repo_name stringclasses 100
values | file_path stringlengths 5 100 | file_content stringlengths 27 51.9k | imported_files_content stringlengths 45 239k | import_relationships dict |
|---|---|---|---|---|
shuishen112/pairwise-rnn | /models/__init__.py | from .QA_CNN_pairwise import QA_CNN_extend as CNN
from .QA_RNN_pairwise import QA_RNN_extend as RNN
from .QA_CNN_quantum_pairwise import QA_CNN_extend as QCNN
def setup(opt):
if opt["model_name"]=="cnn":
model=CNN(opt)
elif opt["model_name"]=="rnn":
model=RNN(opt)
elif opt['model_name']=='qcnn':
model=QCNN(opt... | #coding:utf-8
import tensorflow as tf
import numpy as np
from tensorflow.contrib import rnn
import models.blocks as blocks
# model_type :apn or qacnn
class QA_CNN_extend(object):
# def __init__(self,max_input_left,max_input_right,batch_size,vocab_size,embedding_size,filter_sizes,num_filters,hidden_size,
# dro... | {
"imported_by": [],
"imports": [
"/models/QA_CNN_pairwise.py"
]
} |
shuishen112/pairwise-rnn | /run.py | from tensorflow import flags
import tensorflow as tf
from config import Singleton
import data_helper
import datetime,os
import models
import numpy as np
import evaluation
import sys
import logging
import time
now = int(time.time())
timeArray = time.localtime(now)
timeStamp = time.strftime("%Y%m%d%H%M%S", timeArray)... | #-*- coding:utf-8 -*-
import os
import numpy as np
import tensorflow as tf
import string
from collections import Counter
import pandas as pd
from tqdm import tqdm
import random
from functools import wraps
import time
import pickle
def log_time_delta(func):
@wraps(func)
def _deco(*args, **kwargs):
star... | {
"imported_by": [],
"imports": [
"/data_helper.py",
"/config.py"
]
} |
shuishen112/pairwise-rnn | /test.py | # -*- coding: utf-8 -*-
from tensorflow import flags
import tensorflow as tf
from config import Singleton
import data_helper
import datetime
import os
import models
import numpy as np
import evaluation
from data_helper import log_time_delta,getLogger
logger=getLogger()
args = Singleton().get_rnn_flag()
#args... | #-*- coding:utf-8 -*-
import os
import numpy as np
import tensorflow as tf
import string
from collections import Counter
import pandas as pd
from tqdm import tqdm
import random
from functools import wraps
import time
import pickle
def log_time_delta(func):
@wraps(func)
def _deco(*args, **kwargs):
star... | {
"imported_by": [],
"imports": [
"/data_helper.py",
"/config.py"
]
} |
Sssssbo/SDCNet | /SDCNet.py | import datetime
import os
import time
import torch
from torch import nn
from torch import optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
import pandas as pd
import numpy as np
import joint_transforms
from config import msra10k_path, MTDD_train_path... | import torch
import torch.nn.functional as F
from torch import nn
from resnext import ResNeXt101
class R3Net(nn.Module):
def __init__(self):
super(R3Net, self).__init__()
res50 = ResNeXt101()
self.layer0 = res50.layer0
self.layer1 = res50.layer1
self.layer2 = res50.layer2
... | {
"imported_by": [],
"imports": [
"/model.py",
"/datasets.py",
"/misc.py"
]
} |
Sssssbo/SDCNet | /create_free.py | import numpy as np
import os
import torch
from PIL import Image
from torch.autograd import Variable
from torchvision import transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import pandas as pd
from tqdm import tqdm
import cv2
import numpy as np
from config import ecssd_path, hkuis_pa... | import os
import os.path
import torch.utils.data as data
from PIL import Image
class ImageFolder_joint(data.Dataset):
# image and gt should be in the same folder and have same filename except extended name (jpg and png respectively)
def __init__(self, label_list, joint_transform=None, transform=None, target_... | {
"imported_by": [],
"imports": [
"/datasets.py",
"/misc.py"
]
} |
Sssssbo/SDCNet | /infer_SDCNet.py | import numpy as np
import os
import torch
import torch.nn.functional as F
from PIL import Image
from torch.autograd import Variable
from torchvision import transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import pandas as pd
from tqdm import tqdm
from misc import check_mkdir, AvgMete... | import torch
import torch.nn.functional as F
from torch import nn
from resnext import ResNeXt101
class R3Net(nn.Module):
def __init__(self):
super(R3Net, self).__init__()
res50 = ResNeXt101()
self.layer0 = res50.layer0
self.layer1 = res50.layer1
self.layer2 = res50.layer2
... | {
"imported_by": [],
"imports": [
"/model.py",
"/datasets.py",
"/misc.py"
]
} |
Sssssbo/SDCNet | /model/make_model.py | "import torch\nimport torch.nn as nn\nfrom .backbones.resnet import ResNet, Comb_ResNet, Pure_ResNet(...TRUNCATED) | "import math\n\nimport torch\nfrom torch import nn\n\n\ndef conv3x3(in_planes, out_planes, stride=1)(...TRUNCATED) | {
"imported_by": [],
"imports": [
"/model/backbones/resnet.py"
]
} |
Sssssbo/SDCNet | /resnet/__init__.py | from .make_model import ResNet50, ResNet50_BIN, ResNet50_LowIN | "from .resnet import ResNet, BasicBlock, Bottleneck\nimport torch\nfrom torch import nn\nfrom .confi(...TRUNCATED) | {
"imported_by": [],
"imports": [
"/resnet/make_model.py"
]
} |
riadghorra/whiteboard-oop-project | /src/client.py | "import socket\nimport json\nimport sys\nimport math\nfrom white_board import WhiteBoard, binary_to_(...TRUNCATED) | "import pygame\nimport pygame.draw\nimport json\nimport sys\nfrom functools import reduce\nimport op(...TRUNCATED) | {
"imported_by": [],
"imports": [
"/src/white_board.py"
]
} |
riadghorra/whiteboard-oop-project | /src/main.py | "from white_board import WhiteBoard\nimport json\n\n'''\nThis file is used to run locally or to debu(...TRUNCATED) | "import pygame\nimport pygame.draw\nimport json\nimport sys\nfrom functools import reduce\nimport op(...TRUNCATED) | {
"imported_by": [],
"imports": [
"/src/white_board.py"
]
} |
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