repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
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/__init__.py | 0 | 0 | 0 | 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 |
tinker | tinker-master/tinker-build/tinker-patch-cli/tool_output/merge_mapping.py | #!/usr/bin/python
# coding: utf-8
"""
当工程使用了applymapping之后，会遇到这样的问题
1.类和方法上个版本被keep住了，这个版本不keep
2.类和方法上个版本没有被keep住，这个版本又keep住了
这两个问题会导致proguard报warning，官方建议是手动解决冲突
(http://proguard.sourceforge.net/manual/troubleshooting.html#mappingconflict1)
不解决的话默认以mapping文件为最高优先级处理，这样混淆会带来一些问题
该方案为
简单来说，上个版本的mapping称为mappin... | 6,630 | 38.945783 | 122 | py |
tinker | tinker-master/tinker-build/tinker-patch-cli/tool_output/proguard_warning.py | #!/usr/bin/python
# coding: utf8
import os
import sys
def print_usage():
print >>sys.stderr, \
"""usage: python proguard_warning.py mapping.txt warning.txt
the output mapping file is 'mapping_edit.txt' in the cwd directory
"""
sys.exit(1)
class MappingData:
raw_line = ""
... | 3,821 | 34.06422 | 116 | py |
Cellibrium | Cellibrium-master/Percolibrium/Percolators/python/anomalysensor.py |
import math
import cPickle
FORGETRATE = 0.5
def update_real_Q(qname, newq):
oldav = 0
oldvar = 0.1
[state, oldav, oldvar] = load_special_Q(qname, oldav, oldvar)
if state == True:
if oldvar == 0:
oldvar = 0.5
nextav = w_average(newq, oldav)
newvar ... | 2,249 | 23.456522 | 125 | py |
Cellibrium | Cellibrium-master/Percolibrium/Percolators/python/load_env_graph.py | #!/usr/bin/env python
from lib.neo4j import Neo4j
import urllib, urllib2, json, sys, os, time, pprint, time, pyinotify, glob
config = {}
execfile("conf/config.conf", config)
neo4j = Neo4j(config['neo4j_url'], config['neo4j_user'], config['neo4j_pass'])
pp = pprint.PrettyPrinter(indent=4)
def insert_into_db():
whi... | 1,936 | 23.2125 | 86 | py |
Cellibrium | Cellibrium-master/Percolibrium/Percolators/python/mon_env_graph.py | #!/usr/bin/env python
from lib.neo4j import Neo4j
import urllib, urllib2, json, sys, os, time, pprint, time, pyinotify, glob
config = {}
execfile("conf/config.conf", config)
neo4j = Neo4j(config['neo4j_url'], config['neo4j_user'], config['neo4j_pass'])
pp = pprint.PrettyPrinter(indent=4)
res = []
mylist = []
class... | 1,078 | 28.972222 | 109 | py |
Cellibrium | Cellibrium-master/Percolibrium/Percolators/python/logging.py |
########################################################################################################
#
# Examples, how to encode logs as semantic graphs
#
########################################################################################################
import sys
import time
import socket
from cellibri... | 12,224 | 46.753906 | 291 | py |
Cellibrium | Cellibrium-master/Percolibrium/Percolators/python/env_graph.py | #!/usr/bin/env python
from lib.neo4j import Neo4j
from multiprocessing import Process
import urllib, urllib2, json, sys, os, time, pprint, time, pyinotify, glob
config = {}
execfile("conf/config.conf", config)
neo4j = Neo4j(config['neo4j_url'], config['neo4j_user'], config['neo4j_pass'])
pp = pprint.PrettyPrinter(i... | 3,540 | 27.328 | 220 | py |
Cellibrium | Cellibrium-master/Percolibrium/Percolators/python/test_cellibrium.py |
########################################################################################################
#
# TEST
#
########################################################################################################
import sys
import time
import os
import socket
import re
from cellibrium import Cellibrium
c... | 3,839 | 26.042254 | 138 | py |
Cellibrium | Cellibrium-master/Percolibrium/Percolators/python/cellibrium.py | import sys
import time
import re
import socket
from datetime import datetime
class Cellibrium:
GR_CONTAINS = 3
GR_FOLLOWS = 2 # i.e. influenced by
GR_EXPRESSES = 4 #represents, etc
GR_NEAR = 1 # approx like
GR_CONTEXT = 5 # approx like
ALL_CONTEXTS = "any"
A = {
"a... | 30,510 | 41.494429 | 156 | py |
Cellibrium | Cellibrium-master/Percolibrium/Percolators/python/hello.py | from flask import Flask
app = Flask(__name__)
@app.route("/")
def hello():
return "Hello world"
| 102 | 11.875 | 24 | py |
Cellibrium | Cellibrium-master/Percolibrium/Percolators/python/lib/neo4j.py | # /usr/bin/env python
import urllib, urllib2, json, sys, shlex, re, os, base64
class Neo4j:
def __init__(self, neo4j_url, neo4j_user, neo4j_pass):
self.neo4j_user = neo4j_user
self.neo4j_pass = neo4j_pass
self.neo4j_url = neo4j_url
def neo4j_rest_cypher(self, query_data):
b64 = base64.b64encode('%s:%s' % ... | 3,640 | 31.508929 | 102 | py |
Cellibrium | Cellibrium-master/Percolibrium/Percolators/python/lib/__init__.py | 0 | 0 | 0 | py | |
PrincipledPruningBNN | PrincipledPruningBNN-main/bayesian-tensorflow/setup.py | # Imports
from setuptools import setup, find_packages
import pathlib
# Get the long description from the README file
here = pathlib.Path(__file__).parent.resolve()
long_description = (here / "README.md").read_text(encoding="utf-8")
# Setup
setup(
# Basic info
name='bayesian-tensorflow',
version='1.0.0... | 1,000 | 26.805556 | 67 | py |
PrincipledPruningBNN | PrincipledPruningBNN-main/bayesian-tensorflow/src/bayesian_tensorflow/inference.py | # Imports
import tensorflow as tf
# Local functions
from bayesian_tensorflow import losses
# Custom training step function, for Bayes-by-Backprop
@tf.function
def BBB(model, optim, x_batch, y_batch, n_data):
"""
This function performs gradient descent on a mini-batch of data, when using Bayes-by-Backprop
... | 3,759 | 36.227723 | 105 | 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/__init__.py | # Import activations
from .activations import *
# Import evaluation functions
from .evaluation import *
# Import layers
from .layers import *
# Import inference functions
from .inference import *
# Import losses
from .losses import * | 237 | 16 | 29 | py |
PrincipledPruningBNN | PrincipledPruningBNN-main/bayesian-tensorflow/src/bayesian_tensorflow/evaluation.py | # Imports
import tensorflow as tf
# Local functions
from bayesian_tensorflow import losses
# Custom training step function for Bayes-by-Backprop
@tf.function
def BBB(model, x_batch, y_batch, n_data):
"""
This function evaluation the Variational Free Energy (VFE) when using the Bayes-by-Backprop (BBB)
in... | 1,898 | 29.142857 | 103 | 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 |
PrincipledPruningBNN | PrincipledPruningBNN-main/bayesian-tensorflow/src/bayesian_tensorflow/layers/__init__.py | # Bayes-by-Backprop layers
from .bayes_by_backprop import DenseBBB
from .bayes_by_backprop import GammaBBB
from .bayes_by_backprop import GRUCellBBB
# Variational-Back-Propagation layers
from .variance_backpropagation import DenseVBP
from .variance_backpropagation import GammaVBP
from .variance_backpropagation import ... | 330 | 35.777778 | 48 | py |
PrincipledPruningBNN | PrincipledPruningBNN-main/experiments/figures/__init__.py | # Imports
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
# Custom function for plotting losses after training
def PlotTrainingLosses(kl_theta, kl_tau, acc_loss, figsize=[12,8]):
"""
This function plots the VFE loss and its sperates terms.
"""
# Genera... | 1,865 | 29.590164 | 67 | py |
PrincipledPruningBNN | PrincipledPruningBNN-main/experiments/datasets/uci.py | # Imports
import pandas as pd
# Dataset loader function
def load(name, seed=None):
"""
This function loads the UCI datasets from their respective CSV-files, specified by the `name` input.
- Datasets: boston / concrete / energy / kin8nm / naval / powerplant / wine / yacht
"""
if name ==... | 4,357 | 39.728972 | 110 | py |
PrincipledPruningBNN | PrincipledPruningBNN-main/experiments/datasets/toy.py | # Imports
import numpy as np
import tensorflow as tf
import math
# Custom function to load toy dataset
def load(name):
"""
This function creates the toy dataset specified by the `name` input.
- Datasets: sine / sawtooth / square
"""
# Create training signal
x = np.arange(0, 8, 0.01... | 609 | 23.4 | 72 | py |
PrincipledPruningBNN | PrincipledPruningBNN-main/experiments/datasets/__init__.py | # Import all sub-modules
from . import toy
from . import uci | 60 | 19.333333 | 24 | 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/extract_seconds.py | #!/usr/bin/env python
import datetime
import os
import sys
def extract_datetime_from_line(line, year):
# Expected format: I0210 13:39:22.381027 25210 solver.cpp:204] Iteration 100, lr = 0.00992565
line = line.strip().split()
month = int(line[0][1:3])
day = int(line[0][3:])
timestamp = line[1]
p... | 2,208 | 29.260274 | 97 | py |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/tools/extra/resize_and_crop_images.py | #!/usr/bin/env python
from mincepie import mapreducer, launcher
import gflags
import os
import cv2
from PIL import Image
# gflags
gflags.DEFINE_string('image_lib', 'opencv',
'OpenCV or PIL, case insensitive. The default value is the faster OpenCV.')
gflags.DEFINE_string('input_folder', '',
... | 4,602 | 40.845455 | 99 | 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/web_demo/exifutil.py | """
This script handles the skimage exif problem.
"""
from PIL import Image
import numpy as np
ORIENTATIONS = { # used in apply_orientation
2: (Image.FLIP_LEFT_RIGHT,),
3: (Image.ROTATE_180,),
4: (Image.FLIP_TOP_BOTTOM,),
5: (Image.FLIP_LEFT_RIGHT, Image.ROTATE_90),
6: (Image.ROTATE_270,),
7... | 1,046 | 25.175 | 51 | 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 |
Stochastic-Quantization | Stochastic-Quantization-master/caffe/scripts/copy_notebook.py | #!/usr/bin/env python
"""
Takes as arguments:
1. the path to a JSON file (such as an IPython notebook).
2. the path to output file
If 'metadata' dict in the JSON file contains 'include_in_docs': true,
then copies the file to output file, appending the 'metadata' property
as YAML front-matter, adding the field 'categor... | 1,089 | 32.030303 | 87 | py |
multi-head-attention-labeller | multi-head-attention-labeller-master/variants.py | from modules import *
import collections
import numpy
import pickle
import re
import tensorflow as tf
class Model(object):
"""
Implements several variants of the multi-head attention labeller (MHAL).
These were mainly experimental, so don't take them as granted.
The performances reported are obtained ... | 47,419 | 49.879828 | 110 | py |
multi-head-attention-labeller | multi-head-attention-labeller-master/experiment.py | from collections import Counter
from collections import OrderedDict
from evaluator import Evaluator
from model import Model
# from second_model import Model
# from variants import Model
import gc
import math
import numpy as np
import os
import pandas as pd
import random
import sys
import time
import visualize
import wa... | 31,116 | 43.580229 | 114 | py |
multi-head-attention-labeller | multi-head-attention-labeller-master/modules.py | from math import ceil
import tensorflow as tf
def layer_normalization(layer, epsilon=1e-8):
"""
Implements layer normalization.
:param layer: has 2-dimensional, the first dimension is the batch_size
:param epsilon: a small number to avoid numerical issues, such as zero division.
:return: normalize... | 103,822 | 47.021739 | 102 | py |
multi-head-attention-labeller | multi-head-attention-labeller-master/disable_tokens.py | import random
random.seed(100)
def add_another_column(dataset, extension):
"""
The original dataset file has multiple columns, the first one being
the token and the last one the label. This method builds another file
containing these as well as an additional middle column, representing
the supervi... | 3,741 | 36.79798 | 85 | py |
multi-head-attention-labeller | multi-head-attention-labeller-master/model.py | from math import ceil
from modules import cosine_distance_loss, label_smoothing
import collections
import numpy
import pickle
import re
import tensorflow as tf
class Model(object):
"""
Implements the multi-head attention labeller (MHAL).
"""
def __init__(self, config, label2id_sent, label2id_tok):
... | 45,808 | 48.846572 | 118 | py |
multi-head-attention-labeller | multi-head-attention-labeller-master/conlleval.py | #!/usr/bin/env python
# Python version of the evaluation script from CoNLL'00-
# Originates from: https://github.com/spyysalo/conlleval.py
# Intentional differences:
# - accept any space as delimiter by default
# - optional file argument (default STDIN)
# - option to set boundary (-b argument)
# - LaTeX output (-l a... | 8,967 | 30.914591 | 86 | py |
multi-head-attention-labeller | multi-head-attention-labeller-master/evaluator.py | from collections import OrderedDict
from sklearn.metrics import classification_report
import conlleval
import numpy as np
import time
class Evaluator:
"""
Evaluates the results of a joint text classifier.
"""
def __init__(self, label2id_sent, label2id_tok, conll03_eval):
self.id2label_sent = ... | 14,918 | 46.512739 | 99 | py |
multi-head-attention-labeller | multi-head-attention-labeller-master/second_model.py | from modules import label_smoothing
import collections
import numpy
import pickle
import re
import tensorflow as tf
class Model(object):
"""
Implements the multi-head attention labeller (MHAL)
without keys, queries, and values.
It only uses a simple, soft attention.
"""
def __init__(self, con... | 41,034 | 48.026284 | 105 | py |
multi-head-attention-labeller | multi-head-attention-labeller-master/visualize.py | import matplotlib as mpl
mpl.use("agg")
mpl.rcParams['xtick.labelsize'] = 20
mpl.rcParams['ytick.labelsize'] = 20
import matplotlib.pyplot as plt
import time
from tqdm import tqdm
import numpy as np
html_header = '<!DOCTYPE html>\n<html>\n<font size="3">\n<head>\n<meta charset="UTF-8">\n<body>\n'
html_footer = '</body... | 8,269 | 44.191257 | 98 | 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/h36m_dataset.py |
import numpy as np
import copy
from common.skeleton import Skeleton
from common.mocap_dataset import MocapDataset
from common.camera import normalize_screen_coordinates
h36m_skeleton = Skeleton(parents=[-1, 0, 1, 2, 3, 4, 0, 6, 7, 8, 9, 0, 11, 12, 13, 14, 12,
16, 17, 18, 19, 20, 19, ... | 10,701 | 41.300395 | 119 | py |
P-STMO | P-STMO-main/common/visualization.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 matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, write... | 28,111 | 39.742029 | 119 | py |
P-STMO | P-STMO-main/common/generator_3dhp.py | import numpy as np
class ChunkedGenerator:
def __init__(self, batch_size, cameras, poses_3d, poses_2d, valid_frame,
chunk_length=1, pad=0, causal_shift=0,
shuffle=False, random_seed=1234,
augment=False, reverse_aug= False,kps_left=None, kps_right=None, joints_lef... | 8,837 | 43.19 | 120 | 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/mocap_dataset.py |
class MocapDataset:
def __init__(self, fps, skeleton):
self._skeleton = skeleton
self._fps = fps
self._data = None
self._cameras = None
def remove_joints(self, joints_to_remove):
kept_joints = self._skeleton.remove_joints(joints_to_remove)
for subject in se... | 842 | 22.416667 | 68 | py |
P-STMO | P-STMO-main/common/generator_tds.py | import numpy as np
class ChunkedGenerator:
def __init__(self, batch_size, cameras, poses_3d, poses_2d,
chunk_length=1, pad=0, causal_shift=0,
shuffle=False, random_seed=1234,
augment=False, reverse_aug= False,kps_left=None, kps_right=None, joints_left=None, joint... | 7,836 | 42.06044 | 120 | 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/data_to_npz_3dhp_test.py | import os
import numpy as np
from common.utils_3dhp import *
import h5py
import scipy.io as scio
data_path=r'F:\mpi_inf_3dhp\mpi_inf_3dhp_test_set'
cam_set = [0, 1, 2, 4, 5, 6, 7, 8]
# joint_set = [8, 6, 15, 16, 17, 10, 11, 12, 24, 25, 26, 19, 20, 21, 5, 4, 7]
joint_set = [7, 5, 14, 15, 16, 9, 10, 11, 23, 24, 25, 18... | 1,133 | 20.807692 | 81 | py |
P-STMO | P-STMO-main/common/data_to_npz_3dhp.py | import os
import numpy as np
from common.utils_3dhp import *
import scipy.io as scio
data_path=r'F:\mpi_inf_3dhp\data'
cam_set = [0, 1, 2, 4, 5, 6, 7, 8]
# joint_set = [8, 6, 15, 16, 17, 10, 11, 12, 24, 25, 26, 19, 20, 21, 5, 4, 7]
joint_set = [7, 5, 14, 15, 16, 9, 10, 11, 23, 24, 25, 18, 19, 20, 4, 3, 6]
dic_seq={}... | 1,764 | 25.343284 | 78 | 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/draw_3d_keypoint_3dhp.py | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.image as mpimg
from mpl_toolkits.mplot3d import Axes3D
import scipy.io as scio
parent = [16, 15, 1, 2, 3, 1, 5, 6, 14, 8, 9, 14, 11, 12, 14, 14, 1]
data = scio.loadmat('../checkpoint/inference_data.mat')
joints_right=[2, 3, 4, 8, 9,... | 1,455 | 28.714286 | 141 | py |
P-STMO | P-STMO-main/common/utils_3dhp.py |
def mpii_get_sequence_info(subject_id, sequence):
switcher = {
"1 1": [6416,25],
"1 2": [12430,50],
"2 1": [6502,25],
"2 2": [6081,25],
"3 1": [12488,50],
"3 2": [12283,50],
"4 1": [6171,25],
"4 2": [6675,25],
"5 1": [12820,50],
"5 2... | 547 | 20.92 | 49 | py |
P-STMO | P-STMO-main/common/skeleton.py |
import numpy as np
class Skeleton:
def __init__(self, parents, joints_left, joints_right):
assert len(joints_left) == len(joints_right)
self._parents = np.array(parents)
self._joints_left = joints_left
self._joints_right = joints_right
self._compute_metadata()
def nu... | 2,532 | 29.518072 | 73 | py |
P-STMO | P-STMO-main/common/draw_2d_keypoint_3dhp.py | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.image as mpimg
import scipy.io as scio
keypoints = np.load('../dataset/data_test_3dhp.npz',allow_pickle=True)
image = mpimg.imread(r'..\3dhp_test\TS6\imageSequence\img_000061.jpg')
parents=[1,15,1,2,3,1,5,6,14,8,9,14,11,12,-1,14,15... | 1,532 | 25.894737 | 93 | 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/generators.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.
#
from itertools import zip_longest
import numpy as np
class ChunkedGenerator:
"""
Batched data generator, used for tr... | 20,264 | 46.682353 | 132 | py |
P-STMO | P-STMO-main/in_the_wild/arguments.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 argparse
def parse_args():
parser = argparse.ArgumentParser(description='Training script')
# General argument... | 7,306 | 69.941748 | 156 | py |
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