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 |
|---|---|---|---|---|---|---|
myriad | myriad-main/myriad/nlp_solvers/extra_gradient.py | # (c) 2021 Nikolaus Howe
import jax.numpy as jnp
from jax import jit, grad
from tensorboardX import SummaryWriter # for parameter tuning
writer = SummaryWriter()
def extra_gradient(fun, x0, method, constraints, bounds, jac, options):
del method, jac
print("we're trying exgd with steps:", options['maxiter'])
... | 2,531 | 29.878049 | 106 | py |
myriad | myriad-main/myriad/systems/base.py | # (c) 2021 Nikolaus Howe
from abc import ABC
from dataclasses import dataclass
from typing import Mapping, Optional
import jax.numpy as jnp
from myriad.custom_types import Control, Controls, Cost, DState, Params, State, States
@dataclass
class FiniteHorizonControlSystem(object):
"""
Abstract class describing a ... | 6,308 | 33.47541 | 121 | py |
myriad | myriad-main/myriad/systems/neural_ode/node_system.py | # (c) 2021 Nikolaus Howe
from __future__ import annotations
import typing
if typing.TYPE_CHECKING:
from myriad.neural_ode.create_node import NeuralODE
import jax.numpy as jnp
from myriad.systems.base import FiniteHorizonControlSystem
from myriad.custom_types import Control, Cost, DState, Params, State, Timestep
... | 1,363 | 30.72093 | 106 | py |
myriad | myriad-main/myriad/systems/miscellaneous/tumour.py | import jax.numpy as jnp
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from myriad.custom_types import Params
from myriad.systems import FiniteHorizonControlSystem
class Tumour(FiniteHorizonControlSystem):
"""
Tumour anti-angiogenesis model, from [Practical Methods for Optimal Control ... | 4,802 | 35.386364 | 205 | py |
myriad | myriad-main/myriad/systems/miscellaneous/seir.py | import jax.numpy as jnp
from myriad.systems import FiniteHorizonControlSystem
class SEIR(FiniteHorizonControlSystem):
"""
SEIR epidemic model for COVID-19, inspired by [Perkins and Espana, 2020](https://link.springer.com/article/10.1007/s11538-020-00795-y).
This model is an adaptation of SEIR models, specific... | 4,207 | 35.591304 | 137 | py |
myriad | myriad-main/myriad/systems/miscellaneous/rocket_landing.py | # (c) 2021 Nikolaus Howe
import jax
import jax.numpy as jnp
from typing import Optional
from myriad.custom_types import Control, Cost, DState, Params, State, Timestep
from myriad.systems import FiniteHorizonControlSystem
class RocketLanding(FiniteHorizonControlSystem):
"""
Simulate a starship landing! Inspired ... | 4,647 | 36.184 | 154 | py |
myriad | myriad-main/myriad/systems/miscellaneous/van_der_pol.py | import jax.numpy as jnp
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from myriad.custom_types import Params
from myriad.systems import FiniteHorizonControlSystem
class VanDerPol(FiniteHorizonControlSystem):
"""
Driven Van der Pol oscillator, from [CasADi](http://casadi.sourceforge.ne... | 2,496 | 29.82716 | 113 | py |
myriad | myriad-main/myriad/systems/classical_control/cartpole.py | # (c) 2021 Nikolaus Howe
import jax.numpy as jnp
from typing import Optional
from myriad.systems.base import FiniteHorizonControlSystem
from myriad.custom_types import Control, Cost, DState, Params, State, Timestep
class CartPole(FiniteHorizonControlSystem):
"""
Cart-pole swing-up, from [(Kelly, 2017)](https://... | 5,799 | 39.277778 | 127 | py |
myriad | myriad-main/myriad/systems/classical_control/mountain_car.py | # (c) 2021 Nikolaus Howe
import jax
import jax.numpy as jnp
from typing import Optional
from myriad.custom_types import Control, Cost, DState, Params, State, Timestep
from myriad.systems.base import FiniteHorizonControlSystem
def hill_function(x: float) -> float:
# return jnp.max(jnp.array([-3 * x - jnp.pi, -1/3 ... | 5,788 | 31.706215 | 175 | py |
myriad | myriad-main/myriad/systems/classical_control/pendulum.py | # (c) 2021 Nikolaus Howe
# inspired by https://github.com/openai/gym/blob/master/gym/envs/classic_control/pendulum.py
# and https://github.com/locuslab/mpc.pytorch/blob/07f43da67581b783f4f230ca97b0efbc421773af/mpc/env_dx/pendulum.py
import jax
import jax.numpy as jnp
from typing import Optional
from myriad.systems.... | 5,848 | 30.446237 | 156 | py |
myriad | myriad-main/myriad/systems/lenhart/mould_fungicide.py | from typing import Union, Optional
import gin
import jax.numpy as jnp
import matplotlib.pyplot as plt
import seaborn as sns
from myriad.custom_types import Params
from myriad.systems import IndirectFHCS
@gin.configurable
class MouldFungicide(IndirectFHCS):
"""
Taken from: Optimal Control Applied to Biological... | 3,084 | 37.5625 | 113 | py |
myriad | myriad-main/myriad/systems/lenhart/simple_case.py | from typing import Union, Optional, Dict
import gin
import jax.numpy as jnp
import matplotlib.pyplot as plt
import seaborn as sns
from myriad.systems import IndirectFHCS
@gin.configurable
class SimpleCase(IndirectFHCS):
"""
Taken from: Optimal Control Applied to Biological Models, Lenhart & Workman (Chapter 5... | 2,217 | 34.206349 | 112 | py |
myriad | myriad-main/myriad/systems/lenhart/cancer_treatment.py | import gin
import jax.numpy as jnp
from typing import Optional, Union
from myriad.custom_types import Params
from myriad.systems import IndirectFHCS
@gin.configurable
class CancerTreatment(IndirectFHCS):
"""
Taken from: Optimal Control Applied to Biological Models, Lenhart & Workman (Chapter 10, Lab 5)
... | 4,085 | 43.413043 | 120 | py |
myriad | myriad-main/myriad/systems/lenhart/simple_case_with_bounds.py | from typing import Union, Optional
import gin
import jax.numpy as jnp
import matplotlib.pyplot as plt
import seaborn as sns
from myriad.systems import IndirectFHCS
@gin.configurable
class SimpleCaseWithBounds(IndirectFHCS):
"""
Taken from: Optimal Control Applied to Biological Models, Lenhart & Workman (Chapt... | 2,409 | 35.515152 | 112 | py |
myriad | myriad-main/myriad/systems/lenhart/predator_prey.py | import gin
import jax.numpy as jnp
from typing import Union, Optional
from myriad.custom_types import Params
from myriad.systems import IndirectFHCS
@gin.configurable
class PredatorPrey(IndirectFHCS):
# TODO: there is an error when trying to plot with PredatorPrey
"""
Taken from: Optimal Control Applied to ... | 5,190 | 36.615942 | 132 | py |
myriad | myriad-main/myriad/systems/lenhart/bacteria.py | from typing import Union, Optional
import gin
import jax.numpy as jnp
from myriad.custom_types import Params
from myriad.systems import IndirectFHCS
@gin.configurable
class Bacteria(IndirectFHCS):
"""Taken from: Optimal Control Applied to Biological Models, Lenhart & Workman (Chapter 7, Lab 3)
This environm... | 4,287 | 43.666667 | 120 | py |
myriad | myriad-main/myriad/systems/lenhart/harvest.py | import gin
import jax.numpy as jnp
from typing import Union, Optional
from myriad.systems import IndirectFHCS
@gin.configurable
class Harvest(IndirectFHCS):
"""
Taken from: Optimal Control Applied to Biological Models, Lenhart & Workman (Chapter 11, Lab 6)
The model was was adapted from Wayne M. Getz. Optimal... | 2,859 | 38.722222 | 112 | py |
myriad | myriad-main/myriad/systems/lenhart/bear_populations.py | import gin
import jax.numpy as jnp
from typing import Union, Optional
from myriad.custom_types import Params
from myriad.systems import IndirectFHCS
@gin.configurable
class BearPopulations(IndirectFHCS):
"""
Taken from: Optimal Control Applied to Biological Models, Lenhart & Workman (Chapter 15, Lab 9)
... | 6,332 | 42.675862 | 178 | py |
myriad | myriad-main/myriad/systems/lenhart/invasive_plant.py | import gin
import jax.numpy as jnp
import matplotlib.pyplot as plt
from typing import Union, Optional
from myriad.systems import IndirectFHCS
@gin.configurable
class InvasivePlant(IndirectFHCS):
"""
Taken from: Optimal Control Applied to Biological Models, Lenhart & Workman (Chapter 24, Lab 14)
This probl... | 5,532 | 39.985185 | 151 | py |
myriad | myriad-main/myriad/systems/lenhart/epidemic_seirn.py | import gin
import jax.numpy as jnp
from typing import Union, Optional
from myriad.systems import IndirectFHCS
@gin.configurable
class EpidemicSEIRN(IndirectFHCS): # TODO : Add R calculation at the end
"""
Taken from: Optimal Control Applied to Biological Models, Lenhart & Workman (Chapter 13, Lab 7)
... | 5,092 | 44.473214 | 149 | py |
myriad | myriad-main/myriad/systems/lenhart/hiv_treatment.py | import gin
import jax.numpy as jnp
from typing import Union, Optional
from myriad.custom_types import Params
from myriad.systems import IndirectFHCS
@gin.configurable
class HIVTreatment(IndirectFHCS):
"""
Taken from: Optimal Control Applied to Biological Models, Lenhart & Workman (Chapter 14, Lab 8)
Model... | 5,535 | 40.939394 | 163 | py |
myriad | myriad-main/myriad/systems/lenhart/bioreactor.py | import gin
import jax.numpy as jnp
from typing import Union, Optional
from myriad.custom_types import Params
from myriad.systems import IndirectFHCS
@gin.configurable
class Bioreactor(IndirectFHCS): # TODO: Add resolution for z state after optimization
"""
Taken from: Optimal Control Applied to Biological ... | 4,278 | 40.95098 | 133 | py |
myriad | myriad-main/myriad/systems/lenhart/timber_harvest.py | from typing import Union, Optional
import gin
import jax.numpy as jnp
import matplotlib.pyplot as plt
import seaborn as sns
from myriad.custom_types import Params
from myriad.systems import IndirectFHCS
@gin.configurable
class TimberHarvest(IndirectFHCS):
"""
Taken from: Optimal Control Applied to Biological ... | 4,409 | 41.403846 | 118 | py |
myriad | myriad-main/myriad/systems/lenhart/glucose.py | import gin
import jax.numpy as jnp
from typing import Union, Optional
from myriad.custom_types import Params
from myriad.systems import IndirectFHCS
@gin.configurable
class Glucose(IndirectFHCS):
"""
Taken from: Optimal Control Applied to Biological Models, Lenhart & Workman (Chapter 16, Lab 10)
Model is ... | 4,719 | 36.165354 | 121 | py |
myriad | myriad-main/myriad/trajectory_optimizers/forward_backward_sweep.py | # (c) 2021 Nikolaus Howe
import jax.numpy as jnp
from jax.flatten_util import ravel_pytree
# from jax.ops import index_update
# from ipopt import minimize_ipopt
from scipy.optimize import minimize
from dataclasses import dataclass
from typing import Callable, Dict, Tuple, Union
from myriad.config import Config, HPara... | 6,006 | 36.779874 | 117 | py |
myriad | myriad-main/myriad/trajectory_optimizers/base.py | # (c) 2021 Nikolaus Howe
from __future__ import annotations
import typing
if typing.TYPE_CHECKING:
from myriad.config import Config, HParams
# from myriad.config import
import jax
import jax.numpy as jnp
import numpy as np
from jax import vmap
from jax.flatten_util import ravel_pytree
# from ipopt import minimize... | 5,442 | 37.330986 | 149 | py |
myriad | myriad-main/myriad/trajectory_optimizers/shooting.py | # (c) 2021 Nikolaus Howe
import jax
import jax.numpy as jnp
import numpy as np
from jax.flatten_util import ravel_pytree
from myriad.config import Config, HParams, IntegrationMethod
from myriad.custom_types import Control, Params, Timestep
from myriad.systems import FiniteHorizonControlSystem
from myriad.utils import... | 11,796 | 41.283154 | 120 | py |
myriad | myriad-main/myriad/trajectory_optimizers/collocation/hermite_simpson.py | # (c) 2021 Nikolaus Howe
import jax.numpy as jnp
import numpy as np
from jax import vmap
from jax.flatten_util import ravel_pytree
from typing import Tuple
from myriad.config import Config, HParams
from myriad.custom_types import Control, Controls, Cost, DState, DStates, Params, State, States, Timestep
from myriad.sy... | 15,045 | 41.744318 | 118 | py |
myriad | myriad-main/myriad/trajectory_optimizers/collocation/trapezoidal.py | # (c) 2021 Nikolaus Howe
import jax.numpy as jnp
import numpy as np
from jax import vmap
from jax.flatten_util import ravel_pytree
from myriad.config import Config, HParams
from myriad.custom_types import Control, Cost, DState, Params, State, Timestep, DStates
from myriad.trajectory_optimizers.base import TrajectoryO... | 8,760 | 40.719048 | 110 | py |
myriad | myriad-main/myriad/experiments/e2e_sysid.py | # (c) 2021 Nikolaus Howe
import jax
import jax.numpy as jnp
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import optax
import pickle as pkl
from pathlib import Path
from typing import Tuple
from myriad.config import HParams, Config, SystemType, NLPSolverType
from myriad.custom_types import Para... | 17,345 | 37.892377 | 116 | py |
myriad | myriad-main/myriad/experiments/node_mle_sysid.py | # (c) Nikolaus Howe 2021
from __future__ import annotations
import csv
import jax
import jax.numpy as jnp
import numpy as np
import pickle as pkl
from pathlib import Path
from myriad.config import Config, HParams, IntegrationMethod
from myriad.neural_ode.create_node import NeuralODE
from myriad.neural_ode.node_train... | 8,692 | 42.034653 | 115 | py |
myriad | myriad-main/myriad/experiments/mle_sysid.py | # (c) 2021 Nikolaus Howe
from __future__ import annotations
import csv
import jax
import jax.numpy as jnp
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import optax
import pickle as pkl
from pathlib import Path
from typing import Dict, Tuple, Union
from myriad.config import Config, HParams, In... | 12,626 | 36.247788 | 119 | py |
myriad | myriad-main/myriad/experiments/node_e2e_sysid.py | # (c) 2021 Nikolaus Howe
import jax
import jax.numpy as jnp
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import optax
import pickle as pkl
from pathlib import Path
from typing import Tuple
from myriad.config import HParams, Config, NLPSolverType
from myriad.defaults import learning_rates, para... | 18,018 | 38.342795 | 118 | py |
SkeletonGCL | SkeletonGCL-main/main.py | #!/usr/bin/env python
from __future__ import print_function
import argparse
import inspect
import os
import pickle
import random
import shutil
import sys
import time
from collections import OrderedDict
import traceback
from sklearn.metrics import confusion_matrix
import csv
import numpy as np
import glob
# torch
impo... | 23,323 | 38.2 | 180 | py |
SkeletonGCL | SkeletonGCL-main/torchlight/setup.py | from setuptools import find_packages, setup
setup(
name='torchlight',
version='1.0',
description='A mini framework for pytorch',
packages=find_packages(),
install_requires=[])
| 197 | 21 | 47 | py |
SkeletonGCL | SkeletonGCL-main/torchlight/torchlight/util.py | #!/usr/bin/env python
import argparse
import os
import sys
import traceback
import time
import pickle
from collections import OrderedDict
import yaml
import h5py
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
# from torchpack.runner.hooks import Pav... | 6,649 | 32.756345 | 117 | py |
SkeletonGCL | SkeletonGCL-main/torchlight/torchlight/gpu.py | import os
import torch
def visible_gpu(gpus):
"""
set visible gpu.
can be a single id, or a list
return a list of new gpus ids
"""
gpus = [gpus] if isinstance(gpus, int) else list(gpus)
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(list(map(str, gpus)))
return list(range(... | 750 | 19.861111 | 71 | py |
SkeletonGCL | SkeletonGCL-main/model/agcn.py | import math
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
def import_class(name):
components = name.split('.')
mod = __import__(components[0])
for comp in components[1:]:
mod = getattr(mod, comp)
return mod
def conv_branch_init(conv, branches):
... | 6,153 | 32.086022 | 138 | py |
SkeletonGCL | SkeletonGCL-main/model/loss.py | from importlib_metadata import requires
import torch
import torch.nn as nn
from torch import einsum, positive
import math
import random
class InfoNCEGraph(nn.Module):
def __init__(self, in_channels=128, out_channels=256, mem_size=512, positive_num=128, negative_num=512, T=0.8, class_num=60, label_all=[]):
... | 3,455 | 45.702703 | 143 | py |
SkeletonGCL | SkeletonGCL-main/model/baseline.py | import math
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
def import_class(name):
components = name.split('.')
mod = __import__(components[0])
for comp in components[1:]:
mod = getattr(mod, comp)
return mod
def conv_branch_init(conv, branches):
... | 6,316 | 31.06599 | 110 | py |
SkeletonGCL | SkeletonGCL-main/model/ctrgcn.py | import math
import pdb
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
def import_class(name):
components = name.split('.')
mod = __import__(components[0])
for comp in components[1:]:
mod = getattr(mod, comp)
return mod
def conv_branch_init(conv, bra... | 13,345 | 35.664835 | 132 | py |
SkeletonGCL | SkeletonGCL-main/feeders/feeder_ucla.py | import numpy as np
import pickle
import json
import random
import math
from torch.utils.data import Dataset
class Feeder(Dataset):
def __init__(self, data_path, label_path, repeat=1, random_choose=False, random_shift=False, random_move=False,
window_size=-1, normalization=False, debug=False, use_... | 94,902 | 603.477707 | 61,388 | py |
SkeletonGCL | SkeletonGCL-main/feeders/tools.py | import random
import matplotlib.pyplot as plt
import numpy as np
import pdb
import torch
import torch.nn.functional as F
def valid_crop_resize(data_numpy,valid_frame_num,p_interval,window):
# input: C,T,V,M
C, T, V, M = data_numpy.shape
begin = 0
end = valid_frame_num
valid_size = end - begin
... | 8,189 | 33.851064 | 150 | py |
SkeletonGCL | SkeletonGCL-main/feeders/feeder_ntu.py | import numpy as np
import torch
from torch.utils.data import Dataset
from feeders import tools
class Feeder(Dataset):
def __init__(self, data_path, label_path=None, p_interval=1, split='train', random_choose=False, random_shift=False,
random_move=False, random_rot=False, window_size=-1, normalizat... | 5,311 | 41.496 | 120 | py |
STDEN | STDEN-main/stden_train.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import yaml
from lib.utils import load_graph_data
from model.stden_supervisor import STDENSupervisor
import numpy as np
import torch
def main(args):
with open(args.config_filename) as f... | 1,156 | 29.447368 | 108 | py |
STDEN | STDEN-main/stden_eval.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import yaml
from lib.utils import load_graph_data
from model.stden_supervisor import STDENSupervisor
import numpy as np
import torch
def main(args):
with open(args.config_filename) as f... | 1,577 | 34.863636 | 108 | py |
STDEN | STDEN-main/model/diffeq_solver.py | import torch
import torch.nn as nn
import time
from torchdiffeq import odeint
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class DiffeqSolver(nn.Module):
def __init__(self, odefunc, method, latent_dim,
odeint_rtol = 1e-4, odeint_atol = 1e-5):
nn.Module.__init__(self... | 1,877 | 37.326531 | 119 | py |
STDEN | STDEN-main/model/ode_func.py | import numpy as np
import torch
import torch.nn as nn
from lib import utils
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class LayerParams:
def __init__(self, rnn_network: nn.Module, layer_type: str):
self._rnn_network = rnn_network
self._params_dict = {}
self._bi... | 6,912 | 40.644578 | 135 | py |
STDEN | STDEN-main/model/stden_supervisor.py | import os
import time
from random import SystemRandom
import numpy as np
import pandas as pd
import torch
from torch.utils.tensorboard import SummaryWriter
from lib import utils
from model.stden_model import STDENModel
from lib.metrics import masked_mae_loss, masked_mape_loss, masked_rmse_loss
device = torch.device... | 17,713 | 41.684337 | 154 | py |
STDEN | STDEN-main/model/stden_model.py | import time
import torch
import torch.nn as nn
from torch.nn.modules.rnn import GRU
from model.ode_func import ODEFunc
from model.diffeq_solver import DiffeqSolver
from lib import utils
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def count_parameters(model):
return sum(p.numel() for p... | 8,910 | 42.048309 | 115 | py |
STDEN | STDEN-main/lib/utils.py | import logging
import numpy as np
import os
import time
import scipy.sparse as sp
import sys
import torch
import torch.nn as nn
class DataLoader(object):
def __init__(self, xs, ys, batch_size, pad_with_last_sample=True, shuffle=False):
"""
:param xs:
:param ys:
:param batch_size:
... | 7,962 | 33.925439 | 160 | py |
STDEN | STDEN-main/lib/metrics.py | import torch
def masked_mae_loss(y_pred, y_true):
y_true[y_true < 1e-4] = 0
mask = (y_true != 0).float()
mask /= mask.mean() # assign the sample weights of zeros to nonzero-values
loss = torch.abs(y_pred - y_true)
loss = loss * mask
# trick for nans: https://discuss.pytorch.org/t/how-to-set-na... | 896 | 28.9 | 88 | py |
driver-gaze-yolov5 | driver-gaze-yolov5-main/gaze_prediction_and_evaluation.py | """
The code for computing the saliency metrics is adapted from
https://github.com/tarunsharma1/saliency_metrics/blob/master/salience_metrics.py
"""
import os
import argparse
import time
import shutil
import math
import torch
from torch.utils.data import DataLoader
from torch import nn
from torch.nn import functional... | 22,026 | 36.717466 | 134 | py |
driver-gaze-yolov5 | driver-gaze-yolov5-main/extract_features.py | """
The following code is adapted from the file detect.py of https://github.com/ultralytics/yolov5 (Release 5.0)
"""
import os
import argparse
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from models.experimental import attempt_load
from utils.datasets import Load... | 10,476 | 45.358407 | 118 | py |
driver-gaze-yolov5 | driver-gaze-yolov5-main/network.py | """
The convolutional LSTM is adapted from
https://github.com/yaorong0921/Driver-Intention-Prediction/blob/master/models/convolution_lstm.py
"""
import torch.nn as nn
import torch.nn.functional as F
import torch
from torch.autograd import Variable
class Net(nn.Module):
def __init__(self, gridwidth, gridheight):... | 6,594 | 38.491018 | 119 | py |
driver-gaze-yolov5 | driver-gaze-yolov5-main/bdda.py | import os
import numpy as np
import math
import torch
from torch.utils.data import Dataset
import cv2
from utils.utils import *
import torchvision
from PIL import Image
class VideoRecord(object):
def __init__(self, row):
self._data = row
@property
def img_id(self):
return (self._data[0]) ... | 5,490 | 31.684524 | 134 | py |
driver-gaze-yolov5 | driver-gaze-yolov5-main/More files/evaluation_otherModel.py | import os
import argparse
import time
import shutil
import math
import torch
from torch.utils.data import DataLoader
from torch import nn
from torch.nn import functional as F
import torchvision
import numbers
import network
from bdda_otherModels import BDDA
import numpy as np
from PIL import Image
from sklearn.met... | 9,879 | 34.285714 | 138 | py |
driver-gaze-yolov5 | driver-gaze-yolov5-main/More files/compute_BDDA_baseline.py | import os
from PIL import Image
import numpy as np
import math
import argparse
import os
import numpy as np
import math
import torch
from torch.utils.data import Dataset
import cv2
from utils.utils import *
import torchvision
from PIL import Image
parser = argparse.ArgumentParser(description='Create average baseline... | 1,331 | 22.368421 | 110 | py |
driver-gaze-yolov5 | driver-gaze-yolov5-main/More files/flops_counter.py | '''
Copyright (C) 2019 Sovrasov V. - All Rights Reserved
* You may use, distribute and modify this code under the
* terms of the MIT license.
* You should have received a copy of the MIT license with
* this file. If not visit https://opensource.org/licenses/MIT
'''
# this script can be used to evaluate model compl... | 14,874 | 33.512761 | 139 | py |
driver-gaze-yolov5 | driver-gaze-yolov5-main/More files/evaluation_BDDA_baseline.py | import os
import argparse
import time
import shutil
import math
import torch
from torch.utils.data import DataLoader
from torch import nn
from torch.nn import functional as F
import torchvision
import numbers
import network
from bdda_otherModels import BDDA
import numpy as np
from sklearn.metrics import f1_score,... | 8,598 | 34.097959 | 128 | py |
driver-gaze-yolov5 | driver-gaze-yolov5-main/More files/bdda_otherModels.py | import os
import numpy as np
import math
import torch
from torch.utils.data import Dataset
import cv2
from utils.utils import *
import torchvision
from PIL import Image
class VideoRecord(object):
def __init__(self, row):
self._data = row
@property
def img_id(self):
return (self._data[0]) ... | 1,891 | 24.226667 | 130 | py |
NMTGMinor | NMTGMinor-master/preprocess_classify.py | #!/usr/bin/env python
import onmt
import onmt.markdown
import argparse
import torch
import subprocess
import time, datetime
from onmt.data.binarizer import Binarizer
from onmt.data.binarizer import SpeechBinarizer
from onmt.data.indexed_dataset import IndexedDatasetBuilder
import h5py as h5
import numpy as np
import ... | 33,644 | 42.024297 | 118 | py |
NMTGMinor | NMTGMinor-master/setup.py | #!/usr/bin/env python
from setuptools import setup, find_packages
setup(name='NMTGMinor',
version='0.1',
author='quanpn90',
author_email='ngoc.pham@kit.edu',
url='https://github.com/quanpn90/NMTGMinor',
license='MIT',
scripts=[
'flask_online.py',
'online.py',
... | 532 | 25.65 | 60 | py |
NMTGMinor | NMTGMinor-master/classify.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
import sys
import h5py as h5
import numpy as np
from onmt.inference.predictor import Predictor
parser = argparse.ArgumentParser(description='translate.py... | 17,820 | 39.687215 | 119 | py |
NMTGMinor | NMTGMinor-master/eval_autoencoder.py | #!/usr/bin/env python
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
import sys
import h5py as h5
import numpy as np
from ae.Evaluator import Evaluator
parser = argparse.ArgumentParser(description='translate.py')
onmt.markdown.add_md_help_argumen... | 9,790 | 35.808271 | 102 | py |
NMTGMinor | NMTGMinor-master/translate.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
import sys
import numpy as np
from onmt.inference.fast_translator import FastTranslator
from onmt.inference.stream_translator import StreamTranslator
par... | 35,674 | 43.04321 | 121 | py |
NMTGMinor | NMTGMinor-master/verify_wav2vec2_feat.py | #!/usr/bin/env python
# from fairseq.checkpoint_utils import load_model_ensemble_and_task, load_checkpoint_to_cpu
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
import sys
import h5py as h5
import numpy as np
from onmt.inference.fast_translator ... | 8,036 | 32.348548 | 119 | py |
NMTGMinor | NMTGMinor-master/rematch_language_embedding.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
import sys
import copy
from onmt.model_factory import build_model, build_language_model, optimize_model
from onmt.constants import add_tokenidx
from optio... | 2,213 | 26 | 81 | py |
NMTGMinor | NMTGMinor-master/extract_wav2vec2_codebook.py | #!/usr/bin/env python
# from fairseq.checkpoint_utils import load_model_ensemble_and_task, load_checkpoint_to_cpu
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
import sys
import h5py as h5
import numpy as np
from onmt.inference.fast_translator ... | 7,425 | 32.151786 | 119 | py |
NMTGMinor | NMTGMinor-master/average_checkpoints_auto.py | #!/usr/bin/env python
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
import os, sys
from onmt.model_factory import build_model, build_language_model, build_classifier, optimize_model
from copy import deepcopy
from onmt.utils import checkpoint_path... | 5,784 | 27.925 | 114 | py |
NMTGMinor | NMTGMinor-master/autoencoder.py | #!/usr/bin/env python
from __future__ import division
import onmt
import onmt.markdown
import onmt.modules
import argparse
import torch
import torch.nn as nn
from torch import cuda
from torch.autograd import Variable
import math
import time, datetime
from onmt.modules.loss import NMTLossFunc
from onmt.model_factory im... | 5,533 | 34.703226 | 115 | py |
NMTGMinor | NMTGMinor-master/sample_lm.py | #!/usr/bin/env python
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
from onmt.model_factory import build_model
parser = argparse.ArgumentParser(description='translate.py')
onmt.markdown.add_md_help_argument(parser)
parser.add_argument('-models... | 3,478 | 26.393701 | 96 | py |
NMTGMinor | NMTGMinor-master/rescore.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
import sys
import h5py as h5
import numpy as np
import apex
parser = argparse.ArgumentParser(description='rescore.py')
onmt.markdown.add_md_help_argument... | 13,352 | 38.979042 | 117 | py |
NMTGMinor | NMTGMinor-master/options.py | import argparse
def make_parser(parser):
# Data options
parser.add_argument('-data', required=True,
help='Path to the *-train.pt file from preprocess.py')
parser.add_argument('-data_format', required=False, default='raw',
help='Default data format: raw')
... | 43,910 | 49.822917 | 149 | py |
NMTGMinor | NMTGMinor-master/extend_weight.py | #!/usr/bin/env python
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
import os, sys
from onmt.model_factory import build_model, build_language_model, build_classifier, optimize_model
from copy import deepcopy
from onmt.utils import checkpoint_path... | 5,225 | 31.259259 | 144 | py |
NMTGMinor | NMTGMinor-master/preprocess_triangle.py | #!/usr/bin/env python
import onmt
import onmt.markdown
import argparse
import torch
import subprocess
import time, datetime
from onmt.data.binarizer import Binarizer
from onmt.data.binarizer import SpeechBinarizer
from onmt.data.indexed_dataset import IndexedDatasetBuilder
import numpy as np
import warnings
import os... | 64,908 | 43.397401 | 119 | py |
NMTGMinor | NMTGMinor-master/extract_wav2vec2_tdnn.py | #!/usr/bin/env python
# from fairseq.checkpoint_utils import load_model_ensemble_and_task, load_checkpoint_to_cpu
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
import sys
import h5py as h5
import numpy as np
from onmt.inference.fast_translator ... | 7,353 | 32.733945 | 119 | py |
NMTGMinor | NMTGMinor-master/train_distributed.py | #!/usr/bin/env python
from __future__ import division
import pickle
import types
import onmt
import onmt.markdown
import onmt.modules
import argparse
import torch
import time, datetime
from onmt.data.mmap_indexed_dataset import MMapIndexedDataset
from onmt.data.scp_dataset import SCPIndexDataset
from onmt.data.wav_dat... | 33,419 | 44.345997 | 117 | py |
NMTGMinor | NMTGMinor-master/train_language_model.py | #!/usr/bin/env python
from __future__ import division
import onmt
import onmt.markdown
import onmt.modules
import argparse
import torch
import torch.nn as nn
from torch import cuda
from torch.autograd import Variable
import math
import time, datetime
from onmt.train_utils.trainer import XETrainer
from onmt.modules.los... | 4,807 | 32.158621 | 105 | py |
NMTGMinor | NMTGMinor-master/preprocess.py | #!/usr/bin/env python
import onmt
import onmt.markdown
import argparse
import torch
import subprocess
import time, datetime
from onmt.data.binarizer import Binarizer
from onmt.data.binarizer import SpeechBinarizer
from onmt.data.indexed_dataset import IndexedDatasetBuilder
import numpy as np
import warnings
import os... | 63,944 | 43.498956 | 119 | py |
NMTGMinor | NMTGMinor-master/flask_online.py | #!/usr/bin/env python
from onmt.online_translator import RecognizerParameter, ASROnlineTranslator
from flask import Flask, request
import torch
import numpy as np
import math
import sys
import json
import threading
import queue
import uuid
import traceback
import subprocess
host = sys.argv[1] # 192.168.0.72
port = sy... | 6,118 | 29.595 | 115 | py |
NMTGMinor | NMTGMinor-master/flask_mt.py | #!/usr/bin/env python
# from onmt.online_translator import RecognizerParameter, ASROnlineTranslator
from onmt.online_translator import TranslatorParameter, OnlineTranslator
from flask import Flask, request
import torch
import numpy as np
import math
import sys
import json
import threading
import queue
import uuid
impor... | 6,523 | 27.867257 | 115 | py |
NMTGMinor | NMTGMinor-master/train_classify.py | #!/usr/bin/env python
from __future__ import division
import onmt
import onmt.markdown
import onmt.modules
import argparse
import torch
import time, datetime
from onmt.data.mmap_indexed_dataset import MMapIndexedDataset
from onmt.data.scp_dataset import SCPIndexDataset
from onmt.data.wav_dataset import WavDataset
from... | 20,484 | 44.220751 | 107 | py |
NMTGMinor | NMTGMinor-master/tools/grad_check.py | import torch.nn as nn
import onmt
import torch
from onmt.reversible_models.transformers import ReversibleTransformerEncoderLayer, ReversibleEncoderFunction, \
ReversibleTransformerDecoderLayer, ReversibleDecoderFunction
class TestEncoder(nn.Module):
def __init__(self, layers):
super().__init__()
... | 2,992 | 28.93 | 111 | py |
NMTGMinor | NMTGMinor-master/tools/perplexity_score.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
import sys
import h5py as h5
import numpy as np
import apex
parser = argparse.ArgumentParser(description='translate.py')
onmt.markdown.add_md_help_argume... | 14,590 | 39.30663 | 117 | py |
NMTGMinor | NMTGMinor-master/tools/test_amp.py | import torch
from apex import amp
from apex.normalization.fused_layer_norm import FusedLayerNorm
torch.cuda.set_device(1)
class NeuralNet(torch.nn.Module):
def __init__(self, d_in, d_out):
self.d_in = d_in
self.d_out = d_out
super().__init__()
self.norm = torch.nn.LayerNorm(d_in)
... | 1,464 | 28.3 | 67 | py |
NMTGMinor | NMTGMinor-master/tools/get_best.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
import sys
import h5py as h5
import numpy as np
import apex
parser = argparse.ArgumentParser(description='rescore.py')
onmt.markdown.add_md_help_argument... | 2,055 | 24.382716 | 75 | py |
NMTGMinor | NMTGMinor-master/tools/average_checkpoints.py | from __future__ import division
import onmt
import onmt.markdown
import torch
import argparse
import math
import numpy
from onmt.model_factory import build_model
parser = argparse.ArgumentParser(description='translate.py')
onmt.markdown.add_md_help_argument(parser)
parser.add_argument('-models', required=True,
... | 3,446 | 26.798387 | 96 | py |
NMTGMinor | NMTGMinor-master/tools/grad_check_reversible.py | import torch.nn as nn
import onmt
import torch
# from onmt.reversible_models.transformers import ReversibleTransformerEncoderLayer, ReversibleEncoderFunction, \
# ReversibleTransformerDecoderLayer, ReversibleDecoderFunction
from onmt.reversible_models.relative_transformers import ReversibleTransformerEncoderLayer,... | 3,353 | 30.641509 | 120 | py |
NMTGMinor | NMTGMinor-master/test/test_cmatmul.py | import torch
from time import time
B = 16384
N_in = 1024
N_out = 4096
num_iters = 200
x = torch.randn(B, N_in, dtype=torch.cfloat, requires_grad=True)
r = torch.randn(B, N_in, dtype=torch.float, requires_grad=True)
i = torch.randn(B, N_in, dtype=torch.float, requires_grad=True)
print(r.type())
r.data.copy_(x.real.d... | 2,405 | 20.872727 | 91 | py |
NMTGMinor | NMTGMinor-master/test/test_factorize_linear.py | import torch
import torch.nn.functional as F
from time import time
N_in = 1024
N_out = 4096
B = 16384
num_iters = 512
x = torch.randn(B, N_in, dtype=torch.float, requires_grad=True)
W = torch.randn(N_out, N_in, dtype=torch.float, requires_grad=True)
b = torch.randn(N_out, dtype=torch.float, requires_grad=True)
x =... | 3,043 | 24.579832 | 93 | py |
NMTGMinor | NMTGMinor-master/test/test_self_attention_blaslt.py | import torch
import unittest
from modules.self_multihead_attn import SelfMultiheadAttn
from time import time
class SelfMultiheadAttnTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
self.seq_length = 512
self.sequences ... | 5,583 | 43.672 | 109 | py |
NMTGMinor | NMTGMinor-master/test/test_rotation.py | import torch
import torch
from torch import nn, einsum
from einops import rearrange, repeat
class SinusoidalEmbeddings(nn.Module):
def __init__(self, dim):
super().__init__()
inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer('inv_freq', inv_freq)
... | 3,312 | 25.293651 | 104 | py |
NMTGMinor | NMTGMinor-master/test/test_fmha.py | ###############################################################################
# Copyright (c) 2011-2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistribution... | 14,170 | 32.343529 | 104 | py |
NMTGMinor | NMTGMinor-master/test/test_flattened_weight.py | import torch
import torch.nn.functional as F
from time import time
class ParameterRef(object):
def __init__(self, weight_buf, offset, length, size):
self.weight_buf = weight_buf
self.offset = offset
self.length = length
self.size = size
def __call__(self):
return sel... | 3,501 | 25.330827 | 86 | py |
NMTGMinor | NMTGMinor-master/test/test_multi_linear.py | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Parameter
len_q = 20
input_dim = 128
heads = 8
head_dim = input_dim // heads
output_dim = input_dim
k_proj = nn.Linear(input_dim, input_dim, bias=True)
v_proj = nn.Linear(input_dim, input_dim, bias=True)
q_proj = nn.Linear(input_d... | 3,110 | 32.095745 | 133 | py |
NMTGMinor | NMTGMinor-master/test/test_self_attention.py | import torch
import unittest
from modules.self_multihead_attn import SelfMultiheadAttn
from time import time
class SelfMultiheadAttnTest(unittest.TestCase):
def setUp(self, seed=1234):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
self.seq_length = 512
self.sequences ... | 5,583 | 43.672 | 109 | py |
NMTGMinor | NMTGMinor-master/test/test_softmax.py | import torch
import mask_softmax_dropout_cuda
import copy
BH = 1024 * 8
B = 1024
H = BH // B
Q = 75
K = 56
x = torch.randn((BH, Q, K) , dtype=torch.float16, device=torch.device("cuda"), requires_grad=True) * 100
x_ref = x.clone().detach().requires_grad_(True)
grado = torch.randn((BH, Q, K), dtype=torch.float16, devi... | 1,510 | 24.610169 | 104 | py |
NMTGMinor | NMTGMinor-master/test/modules/fast_self_multihead_attn_func.py | import torch
# import fast_self_multihead_attn
# import fast_self_multihead_attn_bias
# import fast_self_multihead_attn_bias_additive_mask
import self_multihead_attn_cuda as fast_self_multihead_attn_bias_additive_mask
class FastSelfAttnFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, use_time_mas... | 3,260 | 32.96875 | 108 | py |
NMTGMinor | NMTGMinor-master/test/modules/self_multihead_attn.py | import math
import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from .self_multihead_attn_func import self_attn_func
from .fast_self_multihead_attn_func import fast_self_attn_func
# from .fast_self_multihead_attn_norm_add_func import fast_self_attn_norm_add_func
# from ape... | 4,111 | 39.712871 | 118 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.