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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a3t-dev_richard | a3t-dev_richard/egs2/lrs2/lipreading1/local/feature_extract/models/pretrained.py | # coding: utf-8
import math
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(
in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
)
class BasicBlock(nn.Module):
expans... | 8,288 | 31.252918 | 88 | py |
vaele | vaele-master/run_experiment.py | import vaele_config
import tensorflow as tf
# Set up for debugging (uncomment if needed)
# tf.config.experimental_run_functions_eagerly(False)
# tf.config.experimental_functions_run_eagerly()
######################################### Experiment selection #########################################################
tf.rand... | 1,518 | 42.4 | 120 | py |
vaele | vaele-master/vaele_config.py | import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = ""
import gpflow
from gpflow.config import default_float
from gpflow.config import set_default_positive_bijector
import numpy as np
import tensorflow as tf
import tensorflow.keras.backend as backend
set_default_positive_bije... | 1,401 | 24.962963 | 93 | py |
vaele | vaele-master/nnets/models.py | from vaele_config import tf_floatx
import numpy as np
import tensorflow as tf
import gpflow
from magic_numbers import DROPOUT, REC_DROPOUT, BETA, ALPHA, BIDIRECTIONAL
_POSITIVE_TRANSFORMATION = 'softplus'
class DropTimeSteps(tf.keras.layers.Layer):
def __init__(self, rate, dropped_scale=tf.convert_to_tensor(1, dt... | 19,791 | 40.667368 | 125 | py |
vaele | vaele-master/utils/schedulers.py | import matplotlib.pyplot as plt
import tensorflow as tf
from vaele_config import tf_floatx
def plot_scheduler(scheduler, max_epochs=10000):
scheduled_values = []
for epoch in range(max_epochs):
scheduled_values.append(scheduler(epoch))
plt.plot(scheduled_values)
class NatGradScheduler(tf.keras.o... | 3,410 | 38.662791 | 115 | py |
vaele | vaele-master/utils/Trainer.py | import os
import time
import numpy as np
from gpflow.monitor import Monitor, MonitorTaskGroup, ScalarToTensorBoard, ImageToTensorBoard, ModelToTensorBoard
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
from tqdm import tqdm
from experiments import Experiment
from utils.plot_utils... | 11,890 | 43.535581 | 137 | py |
paint2pix | paint2pix-main/demo.py | import base64
import json
import os
import re
import time
import uuid
from io import BytesIO
from pathlib import Path
import glob
import cv2
from cv2 import stylization
import numpy as np
import pandas as pd
import streamlit as st
from PIL import Image
from streamlit_drawable_canvas import st_canvas
#from svgpathtools... | 20,491 | 48.617433 | 346 | py |
paint2pix | paint2pix-main/predict.py | import numpy as np
from collections import deque
import cv2
import pandas as pd
import os,sys
import glob
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import transforms, utils
from PIL import Image
from utils import id_utils
from utils.... | 12,249 | 38.90228 | 230 | py |
paint2pix | paint2pix-main/models/psp.py | """
This file defines the core research contribution
"""
import math
import torch
from torch import nn
from models.stylegan2.model import Generator
from configs.paths_config import model_paths
from models.encoders import fpn_encoders, restyle_psp_encoders
from utils.model_utils import RESNET_MAPPING
class pSp(nn.Mod... | 6,412 | 44.161972 | 120 | py |
paint2pix | paint2pix-main/models/e4e.py | """
This file defines the core research contribution
"""
import math
import torch
from torch import nn
from models.stylegan2.model import Generator
from configs.paths_config import model_paths
from models.encoders import restyle_e4e_encoders
from utils.model_utils import RESNET_MAPPING
from models.stylegan2.model imp... | 7,522 | 46.01875 | 137 | py |
paint2pix | paint2pix-main/models/mtcnn/mtcnn.py | import numpy as np
import torch
from PIL import Image
from models.mtcnn.mtcnn_pytorch.src.get_nets import PNet, RNet, ONet
from models.mtcnn.mtcnn_pytorch.src.box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
from models.mtcnn.mtcnn_pytorch.src.first_stage import run_first_stage
from models.mtcnn.... | 6,220 | 38.624204 | 116 | py |
paint2pix | paint2pix-main/models/mtcnn/mtcnn_pytorch/src/get_nets.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import numpy as np
from configs.paths_config import model_paths
PNET_PATH = model_paths["mtcnn_pnet"]
ONET_PATH = model_paths["mtcnn_onet"]
RNET_PATH = model_paths["mtcnn_rnet"]
class Flatten(nn.Module):
def _... | 4,995 | 28.046512 | 65 | py |
paint2pix | paint2pix-main/models/mtcnn/mtcnn_pytorch/src/align_trans.py | # -*- coding: utf-8 -*-
"""
Created on Mon Apr 24 15:43:29 2017
@author: zhaoy
"""
import numpy as np
import cv2
# from scipy.linalg import lstsq
# from scipy.ndimage import geometric_transform # , map_coordinates
from models.mtcnn.mtcnn_pytorch.src.matlab_cp2tform import get_similarity_transform_for_cv2
# referenc... | 11,036 | 35.186885 | 109 | py |
paint2pix | paint2pix-main/models/mtcnn/mtcnn_pytorch/src/first_stage.py | import torch
import math
from PIL import Image
import numpy as np
from .box_utils import nms, _preprocess
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device = 'cuda:0'
def run_first_stage(image, net, scale, threshold):
"""Run P-Net, generate bounding boxes, and do NMS.
Argument... | 3,147 | 30.168317 | 76 | py |
paint2pix | paint2pix-main/models/mtcnn/mtcnn_pytorch/src/detector.py | import numpy as np
import torch
from .get_nets import PNet, RNet, ONet
from .box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
from .first_stage import run_first_stage
def detect_faces(image, min_face_size=20.0,
thresholds=[0.6, 0.7, 0.8],
nms_thresholds=[0.7, 0... | 4,333 | 33.396825 | 101 | py |
paint2pix | paint2pix-main/models/e4e_modules/discriminator.py | from torch import nn
class LatentCodesDiscriminator(nn.Module):
def __init__(self, style_dim, n_mlp):
super().__init__()
self.style_dim = style_dim
layers = []
for i in range(n_mlp-1):
layers.append(
nn.Linear(style_dim, style_dim)
)
... | 496 | 22.666667 | 47 | py |
paint2pix | paint2pix-main/models/e4e_modules/latent_codes_pool.py | import random
import torch
class LatentCodesPool:
"""This class implements latent codes buffer that stores previously generated w latent codes.
This buffer enables us to update discriminators using a history of generated w's
rather than the ones produced by the latest encoder.
"""
def __init__(se... | 2,349 | 40.964286 | 141 | py |
paint2pix | paint2pix-main/models/stylegan2/model.py | import math
import random
import torch
from torch import nn
from torch.nn import functional as F
from models.stylegan2.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
import streamlit as st
N = 0
class PixelNorm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
... | 19,167 | 26.820029 | 124 | py |
paint2pix | paint2pix-main/models/stylegan2/.ipynb_checkpoints/model-checkpoint.py | import math
import random
import torch
from torch import nn
from torch.nn import functional as F
from models.stylegan2.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
import streamlit as st
N = 0
class PixelNorm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
... | 19,167 | 26.820029 | 124 | py |
paint2pix | paint2pix-main/models/stylegan2/op/upfirdn2d.py | import os
import torch
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
upfirdn2d_op = load(
'upfirdn2d',
sources=[
os.path.join(module_path, 'upfirdn2d.cpp'),
os.path.join(module_path, 'upfirdn2d_kernel.cu'),
],
)
cla... | 5,203 | 27.12973 | 108 | py |
paint2pix | paint2pix-main/models/stylegan2/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.... | 2,378 | 26.662791 | 83 | py |
paint2pix | paint2pix-main/models/encoders/restyle_e4e_encoders.py | from enum import Enum
from torch import nn
from torch.nn import Conv2d, BatchNorm2d, PReLU, Sequential, Module
from torchvision.models import resnet34
from models.encoders.helpers import get_blocks, bottleneck_IR, bottleneck_IR_SE
from models.encoders.map2style import GradualStyleBlock
from models.stylegan2.model imp... | 4,958 | 38.047244 | 109 | py |
paint2pix | paint2pix-main/models/encoders/restyle_psp_encoders.py | import torch
from torch import nn
from torch.nn import Conv2d, BatchNorm2d, PReLU, Sequential, Module
from torchvision.models.resnet import resnet34
from models.encoders.helpers import get_blocks, bottleneck_IR, bottleneck_IR_SE
from models.encoders.map2style import GradualStyleBlock
class BackboneEncoder(Module):
... | 3,505 | 36.297872 | 120 | py |
paint2pix | paint2pix-main/models/encoders/map2style.py | import numpy as np
from torch import nn
from torch.nn import Conv2d, Module
from models.stylegan2.model import EqualLinear
class GradualStyleBlock(Module):
def __init__(self, in_c, out_c, spatial):
super(GradualStyleBlock, self).__init__()
self.out_c = out_c
self.spatial = spatial
... | 907 | 29.266667 | 76 | py |
paint2pix | paint2pix-main/models/encoders/fpn_encoders.py | import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv2d, BatchNorm2d, PReLU, Sequential, Module
from torchvision.models.resnet import resnet34
from models.encoders.helpers import get_blocks, bottleneck_IR, bottleneck_IR_SE
from models.encoders.map2style import GradualStyleBlock
... | 5,672 | 34.45625 | 114 | py |
paint2pix | paint2pix-main/models/encoders/model_irse.py | from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
from models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
"""
Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
"""
class Backbone(Mo... | 2,836 | 32.376471 | 97 | py |
paint2pix | paint2pix-main/models/encoders/helpers.py | from collections import namedtuple
import torch
from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
"""
ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
"""
class Flatten(Module):
def forward(self, input):
return inp... | 3,556 | 28.641667 | 112 | py |
paint2pix | paint2pix-main/models/encoders/.ipynb_checkpoints/restyle_e4e_encoders-checkpoint.py | from enum import Enum
from torch import nn
from torch.nn import Conv2d, BatchNorm2d, PReLU, Sequential, Module
from torchvision.models import resnet34
from models.encoders.helpers import get_blocks, bottleneck_IR, bottleneck_IR_SE
from models.encoders.map2style import GradualStyleBlock
from models.stylegan2.model imp... | 6,131 | 37.325 | 120 | py |
paint2pix | paint2pix-main/models/encoders/.ipynb_checkpoints/map2style-checkpoint.py | import numpy as np
from torch import nn
from torch.nn import Conv2d, Module
from models.stylegan2.model import EqualLinear
class GradualStyleBlock(Module):
def __init__(self, in_c, out_c, spatial):
super(GradualStyleBlock, self).__init__()
self.out_c = out_c
self.spatial = spatial
... | 907 | 29.266667 | 76 | py |
paint2pix | paint2pix-main/models/.ipynb_checkpoints/e4e-checkpoint.py | """
This file defines the core research contribution
"""
import math
import torch
from torch import nn
from models.stylegan2.model import Generator
from configs.paths_config import model_paths
from models.encoders import restyle_e4e_encoders
from utils.model_utils import RESNET_MAPPING
from models.stylegan2.model imp... | 6,231 | 43.834532 | 129 | py |
paint2pix | paint2pix-main/configs/transforms_config.py | from abc import abstractmethod
import torchvision.transforms as transforms
class TransformsConfig(object):
def __init__(self, opts):
self.opts = opts
@abstractmethod
def get_transforms(self):
pass
class EncodeTransforms(TransformsConfig):
def __init__(self, opts):
super(EncodeTransforms, self).__init__... | 2,171 | 29.166667 | 61 | py |
paint2pix | paint2pix-main/criteria/moco_loss.py | import torch
from torch import nn
import torch.nn.functional as F
from configs.paths_config import model_paths
class MocoLoss(nn.Module):
def __init__(self):
super(MocoLoss, self).__init__()
print("Loading MOCO model from path: {}".format(model_paths["moco"]))
self.model = self.__load_mod... | 2,638 | 36.7 | 92 | py |
paint2pix | paint2pix-main/criteria/id_loss.py | import torch
from torch import nn
from configs.paths_config import model_paths
from models.encoders.model_irse import Backbone
class IDLoss(nn.Module):
def __init__(self):
super(IDLoss, self).__init__()
print('Loading ResNet ArcFace')
self.facenet = Backbone(input_size=112, num_layers=50, ... | 1,819 | 35.4 | 92 | py |
paint2pix | paint2pix-main/criteria/w_norm.py | import torch
from torch import nn
class WNormLoss(nn.Module):
def __init__(self, start_from_latent_avg=True):
super(WNormLoss, self).__init__()
self.start_from_latent_avg = start_from_latent_avg
def forward(self, latent, latent_avg=None):
if self.start_from_latent_avg:
latent = latent - latent_avg
retu... | 379 | 24.333333 | 64 | py |
paint2pix | paint2pix-main/criteria/lpips/lpips.py | import torch
import torch.nn as nn
from criteria.lpips.networks import get_network, LinLayers
from criteria.lpips.utils import get_state_dict
class LPIPS(nn.Module):
r"""Creates a criterion that measures
Learned Perceptual Image Patch Similarity (LPIPS).
Arguments:
net_type (str): the network typ... | 1,203 | 32.444444 | 71 | py |
paint2pix | paint2pix-main/criteria/lpips/utils.py | from collections import OrderedDict
import torch
def normalize_activation(x, eps=1e-10):
norm_factor = torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True))
return x / (norm_factor + eps)
def get_state_dict(net_type: str = 'alex', version: str = '0.1'):
# build url
url = 'https://raw.githubusercontent... | 885 | 27.580645 | 79 | py |
paint2pix | paint2pix-main/criteria/lpips/networks.py | from typing import Sequence
from itertools import chain
import torch
import torch.nn as nn
from torchvision import models
from criteria.lpips.utils import normalize_activation
def get_network(net_type: str):
if net_type == 'alex':
return AlexNet()
elif net_type == 'squeeze':
return SqueezeN... | 2,667 | 26.791667 | 79 | py |
paint2pix | paint2pix-main/criteria/.ipynb_checkpoints/id_loss-checkpoint.py | import torch
from torch import nn
from configs.paths_config import model_paths
from models.encoders.model_irse import Backbone
class IDLoss(nn.Module):
def __init__(self):
super(IDLoss, self).__init__()
print('Loading ResNet ArcFace')
self.facenet = Backbone(input_size=112, num_layers=50, ... | 1,819 | 35.4 | 92 | py |
paint2pix | paint2pix-main/utils/inference_utils.py | import torch
def get_average_image(net, opts):
avg_image = net(net.latent_avg.unsqueeze(0),
input_code=True,
randomize_noise=False,
return_latents=False,
average_code=True)[0]
avg_image = avg_image.to('cuda').float().detach()
... | 2,011 | 37.692308 | 98 | py |
paint2pix | paint2pix-main/utils/id_utils.py | from utils.common import tensor2im
from PIL import ImageColor
import torch
import cv2
import numpy as np
from collections import deque
import cv2
import pandas as pd
import os,sys
import glob
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision ... | 5,591 | 35.54902 | 248 | py |
paint2pix | paint2pix-main/utils/.ipynb_checkpoints/id_utils-checkpoint.py | from utils.common import tensor2im
from PIL import ImageColor
import torch
import cv2
import numpy as np
from collections import deque
import cv2
import pandas as pd
import os,sys
import glob
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision ... | 5,591 | 35.54902 | 248 | py |
paint2pix | paint2pix-main/utils/.ipynb_checkpoints/inference_utils-checkpoint.py | import torch
def get_average_image(net, opts):
avg_image = net(net.latent_avg.unsqueeze(0),
input_code=True,
randomize_noise=False,
return_latents=False,
average_code=True)[0]
avg_image = avg_image.to('cuda').float().detach()
... | 1,967 | 36.846154 | 89 | py |
BFA | BFA-master/main.py | from __future__ import division
from __future__ import absolute_import
import os, sys, shutil, time, random
import argparse
import torch
import torch.backends.cudnn as cudnn
import torchvision.datasets as dset
import torchvision.transforms as transforms
from utils import AverageMeter, RecorderMeter, time_string, conve... | 33,044 | 37.379791 | 124 | py |
BFA | BFA-master/utils.py | import os, sys, time, random
import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from torch import nn
import torch
from models.quantization import quan_Conv2d, quan_Linear
def piecewise_clustering(var, lambda_coeff, l_norm):
var1=(var[var.ge(0)]-var[var.ge(0)].mean()).pow(l_n... | 5,722 | 32.273256 | 109 | py |
BFA | BFA-master/attack/random_attack.py | import random
import torch
from models.quantization import quan_Conv2d, quan_Linear, quantize
import operator
from attack.data_conversion import *
class random_flip(object):
def __init__(self, model):
self.module_list = []
for name, m in model.named_modules():
if isinstance(m, quan_Con... | 2,871 | 41.235294 | 85 | py |
BFA | BFA-master/attack/BFA.py | import random
import torch
from models.quantization import quan_Conv2d, quan_Linear, quantize
import operator
from attack.data_conversion import *
class BFA(object):
def __init__(self, criterion, model, k_top=10):
self.criterion = criterion
# init a loss_dict to log the loss w.r.t each layer
... | 10,315 | 43.852174 | 97 | py |
BFA | BFA-master/attack/data_conversion.py | import torch
from models.quantization import quan_Conv2d, quan_Linear
def int2bin(input, num_bits):
'''
convert the signed integer value into unsigned integer (2's complement equivalently).
Note that, the conversion is different depends on number of bit used.
'''
output = input.clone()
if num_... | 2,948 | 36.329114 | 96 | py |
BFA | BFA-master/models/bin_vgg_cifar.py | '''
Modified from https://github.com/pytorch/vision.git
'''
import math
import torch
import torch.nn as nn
import torch.nn.init as init
import math
import torch.nn.functional as F
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
class _bin_func(torch.a... | 5,981 | 29.676923 | 119 | py |
BFA | BFA-master/models/quantization.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import models
import torch.nn.functional as F
class _quantize_func(torch.autograd.Function):
@staticmethod
def forward(ctx, input, step_size, half_lvls):
# ctx is a context object that can be used to stash informat... | 11,969 | 40.275862 | 99 | py |
BFA | BFA-master/models/quan_vgg_cifar.py | '''
Modified from https://github.com/pytorch/vision.git
'''
import math
import torch.nn as nn
import torch.nn.init as init
from .quantization import *
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
class VGG(nn.Module):
'''
VGG model
''... | 3,120 | 27.372727 | 98 | py |
BFA | BFA-master/models/quan_resnet_cifar.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
import math
from .quantization import *
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
assert stride == 2
self.avg = nn.AvgPool2d(kernel_s... | 5,674 | 30.353591 | 95 | py |
BFA | BFA-master/models/bin_resnet_cifar.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
import math
from .quantization import *
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
assert stride == 2
self.avg = nn.AvgPool2d(kernel_s... | 4,746 | 31.737931 | 95 | py |
BFA | BFA-master/models/quan_alexnet_imagenet.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from .quantization import *
__all__ = ['AlexNet', 'alexnet_quan']
model_urls = {
'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}
class AlexNet(nn.Module):
def __init__(self, num_classes=1000):
super(AlexNet,... | 2,188 | 32.166667 | 78 | py |
BFA | BFA-master/models/test_model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
import math
from .quantization import *
defaultcfg = {
11: [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512],
13: [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512],
16: [64, 64, 'M', 1... | 3,428 | 34.71875 | 139 | py |
BFA | BFA-master/models/binarization.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
class _bin_func(torch.autograd.Function):
@staticmethod
def forward(ctx, input, mu):
ctx.mu = mu
output = input.clone().zero_()
output[input.ge(0)] = 1
output[input... | 6,120 | 39.269737 | 97 | py |
BFA | BFA-master/models/quan_resnet_imagenet.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from .quantization import *
__all__ = [
'ResNet', 'resnet18_quan', 'resnet34_quan', 'resnet50', 'resnet101',
'resnet152'
]
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://downlo... | 8,282 | 30.980695 | 106 | py |
BFA | BFA-master/models/quan_mobilenet_imagenet.py | from torch import 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
from .quantization import *
__all__ = ['MobileNetV2', 'mobilenet_v2']
model_urls = {
'mobilenet_v2': 'https://download.pytorch.org/models/m... | 6,882 | 35.036649 | 107 | py |
BFA | BFA-master/models/vanilla_models/vanilla_resnet_imagenet.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f... | 7,277 | 31.061674 | 106 | py |
BFA | BFA-master/models/vanilla_models/vanilla_resnet_cifar.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
import math
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
assert stride == 2
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
de... | 5,874 | 29.921053 | 97 | py |
BFA | BFA-master/models/vanilla_models/vanilla_vgg_cifar.py | '''
Modified from https://github.com/pytorch/vision.git
'''
import math
import torch.nn as nn
import torch.nn.init as init
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
class VGG(nn.Module):
'''
VGG model
'''
def __init__(self, feat... | 3,073 | 27.462963 | 98 | py |
BFA | BFA-master/models/vanilla_models/vanilla_mobilenet_imagenet.py | from torch import 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
__all__ = ['MobileNetV2', 'mobilenet_v2']
model_urls = {
'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}... | 6,583 | 35.375691 | 107 | py |
BFA | BFA-master/visulization/__init__.py | from .torchsummary import summary | 33 | 33 | 33 | py |
BFA | BFA-master/visulization/torchsummary.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from collections import OrderedDict
import numpy as np
def summary(model, input_size, batch_size=-1, device="cuda"):
def register_hook(module):
def hook(module, input, output):
class_name = str(module.__class__).split(".... | 4,359 | 36.913043 | 88 | py |
imSim | imSim-main/doc/conf.py | """Configuration file for the Sphinx documentation builder."""
import os
import sys
import importlib.util
from sphinx.ext.napoleon.docstring import GoogleDocstring
import sphinx.ext.napoleon
sys.path.insert(0, os.path.abspath(".."))
def load_imsim_version():
"""Extract version of imsim without importing the who... | 1,935 | 26.267606 | 92 | py |
TWFR-GMM | TWFR-GMM-main/utils.py | """
functional functions
"""
import math
import os
import re
import itertools
import glob
import torch
import yaml
import csv
import logging
import torchaudio
import numpy as np
import librosa
import sklearn
import random
system_sep = '/'
def load_yaml(file_path='./config.yaml'):
with open(file_path) as f:
... | 15,636 | 36.230952 | 118 | py |
TWFR-GMM | TWFR-GMM-main/gmmer.py | import logging
import os
import sys
import sklearn
import numpy as np
import time
import re
import joblib
import torch.nn.functional as F
import torch
import librosa
import scipy
from sklearn.manifold import TSNE
from sklearn.mixture import GaussianMixture
from sklearn.cluster import DBSCAN
import matplotlib.pyplot as... | 21,143 | 56.770492 | 152 | py |
TWFR-GMM | TWFR-GMM-main/run.py | import os
import time
import argparse
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from extractor import SpecExtractor
from gmmer import GMMer
import utils
def main(args):
# spectrogram
# transform = utils.Wa... | 3,608 | 33.04717 | 76 | py |
TWFR-GMM | TWFR-GMM-main/extractor.py | import os.path
import torch
import librosa
import numpy as np
from collections import OrderedDict
import utils
from imblearn.over_sampling import SMOTE, BorderlineSMOTE
from collections import Counter
class SpecExtractor:
def __init__(self, *args, **kwargs):
self.args = kwargs['args']
self.dim = k... | 3,485 | 36.891304 | 128 | py |
Robust-optimal-transportation | Robust-optimal-transportation-main/RWGAN/Synthetic data/WGAN.py | # -*- coding: utf-8 -*-
"""
Created on Sun Oct 2 22:11:01 2022
@author: 20447
"""
from scipy.spatial.distance import cdist
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import numpy as np
import ot
real_data_num = 1000
batch = 64
def make_data(w, b, data_num,p=0.1,err=3): #p is the proportion ... | 3,235 | 25.308943 | 109 | py |
Robust-optimal-transportation | Robust-optimal-transportation-main/RWGAN/Synthetic data/RWGANB.py | # -*- coding: utf-8 -*-
"""
Created on Fri Jan 6 21:11:53 2023
@author: yiming ma
"""
from scipy.spatial.distance import cdist
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import numpy as np
from torch import autograd
import ot
real_data_num = 1000 #真实数据数量
batch = 64 #每次运算处理多少数据
def make_data... | 3,970 | 27.163121 | 109 | py |
Robust-optimal-transportation | Robust-optimal-transportation-main/RWGAN/Synthetic data/RWGAN1.py | # -*- coding: utf-8 -*-
"""
Created on Sat Sep 10 09:48:53 2022
@author: yiming ma
"""
import ot
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import numpy as np
from torch import autograd
from scipy.spatial.distance import cdist
real_data_num = 1000
batch = 64
def make_data(w, b, data_num,p=0... | 5,323 | 27.021053 | 109 | py |
Robust-optimal-transportation | Robust-optimal-transportation-main/RWGAN/Synthetic data/RWGAN2.py | # -*- coding: utf-8 -*-
"""
Created on Sun Oct 2 22:12:26 2022
@author: 20447
"""
from scipy.spatial.distance import cdist
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import numpy as np
from torch import autograd
import ot
real_data_num = 1000 #真实数据数量
batch = 64 #每次运算处理多少数据
def make_data(w, b... | 3,962 | 27.307143 | 109 | py |
DistCL | DistCL-main/DistCL_code/utils.py | import numpy as np
from numpy import ma
import pandas as pd
from sklearn.utils import check_random_state
from torch.utils.data import Dataset
import torch
import torch.nn as nn
from torch.nn.utils import prune
import torch.nn.functional as F
import random
device = torch.device('cuda:0' if torch.cuda.is_available() else... | 5,487 | 26.717172 | 109 | py |
DistCL | DistCL-main/DistCL_code/distnn.py | import pandas as pd
pd.options.mode.chained_assignment = None
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.nn.utils import prune
from torch.utils.data import DataLoader
from sklearn.model_selection import train_test_split
from utils import... | 4,182 | 33.858333 | 155 | py |
DistCL | DistCL-main/VPP_case/utils.py | import numpy as np
from numpy import ma
import pandas as pd
from sklearn.utils import check_random_state
from torch.utils.data import Dataset
import torch
import torch.nn as nn
from torch.nn.utils import prune
import torch.nn.functional as F
import random
device = torch.device('cuda:0' if torch.cuda.is_available() else... | 5,487 | 26.717172 | 109 | py |
DistCL | DistCL-main/VPP_case/distnn.py | import pandas as pd
pd.options.mode.chained_assignment = None
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.nn.utils import prune
from torch.utils.data import DataLoader
from sklearn.model_selection import train_test_split
from utils import... | 4,182 | 33.858333 | 155 | py |
alignn | alignn-main/setup.py | """ALIGNN: Atomistic LIne Graph Neural Network.
https://jarvis.nist.gov.
"""
import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="alignn",
version="2023.07.10",
author="Kamal Choudhary, Brian DeCost",
author_email="kamal.chou... | 1,946 | 29.904762 | 73 | py |
alignn | alignn-main/alignn/train_folder.py | #!/usr/bin/env python
"""Module to train for a folder with formatted dataset."""
import csv
import os
import sys
import time
from jarvis.core.atoms import Atoms
from alignn.data import get_train_val_loaders
from alignn.train import train_dgl
from alignn.config import TrainingConfig
from jarvis.db.jsonutils import load... | 6,229 | 29.096618 | 77 | py |
alignn | alignn-main/alignn/profile.py | """pytorch profiling script.
from the repository root, run
`PYTHONPATH=$PYTHONPATH:. python jarvisdgl/profile.py`
"""
from functools import partial
# from pathlib import Path
from typing import Any, Dict, Union
# import numpy as np
import torch
import torch.profiler
from torch import nn
from tqdm import tqdm
from ... | 2,454 | 28.22619 | 79 | py |
alignn | alignn-main/alignn/data.py | """Jarvis-dgl data loaders and DGLGraph utilities."""
import random
from pathlib import Path
from typing import Optional
# from typing import Dict, List, Optional, Set, Tuple
import os
import torch
import dgl
import numpy as np
import pandas as pd
from jarvis.core.atoms import Atoms
from alignn.graphs import Graph, ... | 18,041 | 31.217857 | 77 | py |
alignn | alignn-main/alignn/train_folder_ff.py | #!/usr/bin/env python
"""Module to train for a folder with formatted dataset."""
import os
# import numpy as np
import sys
from alignn.data import get_train_val_loaders
from alignn.train import train_dgl
from alignn.config import TrainingConfig
from jarvis.db.jsonutils import loadjson
import argparse
from alignn.mode... | 10,293 | 29.187683 | 78 | py |
alignn | alignn-main/alignn/cli.py | """Ignite training cli."""
import json
import os
import shutil
from pathlib import Path
# from typing import Any, Dict, Optional, Union
from typing import Optional
import torch
import typer
from alignn.config import TrainingConfig
from alignn.profile import profile_dgl
from alignn.train import train_dgl
def cli(
... | 1,871 | 24.297297 | 79 | py |
alignn | alignn-main/alignn/pretrained.py | #!/usr/bin/env python
"""Module to download and load pre-trained ALIGNN models."""
import requests
import os
import zipfile
from tqdm import tqdm
from alignn.models.alignn import ALIGNN, ALIGNNConfig
from alignn.data import get_torch_dataset
from torch.utils.data import DataLoader
import tempfile
import torch
import s... | 13,012 | 28.642369 | 79 | py |
alignn | alignn-main/alignn/train.py | """Ignite training script.
from the repository root, run
`PYTHONPATH=$PYTHONPATH:. python alignn/train.py`
then `tensorboard --logdir tb_logs/test` to monitor results...
"""
from functools import partial
# from pathlib import Path
from typing import Any, Dict, Union
import ignite
import torch
from ignite.contrib.han... | 45,498 | 36.20278 | 93 | py |
alignn | alignn-main/alignn/graphs.py | """Module to generate networkx graphs."""
from jarvis.core.atoms import get_supercell_dims
from jarvis.core.specie import Specie
from jarvis.core.utils import random_colors
import numpy as np
import pandas as pd
from collections import OrderedDict
from jarvis.analysis.structure.neighbors import NeighborsAnalysis
from j... | 28,564 | 32.409357 | 79 | py |
alignn | alignn-main/alignn/models/dense_alignn.py | """Atomistic LIne Graph Neural Network.
A prototype crystal line graph network dgl implementation.
"""
from typing import Tuple, Union
# from typing import List, Optional, Tuple, Union
import dgl
import dgl.function as fn
import numpy as np
import torch
from dgl.nn import AvgPooling
from pydantic import root_validato... | 15,704 | 29.794118 | 79 | py |
alignn | alignn-main/alignn/models/alignn_layernorm.py | """Atomistic LIne Graph Neural Network.
A prototype crystal line graph network dgl implementation.
"""
from typing import Tuple, Union
import dgl
import dgl.function as fn
import numpy as np
import torch
from dgl.nn import AvgPooling
# from dgl.nn.functional import edge_softmax
from pydantic.typing import Literal
fr... | 9,760 | 31.428571 | 79 | py |
alignn | alignn-main/alignn/models/modified_cgcnn.py | """CGCNN: dgl implementation."""
from typing import Tuple
import dgl
import dgl.function as fn
import numpy as np
import torch
import torch.nn.functional as F
from dgl.nn import AvgPooling
from pydantic.typing import Literal
from torch import nn
from alignn.models.utils import RBFExpansion
from alignn.utils import B... | 11,818 | 32.013966 | 79 | py |
alignn | alignn-main/alignn/models/densegcn.py | """A baseline graph convolution network dgl implementation."""
from typing import List, Optional
import dgl
import torch
from dgl.nn import AvgPooling, GraphConv
from pydantic.typing import Literal
from torch import nn
from torch.nn import functional as F
from alignn.utils import BaseSettings
class DenseGCNConfig(B... | 3,937 | 27.536232 | 76 | py |
alignn | alignn-main/alignn/models/utils.py | """Shared model-building components."""
from typing import Optional
import numpy as np
import torch
from torch import nn
class RBFExpansion(nn.Module):
"""Expand interatomic distances with radial basis functions."""
def __init__(
self,
vmin: float = 0,
vmax: float = 8,
bins: ... | 1,234 | 27.72093 | 71 | py |
alignn | alignn-main/alignn/models/icgcnn.py | """CGCNN: dgl implementation."""
from typing import Tuple
import dgl
import dgl.function as fn
# import numpy as np
import torch
import torch.nn.functional as F
from dgl.nn import AvgPooling
from pydantic.typing import Literal
from torch import nn
from alignn.models.utils import RBFExpansion
from alignn.utils import... | 9,671 | 31.24 | 79 | py |
alignn | alignn-main/alignn/models/gcn.py | """A baseline graph convolution network dgl implementation."""
# import dgl
import torch
from dgl.nn import AvgPooling, GraphConv
from pydantic.typing import Literal
from torch import nn
from torch.nn import functional as F
from alignn.utils import BaseSettings
class SimpleGCNConfig(BaseSettings):
"""Hyperparame... | 1,940 | 28.861538 | 76 | py |
alignn | alignn-main/alignn/models/alignn.py | """Atomistic LIne Graph Neural Network.
A prototype crystal line graph network dgl implementation.
"""
from typing import Tuple, Union
import dgl
import dgl.function as fn
import numpy as np
import torch
from dgl.nn import AvgPooling
# from dgl.nn.functional import edge_softmax
from pydantic.typing import Literal
fr... | 9,615 | 31.377104 | 79 | py |
alignn | alignn-main/alignn/models/alignn_cgcnn.py | """CGCNN: dgl implementation."""
from typing import Tuple
import dgl
import dgl.function as fn
import numpy as np
import torch
import torch.nn.functional as F
from dgl.nn import AvgPooling
from pydantic.typing import Literal
from torch import nn
# import torch
from alignn.models.utils import RBFExpansion
from alignn... | 10,668 | 32.977707 | 79 | py |
alignn | alignn-main/alignn/models/alignn_atomwise.py | """Atomistic LIne Graph Neural Network.
A prototype crystal line graph network dgl implementation.
"""
from typing import Tuple, Union
from torch.autograd import grad
import dgl
import dgl.function as fn
import numpy as np
from dgl.nn import AvgPooling
import torch
# from dgl.nn.functional import edge_softmax
from py... | 16,905 | 33.786008 | 82 | py |
alignn | alignn-main/alignn/ff/ff.py | """Module for running ALIGNN-FF."""
from ase.md import MDLogger
from jarvis.core.atoms import Atoms as JarvisAtoms
import os
# import json
import requests
from ase.md.nvtberendsen import NVTBerendsen
from ase.md.nptberendsen import NPTBerendsen
from ase.io import Trajectory
import matplotlib.pyplot as plt
from jarvis.... | 48,964 | 30.549613 | 81 | py |
alignn | alignn-main/alignn/scripts/predict_db_all.py | """Module to predict for all DB's form. enp and gap."""
import torch
# from jarvis.core.atoms import Atoms
# from jarvis.core.graphs import Graph
from alignn.models.alignn import ALIGNN
# from jarvis.analysis.structure.spacegroup import Spacegroup3D
# from jarvis.db.figshare import data
from alignn.data import load_d... | 4,631 | 40.357143 | 79 | py |
alignn | alignn-main/alignn/scripts/final_model.py | from alignn.data import load_dataset
# from alignn.data import get_train_val_loaders
from alignn.config import TrainingConfig
from jarvis.db.jsonutils import loadjson
from alignn.train import train_dgl
from alignn.models.alignn import ALIGNN
import torch
device = "cpu"
if torch.cuda.is_available():
device = torch... | 1,340 | 21.728814 | 78 | py |
alignn | alignn-main/alignn/scripts/defect.py | """Module to check if V_Ef is approx. correct."""
import torch
from jarvis.core.atoms import Atoms
from jarvis.core.graphs import Graph
from alignn.models.alignn import ALIGNN
# from jarvis.analysis.structure.spacegroup import Spacegroup3D
from jarvis.db.figshare import get_jid_data
from jarvis.analysis.defects.vacanc... | 2,188 | 28.186667 | 79 | py |
alignn | alignn-main/alignn/scripts/predict_db.py | """Module to predict for a DB of Atoms."""
import torch
from jarvis.core.atoms import Atoms
from jarvis.core.graphs import Graph
from alignn.models.alignn import ALIGNN
# from jarvis.analysis.structure.spacegroup import Spacegroup3D
from jarvis.db.figshare import data
model_path = "JV15/jv_optb88vdw_bandgap_alignn/c... | 2,283 | 26.190476 | 75 | py |
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