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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more | more-main/log_utils.py | from collections import defaultdict, deque
import datetime
import time
import logging
from termcolor import colored
import sys
import os
import torch
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the global series average.
https://github.co... | 6,646 | 32.741117 | 129 | py |
more | more-main/action_utils_mask.py | import cv2
import imutils
import math
import random
from constants import (
GRIPPER_PUSH_ADD_PIXEL,
colors_lower,
colors_upper,
IMAGE_PAD_SIZE,
IMAGE_SIZE,
IMAGE_PAD_WIDTH,
PUSH_DISTANCE,
GRIPPER_PUSH_RADIUS_PIXEL,
PIXEL_SIZE,
DEPTH_MIN,
IMAGE_SIZE,
CONSECUTIVE_DISTANCE_T... | 39,123 | 40.933548 | 127 | py |
more | more-main/models.py |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from vision.backbone_utils import resnet_fpn_net
from constants import NUM_ROTATION
class PushNet(nn.Module):
"""
The DQN Network.
"""
def __init__(self, pre_train=False):
super().__init__()
self.de... | 17,954 | 40.370968 | 110 | py |
more | more-main/push_net.py | import torch
import torch.nn as nn
from vision.backbone_utils import resent_backbone
from collections import OrderedDict
class PushPredictionNet(nn.Module):
def __init__(self):
super().__init__()
# single object state encoder
self.single_state_encoder = nn.Sequential(
OrderedD... | 4,492 | 33.829457 | 87 | py |
more | more-main/mcts_utils.py | from dataset import LifelongEvalDataset
import math
import random
import torch
from torchvision.transforms import functional as TF
import numpy as np
import cv2
import imutils
from models import reinforcement_net
from action_utils_mask import get_orientation, adjust_push_start_point
import utils
from constants import... | 42,442 | 42.48668 | 107 | py |
more | more-main/train_foreground.py | import torch
from models import reinforcement_net
from dataset import ForegroundDataset
import argparse
import time
import datetime
import os
from constants import PUSH_Q, GRASP_Q, NUM_ROTATION
from torch.utils.tensorboard import SummaryWriter
import log_utils
import torch_utils
def parse_args():
default_params ... | 24,462 | 35.241481 | 111 | py |
more | more-main/push_predictor.py | import copy
import torch
import gc
import numpy as np
import cv2
from torchvision.transforms import functional as TF
import math
from push_net import PushPredictionNet
from models import reinforcement_net
from train_maskrcnn import get_model_instance_segmentation
from dataset import PushPredictionMultiDatasetEvaluatio... | 29,449 | 43.961832 | 198 | py |
more | more-main/trainer.py | import os
import numpy as np
import math
import cv2
import torch
from torch.autograd import Variable
from models import reinforcement_net
from scipy import ndimage
from constants import (
COLOR_MEAN,
COLOR_STD,
DEPTH_MEAN,
DEPTH_STD,
DEPTH_MIN,
IMAGE_PAD_WIDTH,
NUM_ROTATION,
GRIPPER_GRAS... | 39,013 | 41.222944 | 100 | py |
more | more-main/mcts_main.py | """Test"""
import glob
import gc
import os
import time
import datetime
import pybullet as p
import cv2
import numpy as np
from graphviz import Digraph
import argparse
import random
import torch
import pandas as pd
from mcts_utils import MCTSHelper
from mcts.search import MonteCarloTreeSearch
from mcts.nodes import Pu... | 23,696 | 40.793651 | 183 | py |
more | more-main/vision/backbone_utils.py | from collections import OrderedDict
from torch import nn
from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork, LastLevelMaxPool
import torch.nn.functional as F
from torchvision.ops import misc as misc_nn_ops
from ._utils import IntermediateLayerGetter
from . import resnet
from constants import GRIP... | 13,140 | 38.821212 | 118 | py |
more | more-main/vision/_utils.py | from collections import OrderedDict
import torch
from torch import nn
from torch.jit.annotations import Dict
from torch.nn import functional as F
class IntermediateLayerGetter(nn.ModuleDict):
"""
Module wrapper that returns intermediate layers from a model
It has a strong assumption that the modules hav... | 2,641 | 37.289855 | 89 | py |
more | more-main/vision/resnet.py | import torch
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
__all__ = [
"ResNet",
"resnet10",
"resnet18",
"resnet34",
"resnet50",
"resnet101",
"resnet152",
"resnext50_32x4d",
"resnext101_32x8d",
"wide_resnet50_2",
"wide_resnet101_2",
]
... | 14,901 | 34.229314 | 107 | py |
more | more-main/vision/coco_utils.py | import copy
import os
from PIL import Image
import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask
from pycocotools.coco import COCO
class FilterAndRemapCocoCategories(object):
def __init__(self, categories, remap=True):
self.categories = categories
sel... | 7,759 | 34.272727 | 83 | py |
more | more-main/vision/coco_eval.py | import json
import tempfile
import numpy as np
import copy
import time
import torch
import torch._six
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from collections import defaultdict
import old_utils as utils
class CocoEvaluator(object):
def ... | 12,012 | 33.421203 | 107 | py |
more | more-main/vision/transforms.py | import random
import torch
from torchvision.transforms import functional as F
def _flip_coco_person_keypoints(kps, width):
flip_inds = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
flipped_data = kps[:, flip_inds]
flipped_data[..., 0] = width - flipped_data[..., 0]
# Maintain COCO conven... | 1,358 | 28.543478 | 74 | py |
RWP | RWP-main/utils.py | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models_imagenet
import nu... | 14,352 | 38.215847 | 173 | py |
RWP | RWP-main/train_rwp_parallel.py | import argparse
from torch.nn.modules.batchnorm import _BatchNorm
import os
import time
import numpy as np
import random
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
imp... | 16,489 | 34.310493 | 165 | py |
RWP | RWP-main/train_rwp_imagenet.py | import argparse
import os
import random
import shutil
import time
import warnings
import os
import numpy as np
import pickle
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from utils import *
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
imp... | 21,710 | 36.890052 | 118 | py |
RWP | RWP-main/models/resnet.py | """resnet in pytorch
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
Deep Residual Learning for Image Recognition
https://arxiv.org/abs/1512.03385v1
"""
import torch
import torch.nn as nn
class BasicBlock(nn.Module):
"""Basic Block for resnet 18 and resnet 34
"""
#BasicBlock and Bottl... | 5,620 | 32.064706 | 118 | py |
RWP | RWP-main/models/vgg.py | """
VGG model definition
ported from https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
"""
import math
import torch.nn as nn
import torchvision.transforms as transforms
__all__ = ['VGG16', 'VGG16BN', 'VGG19', 'VGG19BN']
def make_layers(cfg, batch_norm=False):
layers = list()
in... | 2,502 | 25.913978 | 97 | py |
RWP | RWP-main/models/wide_resnet.py | """
WideResNet model definition
ported from https://github.com/meliketoy/wide-resnet.pytorch/blob/master/networks/wide_resnet.py
"""
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import math
__all__ = ['WideResNet28x10', 'WideRes... | 5,426 | 38.904412 | 114 | py |
RandomNeuralField | RandomNeuralField-main/train.py | import sys
from pathlib import Path
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch import optim
from torch.utils.data import DataLoader, TensorDataset
from torchvision import datasets, transforms
from src.utils.factory import read_yaml
from src.... | 5,899 | 30.382979 | 112 | py |
RandomNeuralField | RandomNeuralField-main/src/tools/relative_frob.py | import sys
from os.path import join, dirname
import torch
import torch.nn as nn
from torch import optim
sys.path.append(join(dirname(__file__), "../.."))
from src.ntk.generate import generate_ntk
from src.utils.factory import calc_diff_frob, calc_acc
def calc_ntk_frob(cfg, net, lr, train_loader, test_loader):
... | 1,949 | 31.5 | 78 | py |
RandomNeuralField | RandomNeuralField-main/src/tools/train.py | import sys
from os.path import join, dirname
import torch
import torch.nn as nn
from torch import optim
sys.path.append(join(dirname(__file__), "../.."))
from src.utils.factory import calc_acc
def train(cfg, net, lr, database):
n_epochs = cfg.GENERAL.EPOCH
input_shape = cfg.MODEL.INPUT_FEATURES
dev... | 2,554 | 32.181818 | 82 | py |
RandomNeuralField | RandomNeuralField-main/src/dataset/dataset.py | import numpy as np
from sklearn.model_selection import train_test_split
import torch
from torch.utils.data import DataLoader, TensorDataset
from torchvision import datasets, transforms
class MakeDataset:
def __init__(self, cfg):
self.cfg = cfg
self.dataset_name = cfg.DATA.NAME
s... | 2,678 | 30.151163 | 78 | py |
RandomNeuralField | RandomNeuralField-main/src/ntk/generate.py | from tqdm.auto import tqdm
import numpy as np
import torch
from torch.autograd import grad
def generate_ntk(net, label, train, test, cfg, calc_lr=False):
input_shape = cfg.MODEL.INPUT_FEATURES
device_id = cfg.GENERAL.GPUS
if len(train.size()) > 2:
train = train.view(-1, input_shape)
... | 1,997 | 31.754098 | 75 | py |
RandomNeuralField | RandomNeuralField-main/src/models/initializers.py | import sys
from os.path import join, dirname
import numpy as np
import torch.nn as nn
from torch import Tensor
sys.path.append(join(dirname(__file__), "../.."))
from src.models.utils import sym_mat, receptive_mat, weight_correlation, matern_kernel
class Initializers(nn.Module):
def __init__(self, cfg):
... | 3,358 | 31.931373 | 86 | py |
RandomNeuralField | RandomNeuralField-main/src/models/networks.py | import sys
from os.path import join, dirname
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.append(join(dirname(__file__), "../.."))
from src.models.initializers import Initializers
def read_model(cfg):
init_type = cfg.INITIALIZER.TYPE
device_id = cfg.GE... | 3,468 | 28.905172 | 87 | py |
DivMF | DivMF-master/src/main.py | '''
Top-K Diversity Regularizer
This software may be used only for research evaluation purposes.
For other purposes (e.g., commercial), please contact the authors.
'''
import time
import math
import click
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from model ... | 8,840 | 37.947137 | 110 | py |
DivMF | DivMF-master/src/utils.py | '''
Top-K Diversity Regularizer
This software may be used only for research evaluation purposes.
For other purposes (e.g., commercial), please contact the authors.
'''
import random
import numpy as np
from numpy.core.numeric import indices
import pandas as pd
import scipy.sparse as sp
import torch
from torch._C imp... | 6,732 | 30.316279 | 86 | py |
DivMF | DivMF-master/src/model.py | '''
Top-K Diversity Regularizer
This software may be used only for research evaluation purposes.
For other purposes (e.g., commercial), please contact the authors.
'''
import torch.nn as nn
class BPR(nn.Module):
def __init__(self, user_num, item_num, factor_num):
super(BPR, self).__init__()
"""
user_num: nu... | 972 | 25.297297 | 66 | py |
3SD | 3SD-main/data_loader.py | # data loader
from __future__ import print_function, division
import glob
import torch
from skimage import io, transform, color
import numpy as np
import random
import math
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from PIL import Image
#... | 9,040 | 32.609665 | 113 | py |
3SD | 3SD-main/new_data_loader.py | # data loader
from __future__ import print_function, division
import glob
import torch
from skimage import io, transform, color
import numpy as np
import random
import math
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from PIL import Image
#... | 10,287 | 33.756757 | 147 | py |
3SD | 3SD-main/basenet_train.py | import os
import torch
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms as T
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torch.optim as optim
import torchvision.transform... | 21,206 | 37.279783 | 204 | py |
3SD | 3SD-main/3SD_train.py | import os
import torch
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms as T
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torch.optim as optim
import torchvision.transform... | 23,488 | 38.018272 | 204 | py |
3SD | 3SD-main/compute_and_plot.py | import os
import torch
from sklearn.metrics import f1_score, precision_score, recall_score
'''from sklearn.metrics import (precision_recall_curve, PrecisionRecallDisplay)
from sklearn.metrics import precision_recall_curve'''
import cv2
import pdb
import numpy as np
import torch.nn.functional as F
from torch.autograd i... | 9,521 | 33.625455 | 172 | py |
3SD | 3SD-main/u2net_test_pseudo_dino_final.py | import os
from skimage import io, transform
import torch
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms as T
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms#, utils
# import torch.optim a... | 12,334 | 34.14245 | 169 | py |
3SD | 3SD-main/u2net_test.py | import os
from skimage import io, transform
import torch
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms#, utils
# import torch.optim as optim
import numpy as np
from PIL imp... | 4,238 | 32.642857 | 129 | py |
3SD | 3SD-main/smoothness/__init__.py | import torch
import torch.nn.functional as F
# from torch.autograd import Variable
# import numpy as np
def laplacian_edge(img):
laplacian_filter = torch.Tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
filter = torch.reshape(laplacian_filter, [1, 1, 3, 3])
filter = filter.cuda()
lap_edge = F.conv2d(im... | 2,014 | 30.484375 | 78 | py |
3SD | 3SD-main/model/u2net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class REBNCONV(nn.Module):
def __init__(self,in_ch=3,out_ch=3,dirate=1):
super(REBNCONV,self).__init__()
self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate)
self.bn_s1 = nn.BatchNorm2d(out_ch)
... | 14,719 | 26.984791 | 118 | py |
3SD | 3SD-main/model/u2net_transformer_pseudo_dino_final.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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.apach... | 33,498 | 32.499 | 155 | py |
3SD | 3SD-main/model/utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 ... | 21,117 | 33.06129 | 115 | py |
3SD | 3SD-main/model/u2net_refactor.py | import torch
import torch.nn as nn
import math
__all__ = ['U2NET_full', 'U2NET_lite']
def _upsample_like(x, size):
return nn.Upsample(size=size, mode='bilinear', align_corners=False)(x)
def _size_map(x, height):
# {height: size} for Upsample
size = list(x.shape[-2:])
sizes = {}
for h in range(... | 6,097 | 35.08284 | 101 | py |
3SD | 3SD-main/model/u2net_transformer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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.apach... | 30,678 | 31.917382 | 124 | py |
STFTgrad | STFTgrad-main/classifier/classifier_adaptive.py | """
Code for the adaptive classifier with the differentiable STFT front-end
This will be trained on our test input signal, alternating sinusoids of 2 frequencies
"""
# Dependencies
import numpy as np
from tqdm import tqdm
import haiku as hk
import jax.numpy as jnp
import jax
import optax
from dstft import diff_stft
imp... | 3,684 | 25.510791 | 120 | py |
STFTgrad | STFTgrad-main/classifier/classifier_ordinary.py | """
Code for a normal classifier (to obtain the loss function as a function of the window length)
This will be trained on our test input signal, alternating sinusoids of 2 frequencies
"""
# Dependencies
import numpy as np
from tqdm import tqdm
import haiku as hk
import jax.numpy as jnp
import jax
import optax
from dst... | 3,916 | 25.828767 | 120 | py |
STFTgrad | STFTgrad-main/classifier/dstft.py | """
Code for the differentiable STFT front-end
As explained in our paper, we use a Gaussian Window STFT, with N = floor(6\sigma)
"""
# Dependencies
import jax.numpy as jnp
import jax
def diff_stft(xinp,s,hf = 0.5):
"""
Inputs
------
xinp: jnp.array
Input audio signal in time domain
s: jnp.... | 1,290 | 26.468085 | 154 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/adaptive_stft.py | import math
from tqdm import trange
import sys
import pathlib
import torch.autograd
import torch
import numpy as np
import torch.optim
import torch.nn as nn
from celluloid import Camera
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
import torch.nn.functional as F
from adaptive_stft_utils.operator... | 11,853 | 39.875862 | 123 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/MNISTExperiment.py | from models import UMNNMAFFlow
import torch
from lib import dataloader as dl
import lib as transform
import lib.utils as utils
import numpy as np
import os
import pickle
from timeit import default_timer as timer
import torchvision
from tensorboardX import SummaryWriter
writer = SummaryWriter()
def train_mnist(datas... | 13,329 | 49.492424 | 127 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/ToyExperiments.py | from models import UMNNMAFFlow
import torch
import lib.toy_data as toy_data
import numpy as np
import matplotlib.pyplot as plt
from timeit import default_timer as timer
import os
import lib.utils as utils
import lib.visualize_flow as vf
green = '#e15647'
black = '#2d5468'
white_bg = '#ececec'
def summary_plots(x, x_te... | 7,250 | 37.775401 | 128 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/MonotonicMLP.py | import torch
import argparse
import torch.nn as nn
import matplotlib.pyplot as plt
from models.UMNN import MonotonicNN, IntegrandNN
def f(x_1, x_2, x_3):
return .001*(x_1**3 + x_1) + x_2 ** 2 + torch.sin(x_3)
def create_dataset(n_samples):
x = torch.randn(n_samples, 3)
y = f(x[:, 0], x[:, 1], x[:, 2])
... | 3,487 | 35.715789 | 96 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/UCIExperiments.py | from models import UMNNMAFFlow
import torch
import numpy as np
import os
import pickle
import lib.utils as utils
import datasets
from timeit import default_timer as timer
from tensorboardX import SummaryWriter
writer = SummaryWriter()
def batch_iter(X, batch_size, shuffle=False):
"""
X: feature tensor (shape... | 10,219 | 41.941176 | 131 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/TrainVaeFlow.py | # !/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import argparse
import time
import torch
import torch.utils.data
import torch.optim as optim
import numpy as np
import math
import random
import os
import datetime
import lib.utils as utils
from models.vae_lib.models import VAE
fro... | 13,750 | 39.444118 | 124 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/models/VAE.py | from __future__ import print_function
import torch
import torch.nn as nn
from ...vae_lib.models import flows
from ...vae_lib.models.layers import GatedConv2d, GatedConvTranspose2d
class VAE(nn.Module):
"""
The base VAE class containing gated convolutional encoder and decoder architecture.
Can be used as ... | 26,921 | 32.949559 | 136 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/models/CNFVAE.py | import torch
import torch.nn as nn
from train_misc import build_model_tabular
from UMNNMAF import lib as layers
import lib as diffeq_layers
from .VAE import VAE
from lib import NONLINEARITIES
from torchdiffeq import odeint_adjoint as odeint
def get_hidden_dims(args):
return tuple(map(int, args.dims.split("-"))) ... | 14,375 | 33.808717 | 116 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/models/layers.py | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import numpy as np
import torch.nn.functional as F
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
class GatedConv2d(nn.Module):
def __init__(self... | 7,128 | 32.947619 | 115 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/models/flows.py | """
Collection of flow strategies
"""
from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from ...vae_lib.models.layers import MaskedConv2d, MaskedLinear
import sys
sys.path.append("../../")
from models import... | 10,990 | 32.306061 | 118 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/optimization/loss.py | from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
from ...vae_lib.utils.distributions import log_normal_diag, log_normal_standard, log_bernoulli
import torch.nn.functional as F
def binary_loss_function(recon_x, x, z_mu, z_var, z_0, z_k, ldj, beta=1.):
"""
Computes th... | 10,621 | 38.051471 | 116 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/optimization/training.py | from __future__ import print_function
import time
import torch
from ...vae_lib.optimization.loss import calculate_loss
from ...vae_lib.utils.visual_evaluation import plot_reconstructions
from ...vae_lib.utils.log_likelihood import calculate_likelihood
import numpy as np
def train(epoch, train_loader, model, opt, ar... | 5,533 | 30.443182 | 120 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/utils/distributions.py | from __future__ import print_function
import torch
import torch.utils.data
import math
MIN_EPSILON = 1e-5
MAX_EPSILON = 1. - 1e-5
PI = torch.FloatTensor([math.pi])
if torch.cuda.is_available():
PI = PI.cuda()
# N(x | mu, var) = 1/sqrt{2pi var} exp[-1/(2 var) (x-mean)(x-mean)]
# log N(x| mu, var) = -log sqrt(2pi... | 1,768 | 25.80303 | 86 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/utils/load_data.py | from __future__ import print_function
import torch
import torch.utils.data as data_utils
import pickle
from scipy.io import loadmat
import numpy as np
import os
def load_static_mnist(args, **kwargs):
"""
Dataloading function for static mnist. Outputs image data in vectorized form: each image is a vector of... | 7,580 | 35.800971 | 116 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/spectral_normalization.py | # Code from https://github.com/christiancosgrove/pytorch-spectral-normalization-gan/blob/master/spectral_normalization.py
import torch
from torch import nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
def joint_gaussian(n_samp=1000):
x2 = torch.distributions.Norm... | 2,536 | 32.381579 | 121 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/made.py | """
Implements Masked AutoEncoder for Density Estimation, by Germain et al. 2015
Re-implementation by Andrej Karpathy based on https://arxiv.org/abs/1502.03509
Modified by Antoine Wehenkel
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
# -------------------------... | 9,945 | 40.26971 | 151 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/ParallelNeuralIntegral.py | import torch
import numpy as np
import math
def _flatten(sequence):
flat = [p.contiguous().view(-1) for p in sequence]
return torch.cat(flat) if len(flat) > 0 else torch.tensor([])
def compute_cc_weights(nb_steps):
lam = np.arange(0, nb_steps + 1, 1).reshape(-1, 1)
lam = np.cos((lam @ lam.T) * math.... | 4,099 | 38.423077 | 138 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/MonotonicNN.py | import torch
import torch.nn as nn
from .NeuralIntegral import NeuralIntegral
from .ParallelNeuralIntegral import ParallelNeuralIntegral
def _flatten(sequence):
flat = [p.contiguous().view(-1) for p in sequence]
return torch.cat(flat) if len(flat) > 0 else torch.tensor([])
class IntegrandNN(nn.Module):
... | 1,957 | 34.6 | 145 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/NeuralIntegral.py | import torch
import numpy as np
import math
def _flatten(sequence):
flat = [p.contiguous().view(-1) for p in sequence]
return torch.cat(flat) if len(flat) > 0 else torch.tensor([])
def compute_cc_weights(nb_steps):
lam = np.arange(0, nb_steps + 1, 1).reshape(-1, 1)
lam = np.cos((lam @ lam.T) * math.... | 2,840 | 31.284091 | 119 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/UMNNMAF.py | import torch
import torch.nn as nn
from .NeuralIntegral import NeuralIntegral
from .ParallelNeuralIntegral import ParallelNeuralIntegral
import numpy as np
import math
from .made import MADE, ConditionnalMADE
class ELUPlus(nn.Module):
def __init__(self):
super().__init__()
self.elu = nn.ELU()
de... | 10,524 | 38.716981 | 129 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/UMNNMAFFlow.py | import torch
import torch.nn as nn
from .UMNNMAF import EmbeddingNetwork, UMNNMAF
import numpy as np
import math
class ListModule(object):
def __init__(self, module, prefix, *args):
"""
The ListModule class is a container for multiple nn.Module.
:nn.Module module: A module to add in the li... | 5,656 | 36.217105 | 115 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/datasets/download_datasets.py | # -*- coding: utf-8 -*-
"""
Created on Wed Apr 19 15:58:53 2017
@author: Chin-Wei
# some code adapted from https://github.com/yburda/iwae/blob/master/download_mnist.py
LSUN
https://github.com/fyu/lsun
"""
import urllib
import pickle
import os
import struct
import numpy as np
import gzip
import time
import urllib.requ... | 9,226 | 31.60424 | 119 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/adaptive_stft_utils/operators.py | import torch.autograd
import torch
import torch.nn.functional as F
def dithering_int(n):
if n == int(n):
return int(n)
return int(torch.bernoulli((n - int(n))) + int(n))
class SignPassGrad(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input.sign()
@stat... | 1,729 | 23.027778 | 87 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/adaptive_stft_utils/losses.py | import torch.autograd
import torch
import torch.nn.functional as F
from .operators import clip_tensor_norm
def kurtosis(rfft_magnitudes_sq):
epsilon = 1e-7
max_norm = 0.1
kur_part = [
torch.sum(torch.pow(a, 2)) /
(torch.pow(torch.sum(a), 2).unsqueeze(-1) + epsilon)
for a in rfft_ma... | 541 | 24.809524 | 93 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/adaptive_stft_utils/mappings.py | import math
import sys
import pathlib
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.insert(0, pathlib.Path(__file__).parent.parent.parent.absolute())
from UMNN.models.UMNN import MonotonicNN
# Monotonically increasing mapping
class IdxToWindow(nn.Module):
def __init__(self, signal_... | 6,507 | 40.452229 | 216 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/test_tasnet.py | import os
import torch
from data_loader.AudioReader import AudioReader, write_wav
import argparse
from model.model import TSENet,TSENet_one_hot
from logger.set_logger import setup_logger
import logging
from config.option import parse
import torchaudio
from utils.util import handle_scp, handle_scp_inf
def read_wav(fnam... | 7,559 | 48.090909 | 176 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/train_Tasnet.py | import sys
sys.path.append('./')
from torch.utils.data import DataLoader as Loader
from data_loader.Dataset import Datasets
from model.model import TSENet,TSENet_one_hot
from logger import set_logger
import logging
from config import option
import argparse
import torch
from trainer import trainer_Tasnet,trainer_Tasnet_... | 7,435 | 48.245033 | 131 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/create_scp_debug.py | import os
train_mix_scp = '/apdcephfs/private_helinwang/tsss/Dual-Path-RNN-Pytorch/scps_debug/tr_mix.scp'
train_s1_scp = '/apdcephfs/private_helinwang/tsss/Dual-Path-RNN-Pytorch/scps_debug/tr_s1.scp'
train_s2_scp = '/apdcephfs/private_helinwang/tsss/Dual-Path-RNN-Pytorch/scps_debug/tr_s2.scp'
train_re_scp = '/apdcephf... | 5,313 | 33.506494 | 102 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/draw.py | import torchaudio
import matplotlib
import matplotlib.pyplot as plt
[width, height] = matplotlib.rcParams['figure.figsize']
if width < 10:
matplotlib.rcParams['figure.figsize'] = [width * 2.5, height]
if __name__ == "__main__":
# filename = "/apdcephfs/private_helinwang/tsss/tsss_mixed/train/train_1.wav"
f... | 775 | 32.73913 | 95 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/trainer/trainer_Tasnet.py | import sys
sys.path.append('../')
from utils.util import check_parameters
import time
import logging
from model.loss import get_loss
import torch
import os
import matplotlib.pyplot as plt
import numpy as np
import math
def time_to_frame(tm,st=True):
radio = 10.0/624
if st:
n_fame = tm//radio
else:... | 21,031 | 46.58371 | 259 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/trainer/trainer_Tasnet_one_hot.py | import sys
sys.path.append('../')
from utils.util import check_parameters
import time
import logging
from model.loss import get_loss,get_loss_one_hot
import torch
import os
import matplotlib.pyplot as plt
import numpy as np
def time_to_frame(tm,st=True):
radio = 10.0/624
if st:
n_fame = tm//radio
e... | 21,117 | 47.104784 | 259 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/data_loader/AudioData.py | import torch.nn.functional as F
from utils import util
import torch
import torchaudio
import sys
sys.path.append('../')
def read_wav(fname, return_rate=False):
'''
Read wavfile using Pytorch audio
input:
fname: wav file path
return_rate: Whether to return the sampli... | 2,751 | 30.272727 | 87 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/data_loader/Dataset.py | import sys
sys.path.append('../')
import torch
from torch.utils.data import DataLoader, Dataset
import torchaudio
from utils.util import handle_scp, handle_scp_inf
def read_wav(fname, return_rate=False):
'''
Read wavfile using Pytorch audio
input:
fname: wav file path
... | 3,439 | 39.952381 | 134 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/data_loader/AudioReader.py | import sys
sys.path.append('../')
import torchaudio
import torch
from utils.util import handle_scp
def read_wav(fname, return_rate=False):
'''
Read wavfile using Pytorch audio
input:
fname: wav file path
return_rate: Whether to return the sampling rate
outp... | 2,556 | 28.732558 | 82 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/utils/util.py | import torch
import torch.nn as nn
def handle_scp(scp_path):
'''
Read scp file script
input:
scp_path: .scp file's file path
output:
scp_dict: {'key':'wave file path'}
'''
scp_dict = dict()
line = 0
lines = open(scp_path, 'r').readlines()
for l in lines:
... | 1,932 | 26.225352 | 79 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/model/model_t.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class GlobalLayerNorm(nn.Module):
'''
Calculate Global Layer Normalization
dim: (int or list or torch.Size) –
input shape from an expected input of size
eps: a value added to the denominator for numerical stability.... | 21,609 | 37.451957 | 165 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/model/PANNS.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
from torchlibrosa.augmentation import SpecAugmentation
def init_layer(layer):
"""Initialize a Linear or Convolutional layer. """
nn.init.xavier_uniform_(layer.weight)
if hasattr(l... | 4,628 | 38.905172 | 107 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/model/loss.py | import torch
import numpy as np
def nll_loss(output, target):
'''Negative likelihood loss. The output should be obtained using F.log_softmax(x).
Args:
output: (N, classes_num)
target: (N, classes_num)
'''
loss = - torch.mean(target * output)
return loss
def sisnr_loss(x, s, eps=1e-8):
... | 14,345 | 42.34139 | 156 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/model/model.py | import torch
from torch import nn
import torch.nn.functional as F
import sys
sys.path.append('../')
from utils.util import check_parameters
from model.PANNS import CNN10
from model.tsd import TSD
import math
def init_kernel(frame_len,
frame_hop,
num_fft=None,
window="sqr... | 32,316 | 39.497494 | 165 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/model/tsd.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
def init_layer(layer):
"""Initialize a Linear or Convolutional layer. """
nn.init.xavier_uniform_(layer.weight)
if hasattr(layer, 'bias'):
if layer.bias is not None:
... | 5,798 | 33.517857 | 109 | py |
Tim-TSENet | Tim-TSENet-main/generate_dataset/generate_data_fsd_kaggle2.py | import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
import numpy as np
import argparse
import librosa
import matplotlib.pyplot as plt
import torch
import random
from utilities import create_folder, get_filename
from models import *
from pytorch_utils import move_data_to_device
import config
... | 46,868 | 46.874362 | 170 | py |
Tim-TSENet | Tim-TSENet-main/generate_dataset/create_scp.py | import os
train_mix_scp = '/apdcephfs/private_helinwang/tsss/Dual-Path-RNN-Pytorch/scps/tr_mix.scp'
train_s1_scp = '/apdcephfs/private_helinwang/tsss/Dual-Path-RNN-Pytorch/scps/tr_s1.scp'
train_re_scp = '/apdcephfs/private_helinwang/tsss/Dual-Path-RNN-Pytorch/scps/tr_re.scp'
test_mix_scp = '/apdcephfs/private_helinwa... | 2,673 | 34.653333 | 89 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/test_tasnet_one_hot.py | import os
import torch
from data_loader.AudioReader import AudioReader, write_wav
import argparse
from torch.nn.parallel import data_parallel
from model.model import TSDNet,TSDNet_one_hot,TSDNet_plus_one_hot
from logger.set_logger import setup_logger
import logging
from config.option import parse
import torchaudio
from... | 9,953 | 48.034483 | 171 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/test_tasnet_wav.py | import os
import torch
from data_loader.AudioReader import AudioReader, write_wav, read_wav
import argparse
from torch.nn.parallel import data_parallel
from model.model import Conv_TasNet
from logger.set_logger import setup_logger
import logging
from config.option import parse
import tqdm
class Separation():
def ... | 2,864 | 37.2 | 104 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/test_tasnet.py | import os
import torch
from data_loader.AudioReader import AudioReader, write_wav
import argparse
from torch.nn.parallel import data_parallel
from model.model import TSDNet,TSDNet_one_hot
from logger.set_logger import setup_logger
import logging
from config.option import parse
import torchaudio
from utils.util import h... | 9,282 | 48.116402 | 167 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/train_Tasnet.py | import sys
sys.path.append('./')
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader as Loader
from data_loader.Dataset_light import Datasets
from model.model import TSDNet,TSDNet_one_hot, TSDNet_plus_one_hot
from logger import set_logger
import logging
from config import opt... | 7,358 | 46.477419 | 145 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/tsd_utils.py | import collections
import sys
from loguru import logger
from pprint import pformat
import numpy as np
import pandas as pd
import scipy
import six
import sklearn.preprocessing as pre
import torch
import tqdm
import yaml
import augment
#import dataset
from scipy.interpolate import interp1d
def parse_config_or_kwargs(c... | 13,313 | 35.377049 | 169 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/train_rnn.py | import sys
sys.path.append('./')
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader as Loader
from data_loader.Dataset import Datasets
from model import model_rnn
from logger import set_logger
import logging
from config import option
import argparse
import torch
from trainer... | 3,546 | 37.554348 | 114 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/train_Tasnet_tse.py | import sys
sys.path.append('./')
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader as Loader
from data_loader.Dataset_light import Datasets_tse # using add tse results
from model.model import TSDNet_tse,TSDNet_one_hot, TSDNet_plus_one_hot
from logger import set_logger
impor... | 7,255 | 47.373333 | 131 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/dualrnn_test_wav.py | import os
import torch
from data_loader.AudioReader import AudioReader, write_wav, read_wav
import argparse
from torch.nn.parallel import data_parallel
from model.model_rnn import Dual_RNN_model
from logger.set_logger import setup_logger
import logging
from config.option import parse
import tqdm
class Separation():
... | 2,918 | 38.986301 | 105 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/augment.py | import torch
import logging
import torch.nn as nn
import numpy as np
class TimeShift(nn.Module):
def __init__(self, mean, std):
super().__init__()
self.mean = mean
self.std = std
def forward(self, x):
if self.training:
shift = torch.empty(1).normal_(self.mean, self... | 1,333 | 23.703704 | 76 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/test_tasnet_tse.py | import os
import torch
from data_loader.AudioReader import AudioReader, write_wav
import argparse
from torch.nn.parallel import data_parallel
from model.model import TSDNet,TSDNet_one_hot, TSDNet_tse
from logger.set_logger import setup_logger
import logging
from config.option import parse
import torchaudio
from utils.u... | 10,323 | 50.108911 | 214 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/draw.py | import torchaudio
import matplotlib
import matplotlib.pyplot as plt
[width, height] = matplotlib.rcParams['figure.figsize']
if width < 10:
matplotlib.rcParams['figure.figsize'] = [width * 2.5, height]
if __name__ == "__main__":
# filename = "/apdcephfs/private_helinwang/tsss/tsss_mixed/train/train_1.wav"
f... | 775 | 32.73913 | 95 | py |
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