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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xcos | xcos-master/src/worker/tester.py | import os
import time
import torch
from torchvision.utils import save_image
from .worker_template import WorkerTemplate
from data_loader.base_data_loader import BaseDataLoader
from pipeline.base_pipeline import BasePipeline
from utils.global_config import global_config
from utils.logging_config import logger
from utils... | 4,707 | 42.192661 | 118 | py |
xcos | xcos-master/src/worker/worker_template.py | import time
from abc import ABC, abstractmethod
import torch
from torchvision.utils import make_grid
from data_loader.base_data_loader import BaseDataLoader
from pipeline.base_pipeline import BasePipeline
from utils.global_config import global_config
from utils.util import batch_visualize_xcos
class WorkerTemplate(... | 6,516 | 38.981595 | 101 | py |
xcos | xcos-master/src/worker/validator.py | import torch
from .training_worker import TrainingWorker
from pipeline.base_pipeline import BasePipeline
class Validator(TrainingWorker):
"""
Validator class
Note:
Inherited from WorkerTemplate.
"""
def __init__(self, pipeline: BasePipeline, *args):
super().__init__(pipeline, *arg... | 1,686 | 39.166667 | 113 | py |
xcos | xcos-master/src/data_loader/data_loaders.py | import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) # noqa
from torchvision import transforms
from .base_data_loader import BaseDataLoader
from .mnist import MnistDataset
from .mnist_result import MnistResultDataset
from .face_datasets import SiameseImageFolder, InsightF... | 5,081 | 40.655738 | 98 | py |
xcos | xcos-master/src/data_loader/base_data_loader.py | import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data.dataloader import default_collate
from torch.utils.data.sampler import SubsetRandomSampler
# Add this to initialize workers of dataloader to avoid fixed numpy random
# seeds for each training epoch. For a clearer explanation please refer... | 2,242 | 32.477612 | 112 | py |
xcos | xcos-master/src/data_loader/face_datasets.py | import cv2
import os
import os.path as op
import warnings
from glob import glob
import numpy as np
import pandas as pd
from PIL import Image
import bcolz
import torch
import torch.nn as nn
from torchvision import transforms, datasets
from torch.utils.data import Dataset
from torch.utils.data.sampler import BatchSampler... | 49,157 | 36.212718 | 107 | py |
xcos | xcos-master/src/data_loader/mnist_result.py | import numpy as np
from torch.utils.data import Dataset
class MnistResultDataset(Dataset):
"""
Customized MNIST result dataset demo
"""
def __init__(self, result_filename, key='model_output'):
self.key = key
self.results = self._load_data(result_filename, key)
def _load_data(self,... | 661 | 24.461538 | 60 | py |
xcos | xcos-master/src/data_loader/mnist.py | from torchvision import datasets
class MnistDataset(datasets.MNIST):
"""
Customized MNIST dataset demo
"""
def __init__(self, data_dir, train, download, transform):
super().__init__(data_dir, train=train, download=download, transform=transform)
def __getitem__(self, index):
""" Ov... | 532 | 27.052632 | 87 | py |
xcos | xcos-master/src/utils/visualization.py | try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
print("Using tensorboardX instead of built-in tensorboard (need PyTorch 1.2+ with Tensorboard 1.14+)")
from tensorboardX import SummaryWriter
class WriterTensorboard():
def __init__(self, writer_dir, logger, enable):
se... | 1,723 | 37.311111 | 114 | py |
xcos | xcos-master/src/utils/insight2xcos.py | # from model.face_recog import Backbone_FC2Conv, Backbone
# from model.xcos_modules import XCosAttention
# backbone = Backbone_FC2Conv(50, 0.6, 'ir_se')
# attention = XCosAttention(use_softmax=True, softmax_t=1, chw2hwc=True)
# backbone_target = Backbone(50,
# 0.6,
# ... | 2,258 | 43.294118 | 120 | py |
xcos | xcos-master/src/utils/insight_to_normal_face_model.py | # from model.face_recog import Backbone_FC2Conv, Backbone
# from model.xcos_modules import XCosAttention
# backbone = Backbone_FC2Conv(50, 0.6, 'ir_se')
# attention = XCosAttention(use_softmax=True, softmax_t=1, chw2hwc=True)
# backbone_target = Backbone(50,
# 0.6,
# ... | 1,476 | 33.348837 | 86 | py |
xcos | xcos-master/src/utils/util.py | import os
import os.path as op
from glob import glob
import importlib.util
import torch
import numpy as np
import io
import cv2
import base64
import seaborn as sns
from PIL import Image
from torchvision.transforms import ToTensor
from matplotlib import pyplot as plt
lib_path = op.abspath(op.join(__file__, op.pardir... | 11,115 | 31.127168 | 90 | py |
xcos | xcos-master/src/utils/verification.py | import numpy as np
from sklearn.model_selection import KFold
import matplotlib.pyplot as plt
import io
from PIL import Image
from torchvision import transforms
def calculate_accuracy(threshold, dist, actual_issame, useCos=False):
'''
if useCos = True, then view 'dist' variable as cos
'''
if useCos:
... | 4,936 | 33.284722 | 95 | py |
xcos | xcos-master/src/model/base_model.py | import torch.nn as nn
import numpy as np
from utils.logging_config import logger
class BaseModel(nn.Module):
"""
Base class for all models
"""
def __init__(self):
super(BaseModel, self).__init__()
def forward(self, *input):
"""
Forward pass logic
:return: Model ... | 672 | 20.709677 | 79 | py |
xcos | xcos-master/src/model/loss.py | import torch
import torch.nn as nn
class BaseLoss(nn.Module):
def __init__(self, output_key, target_key, nickname=None, weight=1):
super().__init__()
self.output_key = output_key
self.target_key = target_key
self.weight = weight
self.nickname = self.__class__.__name__ if ni... | 4,210 | 32.688 | 90 | py |
xcos | xcos-master/src/model/face_recog.py | from torch.nn import (Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU,
ReLU, Sigmoid, Dropout, MaxPool2d,
AdaptiveAvgPool2d, Sequential, Module, Parameter)
# import torch.nn.functional as F
import torch
from collections import namedtuple
import math
from .networks import nor... | 15,351 | 37.094293 | 112 | py |
xcos | xcos-master/src/model/model.py | import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) # noqa
import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_model import BaseModel
from .networks import MnistGenerator, MnistDiscriminator
from .face_recog import Backbone_FC2Conv, Backbone, A... | 9,784 | 39.26749 | 102 | py |
xcos | xcos-master/src/model/networks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import spectral_norm
def normal_init(m, mean, std):
if isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Conv2d):
m.weight.data.normal_(mean, std)
m.bias.data.zero_()
class MnistGenerator(nn.Module):
#... | 2,779 | 38.714286 | 129 | py |
xcos | xcos-master/src/model/xcos_modules.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .networks import normal_init
cos = nn.CosineSimilarity(dim=1, eps=1e-6)
def l2normalize(x):
return F.normalize(x, p=2, dim=1)
class FrobeniusInnerProduct(nn.Module):
def __init__(self):
super(FrobeniusInnerProduct, self).__in... | 10,474 | 33.916667 | 94 | py |
xcos | xcos-master/src/model/metric.py | import os
import torch
from abc import abstractmethod
import tempfile
import numpy as np
from torchvision import transforms
from utils.util import DeNormalize, lib_path, import_given_path
from utils.verification import evaluate_accuracy
from utils.logging_config import logger
class BaseMetric(torch.nn.Module):
... | 8,283 | 35.982143 | 114 | py |
PrincipledPruningBNN | PrincipledPruningBNN-main/bayesian-tensorflow/src/bayesian_tensorflow/losses.py | # Imports
import math
from keras import backend as K
import tensorflow as tf
# Accuracy loss function for regression models, for Bayes-by-Backprop
@tf.function
def AccLossBBB(y_true, y_pred):
"""
This function computes the accuracy loss term of the Variational Free Energy (VFE) for the
Bayes-by-Backprop ... | 1,705 | 30.592593 | 100 | py |
PrincipledPruningBNN | PrincipledPruningBNN-main/bayesian-tensorflow/src/bayesian_tensorflow/activations.py | # Imports
import math
from keras import backend as K
import tensorflow as tf
# ReLU function
@tf.function
def relu_moments(h_mean, h_var):
"""
This functions computes the first and second (central) moment of a Normal distribution
passing through a ReLU function.
It takes the mean and variance of... | 2,544 | 28.252874 | 94 | py |
PrincipledPruningBNN | PrincipledPruningBNN-main/bayesian-tensorflow/src/bayesian_tensorflow/layers/bayes_by_backprop.py | # Imports
from keras import backend as K
from keras import initializers, activations
import tensorflow as tf
# Dense layer
class DenseBBB(tf.keras.layers.Layer):
"""
Variational fully connected layer (dense), following Bayes-by-Backprop (BBB).
It takes the number of units as its input, all other inp... | 22,719 | 41.706767 | 132 | py |
PrincipledPruningBNN | PrincipledPruningBNN-main/bayesian-tensorflow/src/bayesian_tensorflow/layers/variance_backpropagation.py | # Imports
import math
from keras import backend as K
from keras import initializers
import tensorflow as tf
# Local functions
from bayesian_tensorflow import activations
# Dense layer
class DenseVBP(tf.keras.layers.Layer):
"""
Variational fully connected layer (dense), following Variance Back-Propagation (V... | 21,930 | 41.09405 | 132 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/tools/extra/summarize.py | #!/usr/bin/env python
"""Net summarization tool.
This tool summarizes the structure of a net in a concise but comprehensive
tabular listing, taking a prototxt file as input.
Use this tool to check at a glance that the computation you've specified is the
computation you expect.
"""
from caffe.proto import caffe_pb2
... | 4,880 | 33.617021 | 95 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/tools/extra/parse_log.py | #!/usr/bin/env python
"""
Parse training log
Evolved from parse_log.sh
"""
import os
import re
import extract_seconds
import argparse
import csv
from collections import OrderedDict
def parse_log(path_to_log):
"""Parse log file
Returns (train_dict_list, test_dict_list)
train_dict_list and test_dict_lis... | 7,136 | 32.824645 | 86 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/examples/web_demo/app.py | import os
import time
import cPickle
import datetime
import logging
import flask
import werkzeug
import optparse
import tornado.wsgi
import tornado.httpserver
import numpy as np
import pandas as pd
from PIL import Image
import cStringIO as StringIO
import urllib
import exifutil
import caffe
REPO_DIRNAME = os.path.abs... | 7,793 | 33.184211 | 105 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/examples/pycaffe/caffenet.py | from __future__ import print_function
from caffe import layers as L, params as P, to_proto
from caffe.proto import caffe_pb2
# helper function for common structures
def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
... | 2,112 | 36.732143 | 91 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/examples/pycaffe/tools.py | import numpy as np
class SimpleTransformer:
"""
SimpleTransformer is a simple class for preprocessing and deprocessing
images for caffe.
"""
def __init__(self, mean=[128, 128, 128]):
self.mean = np.array(mean, dtype=np.float32)
self.scale = 1.0
def set_mean(self, mean):
... | 3,457 | 27.344262 | 79 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/examples/pycaffe/layers/pascal_multilabel_datalayers.py | # imports
import json
import time
import pickle
import scipy.misc
import skimage.io
import caffe
import numpy as np
import os.path as osp
from xml.dom import minidom
from random import shuffle
from threading import Thread
from PIL import Image
from tools import SimpleTransformer
class PascalMultilabelDataLayerSync... | 6,846 | 30.552995 | 78 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/examples/pycaffe/layers/pyloss.py | import caffe
import numpy as np
class EuclideanLossLayer(caffe.Layer):
"""
Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
to demonstrate the class interface for developing layers in Python.
"""
def setup(self, bottom, top):
# check input pair
if len(bo... | 1,223 | 31.210526 | 79 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/examples/finetune_flickr_style/assemble_data.py | #!/usr/bin/env python
"""
Form a subset of the Flickr Style data, download images to dirname, and write
Caffe ImagesDataLayer training file.
"""
import os
import urllib
import hashlib
import argparse
import numpy as np
import pandas as pd
from skimage import io
import multiprocessing
# Flickr returns a special image i... | 3,636 | 35.737374 | 94 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/src/caffe/test/test_data/generate_sample_data.py | """
Generate data used in the HDF5DataLayer and GradientBasedSolver tests.
"""
import os
import numpy as np
import h5py
script_dir = os.path.dirname(os.path.abspath(__file__))
# Generate HDF5DataLayer sample_data.h5
num_cols = 8
num_rows = 10
height = 6
width = 5
total_size = num_cols * num_rows * height * width
da... | 2,104 | 24.670732 | 70 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/draw_net.py | #!/usr/bin/env python
"""
Draw a graph of the net architecture.
"""
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from google.protobuf import text_format
import caffe
import caffe.draw
from caffe.proto import caffe_pb2
def parse_args():
"""Parse input arguments
"""
parser = Argument... | 1,934 | 31.79661 | 81 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/detect.py | #!/usr/bin/env python
"""
detector.py is an out-of-the-box windowed detector
callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
Note that this model was trained for image classification and not detection,
and finetuning for detection can be expected to improve results... | 5,734 | 31.95977 | 88 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/classify.py | #!/usr/bin/env python
"""
classify.py is an out-of-the-box image classifer callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
"""
import numpy as np
import os
import sys
import argparse
import glob
import time
import caffe
def main(argv):
pycaffe_dir = os.path.... | 4,262 | 29.669065 | 88 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/train.py | #!/usr/bin/env python
"""
Trains a model using one or more GPUs.
"""
from multiprocessing import Process
import caffe
def train(
solver, # solver proto definition
snapshot, # solver snapshot to restore
gpus, # list of device ids
timing=False, # show timing info for compute and com... | 3,145 | 30.148515 | 85 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/net_spec.py | """Python net specification.
This module provides a way to write nets directly in Python, using a natural,
functional style. See examples/pycaffe/caffenet.py for an example.
Currently this works as a thin wrapper around the Python protobuf interface,
with layers and parameters automatically generated for the "layers"... | 8,277 | 34.835498 | 88 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/classifier.py | #!/usr/bin/env python
"""
Classifier is an image classifier specialization of Net.
"""
import numpy as np
import caffe
class Classifier(caffe.Net):
"""
Classifier extends Net for image class prediction
by scaling, center cropping, or oversampling.
Parameters
----------
image_dims : dimensio... | 3,537 | 34.737374 | 78 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/coord_map.py | """
Determine spatial relationships between layers to relate their coordinates.
Coordinates are mapped from input-to-output (forward), but can
be mapped output-to-input (backward) by the inverse mapping too.
This helps crop and align feature maps among other uses.
"""
from __future__ import division
import numpy as np... | 6,721 | 35.139785 | 79 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/detector.py | #!/usr/bin/env python
"""
Do windowed detection by classifying a number of images/crops at once,
optionally using the selective search window proposal method.
This implementation follows ideas in
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik.
Rich feature hierarchies for accurate object detection... | 8,541 | 38.364055 | 80 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/__init__.py | from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed, solver_count, set_solver_count, solver_rank, set_solver_rank, set_mul... | 552 | 60.444444 | 216 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/pycaffe.py | """
Wrap the internal caffe C++ module (_caffe.so) with a clean, Pythonic
interface.
"""
from collections import OrderedDict
try:
from itertools import izip_longest
except:
from itertools import zip_longest as izip_longest
import numpy as np
from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \... | 11,615 | 32.572254 | 89 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/draw.py | """
Caffe network visualization: draw the NetParameter protobuffer.
.. note::
This requires pydot>=1.0.2, which is not included in requirements.txt since
it requires graphviz and other prerequisites outside the scope of the
Caffe.
"""
from caffe.proto import caffe_pb2
"""
pydot is not supported under p... | 8,789 | 34.877551 | 112 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/io.py | import numpy as np
import skimage.io
from scipy.ndimage import zoom
from skimage.transform import resize
try:
# Python3 will most likely not be able to load protobuf
from caffe.proto import caffe_pb2
except:
import sys
if sys.version_info >= (3, 0):
print("Failed to include caffe_pb2, things mi... | 12,743 | 32.1875 | 110 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/test/test_coord_map.py | import unittest
import numpy as np
import random
import caffe
from caffe import layers as L
from caffe import params as P
from caffe.coord_map import coord_map_from_to, crop
def coord_net_spec(ks=3, stride=1, pad=0, pool=2, dstride=2, dpad=0):
"""
Define net spec for simple conv-pool-deconv pattern common t... | 6,894 | 34.725389 | 79 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/test/test_python_layer_with_param_str.py | import unittest
import tempfile
import os
import six
import caffe
class SimpleParamLayer(caffe.Layer):
"""A layer that just multiplies by the numeric value of its param string"""
def setup(self, bottom, top):
try:
self.value = float(self.param_str)
except ValueError:
... | 2,031 | 31.774194 | 79 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/test/test_io.py | import numpy as np
import unittest
import caffe
class TestBlobProtoToArray(unittest.TestCase):
def test_old_format(self):
data = np.zeros((10,10))
blob = caffe.proto.caffe_pb2.BlobProto()
blob.data.extend(list(data.flatten()))
shape = (1,1,10,10)
blob.num, blob.channels, b... | 1,694 | 28.736842 | 65 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/test/test_solver.py | import unittest
import tempfile
import os
import numpy as np
import six
import caffe
from test_net import simple_net_file
class TestSolver(unittest.TestCase):
def setUp(self):
self.num_output = 13
net_f = simple_net_file(self.num_output)
f = tempfile.NamedTemporaryFile(mode='w+', delete=F... | 2,165 | 33.380952 | 76 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/test/test_layer_type_list.py | import unittest
import caffe
class TestLayerTypeList(unittest.TestCase):
def test_standard_types(self):
#removing 'Data' from list
for type_name in ['Data', 'Convolution', 'InnerProduct']:
self.assertIn(type_name, caffe.layer_type_list(),
'%s not in layer_type_lis... | 338 | 27.25 | 65 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/test/test_net.py | import unittest
import tempfile
import os
import numpy as np
import six
from collections import OrderedDict
import caffe
def simple_net_file(num_output):
"""Make a simple net prototxt, based on test_net.cpp, returning the name
of the (temporary) file."""
f = tempfile.NamedTemporaryFile(mode='w+', delete... | 11,640 | 28.848718 | 82 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/test/test_draw.py | import os
import unittest
from google.protobuf import text_format
import caffe.draw
from caffe.proto import caffe_pb2
def getFilenames():
"""Yields files in the source tree which are Net prototxts."""
result = []
root_dir = os.path.abspath(os.path.join(
os.path.dirname(__file__), '..', '..', '..... | 1,114 | 28.342105 | 79 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/test/test_nccl.py | import sys
import unittest
import caffe
class TestNCCL(unittest.TestCase):
def test_newuid(self):
"""
Test that NCCL uids are of the proper type
according to python version
"""
if caffe.has_nccl():
uid = caffe.NCCL.new_uid()
if sys.version_info.maj... | 457 | 21.9 | 55 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/test/test_net_spec.py | import unittest
import tempfile
import caffe
from caffe import layers as L
from caffe import params as P
def lenet(batch_size):
n = caffe.NetSpec()
n.data, n.label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
dict(dim=[batch_size, 1, 1, 1])],
... | 3,756 | 40.744444 | 80 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/python/caffe/test/test_python_layer.py | import unittest
import tempfile
import os
import six
import caffe
class SimpleLayer(caffe.Layer):
"""A layer that just multiplies by ten"""
def setup(self, bottom, top):
pass
def reshape(self, bottom, top):
top[0].reshape(*bottom[0].data.shape)
def forward(self, bottom, top):
... | 5,510 | 31.609467 | 81 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/scripts/cpp_lint.py | #!/usr/bin/env python
#
# Copyright (c) 2009 Google Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list... | 187,569 | 37.483792 | 93 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/scripts/split_caffe_proto.py | #!/usr/bin/env python
import mmap
import re
import os
import errno
script_path = os.path.dirname(os.path.realpath(__file__))
# a regex to match the parameter definitions in caffe.proto
r = re.compile(r'(?://.*\n)*message ([^ ]*) \{\n(?: .*\n|\n)*\}')
# create directory to put caffe.proto fragments
try:
os.mkdir(... | 941 | 25.166667 | 65 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/scripts/download_model_binary.py | #!/usr/bin/env python
import os
import sys
import time
import yaml
import hashlib
import argparse
from six.moves import urllib
required_keys = ['caffemodel', 'caffemodel_url', 'sha1']
def reporthook(count, block_size, total_size):
"""
From http://blog.moleculea.com/2012/10/04/urlretrieve-progres-indicator/
... | 2,531 | 31.461538 | 78 | py |
P-STMO | P-STMO-main/run_3dhp.py | import os
import glob
import torch
import random
import logging
import numpy as np
from tqdm import tqdm
import torch.nn as nn
import torch.utils.data
import torch.optim as optim
from common.opt import opts
from common.utils import *
from common.camera import get_uvd2xyz
from common.load_data_3dhp_mae import Fusion
fro... | 16,320 | 38.233173 | 170 | py |
P-STMO | P-STMO-main/run.py | import os
import glob
import torch
import random
import logging
import numpy as np
from tqdm import tqdm
import torch.nn as nn
import torch.utils.data
import torch.optim as optim
from common.opt import opts
from common.utils import *
from common.camera import get_uvd2xyz
from common.load_data_hm36_tds import Fusion
fro... | 15,226 | 37.745547 | 168 | py |
P-STMO | P-STMO-main/run_in_the_wild.py | import os
import glob
import torch
import random
import logging
import numpy as np
from tqdm import tqdm
import torch.nn as nn
import torch.utils.data
import torch.optim as optim
from common.opt import opts
from common.utils import *
from common.camera import get_uvd2xyz
from common.load_data_hm36_tds_in_the_wild impor... | 15,554 | 37.790524 | 168 | py |
P-STMO | P-STMO-main/common/load_data_hm36_tds_in_the_wild.py |
import torch.utils.data as data
import numpy as np
from common.utils import deterministic_random
from common.camera import world_to_camera, normalize_screen_coordinates
from common.generator_tds import ChunkedGenerator
class Fusion(data.Dataset):
def __init__(self, opt, dataset, root_path, train=True, MAE=False,... | 9,334 | 50.291209 | 128 | py |
P-STMO | P-STMO-main/common/load_data_3dhp_mae.py |
import torch.utils.data as data
import numpy as np
from common.utils import deterministic_random
from common.camera import world_to_camera, normalize_screen_coordinates
from common.generator_3dhp import ChunkedGenerator
class Fusion(data.Dataset):
def __init__(self, opt, root_path, train=True, MAE=False):
... | 9,051 | 45.420513 | 125 | py |
P-STMO | P-STMO-main/common/camera.py | import sys
import numpy as np
import torch
def normalize_screen_coordinates(X, w, h):
assert X.shape[-1] == 2
return X / w * 2 - [1, h / w]
def image_coordinates(X, w, h):
assert X.shape[-1] == 2
# Reverse camera frame normalization
return (X + [1, h / w]) * w / 2
def world_to_camera(X, R, t):
Rt = ... | 2,451 | 25.652174 | 87 | py |
P-STMO | P-STMO-main/common/utils.py | import torch
import numpy as np
import hashlib
from torch.autograd import Variable
import os
def deterministic_random(min_value, max_value, data):
digest = hashlib.sha256(data.encode()).digest()
raw_value = int.from_bytes(digest[:4], byteorder='little', signed=False)
return int(raw_value / (2 ** 32 - 1... | 7,304 | 31.039474 | 118 | py |
P-STMO | P-STMO-main/common/opt.py | import argparse
import os
import math
import time
import torch
class opts():
def __init__(self):
self.parser = argparse.ArgumentParser()
def init(self):
self.parser.add_argument('--layers', default=3, type=int)
self.parser.add_argument('--channel', default=256, type=int)
self.p... | 5,367 | 42.290323 | 94 | py |
P-STMO | P-STMO-main/common/load_data_hm36_tds.py |
import torch.utils.data as data
import numpy as np
from common.utils import deterministic_random
from common.camera import world_to_camera, normalize_screen_coordinates
from common.generator_tds import ChunkedGenerator
class Fusion(data.Dataset):
def __init__(self, opt, dataset, root_path, train=True, MAE=False,... | 9,325 | 50.241758 | 128 | py |
P-STMO | P-STMO-main/in_the_wild/videopose_PSTMO.py | import os
import time
from common.arguments import parse_args
from common.camera import *
from common.generators import *
from common.loss import *
from common.model import *
from common.utils import Timer, evaluate, add_path
from common.inference_3d import *
from model.block.refine import refine
from model.stmo impo... | 7,170 | 35.217172 | 139 | py |
P-STMO | P-STMO-main/in_the_wild/inference_3d.py | # Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import hashlib
import os
import pathlib
import shutil
import sys
import time
import cv2
import numpy as np
import torch
from to... | 3,586 | 32.523364 | 128 | py |
P-STMO | P-STMO-main/model/stmo.py | import torch
import torch.nn as nn
from model.block.vanilla_transformer_encoder import Transformer
from model.block.strided_transformer_encoder import Transformer as Transformer_reduce
class Linear(nn.Module):
def __init__(self, linear_size, p_dropout=0.25):
super(Linear, self).__init__()
self.l_si... | 4,047 | 30.874016 | 92 | py |
P-STMO | P-STMO-main/model/stmo_pretrain.py | import torch
import torch.nn as nn
from model.block.vanilla_transformer_encoder_pretrain import Transformer, Transformer_dec
from model.block.strided_transformer_encoder import Transformer as Transformer_reduce
import numpy as np
class LayerNorm(nn.Module):
def __init__(self, features, eps=1e-6):
super(Lay... | 5,518 | 32.652439 | 119 | py |
P-STMO | P-STMO-main/model/block/refine.py | import torch
import torch.nn as nn
from torch.autograd import Variable
fc_out = 256
fc_unit = 1024
class refine(nn.Module):
def __init__(self, opt):
super().__init__()
out_seqlen = 1
fc_in = opt.out_channels*2*out_seqlen*opt.n_joints
fc_out = opt.in_channels * opt.n_joints
... | 948 | 24.648649 | 89 | py |
P-STMO | P-STMO-main/model/block/vanilla_transformer_encoder_pretrain.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import math
import os
import copy
def clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
class Encoder(nn.Module):
def __init__(self, layer, N):
sup... | 5,115 | 31.176101 | 98 | py |
P-STMO | P-STMO-main/model/block/strided_transformer_encoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import math
import os
import copy
def clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
class Encoder(nn.Module):
def __init__(self, layer, N, length, d_mo... | 5,685 | 32.05814 | 120 | py |
P-STMO | P-STMO-main/model/block/vanilla_transformer_encoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import math
import os
import copy
def clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
class Encoder(nn.Module):
def __init__(self, layer, N):
sup... | 4,191 | 30.283582 | 98 | py |
InvariantRuleAD | InvariantRuleAD-main/core/model/reconstruction_models/DeepSVDD.py | import numpy as np
import tensorflow as tf
from tensorflow import keras
import tempfile
from .. import BaseModel
import random
def oneclass_loss(z,radius,nu):
dist = tf.reduce_sum(tf.square(z), axis=-1)
loss = tf.maximum(dist - radius ** 2, tf.zeros_like(dist))
loss = radius**2+(1/nu)*tf.reduce_mean(loss)... | 4,639 | 32.623188 | 113 | py |
InvariantRuleAD | InvariantRuleAD-main/core/model/reconstruction_models/vanilla_autoencoder.py | from tensorflow import keras
import tensorflow as tf
import numpy as np
import tempfile
import random
from .. import BaseModel,AnomalyDetector
from ...preprocessing.signals import ContinuousSignal,CategoricalSignal
from ...learning.hp_optimization.Hyperparameter import ConstHyperparameter,UniformIntegerHyperparameter
f... | 10,613 | 35.854167 | 153 | py |
InvariantRuleAD | InvariantRuleAD-main/core/preprocessing/data_handler.py | import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import warnings
from builtins import isinstance
class TSAEDataHandler():
'''
Data Handler for time-series autoencoders
Parameters
----------
sequence_length : int
the length of sequence
feats : list of stri... | 11,215 | 34.381703 | 120 | py |
CLUE | CLUE-master/baselines/models/xlnet/data_utils.py | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import random
from absl import flags
import absl.logging as _logging # pylint: disable=unused-import
import numpy as np
import tensorflow as tf
from prepro_u... | 29,915 | 31.659389 | 97 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/run_classifier.py | # -*- coding: utf-8 -*-
# @Author: bo.shi
# @Date: 2019-12-30 19:26:53
# @Last Modified by: bo.shi
# @Last Modified time: 2019-12-31 19:49:36
""" Finetuning the library models for sequence classification on CLUE (Bert, ERNIE, XLNet, RoBERTa)."""
from __future__ import absolute_import, division, print_function
imp... | 31,505 | 54.273684 | 152 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/convert_albert_original_tf_checkpoint_to_pytorch.py | """Convert ALBERT checkpoint."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import torch
from transformers.modeling_albert import BertConfig, AlbertForPreTraining, load_tf_weights_in_albert
import logging
logging.basicConfig(level=log... | 2,388 | 40.189655 | 110 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/convert_ernie_original_pad_checkpoint_to_pytorch.py | #!/usr/bin/env python
# encoding: utf-8
"""
File Description: https://github.com/nghuyong/ERNIE-Pytorch
Author: nghuyong
Mail: nghuyong@163.com
Created Time: 2020/7/14
"""
import collections
import os
import json
import shutil
import paddle.fluid.dygraph as D
import torch
from paddle import fluid
# downloading paddle... | 5,179 | 52.958333 | 118 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/convert_bert_original_tf_checkpoint_to_pytorch.py | """Convert BERT checkpoint."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
import logging
logging.basicConfig(level=logging.INFO)
def conver... | 1,972 | 36.942308 | 101 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/convert_xlnet_original_tf_checkpoint_to_pytorch.py | """Convert XLNET checkpoint."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import argparse
import torch
from transformers import (CONFIG_NAME, WEIGHTS_NAME,
XLNetConfig,
XLNetLMHeadModel,
... | 2,480 | 39.016129 | 104 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/tools/common.py | import os
import random
import torch
import numpy as np
import json
import pickle
import torch.nn as nn
from collections import OrderedDict
from pathlib import Path
import logging
logger = logging.getLogger()
def print_config(config):
info = "Running with the following configs:\n"
for k, v in config.items():
... | 11,484 | 28.677003 | 128 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/processors/clue.py | # -*- coding: utf-8 -*-
# @Author: bo.shi
# @Date: 2019-12-30 19:26:53
# @Last Modified by: bo.shi
# @Last Modified time: 2020-01-01 11:39:23
""" CLUE processors and helpers """
import logging
import os
import torch
from .utils import DataProcessor, InputExample, InputFeatures
logger = logging.getLogger(__name__)... | 20,143 | 38.114563 | 130 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/optimization.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICEN... | 8,635 | 44.452632 | 130 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/__main__.py | # coding: utf8
def main():
import sys
if (len(sys.argv) < 4 or len(sys.argv) > 6) or sys.argv[1] not in ["bert", "gpt", "transfo_xl", "gpt2", "xlnet", "xlm"]:
print(
"This command line utility let you convert original (author released) model checkpoint to pytorch.\n"
"It should be used a... | 7,082 | 53.484615 | 135 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/configuration_utils.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 10,772 | 50.793269 | 296 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_distilbert.py | # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.or... | 34,864 | 49.237752 | 201 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_utils.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 43,409 | 52.06846 | 472 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_bert.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 59,643 | 50.864348 | 187 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_gpt2.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License... | 33,126 | 48.965309 | 148 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_openai.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License... | 30,836 | 48.57717 | 148 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/tokenization_bert.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICEN... | 22,451 | 43.636183 | 183 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/configuration_ctrl.py | # coding=utf-8
# Copyright 2018 Salesforce and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# htt... | 5,775 | 39.111111 | 120 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/file_utils.py | """
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
import sys
import json
import logging
import os
impor... | 11,622 | 34.763077 | 144 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/__init__.py | __version__ = "2.1.1"
# Work around to update TensorFlow's absl.logging threshold which alters the
# default Python logging output behavior when present.
# see: https://github.com/abseil/abseil-py/issues/99
# and: https://github.com/tensorflow/tensorflow/issues/26691#issuecomment-500369493
try:
import absl.logging... | 5,761 | 58.402062 | 109 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_transfo_xl.py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Lice... | 39,657 | 43.50954 | 157 | py |
CLUE | CLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_albert.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 54,163 | 49.810507 | 153 | py |
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