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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DFG-NAS | DFG-NAS-main/code/search-cora.py | import sys
import time
import random
import argparse
import collections
import numpy as np
import torch
import torch.utils
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from utils import *
from train import *
... | 3,615 | 34.106796 | 96 | py |
DFG-NAS | DFG-NAS-main/code/utils.py | import sys
import time
import torch
import argparse
import numpy as np
import pickle as pkl
import networkx as nx
import scipy.sparse as sp
from scipy.sparse import csgraph
def sparse_mx_to_torch_sparse_tensor(sparse_mx):
"""Convert a scipy sparse matrix to a torch sparse tensor."""
sparse_mx = sparse_mx.tocoo... | 13,892 | 33.219212 | 111 | py |
DFG-NAS | DFG-NAS-main/code/search-citeseer.py | import sys
import time
import random
import argparse
import collections
import numpy as np
import torch
import torch.utils
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from utils import *
from train import *
... | 3,629 | 34.242718 | 99 | py |
DFG-NAS | DFG-NAS-main/code/search-pubmed.py | import sys
import time
import random
import argparse
import collections
import numpy as np
import torch
import torch.utils
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from utils import *
from train import *
... | 3,623 | 34.529412 | 97 | py |
DFG-NAS | DFG-NAS-main/code/search-ogbn.py | import sys
import time
import random
import argparse
import collections
import numpy as np
import torch
import torch.utils
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch_sparse import SparseTensor
fro... | 4,456 | 34.094488 | 99 | py |
DFG-NAS | DFG-NAS-main/code/train.py | import torch
import torch.nn as nn
import torch.optim as optim
import argparse
from utils import *
from operation import *
def train_and_eval(args, arch, data, index):
adj, features, labels = data
record = []
test_record = []
model = ModelOp(arch, adj, features.shape[1], args.hiddim, labels.max().ite... | 1,522 | 34.418605 | 110 | py |
DFG-NAS | DFG-NAS-main/code/operation.py | import torch
import torch.utils
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
class Graph(nn.Module):
def __init__(self, adj):
super(Graph, self).__init__()
... | 3,498 | 33.643564 | 116 | py |
StyleSpeech | StyleSpeech-main/evaluate.py | import argparse
import os
import torch
import yaml
import torch.nn as nn
from torch.utils.data import DataLoader
from utils.model import get_model, get_vocoder
from utils.tools import to_device, log, synth_one_sample
from model import MetaStyleSpeechLossMain
from dataset import Dataset
device = torch.device("cuda" ... | 2,575 | 28.609195 | 144 | py |
StyleSpeech | StyleSpeech-main/dataset.py | import json
import math
import os
import random
import numpy as np
from torch.utils.data import Dataset
from text import text_to_sequence
from utils.tools import pad_1D, pad_2D, expand
random.seed(1234)
class Dataset(Dataset):
def __init__(
self, filename, preprocess_config, train_config, sort=False, d... | 9,674 | 32.710801 | 103 | py |
StyleSpeech | StyleSpeech-main/synthesize.py | import re
import argparse
from string import punctuation
import torch
import yaml
import numpy as np
import os
import json
import librosa
import pyworld as pw
import audio as Audio
from torch.utils.data import DataLoader
from g2p_en import G2p
from pypinyin import pinyin, Style
from utils.model import get_model, get... | 8,567 | 30.851301 | 106 | py |
StyleSpeech | StyleSpeech-main/train.py | import argparse
import os
import torch
import yaml
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from utils.model import get_model, get_vocoder, get_param_num
from utils.tools import to_device, log, synth_one_sample
from model imp... | 8,522 | 36.88 | 255 | py |
StyleSpeech | StyleSpeech-main/audio/stft.py | import torch
import torch.nn.functional as F
import numpy as np
from scipy.signal import get_window
from librosa.util import pad_center, tiny
from librosa.filters import mel as librosa_mel_fn
from audio.audio_processing import (
dynamic_range_compression,
dynamic_range_decompression,
window_sumsquare,
)
... | 6,226 | 33.787709 | 85 | py |
StyleSpeech | StyleSpeech-main/audio/tools.py | import torch
import numpy as np
from scipy.io.wavfile import write
from audio.audio_processing import griffin_lim
def get_mel_from_wav(audio, _stft):
audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1)
audio = torch.autograd.Variable(audio, requires_grad=False)
melspec, energy = _stft.mel_sp... | 1,188 | 32.971429 | 88 | py |
StyleSpeech | StyleSpeech-main/audio/audio_processing.py | import torch
import numpy as np
import librosa.util as librosa_util
from scipy.signal import get_window
def window_sumsquare(
window,
n_frames,
hop_length,
win_length,
n_fft,
dtype=np.float32,
norm=None,
):
"""
# from librosa 0.6
Compute the sum-square envelope of a window func... | 2,613 | 24.881188 | 86 | py |
StyleSpeech | StyleSpeech-main/hifigan/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Conv1d, ConvTranspose1d
from torch.nn.utils import weight_norm, remove_weight_norm
LRELU_SLOPE = 0.1
def init_weights(m, mean=0.0, std=0.01):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
m.wei... | 5,515 | 30.701149 | 83 | py |
StyleSpeech | StyleSpeech-main/utils/model.py | import os
import json
import torch
import numpy as np
import hifigan
from model import StyleSpeech, ScheduledOptimMain, ScheduledOptimDisc
def get_model(args, configs, device, train=False):
(preprocess_config, model_config, train_config) = configs
model = StyleSpeech(preprocess_config, model_config).to(dev... | 2,966 | 29.587629 | 80 | py |
StyleSpeech | StyleSpeech-main/utils/tools.py | import os
import json
import torch
import torch.nn.functional as F
import numpy as np
import matplotlib
from scipy.io import wavfile
from matplotlib import pyplot as plt
matplotlib.use("Agg")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def to_device(data, device):
if len(data) == 17... | 11,414 | 30.796657 | 88 | py |
StyleSpeech | StyleSpeech-main/model/modules.py | import os
import json
import copy
import math
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import numpy as np
import torch.nn.functional as F
from utils.tools import get_mask_from_lengths, pad
from .blocks import (
Mish,
FCBlock,
Conv1DBl... | 24,652 | 32.633015 | 135 | py |
StyleSpeech | StyleSpeech-main/model/StyleSpeech.py | import os
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
from .modules import (
MelStyleEncoder,
PhonemeEncoder,
MelDecoder,
VarianceAdaptor,
PhonemeDiscriminator,
StyleDiscriminator,
)
from utils.tools import get_mask_from_lengths
class StyleSpeech(nn.Module... | 8,563 | 25.7625 | 133 | py |
StyleSpeech | StyleSpeech-main/model/loss.py | import torch
import torch.nn as nn
class MetaStyleSpeechLossMain(nn.Module):
""" Meta-StyleSpeech Loss for naive StyleSpeech and Step 1 """
def __init__(self, preprocess_config, model_config, train_config):
super(MetaStyleSpeechLossMain, self).__init__()
self.pitch_feature_level = preprocess_... | 5,171 | 33.945946 | 100 | py |
StyleSpeech | StyleSpeech-main/model/optimizer.py | import torch
import numpy as np
class ScheduledOptimMain:
""" A simple wrapper class for learning rate scheduling """
def __init__(self, model, train_config, model_config, current_step):
self._optimizer = torch.optim.Adam(
[param for name, param in model.named_parameters()
... | 3,174 | 35.918605 | 122 | py |
StyleSpeech | StyleSpeech-main/model/blocks.py | import torch
import torch.nn as nn
import numpy as np
from torch.nn import functional as F
class Mish(nn.Module):
def forward(self, x):
return x * torch.tanh(F.softplus(x))
class StyleAdaptiveLayerNorm(nn.Module):
""" Style-Adaptive Layer Norm (SALN) """
def __init__(self, w_size, hidden_size, ... | 9,064 | 29.116279 | 115 | py |
HigeNet | HigeNet-main/atten_comps.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
import numpy as np
import global_var
from math import sqrt
from utils.masking import TriangularCausalMask, ProbMask
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import GPUtil
import psutil
from torchstat import st... | 9,941 | 38.452381 | 130 | py |
HigeNet | HigeNet-main/predict.py | import argparse
import os
import torch
from exp.exp_informer import Exp_Informer
parser = argparse.ArgumentParser(description='[Informer] Long Sequences Forecasting')
parser.add_argument('--model', type=str, default='informer',help='model of experiment, options: [informer, informerstack, informerlight(TBD)]')
parser.a... | 6,252 | 64.821053 | 237 | py |
HigeNet | HigeNet-main/train.py | import argparse
import os
import torch
from exp.exp_informer import Exp_Informer
import global_var
import random
import numpy as np
def seed_torch(seed=1029):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_see... | 9,096 | 66.88806 | 245 | py |
HigeNet | HigeNet-main/models/embed.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
import global_var
class PositionalEmbedding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEmbedding, self).__init__()
# Compute the positional encodings once in log space.
... | 8,290 | 42.408377 | 202 | py |
HigeNet | HigeNet-main/models/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.masking import TriangularCausalMask, ProbMask
from models.encoder import Encoder, EncoderLayer, ConvLayer, EncoderStack
from models.decoder import Decoder, DecoderLayer
from models.attn import FullAttention, ProbAttention, AttentionLayer
fro... | 6,372 | 41.771812 | 122 | py |
HigeNet | HigeNet-main/models/encoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import global_var
class ConvLayer(nn.Module):
def __init__(self, c_in):
super(ConvLayer, self).__init__()
padding = 1 if torch.__version__>='1.5.0' else 2
self.downConv = nn.Conv1d(in_channels=c_in,
... | 3,796 | 37.353535 | 90 | py |
HigeNet | HigeNet-main/models/decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class DecoderLayer(nn.Module):
def __init__(self, self_attention, cross_attention, d_model, d_ff=None,
dropout=0.1, activation="relu"):
super(DecoderLayer, self).__init__()
d_ff = d_ff or 4*d_model
self.self... | 1,859 | 34.09434 | 85 | py |
HigeNet | HigeNet-main/models/attn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import global_var
from math import sqrt
from utils.masking import TriangularCausalMask, ProbMask
class FullAttention(nn.Module):
def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=Fa... | 6,917 | 37.865169 | 130 | py |
HigeNet | HigeNet-main/utils/tools.py | import numpy as np
import torch
def adjust_learning_rate(optimizer, epoch, args):
# lr = args.learning_rate * (0.2 ** (epoch // 2))
if args.lradj=='type1':
lr_adjust = {epoch: args.learning_rate * (0.5 ** ((epoch-1) // 1))}
elif args.lradj=='type2':
lr_adjust = {
2: 5e-5, 4: 1e-5... | 2,725 | 36.861111 | 112 | py |
HigeNet | HigeNet-main/utils/masking.py | import torch
class TriangularCausalMask():
def __init__(self, B, L, device="cpu"):
mask_shape = [B, 1, L, L]
with torch.no_grad():
self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device)
@property
def mask(self):
return self._mask
class... | 851 | 34.5 | 100 | py |
HigeNet | HigeNet-main/data/data_loader.py | import os
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
# from sklearn.preprocessing import StandardScaler
from utils.tools import StandardScaler
from utils.timefeatures import time_features
import warnings
warnings.filterwarnings('ignore')
class Dataset_ETT_ho... | 13,354 | 34.518617 | 105 | py |
HigeNet | HigeNet-main/exp/exp_informer.py | from data.data_loader import Dataset_ETT_hour, Dataset_ETT_minute, Dataset_Custom, Dataset_Pred
from exp.exp_basic import Exp_Basic
from models.model import Informer, InformerStack
from utils.tools import EarlyStopping, adjust_learning_rate
from utils.metrics import metric
import GPUtil
import psutil
import numpy as n... | 14,767 | 42.307918 | 206 | py |
HigeNet | HigeNet-main/exp/exp_basic.py | import os
import torch
import numpy as np
class Exp_Basic(object):
def __init__(self, args):
self.args = args
self.device = self._acquire_device()
self.model = self._build_model().to(self.device)
def _build_model(self):
raise NotImplementedError
return None
def... | 875 | 23.333333 | 121 | py |
COPS-camera_ready | COPS-camera_ready/affinityNet/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... | 7,844 | 34.497738 | 96 | py |
COPS-camera_ready | COPS-camera_ready/affinityNet/train_net.py | #!/usr/bin/env python3
"""
Panoptic-affinity Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
import os
import torch
from typing import List, Set
import detectron2.data.transforms as T
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionC... | 15,047 | 40.454545 | 125 | py |
COPS-camera_ready | COPS-camera_ready/affinityNet/panoptic_affinity/post_processing.py | import torch
import torch.nn.functional as F
import numpy as np
# import matplotlib as mpl
# mpl.use('Agg')
import matplotlib.pyplot as plt
import time, sys
import logging
from . import utils
import torch.multiprocessing as mp
logger = logging.getLogger(__name__)
def get_panoptic_segmentation_multicut_batch(panoptic_... | 2,482 | 44.981481 | 199 | py |
COPS-camera_ready | COPS-camera_ready/affinityNet/panoptic_affinity/losses.py | import torch
import torch.nn as nn
from panopticapi.evaluation import PQStat
import numpy as np
from .multicut_solvers import solve_mc_grad_avg_batch
OFFSET = 256 * 256 * 256
VOID = 0
def iou_batch(pred, target, weight, pixel_dims):
eps = 1e-1
intersection_dense = pred * target
intersection = (intersec... | 27,755 | 50.116022 | 226 | py |
COPS-camera_ready | COPS-camera_ready/affinityNet/panoptic_affinity/multicut_solvers.py | import torch
from lpmp_mc.raw_solvers import amwc_solver, mwc_solver
import numpy as np
import torch.multiprocessing as mp
def get_edge_indices(image_shape, edge_distances, edge_sampling_intervals):
indices = np.arange(np.prod(image_shape)).reshape(image_shape).astype(np.int32)
edge_indices = {}
current_s... | 21,076 | 54.32021 | 353 | py |
COPS-camera_ready | COPS-camera_ready/affinityNet/panoptic_affinity/panoptic_seg_affinity.py | # import matplotlib as mpl
# mpl.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import os, shutil, time
from collections import defaultdict
from typing import Callable, Dict, List, Union
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
import logging
from torch.nn import fun... | 43,449 | 50.057579 | 220 | py |
COPS-camera_ready | COPS-camera_ready/affinityNet/panoptic_affinity/dataset_mapper.py | import copy
import logging
import numpy as np
from typing import Callable, List, Union
import torch
from panopticapi.utils import rgb2id
from detectron2.config import configurable
from detectron2.data import MetadataCatalog
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as ... | 4,869 | 38.918033 | 129 | py |
COPS-camera_ready | COPS-camera_ready/affinityNet/panoptic_affinity/utils.py | import numpy as np
import os, sys
from detectron2.data.detection_utils import convert_image_to_rgb
from torch._C import dtype
from torch.nn import functional as F
import torch
from scipy.optimize import linear_sum_assignment
from detectron2.utils.events import get_event_storage
from detectron2.modeling.postprocessing... | 23,681 | 46.554217 | 203 | py |
COPS-camera_ready | COPS-camera_ready/affinityNet/panoptic_affinity/target_generator.py | import numpy as np
import torch
from PIL import Image
from detectron2.structures import ImageList
from skimage.util.shape import view_as_windows
from scipy import stats
class PanopticAffinityTargetGenerator(object):
"""
Generates training targets for Panoptic-AffinityNet.
"""
def __init__(
sel... | 12,031 | 52.955157 | 205 | py |
Mixed_supervision | Mixed_supervision-master/main.py | __author__="Hao Bian"
import argparse
import random
import numpy as np
from numpy.core.arrayprint import DatetimeFormat
import pandas as pd
import yaml
from addict import Dict
from pathlib import Path
import pprint
from mmcv import Config
import sys
import os.path as osp
# print(sys.path)
parentdir = osp.dirname(osp.d... | 3,666 | 27.874016 | 99 | py |
Mixed_supervision | Mixed_supervision-master/callbacks/mixed_loss_callbacks.py | from typing import Optional, List
from pytorch_lightning.callbacks import Callback
import copy
import torch
from omegaconf import OmegaConf
from utils.util import dynamic_import_from
from loss.common_loss import get_loss
class CombinedCriterion(torch.nn.Module):
def __init__(self, loss: dict, device) -> None:
... | 4,646 | 39.408696 | 96 | py |
Mixed_supervision | Mixed_supervision-master/callbacks/common_callbacks.py | import argparse
from gc import callbacks
from numpy.core.arrayprint import DatetimeFormat
import yaml
from addict import Dict
from pathlib import Path
import pprint
# from experiment.models.model_interface import ModelInterface
import sys
import importlib
from utils.util import dynamic_import_from
# pytorch_lightning... | 3,264 | 33.010417 | 99 | py |
Mixed_supervision | Mixed_supervision-master/models/Mixed_supervision.py | __author__="Hao Bian"
import math
import numpy as np
import torch
import torch.nn as nn
from timm.models import create_model
from timm.models.layers import trunc_normal_
from .builder import MODELS
from utils.util import read_yaml
from .layers import *
def get_block(block_type, **kargs):
if block_type == 'mha':... | 9,287 | 35.857143 | 139 | py |
Mixed_supervision | Mixed_supervision-master/models/layers.py | import torch
import torch.nn as nn
import numpy as np
from functools import partial
import torch.nn.init as init
import torch.nn.functional as F
import math
from timm.models.layers import DropPath, to_2tuple
DROPOUT_FLOPS = 4
LAYER_NORM_FLOPS = 5
ACTIVATION_FLOPS = 8
SOFTMAX_FLOPS = 5
class GroupLinear(nn.Module):
... | 16,369 | 33.608879 | 159 | py |
Mixed_supervision | Mixed_supervision-master/models/test_model.py | import os.path as osp
import sys
from addict import Dict
parentdir = osp.dirname(osp.dirname(__file__))
sys.path.insert(0, parentdir)
from models.builder import build_model
from utils.config import load_config
import torch
from utils.util import dynamic_import_from
config_name = 'configs/SICAPv2.yaml'
cfg = load_conf... | 566 | 21.68 | 72 | py |
Mixed_supervision | Mixed_supervision-master/models/model_interface.py | __author__ = "Hao Bian"
import argparse
from functools import partial
import inspect
from pathlib import Path
from cv2 import phase
import pandas as pd
import sys
import os
import numpy as np
import importlib
import copy
from os.path import join as opj
import matplotlib.pyplot as plt
import openslide
from pytorch_ligh... | 6,750 | 33.269036 | 302 | py |
Mixed_supervision | Mixed_supervision-master/datasets/data_utils.py | from albumentations.pytorch import ToTensorV2
import albumentations as A
import numpy as np
import cv2
import matplotlib.pyplot as plt
import tifffile as tiff
MEAN = np.array([0.485, 0.456, 0.406])
STD = np.array([0.229, 0.224, 0.225])
def visulize(input_img, mode='opencv'):
if mode == 'opencv':
plt.fig... | 3,514 | 28.537815 | 107 | py |
Mixed_supervision | Mixed_supervision-master/datasets/Mixed_data.py | __author__ = "Hao Bian"
import glob
import os
import sys
sys.path.append('.')
from tqdm import tqdm
from utils.util import read_yaml
import random
import numpy as np
import torch
import pandas as pd
from pathlib import Path
import torch
import torch.utils.data as data
from torch.utils.data import random_split, DataLo... | 7,820 | 33.004348 | 111 | py |
Mixed_supervision | Mixed_supervision-master/datasets/data_interface.py |
__author__ = "Hao Bian"
import inspect
import importlib # In order to dynamically import the library
import pytorch_lightning as pl
from torch.utils.data import random_split, DataLoader
class DataInterface(pl.LightningDataModule):
def __init__(self, cfg, **kwargs):
"""[summary]
Args:
... | 4,066 | 37.009346 | 154 | py |
Mixed_supervision | Mixed_supervision-master/loss/dice_loss.py | """
get_tp_fp_fn, SoftDiceLoss, and DC_and_CE/TopK_loss are from https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/training/loss_functions
"""
import torch
from .ND_Crossentropy import CrossentropyND, TopKLoss, WeightedCrossEntropyLoss
from torch import nn
from torch.autograd import Variable
from torch import eins... | 17,461 | 33.374016 | 138 | py |
Mixed_supervision | Mixed_supervision-master/loss/lovasz_loss.py | import torch
import torch.nn as nn
#from torch.autograd import Function
# copy from: https://github.com/Hsuxu/Loss_ToolBox-PyTorch/blob/master/LovaszSoftmax/lovasz_loss.py
def lovasz_grad(gt_sorted):
"""
Computes gradient of the Lovasz extension w.r.t sorted errors
See Alg. 1 in paper
"""
p = len(... | 2,458 | 34.128571 | 106 | py |
Mixed_supervision | Mixed_supervision-master/loss/ND_Crossentropy.py | """
CrossentropyND and TopKLoss are from: https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/training/loss_functions/ND_Crossentropy.py
"""
import torch
import torch.nn.functional as F
from scipy.ndimage import distance_transform_edt
import numpy as np
class CrossentropyND(torch.nn.CrossEntropyLoss):
"""
... | 7,162 | 31.411765 | 134 | py |
Mixed_supervision | Mixed_supervision-master/loss/common_loss.py | from typing import List
import torch
from torch import nn
from utils.util import dynamic_import_from
class MultiLabelBCELoss(nn.Module):
"""Binary Cross Entropy loss over each label seperately, then averaged"""
def __init__(self, weight=None) -> None:
super().__init__()
self.weight = weight
... | 4,509 | 39.630631 | 114 | py |
Mixed_supervision | Mixed_supervision-master/loss/boundary_loss.py | import torch
from .ND_Crossentropy import CrossentropyND, TopKLoss
from torch import nn
from scipy.ndimage import distance_transform_edt
import numpy as np
def softmax_helper(x):
# copy from: https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/utilities/nd_softmax.py
rpt = [1 for _ in range(len(x.size()))]... | 9,819 | 32.515358 | 133 | py |
Mixed_supervision | Mixed_supervision-master/loss/loss_factory.py | __author__ = 'Hao Bian'
import torch
import torch.nn as nn
import torchmetrics
from torchmetrics import metric
# from .boundary_loss import BDLoss, SoftDiceLoss, DC_and_BD_loss, HDDTBinaryLoss,\
# DC_and_HDBinary_loss, DistBinaryDiceLoss
# from .dice_loss import GDiceLoss, GDiceLossV2, SSLoss, SoftDiceLoss,\
# ... | 3,872 | 36.970588 | 83 | py |
Mixed_supervision | Mixed_supervision-master/loss/focal_loss.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
"""
copy from: https://github.com/Hsuxu/Loss_ToolBox-PyTorch/blob/master/FocalLoss/FocalLoss.py
This is a implementation of Focal Loss with smooth label cross entropy supported which is propos... | 3,479 | 36.021277 | 118 | py |
Mixed_supervision | Mixed_supervision-master/loss/hausdorff.py | import cv2 as cv
import numpy as np
import torch
from torch import nn
from scipy.ndimage.morphology import distance_transform_edt as edt
from scipy.ndimage import convolve
"""
Hausdorff loss implementation based on paper:
https://arxiv.org/pdf/1904.10030.pdf
copy pasted from - all credit goes to original authors:
h... | 5,152 | 28.614943 | 87 | py |
Mixed_supervision | Mixed_supervision-master/utils/utils_mixed.py | import argparse
from turtle import width
import yaml
import importlib
from sklearn.metrics import confusion_matrix
from typing import Optional, Any, Union
import itertools
from matplotlib.colors import ListedColormap
import pandas as pd
import numpy as np
import torch
from matplotlib import pyplot as plt
import os
from... | 11,474 | 32.357558 | 127 | py |
Mixed_supervision | Mixed_supervision-master/utils/util.py | import importlib
from typing import Optional, Any, Union
import yaml
from addict import Dict
import os
import torch.nn.functional as F
import torch
def dynamic_import_from(source_file: str, class_name: str) -> Any:
"""Do a from source_file import class_name dynamically
Args:
source_file (str): Where ... | 2,788 | 28.670213 | 92 | py |
Mixed_supervision | Mixed_supervision-master/utils/metrics.py | from functools import partial
import logging
from abc import abstractmethod
from typing import List, Any, Union
import numpy as np
import sklearn.metrics
import torch
class Metric:
def __init__(self, *args, **kwargs) -> None:
pass
@staticmethod
def is_better(value: Any, comparison: Any) -> bool:
... | 25,788 | 34.375857 | 125 | py |
EasyNMT | EasyNMT-main/setup.py | from setuptools import setup, find_packages
with open("README.md", mode="r", encoding="utf-8") as readme_file:
readme = readme_file.read()
setup(
name="EasyNMT",
version="2.0.2",
author="Nils Reimers",
author_email="info@nils-reimers.de",
description="Easy to use state-of-the-art Neural Machin... | 1,136 | 30.583333 | 74 | py |
EasyNMT | EasyNMT-main/easynmt/EasyNMT.py | import os
import torch
from .util import http_get, import_from_string, fullname
import json
from . import __DOWNLOAD_SERVER__
from typing import List, Union, Dict, FrozenSet, Set, Iterable
import numpy as np
import tqdm
import nltk
import torch.multiprocessing as mp
import queue
import math
import re
import logging
imp... | 22,217 | 43.170974 | 227 | py |
EasyNMT | EasyNMT-main/easynmt/models/AutoModel.py | from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
from typing import List
import logging
logger = logging.getLogger(__name__)
class AutoModel:
def __init__(self, model_name: str, tokenizer_name: str = None, easynmt_path: str = None, lang_map=None, tokenizer_args=None):
if token... | 2,231 | 31.823529 | 135 | py |
EasyNMT | EasyNMT-main/easynmt/models/OpusMT.py | import time
from transformers import MarianMTModel, MarianTokenizer
import torch
from typing import List
import logging
logger = logging.getLogger(__name__)
class OpusMT:
def __init__(self, easynmt_path: str = None, max_loaded_models: int = 10):
self.models = {}
self.max_loaded_models = max_loade... | 2,236 | 38.245614 | 135 | py |
markup2im | markup2im-main/upload.py | import math
import os
import numpy as np
from dataclasses import dataclass
import torch
import torch.nn
import torch.nn.functional as F
from torch.utils.data._utils.collate import default_collate
from torchvision import transforms
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModel
from ... | 12,445 | 39.148387 | 177 | py |
markup2im | markup2im-main/test_inf.py | import math
import os
import numpy as np
from dataclasses import dataclass
import torch
import torch.nn
import torch.nn.functional as F
from torch.utils.data._utils.collate import default_collate
from torchvision import transforms
from datasets import load_dataset, concatenate_datasets
from transformers import AutoToke... | 11,322 | 39.584229 | 178 | py |
markup2im | markup2im-main/test_inf_html.py | import math
import os
import numpy as np
from dataclasses import dataclass
import torch
import torch.nn
import torch.nn.functional as F
from torch.utils.data._utils.collate import default_collate
from torchvision import transforms
from datasets import load_dataset, concatenate_datasets
from transformers import AutoToke... | 11,563 | 39.575439 | 178 | py |
markup2im | markup2im-main/src/markup2im_models.py | import os
import torch
from diffusers import UNet2DConditionModel
def create_image_decoder(image_size, color_channels, cross_attention_dim):
image_decoder = UNet2DConditionModel(
sample_size=image_size,
in_channels=color_channels,
out_channels=color_channels,
layers_per_block=2,
... | 2,090 | 40 | 86 | py |
markup2im | markup2im-main/src/train.py | import os
import sys
import random
import argparse
import torch
import numpy as np
import torch.nn.functional as F
from torch.utils.data._utils.collate import default_collate
from torchvision import transforms
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModel
from diffusers import D... | 18,318 | 48.915531 | 167 | py |
markup2im | markup2im-main/eval_utils/clip_utils.py | import torch
import clip as clip_utils
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip_utils.load("ViT-B/32", device=device)
def clip_score(pred_img: Image, gold_img: Image) -> float:
pred_img, gold_img = preprocess(pred_img).to(device), preprocess(gold_img)... | 759 | 35.190476 | 89 | py |
markup2im | markup2im-main/scripts/generate.py | import math
import random
import sys
import os
import torch
import tqdm
import argparse
import torch.nn
import numpy as np
from torch.utils.data._utils.collate import default_collate
from torchvision import transforms
from datasets import load_dataset, concatenate_datasets
from transformers import AutoTokenizer, AutoM... | 11,884 | 44.711538 | 173 | py |
ToST | ToST-main/label-smoothening/train_ticket.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 18,496 | 39.123644 | 179 | py |
ToST | ToST-main/label-smoothening/cifar_baseline.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets... | 16,640 | 38.527316 | 180 | py |
ToST | ToST-main/label-smoothening/cifar_prune.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 14,232 | 40.616959 | 179 | py |
ToST | ToST-main/label-smoothening/activations.py | import torch
from torch import nn
from torch.nn import functional as F
class SwishParameteric(nn.Module):
def __init__(self, inplace=True):
super().__init__()
def forward(self, x, beta = 2):
return x * torch.sigmoid(beta*x)
class GeLU(nn.Module):
def __init__(self, inplace=True):
... | 3,869 | 29.96 | 101 | py |
ToST | ToST-main/label-smoothening/models/resnet.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from activations import *
activation_list =... | 5,168 | 34.648276 | 102 | py |
ToST | ToST-main/label-smoothening/models/oresnet.py | from __future__ import absolute_import
import math
import torch.nn as nn
from activations import *
activation_list = {'relu': nn.ReLU,
'swish': nn.SiLU,
'softplus': nn.Softplus,
'elu': nn.ELU,
'swish_parametric' : SwishParameteric,
... | 5,110 | 29.975758 | 94 | py |
ToST | ToST-main/label-smoothening/models/.ipynb_checkpoints/resnet-checkpoint.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from activations import *
activation_list =... | 5,168 | 34.648276 | 102 | py |
ToST | ToST-main/label-smoothening/models/.ipynb_checkpoints/oresnet-checkpoint.py | from __future__ import absolute_import
import math
import torch.nn as nn
from activations import *
activation_list = {'relu': nn.ReLU,
'swish': nn.SiLU,
'softplus': nn.Softplus,
'elu': nn.ELU,
'swish_parametric' : SwishParameteric,
... | 5,110 | 29.975758 | 94 | py |
ToST | ToST-main/label-smoothening/.ipynb_checkpoints/cifar_baseline-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets... | 16,640 | 38.527316 | 180 | py |
ToST | ToST-main/label-smoothening/.ipynb_checkpoints/cifar_prune-checkpoint.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 14,232 | 40.616959 | 179 | py |
ToST | ToST-main/label-smoothening/.ipynb_checkpoints/train_ticket-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 18,496 | 39.123644 | 179 | py |
ToST | ToST-main/label-smoothening/.ipynb_checkpoints/activations-checkpoint.py | import torch
from torch import nn
from torch.nn import functional as F
class SwishParameteric(nn.Module):
def __init__(self, inplace=True):
super().__init__()
def forward(self, x, beta = 2):
return x * torch.sigmoid(beta*x)
class GeLU(nn.Module):
def __init__(self, inplace=True):
... | 3,869 | 29.96 | 101 | py |
ToST | ToST-main/label-smoothening/utils/misc.py | '''Some helper functions for PyTorch, including:
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import errno
import os
import sys
import time
import torch
import math
import torch.nn as nn
impo... | 3,085 | 29.554455 | 110 | py |
ToST | ToST-main/label-smoothening/utils/logger.py | from __future__ import absolute_import
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def plot_overlap(logger, names=None):
names = lo... | 4,349 | 33.52381 | 100 | py |
ToST | ToST-main/label-smoothening/utils/visualize.py | import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
from .misc import *
__all__ = ['make_image', 'show_batch', 'show_mask', 'show_mask_single']
# functions to show an image
def make_image(img, mean=(0,0,0), std=(1,1,1)... | 3,795 | 33.509091 | 95 | py |
ToST | ToST-main/label-smoothening/utils/.ipynb_checkpoints/logger-checkpoint.py | from __future__ import absolute_import
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def plot_overlap(logger, names=None):
names = lo... | 4,349 | 33.52381 | 100 | py |
ToST | ToST-main/label-smoothening/utils/.ipynb_checkpoints/misc-checkpoint.py | '''Some helper functions for PyTorch, including:
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import errno
import os
import sys
import time
import torch
import math
import torch.nn as nn
impo... | 3,085 | 29.554455 | 110 | py |
ToST | ToST-main/label-smoothening/utils/.ipynb_checkpoints/visualize-checkpoint.py | import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
from .misc import *
__all__ = ['make_image', 'show_batch', 'show_mask', 'show_mask_single']
# functions to show an image
def make_image(img, mean=(0,0,0), std=(1,1,1)... | 3,795 | 33.509091 | 95 | py |
ToST | ToST-main/skip_connection/train_ticket.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 17,517 | 39.178899 | 179 | py |
ToST | ToST-main/skip_connection/cifar_baseline.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.dataset... | 16,423 | 38.671498 | 180 | py |
ToST | ToST-main/skip_connection/cifar_prune.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 13,498 | 40.79257 | 179 | py |
ToST | ToST-main/skip_connection/skip_lottery_ticket.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets... | 17,619 | 39.320366 | 179 | py |
ToST | ToST-main/skip_connection/ls_skip_lottery_ticket.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.dataset... | 18,159 | 39.176991 | 179 | py |
ToST | ToST-main/skip_connection/activations.py | import torch
from torch import nn
from torch.nn import functional as F
class SwishParameteric(nn.Module):
def __init__(self, inplace=True):
super().__init__()
def forward(self, x, beta = 2):
return x * torch.sigmoid(beta*x)
class GeLU(nn.Module):
def __init__(self, inplace=True):
... | 3,869 | 29.96 | 101 | py |
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