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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kymatio | kymatio-master/kymatio/frontend/keras_frontend.py | from tensorflow.keras.layers import Layer
class ScatteringKeras(Layer):
def __init__(self):
Layer.__init__(self)
self.frontend_name = 'keras'
def build(self, input_shape):
self.shape = input_shape
Layer.build(self, input_shape)
def scattering(self, x):
return self... | 806 | 20.810811 | 77 | py |
kymatio | kymatio-master/kymatio/frontend/torch_frontend.py | import torch.nn as nn
from ..backend.torch_backend import input_checks
class ScatteringTorch(nn.Module):
def __init__(self):
super(ScatteringTorch, self).__init__()
self.frontend_name = 'torch'
def register_filters(self):
""" This function should be called after filters are generated,
... | 1,276 | 26.76087 | 75 | py |
kymatio | kymatio-master/kymatio/scattering2d/backend/torch_backend.py | # Authors: Edouard Oyallon, Sergey Zagoruyko
import torch
from torch.nn import ReflectionPad2d
from collections import namedtuple
from packaging import version
BACKEND_NAME = 'torch'
from ...backend.torch_backend import _is_complex, cdgmm, type_checks, Modulus, concatenate
from ...backend.base_backend import FFT
... | 5,422 | 31.668675 | 120 | py |
kymatio | kymatio-master/kymatio/scattering2d/backend/torch_skcuda_backend.py | # Authors: Edouard Oyallon, Sergey Zagoruyko, Muawiz Chaudhary
from collections import namedtuple
import torch
import cupy
from string import Template
BACKEND_NAME = 'torch_skcuda'
from ...backend.torch_backend import _is_complex
from ...backend.torch_skcuda_backend import cdgmm
# As of v8, cupy.util has been rena... | 6,347 | 30.425743 | 120 | py |
kymatio | kymatio-master/kymatio/scattering2d/frontend/keras_frontend.py | from ...frontend.keras_frontend import ScatteringKeras
from ...scattering2d.frontend.base_frontend import ScatteringBase2D
from ...tensorflow import Scattering2D as ScatteringTensorFlow2D
from tensorflow.python.framework import tensor_shape
class ScatteringKeras2D(ScatteringKeras, ScatteringBase2D):
def __init_... | 1,558 | 41.135135 | 77 | py |
kymatio | kymatio-master/kymatio/scattering2d/frontend/torch_frontend.py | import torch
from .base_frontend import ScatteringBase2D
from ...scattering2d.core.scattering2d import scattering2d
from ...frontend.torch_frontend import ScatteringTorch
class ScatteringTorch2D(ScatteringTorch, ScatteringBase2D):
def __init__(self, J, shape, L=8, max_order=2, pre_pad=False,
backend=... | 4,192 | 32.015748 | 113 | py |
kymatio | kymatio-master/kymatio/scattering3d/filter_bank.py | """
Authors: Louis Thiry, Georgios Exarchakis and Michael Eickenberg
All rights reserved, 2017.
"""
__all__ = ['solid_harmonic_filter_bank']
import numpy as np
from scipy.special import sph_harm, factorial
from .utils import get_3d_angles, double_factorial, sqrt
def solid_harmonic_filter_bank(M, N, O, J, L, sigma_0... | 6,008 | 30.626316 | 84 | py |
kymatio | kymatio-master/kymatio/scattering3d/core/scattering3d.py | # Authors: Louis Thiry, Georgios Exarchakis
# Scientific Ancestry: Louis Thiry, Georgios Exarchakis, Matthew Hirn, Michael Eickenberg
def scattering3d(x, filters, rotation_covariant, L, J, max_order, backend, averaging):
"""
The forward pass of 3D solid harmonic scattering
Parameters
----------
inp... | 2,708 | 33.730769 | 89 | py |
kymatio | kymatio-master/kymatio/scattering3d/backend/torch_backend.py | import torch
import warnings
BACKEND_NAME = 'torch'
from collections import namedtuple
from packaging import version
def _is_complex(input):
"""Checks if input is complex.
Parameters
----------
input : tensor
Input to be checked if complex.
Returns
-------
... | 7,964 | 31.246964 | 102 | py |
kymatio | kymatio-master/kymatio/scattering3d/backend/torch_skcuda_backend.py | import torch
import warnings
from skcuda import cublas
BACKEND_NAME = 'torch_skcuda'
from collections import namedtuple
def _is_complex(input):
return input.shape[-1] == 2
def cdgmm3d(A, B, inplace=False):
"""Complex pointwise multiplication.
Complex pointwise multiplication between (batched) ten... | 3,136 | 31.677083 | 99 | py |
kymatio | kymatio-master/kymatio/scattering3d/backend/numpy_backend.py | import numpy as np
from collections import namedtuple
from scipy.fftpack import fftn, ifftn
BACKEND_NAME = 'numpy'
def _iscomplex(x):
return x.dtype == np.complex64 or x.dtype == np.complex128
def complex_modulus(input_array):
"""Computes complex modulus.
Parameters
----------
inp... | 5,751 | 29.115183 | 94 | py |
kymatio | kymatio-master/kymatio/scattering3d/frontend/torch_frontend.py | # Authors: Louis Thiry, Georgios Exarchakis
# Scientific Ancestry: Louis Thiry, Georgios Exarchakis, Matthew Hirn, Michael Eickenberg
import torch
from ...frontend.torch_frontend import ScatteringTorch
from ..core.scattering3d import scattering3d
from .base_frontend import ScatteringBase3D
class HarmonicScatteringTo... | 3,573 | 38.274725 | 118 | py |
kymatio | kymatio-master/examples/3d/scattering3d_qm7_torch.py | """
3D scattering quantum chemistry regression
==========================================
Description:
This example trains a classifier combined with a scattering transform to
regress molecular atomization energies on the QM7 dataset. Here, we use full
charges, valence charges and core charges. A linear regression is ... | 12,151 | 36.856698 | 79 | py |
kymatio | kymatio-master/examples/1d/classif_keras.py | """
Classification of spoken digit recordings
=========================================
In this example we use the 1D scattering transform to represent spoken digits,
which we then classify using a simple classifier. This shows that 1D scattering
representations are useful for this type of problem.
This dataset is au... | 7,434 | 35.625616 | 80 | py |
kymatio | kymatio-master/examples/1d/reconstruct_torch.py | """
Reconstruct a synthetic signal from its scattering transform
============================================================
In this example we generate a harmonic signal of a few different frequencies,
analyze it with the 1D scattering transform, and reconstruct the scattering
transform back to the harmonic signal.
"... | 4,377 | 29.830986 | 79 | py |
kymatio | kymatio-master/examples/1d/plot_classif_torch.py | """
Classification of spoken digit recordings
=========================================
In this example we use the 1D scattering transform to represent spoken digits,
which we then classify using a simple classifier. This shows that 1D scattering
representations are useful for this type of problem.
This dataset is au... | 12,168 | 34.896755 | 80 | py |
kymatio | kymatio-master/examples/2d/plot_invert_scattering_torch.py | """
Inverting scattering via mse
============================
This script aims to quantify the information loss for natural images by
performing a reconstruction of an image from its scattering coefficients via a
L2-norm minimization.
"""
###############################################################################
... | 2,813 | 31.72093 | 85 | py |
kymatio | kymatio-master/examples/2d/long_mnist_classify_torch.py | """
Classification of Few Sample MNIST with Scattering
=====================================================================
Here we demonstrate a simple application of scattering on the MNIST dataset.
We use 5000 MNIST samples to train a linear classifier. Features are normalized by batch normalization.
Please also se... | 4,494 | 33.312977 | 103 | py |
kymatio | kymatio-master/examples/2d/cifar_small_sample.py | """
Classification on CIFAR10 (ResNet)
==================================
Based on pytorch example for CIFAR10
"""
import torch.optim
from torchvision import datasets, transforms
import torch.nn.functional as F
from kymatio import Scattering2D
import torch
import argparse
import kymatio.datasets as scattering_datase... | 8,450 | 33.353659 | 127 | py |
kymatio | kymatio-master/examples/2d/cifar_torch.py | """
Classification on CIFAR10
=========================
Based on pytorch example for MNIST
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
from torchvision import datasets, transforms
from kymatio.torch import Scattering2D
import kymatio.datasets as scattering_datasets
import... | 6,195 | 33.808989 | 119 | py |
kymatio | kymatio-master/examples/2d/mnist_keras.py | """
Classification of MNIST with scattering
=======================================
Here we demonstrate a simple application of scattering on the MNIST dataset.
We use 10000 images to train a linear classifier. Features are normalized by
batch normalization.
"""
########################################################... | 2,337 | 32.884058 | 79 | py |
kymatio | kymatio-master/examples/2d/cifar_resnet_torch.py | """
Classification on CIFAR10 (ResNet)
==================================
Based on pytorch example for CIFAR10
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
from torchvision import datasets, transforms
from kymatio.torch import Scattering2D
import kymatio.datasets as scatt... | 6,924 | 32.616505 | 119 | py |
kymatio | kymatio-master/examples/2d/regularized_inverse_scattering_MNIST_torch.py | """
Regularized inverse of a scattering transform on MNIST
======================================================
Description:
This example trains a convolutional network to invert the scattering transform at scale 2 of MNIST digits.
After only two epochs, it produces a network that transforms a linear interpolation i... | 6,497 | 38.144578 | 119 | py |
kymatio | kymatio-master/tests/general/test_torch_backend.py | import torch
import pytest
from kymatio.backend.torch_backend import ModulusStable, modulus
def test_modulus(random_state=42):
"""
Tests the stability and differentiability of modulus
"""
x = torch.randn(100, 4, 128, 2, requires_grad=True)
x_grad = x.clone()
x_abs = modulus(x)
x_grad[...... | 690 | 24.592593 | 64 | py |
kymatio | kymatio-master/tests/scattering1d/test_torch_scattering1d.py | import pytest
import torch
from kymatio import Scattering1D
import math
import os
import io
import numpy as np
backends = []
skcuda_available = False
try:
if torch.cuda.is_available():
from skcuda import cublas
import cupy
skcuda_available = True
except:
Warning('torch_skcuda backend n... | 16,574 | 31.184466 | 117 | py |
kymatio | kymatio-master/tests/scattering1d/test_utils_scattering1d.py | import numpy as np
import pytest
from kymatio import Scattering1D
from kymatio.scattering1d.frontend.torch_frontend import ScatteringTorch1D
from kymatio.scattering1d.utils import compute_border_indices, compute_padding
def test_compute_padding():
"""
Test the compute_padding function
"""
pad_left, p... | 2,002 | 30.296875 | 87 | py |
kymatio | kymatio-master/tests/scattering2d/test_keras_scattering2d.py | import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Flatten, Dense
from kymatio.keras import Scattering2D
import os, io
import numpy as np
def test_Scattering2D():
test_data_dir = os.path.dirname(__file__)
data = None
with open(os.path.join(test_data... | 1,020 | 24.525 | 74 | py |
kymatio | kymatio-master/tests/scattering2d/test_torch_scattering2d.py | """ This script will test the submodules used by the scattering module"""
import os
import io
import numpy as np
import torch
import pytest
from kymatio import Scattering2D
from torch.autograd import gradcheck
from collections import namedtuple
devices = ['cpu']
if torch.cuda.is_available():
devices.append('cuda'... | 18,377 | 31.527434 | 79 | py |
kymatio | kymatio-master/tests/scattering2d/test_frontend_scattering2d.py | import pytest
from kymatio import Scattering2D
from kymatio.scattering2d.frontend.torch_frontend import ScatteringTorch2D
# Check that the default frontend is Torch and that errors are correctly launched.
def test_scattering2d_frontend():
scattering = Scattering2D(2, shape=(10, 10))
assert isinstance(scatteri... | 839 | 41 | 97 | py |
kymatio | kymatio-master/tests/scattering3d/test_torch_scattering3d.py | """ This script will test the submodules used by the scattering module"""
import torch
import os
import io
import numpy as np
import pytest
from kymatio import HarmonicScattering3D
from kymatio.scattering3d.utils import generate_weighted_sum_of_gaussians
backends = []
skcuda_available = False
try:
if torch.cuda.i... | 10,141 | 32.694352 | 127 | py |
kymatio | kymatio-master/doc/source/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 6,349 | 29.238095 | 171 | py |
tau-ResNet | tau-ResNet-master/imagenet/imagenet_train.py | from __future__ import print_function
import warnings
warnings.filterwarnings("ignore")
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
import ... | 8,364 | 35.528384 | 138 | py |
tau-ResNet | tau-ResNet-master/imagenet/utils.py | '''Some helper functions for PyTorch, including:
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import os
import sys
import time
import math
import torch.nn as nn
import torch.nn.init as init
i... | 13,331 | 33.272494 | 115 | py |
tau-ResNet | tau-ResNet-master/imagenet/models/resnet_imagenet.py | import torch
import torch.nn as nn
import numpy as np
import math
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):... | 4,775 | 27.260355 | 96 | py |
tau-ResNet | tau-ResNet-master/imagenet/models/resnet_imagenet_nobn.py | import torch
import torch.nn as nn
import numpy as np
import math
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):... | 5,390 | 28.95 | 99 | py |
tau-ResNet | tau-ResNet-master/cifar/utils.py | '''Some helper functions for PyTorch, including:
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import os
import sys
import time
import math
import torch.nn as nn
import torch.nn.init as init
i... | 3,512 | 26.023077 | 96 | py |
tau-ResNet | tau-ResNet-master/cifar/cifar_train.py | '''Train CIFAR10 with PyTorch.'''
from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
import csv
from models ... | 7,589 | 34.302326 | 123 | py |
tau-ResNet | tau-ResNet-master/cifar/models/resnet_cifar.py | import torch
import torch.nn as nn
import numpy as np
import math
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
... | 3,914 | 24.258065 | 90 | py |
tau-ResNet | tau-ResNet-master/cifar/models/resnet_cifar_nobn.py | import torch
import torch.nn as nn
import numpy as np
import math
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock_NoBN(nn.Module):
expansion... | 4,512 | 27.20625 | 99 | py |
scikit-beam | scikit-beam-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# scikit-beam documentation build configuration file, created by
# sphinx-quickstart on Mon Sep 17 09:43:12 2012.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
#... | 11,192 | 30.888889 | 82 | py |
ld-metric | ld-metric-master/PolyLaneNet/test.py | import os
import sys
import random
import logging
import argparse
import subprocess
from time import time
import cv2
import numpy as np
import torch
from lib.config import Config
from utils.evaluator import Evaluator
def test(model, test_loader, evaluator, exp_root, cfg, view, epoch, max_batches=None, verbose=True)... | 6,176 | 33.316667 | 117 | py |
ld-metric | ld-metric-master/PolyLaneNet/train.py | import os
import sys
import random
import shutil
import logging
import argparse
import subprocess
from time import time
import numpy as np
import torch
from test import test
from lib.config import Config
from utils.evaluator import Evaluator
def train(model, train_loader, exp_dir, cfg, val_loader, train_state=None)... | 9,332 | 33.3125 | 113 | py |
ld-metric | ld-metric-master/PolyLaneNet/lib/config.py | import yaml
import torch
import lib.models as models
import lib.datasets as datasets
class Config(object):
def __init__(self, config_path):
self.config = {}
self.load(config_path)
def load(self, path):
with open(path, 'r') as file:
self.config_str = file.read()
se... | 1,518 | 31.319149 | 115 | py |
ld-metric | ld-metric-master/PolyLaneNet/lib/models.py | import torch
import torch.nn as nn
from torchvision.models import resnet34, resnet50, resnet101
from efficientnet_pytorch import EfficientNet
class OutputLayer(nn.Module):
def __init__(self, fc, num_extra):
super(OutputLayer, self).__init__()
self.regular_outputs_layer = fc
self.num_extra ... | 7,208 | 43.776398 | 118 | py |
ld-metric | ld-metric-master/PolyLaneNet/lib/datasets/lane_dataset.py | import cv2
import numpy as np
import imgaug.augmenters as iaa
from imgaug.augmenters import Resize
from torchvision.transforms import ToTensor
from torch.utils.data.dataset import Dataset
from imgaug.augmentables.lines import LineString, LineStringsOnImage
from .elas import ELAS
from .llamas import LLAMAS
from .tusimp... | 8,885 | 35.871369 | 119 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/export.py | import torch, os, cv2
from model.model import parsingNet
from utils.common import merge_config
from utils.dist_utils import dist_print
import torch
import scipy.special, tqdm
import numpy as np
import torchvision.transforms as transforms
from data.dataset import LaneTestDataset
from data.constant import culane_row_anch... | 1,430 | 29.446809 | 96 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/test.py | import torch, os
from model.model import parsingNet
from utils.common import merge_config
from utils.dist_utils import dist_print
from evaluation.eval_wrapper import eval_lane
import torch
if __name__ == "__main__":
torch.backends.cudnn.benchmark = True
args, cfg = merge_config()
distributed = False
i... | 1,711 | 34.666667 | 125 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/speed_real.py | # Thanks for the contribution of KopiSoftware https://github.com/KopiSoftware
import torch
import time
import numpy as np
from model.model import parsingNet
import torchvision.transforms as transforms
import cv2
from matplotlib import pyplot as plt
from PIL import Image
img_transforms = transforms.Compose([
tran... | 4,359 | 27.496732 | 101 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/demo.py | import torch, os, cv2
from model.model import parsingNet
from utils.common import merge_config
from utils.dist_utils import dist_print
import torch
import scipy.special, tqdm
import numpy as np
import torchvision.transforms as transforms
from data.dataset import LaneTestDataset
from data.constant import culane_row_anch... | 4,119 | 40.2 | 192 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/speed_simple.py | import torch
import time
import numpy as np
from model.model import parsingNet
# torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
net = parsingNet(pretrained = False, backbone='18',cls_dim = (100+1,56,4),use_aux=False).cuda()
# net = parsingNet(pretrained = False, backbone='18',cls_dim... | 802 | 24.09375 | 97 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/train.py | import torch, os, datetime
import numpy as np
from model.model import parsingNet
from data.dataloader import get_train_loader
from utils.dist_utils import dist_print, dist_tqdm, is_main_process, DistSummaryWriter
from utils.factory import get_metric_dict, get_loss_dict, get_optimizer, get_scheduler
from utils.metrics... | 5,600 | 35.135484 | 155 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/evaluation/eval_wrapper.py |
from data.dataloader import get_test_loader
from evaluation.tusimple.lane import LaneEval
from utils.dist_utils import is_main_process, dist_print, get_rank, get_world_size, dist_tqdm, synchronize
import os, json, torch, scipy
import numpy as np
import platform
def generate_lines(out, shape, names, output_path, gridi... | 11,756 | 48.39916 | 157 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/utils/loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class OhemCELoss(nn.Module):
def __init__(self, thresh, n_min, ignore_lb=255, *args, **kwargs):
super(OhemCELoss, self).__init__()
self.thresh = -torch.log(torch.tensor(thresh, dtype=torch.float)).cuda()
... | 2,506 | 32.426667 | 86 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/utils/dist_utils.py | import torch
import torch.distributed as dist
import pickle
def get_world_size():
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
return dist.get_world_size()
def to_python_float(t):
if hasattr(t, 'item'):
return t.item()
else:
return t... | 4,623 | 25.574713 | 77 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/utils/common.py | import os, argparse
from utils.dist_utils import is_main_process, dist_print, DistSummaryWriter
from utils.config import Config
import torch
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n... | 4,670 | 42.25 | 100 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/utils/factory.py | from utils.loss import SoftmaxFocalLoss, ParsingRelationLoss, ParsingRelationDis
from utils.metrics import MultiLabelAcc, AccTopk, Metric_mIoU
from utils.dist_utils import DistSummaryWriter
import torch
def get_optimizer(net,cfg):
training_params = filter(lambda p: p.requires_grad, net.parameters())
if cfg.o... | 5,179 | 39.155039 | 160 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/utils/metrics.py | import numpy as np
import torch
import time,pdb
def converter(data):
if isinstance(data,torch.Tensor):
data = data.cpu().data.numpy().flatten()
return data.flatten()
def fast_hist(label_pred, label_true,num_classes):
#pdb.set_trace()
hist = np.bincount(num_classes * label_true.astype(int) + lab... | 3,280 | 30.854369 | 111 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/data/mytransforms.py | import numbers
import random
import numpy as np
from PIL import Image, ImageOps, ImageFilter
#from config import cfg
import torch
import pdb
import cv2
# ===============================img tranforms============================
class Compose2(object):
def __init__(self, transforms):
self.transforms = trans... | 5,085 | 29.27381 | 129 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/data/dataloader.py | import torch, os
import numpy as np
import torchvision.transforms as transforms
import data.mytransforms as mytransforms
from data.constant import tusimple_row_anchor, culane_row_anchor
from data.dataset import LaneClsDataset, LaneTestDataset
def get_train_loader(batch_size, data_root, griding_num, dataset, use_aux, ... | 4,784 | 42.899083 | 121 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/data/dataset.py | import torch
from PIL import Image
import os
import pdb
import numpy as np
import cv2
from data.mytransforms import find_start_pos
def loader_func(path):
return Image.open(path)
class LaneTestDataset(torch.utils.data.Dataset):
def __init__(self, path, list_path, img_transform=None):
super(LaneTestDa... | 5,672 | 33.174699 | 142 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/model/model.py | import torch
from model.backbone import resnet
import numpy as np
class conv_bn_relu(torch.nn.Module):
def __init__(self,in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1,bias=False):
super(conv_bn_relu,self).__init__()
self.conv = torch.nn.Conv2d(in_channels,out_channels, ker... | 4,814 | 39.125 | 167 | py |
ld-metric | ld-metric-master/Ultra-Fast-Lane-Detection/model/backbone.py | import torch,pdb
import torchvision
import torch.nn.modules
class vgg16bn(torch.nn.Module):
def __init__(self,pretrained = False):
super(vgg16bn,self).__init__()
model = list(torchvision.models.vgg16_bn(pretrained=pretrained).features.children())
model = model[:33]+model[34:43]
self... | 2,086 | 35.614035 | 92 | py |
ld-metric | ld-metric-master/LaneATT/main.py | import logging
import argparse
import torch
import nms
from lib.config import Config
from lib.runner import Runner
from lib.experiment import Experiment
def parse_args():
parser = argparse.ArgumentParser(description="Train lane detector")
parser.add_argument("mode", choices=["train", "test"], help="Train or ... | 2,600 | 42.35 | 115 | py |
ld-metric | ld-metric-master/LaneATT/utils/gen_anchor_mask.py | import random
import argparse
import cv2
import torch
import numpy as np
from tqdm import trange
from lib.config import Config
from lib.models.matching import match_proposals_with_targets
def get_anchors_use_frequency(cfg, split='train', t_pos=15., t_neg=20.):
model = cfg.get_model()
anchors_frequency = tor... | 2,332 | 30.958904 | 105 | py |
ld-metric | ld-metric-master/LaneATT/utils/viz_dataset.py | import argparse
import cv2
import torch
import random
import numpy as np
from lib.config import Config
def parse_args():
parser = argparse.ArgumentParser(description="Visualize a dataset")
parser.add_argument("--cfg", help="Config file")
parser.add_argument("--split",
choices=["t... | 864 | 21.763158 | 71 | py |
ld-metric | ld-metric-master/LaneATT/utils/speed.py | import time
import argparse
import torch
from thop import profile, clever_format
from lib.config import Config
def parse_args():
parser = argparse.ArgumentParser(description="Tool to measure a model's speed")
parser.add_argument("--cfg", default="config.yaml", help="Config file")
parser.add_argument("--... | 1,662 | 25.396825 | 118 | py |
ld-metric | ld-metric-master/LaneATT/lib/experiment.py | import os
import re
import json
import logging
import subprocess
import torch
from torch.utils.tensorboard import SummaryWriter
class Experiment:
def __init__(self, exp_name, args=None, mode='train', exps_basedir='experiments', tensorboard_dir='tensorboard'):
self.name = exp_name
self.exp_dirpath... | 6,817 | 43.272727 | 117 | py |
ld-metric | ld-metric-master/LaneATT/lib/config.py | import yaml
import torch
import lib.models as models
import lib.datasets as datasets
class Config:
def __init__(self, config_path):
self.config = {}
self.config_str = ""
self.load(config_path)
def load(self, path):
with open(path, 'r') as file:
self.config_str = fi... | 1,712 | 31.320755 | 115 | py |
ld-metric | ld-metric-master/LaneATT/lib/runner.py | import pickle
import random
import logging
import cv2
import torch
import numpy as np
from tqdm import tqdm, trange
class Runner:
def __init__(self, cfg, exp, device, resume=False, view=None, deterministic=False):
self.cfg = cfg
self.exp = exp
self.device = device
self.resume = re... | 6,411 | 40.908497 | 113 | py |
ld-metric | ld-metric-master/LaneATT/lib/focal_loss.py | # pylint: disable-all
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
# Source: https://github.com/kornia/kornia/blob/f4f70fefb63287f72bc80cd96df9c061b1cb60dd/kornia/losses/focal.py
def one_hot(labels: torch.Tensor,
num_classes: int,
device: Opt... | 6,021 | 38.103896 | 118 | py |
ld-metric | ld-metric-master/LaneATT/lib/models/matching.py | import torch
INFINITY = 987654.
def match_proposals_with_targets(model, proposals, targets, t_pos=15., t_neg=20.):
# repeat proposals and targets to generate all combinations
num_proposals = proposals.shape[0]
num_targets = targets.shape[0]
# pad proposals and target for the valid_offset_mask's trick... | 3,255 | 48.333333 | 119 | py |
ld-metric | ld-metric-master/LaneATT/lib/models/resnet.py | # pylint: disable-all
'''
Source: https://github.com/akamaster/pytorch_resnet_cifar10
Properly implemented ResNet-s for CIFAR10 as described in paper [1].
The implementation and structure of this file is hugely influenced by [2]
which is implemented for ImageNet and doesn't have option A for identity.
Moreover, most ... | 5,112 | 29.616766 | 116 | py |
ld-metric | ld-metric-master/LaneATT/lib/models/laneatt.py | import math
import cv2
import torch
import numpy as np
import torch.nn as nn
from torchvision.models import resnet18, resnet34
from nms import nms
from lib.lane import Lane
from lib.focal_loss import FocalLoss
from .resnet import resnet122 as resnet122_cifar
from .matching import match_proposals_with_targets
clas... | 20,592 | 45.276404 | 126 | py |
ld-metric | ld-metric-master/LaneATT/lib/datasets/lane_dataset.py | import logging
import cv2
import numpy as np
import imgaug.augmenters as iaa
from imgaug.augmenters import Resize
from torchvision.transforms import ToTensor
from torch.utils.data.dataset import Dataset
from scipy.interpolate import InterpolatedUnivariateSpline
from imgaug.augmentables.lines import LineString, LineStr... | 11,864 | 39.220339 | 115 | py |
ld-metric | ld-metric-master/LaneATT/lib/nms/setup.py | from setuptools import setup
from torch.utils.cpp_extension import CUDAExtension, BuildExtension
setup(name='nms', packages=['nms'],
package_dir={'':'src'},
ext_modules=[CUDAExtension('nms.details', ['src/nms.cpp', 'src/nms_kernel.cu'])],
cmdclass={'build_ext': BuildExtension})
| 305 | 33 | 89 | py |
ld-metric | ld-metric-master/scnn/demo_test.py | import argparse
import cv2
import torch
from model import SCNN
from utils.prob2lines import getLane
from utils.transforms import *
net = SCNN(input_size=(800, 288), pretrained=False)
mean = (0.3598, 0.3653, 0.3662) # CULane mean, std
std = (0.2573, 0.2663, 0.2756)
transform_img = Resize((800, 288))
transform_to_net ... | 2,216 | 31.130435 | 109 | py |
ld-metric | ld-metric-master/scnn/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
class SCNN(nn.Module):
def __init__(
self,
input_size,
ms_ks=9,
pretrained=True,
):
"""
Argument
ms_ks: kernel size in message pass... | 5,435 | 35.72973 | 120 | py |
ld-metric | ld-metric-master/scnn/test_tusimple.py | import argparse
import json
import os
import torch.nn.functional as F
from torch.utils.data import DataLoader
from tqdm import tqdm
import dataset
from config import *
from model import SCNN
from utils.prob2lines import getLane
from utils.transforms import *
def parse_args():
parser = argparse.ArgumentParser()
... | 4,787 | 32.957447 | 112 | py |
ld-metric | ld-metric-master/scnn/train.py | import argparse
import json
import os
import shutil
import time
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import tqdm
from config import *
import dataset
from model import SCNN
from utils.tensorboard import TensorBoard
from utils.transforms import *
from utils.lr_scheduler import P... | 8,312 | 36.278027 | 169 | py |
ld-metric | ld-metric-master/scnn/test_CULane.py | import argparse
import json
import os
import torch.nn.functional as F
from torch.utils.data import DataLoader
from tqdm import tqdm
import dataset
from config import *
from model import SCNN
from utils.prob2lines import getLane
from utils.transforms import *
def parse_args():
parser = argparse.ArgumentParser()
... | 3,604 | 30.902655 | 110 | py |
ld-metric | ld-metric-master/scnn/onnx_export.py | import argparse
import cv2
import torch
from model import SCNN
from utils.prob2lines import getLane
from utils.transforms import *
net = SCNN(input_size=(800, 288), pretrained=False)
mean=(0.3598, 0.3653, 0.3662) # CULane mean, std
std=(0.2573, 0.2663, 0.2756)
transform_img = Resize((800, 288))
transform_to_net = Com... | 2,171 | 30.941176 | 109 | py |
ld-metric | ld-metric-master/scnn/dataset/CULane.py | import cv2
import os
import numpy as np
import torch
from torch.utils.data import Dataset
class CULane(Dataset):
def __init__(self, path, image_set, transforms=None):
super(CULane, self).__init__()
assert image_set in ('train', 'val', 'test'), "image_set is not valid!"
self.data_dir_path ... | 3,021 | 32.955056 | 142 | py |
ld-metric | ld-metric-master/scnn/dataset/Tusimple.py | import json
import os
import cv2
import numpy as np
import torch
from torch.utils.data import Dataset
class Tusimple(Dataset):
"""
image_set is splitted into three partitions: train, val, test.
train includes label_data_0313.json, label_data_0601.json
val includes label_data_0531.json
test includ... | 7,855 | 39.081633 | 141 | py |
ld-metric | ld-metric-master/scnn/experiments/vgg_SCNN_DULR_w9/t7_to_pt.py | import sys
import os
abs_file_path = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(abs_file_path, "..", "..")) # add path
import torch
import torch.nn as nn
import collections
from torch.utils.serialization import load_lua
from model import SCNN
model1 = load_lua('experiments/vgg_SCNN_DULR_w... | 6,723 | 50.723077 | 161 | py |
ld-metric | ld-metric-master/scnn/utils/lr_scheduler.py | from torch.optim.lr_scheduler import _LRScheduler
class PolyLR(_LRScheduler):
def __init__(self, optimizer, pow, max_iter, min_lrs=1e-20, last_epoch=-1, warmup=0):
"""
:param warmup: how many steps for linearly warmup lr
"""
self.pow = pow
self.max_iter = max_iter
i... | 1,120 | 37.655172 | 102 | py |
ld-metric | ld-metric-master/scnn/utils/tensorboard.py | # Code copied from pytorch-tutorial https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/04-utils/tensorboard/logger.py
import tensorflow as tf
import numpy as np
from PIL import Image
import scipy.misc
try:
from StringIO import StringIO # Python 2.7
except ImportError:
from io import BytesIO ... | 2,598 | 33.653333 | 132 | py |
ld-metric | ld-metric-master/scnn/utils/transforms/transforms.py | import cv2
import numpy as np
import torch
from torchvision.transforms import Normalize as Normalize_th
class CustomTransform:
def __call__(self, *args, **kwargs):
raise NotImplementedError
def __str__(self):
return self.__class__.__name__
def __eq__(self, name):
return str(self... | 4,536 | 26.49697 | 115 | py |
ld-metric | ld-metric-master/car_motion_attack/model_scnn.py | import numpy as np
import cv2
import torch
from scipy.interpolate import CubicSpline
from scnn.utils.transforms import Resize, Compose, Normalize, ToTensor
from scnn.model import SCNN
from scipy.interpolate import InterpolatedUnivariateSpline
from car_motion_attack.config import (DTYPE, PIXELS_PER_METER, SKY_HEIGHT, I... | 10,256 | 33.887755 | 113 | py |
ld-metric | ld-metric-master/car_motion_attack/model_ultrafast.py | import numpy as np
import cv2
import torch
import scipy
from scipy.interpolate import CubicSpline
from model.model import parsingNet
import torchvision.transforms as transforms
from car_motion_attack.config import (DTYPE, PIXELS_PER_METER, SKY_HEIGHT, IMG_INPUT_SHAPE,
IMG_INPUT_MA... | 12,612 | 34.133705 | 113 | py |
ld-metric | ld-metric-master/car_motion_attack/car_motion.py | from logging import getLogger
import cv2
import sys
import numpy as np
from car_motion_attack.model_input_preprocess import ModelInPreprocess
from car_motion_attack.manage_mask_scnn import FrameMask
from car_motion_attack.perspective_transform import PerspectiveTransform
from car_motion_attack.model_output_postproces... | 28,346 | 35.064885 | 127 | py |
ld-metric | ld-metric-master/car_motion_attack/attack.py | import os
import pickle
from logging import getLogger
import numpy as np
import pandas as pd
import cv2
import torch
from tqdm import tqdm
from scipy.interpolate import InterpolatedUnivariateSpline
from car_motion_attack.model_scnn import SCNNOpenPilot
from car_motion_attack.model_ultrafast import UltraFastOpenPilot
... | 19,157 | 37.625 | 157 | py |
ld-metric | ld-metric-master/car_motion_attack/model_polylanenet.py | import numpy as np
import cv2
import torch
import torch.nn as nn
from scipy.interpolate import CubicSpline
import torch
import torchvision.transforms as transforms
from scipy.interpolate import InterpolatedUnivariateSpline
from PolyLaneNet.lib.models import PolyRegression
from car_motion_attack.config import (DTYPE, ... | 12,419 | 35.637168 | 139 | py |
ld-metric | ld-metric-master/car_motion_attack/replay_bicycle.py | import pickle
import os
import sys
from logging import getLogger
import numpy as np
import pandas as pd
import cv2
from scipy.interpolate import InterpolatedUnivariateSpline
#import tensorflow as tf
#from keras import backend as K
from tqdm import tqdm
from car_motion_attack.model_scnn import SCNNOpenPilot
from car_m... | 5,276 | 33.717105 | 112 | py |
ld-metric | ld-metric-master/car_motion_attack/model_laneatt.py | import numpy as np
import cv2
import torch
import torch.nn as nn
from scipy.interpolate import CubicSpline
import torch
import torchvision.transforms as transforms
from scipy.interpolate import InterpolatedUnivariateSpline
from functools import lru_cache
from lib.models import LaneATT
from lib.datasets import LaneData... | 15,062 | 37.134177 | 169 | py |
ld-metric | ld-metric-master/car_motion_attack/models.py | #!/usr/bin/env python
from keras.utils import plot_model
from keras import backend as K
from keras.optimizers import RMSprop, Adam
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers import Dense, Activation, ELU, Flatten, Add, Multiply, ReLU, Reshape, Softmax
from keras.layers import Inp... | 27,013 | 62.562353 | 152 | py |
ld-metric | ld-metric-master/car_motion_attack/scnn.py |
import numpy as np
import cv2
import torch
from scipy.interpolate import CubicSpline
from scnn.model import SCNN
from scnn.utils.transforms import Resize, Compose, Normalize, ToTensor
from car_motion_attack.config import PIXELS_PER_METER
from car_motion_attack.config import (DTYPE, PIXELS_PER_METER, SKY_HEIGHT, IMG_I... | 6,106 | 36.931677 | 135 | py |
ld-metric | ld-metric-master/car_motion_attack/replay_metric.py | import pickle
import os
import sys
from logging import getLogger
import numpy as np
import pandas as pd
import cv2
from scipy.interpolate import InterpolatedUnivariateSpline
#import tensorflow as tf
#from keras import backend as K
from tqdm import tqdm
from car_motion_attack.model_scnn import SCNNOpenPilot
from car_m... | 6,897 | 36.693989 | 111 | py |
ld-metric | ld-metric-master/car_motion_attack/replay_follow.py | import pickle
import os
import sys
from logging import getLogger
import numpy as np
import pandas as pd
import cv2
from scipy.interpolate import InterpolatedUnivariateSpline
#import tensorflow as tf
#from keras import backend as K
from tqdm import tqdm
from car_motion_attack.model_scnn import SCNNOpenPilot
from car_m... | 6,244 | 35.520468 | 111 | py |
ld-metric | ld-metric-master/car_motion_attack/polyfuzz/utils/keras_model.py | #!/usr/bin/env python
from __future__ import print_function
import os
os.environ['GLOG_minloglevel'] = '2'
import sys
import argparse
import numpy as np
import keras
from keras.models import Model
from keras.layers import Dense, Activation, Flatten, Permute
from keras.layers import Input, Lambda, Concatenate
from kera... | 5,364 | 42.97541 | 127 | py |
ld-metric | ld-metric-master/car_motion_attack/polyfuzz/utils/vehicle_control_torch.py | import math
import torch
DTYPE = torch.float32
@torch.jit.script
def xdot(v, yaw):
return v * torch.cos(yaw)
@torch.jit.script
def ydot(v, yaw):
return v * torch.sin(yaw)
@torch.jit.script
def yawdot(v, delta, L):
return v / L * torch.tan(delta)
def vdot(a):
return 0 # Assume Acceleration is zer... | 2,606 | 29.313953 | 73 | py |
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