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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pyGAT | pyGAT-master/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from layers import GraphAttentionLayer, SpGraphAttentionLayer
class GAT(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
"""Dense version of GAT."""
super(GAT, self).__init__()
self.dropout = dro... | 2,251 | 40.703704 | 126 | py |
pyGAT | pyGAT-master/train.py | from __future__ import division
from __future__ import print_function
import os
import glob
import time
import random
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from utils import load_data, accur... | 5,077 | 31.76129 | 109 | py |
pyGAT | pyGAT-master/visualize_graph.py | from graphviz import Digraph
import torch
import models
def make_dot(var, params):
""" Produces Graphviz representation of PyTorch autograd graph
Blue nodes are the Variables that require grad, orange are Tensors
saved for backward in torch.autograd.Function
Args:
var: output Variabl... | 2,096 | 32.285714 | 85 | py |
M3ViT | M3ViT-main/main.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
import argparse
import cv2
import os
import numpy as np
import sys
import torch
from utils.config import create_config
from utils.common_config import get_train_dataset, get_transformations,\
... | 6,157 | 36.779141 | 165 | py |
M3ViT | M3ViT-main/train_fastmoe.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
import argparse
import cv2
import os
import numpy as np
import sys
import torch
from torch.nn.parallel import DistributedDataParallel
from utils.config import create_config
from utils.common_confi... | 22,951 | 44.995992 | 165 | py |
M3ViT | M3ViT-main/train_vit.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
import argparse
import cv2
import os
import numpy as np
import sys
import torch
from torch.nn.parallel import DistributedDataParallel
from utils.config import create_config
from utils.common_confi... | 11,637 | 37.664452 | 165 | py |
M3ViT | M3ViT-main/evaluation/eval_semseg.py | # This code is referenced from
# https://github.com/facebookresearch/astmt/
#
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# License: Attribution-NonCommercial 4.0 International
import warnings
import cv2
import os.path
import glob
import json
import numpy as np
import torch
from PIL ... | 7,002 | 33.497537 | 120 | py |
M3ViT | M3ViT-main/evaluation/eval_normals.py | # This code is referenced from
# https://github.com/facebookresearch/astmt/
#
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# License: Attribution-NonCommercial 4.0 International
import warnings
import cv2
import os.path
import numpy as np
import glob
import math
import torch
import js... | 5,486 | 35.098684 | 106 | py |
M3ViT | M3ViT-main/evaluation/eval_edge.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
import os
import glob
import json
import torch
import numpy as np
from utils.utils import mkdir_if_missing
from losses.loss_functions import BalancedCrossEntropyLoss
from utils.mypath import MyPat... | 5,634 | 37.074324 | 181 | py |
M3ViT | M3ViT-main/evaluation/eval_human_parts.py | # This code is referenced from
# https://github.com/facebookresearch/astmt/
#
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# License: Attribution-NonCommercial 4.0 International
import warnings
import cv2
import glob
import json
import os.path
import numpy as np
import torch
from PIL ... | 5,293 | 32.295597 | 120 | py |
M3ViT | M3ViT-main/evaluation/evaluate_utils.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
from audioop import mul
import os
import cv2
import imageio
import numpy as np
import json
import torch
import scipy.io as sio
from utils.utils import get_output, mkdir_if_missing
import numpy as ... | 17,462 | 44.007732 | 161 | py |
M3ViT | M3ViT-main/evaluation/eval_depth.py | # This code is referenced from
# https://github.com/facebookresearch/astmt/
#
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# License: Attribution-NonCommercial 4.0 International
import warnings
import cv2
import os.path
import numpy as np
import glob
import torch
import json
import sc... | 4,173 | 29.028777 | 99 | py |
M3ViT | M3ViT-main/evaluation/eval_sal.py | # This code is referenced from
# https://github.com/facebookresearch/astmt/
#
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# License: Attribution-NonCommercial 4.0 International
import warnings
import cv2
import os.path
import numpy as np
import glob
import json
import torch
from PIL ... | 5,771 | 34.62963 | 108 | py |
M3ViT | M3ViT-main/models/seg_hrnet.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Ke Sun (sunk@mail.ustc.edu.cn)
# Minor changes made by Simon Vandenhende
# ------------------------------------------------------------------------------
from __futu... | 20,391 | 37.621212 | 207 | py |
M3ViT | M3ViT-main/models/aspp.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
import torch
import torch.nn as nn
import torch.nn.functional as F
class DeepLabHead(nn.Sequential):
def __init__(self, in_channels, num_classes):
super(DeepLabHead, self).__init__(
... | 2,423 | 31.32 | 101 | py |
M3ViT | M3ViT-main/models/nddr_cnn.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
Implementation of NDDR-CNN
https://arxiv.org/abs/1801.08297
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
class NDDRLayer(nn.Module):
def __in... | 4,239 | 37.899083 | 213 | py |
M3ViT | M3ViT-main/models/vit_up_head.py | import torch.nn as nn
import torch.nn.functional as F
from functools import partial
import math
# from .layers import trunc_normal_
# from ..builder import HEADS
# from .decode_head import BaseDecodeHead
from mmcv.cnn import build_norm_layer
from models.decoder_head import BaseDecodeHead
def _no_grad_trunc_normal_... | 9,541 | 41.221239 | 119 | py |
M3ViT | M3ViT-main/models/custom_moe_layer.py | r"""
Adaption to act as the MLP layer using an MoE MLP layer in transformer.
"""
import torch
import torch.nn as nn
from fmoe.layers import FMoE, _fmoe_general_global_forward
from fmoe.linear import FMoELinear
from functools import partial
import tree
import torch
import torch.nn as nn
from fmoe.functions import prepa... | 12,708 | 39.346032 | 175 | py |
M3ViT | M3ViT-main/models/model_utils.py | def cal_flops(w, h, k, c_in, c_out):
""" calculate the actual flops of one example
c_in and c_out are vector across the whole batch
"""
return w * h * k * k * c_in * c_out
def load_pretrained_v2(model, path):
ckpt = torch.load(path)['state_dict']
model_state = model.state_dict()
new_ckpt ... | 559 | 25.666667 | 52 | py |
M3ViT | M3ViT-main/models/resnet.py | #
# Code referenced from Torchvision
import torch
import torch.nn as nn
# from torchvision.models.utils import load_state_dict_from_url
from torch.hub import load_state_dict_from_url
from models.model_utils import cal_flops
import math
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'... | 30,315 | 37.569975 | 113 | py |
M3ViT | M3ViT-main/models/Jtrl.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
Implementation of PAD-Net.
https://arxiv.org/abs/1805.04409
"""
from re import A
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.resnet import Bottleneck... | 11,978 | 43.366667 | 128 | py |
M3ViT | M3ViT-main/models/cross_stitch.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
Implementation of cross-stitch networks
https://arxiv.org/abs/1604.03539
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
class ChannelWiseMultiply(... | 4,052 | 35.513514 | 136 | py |
M3ViT | M3ViT-main/models/vits_gate.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
from functools import partial, reduce
from operator import mul
from timm.mod... | 6,404 | 37.125 | 120 | py |
M3ViT | M3ViT-main/models/vision_transformer_moe.py | import torch
import torch.nn as nn
from functools import partial
import math
from itertools import repeat
# from torch._six import container_abcs
import collections.abc
import warnings
from collections import OrderedDict
from utils.helpers import load_pretrained,load_pretrained_pos_emb
from models.custom_moe_layer impo... | 27,705 | 45.099834 | 171 | py |
M3ViT | M3ViT-main/models/vit.py | import torch
import torch.nn as nn
from functools import partial
import math
from itertools import repeat
# from torch._six import container_abcs
import collections.abc
import warnings
from utils.helpers import load_pretrained
# from .layers import DropPath, to_2tuple, trunc_normal_
# from ..builder import BACKBONES
... | 20,796 | 40.263889 | 164 | py |
M3ViT | M3ViT-main/models/papnet_new.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
Implementation of PAD-Net.
https://arxiv.org/abs/1805.04409
"""
from re import A
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.resnet import Bottleneck... | 14,599 | 46.402597 | 219 | py |
M3ViT | M3ViT-main/models/mobilenetv3.py | """
Creates a MobileNetV3 Model as defined in:
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam. (2019).
Searching for MobileNetV3
arXiv preprint arXiv:1905.02244.
"""
import torch.nn as nn
import math
__a... | 7,613 | 31.538462 | 169 | py |
M3ViT | M3ViT-main/models/layers.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
import torch
import torch.nn as nn
class SEBlock(nn.Module):
""" Squeeze-and-excitation block """
def __init__(self, channels, r=16):
super(SEBlock, self).__init__()
self... | 1,297 | 34.081081 | 102 | py |
M3ViT | M3ViT-main/models/resnet_dilated.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
import torch.nn as nn
class ResnetDilated(nn.Module):
""" ResNet backbone with dilated convolutions """
def __init__(self, orig_resnet, dilate_scale=8):
super(ResnetDilated, self... | 3,634 | 31.168142 | 92 | py |
M3ViT | M3ViT-main/models/models.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import build_norm_layer
class TamModule(nn.Module):
def __init__(self, p, tasks, input_channels, nor... | 13,792 | 47.911348 | 202 | py |
M3ViT | M3ViT-main/models/padnet.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
Implementation of PAD-Net.
https://arxiv.org/abs/1805.04409
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.resnet import Bottleneck
from models.laye... | 10,861 | 41.596078 | 163 | py |
M3ViT | M3ViT-main/models/decoder_head.py | from abc import ABCMeta, abstractmethod
import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmcv.runner import auto_fp16, force_fp32
# from mmseg.core import build_pixel_sampler
# from mmseg.ops import resize
# from ..builder import build_loss
# from ..losses import accuracy
class BaseDecodeHea... | 9,089 | 37.846154 | 78 | py |
M3ViT | M3ViT-main/models/mtan.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
Implementation of MTAN
https://arxiv.org/abs/1803.10704
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.resnet import ResNet, conv1x1, Bottleneck... | 7,140 | 46.926174 | 149 | py |
M3ViT | M3ViT-main/models/mti_net.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
MTI-Net implementation based on HRNet backbone
https://arxiv.org/pdf/2001.06902.pdf
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.resnet import B... | 7,605 | 43.22093 | 162 | py |
M3ViT | M3ViT-main/models/papnet.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
Implementation of PAD-Net.
https://arxiv.org/abs/1805.04409
"""
from re import A
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.resnet import Bottleneck... | 13,171 | 45.875445 | 219 | py |
M3ViT | M3ViT-main/models/gate_funs/noisy_gate_vmoe.py | r"""
Noisy gate for gshard and switch
"""
from fmoe.gates.base_gate import BaseGate
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.normal import Normal
import math
import numpy as np
from collections import Counter
from pdb import set_trace
class NoisyGate_VMoE(BaseGate):
... | 13,246 | 41.594855 | 165 | py |
M3ViT | M3ViT-main/models/gate_funs/noisy_gate.py | r"""
Noisy gate for gshard and switch
"""
from fmoe.gates.base_gate import BaseGate
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.normal import Normal
import math
from pdb import set_trace
class NoisyGate(BaseGate):
def __init__(self, d_model, num_expert, world_size,... | 8,344 | 35.441048 | 144 | py |
M3ViT | M3ViT-main/train/train_utils.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
from evaluation.evaluate_utils import PerformanceMeter
from utils.utils import AverageMeter, ProgressMeter, get_output
import numpy as np
from collections import Counter
import torch.nn.functional... | 11,942 | 41.806452 | 117 | py |
M3ViT | M3ViT-main/utils/moe_utils.py | from collections import OrderedDict
from models.gate_funs.noisy_gate import NoisyGate
from models.gate_funs.noisy_gate_vmoe import NoisyGate_VMoE
from models.custom_moe_layer import FMoETransformerMLP
import torch.distributed
import os
from pdb import set_trace
import torch.nn.functional as F
import shutil
def gathe... | 8,324 | 35.195652 | 129 | py |
M3ViT | M3ViT-main/utils/common_config.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
import os
import copy
import torch
import torch.nn.functional as F
import math
import numpy as np
from torchvision import transforms
from torch.utils.data import DataLoader
from utils.custom_colla... | 40,973 | 47.894988 | 235 | py |
M3ViT | M3ViT-main/utils/sampler.py | from __future__ import division
import math
import numpy as np
import torch
import math
import torch.distributed as dist
from torch.utils.data.sampler import Sampler
from .helpers import get_dist_info
from torch.utils.data import DistributedSampler as _DistributedSampler
# from torch.utils.data import Sampler
clas... | 7,997 | 34.705357 | 87 | py |
M3ViT | M3ViT-main/utils/custom_collate.py | # This code is referenced from
# https://github.com/facebookresearch/astmt/
#
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# License: Attribution-NonCommercial 4.0 International
import torch
import collections
import re
# from torch._six import string_classes, int_classes
string_class... | 2,785 | 32.97561 | 111 | py |
M3ViT | M3ViT-main/utils/utils.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
import os
import torch
import torch.nn.functional as F
import errno
def mkdir_if_missing(directory):
if not os.path.exists(directory):
try:
os.makedirs(directory)
#... | 2,115 | 25.78481 | 91 | py |
M3ViT | M3ViT-main/utils/helpers.py | # This code is referenced from
# https://github.com/facebookresearch/astmt/
#
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# License: Attribution-NonCommercial 4.0 International
from collections import OrderedDict
import torch
import cv2
import numpy as np
import torch.nn.functional ... | 13,546 | 40.176292 | 190 | py |
M3ViT | M3ViT-main/data/custom_transforms.py | # This code is referenced from
# https://github.com/facebookresearch/astmt/
#
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# License: Attribution-NonCommercial 4.0 International
import numpy.random as random
import numpy as np
import torch
import cv2
import math
import utils.helpe... | 10,478 | 31.84953 | 144 | py |
M3ViT | M3ViT-main/data/cityscapes.py | from torch.utils.data.dataset import Dataset
import os
import torch
import torch.nn.functional as F
import fnmatch
import numpy as np
import random
from utils.mypath import MyPath
import cv2
class RandomScaleCrop(object):
"""
Credit to Jialong Wu from https://github.com/lorenmt/mtan/issues/34.
"""
def ... | 11,485 | 45.502024 | 154 | py |
M3ViT | M3ViT-main/data/pascal_context.py | # This code is referenced from
# https://github.com/facebookresearch/astmt/
#
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# License: Attribution-NonCommercial 4.0 International
import os
import sys
import tarfile
import json
import cv2
import numpy as np
import scipy.io as sio
impor... | 20,820 | 40.148221 | 118 | py |
M3ViT | M3ViT-main/data/nyud.py | # This code is referenced from
# https://github.com/facebookresearch/astmt/
#
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# License: Attribution-NonCommercial 4.0 International
import os
import sys
import tarfile
import cv2
from PIL import Image
import numpy as np
import torch.utils... | 10,411 | 34.780069 | 106 | py |
M3ViT | M3ViT-main/losses/loss_functions.py | # This code is referenced from
# https://github.com/facebookresearch/astmt/
#
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# License: Attribution-NonCommercial 4.0 International
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.module import Modu... | 6,352 | 31.085859 | 121 | py |
M3ViT | M3ViT-main/losses/loss_schemes.py | #
# Authors: Simon Vandenhende
# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
import torch
import torch.nn as nn
import torch.nn.functional as F
class SingleTaskLoss(nn.Module):
def __init__(self, loss_ft, task):
super(SingleTaskLoss, self).__init__()
... | 9,691 | 38.080645 | 114 | py |
mechamodlearn | mechamodlearn-master/setup.py | from setuptools import setup, find_packages
def get_long_description():
with open("README.md", "r") as readme_file:
return readme_file.read()
setup(
name='mechamodlearn',
description='PyTorch framework for learning mechanical systems',
long_description=get_long_description(),
long_descri... | 968 | 25.189189 | 68 | py |
mechamodlearn | mechamodlearn-master/mechamodlearn/rigidbody.py | # File: rigidbody.py
import abc
import torch
from mechamodlearn import nn, utils
from mechamodlearn.models import CholeskyMMNet, PotentialNet, GeneralizedForceNet
class AbstractRigidBody:
@property
@abc.abstractmethod
def thetamask(self):
"""Returns theta mask for configuration q.
These... | 5,598 | 29.102151 | 100 | py |
mechamodlearn | mechamodlearn-master/mechamodlearn/viz_utils.py | #
# File: viz_utils.py
#
import matplotlib.pyplot as plt
import torch
from mechamodlearn.odesolver import odeint
from mechamodlearn import utils
def plot_traj(system_x_T_B, model_x_T_B, tstar):
D = system_x_T_B.shape[-1]
cm = plt.get_cmap('tab10')
fig, axs = plt.subplots(1, D, figsize=(6 * D, 6), squeeze... | 1,915 | 33.836364 | 99 | py |
mechamodlearn | mechamodlearn-master/mechamodlearn/transform.py | # File: transform.py
#
import functools
from more_itertools import windowed
import torch
from mechamodlearn.dataset import ActuatedTrajectoryDataset, ODEPredDataset
def fill_windowed(ls, traj_T_D, chunk_size, step):
qs_ms = windowed(traj_T_D, chunk_size, step=step)
for q in qs_ms:
for i, cq in enum... | 1,694 | 32.235294 | 98 | py |
mechamodlearn | mechamodlearn-master/mechamodlearn/utils.py | # File: utils.py
#
import random
import sys
from contextlib import contextmanager
try:
import resource
except ImportError:
# resource not available on Windows
resource = None
import numpy as np
from numba import jit
import timeit
import torch
def bfill_lowertriangle(A: torch.Tensor, vec: torch.Tensor):
... | 3,699 | 23.666667 | 106 | py |
mechamodlearn | mechamodlearn-master/mechamodlearn/dataset.py | # File: dataset.py
#
import torch
from torch.utils.data import dataset
from mechamodlearn.odesolver import odeint, ActuatedODEWrapper
from mechamodlearn import utils
class ActuatedTrajectoryDataset(dataset.TensorDataset):
def __init__(self, traj_q_T_B, traj_v_T_B, traj_u_T_B):
"""
Arguments:
... | 2,746 | 33.772152 | 97 | py |
mechamodlearn | mechamodlearn-master/mechamodlearn/nn.py | # File: nn.py
#
import torch
class Identity(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x
ACTIVATIONS = {
'tanh': torch.nn.Tanh,
'relu': torch.nn.ReLU,
'elu': torch.nn.ELU,
'identity': Identity
}
class LNMLP(torch.nn.Module):
... | 1,728 | 24.80597 | 90 | py |
mechamodlearn | mechamodlearn-master/mechamodlearn/models.py | # File: models.py
#
import torch
from mechamodlearn import nn, utils
class SharedMMVEmbed(torch.nn.Module):
def __init__(self, qdim, hidden_sizes):
self._qdim = qdim
self._hidden_sizes = hidden_sizes
super().__init__()
self._lnet = nn.LNMLP(qdim, hidden_sizes[:-1], hidden_sizes[-... | 4,469 | 26.592593 | 95 | py |
mechamodlearn | mechamodlearn-master/mechamodlearn/odesolver.py | #
# File: odesolver.py
#
import abc
import torch
class FixedGridODESolver(metaclass=abc.ABCMeta):
def __init__(self, func, y0, grid_constructor=None, transforms=None):
self.func = func
self.y0 = y0
if grid_constructor is None:
grid_constructor = lambda f, y0, t: t
s... | 5,822 | 27.684729 | 100 | py |
mechamodlearn | mechamodlearn-master/mechamodlearn/trainer.py | #
# File: trainer.py
#
from typing import Dict, List, Optional, Tuple
import abc
import datetime
from pathlib import Path
import re
import os
import shutil
import time
import traceback
import numpy as np
import torch
import torch.nn.functional as F
import tqdm
from mechamodlearn import dataset, logger, nested, trans... | 21,179 | 38.887006 | 99 | py |
mechamodlearn | mechamodlearn-master/mechamodlearn/systems/mlacrobot.py | #
# File: mlacrobot.py
#
import torch
from mechamodlearn.rigidbody import AbstractRigidBody
from mechamodlearn.models import ControlAffineLinearForce, ViscousJointDampingForce, GeneralizedForces
class MultiLinkAcrobotMM(torch.nn.Module):
def __init__(self, qdim, params=None):
super().__init__()
... | 4,875 | 31.078947 | 102 | py |
mechamodlearn | mechamodlearn-master/mechamodlearn/systems/pendulum.py | # File: pendulum.py
#
import torch
from mechamodlearn.rigidbody import AbstractRigidBody
from mechamodlearn.models import ControlAffineLinearForce, ViscousJointDampingForce, GeneralizedForces
class SimplePendulumMM(torch.nn.Module):
def __init__(self, l):
"""
Arguments:
- `l`: length of... | 2,694 | 23.724771 | 102 | py |
mechamodlearn | mechamodlearn-master/mechamodlearn/systems/__init__.py | import torch
from .pendulum import ActuatedDampedPendulum, ActuatedSimplePendulum
from .mlacrobot import MultiLinkAcrobot, DampedMultiLinkAcrobot
DEFAULT_SYS_PARAMS = {
'simplependulum': torch.tensor([1.0, 1.0, 1.0]),
'dampedpendulum': torch.tensor([1.0, 10.0, 1.0, -0.5]),
'2linkdampedacrobot': torch.tens... | 379 | 33.545455 | 92 | py |
mechamodlearn | mechamodlearn-master/experiments/simple.py | #!/usr/bin/env python3
#
# File: simple.py
#
from datetime import datetime
from pathlib import Path
import click
import torch
from mechamodlearn import dataset, utils, viz_utils
from mechamodlearn.trainer import OfflineTrainer
from mechamodlearn.systems import ActuatedDampedPendulum, DampedMultiLinkAcrobot, DEFAULT_S... | 3,755 | 37.721649 | 100 | py |
linlearn | linlearn-master/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 4,773 | 29.602564 | 115 | py |
PerturbationSaliencyEvaluation | PerturbationSaliencyEvaluation-main/applications/atari/custom_occlusion_sensitvity.py | """
This module was adapted from a module in
https://github.com/sicara/tf-explain
Date: 2020
commit: 8dff129ff7b1012dba2761a61e3c3e68e9ecbec2
License: MIT
"""
import math
import cv2
import numpy as np
from tf_explain.utils.display import grid_display, heatmap_display
from tf_explain.utils.saver import save_rgb
from... | 5,201 | 31.5125 | 125 | py |
PerturbationSaliencyEvaluation | PerturbationSaliencyEvaluation-main/applications/atari/highlights_stream_generator.py | """
Generates a stream of gameplay for a given agent.
A folder 'stream' is created whose subfolders contain all the states, visually displayed frames, Q-values,
saliency maps and features (output of the second to last layer).
This module was adapted from a module in https://github.com/HuTobias/HIGHLIG... | 5,713 | 34.271605 | 149 | py |
PerturbationSaliencyEvaluation | PerturbationSaliencyEvaluation-main/applications/atari/ImageGeneration.py | """
Module for creating example saliency map images for the HIGHLIGHT states.
Some functions are adapted from https://github.com/HuTobias/HIGHLIGHTS-LRP
Date: 2020
commit: 834bf795ee37a74b611beb79851438e9a8afd676
License: MIT
"""
import keras
import numpy as np
from PIL import Image
from matplotlib import pyplot as p... | 17,546 | 41.9022 | 119 | py |
PerturbationSaliencyEvaluation | PerturbationSaliencyEvaluation-main/applications/atari/parameter_search/parameter_test.py | """ Module for testing different parameters for the perturbation-based saliency maps."""
import skimage.segmentation as seg
import numpy as np
import os
import re
import keras
import timeit
import applications.atari.rise as rise
from applications.atari.explanation import explainer
from applications.atari.custom_lime ... | 13,440 | 43.213816 | 132 | py |
PerturbationSaliencyEvaluation | PerturbationSaliencyEvaluation-main/applications/atari/parameter_search/Verification/highlights_occlusion_search.py | """ Module for doing a parameter search on different subsets of 10 states of the game Pacman with fast parameter configurations of occlusion.
This can then be used to verify which states are suited to search for good parameters."""
import numpy as np
import os
import re
import keras
import timeit
import applications.... | 3,958 | 40.239583 | 141 | py |
PerturbationSaliencyEvaluation | PerturbationSaliencyEvaluation-main/applications/atari/parameter_search/Verification/full_occlusion_search.py | """
Module for doing a full parameter search for 1000 states of the game Pacman with fast parameter configurations of occlusion.
This can then be used to verify which states are suited to search for good parameters.
This script needs to be run from the insertion_metric folder, since the insertion results are saved the... | 2,839 | 44.079365 | 139 | py |
PerturbationSaliencyEvaluation | PerturbationSaliencyEvaluation-main/applications/atari/insertion_metric/insertion_metric_main.py | """
This module does the insertion metric experiments and measures the run-time of the saliency map generation approaches.
"""
from applications.atari.custom_atari_wrapper import atari_wrapper
from applications.atari.explanation import explainer
import applications.atari.rise as rise
import gym
import keras
import num... | 11,449 | 45.734694 | 135 | py |
PerturbationSaliencyEvaluation | PerturbationSaliencyEvaluation-main/applications/atari/sanity_checks/sanity_checks_main.py | '''
This module was adapted from a module in https://github.com/HuTobias/HIGHLIGHTS-LRP
Date: 2020
commit: 834bf795ee37a74b611beb79851438e9a8afd676
License: MIT
This module implements sanity checks for saliency maps.
To this end the layers in the model are cascadingly randomized and for each step we create a copy of ... | 21,348 | 41.783567 | 131 | py |
PerturbationSaliencyEvaluation | PerturbationSaliencyEvaluation-main/applications/affectnet/main_affectnet.py | from applications.affectnet.vgg_face_batch_norm import get_model, get_preprocess
import cv2
from applications.atari.explanation import explainer, create_saliency_image
import applications.atari.rise as rise
import timeit
import os
import numpy as np
from matplotlib import pyplot as plt
from skimage.segmentation import ... | 8,179 | 42.743316 | 124 | py |
PerturbationSaliencyEvaluation | PerturbationSaliencyEvaluation-main/applications/affectnet/vgg_face_batch_norm.py | import os
from keras.layers import Dense, GlobalMaxPooling2D, Dropout, Conv2D, MaxPooling2D, BatchNormalization, ZeroPadding2D, Input
from keras.models import Model
from keras.applications.imagenet_utils import preprocess_input
n_output = 8#config.corpus.value # if not config.label_indices else len(config.label_indice... | 2,174 | 30.071429 | 123 | py |
PerturbationSaliencyEvaluation | PerturbationSaliencyEvaluation-main/applications/affectnet/generate_no_softmax_model.py | from applications.affectnet.vgg_face_batch_norm import get_model, get_preprocess
import keras
if __name__ == '__main__':
model = get_model()
target_size = (224, 224)
model.load_weights('vgg_face_batch_norm_e_74_l_1.18.h5')
idx_of_layer_to_change = -1
model.layers[idx_of_layer_to_change].activation ... | 387 | 37.8 | 80 | py |
Oort | Oort-master/training/testlibs.py | # Standard libs
import os, re, shutil, sys, time, datetime, logging, pickle, json, socket
import random, math, gc, copy
from collections import OrderedDict
from ctypes import c_bool
from multiprocessing import Process, Value
from multiprocessing.managers import BaseManager
import multiprocessing, threading
import numpy... | 1,597 | 31.612245 | 138 | py |
Oort | Oort-master/training/learner.py | # -*- coding: utf-8 -*-
from fl_client_libs import *
initiate_client_setting()
for i in range(torch.cuda.device_count()):
try:
device = torch.device('cuda:'+str(i))
torch.cuda.set_device(i)
logging.info(f'End up with cuda device {torch.rand(1).to(device=device)}')
break
except ... | 26,929 | 37.252841 | 174 | py |
Oort | Oort-master/training/param_server.py | # -*- coding: utf-8 -*-
from fl_aggregator_libs import *
from random import Random
initiate_aggregator_setting()
for i in range(torch.cuda.device_count()):
try:
device = torch.device('cuda:'+str(i))
torch.cuda.set_device(i)
logging.info(f'End up with cuda device {torch.rand(1).to(device=de... | 23,390 | 43.810345 | 164 | py |
Oort | Oort-master/training/flLibs.py | # Standard libs
import os, re, shutil, sys, time, datetime, logging, pickle, json, socket
import random, math, gc, copy
from collections import OrderedDict
from ctypes import c_bool
from multiprocessing import Process, Value
from multiprocessing.managers import BaseManager
import multiprocessing, threading
import numpy... | 11,520 | 48.446352 | 194 | py |
Oort | Oort-master/training/utils/voice_data_loader.py | import math
import os
import pickle
from tempfile import NamedTemporaryFile
import librosa
import numpy as np
import scipy.signal
import soundfile as sf
import sox
import torch
from torch.utils.data import Dataset, Sampler, DistributedSampler, DataLoader
from .spec_augment import spec_augment
windows = {
'hammin... | 14,952 | 37.940104 | 120 | py |
Oort | Oort-master/training/utils/divide_data.py | # -*- coding: utf-8 -*-
from random import Random
#from core.dataloader import DataLoader
from torch.utils.data import DataLoader
import numpy as np
from math import *
import logging
from scipy import stats
import numpy as np
from pyemd import emd
from collections import OrderedDict
import time
import pickle, random
fr... | 22,021 | 39.186131 | 214 | py |
Oort | Oort-master/training/utils/openImg.py | from __future__ import print_function
import warnings
from PIL import Image
import os
import os.path
import numpy as np
import torch
import codecs
import string
import time
class OPENIMG():
"""
Args:
root (string): Root directory of dataset where ``MNIST/processed/training.pt``
and ``MNIST... | 4,302 | 29.735714 | 108 | py |
Oort | Oort-master/training/utils/utils_data.py | # -*- coding: utf-8 -*-
import sys
from torchvision import transforms
def get_data_transform(data: str):
if data == 'mnist':
train_transform = transforms.Compose([
#transforms.Grayscale(num_output_channels=1),
transforms.Resize((28,28)),
transforms.RandomHorizontalFlip(),
... | 5,916 | 42.189781 | 96 | py |
Oort | Oort-master/training/utils/inception.py | from collections import namedtuple
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
#from torch.jit.annotations import Optional
from torch import Tensor
__all__ = ['Inception3', 'inception_v3', 'InceptionOutputs', '_InceptionOutputs']
kernel_size = 1
model_urls = {
# Inception v... | 15,687 | 36.441527 | 102 | py |
Oort | Oort-master/training/utils/resnet_speech.py | """Imported from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
and added support for the 1x32x32 mel spectrogram for the speech recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Deep Residual Learning for Image Recognition
https://arxiv.org/abs/1512.03385
"""
import torch.nn... | 6,915 | 30.294118 | 95 | py |
Oort | Oort-master/training/utils/stackoverflow.py | from __future__ import print_function
import warnings
import os
import os.path
import torch
import time
import pickle
import h5py as h5
import torch.nn.functional as F
#import logging
class stackoverflow():
"""
Args:
root (string): Root directory of dataset where ``MNIST/processed/training.pt``
... | 8,611 | 34.73444 | 162 | py |
Oort | Oort-master/training/utils/femnist.py | from __future__ import print_function
import warnings
from PIL import Image
import os
import os.path
import numpy as np
import torch
import codecs
import string
import time
import pickle
class FEMNIST():
"""
Args:
root (string): Root directory of dataset where ``MNIST/processed/training.pt``
... | 3,678 | 28.432 | 95 | py |
Oort | Oort-master/training/utils/spec_augment.py | # Copyright 2019 RnD at Spoon Radio
#
# 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 or agreed to in wr... | 4,866 | 36.152672 | 127 | py |
Oort | Oort-master/training/utils/nlp.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 37,352 | 42.132794 | 165 | py |
Oort | Oort-master/training/utils/yogi.py | import torch
class YoGi():
def __init__(self, eta=1e-2, tau=1e-3, beta=0.999, beta2=-1):
self.eta = eta
self.tau = tau
self.beta = beta
self.v_t = []
self.m_t = []
self.beta2 = beta2
def update(self, gradients):
update_gradients = []
for idx, gr... | 1,310 | 31.775 | 126 | py |
Oort | Oort-master/training/utils/sparse_image_warp.py | # Copyright 2019 RnD at Spoon Radio
#
# 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 or agreed to in wr... | 16,253 | 38.547445 | 113 | py |
Oort | Oort-master/training/utils/dataloaders.py | from torch.utils.data import Dataset
import torch
import os
import pandas as pd
# This loader works for Amazon/Yelp Review
class TextSentimentDataset(Dataset):
def __init__(self, data_path, max_len, train=True, tokenizer=None):
file = 'training.csv' if train else 'testing.csv'
self.df = pd.read_c... | 2,226 | 34.919355 | 100 | py |
Oort | Oort-master/training/utils/utils_model.py | # -*- coding: utf-8 -*-
import math
import random
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import numpy as np
import logging
from argParser import args
from utils.nlp import mask_tokens
from utils.decoder import GreedyDecoder
class MySGD(optim.SGD):
def ... | 10,219 | 33.295302 | 126 | py |
Oort | Oort-master/training/utils/decoder.py | #!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright 2015-2016 Nervana Systems Inc.
# 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
#... | 8,145 | 40.141414 | 120 | py |
Oort | Oort-master/training/utils/reddit.py | from __future__ import print_function
import warnings
import os
import os.path
import torch
import glob
import time
import json
import pickle
import torch.nn.functional as F
class reddit():
classes = []
MAX_SEQ_LEN = 20000
@property
def train_labels(self):
warnings.warn("train_labels has been ... | 6,070 | 30.78534 | 166 | py |
Oort | Oort-master/training/utils/models.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import math
from transformers import BertModel
class MnistCNN(nn.Module):
""" CNN Network architecture. """
def __init__(self):
super(MnistCNN, self).__init__()
self.c... | 23,019 | 32.902798 | 130 | py |
Oort | Oort-master/training/utils/speech.py | from __future__ import print_function
import warnings
import os
import os.path
import numpy as np
import torch
import codecs
import string
import time
import numba
import librosa
from torch.utils.data import Dataset
CLASSES = ['up', 'two', 'sheila', 'zero', 'yes', 'five', 'one', 'happy', 'marvin', 'no', 'go', 'seven',... | 5,615 | 31.091429 | 295 | py |
Oort | Oort-master/training/utils/mobilenet.py | from torch import nn
import torch.utils.model_zoo as model_zoo
__all__ = ['MobileNetV2', 'mobilenet_v2']
model_urls = {
'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf re... | 6,560 | 35.653631 | 107 | py |
Oort | Oort-master/training/utils/transforms_stft.py | """Transforms on the short time fourier transforms of wav samples."""
__author__ = 'Erdene-Ochir Tuguldur'
import random
import numpy as np
import librosa
from torch.utils.data import Dataset
from .transforms_wav import should_apply_transform
random.seed(233)
class ToSTFT(object):
"""Applies on an audio the ... | 4,134 | 30.325758 | 121 | py |
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