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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spikingjelly | spikingjelly-master/spikingjelly/activation_based/model/spike_dhs.py | import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.fusion import *
from torch.autograd import Function
from torch import Tensor
from collections import namedtuple
from ...activation_based import layer
from ..neuron import LIFNode
from torch.nn.functional import interpolate
from ..surrogate import... | 24,737 | 37.592824 | 138 | py |
spikingjelly | spikingjelly-master/spikingjelly/activation_based/model/train_imagenet_example.py | import torch
from spikingjelly.activation_based import surrogate, neuron, functional
from spikingjelly.activation_based.model import spiking_resnet, train_classify
class SResNetTrainer(train_classify.Trainer):
def preprocess_train_sample(self, args, x: torch.Tensor):
# define how to process train sample b... | 2,202 | 45.87234 | 200 | py |
spikingjelly | spikingjelly-master/spikingjelly/activation_based/model/parametric_lif_net.py | import torch
import torch.nn as nn
from copy import deepcopy
from .. import layer
# Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks https://arxiv.org/abs/2007.05785
__all__ = ['MNISTNet', 'FashionMNISTNet', 'NMNISTNet', 'CIFAR10Net', 'CIFAR10DVSNet', 'DVSGestureNet']
cla... | 6,807 | 28.344828 | 128 | py |
spikingjelly | spikingjelly-master/spikingjelly/activation_based/model/spiking_resnet.py | import torch
import torch.nn as nn
from copy import deepcopy
try:
from torchvision.models.utils import load_state_dict_from_url
except ImportError:
from torchvision._internally_replaced_utils import load_state_dict_from_url
from .. import layer
__all__ = ['SpikingResNet', 'spiking_resnet18', 'spiking_resnet3... | 18,386 | 42.883055 | 135 | py |
spikingjelly | spikingjelly-master/spikingjelly/activation_based/model/train_classify.py | import datetime
import os
import time
import warnings
from .tv_ref_classify import presets, transforms, utils
import torch
import torch.utils.data
import torchvision
from .tv_ref_classify.sampler import RASampler
from torch import nn
from torch.utils.data.dataloader import default_collate
from torchvision.transforms.fu... | 34,412 | 45.566982 | 194 | py |
spikingjelly | spikingjelly-master/spikingjelly/activation_based/model/tv_ref_classify/sampler.py | import math
import torch
import torch.distributed as dist
class RASampler(torch.utils.data.Sampler):
"""Sampler that restricts data loading to a subset of the dataset for distributed,
with repeated augmentation.
It ensures that different each augmented version of a sample will be visible to a
differe... | 2,395 | 37.031746 | 103 | py |
spikingjelly | spikingjelly-master/spikingjelly/activation_based/model/tv_ref_classify/presets.py | import torch
from torchvision.transforms import autoaugment, transforms
from torchvision.transforms.functional import InterpolationMode
class ClassificationPresetTrain:
def __init__(
self,
crop_size,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
interpolation=Inter... | 2,252 | 33.136364 | 100 | py |
spikingjelly | spikingjelly-master/spikingjelly/activation_based/model/tv_ref_classify/utils.py | import copy
import datetime
import errno
import hashlib
import os
import time
from collections import defaultdict, deque, OrderedDict
import torch
import torch.distributed as dist
class SmoothedValue:
"""Track a series of values and provide access to smoothed values over a
window or the global series average... | 14,077 | 33.169903 | 120 | py |
spikingjelly | spikingjelly-master/spikingjelly/activation_based/model/tv_ref_classify/__init__.py | # https://github.com/pytorch/vision/tree/release/0.12/references/classification | 79 | 79 | 79 | py |
spikingjelly | spikingjelly-master/spikingjelly/activation_based/model/tv_ref_classify/transforms.py | import math
from typing import Tuple
import torch
from torch import Tensor
from torchvision.transforms import functional as F
class RandomMixup(torch.nn.Module):
"""Randomly apply Mixup to the provided batch and targets.
The class implements the data augmentations as described in the paper
`"mixup: Beyon... | 6,608 | 36.551136 | 108 | py |
spikingjelly | spikingjelly-master/spikingjelly/timing_based/neuron.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class Tempotron(nn.Module):
def __init__(self, in_features, out_features, T, tau=15.0, tau_s=15.0 / 4, v_threshold=1.0):
'''
:param in_features: 输入数量，含义与nn.Linear的in_features参数相同
:param out_features: 输出数量，含义与nn.L... | 4,629 | 43.095238 | 138 | py |
spikingjelly | spikingjelly-master/spikingjelly/timing_based/encoding.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class GaussianTuning:
def __init__(self, n, m, x_min: torch.Tensor, x_max: torch.Tensor):
'''
:param n: 特征的数量，int
:param m: 编码一个特征所使用的神经元数量，int
:param x_min: n个特征的最小值，shape=[n]的tensor
:param x_max: n个特征的最大值，s... | 2,256 | 42.403846 | 115 | py |
spikingjelly | spikingjelly-master/spikingjelly/timing_based/examples/tempotron_mnist.py | import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
import torchvision
from torch.utils.tensorboard import SummaryWriter
import spikingjelly.timing_based.encoding as encoding
import spikingjelly.timing_based.neuron as neuron
from tqdm import tqdm
parser =... | 6,356 | 37.762195 | 167 | py |
spikingjelly | spikingjelly-master/docs/source/conf.py | # sphinx-apidoc -o ./docs/source ./spikingjelly
# 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 -----------------------... | 3,190 | 34.853933 | 184 | py |
spikingjelly | spikingjelly-master/docs/source/_static/tutorials/activation_based/plot1.py | import torch
from matplotlib import pyplot as plt
plt.style.use(['science','muted'])
def reset_v(h, s):
return h * (1 - s)
x = torch.arange(-1, 1.01, 0.01)
figure = plt.figure(dpi=200)
fig0 = plt.subplot(1, 2, 1)
plt.xlabel('$x$')
plt.ylabel('$y$')
plt.title('$\\Theta(x)$ and $\\sigma(\\alpha x)$')
plt.plot(x, (x... | 1,337 | 38.352941 | 111 | py |
spikingjelly | spikingjelly-master/docs/source/_static/tutorials/activation_based/4_conv_fashion_mnist/plot_logs.py | from matplotlib import pyplot as plt
import numpy as np
from spikingjelly.activation_based.examples.conv_fashion_mnist import Net
from spikingjelly import visualizing
import torch
import torch.nn as nn
import torchvision
def plot_log(csv_file, title, x_label, y_label, figsize=(12, 8), plot_max=False):
log_data = np... | 3,691 | 45.734177 | 144 | py |
spikingjelly | spikingjelly-master/docs/source/_static/tutorials/activation_based/11_cext_neuron_with_lbl/plot_bar.py | import numpy as np
from matplotlib import pyplot as plt
def plot_bar_and_text(x, y, width, label):
plt.bar(x, y, width=width, label=label)
for i in range(x.shape[0]):
plt.text(x[i], y[i] + 0.2, str(round(y[i], 2)), ha='center')
plt.style.use(['science'])
fig = plt.figure(figsize=(8, 4), dpi=250)
csv_ar... | 2,072 | 48.357143 | 138 | py |
spikingjelly | spikingjelly-master/docs/source/_static/tutorials/activation_based/14_classify_dvsg/plot_logs.py | from matplotlib import pyplot as plt
import numpy as np
from spikingjelly import visualizing
import torch
import torch.nn as nn
import torchvision
def plot_log(csv_file, title, x_label, y_label, figsize=(12, 8), plot_max=False):
log_data = np.loadtxt(csv_file, delimiter=',', skiprows=1, usecols=(1, 2))
x = log_... | 1,909 | 36.45098 | 141 | py |
spikingjelly | spikingjelly-master/docs/source/_static/tutorials/activation_based/5_ann2snn/polt012.py | import torch
from spikingjelly.activation_based import neuron
from spikingjelly import visualizing
from matplotlib import pyplot as plt
import numpy as np
plt.style.use(['science', 'muted'])
plt.rcParams['figure.dpi'] = 200
if_node = neuron.IFNode(v_reset=None, monitor_state=True)
T = 128
x = torch.arange(-0.2, 1.2, 0.... | 1,076 | 25.268293 | 86 | py |
spikingjelly | spikingjelly-master/docs/source/_static/tutorials/activation_based/15_recurrent_connection_and_stateful_synapse/plot_logs.py | from matplotlib import pyplot as plt
import numpy as np
from spikingjelly import visualizing
import torch
import torch.nn as nn
import torchvision
def plot_log(csv_file, title, x_label, y_label, plot_max=False, label=None):
log_data = np.loadtxt(csv_file, delimiter=',', skiprows=1, usecols=(1, 2))
x = log_data[... | 1,850 | 32.654545 | 109 | py |
spikingjelly | spikingjelly-master/docs/source/_static/tutorials/activation_based/15_recurrent_connection_and_stateful_synapse/save_gif.py | import torch
import torch.nn.functional as F
import torchvision.transforms
from torchvision.datasets import FashionMNIST
from matplotlib import pyplot as plt
train_set = FashionMNIST('D:/datasets/FashionMNIST', train=True, transform=torchvision.transforms.ToTensor())
to_img = torchvision.transforms.ToPILImage()
image... | 822 | 38.190476 | 109 | py |
spikingjelly | spikingjelly-master/docs/source/_static/logo/demo.py | from matplotlib import pyplot as plt
import torch
from spikingjelly.activation_based import neuron
from spikingjelly import visualizing
import numpy as np
import matplotlib
with torch.no_grad():
# Requires SciencePlots package: https://github.com/garrettj403/SciencePlots
plt.style.use(['science'])
if_node = neuro... | 3,737 | 33.934579 | 168 | py |
implicit_q_learning | implicit_q_learning-master/actor.py | from typing import Tuple
import jax
import jax.numpy as jnp
from common import Batch, InfoDict, Model, Params, PRNGKey
def update(key: PRNGKey, actor: Model, critic: Model, value: Model,
batch: Batch, temperature: float) -> Tuple[Model, InfoDict]:
v = value(batch.observations)
q1, q2 = critic(ba... | 986 | 30.83871 | 76 | py |
implicit_q_learning | implicit_q_learning-master/learner.py | """Implementations of algorithms for continuous control."""
from typing import Optional, Sequence, Tuple
import jax
import jax.numpy as jnp
import numpy as np
import optax
import policy
import value_net
from actor import update as awr_update_actor
from common import Batch, InfoDict, Model, PRNGKey
from critic import... | 5,114 | 36.065217 | 107 | py |
implicit_q_learning | implicit_q_learning-master/policy.py | import functools
from typing import Optional, Sequence, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from tensorflow_probability.substrates import jax as tfp
tfd = tfp.distributions
tfb = tfp.bijectors
from common import MLP, Params, PRNGKey, default_init
LOG_STD_MIN = -10.0
L... | 2,998 | 34.702381 | 79 | py |
implicit_q_learning | implicit_q_learning-master/common.py | import collections
import os
from typing import Any, Callable, Dict, Optional, Sequence, Tuple
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
import optax
Batch = collections.namedtuple(
'Batch',
['observations', 'actions', 'rewards', 'masks', 'next_observations'])
def default_init(s... | 3,226 | 30.637255 | 78 | py |
implicit_q_learning | implicit_q_learning-master/value_net.py | from typing import Callable, Sequence, Tuple
import jax.numpy as jnp
from flax import linen as nn
from common import MLP
class ValueCritic(nn.Module):
hidden_dims: Sequence[int]
@nn.compact
def __call__(self, observations: jnp.ndarray) -> jnp.ndarray:
critic = MLP((*self.hidden_dims, 1))(observ... | 1,360 | 30.651163 | 77 | py |
implicit_q_learning | implicit_q_learning-master/evaluation.py | from typing import Dict
import flax.linen as nn
import gym
import numpy as np
def evaluate(agent: nn.Module, env: gym.Env,
num_episodes: int) -> Dict[str, float]:
stats = {'return': [], 'length': []}
for _ in range(num_episodes):
observation, done = env.reset(), False
while not... | 617 | 22.769231 | 71 | py |
implicit_q_learning | implicit_q_learning-master/critic.py | from typing import Tuple
import jax.numpy as jnp
from common import Batch, InfoDict, Model, Params
def loss(diff, expectile=0.8):
weight = jnp.where(diff > 0, expectile, (1 - expectile))
return weight * (diff**2)
def update_v(critic: Model, value: Model, batch: Batch,
expectile: float) -> Tup... | 1,574 | 29.882353 | 78 | py |
backtranslated-imdb | backtranslated-imdb-master/cache_backtranslations.py | import textblob
import itertools
import glob
import time
from tqdm import tqdm_notebook, tqdm
# from fastai.text import *
# from fastai.core import save_texts
import pickle
from pathlib import Path
import pathlib
import fire
import shutil
def pickle_save(obj, path):
with open(path, 'wb') as f:
pickle.dump... | 4,140 | 29.674074 | 116 | py |
abr_control | abr_control-main/abr_control/utils/transformations.py | # pylint: skip-file
# -*- coding: utf-8 -*-
# transformations.py
# Copyright (c) 2006-2015, Christoph Gohlke
# Copyright (c) 2006-2015, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
# Redistribution and use in source and binary forms, with ... | 65,920 | 35.541574 | 88 | py |
GRU4REC-pytorch | GRU4REC-pytorch-master/main.py | import argparse
import torch
import lib
import numpy as np
import os
import datetime
parser = argparse.ArgumentParser()
parser.add_argument('--hidden_size', default=100, type=int) #Literature uses 100 / 1000 --> better is 100
parser.add_argument('--num_layers', default=3, type=int) #1 hidden layer
parser.add_argument(... | 7,099 | 46.972973 | 176 | py |
GRU4REC-pytorch | GRU4REC-pytorch-master/lib/model.py | from torch import nn
import torch
class GRU4REC(nn.Module):
def __init__(self, input_size, hidden_size, output_size, num_layers=1, final_act='tanh',
dropout_hidden=.5, dropout_input=0, batch_size=50, embedding_dim=-1, use_cuda=False):
super(GRU4REC, self).__init__()
self.input_size... | 4,363 | 40.561905 | 116 | py |
GRU4REC-pytorch | GRU4REC-pytorch-master/lib/dataset.py | import pandas as pd
import numpy as np
import torch
class Dataset(object):
def __init__(self, path, sep=',', session_key='SessionID', item_key='ItemID', time_key='Time', n_sample=-1, itemmap=None, itemstamp=None, time_sort=False):
# Read csv
self.df = pd.read_csv(path, sep=sep, dtype={session_key:... | 5,210 | 40.688 | 159 | py |
GRU4REC-pytorch | GRU4REC-pytorch-master/lib/lossfunction.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
class LossFunction(nn.Module):
def __init__(self, loss_type='TOP1', use_cuda=False):
""" An abstract loss function that can supports custom loss functions compatible with PyTorch."""
super(LossFu... | 3,480 | 33.127451 | 115 | py |
GRU4REC-pytorch | GRU4REC-pytorch-master/lib/evaluation.py | import lib
import numpy as np
import torch
from tqdm import tqdm
class Evaluation(object):
def __init__(self, model, loss_func, use_cuda, k=20):
self.model = model
self.loss_func = loss_func
self.topk = k
self.device = torch.device('cuda' if use_cuda else 'cpu')
def eval(self, ... | 1,477 | 37.894737 | 149 | py |
GRU4REC-pytorch | GRU4REC-pytorch-master/lib/trainer.py | import os
import lib
import time
import torch
import numpy as np
from tqdm import tqdm
class Trainer(object):
def __init__(self, model, train_data, eval_data, optim, use_cuda, loss_func, batch_size, args):
self.model = model
self.train_data = train_data
self.eval_data = eval_data
s... | 2,872 | 36.802632 | 166 | py |
GRU4REC-pytorch | GRU4REC-pytorch-master/lib/optimizer.py | import torch.optim as optim
class Optimizer:
def __init__(self, params, optimizer_type='Adagrad', lr=.05,
momentum=0, weight_decay=0, eps=1e-6):
'''
An abstract optimizer class for handling various kinds of optimizers.
You can specify the optimizer type and related paramet... | 1,717 | 43.051282 | 112 | py |
GRU4REC-pytorch | GRU4REC-pytorch-master/lib/metric.py | import torch
def get_recall(indices, targets): #recall --> wether next item in session is within top K=20 recommended items or not
"""
Calculates the recall score for the given predictions and targets
Args:
indices (Bxk): torch.LongTensor. top-k indices predicted by the model.
targets (B):... | 1,833 | 31.175439 | 117 | py |
CEFR-SP | CEFR-SP-main/src/model_base.py | import torch, transformers
import numpy as np
from torch import nn
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from transformers import AutoTokenizer, AutoModel
from sklearn.metrics import f1_score
from util import mean_pooling, token_embeddings_filtering_padding, read_corpus, CEFRDataset, ev... | 8,222 | 44.683333 | 121 | py |
CEFR-SP | CEFR-SP-main/src/split_dataset.py | from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
from util import mean_pooling
import numpy as np
import tqdm
def read_cefr_corpus(corpus_path):
levels, sents = [], []
lv_indices = {0: [], 1: [], 2: [], 3: [], 4: [], 5: []}
with open(corpus_path) as f:
... | 5,429 | 43.876033 | 114 | py |
CEFR-SP | CEFR-SP-main/src/model.py | import torch, random, itertools, tqdm
import numpy as np
from torch import nn
from torch.utils.data import DataLoader
from util import mean_pooling, read_corpus, CEFRDataset, convert_numeral_to_six_levels
from model_base import LevelEstimaterBase
class LevelEstimaterClassification(LevelEstimaterBase):
def __init_... | 10,665 | 44.004219 | 120 | py |
CEFR-SP | CEFR-SP-main/src/util.py | import torch, scipy
import numpy as np
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.metrics import classification_report
import seaborn as sns
import matplotlib.pyplot as plt
class TextDataset(torch.utils.data.Dataset):
def __init__(self, encodings):
self.e... | 3,759 | 33.495413 | 115 | py |
CEFR-SP | CEFR-SP-main/src/baseline.py | import torch
import numpy as np
from torch import nn
from util import mean_pooling, convert_numeral_to_six_levels
from model_base import LevelEstimaterBase
class BaselineClassification(LevelEstimaterBase):
def __init__(self, corpus_path, test_corpus_path, pretrained_model, problem_type, attach_wlv, num_labels,
... | 3,805 | 41.288889 | 126 | py |
CEFR-SP | CEFR-SP-main/src/level_estimator.py | import random
import tqdm
import torch, glob, os, argparse
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks import LearningRateMonitor
from... | 9,197 | 60.731544 | 117 | py |
Parrot_Paraphraser | Parrot_Paraphraser-main/parrot/filters.py | import torch
class Adequacy():
def __init__(self, model_tag='prithivida/parrot_adequacy_model'):
from transformers import AutoModelForSequenceClassification, AutoTokenizer
self.adequacy_model = AutoModelForSequenceClassification.from_pretrained(model_tag)
self.tokenizer = AutoTokenizer.from_pretrained... | 5,950 | 41.507143 | 108 | py |
WarpGAN | WarpGAN-master/align/mtcnntf/detect_face.py | """ Tensorflow implementation of the face detection / alignment algorithm found at
https://github.com/kpzhang93/MTCNN_face_detection_alignment
"""
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated docu... | 31,697 | 39.534527 | 150 | py |
SoftCTC | SoftCTC-main/soft_ctc/soft_ctc_loss_cuda.py | import torch
import importlib
import numpy as np
import sys
from soft_ctc.models import BatchConnections
soft_ctc_cuda = None
static_cuda_path = "soft_ctc.libs.cuda.soft_ctc_cuda"
dynamic_cuda_path = "soft_ctc.libs.cuda_" + torch.version.cuda.split(".")[0] + ".soft_ctc_cuda"
try:
soft_ctc_cuda = importlib.import... | 3,848 | 41.296703 | 317 | py |
SoftCTC | SoftCTC-main/soft_ctc/soft_ctc_loss_opencl.py | import torch
import numpy as np
import importlib
import sys
from soft_ctc.models import BatchConnections
static_opencl_path = "soft_ctc.libs.opencl.soft_ctc_opencl"
try:
soft_ctc_opencl = importlib.import_module(static_opencl_path)
except:
print("Error: Unable to load precompiled OpenCL SoftCTC library.", fi... | 3,550 | 42.304878 | 328 | py |
SoftCTC | SoftCTC-main/soft_ctc/multi_ctc_loss.py | import torch
from torch.nn.modules.loss import _Loss
class MultiCTCLoss(_Loss):
def __init__(self, blank=0, zero_infinity=True):
super().__init__(reduction='none')
self.blank = blank
self.zero_infinity = zero_infinity
self.ctc_loss = torch.nn.CTCLoss(blank=self.blank, reduction='no... | 842 | 34.125 | 110 | py |
SoftCTC | SoftCTC-main/soft_ctc/soft_ctc_loss.py | import torch
from soft_ctc.models import BatchConnections
class SoftCTCLoss(torch.autograd.Function):
def __init__(self, norm_step=10, zero_infinity=True):
self._norm_step = norm_step
self._zero_infinity = zero_infinity
def __call__(self, logits, connections: BatchConnections, labels):
... | 4,473 | 31.656934 | 126 | py |
SoftCTC | SoftCTC-main/soft_ctc/models/batch_connections.py | import torch
import numpy as np
from typing import Optional, List, Dict
from soft_ctc.models.connections import Connections, convert_confusion_network_to_connections
class BatchConnections():
def __init__(self, forward, backward, forward_start, forward_end, backward_start, backward_end):
self.forward = f... | 6,531 | 36.54023 | 114 | py |
LSAE | LSAE-main/train_lsae.py | import argparse
import math
import random
import os
import cv2
from functools import partial
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 tqdm impor... | 24,311 | 33.002797 | 142 | py |
LSAE | LSAE-main/model.py | import math
from packaging import version
import torch
from torch import nn
from torch.nn import functional as F
from stylegan2.model import StyledConv, Blur, EqualLinear, EqualConv2d, ScaledLeakyReLU
from stylegan2.op import FusedLeakyReLU
from models.resnet import resnet50
class EqualConvTranspose2d(nn.Module):
... | 32,552 | 29.652542 | 114 | py |
LSAE | LSAE-main/dataset.py | import os
import pdb
import numpy as np
from PIL import Image, ImageChops
from sklearn.model_selection import train_test_split
import torch
from torchvision import transforms
from torch.utils import data
from torch.utils.data import Dataset
import cv2
from tqdm import tqdm
import pdb
class CXR14Dataset(Dataset):
... | 8,672 | 33.692 | 152 | py |
LSAE | LSAE-main/train_texencoder_cxr14.py | import argparse
import math
import random
import os
import numpy as np
from sklearn.metrics import roc_auc_score
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 tqdm impo... | 10,920 | 30.202857 | 121 | py |
LSAE | LSAE-main/stylegan2/non_leaking.py | import math
import torch
from torch.nn import functional as F
from op import upfirdn2d
SYM6 = (
0.015404109327027373,
0.0034907120842174702,
-0.11799011114819057,
-0.048311742585633,
0.4910559419267466,
0.787641141030194,
0.3379294217276218,
-0.07263752278646252,
-0.0210602925123... | 10,140 | 24.41604 | 87 | py |
LSAE | LSAE-main/stylegan2/ppl.py | import argparse
import torch
from torch.nn import functional as F
import numpy as np
from tqdm import tqdm
import lpips
from model import Generator
def normalize(x):
return x / torch.sqrt(x.pow(2).sum(-1, keepdim=True))
def slerp(a, b, t):
a = normalize(a)
b = normalize(b)
d = (a * b).sum(-1, keep... | 2,992 | 27.504762 | 85 | py |
LSAE | LSAE-main/stylegan2/projector.py | import argparse
import math
import os
import torch
from torch import optim
from torch.nn import functional as F
from torchvision import transforms
from PIL import Image
from tqdm import tqdm
import lpips
from model import Generator
def noise_regularize(noises):
loss = 0
for noise in noises:
size = ... | 5,943 | 26.646512 | 90 | py |
LSAE | LSAE-main/stylegan2/calc_inception.py | import argparse
import pickle
import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.models import inception_v3, Inception3
import numpy as np
from tqdm import tqdm
from inception import InceptionV3
f... | 3,724 | 30.837607 | 88 | py |
LSAE | LSAE-main/stylegan2/inception.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
try:
from torchvision.models.utils import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
# Inception weights ported to Pytorch from
# http://do... | 11,623 | 36.376206 | 126 | py |
LSAE | LSAE-main/stylegan2/generate.py | import argparse
import torch
from torchvision import utils
from model import Generator
from tqdm import tqdm
def generate(args, g_ema, device, mean_latent):
with torch.no_grad():
g_ema.eval()
for i in tqdm(range(args.pics)):
sample_z = torch.randn(args.sample, args.latent, device=device... | 1,656 | 28.589286 | 99 | py |
LSAE | LSAE-main/stylegan2/model.py | import math
import random
import functools
import operator
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from .op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
class PixelNorm(nn.Module):
def __init__(self):
super().__init__()
def fo... | 18,345 | 26.058997 | 100 | py |
LSAE | LSAE-main/stylegan2/dataset.py | from io import BytesIO
import os
import lmdb
from PIL import Image
from torch.utils.data import Dataset
class MultiResolutionDataset(Dataset):
def __init__(self, path, transform, resolution=256):
self.env = lmdb.open(
path,
max_readers=32,
readonly=True,
loc... | 1,584 | 26.327586 | 80 | py |
LSAE | LSAE-main/stylegan2/fid.py | import argparse
import pickle
import torch
from torch import nn
import numpy as np
from scipy import linalg
from tqdm import tqdm
from model import Generator
from calc_inception import load_patched_inception_v3
@torch.no_grad()
def extract_feature_from_samples(
generator, inception, truncation, truncation_laten... | 3,148 | 28.157407 | 88 | py |
LSAE | LSAE-main/stylegan2/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(... | 2,715 | 20.385827 | 76 | py |
LSAE | LSAE-main/stylegan2/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 tqdm import tqdm
try:
import wandb
except Imp... | 14,121 | 29.7 | 89 | py |
LSAE | LSAE-main/stylegan2/convert_weight.py | import argparse
import os
import sys
import pickle
import math
import torch
import numpy as np
from torchvision import utils
from model import Generator, Discriminator
def convert_modconv(vars, source_name, target_name, flip=False):
weight = vars[source_name + "/weight"].value().eval()
mod_weight = vars[sou... | 7,934 | 27.238434 | 88 | py |
LSAE | LSAE-main/stylegan2/lpips/base_model.py | import os
import numpy as np
import torch
from torch.autograd import Variable
from pdb import set_trace as st
from IPython import embed
class BaseModel():
def __init__(self):
pass;
def name(self):
return 'BaseModel'
def initialize(self, use_gpu=True, gpu_ids=[0]):
self.use... | 1,618 | 26.440678 | 77 | py |
LSAE | LSAE-main/stylegan2/lpips/pretrained_networks.py | from collections import namedtuple
import torch
from torchvision import models as tv
from IPython import embed
class squeezenet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(squeezenet, self).__init__()
pretrained_features = tv.squeezenet1_1(pretrained=pretrained... | 6,533 | 34.901099 | 109 | py |
LSAE | LSAE-main/stylegan2/lpips/networks_basic.py |
from __future__ import absolute_import
import sys
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable
import numpy as np
from pdb import set_trace as st
from skimage import color
from IPython import embed
from . import pretrained_networks as pn
import lpips as util
de... | 7,483 | 38.808511 | 134 | py |
LSAE | LSAE-main/stylegan2/lpips/__init__.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from skimage.measure import compare_ssim
import torch
from torch.autograd import Variable
from lpips import dist_model
class PerceptualLoss(torch.nn.Module):
def __init__(self, model='... | 5,720 | 34.534161 | 172 | py |
LSAE | LSAE-main/stylegan2/lpips/dist_model.py |
from __future__ import absolute_import
import sys
import numpy as np
import torch
from torch import nn
import os
from collections import OrderedDict
from torch.autograd import Variable
import itertools
from .base_model import BaseModel
from scipy.ndimage import zoom
import fractions
import functools
import skimage.tr... | 11,773 | 40.312281 | 177 | py |
LSAE | LSAE-main/stylegan2/op/upfirdn2d.py | import os
import torch
from torch.nn import functional as F
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, ... | 5,672 | 27.223881 | 108 | py |
LSAE | LSAE-main/stylegan2/op/fused_act.py | import os
import torch
from torch import nn
from torch.nn import functional as F
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.joi... | 2,694 | 26.5 | 83 | py |
crfill | crfill-master/test.py | import numpy as np
import cv2
import torch
import data
from options.test_options import TestOptions
import models
opt = TestOptions().parse()
dataloader = data.create_dataloader(opt)
model = models.create_model(opt)
model.eval()
# test
num = 0
psnr_total = 0
for i, data_i in enumerate(dataloader):
if i * opt.ba... | 801 | 24.870968 | 56 | py |
crfill | crfill-master/demo.py | import pdb
import cv2
import os
from collections import OrderedDict
import numpy as np
from werkzeug.utils import secure_filename
from flask import Flask, url_for, render_template, request, redirect, send_from_directory
from PIL import Image
import base64
import io
import random
from options.test_options import Test... | 5,329 | 37.623188 | 116 | py |
crfill | crfill-master/train.py | import pdb
import sys
import torch
import numpy as np
from collections import OrderedDict
from options.train_options import TrainOptions
import data
from util.iter_counter import IterationCounter
from logger import Logger
from torchvision.utils import make_grid
from trainers import create_trainer
# parse options
opt =... | 4,864 | 38.233871 | 92 | py |
crfill | crfill-master/options/base_options.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import pdb
import sys
import argparse
import os
from util import util
import torch
import models
import data
import pickle
class BaseOptions():... | 8,189 | 46.068966 | 283 | py |
crfill | crfill-master/models/arrange_model.py | import pdb
import torch
from models.inpaint_model import InpaintModel
import util.util as util
class ArrangeModel(InpaintModel):
@staticmethod
def modify_commandline_options(parser, is_train):
InpaintModel.modify_commandline_options(parser, is_train)
parser.add_argument('--load_base_g', type=st... | 5,774 | 42.75 | 96 | py |
crfill | crfill-master/models/__init__.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import importlib
import torch
def find_model_using_name(model_name):
# Given the option --model [modelname],
# the file "models/modeln... | 1,417 | 30.511111 | 156 | py |
crfill | crfill-master/models/inpaint_model.py | import pdb
import torch
import models.networks as networks
import util.util as util
from models.create_mask import MaskCreator
import random
import numpy as np
class InpaintModel(torch.nn.Module):
@staticmethod
def modify_commandline_options(parser, is_train):
networks.modify_commandline_options(parse... | 11,242 | 40.640741 | 121 | py |
crfill | crfill-master/models/networks/loss.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
# Defines the GAN loss which uses either LSGAN or the regular GAN.
# When L... | 4,283 | 39.415094 | 105 | py |
crfill | crfill-master/models/networks/utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
import numpy as np
from torch.nn.functional import normalize
class gen_conv(nn.Conv2d):
def __init__(self, cin, cout, ksize, stride=1, rate=1, activation=nn.ELU()):
"""Define conv for generator
Args:
cin: In... | 11,245 | 40.962687 | 155 | py |
crfill | crfill-master/models/networks/inpaint_d.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
import numpy as np
from torch.nn.functional import normalize
from models.networks.base_network import BaseNetwork
from models.networks.utils import dis_conv
class DeepFillDiscriminator(BaseNetwork):
def __init__(self, opt):
super... | 1,255 | 31.205128 | 69 | py |
crfill | crfill-master/models/networks/inpaint_g.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
import numpy as np
from torch.nn.functional import normalize
from models.networks.base_network import BaseNetwork
from models.networks.utils import gen_conv, gen_deconv, dis_conv
from models.networks.splitcam import ReduceContextAttentionP1, ... | 16,072 | 37.178147 | 89 | py |
crfill | crfill-master/models/networks/__init__.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch
from models.networks.base_network import BaseNetwork
from models.networks.loss import *
from models.networks.discriminator import *... | 1,689 | 29.178571 | 105 | py |
crfill | crfill-master/models/networks/splitcam.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from models.networks.utils import batch_conv2d, batch_transposeconv2d
import pdb
def hardmax(similar):
val_max, id_max = torch.max(similar, 1)
num = similar.size(1)
sb = torch.Tensor(range(num)).long().to(similar.device)
id_max = id_ma... | 11,824 | 45.372549 | 120 | py |
crfill | crfill-master/models/networks/base_network.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch.nn as nn
from torch.nn import init
class BaseNetwork(nn.Module):
def __init__(self):
super(BaseNetwork, self).__init_... | 2,609 | 39.153846 | 107 | py |
crfill | crfill-master/util/util.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import re
import pdb
import importlib
import torch
from argparse import Namespace
import numpy as np
from PIL import Image
import os
import argp... | 10,380 | 32.814332 | 139 | py |
crfill | crfill-master/data/base_dataset.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
import random
class BaseD... | 4,080 | 30.635659 | 108 | py |
crfill | crfill-master/data/valimage_dataset.py | import torchvision.transforms as transforms
import torch
from data.base_dataset import get_params, get_transform, BaseDataset
from PIL import Image
from data.image_folder import make_dataset
import os
import pdb
class ValImageDataset(BaseDataset):
@staticmethod
def modify_commandline_options(parser, is_train)... | 3,245 | 39.074074 | 90 | py |
crfill | crfill-master/data/image_folder.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
###############################################################################
# Code from
# https://github.com/pytorch/vision/blob/master/torc... | 3,137 | 30.69697 | 105 | py |
crfill | crfill-master/data/__init__.py | """
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import importlib
import torch.utils.data
from data.base_dataset import BaseDataset
def find_dataset_using_name(dataset_name):
# Given the ... | 2,822 | 33.012048 | 105 | py |
crfill | crfill-master/data/testimage_dataset.py | import torchvision.transforms as transforms
import torch
from data.base_dataset import get_params, get_transform, BaseDataset
from PIL import Image
from data.image_folder import make_dataset
import os
class TestImageDataset(BaseDataset):
@staticmethod
def modify_commandline_options(parser, is_train):
... | 2,801 | 35.38961 | 84 | py |
crfill | crfill-master/trainers/stylegan2_trainer.py | import pdb
import torch
from models.networks.sync_batchnorm import DataParallelWithCallback
import models
#from models.pix2pix_model import Pix2PixModel
class StyleGAN2Trainer():
def __init__(self, opt):
self.opt = opt
self.pix2pix_model = models.create_model(opt)
if len(opt.gpu_ids) > 0:
... | 4,294 | 35.398305 | 82 | py |
crfill | crfill-master/trainers/__init__.py | import importlib
def find_trainer_using_name(model_name):
model_filename = "trainers." + model_name + "_trainer"
modellib = importlib.import_module(model_filename)
# In the file, the class called ModelNameModel() will
# be instantiated. It has to be a subclass of torch.nn.Module,
# and it is case-... | 941 | 31.482759 | 156 | py |
meta-ot | meta-ot-main/plot_world_pair.py | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
import argparse
import pickle as pkl
import numpy as np
from matplotlib import pyplot as plt
plt.style.use('bmh')
import os
import jax
import jax.numpy as jnp
from ott.core import quad_problems, problems, sinkhorn
# from ott.tools import ... | 3,184 | 30.534653 | 114 | py |
meta-ot | meta-ot-main/train_color_meta.py | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# https://github.com/iamalexkorotin/Wasserstein2GenerativeNetworks/blob/master/notebooks/W2GN_color_transfer.ipynb
import hydra
from hydra.utils import instantiate
import csv
import copy
import glob
import os
import random
import functools
... | 16,280 | 40.113636 | 176 | py |
meta-ot | meta-ot-main/create_video_color.py | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
import argparse
import copy
from omegaconf import OmegaConf
import pandas as pd
import jax
import jax.numpy as jnp
import torch
from torch import nn
import numpy as np
from meta_ot.data import ImageSampler, ImagePairSampler
from meta_ot impo... | 5,474 | 32.384146 | 176 | py |
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