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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AFC | AFC-master/inclearn/convnet/my_resnet_mtl.py | """Pytorch port of the resnet used for CIFAR100 by iCaRL.
https://github.com/srebuffi/iCaRL/blob/master/iCaRL-TheanoLasagne/utils_cifar100.py
"""
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from inclearn.convnet.tools.conv_mtl import Conv2dMtl
from inclearn.lib import pooling
fro... | 10,928 | 29.190608 | 97 | py |
AFC | AFC-master/inclearn/convnet/resnet.py | """Taken & slightly modified from:
* https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
model_urls = {
... | 8,824 | 30.294326 | 106 | py |
AFC | AFC-master/inclearn/convnet/vgg.py | import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = [
'VGG',
'vgg11',
'vgg11_bn',
'vgg13',
'vgg13_bn',
'vgg16',
'vgg16_bn',
'vgg19_bn',
'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': '... | 7,286 | 36.953125 | 113 | py |
AFC | AFC-master/inclearn/convnet/densenet.py | import re
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
model_urls = {
'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
'densenet169': 'htt... | 7,713 | 41.384615 | 95 | py |
AFC | AFC-master/inclearn/convnet/resnet_mtl.py | """Taken & slightly modified from:
* https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import logging
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from inclearn.convnet.tools.conv_mtl import Conv2dMtl
from torch.nn import functional as F
logger = logging.getLogger(__nam... | 9,970 | 30.85623 | 106 | py |
AFC | AFC-master/inclearn/convnet/my_resnet_brn.py | ''' Incremental-Classifier Learning
Authors : Khurram Javed, Muhammad Talha Paracha
Maintainer : Khurram Javed
Lab : TUKL-SEECS R&D Lab
Email : 14besekjaved@seecs.edu.pk '''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from inclearn.lib import pooling
... | 9,785 | 29.58125 | 97 | py |
AFC | AFC-master/inclearn/convnet/resnet_importance.py | """Taken & slightly modified from:
* https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
import torch
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
model... | 12,403 | 33.648045 | 106 | py |
AFC | AFC-master/inclearn/convnet/cifar_resnet.py | ''' Incremental-Classifier Learning
Authors : Khurram Javed, Muhammad Talha Paracha
Maintainer : Khurram Javed
Lab : TUKL-SEECS R&D Lab
Email : 14besekjaved@seecs.edu.pk '''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
class DownsampleA(nn.Module):
... | 5,946 | 29.035354 | 102 | py |
AFC | AFC-master/inclearn/convnet/my_resnet.py | """Pytorch port of the resnet used for CIFAR100 by iCaRL.
https://github.com/srebuffi/iCaRL/blob/master/iCaRL-TheanoLasagne/utils_cifar100.py
"""
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from inclearn.lib import pooling
logger = logging.getLogger(__... | 9,282 | 28.469841 | 97 | py |
AFC | AFC-master/inclearn/convnet/my_resnet_mcbn.py | """Pytorch port of the resnet used for CIFAR100 by iCaRL.
https://github.com/srebuffi/iCaRL/blob/master/iCaRL-TheanoLasagne/utils_cifar100.py
"""
import logging
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from inclearn.lib import pooling
logger = loggin... | 11,898 | 28.972292 | 98 | py |
AFC | AFC-master/inclearn/convnet/my_resnet_importance.py | """Pytorch port of the resnet used for CIFAR100 by iCaRL.
https://github.com/srebuffi/iCaRL/blob/master/iCaRL-TheanoLasagne/utils_cifar100.py
"""
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from inclearn.lib import pooling
import pdb
logger = logging.g... | 13,049 | 31.625 | 106 | py |
AFC | AFC-master/inclearn/convnet/my_resnet_imagenet.py | """Pytorch port of the resnet used for CIFAR100 by iCaRL.
https://github.com/srebuffi/iCaRL/blob/master/iCaRL-TheanoLasagne/utils_cifar100.py
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from inclearn.lib import pooling
class DownsampleStride(nn.Module):
def ... | 7,242 | 27.972 | 97 | py |
AFC | AFC-master/inclearn/convnet/tools/conv_mtl.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/pytorch/pytorch
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in th... | 4,162 | 33.122951 | 99 | py |
AFC | AFC-master/inclearn/models/base.py | import abc
import logging
import os
import torch
LOGGER = logging.Logger("IncLearn", level="INFO")
logger = logging.getLogger(__name__)
class IncrementalLearner(abc.ABC):
"""Base incremental learner.
Methods are called in this order (& repeated for each new task):
1. set_task_info
2. before_task
... | 3,228 | 24.832 | 77 | py |
AFC | AFC-master/inclearn/models/afc.py | import copy
import logging
import math
import numpy as np
import torch
from torch.nn import functional as F
from inclearn.lib import data, factory, losses, network, utils
from inclearn.lib.data import samplers
from inclearn.models.icarl import ICarl
logger = logging.getLogger(__name__)
class AFC(ICarl):
def _... | 15,486 | 36.773171 | 100 | py |
AFC | AFC-master/inclearn/models/icarl.py | import collections
import copy
import logging
import os
import pickle
import numpy as np
import torch
from scipy.spatial.distance import cdist
from torch import nn
from torch.nn import functional as F
from tqdm import tqdm
from inclearn.lib import factory, herding, losses, network, schedulers, utils
from inclearn.lib... | 19,231 | 37.007905 | 121 | py |
AFC | AFC-master/inclearn/lib/pooling.py | import torch
import torch.nn as nn
from torch.autograd import Function
class WeldonPool2d(nn.Module):
def __init__(self, kmax=1, kmin=None, **kwargs):
super(WeldonPool2d, self).__init__()
self.kmax = kmax
self.kmin = kmin
if self.kmin is None:
self.kmin = self.kmax
... | 4,029 | 37.75 | 153 | py |
AFC | AFC-master/inclearn/lib/callbacks.py | import copy
import torch
class Callback:
def __init__(self):
self._iteration = 0
self._in_training = True
@property
def in_training(self):
return self._in_training
def on_epoch_begin(self):
pass
def on_epoch_end(self, metric=None):
self._iteration += 1
... | 2,331 | 25.5 | 86 | py |
AFC | AFC-master/inclearn/lib/schedulers.py | import warnings
import numpy as np
from torch.optim.lr_scheduler import ReduceLROnPlateau, _LRScheduler
class GradualWarmupScheduler(_LRScheduler):
""" Gradually warm-up(increasing) learning rate in optimizer.
Proposed in 'Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour'.
From: https://github... | 5,695 | 36.228758 | 98 | py |
AFC | AFC-master/inclearn/lib/utils.py | import datetime
import logging
import os
import warnings
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import torch
from sklearn import manifold
from sklearn.cluster import KMeans
from sklearn.neighbors import KNeighborsClassifier
logger = logging.getLogger(__name__)
def to_onehot(targ... | 7,556 | 30.098765 | 100 | py |
AFC | AFC-master/inclearn/lib/calibration.py | import torch
from torch import optim
from torch.nn import functional as F
from inclearn.lib.network import (CalibrationWrapper, LinearModel, TemperatureScaling)
def calibrate(network, loader, device, indexes, calibration_type="linear"):
"""Corrects the bias for new classes.
:param network: The logits extrac... | 2,171 | 33.47619 | 86 | py |
AFC | AFC-master/inclearn/lib/vizualization.py | import torch
def grad_cam(spatial_features, selected_logits):
batch_size = spatial_features.shape[0]
assert batch_size == len(selected_logits)
formated_logits = [selected_logits[i] for i in range(batch_size)]
import pdb
pdb.set_trace()
grads = torch.autograd.grad(
formated_logits, sp... | 447 | 22.578947 | 79 | py |
AFC | AFC-master/inclearn/lib/factory.py | import warnings
import torch
from torch import optim
from inclearn import models
from inclearn.convnet import (
densenet, my_resnet, my_resnet_importance, my_resnet2, my_resnet_brn,
my_resnet_mcbn, my_resnet_mtl, resnet, resnet_importance,
resnet_mtl, ucir_resnet, vgg
)
from inclearn.lib import data, sche... | 6,414 | 34.247253 | 97 | py |
AFC | AFC-master/inclearn/lib/metrics.py | import collections
import numpy as np
import torch
class MetricLogger:
def __init__(self, nb_tasks, nb_classes, increments):
self.metrics = collections.defaultdict(list)
self.nb_tasks = nb_tasks
self.nb_classes = nb_classes
self.increments = increments
self._accuracy_ma... | 8,076 | 33.370213 | 99 | py |
AFC | AFC-master/inclearn/lib/distance.py | import torch
from torch.nn import functional as F
def squared_euclidian_distance(a, b):
return torch.cdist(a, b)**2
def cosine_similarity(a, b):
return torch.mm(F.normalize(a, p=2, dim=-1), F.normalize(b, p=2, dim=-1).T)
def stable_cosine_distance(a, b, squared=True):
"""Computes the pairwise distance... | 1,492 | 34.547619 | 99 | py |
AFC | AFC-master/inclearn/lib/herding.py | import numpy as np
import torch
from sklearn.cluster import KMeans
from torch.nn import functional as F
from inclearn.lib import utils
def closest_to_mean(features, nb_examplars):
features = features / (np.linalg.norm(features, axis=0) + 1e-8)
class_mean = np.mean(features, axis=0)
return _l2_distance(f... | 6,268 | 29.881773 | 98 | py |
AFC | AFC-master/inclearn/lib/loops/generators.py | import collections
import itertools
import logging
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from tqdm import tqdm
from .loops import _print_metrics
logger = logging.getLogger(__name__)
def perclass_loop(
inc_dataset,
class_ids,
devices,
n_epochs,
... | 15,274 | 30.690871 | 99 | py |
AFC | AFC-master/inclearn/lib/loops/loops.py | import collections
import logging
import torch
from torch import nn
from inclearn.lib.network import hook
from tqdm import tqdm
logger = logging.getLogger(__name__)
def single_loop(
train_loader,
val_loader,
devices,
network,
n_epochs,
optimizer,
train_function,
eval_function,
t... | 3,951 | 29.4 | 95 | py |
AFC | AFC-master/inclearn/lib/network/postprocessors.py | import torch
from torch import nn
class ConstantScalar(nn.Module):
def __init__(self, constant=1., bias=0., **kwargs):
super().__init__()
self.factor = constant
self.bias = bias
def on_task_end(self):
pass
def on_epoch_end(self):
pass
def forward(self, x):
... | 2,436 | 21.357798 | 98 | py |
AFC | AFC-master/inclearn/lib/network/hook.py | import torch
import torch.nn as nn
def get_gradcam_hook(model):
if isinstance(model, nn.DataParallel):
gradients = [None for _ in model.device_ids]
activations = [None for _ in model.device_ids]
def backward_hook(module, grad_input, grad_output):
gradients[model.device_ids.ind... | 913 | 29.466667 | 91 | py |
AFC | AFC-master/inclearn/lib/network/memory.py | import torch
from torch import nn
from torch.nn import functional as F
class MemoryBank:
def __init__(self, device, momentum=0.5):
self.features = None
self.targets = None
self.momentum = momentum
self.device = device
def add(self, features, targets):
if self.featur... | 1,146 | 29.184211 | 87 | py |
AFC | AFC-master/inclearn/lib/network/calibrators.py | import torch
from torch import nn
class CalibrationWrapper(nn.Module):
"""Wraps several calibration models, each being applied on different targets."""
def __init__(self):
super().__init__()
self.start_indexes = []
self.end_indexes = []
self.models = nn.ModuleList([])
de... | 2,150 | 27.68 | 100 | py |
AFC | AFC-master/inclearn/lib/network/mlp.py | from torch import nn
class MLP(nn.Module):
def __init__(self, input_dim, hidden_dims, use_bn=True, input_dropout=0., hidden_dropout=0.):
super().__init__()
layers = []
for index, dim in enumerate(hidden_dims[:-1]):
layers.append(nn.Linear(input_dim, dim, bias=True))
... | 1,129 | 32.235294 | 97 | py |
AFC | AFC-master/inclearn/lib/network/autoencoder.py | import logging
import torch
from torch import nn
from .mlp import MLP
from .word import get_embeddings
logger = logging.getLogger(__name__)
class AdvAutoEncoder(nn.Module):
def __init__(
self,
dataset,
embeddings=None,
encoder_config=None,
decoder_config=None,
d... | 2,431 | 28.658537 | 79 | py |
AFC | AFC-master/inclearn/lib/network/classifiers.py | import copy
import logging
import numpy as np
import torch
from sklearn.cluster import KMeans
from torch import nn
from torch.nn import functional as F
from inclearn.lib import distance as distance_lib
from inclearn.lib import utils
from .postprocessors import FactorScalar, HeatedUpScalar
logger = logging.getLogger... | 23,762 | 35.671296 | 100 | py |
AFC | AFC-master/inclearn/lib/network/word.py | import logging
import os
import pickle
import numpy as np
import torch
from scipy.io import loadmat
from torch import nn
from torch.nn import functional as F
import gensim
from inclearn.lib.data import fetch_word_embeddings
from .mlp import MLP
logger = logging.getLogger(__name__)
class Word2vec(nn.Module):
... | 9,761 | 32.662069 | 110 | py |
AFC | AFC-master/inclearn/lib/network/basenet.py | import copy
import logging
import torch
from torch import nn
from inclearn.lib import factory
from .classifiers import (Classifier, CosineClassifier, DomainClassifier, MCCosineClassifier)
from .postprocessors import FactorScalar, HeatedUpScalar, InvertedFactorScalar
from .word import Word2vec
logger = logging.getLo... | 9,766 | 35.580524 | 99 | py |
AFC | AFC-master/inclearn/lib/data/datasets.py | import collections
import glob
import logging
import math
import os
import warnings
import numpy as np
from torchvision import datasets, transforms
logger = logging.getLogger(__name__)
class DataHandler:
base_dataset = None
train_transforms = []
test_transforms = []
common_transforms = [transforms.T... | 18,153 | 31.82821 | 100 | py |
AFC | AFC-master/inclearn/lib/data/incdataset.py | import logging
import random
import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import transforms
from .datasets import (
APY, CUB200, LAD, AwA2, ImageNet100, ImageNet100UCIR, ImageNet1000, TinyImageNet200, iCIFAR10,
iCIFAR100
)
logger = logging.get... | 17,267 | 36.457701 | 101 | py |
AFC | AFC-master/inclearn/lib/data/samplers.py | import numpy as np
from torch.utils.data.sampler import BatchSampler
class MemoryOverSampler(BatchSampler):
def __init__(self, y, memory_flags, batch_size=128, **kwargs):
self.indexes = self._oversample(y, memory_flags)
self.batch_size = batch_size
def __len__(self):
return len(self.... | 6,690 | 31.014354 | 97 | py |
AFC | AFC-master/inclearn/lib/losses/base.py | import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
def binarize_and_smooth_labels(T, nb_classes, smoothing_const=0.1):
import sklearn.preprocessing
T = T.cpu().numpy()
T = sklearn.preprocessing.label_binarize(T, classes=range(0, nb_classes))
T = T * (1 - smoothin... | 5,013 | 33.819444 | 94 | py |
AFC | AFC-master/inclearn/lib/losses/unsupervised.py | import torch
from torch.nn import functional as F
def unsupervised_rotations(inputs, memory_flags, network, apply_on="all", factor=1.0, **kwargs):
"""Rotates inputs by 90° four times, and predict the angles.
References:
* Spyros Gidaris, Praveer Singh, Nikos Komodakis
Unsupervised Represent... | 1,553 | 34.318182 | 96 | py |
AFC | AFC-master/inclearn/lib/losses/metrics.py | import itertools
import numpy as np
import torch
from torch.nn import functional as F
def triplet_loss(
features,
targets,
squaredl2=False,
triplet_selection="all",
margin=0.2,
factor=1.,
normalize=False,
aggreg="mean",
harmonic_embeddings=None,
old_features=None,
memory_f... | 13,211 | 34.045093 | 102 | py |
AFC | AFC-master/inclearn/lib/losses/distillation.py | import functools
import math
import torch
from torch.nn import functional as F
from inclearn.lib import vizualization
def mer_loss(new_logits, old_logits):
"""Distillation loss that is less important if the new model is unconfident.
Reference:
* Kim et al.
Incremental Learning with Maximu... | 13,152 | 32.047739 | 97 | py |
AFC | AFC-master/inclearn/lib/losses/regularizations.py | import functools
import numpy as np
import torch
from torch.nn import functional as F
from inclearn.lib import utils
def weights_orthogonality(weights, margin=0.):
"""Regularization forcing the weights to be disimilar.
:param weights: Learned parameters of shape (n_classes, n_features).
:param margin: ... | 13,746 | 33.027228 | 99 | py |
caffe-model | caffe-model-master/nin.py | from caffe import layers as L
from caffe import params as P
import caffe
def conv_relu(bottom, num_output=64, kernel_size=3, stride=1, pad=0):
conv = L.Convolution(bottom, num_output=num_output, kernel_size=kernel_size, stride=stride, pad=pad,
param=[dict(lr_mult=1, decay_mult=1), dict(lr... | 7,811 | 55.201439 | 120 | py |
caffe-model | caffe-model-master/inception_resnet.py | import caffe
from caffe import layers as L
from caffe import params as P
def fc_relu_drop(bottom, num_output=1024, dropout_ratio=0.5):
fc = L.InnerProduct(bottom, num_output=num_output,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_fil... | 26,493 | 61.781991 | 153 | py |
caffe-model | caffe-model-master/resnet.py | import caffe
from caffe import layers as L
from caffe import params as P
def conv_bn_scale_relu(bottom, num_output=64, kernel_size=3, stride=1, pad=0):
conv = L.Convolution(bottom, num_output=num_output, kernel_size=kernel_size, stride=stride, pad=pad,
param=[dict(lr_mult=1, decay_mult=1)... | 8,064 | 50.369427 | 130 | py |
caffe-model | caffe-model-master/resnext.py | import caffe
from caffe import layers as L
from caffe import params as P
def resnext_block(bottom, base_output=64, card=32):
"""
input:4*base_output x n x n
output:4*base_output x n x n
:param base_output: base num_output of branch2
:param bottom: bottom layer
:return: layers
Args:
... | 8,469 | 53.645161 | 122 | py |
caffe-model | caffe-model-master/inception_v4.py | import caffe
from caffe import layers as L
from caffe import params as P
def fc_relu_drop(bottom, num_output=1024, dropout_ratio=0.5):
fc = L.InnerProduct(bottom, num_output=num_output,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_fil... | 26,845 | 65.947631 | 159 | py |
caffe-model | caffe-model-master/inception_v3.py | import caffe
from caffe import layers as L
from caffe import params as P
def fc_relu_drop(bottom, num_output=1024, dropout_ratio=0.5):
fc = L.InnerProduct(bottom, num_output=num_output,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_fil... | 29,733 | 70.995157 | 140 | py |
caffe-model | caffe-model-master/vggnet.py | from caffe import layers as L
from caffe import params as P
import caffe
def conv_relu(bottom, num_output=64, kernel_size=3, stride=1, pad=1):
conv = L.Convolution(bottom, num_output=num_output, kernel_size=kernel_size, stride=stride, pad=pad,
param=[dict(lr_mult=1, decay_mult=1), dict(lr... | 13,689 | 53.110672 | 112 | py |
caffe-model | caffe-model-master/lenet.py | from caffe import layers as L
from caffe import params as P
import caffe
class LeNet(object):
def __init__(self, lmdb_train, lmdb_test, num_output):
self.train_data = lmdb_train
self.test_data = lmdb_test
self.classifier_num = num_output
def lenet_proto(self, batch_size, phase='TRAIN'... | 3,797 | 45.888889 | 104 | py |
caffe-model | caffe-model-master/fractalnet.py | import caffe
from caffe import layers as L
from caffe import params as P
def conv_bn_scale_relu(bottom, num_output=64, kernel_size=1, stride=1, pad=0):
conv = L.Convolution(bottom, num_output=num_output, kernel_size=kernel_size, stride=stride, pad=pad,
param=[dict(lr_mult=1, decay_mult=1)... | 9,757 | 61.152866 | 117 | py |
caffe-model | caffe-model-master/inception_v1.py | import caffe
from caffe import layers as L
from caffe import params as P
def fc_relu_drop(bottom, fc_param, dropout_ratio=0.5):
fc = L.InnerProduct(bottom, num_output=fc_param['num_output'],
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weigh... | 32,045 | 75.665072 | 118 | py |
caffe-model | caffe-model-master/alexnet.py | import caffe
from caffe import layers as L
from caffe import params as P
def conv_relu(bottom, num_output=64, kernel_size=3, stride=1, pad=0, group=1):
conv = L.Convolution(bottom, num_output=num_output, kernel_size=kernel_size, stride=stride, pad=pad, group=group,
param=[dict(lr_mult=1, ... | 6,674 | 49.954198 | 117 | py |
caffe-model | caffe-model-master/seg/evaluation_seg.py | import sys
sys.path.append('/home/prmct/workspace/PSPNet-0120/python/')
import caffe
import cv2
import numpy as np
import datetime
gpu_mode = True
gpu_id = 3
data_root = '/home/prmct/Database/VOC_PASCAL/VOC2012_test/JPEGImages/'
val_file = 'test_205.txt'
save_root = './predict205_40000_ms/'
model_weights = 'psp_resn... | 5,801 | 34.595092 | 116 | py |
caffe-model | caffe-model-master/cls/evaluation_cls.py | import sys
sys.path.append('~/caffe-master-0116/python')
import numpy as np
import caffe
import cv2
import datetime
gpu_mode = True
gpu_id = 0
data_root = '~/Database/ILSVRC2012'
val_file = 'ILSVRC2012_val.txt'
save_log = 'log{}.txt'.format(datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
model_weights = 'resnet-v2... | 5,974 | 35.212121 | 122 | py |
caffe-model | caffe-model-master/cls/inception-v3/inception_v3.py | import caffe
from caffe import layers as L
from caffe import params as P
def fc_relu_drop(bottom, num_output=1024, dropout_ratio=0.5):
fc = L.InnerProduct(bottom, num_output=num_output,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_fil... | 29,733 | 70.995157 | 140 | py |
caffe-model | caffe-model-master/cls/resnet-v1/resnet_v1.py | import caffe
from caffe import layers as L
from caffe import params as P
def conv_bn_scale_relu(bottom, num_output=64, kernel_size=3, stride=1, pad=0):
conv = L.Convolution(bottom, num_output=num_output, kernel_size=kernel_size, stride=stride, pad=pad,
param=[dict(lr_mult=1, decay_mult=1)... | 8,064 | 50.369427 | 130 | py |
caffe-model | caffe-model-master/cls/inception-resnet-v2/inception_resnet.py | import caffe
from caffe import layers as L
from caffe import params as P
def fc_relu_drop(bottom, num_output=1024, dropout_ratio=0.5):
fc = L.InnerProduct(bottom, num_output=num_output,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_fil... | 26,493 | 61.781991 | 153 | py |
tt-pytorch | tt-pytorch-master/setup.py |
from setuptools import setup
setup(
name='t3nsor',
version='1.0',
description='TT decomposition on Pytorch',
author='V. Khrulkov, L. Mirvakhabova, A. Grinchuk',
author_email='khrulkov.v@gmail.com',
packages=['t3nsor'], #same as name
install_requires=['numpy', 'sympy', 'scipy'], #external package... | 340 | 25.230769 | 83 | py |
tt-pytorch | tt-pytorch-master/sentiment/utils.py | import torch
import torch.nn as nn
import subprocess
import pandas as pd
import pickle
def binary_accuracy(preds, y):
"""
Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8
"""
#round predictions to the closest integer
if len(preds.shape) == 1:
rounded_preds =... | 2,334 | 23.840426 | 83 | py |
tt-pytorch | tt-pytorch-master/sentiment/models.py | import sys
import torch
import numpy as np
import torch.nn as nn
import t3nsor as t3
class LSTM_Classifier(nn.Module):
def __init__(
self,
embedding_dim,
hidden_dim,
output_dim,
n_layers,
bidirectional,
dropout
):
super().__init__()
self.... | 1,074 | 25.219512 | 71 | py |
tt-pytorch | tt-pytorch-master/sentiment/train.py | import argparse
import sys
sys.path.insert(0, '..')
parser = argparse.ArgumentParser()
parser.add_argument(
'--embedding',
default='tt',
choices=['tt', 'tr', 'full'],
type=str)
parser.add_argument('--ranks', type=int, default=8)
parser.add_argument('--d', type=int, default=3)
parser.add_argument('--em... | 6,304 | 29.606796 | 226 | py |
tt-pytorch | tt-pytorch-master/t3nsor/initializers.py | import numpy as np
import torch
from t3nsor.tensor_train import TensorTrain
from t3nsor.tensor_train import TensorTrainBatch
def _validate_input_parameters(is_tensor, shape, **params):
"""Internal function for validating input parameters
Args:
is_tensor: bool, determines whether we attempt to construct... | 17,103 | 39.72381 | 119 | py |
tt-pytorch | tt-pytorch-master/t3nsor/initializers_tr.py | import numpy as np
import torch
from t3nsor.tensor_ring import TensorRing
# from t3nsor.tensor_ring import TensorRingBatch
def _validate_input_parameters_tr(is_tensor, shape, **params):
"""Internal function for validating input parameters
Args:
is_tensor: bool, determines whether we attempt to construc... | 17,196 | 39.945238 | 122 | py |
tt-pytorch | tt-pytorch-master/t3nsor/utils.py | from scipy.stats import entropy
import numpy as np
from sympy.utilities.iterables import multiset_partitions
from sympy.ntheory import factorint
from itertools import cycle, islice
import torch
MODES = ['ascending', 'descending', 'mixed']
CRITERIONS = ['entropy', 'var']
def _to_list(p):
res = []
for k, v i... | 3,321 | 25.576 | 99 | py |
tt-pytorch | tt-pytorch-master/t3nsor/decompositions.py | import numpy as np
import torch
from t3nsor.tensor_train import TensorTrain
from t3nsor.utils import svd_fix
def to_tt_tensor(tens, max_tt_rank=10, epsilon=None):
shape = tens.shape
d = len(shape)
max_tt_rank = np.array(max_tt_rank).astype(np.int32)
if max_tt_rank.size == 1:
max_tt_rank = [i... | 2,562 | 31.443038 | 92 | py |
tt-pytorch | tt-pytorch-master/t3nsor/layers.py | import torch
import numpy as np
import torch.nn as nn
import t3nsor as t3
class TTEmbedding(nn.Module):
def __init__(self,
init=None,
shape=None,
voc_size=None,
emb_size=None,
auto_shapes=None,
auto_shape_mode='a... | 8,890 | 31.213768 | 95 | py |
tt-pytorch | tt-pytorch-master/t3nsor/tensor_train.py | import torch
import numpy as np
import torch.nn as nn
class TensorTrain(object):
def __init__(self, tt_cores, shape=None, tt_ranks=None, convert_to_tensors=True):
#tt_cores = list(tt_cores)
if convert_to_tensors:
for i in range(len(tt_cores)):
tt_cores[i] = torch.Tensor... | 9,783 | 30.869707 | 111 | py |
tt-pytorch | tt-pytorch-master/t3nsor/ops.py | from t3nsor import TensorTrainBatch
from t3nsor import TensorTrain
from torch.autograd import Function
import torch
def gather_rows(tt_mat, inds):
"""
inds -- list of indices of shape batch_size x d
d = len(tt_mat.raw_shape[1])
"""
cores = tt_mat.cores
slices = []
batch_size = int(inds.shap... | 8,157 | 32.850622 | 124 | py |
tt-pytorch | tt-pytorch-master/t3nsor/tensor_ring.py | import torch
import numpy as np
import torch.nn as nn
import t3nsor as t3
class TensorRing(object):
def __init__(self, tr_cores, shape=None, tr_ranks=None, convert_to_tensors=True):
#tr_cores = list(tr_cores)
if convert_to_tensors:
for i in range(len(tr_cores)):
tr_cores... | 3,719 | 26.969925 | 94 | py |
FinQA | FinQA-main/code/generator/Test.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Main script
"""
from tqdm import tqdm
import json
import os
from datetime import datetime
import time
import logging
from utils import *
from config import parameters as conf
from torch import nn
import torch
import torch.optim as optim
from Model_new import Bert_mode... | 6,866 | 34.95288 | 126 | py |
FinQA | FinQA-main/code/generator/utils.py | import time
import os
import sys
import shutil
import io
import subprocess
import re
import zipfile
import json
import copy
import torch
import random
import collections
import math
import numpy as np
from tqdm import tqdm
import torch.nn.functional as F
from config import parameters as conf
from transformers import Be... | 25,374 | 28.747948 | 119 | py |
FinQA | FinQA-main/code/generator/Main.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Main script
"""
from tqdm import tqdm
import json
import os
from datetime import datetime
import time
import logging
from utils import *
from config import parameters as conf
from torch import nn
import torch
import torch.optim as optim
from Model_new import Bert_mode... | 11,036 | 36.927835 | 129 | py |
FinQA | FinQA-main/code/generator/Model_new.py | import torch
from torch import nn
import torch.optim as optim
import torch.nn.functional as F
import math
import numpy as np
from config import parameters as conf
if conf.pretrained_model == "bert":
from transformers import BertModel
elif conf.pretrained_model == "roberta":
from transformers import RobertaMode... | 13,570 | 41.01548 | 119 | py |
FinQA | FinQA-main/code/retriever/Test.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Main script
"""
from tqdm import tqdm
import json
import os
from datetime import datetime
import time
import logging
from utils import *
from config import parameters as conf
from torch import nn
import torch
import torch.optim as optim
from Model import Bert_model
i... | 4,590 | 31.104895 | 106 | py |
FinQA | FinQA-main/code/retriever/utils.py | import time
import os
import sys
import shutil
import io
import subprocess
import re
import zipfile
import json
import copy
import torch
import random
import collections
import math
import numpy as np
import torch.nn.functional as F
from config import parameters as conf
from tqdm import tqdm
from transformers import Be... | 13,173 | 26.106996 | 110 | py |
FinQA | FinQA-main/code/retriever/Main.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Main script
"""
from tqdm import tqdm
import json
import os
from datetime import datetime
import time
import logging
from utils import *
from config import parameters as conf
from torch import nn
import torch
import torch.optim as optim
from Model import Bert_model
i... | 8,275 | 34.519313 | 106 | py |
FinQA | FinQA-main/code/retriever/Model.py | import torch
from torch import nn
import torch.optim as optim
import torch.nn.functional as F
import math
import numpy as np
from config import parameters as conf
if conf.pretrained_model == "bert":
from transformers import BertModel
elif conf.pretrained_model == "roberta":
from transformers import RobertaMode... | 1,504 | 29.1 | 87 | py |
hawp | hawp-master/setup.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#!/usr/bin/env python
import glob
import os
import torch
from setuptools import find_packages
from setuptools import setup
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_ext... | 2,037 | 27.305556 | 73 | py |
hawp | hawp-master/parsing/detector.py | import torch
from torch import nn
from parsing.backbones import build_backbone
from parsing.encoder.hafm import HAFMencoder
# from epnet.structures.linelist_ops import linesegment_distance
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
import time
PRETRAINED = {
'url': 'https:/... | 17,610 | 39.025 | 293 | py |
hawp | hawp-master/parsing/solver.py | import torch
def make_optimizer(cfg, model):
params = []
for key, value in model.named_parameters():
if not value.requires_grad:
continue
lr=cfg.SOLVER.BASE_LR
weight_decay = cfg.SOLVER.WEIGHT_DECAY
# if 'md_predictor' in key or 'st_predictor' in key or 'ed_predic... | 1,199 | 35.363636 | 109 | py |
hawp | hawp-master/parsing/dataset/test_dataset.py | import torch
from torch.utils.data import Dataset
from torch.utils.data.dataloader import default_collate
import json
import copy
from PIL import Image
from skimage import io
import os
import os.path as osp
import numpy as np
class TestDatasetWithAnnotations(Dataset):
'''
Format of the annotation file
annot... | 1,711 | 33.24 | 84 | py |
hawp | hawp-master/parsing/dataset/train_dataset.py | import torch
from torch.utils.data import Dataset
import os.path as osp
import json
import cv2
from skimage import io
from PIL import Image
import numpy as np
import random
from torch.utils.data.dataloader import default_collate
from torch.utils.data.dataloader import DataLoader
import matplotlib.pyplot as plt
from to... | 2,175 | 33.539683 | 84 | py |
hawp | hawp-master/parsing/dataset/build.py | import torch
from .transforms import *
from . import train_dataset
from parsing.config.paths_catalog import DatasetCatalog
from . import test_dataset
def build_transform(cfg):
transforms = Compose(
[ResizeImage(cfg.DATASETS.IMAGE.HEIGHT,
cfg.DATASETS.IMAGE.WIDTH),
ToTensor(),
... | 2,631 | 38.283582 | 83 | py |
hawp | hawp-master/parsing/dataset/transforms.py | import torch
import torchvision
from torchvision.transforms import functional as F
from skimage.transform import resize
import numpy as np
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, image, ann=None):
if ann is None:
for t... | 2,395 | 32.277778 | 90 | py |
hawp | hawp-master/parsing/encoder/hafm.py | import torch
import numpy as np
from torch.utils.data.dataloader import default_collate
from parsing import _C
class HAFMencoder(object):
def __init__(self, cfg):
self.dis_th = cfg.ENCODER.DIS_TH
self.ang_th = cfg.ENCODER.ANG_TH
self.num_static_pos_lines = cfg.ENCODER.NUM_STATIC_POS_LINES
... | 5,407 | 39.358209 | 109 | py |
hawp | hawp-master/parsing/utils/c2_model_loading.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import pickle
from collections import OrderedDict
import torch
from parsing.utils.model_serialization import load_state_dict
from parsing.utils.registry import Registry
def _rename_basic_resnet_weights(layer_keys):
layer_keys... | 8,543 | 39.880383 | 129 | py |
hawp | hawp-master/parsing/utils/metric_logger.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import defaultdict
from collections import deque
import torch
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the global series average.
"""
def __... | 2,379 | 29.512821 | 98 | py |
hawp | hawp-master/parsing/utils/checkpoint.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import os
import torch
from parsing.utils.model_serialization import load_state_dict
from parsing.utils.c2_model_loading import load_c2_format
from parsing.utils.imports import import_file
from parsing.utils.model_zoo import cache_... | 4,773 | 33.1 | 84 | py |
hawp | hawp-master/parsing/utils/comm.py | """
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import pickle
import time
import torch
import torch.distributed as dist
def get_world_size():
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
ret... | 3,764 | 28.186047 | 84 | py |
hawp | hawp-master/parsing/utils/model_zoo.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
import sys
try:
from torch.hub import _download_url_to_file
from torch.hub import urlparse
from torch.hub import HASH_REGEX
except ImportError:
from torch.utils.model_zoo import _download_url_to_file
from torch.utils.... | 3,023 | 47.774194 | 135 | py |
hawp | hawp-master/parsing/utils/model_serialization.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import OrderedDict
import logging
import torch
from parsing.utils.imports import import_file
def align_and_update_state_dicts(model_state_dict, loaded_state_dict):
"""
Strategy: suppose that the models that we will crea... | 3,823 | 39.680851 | 92 | py |
hawp | hawp-master/parsing/utils/imports.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import importlib
import importlib.util
import sys
def import_file(module_name, file_path, make_importable=False):
spec = importlib.util.spec_from_file_location(module_name, file_path)
module = importlib.util.module_from_spec(... | 446 | 28.8 | 73 | py |
hawp | hawp-master/parsing/backbones/stacked_hg.py | """
Hourglass network inserted in the pre-activated Resnet
Use lr=0.01 for current version
(c) Nan Xue (HAWP)
(c) Yichao Zhou (LCNN)
(c) YANG, Wei
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ["HourglassNet", "hg"]
class Bottleneck2D(nn.Module):
expansion = 2
def __init__... | 6,111 | 31.684492 | 101 | py |
hawp | hawp-master/parsing/backbones/multi_task_head.py | import torch
import torch.nn as nn
class MultitaskHead(nn.Module):
def __init__(self, input_channels, num_class, head_size):
super(MultitaskHead, self).__init__()
m = int(input_channels / 4)
heads = []
for output_channels in sum(head_size, []):
heads.append(
... | 1,816 | 34.627451 | 79 | py |
hawp | hawp-master/scripts/test.py | import torch
from parsing.config import cfg
from parsing.utils.comm import to_device
from parsing.dataset import build_test_dataset
from parsing.detector import get_hawp_model
from parsing.utils.logger import setup_logger
from parsing.utils.checkpoint import DetectronCheckpointer
from parsing.config.paths_catalog impor... | 6,703 | 35.63388 | 124 | py |
hawp | hawp-master/scripts/predict.py | import torch
from parsing.config import cfg
from parsing.utils.comm import to_device
from parsing.dataset.build import build_transform
from parsing.detector import get_hawp_model
from parsing.utils.logger import setup_logger
from skimage import io
import argparse
import matplotlib.pyplot as plt
parser = argparse.Argum... | 1,725 | 27.295082 | 68 | py |
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