer_utils
from tensorflow.python.ops import math_ops
from tensorflow.keras.layers import Layer
from tensorflow.keras.layers import Dense, Conv2D
from tensorflow.python.keras.... | 9,361 | 36.150794 | 86 | py |
downscaling-rnn-gan | downscaling-rnn-gan-master/dsrnngan/models.py | import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Concatenate
from tensorflow.keras.layers import Activation, LeakyReLU
from tensorflow.keras.layers import ConvLSTM2D, Conv2D, UpSampling2D, Layer
from tensorflow.keras.layers import GlobalAver... | 6,752 | 33.454082 | 84 | py |
downscaling-rnn-gan | downscaling-rnn-gan-master/dsrnngan/rnn.py | from tensorflow.keras.layers import Layer
import tensorflow as tf
class CustomGateGRU(Layer):
def __init__(self,
update_gate=None, reset_gate=None, output_gate=None,
return_sequences=False, time_steps=1,
**kwargs):
super().__init__(**kwargs)
self.update_gate = update_gat... | 989 | 28.117647 | 69 | py |
downscaling-rnn-gan | downscaling-rnn-gan-master/dsrnngan/meta.py | import h5py
from tensorflow.keras import backend as K
class Nontrainable(object):
def __init__(self, models):
if not isinstance(models, list):
models = [models]
self.models = models
def __enter__(self):
self.trainable_status = [m.trainable for m in self.models]
... | 2,752 | 33.848101 | 76 | py |
downscaling-rnn-gan | downscaling-rnn-gan-master/dsrnngan/train.py | import gc
import os
import netCDF4
import numpy as np
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.optimizers import Adam
import gan
import data
import models
import noise
import plots
path = os.path.dirname(os.path.abspath... | 6,268 | 34.022346 | 77 | py |
downscaling-rnn-gan | downscaling-rnn-gan-master/dsrnngan/blocks.py | import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Add, Conv2D, Dense, Input
from tensorflow.keras.layers import ELU, LeakyReLU, ReLU, ThresholdedReLU
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import AveragePooling2D
from tensorflow... | 2,410 | 34.455882 | 73 | py |
Verbal-ConvQuestions | Verbal-ConvQuestions-main/experiments/test.py | import math
import torch
import random
import logging
import numpy as np
from tqdm import tqdm
from torch.utils.data import DataLoader
from constants import *
from models import models
from utils import evaluate, Predictor
from data.conv_data import ConversationalAnswerVerbalizationData
logging.basicConfig(format='%(... | 2,729 | 36.39726 | 109 | py |
Verbal-ConvQuestions | Verbal-ConvQuestions-main/experiments/constants.py | import os
import torch
from pathlib import Path
from args import get_parser
from accelerate import Accelerator
# set root path
ROOT_PATH = Path(os.path.dirname(__file__))
# read parser
parser = get_parser()
args = parser.parse_args()
# define device
CUDA = 'cuda'
CPU = 'cpu'
DEVICE = torch.device(CUDA if torch.cuda.... | 1,533 | 17.938272 | 65 | py |
Verbal-ConvQuestions | Verbal-ConvQuestions-main/experiments/utils.py | from __future__ import division
import os
import re
import json
import nltk
import glob
import torch
import logging
import torch.nn as nn
from tqdm import tqdm
from constants import *
logger = logging.getLogger(__name__)
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
... | 5,887 | 36.503185 | 120 | py |
Verbal-ConvQuestions | Verbal-ConvQuestions-main/experiments/train.py | import time
import torch
import random
import logging
import numpy as np
from tqdm import tqdm
from transformers import AdamW
from torch.utils.data import DataLoader
from constants import *
from models import models
from utils import AverageMeter, save_checkpoint, evaluate
from data.conv_data import ConversationalAnsw... | 3,632 | 34.271845 | 120 | py |
Verbal-ConvQuestions | Verbal-ConvQuestions-main/experiments/models/bert.py | import torch.nn as nn
from transformers import BertGenerationEncoder, BertGenerationDecoder, EncoderDecoderModel
from constants import *
from utils import init_weights
class Bert(nn.Module):
def __init__(self, vocab):
super(Bert, self).__init__()
self.vocab = vocab
self.encoder = BertGener... | 975 | 31.533333 | 120 | py |
Verbal-ConvQuestions | Verbal-ConvQuestions-main/experiments/models/convolutional.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from constants import *
from utils import init_weights
class Convolutional(nn.Module):
def __init__(self, vocab):
super(Convolutional, self).__init__()
self.vocab = vocab
self.encoder = Encoder(vocab, DEVICE)
... | 11,804 | 38.089404 | 125 | py |
Verbal-ConvQuestions | Verbal-ConvQuestions-main/experiments/models/transformer.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
# import constants
from constants import *
from utils import init_weights
class Transformer(nn.Module):
def __init__(self, vocab):
super(Transformer, self).__init__()
self.vocab = voc... | 8,608 | 37.433036 | 125 | py |
Verbal-ConvQuestions | Verbal-ConvQuestions-main/experiments/models/t5.py | import torch.nn as nn
from transformers import T5ForConditionalGeneration
from constants import *
from utils import init_weights
class T5(nn.Module):
def __init__(self, vocab):
super(T5, self).__init__()
self.vocab = vocab
self.t5 = T5ForConditionalGeneration.from_pretrained(T5_BASE)
... | 690 | 23.678571 | 81 | py |
Verbal-ConvQuestions | Verbal-ConvQuestions-main/experiments/models/bart.py | import torch.nn as nn
from transformers import BartForConditionalGeneration
from constants import *
from utils import init_weights
class Bart(nn.Module):
def __init__(self, vocab):
super(Bart, self).__init__()
self.vocab = vocab
self.bart = BartForConditionalGeneration.from_pretrained(BAR... | 706 | 24.25 | 83 | py |
Verbal-ConvQuestions | Verbal-ConvQuestions-main/experiments/data/conv_data.py | import os
import re
import glob
import json
from torch.utils.data import Dataset
from transformers import AutoTokenizer
from constants import *
from models import tokenizers
class PtDataset(Dataset):
def __init__(self, questions, answers, domains):
self.question_ids = torch.LongTensor(questions.data['inpu... | 3,997 | 37.815534 | 115 | py |
FIT | FIT-main/convert_complex_pretrain_ckpts.py | import os
import torch
from src.structure.knowledge_graph_index import KGIndex
from src.structure.neural_binary_predicate import ComplEx
kgs = ['FB15k-237', 'FB15k', 'NELL']
if __name__ == "__main__":
for kgname in kgs:
kgidx = KGIndex.load(os.path.join('data', kgname + '-betae', 'kgindex.json'))
... | 1,564 | 37.170732 | 116 | py |
FIT | FIT-main/brutal_search.py | import argparse
import json
import logging
import os
import os.path as osp
import random
from collections import defaultdict
from typing import List
import copy
import numpy as np
import scipy.sparse
import torch
import torch.nn.functional as F
import tqdm
import pickle
from torch import nn
from scipy.sparse import cs... | 16,205 | 49.802508 | 121 | py |
FIT | FIT-main/lifted_embedding_estimation_with_truth_value.py | import argparse
import json
import logging
import os
import os.path as osp
import random
from collections import defaultdict
from typing import List
import numpy as np
import torch
import torch.nn.functional as F
import tqdm
from torch import nn
from src.language.tnorm import GodelTNorm, ProductTNorm, Tnorm
from src.... | 39,140 | 39.81439 | 103 | py |
FIT | FIT-main/solve_EFO1_v2.py | import argparse
import json
import logging
import os
import os.path as osp
import random
from collections import defaultdict
from typing import List
import copy
import numpy as np
import scipy.sparse
import torch
import torch.nn.functional as F
import tqdm
import pickle
from torch import nn
from scipy.sparse import cs... | 20,558 | 51.048101 | 250 | py |
FIT | FIT-main/compute_score.py | import argparse
import json
import logging
import os
import os.path as osp
import random
from collections import defaultdict
from typing import List
from math import ceil
import torch
from create_matrix import create_matrix_from_ckpt
from src.structure.knowledge_graph import KnowledgeGraph
from src.structure.knowledg... | 4,155 | 40.56 | 113 | py |
FIT | FIT-main/create_matrix.py | import argparse
import json
import logging
import os
import os.path as osp
import random
from collections import defaultdict
from typing import List
import torch
import pickle
from src.structure.knowledge_graph import KnowledgeGraph
from src.structure.knowledge_graph_index import KGIndex
parser = argparse.ArgumentPa... | 4,722 | 42.731481 | 120 | py |
FIT | FIT-main/solve_EFO1.py | import argparse
import json
import logging
import os
import os.path as osp
import random
from collections import defaultdict
from typing import List
import copy
import numpy as np
import torch
import torch.nn.functional as F
import tqdm
import pickle
from torch import nn
from src.language.fof import ConjunctiveFormul... | 20,639 | 50.6 | 212 | py |
FIT | FIT-main/src/trainer.py | import logging
import os
import torch
from .evaluation import Evaluator
from .structure import KnowledgeGraph, NeuralBinaryPredicate
from .learner import Learner, LearnerForwardOutput
from .utils.recorder import TrainRecorder
from .utils.config import ExperimentConfigCollection
class Trainer:
"""
Basic inte... | 7,055 | 33.419512 | 162 | py |
FIT | FIT-main/src/pipeline/sampler.py | import torch
from ..utils.data_util import tensorize_batch_entities
from ..structure import KnowledgeGraph, NeuralBinaryPredicate
from ..utils import RaggedBatch
class TripleSampler:
def __init__(self) -> None:
pass
def __call__(self, batch_input: torch.Tensor) -> RaggedBatch:
# assert the b... | 6,295 | 35.818713 | 84 | py |
FIT | FIT-main/src/pipeline/reasoning_machine.py | """
A file maintains various reasoners
"""
from abc import ABC, abstractmethod
from collections import defaultdict
from curses import termname
from imp import is_frozen
import math
from typing import Dict, List
from random import sample
import torch
from torch import nn
from src.language.fof import (BinaryPredicate, C... | 39,544 | 37.580488 | 99 | py |
FIT | FIT-main/src/pipeline/.ipynb_checkpoints/sampler-checkpoint.py | import torch
from src.utils.data_util import tensorize_batch_entities
from ..structure import KnowledgeGraph, NeuralBinaryPredicate
from ..utils import RaggedBatch
class TripleSampler:
def __init__(self) -> None:
pass
def __call__(self, batch_input: torch.Tensor) -> RaggedBatch:
# assert the... | 6,297 | 35.830409 | 84 | py |
FIT | FIT-main/src/pipeline/.ipynb_checkpoints/reasoning_machine-checkpoint.py | """
A file maintains various reasoners
"""
from abc import ABC, abstractmethod
from collections import defaultdict
from curses import termname
from imp import is_frozen
import math
from typing import Dict, List
from random import sample
import torch
from torch import nn
from src.language.fof import (BinaryPredicate, C... | 39,558 | 37.518987 | 99 | py |
FIT | FIT-main/src/evaluation/link_prediction.py | from collections import defaultdict
from tqdm import tqdm
import torch
import numpy as np
from .abstract_task import AbstractTask
from ..structure import KnowledgeGraph, NeuralBinaryPredicate
class LinkPrediction(AbstractTask):
def __init__(self, kg: KnowledgeGraph, observed_kg: KnowledgeGraph):
"""
... | 5,939 | 39.408163 | 90 | py |
FIT | FIT-main/src/evaluation/query_answering.py | from .abstract_task import AbstractTask
from typing import Dict
import json
from torch.utils.data import DataLoader
# from ..utils.data import collate_qaa_into_first_order_formula
class QueryAnsweringTV(AbstractTask):
def __init__(self, qaafile, **dataloader_kwargs) -> None:
with open(qaafile, 'rt') as f... | 558 | 28.421053 | 63 | py |
FIT | FIT-main/src/structure/nbp_complex.py | import torch
from torch import nn
from .neural_binary_predicate import NeuralBinaryPredicate
class ComplEx(NeuralBinaryPredicate, nn.Module):
def __init__(self,
num_entities: int,
num_relations: int,
embedding_dim: int,
scale: float = 1,
... | 4,239 | 35.869565 | 114 | py |
FIT | FIT-main/src/structure/geometric_graph.py | from typing import List
from collections import defaultdict, OrderedDict
import torch
import torch_geometric
from torch_geometric.data import Data
from src.language.fof import ConjunctiveFormula
from src.structure.knowledge_graph import KnowledgeGraph
class QueryGraph(Data):
def __init__(self, input_formula: Co... | 2,042 | 39.058824 | 109 | py |
FIT | FIT-main/src/structure/knowledge_graph.py | import copy
import random
import time
from collections import defaultdict
from typing import List, Tuple, Union, Any
from copy import deepcopy
import numpy as np
import torch
from torch.utils.data import DataLoader
from src.utils.config import KnowledgeGraphConfig
from .knowledge_graph_index import KGIndex
from ..uti... | 29,681 | 45.817035 | 187 | py |
FIT | FIT-main/src/structure/neural_binary_predicate.py | from abc import abstractmethod
import torch
class NeuralBinaryPredicate:
num_entities: int
num_relations: int
device: torch.device
scale: float
@abstractmethod
def embedding_score(self, head_emb, rel_emb, tail_emb):
"""
This method computes the score for the triple given the ... | 5,231 | 37.755556 | 116 | py |
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