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
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tensorsketch | tensorsketch-master/tensorsketch/temp.py | def f(x, y, **kwargs):
print(f"{x}->{kwargs['var1']}")
# f(1,2, **{'var1':10, 'var2':20})
def g(**kwargs):
a = kwargs
f(100, 2, **a)
g(**{'var1':10})
| 166 | 12.916667 | 35 | py |
tensorsketch | tensorsketch-master/tensorsketch/util.py | import numpy as np
from scipy import fftpack
import tensorly as tl
from tensorly.base import unfold, fold
from scipy.sparse.linalg import svds
from collections.abc import Iterable
tl.set_backend('numpy')
def random_matrix_generator(m, n, Rinfo_bucket):
'''
Generate random matrix of size m x n
:param m:... | 10,216 | 35.230496 | 133 | py |
tensorsketch | tensorsketch-master/tensorsketch/__init__.py | name = "tensorsketch"
| 22 | 10.5 | 21 | py |
tensorsketch | tensorsketch-master/tensorsketch/sketch.py | import tensorly as tl
import numpy as np
from .util import random_matrix_generator, square_tensor_gen
from .util import ssrft_modeprod, gprod, sp0prod
from .random_projection import random_matrix_generator, tensor_random_matrix_generator
from sklearn.decomposition import TruncatedSVD
def fetch_arm_sketch(X, ks, tenso... | 2,591 | 31.810127 | 86 | py |
tensorsketch | tensorsketch-master/tensorsketch/tensor_approx.py | #######################
# *
# Yiming Sun *
# 11/2019 *
# *
#######################
import numpy as np
from scipy import fftpack
import tensorly as tl
from .util import square_tensor_gen, st_hosvd
from .sketch import fetch_arm_sketch, fetch_core_sketch
import... | 8,797 | 38.809955 | 126 | py |
tensorsketch | tensorsketch-master/tensorsketch/tests/test_tucker.py | import numpy as np
from scipy import fftpack
import tensorly as tl
from unittest import TestCase
from ..util import square_tensor_gen, TensorInfoBucket, RandomInfoBucket, eval_rerr
from ..sketch import Sketch
import time
from tensorly.decomposition import tucker
from ..recover_from_sketches import SketchTwoPassRecover... | 1,368 | 30.837209 | 93 | py |
tensorsketch | tensorsketch-master/tensorsketch/tests/test_tensor_recover.py | import numpy as np
from scipy import fftpack
import tensorly as tl
from unittest import TestCase
from ..util import *
from ..sketch import *
import time
from tensorly.decomposition import tucker
from ..recover_from_sketches import SketchTwoPassRecover
from ..recover_from_sketches import SketchOnePassRecover
from sklea... | 1,772 | 39.295455 | 89 | py |
deep_gen_msm | deep_gen_msm-master/prinz/deep_ml_0.py | import torch
import torch.nn as nn
from torch.autograd import Variable, grad, backward
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
import torch.utils.data as Data
from math import pi,inf,log
import copy
from pyemma.plots import scatter_contour
from py... | 9,513 | 33.471014 | 146 | py |
deep_gen_msm | deep_gen_msm-master/prinz/deep_ed_0.py | import torch
import torch.nn as nn
from torch.autograd import Variable, grad, backward
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
import torch.utils.data as Data
from math import pi,inf,log
import copy
from pyemma.plots import scatter_contour
from py... | 11,120 | 37.085616 | 166 | py |
HPLFlowNet | HPLFlowNet-master/main.py | import os, sys
import os.path as osp
import time
from functools import partial
import gc
import traceback
import numpy as np
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import transforms
import datasets
import models
import cmd_args
from main_... | 10,414 | 34.790378 | 99 | py |
HPLFlowNet | HPLFlowNet-master/visualization.py | # Usage: python xxx.py path_to_pc1/pc2/output/epe3d/path_list [pc2]
import numpy as np
import sys
import mayavi.mlab as mlab
import os.path as osp
import pickle
SCALE_FACTOR = 0.05
MODE = 'sphere'
DRAW_LINE = True
if '-h' in ' '.join(sys.argv):
print('Usage: python3 visu_new.py VISU_PATH')
sys.exit(0)
visu_path =... | 2,870 | 25.831776 | 121 | py |
HPLFlowNet | HPLFlowNet-master/main_utils.py | # helper functions for training
import os, sys
import shutil
import torch
from torch.nn import init
def reset_learning_rate(optimizer, args):
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr
def adjust_learning_rate(optimizer, epoch, args):
# old_lr = optimizer.param_groups[0]... | 4,763 | 30.342105 | 98 | py |
HPLFlowNet | HPLFlowNet-master/cmd_args.py | import socket
import numpy as np
import yaml
import os, sys
import os.path as osp
import datasets
import models
from utils.easydict import EasyDict
model_names = sorted(name for name in models.__dict__
if (not name.startswith("__"))
and ('Args' not in name)
... | 2,192 | 32.227273 | 111 | py |
HPLFlowNet | HPLFlowNet-master/evaluation_utils.py | import numpy as np
def evaluate_3d(sf_pred, sf_gt):
"""
sf_pred: (N, 3)
sf_gt: (N, 3)
"""
l2_norm = np.linalg.norm(sf_gt - sf_pred, axis=-1)
EPE3D = l2_norm.mean()
sf_norm = np.linalg.norm(sf_gt, axis=-1)
relative_err = l2_norm / (sf_norm + 1e-4)
acc3d_strict = (np.logical_or(l2_... | 1,018 | 26.540541 | 95 | py |
HPLFlowNet | HPLFlowNet-master/evaluation_bnn.py | import os, sys
import os.path as osp
import numpy as np
import pickle
import torch
import torch.optim
import torch.utils.data
from main_utils import *
from utils import geometry
from evaluation_utils import evaluate_2d, evaluate_3d
TOTAL_NUM_SAMPLES = 0
def evaluate(val_loader, model, logger, args):
save_idx =... | 4,786 | 36.108527 | 90 | py |
HPLFlowNet | HPLFlowNet-master/models/HPLFlowNet.py | import torch
import torch.nn as nn
from .bilateralNN import BilateralConvFlex
from .bnn_flow import BilateralCorrelationFlex
from .module_utils import Conv1dReLU
__all__ = ['HPLFlowNet']
class HPLFlowNet(nn.Module):
def __init__(self, args):
super(HPLFlowNet, self).__init__()
self.scales_filter_... | 25,890 | 59.071926 | 117 | py |
HPLFlowNet | HPLFlowNet-master/models/HPLFlowNet_shallow.py | import torch
import torch.nn as nn
from .bilateralNN import BilateralConvFlex
from .bnn_flow import BilateralCorrelationFlex
from .module_utils import Conv1dReLU
__all__ = ['HPLFlowNetShallow']
class HPLFlowNetShallow(nn.Module):
def __init__(self, args):
super(HPLFlowNetShallow, self).__init__()
... | 18,315 | 57.705128 | 117 | py |
HPLFlowNet | HPLFlowNet-master/models/bilateralNN.py | import torch
import torch.nn as nn
from .module_utils import Conv2dReLU
DELETE_TMP_VARIABLES = False
class SparseSum(torch.autograd.Function):
@staticmethod
def forward(ctx, indices, values, size, cuda):
"""
:param ctx:
:param indices: (1, B*d1*N)
:param values: (B*d1*N, fea... | 10,021 | 40.933054 | 116 | py |
HPLFlowNet | HPLFlowNet-master/models/bnn_flow.py | import torch
import torch.nn as nn
from .bilateralNN import sparse_sum
from .module_utils import Conv2dReLU, Conv3dReLU
DELETE_TMP_VARIABLES = False
class BilateralCorrelationFlex(nn.Module):
def __init__(self, d,
corr_filter_radius, corr_corr_radius,
num_input, num_corr_output... | 9,641 | 44.267606 | 120 | py |
HPLFlowNet | HPLFlowNet-master/models/epe3d_loss.py | import torch
import torch.nn as nn
class EPE3DLoss(nn.Module):
def __init__(self):
super(EPE3DLoss, self).__init__()
def forward(self, input, target):
return torch.norm(input - target, p=2, dim=1) | 223 | 21.4 | 53 | py |
HPLFlowNet | HPLFlowNet-master/models/__init__.py | from .epe3d_loss import *
from .HPLFlowNet import *
from .HPLFlowNet_shallow import *
| 87 | 16.6 | 33 | py |
HPLFlowNet | HPLFlowNet-master/models/build_khash_cffi.py | import glob
import os
from cffi import FFI
# include_dirs = [os.path.join('libraries', 'Rmath', 'src'),
# os.path.join('libraries', 'Rmath', 'include')]
# rmath_src = glob.glob(os.path.join('libraries', 'Rmath', 'src', '*.c'))
ffi = FFI()
ffi.set_source('_khash_ffi', '#include "khash_int2int.h"')
... | 653 | 24.153846 | 73 | py |
HPLFlowNet | HPLFlowNet-master/models/module_utils.py | import torch
import torch.nn as nn
__all__ = ['Conv1dReLU', 'Conv2dReLU', 'Conv3dReLU']
LEAKY_RATE = 0.1
class Conv1dReLU(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, use_leaky=False, bias=True):
super(Conv1dReLU, self).__init__()
self.in_channels... | 2,027 | 31.190476 | 117 | py |
HPLFlowNet | HPLFlowNet-master/datasets/kitti.py | import sys, os
import os.path as osp
import numpy as np
import torch.utils.data as data
__all__ = ['KITTI']
class KITTI(data.Dataset):
"""
Args:
train (bool): If True, creates dataset from training set, otherwise creates from test set.
transform (callable):
gen_func (callable):
... | 3,701 | 33.277778 | 105 | py |
HPLFlowNet | HPLFlowNet-master/datasets/flyingthings3d_subset.py | import sys, os
import os.path as osp
import numpy as np
import torch.utils.data as data
__all__ = ['FlyingThings3DSubset']
class FlyingThings3DSubset(data.Dataset):
"""
Args:
train (bool): If True, creates dataset from training set, otherwise creates from test set.
transform (callable):
... | 3,536 | 33.676471 | 120 | py |
HPLFlowNet | HPLFlowNet-master/datasets/__init__.py | from .flyingthings3d_subset import *
from .kitti import *
| 58 | 18.666667 | 36 | py |
HPLFlowNet | HPLFlowNet-master/data_preprocess/process_flyingthings3d_subset.py | import numpy as np
import sys, os
import os.path as osp
from multiprocessing import Pool
import argparse
import IO
from flyingthings3d_utils import *
parser = argparse.ArgumentParser()
parser.add_argument('--raw_data_path', type=str, help="path to the raw data")
parser.add_argument('--save_path', type=str, help="save... | 2,962 | 36.506329 | 116 | py |
HPLFlowNet | HPLFlowNet-master/data_preprocess/flyingthings3d_utils.py | import numpy as np
def next_pixel2pc(flow, disparity, save_path=None, f=-1050., cx=479.5, cy=269.5):
height, width = disparity.shape
BASELINE = 1.0
depth = -1. * f * BASELINE / disparity
x = ((np.tile(np.arange(width, dtype=np.float32)[None, :], (height, 1)) - cx + flow[..., 0]) * -1. / disparity)[:... | 1,143 | 33.666667 | 118 | py |
HPLFlowNet | HPLFlowNet-master/data_preprocess/process_kitti.py | import os, sys
import os.path as osp
import numpy as np
from multiprocessing import Pool
from kitti_utils import *
calib_root = './utils/calib_cam_to_cam/'
data_root = sys.argv[1]
disp1_root = osp.join(data_root, 'training/disp_occ_0')
disp2_root = osp.join(data_root, 'training/disp_occ_1')
op_flow_root = osp.join(da... | 2,644 | 31.256098 | 96 | py |
HPLFlowNet | HPLFlowNet-master/data_preprocess/IO.py | #!/usr/bin/env python3.4
import os
import re
import numpy as np
import uuid
from scipy import misc
import numpy as np
from PIL import Image
import sys
def read(file):
if file.endswith('.float3'): return readFloat(file)
elif file.endswith('.flo'): return readFlow(file)
elif file.endswith('.ppm'): return r... | 5,244 | 26.898936 | 92 | py |
HPLFlowNet | HPLFlowNet-master/data_preprocess/python_pfm.py | import re
import numpy as np
import sys
def readPFM(file):
file = open(file, 'rb')
color = None
width = None
height = None
scale = None
endian = None
header = file.readline().rstrip()
if header == 'PF':
color = True
elif header == 'Pf':
color = False
else:
... | 1,721 | 22.916667 | 92 | py |
HPLFlowNet | HPLFlowNet-master/data_preprocess/kitti_utils.py | import numpy as np
import png
def pixel2xyz(depth, P_rect, px=None, py=None):
assert P_rect[0,1] == 0
assert P_rect[1,0] == 0
assert P_rect[2,0] == 0
assert P_rect[2,1] == 0
assert P_rect[0,0] == P_rect[1,1]
focal_length_pixel = P_rect[0,0]
height, width = depth.shape[:2]
if px is... | 1,904 | 29.238095 | 82 | py |
HPLFlowNet | HPLFlowNet-master/utils/easydict.py | class EasyDict(dict):
"""
Get attributes
>>> d = EasyDict({'foo':3})
>>> d['foo']
3
>>> d.foo
3
>>> d.bar
Traceback (most recent call last):
...
AttributeError: 'EasyDict' object has no attribute 'bar'
>>> #Works recursively
>>> d = EasyDict({'foo':3, 'bar':{'x':1, 'y... | 2,438 | 23.148515 | 79 | py |
HPLFlowNet | HPLFlowNet-master/utils/geometry.py | import numpy as np
import os
import os.path as osp
def get_batch_2d_flow(pc1, pc2, predicted_pc2, paths):
if 'KITTI' in paths[0] or 'kitti' in paths[0]:
focallengths = []
cxs = []
cys = []
constx = []
consty = []
constz = []
for path in paths:
fn... | 2,600 | 38.409091 | 103 | py |
HPLFlowNet | HPLFlowNet-master/utils/__init__.py | 0 | 0 | 0 | py | |
HPLFlowNet | HPLFlowNet-master/transforms/functional.py | import torch
def to_tensor(array):
"""Convert a 2D `numpy.ndarray`` to tensor, do transpose first.
See ``ToTensor`` for more details.
Args:
array (numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
assert len(array.shape) == 2
array = a... | 381 | 18.1 | 67 | py |
HPLFlowNet | HPLFlowNet-master/transforms/__init__.py | from .transforms import *
| 26 | 12.5 | 25 | py |
HPLFlowNet | HPLFlowNet-master/transforms/transforms.py | import os, sys
import os.path as osp
from collections import defaultdict
import numbers
import math
import numpy as np
import traceback
import time
import torch
import numba
from numba import njit, cffi_support
from . import functional as F
sys.path.append(osp.join(osp.dirname(osp.dirname(osp.abspath(__file__))), '... | 29,266 | 43.010526 | 127 | py |
fact-checkers-fact-check | fact-checkers-fact-check-main/analyses/utils.py | from tempfile import NamedTemporaryFile
from matplotlib.image import imread
def get_size(fig, dpi=100):
with NamedTemporaryFile(suffix='.png') as f:
fig.savefig(f.name, bbox_inches='tight', dpi=dpi)
height, width, _channels = imread(f.name).shape
return width / dpi, height / dpi
def set... | 1,131 | 38.034483 | 92 | py |
arx | arx-master/setup.py | """ARX paper code
Confidential code not for distribution.
Alex Cooper <alex@acooper.org>
"""
from codecs import open
from os import path
from setuptools import find_packages, setup
here = path.abspath(path.dirname(__file__))
# Get the long description from the README file
with open(path.join(here, "README.md"), enc... | 1,311 | 25.24 | 66 | py |
arx | arx-master/arx/cv.py |
import chex
import jax.numpy as jnp
class CVScheme:
"""Generic CV scheme class
Methods:
name: name of the scheme suitable for plots and output
n_folds: number of folds, always numbered from 0
test_mask: boolean mask for test data for fold i
train_mask: boolean mask for train ... | 5,192 | 26.331579 | 92 | py |
arx | arx-master/arx/sarx_test.py | import unittest
import cv
import jax.numpy as jnp
from sarx import *
class TestSARX(unittest.TestCase):
def setUp(self) -> None:
self.T = 100
self.phi_star = jnp.array([0.4])
self.sigsq_star = 1.5
self.beta_star = jnp.array([1.0, 2.0, 0.5])
self.Z = make_Z(q=3, T=self.T, p... | 9,344 | 38.935897 | 88 | py |
arx | arx-master/arx/sarx_experiments.py | import jax.numpy as jnp
import pandas as pd
from arx.cv import *
from arx.experiments import *
from arx.sarx import *
def by_excluded_effect(filename, ex_no: int, variant: str, T: int = 100, seed: int = 0):
"""Simplified model selection experiment, varying beta2 (the excluded effect)
Args:
filename:... | 26,848 | 41.149137 | 136 | py |
arx | arx-master/arx/sarx.py | from typing import Tuple
import chex
import jax
from jax import numpy as jnp
from jax.numpy.linalg import inv, slogdet, solve
from jax.scipy.linalg import solve_triangular
from jax.scipy.stats import multivariate_normal
from scipy import optimize
from arx.arx import make_L
from arx.cv import CVScheme
class Poly:
... | 17,979 | 35.470588 | 97 | py |
arx | arx-master/arx/experiments.py | from typing import Any, Callable, Dict
import jax.numpy as jnp
from chex import Array, PRNGKey
import numpy as np
import os
import jax
import chex
from arx import sarx, arx
EFFECT_SIZES = jnp.array([0.0, 0.5, 1.0, 2.0, 5.0, 10.0])
# \beta_*^{easy}
EASY_EFFECTS = jnp.array([1.0, 2.0, 1.0])
# \beta_*^{hard}
HARD_EFFE... | 6,039 | 25.964286 | 73 | py |
arx | arx-master/arx/arx_test.py | from jax.config import config
config.update("jax_enable_x64", True)
import unittest
import cv
import jax
import jax.numpy as jnp
from chex import assert_equal, assert_shape, assert_tree_all_finite
from scipy.integrate import quad
import arx
class TestArx(unittest.TestCase):
def setUp(self) -> None:
ke... | 9,391 | 32.070423 | 82 | py |
arx | arx-master/arx/cv_test.py | import unittest
import cv
from chex import assert_equal, assert_shape, assert_tree_all_close
from jax import numpy as jnp
from tree import assert_same_structure
class TestCVSchemes(unittest.TestCase):
def test_loo(self):
loo = cv.LOOCVScheme(120)
assert_equal(loo.n_folds(), 120)
tstm = jn... | 2,115 | 37.472727 | 82 | py |
arx | arx-master/arx/cli.py | #!.venv/bin/python3
from jax.config import config
config.update("jax_enable_x64", True)
import glob
import os
import pandas as pd
import click
import arx.arx_experiments as arxex
import arx.sarx_experiments as sarxex
RESULTS = 'results'
def ensure_results_dir():
if not os.path.exists(RESULTS):
os.mkd... | 12,401 | 44.933333 | 156 | py |
arx | arx-master/arx/arx_experiments.py | from typing import List
import click
import jax
import pandas as pd
import arx.experiments as ex
from arx import cv
def full_bayes(
experiment_no: int,
experiment_variant: str,
filename: str,
T: int = 100,
alternative: str = '10-fold',
n_posts=10,
mc_reps=500,
n_warmup=400,
n_cha... | 9,112 | 39.323009 | 137 | py |
arx | arx-master/arx/arx.py | """Full ARX(p,q) model, using quadrature for inference.
This limited first version can only do inference for p=1,
but can simulate from any ARX(p,q) dgp.
"""
f"This script needs python 3.x"
from jax.config import config
from arx.cv import CVScheme
config.update("jax_enable_x64", True)
from collections import nam... | 38,201 | 35.732692 | 127 | py |
arx | arx-master/arx/experiments_test.py | import os
import unittest
import experiments as ex
import jax
import sarx_experiments as sx
import arx
class TestExperimentInstance(unittest.TestCase):
def setUp(self) -> None:
self.ex1 = ex.make_full_experiment(1, "hard", simplified=False)
def test_experiment_instance(self) -> None:
key = ... | 2,784 | 30.647727 | 87 | py |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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... | 1,966 | 29.261538 | 97 | py |
ADaPTION | ADaPTION-master/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', '',
... | 4,541 | 40.290909 | 99 | py |
ADaPTION | ADaPTION-master/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... | 6,688 | 32.613065 | 86 | py |
ADaPTION | ADaPTION-master/examples/create_prototxt/create_prototxt.py | import collections as c
base_dir = './'
layer_dir = base_dir + 'layers/'
# lp = False # use lp version of the layers
lp = True # use lp version of the layers
# deploy = False
deploy = True
visualize = False
# visualize = True
# VGG 16
# net_descriptor = ['64C3S1', 'A', 'ReLU', '64C3S1', 'A', 'ReLU', '2P2',
# ... | 25,917 | 58.718894 | 169 | py |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/examples/low_precision/imagenet/visualization/Visualization_weights.py | import caffe
import matplotlib.pyplot as plt
import numpy as np
from collections import defaultdict
plt.rcParams['font.size'] = 20
# plt.rcParams['xtick.labelzie'] = 18
def make_2d(data):
return np.reshape(data, (data.shape[0], -1))
caffe.set_mode_gpu()
caffe.set_device(0)
caffe_root = '/home/moritz/Repositori... | 19,380 | 31.463987 | 98 | py |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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"... | 8,048 | 34.45815 | 88 | py |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/python/caffe/__init__.py | from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed
from ._caffe import __version__
from .proto.caffe_pb2 import TRAIN, TEST
from .classifier import C... | 434 | 47.333333 | 111 | py |
ADaPTION | ADaPTION-master/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, \... | 11,243 | 32.564179 | 89 | py |
ADaPTION | ADaPTION-master/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... | 8,813 | 34.97551 | 120 | py |
ADaPTION | ADaPTION-master/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... | 12,729 | 32.151042 | 110 | py |
ADaPTION | ADaPTION-master/python/caffe/nullhop/caffe2nullhop.py | '''
TODO: remove pixels from the network file
TODO: support different fixed point representations other than q7.8
TODO: check kernels arrangement
TODO: Currently only for LP version of convolutional layers. Extend also to normal convolution?
To be used only with low precision (LP) version of caffe (caffe_lp/ ), beca... | 11,262 | 38.658451 | 290 | py |
ADaPTION | ADaPTION-master/python/caffe/imagenet/cnn_to_NullHop.py | #!/usr/bin/env python
"""
Authors: federico.corradi@inilabs.com, diederikmoeys@live.com
Converts caffe networks into jAER xml format
this script requires command line arguments:
model file -> network.prototxt
weights file -> caffenet.model
... | 31,660 | 49.335453 | 181 | py |
ADaPTION | ADaPTION-master/python/caffe/imagenet/convert_caffemodel_nullhop.py | import glob
import os
import numpy as np
import matplotlib.pyplot as plt
import caffe
caffe_root = '../../../'
caffe.set_mode_gpu()
caffe.set_device(0)
modelName = 'LP_VGG16'
if modelName == 'resnets':
model_def = '/users/hesham/trained_models/resNets/ResNet-50-deploy.prototxt'
model_weights = '/users/hesham/... | 7,417 | 41.632184 | 147 | py |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/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... | 9,656 | 26.910405 | 78 | py |
ADaPTION | ADaPTION-master/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])],
... | 3,287 | 39.097561 | 77 | py |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/python/caffe/quantization/convert_weights.py | '''
This script converts weights, which are trained without rounding, to match the size of the
data blobs of low precison rounded weights.
In high precision each conv layer has two blob allocated for the weights and the biases
However in low precision, since we are using dual copy roudning/pow2quantization, we basicall... | 9,219 | 49.382514 | 137 | py |
ADaPTION | ADaPTION-master/python/caffe/quantization/__init__.py | 0 | 0 | 0 | py | |
ADaPTION | ADaPTION-master/python/caffe/quantization/qmf_check.py | '''
This script loads an already trained CNN and prepares the Qm.f notation for each layer. Weights and activation are considered.
This distribution is used by net_descriptor to build a new prototxt file to finetune the quantized weights and activations
List of functions, for further details see below
- forward_pa... | 14,568 | 48.386441 | 136 | py |
ADaPTION | ADaPTION-master/python/caffe/quantization/net_descriptor.py | '''
This script reads out a given prototxt file to extract the network layout
Based on this network layout we create a new prototxt for either training or testing
List of functions, for further details see below
- get_model
- extract
- create
Author: Moritz Milde
Date: 02.11.2016
E-Mail: mmilde@ini.uzh.c... | 42,894 | 58.825662 | 155 | py |
ADaPTION | ADaPTION-master/scripts/cpp_lint.py | #!/usr/bin/python2
#
# 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 of... | 187,448 | 37.49846 | 93 | py |
ADaPTION | ADaPTION-master/scripts/download_model_binary.py | #!/usr/bin/env python
import os
import sys
import time
import yaml
import urllib
import hashlib
import argparse
required_keys = ['caffemodel', 'caffemodel_url', 'sha1']
def reporthook(count, block_size, total_size):
"""
From http://blog.moleculea.com/2012/10/04/urlretrieve-progres-indicator/
"""
glob... | 2,507 | 31.571429 | 78 | py |
ADaPTION | ADaPTION-master/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 |
ADaPTION | ADaPTION-master/frcnn/tools/compress_net.py | #!/usr/bin/env python
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Compress a Fast R-CNN network using truncated... | 3,918 | 30.103175 | 81 | py |
ADaPTION | ADaPTION-master/frcnn/tools/train_faster_rcnn_alt_opt.py | #!/usr/bin/env python
# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Train a Faster R-CNN network using alternat... | 12,767 | 36.116279 | 80 | py |
ADaPTION | ADaPTION-master/frcnn/tools/reval.py | #!/usr/bin/env python
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Reval = re-eval. Re-evaluate saved detections... | 2,126 | 30.746269 | 76 | py |
ADaPTION | ADaPTION-master/frcnn/tools/test_net.py | #!/usr/bin/env python
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Test a Fast R-CNN network on an image databas... | 3,165 | 33.791209 | 77 | py |
ADaPTION | ADaPTION-master/frcnn/tools/_init_paths.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Set up paths for Fast R-CNN."""
import os.path as osp
import sys
... | 690 | 24.592593 | 68 | py |
ADaPTION | ADaPTION-master/frcnn/tools/demo.py | #!/usr/bin/env python
# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""
Demo script showing detections in sample i... | 5,067 | 31.075949 | 80 | py |
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