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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FasterSeg | FasterSeg-master/tools/datasets/BaseDataset.py | import os
import cv2
cv2.setNumThreads(0)
import torch
import numpy as np
from random import shuffle
import torch.utils.data as data
#own imports
import os
import csv
from collections import namedtuple
class BaseDataset(data.Dataset):
#*************calss mebers*************************
isCustomData = False... | 11,827 | 37.278317 | 267 | py |
FasterSeg | FasterSeg-master/tools/engine/tester.py | import os
import os.path as osp
import cv2
import numpy as np
import time
from tqdm import tqdm
import torch
import torch.nn.functional as F
import torch.multiprocessing as mp
from engine.logger import get_logger
from utils.pyt_utils import load_model, link_file, ensure_dir
from utils.img_utils import pad_image_to_sh... | 11,687 | 36.341853 | 97 | py |
FasterSeg | FasterSeg-master/tools/engine/evaluator.py | import os
import cv2
import numpy as np
import time
from tqdm import tqdm
import torch
import torch.multiprocessing as mp
from engine.logger import get_logger
from utils.pyt_utils import load_model, link_file, ensure_dir
from utils.img_utils import pad_image_to_shape, normalize
logger = get_logger()
class Evaluato... | 12,909 | 36.970588 | 97 | py |
FasterSeg | FasterSeg-master/tools/utils/img_utils.py | import cv2
import numpy as np
import numbers
import random
import collections
def get_2dshape(shape, *, zero=True):
if not isinstance(shape, collections.Iterable):
shape = int(shape)
shape = (shape, shape)
else:
h, w = map(int, shape)
shape = (h, w)
if zero:
minv = ... | 4,898 | 25.33871 | 79 | py |
FasterSeg | FasterSeg-master/tools/utils/pyt_utils.py | # encoding: utf-8
import os
import time
import argparse
from collections import OrderedDict
import torch
import torch.distributed as dist
from engine.logger import get_logger
logger = get_logger()
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://do... | 3,836 | 28.068182 | 79 | py |
FasterSeg | FasterSeg-master/tools/utils/darts_utils.py | import os
import math
import numpy as np
import torch
import shutil
from torch.autograd import Variable
import time
from tqdm import tqdm
from genotypes import PRIMITIVES
import matplotlib
# Force matplotlib to not use any Xwindows backend.
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from pdb import set_... | 13,141 | 36.764368 | 196 | py |
FasterSeg | FasterSeg-master/tools/utils/init_func.py | import torch
import torch.nn as nn
def __init_weight(feature, conv_init, norm_layer, bn_eps, bn_momentum,
**kwargs):
for name, m in feature.named_modules():
if isinstance(m, (nn.Conv2d, nn.Conv3d)):
conv_init(m.weight, **kwargs)
elif isinstance(m, norm_layer):
... | 1,958 | 34.618182 | 78 | py |
FasterSeg | FasterSeg-master/latency/slimmable_ops.py | import torch.nn as nn
from pdb import set_trace as bp
def make_divisible(v, divisor=8, min_value=1):
"""
forked from slim:
https://github.com/tensorflow/models/blob/\
0344c5503ee55e24f0de7f37336a6e08f10976fd/\
research/slim/nets/mobilenet/mobilenet.py#L62-L69
"""
if min_value is None:
... | 2,807 | 38.549296 | 156 | py |
FasterSeg | FasterSeg-master/latency/seg_oprs.py | import numpy as np
try:
from utils.darts_utils import compute_latency_ms_tensorrt as compute_latency
print("use TensorRT for latency test")
except:
from utils.darts_utils import compute_latency_ms_pytorch as compute_latency
print("use PyTorch for latency test")
import torch
import torch.nn as nn
import... | 10,508 | 37.214545 | 134 | py |
FasterSeg | FasterSeg-master/latency/run_latency.py | from __future__ import division
import os
import sys
import time
import glob
import logging
import torch
import numpy as np
from thop import profile
from config import config
config.save = 'latency-{}-{}'.format(config.save, time.strftime("%Y%m%d-%H%M%S"))
from utils.darts_utils import create_exp_dir, plot_op, plot... | 4,160 | 46.827586 | 262 | py |
FasterSeg | FasterSeg-master/latency/model_seg.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from operations import *
from genotypes import PRIMITIVES
from pdb import set_trace as bp
from seg_oprs import FeatureFusion, Head
BatchNorm2d = nn.BatchNorm2d
def softmax(x):
return np.exp(x) / (np.exp(x).sum() + np.spacing(1))... | 20,590 | 49.592138 | 238 | py |
FasterSeg | FasterSeg-master/latency/operations.py | __all__ = ['ConvNorm', 'BasicResidual1x', 'BasicResidual_downup_1x', 'BasicResidual2x', 'BasicResidual_downup_2x', 'FactorizedReduce', 'OPS', 'OPS_name', 'OPS_Class']
from pdb import set_trace as bp
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from thop import profile
import sy... | 24,110 | 43.321691 | 212 | py |
FasterSeg | FasterSeg-master/train/slimmable_ops.py | import torch.nn as nn
from pdb import set_trace as bp
def make_divisible(v, divisor=8, min_value=1):
"""
forked from slim:
https://github.com/tensorflow/models/blob/\
0344c5503ee55e24f0de7f37336a6e08f10976fd/\
research/slim/nets/mobilenet/mobilenet.py#L62-L69
"""
if min_value is None:
... | 2,807 | 38.549296 | 156 | py |
FasterSeg | FasterSeg-master/train/test.py | #!/usr/bin/env python3
# encoding: utf-8
import os
import time
import cv2
cv2.setNumThreads(0)
import torchvision
from PIL import Image
import argparse
import numpy as np
import torch
import torch.multiprocessing as mp
from utils.pyt_utils import ensure_dir, link_file, load_model, parse_devices
from utils.visualize i... | 7,399 | 35.27451 | 247 | py |
FasterSeg | FasterSeg-master/train/seg_oprs.py | import numpy as np
try:
from utils.darts_utils import compute_latency_ms_tensorrt as compute_latency
print("use TensorRT for latency test")
except:
from utils.darts_utils import compute_latency_ms_pytorch as compute_latency
print("use PyTorch for latency test")
import torch
import torch.nn as nn
import... | 10,508 | 37.214545 | 134 | py |
FasterSeg | FasterSeg-master/train/dataloader.py | import cv2
cv2.setNumThreads(0)
from torch.utils import data
from utils.img_utils import random_scale, random_mirror, normalize, generate_random_crop_pos, random_crop_pad_to_shape
class TrainPre(object):
def __init__(self, config, img_mean, img_std):
self.img_mean = img_mean
self.img_std = img_st... | 2,604 | 40.349206 | 181 | py |
FasterSeg | FasterSeg-master/train/loss.py | import torch.nn as nn
import torch.nn.functional as F
import torch
class CrossEntropyLoss2d(nn.Module):
def __init__(self, weight=None, size_average=True, ignore_index=-100):
super(CrossEntropyLoss2d, self).__init__()
self.nll_loss = nn.NLLLoss(weight, size_average, ignore_index)
def forward(... | 3,234 | 38.938272 | 112 | py |
FasterSeg | FasterSeg-master/train/seg_metrics.py | import numpy as np
import torch
class Seg_Metrics(object):
def __init__(self, n_classes=19):
self.n_classes = n_classes
self.total_inter = np.zeros(n_classes)
self.total_union = np.zeros(n_classes)
def update(self, pred, target):
inter, union = batch_intersection_union(pred, t... | 3,565 | 34.66 | 90 | py |
FasterSeg | FasterSeg-master/train/model_seg.py | import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
from operations import *
from genotypes import PRIMITIVES
from pdb import set_trace as bp
from seg_oprs import FeatureFusion, Head
BatchNorm2d = nn.BatchNorm2d
def softmax(x):
return np.exp(x) / (np.exp(x).sum() + np.spacin... | 21,098 | 50.460976 | 238 | py |
FasterSeg | FasterSeg-master/train/train.py | from __future__ import division
import os
import sys
import time
import glob
import logging
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
from tensorboardX import SummaryWriter
import numpy as np
from thop import profile
from config_train import config
if... | 14,230 | 45.659016 | 262 | py |
FasterSeg | FasterSeg-master/train/operations.py | __all__ = ['ConvNorm', 'BasicResidual1x', 'BasicResidual_downup_1x', 'BasicResidual2x', 'BasicResidual_downup_2x', 'FactorizedReduce', 'OPS', 'OPS_name', 'OPS_Class']
from pdb import set_trace as bp
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from thop import profile
import sy... | 24,542 | 43.381555 | 212 | py |
FasterSeg | FasterSeg-master/search/slimmable_ops.py | import torch.nn as nn
from pdb import set_trace as bp
def make_divisible(v, divisor=8, min_value=1):
"""
forked from slim:
https://github.com/tensorflow/models/blob/\
0344c5503ee55e24f0de7f37336a6e08f10976fd/\
research/slim/nets/mobilenet/mobilenet.py#L62-L69
"""
if min_value is None:
... | 2,807 | 38.549296 | 156 | py |
FasterSeg | FasterSeg-master/search/seg_oprs.py | import numpy as np
try:
from utils.darts_utils import compute_latency_ms_tensorrt as compute_latency
print("use TensorRT for latency test")
except:
from utils.darts_utils import compute_latency_ms_pytorch as compute_latency
print("use PyTorch for latency test")
import torch
import torch.nn as nn
import... | 10,527 | 37.283636 | 134 | py |
FasterSeg | FasterSeg-master/search/architect.py | import torch
import numpy as np
from torch import nn
from torch.autograd import Variable
from pdb import set_trace as bp
from operations import *
def _concat(xs):
return torch.cat([x.view(-1) for x in xs])
class Architect(object):
def __init__(self, model, args, distill=False):
self.network_momentu... | 5,591 | 42.348837 | 136 | py |
FasterSeg | FasterSeg-master/search/dataloader.py | import cv2
cv2.setNumThreads(0)
from torch.utils import data
from utils.img_utils import random_scale, random_mirror, normalize, generate_random_crop_pos, random_crop_pad_to_shape
class TrainPre(object):
def __init__(self, config, img_mean, img_std):
self.img_mean = img_mean
self.img_std = img_st... | 2,319 | 39 | 181 | py |
FasterSeg | FasterSeg-master/search/loss.py | import torch.nn as nn
import torch.nn.functional as F
import torch
class CrossEntropyLoss2d(nn.Module):
def __init__(self, weight=None, size_average=True, ignore_index=-100):
super(CrossEntropyLoss2d, self).__init__()
self.nll_loss = nn.NLLLoss(weight, size_average, ignore_index)
def forward(... | 3,235 | 38.463415 | 112 | py |
FasterSeg | FasterSeg-master/search/seg_metrics.py | # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Created by: Hang Zhang
# ECE Department, Rutgers University
## Email: zhang.hang@rutgers.edu
# Copyright (c) 2017
##
# This source code is licensed under the MIT-style license found in the
# LICENSE file in the root directory of this source t... | 3,966 | 35.063636 | 90 | py |
FasterSeg | FasterSeg-master/search/model_search.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from operations import *
from torch.autograd import Variable
from genotypes import PRIMITIVES
# from utils.darts_utils import drop_path, compute_speed, compute_speed_tensorrt
from pdb import set_trace as bp
from seg_oprs import Head
import numpy as np
... | 28,684 | 51.249545 | 204 | py |
FasterSeg | FasterSeg-master/search/train_search.py | from __future__ import division
import os
import sys
import time
import glob
import logging
from tqdm import tqdm
from random import shuffle
import torch
import torch.nn as nn
import torch.utils
from tensorboardX import SummaryWriter
import numpy as np
import matplotlib
# Force matplotlib to not use any Xwindows back... | 15,633 | 49.75974 | 252 | py |
FasterSeg | FasterSeg-master/search/model_seg.py | import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
from operations import *
from genotypes import PRIMITIVES
from pdb import set_trace as bp
from seg_oprs import FeatureFusion, Head
BatchNorm2d = nn.BatchNorm2d
def softmax(x):
return np.exp(x) / (np.exp(x).sum() + np.spacin... | 20,345 | 49.61194 | 238 | py |
FasterSeg | FasterSeg-master/search/operations.py | __all__ = ['ConvNorm', 'BasicResidual1x', 'BasicResidual_downup_1x', 'BasicResidual2x', 'BasicResidual_downup_2x', 'FactorizedReduce', 'OPS', 'OPS_name', 'OPS_Class']
from pdb import set_trace as bp
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from thop import profile
import sy... | 24,561 | 43.415913 | 212 | py |
TCL | TCL-master/main.py | # -*- coding: UTF-8 -*-
'''
@Project : ProPos
@File : main.py
@Author : Zhizhong Huang from Fudan University
@Homepage: https://hzzone.github.io/
@Email : zzhuang19@fudan.edu.cn
@Date : 2022/10/19 9:24 PM
'''
import torch
import torch.distributed as dist
import numpy as np
import random
from PIL import Imag... | 1,789 | 29.862069 | 93 | py |
TCL | TCL-master/models/basic_template.py | from __future__ import print_function
import os
import os.path as osp
import argparse
import warnings
import torch
import torchvision.datasets
from torchvision import transforms
import numpy as np
import torch.distributed as dist
import tqdm
from utils import TwoCropTransform, extract_features
from utils.ops import ... | 23,637 | 41.66787 | 119 | py |
TCL | TCL-master/models/tcl/tcl.py | import matplotlib.pyplot as plt
import torch
import argparse
import copy
import torch.nn.functional as F
import tqdm
import numpy as np
import torch.nn as nn
import torch.distributed as dist
from models.basic_template import TrainTask
from network import backbone_dict
from .tcl_wrapper import SimCLRWrapper
from utils.... | 11,090 | 40.695489 | 115 | py |
TCL | TCL-master/models/tcl/tcl_plus.py | import torch
import argparse
import copy
import torch.nn as nn
from models.basic_template import TrainTask
from network import backbone_dict
from utils.ops import convert_to_ddp, convert_to_cuda, load_network
from models import model_dict
@model_dict.register('tcl_plus')
class TCL(TrainTask):
def set_model(self... | 6,096 | 38.590909 | 122 | py |
TCL | TCL-master/models/tcl/tcl_wrapper.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def mixup(input, alpha=1.0):
bs = input.size(0)
randind = torch.randperm(bs).to(input.device)
# beta = torch.distributions.beta.Beta(alpha, alpha)
# lam = beta.sample([bs]).to(input.device)
import numpy as np
lam = np.random.be... | 8,774 | 41.391304 | 116 | py |
TCL | TCL-master/models/tcl/data/create_noise.py | import numpy as np
import torchvision
################# Symmetric noise #########################
def random_in_noise(targets, noise_ratio=0.2):
targets = np.copy(np.array(targets))
num_classes = len(np.unique(targets))
_num = int(len(targets) * noise_ratio)
clean_labels = np.copy(targets)
# to b... | 2,397 | 39.644068 | 118 | py |
TCL | TCL-master/network/resnet.py | # -*- coding: UTF-8 -*-
'''
@Project : ProPos
@File : resnet18.py
@Author : Zhizhong Huang from Fudan University
@Homepage: https://hzzone.github.io/
@Email : zzhuang19@fudan.edu.cn
@Date : 2022/10/19 9:25 PM
'''
import torch.nn as nn
from torchvision.models import resnet
from torchvision.models.resnet impo... | 4,342 | 30.933824 | 114 | py |
TCL | TCL-master/network/preact_resnet.py | # -*- coding: UTF-8 -*-
'''
@Project : ProPos
@File : preact_resnet.py
@Author : Zhizhong Huang from Fudan University
@Homepage: https://hzzone.github.io/
@Email : zzhuang19@fudan.edu.cn
@Date : 2022/10/19 9:25 PM
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_planes... | 3,999 | 32.613445 | 103 | py |
TCL | TCL-master/utils/multicrop_transform.py | # -*- coding: UTF-8 -*-
'''
@Project : ProPos
@File : multicrop_transform.py
@Author : Zhizhong Huang from Fudan University
@Homepage: https://hzzone.github.io/
@Email : zzhuang19@fudan.edu.cn
@Date : 2022/10/19 9:24 PM
'''
import random
import cv2
from PIL import ImageFilter
import numpy as np
import torch... | 1,461 | 29.458333 | 72 | py |
TCL | TCL-master/utils/sampler.py | import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler
class RandomSampler(Sampler):
def __init__(self, dataset=None, batch_size=0, num_iter=None, restore_iter=0,
weights=None, replacement=True, seed=0, shuffle=True, num_replicas=None, rank=None):
supe... | 2,429 | 36.96875 | 117 | py |
TCL | TCL-master/utils/optimizers.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.optim import * # noqa: F401,F403
from torch.optim.optimizer import Optimizer
class LARS(Optimizer):
"""Implements layer-wise adaptive rate scaling for SGD.
Args:
params (iterable): Iterable of parameters to optimize or dicts def... | 4,728 | 35.658915 | 79 | py |
TCL | TCL-master/utils/knn_monitor.py | # -*- coding: UTF-8 -*-
'''
@Project : ProPos
@File : knn_monitor.py
@Author : Zhizhong Huang from Fudan University
@Homepage: https://hzzone.github.io/
@Email : zzhuang19@fudan.edu.cn
@Date : 2022/10/19 9:23 PM
'''
import torch
import tqdm
import torch.nn.functional as F
import torch.distributed as dist
... | 2,388 | 35.19697 | 115 | py |
TCL | TCL-master/utils/grad_scaler.py | # -*- coding: UTF-8 -*-
'''
@Project : ICLR2022_Codes
@File : grad_scaler.py
@Author : Zhizhong Huang from Fudan University
@Homepage: https://hzzone.github.io/
@Email : zzhuang19@fudan.edu.cn
@Date : 2022/1/18 8:29 PM
'''
import torch
from torch._six import inf
def get_grad_norm_(parameters, norm_type: fl... | 2,497 | 32.306667 | 117 | py |
TCL | TCL-master/utils/loggerx.py | # -*- coding: UTF-8 -*-
'''
@Project : ProPos
@File : loggerx.py
@Author : Zhizhong Huang from Fudan University
@Homepage: https://hzzone.github.io/
@Email : zzhuang19@fudan.edu.cn
@Date : 2022/10/19 8:47 PM
'''
import torch
import numpy as np
from torch import nn
import torch
import torch.nn.functional as F... | 4,557 | 31.791367 | 116 | py |
TCL | TCL-master/utils/gather_layer.py | # -*- coding: UTF-8 -*-
'''
@Project : ProPos
@File : gater_layer.py
@Author : Zhizhong Huang from Fudan University
@Homepage: https://hzzone.github.io/
@Email : zzhuang19@fudan.edu.cn
@Date : 2022/10/19 9:22 PM
'''
import torch
import torch.distributed as dist
class GatherLayer(torch.autograd.Function):
... | 937 | 27.424242 | 106 | py |
TCL | TCL-master/utils/infonce.py | import torch
import torch.nn.functional as F
import torch.nn as nn
def mask_correlated_samples(batch_size):
N = 2 * batch_size
mask = torch.ones((N, N))
mask = mask.fill_diagonal_(0)
for i in range(batch_size):
mask[i, batch_size + i] = 0
mask[batch_size + i, i] = 0
mask = mask.boo... | 2,521 | 30.924051 | 77 | py |
TCL | TCL-master/utils/__init__.py | # -*- coding: UTF-8 -*-
'''
@Project : ProPos
@File : __init__.py
@Author : Zhizhong Huang from Fudan University
@Homepage: https://hzzone.github.io/
@Email : zzhuang19@fudan.edu.cn
@Date : 2022/10/19 9:22 PM
'''
from torchvision import transforms
from PIL import Image
import torch
import torch.nn.function... | 4,559 | 28.230769 | 85 | py |
TCL | TCL-master/utils/ops.py | # -*- coding: UTF-8 -*-
'''
@Project : ProPos
@File : ops.py
@Author : Zhizhong Huang from Fudan University
@Homepage: https://hzzone.github.io/
@Email : zzhuang19@fudan.edu.cn
@Date : 2022/10/19 8:43 PM
'''
import math
from torch import nn
import torch
from typing import Union
import torch.distributed as d... | 3,941 | 31.04878 | 92 | py |
giraffe | giraffe-main/render.py | import torch
import os
import argparse
from im2scene import config
from im2scene.checkpoints import CheckpointIO
parser = argparse.ArgumentParser(
description='Render images of a GIRAFFE model.'
)
parser.add_argument('config', type=str, help='Path to config file.')
parser.add_argument('--no-cuda', action='store_tr... | 1,053 | 29.114286 | 78 | py |
giraffe | giraffe-main/eval_files.py | import torch
import os
import argparse
import numpy as np
from im2scene.eval import (
calculate_activation_statistics, calculate_frechet_distance)
from torchvision.utils import save_image, make_grid
parser = argparse.ArgumentParser(
description='Evaluate your own generated images (see ReadMe for more\
... | 1,591 | 31.489796 | 79 | py |
giraffe | giraffe-main/eval.py | import torch
import os
import argparse
from tqdm import tqdm
import time
from im2scene import config
from im2scene.checkpoints import CheckpointIO
import numpy as np
from im2scene.eval import (
calculate_activation_statistics, calculate_frechet_distance)
from math import ceil
from torchvision.utils import save_imag... | 2,412 | 28.790123 | 79 | py |
giraffe | giraffe-main/train.py | import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
import numpy as np
import os
import argparse
import time
from im2scene import config
from im2scene.checkpoints import CheckpointIO
import logging
logger_py = logging.getLogger(__name__)
np.random.seed(0)
torch.manual_seed(0)
# A... | 6,822 | 36.284153 | 79 | py |
giraffe | giraffe-main/scripts/precalc_fid.py | #!/usr/bin/env python3
'''
Code is mainly adopted from :
https://github.com/mseitzer/pytorch-fid/blob/master/src/pytorch_fid/fid_score.py
'''
"""Calculates the Frechet Inception Distance (FID) to evalulate GANs
The FID metric calculates the distance between two distributions of images.
Typically, we have summary stat... | 10,559 | 39.772201 | 146 | py |
giraffe | giraffe-main/im2scene/camera.py | import numpy as np
import torch
from scipy.spatial.transform import Rotation as Rot
def get_camera_mat(fov=49.13, invert=True):
# fov = 2 * arctan( sensor / (2 * focal))
# focal = (sensor / 2) * 1 / (tan(0.5 * fov))
# in our case, sensor = 2 as pixels are in [-1, 1]
focal = 1. / np.tan(0.5 * fov * np... | 4,092 | 29.544776 | 79 | py |
giraffe | giraffe-main/im2scene/inception.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
try:
from torchvision.models.utils import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
# ACKNOWLEDGEMENT: Code adapted from : https://github.com/mseitzer... | 12,262 | 35.60597 | 126 | py |
giraffe | giraffe-main/im2scene/checkpoints.py | import os
import urllib
import torch
from torch.utils import model_zoo
import shutil
import datetime
class CheckpointIO(object):
''' CheckpointIO class.
It handles saving and loading checkpoints.
Args:
checkpoint_dir (str): path where checkpoints are saved
'''
def __init__(self, checkpo... | 3,613 | 28.622951 | 79 | py |
giraffe | giraffe-main/im2scene/training.py | from collections import defaultdict
from torch import autograd
import torch.nn.functional as F
import numpy as np
class BaseTrainer(object):
''' Base trainer class.
'''
def evaluate(self, *args, **kwargs):
''' Performs an evaluation.
'''
eval_list = defaultdict(list)
# fo... | 2,009 | 26.162162 | 66 | py |
giraffe | giraffe-main/im2scene/layers.py | import torch.nn as nn
import torch.nn.functional as F
from kornia.filters import filter2D
import torch
# Resnet Blocks
class ResnetBlockFC(nn.Module):
''' Fully connected ResNet Block class.
Args:
size_in (int): input dimension
size_out (int): output dimension
size_h (int): hidden dim... | 2,738 | 25.336538 | 79 | py |
giraffe | giraffe-main/im2scene/config.py | import yaml
from im2scene import data
from im2scene import gan2d, giraffe
import logging
import os
# method directory; for this project we only use giraffe
method_dict = {
'gan2d': gan2d,
'giraffe': giraffe,
}
# General config
def load_config(path, default_path=None):
''' Loads config file.
Args:
... | 4,421 | 27.714286 | 78 | py |
giraffe | giraffe-main/im2scene/common.py | import torch
import numpy as np
import logging
logger_py = logging.getLogger(__name__)
def arange_pixels(resolution=(128, 128), batch_size=1, image_range=(-1., 1.),
subsample_to=None, invert_y_axis=False):
''' Arranges pixels for given resolution in range image_range.
The function returns t... | 7,423 | 33.530233 | 83 | py |
giraffe | giraffe-main/im2scene/eval.py | import numpy as np
import torch
from scipy import linalg
from torch.nn.functional import adaptive_avg_pool2d
from PIL import Image
from tqdm import tqdm
from im2scene.inception import InceptionV3
'''
NOTE: The code is largely adopted from:
https://github.com/mseitzer/pytorch-fid/blob/master/pytorch_fid/fid_score.py
''... | 7,228 | 36.455959 | 85 | py |
giraffe | giraffe-main/im2scene/discriminator/conv.py | import torch.nn as nn
from math import log2
from im2scene.layers import ResnetBlock
class DCDiscriminator(nn.Module):
''' DC Discriminator class.
Args:
in_dim (int): input dimension
n_feat (int): features of final hidden layer
img_size (int): input image size
'''
def __init__(... | 2,661 | 28.577778 | 70 | py |
giraffe | giraffe-main/im2scene/gan2d/training.py | from im2scene.training import (
toggle_grad, compute_grad2, compute_bce, update_average)
from torchvision.utils import save_image, make_grid
from im2scene.eval import (
calculate_activation_statistics, calculate_frechet_distance)
import os
import torch
from im2scene.training import BaseTrainer
from tqdm import ... | 5,934 | 29.280612 | 79 | py |
giraffe | giraffe-main/im2scene/gan2d/config.py | import os
from im2scene.discriminator import discriminator_dict
from im2scene.gan2d import models, training
from torch import randn
from copy import deepcopy
import numpy as np
def get_model(cfg, device=None, len_dataset=0, **kwargs):
''' Returns the model.
Args:
cfg (dict): imported yaml config
... | 2,384 | 31.671233 | 77 | py |
giraffe | giraffe-main/im2scene/gan2d/models/__init__.py | import torch.nn as nn
from im2scene.gan2d.models import generator
generator_dict = {
'simple': generator.Generator,
}
class GAN2D(nn.Module):
''' 2D-GAN model class.
Args:
device (device): torch device
discriminator (nn.Module): discriminator network
generator (nn.Module): gener... | 1,678 | 25.650794 | 71 | py |
giraffe | giraffe-main/im2scene/gan2d/models/generator.py | import torch.nn as nn
import torch.nn.functional as F
import torch
import numpy as np
from im2scene.layers import ResnetBlock
'''
ACKNOWLEDGEMENT: This code is largely adopted from:
https://github.com/LMescheder/GAN_stability
'''
def actvn(x):
out = F.leaky_relu(x, 2e-1)
return out
class Generator(nn.Modul... | 1,753 | 23.704225 | 70 | py |
giraffe | giraffe-main/im2scene/giraffe/training.py | from im2scene.eval import (
calculate_activation_statistics, calculate_frechet_distance)
from im2scene.training import (
toggle_grad, compute_grad2, compute_bce, update_average)
from torchvision.utils import save_image, make_grid
import os
import torch
from im2scene.training import BaseTrainer
from tqdm import ... | 6,247 | 29.778325 | 79 | py |
giraffe | giraffe-main/im2scene/giraffe/config.py | import os
from im2scene.discriminator import discriminator_dict
from im2scene.giraffe import models, training, rendering
from copy import deepcopy
import numpy as np
def get_model(cfg, device=None, len_dataset=0, **kwargs):
''' Returns the giraffe model.
Args:
cfg (dict): imported yaml config
... | 4,152 | 33.89916 | 77 | py |
giraffe | giraffe-main/im2scene/giraffe/rendering.py | import torch
import numpy as np
from im2scene.common import interpolate_sphere
from torchvision.utils import save_image, make_grid
import imageio
from math import sqrt
from os import makedirs
from os.path import join
class Renderer(object):
''' Render class for GIRAFFE.
It provides functions to render the r... | 23,026 | 37.314476 | 79 | py |
giraffe | giraffe-main/im2scene/giraffe/models/bounding_box_generator.py | import numpy as np
import torch.nn as nn
import torch
from scipy.spatial.transform import Rotation as Rot
from im2scene.camera import get_rotation_matrix
class BoundingBoxGenerator(nn.Module):
''' Bounding box generator class
Args:
n_boxes (int): number of bounding boxes (excluding background)
... | 6,719 | 41.531646 | 124 | py |
giraffe | giraffe-main/im2scene/giraffe/models/decoder.py |
import torch.nn as nn
import torch.nn.functional as F
import torch
import numpy as np
from numpy import pi
class Decoder(nn.Module):
''' Decoder class.
Predicts volume density and color from 3D location, viewing
direction, and latent code z.
Args:
hidden_size (int): hidden size of Decoder n... | 6,603 | 40.534591 | 79 | py |
giraffe | giraffe-main/im2scene/giraffe/models/__init__.py | import torch.nn as nn
from im2scene.giraffe.models import (
decoder, generator, bounding_box_generator, neural_renderer)
# Dictionaries
decoder_dict = {
'simple': decoder.Decoder,
}
generator_dict = {
'simple': generator.Generator,
}
background_generator_dict = {
'simple': decoder.Decoder,
}
boundi... | 1,914 | 23.551282 | 73 | py |
giraffe | giraffe-main/im2scene/giraffe/models/neural_renderer.py | import torch.nn as nn
import torch
from math import log2
from im2scene.layers import Blur
class NeuralRenderer(nn.Module):
''' Neural renderer class
Args:
n_feat (int): number of features
input_dim (int): input dimension; if not equal to n_feat,
it is projected to n_feat with a 1x... | 3,705 | 35.333333 | 78 | py |
giraffe | giraffe-main/im2scene/giraffe/models/generator.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from im2scene.common import (
arange_pixels, image_points_to_world, origin_to_world
)
import numpy as np
from scipy.spatial.transform import Rotation as Rot
from im2scene.camera import get_camera_mat, get_random_pose, get_camera_pose
class Generat... | 20,917 | 40.339921 | 79 | py |
giraffe | giraffe-main/im2scene/data/datasets.py | import os
import logging
from torch.utils import data
import numpy as np
import glob
from PIL import Image
from torchvision import transforms
import lmdb
import pickle
import string
import io
import random
# fix for broken images
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
logger = logging.getLogg... | 5,985 | 30.840426 | 76 | py |
NAT | NAT-master/derive.py | import os
import sys
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from search_model import NASNetwork as Network
import random
import genotypes
parser = argpar... | 5,624 | 45.487603 | 427 | py |
NAT | NAT-master/search_model.py | import genotypes
from operations import *
import utils
import numpy as np
from utils import arch_to_genotype, draw_genotype
import os
from pygcn.layers import GraphConvolution
class NASOp(nn.Module):
def __init__(self, C, stride, op_type):
super(NASOp, self).__init__()
self._ops = nn.ModuleList()
... | 17,400 | 44.197403 | 345 | py |
NAT | NAT-master/utils.py | import os
import numpy as np
import torch
import shutil
import torchvision.transforms as transforms
from torch.autograd import Variable
import itertools
from genotypes import LOOSE_END_PRIMITIVES, FULLY_CONCAT_PRIMITIVES, TRANSFORM_PRIMITIVES, Genotype
from graphviz import Digraph
from collections import defaultdict
im... | 11,899 | 28.67581 | 110 | py |
NAT | NAT-master/nat_learner.py | import torch
class Transformer(object):
def __init__(self, model, args):
self.args = args
self.model = model
self.optimizer = torch.optim.Adam(self.model.transformer_parameters(),
lr=args.transformer_learning_rate, betas=(0.5, 0.999),
... | 934 | 32.392857 | 133 | py |
NAT | NAT-master/train_search.py | import os
import sys
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from search_model import NASNetwork as Network
from nat_learner import Transformer
import random... | 8,522 | 42.707692 | 427 | py |
NAT | NAT-master/evaluate_model.py | from operations import *
from utils import drop_path
class Cell(nn.Module):
def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev):
super(Cell, self).__init__()
print(C_prev_prev, C_prev, C)
if reduction_prev:
self.preprocess0 = FactorizedReduce(C_prev... | 7,604 | 34.872642 | 96 | py |
NAT | NAT-master/operations.py | import torch
import torch.nn as nn
import torch.nn.functional as F
OPS = {
'null': lambda C, stride, affine: Zero(stride),
'avg_pool_2x2': lambda C, stride, affine: nn.AvgPool2d(2, stride=stride, padding=0, count_include_pad=False),
'avg_pool_3x3': lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, p... | 5,289 | 41.66129 | 116 | py |
librosa | librosa-main/docs/conf.py | # -*- coding: utf-8 -*-
#
# librosa documentation build configuration file, created by
# sphinx-quickstart on Tue Jun 25 13:12:33 2013.
#
# 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.
#
# All... | 12,135 | 28.965432 | 97 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/data.py | from __future__ import print_function
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
import glob
import skimage.io as io
import skimage.transform as trans
Black = [0,0,0]
Red = [255,0,0]
Green = [0,255,0]
Blue = [0,0,255]
Sky = [128,128,128]
Building = [128,0,0]
Pole = [192,192... | 5,880 | 38.206667 | 217 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/xnet/model.py | from .builder import build_xnet
from ..utils import freeze_model
from ..backbones import get_backbone
DEFAULT_SKIP_CONNECTIONS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3', 'block2_conv2', 'block1_conv2',
'block5_pool', 'block4_pool', 'block3_pool', 'block2_pool... | 5,117 | 46.388889 | 115 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/xnet/builder.py | from keras.layers import Conv2D
from keras.layers import Activation
from keras.models import Model
from .blocks import Transpose2D_block
from .blocks import Upsample2D_block
from ..utils import get_layer_number, to_tuple
import copy
def build_xnet(backbone, classes, skip_connection_layers,
decoder_fi... | 7,554 | 41.926136 | 112 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/xnet/blocks.py | from keras.layers import Conv2DTranspose
from keras.layers import UpSampling2D
from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Concatenate
def handle_block_names(stage, cols):
conv_name = 'decoder_stage{}-{}_conv'.format(stag... | 3,112 | 38.910256 | 106 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/backbones/preprocessing.py | """
Image pre-processing functions.
Images are assumed to be read in uint8 format (range 0-255).
"""
from keras.applications import vgg16
from keras.applications import vgg19
from keras.applications import densenet
from keras.applications import inception_v3
from keras.applications import inception_resnet_v2
identica... | 1,019 | 28.142857 | 62 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/backbones/inception_v3.py | # -*- coding: utf-8 -*-
"""Inception V3 model for Keras.
Note that the input image format for this model is different than for
the VGG16 and ResNet models (299x299 instead of 224x224),
and that the input preprocessing function is also different (same as Xception).
# Reference
- [Rethinking the Inception Architecture fo... | 15,272 | 36.898263 | 152 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/backbones/inception_resnet_v2.py | # -*- coding: utf-8 -*-
"""Inception-ResNet V2 model for Keras.
Model naming and structure follows TF-slim implementation (which has some additional
layers and different number of filters from the original arXiv paper):
https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py
Pre-train... | 16,002 | 42.134771 | 92 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/backbones/backbones.py |
from .classification_models.classification_models import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
from .classification_models.classification_models import ResNeXt50, ResNeXt101
from .inception_resnet_v2 import InceptionResNetV2
from .inception_v3 import InceptionV3
from keras.applications import DenseNet12... | 930 | 28.09375 | 107 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/backbones/classification_models/classification_models/utils.py | from keras.utils import get_file
def find_weights(weights_collection, model_name, dataset, include_top):
w = list(filter(lambda x: x['model'] == model_name, weights_collection))
w = list(filter(lambda x: x['dataset'] == dataset, w))
w = list(filter(lambda x: x['include_top'] == include_top, w))
return... | 1,263 | 38.5 | 91 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/backbones/classification_models/classification_models/resnext/builder.py | import keras.backend as K
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import GlobalAveragePooling2D
from keras.layers import ZeroPadding2D
from keras.layers import D... | 3,364 | 31.355769 | 92 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/backbones/classification_models/classification_models/resnext/blocks.py | from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Add
from keras.layers import Lambda
from keras.layers import Concatenate
from keras.layers import ZeroPadding2D
from .params import get_conv_params
from .params import get_bn_params
... | 4,292 | 36.657895 | 107 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/backbones/classification_models/classification_models/resnet/builder.py | import keras.backend as K
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import GlobalAveragePooling2D
from keras.layers import ZeroPadding2D
from keras.layers import D... | 3,750 | 32.491071 | 92 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/backbones/classification_models/classification_models/resnet/blocks.py | from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Add
from keras.layers import ZeroPadding2D
from .params import get_conv_params
from .params import get_bn_params
def handle_block_names(stage, block):
name_base = 'stage{}_unit... | 6,363 | 37.569697 | 100 | py |
ct-angel | ct-angel-master/ct-angel-train/unet_pp/backbones/classification_models/tests/test_imagenet.py | import numpy as np
from skimage.io import imread
from keras.applications.imagenet_utils import decode_predictions
import sys
sys.path.insert(0, '..')
from classification_models import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
from classification_models import ResNeXt50, ResNeXt101
from classification_models ... | 4,964 | 29.838509 | 135 | py |
harth-ml-experiments | harth-ml-experiments-main/experiments/deep_learning/src/data_generator.py | import os
import math
import tqdm
import numpy as np
import pandas as pd
import tensorflow as tf
from collections import Counter
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import Sequence
class AccelerometerDataGenerator(Sequence):
def __init__(self, subject_fi... | 6,733 | 38.846154 | 79 | py |
harth-ml-experiments | harth-ml-experiments-main/experiments/deep_learning/src/model.py | import os
import numpy as np
import tensorflow as tf
import tensorflow_addons as tfa
from datetime import datetime
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Conv2D, Conv1D
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers... | 12,249 | 39.03268 | 77 | py |
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