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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WordArt | WordArt-main/mmocr/models/textrecog/backbones/very_deep_vgg.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule, Sequential
from mmocr.models.builder import BACKBONES
@BACKBONES.register_module()
class VeryDeepVgg(BaseModule):
"""Implement VGG-VeryDeep backbone for text recognition, modified from
`VGG-VeryDeep <htt... | 2,587 | 31.35 | 79 | py |
WordArt | WordArt-main/mmocr/models/textrecog/backbones/resnet31_ocr.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule, Sequential
import mmocr.utils as utils
from mmocr.models.builder import BACKBONES
from mmocr.models.textrecog.layers import BasicBlock
@BACKBONES.register_module()
class ResNet31OCR(BaseModule):
"""Implement... | 5,649 | 37.69863 | 79 | py |
WordArt | WordArt-main/mmocr/models/textrecog/heads/seg_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from torch import nn
from mmocr.models.builder import HEADS
@HEADS.register_module()
class SegHead(BaseModule):
"""Head for segmentation based text recognition.
... | 2,022 | 30.123077 | 78 | py |
WordArt | WordArt-main/mmocr/datasets/kie_dataset.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
from os import path as osp
import numpy as np
import torch
from mmdet.datasets.builder import DATASETS
from mmocr.core import compute_f1_score
from mmocr.datasets.base_dataset import BaseDataset
from mmocr.datasets.pipelines import sort_verte... | 8,705 | 35.734177 | 79 | py |
WordArt | WordArt-main/mmocr/datasets/base_dataset.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmcv.utils import print_log
from mmdet.datasets.builder import DATASETS
from mmdet.datasets.pipelines import Compose
from torch.utils.data import Dataset
from mmocr.datasets.builder import build_loader
@DATASETS.register_module()
class BaseDatas... | 5,469 | 31.176471 | 75 | py |
WordArt | WordArt-main/mmocr/datasets/openset_kie_dataset.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import numpy as np
import torch
from mmdet.datasets.builder import DATASETS
from mmocr.datasets import KIEDataset
@DATASETS.register_module()
class OpensetKIEDataset(KIEDataset):
"""Openset KIE classifies the nodes (i.e. text boxes) into bg/key/value
... | 12,082 | 37.977419 | 79 | py |
WordArt | WordArt-main/mmocr/datasets/pipelines/transform_wrappers.py | # Copyright (c) OpenMMLab. All rights reserved.
import inspect
import random
import mmcv
import numpy as np
import torchvision.transforms as torchvision_transforms
from mmcv.utils import build_from_cfg
from mmdet.datasets.builder import PIPELINES
from mmdet.datasets.pipelines import Compose
from PIL import Image
@PI... | 4,022 | 30.186047 | 79 | py |
WordArt | WordArt-main/mmocr/datasets/pipelines/ner_transforms.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.datasets.builder import PIPELINES
from mmocr.models.builder import build_convertor
@PIPELINES.register_module()
class NerTransform:
"""Convert text to ID and entity in ground truth to label ID. The masks and
tokens are generated at the s... | 2,051 | 31.0625 | 79 | py |
WordArt | WordArt-main/mmocr/datasets/pipelines/ocr_transforms.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import mmcv
import numpy as np
import torch
import torchvision.transforms.functional as TF
from mmcv.runner.dist_utils import get_dist_info
from mmdet.datasets.builder import PIPELINES
from PIL import Image
from shapely.geometry import Polygon
from shapely.ge... | 15,708 | 33.525275 | 79 | py |
WordArt | WordArt-main/mmocr/datasets/pipelines/transforms.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import cv2
import mmcv
import numpy as np
import torchvision.transforms as transforms
from mmdet.core import BitmapMasks, PolygonMasks
from mmdet.datasets.builder import PIPELINES
from mmdet.datasets.pipelines.transforms import Resize
from PIL import Image
fr... | 37,543 | 35.771792 | 79 | py |
WordArt | WordArt-main/mmocr/datasets/pipelines/textdet_targets/textsnake_targets.py | # Copyright (c) OpenMMLab. All rights reserved.
import cv2
import numpy as np
from mmdet.core import BitmapMasks
from mmdet.datasets.builder import PIPELINES
from numpy.linalg import norm
import mmocr.utils.check_argument as check_argument
from . import BaseTextDetTargets
@PIPELINES.register_module()
class TextSnake... | 20,827 | 40.907445 | 79 | py |
WordArt | WordArt-main/mmocr/datasets/pipelines/textdet_targets/panet_targets.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core import BitmapMasks
from mmdet.datasets.builder import PIPELINES
from . import BaseTextDetTargets
@PIPELINES.register_module()
class PANetTargets(BaseTextDetTargets):
"""Generate the ground truths for PANet: Efficient and Accurate Arbitrary-
Shap... | 2,321 | 34.181818 | 78 | py |
WordArt | WordArt-main/mmocr/utils/setup_env.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
def setup_multi_processes(cfg):
"""Setup multi-processing environment variables."""
# set multi-process start method as `fork` to speed up the training
if platform.syste... | 2,219 | 45.25 | 112 | py |
WordArt | WordArt-main/mmocr/utils/model.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
class _BatchNormXd(torch.nn.modules.batchnorm._BatchNorm):
"""A general BatchNorm layer without input dimension check.
Reproduced from @kapily's work:
(https://github.com/pytorch/pytorch/issues/41081#issuecomment-783961547)
The only differe... | 1,976 | 37.019231 | 79 | py |
WordArt | WordArt-main/mmocr/utils/box_util.py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import numpy as np
from mmocr.utils.check_argument import is_2dlist, is_type_list
def is_on_same_line(box_a, box_b, min_y_overlap_ratio=0.8):
"""Check if two boxes are on the same line by their y-axis coordinates.
Two boxes are on the same li... | 6,979 | 33.9 | 79 | py |
WordArt | WordArt-main/mmocr/utils/ocr.py | #!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
import mmcv
import numpy as np
import torch
from mmcv.image.misc import tensor2imgs
from mmcv.runner import load_checkpoint
from mmcv.utils... | 33,609 | 37.236633 | 79 | py |
KnowledgeablePromptTuning | KnowledgeablePromptTuning-main/contextualize_calibration.py |
from yacs.config import CfgNode
from openprompt.data_utils import FewShotSampler
from torch.utils.data.dataset import Dataset
from transformers.data.processors.utils import InputExample
from openprompt.pipeline_base import PromptDataLoader, PromptModel, PromptForClassification
from typing import *
import torch
# from ... | 1,281 | 37.848485 | 140 | py |
KnowledgeablePromptTuning | KnowledgeablePromptTuning-main/fewshot_softpilot.py |
from tqdm import tqdm
from openprompt.data_utils.text_classification_dataset import AgnewsProcessor, DBpediaProcessor, ImdbProcessor, AmazonProcessor
from openprompt.data_utils.huggingface_dataset import YahooAnswersTopicsProcessor
import torch
from openprompt.data_utils.utils import InputExample
import argparse
impor... | 16,244 | 40.230964 | 261 | py |
KnowledgeablePromptTuning | KnowledgeablePromptTuning-main/filter_method.py | import torch
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
def tfidf_filter(myverbalizer, cc_logits, class_labels):
myrecord = ""
class_num = len(class_labels)
norm_ord = 10/(class_num-2+1e-2) +1
print("norm_ord", norm_ord)
context_size = cc_logits.shape[0]
tobep... | 4,022 | 44.202247 | 155 | py |
KnowledgeablePromptTuning | KnowledgeablePromptTuning-main/zeroshot.py |
from tqdm import tqdm
from openprompt.data_utils.text_classification_dataset import AgnewsProcessor, DBpediaProcessor, ImdbProcessor, AmazonProcessor
from openprompt.data_utils.huggingface_dataset import YahooAnswersTopicsProcessor
import torch
from openprompt.data_utils.utils import InputExample
import argparse
impo... | 8,753 | 41.289855 | 235 | py |
KnowledgeablePromptTuning | KnowledgeablePromptTuning-main/fewshot.py |
from tqdm import tqdm
from openprompt.data_utils.text_classification_dataset import AgnewsProcessor, DBpediaProcessor, ImdbProcessor, AmazonProcessor
from openprompt.data_utils.huggingface_dataset import YahooAnswersTopicsProcessor
import torch
from openprompt.data_utils.utils import InputExample
import argparse
impor... | 15,822 | 40.859788 | 261 | py |
violin | violin-master/main.py | # this code is developed based on https://github.com/jayleicn/TVQA
import os
import numpy as np
from tqdm import tqdm
import json
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from violin_dataset import ViolinDataset, pad_collate, preprocess_batch
from... | 7,996 | 45.766082 | 132 | py |
violin | violin-master/violin_dataset.py | # this code is developed based on https://github.com/jayleicn/TVQA
import numpy as np
import h5py
import os
import json
import re
import torch
import pickle
from collections import Counter
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
from tqdm import tqdm
f... | 7,207 | 39.268156 | 148 | py |
violin | violin-master/config.py | import os
import time
import torch
import argparse
def get_argparse():
parser = argparse.ArgumentParser()
parser.add_argument("--results_dir_base", type=str, default="results/results")
parser.add_argument("--feat_dir", type=str, default="../../feat")
parser.add_argument("--bert_dir", type=str, default=... | 3,208 | 59.54717 | 174 | py |
violin | violin-master/model/bidaf.py | # this code is developed based on https://github.com/jayleicn/TVQA
import torch
import torch.nn as nn
import torch.nn.functional as F
class BidafAttn(nn.Module):
"""from the BiDAF paper https://arxiv.org/abs/1611.01603.
Implemented by @easonnie and @jayleicn
"""
def __init__(self, channel_size, metho... | 2,985 | 35.864198 | 110 | py |
violin | violin-master/model/rnn.py | # this code is developed based on https://github.com/jayleicn/TVQA
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
class RNNEncoder(nn.Module):
"""A RNN wrapper handles variable length inputs, always set batch_first=True.
Supports LSTM, GRU and RNN.... | 3,028 | 45.6 | 111 | py |
violin | violin-master/model/ViolinBase.py | # this code is developed based on https://github.com/jayleicn/TVQA
import torch
from torch import nn
from .rnn import RNNEncoder
from .bidaf import BidafAttn
import pickle
class ViolinBase(nn.Module):
def __init__(self, opt):
super(ViolinBase, self).__init__()
hsize1 = opt.hsize1
hsize2 = ... | 3,971 | 37.941176 | 127 | py |
gaussian-equiv-2layer | gaussian-equiv-2layer-master/generators.py | #!/usr/bin/env python3
#
# A deep, fully-connected deep generative neural network.
#
# Author: Sebastian Goldt <goldt.sebastian@gmail.com>
#
# Date: April 2020
from collections import OrderedDict
import math
import torch
import torch.nn as nn
class Sign(nn.Module):
r"""Applies the sign function element-wise:
... | 3,014 | 31.074468 | 86 | py |
gaussian-equiv-2layer | gaussian-equiv-2layer-master/covariance_generator.py | #!/usr/bin/env python3
#
# Robust estimation of the mean and covariance of an arbitray generator.
#
# Author: Sebastian Goldt <goldt.sebastian@gmail.com>
#
# Date: May 2020
import argparse
import numpy as np
import torch
from tqdm import tqdm
from dcgan import Generator
from generators import RandomGenerator, Sign
... | 7,492 | 35.91133 | 118 | py |
gaussian-equiv-2layer | gaussian-equiv-2layer-master/twolayer.py | #!/usr/bin/env python3
#
# Simple class for two-layer fully connected neural networks.
#
# Author: Sebastian Goldt <goldt.sebastian@gmail.com>
#
# Date: February 2020
import math
import torch
import torch.nn as nn
SQRT2 = 1.414213562
def identity(x):
"""
Identity function, can be used as an activation func... | 4,011 | 27.055944 | 87 | py |
gaussian-equiv-2layer | gaussian-equiv-2layer-master/data_utils.py | # Utility functions for the real NVP model.
#
# Author: Fangzhou Mu <fmu2@wisc.edu>
#
# https://github.com/fmu2/realNVP
"""Utility functions for real NVP.
"""
import torch
import torch.nn.functional as F
import torch.distributions as distributions
import torch.utils.data as data
import torchvision.datasets as datase... | 5,359 | 33.805195 | 82 | py |
gaussian-equiv-2layer | gaussian-equiv-2layer-master/dcgan.py | #!/usr/bin/env python3
#
# Code to train a deep convolutional GAN, from pyTorch examples.
#
# Original source can be found here:
# https://raw.githubusercontent.com/pytorch/examples/master/dcgan/main.py
from __future__ import print_function
import argparse
import os
import random
import torch
import torch.nn as nn... | 9,548 | 33.348921 | 163 | py |
gaussian-equiv-2layer | gaussian-equiv-2layer-master/realnvp.py | # Utility class implementing the real NVP model.
#
# Author: Fangzhou Mu <fmu2@wisc.edu>
#
# https://github.com/fmu2/realNVP
"""
Utility classes for real NVP.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class DataInfo():
def __init__(self, name, channel, size):
... | 38,895 | 38.448276 | 96 | py |
gaussian-equiv-2layer | gaussian-equiv-2layer-master/deepgen_online.py | #!/usr/bin/env python3
#
# Training two-layer networks on inputs coming from various deep generators.
#
# Date: May 2020
#
# Author: Sebastian Goldt <goldt.sebastian@gmail.com>
import argparse
import math
import numpy as np # for storing tensors in CSV format
import torch
import torch.distributions as distributions... | 14,533 | 33.359338 | 98 | py |
RoMe | RoMe-main/rome.py | import torch
from args import get_args
from components.model import ScorerNN
from components.grammar import Grammar
from components.emd import EMD_mod
from components.ted_se import TEDse
class RoMe:
def __init__(self):
self.args = get_args()
self.emd = EMD_mod(self.args) # semantic similarit... | 1,290 | 32.102564 | 92 | py |
RoMe | RoMe-main/components/emd.py | # part of the code is adopted from https://github.com/AIPHES/emnlp19-moverscore/blob/master/moverscore_v2.py
from sklearn.metrics.pairwise import cosine_similarity
from collections import defaultdict
import numpy as np
import os, torch
import pulp
import string
from transformers import AlbertModel, AlbertConfig, Alber... | 11,112 | 35.920266 | 142 | py |
RoMe | RoMe-main/components/model.py | import torch
import torch.nn as nn
torch.manual_seed(123)
class ScorerNN(nn.Module):
def __init__(self, feat_size=3, hidden1=30, hidden2=20, drop1=0.3, drop2=0.2):
super(ScorerNN, self).__init__()
self.hid1 = nn.Linear(feat_size, hidden1)
self.drop1 = nn.Dropout(drop1)
self.hid2 = n... | 617 | 31.526316 | 82 | py |
RoMe | RoMe-main/components/grammar.py | from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
class Grammar:
def __init__(self):
self.tokenizer = AutoTokenizer.from_pretrained("saved_model/grammar/", use_auth_token=False)
self.model = AutoModelForSequenceClassification.from_pretrained("saved_model/gramm... | 1,181 | 37.129032 | 116 | py |
MTL | MTL-master/optimize_toy.py | import argparse
import os
from collections import defaultdict
from os import path as osp
from tqdm import tqdm
import numpy as np
import torch
from code.optim import *
import code.utils.utils as utils
import code.utils.toy as toy_problem
import code.utils.toy_plot as toy_plot
# Adapted from Nash-MTL: https://github.... | 2,161 | 29.450704 | 99 | py |
MTL | MTL-master/train.py | import os
from collections import defaultdict
from os import path as osp
import time
from tqdm import tqdm
import numpy as np
import torch
from code.optim import *
import code.utils.utils as utils
from code.benchmarks.mtl_benchmark import get_benchmark_class
class MTLTrainer:
def __init__(self, args):
s... | 6,171 | 34.268571 | 113 | py |
MTL | MTL-master/code/evaluation/nyu2.py | import numpy as np
import torch
from tqdm import tqdm
# New mIoU and Acc. formula: accumulate every pixel and average across all pixels in all images
class ConfMatrix(object):
def __init__(self, num_classes=13):
self.num_classes = num_classes
self.mat = None
def update(self, pred, target):
... | 4,659 | 33.264706 | 112 | py |
MTL | MTL-master/code/evaluation/cityscapes.py | import numpy as np
import torch
class CityScapesEvaluator:
@staticmethod
def _compute_stats(model, data_loader, device):
ss_hist = np.zeros((19, 19))
is_enum = 0.0
is_denum = 0.0
de_enum = 0.0
de_denum = 0.0
for data in data_loader:
input = data[0]... | 2,334 | 30.133333 | 84 | py |
MTL | MTL-master/code/evaluation/posenet.py | from code.data.datasets.posenet import cal_quat_angle_error
import numpy as np
import torch
from tqdm import tqdm
class SevenScenesEvaluator:
@staticmethod
def evaluate(model, data_loader, device):
model.eval()
with torch.no_grad():
q_err_all, t_err_all = [], []
encode... | 1,372 | 35.131579 | 81 | py |
MTL | MTL-master/code/evaluation/celeba.py | from collections import defaultdict
import numpy as np
import torch
import torch.nn.functional as F
class CelebAEvaluator:
@staticmethod
def evaluate(model, data_loader, device):
model.eval()
with torch.no_grad():
correct = np.zeros(40)
total = len(data_loader.dataset... | 911 | 26.636364 | 76 | py |
MTL | MTL-master/code/evaluation/mmnist.py | import numpy as np
import torch
class MMnistEvaluator:
@staticmethod
def _compute_hist(model, data_loader, device):
hist_left = np.zeros((10, 10))
hist_right = np.zeros((10, 10))
for input, t1, t2 in data_loader:
hrepr = model["encoder"](input.to(device))
p1 = ... | 1,319 | 35.666667 | 82 | py |
MTL | MTL-master/code/benchmarks/mtl_benchmark.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class MTLModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.encoder = None
self.decoders = torch.nn.ModuleDict()
self.last_shared_layer = None
def forward(self, img):
hrepr = self.encod... | 1,605 | 24.492063 | 96 | py |
MTL | MTL-master/code/benchmarks/cityscapes_three.py | import torch
import torch.nn.functional as F
from . import mtl_benchmark
from code.data.augmentation.cityscapes import *
from code.data.datasets.cityscapes import CITYSCAPES
from code.evaluation.cityscapes import CityScapesEvaluator
from code.models.cityscapes import ResNet50Dilated, SegmentationDecoder
def l1_loss_... | 3,450 | 30.953704 | 119 | py |
MTL | MTL-master/code/benchmarks/nyuv2.py | import torch
import torch.nn.functional as F
from . import mtl_benchmark
from code.models.nyu2 import (
DepthDecoder,
NormalDecoder,
ResNet50Dilated,
SemanticDecoder,
)
from code.models.segnet_mtan import MTANEncoder, MTANDepthDecoder, MTANNormalDecoder, MTANSemanticDecoder
from code.data.datasets imp... | 4,859 | 33.225352 | 116 | py |
MTL | MTL-master/code/models/nyu2.py | import math
import os
import sys
from turtle import forward
import torch
import torch.nn as nn
try:
from urllib import urlretrieve
except ImportError:
from urllib.request import urlretrieve
model_urls = {
"resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth",
"resnet101": "http://s... | 9,640 | 29.801917 | 100 | py |
MTL | MTL-master/code/models/segnet_mtan.py | from typing import Iterator
import torch
import torch.nn as nn
import torch.nn.functional as F
class MTANEncoder(nn.Module):
"""SegNet MTAN"""
filter = [64, 128, 256, 512, 512]
def __init__(self):
super().__init__()
filter = MTANEncoder.filter
# define encoder decoder layers
... | 11,488 | 37.043046 | 93 | py |
MTL | MTL-master/code/models/cityscapes.py | import math
import os
import sys
import torch
import torch.nn as nn
try:
from urllib import urlretrieve
except ImportError:
from urllib.request import urlretrieve
model_urls = {
"resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth",
"resnet101": "http://sceneparsing.csail.mit.edu/m... | 8,788 | 30.27758 | 100 | py |
MTL | MTL-master/code/models/posenet.py | import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
class PoseNetEncoder(nn.Module):
def __init__(self, dropout_rate=0.5):
super(PoseNetEncoder, self).__init__()
self.base_model = models.resnet34(pretrained=True)
self.dropout_rate = dropout_rate
fe... | 1,617 | 28.962963 | 79 | py |
MTL | MTL-master/code/models/celeba.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes, planes, kernel_size=3, stride=stride, padding=1, bia... | 3,928 | 31.741667 | 85 | py |
MTL | MTL-master/code/models/mmnist.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class LeNetEncoder(nn.Module):
def __init__(self):
super(LeNetEncoder, self).__init__()
self.cnn = nn.Sequential(
nn.Conv2d(1, 10, 5, 1),
nn.MaxPool2d(2),
nn.ReLU(),
nn.Conv2d(10, 20,... | 834 | 22.194444 | 80 | py |
MTL | MTL-master/code/optim/mtl_metrics.py | import torch
def compute_metrics(G):
"""
Arguments:
G (torch.Tensor): Matrix of shape TxN
Returns:
svals (list[float], T): Singular values
cn (float): Condition number
cos (torch.Tensor, TxT): Pair-wise task gradient cosine distance
gms (torch.Tensor, TxT): Gradient... | 1,667 | 34.489362 | 86 | py |
MTL | MTL-master/code/optim/basic_balancer.py | from collections import defaultdict
import torch
from . import mtl_metrics
class BasicBalancer(torch.nn.Module):
def __init__(self, compute_stats=False):
super().__init__()
self.compute_stats = compute_stats
self.info = None
self.losses = defaultdict(float)
def set_losses(sel... | 6,874 | 38.511494 | 119 | py |
MTL | MTL-master/code/optim/graddrop/balancer.py | import torch
from .. import basic_balancer
from .. import balancers
@balancers.register("graddrop")
class GradDropBalancer(basic_balancer.BasicBalancer):
"""
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
Arxiv: https://arxiv.org/abs/2010.06808
"""
def __init__(sel... | 1,339 | 36.222222 | 115 | py |
MTL | MTL-master/code/optim/pcgrad/solver.py | import torch
class RandomProjectionSolver:
@staticmethod
def apply(grads):
assert (
len(grads.shape) == 2
), f"Invalid shape of 'grads': {grads.shape}. Only 2D tensors are applicable"
with torch.no_grad():
order = torch.randperm(grads.shape[0])
grad... | 1,099 | 31.352941 | 85 | py |
MTL | MTL-master/code/optim/gradnorm/balancer.py | import torch
from .. import basic_balancer
from .. import balancers
@balancers.register("gradnorm")
class GradNormBalancer(basic_balancer.BasicBalancer):
"""
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Arxiv: https://arxiv.org/pdf/1711.02257.pdf
"""
def... | 2,206 | 37.719298 | 118 | py |
MTL | MTL-master/code/optim/aligned/balancer.py | import torch
from .solver import ProcrustesSolver
from .. import basic_balancer
from .. import balancers
@balancers.register("amtl")
class AlignedMTLBalancer(basic_balancer.BasicBalancer):
def __init__(self, scale_mode='min', scale_decoder_grad=False, **kwargs):
super().__init__(**kwargs)
self.sc... | 3,057 | 37.708861 | 100 | py |
MTL | MTL-master/code/optim/aligned/solver.py | import torch
class ProcrustesSolver:
@staticmethod
def apply(grads, scale_mode='min'):
assert (
len(grads.shape) == 3
), f"Invalid shape of 'grads': {grads.shape}. Only 3D tensors are applicable"
with torch.no_grad():
cov_grad_matrix_e = torch.matmul(grads.perm... | 1,396 | 35.763158 | 85 | py |
MTL | MTL-master/code/optim/uncertainty/balancer.py | import torch
from .. import basic_balancer
from .. import balancers
@balancers.register("uncertainty")
class HomoscedasticUncertaintyBalancer(basic_balancer.BasicBalancer):
"""
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Arxiv: https://arxiv.org/abs/1705.07115
... | 1,362 | 36.861111 | 115 | py |
MTL | MTL-master/code/optim/cagrad/balancer.py | import numpy as np
import torch
from scipy.optimize import minimize
from .. import basic_balancer
from .. import balancers
@balancers.register("cagrad")
class CAGradBalancer(basic_balancer.BasicBalancer):
"""
Conflict-Averse Gradient Descent for Multitask Learning (CAGrad)
Arxiv: https://arxiv.org/abs/211... | 2,386 | 35.723077 | 116 | py |
MTL | MTL-master/code/optim/gradvac/balancer.py | import random
import numpy as np
import torch
from .. import basic_balancer
from .. import balancers
@balancers.register("gradvac")
class GradVacBalancer(basic_balancer.BasicBalancer):
"""
Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models
Arxiv: https:... | 2,109 | 38.074074 | 151 | py |
MTL | MTL-master/code/optim/ls/balancer.py | import torch
from .. import basic_balancer
from .. import balancers
@balancers.register("ls")
class LinearScalarization(basic_balancer.BasicBalancer):
"""
Uniform task weighting
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
def step(self, losses, shared_params, task_specifi... | 665 | 26.75 | 91 | py |
MTL | MTL-master/code/optim/si/balancer.py | import torch
from .. import basic_balancer
from .. import balancers
@balancers.register("si")
class ScaleInvariantLinearScalarization(basic_balancer.BasicBalancer):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def step_with_model(self, data: torch.Tensor, targets: dict, model: torch.nn.Mo... | 854 | 30.666667 | 115 | py |
MTL | MTL-master/code/optim/dwa/balancer.py | import torch
import numpy as np
from .. import basic_balancer
from .. import balancers
@balancers.register("dwa")
class DynamicWeightAveraging(basic_balancer.BasicBalancer):
"""Dynamic Weight Average from `End-to-End Multi-Task Learning with Attention`.
Arxiv: https://arxiv.org/abs/1803.10704
Modifi... | 1,933 | 36.921569 | 110 | py |
MTL | MTL-master/code/optim/nash/balancer.py | import torch
import numpy as np
import cvxpy as cp
from .. import balancers
from .. import basic_balancer
@balancers.register("nash")
class NashMTL(basic_balancer.BasicBalancer):
"""
Multi-Task Learning as a Bargaining Game
Arxiv: https://arxiv.org/abs/2202.01017
Modification of: https://g... | 4,247 | 33.819672 | 98 | py |
MTL | MTL-master/code/optim/imtl/balancer.py | import torch
from .. import basic_balancer
from .. import balancers
@balancers.register("imtl")
class IMTLG(basic_balancer.BasicBalancer):
"""
Towards Impartial Multi-task Learning
Paper: https://openreview.net/forum?id=IMPnRXEWpvr
Modification of:
https://github.com/AvivNavon/nash-mtl/blob/7cc16... | 1,570 | 34.704545 | 118 | py |
MTL | MTL-master/code/optim/rlw/balancer.py | import torch
import torch.nn.functional as F
from .. import basic_balancer
from .. import balancers
@balancers.register("rlw")
class RandomLossWeighting(basic_balancer.BasicBalancer):
"""
Random loss weighting with normal distribution
"Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi... | 1,105 | 33.5625 | 115 | py |
MTL | MTL-master/code/optim/mgda/balancer.py | import torch
from .. import balancers
from .. import basic_balancer
from .solver import MinNormSolver
@balancers.register("mgda")
class MGDABalancer(basic_balancer.BasicBalancer):
"""
Multi-Task Learning as Multi-Objective Optimization
Arxiv: https://arxiv.org/abs/1810.04650
Modification... | 2,621 | 34.432432 | 115 | py |
MTL | MTL-master/code/optim/mgda/solver.py | import torch
class MinNormSolver:
MAX_ITER = 250
STOP_CRIT = 1e-5
def _min_norm_element_from2(v1v1, v1v2, v2v2):
"""
Analytical solution for min_{c} |cx_1 + (1-c)x_2|_2^2
d is the distance (objective) optimzed
v1v1 = <x1,x1>
v1v2 = <x1,x2>
v2v2 = <x2,x2>
... | 5,141 | 35.992806 | 147 | py |
MTL | MTL-master/code/utils/toy.py | import torch
import torch.nn as nn
LOWER = 0.000005
class Toy(nn.Module):
def __init__(self, scale=0.5):
super(Toy, self).__init__()
self.centers = torch.Tensor([[-3.0, 0], [3.0, 0]])
self.scale = scale
def forward(self, x, compute_grad=False):
x1 = x[0]
x2 = x[1]
... | 1,987 | 32.133333 | 80 | py |
MTL | MTL-master/code/utils/utils.py | import random
import numpy as np
import torch
def fix_seed(seed: int):
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
def strfy(t... | 2,324 | 43.711538 | 105 | py |
MTL | MTL-master/code/utils/toy_plot.py | import matplotlib
import numpy as np
import seaborn as sns
import torch
from matplotlib import pyplot as plt
from code.utils.toy import Toy
# Sourced from: https://raw.githubusercontent.com/AvivNavon/nash-mtl/main/experiments/toy/utils.py
def get_opt(scale):
F = Toy(scale=scale)
yy = -8.3552
x = np.lins... | 2,148 | 26.202532 | 98 | py |
MTL | MTL-master/code/data/datasets/nyu2.py | import fnmatch
import os
import random
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data.dataset import Dataset
"""
Source: https://github.com/Cranial-XIX/CAGrad/blob/main/nyuv2/create_dataset.py
"""
class RandomScaleCrop(object):
"""
Credit to Jialong Wu from https:/... | 3,387 | 29.8 | 86 | py |
MTL | MTL-master/code/data/datasets/cityscapes.py | import os
from code.data.augmentation.cityscapes import *
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image
from torch.utils import data
def recursive_glob(rootdir=".", suffix=""):
"""Performs recursive glob with given suffix and rootdir
:param rootdir is the ro... | 9,806 | 30.332268 | 94 | py |
MTL | MTL-master/code/data/datasets/posenet.py | from code.data.augmentation.posenet import get_7scenes_img_augmentations
from os import path as osp
import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset
def cal_quat_angle_error(label, pred):
if len(label.shape) == 1:
label = np.expand_dims(label, axis=0)
if len(... | 6,237 | 34.850575 | 88 | py |
MTL | MTL-master/code/data/datasets/celeba.py | import glob
import os
import re
import cv2
import numpy as np
import torch
from PIL import Image
from torch.utils import data
class CELEBA(data.Dataset):
class_names = [
"5_o_Clock_Shadow",
"Arched_Eyebrows",
"Attractive",
"Bags_Under_Eyes",
"Bald",
"Bangs",
... | 4,207 | 27.432432 | 83 | py |
MTL | MTL-master/code/data/datasets/mmnist.py | from __future__ import print_function
import codecs
import errno
import os
import os.path
import numpy as np
import torch
import torch.utils.data as data
from PIL import Image
class MNIST(data.Dataset):
urls = [
"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz",
"http://yann.lecun.co... | 10,415 | 34.671233 | 88 | py |
MTL | MTL-master/code/data/augmentation/posenet.py | import albumentations as A
from albumentations.pytorch import ToTensorV2
def get_imagenet_mean_std():
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
return mean, std
def test_7scenes_img_augmentations():
mean, std = get_imagenet_mean_std()
crop_size = 224
augs = A.Compose(
[
... | 1,782 | 26.430769 | 88 | py |
DE-RRD_CIKM20 | DE-RRD_CIKM20-master/main.py | import argparse
from Models.BPR import BPR
from Utils.dataset import implicit_CF_dataset, implicit_CF_dataset_test
from Utils.data_utils import read_LOO_settings
import torch
import torch.utils.data as data
import torch.optim as optim
from run import LOO_run
def run():
# gpu setting
gpu = torch.device('cuda:' +... | 2,745 | 33.759494 | 170 | py |
DE-RRD_CIKM20 | DE-RRD_CIKM20-master/main_DE.py | import argparse
import os
from Models.BPR import BPR
from Models.DE import BPR_DE
from Utils.dataset import implicit_CF_dataset, implicit_CF_dataset_test
from Utils.data_utils import read_LOO_settings
import torch
import torch.utils.data as data
import torch.optim as optim
from run import LOO_DE_run
def run():
... | 3,108 | 32.793478 | 143 | py |
DE-RRD_CIKM20 | DE-RRD_CIKM20-master/run.py | import time
from copy import deepcopy
import torch
import torch.optim as optim
from Utils.evaluation import evaluation, LOO_print_result, print_final_result
from Utils.loss import relaxed_ranking_loss
from Utils.data_utils import T_annealing
def LOO_IR_RRD_run(opt, model, gpu, optimizer, train_loader, test_dataset,... | 10,033 | 30.258567 | 145 | py |
DE-RRD_CIKM20 | DE-RRD_CIKM20-master/main_URRD.py | import argparse
from Models.BPR import BPR
from Utils.dataset import implicit_CF_dataset_URRD, implicit_CF_dataset_test
from Utils.data_utils import read_LOO_settings, load_pickle
import torch
import torch.utils.data as data
import torch.optim as optim
from run import LOO_URRD_run
def run():
# gpu setting
gpu = ... | 2,744 | 34.649351 | 152 | py |
DE-RRD_CIKM20 | DE-RRD_CIKM20-master/Models/BPR.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class BPR(nn.Module):
def __init__(self, user_count, item_count, dim, gpu):
"""
Parameters
----------
user_count : int
item_count : int
dim : int
embedding dimension
gpu : if available
"""
super(BPR, self).__init__()
self.user_c... | 3,317 | 22.366197 | 67 | py |
DE-RRD_CIKM20 | DE-RRD_CIKM20-master/Models/DE.py | import torch.nn.functional as F
import torch.nn as nn
import torch
from pdb import set_trace as bp
import numpy as np
from Utils.data_utils import count_parameters
import math
from Models.BPR import BPR
class Expert(nn.Module):
def __init__(self, dims):
super(Expert, self).__init__()
self.mlp = nn.Sequential(nn... | 3,953 | 34.945455 | 146 | py |
DE-RRD_CIKM20 | DE-RRD_CIKM20-master/Utils/loss.py | import torch
def relaxed_ranking_loss(S1, S2):
above = S1.sum(1, keepdims=True)
below1 = S1.flip(-1).exp().cumsum(1)
below2 = S2.exp().sum(1, keepdims=True)
below = (below1 + below2).log().sum(1, keepdims=True)
return -(above - below).sum()
| 257 | 17.428571 | 54 | py |
DE-RRD_CIKM20 | DE-RRD_CIKM20-master/Utils/data_utils.py | import numpy as np
import os
import random
import pickle
import time
import torch
########################################################################################################################
# Helper Functions
################################################################################################... | 3,125 | 22.862595 | 120 | py |
DE-RRD_CIKM20 | DE-RRD_CIKM20-master/Utils/dataset.py | import torch
import torch.nn as nn
import torch.utils.data as data
import torch.nn.functional as F
import numpy as np
from Utils.data_utils import *
from pdb import set_trace as bp
#################################################################################################################
# For training
######... | 12,703 | 31.3257 | 118 | py |
DE-RRD_CIKM20 | DE-RRD_CIKM20-master/Utils/evaluation.py | import torch
import math
import copy
import time
from Utils.data_utils import to_np, Euclidian_dist
import numpy as np
from pdb import set_trace as bp
def LOO_check(ranking_list, target_item, topk=10):
"""Calculate three ranking metrics: HR, NDCG, MRR
Parameters
----------
ranking_list : 1-D array
a recomm... | 10,813 | 28.227027 | 142 | py |
meta-sgld | meta-sgld-master/src/utils/data_gen.py |
from __future__ import absolute_import, division, print_function
import torch
from torchvision import datasets, transforms
import torch.utils.data as data_utils
import os
import numpy as np
from Utils import omniglot
from Utils import imagenet_data
import multiprocessing
# ------------------------------------------... | 13,550 | 36.746518 | 126 | py |
meta-sgld | meta-sgld-master/src/utils/omniglot.py | import torch.utils.data as data
import os
import os.path
import errno
class Omniglot(data.Dataset):
urls = [
'https://github.com/brendenlake/omniglot/raw/master/python/images_background.zip',
'https://github.com/brendenlake/omniglot/raw/master/python/images_evaluation.zip'
]
raw_folder... | 3,630 | 32.009091 | 104 | py |
meta-sgld | meta-sgld-master/src/utils/test.py | import torch
from torch import nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.a = nn.ParameterList([nn.Parameter(torch.zeros(3, 4))])
b = [torch.ones(2, 3), torch.ones(2, 3)]
for i in range(2):
self.register_buffer('b%d' % i, b[i])
... | 708 | 17.179487 | 68 | py |
meta-sgld | meta-sgld-master/src/utils/omniglotNShot.py | from src.utils.omniglot import Omniglot
import torchvision.transforms as transforms
from PIL import Image
import os.path
import numpy as np
class OmniglotNShot:
def __init__(self, root, batchsz, n_way, k_shot, k_query, imgsz):
"""
Different from mnistNShot, the
:param root:
... | 8,150 | 39.152709 | 116 | py |
meta-sgld | meta-sgld-master/src/utils/Bayes_utils.py |
from __future__ import absolute_import, division, print_function
import torch
from Utils import common as cmn, data_gen
from Utils.common import count_correct
from Models.stochastic_layers import StochasticLayer
from Utils.Losses import get_loss_func
# --------------------------------------------------------------... | 7,227 | 39.606742 | 118 | py |
meta-sgld | meta-sgld-master/src/utils/common.py |
from __future__ import absolute_import, division, print_function
from datetime import datetime
import os
import torch.nn as nn
import torch
import numpy as np
import random
import sys
import pickle
from Utils.data_gen import get_info
from functools import reduce
# --------------------------------------------------... | 11,231 | 38 | 132 | py |
meta-sgld | meta-sgld-master/src/utils/complexity_terms.py |
from __future__ import absolute_import, division, print_function
import torch
from torch.autograd import Variable
import math
from Utils import common as cmn
import torch.nn.functional as F
from Models.stochastic_layers import StochasticLayer
from Utils.common import net_weights_magnitude, count_correct
# ----------... | 8,460 | 42.389744 | 159 | py |
meta-sgld | meta-sgld-master/src/utils/Losses.py | from __future__ import absolute_import, division, print_function
import torch
from torch.nn.modules.module import Module
import torch.nn as nn
import math
from Utils.data_gen import get_info
# -----------------------------------------------------------------------------------------------------------#
# Returns loss fu... | 5,831 | 38.673469 | 110 | py |
meta-sgld | meta-sgld-master/src/algo/learner.py | import torch
from torch import nn
from torch.nn import functional as F
import numpy as np
class Learner(nn.Module):
"""
"""
def __init__(self, config, imgc, imgsz):
"""
:param config: network config file, type:list of (string, list)
:param imgc: 1 or 3
:param im... | 8,070 | 35.520362 | 111 | py |
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