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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AMP | AMP-main/DeepSpeed/DeepSpeedExamples/Megatron-LM-v1.1.5-3D_parallelism/megatron/model/trans_gan_dis.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
from .ViT_helper import DropPath, to_2tuple, trunc_normal_
from .diff_aug import DiffAugment
from .utils import make_grid, save_image
from .ada import *
import scipy.signal
from torch_utils.ops import upfirdn2d
wavelets... | 27,407 | 46.336788 | 366 | py |
AMP | AMP-main/DeepSpeed/DeepSpeedExamples/Megatron-LM-v1.1.5-3D_parallelism/megatron/model/distributed.py | # coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# 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 re... | 5,039 | 43.60177 | 103 | py |
AMP | AMP-main/DeepSpeed/DeepSpeedExamples/Megatron-LM-v1.1.5-3D_parallelism/megatron/model/ViT_helper.py | import torch
from torch import nn
def drop_path(x, drop_prob: float = 0., training: bool = False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is mis... | 4,078 | 35.419643 | 108 | py |
AMP | AMP-main/DeepSpeed/DeepSpeedExamples/Megatron-LM-v1.1.5-3D_parallelism/megatron/model/fused_softmax.py | # coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# 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 re... | 4,981 | 37.921875 | 103 | py |
AMP | AMP-main/DeepSpeed/DeepSpeedExamples/Megatron-LM-v1.1.5-3D_parallelism/megatron/model/trans_gan_gen.py | from megatron import get_args
from megatron import mpu
from megatron.module import MegatronModule
import torch.nn.functional as F
import torch
import torch.nn as nn
import math
import numpy as np
from .ViT_helper import DropPath, to_2tuple, trunc_normal_
from .diff_aug import DiffAugment
import torch.utils.checkpoint ... | 61,139 | 43.791209 | 285 | py |
AMP | AMP-main/DeepSpeed/DeepSpeedExamples/Megatron-LM-v1.1.5-3D_parallelism/megatron/model/language_model.py | # coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# 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 re... | 15,339 | 38.740933 | 93 | py |
MagneticKP | MagneticKP-main/MagneticKP-python/magnetickp/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 2,029 | 33.40678 | 79 | py |
MAtt | MAtt-main/mAtt_bci.py | import torch
import torch.nn as nn
from utils.functions import trainNetwork, testNetwork
from mAtt.mAtt import mAtt_bci
from utils.GetBci2a import getAllDataloader
import os
import argparse
if __name__=='__main__':
ap = argparse.ArgumentParser()
ap.add_argument('--repeat', type=int, default=1, help='No.xxx re... | 1,944 | 45.309524 | 158 | py |
MAtt | MAtt-main/mAtt_mamem.py | import torch
import torch.nn as nn
from utils.functions import trainNetwork, testNetwork
from mAtt.mAtt import mAtt_mamem
from utils.GetMamem import getAllDataloader
import os
import argparse
if __name__=='__main__':
ap = argparse.ArgumentParser()
ap.add_argument('--repeat', type=int, default=1, help='No.xxx ... | 1,939 | 39.416667 | 154 | py |
MAtt | MAtt-main/mAtt_bcicha.py | import torch
import torch.nn as nn
from utils.functions import trainNetwork, testNetwork, testNetwork_auc
from mAtt.mAtt import mAtt_cha
from utils.GetBCIcha import getAllDataloader
import os
import argparse
if __name__=='__main__':
ap = argparse.ArgumentParser()
ap.add_argument('--repeat', type=int, default=... | 1,888 | 39.191489 | 154 | py |
MAtt | MAtt-main/utils/GetBCIcha.py | import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.utils.data as Data
from scipy import io
import os
# session 123 is training set, session4 is validation set, and session5 is testing set.
def getAllDataloader(subject, data_path='./data/B... | 1,879 | 25.857143 | 88 | py |
MAtt | MAtt-main/utils/functions.py | import torch
import torch.nn as nn
import sys
import os
sys.path.append("..")
from mAtt.optimizer import MixOptimizer
from sklearn.metrics import roc_auc_score as ras
import numpy as np
def trainNetwork(net, trainloader, validloader, testloader, model_path=None, iterations=500, lr=5*1e-4, wd=None, repeat=None, sub=No... | 2,868 | 31.602273 | 149 | py |
MAtt | MAtt-main/utils/GetMamem.py | import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.utils.data as Data
from scipy import io
import os
# session 123 is training set, session4 is validation set, and session5 is testing set.
def getAllDataloader(subject, ratio=8, data_path=... | 1,849 | 25.811594 | 88 | py |
MAtt | MAtt-main/utils/GetBci2a.py | import torch
import torch.utils.data as Data
from scipy import io
import numpy as np
import os
def split_train_valid_set(x_train, y_train, ratio):
s = y_train.argsort()
x_train = x_train[s]
y_train = y_train[s]
cL = int(len(x_train) / 4)
class1_x = x_train[ 0 * cL : 1 * cL ]
class2_x = x_trai... | 2,773 | 30.168539 | 93 | py |
MAtt | MAtt-main/mAtt/mAtt.py | import torch
import torch.nn as nn
from mAtt.spd import SPDTransform, SPDTangentSpace, SPDRectified
class signal2spd(nn.Module):
# convert signal epoch to SPD matrix
def __init__(self):
super().__init__()
self.dev = torch.device('cpu')
def forward(self, x):
x = x.squeeze()
... | 7,682 | 31.281513 | 128 | py |
MAtt | MAtt-main/mAtt/utils.py | import torch
import numpy as np
def symmetric(A):
size = list(range(len(A.shape)))
temp = size[-1]
size.pop()
size.insert(-1, temp)
return 0.5 * (A + A.permute(*size))
def is_nan_or_inf(A):
C1 = torch.nonzero(A == float('inf'))
C2 = torch.nonzero(A != A)
if len(C1.size()) > 0 or len(C2... | 1,809 | 25.231884 | 105 | py |
MAtt | MAtt-main/mAtt/spd.py | import torch
from torch import nn
from torch.optim.optimizer import Optimizer
from torch.autograd import Function
import numpy as np
from mAtt.utils import *
from mAtt import StiefelParameter
class SPDTransform(nn.Module):
def __init__(self, input_size, output_size):
super(SPDTransform, self).__init__()
... | 12,266 | 31.112565 | 118 | py |
MAtt | MAtt-main/mAtt/__init__.py | from torch import nn
class StiefelParameter(nn.Parameter):
"""A kind of Variable that is to be considered a module parameter on the space of
Stiefel manifold.
"""
def __new__(cls, data=None, requires_grad=True):
return super(StiefelParameter, cls).__new__(cls, data, requires_grad=requires_... | 413 | 33.5 | 91 | py |
PixelFolder | PixelFolder-main/calculate_fid.py | import argparse
import os
import torch
import torchvision
from torch_fidelity import calculate_metrics
import numpy as np
from ipdb import set_trace
import shutil
import model
from dataset import ImageDataset
from tensor_transforms import convert_to_coord_format
@torch.no_grad()
def calculate_fid(model, fid_dataset... | 3,784 | 40.593407 | 131 | py |
PixelFolder | PixelFolder-main/prepare_data.py | #!/usr/bin/env python3
from glob import glob
import os
from io import BytesIO
import argparse
import multiprocessing
from ipdb import set_trace
# import cv2
import lmdb
from tqdm import tqdm
from PIL import Image
from torchvision.transforms import functional as trans_fn
def format_for_lmdb(*args):
key_parts = []
... | 4,468 | 42.38835 | 106 | py |
PixelFolder | PixelFolder-main/dataset.py | __all__ = ['MultiScaleDataset',
'ImageDataset'
]
from io import BytesIO
import math
import lmdb
from PIL import Image
from torch.utils.data import Dataset
import torch
import numpy as np
from ipdb import set_trace
import tensor_transforms as tt
class MultiScaleDataset(Dataset):
def __init... | 3,579 | 27.870968 | 111 | py |
PixelFolder | PixelFolder-main/distributed.py | import math
import pickle
import torch
from torch import distributed as dist
from torch.utils.data.sampler import Sampler
def get_rank():
if not dist.is_available():
return 0
if not dist.is_initialized():
return 0
return dist.get_rank()
def synchronize():
if not dist.is_available()... | 2,714 | 20.547619 | 76 | py |
PixelFolder | PixelFolder-main/train.py | import argparse
import math
import random
import os
import numpy as np
import torch
from torch import nn, autograd, optim
from torch.nn import functional as F
from torch.utils import data
import torch.distributed as dist
from torchvision import transforms, utils
from torch.utils.tensorboard import SummaryWriter
from t... | 17,404 | 35.565126 | 125 | py |
PixelFolder | PixelFolder-main/tensor_transforms.py | import random
import torch
def convert_to_coord_format(b, h, w, device='cpu', integer_values=False):
if integer_values:
x_channel = torch.arange(w, dtype=torch.float, device=device).view(1, 1, 1, -1).repeat(b, 1, w, 1)
y_channel = torch.arange(h, dtype=torch.float, device=device).view(1, 1, -1, 1... | 1,277 | 29.428571 | 106 | py |
PixelFolder | PixelFolder-main/op/upfirdn2d.py | import os
import torch
from torch.autograd import Function
from torch.utils.cpp_extension import load
from torch.nn import functional as F
from ipdb import set_trace
module_path = os.path.dirname(__file__)
upfirdn2d_op = load(
'upfirdn2d',
sources=[
os.path.join(module_path, 'upfirdn2d.cpp'),
... | 6,562 | 27.785088 | 108 | py |
PixelFolder | PixelFolder-main/op/fused_act.py | import os
import torch
from torch import nn
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
fused = load(
'fused',
sources=[
os.path.join(module_path, 'fused_bias_act.cpp'),
os.path.join(module_path, 'fused_bias_act_kernel... | 2,379 | 26.356322 | 83 | py |
PixelFolder | PixelFolder-main/model/GeneratorStyleganv2.py | __all__ = ['GeneratorOriginal'
]
import math
import random
import torch
from torch import nn
from .blocks import ConstantInput, StyledConv, ToRGB, PixelNorm, EqualLinear
class GeneratorOriginal(nn.Module):
def __init__(
self,
size,
style_dim,
n_mlp,
channel_mul... | 5,663 | 26.629268 | 91 | py |
PixelFolder | PixelFolder-main/model/GeneratorsCIPS.py | __all__ = ['CIPSskip',
'CIPSres',
]
import math
import torch
from torch import nn
import torch.nn.functional as F
from .blocks import ConstantInput, LFF, StyledConv, ToRGB, PixelNorm, EqualLinear, StyledResBlock
class CIPSskip(nn.Module):
def __init__(self, size=256, hidden_size=512, n_ml... | 6,684 | 30.238318 | 119 | py |
PixelFolder | PixelFolder-main/model/GeneratorPixelFolder.py | __all__ = ['PixelFolder'
]
import math
import random
import torch
from torch import nn
from .blocks import ConstantInput, StyledConv, ToRGB, PixelNorm, EqualLinear, Unfold, LFF, PosFold
import tensor_transforms as tt
class PixelFolder(nn.Module):
def __init__(
self,
size,
sty... | 5,962 | 31.232432 | 152 | py |
PixelFolder | PixelFolder-main/model/Discriminators.py | __all__ = [
'Discriminator'
]
import math
import torch
from torch import nn
from .blocks import ConvLayer, ResBlock, EqualLinear
class Discriminator(nn.Module):
def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], input_size=3, n_first_layers=0, **kwargs):
sup... | 7,509 | 31.23176 | 119 | py |
PixelFolder | PixelFolder-main/model/blocks.py | import math
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
class PixelNorm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return input * torch.rsqrt(torch.mean(... | 19,139 | 29.09434 | 138 | py |
LeNet-5 | LeNet-5-master/run.py | from lenet import LeNet5
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision.datasets.mnist import MNIST
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import visdom
import onnx
viz = visdom.Visdom()
data_train = MNIST('./data/mnist',
... | 2,904 | 27.762376 | 119 | py |
LeNet-5 | LeNet-5-master/lenet.py | import torch.nn as nn
from collections import OrderedDict
class C1(nn.Module):
def __init__(self):
super(C1, self).__init__()
self.c1 = nn.Sequential(OrderedDict([
('c1', nn.Conv2d(1, 6, kernel_size=(5, 5))),
('relu1', nn.ReLU()),
('s1', nn.MaxPool2d(kernel_siz... | 2,330 | 21.2 | 62 | py |
BASPRO | BASPRO-main/perplexity.py | import os
import json
import numpy as np
import pandas as pd
import torch
from pytorch_transformers import BertTokenizer, BertModel, BertForMaskedLM
import logging
from tqdm import tqdm
logging.basicConfig(level=logging.INFO)
class Perplexity_Checker(object):
def __init__(self, MODEL_PATH, DEVICE):
self.... | 3,339 | 37.837209 | 106 | py |
GraphFit | GraphFit-master/dataset.py | from __future__ import print_function
import os
import os.path
import sys
import torch
import torch.utils.data as data
import numpy as np
import scipy.spatial as spatial
# do NOT modify the returned points! kdtree uses a reference, not a copy of these points,
# so modifying the points would make the kdtree give incorr... | 19,069 | 42.24263 | 143 | py |
GraphFit | GraphFit-master/test_n_est_ms.py | from __future__ import print_function
import argparse
import os
import sys
import random
import numpy as np
import torch
import torch.nn.parallel
import torch.utils.data
import importlib.util
import time
from pathlib import Path
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
BASE_DIR_PATH = Path(BASE_DIR)
sys.p... | 12,363 | 45.307116 | 178 | py |
GraphFit | GraphFit-master/train_n_est_ms.py | from __future__ import print_function
import argparse
import os
import sys
import random
import math
import shutil
import torch
import torch.nn.parallel
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.utils.data
from tensorboardX import SummaryWriter
from pathlib import Path
B... | 29,697 | 50.291883 | 186 | py |
GraphFit | GraphFit-master/models/GraphFit_ms.py | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import numpy as np
import torch.nn.functional as F
import normal_estimation_utils
def _get_device(device_string):
if device_string.lower() == 'cpu':
return torch.device('cpu')
if device_string.lower() == 'cuda':
if ... | 23,025 | 41.327206 | 235 | py |
GraphFit | GraphFit-master/utils/visualization.py | from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
import torch
import os
# from plane_dataset import PlaneSet as Dataset
from matplotlib import rc
rc('font', **{'family':'DejaVu Sans Mono'})
import matplotlib.pyplot as plt
from matplotlib import cm
# rc('text', usetex=True)
... | 7,575 | 35.956098 | 126 | py |
GraphFit | GraphFit-master/utils/normal_estimation_utils.py | from sklearn.mixture import GaussianMixture
from sklearn.preprocessing import normalize
import os
import pickle
import numpy as np
import torch
import provider
def get_gmm(points, n_gaussians, NUM_POINT, type='grid', variance=0.05, n_scales=3, D=3):
"""
Compute weights, means and covariances for a gmm with t... | 18,052 | 40.983721 | 175 | py |
GraphFit | GraphFit-master/utils/pcpnet_dataset.py | from __future__ import print_function
import os
import os.path
import sys
import torch
import torch.utils.data as data
import numpy as np
import scipy.spatial as spatial
# do NOT modify the returned points! kdtree uses a reference, not a copy of these points,
# so modifying the points would make the kdtree give incor... | 18,625 | 40.85618 | 158 | py |
GraphFit | GraphFit-master/utils/provider.py | import os
import sys
import numpy as np
import torch
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, './utils'))
from pcpnet_dataset import PointcloudPatchDataset, RandomPointcloudPatchSampler, SequentialShapeRandomPointcloudPatchSampler,\
Sequ... | 15,862 | 43.810734 | 128 | py |
STSN | STSN-main/train_slot_transformer_raven.py | import os
import argparse
from slot_transformer_v2 import *
import time
import datetime
import torch.optim as optim
import torch
from PIL import Image
from torchvision.transforms import transforms
from util import log
from torch.utils.data import Dataset, DataLoader
import glob
import torchvision.transforms.functional ... | 23,503 | 43.431002 | 551 | py |
STSN | STSN-main/train_slot_transformer_clevr_multigpu.py | import os
import argparse
from clevr_slot_transformer import *
import time
import datetime
import torch.optim as optim
import torch
from PIL import Image
from torchvision.transforms import transforms
from util import log
from torch.utils.data import Dataset, DataLoader
import glob
import torchvision.transforms.function... | 22,449 | 45.098563 | 551 | py |
STSN | STSN-main/train_slot_transformer_pgm_multigpu.py | import os
import argparse
from slot_transformer_pgm import *
import time
import datetime
import torch.optim as optim
import torch
from PIL import Image
from torchvision.transforms import transforms
from util import log
from torch.utils.data import Dataset, DataLoader
import torch.distributed as dist
from torch.nn.paral... | 20,484 | 40.720978 | 386 | py |
STSN | STSN-main/clevr_slot_transformer.py | import numpy as np
from torch import nn
import torch
import torch.nn.functional as F
import math
from util import log
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
class SlotAttention(nn.Module):
def __init__(self, num_slots, dim, iters = 3, eps = 1e-8, hidden_dim = 128):
... | 18,221 | 39.314159 | 286 | py |
STSN | STSN-main/slot_transformer_pgm.py | import numpy as np
from torch import nn
import torch
import torch.nn.functional as F
import math
from util import log
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
class SlotAttention(nn.Module):
def __init__(self, num_slots, dim, iters = 3, eps = 1e-8, hidden_dim = 128):
... | 18,252 | 39.293598 | 286 | py |
STSN | STSN-main/slot_transformer_v2.py | import numpy as np
from torch import nn
import torch
import torch.nn.functional as F
import math
from util import log
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
class SlotAttention(nn.Module):
def __init__(self, num_slots, dim, iters = 3, eps = 1e-8, hidden_dim = 128):
... | 18,252 | 39.293598 | 286 | py |
FineD-Eval | FineD-Eval-main/run.py | import argparse
import json
from pathlib import Path
import random
import numpy as np
import torch
from src.models import fined
from src.utils import data_utils, logging, metrics, model_utils
logger = logging.get_logger(__name__)
def parse_args(use_args=None):
parser = argparse.ArgumentParser()
# Name the ... | 11,841 | 29.599483 | 80 | py |
FineD-Eval | FineD-Eval-main/src/models/fined.py | from collections import Counter
from dataclasses import asdict, dataclass, field
from typing import Any
import torch
from torch import nn
from torch.nn import functional as F
from transformers import AutoModel, AutoTokenizer
from src.utils import data_utils, logging
from src.models.trainer import Trainer
logger = lo... | 13,058 | 42.385382 | 111 | py |
FineD-Eval | FineD-Eval-main/src/models/trainer.py | import collections
import copy
from dataclasses import asdict
import json
from pathlib import Path
import torch
from torch import nn
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader, BatchSampler, RandomSampler
from tqdm import tqdm
from transformers import (
Adafacto... | 14,457 | 34.698765 | 118 | py |
FineD-Eval | FineD-Eval-main/src/utils/model_utils.py | import torch
def device():
return (
torch.device("cuda")
if torch.cuda.is_available()
else torch.device("cpu")
)
def to_device(d, device_=None):
if device_ is None:
device_ = device()
for k in d:
if type(d[k]) == dict:
d[k] = to_device(d[k], device... | 1,080 | 19.788462 | 70 | py |
FineD-Eval | FineD-Eval-main/src/utils/data_utils.py | from dataclasses import asdict, dataclass, field
from typing import Any, Dict, List, Optional
from pathlib import Path
import pickle
import random
import codecs
from tqdm import tqdm
import torch
from torch.utils.data import Dataset, Sampler, RandomSampler
from torch.nn.utils.rnn import pad_sequence
from src.utils imp... | 13,475 | 33.116456 | 89 | py |
dynconv | dynconv-master/classification/main_cifar.py | import argparse
import os.path
import matplotlib.pyplot as plt
import dynconv
import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import tqdm
import utils.flopscounter as flopscounter
import utils.logger as l... | 8,873 | 37.25 | 141 | py |
dynconv | dynconv-master/classification/main_imagenet.py | import argparse
import os.path
import matplotlib.pyplot as plt
import dataloader.imagenet
import dynconv
import torch
import models
import torch.nn as nn
import torch.optim
import torch.utils.data
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import tqdm
import utils.flopscounter... | 8,980 | 36.577406 | 141 | py |
dynconv | dynconv-master/classification/models/resnet_224x224.py | import torch
import torch.nn as nn
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
import dynconv
from models.resnet_util import *
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
're... | 10,881 | 41.507813 | 107 | py |
dynconv | dynconv-master/classification/models/resnet_util.py | import torch
import torch.nn as nn
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
import dynconv
import models.resnet_util
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution ... | 4,825 | 34.226277 | 101 | py |
dynconv | dynconv-master/classification/models/resnet_32x32.py | """
ResNet for 32 by 32 images (CIFAR)
"""
import math
import dynconv
import torch
import torch.autograd as autograd
import torch.nn as nn
from torch.autograd import Variable
from models.resnet_util import *
########################################
# Original ResNet #
##########################... | 3,040 | 32.054348 | 91 | py |
dynconv | dynconv-master/classification/utils/utils.py | import os.path
import torch
from torchvision import transforms
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def upda... | 2,139 | 26.435897 | 108 | py |
dynconv | dynconv-master/classification/utils/logger.py | """ Logger for printing """
# Settings
interval = 50
# Logger
loggers = {}
step = 0
def add(name, val):
global loggers, step
loggers[name] = val
def tick():
global loggers, step
if step > 0 and step % interval == 0:
# print(f'\n $$$ Step {step}')
print('')
for name in sorted... | 537 | 16.354839 | 57 | py |
dynconv | dynconv-master/classification/utils/flopscounter.py | import numpy as np
import torch
import torch.nn as nn
def flops_to_string(flops):
if flops // 10**9 > 0:
return str(round(flops / 10.**9, 2)) + 'GMac'
elif flops // 10**6 > 0:
return str(round(flops / 10.**6, 2)) + 'MMac'
elif flops // 10**3 > 0:
return str(round(flops / 10.**3, 2))... | 9,061 | 31.952727 | 106 | py |
dynconv | dynconv-master/classification/utils/viz.py | import math
import matplotlib.pyplot as plt
import utils.utils as utils
import dynconv
mean = (0.4914, 0.4822, 0.4465)
std = (0.2023, 0.1994, 0.2010)
unnormalize = utils.UnNormalize(mean, std)
def plot_image(input):
''' shows the first image of a 4D pytorch batch '''
assert input.dim() == 4
plt.figure('Im... | 1,535 | 25.033898 | 70 | py |
dynconv | dynconv-master/classification/dynconv/loss.py | import utils.logger as logger
import math
import torch
import torch.nn as nn
class SparsityCriterion(nn.Module):
'''
Defines the sparsity loss, consisting of two parts:
- network loss: MSE between computational budget used for whole network and target
- block loss: sparsity (percentage of used FLOPS... | 2,243 | 37.689655 | 121 | py |
dynconv | dynconv-master/classification/dynconv/utils.py | import torch.nn.functional as F
def apply_mask(x, mask):
mask_hard = mask.hard
assert mask_hard.shape[0] == x.shape[0]
assert mask_hard.shape[2:4] == x.shape[2:4], (mask_hard.shape, x.shape)
return mask_hard.float().expand_as(x) * x
def ponder_cost_map(masks):
""" takes in the mask list and retur... | 784 | 31.708333 | 94 | py |
dynconv | dynconv-master/classification/dynconv/layers.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# these wrappers register the FLOPS of each layer, which
# will be used in the sparsity criterion to restrict the
# amount of executed conditoinal operations.
# In the pose repository, these wrappers are also used to
# efficiently execute sparse laye... | 1,261 | 28.348837 | 74 | py |
dynconv | dynconv-master/classification/dynconv/maskunit.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from utils import logger
class Mask():
'''
Class that holds the mask properties
hard: the hard/binary mask (1 or 0), 4-dim tensor
soft (optional): the float mask, same shape as hard
active_positions: the amount of positions where ... | 4,111 | 31.125 | 146 | py |
dynconv | dynconv-master/classification/dataloader/imagenet.py | import logging
import os
from glob import glob
from os import path as osp
import mat4py
import numpy as np
import torch
from PIL import Image
def default_loader(path):
return Image.open(path).convert('RGB')
class IN1K(torch.utils.data.Dataset):
"""
ImageNet 1K dataset
Classes numbered from 0 to 999 ... | 4,356 | 38.252252 | 102 | py |
dynconv | dynconv-master/pose/tools/test.py | # ------------------------------------------------------------------------------
# pose.pytorch
# Copyright (c) 2018-present Microsoft
# Licensed under The Apache-2.0 License [see LICENSE for details]
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ----------------------------------------------------------------------... | 5,643 | 31.068182 | 110 | py |
dynconv | dynconv-master/pose/tools/speedtest.py | # ------------------------------------------------------------------------------
# pose.pytorch
# Copyright (c) 2018-present Microsoft
# Licensed under The Apache-2.0 License [see LICENSE for details]
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ----------------------------------------------------------------------... | 5,789 | 31.346369 | 110 | py |
dynconv | dynconv-master/pose/tools/_init_paths.py | # ------------------------------------------------------------------------------
# pose.pytorch
# Copyright (c) 2018-present Microsoft
# Licensed under The Apache-2.0 License [see LICENSE for details]
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ----------------------------------------------------------------------... | 739 | 25.428571 | 80 | py |
dynconv | dynconv-master/pose/tools/train.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __futu... | 8,030 | 31.124 | 110 | py |
dynconv | dynconv-master/pose/lib/core/loss.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __futu... | 3,068 | 35.105882 | 80 | py |
dynconv | dynconv-master/pose/lib/core/function.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Written by Feng Zhang & Hong Hu
#
#
# modified by Thomas Verelst
# ESAT-PSI, KU LEUVEN
# -----------------------------------------... | 17,495 | 36.384615 | 229 | py |
dynconv | dynconv-master/pose/lib/dataset/JointsDataset.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __futu... | 11,409 | 33.264264 | 85 | py |
dynconv | dynconv-master/pose/lib/models/hourglass_mn.py | '''
Hourglass network inserted in the pre-activated Resnet
Use lr=0.01 for current version
(c) YANG, Wei
modified by Thomas Verelst
ESAT-PSI, KU LEUVEN, 2020
'''
from __future__ import absolute_import, division, print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import dynconv
BN... | 8,643 | 36.419913 | 144 | py |
dynconv | dynconv-master/pose/lib/utils/vis.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __futu... | 7,451 | 32.872727 | 119 | py |
dynconv | dynconv-master/pose/lib/utils/misc.py | # import importlib
# import numpy as np
# import torch
# from easydict import EasyDict as edict
# #### CUDA
# def cudafy(cfg, var):
# '''
# moves tensor to cuda
# '''
# return var.to(cfg.device, non_blocking=True)
# def cudafy_list(cfg, var_list):
# '''
# moves list of tensors to cuda
# '... | 4,696 | 30.52349 | 126 | py |
dynconv | dynconv-master/pose/lib/utils/utils.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft and ilovepose
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Written by Feng Zhang & Hong Hu
# ------------------------------------------------------------------------------
fr... | 10,650 | 36.769504 | 120 | py |
dynconv | dynconv-master/pose/lib/utils/flopscounter_old.py | import numpy as np
import torch
import torch.nn as nn
def flops_to_string(flops):
if flops // 10**9 > 0:
return str(round(flops / 10.**9, 2)) + 'GMac'
elif flops // 10**6 > 0:
return str(round(flops / 10.**6, 2)) + 'MMac'
elif flops // 10**3 > 0:
return str(round(flops / 10.**3, 2)... | 8,877 | 32.37594 | 105 | py |
dynconv | dynconv-master/pose/lib/utils/flopscounter.py | import numpy as np
import torch
import torch.nn as nn
def flops_to_string(flops):
if flops // 10**9 > 0:
return str(round(flops / 10.**9, 2)) + 'GMac'
elif flops // 10**6 > 0:
return str(round(flops / 10.**6, 2)) + 'MMac'
elif flops // 10**3 > 0:
return str(round(flops / 10.**3, 2))... | 9,061 | 31.952727 | 106 | py |
dynconv | dynconv-master/pose/lib/utils/viz.py | import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
from torchvision import transforms
def showKey():
'''
shows a plot, closable by pressing a key
'''
plt.draw()
plt.pause(1)
input("<Hit Enter To Close>")
plt.clf()
plt.cla()
plt.close('all')
def frame2mpl(... | 3,306 | 27.508621 | 138 | py |
dynconv | dynconv-master/pose/lib/dynconv/loss_cost.py | # import utils.logger as logger
import math
import torch
import torch.nn as nn
class SparsityCriterion(nn.Module):
'''
Defines the sparsity loss, consisting of two parts:
- network loss: MSE between computational budget used for whole network and target
- block loss: sparsity (percentage of used FLO... | 2,546 | 37.014925 | 94 | py |
dynconv | dynconv-master/pose/lib/dynconv/cuda.py | import time
from collections import namedtuple
from string import Template
import cupy
import torch
import math
# HELPER FUNCTIONS
Stream = namedtuple('Stream', ['ptr'])
ROUNDING = 8
CUDA_NUM_THREADS = 256
CHANNELS_PER_THREAD = 2
def Dtype(t):
if isinstance(t, torch.cuda.FloatTensor):
return 'float'
... | 11,170 | 37.12628 | 177 | py |
dynconv | dynconv-master/pose/lib/dynconv/utils.py | import torch
import torch.nn.functional as F
def apply_mask(x, mask):
mask_hard = mask.hard
assert mask_hard.shape[0] == x.shape[0]
assert mask_hard.shape[2:4] == x.shape[2:4], (mask_hard.shape, x.shape)
return mask_hard.float().expand_as(x) * x
def ponder_cost_map(masks):
""" takes in the mask li... | 1,799 | 34.294118 | 113 | py |
dynconv | dynconv-master/pose/lib/dynconv/layers.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dynconv.cuda
## CONVOLUTIONS
def conv1x1(conv_module, x, mask, fast=False):
# Conv1x1 gets slow when batch size is large, using linear per ppixel is equivalent
w = conv_module.weight.data
mask.flops_per_position += w.shape[0]*w.sh... | 3,461 | 34.690722 | 131 | py |
dynconv | dynconv-master/pose/lib/dynconv/maskunit.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Mask():
'''
Class that holds the mask properties
hard: the hard/binary mask (1 or 0), 4-dim tensor
soft (optional): the float mask, same shape as hard
active_positions: the amount of positions where hard == 1
total_posit... | 5,931 | 35.84472 | 169 | py |
pix2pixHD | pix2pixHD-master/test.py | import os
from collections import OrderedDict
from torch.autograd import Variable
from options.test_options import TestOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
import util.util as util
from util.visualizer import Visualizer
from util import html
import torch
opt = Te... | 2,450 | 35.044118 | 112 | py |
pix2pixHD | pix2pixHD-master/run_engine.py | import os
import sys
from random import randint
import numpy as np
import tensorrt
try:
from PIL import Image
import pycuda.driver as cuda
import pycuda.gpuarray as gpuarray
import pycuda.autoinit
import argparse
except ImportError as err:
sys.stderr.write("""ERROR: failed to import module ({})... | 5,850 | 32.626437 | 111 | py |
pix2pixHD | pix2pixHD-master/precompute_feature_maps.py | from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
import os
import util.util as util
from torch.autograd import Variable
import torch.nn as nn
opt = TrainOptions().parse()
opt.nThreads = 1
opt.batchSize = 1
opt.serial_batches = True
op... | 1,141 | 33.606061 | 105 | py |
pix2pixHD | pix2pixHD-master/train.py | import time
import os
import numpy as np
import torch
from torch.autograd import Variable
from collections import OrderedDict
from subprocess import call
import fractions
def lcm(a,b): return abs(a * b)/fractions.gcd(a,b) if a and b else 0
from options.train_options import TrainOptions
from data.data_loader import Cre... | 5,772 | 39.65493 | 130 | py |
pix2pixHD | pix2pixHD-master/options/base_options.py | import argparse
import os
from util import util
import torch
class BaseOptions():
def __init__(self):
self.parser = argparse.ArgumentParser()
self.initialized = False
def initialize(self):
# experiment specifics
self.parser.add_argument('--name', type=str, default='label2ci... | 6,690 | 65.91 | 228 | py |
pix2pixHD | pix2pixHD-master/models/base_model.py | import os
import torch
import sys
class BaseModel(torch.nn.Module):
def name(self):
return 'BaseModel'
def initialize(self, opt):
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor
... | 3,381 | 35.76087 | 126 | py |
pix2pixHD | pix2pixHD-master/models/ui_model.py | import torch
from torch.autograd import Variable
from collections import OrderedDict
import numpy as np
import os
from PIL import Image
import util.util as util
from .base_model import BaseModel
from . import networks
class UIModel(BaseModel):
def name(self):
return 'UIModel'
def initialize(self, opt)... | 17,626 | 49.798271 | 137 | py |
pix2pixHD | pix2pixHD-master/models/pix2pixHD_model.py | import numpy as np
import torch
import os
from torch.autograd import Variable
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
class Pix2PixHDModel(BaseModel):
def name(self):
return 'Pix2PixHDModel'
def init_loss_filter(self, use_gan_feat_loss, use_vg... | 14,036 | 45.022951 | 139 | py |
pix2pixHD | pix2pixHD-master/models/networks.py | import torch
import torch.nn as nn
import functools
from torch.autograd import Variable
import numpy as np
###############################################################################
# Functions
###############################################################################
def weights_init(m):
classname = m._... | 18,316 | 42.925659 | 144 | py |
pix2pixHD | pix2pixHD-master/models/models.py | import torch
def create_model(opt):
if opt.model == 'pix2pixHD':
from .pix2pixHD_model import Pix2PixHDModel, InferenceModel
if opt.isTrain:
model = Pix2PixHDModel()
else:
model = InferenceModel()
else:
from .ui_model import UIModel
model = UIModel()
... | 567 | 26.047619 | 68 | py |
pix2pixHD | pix2pixHD-master/util/image_pool.py | import random
import torch
from torch.autograd import Variable
class ImagePool():
def __init__(self, pool_size):
self.pool_size = pool_size
if self.pool_size > 0:
self.num_imgs = 0
self.images = []
def query(self, images):
if self.pool_size == 0:
retu... | 1,090 | 33.09375 | 67 | py |
pix2pixHD | pix2pixHD-master/util/util.py | from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import numpy as np
import os
# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
if isinstance(image_tensor, list):
... | 4,029 | 38.90099 | 129 | py |
pix2pixHD | pix2pixHD-master/data/custom_dataset_data_loader.py | import torch.utils.data
from data.base_data_loader import BaseDataLoader
def CreateDataset(opt):
dataset = None
from data.aligned_dataset import AlignedDataset
dataset = AlignedDataset()
print("dataset [%s] was created" % (dataset.name()))
dataset.initialize(opt)
return dataset
class CustomD... | 886 | 26.71875 | 64 | py |
pix2pixHD | pix2pixHD-master/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__()
def name(self):
return 'BaseDataset'
def initialize(self, opt):
... | 2,882 | 30.681319 | 107 | py |
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