repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
dfd_benchmark | dfd_benchmark-master/pytorch_model/eval_torch.py | import torch
import random
import os
from sklearn import metrics
import numpy as np
from torch.autograd import Variable
from pytorch_model.capsule_pytorch.model import VggExtractor,CapsuleNet,CapsuleLoss
import torch.nn as nn
import time
from tqdm import tqdm
from sklearn.metrics import recall_score,accuracy_score,pre... | 14,189 | 42.931889 | 144 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/detect_torch.py | import torch
import numpy as np
from torch.autograd import Variable
from pytorch_model.capsule_pytorch.model import VggExtractor,CapsuleNet,CapsuleLoss
import torch.nn as nn
def detect_capsule(img,gpu_id=-1,model_path="checkpoint"):
device = torch.device("cuda" if torch.cuda.is_available()
... | 1,828 | 28.983607 | 83 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/focal_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
# https://github.com/clcarwin/focal_loss_pytorch
"""
class FocalLoss(nn.Module):
def __init__(self, gamma=0, alpha=None, size_average=True):
super(FocalLoss, self).__init__()
self.gamma = gamma
... | 3,930 | 32.887931 | 97 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/model_cnn_pytorch.py | import os, sys, random
import torch
import torch.nn as nn
import torchvision.models as models
class MyResNeXt(models.resnet.ResNet):
def __init__(self, training=True):
super(MyResNeXt, self).__init__(block=models.resnet.Bottleneck,
layers=[3, 4, 6, 3],
... | 3,405 | 32.067961 | 74 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/xception.py | from pytorchcv.model_provider import get_model
import torch.nn as nn
import torch
class Pooling(nn.Module):
def __init__(self):
super(Pooling, self).__init__()
self.p1 = nn.AdaptiveAvgPool2d((1,1))
self.p2 = nn.AdaptiveMaxPool2d((1,1))
def forward(self, x):
x1 = self.p1(x)
... | 2,363 | 26.172414 | 86 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/siamese.py | import os
from torch.utils.data import DataLoader, Dataset
import numpy as np
import random
from PIL import Image
import torch
# from torch.autograd import Variable
# import PIL.ImageOps
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
import glob
from torchvision import transforms, datase... | 7,366 | 36.974227 | 172 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/data_generate.py | import torch
import torchvision.transforms as transforms
# import torchtoolbox.transform as transforms
import torchvision.datasets as datasets
import glob
from torch.utils.data import Dataset
import numpy as np
from PIL import Image,ImageEnhance
import cv2
from albumentations.augmentations.transforms import ImageCompr... | 24,064 | 48.01222 | 151 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/efficientnet/train_pairwise.py | import os
import torch
import torch.nn as nn
from torch import optim
from pytorch_model.pairwise.contrastive_loss import ContrastiveLoss
from pytorch_model.pairwise.data_generate import get_generate_pairwise
import torch.backends.cudnn as cudnn
from tqdm import tqdm
from pytorch_model.pairwise.eval_pairwise import eva... | 2,817 | 38.690141 | 108 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/efficientnet/extract_feature_conbine.py | import os
import torch
import torch.backends.cudnn as cudnn
from pytorch_model.efficientnet import EfficientNet
from pytorch_model.efficientnet.model import Identity
from feature_model.visual_artifact.pipeline.eyecolor import extract_eyecolor_features
from feature_model.visual_artifact.process_data import load_facedet... | 1,233 | 40.133333 | 153 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/efficientnet/utils.py | """
This file contains helper functions for building the model and for loading model parameters.
These helper functions are built to mirror those in the official TensorFlow implementation.
"""
import re
import math
import collections
from functools import partial
import torch
from torch import nn
from torch.nn import ... | 14,147 | 41.486486 | 130 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/efficientnet/model.py | import torch
from torch import nn
from torch.nn import functional as F
from .utils import (
round_filters,
round_repeats,
drop_connect,
get_same_padding_conv2d,
get_model_params,
efficientnet_params,
load_pretrained_weights,
Swish,
MemoryEfficientSwish,
)
class MBConvBlock(nn.Modu... | 11,381 | 40.540146 | 107 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/efficientnet/model_pairwise.py | from pytorch_model.efficientnet import EfficientNet
import torch.nn as nn
from pytorch_model.efficientnet.model import Identity
model = EfficientNet.from_pretrained('efficientnet-b3',num_classes=1)
class EfficientPairwise(nn.Module):
def __init__(self):
super(EfficientPairwise, self).__init__()
se... | 1,315 | 31.9 | 87 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/pairwise/contrastive_loss.py | import torch
import torch.nn.functional as F
class ContrastiveLoss(torch.nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self,device, margin=2.0):
super(ContrastiveLoss, self).__init__()
self.ma... | 989 | 46.142857 | 172 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/pairwise/train_pairwise.py | import os
import torch
import torch.nn as nn
from torch import optim
from pytorch_model.pairwise.contrastive_loss import ContrastiveLoss
from pytorch_model.pairwise.data_generate import get_generate_pairwise
import torch.backends.cudnn as cudnn
from tqdm import tqdm
from pytorch_model.pairwise.eval_pairwise import eva... | 3,153 | 41.053333 | 108 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/pairwise/eval_pairwise.py |
import os
import torch
import torch.nn as nn
from torch import optim
from pytorch_model.pairwise.contrastive_loss import ContrastiveLoss
from pytorch_model.pairwise.data_generate import get_generate_pairwise
import torch.backends.cudnn as cudnn
from tqdm import tqdm
def eval_pairwise(model,criterion,text_writer,dat... | 1,240 | 33.472222 | 99 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/pairwise/model.py | import os
from torch.utils.data import DataLoader
import torch
import torch.nn as nn
from torch import optim
import glob
from torchvision import transforms, datasets, models
from pytorch_model.pairwise.dense_block import DenseBlock,TransitionBlock,BasicBlock,BottleneckBlock
class cffn(nn.Module):
def __init__(se... | 5,546 | 34.557692 | 188 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/pairwise/dense_block.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu = nn.ReLU(inplace=True)
self.conv1 ... | 2,885 | 42.074627 | 89 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/pairwise/data_generate.py | import glob
import numpy as np
import random
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms
import torch
class PairwiseDataset(Dataset):
def __init__(self, path, transform=None, should_invert=True,shuffle=True):
self.path = path
self.transform... | 4,074 | 37.443396 | 105 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/DSP_FWA/models/classifier.py |
import torch
from torch import nn
from torchvision import models
import torch.nn.functional as F
import os, math
class ResNet(nn.Module):
def __init__(self, layers=18, num_class=2, pretrained=True):
super(ResNet, self).__init__()
if layers == 18:
self.resnet = models.resnet18(pretrain... | 3,608 | 33.04717 | 92 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/capsule_pytorch/loss.py |
from torch import nn
class CapsuleLoss(nn.Module):
def __init__(self):
super(CapsuleLoss, self).__init__()
self.cross_entropy_loss = nn.CrossEntropyLoss()
# if gpu_id >= 0:
# self.cross_entropy_loss.cuda(gpu_id)
def forward(self, classes, labels):
loss_t = self.cr... | 516 | 26.210526 | 83 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/capsule_pytorch/model.py | """
Copyright (c) 2019, National Institute of Informatics
All rights reserved.
Author: Huy H. Nguyen
-----------------------------------------------------
Script for training Capsule-Forensics-v2 on FaceForensics++ database (Real, DeepFakes, Face2Face, FaceSwap)
"""
import sys
sys.setrecursionlimit(15000)
import os
im... | 14,487 | 33.089412 | 135 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/drn/drn.py | import pdb
import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
torch.backends.cudnn.benchmark = True
BatchNorm = nn.BatchNorm2d
# __all__ = ['DRN', 'drn26', 'drn42', 'drn58']
webroot = 'https://tigress-web.princeton.edu/~fy/drn/models/'
model_urls = {
'resnet50': 'https:/... | 14,255 | 33.269231 | 88 | py |
dfd_benchmark | dfd_benchmark-master/pytorch_model/drn/drn_seg.py | import math
import torch
import torch.nn as nn
from pytorch_model.drn.drn import drn_c_26
def fill_up_weights(up):
w = up.weight.data
f = math.ceil(w.size(2) / 2)
c = (2 * f - 1 - f % 2) / (2. * f)
for i in range(w.size(2)):
for j in range(w.size(3)):
w[0, 0, i, j] = \
... | 5,354 | 32.679245 | 101 | py |
scGNN | scGNN-master/model.py | import torch
from torch import nn, optim
from torch.nn import functional as F
class AE(nn.Module):
''' Autoencoder for dimensional reduction'''
def __init__(self,dim):
super(AE, self).__init__()
self.dim = dim
self.fc1 = nn.Linear(dim, 512)
self.fc2 = nn.Linear(512, 128)
... | 1,575 | 27.654545 | 60 | py |
scGNN | scGNN-master/util_function.py | import sys
import os
import numpy as np
import pickle as pkl
import networkx as nx
import scipy.sparse as sp
import scipy.io
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import Dataset, DataLoader
from benchmark_util import *
from igraph import *
dir_path = os.path... | 25,896 | 35.372191 | 317 | py |
scGNN | scGNN-master/scGNN.py | import time
import os
import argparse
import sys
import numpy as np
import pickle as pkl
import networkx as nx
import scipy.sparse as sp
import resource
import datetime
import torch
from torch.utils.data import Dataset, DataLoader
from torch import nn, optim
from torch.nn import functional as F
from sklearn.decompositi... | 41,917 | 50.814586 | 257 | py |
scGNN | scGNN-master/main_benchmark.py | import time
import resource
import datetime
import argparse
import sys
import numpy as np
import pickle as pkl
import networkx as nx
import scipy.sparse as sp
import torch
from torch.utils.data import Dataset, DataLoader
from torch import nn, optim
from torch.nn import functional as F
from sklearn.decomposition import ... | 42,982 | 51.675245 | 257 | py |
scGNN | scGNN-master/gae_embedding.py | import os, sys
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir))
# For replicating the experiments
SEED = 42
import argparse
import time
import random
import numpy as np
import scipy.sparse as sp
import torch
np.random.seed(SEED)
torch.manual_seed(SEED)
from torch import optim
import... | 6,544 | 45.41844 | 171 | py |
scGNN | scGNN-master/gae/utils.py | import pickle as pkl
import networkx as nx
import numpy as np
import scipy.sparse as sp
import torch
from sklearn.metrics import roc_auc_score, average_precision_score
def sample_mask(idx, l):
"""Create mask."""
mask = np.zeros(l)
mask[idx] = 1
return np.array(mask, dtype=np.bool)
def load_data(dat... | 8,312 | 35.460526 | 114 | py |
scGNN | scGNN-master/gae/model.py | '''GAE models'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from gae.layers import GraphConvolution
class GCNModelVAE(nn.Module):
def __init__(self, input_feat_dim, hidden_dim1, hidden_dim2, dropout):
super(GCNModelVAE, self).__init__()
self.gc1 = GraphConvolution(input_fe... | 2,102 | 32.380952 | 87 | py |
scGNN | scGNN-master/gae/layers.py | import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0., act=F.relu):... | 1,269 | 32.421053 | 80 | py |
scGNN | scGNN-master/gae/train.py | import os, sys
# Main entrance from https://github.com/MysteryVaibhav/RWR-GAE
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir))
# For replicating the experiments
SEED = 42
import argparse
import time
import random
import numpy as np
import scipy.sparse as sp
import torch
np.random.s... | 5,829 | 46.786885 | 112 | py |
scGNN | scGNN-master/gae/optimizer.py | import torch
import torch.nn.modules.loss
import torch.nn.functional as F
def loss_function(preds, labels, mu, logvar, n_nodes, norm, pos_weight):
cost = norm * F.binary_cross_entropy_with_logits(preds, labels, pos_weight=labels * pos_weight)
# Check if the model is simple Graph Auto-encoder
if logvar is... | 677 | 32.9 | 99 | py |
mAP | mAP-master/scripts/extra/convert_keras-yolo3.py | '''
ABOUT THIS SCRIPT:
Converts ground-truth from the annotation files
according to the https://github.com/qqwweee/keras-yolo3
or https://github.com/gustavovaliati/keras-yolo3 format.
And converts the detection-results from the annotation files
according to the https://github.com/gustavovaliati/keras-yolo3 format.
'''... | 3,677 | 40.325843 | 99 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/attack_and_counter_game/attack_and_counter_hparam_search_dqn.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.typing import PolicyID
from ray.rllib.models import MODEL_DEFAULTS
from ray import tune
fro... | 10,509 | 40.377953 | 178 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/attack_and_counter_game/attack_and_counter_game_ppo_hparam_search.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.typing import PolicyID
from ray.rllib.models import MODEL_DEFAULTS
from ray import tune
fro... | 8,159 | 39 | 182 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/oshi_zumo/oshi_zumo_hyperparam_search_sac_cont_check.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils.typing import PolicyID
from ray import tune
from ray.rllib.agents.callbacks import DefaultCallbacks
from ray.rllib.evaluation impor... | 10,114 | 42.412017 | 186 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/oshi_zumo/oshi_zumo_hyperparam_search_sac_cont.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils.typing import PolicyID
from ray import tune
from ray.tune import choice
from ray.rllib.agents.callbacks import DefaultCallbacks
fro... | 9,861 | 41.878261 | 186 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/oshi_zumo/oshi_zumo_hyperparam_search_sac_discrete.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils.typing import PolicyID
from ray import tune
from ray.tune import choice
from ray.rllib.agents.callbacks import DefaultCallbacks
fro... | 9,854 | 41.662338 | 186 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/oshi_zumo/oshi_zumo_hyperparam_search_ppo_check.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.typing import PolicyID
from ray.rllib.models import MODEL_DEFAULTS
from ray import tune
# f... | 8,116 | 38.21256 | 186 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/oshi_zumo/oshi_zumo_hyperparam_search_sac_cont_check2.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils.typing import PolicyID
from ray import tune
from ray.rllib.agents.callbacks import DefaultCallbacks
from ray.rllib.evaluation impor... | 10,158 | 42.600858 | 186 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/oshi_zumo/oshi_zumo_hyperparam_search_sac_cont_check3.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils.typing import PolicyID
from ray import tune
from ray.rllib.agents.callbacks import DefaultCallbacks
from ray.rllib.evaluation impor... | 10,159 | 42.418803 | 186 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/oshi_zumo/oshi_zumo_hyperparam_search_ppo_discrete.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.typing import PolicyID
from ray.rllib.models import MODEL_DEFAULTS
from ray import tune
fro... | 7,968 | 39.247475 | 186 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/oshi_zumo/tiny_oshi_zumo_hyperparam_search_dqn.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.typing import PolicyID
from ray.rllib.models import MODEL_DEFAULTS
from ray import tune
fro... | 10,154 | 39.947581 | 173 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/oshi_zumo/oshi_zumo_hyperparam_search_ppo_cont.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.typing import PolicyID
from ray.rllib.models import MODEL_DEFAULTS
from ray import tune
fro... | 8,421 | 39.104762 | 186 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/oshi_zumo/medium_oshi_zumo_hyperparam_search_dqn.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.typing import PolicyID
from ray.rllib.models import MODEL_DEFAULTS
from ray import tune
fro... | 10,162 | 39.979839 | 175 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/oshi_zumo/oshi_zumo_hyperparam_search_ppo_discrete_check.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.typing import PolicyID
from ray.rllib.models import MODEL_DEFAULTS
from ray import tune
fro... | 7,598 | 38.373057 | 186 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/oshi_zumo/oshi_zumo_hyperparam_search_sac_cont_larger.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils.typing import PolicyID
from ray import tune
from ray.tune import choice
from ray.rllib.agents.callbacks import DefaultCallbacks
fro... | 9,921 | 42.13913 | 186 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/loss_game/loss_game_hyperparam_search_dqn.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.typing import PolicyID
from ray.rllib.models import MODEL_DEFAULTS
from ray import tune
fro... | 9,782 | 39.259259 | 181 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/loss_game/loss_game_ppo_hparam_search.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.typing import PolicyID
from ray.rllib.models import MODEL_DEFAULTS
from ray import tune
fro... | 8,114 | 38.393204 | 179 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/misc/rnn_test.py | import logging
import os
import ray
from ray.rllib.utils import merge_dicts, try_import_torch
torch, _ = try_import_torch()
from ray.rllib.agents.ppo import PPOTrainer, PPOTorchPolicy
from grl.utils.common import pretty_dict_str
from grl.rllib_tools.space_saving_logger import get_trainer_logger_creator
from grl.uti... | 2,725 | 33.948718 | 108 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/poker/leduc_hyperparam_search_dqn.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.typing import PolicyID
from ray.rllib.models import MODEL_DEFAULTS
from ray import tune
fro... | 10,009 | 39.526316 | 163 | py |
nxdo | nxdo-master/grl/rl_apps/single_agent/poker/leduc_hyperparam_search_dqn_compare_with_original.py | import logging
import os
from typing import Dict
import ray
from ray.rllib import BaseEnv
from ray.rllib.utils import try_import_torch
torch, _ = try_import_torch()
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.typing import PolicyID
from ray.rllib.models import MODEL_DEFAULTS
from ray import tune
fro... | 5,710 | 37.328859 | 163 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/catalog/loss_game_scenarios.py | from ray.rllib.agents.dqn import DQNTrainer
from ray.rllib.agents.ppo import PPOTrainer, PPOTorchPolicy
from grl.algos.nfsp_rllib.nfsp import NFSPTrainer, NFSPTorchAveragePolicy
from grl.envs.loss_game_alpha_multi_agent_env import LossGameAlphaMultiAgentEnv
from grl.envs.loss_game_multi_agent_env import LossGameMultiA... | 26,570 | 44.498288 | 152 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/catalog/poker_nxdo_scenarios.py | from ray.rllib.agents.dqn import DQNTrainer
from ray.rllib.agents.ppo import PPOTrainer, PPOTorchPolicy
from grl.algos.nfsp_rllib.nfsp import NFSPTrainer, NFSPTorchAveragePolicy
from grl.envs.poker_multi_agent_env import PokerMultiAgentEnv
from grl.rl_apps.nxdo.solve_restricted_game_fns import *
from grl.rl_apps.scena... | 24,301 | 46.37232 | 140 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/catalog/oshi_zumo_nfsp_scenarios.py | from ray.rllib.agents.dqn import DQNTrainer
from grl.algos.nfsp_rllib.nfsp import NFSPTrainer, NFSPTorchAveragePolicy
from grl.envs.oshi_zumo_multi_agent_env import OshiZumoMultiAgentEnv, ThousandActionOshiZumoMultiAgentEnv, \
TinyOshiZumoMultiAgentEnv, MediumOshiZumoMultiAgentEnv
from grl.rl_apps.scenarios.catalo... | 4,597 | 37.316667 | 108 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/catalog/poker_psro_scenarios.py | from ray.rllib.agents.dqn import DQNTrainer
from ray.rllib.agents.ppo import PPOTrainer, PPOTorchPolicy
from grl.envs.poker_multi_agent_env import PokerMultiAgentEnv
from grl.rl_apps.scenarios.catalog import scenario_catalog
from grl.rl_apps.scenarios.catalog.common import default_if_creating_ray_head
from grl.rl_apps... | 12,777 | 40.62215 | 97 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/catalog/oshi_zumo_nxdo_scenarios.py | from ray.rllib.agents.dqn import DQNTrainer
from ray.rllib.agents.ppo import PPOTrainer, PPOTorchPolicy
from grl.algos.nfsp_rllib.nfsp import NFSPTrainer, NFSPTorchAveragePolicy
from grl.envs.oshi_zumo_multi_agent_env import OshiZumoMultiAgentEnv, TinyOshiZumoMultiAgentEnv, \
ThousandActionOshiZumoMultiAgentEnv, M... | 13,983 | 44.255663 | 129 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/catalog/oshi_zumo_psro_scenarios.py | from ray.rllib.agents.dqn import DQNTrainer
from ray.rllib.agents.ppo import PPOTrainer, PPOTorchPolicy
from grl.envs.oshi_zumo_multi_agent_env import OshiZumoMultiAgentEnv, TinyOshiZumoMultiAgentEnv, \
ThousandActionOshiZumoMultiAgentEnv, MediumOshiZumoMultiAgentEnv
from grl.rl_apps.scenarios.catalog import scena... | 7,431 | 37.507772 | 98 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/catalog/poker_nfsp_scenarios.py | from ray.rllib.agents.dqn import DQNTrainer
from grl.algos.nfsp_rllib.nfsp import NFSPTrainer, NFSPTorchAveragePolicy
from grl.envs.poker_multi_agent_env import PokerMultiAgentEnv
from grl.rl_apps.scenarios.catalog import scenario_catalog
from grl.rl_apps.scenarios.catalog.common import default_if_creating_ray_head
fr... | 5,051 | 38.162791 | 93 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/catalog/attack_and_counter_game_scenarios.py | from ray.rllib.agents.dqn import DQNTrainer
from ray.rllib.agents.ppo import PPOTrainer, PPOTorchPolicy
from grl.algos.nfsp_rllib.nfsp import NFSPTrainer, NFSPTorchAveragePolicy
from grl.envs.attack_and_counter_game import AttackCounterGameMultiAgentEnv
from grl.rl_apps.nxdo.solve_restricted_game_fns import *
from grl... | 5,696 | 46.082645 | 97 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/trainer_configs/loss_game_configs.py | import os
from typing import Dict, Any
from ray.rllib.env import MultiAgentEnv
from ray.rllib.models import MODEL_DEFAULTS
from ray.rllib.utils import merge_dicts
from grl.rl_apps.scenarios.trainer_configs.defaults import GRL_DEFAULT_OSHI_ZUMO_MEDIUM_DQN_PARAMS
from grl.rl_apps.scenarios.trainer_configs.defaults impo... | 6,391 | 36.6 | 98 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/trainer_configs/defaults.py | import os
from ray.rllib.models import MODEL_DEFAULTS
from ray.rllib.utils import merge_dicts
from grl.rllib_tools.stochastic_sampling_ignore_kwargs import StochasticSamplingIgnoreKwargs
from grl.rllib_tools.valid_actions_epsilon_greedy import ValidActionsEpsilonGreedy
# To use different params, please make define a... | 10,751 | 33.683871 | 92 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/trainer_configs/oshi_zumo_configs.py | import os
from typing import Dict, Any
from ray.rllib.env import MultiAgentEnv
from ray.rllib.models import MODEL_DEFAULTS
from ray.rllib.utils import merge_dicts
from grl.rl_apps.scenarios.trainer_configs.defaults import GRL_DEFAULT_OSHI_ZUMO_MEDIUM_DQN_PARAMS, \
GRL_DEFAULT_OSHI_ZUMO_TINY_DQN_PARAMS
from grl.rl... | 4,008 | 34.794643 | 101 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/trainer_configs/poker_nfsp_configs.py | import os
from typing import Dict, Any
from ray.rllib.env import MultiAgentEnv
from ray.rllib.models import MODEL_DEFAULTS
from ray.rllib.utils import merge_dicts
from grl.rl_apps.scenarios.trainer_configs.defaults import GRL_DEFAULT_OPENSPIEL_POKER_DQN_PARAMS
from grl.rllib_tools.models.valid_actions_fcnet import ge... | 8,799 | 36.288136 | 166 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/trainer_configs/poker_psro_configs.py | import os
from typing import Dict, Any
from ray.rllib.env import MultiAgentEnv
from ray.rllib.models import MODEL_DEFAULTS
from ray.rllib.models.torch.torch_action_dist import TorchBeta
from ray.rllib.utils import merge_dicts
from ray.tune.registry import RLLIB_ACTION_DIST, _global_registry
from grl.rl_apps.scenarios... | 7,192 | 37.672043 | 108 | py |
nxdo | nxdo-master/grl/rl_apps/scenarios/trainer_configs/attack_and_counter_game_configs.py | import os
from typing import Dict, Any
from ray.rllib.env import MultiAgentEnv
from ray.rllib.models import MODEL_DEFAULTS
from ray.rllib.utils import merge_dicts
from grl.rl_apps.scenarios.trainer_configs.defaults import GRL_DEFAULT_OSHI_ZUMO_MEDIUM_DQN_PARAMS
from grl.rl_apps.scenarios.trainer_configs.defaults impo... | 2,657 | 37.521739 | 98 | py |
nxdo | nxdo-master/grl/rl_apps/psro/general_psro_br.py | import argparse
import logging
import os
import time
from typing import Dict, Type, List
import ray
from ray.rllib.agents import Trainer
from ray.rllib.agents.callbacks import DefaultCallbacks
from ray.rllib.agents.dqn import DQNTrainer
from ray.rllib.env import BaseEnv
from ray.rllib.evaluation import MultiAgentEpiso... | 23,205 | 52.470046 | 141 | py |
nxdo | nxdo-master/grl/rl_apps/psro/approx_exploitability_psro_logger.py | import json
import logging
import os
import numpy as np
from ray.rllib.utils import try_import_torch
from termcolor import colored
from grl.algos.p2sro.p2sro_manager.logger import SimpleP2SROManagerLogger
from grl.rl_apps.scenarios.psro_scenario import PSROScenario
from grl.utils.common import ensure_dir
from grl.uti... | 4,625 | 48.212766 | 130 | py |
nxdo | nxdo-master/grl/rl_apps/psro/general_psro_approx_exploitability.py | import logging
import os
import time
from typing import Dict, Type
import deepdish
import numpy as np
import ray
from ray.rllib.agents.callbacks import DefaultCallbacks
from ray.rllib.agents.dqn import DQNTrainer
from ray.rllib.env import BaseEnv
from ray.rllib.evaluation import MultiAgentEpisode, RolloutWorker
from r... | 9,061 | 42.777778 | 196 | py |
nxdo | nxdo-master/grl/rl_apps/psro/exploitability_psro_logger.py | import json
import logging
import os
from typing import Dict
import numpy as np
from ray.rllib.agents.trainer import with_common_config
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.policy import Policy
from ray.rllib.utils import try_import_torch
from ray.rllib.utils.typing import TensorT... | 9,413 | 52.488636 | 154 | py |
nxdo | nxdo-master/grl/rl_apps/psro/simple_self_play.py | import argparse
import logging
import os
from typing import Type, Dict
from ray.rllib.agents import Trainer
from ray.rllib.agents.callbacks import DefaultCallbacks
from ray.rllib.policy import Policy
from ray.rllib.utils import merge_dicts, try_import_torch
import grl
from grl.rl_apps.scenarios.catalog import scenari... | 7,367 | 41.344828 | 115 | py |
nxdo | nxdo-master/grl/rl_apps/psro/poker_utils.py | import os
from typing import Dict, Callable, List, Tuple, Union
import deepdish
import numpy as np
import pyspiel
import ray
from open_spiel.python import rl_environment
from open_spiel.python.algorithms.exploitability import exploitability
from open_spiel.python.policy import Policy as OpenSpielPolicy, tabular_policy... | 23,643 | 48.053942 | 138 | py |
nxdo | nxdo-master/grl/rl_apps/nxdo/general_nxdo_br.py | import argparse
import logging
import os
import time
from typing import Dict, List, Type
import deepdish
import ray
from gym.spaces import Discrete
from ray.rllib.agents import Trainer
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.evaluation import RolloutWorker
from ray.rllib.policy import Po... | 18,537 | 45.931646 | 131 | py |
nxdo | nxdo-master/grl/rl_apps/nxdo/general_nxdo_approx_exploitability.py | import logging
import time
from typing import Dict, List, Type, Callable
import deepdish
import ray
from gym.spaces import Discrete
from ray.rllib.agents import Trainer
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.evaluation import RolloutWorker
from ray.rllib.policy import Policy
from ray.rl... | 12,839 | 44.531915 | 116 | py |
nxdo | nxdo-master/grl/rl_apps/nxdo/general_nxdo_nfsp_metanash.py | import copy
import logging
import os
import time
from copy import deepcopy
from typing import List, Any, Tuple, Type, Dict, Union
import deepdish
import numpy as np
import ray
from gym.spaces import Discrete
from ray.rllib.agents import Trainer
from ray.rllib.agents.callbacks import DefaultCallbacks
from ray.rllib.env... | 25,425 | 52.868644 | 132 | py |
nxdo | nxdo-master/grl/rl_apps/nfsp/general_approx_exploitability_nfsp.py | import logging
import os
from typing import Type, Dict
import deepdish
from ray.rllib.agents import Trainer
from ray.rllib.evaluation import RolloutWorker
from ray.rllib.policy import Policy
from ray.rllib.utils import merge_dicts, try_import_torch
from grl.rl_apps.scenarios.nfsp_scenario import NFSPScenario
from grl... | 6,012 | 40.468966 | 119 | py |
nxdo | nxdo-master/grl/rl_apps/nfsp/general_nfsp.py | import argparse
import copy
import logging
import os
import time
from typing import Any, Tuple, Type, Dict, Union
import deepdish
import numpy as np
import ray
from ray.rllib.agents import Trainer
from ray.rllib.agents.callbacks import DefaultCallbacks
from ray.rllib.env import BaseEnv
from ray.rllib.evaluation import... | 20,006 | 47.09375 | 124 | py |
nxdo | nxdo-master/grl/rl_apps/nfsp/mixed_discrete_cont_actions_approx_expl_nfsp.py | import logging
import os
from typing import Type, Dict
import deepdish
from ray.rllib.agents import Trainer
from ray.rllib.evaluation import RolloutWorker
from ray.rllib.policy import Policy
from ray.rllib.utils import merge_dicts, try_import_torch
from grl.rl_apps.scenarios.ray_setup import init_ray_for_scenario
fro... | 6,606 | 40.29375 | 128 | py |
nxdo | nxdo-master/grl/rl_apps/nfsp/general_approx_exploitability_self_play.py | import logging
from typing import Type, Dict
import deepdish
from ray.rllib.evaluation import RolloutWorker
from ray.rllib.policy import Policy
from ray.rllib.utils import merge_dicts, try_import_torch
from grl.rl_apps.scenarios.ray_setup import init_ray_for_scenario
from grl.rl_apps.scenarios.stopping_conditions imp... | 5,532 | 41.891473 | 118 | py |
nxdo | nxdo-master/grl/algos/nfsp_rllib/nfsp_torch_avg_policy.py | import logging
from typing import Dict, Tuple, Type, List
import gym
import numpy as np
from ray.rllib.models import ModelCatalog
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.tf_action_dist import (Categorical)
from ray.rllib.models.torch.torch_action_dist import TorchCategorical
from ray.rlli... | 7,543 | 36.162562 | 110 | py |
nxdo | nxdo-master/grl/algos/nfsp_rllib/nfsp.py | import logging
from typing import Optional, Type, Callable
import ray
from ray.rllib.agents.trainer import with_common_config
from ray.rllib.agents.trainer_template import build_trainer
from ray.rllib.evaluation.worker_set import WorkerSet
from ray.rllib.execution.metric_ops import StandardMetricsReporting
from ray.rl... | 5,849 | 33.210526 | 113 | py |
nxdo | nxdo-master/grl/algos/nfsp_rllib/__init__.py | from grl.algos.nfsp_rllib.nfsp import NFSPTrainer, DEFAULT_CONFIG
from grl.algos.nfsp_rllib.nfsp_torch_avg_policy import NFSPTorchAveragePolicy
__all__ = [
"DEFAULT_CONFIG",
"NFSPTrainer",
"NFSPTorchAveragePolicy"
]
| 229 | 24.555556 | 77 | py |
nxdo | nxdo-master/grl/rllib_tools/action_dists.py | import gym
import numpy as np
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try... | 7,353 | 37.302083 | 79 | py |
nxdo | nxdo-master/grl/rllib_tools/valid_actions_epsilon_greedy.py | import numpy as np
from typing import Union, Optional
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.utils.annotations import override
from ray.rllib.utils.exploration.exploration import Exploration, TensorType
from ray.rllib.utils.framework import try_import_tf, try_import_torch, \
get... | 5,394 | 39.871212 | 118 | py |
nxdo | nxdo-master/grl/rllib_tools/safe_convert_to_torch_tensor.py | import numpy as np
import tree
from ray.rllib.models.repeated_values import RepeatedValues
from ray.rllib.utils.framework import try_import_torch
torch, nn = try_import_torch()
def safe_convert_to_torch_tensor(x, device=None):
"""Converts any struct to torch.Tensors.
Modified from original RLlib implementa... | 1,538 | 33.977273 | 74 | py |
nxdo | nxdo-master/grl/rllib_tools/policy_checkpoints.py | import logging
import os
import time
from typing import Dict, Any
import cloudpickle
import deepdish
from ray.rllib.agents import Trainer
from ray.rllib.policy import Policy, TorchPolicy
from ray.rllib.utils import try_import_torch
from ray.rllib.utils.typing import PolicyID
from tables.exceptions import HDF5ExtError
... | 4,032 | 35.333333 | 133 | py |
nxdo | nxdo-master/grl/rllib_tools/stochastic_sampling_ignore_kwargs.py | """
Modified Ray implementation (ray==1.0.1.post1) to ignore certain kwargs for compatibility when
instantiating Policies for one RL algo in the Trainer of another.
"""
import gym
import numpy as np
import tree
from typing import Union
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.models.... | 6,143 | 38.384615 | 94 | py |
nxdo | nxdo-master/grl/rllib_tools/modified_policies/safe_set_weights_policy_mixin.py | from ray.rllib.utils.typing import ModelWeights
from grl.rllib_tools.safe_convert_to_torch_tensor import safe_convert_to_torch_tensor
class SafeSetWeightsPolicyMixin:
"""
Prevents occasional crashing seen in the default RLLib (version 1.0.1.post1) set_weights implementation
due to unwritable Numpy array... | 543 | 33 | 107 | py |
nxdo | nxdo-master/grl/rllib_tools/modified_policies/simple_q_torch_policy.py | """PyTorch policy class used for Simple Q-Learning"""
import logging
from typing import Dict, Tuple
import gym
import ray
from ray.rllib.agents.dqn.simple_q_tf_policy import Q_SCOPE, Q_TARGET_SCOPE
from ray.rllib.agents.dqn.simple_q_tf_policy import (compute_q_values, get_distribution_inputs_and_class)
from ray.rllib... | 7,570 | 37.431472 | 105 | py |
nxdo | nxdo-master/grl/rllib_tools/modified_policies/sac_torch_policy_squashed.py | """
PyTorch policy class used for SAC. Modified to always squash action outputs.
"""
import gym
from gym.spaces import Discrete
import logging
from typing import Dict, List, Optional, Tuple, Type, Union
import ray
import ray.experimental.tf_utils
from ray.rllib.agents.a3c.a3c_torch_policy import apply_grad_clipping
f... | 21,379 | 39.879541 | 91 | py |
nxdo | nxdo-master/grl/rllib_tools/models/valid_actions_fcnet.py | import logging
from typing import Type, Union
from gym.spaces import Box, Discrete
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.models.torch.fcnet import FullyConnectedNetwork
from ray.rllib.env import MultiAgentEnv
from grl.envs.valid_actions... | 4,487 | 44.795918 | 127 | py |
nxdo | nxdo-master/grl/rllib_tools/models/cnn_lstm.py | import numpy as np
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.preprocessors import get_preprocessor
from ray.rllib.models.tf.recurrent_net import RecurrentNetwork
from ray.rllib.models.torch.recurrent_net import RecurrentNetwork as TorchRNN
from ray.rllib.utils.annotations import override
from ... | 5,340 | 38.858209 | 79 | py |
nxdo | nxdo-master/grl/rllib_tools/trainers/ppo_multiagent_batch.py | """
Proximal Policy Optimization (PPO)
==================================
This file defines the distributed Trainer class for proximal policy
optimization.
See `ppo_[tf|torch]_policy.py` for the definition of the policy loss.
Detailed documentation: https://docs.ray.io/en/master/rllib-algorithms.html#ppo
(JB) Modif... | 12,883 | 40.831169 | 111 | py |
nback_align | nback_align-master/cnn_session.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
import torch.optim as optim
np.set_printoptions(threshold=1000)
device = "cuda" if torch.cuda.is_available() else "cpu"
def idx2onehot(idx, n):
idx = idx.type(torch.LongTenso... | 6,100 | 26.986239 | 102 | py |
nback_align | nback_align-master/cnn_sub.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
import torch.optim as optim
np.set_printoptions(threshold=1000)
device = "cuda" if torch.cuda.is_available() else "cpu"
def idx2onehot(idx, n):
idx = idx.type(torch.LongTenso... | 7,811 | 30.756098 | 157 | py |
nback_align | nback_align-master/rnn_sess.py | import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
import torch.optim as optim
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
... | 7,166 | 29.497872 | 123 | py |
nback_align | nback_align-master/rnn_sub.py | import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
import torch.optim as optim
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
... | 8,695 | 33.101961 | 157 | py |
pabst | pabst-main/pabst/run_pplm.py | import argparse
import json
from operator import add
from typing import List, Optional, Tuple, Union
from collections import OrderedDict
import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from tqdm import trange
from transformers import GPT2Tokenizer
from transformers.f... | 31,049 | 31.684211 | 148 | py |
pabst | pabst-main/pabst/comac_compatibility.py | from transformers import (GPT2LMHeadModel, GPT2Tokenizer, WEIGHTS_NAME, CONFIG_NAME)
from models.reinforce_model.data import PADDED_INPUTS, ATTR_TO_SPECIAL_TOKEN
from models.reinforce_model.model_with_inferencenw import LatentVariableInferenceModel
import torch
import os
training_args_path = '/data3/bodhi/projects/pe... | 1,851 | 44.170732 | 173 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.