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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synboost | synboost-master/image_synthesis/models/networks/__init__.py | import importlib
import torch
import torch.nn as nn
import functools
import numpy as np
import torch.nn.functional as F
from models.networks.base_network import BaseNetwork
from models.networks.loss import *
from models.networks.discriminator import *
from models.networks.generator import *
from models.networks.encoder... | 1,874 | 30.25 | 71 | py |
synboost | synboost-master/image_synthesis/models/networks/generator.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.networks.base_network import BaseNetwork
from models.networks.normalization import get_nonspade_norm_layer
from models.networks.architecture import DepthsepCCBlock as DepthsepCCBlock
import pdb
class CondConvGenerator(Bas... | 8,835 | 49.204545 | 137 | py |
synboost | synboost-master/image_synthesis/models/networks/normalization.py | import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.spectral_norm as spectral_norm
## Returns a function that creates a normalization function
## that does not condition on semantic map
def get_nonspade_norm_layer(opt, norm_type='instance'):
# helper function to get ... | 3,364 | 36.388889 | 88 | py |
synboost | synboost-master/image_synthesis/models/networks/base_network.py | import torch
import torch.nn as nn
from torch.nn import init
class BaseNetwork(nn.Module):
def __init__(self):
super(BaseNetwork, self).__init__()
@staticmethod
def modify_commandline_options(parser, is_train):
return parser
def print_network(self):
if isinstance(self, list):
... | 2,313 | 40.321429 | 107 | py |
synboost | synboost-master/image_synthesis/util/util.py |
import re
import importlib
import torch
from argparse import Namespace
import numpy as np
from PIL import Image
import os
import argparse
import dill as pickle
import util.coco
import pdb
def save_obj(obj, name ):
with open(name, 'wb') as f:
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
def load_obj(name... | 10,916 | 35.757576 | 132 | py |
synboost | synboost-master/image_synthesis/data/base_dataset.py | import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
import random
class BaseDataset(data.Dataset):
def __init__(self):
super(BaseDataset, self).__init__()
@staticmethod
def modify_commandline_options(parser, is_train):
retur... | 3,870 | 30.217742 | 108 | py |
synboost | synboost-master/image_synthesis/data/image_folder.py | ###############################################################################
# Code from
# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
# Modified the original code so that it also loads images from the current
# directory as well as the subdirectories
################################... | 2,997 | 30.557895 | 93 | py |
synboost | synboost-master/image_synthesis/data/__init__.py | import importlib
import torch.utils.data
from data.base_dataset import BaseDataset
import pdb
def find_dataset_using_name(dataset_name):
# Given the option --dataset [datasetname],
# the file "datasets/datasetname_dataset.py"
# will be imported.
dataset_filename = "data." + dataset_name + "_dataset"
... | 2,437 | 41.034483 | 117 | py |
synboost | synboost-master/image_synthesis/trainers/pix2pix_trainer.py | from models.pix2pix_model import Pix2PixModel
import torch
class Pix2PixTrainer():
"""
Trainer creates the model and optimizers, and uses them to
updates the weights of the network while reporting losses
and the latest visuals to visualize the progress in training.
"""
def __init__(self, opt):... | 2,610 | 34.283784 | 101 | py |
synboost | synboost-master/image_dissimilarity/test.py | import argparse
import yaml
import torch.backends.cudnn as cudnn
import torch
from PIL import Image
import numpy as np
import os
from sklearn import metrics
import matplotlib.pyplot as plt
from tqdm import tqdm
from util import trainer_util, metrics
from util.iter_counter import IterationCounter
from models.dissimilar... | 5,651 | 35.230769 | 77 | py |
synboost | synboost-master/image_dissimilarity/test_multiple.py | import argparse
import yaml
import torch.backends.cudnn as cudnn
import torch
from PIL import Image
import numpy as np
import os
from sklearn import metrics
import matplotlib.pyplot as plt
from tqdm import tqdm
from util import trainer_util, metrics
from util.iter_counter import IterationCounter
from models.dissimilar... | 6,221 | 43.442857 | 118 | py |
synboost | synboost-master/image_dissimilarity/scaling_trainer.py | import argparse
import yaml
import os
import torch.backends.cudnn as cudnn
import torch
from util import trainer_util, metrics
from util.temperature_scaling import ModelWithTemperature
from models.dissimilarity_model import DissimNet, GuidedDissimNet, ResNetDissimNet, CorrelatedDissimNet
parser = argparse.ArgumentPa... | 3,196 | 43.402778 | 152 | py |
synboost | synboost-master/image_dissimilarity/test_ensemble.py | import argparse
import yaml
import torch.backends.cudnn as cudnn
import torch
from PIL import Image
import numpy as np
import os
from sklearn import metrics
import matplotlib.pyplot as plt
from tqdm import tqdm
import ast
from itertools import product
from numpy.linalg import norm
from util import trainer_util, metric... | 6,733 | 41.0875 | 145 | py |
synboost | synboost-master/image_dissimilarity/train.py | import argparse
import yaml
import os
from tqdm import tqdm
import numpy as np
import shutil
from PIL import Image
import torch.backends.cudnn as cudnn
import torch
from torch.utils.tensorboard import SummaryWriter
from torchvision.utils import make_grid
from torchvision.transforms import ToPILImage, ToTensor
from tr... | 21,638 | 47.086667 | 141 | py |
synboost | synboost-master/image_dissimilarity/models/vgg_features.py | import torch.nn as nn
import torchvision.models
import torch
import sys
from torch.nn.modules.upsampling import Upsample
sys.path.append("..")
from image_dissimilarity.models.normalization import SPADE
class VGGFeatures(nn.Module):
def __init__(self, architecture='vgg16', pretrained=True):
super(VGGFeatures, self)... | 7,506 | 36.348259 | 175 | py |
synboost | synboost-master/image_dissimilarity/models/resnet_features.py | import torch
import torch.nn as nn
from torch.utils.model_zoo import load_url as load_state_dict_from_url
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/mo... | 9,957 | 36.577358 | 106 | py |
synboost | synboost-master/image_dissimilarity/models/dissimilarity_model.py | import torch.nn as nn
import torch
import torchvision.models
import sys
sys.path.append("..")
from image_dissimilarity.models.semantic_encoder import SemanticEncoder, ResNetSemanticEncoder
from image_dissimilarity.models.vgg_features import VGGFeatures, VGGSPADE
from image_dissimilarity.models.resnet_features import r... | 55,266 | 44.562242 | 121 | py |
synboost | synboost-master/image_dissimilarity/models/semantic_encoder.py | import torch.nn as nn
class SemanticEncoder(nn.Module):
''' Semantic Encoder as described in Detecting the Unexpected via Image Resynthesis '''
def __init__(self, architecture='vgg16', in_channels=19, num_hidden_layers=4, base_feature_size=32):
super(SemanticEncoder, self).__init__()
self.hidden_layers = nn... | 4,064 | 30.757813 | 101 | py |
synboost | synboost-master/image_dissimilarity/models/normalization.py | # CODE FROM NVIDIA Segmentation repositories
import torch.nn as nn
import torch.nn.functional as F
## Creates SPADE normalization layer based on the given configuration
## SPADE consists of two steps. First, it normalizes the activations using
## your favorite normalization method, such as Batch Norm or Instance Norm.... | 4,542 | 33.416667 | 88 | py |
synboost | synboost-master/image_dissimilarity/util/trainer_util.py | import torch
import random
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import os
import cv2
import sys
sys.path.append("..")
from image_dissimilarity.data.cityscapes_dataset import CityscapesDataset
import image_dissimilarity.data.cityscapes_labels as cityscapes_labels
def activate_gpus(... | 3,347 | 30.584906 | 96 | py |
synboost | synboost-master/image_dissimilarity/util/image_logging.py | import numpy as np
import torch
from torchvision.transforms import ToPILImage
class DenormalizeImage(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, tensor):
"""
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be n... | 3,080 | 31.776596 | 109 | py |
synboost | synboost-master/image_dissimilarity/util/image_decoders.py | from torchvision.transforms import ToPILImage
class DenormalizeImage(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, tensor):
"""
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
Returns:
... | 529 | 26.894737 | 77 | py |
synboost | synboost-master/image_dissimilarity/util/temperature_scaling.py | import torch
from torch import nn, optim
from torch.nn import functional as F
class ModelWithTemperature(nn.Module):
"""
A thin decorator, which wraps a model with temperature scaling
model (nn.Module):
A classification neural network
NB: Output of the neural network should be the classifi... | 4,951 | 38.935484 | 109 | py |
synboost | synboost-master/image_dissimilarity/data/troublesheet_data.py | import torch.utils.data as data
from torch.utils.data import Dataset
import os
from PIL import Image
import numpy as np
from natsort import natsorted
from torchvision import transforms
import torch
import sys
sys.path.append("../..")
import image_dissimilarity.data.cityscapes_labels as cityscapes_labels
from image_dis... | 7,179 | 40.50289 | 142 | py |
synboost | synboost-master/image_dissimilarity/data/augmentations.py | import numpy as np
from PIL import Image
from PIL import ImageFile
from torchvision import transforms
from imgaug import augmenters as iaa
from imgaug import parameters as iap
ImageFile.LOAD_TRUNCATED_IMAGES = True
# defines all the different types of transformations
class OnlyApplyBlurs:
def __init__(self):
... | 13,433 | 46.638298 | 121 | py |
synboost | synboost-master/image_dissimilarity/data/cityscapes_dataset.py | import torch.utils.data as data
from torch.utils.data import Dataset
import os
from PIL import Image
import numpy as np
from natsort import natsorted
from torchvision import transforms
import torch
import random
import sys
sys.path.append("..")
import image_dissimilarity.data.cityscapes_labels as cityscapes_labels
fro... | 12,277 | 43.32491 | 142 | py |
synboost | synboost-master/image_dissimilarity/trainers/dissimilarity_trainer.py | import torch
import os
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.optim.lr_scheduler import ReduceLROnPlateau
import sys
sys.path.append("..")
from image_dissimilarity.util import trainer_util
from image_dissimilarity.models.dissimilarity_model import DissimNet, DissimNetPrior
class Dissimi... | 7,689 | 44.235294 | 160 | py |
synboost | synboost-master/options/base_options.py | import argparse
import os
import sys
sys.path.insert(0, os.path.join(os.getcwd(), os.path.dirname(os.path.dirname(__file__)), 'image_synthesis'))
from util import util
import torch
import models
import data
import pickle
class BaseOptions():
def __init__(self):
self.initialized = False
def initialize... | 9,263 | 50.75419 | 283 | py |
synboost | synboost-master/image_segmentation_icnet/train_distribute.py | # Author: Xiangtai Li
# Email: lxtpku@pku.edu.cn
"""
Distribute Training Code For Fast training.
"""
import argparse
import os
import os.path as osp
import timeit
import numpy as np
import torch
from torch.utils import data
import torch.optim as optim
import torch.backends.cudnn as cudnn
from libs.utils.logger ... | 11,694 | 42.314815 | 124 | py |
synboost | synboost-master/image_segmentation_icnet/val.py | import argparse
from scipy import ndimage
import numpy as np
import json
import torch
from torch.utils import data
import torch.nn as nn
import os
from math import ceil
from PIL import Image as PILImage
from libs.datasets.cityscapes import Cityscapes
DATA_DIRECTORY = 'cityscapes'
DATA_LIST_PATH = './data/cityscapes... | 10,805 | 38.727941 | 112 | py |
synboost | synboost-master/image_segmentation_icnet/prediction_test_different_size.py | import os
import argparse
import torch
import torch.nn.functional as F
import cv2
import numpy as np
from shutil import copyfile
import datetime
import libs.models as models
N_CLASS = 19
color_list = [7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33]
color_map = [(128, 64, 128), (244, 35, 23... | 7,204 | 40.408046 | 154 | py |
synboost | synboost-master/image_segmentation_icnet/libs/core/operators.py | # Common Segmentation Operator implemented by Pytorch
# XiangtaiLi(lxtpku@pku.edu.cn)
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import BatchNorm2d
upsample = lambda x, size: F.interpolate(x, size, mode='bilinear', align_corners=True)
def conv3x3(in_planes, out_planes, stride=... | 10,251 | 35.614286 | 134 | py |
synboost | synboost-master/image_segmentation_icnet/libs/core/loss.py | # CE-loss
import torch.nn as nn
import torch
import torch.nn.functional as F
class OhemCrossEntropy2dTensor(nn.Module):
def __init__(self, ignore_label, reduction='elementwise_mean', thresh=0.6, min_kept=256,
down_ratio=1, use_weight=False):
super(OhemCrossEntropy2dTensor, self).__init__(... | 6,062 | 37.617834 | 98 | py |
synboost | synboost-master/image_segmentation_icnet/libs/models/BiSegNet.py | # @Author: yuchangqian
# Modified: XiangtaiLi
# BiSeg uses deeply based backbone.
import torch
import torch.nn as nn
import torch.nn.functional as F
from libs.models.backbone.resnet import resnet18
from libs.core.operators import ConvBnRelu, FeatureFusion, AttentionRefinement
class SpatialPath(nn.Module):
def _... | 6,657 | 38.164706 | 80 | py |
synboost | synboost-master/image_segmentation_icnet/libs/models/FastSCNN.py | # Author: Xiangtai Li
# Email: lxtpku@pku.edu.cn
# FastSCNN doesn't use pretrained backbone network while usually takes longer training time.
import torch
import torch.nn as nn
import torch.nn.functional as F
class FastSCNN(nn.Module):
def __init__(self, num_classes, aux=False):
super(FastSCNN, self).__in... | 8,244 | 34.847826 | 120 | py |
synboost | synboost-master/image_segmentation_icnet/libs/models/ICNet.py | # Author: Xiangtai Li
# Email: lxtpku@pku.edu.cn
import torch
import torch.nn as nn
import torch.nn.functional as F
from libs.core.operators import ConvBnRelu
from libs.models.PSPNet import PSPHead_res50
class CascadeFeatureFusion(nn.Module):
"""CFF Unit"""
def __init__(self, low_channels, high_channels, out... | 3,822 | 31.398305 | 101 | py |
synboost | synboost-master/image_segmentation_icnet/libs/models/ESPNet.py | # Author: "Sachin Mehta"
# ESPNet doesn't use pretrained backbone network while usually takes longer training time.
import torch
import torch.nn as nn
class CBR(nn.Module):
'''
This class defines the convolution layer with batch normalization and PReLU activation
'''
def __init__(self, nIn, nOut, kSi... | 13,332 | 31.283293 | 120 | py |
synboost | synboost-master/image_segmentation_icnet/libs/models/MSFNet.py | # Author: Xiangtai Li
# Email: lxtpku@pku.edu.cn
# Pytorch Implementation Of MSFNet: Real-Time Semantic Segmentation via Multiply Spatial Fusion Network(face++)
# I didn't include the boundaries information
import torch
import torch.nn as nn
class MSFNet(nn.Module):
def __init__(self):
super(MSFNet, sel... | 558 | 19.703704 | 111 | py |
synboost | synboost-master/image_segmentation_icnet/libs/models/SwiftNet.py | # Author: Xiangtai Li
# Email: lxtpku@pku.edu.cn
"""
SwiftNet is a little different
1. because it use the pre-activation input as lateral feature input.
The backbone need writing for easier experiment
2. I also add dsn head for easier training during the decoder upsample process.
3. SwiftNet use tor... | 9,847 | 35.746269 | 119 | py |
synboost | synboost-master/image_segmentation_icnet/libs/models/DFANet.py | # Author: Xiangtai Li
# Email: lxtpku@pku.edu.cn
"""
Implementation of DFANet: a little different from the origin paper, I add more dsn loss for training.
DFANet uses modified Xception backbone pretrained on ImageNet.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from libs.models.backb... | 4,577 | 42.188679 | 120 | py |
synboost | synboost-master/image_segmentation_icnet/libs/models/DFSegNet.py | # Author: Xiangtai Li
# Email: lxtpku@pku.edu.cn
# Pytorch Implementation of DongFeng SegNet:
# Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search.
# The backbone is pretrained on ImageNet
import torch
import torch.nn as nn
import torch.nn.functional as F
from libs.core.operators i... | 1,910 | 24.824324 | 89 | py |
synboost | synboost-master/image_segmentation_icnet/libs/models/PSPNet.py | import torch.nn as nn
from torch.nn import functional as F
import torch
affine_par = True
from torch.nn import BatchNorm2d
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=Fa... | 8,605 | 36.255411 | 147 | py |
synboost | synboost-master/image_segmentation_icnet/libs/models/backbone/resnet.py | from __future__ import print_function, division, absolute_import
import torch.nn as nn
from libs.utils.tools import load_model
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101']
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, ... | 7,569 | 33.724771 | 80 | py |
synboost | synboost-master/image_segmentation_icnet/libs/models/backbone/dfnet.py | from __future__ import print_function, division, absolute_import
import math
import torch
import torch.nn as nn
from torch.nn import BatchNorm2d
__all__ = ["dfnetv1", "dfnetv2"]
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=... | 5,655 | 32.270588 | 78 | py |
synboost | synboost-master/image_segmentation_icnet/libs/models/backbone/xception.py | from __future__ import print_function, division, absolute_import
import torch.nn as nn
from libs.core.operators import ConvBnRelu, SeparableConv2d
from libs.utils.tools import load_model
__all__ = ['Xception', 'Xception39','XceptionA']
class SeparableConvBnRelu(nn.Module):
def __init__(self, in_channels, out_ch... | 8,073 | 34.725664 | 121 | py |
synboost | synboost-master/image_segmentation_icnet/libs/datasets/cityscapes.py | # Author: Xiangtai Li
# Email: lxtpku@pku.edu.cn
import os.path as osp
import numpy as np
import random
import cv2
from torch.utils import data
class Cityscapes(data.Dataset):
def __init__(self, root, list_path="./list/cityscapes/train.txt", max_iters=None, crop_size=(321, 321),
mean=(128, 128... | 4,363 | 39.036697 | 115 | py |
synboost | synboost-master/image_segmentation_icnet/libs/datasets/camvid.py | # Author: Xiangtai Li
# Email: lxtpku@pku.edu.cn
import os.path as osp
import numpy as np
import random
import cv2
from torch.utils import data
"""
CamVid is a road scene understanding dataset with 367 training images and 233 testing images of day and dusk scenes.
The challenge is to segment 11 classes such as road... | 6,750 | 33.443878 | 117 | py |
synboost | synboost-master/image_segmentation_icnet/libs/datasets/mapillary.py | # Author: Xiangtai Li
# Email: lxtpku@pku.edu.cn
import os
import numpy as np
import random
import cv2
from torch.utils import data
class MapDataSet(data.Dataset):
def __init__(self, root, split="train", max_iters=80000, crop_size=(321, 321), mean=(128, 128, 128), vars=(1, 1, 1), scale=True,
mir... | 6,019 | 42.941606 | 132 | py |
synboost | synboost-master/image_segmentation_icnet/libs/utils/image_utils.py | import cv2
import numpy as np
import numbers
import random
import collections
import torch
import torch.nn.functional as F
def get_2dshape(shape, *, zero=True):
if not isinstance(shape, collections.Iterable):
shape = int(shape)
shape = (shape, shape)
else:
h, w = map(int, shape)
... | 5,871 | 25.812785 | 87 | py |
synboost | synboost-master/image_segmentation_icnet/libs/utils/tools.py | # some tools for network training
import argparse
import time
from collections import OrderedDict
import torch
import torch.distributed as dist
def all_reduce_tensor(tensor, op=dist.ReduceOp.SUM, world_size=1):
tensor = tensor.clone()
dist.all_reduce(tensor, op)
tensor.div_(world_size)
return tensor... | 2,315 | 25.930233 | 78 | py |
synboost | synboost-master/data_preparation/prior_probability_estimation.py | import os
from PIL import Image
import numpy as np
import cv2
from collections import OrderedDict
import shutil
import torch
from torch.backends import cudnn
import torchvision.transforms as transforms
from options.test_options import TestOptions
import sys
sys.path.insert(0, './image_segmentation')
import network
fro... | 3,145 | 29.25 | 99 | py |
synboost | synboost-master/data_preparation/data_preprocess.py | import os
from PIL import Image
import numpy as np
import cv2
from collections import OrderedDict
import shutil
import torch
from torch.backends import cudnn
import torchvision.transforms as transforms
from options.test_options import TestOptions
import sys
sys.path.insert(0, './image_segmentation')
import network
fr... | 6,546 | 34.775956 | 123 | py |
synboost | synboost-master/data_preparation/mean_absolute_features.py | import yaml
import torch
from torchvision.transforms import ToPILImage, ToTensor
import torchvision
import os
import sys
sys.path.append("./image_dissimilarity")
from util import trainer_util, metrics
def mae_features(config_file_path, gpu_ids, dataroot, data_origin):
soft_fdr = os.path.join(dataroot, 'mae_f... | 4,635 | 38.288136 | 115 | py |
synboost | synboost-master/image_segmentation/demo_folder.py | import os
import sys
import time
import argparse
from PIL import Image
import numpy as np
import cv2
import torch
from torch.backends import cudnn
import torchvision.transforms as transforms
import network
from optimizer import restore_snapshot
from datasets import cityscapes
from config import assert_and_infer_cfg
... | 3,198 | 36.635294 | 162 | py |
synboost | synboost-master/image_segmentation/loss.py | """
Loss.py
"""
import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from config import cfg
def get_loss(args):
"""
Get the criterion based on the loss function
args: commandline arguments
return: criterion, criterion_val
"""
if args.img_wt_los... | 6,799 | 34.602094 | 99 | py |
synboost | synboost-master/image_segmentation/config.py | """
# Code adapted from:
# https://github.com/facebookresearch/Detectron/blob/master/detectron/core/config.py
Source License
# Copyright (c) 2017-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obta... | 3,886 | 30.601626 | 90 | py |
synboost | synboost-master/image_segmentation/demo.py | import os
import sys
import argparse
from PIL import Image
import numpy as np
import cv2
import torch
from torch.backends import cudnn
import torchvision.transforms as transforms
import network
from optimizer import restore_snapshot
from datasets import cityscapes
from config import assert_and_infer_cfg
parser = arg... | 2,091 | 32.741935 | 140 | py |
synboost | synboost-master/image_segmentation/eval.py | """
Evaluation Script
Support Two Modes: Pooling based inference and sliding based inference
Pooling based inference is simply whole image inference.
"""
import os
import logging
import sys
import argparse
import re
import queue
import threading
from math import ceil
from datetime import datetime
from tqdm import tqdm
... | 20,447 | 34.013699 | 92 | py |
synboost | synboost-master/image_segmentation/train.py | """
training code
"""
from __future__ import absolute_import
from __future__ import division
import argparse
import logging
import os
import torch
from apex import amp
from config import cfg, assert_and_infer_cfg
from utils.misc import AverageMeter, prep_experiment, evaluate_eval, fast_hist
import datasets
import loss... | 12,961 | 39.633229 | 100 | py |
synboost | synboost-master/image_segmentation/optimizer.py | """
Pytorch Optimizer and Scheduler Related Task
"""
import math
import logging
import torch
from torch import optim
from image_segmentation.config import cfg
def get_optimizer(args, net):
"""
Decide Optimizer (Adam or SGD)
"""
param_groups = net.parameters()
if args.sgd:
optimizer = opti... | 3,413 | 33.836735 | 92 | py |
synboost | synboost-master/image_segmentation/datasets/kitti.py | """
KITTI Dataset Loader
"""
import os
import sys
import numpy as np
from PIL import Image
from torch.utils import data
import logging
import datasets.uniform as uniform
import datasets.cityscapes_labels as cityscapes_labels
import json
from config import cfg
trainid_to_name = cityscapes_labels.trainId2name
id_to_tr... | 8,750 | 33.72619 | 133 | py |
synboost | synboost-master/image_segmentation/datasets/sampler.py | """
# Code adapted from:
# https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py
#
# BSD 3-Clause License
#
# Copyright (c) 2017,
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions ar... | 4,347 | 38.527273 | 118 | py |
synboost | synboost-master/image_segmentation/datasets/cityscapes.py | """
Cityscapes Dataset Loader
"""
import logging
import json
import os
import numpy as np
from PIL import Image
from torch.utils import data
import torchvision.transforms as transforms
import datasets.uniform as uniform
import datasets.cityscapes_labels as cityscapes_labels
from config import cfg
trainid_to_name = c... | 19,309 | 38.488753 | 100 | py |
synboost | synboost-master/image_segmentation/datasets/nullloader.py | """
Null Loader
"""
import numpy as np
import torch
from torch.utils import data
num_classes = 19
ignore_label = 255
class NullLoader(data.Dataset):
"""
Null Dataset for Performance
"""
def __init__(self,crop_size):
self.imgs = range(200)
self.crop_size = crop_size
def __getitem__... | 576 | 23.041667 | 158 | py |
synboost | synboost-master/image_segmentation/datasets/camvid.py | """
Camvid Dataset Loader
"""
import os
import sys
import numpy as np
from PIL import Image
from torch.utils import data
import logging
import datasets.uniform as uniform
import json
from config import cfg
# trainid_to_name = cityscapes_labels.trainId2name
# id_to_trainid = cityscapes_labels.label2trainid
num_classe... | 10,286 | 35.349823 | 133 | py |
synboost | synboost-master/image_segmentation/datasets/__init__.py | """
Dataset setup and loaders
"""
from datasets import cityscapes
from datasets import mapillary
from datasets import kitti
from datasets import camvid
import torchvision.transforms as standard_transforms
import transforms.joint_transforms as joint_transforms
import transforms.transforms as extended_transforms
from to... | 10,943 | 39.533333 | 133 | py |
synboost | synboost-master/image_segmentation/datasets/mapillary.py | """
Mapillary Dataset Loader
"""
from PIL import Image
from torch.utils import data
import os
import numpy as np
import json
import datasets.uniform as uniform
from config import cfg
num_classes = 65
ignore_label = 65
root = cfg.DATASET.MAPILLARY_DIR
config_fn = os.path.join(root, 'config.json')
id_to_ignore_or_group ... | 6,713 | 33.430769 | 86 | py |
synboost | synboost-master/image_segmentation/network/Resnet.py | """
# Code Adapted from:
# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
#
# BSD 3-Clause License
#
# Copyright (c) 2017,
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are me... | 8,386 | 31.890196 | 90 | py |
synboost | synboost-master/image_segmentation/network/wider_resnet.py | """
# Code adapted from:
# https://github.com/mapillary/inplace_abn/
#
# BSD 3-Clause License
#
# Copyright (c) 2017, mapillary
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistribution... | 14,157 | 34.752525 | 87 | py |
synboost | synboost-master/image_segmentation/network/deepv3.py | """
# Code Adapted from:
# https://github.com/sthalles/deeplab_v3
#
# MIT License
#
# Copyright (c) 2018 Thalles Santos Silva
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restric... | 10,730 | 33.728155 | 138 | py |
synboost | synboost-master/image_segmentation/network/SEresnext.py | """
# Code adapted from:
# https://github.com/Cadene/pretrained-models.pytorch
#
# BSD 3-Clause License
#
# Copyright (c) 2017, Remi Cadene
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Re... | 15,124 | 36.071078 | 98 | py |
synboost | synboost-master/image_segmentation/network/mynn.py | """
Custom Norm wrappers to enable sync BN, regular BN and for weight initialization
"""
import torch.nn as nn
from config import cfg
from apex import amp
def Norm2d(in_channels):
"""
Custom Norm Function to allow flexible switching
"""
layer = getattr(cfg.MODEL, 'BNFUNC')
normalization_layer = la... | 1,076 | 25.925 | 80 | py |
synboost | synboost-master/image_segmentation/network/__init__.py | """
Network Initializations
"""
import logging
import importlib
import torch
def get_net(args, criterion):
"""
Get Network Architecture based on arguments provided
"""
net = get_model(network=args.arch, num_classes=args.dataset_cls.num_classes,
criterion=criterion)
num_params... | 1,130 | 23.586957 | 80 | py |
synboost | synboost-master/image_segmentation/utils/misc.py | """
Miscellanous Functions
"""
import sys
import re
import os
import shutil
import torch
from datetime import datetime
import logging
from subprocess import call
import shlex
from tensorboardX import SummaryWriter
import numpy as np
import torchvision.transforms as standard_transforms
import torchvision.utils as vutil... | 11,777 | 37.616393 | 102 | py |
synboost | synboost-master/image_segmentation/utils/my_data_parallel.py |
"""
# Code adapted from:
# https://github.com/pytorch/pytorch/blob/master/torch/nn/parallel/data_parallel.py
#
# BSD 3-Clause License
#
# Copyright (c) 2017,
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following condition... | 8,606 | 40.985366 | 111 | py |
synboost | synboost-master/image_segmentation/sdcnet/main.py | #!/usr/bin/env python
import argparse
import os
import numpy as np
import shutil
import torch
import torch.backends.cudnn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from tensorboardX import SummaryWriter
import cv2
from tqdm import tqdm
### masks warning : RuntimeError: Set changed size duri... | 26,977 | 40.062405 | 127 | py |
synboost | synboost-master/image_segmentation/sdcnet/sdc_aug.py | import os
import sys
import argparse
import cv2
import numpy as np
from PIL import Image
import shutil
import torch
import torch.nn as nn
from torch.autograd import Variable
from models.sdc_net2d import *
parser = argparse.ArgumentParser()
parser.add_argument('--pretrained', default='', type=str, metavar='PATH', he... | 15,478 | 45.623494 | 131 | py |
synboost | synboost-master/image_segmentation/sdcnet/utility/tools.py | import os
import subprocess
import time
from inspect import isclass
class TimerBlock:
def __init__(self, title):
print(("{}".format(title)))
def __enter__(self):
self.start = time.clock()
return self
def __exit__(self, exc_type, exc_value, traceback):
self.end = time.clock... | 3,701 | 37.164948 | 155 | py |
synboost | synboost-master/image_segmentation/sdcnet/models/model_utils.py | from __future__ import division
from __future__ import print_function
import torch.nn as nn
def conv2d(channels_in, channels_out, kernel_size=3, stride=1, bias = True):
return nn.Sequential(
nn.Conv2d(channels_in, channels_out, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, bias=bias)... | 649 | 39.625 | 124 | py |
synboost | synboost-master/image_segmentation/sdcnet/models/sdc_net2d.py | '''
Portions of this code are adapted from:
https://github.com/NVIDIA/flownet2-pytorch/blob/master/networks/FlowNetS.py
https://github.com/ClementPinard/FlowNetPytorch/blob/master/models/FlowNetS.py
'''
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
from torch.... | 9,288 | 40.842342 | 119 | py |
synboost | synboost-master/image_segmentation/sdcnet/datasets/frame_loader.py | from __future__ import division
from __future__ import print_function
import os
import natsort
import numpy as np
import cv2
import torch
from torch.utils import data
from datasets.dataset_utils import StaticRandomCrop
class FrameLoader(data.Dataset):
def __init__(self, args, root, is_training = False, transfor... | 3,766 | 35.931373 | 107 | py |
synboost | synboost-master/image_segmentation/sdcnet/datasets/dataset_utils.py | from __future__ import division
from __future__ import print_function
import torch
class StaticRandomCrop(object):
"""
Helper function for random spatial crop
"""
def __init__(self, size, image_shape):
h, w = image_shape
self.th, self.tw = size
self.h1 = torch.randint(0, h - se... | 519 | 27.888889 | 79 | py |
synboost | synboost-master/image_segmentation/sdcnet/spatialdisplconv_package/test_spatialdisplconv.py | import torch
import time
from spatialdisplconv import SpatialDisplConv
assert torch.cuda.is_available()
cuda_device = torch.device("cuda") # device object representing GPU
n = 8
h = 224
w = 224
offset = 9 # 11
#input1 = N, 3, H + 11, W + 11
#input2 = N, 11, H, W
#input3 = N, 11, H, W
#input4 = N, 2, H, W
# Note t... | 1,305 | 23.185185 | 98 | py |
synboost | synboost-master/image_segmentation/sdcnet/spatialdisplconv_package/spatialdisplconv.py | from torch.nn.modules.module import Module
from torch.autograd import Function, Variable
import spatialdisplconv_cuda
class SpatialDisplConvFunction(Function):
@staticmethod
def forward(ctx, input1, input2, input3, input4, kernel_size = 1):
assert input1.is_contiguous(), "spatialdisplconv forward - in... | 2,270 | 32.397059 | 95 | py |
synboost | synboost-master/image_segmentation/sdcnet/spatialdisplconv_package/setup.py | #!/usr/bin/env python3
import os
import torch
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
cxx_args = ['-std=c++11']
nvcc_args = [
'-gencode', 'arch=compute_50,code=sm_50',
'-gencode', 'arch=compute_52,code=sm_52',
'-gencode', 'arch=compute_60,code=sm_6... | 791 | 25.4 | 67 | py |
synboost | synboost-master/image_segmentation/transforms/joint_transforms.py | """
# Code borrowded from:
# https://github.com/zijundeng/pytorch-semantic-segmentation/blob/master/utils/joint_transforms.py
#
#
# MIT License
#
# Copyright (c) 2017 ZijunDeng
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "So... | 22,631 | 35.8 | 109 | py |
synboost | synboost-master/image_segmentation/transforms/transforms.py | """
# Code borrowded from:
# https://github.com/zijundeng/pytorch-semantic-segmentation/blob/master/utils/transforms.py
#
#
# MIT License
#
# Copyright (c) 2017 ZijunDeng
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software... | 11,778 | 32.274011 | 94 | py |
synboost | synboost-master/image_synthesis_spade/options/base_options.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import sys
import argparse
import os
from util import util
import torch
import models
import data
import pickle
class BaseOptions():
def _... | 9,040 | 49.50838 | 283 | py |
synboost | synboost-master/image_synthesis_spade/models/pix2pix_model.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch
import models.networks as networks
import util.util as util
class Pix2PixModel(torch.nn.Module):
@staticmethod
def modify... | 9,780 | 37.813492 | 105 | py |
synboost | synboost-master/image_synthesis_spade/models/__init__.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import importlib
import torch
def find_model_using_name(model_name):
# Given the option --model [modelname],
# the file "models/modeln... | 1,417 | 30.511111 | 156 | py |
synboost | synboost-master/image_synthesis_spade/models/networks/architecture.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torch.nn.utils.spectral_norm as spectral_norm
from ... | 4,536 | 35.58871 | 105 | py |
synboost | synboost-master/image_synthesis_spade/models/networks/discriminator.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from models.networks.base_network import BaseNetwork
from models.networ... | 4,386 | 35.256198 | 105 | py |
synboost | synboost-master/image_synthesis_spade/models/networks/loss.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.networks.architecture import VGG19
# Defines the GAN loss which... | 4,783 | 38.53719 | 105 | py |
synboost | synboost-master/image_synthesis_spade/models/networks/encoder.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from models.networks.base_network import BaseNetwork
from models.networ... | 1,975 | 34.285714 | 105 | py |
synboost | synboost-master/image_synthesis_spade/models/networks/__init__.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch
from models.networks.base_network import BaseNetwork
from models.networks.loss import *
from models.networks.discriminator import *... | 1,965 | 29.71875 | 105 | py |
synboost | synboost-master/image_synthesis_spade/models/networks/generator.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.networks.base_network import BaseNetwork
from models.networks.nor... | 6,790 | 36.10929 | 189 | py |
synboost | synboost-master/image_synthesis_spade/models/networks/normalization.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.networks.sync_batchnorm import SynchronizedBatchNorm2d
... | 4,466 | 39.243243 | 105 | py |
synboost | synboost-master/image_synthesis_spade/models/networks/base_network.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch.nn as nn
from torch.nn import init
class BaseNetwork(nn.Module):
def __init__(self):
super(BaseNetwork, self).__init_... | 2,469 | 40.166667 | 107 | py |
synboost | synboost-master/image_synthesis_spade/util/util.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import re
import importlib
import torch
from argparse import Namespace
import numpy as np
from PIL import Image
import os
import argparse
import... | 9,449 | 32.992806 | 139 | py |
synboost | synboost-master/image_synthesis_spade/data/base_dataset.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
import random
class BaseD... | 4,046 | 30.372093 | 108 | py |
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