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
value |
|---|---|---|---|---|---|---|
DiffDVR | DiffDVR-master/pytests/tests/stepsize/vis_stepsize.py | import os
import sys
sys.path.append(os.getcwd())
import h5py
import tests.vis_gui
import torch
import numpy as np
import skimage.transform
import matplotlib.colors
import matplotlib.pyplot
import pyrenderer
class UIStepsize(tests.vis_gui.UI):
def __init__(self, folder):
keys = [
"filename"... | 4,080 | 35.765766 | 99 | py |
DiffDVR | DiffDVR-master/pytests/tests/camera/run_image1d.py | import numpy as np
import torch
import os
import matplotlib.pyplot as plt
from matplotlib import gridspec
import tqdm
import imageio
from diffdvr import Renderer, CameraOnASphere, Entropy, ColorMatches, Settings, setup_default_settings, \
fibonacci_sphere, renderer_dtype_torch, renderer_dtype_np, ProfileRenderer
f... | 13,803 | 41.473846 | 119 | py |
DiffDVR | DiffDVR-master/pytests/tests/camera/run_entropy2d.py | import numpy as np
import torch
import os
import matplotlib.pyplot as plt
from matplotlib import gridspec
import matplotlib.ticker as mticker
import matplotlib
import matplotlib.colors
import tqdm
import imageio
from diffdvr import Renderer, CameraOnASphere, Entropy, ColorMatches, Settings, setup_default_settings, \
... | 33,526 | 45.760112 | 149 | py |
DiffDVR | DiffDVR-master/pytests/tests/camera/test_camera_optimization.py | import numpy as np
import torch
import sys
import os
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import matplotlib.animation
import tqdm
# load pyrenderer
from diffdvr import renderer_dtype_torch
import pyrenderer
from vis import lossvis
from vis import cameravis
def make_real3(vector):
... | 10,423 | 37.043796 | 151 | py |
DiffDVR | DiffDVR-master/pytests/tests/camera/test_viewport_optimization.py | import numpy as np
import torch
import matplotlib.pyplot as plt
import matplotlib
import tqdm
import re
from typing import Optional
from diffdvr import Renderer, CameraOnASphere, Entropy, ColorMatches, Settings, setup_default_settings, \
fibonacci_sphere, renderer_dtype_torch, renderer_dtype_np
import pyrenderer
de... | 20,734 | 41.752577 | 124 | py |
DiffDVR | DiffDVR-master/pytests/tests/tf/test_tf_optimization.py | import numpy as np
import torch
import sys
import os
import matplotlib.pyplot as plt
import matplotlib.animation
import tqdm
# load pyrenderer
from diffdvr import make_real3
import pyrenderer
from vis import tfvis
# TF parameterization:
# color by Sigmoid, opacity by SoftPlus
class TransformTF(torch.nn.Module):
de... | 8,224 | 34 | 112 | py |
DiffDVR | DiffDVR-master/pytests/tests/tf/run_reconstruction.py |
import sys
import os
sys.path.insert(0, os.getcwd())
import numpy as np
import torch
import os
import matplotlib.pyplot as plt
from matplotlib import gridspec
from matplotlib.patches import Polygon
import tqdm
import imageio
from diffdvr import Renderer, CameraOnASphere, Settings, setup_default_settings, \
fibon... | 32,858 | 43.584803 | 146 | py |
DiffDVR | DiffDVR-master/pytests/tests/tf/train_styletransfer.py | """
Large hyperparameter training session
"""
import sys
import os
sys.path.append(os.getcwd())
import numpy as np
import torch
import os
import tqdm
import time
import h5py
import argparse
import json
from collections import defaultdict
import subprocess
import imageio
from diffdvr import Renderer, CameraOnASphere,... | 14,153 | 41.504505 | 130 | py |
DiffDVR | DiffDVR-master/pytests/tests/tf/train_meta.py | """
Large hyperparameter training session
"""
import sys
import os
sys.path.append(os.getcwd())
import numpy as np
import torch
import os
import tqdm
import time
import h5py
import argparse
import json
from collections import defaultdict
import subprocess
from diffdvr import Renderer, CameraOnASphere, Settings, setu... | 11,824 | 39.635739 | 111 | py |
DiffDVR | DiffDVR-master/pytests/diffdvr/renderer.py | import torch
import torch.nn as nn
import numpy as np
import time
from diffdvr.utils import implies
import pyrenderer
class ProfileRenderer:
def __init__(self):
self.forward_ms = 0.0
self.forward_bytes = 0
self.backward_ms = 0.0
class Timer:
def __init__(self, enable, cuda):
s... | 17,146 | 46.630556 | 140 | py |
DiffDVR | DiffDVR-master/pytests/diffdvr/settings.py | import torch
import numpy as np
import json
import os
from typing import Optional, NamedTuple
from diffdvr.utils import make_real3, renderer_dtype_torch, renderer_dtype_np
import pyrenderer
"""
Loads settings from .json file exported by the GUI
"""
class Settings:
def __init__(self, file):
self._filepat... | 7,446 | 39.254054 | 96 | py |
DiffDVR | DiffDVR-master/pytests/diffdvr/priors.py | import torch
from typing import Union, Sequence
class SmoothnessPrior(torch.nn.Module):
"""
n-dimensional smoothness prior loss.
For each dimension i, the value $\int (f'_i(x))^2 dx$ is computed,
i.e. the first derivative along dimension i, squared and summed/averaged over
the image
"""
def __init__(sel... | 1,083 | 29.111111 | 85 | py |
DiffDVR | DiffDVR-master/pytests/diffdvr/utils.py | import atexit
import torch
import sys
import os
import numpy as np
from typing import Tuple, Union
try:
import pyrenderer
except ModuleNotFoundError:
__newpath = os.path.abspath(os.path.join(os.path.split(__file__)[0], '../../bin'))
sys.path.append(__newpath)
print("Search pyrenderer in '%s'"%__newpath)
impo... | 2,830 | 30.808989 | 92 | py |
DiffDVR | DiffDVR-master/pytests/diffdvr/parametrizations.py | import torch
import torch.nn.functional as F
import numpy as np
from typing import Sequence
from diffdvr.utils import inverseSigmoid, inverseSoftplus
import pyrenderer
class VolumeDensities(torch.nn.Module):
"""
Default parametrization of the density volume:
The input which is optimized for is in the full... | 6,979 | 35.165803 | 86 | py |
DiffDVR | DiffDVR-master/pytests/diffdvr/__init__.py |
from .utils import make_real3, make_real4, \
inverseSigmoid, InverseSigmoid, \
inverseSoftplus, InverseSoftplus, \
implies, toCHW, fibonacci_sphere, \
renderer_dtype_torch, renderer_dtype_np, \
cvector_to_numpy
from .entropy import Entropy, ColorMatches
from .settings import Settings, setup_default_setting... | 524 | 25.25 | 65 | py |
DiffDVR | DiffDVR-master/pytests/diffdvr/entropy.py | import torch
import numpy as np
from typing import Optional, Union, Sequence
import diffdvr.utils
import pyrenderer
class Entropy(torch.nn.Module):
"""
Computes the entropy of the input tensor:
$H(x) = sum_i (p_i log_2(p_i) )$ where $p_i$ is the input tensor.
"""
def __init__(self,
dim : Opti... | 3,647 | 32.163636 | 83 | py |
DiffDVR | DiffDVR-master/pytests/losses/ssim.py | # Source:
# https://github.com/jorge-pessoa/pytorch-msssim/blob/master/pytorch_msssim/__init__.py
import torch
import torch.nn.functional as F
from math import exp
import numpy as np
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size... | 4,707 | 32.15493 | 118 | py |
DiffDVR | DiffDVR-master/pytests/losses/tecogan.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
# Input: B x C=7 x W x H
# with B split in half between ground truth and prediction
# with C=7, first three layers: RGB of the prediction/ground truth.
# Last four layers: bilinear upscaled input image
class TecoGANDiscriminator... | 2,654 | 36.394366 | 102 | py |
DiffDVR | DiffDVR-master/pytests/losses/lossbuilder.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
import torchvision.models as models
from .ssim import MSSSIM, SSIM
import losses.lpips as lpips
class LossBuilder:
def __init__(self, device):
self.vgg_path = 'https://download.pytorch.... | 21,033 | 42.458678 | 140 | py |
DiffDVR | DiffDVR-master/pytests/losses/enhancenetlarge.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from .makelayers import _make_layers
# Input: B x C=7 x W x H
# with B split in half between ground truth and prediction
# with C=7, first three layers: RGB of the prediction/ground truth.
# Last four layers: bilinear upscaled ... | 2,222 | 37.327586 | 102 | py |
DiffDVR | DiffDVR-master/pytests/losses/enhancenetsmall.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from .makelayers import _make_layers
# Input: B x C=7 x W x H
# with B split in half between ground truth and prediction
# with C=7, first three layers: RGB of the prediction/ground truth.
# Last four layers: bilinear upscaled ... | 2,205 | 37.034483 | 102 | py |
DiffDVR | DiffDVR-master/pytests/losses/lpips/base_model.py | import os
import torch
from torch.autograd import Variable
class BaseModel():
def __init__(self):
pass;
def name(self):
return 'BaseModel'
def initialize(self, use_gpu=True, gpu_ids=[0]):
self.use_gpu = use_gpu
self.gpu_ids = gpu_ids
def forward(self):
... | 1,542 | 26.070175 | 77 | py |
DiffDVR | DiffDVR-master/pytests/losses/lpips/utils.py | import numpy as np
#from skimage.measure import compare_ssim
import torch
from torch.autograd import Variable
def normalize_tensor(in_feat,eps=1e-10):
norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1,keepdim=True))
return in_feat/(norm_factor+eps)
def l2(p0, p1, range=255.):
return .5*np.mean((p0 / ran... | 4,288 | 33.312 | 80 | py |
DiffDVR | DiffDVR-master/pytests/losses/lpips/pretrained_networks.py | from collections import namedtuple
import torch
from torchvision import models as tv
class squeezenet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(squeezenet, self).__init__()
pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features
self.sl... | 6,507 | 34.955801 | 109 | py |
DiffDVR | DiffDVR-master/pytests/losses/lpips/networks_basic.py |
from __future__ import absolute_import
import torch
import torch.nn as nn
from torch.autograd import Variable
from . import pretrained_networks as pn
from .utils import normalize_tensor, l2, tensor2tensorlab, tensor2np, tensor2im
def spatial_average(in_tens, keepdim=True):
return in_tens.mean([2,3],keepdim=kee... | 7,325 | 39.032787 | 134 | py |
DiffDVR | DiffDVR-master/pytests/losses/lpips/__init__.py |
import torch
from . import dist_model
#Source: https://github.com/richzhang/PerceptualSimilarity
class PerceptualLoss(torch.nn.Module):
def __init__(self, model='net-lin', net='alex', colorspace='rgb', spatial=False, use_gpu=True, gpu_ids=[0]): # VGG using our perceptually-learned weights (LPIPS metric)
#... | 1,398 | 33.975 | 172 | py |
DiffDVR | DiffDVR-master/pytests/losses/lpips/dist_model.py |
from __future__ import absolute_import
import os
from collections import OrderedDict
import numpy as np
import torch
from scipy.ndimage import zoom
from torch.autograd import Variable
from tqdm import tqdm
from . import networks_basic as networks
from .base_model import BaseModel
from .utils import voc_ap, tensor2i... | 11,636 | 40.709677 | 177 | py |
MoGPT | MoGPT-main/src/run_trainer_utterance_reordering.py | import datetime
import os
import pprint
from argparse import ArgumentParser
from pathlib import Path
import pytorch_lightning as pl
import torch
from huggingface_hub import Repository
from pytorch_lightning.loggers import WandbLogger
from transformers import AutoTokenizer
from transformers.utils import get_full_repo_n... | 16,616 | 39.137681 | 153 | py |
MoGPT | MoGPT-main/src/run_trainer_vanilla_gpt2.py | import datetime
import os
import pprint
from argparse import ArgumentParser
from pathlib import Path
import pytorch_lightning as pl
import torch
from huggingface_hub import Repository
from pytorch_lightning.loggers import WandbLogger
from transformers import AutoTokenizer
from transformers.utils import get_full_repo_n... | 13,800 | 36.810959 | 152 | py |
MoGPT | MoGPT-main/src/run_trainer_utterance_masking.py | import datetime
import os
import pprint
from argparse import ArgumentParser
from pathlib import Path
import pytorch_lightning as pl
import torch
import wandb
from huggingface_hub import Repository
from pytorch_lightning.loggers import WandbLogger
from transformers import AutoTokenizer
from transformers.utils import ge... | 16,784 | 39.155502 | 190 | py |
MoGPT | MoGPT-main/src/data_modules/base.py | import json
import os
from pathlib import Path
from typing import Callable, Dict, Optional, Union
import pytorch_lightning as pl
# from datasets import Dataset
from torch.utils.data import DataLoader, Dataset
from transformers import PreTrainedTokenizerBase
class LoadDataset(Dataset):
def __init__(
self... | 11,395 | 36.486842 | 161 | py |
MoGPT | MoGPT-main/src/data_modules/token_utterance_reordering.py | import copy
from itertools import chain
from itertools import repeat
from typing import Callable, Optional
from typing import Dict, List
import numpy as np
import torch
from torch.nn.utils.rnn import pad_sequence
from data_modules.base import TransformerDataModule
from data_modules.vanilla_gpt2 import DataCollatorWit... | 21,315 | 41.209901 | 163 | py |
MoGPT | MoGPT-main/src/data_modules/vanilla_gpt2.py | from itertools import chain
from itertools import repeat
from typing import Callable, Optional
from typing import Dict, List
import torch
from torch.nn.utils.rnn import pad_sequence
from data_modules.base import TransformerDataModule
class DataCollatorWithPadding:
def __init__(
self,
padding_ind... | 11,982 | 37.905844 | 153 | py |
MoGPT | MoGPT-main/src/data_modules/binary_utterance_masking.py | import copy
import numpy as np
import torch
from data_modules.base import TransformerDataModule
from data_modules.vanilla_gpt2 import DataCollatorWithPadding
from itertools import chain
from itertools import repeat
from nltk.corpus import wordnet
from torch.nn.utils.rnn import pad_sequence
from transformers import pipe... | 26,630 | 42.729064 | 176 | py |
MoGPT | MoGPT-main/src/data_modules/binary_utterance_reordering.py | import copy
from itertools import chain
from itertools import repeat
from typing import Callable, Optional
from typing import Dict, List
import numpy as np
import torch
from torch.nn.utils.rnn import pad_sequence
from data_modules.base import TransformerDataModule
from data_modules.vanilla_gpt2 import DataCollatorWit... | 21,490 | 41.556436 | 163 | py |
MoGPT | MoGPT-main/src/data_modules/token_utterance_masking.py | import copy
from itertools import chain
from itertools import repeat
from typing import Callable, Optional
from typing import Dict, List
import numpy as np
import torch
from torch.nn.utils.rnn import pad_sequence
from data_modules.base import TransformerDataModule
from data_modules.vanilla_gpt2 import DataCollatorWit... | 24,203 | 41.537786 | 176 | py |
MoGPT | MoGPT-main/src/models/base.py | from pathlib import Path
from typing import Any, Callable, Dict, IO, List, Optional, Tuple, Union
import pytorch_lightning as pl
import torch
import transformers
from pytorch_lightning.utilities import rank_zero_warn
from transformers import AutoConfig
from transformers import AutoModel
from transformers import PreTra... | 8,107 | 39.338308 | 127 | py |
MoGPT | MoGPT-main/src/models/lite_gpt2.py | import math
import torch
from transformers import AutoModel
from transformers import GPT2LMHeadModel
from models.base import LiteTransformer
class LiteGPT2LMHeadModel(LiteTransformer):
def __init__(
self,
downstream_model_type: AutoModel = GPT2LMHeadModel,
*args,
**kwargs
) ... | 3,182 | 28.747664 | 84 | py |
MoGPT | MoGPT-main/src/models/gpt2_dh.py | from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint
from torch.nn import CrossEntropyLoss
from transformers.activations import gelu
from transformers.models.gpt2.modeling_gpt2 import GPT2Model
from transformers.models.gpt2.modelin... | 8,390 | 38.21028 | 119 | py |
MoGPT | MoGPT-main/src/models/lite_gpt2_dh.py | import math
import torch
from transformers import AutoModel
from models.base import LiteTransformer
from models.gpt2_dh import GPT2DoubleHeadsModel
class LiteGPT2DoubleHeadsModel(LiteTransformer):
def __init__(
self,
*args,
downstream_model_type: AutoModel = GPT2DoubleHeadsModel,
... | 4,061 | 33.423729 | 94 | py |
MoGPT | MoGPT-main/src/utils/callbacks.py | import json
import os
import sys
from argparse import Namespace
from typing import Optional
import pytorch_lightning as pl
from huggingface_hub import Repository
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.loggers import WandbLogger
from transformers import PreTrainedTokenizerBase
class G... | 3,294 | 38.698795 | 143 | py |
MoGPT | MoGPT-main/src/utils/deepspeed.py | # Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | 1,176 | 34.666667 | 110 | py |
MoGPT | MoGPT-main/src/utils/__init__.py | import torch
from nltk.corpus import wordnet
def word_synonym(token, sentence=None, by="None", model=None, tokenizer=None, device="cpu", topk=5):
synonyms = []
antonyms = []
if by.upper() == "TRANSFORMER":
model = model
sent_token = tokenizer.tokenize(sentence)
mask_index = sent_t... | 928 | 34.730769 | 100 | py |
MoGPT | MoGPT-main/src/utils/imports.py | # Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | 1,047 | 44.565217 | 113 | py |
EMSAFormer | EMSAFormer-main/main.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Soehnke Fischedick <soehnke-benedikt.fischedick@tu-ilmenau.de>
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
.. codeauthor:: Mona Koehler <mona.koehler@tu-ilmenau.de>
"""
from typing import Tuple
from copy import deepcopy
from datetime import datetime
impor... | 26,219 | 37.110465 | 80 | py |
EMSAFormer | EMSAFormer-main/inference_dataset.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
from copy import deepcopy
from datetime import datetime
from functools import partial
import getpass
import json
import os
from pprint import pprint
import sys
from time import time
import warnings
import cv2
import numpy... | 30,738 | 38.05845 | 153 | py |
EMSAFormer | EMSAFormer-main/inference_samples.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Mona Koehler <mona.koehler@tu-ilmenau.de>
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
.. codeauthor:: Soehnke Fischedick <soehnke-benedikt.fischedick@tu-ilmenau.de>
"""
from glob import glob
import os
import cv2
import matplotlib.pyplot as plt
import nump... | 8,393 | 31.534884 | 96 | py |
EMSAFormer | EMSAFormer-main/emsaformer/lr_scheduler.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
from torch.optim.lr_scheduler import OneCycleLR
KNOWN_LR_SCHEDULERS = ('onecycle', )
LrSchedulerType = OneCycleLR
def get_lr_scheduler(args, optimizer) -> LrSchedulerType:
name = args.learning_rate_scheduler
n_... | 818 | 23.088235 | 70 | py |
EMSAFormer | EMSAFormer-main/emsaformer/preprocessing.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Soehnke Fischedick <soehnke-benedikt.fischedick@tu-ilmenau.de>
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
from typing import Optional, Tuple
from nicr_mt_scene_analysis.data.preprocessing import CloneEntries
from nicr_mt_scene_analysis.data.preproces... | 9,116 | 37.795745 | 84 | py |
EMSAFormer | EMSAFormer-main/emsaformer/weights.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
import torch
from nicr_scene_analysis_datasets import ScanNet
def load_weights(args, model, state_dict, verbose=True):
# this function accounts for:
# - renamed keys, e.g., fused_encoders.* -> encoder.*
# - m... | 5,946 | 47.349593 | 82 | py |
EMSAFormer | EMSAFormer-main/emsaformer/args.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Soehnke Fischedick <soehnke-benedikt.fischedick@tu-ilmenau.de>
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
.. codeauthor:: Mona Koehler <mona.koehler@tu-ilmenau.de>
"""
import argparse as ap
import json
import os
import shlex
import shutil
import socket
f... | 60,757 | 40.930987 | 93 | py |
EMSAFormer | EMSAFormer-main/emsaformer/model.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Soehnke Fischedick <soehnke-benedikt.fischedick@tu-ilmenau.de>
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
from typing import Any, Dict
from collections import ChainMap
from nicr_mt_scene_analysis.model.block import get_block_class
from nicr_mt_scene... | 9,460 | 39.431624 | 96 | py |
EMSAFormer | EMSAFormer-main/emsaformer/data.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Soehnke Fischedick <soehnke-benedikt.fischedick@tu-ilmenau.de>
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
from typing import Optional, Iterable, Tuple
from collections import OrderedDict
from copy import deepcopy
from dataclasses import asdict
from f... | 19,253 | 38.946058 | 87 | py |
EMSAFormer | EMSAFormer-main/emsaformer/decoder.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Soehnke Fischedick <soehnke-benedikt.fischedick@tu-ilmenau.de>
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
from typing import Tuple, Union
from torch import nn
from nicr_mt_scene_analysis.model.activation import get_activation_class
from nicr_mt_scen... | 9,414 | 45.608911 | 96 | py |
EMSAFormer | EMSAFormer-main/emsaformer/optimizer.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
from typing import Union
from torch.optim import Adam
from torch.optim import AdamW
from torch.optim import RAdam
from torch.optim import SGD
KNOWN_OPTIMIZERS = ('adam', 'adamw', 'radam', 'sgd')
OptimizerType = Union[Ad... | 1,424 | 22.75 | 63 | py |
EMSAFormer | EMSAFormer-main/emsaformer/tests/test_interface_emsaformer_model.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
.. codeauthor:: Soehnke Fischedick <soehnke-benedikt.fischedick@tu-ilmenau.de>
"""
import os
from nicr_mt_scene_analysis.testing.onnx import export_onnx_model
import pytest
import torch
from emsaformer.args import ArgParserEMS... | 6,571 | 36.554286 | 105 | py |
EMSAFormer | EMSAFormer-main/emsaformer/tests/test_interface_emsanet_model.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
import os
from nicr_mt_scene_analysis.testing.onnx import export_onnx_model
import pytest
import torch
from emsaformer.args import ArgParserEMSAFormer
from emsaformer.data import get_dataset
from emsaformer.model import EM... | 6,470 | 35.559322 | 86 | py |
EMSAFormer | EMSAFormer-main/emsaformer/tests/test_emsanet_model_weights.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Mona Koehler <mona.koehler@tu-ilmenau.de>
"""
from nicr_mt_scene_analysis.testing.onnx import export_onnx_model
import onnx
import torch
from emsaformer.args import ArgParserEMSAFormer
from emsaformer.data import get_datahelper
from emsaformer.model import EMSAFormer
def t... | 2,485 | 33.054795 | 80 | py |
EMSAFormer | EMSAFormer-main/emsaformer/tests/test_interface_preprocessing.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
from functools import partial
from nicr_mt_scene_analysis.data import mt_collate
from nicr_mt_scene_analysis.data import CollateIgnoredDict
from nicr_mt_scene_analysis.testing.preprocessing import show_results
from nicr_mt_... | 3,402 | 35.98913 | 75 | py |
EMSAFormer | EMSAFormer-main/emsaformer/tests/test_interface_decoders.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Soehnke Fischedick <soehnke-benedikt.fischedick@tu-ilmenau.de>
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
import os
import pytest
import torch
from nicr_mt_scene_analysis.testing.onnx import export_onnx_model
from emsaformer.args import ArgParserE... | 8,676 | 36.240343 | 79 | py |
EMSAFormer | EMSAFormer-main/emsaformer/tests/test_semantic_loss.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Mona Koehler <mona.koehler@tu-ilmenau.de>
"""
import numpy as np
import torch
from torch import nn
from nicr_mt_scene_analysis.loss.ce import CrossEntropyLossSemantic
DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
# copied from: https://github.com/TUI-NICR/ESAN... | 3,614 | 33.759615 | 80 | py |
EMSAFormer | EMSAFormer-main/emsaformer/tests/test_metrics_with_model.py | # -*- coding: utf-8 -*-
"""
.. codeauthor:: Soehnke Fischedick <soehnke-benedikt.fischedick@tu-ilmenau.de>
"""
import json
import os
import torch
import numpy as np
import pytest
import PIL.Image as Image
from tqdm import tqdm
from nicr_mt_scene_analysis import metric
from nicr_mt_scene_analysis.data import move_batc... | 9,468 | 39.465812 | 120 | py |
dilation | dilation-master/test.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function, division
import argparse
import caffe
import cv2
import numpy as np
import os
from os.path import exists, join, split, splitext
import network
import util
__author__ = 'Fisher Yu'
__copyright__ = 'Copyright (c) 2016, Fisher Yu'
__em... | 15,221 | 37.536709 | 90 | py |
dilation | dilation-master/network.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function, division
from caffe import layers as L
from caffe import params as P
__author__ = 'Fisher Yu'
__copyright__ = 'Copyright (c) 2016, Fisher Yu'
__email__ = 'i@yf.io'
__license__ = 'MIT'
def make_image_label_data(image_list_path, lab... | 8,068 | 40.80829 | 78 | py |
dilation | dilation-master/predict.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function, division
import argparse
import caffe
import cv2
import json
import numba
import numpy as np
from os.path import dirname, exists, join, splitext
import sys
import util
__author__ = 'Fisher Yu'
__copyright__ = 'Copyright (c) 2016, Fi... | 5,214 | 37.91791 | 80 | py |
dilation | dilation-master/train.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function, division
import argparse
import caffe
from caffe.proto import caffe_pb2
import os
from os.path import dirname, exists, join
import subprocess
import network
__author__ = 'Fisher Yu'
__copyright__ = 'Copyright (c) 2016, Fisher Yu'
_... | 9,918 | 37.898039 | 79 | py |
mvgrl | mvgrl-master/utils.py | import numpy as np
import networkx as nx
import torch
from scipy.linalg import fractional_matrix_power, inv
import scipy.sparse as sp
def compute_ppr(graph: nx.Graph, alpha=0.2, self_loop=True):
a = nx.convert_matrix.to_numpy_array(graph)
if self_loop:
a = a + np.eye(a.shape[0]) ... | 2,725 | 33.948718 | 97 | py |
mvgrl | mvgrl-master/node/train.py | import numpy as np
import scipy.sparse as sp
import torch
import torch.nn as nn
from utils import sparse_mx_to_torch_sparse_tensor
from node.dataset import load
# Borrowed from https://github.com/PetarV-/DGI
class GCN(nn.Module):
def __init__(self, in_ft, out_ft, bias=True):
super(GCN, self).__init__()
... | 8,554 | 27.708054 | 82 | py |
mvgrl | mvgrl-master/graph/train.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.model_selection import GridSearchCV, StratifiedKFold
from graph.dataset import load
class GCNLayer(nn.Module):
def __init__(self, in_ft, out_ft, bias=True):
super(GCNLayer, self).__init__()
self.fc =... | 10,865 | 30.314121 | 95 | py |
MLCVNet | MLCVNet-master/demo.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
""" Demo of using MLCVNet 3D object detector to detect objects from a point cloud.
"""
import os
import sys
import numpy as np
import argpar... | 4,080 | 40.642857 | 133 | py |
MLCVNet | MLCVNet-master/eval.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
""" Evaluation routine for 3D object detection with SUN RGB-D and ScanNet.
"""
import os
import sys
import numpy as np
from datetime import ... | 8,639 | 44.957447 | 153 | py |
MLCVNet | MLCVNet-master/train.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
""" Training routine for 3D object detection with SUN RGB-D or ScanNet.
Sample usage:
python train.py --dataset sunrgbd --log_dir log_sunrgb... | 14,735 | 43.385542 | 153 | py |
MLCVNet | MLCVNet-master/scannet/scannet_detection_dataset.py | # coding: utf-8
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
""" Dataset for object bounding box regression.
An axis aligned bounding box is parameterized by (cx,cy,cz) and (dx,dy,dz)
wh... | 10,395 | 45.204444 | 108 | py |
MLCVNet | MLCVNet-master/models/voting_module.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
''' Voting module: generate votes from XYZ and features of seed points.
Date: July, 2019
Author: Charles R. Qi and Or Litany
'''
import tor... | 3,026 | 39.36 | 93 | py |
MLCVNet | MLCVNet-master/models/dump_helper.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DI... | 6,654 | 48.664179 | 153 | py |
MLCVNet | MLCVNet-master/models/backbone_module.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import sys
import os
BASE_DIR = os.path.dirname(os.pat... | 4,912 | 33.356643 | 129 | py |
MLCVNet | MLCVNet-master/models/mlcvnet.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
""" Deep hough voting network for 3D object detection in point clouds.
Author: Charles R. Qi and Or Litany
"""
import torch
import torch.nn... | 5,057 | 34.87234 | 119 | py |
MLCVNet | MLCVNet-master/models/loss_helper.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import numpy as np
import sys
import os
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = o... | 12,245 | 47.788845 | 185 | py |
MLCVNet | MLCVNet-master/models/ap_helper.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
""" Helper functions and class to calculate Average Precisions for 3D object detection.
"""
import os
import sys
import numpy as np
import to... | 14,467 | 48.547945 | 177 | py |
MLCVNet | MLCVNet-master/models/CGNL.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 17 22:59:27 2019
@author: qian
"""
# Non-local block using embedded gaussian
# Code from
# https://github.com/AlexHex7/Non-local_pytorch/blob/master/Non-Local_pytorch_0.3.1/lib/non_local_embedded_gaussian.py
import math
import torch
from torch impo... | 10,949 | 29.082418 | 118 | py |
MLCVNet | MLCVNet-master/models/proposal_module.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import os
import sys
BASE_DIR = os.path.dirname(os.path... | 6,740 | 50.068182 | 217 | py |
MLCVNet | MLCVNet-master/pointnet2/setup.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
import glob
_ext_src_root = "_ext_src"
_ext... | 928 | 28.03125 | 83 | py |
MLCVNet | MLCVNet-master/pointnet2/pointnet2_utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
''' Modified based on: https://github.com/erikwijmans/Pointnet2_PyTorch '''
from __future__ import (
division,
absolute_import,
w... | 12,071 | 27.606635 | 144 | py |
MLCVNet | MLCVNet-master/pointnet2/pointnet2_test.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
''' Testing customized ops. '''
import torch
from torch.autograd import gradcheck
import numpy as np
import os
import sys
BASE_DIR = os.pat... | 1,011 | 28.764706 | 83 | py |
MLCVNet | MLCVNet-master/pointnet2/pointnet2_modules.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
''' Pointnet2 layers.
Modified based on: https://github.com/erikwijmans/Pointnet2_PyTorch
Extended with the following:
1. Uniform sampling in... | 17,609 | 32.930636 | 135 | py |
MLCVNet | MLCVNet-master/pointnet2/pytorch_utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
''' Modified based on Ref: https://github.com/erikwijmans/Pointnet2_PyTorch '''
import torch
import torch.nn as nn
from typing import List, T... | 7,501 | 24.090301 | 79 | py |
MLCVNet | MLCVNet-master/utils/tf_visualizer.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
'''Code adapted from https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix'''
import os
import time
BASE_DIR = os.path.dirname(os.path.absp... | 1,874 | 36.5 | 90 | py |
MLCVNet | MLCVNet-master/utils/metric_util.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
""" Utility functions for metric evaluation.
Author: Or Litany and Charles R. Qi
"""
import os
import sys
import torch
BASE_DIR = os.path.d... | 5,891 | 33.057803 | 106 | py |
MLCVNet | MLCVNet-master/utils/nn_distance.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
""" Chamfer distance in Pytorch.
Author: Charles R. Qi
"""
import torch
import torch.nn as nn
import numpy as np
def huber_loss(error, del... | 2,924 | 29.789474 | 89 | py |
pFedGate | pFedGate-main/run_experiment.py | """Run Experiment
This script allows to run one federated learning experiment; the experiment name, the method and the
number of clients/tasks should be precised along side with the hyper-parameters of the experiment.
The results of the experiment (i.e., training logs) are written to ./logs/ folder.
This file can al... | 11,307 | 36.197368 | 117 | py |
pFedGate | pFedGate-main/aggregator.py | import logging
import os
import time
import random
from abc import ABC, abstractmethod
from copy import deepcopy
import numpy as np
import numpy.linalg as LA
import wandb
from sklearn.metrics import pairwise_distances
from sklearn.cluster import AgglomerativeClustering
from utils.torch_utils import *
class Aggreg... | 28,793 | 32.716628 | 122 | py |
pFedGate | pFedGate-main/datasets.py | import os
import pickle
import string
import torch
from torchvision.datasets import CIFAR10, CIFAR100, EMNIST
from torchvision.transforms import Compose, ToTensor, Normalize
from torch.utils.data import Dataset
import numpy as np
from PIL import Image
class TabularDataset(Dataset):
"""
Constructs a torch.ut... | 11,379 | 25.588785 | 120 | py |
pFedGate | pFedGate-main/client.py | import logging
import torch.nn.functional as F
from copy import deepcopy
import wandb
from utils.torch_utils import *
class Client(object):
r"""Implements one clients
Attributes
----------
learners_ensemble
n_learners
train_iterator
val_iterator
test_iterator
train_loader
... | 8,689 | 30.258993 | 118 | py |
pFedGate | pFedGate-main/pFedGate/gated_learner.py | import copy
import pickle
import torch
from torch.nn.functional import gumbel_softmax
import numpy as np
from learners.learner import Learner
from models.knapsack_solver import KnapsackSolver01, KnapsackSolverFractional
from utils.sparse_factor_schedule import SparsityReduceLROnPlateauScheduler
from utils.torch_utils... | 24,477 | 51.527897 | 134 | py |
pFedGate | pFedGate-main/pFedGate/gate_aggregator.py | import copy
import logging
import os
import numpy as np
import torch
import wandb
from aggregator import Aggregator
from utils.torch_utils import average_model_of_learners, average_torch_modules, average_torch_state_dict_online, \
mean_torch_state_dict, \
copy_side_info, copy_model
class pFedGateAggregator(... | 30,279 | 56.348485 | 187 | py |
pFedGate | pFedGate-main/pFedGate/gated_client.py | import logging
import torch
import wandb
from client import Client
class pFedGateClient(Client):
r"""
Implements client for the proposed pFedGate method
"""
def __init__(
self,
learners_ensemble,
train_iterator,
val_iterator,
test_iterator... | 10,023 | 42.393939 | 123 | py |
pFedGate | pFedGate-main/models/nn_nets.py | import torch.autograd
import torch.nn.functional as F
import torchvision.models as models
import torch
from torch import nn
from torch.hub import load_state_dict_from_url
from models.adapted_op import AdaptedLinear
class DifferentiableRoundFun(torch.autograd.Function):
@staticmethod
def forward(ctx, input... | 1,702 | 25.609375 | 68 | py |
pFedGate | pFedGate-main/models/knapsack_solver.py | import numpy as np
import torch
import itertools
from numba import jit
class KnapsackSolver01(object):
"""
A knapsack problem solver implementation for 0-1 Knapsack with large Weights,
ref: https://www.geeksforgeeks.org/knapsack-with-large-weights/
time complexity: O(value_sum_max * item_num_max) = O... | 8,514 | 40.536585 | 118 | py |
pFedGate | pFedGate-main/models/adapted_op.py | """
Including adapted forward using adapted parameters, such that the gradients can bp to
gating layers that change the original model parameters
"""
from typing import Optional, Callable, List
import torch
from torch import nn, Tensor
from torch.nn import functional as F
from torch.nn.modules.utils import _pair
fr... | 9,275 | 43.171429 | 114 | py |
pFedGate | pFedGate-main/models/switchable_norm.py | # Switchable-Norm from official implementation
# https://github.com/switchablenorms/Switchable-Normalization/blob/master/devkit/ops/switchable_norm.py
import torch
import torch.nn as nn
class SwitchNorm1d(nn.Module):
def __init__(self, num_features, eps=1e-5, momentum=0.997, using_moving_average=True):
s... | 8,988 | 39.129464 | 104 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.